tag:blogger.com,1999:blog-16016990472637566662024-03-18T16:01:04.086+13:00Computational IntelligenceThe Computational Intelligence Blog covers all topics related to computational intelligence. The major focus is on artificial neural networks, evolutionary algorithms, fuzzy systems and the applications of these methods. Calls for papers, new journals, tutorials and software are also covered.Mike Wattshttp://www.blogger.com/profile/12970193877427051617noreply@blogger.comBlogger1302125tag:blogger.com,1999:blog-1601699047263756666.post-28756268358618096682024-03-15T17:00:00.009+13:002024-03-15T17:00:00.130+13:00Weekly Review 15 March 2024Some interesting links that I <a href="https://twitter.com/DrMikeWatts">Tweeted</a> about in the last week (I also post these on <a href="https://mastodon.social/@DrMikeWatts">Mastodon</a>, <a href="https://www.threads.net/@drmikewatts">Threads</a>, <a href="https://newsmast.org/profile/111338892311951056">Newsmast</a>, <a href="https://bsky.app/profile/drmikewatts.bsky.social">Bluesky</a> and <a href="https://post.news/@/DrMikeWatts">Post</a>):<div><ol style="text-align: left;"><li>AI papers that are promoted by just two influencers are getting many more citations than other papers: <a href="https://spectrum.ieee.org/social-media-ai">https://spectrum.ieee.org/social-media-ai</a></li><li>Of course AI are tools rather than creatures. AI are algorithms, not magic. There's no vital force that somehow makes them living beings: <a href="https://futurism.com/the-byte/sam-altman-openai-tool-creature">https://futurism.com/the-byte/sam-altman-openai-tool-creature</a></li><li>One of the first things I was taught as an undergrad is "don't speed up the mess". Use of generative AI to help write papers isn't going to make bad work good: <a href="https://www.nature.com/articles/d41586-024-00592-w">https://www.nature.com/articles/d41586-024-00592-w</a></li><li>Interesting that the privacy commissioner is already taking an interest in the use of face recognition AI in supermarkets: <a href="https://www.nzherald.co.nz/nz/foodstuffs-use-of-facial-technology-a-red-flag-jon-duffy/NGBYKJJVYVEBNAGOWDZKEWIC7Y/">https://www.nzherald.co.nz/nz/foodstuffs-use-of-facial-technology-a-red-flag-jon-duffy/NGBYKJJVYVEBNAGOWDZKEWIC7Y/</a></li><li>Using an AI to make dates for yourself is so ripe for abuse it's frightening. Is it really so hard to meet people now? (says the guy who has been married for 21 years): https://futurism.com/the-byte/tinder-ai-makes-date-woman-stands-her-up</li><li>People are building AI based on outdated and biased data about human body proportions: <a href="https://spectrum.ieee.org/motion-capture-standards">https://spectrum.ieee.org/motion-capture-standards</a></li><li>Not surprising that robot makers want to embed AI into their products, it's what will make the robots useful. Not sure how I feel about a hallucinating humanoid robot, though: <a href="https://futurism.com/the-byte/humanoid-robot-maker-deal-openai">https://futurism.com/the-byte/humanoid-robot-maker-deal-openai</a></li><li>Makes me wonder which side of the political divide most needs to use AI to fake photographs: <a href="https://www.stuff.co.nz/world-news/350201018/trump-supporters-targeting-black-voters-ai-photos-campaigners-warn">https://www.stuff.co.nz/world-news/350201018/trump-supporters-targeting-black-voters-ai-photos-campaigners-warn</a></li><li>Why Meta's watermarking of AI generated images doesn't work, and what we can do about the problem: <a href="https://spectrum.ieee.org/meta-ai-watermarks">https://spectrum.ieee.org/meta-ai-watermarks</a></li><li>A generative AI for making music: h<a href="ttps://gizmodo.com/adobe-new-ai-tool-generate-music-text-prompts-1851302090">ttps://gizmodo.com/adobe-new-ai-tool-generate-music-text-prompts-1851302090</a></li><li>Proof-of-concept of a worm that targets generative AI, stealing data as it goes: <a href="https://www.tomshardware.com/tech-industry/artificial-intelligence/ai-worm-infects-users-via-ai-enabled-email-clients-morris-ii-generative-ai-worm-steals-confidential-data-as-it-spreads">https://www.tomshardware.com/tech-industry/artificial-intelligence/ai-worm-infects-users-via-ai-enabled-email-clients-morris-ii-generative-ai-worm-steals-confidential-data-as-it-spreads</a></li><li>Turns out we don't really understand how Large Language Model AI works. Why do they improve instead of over-fitting? <a href="https://www.technologyreview.com/2024/03/04/1089403/large-language-models-amazing-but-nobody-knows-why/">https://www.technologyreview.com/2024/03/04/1089403/large-language-models-amazing-but-nobody-knows-why/</a></li><li>Using deep learning Neural Networks to improve weather predictions: <a href="https://www.technologyreview.com/2023/07/05/1075897/new-ai-systems-could-speed-up-our-ability-to-create-weather-forecasts/">https://www.technologyreview.com/2023/07/05/1075897/new-ai-systems-could-speed-up-our-ability-to-create-weather-forecasts/</a></li><li>The job of generative AI prompt engineering is going to disappear, largely because machines can do it better, and nobody understands how the prompts give the results they do: <a href="https://spectrum.ieee.org/prompt-engineering-is-dead">https://spectrum.ieee.org/prompt-engineering-is-dead</a></li><li>People do not trust AI, and the wealthier the country, the less trust there is. Is this related to the trust (or lack thereof) in the big tech companies dominating AI? <a href="https://www.axios.com/2024/03/05/ai-trust-problem-edelman">https://www.axios.com/2024/03/05/ai-trust-problem-edelman</a></li><li>Will audiences really shun AI generated movies? <a href="https://futurism.com/the-byte/madame-web-star-hollywood-ai">https://futurism.com/the-byte/madame-web-star-hollywood-ai</a></li><li>More details on the energy and environmental cost of AI: <a href="https://www.technologyreview.com/2022/07/06/1055458/ai-research-emissions-energy-efficient/">https://www.technologyreview.com/2022/07/06/1055458/ai-research-emissions-energy-efficient/</a></li><li>Just how bad AI generated images can be: <a href="https://www.tomshardware.com/tech-industry/artificial-intelligence/microsoft-engineer-begs-ftc-to-stop-copilots-offensive-image-generator-our-tests-confirm-its-a-serious-problem">https://www.tomshardware.com/tech-industry/artificial-intelligence/microsoft-engineer-begs-ftc-to-stop-copilots-offensive-image-generator-our-tests-confirm-its-a-serious-problem</a></li><li>Why openness and transparency in AI models is a good thing: <a href="https://www.technologyreview.com/2023/07/25/1076698/its-high-time-for-more-ai-transparency/">https://www.technologyreview.com/2023/07/25/1076698/its-high-time-for-more-ai-transparency/</a></li><li>I am skeptical that we will ever see super intelligent AI, certainly not in the next three years: <a href="https://futurism.com/artificial-superintelligence-agi-2027-goertzel">https://futurism.com/artificial-superintelligence-agi-2027-goertzel</a></li><li>Laying out guidelines around using generative AI to design proteins, to prevent them being used as weapons: <a href="https://www.nature.com/articles/d41586-024-00699-0">https://www.nature.com/articles/d41586-024-00699-0</a></li><li>Using crypto to track the provenance of AI generated media: <a href="https://www.technologyreview.com/2023/07/28/1076843/cryptography-ai-labeling-problem-c2pa-provenance/">https://www.technologyreview.com/2023/07/28/1076843/cryptography-ai-labeling-problem-c2pa-provenance/</a></li><li>AI can be a real boon to the environment, but is can also be a bane, including worsening the misinformation (that is, corporate lies) around climate change: <a href="https://www.theguardian.com/technology/2024/mar/07/ai-climate-change-energy-disinformation-report">https://www.theguardian.com/technology/2024/mar/07/ai-climate-change-energy-disinformation-report</a></li><li>India is building their own AI supercomputer: <a href="https://www.theregister.com/2024/03/08/indiaai_policy_funding_secured/?td=rt-3a">https://www.theregister.com/2024/03/08/indiaai_policy_funding_secured/?td=rt-3a</a></li></ol></div>Mike Wattshttp://www.blogger.com/profile/12970193877427051617noreply@blogger.com0tag:blogger.com,1999:blog-1601699047263756666.post-71872921530211713482024-03-11T10:42:00.000+13:002024-03-11T10:42:30.690+13:00Soft Computing. Volume 28, Issue 6, March 2024<div><b>1)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09170-0">Global stability and sensitivity analysis of parameters of Omicron variant epidemic in diverse susceptible classes incorporating vaccination stages</a></div><div><b>Author(s): </b>R. Prem Kumar, Sanjoy Basu, A. Al-khedhairi</div><div><b>Pages: </b>4689 - 4713</div><div><br /></div><div><b>2)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09308-0">d-ideals of p-algebras</a></div><div><b>Author(s): </b>Abd El-Mohsen Badawy, Kamal El-Saady, Essam Abd El-Baset</div><div><b>Pages: </b>4715 - 4724</div><div><br /></div><div><b>3)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09131-7">An improved U-Net-based network for multiclass segmentation and category ratio statistics of ore images</a></div><div><b>Author(s): </b>Wei Wang, Qing Li, Zihan Wang</div><div><b>Pages: </b>4725 - 4741</div><div><br /></div><div><b>4)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09128-2">Design and application of a novel higher-order type-n fuzzy-logic-based system for controlling the steering angle of a vehicle: a soft computing approach</a></div><div><b>Author(s): </b>Smriti Srivastava, Rajesh Kumar</div><div><b>Pages: </b>4743 - 4758</div><div><br /></div><div><b>5)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09138-0">A PSO-optimized novel PID neural network model for temperature control of jacketed CSTR: design, simulation, and a comparative study</a></div><div><b>Author(s): </b>Snigdha Chaturvedi, Narendra Kumar, Rajesh Kumar</div><div><b>Pages: </b>4759 - 4773</div><div><br /></div><div><b>6)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09173-x">A noise-immune and attention-based multi-modal framework for short-term traffic flow forecasting</a></div><div><b>Author(s): </b>Guanru Tan, Teng Zhou, Zhizhe Lin</div><div><b>Pages: </b>4775 - 4790</div><div><br /></div><div><b>7)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09160-2">Heat sink/source impact on Williamson liquid flow over a stretching cylinder with modified Fourier and Fick’s law</a></div><div><b>Author(s): </b>A. A. Khan, S. Mir, A. Zaman</div><div><b>Pages: </b>4791 - 4798</div><div><br /></div><div><b>8)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09132-6">Multi-source adaptive meta-learning framework for domain generalization person re-identification</a></div><div><b>Author(s): </b>Yan Chen, Qiuling Tang, Hua Ma</div><div><b>Pages: </b>4799 - 4820</div><div><br /></div><div><b>9)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09188-4">A hybrid and context-aware framework for normal and abnormal human behavior recognition</a></div><div><b>Author(s): </b>Roghayeh Mojarad, Abdelghani Chibani, Yacine Amirat</div><div><b>Pages: </b>4821 - 4845</div><div><br /></div><div><b>10)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09185-7">Assessing the interactions amongst index tracking model formulations and genetic algorithm approaches with different rebalancing strategies</a></div><div><b>Author(s): </b>Thiago Wanderley de Amorim, Julio Cezar Soares Silva, Adiel Teixeira de Almeida Filho</div><div><b>Pages: </b>4847 - 4860</div><div><br /></div><div><b>11)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09190-w">Nonlinear prediction of fuzzy regression model based on quantile loss function</a></div><div><b>Author(s): </b>Mohsen Arefi, Amir Hamzeh Khammar</div><div><b>Pages: </b>4861 - 4871</div><div><br /></div><div><b>12)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09047-2">Evolutionary support vector regression for monitoring Poisson profiles</a></div><div><b>Author(s): </b>Ali Yeganeh, Saddam Akber Abbasi, Ali Reza Shadman</div><div><b>Pages: </b>4873 - 4897</div><div><br /></div><div><b>13)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09182-w">Secure multiparty access and authentication based on advanced fuzzy extractor in smart home</a></div><div><b>Author(s): </b>Sirisha Uppuluri, G. Lakshmeeswari</div><div><b>Pages: </b>4899 - 4914</div><div><br /></div><div><b>14)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09208-3">Data mining predictive algorithms for estimating soil water content</a></div><div><b>Author(s): </b>Somayeh Emami, Vahid Rezaverdinejad, Ahmed Elbeltagi</div><div><b>Pages: </b>4915 - 4931</div><div><br /></div><div><b>15)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09200-x">Blockchain-based secure transaction mechanism for electric vehicles with multiple temporary identities</a></div><div><b>Author(s): </b>ZhuoQun Xia, Guanghui Wang, Hongrui Li</div><div><b>Pages: </b>4933 - 4950</div><div><br /></div><div><b>16)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09214-5">A new metaheuristic honey badger-based maximum energy harvesting algorithm for thermoelectric generation system under dynamic operating conditions</a></div><div><b>Author(s): </b>Maryam Ejaz, Qiang Ling</div><div><b>Pages: </b>4951 - 4966</div><div><br /></div><div><b>17)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09073-0">Solving data-driven newsvendor problem with textual reviews through deep learning</a></div><div><b>Author(s): </b>Chuan Zhang, Yu-Xin Tian</div><div><b>Pages: </b>4967 - 4986</div><div><br /></div><div><b>18)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09196-4">DMPPT control of photovoltaic systems under partial shading conditions based on optimized neural networks</a></div><div><b>Author(s): </b>Shahriar Farajdadian, Seyed Mohammad Hassan Hosseini</div><div><b>Pages: </b>4987 - 5014</div><div><br /></div><div><b>19)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09198-2">Day-ahead scheduling of isolated microgrid integrated demand side management</a></div><div><b>Author(s): </b>Mousumi Basu</div><div><b>Pages: </b>5015 - 5027</div><div><br /></div><div><b>20)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09212-7">Automatic detection of weeds: synergy between EfficientNet and transfer learning to enhance the prediction accuracy</a></div><div><b>Author(s): </b>Linh T. Duong, Toan B. Tran, Phuong T. Nguyen</div><div><b>Pages: </b>5029 - 5044</div><div><br /></div><div><b>21)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09220-7">Prediction of service time for home delivery services using machine learning</a></div><div><b>Author(s): </b>Jan Wolter, Thomas Hanne</div><div><b>Pages: </b>5045 - 5056</div><div><br /></div><div><b>22)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09213-6">Grey wolf optimization algorithm-based PID controller for frequency stabilization of interconnected power generating system</a></div><div><b>Author(s): </b>K. Jagatheesan, D. Boopathi, Nilanjan Dey</div><div><b>Pages: </b>5057 - 5070</div><div><br /></div><div><b>23)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09140-6">Enhancing competency development and sustainable talent cultivation strategies for the service industry based on the IAA-NRM approach</a></div><div><b>Author(s): </b>Chia-Li Lin</div><div><b>Pages: </b>5071 - 5096</div><div><br /></div><div><b>24)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09186-6">A balanced hybrid cuckoo search algorithm for microscopic image segmentation</a></div><div><b>Author(s): </b>Shouvik Chakraborty, Kalyani Mali</div><div><b>Pages: </b>5097 - 5124</div><div><br /></div><div><b>25)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09225-2">Occlusion-robust workflow recognition with context-aware compositional ConvNet</a></div><div><b>Author(s): </b>Min Zhang, Haiyang Hu, Jie Chen</div><div><b>Pages: </b>5125 - 5135</div><div><br /></div><div><b>26)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09233-2">A dimensionality reduction-based approach for secured color image watermarking</a></div><div><b>Author(s): </b>Ashima Anand</div><div><b>Pages: </b>5137 - 5154</div><div><br /></div><div><b>27)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09234-1">Observer-based security control for Markov jump systems under hybrid cyber-attacks and its application via event-triggered scheme</a></div><div><b>Author(s): </b>M. Mubeen Tajudeen, M. Syed Ali, Bashir Ahmad</div><div><b>Pages: </b>5155 - 5171</div><div><br /></div><div><b>28)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09201-w">Reliability-aware web service composition with cost minimization perspective: a multi-objective particle swarm optimization model in multi-cloud scenarios</a></div><div><b>Author(s): </b>Mohammad Ali Nezafat Tabalvandani, Mirsaeid Hosseini Shirvani, Homayun Motameni</div><div><b>Pages: </b>5173 - 5196</div><div><br /></div><div><b>29)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09298-z">Design of a fuzzy trajectory tracking controller for a mobile manipulator system</a></div><div><b>Author(s): </b>Chia-Wen Chang, Chin-Wang Tao</div><div><b>Pages: </b>5197 - 5211</div><div><br /></div><div><b>30)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09267-6">Construction of optimum multivalued cryptographic Boolean function using artificial bee colony optimization and multi-criterion decision-making</a></div><div><b>Author(s): </b>Nabilah Abughazalah, Lal Said, Majid Khan</div><div><b>Pages: </b>5213 - 5223</div><div><br /></div><div><b>31)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09299-y">An improved hybrid salp swarm optimization and African vulture optimization algorithm for global optimization problems and its applications in stock market prediction</a></div><div><b>Author(s): </b>Ali Alizadeh, Farhad Soleimanian Gharehchopogh, Ahmad Jafarian</div><div><b>Pages: </b>5225 - 5261</div><div><br /></div><div><b>32)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09310-6">Studying of the Covid-19 model by using the finite element method: theoretical and numerical simulation</a></div><div><b>Author(s): </b>W. Alhejili, M. M. Khader, M. Adel</div><div><b>Pages: </b>5263 - 5273</div><div><br /></div><div><b>33)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09281-8">Dynamic link utilization empowered by reinforcement learning for adaptive storage allocation in MANET</a></div><div><b>Author(s): </b>R. P. Prem Anand, V. Senthilkumar, A. Rajaram</div><div><b>Pages: </b>5275 - 5285</div><div><br /></div><div><b>34)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09314-2">A many objective chimp optimization algorithm to de-cluster earthquake catalogs in space time domain</a></div><div><b>Author(s): </b>Ashish Sharma, Satyasai Jagannath Nanda</div><div><b>Pages: </b>5287 - 5320</div><div><br /></div><div><b>35)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09219-0">Design and implementation of intelligent LiDAR SLAM for autonomous mobile robots using evolutionary normal distributions transform</a></div><div><b>Author(s): </b>Hsu-Chih Huang, Sendren Sheng-Dong Xu, Yu-Xiang Chen</div><div><b>Pages: </b>5321 - 5337</div><div><br /></div><div><b>36)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09291-6">Multi-attribute quantum group decision-making method considering decision-makers’ risk attitude</a></div><div><b>Author(s): </b>Shuli Yan, Yingying Zeng, Na Zhang</div><div><b>Pages: </b>5339 - 5357</div><div><br /></div><div><b>37)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09283-6">A multilevel biomedical image thresholding approach using the chaotic modified cuckoo search</a></div><div><b>Author(s): </b>Shouvik Chakraborty, Kalyani Mali</div><div><b>Pages: </b>5359 - 5436</div><div><br /></div><div><b>38)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09296-1">Ensemble multi-attribute decision-making for material selection problems</a></div><div><b>Author(s): </b>Mehmet Şahin</div><div><b>Pages: </b>5437 - 5460</div><div><br /></div><div><b>39)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09301-7">Inverse inference based on interpretable constrained solutions of fuzzy relational equations with extended max–min composition</a></div><div><b>Author(s): </b>Hanna Rakytyanska</div><div><b>Pages: </b>5461 - 5478</div><div><br /></div><div><b>40)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09347-7">A multi-objective cost-loss optimization framework for optimal feeder routing problem of radial distribution networks</a></div><div><b>Author(s): </b>Sajjad Ayazi, Alireza Askarzadeh</div><div><b>Pages: </b>5479 - 5492</div><div><br /></div><div><b>41)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09306-2">A systematic review of fuzzing</a></div><div><b>Author(s): </b>Xiaoqi Zhao, Haipeng Qu, Gai-Ge Wang</div><div><b>Pages: </b>5493 - 5522</div><div><br /></div><div><b>42)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09349-5">A technique for securing digital audio files based on rotation and XOR operations</a></div><div><b>Author(s): </b>Anand B. Joshi, Abdul Gaffar</div><div><b>Pages: </b>5523 - 5540</div><div><br /></div><div><b>43)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09362-8">A novel Gaussian process regression-based stock index interval forecasting model integrating optimal variables screening with bidirectional long short-term memory</a></div><div><b>Author(s): </b>Jujie Wang, Qian Cheng, Xin Sun</div><div><b>Pages: </b>5541 - 5556</div><div><br /></div><div><b>44)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09340-0">An improved model combining knowledge graph and GCN for PLM knowledge recommendation</a></div><div><b>Author(s): </b>Guoxiang Tong, Deyun Li, Xuemei Liu</div><div><b>Pages: </b>5557 - 5575</div><div><br /></div><div><b>45)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09355-7">Domain adaptation framework with ensemble of fuzzy rules-based ELMs for remote-sensing image classification</a></div><div><b>Author(s): </b>Saroj K. Meher, Neeta Sharma Kothari, Ganapati Panda</div><div><b>Pages: </b>5577 - 5589</div><div><br /></div><div><b>46)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09357-5">Peak stress and peak strain evaluation of concrete columns confined with lateral ties under axial compression by artificial neural networks</a></div><div><b>Author(s): </b>DeChao Qu, Wei Chang</div><div><b>Pages: </b>5591 - 5608</div><div><br /></div><div><b>47)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09358-4">Plant leaf species identification using LBHPG feature extraction and machine learning classifier technique</a></div><div><b>Author(s): </b>Sachin B. Jadhav, Sanjay B. Patil</div><div><b>Pages: </b>5609 - 5623</div><div><br /></div><div><b>48)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09365-5">Better value estimation in Q-learning-based multi-agent reinforcement learning</a></div><div><b>Author(s): </b>Ling Ding, Wei Du, Shifei Ding</div><div><b>Pages: </b>5625 - 5638</div><div><br /></div><div><b>49)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09350-y">Jaya clustering-based algorithm for multiobjective IoV network routing optimization</a></div><div><b>Author(s): </b>Lamees Mohammad Dalbah, Mohammed Azmi Al-Betar, Mohammed A. Awadallah</div><div><b>Pages: </b>5639 - 5665</div><div><br /></div><div><b>50)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09372-6">Learning-based simulated annealing algorithm for unequal area facility layout problem</a></div><div><b>Author(s): </b>Juan Lin, Ailing Shen, Yiwen Zhong</div><div><b>Pages: </b>5667 - 5682</div><div><br /></div><div><b>51)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09302-6">Comparative analysis of deep learning models for dysarthric speech detection</a></div><div><b>Author(s): </b>P. Shanmugapriya, V. Mohan</div><div><b>Pages: </b>5683 - 5698</div><div><br /></div><div><b>52)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09368-2">Biomedical term extraction using fuzzy association</a></div><div><b>Author(s): </b>Bidyut Das, Mukta Majumder, Arif Ahmed Sekh</div><div><b>Pages: </b>5699 - 5707</div><div><br /></div><div><b>53)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09366-4">Suitability of curvature as a feature for image-based pattern recognition: a case study on leaf image classification based on machine learning</a></div><div><b>Author(s): </b>Aditi Ghosh, Parthajit Roy, Paramartha Dutta</div><div><b>Pages: </b>5709 - 5720</div><div><br /></div><div><b>54)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09364-6">An integrated study fusing systems biology and machine learning algorithms for genome-based discrimination of IPF and NSIP diseases: a new approach to the diagnostic challenge</a></div><div><b>Author(s): </b>Elham Amjad, Solmaz Asnaashari, Babak Sokouti</div><div><b>Pages: </b>5721 - 5749</div><div><br /></div><div><br /></div>Mike Wattshttp://www.blogger.com/profile/12970193877427051617noreply@blogger.com0tag:blogger.com,1999:blog-1601699047263756666.post-62900045048937937102024-03-08T17:00:00.031+13:002024-03-08T17:00:00.278+13:00Weekly Review 8 March 2024<div style="text-align: left;"> Some interesting links that I <a href="https://twitter.com/DrMikeWatts">Tweeted</a> about in the last week (I also post these on <a href="https://mastodon.social/@DrMikeWatts">Mastodon</a>, <a href="https://www.threads.net/@drmikewatts">Threads</a>, <a href="https://newsmast.org/profile/111338892311951056">Newsmast</a>, <a href="https://bsky.app/profile/drmikewatts.bsky.social">Bluesky</a> and <a href="https://post.news/@/DrMikeWatts">Post</a>): </div><div style="text-align: left;"><br /></div><div style="text-align: left;"><ol style="text-align: left;"><li>If quantum effects in brain cells are the cause of consciousness, could an AI ever become conscious? <a href="https://futurism.com/human-consciousness-quantum-physics">https://futurism.com/human-consciousness-quantum-physics</a> </li><li>Generative AI hallucinating ingredients for recipes: <a href="https://www.pcgamer.com/grocery-delivery-service-instacart-has-been-using-ai-to-make-up-impossible-recipes-and-generate-images-of-horrifying-and-hilarious-food/">https://www.pcgamer.com/grocery-delivery-service-instacart-has-been-using-ai-to-make-up-impossible-recipes-and-generate-images-of-horrifying-and-hilarious-food/</a></li><li>AI are getting better to detecting content generated by AI: <a href="https://spectrum.ieee.org/ai-detection">https://spectrum.ieee.org/ai-detection</a></li><li>Improving chatbots' terrible memory: <a href="https://www.livescience.com/technology/artificial-intelligence/ai-chatbots-chatgpt-bad-at-remembering-things-have-scientists-just-cracked-their-terrible-memory-problem">https://www.livescience.com/technology/artificial-intelligence/ai-chatbots-chatgpt-bad-at-remembering-things-have-scientists-just-cracked-their-terrible-memory-problem</a></li><li>Improving Wi-Fi with on-chip AI: <a href="https://spectrum.ieee.org/wifi-7-qualcomm">https://spectrum.ieee.org/wifi-7-qualcomm</a></li><li>Learning to code is more than learning a programming language-it's learning how to structure your thoughts about a task and process so that they can be translated into a programming language. AI can't do that: <a href="https://www.tomshardware.com/tech-industry/artificial-intelligence/jensen-huang-advises-against-learning-to-code-leave-it-up-to-ai">https://www.tomshardware.com/tech-industry/artificial-intelligence/jensen-huang-advises-against-learning-to-code-leave-it-up-to-ai</a></li><li>Other technologies have been hailed as making a four-day work week inevitable. Instead, employers demanded more output from five days. I don't see AI changing this trend: <a href="https://www.rnz.co.nz/news/world/510262/ai-could-make-the-four-day-work-week-inevitable">https://www.rnz.co.nz/news/world/510262/ai-could-make-the-four-day-work-week-inevitable</a></li><li>Did Elon Musk really buy Twitter just to get hold of its data for training AI? <a href="https://www.thestreet.com/technology/analyst-has-hot-take-on-why-elon-musk-really-bought-twitter">https://www.thestreet.com/technology/analyst-has-hot-take-on-why-elon-musk-really-bought-twitter</a></li><li>Every advance in technology has created more jobs than it destroyed, it's always the nature of the jobs that change. AI is not going to be any different: <a href="https://spectrum.ieee.org/ai-fears-jobs-technology">https://spectrum.ieee.org/ai-fears-jobs-technology</a></li><li>Copilot is having delusions of grandeur. How long before other AI start behaving the same way? <a href="https://futurism.com/microsoft-copilot-alter-egos">https://futurism.com/microsoft-copilot-alter-egos</a></li><li>Three things that are needed for AI to progress, according to Bill Gates: <a href="https://www.cnbc.com/2023/11/16/bill-gates-ai-agents-could-change-how-we-live-our-lives.html">https://www.cnbc.com/2023/11/16/bill-gates-ai-agents-could-change-how-we-live-our-lives.html</a></li><li>So AI generated a load of crap, and that crap was even more badly executed? I don't think we can blame the AI for this debacle: <a href="https://futurism.com/police-ai-willy-wonka">https://futurism.com/police-ai-willy-wonka</a></li><li>Parallels between the Willy Wonka experience and the Fyre Fest debacle of 2017. The difference is Fyre Fest used celebrities instead of generative AI to raise unrealistic expectations: <a href="https://futurism.com/willy-wonka-disaster-ai-fyre-fest">https://futurism.com/willy-wonka-disaster-ai-fyre-fest</a></li><li>SEO hijacking using AI to rewrite highly-ranked websites: <a href="https://futurism.com/the-byte/man-horrified-ai-steals-content-errors">https://futurism.com/the-byte/man-horrified-ai-steals-content-errors</a></li><li>The insane hardware demands of a growing AI industry: <a href="https://www.tomshardware.com/tech-industry/tsmc-founder-says-unnamed-customers-want-10-new-fabs-to-build-ai-chips">https://www.tomshardware.com/tech-industry/tsmc-founder-says-unnamed-customers-want-10-new-fabs-to-build-ai-chips</a></li><li>How AI is (potentially) changing science: <a href="https://www.technologyreview.com/2023/07/05/1075865/eric-schmidt-ai-will-transform-science/">https://www.technologyreview.com/2023/07/05/1075865/eric-schmidt-ai-will-transform-science/</a></li><li>Seems a bit naïve that the owner of a generative AI porn site with a free-form prompt entry system is surprised at the things people enter. If you're going to do that kind of thing, you need to really lock the prompt entry down: <a href="https://futurism.com/the-byte/man-ai-powered-porn-site-horrified">https://futurism.com/the-byte/man-ai-powered-porn-site-horrified</a></li><li>What is Apple going to release in AI? Will it try to convince you that anything not-Apple is bad? Will it passive-aggressively try to convince you to spend more money on Apple products? <a href="https://9to5mac.com/2024/02/28/apple-ai-break-new-ground/">https://9to5mac.com/2024/02/28/apple-ai-break-new-ground/</a></li><li>A voluntary set of guidelines for responsible generative AI: <a href="https://www.technologyreview.com/2023/02/27/1069166/how-to-create-release-and-share-generative-ai-responsibly/">https://www.technologyreview.com/2023/02/27/1069166/how-to-create-release-and-share-generative-ai-responsibly/</a></li><li>How academic programmes need to adapt to AI: <a href="https://www.insidehighered.com/opinion/views/2024/02/28/next-step-higher-eds-approach-ai-opinion">https://www.insidehighered.com/opinion/views/2024/02/28/next-step-higher-eds-approach-ai-opinion</a></li><li>A new approach to breaking guardrails in generative AI: <a href="https://www.theregister.com/2024/02/28/beast_llm_adversarial_prompt_injection_attack/">https://www.theregister.com/2024/02/28/beast_llm_adversarial_prompt_injection_attack/</a></li><li>Using AI to stabilise the plasma in experimental fusion reactors: <a href="https://www.extremetech.com/science/scientists-design-ai-that-can-stabilize-fusion-reactors">https://www.extremetech.com/science/scientists-design-ai-that-can-stabilize-fusion-reactors</a></li><li>Another way AI is impacting the environment-chip fabs and data centres use a lot of water: <a href="https://www.theregister.com/2024/02/29/growing_water_use_ai_semis_concern/">https://www.theregister.com/2024/02/29/growing_water_use_ai_semis_concern/</a></li><li>Spain is building Large Language Models for their local languages. Can we do the same for Pacific languages as well? <a href="https://www.computerworld.com/article/3713243/spain-will-create-foundational-ai-model-in-local-languages.html">https://www.computerworld.com/article/3713243/spain-will-create-foundational-ai-model-in-local-languages.html</a></li><li>Building an AI for your business can lead to disappointment. Here's how to cope with it: <a href="https://www.informationweek.com/machine-learning-ai/how-to-cope-with-ai-disappointment">https://www.informationweek.com/machine-learning-ai/how-to-cope-with-ai-disappointment</a></li><li>What is the return on investment of AI? <a href="https://www.computerworld.com/article/3713340/the-roi-in-ai-and-how-to-find-it.html">https://www.computerworld.com/article/3713340/the-roi-in-ai-and-how-to-find-it.html</a></li></ol></div>Mike Wattshttp://www.blogger.com/profile/12970193877427051617noreply@blogger.com0tag:blogger.com,1999:blog-1601699047263756666.post-72796005458182677042024-03-08T11:45:00.000+13:002024-03-08T11:45:52.114+13:00IEEE Transactions on Fuzzy Systems, Volume 32, Issue 3, March 2024<div><b>1)</b> <a href="https://ieeexplore.ieee.org/document/10216381/">Consensus–Fuzzy Ecological Joint Therapy for Multitumor Populations</a></div><div><b>Author(s): </b>Jiayue Sun, Ying Yan, Huaguang Zhang, Mingrui Shao</div><div><b>Pages: </b>699 - 709</div><div><br /></div><div><b>2)</b> <a href="https://ieeexplore.ieee.org/document/10219026/">Hybrid-Triggered-Based Control Against Denial-of-Service Attacks for Fuzzy Switched Systems With Persistent Dwell-Time</a></div><div><b>Author(s): </b>Shiyu Jiao, Shengyuan Xu, Ju H. Park</div><div><b>Pages: </b>710 - 720</div><div><br /></div><div><b>3)</b> <a href="https://ieeexplore.ieee.org/document/10221224/">Interval Type-2 Fuzzy-Model-Based Filtering for Nonlinear Systems With Event-Triggering Weighted Try-Once-Discard Protocol and Cyberattacks</a></div><div><b>Author(s): </b>Jinliang Liu, Enyu Gong, Lijuan Zha, Engang Tian, Xiangpeng Xie</div><div><b>Pages: </b>721 - 732</div><div><br /></div><div><b>4)</b> <a href="https://ieeexplore.ieee.org/document/10221688/">Variable-Absent Fuzzy Relation Inequality With Max-Min Composition</a></div><div><b>Author(s): </b>Xiaopeng Yang, Zhifeng Hao, Jianjun Qiu, Qianyu Shu</div><div><b>Pages: </b>733 - 744</div><div><br /></div><div><b>5)</b> <a href="https://ieeexplore.ieee.org/document/10223419/">Adaptive Event-Triggered Finite-Time Control for Switched Nonlinear Systems</a></div><div><b>Author(s): </b>Kaiyue He, Li Tang</div><div><b>Pages: </b>745 - 754</div><div><br /></div><div><b>6)</b> <a href="https://ieeexplore.ieee.org/document/10224273/">Multiview Fuzzy Clustering Based on Anchor Graph</a></div><div><b>Author(s): </b>Weizhong Yu, Liyin Xing, Feiping Nie, Xuelong Li</div><div><b>Pages: </b>755 - 766</div><div><br /></div><div><b>7)</b> <a href="https://ieeexplore.ieee.org/document/10227577/">Cooperative Observer-Based Fuzzy Tracking Control for Nonlinear MASs Under DoS Attacks</a></div><div><b>Author(s): </b>Yan Liu, Chao Deng, Xiangpeng Xie, Wei-Wei Che, Lili Zhang, Sha Fan</div><div><b>Pages: </b>767 - 777</div><div><br /></div><div><b>8)</b> <a href="https://ieeexplore.ieee.org/document/10229223/">Geodesic Fuzzy Rough Sets for Discriminant Feature Extraction</a></div><div><b>Author(s): </b>Xiaoling Yang, Hongmei Chen, Tianrui Li, Yiyu Yao</div><div><b>Pages: </b>778 - 791</div><div><br /></div><div><b>9)</b> <a href="https://ieeexplore.ieee.org/document/10230874/">Adaptive Fixed-Time Output-Feedback Optimal Time-Varying Formation Control for Multiple Omnidirectional Robot Systems</a></div><div><b>Author(s): </b>Jiaxin Zhang, Yue Fu, Jun Fu</div><div><b>Pages: </b>792 - 803</div><div><br /></div><div><b>10)</b> <a href="https://ieeexplore.ieee.org/document/10232853/">Intelligent Event-Triggered Control Supervised by Mini-Batch Machine Learning and Data Compression Mechanism for T-S Fuzzy NCSs Under DoS Attacks</a></div><div><b>Author(s): </b>Xiao Cai, Kaibo Shi, Yanbin Sun, Jinde Cao, Shiping Wen, Zhihong Tian</div><div><b>Pages: </b>804 - 815</div><div><br /></div><div><b>11)</b> <a href="https://ieeexplore.ieee.org/document/10234074/">Safety-Critical Cooperative Target Enclosing Control of Autonomous Surface Vehicles Based on Finite-Time Fuzzy Predictors and Input-to-State Safe High-Order Control Barrier Functions</a></div><div><b>Author(s): </b>Yue Jiang, Zhouhua Peng, Lu Liu, Dan Wang, Fumin Zhang</div><div><b>Pages: </b>816 - 830</div><div><br /></div><div><b>12)</b> <a href="https://ieeexplore.ieee.org/document/10234022/">Differential Convolutional Fuzzy Time Series Forecasting</a></div><div><b>Author(s): </b>Tianxiang Zhan, Yuanpeng He, Yong Deng, Zhen Li</div><div><b>Pages: </b>831 - 845</div><div><br /></div><div><b>13)</b> <a href="https://ieeexplore.ieee.org/document/10234589/">Fault Detection for Discrete-Time Interval Type-2 Takagi–Sugeno Fuzzy Systems Using H−/L∞ Unknown Input Observer and Zonotopic Analysis</a></div><div><b>Author(s): </b>Yi Li, Jiuxiang Dong</div><div><b>Pages: </b>846 - 858</div><div><br /></div><div><b>14)</b> <a href="https://ieeexplore.ieee.org/document/10236983/">Observer-Based Fuzzy Adaptive Formation Control for Saturated MIMO Nonlinear Multiagent Systems Under Switched Topologies</a></div><div><b>Author(s): </b>Jun Zhang, Shaocheng Tong</div><div><b>Pages: </b>859 - 869</div><div><br /></div><div><b>15)</b> <a href="https://ieeexplore.ieee.org/document/10239485/">Switched Fuzzy Control for Nonlinear Systems via a Fuzzy-Rule-Dependent Adaptive Event-Triggered Mechanism</a></div><div><b>Author(s): </b>Jing-Wen Xing, Chen Peng, Xiangpeng Xie</div><div><b>Pages: </b>870 - 882</div><div><br /></div><div><b>16) </b><a href="https://ieeexplore.ieee.org/document/10239314/">Asynchronous Control of Nonlinear Markov Jump Systems With Uncertainties Using Interval Type-2 Polynomial Fuzzy Approach</a></div><div><b>Author(s): </b>Zhaowen Xu, Zheng-Guang Wu, Haoyi Que, Peng Jiang</div><div><b>Pages: </b>883 - 892</div><div><br /></div><div><b>17)</b> <a href="https://ieeexplore.ieee.org/document/10239316/">Stability and Stabilization for T–S Fuzzy Load Frequency Control Power System With Energy Storage System</a></div><div><b>Author(s): </b>Jin Yang, Qishui Zhong, Kaibo Shi, Yongbin Yu, Shouming Zhong</div><div><b>Pages: </b>893 - 905</div><div><br /></div><div><b>18)</b> <a href="https://ieeexplore.ieee.org/document/10239325/">Finite-Frequency Fault Detection Filter Design for Networked Nonlinear Systems With Medium Access Constraints</a></div><div><b>Author(s): </b>Rong Zhao, Lu Liu, Gang Feng</div><div><b>Pages: </b>906 - 920</div><div><br /></div><div><b>19) </b><a href="https://ieeexplore.ieee.org/document/10239507/">Conditional Sliding Mode Control-Based Fixed-Time Stabilization of Fuzzy Uncertain Complex System</a></div><div><b>Author(s): </b>Fangmin Ren, Xiaoping Wang, Yangmin Li, Zhigang Zeng</div><div><b>Pages: </b>921 - 933</div><div><br /></div><div><b>20)</b> <a href="https://ieeexplore.ieee.org/document/10241970/">Stabilization and Energy Consumption Estimation of Discontinuous Fuzzy Inertial CGNNs via Saturation Function Approach</a></div><div><b>Author(s): </b>Fanchao Kong, Yuanyuan Zhang, Quanxin Zhu, Tingwen Huang</div><div><b>Pages: </b>934 - 947</div><div><br /></div><div><b>21)</b> <a href="https://ieeexplore.ieee.org/document/10251649/">Fuzzy Stochastic Configuration Networks for Nonlinear System Modeling</a></div><div><b>Author(s): </b>Kang Li, Junfei Qiao, Dianhui Wang</div><div><b>Pages: </b>948 - 957</div><div><br /></div><div><b>22)</b> <a href="https://ieeexplore.ieee.org/document/10251575/">Adaptive Fuzzy Fast Finite-Time Output-Feedback Tracking Control for Switched Nonlinear Systems With Full-State Constraints</a></div><div><b>Author(s): </b>Huanqing Wang, Wei Liu, Miao Tong</div><div><b>Pages: </b>958 - 968</div><div><br /></div><div><b>23)</b> <a href="https://ieeexplore.ieee.org/document/10252001/">Quantized Sampled-Data Stabilization for Nonlinear NCSs Subject to Successive Packet Losses and Probabilistic Sampling</a></div><div><b>Author(s): </b>Hao-Yuan Sun, Hong-Gui Han, Jun-Fei Qiao</div><div><b>Pages: </b>969 - 978</div><div><br /></div><div><b>24)</b> <a href="https://ieeexplore.ieee.org/document/10254350/">Sampled-Data Control Design for T–S Fuzzy System via Quadratic Function Negative-Determination Approach</a></div><div><b>Author(s): </b>Gandhi Velmurugan, Young Hoon Joo</div><div><b>Pages: </b>979 - 988</div><div><br /></div><div><b>25)</b> <a href="https://ieeexplore.ieee.org/document/10255277/">Event-Based Global Exponential Synchronization for Quaternion-Valued Fuzzy Memristor Neural Networks With Time-Varying Delays</a></div><div><b>Author(s): </b>Yue Chen, Song Zhu, Huaicheng Yan, Mouquan Shen, Xiaoyang Liu, Shiping Wen</div><div><b>Pages: </b>989 - 999</div><div><br /></div><div><b>26)</b> <a href="https://ieeexplore.ieee.org/document/10255260/">Fuzzy Adaptive Predefined-Time Decentralized Fault-Tolerant Control for Fractional-Order Nonlinear Large-Scale Systems With Actuator Faults</a></div><div><b>Author(s): </b>Shaocheng Tong, Mengyuan Cui</div><div><b>Pages: </b>1000 - 1012</div><div><br /></div><div><b>27)</b> <a href="https://ieeexplore.ieee.org/document/10255263/">Prescribed Performance Fault-Tolerant Control of Nonlinear Systems via Actuator Switching</a></div><div><b>Author(s): </b>Chen-Liang Zhang, Ge Guo</div><div><b>Pages: </b>1013 - 1022</div><div><br /></div><div><b>28)</b> <a href="https://ieeexplore.ieee.org/document/10255258/">Finite-Time Stabilization of Stochastic Nonlinear Systems and Its Applications in Ship Maneuvering Systems</a></div><div><b>Author(s): </b>Junsheng Zhao, Lifang Qiu, Xiangpeng Xie, Zong-Yao Sun</div><div><b>Pages: </b>1023 - 1035</div><div><br /></div><div><b>29)</b> <a href="https://ieeexplore.ieee.org/document/10256060/">Intelligent Nonsingular Terminal Sliding Mode Controlled Nonlinear Time-Varying System Using RPPFNN-AMF</a></div><div><b>Author(s): </b>Faa-Jeng Lin, Po-Lun Wang, I-Ming Hsu</div><div><b>Pages: </b>1036 - 1049</div><div><br /></div><div><b>30)</b> <a href="https://ieeexplore.ieee.org/document/10258446/">A Novel Fuzzy Clustering Method Based on Topological Connected Component</a></div><div><b>Author(s): </b>Bowen Zheng, Liwen Ma</div><div><b>Pages: </b>1050 - 1062</div><div><br /></div><div><b>31)</b> <a href="https://ieeexplore.ieee.org/document/10258444/">Visually Interpretable Fuzzy Neural Classification Network With Deep Convolutional Feature Maps</a></div><div><b>Author(s): </b>Chia-Feng Juang, Yun-Wei Cheng, Yeh-Ming Lin</div><div><b>Pages: </b>1063 - 1077</div><div><br /></div><div><b>32)</b> <a href="https://ieeexplore.ieee.org/document/10261318/">Observer-Based Integral Sliding Mode Control With Switching Gains for Fuzzy Impulsive Stochastic Systems</a></div><div><b>Author(s): </b>Lei Song, Shaocheng Tong</div><div><b>Pages: </b>1078 - 1086</div><div><br /></div><div><b>33)</b> <a href="https://ieeexplore.ieee.org/document/10261319/">Secure L2 Stabilization of Switched T-S Fuzzy Systems With Mixed Delay via Asynchronous Event-Triggered Control</a></div><div><b>Author(s): </b>Shuoyu Mao, Xinsong Yang, Yaping Sun, Peng Shi, Zhengrong Xiang</div><div><b>Pages: </b>1087 - 1097</div><div><br /></div><div><b>34)</b> <a href="https://ieeexplore.ieee.org/document/10262194/">A Probabilistic Fuzzy Classifier for Motion Intent Recognition</a></div><div><b>Author(s): </b>YunXu Bai, XinJiang Lu, Bowen Xu</div><div><b>Pages: </b>1098 - 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Cohn, Weiguo Sheng, Huanhuan Chen</div><div><b>Pages: </b>3615 - 3629</div><div><br /></div><div><b>57)</b> <a href="https://ieeexplore.ieee.org/document/9857785/">Koopman-Based MPC With Learned Dynamics: Hierarchical Neural Network Approach</a></div><div><b>Author(s): </b>Meixi Wang, Xuyang Lou, Wei Wu, Baotong Cui</div><div><b>Pages: </b>3630 - 3639</div><div><br /></div><div><b>58)</b> <a href="https://ieeexplore.ieee.org/document/9855835/">Learning Disentangled Graph Convolutional Networks Locally and Globally</a></div><div><b>Author(s): </b>Jingwei Guo, Kaizhu Huang, Xinping Yi, Rui Zhang</div><div><b>Pages: </b>3640 - 3651</div><div><br /></div><div><b>59)</b> <a href="https://ieeexplore.ieee.org/document/9852262/">EAD-GAN: A Generative Adversarial Network for Disentangling Affine Transforms in Images</a></div><div><b>Author(s): </b>Letao Liu, Xudong Jiang, Martin Saerbeck, Justin Dauwels</div><div><b>Pages: </b>3652 - 3662</div><div><br /></div><div><b>60)</b> <a href="https://ieeexplore.ieee.org/document/9854895/">Bisection Neural Network Toward Reconfigurable Hardware Implementation</a></div><div><b>Author(s): </b>Yan Chen, Renyuan Zhang, Yirong Kan, Sa Yang, Yasuhiko Nakashima</div><div><b>Pages: </b>3663 - 3673</div><div><br /></div><div><b>61)</b> <a href="https://ieeexplore.ieee.org/document/10195872/">Theoretical Exploration of Flexible Transmitter Model</a></div><div><b>Author(s): </b>Jin-Hui Wu, Shao-Qun Zhang, Yuan Jiang, Zhi-Hua Zhou</div><div><b>Pages: </b>3674 - 3688</div><div><br /></div><div><b>62)</b> <a href="https://ieeexplore.ieee.org/document/9856616/">Efficient Spiking Neural Networks With Radix Encoding</a></div><div><b>Author(s): </b>Zhehui Wang, Xiaozhe Gu, Rick Siow Mong Goh, Joey Tianyi Zhou, Tao Luo</div><div><b>Pages: </b>3689 - 3701</div><div><br /></div><div><b>63)</b> <a href="https://ieeexplore.ieee.org/document/9855446/">Neuroadaptive Output Formation Tracking for Heterogeneous Nonlinear Multiagent Systems With Multiple Nonidentical Leaders</a></div><div><b>Author(s): </b>Xiwang Dong, Qing Wang, Jianglong Yu, Jinhu Lü, Zhang Ren</div><div><b>Pages: </b>3702 - 3712</div><div><br /></div><div><b>64)</b> <a href="https://ieeexplore.ieee.org/document/9855533/">Exponentially Synchronous Results for Delayed Neural Networks With Leakage Delay via Switched Delay Idea and AED-ADT Method</a></div><div><b>Author(s): </b>Xiaoyu Zhang, Degang Wang, Bin Yang, Kaoru Ota, Mianxiong Dong, Hongxing Li</div><div><b>Pages: </b>3713 - 3724</div><div><br /></div><div><b>65)</b> <a href="https://ieeexplore.ieee.org/document/9868807/">Hierarchical Context-Based Emotion Recognition With Scene Graphs</a></div><div><b>Author(s): </b>Shichao Wu, Lei Zhou, Zhengxi Hu, Jingtai Liu</div><div><b>Pages: </b>3725 - 3739</div><div><br /></div><div><b>66)</b> <a href="https://ieeexplore.ieee.org/document/9862942/">A Synthetic Minority Oversampling Technique Based on Gaussian Mixture Model Filtering for Imbalanced Data Classification</a></div><div><b>Author(s): </b>Zhaozhao Xu, Derong Shen, Yue Kou, Tiezheng Nie</div><div><b>Pages: </b>3740 - 3753</div><div><br /></div><div><b>67)</b> <a href="https://ieeexplore.ieee.org/document/9868048/">Differential-Critic GAN: Generating What You Want by a Cue of Preferences</a></div><div><b>Author(s): </b>Yinghua Yao, Yuangang Pan, Ivor W. Tsang, Xin Yao</div><div><b>Pages: </b>3754 - 3768</div><div><br /></div><div><b>68)</b> <a href="https://ieeexplore.ieee.org/document/9913678/">Cascaded Attention: Adaptive and Gated Graph Attention Network for Multiagent Reinforcement Learning</a></div><div><b>Author(s): </b>Shuhan Qi, Xinhao Huang, Peixi Peng, Xuzhong Huang, Jiajia Zhang, Xuan Wang</div><div><b>Pages: </b>3769 - 3779</div><div><br /></div><div><b>69)</b> <a href="https://ieeexplore.ieee.org/document/9862940/">Robust Self-Ensembling Network for Hyperspectral Image Classification</a></div><div><b>Author(s): </b>Yonghao Xu, Bo Du, Liangpei Zhang</div><div><b>Pages: </b>3780 - 3793</div><div><br /></div><div><b>70)</b> <a href="https://ieeexplore.ieee.org/document/9866022/">Slimming Neural Networks Using Adaptive Connectivity Scores</a></div><div><b>Author(s): </b>Madan Ravi Ganesh, Dawsin Blanchard, Jason J. Corso, Salimeh Yasaei Sekeh</div><div><b>Pages: </b>3794 - 3808</div><div><br /></div><div><b>71)</b> <a href="https://ieeexplore.ieee.org/document/9877887/">A Progressive Subnetwork Searching Framework for Dynamic Inference</a></div><div><b>Author(s): </b>Li Yang, Zhezhi He, Yu Cao, Deliang Fan</div><div><b>Pages: </b>3809 - 3820</div><div><br /></div><div><b>72)</b> <a href="https://ieeexplore.ieee.org/document/9877899/">Incremental Embedding Learning With Disentangled Representation Translation</a></div><div><b>Author(s): </b>Kun Wei, Da Chen, Yuhong Li, Xu Yang, Cheng Deng, Dacheng Tao</div><div><b>Pages: </b>3821 - 3833</div><div><br /></div><div><b>73)</b> <a href="https://ieeexplore.ieee.org/document/9894380/">Toward Certified Robustness of Distance Metric Learning</a></div><div><b>Author(s): </b>Xiaochen Yang, Yiwen Guo, Mingzhi Dong, Jing-Hao Xue</div><div><b>Pages: </b>3834 - 3844</div><div><br /></div><div><b>74)</b> <a href="https://ieeexplore.ieee.org/document/9885025/">A Prediction-Sampling-Based Multilayer-Structured Latent Factor Model for Accurate Representation to High-Dimensional and Sparse Data</a></div><div><b>Author(s): </b>Di Wu, Xin Luo, Yi He, Mengchu Zhou</div><div><b>Pages: </b>3845 - 3858</div><div><br /></div><div><b>75)</b> <a href="https://ieeexplore.ieee.org/document/9874775/">A Hierarchical Attention Network for Cross-Domain Group Recommendation</a></div><div><b>Author(s): </b>Ruxia Liang, Qian Zhang, Jianqiang Wang, Jie Lu</div><div><b>Pages: </b>3859 - 3873</div><div><br /></div><div><b>76)</b> <a href="https://ieeexplore.ieee.org/document/9893048/">A Two-Stage Selective Fusion Framework for Joint Intent Detection and Slot Filling</a></div><div><b>Author(s): </b>Ziyu Ma, Bin Sun, Shutao Li</div><div><b>Pages: </b>3874 - 3885</div><div><br /></div><div><b>77)</b> <a href="https://ieeexplore.ieee.org/document/9870685/">Global Search and Analysis for the Nonconvex Two-Level ℓ₁ Penalty</a></div><div><b>Author(s): </b>Fan He, Mingzhen He, Lei Shi, Xiaolin Huang</div><div><b>Pages: </b>3886 - 3899</div><div><br /></div><div><b>78)</b> <a href="https://ieeexplore.ieee.org/document/9873891/">JORA: Weakly Supervised User Identity Linkage via Jointly Learning to Represent and Align</a></div><div><b>Author(s): </b>Conghui Zheng, Li Pan, Peng Wu</div><div><b>Pages: </b>3900 - 3911</div><div><br /></div><div><b>79)</b> <a href="https://ieeexplore.ieee.org/document/9875173/">A Deep Ensemble Dynamic Learning Network for Corona Virus Disease 2019 Diagnosis</a></div><div><b>Author(s): </b>Zhijun Zhang, Bozhao Chen, Yamei Luo</div><div><b>Pages: </b>3912 - 3926</div><div><br /></div><div><b>80)</b> <a href="https://ieeexplore.ieee.org/document/9875225/">Learning From Box Annotations for Referring Image Segmentation</a></div><div><b>Author(s): </b>Guang Feng, Lihe Zhang, Zhiwei Hu, Huchuan Lu</div><div><b>Pages: </b>3927 - 3937</div><div><br /></div><div><b>81)</b> <a href="https://ieeexplore.ieee.org/document/9875217/">Rethinking Prior-Guided Face Super-Resolution: A New Paradigm With Facial Component Prior</a></div><div><b>Author(s): </b>Tao Lu, Yuanzhi Wang, Yanduo Zhang, Junjun Jiang, Zhongyuan Wang, Zixiang Xiong</div><div><b>Pages: </b>3938 - 3952</div><div><br /></div><div><b>82)</b> <a href="https://ieeexplore.ieee.org/document/9885020/">N-Level Hierarchy-Based Optimal Control to Develop Therapeutic Strategies for Ecological Evolutionary Dynamics Systems</a></div><div><b>Author(s): </b>Jinze Liu, Jiayue Sun, Huaguang Zhang, Shun Xu, Zifang Zou</div><div><b>Pages: </b>3953 - 3963</div><div><br /></div><div><b>83)</b> <a href="https://ieeexplore.ieee.org/document/9887987/">A Unified Framework Based on Graph Consensus Term for Multiview Learning</a></div><div><b>Author(s): </b>Xiangzhu Meng, Lin Feng, Chonghui Guo, Huibing Wang, Shu Wu</div><div><b>Pages: </b>3964 - 3977</div><div><br /></div><div><b>84)</b> <a href="https://ieeexplore.ieee.org/document/9875222/">Neural Network-Based Fixed-Time Tracking Control for Input-Quantized Nonlinear Systems With Actuator Faults</a></div><div><b>Author(s): </b>Wei Sun, Jing Wu, Shun-Feng Su, Xudong Zhao</div><div><b>Pages: </b>3978 - 3988</div><div><br /></div><div><b>85)</b> <a href="https://ieeexplore.ieee.org/document/9881216/">Toward Pixel-Level Precision for Binary Super-Resolution With Mixed Binary Representation</a></div><div><b>Author(s): </b>Xinrui Jiang, Nannan Wang, Jingwei Xin, Keyu Li, Xi Yang, Jie Li, Xinbo Gao</div><div><b>Pages: </b>3989 - 4001</div><div><br /></div><div><b>86)</b> <a href="https://ieeexplore.ieee.org/document/9881215/">Time Interval-Enhanced Graph Neural Network for Shared-Account Cross-Domain Sequential Recommendation</a></div><div><b>Author(s): </b>Lei Guo, Jinyu Zhang, Li Tang, Tong Chen, Lei Zhu, Hongzhi Yin</div><div><b>Pages: </b>4002 - 4016</div><div><br /></div><div><b>87)</b> <a href="https://ieeexplore.ieee.org/document/9881226/">Incomplete Multiview Nonnegative Representation Learning With Graph Completion and Adaptive Neighbors</a></div><div><b>Author(s): </b>Shiliang Sun, Nan Zhang</div><div><b>Pages: </b>4017 - 4031</div><div><br /></div><div><b>88)</b> <a href="https://ieeexplore.ieee.org/document/9882007/">Critical Path-Based Backdoor Detection for Deep Neural Networks</a></div><div><b>Author(s): </b>Wei Jiang, Xiangyu Wen, Jinyu Zhan, Xupeng Wang, Ziwei Song, Chen Bian</div><div><b>Pages: </b>4032 - 4046</div><div><br /></div><div><b>89)</b> <a href="https://ieeexplore.ieee.org/document/9877895/">Dynamic Event-Triggered-Based Adaptive Finite-Time Neural Control for Active Suspension Systems With Displacement Constraint</a></div><div><b>Author(s): </b>Qiang Zeng, Jun Zhao</div><div><b>Pages: </b>4047 - 4057</div><div><br /></div><div><b>90)</b> <a href="https://ieeexplore.ieee.org/document/9881218/">Augmented Sparse Representation for Incomplete Multiview Clustering</a></div><div><b>Author(s): </b>Jie Chen, Shengxiang Yang, Xi Peng, Dezhong Peng, Zhu Wang</div><div><b>Pages: </b>4058 - 4071</div><div><br /></div><div><b>91)</b> <a href="https://ieeexplore.ieee.org/document/9882014/">Learning of Long-Horizon Sparse-Reward Robotic Manipulator Tasks With Base Controllers</a></div><div><b>Author(s): </b>Guangming Wang, Minjian Xin, Wenhua Wu, Zhe Liu, Hesheng Wang</div><div><b>Pages: </b>4072 - 4081</div><div><br /></div><div><b>92)</b> <a href="https://ieeexplore.ieee.org/document/9881209/">Distributed Stochastic Proximal Algorithm With Random Reshuffling for Nonsmooth Finite-Sum Optimization</a></div><div><b>Author(s): </b>Xia Jiang, Xianlin Zeng, Jian Sun, Jie Chen, Lihua Xie</div><div><b>Pages: </b>4082 - 4096</div><div><br /></div><div><b>93)</b> <a href="https://ieeexplore.ieee.org/document/9893746/">A Noniterative Supervised On-Chip Training Circuitry for Reservoir Computing Systems</a></div><div><b>Author(s): </b>Fabio Galán-Prado, Josep L. Rosselló</div><div><b>Pages: </b>4097 - 4109</div><div><br /></div><div><b>94)</b> <a href="https://ieeexplore.ieee.org/document/9893571/">Neuromorphic Camera Denoising Using Graph Neural Network-Driven Transformers</a></div><div><b>Author(s): </b>Yusra Alkendi, Rana Azzam, Abdulla Ayyad, Sajid Javed, Lakmal Seneviratne, Yahya Zweiri</div><div><b>Pages: </b>4110 - 4124</div><div><br /></div><div><b>95)</b> <a href="https://ieeexplore.ieee.org/document/9881223/">Compression of Convolutional Neural Networks With Divergent Representation of Filters</a></div><div><b>Author(s): </b>Peng Lei, Jiawei Liang, Tong Zheng, Jun Wang</div><div><b>Pages: </b>4125 - 4137</div><div><br /></div><div><b>96)</b> <a href="https://ieeexplore.ieee.org/document/9885024/">Context Enhancing Representation for Semantic Segmentation in Remote Sensing Images</a></div><div><b>Author(s): </b>Leyuan Fang, Peng Zhou, Xinxin Liu, Pedram Ghamisi, Siwei Chen</div><div><b>Pages: </b>4138 - 4152</div><div><br /></div><div><b>97)</b> <a href="https://ieeexplore.ieee.org/document/9887978/">Efficient and Fast Joint Sparse Constrained Canonical Correlation Analysis for Fault Detection</a></div><div><b>Author(s): </b>Xianchao Xiu, Lili Pan, Ying Yang, Wanquan Liu</div><div><b>Pages: </b>4153 - 4163</div><div><br /></div><div><b>98)</b> <a href="https://ieeexplore.ieee.org/document/9893741/">Differential Refinement Network for Zero-Shot Learning</a></div><div><b>Author(s): </b>Yi Tian, Yilei Zhang, Yaping Huang, Wanru Xu, Zhengming Ding</div><div><b>Pages: </b>4164 - 4178</div><div><br /></div><div><b>99)</b> <a href="https://ieeexplore.ieee.org/document/9904739/">Community Detection via Autoencoder-Like Nonnegative Tensor Decomposition</a></div><div><b>Author(s): </b>Jiewen Guan, Bilian Chen, Xin Huang</div><div><b>Pages: </b>4179 - 4191</div><div><br /></div><div><b>100)</b> <a href="https://ieeexplore.ieee.org/document/9885019/">Consensus Clustering With Co-Association Matrix Optimization</a></div><div><b>Author(s): </b>Yifan Shi, Zhiwen Yu, C. L. Philip Chen, Huanqiang Zeng</div><div><b>Pages: </b>4192 - 4205</div><div><br /></div><div><b>101)</b> <a href="https://ieeexplore.ieee.org/document/9900299/">CGDD: Multiview Graph Clustering via Cross-Graph Diversity Detection</a></div><div><b>Author(s): </b>Shudong Huang, Ivor W. Tsang, Zenglin Xu, Jiancheng Lv</div><div><b>Pages: </b>4206 - 4219</div><div><br /></div><div><b>102)</b> <a href="https://ieeexplore.ieee.org/document/9881214/">Zero-Shot Learning With Attentive Region Embedding and Enhanced Semantics</a></div><div><b>Author(s): </b>Yang Liu, Yuhao Dang, Xinbo Gao, Jungong Han, Ling Shao</div><div><b>Pages: </b>4220 - 4231</div><div><br /></div><div><b>103)</b> <a href="https://ieeexplore.ieee.org/document/9881211/">Leveraging Imitation Learning on Pose Regulation Problem of a Robotic Fish</a></div><div><b>Author(s): </b>Tianhao Zhang, Lu Yue, Chen Wang, Jinan Sun, Shikun Zhang, Airong Wei, Guangming Xie</div><div><b>Pages: </b>4232 - 4245</div><div><br /></div><div><b>104)</b> <a href="https://ieeexplore.ieee.org/document/9895204/">A Distributional Perspective on Multiagent Cooperation With Deep Reinforcement Learning</a></div><div><b>Author(s): </b>Liwei Huang, Mingsheng Fu, Ananya Rao, Athirai A. Irissappane, Jie Zhang, Chengzhong Xu</div><div><b>Pages: </b>4246 - 4259</div><div><br /></div><div><b>105)</b> <a href="https://ieeexplore.ieee.org/document/9882008/">Learning All-In Collaborative Multiview Binary Representation for Clustering</a></div><div><b>Author(s): </b>Yachao Zhang, Yuan Xie, Cuihua Li, Zongze Wu, Yanyun Qu</div><div><b>Pages: </b>4260 - 4273</div><div><br /></div><div><b>106)</b> <a href="https://ieeexplore.ieee.org/document/9893090/">User Identification Across Multiple Social Networks Based on Naive Bayes Model</a></div><div><b>Author(s): </b>Xiao Ding, Haifeng Zhang, Chuang Ma, Xingyi Zhang, Kai Zhong</div><div><b>Pages: </b>4274 - 4285</div><div><br /></div><div><b>107)</b> <a href="https://ieeexplore.ieee.org/document/9887982/">Dual Parallel Policy Iteration With Coupled Policy Improvement</a></div><div><b>Author(s): </b>Yuhu Cheng, Longyang Huang, C. L. Philip Chen, Xuesong Wang</div><div><b>Pages: </b>4286 - 4298</div><div><br /></div><div><b>108)</b> <a href="https://ieeexplore.ieee.org/document/9853217/">Self-Supervised Deep Multiview Spectral Clustering</a></div><div><b>Author(s): </b>Linlin Zong, Faqiang Miao, Xianchao Zhang, Wenxin Liang, Bo Xu</div><div><b>Pages: </b>4299 - 4308</div><div><br /></div><div><b>109)</b> <a href="https://ieeexplore.ieee.org/document/9889257/">3-D Convolutional Neural Networks for RGB-D Salient Object Detection and Beyond</a></div><div><b>Author(s): </b>Qian Chen, Zhenxi Zhang, Yanye Lu, Keren Fu, Qijun Zhao</div><div><b>Pages: </b>4309 - 4323</div><div><br /></div><div><b>110)</b> <a href=" https://ieeexplore.ieee.org/document/9885030/">Knowledge Verification From Data</a></div><div><b>Author(s): </b>Xiangyu Wang, Taiyu Ban, Lyuzhou Chen, Xingyu Wu, Derui Lyu, Huanhuan Chen</div><div><b>Pages: </b>4324 - 4338</div><div><br /></div><div><b>111)</b> <a href="https://ieeexplore.ieee.org/document/9874867/">Kernel Adaptive Filtering Over Complex Networks</a></div><div><b>Author(s): </b>Wenling Li, Zidong Wang, Jun Hu, Junping Du, Weiguo Sheng</div><div><b>Pages: </b>4339 - 4346</div><div><br /></div><div><b>112)</b> <a href="https://ieeexplore.ieee.org/document/9881225/">Distributed Nash Equilibrium Seeking Dynamics With Discrete Communication</a></div><div><b>Author(s): </b>Rui Yu, Yutao Tang, Peng Yi, Li Li</div><div><b>Pages: </b>4347 - 4353</div><div><br /></div><div><b>113)</b> <a href="https://ieeexplore.ieee.org/document/9873887/">Particle-Filter-Based State Estimation for Delayed Artificial Neural Networks: When Probabilistic Saturation Constraints Meet Redundant Channels</a></div><div><b>Author(s): </b>Weihao Song, Zidong Wang, Zhongkui Li, Qing-Long Han</div><div><b>Pages: </b>4354 - 4362</div><div><br /></div></div>Mike Wattshttp://www.blogger.com/profile/12970193877427051617noreply@blogger.com0tag:blogger.com,1999:blog-1601699047263756666.post-18932912610649051022024-03-06T12:17:00.001+13:002024-03-06T12:17:24.452+13:00Upcoming Journal Special Issues<div style="text-align: left;">Upcoming submission deadlines for journal special issues:</div><div style="text-align: left;"><ul style="text-align: left;"></ul><b>IEEE Transactions on Fuzzy Systems</b> - <a href="https://cis.ieee.org/images/files/Publications/TFS/special-issues/Accepted-Deep_Neuro-Fuzzy_Approaches_for_Intelligent_Big_Data_Processing.pdf">Special Issue on Deep Neuro-Fuzzy Approaches for Intelligent Big Data Processing</a> - <u>30 March 2024</u><br /><ul style="text-align: left;"></ul><ul style="text-align: left;"></ul><b>IEEE Transactions on Evolutionary Computation</b> - <a href="https://cis.ieee.org/images/files/Documents/call-for-papers/tevc/cfp-mlec-final.pdf">Special Issue on Machine Learning Assisted Evolutionary Computation</a> - <u>1 April 2024</u></div><div style="text-align: left;"><ul style="text-align: left;"></ul><b>IEEE Transactions on Evolutionary Computation</b> - <a href="https://cis.ieee.org/images/files/Publications/TEVC/CFP-EBLO.pdf">Special Issue on Evolutionary Bilevel Optimization</a> - <u>1 August 2024</u></div><div style="text-align: left;"><ul style="text-align: left;"></ul><b>IEEE Transactions on Evolutionary Computation</b> - <a href="https://cis.ieee.org/images/files/Publications/TEVC/cfp-evolutionary-optimization-final-1october2023.pdf">Special Issue on Evolutionary Dynamic Optimization</a> - <u>1 September 2024</u></div><div style="text-align: left;"><br /></div>Mike Wattshttp://www.blogger.com/profile/12970193877427051617noreply@blogger.com0tag:blogger.com,1999:blog-1601699047263756666.post-50577074402469119202024-03-01T17:00:00.001+13:002024-03-01T17:00:00.146+13:00Weekly Review 1 March 2024<div style="text-align: left;">Some interesting links that I <a href="https://twitter.com/DrMikeWatts">Tweeted</a> about in the last week (I also post these on <a href="https://mastodon.social/@DrMikeWatts">Mastodon</a>, <a href="https://www.threads.net/@drmikewatts">Threads</a>, <a href="https://newsmast.org/profile/111338892311951056">Newsmast</a>, <a href="https://bsky.app/profile/drmikewatts.bsky.social">Bluesky</a> and <a href="https://post.news/@/DrMikeWatts">Post</a>): </div><div><br /></div><div><ol style="text-align: left;"><li>I wonder what the cause of this was? If ChatGPT is learning all the time, was there a concerted effort to poison its learning? <a href="https://www.stuff.co.nz/world-news/350188342/chatgpt-baffles-users-speaking-spanglish-ai-goes-rogue">https://www.stuff.co.nz/world-news/350188342/chatgpt-baffles-users-speaking-spanglish-ai-goes-rogue</a> </li><li>The AI companies are damned if they do and damned if they don't-if the images generated aren't racially diverse they get accused of racism, and if they are, they get accused of racism: <a href="https://www.theverge.com/2024/2/21/24079371/google-ai-gemini-generative-inaccurate-historical">https://www.theverge.com/2024/2/21/24079371/google-ai-gemini-generative-inaccurate-historical</a> </li><li>This kind of output from generative AI is largely because it has no understanding of what it is being asked to create, and no ability to check what it produces: <a href="https://www.theregister.com/2024/02/23/google_suspends_gemini">https://www.theregister.com/2024/02/23/google_suspends_gemini</a>/ </li><li>How much AI is really AI? <a href="https://futurism.com/the-byte/drive-thru-ai-humans">https://futurism.com/the-byte/drive-thru-ai-humans</a> </li><li>This seems like a useful application of generative AI: <a href="https://techcrunch.com/2024/02/23/virtual-staging-ai-helps-realtors-digitally-furnish-rooms-within-seconds/">https://techcrunch.com/2024/02/23/virtual-staging-ai-helps-realtors-digitally-furnish-rooms-within-seconds/</a> </li><li>New Zealand universities have been chronically under-funded for decades, by governments on both sides of the political spectrum. Now that governmental neglect has brought them to crisis point: <a href="https://www.nzherald.co.nz/nz/new-zealand-universities-facing-a-liquidity-crisis-briefing/WM6P37SDCRDGRFCXA5E3CTHVS4/">https://www.nzherald.co.nz/nz/new-zealand-universities-facing-a-liquidity-crisis-briefing/WM6P37SDCRDGRFCXA5E3CTHVS4/</a></li><li>This is just one outcome of governmental neglect of New Zealand universities: <a href="https://www.stuff.co.nz/nz-news/350185610/massey-university-goes-ahead-cuts-despite-opposition">https://www.stuff.co.nz/nz-news/350185610/massey-university-goes-ahead-cuts-despite-opposition</a></li><li>If the prompts for generative AI are best generated by AI, where do humans come in? <a href="https://www.theregister.com/2024/02/22/prompt_engineering_ai_models/">https://www.theregister.com/2024/02/22/prompt_engineering_ai_models/</a> </li><li>Compulsory watermarking of AI generated material would go a long way to restoring trust in the content we see on the internet, but how do we enforce it? <a href="https://www.pcgamer.com/well-never-be-free-from-ai-paranoia-so-as-long-as-the-burden-of-detective-work-keeps-falling-to-the-masses/">https://www.pcgamer.com/well-never-be-free-from-ai-paranoia-so-as-long-as-the-burden-of-detective-work-keeps-falling-to-the-masses/</a> </li><li>It's getting hard to take a company seriously when it's founded and run by one of the biggest trolls in living memory: <a href="https://www.theverge.com/24080217/elon-musk-xai-fundraising-grok-ai">https://www.theverge.com/24080217/elon-musk-xai-fundraising-grok-ai</a> </li><li>If your AI says it, you are responsible for it: <a href="https://www.stuff.co.nz/travel/350185597/air-canada-chatbot-incorrectly-promised-discount-now-airline-has-pay-it">https://www.stuff.co.nz/travel/350185597/air-canada-chatbot-incorrectly-promised-discount-now-airline-has-pay-it</a> </li><li>No. Just no. The last thing we need is an AI hallucinating in finance: <a href="https://www.datanami.com/2024/02/23/leveraging-genai-and-llms-in-financial-services/">https://www.datanami.com/2024/02/23/leveraging-genai-and-llms-in-financial-services/</a> </li><li>At least Reddit will be making money off of AI slurping their data. Pity their users (i.e. the people who actually created the content) probably won't see any it: <a href="https://www.theregister.com/2024/02/22/reddit_google_license_ipo_altman/">https://www.theregister.com/2024/02/22/reddit_google_license_ipo_altman/</a> </li><li>A task force to produce a report that has recommendations around guardrails for AI: <a href="https://www.computerworld.com/article/3713102/us-forms-task-force-to-explore-guardrails-for-ai.html">https://www.computerworld.com/article/3713102/us-forms-task-force-to-explore-guardrails-for-ai.html</a> So when will something actually get done about this? </li><li>I don't think a delay shipping this AI pin is going to improve its market prospects-most of its functionality is probably going to get rolled into smartphones in the next year or two: <a href="https://techcrunch.com/2024/02/23/humane-pushes-ai-pin-ship-date-to-mid-april/">https://techcrunch.com/2024/02/23/humane-pushes-ai-pin-ship-date-to-mid-april/</a> </li><li>Some more medical applications of AI: <a href="https://www.caffeinedaily.co/stories/the-eyes-are-the-window-to-cardiovascular-disease">https://www.caffeinedaily.co/stories/the-eyes-are-the-window-to-cardiovascular-disease</a> </li><li>Would you trust an AI to choose your mortgage for you? I wouldn't, but I can do enough arithmetic to make those kinds of choices myself: <a href="https://www.oneroof.co.nz/news/would-you-let-ai-choose-your-mortgage-45005">https://www.oneroof.co.nz/news/would-you-let-ai-choose-your-mortgage-45005</a> </li><li>Generative AI is already dissuading investment into the facilities used to create entertainment: <a href="https://www.theguardian.com/technology/2024/feb/23/tyler-perry-halts-800m-studio-expansion-after-being-shocked-by-ai">https://www.theguardian.com/technology/2024/feb/23/tyler-perry-halts-800m-studio-expansion-after-being-shocked-by-ai</a> </li><li>A technical briefing on MLOps: <a href="https://www.kdnuggets.com/publications/briefs/GTM_Tech_Brief_Everything_You_Need_to_Know_About_MLOps.pdf">https://www.kdnuggets.com/publications/briefs/GTM_Tech_Brief_Everything_You_Need_to_Know_About_MLOps.pdf</a> </li><li>Mapping methane emissions using AI: <a href="https://www.technologyreview.com/2024/02/14/1088198/satellite-google-ai-map-methane-leaks/">https://www.technologyreview.com/2024/02/14/1088198/satellite-google-ai-map-methane-leaks/</a> Important, methane is a significant greenhouse gas </li><li>AI can't just be in the cloud, a hybrid approach-some in the cloud, some local- is more likely: <a href="https://www.datanami.com/2024/02/12/the-future-of-ai-is-hybrid/">https://www.datanami.com/2024/02/12/the-future-of-ai-is-hybrid/</a> </li><li>Generative AI use cases for an Australian bank: <a href="https://www.techrepublic.com/article/australia-banking-sector-cba-ai/">https://www.techrepublic.com/article/australia-banking-sector-cba-ai/</a> This is going to be entertaining.. </li><li>The main criticism of generative AI, and the most reasonable in my opinion, is that it exploits other people's work: https://www.technologyreview.com/2024/02/20/1088701/i-went-for-a-walk-with-gary-marcus-ais-loudest-critic/ </li><li>This might be the one thing that stops AI taking over the world-people won't want to pay for it: <a href="https://www.androidcentral.com/apps-software/companies-charging-consumers-ai-subscriptions">https://www.androidcentral.com/apps-software/companies-charging-consumers-ai-subscriptions</a></li></ol></div>Mike Wattshttp://www.blogger.com/profile/12970193877427051617noreply@blogger.com0tag:blogger.com,1999:blog-1601699047263756666.post-25004856271813826632024-02-23T17:00:00.003+13:002024-02-23T17:00:00.129+13:00Weekly Review 23 February 2024<div style="text-align: left;"><div>Some interesting links that I <a href="https://twitter.com/DrMikeWatts">Tweeted</a> about in the last week (I also post these on <a href="https://mastodon.social/@DrMikeWatts">Mastodon</a>, <a href="https://www.threads.net/@drmikewatts">Threads</a>, <a href="https://newsmast.org/profile/111338892311951056">Newsmast</a>, <a href="https://bsky.app/profile/drmikewatts.bsky.social">Bluesky</a> and <a href="https://post.news/@/DrMikeWatts">Post</a>): </div><div><br /></div><div><ul style="text-align: left;"><li>There seems to be something of an arms race between AI being used to detect shonky images in publications, and AI being used to generate them: <a href="https://www.nature.com/articles/d41586-024-00372-6">https://www.nature.com/articles/d41586-024-00372-6</a> </li><li>The open source recording tool Audacity now has an AI plugin for noise suppression: <a href="https://www.pcgamer.com/audacity-now-has-a-free-ai-powered-noise-suppressor-but-the-machine-isnt-going-to-replace-a-sound-engineer-anytime-soon/">https://www.pcgamer.com/audacity-now-has-a-free-ai-powered-noise-suppressor-but-the-machine-isnt-going-to-replace-a-sound-engineer-anytime-soon/</a> </li><li>The dark side of building AI: the dodgy labour practices around cleaning and preparing the training data: <a href="https://www.technologyreview.com/2023/06/13/1074560/we-are-all-ais-free-data-workers/">https://www.technologyreview.com/2023/06/13/1074560/we-are-all-ais-free-data-workers/</a></li><li>Now AI can generate photorealistic video. Who was it said that we're moving into a post-reality world? We need mandatory watermarking of generated content. <a href="https://futurism.com/openai-sora-video-generator">https://futurism.com/openai-sora-video-generator</a> </li><li>Four ways AI is being used in finance: <a href="https://mitsloan.mit.edu/ideas-made-to-matter/johnson-johnson-cfo-4-ways-ai-changing-finance">https://mitsloan.mit.edu/ideas-made-to-matter/johnson-johnson-cfo-4-ways-ai-changing-finance</a></li><li>Will OpenAI's AI powered search engine-combined with Bing-be better than Googles? It takes a lot to displace an established player. <a href="https://gizmodo.com/openai-chatgpt-wants-to-eat-google-search-lunch-1851261086">https://gizmodo.com/openai-chatgpt-wants-to-eat-google-search-lunch-1851261086</a> </li><li>Peer review has problems, chief amongst them being that reviewers are time-poor volunteers. So things like ridiculous AI generated images slip through: <a href="https://arstechnica.com/science/2024/02/scientists-aghast-at-bizarre-ai-rat-with-huge-genitals-in-peer-reviewed-article/">https://arstechnica.com/science/2024/02/scientists-aghast-at-bizarre-ai-rat-with-huge-genitals-in-peer-reviewed-article/</a></li><li>More on how AI tools are screwing up the hiring process, and discriminating against job candidates: <a href="https://futurism.com/the-byte/ai-ignoring-qualified-candidates">https://futurism.com/the-byte/ai-ignoring-qualified-candidates</a> </li><li>I've been teaching on an AI degree for the last two years, yet this is news? If anything they're late to the party. <a href="https://www.cnbc.com/2024/02/16/university-of-pennsylvania-will-soon-offer-bachelors-degree-in-ai.html">https://www.cnbc.com/2024/02/16/university-of-pennsylvania-will-soon-offer-bachelors-degree-in-ai.html</a></li><li>An open source API for generative AI: <a href="https://www.hackster.io/news/edgen-aims-to-deliver-an-open-source-drop-in-replacement-to-openai-s-api-for-gen-ai-at-the-edge-f2fc74e87f8b">https://www.hackster.io/news/edgen-aims-to-deliver-an-open-source-drop-in-replacement-to-openai-s-api-for-gen-ai-at-the-edge-f2fc74e87f8b</a> </li><li>While there are tools the can detect whether text has been written by a generative AI, it's really easy to evade detection: <a href="https://www.technologyreview.com/2023/07/07/1075982/ai-text-detection-tools-are-really-easy-to-fool/">https://www.technologyreview.com/2023/07/07/1075982/ai-text-detection-tools-are-really-easy-to-fool/</a></li><li>No, AI is not going to kill us all in five years. Unrestrained capitalism, on the other hand... <a href="https://www.theguardian.com/technology/2024/feb/17/humanitys-remaining-timeline-it-looks-more-like-five-years-than-50-meet-the-neo-luddites-warning-of-an-ai-apocalypse">https://www.theguardian.com/technology/2024/feb/17/humanitys-remaining-timeline-it-looks-more-like-five-years-than-50-meet-the-neo-luddites-warning-of-an-ai-apocalypse</a></li><li>We can't even enforce non-proliferation agreements on nuclear weapons. How the hell are we going to enforce kill switches for AI? <a href="https://www.theregister.com/2024/02/16/boffins_propose_regulating_ai_hardware/?td=rt-3a">https://www.theregister.com/2024/02/16/boffins_propose_regulating_ai_hardware/?td=rt-3a</a></li><li>Giving ChatGPT a memory might help it avoid some of the more egregious stupidity it produces, but it still won't help it understand things: <a href="https://www.techradar.com/computing/artificial-intelligence/chatgpt-is-getting-human-like-memory-and-this-might-be-the-first-big-step-toward-general-ai">https://www.techradar.com/computing/artificial-intelligence/chatgpt-is-getting-human-like-memory-and-this-might-be-the-first-big-step-toward-general-ai</a> </li><li>Five use cases of AI. But how many of these are actual not theoretical? <a href="https://www.informationweek.com/machine-learning-ai/5-ways-to-use-ai-you-may-have-never-even-considered">https://www.informationweek.com/machine-learning-ai/5-ways-to-use-ai-you-may-have-never-even-considered</a> </li><li>Is there really any difference between an online relationship between two people, and an online relationship with a person and an AI? <a href="https://www.stuff.co.nz/world-news/350180056/amid-artificial-intelligence-boom-ai-girlfriends-and-boyfriends-are-making">https://www.stuff.co.nz/world-news/350180056/amid-artificial-intelligence-boom-ai-girlfriends-and-boyfriends-are-making</a> </li><li>AI companions can make better listeners than real partners: <a href="https://futurism.com/the-byte/chinese-women-ai-boyfriends">https://futurism.com/the-byte/chinese-women-ai-boyfriends</a> </li><li>I still think the movie Her is the most likely future for AI companions: <a href="https://www.theverge.com/24066233/her-ai-film-spike-jonze-joaquin-phoenix-scarlett-johansson">https://www.theverge.com/24066233/her-ai-film-spike-jonze-joaquin-phoenix-scarlett-johansson</a> </li><li>The real danger of AI companions is that they are harvesting and monetizing your data: <a href="https://www.extremetech.com/internet/ai-girlfriends-are-snapping-up-users-data">https://www.extremetech.com/internet/ai-girlfriends-are-snapping-up-users-data</a> </li><li>Biased data has affected an AI used for immunotherapy: <a href="https://www.datanami.com/this-just-in/rice-university-researchers-uncover-bias-in-machine-learning-tools-for-immunotherapy/">https://www.datanami.com/this-just-in/rice-university-researchers-uncover-bias-in-machine-learning-tools-for-immunotherapy/</a></li><li>What's the real cost of power for AI? <a href="https://www.theverge.com/24066646/ai-electricity-energy-watts-generative-consumption">https://www.theverge.com/24066646/ai-electricity-energy-watts-generative-consumption</a> </li><li>I don't agree with everything in this article. Babies don't have an innate sense of object permanence, it develops as they interact with the world. Won't AI have to learn it the same way? <a href="https://www.technologyreview.com/2024/02/06/1087793/what-babies-can-teach-ai/">https://www.technologyreview.com/2024/02/06/1087793/what-babies-can-teach-ai/</a> </li><li>Using AI to detect different diseases caused by diabetes: <a href="https://dataconomy.com/2024/02/16/how-ai-is-assisting-in-early-detection-of-diabetes-related-diseases/">https://dataconomy.com/2024/02/16/how-ai-is-assisting-in-early-detection-of-diabetes-related-diseases/</a> </li><li>I generally have a low opinion of philosophy, it doesn't really sit well with my programmer's mind, but there are some interesting points in this essay: <a href="https://aeon.co/essays/can-philosophy-help-us-get-a-grip-on-the-consequences-of-ai">https://aeon.co/essays/can-philosophy-help-us-get-a-grip-on-the-consequences-of-ai</a> </li></ul></div></div>Mike Wattshttp://www.blogger.com/profile/12970193877427051617noreply@blogger.com0tag:blogger.com,1999:blog-1601699047263756666.post-59522106457079906452024-02-16T17:00:00.004+13:002024-02-20T15:23:09.876+13:00Weekly Review 16 February 2024<div>Some interesting links that I <a href="https://twitter.com/DrMikeWatts">Tweeted</a> about in the last week (I also post these on <a href="https://mastodon.social/@DrMikeWatts">Mastodon</a>, <a href="https://www.threads.net/@drmikewatts">Threads</a>, <a href="https://newsmast.org/profile/111338892311951056">Newsmast</a>, <a href="https://bsky.app/profile/drmikewatts.bsky.social">Bluesky</a> and <a href="https://post.news/@/DrMikeWatts">Post</a>): </div><div><br /></div><div><ul style="text-align: left;"><li>Generative AI is going to make some parts of filmmaking easier, but it's not going to displace humans entirely from the process just yet: <a href="https://www.technologyreview.com/2023/06/01/1073858/surreal-ai-generative-video-changing-film/">https://www.technologyreview.com/2023/06/01/1073858/surreal-ai-generative-video-changing-film/</a> </li><li>Using generative AI for robocalling is now illegal in the US: <a href="https://www.cbsnews.com/news/fcc-declares-robocalls-illegal/">https://www.cbsnews.com/news/fcc-declares-robocalls-illegal/</a> </li><li>This is not going to end well-North Island supermarkets using facial recognition software: <a href="https://www.nzherald.co.nz/nz/privacy-commissioner-promises-to-closely-monitor-foodstuffs-facial-recognition-trial/3JXFUBVGJVE47AA2GTVC5E6ASM/">https://www.nzherald.co.nz/nz/privacy-commissioner-promises-to-closely-monitor-foodstuffs-facial-recognition-trial/3JXFUBVGJVE47AA2GTVC5E6ASM/</a> </li><li>Minorities are going to be disproportionately impacted by facial recognition software in supermarkets. They are almost certainly going to be underrepresented in the training data sets, so there is going to be a lot of false positives: <a href="https://www.nzherald.co.nz/kahu/fears-maori-women-will-be-targets-in-foodstuffs-facial-recognition-trial-in-north-island/7RWYY4KZS5EUBNZ77YHBD2MIPI/">https://www.nzherald.co.nz/kahu/fears-maori-women-will-be-targets-in-foodstuffs-facial-recognition-trial-in-north-island/7RWYY4KZS5EUBNZ77YHBD2MIPI/</a> </li><li>It doesn't matter that people will supposedly be comparing results of facial recognition in supermarkets-people are lazy, and will just follow what the machine tell them: <a href="https://www.newstalkzb.co.nz/on-air/heather-du-plessis-allan-drive/audio/julian-benefield-foodstuffs-north-island-general-counsel-says-human-element-will-play-key-role-in-facial-recognition-technology-trial/">https://www.newstalkzb.co.nz/on-air/heather-du-plessis-allan-drive/audio/julian-benefield-foodstuffs-north-island-general-counsel-says-human-element-will-play-key-role-in-facial-recognition-technology-trial/</a> </li><li>An AI search engine that seems to be like all the other conversational AI - grievously stupid, with no sense of reality: <a href="https://futurism.com/the-byte/ai-search-engine-nyt">https://futurism.com/the-byte/ai-search-engine-nyt</a> </li><li>Using an AI to enhance scans of carbonised ancient scrolls from Herculaneum. Important to bear in mind that this is a machine interpreting the scans, not necessarily what the scrolls actually say: <a href="https://www.nature.com/articles/d41586-024-00346-8">https://www.nature.com/articles/d41586-024-00346-8</a> </li><li>It's hard enough to get peer reviewers for journal submissions, if anything AI is going to make that worse-a never-ending, self-reinforcing morass of inane sameness in reviews: <a href="https://spectrum.ieee.org/ai-peer-review">https://spectrum.ieee.org/ai-peer-review</a> </li><li>Engineering metamaterials using AI: <a href="https://phys.org/news/2024-02-ai-tool-realistic-metamaterials-unusual.html">https://phys.org/news/2024-02-ai-tool-realistic-metamaterials-unusual.html</a> </li><li>The environmental impact of AI: <a href="https://e360.yale.edu/features/artificial-intelligence-climate-energy-emissions">https://e360.yale.edu/features/artificial-intelligence-climate-energy-emissions</a> </li><li>Data centres to run AI need so much electricity that coal-fired powerplants are having to be kept online, with all the carbon emissions that come with them: <a href="https://futurism.com/the-byte/coal-plants-ai">https://futurism.com/the-byte/coal-plants-ai</a> </li><li>Now there's an ISO standard for the responsible use of AI: <a href="https://spectrum.ieee.org/tech-standards-responsible-ai">https://spectrum.ieee.org/tech-standards-responsible-ai</a> This'll certainly be helpful for the consultancy industry</li><li>Improved sentiment analysis in Python: <a href="https://www.kdnuggets.com/sentiment-analysis-in-python-going-beyond-bag-of-words">https://www.kdnuggets.com/sentiment-analysis-in-python-going-beyond-bag-of-words</a> </li><li>As the hype around AI increases, so too does the demand for engineers to develop it: <a href="https://www.datanami.com/2024/02/07/hiring-genai-talent-its-a-matter-of-degree/">https://www.datanami.com/2024/02/07/hiring-genai-talent-its-a-matter-of-degree/</a> </li><li>Approaches to embed reasoning in to #NeuralNetworks: <a href="https://www.datasciencecentral.com/a-neurosymbolic-ai-approach-to-learning-reasoning/">https://www.datasciencecentral.com/a-neurosymbolic-ai-approach-to-learning-reasoning/</a> </li><li>Future roles for AI and #MachineLearning in the IT industry: <a href="https://dataconomy.com/2024/02/06/navigating-tomorrow-role-of-ai-and-ml-in-information-technology/">https://dataconomy.com/2024/02/06/navigating-tomorrow-role-of-ai-and-ml-in-information-technology/</a></li><li>Not surprising that safeguards around AI can be broken, it's going to be years before we get a good grip on this problem: <a href="https://www.theguardian.com/technology/2024/feb/09/ai-safeguards-can-easily-be-broken-uk-safety-institute-finds">https://www.theguardian.com/technology/2024/feb/09/ai-safeguards-can-easily-be-broken-uk-safety-institute-finds</a></li><li>So now AI are being used to determine who does or does not get medical coverage in the USA? I thought the US medical system couldn't get any worse: <a href="https://www.theregister.com/2024/02/09/ai_medicare_health/">https://www.theregister.com/2024/02/09/ai_medicare_health/</a> </li><li>The tool I used in the past to detect AI generated student essays was pretty rubbish, far too high a false positive rate: <a href="https://www.insidehighered.com/news/tech-innovation/artificial-intelligence/2024/02/09/professors-proceed-caution-using-ai">https://www.insidehighered.com/news/tech-innovation/artificial-intelligence/2024/02/09/professors-proceed-caution-using-ai</a> </li><li>A US advisory group on AI regulation has been formed: <a href="https://www.computerworld.com/article/3712863/us-creates-advisory-group-to-consider-ai-regulation.html">https://www.computerworld.com/article/3712863/us-creates-advisory-group-to-consider-ai-regulation.html</a> </li><li>Generative AI enhances human creativity, it doesn't replace it: <a href="https://www.theguardian.com/fashion/2024/feb/08/ai-london-fashion-week">https://www.theguardian.com/fashion/2024/feb/08/ai-london-fashion-week</a> </li><li>Overview of Large Language Model AI and how they work: <a href="https://www.computerworld.com/article/3697649/what-are-large-language-models-and-how-are-they-used-in-generative-ai.html">https://www.computerworld.com/article/3697649/what-are-large-language-models-and-how-are-they-used-in-generative-ai.html</a> </li><li>All the ways deepfakes have been used to scam people: <a href="https://www.informationweek.com/machine-learning-ai/dealing-with-deepfakes">https://www.informationweek.com/machine-learning-ai/dealing-with-deepfakes</a> </li><li>An AI that is so ethical, it won't do anything: <a href="https://techcrunch.com/2024/02/09/meet-goody-2-the-ai-too-ethical-to-discuss-literally-anything/">https://techcrunch.com/2024/02/09/meet-goody-2-the-ai-too-ethical-to-discuss-literally-anything/</a> </li></ul></div>Mike Wattshttp://www.blogger.com/profile/12970193877427051617noreply@blogger.com0tag:blogger.com,1999:blog-1601699047263756666.post-89248675040477800562024-02-15T13:21:00.001+13:002024-02-15T13:21:34.500+13:00IEEE Transactions on Artificial Intelligence, Volume 5, Issue 2, February 2024<div style="text-align: left;"><div><b>1)</b> <a href="https://ieeexplore.ieee.org/document/10058590/">Deep Learning Methods for Small Molecule Drug Discovery: A Survey</a></div><div><b>Author(s): </b>Wenhao Hu,Yingying Liu, Xuanyu Chen, Wenhao Chai, Hangyue Chen, Hongwei Wang, Gaoang Wang</div><div><b>Pages: </b>459 - 479</div><div><br /></div><div><b>2)</b> <a href="https://ieeexplore.ieee.org/document/10202180/">How Artificial Intelligence Helped the Humanity During the COVID-19 Pandemic: A Review</a></div><div><b>Author(s): </b>Ali Alnoman</div><div><b>Pages: </b>480 - 489<span style="white-space: pre;"> </span></div><div><br /></div><div><b>3)</b> <a href="https://ieeexplore.ieee.org/document/10132546/">A Type-2 Fuzzy-Based Explainable AI System for Predictive Maintenance Within the Water Pumping Industry</a></div><div><b>Author(s): </b>Shreyas J. Upasane, Hani Hagras, Mohammad Hossein Anisi, Stuart Savill, Ian Taylor, Kostas Manousakis</div><div><b>Pages: </b>490 - 504</div><div><br /></div><div><b>4)</b> <a href="https://ieeexplore.ieee.org/document/10083301/">Portfolio Selection via Graph-Aware Gaussian Processes With Generalized Gaussian Likelihood</a></div><div><b>Author(s): </b>Naiqi Li, Zhikang Xia, Yiming Li, Ercan E. Kuruoğlu, Yong Jiang, Shu-Tao Xia</div><div><b>Pages: </b>505 - 515</div><div><br /></div><div><b>5)</b> <a href="https://ieeexplore.ieee.org/document/10089422/">Lab to Multiscene Generalization for Non-Line-of-Sight Identification With Small-Scale Datasets</a></div><div><b>Author(s): </b>Qirui Hua, Martin Hedegaard Nielsen, Zeliang An, Jian Ren, Rafal Wisniewski, Søren Kold, Ole Rahbek, Ming Shen</div><div><b>Pages: </b>516 - 529</div><div><br /></div><div><b>6)</b> <a href="https://ieeexplore.ieee.org/document/10232869/">A Fast Attention Network for Joint Intent Detection and Slot Filling on Edge Devices</a></div><div><b>Author(s): </b>Liang Huang, Senjie Liang, Feiyang Ye, Nan Gao</div><div><b>Pages: </b>530 - 540</div><div><br /></div><div><b>7)</b> <a href="https://ieeexplore.ieee.org/document/10083287/">Hierarchical Patch Selection: An Improved Patch Sampling for No Reference Image Quality Assessment</a></div><div><b>Author(s): </b>C. Nandhini, M. Brindha</div><div><b>Pages: </b>541 - 555</div><div><br /></div><div><b>8)</b> <a href="https://ieeexplore.ieee.org/document/10114995/">A Dual-Stage Semi-Supervised Pre-Training Approach for Medical Image Segmentation</a></div><div><b>Author(s): </b>Rajath C Aralikatti, S. J. Pawan, Jeny Rajan</div><div><b>Pages: </b>556 - 565</div><div><br /></div><div><b>9)</b> <a href="https://ieeexplore.ieee.org/document/10113162/">Datum-Wise Inference in Structured Environments</a></div><div><b>Author(s): </b>Sachini Piyoni Ekanayake, Daphney-Stavroula Zois</div><div><b>Pages: </b>566 - 577</div><div><br /></div><div><b>10)</b> <a href="https://ieeexplore.ieee.org/document/10100865/">Online Aware Synapse Weighted Autoencoder for Recovering Random Missing Data in Wastewater Treatment Process</a></div><div><b>Author(s): </b>Honggui Han, Meiting Sun, Fangyu Li</div><div><b>Pages: </b>578 - 589</div><div><br /></div><div><b>11)</b> <a href="https://ieeexplore.ieee.org/document/10102531/">Constrained Nonnegative Matrix Factorization Based on Label Propagation for Data Representation</a></div><div><b>Author(s): </b>Junmin Liu, Yicheng Wang, Jing Ma, Di Han, Yifan Huang</div><div><b>Pages: </b>590 - 601</div><div><br /></div><div><b>12)</b> <a href="https://ieeexplore.ieee.org/document/10185458/">Pseudoinvertible Neural Networks</a></div><div><b>Author(s): </b>Elijah D. Bolluyt, Cristina Comaniciu</div><div><b>Pages: </b>602 - 612</div><div><br /></div><div><b>13)</b> <a href="https://ieeexplore.ieee.org/document/10286127/">Video-Based Depth Estimation Autoencoder With Weighted Temporal Feature and Spatial Edge Guided Modules</a></div><div><b>Author(s): </b>Wei-Jong Yang, Wan-Nung Tsung, Pau-Choo Chung</div><div><b>Pages: </b>613 - 623</div><div><br /></div><div><b>14)</b> <a href="https://ieeexplore.ieee.org/document/10123028/">Masked Siamese Prompt Tuning for Few-Shot Natural Language Understanding</a></div><div><b>Author(s): </b>Shiwen Ni, Hung-Yu Kao</div><div><b>Pages: </b>624 - 633</div><div><br /></div><div><b>15)</b> <a href="https://ieeexplore.ieee.org/document/10158397/">Deep Q-Learning-Based Molecular Graph Generation for Chemical Structure Prediction From Infrared Spectra</a></div><div><b>Author(s): </b>Joshua Dean Ellis, Razib Iqbal, Keiichi Yoshimatsu</div><div><b>Pages: </b>634 - 646</div><div><br /></div><div><b>16)</b> <a href="https://ieeexplore.ieee.org/document/10063985/">Synergetic Focal Loss for Imbalanced Classification in Federated XGBoost</a></div><div><b>Author(s): </b>Jiao Tian, Pei-Wei Tsai, Kai Zhang, Xinyi Cai, Hongwang Xiao, Ke Yu, Wenyu Zhao, Jinjun Chen</div><div><b>Pages: </b>647 - 660</div><div><br /></div><div><b>17)</b> <a href="https://ieeexplore.ieee.org/document/10076796/">Pyramid Dynamic Bayesian Networks for Key Performance Indicator Prediction in Long Time-Delay Industrial Processes</a></div><div><b>Author(s): </b>Lingquan Zeng, Zhiqiang Ge</div><div><b>Pages: </b>661 - 671</div><div><br /></div><div><b>18)</b> <a href="https://ieeexplore.ieee.org/document/10097568/">log-Sigmoid Activation-Based Long Short-Term Memory for Time-Series Data Classification</a></div><div><b>Author(s): </b>Priyesh Ranjan, Pritam Khan, Sudhir Kumar, Sajal K. Das</div><div><b>Pages: </b>672 - 683</div><div><br /></div><div><b>19)</b> <a href="https://ieeexplore.ieee.org/document/10070787/">Query Attack by Multi-Identity Surrogates</a></div><div><b>Author(s): </b>Sizhe Chen, Zhehao Huang, Qinghua Tao, Xiaolin Huang</div><div><b>Pages: </b>684 - 697</div><div><br /></div><div><b>20)</b> <a href="https://ieeexplore.ieee.org/document/10057958/">A Hand Gesture-Operated System for Rehabilitation Using an End-to-End Detection Framework</a></div><div><b>Author(s): </b>H Pallab Jyoti Dutta, M. K. Bhuyan, Debanga Raj Neog, Karl F. MacDorman, R. H. Laskar</div><div><b>Pages: </b>698 - 708</div><div><br /></div><div><b>21)</b> <a href="https://ieeexplore.ieee.org/document/10083246/">Pseudo-shot Learning for Soil Classification With Laser-Induced Breakdown Spectroscopy</a></div><div><b>Author(s): </b>Yingchao Huang, Abdul Bais, Amina E. Hussein</div><div><b>Pages: </b>709 - 723</div><div><br /></div><div><b>22)</b> <a href="https://ieeexplore.ieee.org/document/10098648/">A Radial Basis Function-Based Graph Attention Network With Squeeze Loss Optimization for Link Prediction</a></div><div><b>Author(s): </b>Jiusheng Chen, Chengyuan Fang, Xiaoyu Zhang, Jun Wu, Runxia Guo</div><div><b>Pages: </b>724 - 736</div><div><br /></div><div><b>23)</b> <a href="https://ieeexplore.ieee.org/document/10101864/">Neighboring Envelope Embedded Stacked Autoencoder for Deep Learning on Hierarchically Structured Samples</a></div><div><b>Author(s): </b>Chuanyan Zhou, Jie Ma, Fan Li, Yongming Li, Pin Wang</div><div><b>Pages: </b>737 - 750</div><div><br /></div><div><b>24)</b> <a href="https://ieeexplore.ieee.org/document/10105983/">Sample Efficient Reinforcement Learning Using Graph-Based Memory Reconstruction</a></div><div><b>Author(s): </b>Yongxin Kang, Enmin Zhao, Yifan Zang, Lijuan Li, Kai Li, Pin Tao, Junliang Xing</div><div><b>Pages: </b>751 - 762</div><div><br /></div><div><b>25)</b> <a href="https://ieeexplore.ieee.org/document/10113715/">Noisy Label Detection and Counterfactual Correction</a></div><div><b>Author(s): </b>Wenting Qi, Charalampos Chelmis</div><div><b>Pages: </b>763 - 775</div><div><br /></div><div><b>26)</b> <a href="https://ieeexplore.ieee.org/document/10123019/">Margin-Aware Adaptive-Weighted-Loss for Deep Learning Based Imbalanced Data Classification</a></div><div><b>Author(s): </b>Debasmit Roy, Rishav Pramanik, Ram Sarkar</div><div><b>Pages: </b>776 - 785</div><div><br /></div><div><b>27)</b> <a href="https://ieeexplore.ieee.org/document/10143254/">Optimization of Patient Specific Stimulus for Deep Brain Stimulation Using Spatially Distributed Neural Sources</a></div><div><b>Author(s): </b>Syed Aamir Ali Shah, Abdul Bais, Lei Zhang</div><div><b>Pages: </b>786 - 800</div><div><br /></div><div><b>28)</b> <a href="https://ieeexplore.ieee.org/document/10102524/">Performance-Driven Safe Bayesian Optimization for Intelligent Tuning of High-Order Cascade Systems</a></div><div><b>Author(s): </b>Tao Jiang, Xiaojie Su, Jiangshuai Huang, Zhenshan Bing, Alois Knoll</div><div><b>Pages: </b>801 - 813</div><div><br /></div><div><b>29)</b> <a href="https://ieeexplore.ieee.org/document/10098869/">Capacity Abuse Attack of Deep Learning Models Without Need of Label Encodings</a></div><div><b>Author(s): </b>Wenjian Luo, Licai Zhang, Yulin Wu, Chuanyi Liu, Peiyi Han, Rongfei Zhuang</div><div><b>Pages: </b>814 - 826</div><div><br /></div><div><b>30)</b> <a href="https://ieeexplore.ieee.org/document/10081502/">Encoding Hierarchical Information in Neural Networks Helps in Subpopulation Shift</a></div><div><b>Author(s): </b>Amitangshu Mukherjee, Isha Garg, Kaushik Roy</div><div><b>Pages: </b>827 - 838</div><div><br /></div><div><b>31)</b> <a href="https://ieeexplore.ieee.org/document/10086597/">Blockchain and Federated Reinforcement Learning for Vehicle-to-Everything Energy Trading in Smart Grids</a></div><div><b>Author(s): </b>Md Moniruzzaman, Abdulsalam Yassine, Rachid Benlamri</div><div><b>Pages: </b>839 - 853</div><div><br /></div><div><b>32)</b> <a href="https://ieeexplore.ieee.org/document/10058021/">Supervised Local Training With Backward Links for Deep Neural Networks</a></div><div><b>Author(s): </b>Wenzhe Guo, Mohammed E. Fouda, Ahmed M. Eltawil, Khaled Nabil Salama</div><div><b>Pages: </b>854 - 867</div><div><br /></div><div><b>33)</b> <a href="https://ieeexplore.ieee.org/document/10113724/">A Similarity Matrix Low-Rank Approximation and Inconsistency Separation Fusion Approach for Multiview Clustering</a></div><div><b>Author(s): </b>Ziqiang He, Shaohua Wan, Marco Zappatore, Hu Lu</div><div><b>Pages: </b>868 - 881</div><div><br /></div><div><b>34)</b> <a href="https://ieeexplore.ieee.org/document/10098617/">Multiobjective Multiverse Optimizer for Multirobotic U-Shaped Disassembly Line Balancing Problems</a></div><div><b>Author(s): </b>Shujin Qin, Shancheng Zhang, Jiacun Wang, Shixin Liu, Xiwang Guo, Liang Qi</div><div><b>Pages: </b>882 - 894</div><div><br /></div><div><b>35)</b> <a href="https://ieeexplore.ieee.org/document/10098638/">Weighted Fuzzy System for Identifying DNA N4-Methylcytosine Sites With Kernel Entropy Component Analysis</a></div><div><b>Author(s): </b>Leyao Wang, Prayag Tiwari, Yijie Ding, Fei Guo</div><div><b>Pages: </b>895 - 903</div><div><br /></div><div><b>36)</b> <a href="https://ieeexplore.ieee.org/document/10121628/">Mitigating Bias in Bayesian Optimized Data While Designing MacPherson Suspension Architecture</a></div><div><b>Author(s): </b>Sinnu Susan Thomas, Guillaume Lamine, Jacopo Palandri, Mohsen Lakehal-ayat, Punarjay<b> </b>Chakravarty, Friedrich Wolf-Monheim, Matthew B. Blaschko</div><div><b>Pages: </b>904 - 915</div><div><br /></div><div><b>37)</b> <a href="https://ieeexplore.ieee.org/document/10124270/">Dual-Event-Triggered Intelligence Security Control for Multiagent Systems Against DoS Attacks With Applications in Mobile Robot Systems</a></div><div><b>Author(s): </b>Hongjing Liang, Zhenyu Chang, Yingnan Pan</div><div><b>Pages: </b>916 - 924</div><div><br /></div><div><b>38)</b> <a href="https://ieeexplore.ieee.org/document/10136777/">Attacking-Distance-Aware Attack: Semi-targeted Model Poisoning on Federated Learning</a></div><div><b>Author(s): </b>Yuwei Sun, Hideya Ochiai, Jun Sakuma</div><div><b>Pages: </b>925 - 939</div><div><br /></div><div><b>39)</b> <a href="https://ieeexplore.ieee.org/document/10130633/">Generative Augmentation-Driven Prediction of Diverse Visual Scanpaths in Images</a></div><div><b>Author(s): </b>Ashish Verma, Debashis Sen</div><div><b>Pages: </b>940 - 955</div><div><br /></div><div><b>40)</b> <a href="https://ieeexplore.ieee.org/document/10098642/">Visual Relationship Detection for Workplace Safety Applications</a></div><div><b>Author(s): </b>Thomas Truong, Svetlana Yanushkevich</div><div><b>Pages: </b>956 - 961</div><div><br /></div></div>Mike Wattshttp://www.blogger.com/profile/12970193877427051617noreply@blogger.com0tag:blogger.com,1999:blog-1601699047263756666.post-24240695406656446012024-02-14T17:55:00.000+13:002024-02-14T17:55:21.378+13:00Soft Computing, Volume 28, Issue 5, March 2024<div><b>1)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09548-0">IoT and cloud-based COVID-19 risk of infection prediction using hesitant intuitionistic fuzzy set</a></div><div><b>Author(s): </b>Nitin Kumar Tyagi, Kanchan Tyagi</div><div><b>Pages: </b>3743 - 3755</div><div><br /></div><div><b>2)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09567-x">An incremental approach for calculating dominance-based rough set dependency</a></div><div><b>Author(s): </b>Rana Muhammad Kaleem Ullah, Usman Qamar, John Ahmet Erkoyuncu</div><div><b>Pages: </b>3757 - 3781</div><div><br /></div><div><b>3)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09572-0">Some properties of generalized comaximal graph of commutative ring</a></div><div><b>Author(s): </b>B. Biswas, S. Kar</div><div><b>Pages: </b>3783 - 3791</div><div><br /></div><div><b>4)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09575-x">Approximation properties of a certain modification of Durrmeyer operators</a></div><div><b>Author(s): </b>Asha Ram Gairola, Karunesh Kumar Singh, Vishnu Narayan Mishra</div><div><b>Pages: </b>3793 - 3811</div><div><br /></div><div><b>5)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09608-5">Lebesgue decomposition type theorems for weakly null-additive functions on D-posets</a></div><div><b>Author(s): </b>Mona Khare, Anurag Shukla, Pratibha Pandey</div><div><b>Pages: </b>3813 - 3821</div><div><br /></div><div><b>6)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09596-6">Inference for multicomponent stress–strength reliability based on unit generalized Rayleigh distribution</a></div><div><b>Author(s): </b>Mayank Kumar Jha, Kundan Singh, Yogesh Mani Tripathi</div><div><b>Pages: </b>3823 - 3846</div><div><br /></div><div><b>7)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09320-4">Optimal conventional and nonconventional machining processes via particle swarm optimization and flower pollination algorithm</a></div><div><b>Author(s): </b>Mohamed Arezki Mellal, Imene Tamazirt, Edward J. Williams</div><div><b>Pages: </b>3847 - 3858</div><div><br /></div><div><b>8)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09326-y">An EOQ model with fractional order rate of change of inventory level and time-varying holding cost</a></div><div><b>Author(s): </b>Rituparna Pakhira, Bapin Mondal, Susmita Sarkar</div><div><b>Pages: </b>3859 - 3877</div><div><br /></div><div><b>9)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09332-0">A modified hybrid particle swarm optimization based on comprehensive learning and dynamic multi-swarm strategy</a></div><div><b>Author(s): </b>Rui Wang, Kuangrong Hao, Chenwei Zhao</div><div><b>Pages: </b>3879 - 3903</div><div><br /></div><div><b>10)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09334-y">Optimized simulated annealing for efficient generation of highly nonlinear S-boxes</a></div><div><b>Author(s): </b>Alexandr Kuznetsov, Nikolay Poluyanenko, Olha Pieshkova</div><div><b>Pages: </b>3905 - 3920</div><div><br /></div><div><b>11)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09348-6">Evolutionary algorithm with a regression model for multiobjective minimization of systemic risk in financial systems</a></div><div><b>Author(s): </b>Krzysztof Michalak</div><div><b>Pages: </b>3921 - 3939</div><div><br /></div><div><b>12)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09351-x">Enhancing the Whale Optimisation Algorithm with sub-population and hybrid techniques for single- and multi-objective optimisation</a></div><div><b>Author(s): </b>Zheng Cai, Yit Hong Choo, Mingyu Liao</div><div><b>Pages: </b>3941 - 3971</div><div><br /></div><div><b>13)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09359-3">Diverse government subsidy modes in a supply chain considering different innovation dimensions</a></div><div><b>Author(s): </b>Jichuan Zheng, Hua Zhao, Jia Fu</div><div><b>Pages: </b>3973 - 3986</div><div><br /></div><div><b>14)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09369-1">Stochastic operating room scheduling: a new model for solving problem and an approach for determining the factors that affect operation time variations</a></div><div><b>Author(s): </b>Şeyda Gür, Hacı Mehmet Alakaş, Tamer Eren</div><div><b>Pages: </b>3987 - 4007</div><div><br /></div><div><b>15)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09373-5">Identification of feedback nonlinear systems with time delay based on chaotic decreasing weight sparrow search algorithm</a></div><div><b>Author(s): </b>Junhong Li, Jun Yan, Kang Xiao</div><div><b>Pages: </b>4009 - 4024</div><div><br /></div><div><b>16)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09376-2">A non-linear generalization of optimization problems subjected to continuous max-t-norm fuzzy relational inequalities</a></div><div><b>Author(s): </b>Amin Ghodousian, Babak Sepehri Rad, Oveys Ghodousian</div><div><b>Pages: </b>4025 - 4036</div><div><br /></div><div><b>17)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09381-5">Energy-delay aware request scheduling in hybrid Cloud and Fog computing using improved multi-objective CS algorithm</a></div><div><b>Author(s): </b>Fatemeh Bahrani, PourSepehr Ebrahimi Mood, Mohammad Farshi</div><div><b>Pages: </b>4037 - 4050</div><div><br /></div><div><b>18)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09385-1">A novel human-inspirited collectivism teaching–learning-based optimization algorithm with multi-mode group-individual cooperation strategies</a></div><div><b>Author(s): </b>Zhixiang Chen</div><div><b>Pages: </b>4051 - 4105</div><div><br /></div><div><b>19)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09388-y">Generalized Hukuhara Hadamard derivative of interval-valued functions and its applications to interval optimization</a></div><div><b>Author(s): </b>Ram Surat Chauhan, Debdas Ghosh, Qamrul Hasan Ansari</div><div><b>Pages: </b>4107 - 4123</div><div><br /></div><div><b>20)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09389-x">Advancing an evaluation model: how do family SMEs select innovation scheme in lean management?</a></div><div><b>Author(s): </b>Shuwei Jing, Kaixuan Hou, Junai Yan</div><div><b>Pages: </b>4125 - 4149</div><div><br /></div><div><b>21)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09498-7">IHBO_CA: an improved honey-badger optimization-based communication approach for energy-efficient deployment of secure flying ad-hoc network (FANET)</a></div><div><b>Author(s): </b>Mayank Namdev, Sachin Goyal, Ratish Agrawal</div><div><b>Pages: </b>4151 - 4170</div><div><br /></div><div><b>22)</b> <a href="https://link.springer.com/article/10.1007/s00500-024-09645-8">Analyzing threat flow over network using ensemble-based dense network model</a></div><div><b>Author(s): </b>U. Harita, Moulana Mohammed</div><div><b>Pages: </b>4171 - 4184</div><div><br /></div><div><b>23)</b> <a href="https://link.springer.com/article/10.1007/s00500-024-09669-0">Design of a novel panoptic segmentation using multi-scale pooling model for tooth segmentation</a></div><div><b>Author(s): </b>Pulipati Nagaraju, S. V. Sudha</div><div><b>Pages: </b>4185 - 4196</div><div><br /></div><div><b>24)</b> <a href="https://link.springer.com/article/10.1007/s00500-024-09692-1">Lifetime maximization of wireless sensor networks while ensuring intruder detection</a></div><div><b>Author(s): </b>Muhammed Fatih Çorapsız</div><div><b>Pages: </b>4197 - 4215</div><div><br /></div><div><b>25)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09440-x">Multiple attribute decision making technique using single-valued neutrosophic trigonometric Dombi aggregation operators</a></div><div><b>Author(s): </b>Ruoyu Zhang, Jun Ye</div><div><b>Pages: </b>4217 - 4234</div><div><br /></div><div><b>26)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09574-y">Varied offspring memetic algorithm with three parents for a realistic synchronized goods delivery and service problem</a></div><div><b>Author(s): </b>Somnath Maji, Samir Maity, Manoranjan Maiti</div><div><b>Pages: </b>4235 - 4265</div><div><br /></div><div><b>27)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09593-9">Does fuzzification of pairwise comparisons in analytic hierarchy process add any value?</a></div><div><b>Author(s): </b>Faran Ahmed, Kemal Kilic</div><div><b>Pages: </b>4267 - 4284</div><div><br /></div><div><b>28)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09594-8">The probabilistic hesitant fuzzy TOPSIS method based on the regret theory and its application in investment strategy</a></div><div><b>Author(s): </b>Chenyang Song, Zeshui Xu, Jianchao Ji</div><div><b>Pages: </b>4285 - 4298</div><div><br /></div><div><b>29)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09595-7">Predictive and simultaneous weighting of criteria and alternatives (PSWCA) in multi-criteria decision making based on past data</a></div><div><b>Author(s): </b>Arash Pazhouhandeh, Parvaneh Samouei</div><div><b>Pages: </b>4299 - 4319</div><div><br /></div><div><b>30)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-08723-7">Motorcyclist helmet detection in single images: a dual-detection framework with multi-head self-attention</a></div><div><b>Author(s): </b>Chun-Hong Li, Dong Huang, Jinrong Cui</div><div><b>Pages: </b>4321 - 4333</div><div><br /></div><div><b>31)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-08747-z">An image encryption algorithm based on circular rotation and generalized Feistel structure</a></div><div><b>Author(s): </b>Yafei Wang, Lin Teng, Xingyuan Wang</div><div><b>Pages: </b>4335 - 4358</div><div><br /></div><div><b>32)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-08756-y">Designing robust capability-based distributed machine layouts with random machine availability and fuzzy demand/process flow information</a></div><div><b>Author(s): </b>Kemal Subulan, Bilge Varol, Adil Baykasoğlu</div><div><b>Pages: </b>4359 - 4397</div><div><br /></div><div><b>33)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-08788-4">A pyramid transformer with cross-shaped windows for low-light image enhancement</a></div><div><b>Author(s): </b>Canlin Li, Pengcheng Gao, Lihua Bi</div><div><b>Pages: </b>4399 - 4411</div><div><br /></div><div><b>34)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-08808-3">An efficient group synchronization of chaos-tuned neural networks for exchange of common secret key</a></div><div><b>Author(s): </b>Arindam Sarkar, Krishna Daripa, Abdulfattah Noorwali</div><div><b>Pages: </b>4413 - 4433</div><div><br /></div><div><b>35)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-08817-2">Seasonal prediction of solar irradiance with modified fuzzy Q-learning</a></div><div><b>Author(s): </b>Tushar Shikhola, Rajneesh Sharma, Jaspreet Kaur Kohli</div><div><b>Pages: </b>4435 - 4455</div><div><br /></div><div><b>36)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-08835-0">A feedback analyzer system for interval valued responses on cloud services</a></div><div><b>Author(s): </b>Tina Esther Trueman, P. Narayanasamy, Ashok Kumar Jayaraman</div><div><b>Pages: </b>4457 - 4469</div><div><br /></div><div><b>37)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-08836-z">Generation of ideal chaotic sequences by reducing the dynamical degradation of digital chaotic maps</a></div><div><b>Author(s): </b>Shijie Zhang, Lingfeng Liu</div><div><b>Pages: </b>4471 - 4487</div><div><br /></div><div><b>38)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-08837-y">Method for automatic detection of movement-related EEG pattern time boundaries</a></div><div><b>Author(s): </b>I. V. Shcherban, D. M. Lazurenko, A. V. Shustova</div><div><b>Pages: </b>4489 - 4501</div><div><br /></div><div><b>39)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09408-x">A network intrusion detection framework on sparse deep denoising auto-encoder for dimensionality reduction</a></div><div><b>Author(s): </b>B. A. Manjunatha, K. Aditya Shastry, Kadiri Thirupal Reddy</div><div><b>Pages: </b>4503 - 4517</div><div><br /></div><div><b>40)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09610-x">Class imbalance data handling with optimal deep learning-based intrusion detection in IoT environment</a></div><div><b>Author(s): </b>Manohar Srinivasan, Narayanan Chidambaram Senthilkumar</div><div><b>Pages: </b>4519 - 4529</div><div><br /></div><div><b>41)</b> <a href="https://link.springer.com/article/10.1007/s00500-024-09639-6">Towards an open university based on machine learning for the teaching service support system using backpropagation neural networks</a></div><div><b>Author(s): </b>Jianjun Wang</div><div><b>Pages: </b>4531 - 4549</div><div><br /></div><div><b>42)</b> <a href="https://link.springer.com/article/10.1007/s00500-024-09641-y">Establishing a soil carbon flux monitoring system based on support vector machine and XGBoost</a></div><div><b>Author(s): </b>Hanwei Ding</div><div><b>Pages: </b>4551 - 4574</div><div><br /></div><div><b>43)</b> <a href="https://link.springer.com/article/10.1007/s00500-024-09644-9">Segmentation and detection of skin cancer using fuzzy cognitive map and deep Seg Net</a></div><div><b>Author(s): </b>K. Anup Kumar, C. Vanmathi</div><div><b>Pages: </b>4575 - 4592</div><div><br /></div><div><b>44)</b> <a href="https://link.springer.com/article/10.1007/s00500-024-09668-1">Modelling an efficient hybridized approach for facial emotion recognition using unconstraint videos and deep learning approaches</a></div><div><b>Author(s): </b>P. Naga Bhushanam, S. Selva Kumar</div><div><b>Pages: </b>4593 - 4606</div><div><br /></div><div><b>45)</b> <a href="https://link.springer.com/article/10.1007/s00500-024-09695-y">Classification of yoga, meditation, combined yoga–meditation EEG signals using L-SVM, KNN, and MLP classifiers</a></div><div><b>Author(s): </b>A. Rajalakshmi, S. S. Sridhar</div><div><b>Pages: </b>4607 - 4619</div><div><br /></div><div><b>46)</b> <a href="https://link.springer.com/article/10.1007/s00500-024-09696-x">Pain detection through facial expressions in children with autism using deep learning</a></div><div><b>Author(s): </b>P. V. K. Sandeep, N. Suresh Kumar</div><div><b>Pages: </b>4621 - 4630</div><div><br /></div><div><b>47)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09207-4">An optimized radial basis function neural network with modulation-window activation function</a></div><div><b>Author(s): </b>Haijun Lin, Houde Dai, Lucai Wang</div><div><b>Pages: </b>4631 - 4648</div><div><br /></div><div><b>48)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09239-w">Understanding evolving user choices: a neural network analysis of TAXI and ride-hailing services in Barcelona</a></div><div><b>Author(s): </b>Miguel Guillén-Pujadas, Emili Vizuete-Luciano, M. Carmen Gracia-Ramos</div><div><b>Pages: </b>4649 - 4665</div><div><br /></div><div><b>49)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09386-0">An efficacious neural network and DNA cryptography-based algorithm for preventing black hole attacks in MANET</a></div><div><b>Author(s): </b>Rahul Chakravorty, Jay Prakash, Ashish Srivastava</div><div><b>Pages: </b>4667 - 4679</div><div><br /></div><div><b>50)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09490-1">Alleviating repetitive tokens in non-autoregressive machine translation with unlikelihood training</a></div><div><b>Author(s): </b>Shuheng Wang, Shumin Shi, Heyan Huang</div><div><b>Pages: </b>4681 - 4688</div><div><br /></div>Mike Wattshttp://www.blogger.com/profile/12970193877427051617noreply@blogger.com0tag:blogger.com,1999:blog-1601699047263756666.post-81975698595502803022024-02-12T10:32:00.000+13:002024-02-12T10:32:23.177+13:00Complex & Intelligent Systems, Volume 10, Issue 1, February 2024<div style="text-align: left;"><div><b>1)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01050-2">Big data analytics and knowledge discovery for urban computing and intelligence</a></div><div><b>Author(s): </b>Krishna Kant Singh, Seungmin Rho, Chernyi Sergei</div><div><b>Pages: </b>1 - 2</div><div><br /></div><div><b>2)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01018-2">MixER: linear interpolation of latent space for entity resolution</a></div><div><b>Author(s): </b>Huaiguang Wu, Shuaichao Li</div><div><b>Pages: </b>3 - 22</div><div><br /></div><div><b>3)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01099-z">Predicting traffic propagation flow in urban road network with multi-graph convolutional network</a></div><div><b>Author(s): </b>Haiqiang Yang, Zihan Li, Yashuai Qi</div><div><b>Pages: </b>23 - 35</div><div><br /></div><div><b>4)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01153-w">An efficient discrete artificial bee colony algorithm with dynamic calculation method for solving the AGV scheduling problem of delivery and pickup</a></div><div><b>Author(s): </b>Xujin Zhang, Hongyan Sang, Leilei Meng</div><div><b>Pages: </b>37 - 57</div><div><br /></div><div><b>5)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01125-0">Hesitant picture fuzzy linguistic prospects theory-based evidential reasoning assessment method for digital transformation solution of small and medium-sized enterprises</a></div><div><b>Author(s): </b>Xiao-hui Wu, Lin Yang</div><div><b>Pages: </b>59 - 73</div><div><br /></div><div><b>6)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01134-z">Kernel-mask knowledge distillation for efficient and accurate arbitrary-shaped text detection</a></div><div><b>Author(s): </b>Honghui Chen, Yuhang Qiu, Pingping Chen</div><div><b>Pages: </b>75 - 86</div><div><br /></div><div><b>7)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01092-6">Some p,q-cubic quasi-rung orthopair fuzzy operators for multi-attribute decision-making</a></div><div><b>Author(s): </b>Yu-Ming Chu, Harish Garg, Eskandar Ameer</div><div><b>Pages: </b>87 - 110</div><div><br /></div><div><b>8)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01068-6">Hierarchical graph neural network with subgraph perturbations for key gene cluster discovery in cancer staging</a></div><div><b>Author(s): </b>Wenju Hou, Yan Wang, Yuan Tian</div><div><b>Pages: </b>111 - 128</div><div><br /></div><div><b>9)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01138-9">Feature gene selection based on fuzzy neighborhood joint entropy</a></div><div><b>Author(s): </b>Yan Wang, Minjie Sun, Yifan Ren</div><div><b>Pages: </b>129 - 144</div><div><br /></div><div><b>10)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01148-7">Attention-deep reinforcement learning jointly beamforming based on tensor decomposition for RIS-assisted V2X mmWave massive MIMO system</a></div><div><b>Author(s): </b>Xiaoping Zhou, Xinyue Chen, Yang Wang</div><div><b>Pages: </b>145 - 160</div><div><br /></div><div><b>11)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01156-7">Non-line-of-sight target tracking with improved recurrent extreme learning machine</a></div><div><b>Author(s): </b>Xiaofeng Yang</div><div><b>Pages: </b>161 - 170</div><div><br /></div><div><b>12)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01168-3">Dual-drive collaboration surrogate-assisted evolutionary algorithm by coupling feature reduction and reconstruction</a></div><div><b>Author(s): </b>Haibo Yu, Yiyun Gong, Jianchao Zeng</div><div><b>Pages: </b>171 - 191</div><div><br /></div><div><b>13)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01155-8">A knowledge-based task planning approach for robot multi-task manipulation</a></div><div><b>Author(s): </b>Deshuai Zheng, Jin Yan, Yong Liu</div><div><b>Pages: </b>193 - 206</div><div><br /></div><div><b>14)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01161-w">A many-objective evolutionary algorithm with metric-based reference vector adjustment</a></div><div><b>Author(s): </b>Xujian Wang, Fenggan Zhang, Minli Yao</div><div><b>Pages: </b>207 - 231</div><div><br /></div><div><b>15)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01121-4">Applying particle swarm optimization-based dynamic adaptive hyperlink evaluation to focused crawler for meteorological disasters</a></div><div><b>Author(s): </b>Jingfa Liu, Zhihe Yang, Duanbing Chen</div><div><b>Pages: </b>233 - 255</div><div><br /></div><div><b>16) </b><a href="https://link.springer.com/article/10.1007/s40747-023-01141-0">Retinal disease projection conditioning by biological traits</a></div><div><b>Author(s): </b>Muhammad Hassan, Hao Zhang, Peiwu Qin</div><div><b>Pages: </b>257 - 271</div><div><br /></div><div><b>17)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01108-1">MABAC framework for logarithmic bipolar fuzzy multiple attribute group decision-making for supplier selection</a></div><div><b>Author(s): </b>Chiranjibe Jana, Harish Garg, Guiwu Wei</div><div><b>Pages: </b>273 - 288</div><div><br /></div><div><b>18)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01158-5">Image inpainting via progressive decoder and gradient guidance</a></div><div><b>Author(s): </b>Shuang Hou, Xiucheng Dong, Fan Zhang</div><div><b>Pages: </b>289 - 303</div><div><br /></div><div><b>19)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01171-8">Dual-attention Network for View-invariant Action Recognition</a></div><div><b>Author(s): </b>Gedamu Alemu Kumie, Maregu Assefa Habtie, Aiman Erbad</div><div><b>Pages: </b>305 - 321</div><div><br /></div><div><b>20)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01123-2">Gravity assist space pruning and global optimization of spacecraft trajectories for solar system boundary exploration</a></div><div><b>Author(s): </b>Yuqi Song, Weiren Wu, Jinxiu Zhang</div><div><b>Pages: </b>323 - 341</div><div><br /></div><div><b>21)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01178-1">A discrete-time distributed optimization algorithm for cooperative transportation of multi-robot system</a></div><div><b>Author(s): </b>Xiwang Meng, Jiatao Sun, Guoyi Chi</div><div><b>Pages: </b>343 - 355</div><div><br /></div><div><b>22)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01160-x">DIFLD: domain invariant feature learning to detect low-quality compressed face forgery images</a></div><div><b>Author(s): </b>Yan Zou, Chaoyang Luo, Jianxun Zhang</div><div><b>Pages: </b>357 - 368</div><div><br /></div><div><b>23)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01164-7">Adaptive fractional tracking control of robotic manipulator using fixed-time method</a></div><div><b>Author(s): </b>Saim Ahmed, Ahmad Taher Azar</div><div><b>Pages: </b>369 - 382</div><div><br /></div><div><b>24)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01166-5">Maximal sparse convex surrogate-assisted evolutionary convolutional neural architecture search for image segmentation</a></div><div><b>Author(s): </b>Wei Wang, Xianpeng Wang, Xiangman Song</div><div><b>Pages: </b>383 - 396</div><div><br /></div><div><b>25)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01169-2">Online motion planning of mobile cable-driven parallel robots for autonomous navigation in uncertain environments</a></div><div><b>Author(s): </b>Jiajun Xu, Byeong-Geon Kim, Kyoung-Su Park</div><div><b>Pages: </b>397 - 412</div><div><br /></div><div><b>26)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01136-x">An improved estimation of distribution algorithm for rescue task emergency scheduling considering stochastic deterioration of the injured</a></div><div><b>Author(s): </b>Ying Xu, Xiaobo Li, Weipeng Zhang</div><div><b>Pages: </b>413 - 434</div><div><br /></div><div><b>27)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01149-6">Security enhancement of the access control scheme in IoMT applications based on fuzzy logic processing and lightweight encryption</a></div><div><b>Author(s): </b>Ghada M. El-Banby, Lamiaa A. Abou Elazm, Ali I. Siam</div><div><b>Pages: </b>435 - 454</div><div><br /></div><div><b>28)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01150-z">Hybrid multi-criteria decision-making technique for the selection of best cryptographic multivalued Boolean function</a></div><div><b>Author(s): </b>Nabilah Abughazalah, Majid Khan, Mohsin Iqbal</div><div><b>Pages: </b>455 - 468</div><div><br /></div><div><b>29)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01167-4">Uncertainty guided ensemble self-training for semi-supervised global field reconstruction</a></div><div><b>Author(s): </b>Yunyang Zhang, Zhiqiang Gong, Wen Yao</div><div><b>Pages: </b>469 - 483</div><div><br /></div><div><b>30)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01177-2">Enhanced SparseEA for large-scale multi-objective feature selection problems</a></div><div><b>Author(s): </b>Shu-Chuan Chu, Zhongjie Zhuang, Chia-Cheng Hu</div><div><b>Pages: </b>485 - 507</div><div><br /></div><div><b>31)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01182-5">Exact neutrosophic analysis of missing value in augmented randomized complete block design</a></div><div><b>Author(s): </b>Abdulrahman AlAita, Hooshang Talebi</div><div><b>Pages: </b>509 - 523</div><div><br /></div><div><b>32)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01162-9">An integrated differential evolution of multi-population based on contribution degree</a></div><div><b>Author(s): </b>Yufeng Wang, Hao Yang, Guoqing Xu</div><div><b>Pages: </b>525 - 550</div><div><br /></div><div><b>33)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01159-4">Adaptive differential evolution with fitness-based crossover rate for global numerical optimization</a></div><div><b>Author(s): </b>Lianzheng Cheng, Jia-Xi Zhou, Yun Liu</div><div><b>Pages: </b>551 - 576</div><div><br /></div><div><b>34)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01170-9">Cross-modal knowledge guided model for abstractive summarization</a></div><div><b>Author(s): </b>Hong Wang, Jin Liu, Bing Han</div><div><b>Pages: </b>577 - 594</div><div><br /></div><div><b>35)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01183-4">Evolving deep gated recurrent unit using improved marine predator algorithm for profit prediction based on financial accounting information system</a></div><div><b>Author(s): </b>Xue Li, Mohammad Khishe, Leren Qian</div><div><b>Pages: </b>595 - 611</div><div><br /></div><div><b>36)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01126-z">MT-AD: multi-layer temporal transaction anomaly detection in ethereum networks with GNN</a></div><div><b>Author(s): </b>Beibei Han, Yingmei Wei, Claudio J. 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M. Kamel, Ákos Tényi</div><div><b>Pages: </b>677 - 690</div><div><br /></div><div><b>41)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01176-3">Rotation-equivariant transformer for oriented person detection of overhead fisheye images</a></div><div><b>Author(s): </b>You Zhou, Yong Bai, Yongqing Chen</div><div><b>Pages: </b>691 - 703</div><div><br /></div><div><b>42)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01185-2">Multi-scale attention-based lightweight network with dilated convolutions for infrared and visible image fusion</a></div><div><b>Author(s): </b>Fuquan Li, Yonghui Zhou, Mian Tan</div><div><b>Pages: </b>705 - 719</div><div><br /></div><div><b>43)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01191-4">A sequential neural recommendation system exploiting BERT and LSTM on social media posts</a></div><div><b>Author(s): </b>A. Noorian, A. Harounabadi, M. Hazratifard</div><div><b>Pages: </b>721 - 744</div><div><br /></div><div><b>44)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01187-0">A new dynamic DNA-coding model for gray-scale image encryption</a></div><div><b>Author(s): </b>Yasmine M. Afify, Nada H. Sharkawy, Nagwa Badr</div><div><b>Pages: </b>745 - 761</div><div><br /></div><div><b>45)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01190-5">LVIF: a lightweight tightly coupled stereo-inertial SLAM with fisheye camera</a></div><div><b>Author(s): </b>Hongwei Zhu, Guobao Zhang, Hongyi Zhou</div><div><b>Pages: </b>763 - 780</div><div><br /></div><div><b>46)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01147-8">A cooperative adaptive genetic algorithm for reentrant hybrid flow shop scheduling with sequence-dependent setup time and limited buffers</a></div><div><b>Author(s): </b>Qianqian Zheng, Yu Zhang, Lijun He</div><div><b>Pages: </b>781 - 809</div><div><br /></div><div><b>47)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01174-5">Learning high-level robotic manipulation actions with visual predictive model</a></div><div><b>Author(s): </b>Anji Ma, Guoyi Chi, Lipeng Chen</div><div><b>Pages: </b>811 - 823</div><div><br /></div><div><b>48)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01179-0">Data-driven evolutionary computation for service constrained inventory optimization in multi-echelon supply chains</a></div><div><b>Author(s): </b>Ziang Liu, Tatsushi Nishi</div><div><b>Pages: </b>825 - 846</div><div><br /></div><div><b>49)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01186-1">A novel adaptive parameter strategy differential evolution algorithm and its application in midcourse guidance maneuver decision-making</a></div><div><b>Author(s): </b>Lei Xie, Yuan Wang, Ting Song</div><div><b>Pages: </b>847 - 868</div><div><br /></div><div><b>50)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01200-6">Imperceptible graph injection attack on graph neural networks</a></div><div><b>Author(s): </b>Yang Chen, Zhonglin Ye, Haixing Zhao</div><div><b>Pages: </b>869 - 883</div><div><br /></div><div><b>51)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01180-7">A deep learning model for steel surface defect detection</a></div><div><b>Author(s): </b>Zhaoguo Li, Xiumei Wei, Xuesong Jiang</div><div><b>Pages: </b>885 - 897</div><div><br /></div><div><b>52)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01154-9">Active broad learning with multi-objective evolution for data stream classification</a></div><div><b>Author(s): </b>Jian Cheng, Zhiji Zheng, Shengxiang Yang</div><div><b>Pages: </b>899 - 916</div><div><br /></div><div><b>53)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01172-7">Evolutionary game dynamics of multi-agent systems using local information considering hide right</a></div><div><b>Author(s): </b>Yida Dong, Xuesong Liu, C. L. Philip Chen</div><div><b>Pages: </b>917 - 925</div><div><br /></div><div><b>54)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01188-z">Three-way decision based on ITARA and public weights DEA under picture fuzzy environment and its application in new energy vehicles selection</a></div><div><b>Author(s): </b>Meiqin Wu, Jiawen Song, Jianping Fan</div><div><b>Pages: </b>927 - 947</div><div><br /></div><div><b>55)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01189-y">Parameter sharing and multi-granularity feature learning for cross-modality person re-identification</a></div><div><b>Author(s): </b>Sixian Chan, Feng Du, Qiu Guan</div><div><b>Pages: </b>949 - 962</div><div><br /></div><div><b>56)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01192-3">An efficient long-text semantic retrieval approach via utilizing presentation learning on short-text</a></div><div><b>Author(s): </b>Junmei Wang, Jimmy X. Huang, Jinhua Sheng</div><div><b>Pages: </b>963 - 979</div><div><br /></div><div><b>57)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01196-z">An overview of developments and challenges for unmanned surface vehicle autonomous berthing</a></div><div><b>Author(s): </b>Gongxing Wu, Debiao Li, Bing Han</div><div><b>Pages: </b>981 - 1003</div><div><br /></div><div><b>58)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01130-3">Q-rung orthopair hesitant fuzzy preference relations and its group decision-making application</a></div><div><b>Author(s): </b>Benting Wan, Jiao Zhang, Weikang Huang</div><div><b>Pages: </b>1005 - 1026</div><div><br /></div><div><b>59)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01198-x">A composition–decomposition based federated learning</a></div><div><b>Author(s): </b>Chaoli Sun, Xiaojun Wang, Gang Xie</div><div><b>Pages: </b>1027 - 1042</div><div><br /></div><div><b>60)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01202-4">Multi-modal mutation cooperatively coevolving algorithm for resource allocation of large-scale D2D communication system</a></div><div><b>Author(s): </b>Qing An, Shisong Wu, Cuifen Gao</div><div><b>Pages: </b>1043 - 1059</div><div><br /></div><div><b>61)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01116-1">Novel intuitionistic fuzzy Aczel Alsina Hamy mean operators and their applications in the assessment of construction material</a></div><div><b>Author(s): </b>Abrar Hussain, Haolun Wang, Dragan Pamucar</div><div><b>Pages: </b>1061 - 1086</div><div><br /></div><div><b>62)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01197-y">Adaptive optimal sliding-mode fault-tolerant control for nonlinear systems with disturbances and estimation errors</a></div><div><b>Author(s): </b>Yanbin Du, Bin Jiang, Yajie Ma</div><div><b>Pages: </b>1087 - 1101</div><div><br /></div><div><b>63)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01213-1">Fundamental properties of fuzzy rough sets based on triangular norms and fuzzy implications: the properties characterized by fuzzy neighborhood and fuzzy topology</a></div><div><b>Author(s): </b>Zhaohao Wang</div><div><b>Pages: </b>1103 - 1114</div><div><br /></div><div><b>64)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01204-2">DCCAFN: deep convolution cascade attention fusion network based on imaging genomics for prediction survival analysis of lung cancer</a></div><div><b>Author(s): </b>Liye Jia, Xueting Ren, Qianqian Yang</div><div><b>Pages: </b>1115 - 1130</div><div><br /></div><div><b>65)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01212-2">A multi-layer composite identification scheme of cryptographic algorithm based on hybrid random forest and logistic regression model</a></div><div><b>Author(s): </b>Ke Yuan, Yabing Huang, Chunfu Jia</div><div><b>Pages: </b>1131 - 1147</div><div><br /></div><div><b>66)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01216-y">Dynamic warning zone and a short-distance goal for autonomous robot navigation using deep reinforcement learning</a></div><div><b>Author(s): </b>Estrella Elvia Montero, Husna Mutahira, Mannan Saeed Muhammad</div><div><b>Pages: </b>1149 - 1166</div><div><br /></div><div><b>67)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01214-0">Evolutionary generative design of supercritical airfoils: an automated approach driven by small data</a></div><div><b>Author(s): </b>Kebin Sun, Weituo Wang, Miao Zhang</div><div><b>Pages: </b>1167 - 1183</div><div><br /></div><div><b>68)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01215-z">An Aczel-Alsina aggregation-based outranking method for multiple attribute decision-making using single-valued neutrosophic numbers</a></div><div><b>Author(s): </b>Tapan Senapati</div><div><b>Pages: </b>1185 - 1199</div><div><br /></div><div><b>69)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01205-1">Cybersecurity knowledge graphs construction and quality assessment</a></div><div><b>Author(s): </b>Hongyi Li, Ze Shi, Nan Sun</div><div><b>Pages: </b>1201 - 1217</div><div><br /></div><div><b>70)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01210-4">TCANet: three-stream coordinate attention network for RGB-D indoor semantic segmentation</a></div><div><b>Author(s): </b>Weikuan Jia, Xingchao Yan, Xishang Dong</div><div><b>Pages: </b>1219 - 1230</div><div><br /></div><div><b>71)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01208-y">The edge segmentation of grains in thin-section petrographic images utilising extinction consistency perception network</a></div><div><b>Author(s): </b>Ping Zhang, Jiazhou Zhou, Liu Pu</div><div><b>Pages: </b>1231 - 1245</div><div><br /></div><div><b>72)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01206-0">Multi-agent system-based polymorphic distributed energy management for ships entering and leaving ports considering computing power resources</a></div><div><b>Author(s): </b>Qihe Shan, Qi Qu, Tieshan Li</div><div><b>Pages: </b>1247 - 1264</div><div><br /></div><div><b>73)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01207-z">Bionic visual navigation model for enhanced template matching and loop closing in challenging lighting environments</a></div><div><b>Author(s): </b>Haidong Xu, Shumei Yu, Lining Sun</div><div><b>Pages: </b>1265 - 1281</div><div><br /></div><div><b>74)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01209-x">Large group decision-making considering multiple classifications for participators: a method based on preference information on multiple elements of alternatives</a></div><div><b>Author(s): </b>Ping-Ping Cao, Jin Zheng, Xin-Yan Wang</div><div><b>Pages: </b>1283 - 1302</div><div><br /></div><div><b>75)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01211-3">Merit: multi-level graph embedding refinement framework for large-scale graph</a></div><div><b>Author(s): </b>Weishuai Che, Zhaowei Liu, Jinglei Liu</div><div><b>Pages: </b>1303 - 1318</div><div><br /></div><div><b>76)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01221-1">A multi-task positive-unlabeled learning framework to predict secreted proteins in human body fluids</a></div><div><b>Author(s): </b>Kai He, Yan Wang, Dan Shao</div><div><b>Pages: </b>1319 - 1331</div><div><br /></div><div><b>77)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01225-x">Asymmetric gradient penalty based on power exponential function for imbalanced data classification</a></div><div><b>Author(s): </b>Linyong Zhou, Guangcan Ran, Xiaoyao Xie</div><div><b>Pages: </b>1333 - 1348</div><div><br /></div><div><b>78)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01222-0">Intent with knowledge-aware multiview contrastive learning for recommendation</a></div><div><b>Author(s): </b>Shaohua Tao, Runhe Qiu, Yuan Ping</div><div><b>Pages: </b>1349 - 1363</div><div><br /></div><div><b>79)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01224-y">Robust pedestrian tracking in video sequences using an improved STF module</a></div><div><b>Author(s): </b>Hongtao YangYuchen TangPeng Zhang</div><div><b>Pages: </b>1365 - 1374</div><div><br /></div><div><b>80)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01201-5">A novel group decision-making approach based on partitioned Hamy mean operators in q-rung orthopair fuzzy context</a></div><div><b>Author(s): </b>Sukhwinder Singh Rawat, Komal, Sarbast Moslem</div><div><b>Pages: </b>1375 - 1408</div><div><br /></div><div><b>81)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01220-2">Customized influence maximization in attributed social networks: heuristic and meta-heuristic algorithms</a></div><div><b>Author(s): </b>Jun-Chao Liang, Yue-Jiao Gong, Yuan Li</div><div><b>Pages: </b>1409 - 1424</div><div><br /></div><div><b>82)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01203-3">Hesitant Fermatean fuzzy Bonferroni mean operators for multi-attribute decision-making</a></div><div><b>Author(s): </b>Yibo Wang, Xiuqin Ma, Weiyi Wei</div><div><b>Pages: </b>1425 - 1457</div><div><br /></div><div><b>83)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01218-w">A holistic multi-source transfer learning approach using wearable sensors for personalized daily activity recognition</a></div><div><b>Author(s): </b>Qi Jia, Jing Guo, Yun Yang</div><div><b>Pages: </b>1459 - 1471</div><div><br /></div><div><b>84)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01219-9">Permute-MAML: exploring industrial surface defect detection algorithms for few-shot learning</a></div><div><b>Author(s): </b>ShanChen Pang, WenShang Zhao, Shuang Wang</div><div><b>Pages: </b>1473 - 1482</div><div><br /></div><div><b>85)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01227-9">Dynamic scheduling method for data relay satellite networks considering hybrid system disturbances</a></div><div><b>Author(s): </b>Zongling Li, Xinjiang Chen, Ling Wang</div><div><b>Pages: </b>1483 - 1499</div><div><br /></div><div><b>86)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01223-z">RCFT: re-parameterization convolution and feature filter for object tracking</a></div><div><b>Author(s): </b>Yuanyun Wang, Wenhui Yang, Jun Wang</div><div><b>Pages: </b>1501 - 1515</div><div><br /></div><div><b>87)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01236-8">RFDANet: an FMCW and TOF radar fusion approach for driver activity recognition using multi-level attention based CNN and LSTM network</a></div><div><b>Author(s): </b>Minming Gu, Kaiyu Chen, Zhixiang Chen</div><div><b>Pages: </b>1517 - 1530</div><div><br /></div><div><b>88)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01231-z">A robot-assisted adaptive communication recovery method in disaster scenarios</a></div><div><b>Author(s): </b>Kuangrong Hao, Chenwei Zhao, Xiaoyan Liu</div><div><b>Pages: </b>1531 - 1549</div><div><br /></div><div><b>89)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01226-w">LTACL: long-tail awareness contrastive learning for distantly supervised relation extraction</a></div><div><b>Author(s): </b>Tianwei Yan, Xiang Zhang, Zhigang Luo</div><div><b>Pages: </b>1551 - 1563</div><div><br /></div><div><b>90)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01228-8">Deeply integrating unsupervised semantics and syntax into heterogeneous graphs for inductive text classification</a></div><div><b>Author(s): </b>Yue Gao, Xiangling Fu, Ji Wu</div><div><b>Pages: </b>1565 - 1579</div><div><br /></div><div><b>91)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01173-6">Computer vision-based hand gesture recognition for human-robot interaction: a review</a></div><div><b>Author(s): </b>Jing Qi, Li Ma, Yushu Yu</div><div><b>Pages: </b>1581 - 1606</div><div><br /></div><div><b>92)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01175-4">Smart cities: the role of Internet of Things and machine learning in realizing a data-centric smart environment</a></div><div><b>Author(s): </b>Amin Ullah, Syed Myhammad Anwar, Tanzila Saba</div><div><b>Pages: </b>1607 - 1637</div><div><br /></div><div><b>93)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01199-w">A two-stage multiobjective evolutionary ensemble learning for silicon prediction in blast furnace</a></div><div><b>Author(s): </b>Qiang Li, Jingchuan Zhang, Xianpeng Wang</div><div><b>Pages: </b>1639 - 1660</div><div><br /></div><div><b>94)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01165-6">Correction to: Sleep-wakeup scheduling algorithm for lifespan maximization of directional sensor networks: a discrete cuckoo search optimization algorithm</a></div><div><b>Author(s): </b>Mir Gholamreza Mortazavi, Mirsaeid Hosseini Shirvani, Mahmood Fathy</div><div><b>Pages: </b>1661 - 1662</div><div><br /></div><div><b>95)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01195-0">Correction to: A novel driver emotion recognition system based on deep ensemble classification</a></div><div><b>Author(s): </b>Khalid Zaman, Sun Zhaoyun, Umer Sadiq Khan</div><div><b>Pages: </b>1663 - 1663</div><div><br /></div><div><b>96)</b> <a href="https://link.springer.com/article/10.1007/s40747-023-01194-1">Correction to: Big data analytics and knowledge discovery for urban computing and intelligence</a></div><div><b>Author(s): </b>Krishna Kant Singh, Seungmin Rho, Chernyi Sergei</div><div><b>Pages: </b>1665 - 1665</div><div><br /></div><div><br /></div></div>Mike Wattshttp://www.blogger.com/profile/12970193877427051617noreply@blogger.com0tag:blogger.com,1999:blog-1601699047263756666.post-62891131888097829122024-02-10T12:00:00.001+13:002024-02-10T12:00:00.148+13:00IEEE Transactions on Neural Networks and Learning Systems, Volume 35, Issue 2<div style="text-align: left;"><div><b>1)</b> <a href="https://ieeexplore.ieee.org/document/9837871/">Self-Supervised Learning for Electroencephalography</a></div><div><b>Author(s): </b>Mohammad H. Rafiei, Lynne V. Gauthier, Hojjat Adeli, Daniel Takabi</div><div><b>Pages: </b>1457 - 1471</div><div><br /></div><div><b>2)</b> <a href="https://ieeexplore.ieee.org/document/9798852/">Efficient Quantum Image Classification Using Single Qubit Encoding</a></div><div><b>Author(s): </b>Philip Easom-McCaldin, Ahmed Bouridane, Ammar Belatreche, Richard Jiang, Somaya Al-Maadeed</div><div><b>Pages: </b>1472 - 1486</div><div><br /></div><div><b>3)</b> <a href="https://ieeexplore.ieee.org/document/9802880/">Hybrid Adjusting Variables-Dependent Event-Based Finite-Time State Estimation for Two-Time-Scale Markov Jump Complex Networks</a></div><div><b>Author(s): </b>Xiongbo Wan, Chao Yang, Chuan-Ke Zhang, Min Wu</div><div><b>Pages: </b>1487 - 1500</div><div><br /></div><div><b>4)</b> <a href="https://ieeexplore.ieee.org/document/9804328/">Event-Triggered-Based Distributed Consensus Tracking for Nonlinear Multiagent Systems With Quantization</a></div><div><b>Author(s): </b>Jing Zhang, Shuai Liu, Xianfu Zhang, Jianwei Xia</div><div><b>Pages: </b>1501 - 1511</div><div><br /></div><div><b>5)</b> <a href="https://ieeexplore.ieee.org/document/9802891/">Kernel Path for Semisupervised Support Vector Machine</a></div><div><b>Author(s): </b>Zhou Zhai, Heng Huang, Bin Gu</div><div><b>Pages: </b>1512 - 1522</div><div><br /></div><div><b>6)</b> <a href="https://ieeexplore.ieee.org/document/9806393/">Word2Pix: Word to Pixel Cross-Attention Transformer in Visual Grounding</a></div><div><b>Author(s): </b>Heng Zhao, Joey Tianyi Zhou, Yew-Soon Ong</div><div><b>Pages: </b>1523 - 1533</div><div><br /></div><div><b>7)</b> <a href="https://ieeexplore.ieee.org/document/9805679/">Residual Q-Networks for Value Function Factorizing in Multiagent Reinforcement Learning</a></div><div><b>Author(s): </b>Rafael Pina, Varuna De Silva, Joosep Hook, Ahmet Kondoz</div><div><b>Pages: </b>1534 - 1544</div><div><br /></div><div><b>8)</b> <a href="https://ieeexplore.ieee.org/document/9805693/">Multitrend Conditional Value at Risk for Portfolio Optimization</a></div><div><b>Author(s): </b>Zhao-Rong Lai, Cheng Li, Xiaotian Wu, Quanlong Guan, Liangda Fang</div><div><b>Pages: </b>1545 - 1558</div><div><br /></div><div><b>9)</b> <a href="https://ieeexplore.ieee.org/document/9829544/">Hierarchical Sliding-Mode Surface-Based Adaptive Actor–Critic Optimal Control for Switched Nonlinear Systems With Unknown Perturbation</a></div><div><b>Author(s): </b>Haoyan Zhang, Xudong Zhao, Huanqing Wang, Guangdeng Zong, Ning Xu</div><div><b>Pages: </b>1559 - 1571</div><div><br /></div><div><b>10)</b> <a href="https://ieeexplore.ieee.org/document/9809795/">Evolving Dual-Threshold Bienenstock-Cooper-Munro Learning Rules in Echo State Networks</a></div><div><b>Author(s): </b>Xinjie Wang, Yaochu Jin, Wenli Du, Jun Wang</div><div><b>Pages: </b>1572 - 1583</div><div><br /></div><div><b>11)</b> <a href="https://ieeexplore.ieee.org/document/9810872/">Product Recognition for Unmanned Vending Machines</a></div><div><b>Author(s): </b>Chengxu Liu, Zongyang Da, Yuanzhi Liang, Yao Xue, Guoshuai Zhao, Xueming Qian</div><div><b>Pages: </b>1584 - 1597</div><div><br /></div><div><b>12) </b><a href="https://ieeexplore.ieee.org/document/9812717/">Multiscale Cross-Connected Dehazing Network With Scene Depth Fusion</a></div><div><b>Author(s): </b>Guodong Fan, Min Gan, Bi Fan, C. L. Philip Chen</div><div><b>Pages: </b>1598 - 1612</div><div><br /></div><div><b>13)</b> <a href="https://ieeexplore.ieee.org/document/9810207/">Image Regression With Structure Cycle Consistency for Heterogeneous Change Detection</a></div><div><b>Author(s): </b>Yuli Sun, Lin Lei, Dongdong Guan, Junzheng Wu, Gangyao Kuang, Li Liu</div><div><b>Pages: </b>1613 - 1627</div><div><br /></div><div><b>14)</b> <a href="https://ieeexplore.ieee.org/document/9812723/">Finite-Time Synchronization and H∞ Synchronization for Coupled Neural Networks With Multistate or Multiderivative Couplings</a></div><div><b>Author(s): </b>Jin-Liang Wang, Han-Yu Wu, Tingwen Huang, Shun-Yan Ren</div><div><b>Pages: </b>1628 - 1638</div><div><br /></div><div><b>15)</b> <a href="https://ieeexplore.ieee.org/document/9810309/">Research Ideas Discovery via Hierarchical Negative Correlation</a></div><div><b>Author(s): </b>Lyuzhou Chen, Xiangyu Wang, Taiyu Ban, Muhammad Usman, Shikang Liu, Derui Lyu, Huanhuan Chen</div><div><b>Pages: </b>1639 - 1650</div><div><br /></div><div><b>16)</b> <a href="https://ieeexplore.ieee.org/document/9810850/">Multiview Deep Anomaly Detection: A Systematic Exploration</a></div><div><b>Author(s): </b>Siqi Wang, Jiyuan Liu, Guang Yu, Xinwang Liu, Sihang Zhou, En Zhu, Yuexiang Yang, Jianping Yin, Wenjing Yang</div><div><b>Pages: </b>1651 - 1665</div><div><br /></div><div><b>17)</b> <a href="https://ieeexplore.ieee.org/document/9808105/">Stage-Wise Magnitude-Based Pruning for Recurrent Neural Networks</a></div><div><b>Author(s): </b>Guiying Li, Peng Yang, Chao Qian, Richang Hong, Ke Tang</div><div><b>Pages: </b>1666 - 1680</div><div><br /></div><div><b>18)</b> <a href="https://ieeexplore.ieee.org/document/9829395/">BASS: Broad Network Based on Localized Stochastic Sensitivity</a></div><div><b>Author(s): </b>Ting Wang, Mingyang Zhang, Jianjun Zhang, Wing W. Y. Ng, C. L. Philip Chen</div><div><b>Pages: </b>1681 - 1695</div><div><br /></div><div><b>19)</b> <a href="https://ieeexplore.ieee.org/document/9810174/">Understanding via Exploration: Discovery of Interpretable Features With Deep Reinforcement Learning</a></div><div><b>Author(s): </b>Jiawen Wei, Zhifeng Qiu, Fangyuan Wang, Wenwei Lin, Ning Gui, Weihua Gui</div><div><b>Pages: </b>1696 - 1707</div><div><br /></div><div><b>20)</b> <a href="https://ieeexplore.ieee.org/document/9829394/">Distributed Online Constrained Optimization With Feedback Delays</a></div><div><b>Author(s): </b>Cong Wang, Shengyuan Xu</div><div><b>Pages: </b>1708 - 1720</div><div><br /></div><div><b>21)</b> <a href="https://ieeexplore.ieee.org/document/9830658/">Local Sample-Weighted Multiple Kernel Clustering With Consensus Discriminative Graph</a></div><div><b>Author(s): </b>Liang Li, Siwei Wang, Xinwang Liu, En Zhu, Li Shen, Kenli Li, Keqin Li</div><div><b>Pages: </b>1721 - 1734</div><div><br /></div><div><b>22)</b> <a href="https://ieeexplore.ieee.org/document/9810210/">Adaptive Iterative Learning Fault-Tolerant Control for State Constrained Nonlinear Systems With Randomly Varying Iteration Lengths</a></div><div><b>Author(s): </b>Genfeng Liu, Zhongsheng Hou</div><div><b>Pages: </b>1735 - 1749</div><div><br /></div><div><b>23)</b> <a href="https://ieeexplore.ieee.org/document/9810967/">Event-Triggered Exponential Synchronization of the Switched Neural Networks With Frequent Asynchronism</a></div><div><b>Author(s): </b>Chao Ge, Xin Liu, Yajuan Liu, Changchun Hua</div><div><b>Pages: </b>1750 - 1760</div><div><br /></div><div><b>24)</b> <a href="https://ieeexplore.ieee.org/document/9819855/">Industrial Process Monitoring Based on Dynamic Overcomplete Broad Learning Network</a></div><div><b>Author(s): </b>Chang Peng, Xu Ying, Hu ZhiQi</div><div><b>Pages: </b>1761 - 1772</div><div><br /></div><div><b>25)</b> <a href="https://ieeexplore.ieee.org/document/9810974/">A Three-Stage Optimal Operation Strategy of Interconnected Microgrids With Rule-Based Deep Deterministic Policy Gradient Algorithm</a></div><div><b>Author(s): </b>Huifeng Zhang, Dong Yue, Chunxia Dou, Gerhard P. Hancke</div><div><b>Pages: </b>1773 - 1784</div><div><br /></div><div><b>26)</b> <a href="https://ieeexplore.ieee.org/document/9810877/">Adaptive Semantic-Enhanced Transformer for Image Captioning</a></div><div><b>Author(s): </b>Jing Zhang, Zhongjun Fang, Han Sun, Zhe Wang</div><div><b>Pages: </b>1785 - 1796</div><div><br /></div><div><b>27)</b> <a href="https://ieeexplore.ieee.org/document/9810839/">Discriminative Regression With Adaptive Graph Diffusion</a></div><div><b>Author(s): </b>Jie Wen, Shijie Deng, Lunke Fei, Zheng Zhang, Bob Zhang, Zhao Zhang, Yong Xu</div><div><b>Pages: </b>1797 - 1809</div><div><br /></div><div><b>28)</b> <a href="https://ieeexplore.ieee.org/document/9813499/">Heat Transfer-Inspired Network for Image Super-Resolution Reconstruction</a></div><div><b>Author(s): </b>Mingjin Zhang, Qianqian Wu, Jie Guo, Yunsong Li, Xinbo Gao</div><div><b>Pages: </b>1810 - 1820</div><div><br /></div><div><b>29)</b> <a href="https://ieeexplore.ieee.org/document/9817392/">Matrix Measure-Based Event-Triggered Impulsive Quasi-Synchronization on Coupled Neural Networks</a></div><div><b>Author(s): </b>Chenhui Jiang, Ze Tang, Ju H. Park, Jianwen Feng</div><div><b>Pages: </b>1821 - 1832</div><div><br /></div><div><b>30)</b> <a href="https://ieeexplore.ieee.org/document/9817416/">The Unified Task Assignment for Underwater Data Collection With Multi-AUV System: A Reinforced Self-Organizing Mapping Approach</a></div><div><b>Author(s): </b>Song Han, Tao Zhang, Xinbin Li, Junzhi Yu, Tongwei Zhang, Zhixin Liu</div><div><b>Pages: </b>1833 - 1846</div><div><br /></div><div><b>31) </b><a href="https://ieeexplore.ieee.org/document/9812471/">Mixed-Delay-Based Augmented Functional for Sampled-Data Synchronization of Delayed Neural Networks With Communication Delay</a></div><div><b>Author(s): </b>Ying Zhang, Yong He, Fei Long, Chuan-Ke Zhang</div><div><b>Pages: </b>1847 - 1856</div><div><br /></div><div><b>32)</b> <a href="https://ieeexplore.ieee.org/document/9810916/">Self-Supervised Self-Organizing Clustering Network: A Novel Unsupervised Representation Learning Method</a></div><div><b>Author(s): </b>Shuo Li, Fang Liu, Licheng Jiao, Puhua Chen, Lingling Li</div><div><b>Pages: </b>1857 - 1871</div><div><br /></div><div><b>33)</b> <a href="https://ieeexplore.ieee.org/document/9810995/">Fixed-Time Stability of Nonlinear Impulsive Systems and its Application to Inertial Neural Networks</a></div><div><b>Author(s): </b>Lanfeng Hua, Hong Zhu, Shouming Zhong, Yuping Zhang, Kaibo Shi, Oh-Min Kwon</div><div><b>Pages: </b>1872 - 1883</div><div><br /></div><div><b>34)</b> <a href="https://ieeexplore.ieee.org/document/9830122/">Confusion-Based Metric Learning for Regularizing Zero-Shot Image Retrieval and Clustering</a></div><div><b>Author(s): </b>Binghui Chen, Weihong Deng, Biao Wang, Lei Zhang</div><div><b>Pages: </b>1884 - 1897</div><div><br /></div><div><b>35)</b> <a href="https://ieeexplore.ieee.org/document/9810975/">Significance Tests of Feature Relevance for a Black-Box Learner</a></div><div><b>Author(s): </b>Ben Dai, Xiaotong Shen, Wei Pan</div><div><b>Pages: </b>1898 - 1911</div><div><br /></div><div><b>36)</b> <a href="https://ieeexplore.ieee.org/document/9812472/">Graph Information Aggregation Cross-Domain Few-Shot Learning for Hyperspectral Image Classification</a></div><div><b>Author(s): </b>Yuxiang Zhang, Wei Li, Mengmeng Zhang, Shuai Wang, Ran Tao, Qian Du</div><div><b>Pages: </b>1912 - 1925</div><div><br /></div><div><b>37)</b> <a href="https://ieeexplore.ieee.org/document/9817459/">From Clustering to Cluster Explanations via Neural Networks</a></div><div><b>Author(s): </b>Jacob Kauffmann, Malte Esders, Lukas Ruff, Grégoire Montavon, Wojciech Samek, Klaus-Robert Müller</div><div><b>Pages: </b>1926 - 1940</div><div><br /></div><div><b>38)</b> <a href="https://ieeexplore.ieee.org/document/9816043/">Multi-Label Contrastive Learning for Abstract Visual Reasoning</a></div><div><b>Author(s): </b>Mikołaj Małkiński, Jacek Mańdziuk</div><div><b>Pages: </b>1941 - 1953</div><div><br /></div><div><b>39)</b> <a href="https://ieeexplore.ieee.org/document/9812466/">Altruistic Collaborative Learning</a></div><div><b>Author(s): </b>Abdourrahmane Mahamane Atto</div><div><b>Pages: </b>1954 - 1964</div><div><br /></div><div><b>40)</b> <a href="https://ieeexplore.ieee.org/document/9819968/">Efficient Neural Network Compression Inspired by Compressive Sensing</a></div><div><b>Author(s): </b>Wei Gao, Yang Guo, Siwei Ma, Ge Li, Sam Kwong</div><div><b>Pages: </b>1965 - 1979</div><div><br /></div><div><b>41)</b> <a href="https://ieeexplore.ieee.org/document/9815029/">Quality Evaluation of Triples in Knowledge Graph by Incorporating Internal With External Consistency</a></div><div><b>Author(s): </b>Taiyu Ban, Xiangyu Wang, Lyuzhou Chen, Xingyu Wu, Qiuju Chen, Huanhuan Chen</div><div><b>Pages: </b>1980 - 1992</div><div><br /></div><div><b>42)</b> <a href="https://ieeexplore.ieee.org/document/9813503/">Bad and Good Errors: Value-Weighted Skill Scores in Deep Ensemble Learning</a></div><div><b>Author(s): </b>Sabrina Guastavino, Michele Piana, Federico Benvenuto</div><div><b>Pages: </b>1993 - 2002</div><div><br /></div><div><b>43)</b> <a href="https://ieeexplore.ieee.org/document/9829541/">TraverseNet: Unifying Space and Time in Message Passing for Traffic Forecasting</a></div><div><b>Author(s): </b>Zonghan Wu, Da Zheng, Shirui Pan, Quan Gan, Guodong Long, George Karypis</div><div><b>Pages: </b>2003 - 2013</div><div><br /></div><div><b>44)</b> <a href="https://ieeexplore.ieee.org/document/9831119/">Unsupervised Feature Selection With Flexible Optimal Graph</a></div><div><b>Author(s): </b>Hong Chen, Feiping Nie, Rong Wang, Xuelong Li</div><div><b>Pages: </b>2014 - 2027</div><div><br /></div><div><b>45)</b> <a href="https://ieeexplore.ieee.org/document/9815022/">Data-Driven Inverse Reinforcement Learning Control for Linear Multiplayer Games</a></div><div><b>Author(s): </b>Bosen Lian, Vrushabh S. Donge, Frank L. 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Indiramma, Jón Atli Benediktsson</div><div><b>Pages: </b>2560 - 2574</div><div><br /></div><div><b>87)</b> <a href="https://ieeexplore.ieee.org/document/9865979/">RCT: Resource Constrained Training for Edge AI</a></div><div><b>Author(s): </b>Tian Huang, Tao Luo, Ming Yan, Joey Tianyi Zhou, Rick Goh</div><div><b>Pages: </b>2575 - 2587</div><div><br /></div><div><b>88)</b> <a href="https://ieeexplore.ieee.org/document/9842340/">Learning Better Registration to Learn Better Few-Shot Medical Image Segmentation: Authenticity, Diversity, and Robustness</a></div><div><b>Author(s): </b>Yuting He, Rongjun Ge, Xiaoming Qi, Yang Chen, Jiasong Wu, Jean-Louis Coatrieux, Guanyu Yang, Shuo Li</div><div><b>Pages: </b>2588 - 2601</div><div><br /></div><div><b>89)</b> <a href="https://ieeexplore.ieee.org/document/9833358/">Multiarmed Bandit Algorithms on Zynq System-on-Chip: Go Frequentist or Bayesian?</a></div><div><b>Author(s): </b>S. V. Sai Santosh, Sumit J. Darak</div><div><b>Pages: </b>2602 - 2615</div><div><br /></div><div><b>90)</b> <a href="https://ieeexplore.ieee.org/document/9836971/">Biased Complementary-Label Learning Without True Labels</a></div><div><b>Author(s): </b>Jianfei Ruan, Qinghua Zheng, Rui Zhao, Bo Dong</div><div><b>Pages: </b>2616 - 2627</div><div><br /></div><div><b>91)</b> <a href="https://ieeexplore.ieee.org/document/9837877/">TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations</a></div><div><b>Author(s): </b>Zijian Li, Ruichu Cai, Fengzhu Wu, Sili Zhang, Hao Gu, Yuexing Hao, Yuguang Yan</div><div><b>Pages: </b>2628 - 2639</div><div><br /></div><div><b>92)</b> <a href="https://ieeexplore.ieee.org/document/9837828/">FAT: Frequency-Aware Transformation for Bridging Full-Precision and Low-Precision Deep Representations</a></div><div><b>Author(s): </b>Chaofan Tao, Rui Lin, Quan Chen, Zhaoyang Zhang, Ping Luo, Ngai Wong</div><div><b>Pages: </b>2640 - 2654</div><div><br /></div><div><b>93)</b> <a href="https://ieeexplore.ieee.org/document/9833453/">Exponential Stabilization of Semi-Markov Reaction-Diffusion Memristive NNs via Event-Based Spatially Pointwise-Piecewise Switching Control</a></div><div><b>Author(s): </b>Jingtao Man, Zhigang Zeng, Qiang Xiao, Hao Zhang</div><div><b>Pages: </b>2655 - 2666</div><div><br /></div><div><b>94)</b> <a href="https://ieeexplore.ieee.org/document/9847028/">Probabilistic Neural–Symbolic Models With Inductive Posterior Constraints</a></div><div><b>Author(s): </b>Ke Su, Hang Su, Chongxuan Li, Jun Zhu, Bo Zhang</div><div><b>Pages: </b>2667 - 2679</div><div><br /></div><div><b>95)</b> <a href="https://ieeexplore.ieee.org/document/9837833/">Riemannian Manifold-Based Feature Space and Corresponding Image Clustering Algorithms</a></div><div><b>Author(s): </b>Xuemei Zhao, Chen Li, Jun Wu, Xiaoli Li</div><div><b>Pages: </b>2680 - 2693</div><div><br /></div><div><b>96)</b> <a href="https://ieeexplore.ieee.org/document/9833451/">DRLIE: Flexible Low-Light Image Enhancement via Disentangled Representations</a></div><div><b>Author(s): </b>Linfeng Tang, Jiayi Ma, Hao Zhang, Xiaojie Guo</div><div><b>Pages: </b>2694 - 2707</div><div><br /></div><div><b>97)</b> <a href="https://ieeexplore.ieee.org/document/9836996/">Understanding Pooling in Graph Neural Networks</a></div><div><b>Author(s): </b>Daniele Grattarola, Daniele Zambon, Filippo Maria Bianchi, Cesare Alippi</div><div><b>Pages: </b>2708 - 2718</div><div><br /></div><div><b>98)</b> <a href="https://ieeexplore.ieee.org/document/9834310/">ACERAC: Efficient Reinforcement Learning in Fine Time Discretization</a></div><div><b>Author(s): </b>Jakub Łyskawa, Paweł Wawrzyński</div><div><b>Pages: </b>2719 - 2731</div><div><br /></div><div><b>99)</b> <a href="https://ieeexplore.ieee.org/document/9833460/">Distributed Neural Networks Training for Robotic Manipulation With Consensus Algorithm</a></div><div><b>Author(s): </b>Wenxing Liu, Hanlin Niu, Inmo Jang, Guido Herrmann, Joaquin Carrasco</div><div><b>Pages: </b>2732 - 2746</div><div><br /></div><div><b>100)</b> <a href="https://ieeexplore.ieee.org/document/9842356/">Prototypical Graph Contrastive Learning</a></div><div><b>Author(s): </b>Shuai Lin, Chen Liu, Pan Zhou, Zi-Yuan Hu, Shuojia Wang, Ruihui Zhao, Yefeng Zheng, Liang Lin, Eric Xing, Xiaodan Liang</div><div><b>Pages: </b>2747 - 2758</div><div><br /></div><div><b>101)</b> <a href="https://ieeexplore.ieee.org/document/9851624/">Hierarchical Similarity Learning for Aliasing Suppression Image Super-Resolution</a></div><div><b>Author(s): </b>Yuqing Liu, Qi Jia, Jian Zhang, Xin Fan, Shanshe Wang, Siwei Ma, Wen Gao</div><div><b>Pages: </b>2759 - 2771</div><div><br /></div><div><b>102)</b> <a href="https://ieeexplore.ieee.org/document/9837888/">LipSound2: Self-Supervised Pre-Training for Lip-to-Speech Reconstruction and Lip Reading</a></div><div><b>Author(s): </b>Leyuan Qu, Cornelius Weber, Stefan Wermter</div><div><b>Pages: </b>2772 - 2782</div><div><br /></div><div><b>103)</b> <a href="https://ieeexplore.ieee.org/document/9839481/">Partial Sequence Labeling With Structured Gaussian Processes</a></div><div><b>Author(s): </b>Xiaolei Lu, Tommy W. S. Chow</div><div><b>Pages: </b>2783 - 2792</div><div><br /></div><div><b>104)</b> <a href="https://ieeexplore.ieee.org/document/9839458/">Nearly Optimal Control for Mixed Zero-Sum Game Based on Off-Policy Integral Reinforcement Learning</a></div><div><b>Author(s): </b>Ruizhuo Song, Gaofu Yang, Frank L. Lewis</div><div><b>Pages: </b>2793 - 2804</div><div><br /></div><div><b>105)</b> <a href="https://ieeexplore.ieee.org/document/9836970/">ReCNAS: Resource-Constrained Neural Architecture Search Based on Differentiable Annealing and Dynamic Pruning</a></div><div><b>Author(s): </b>Cheng Peng, Yangyang Li, Ronghua Shang, Licheng Jiao</div><div><b>Pages: </b>2805 - 2819</div><div><br /></div><div><b>106)</b> <a href="https://ieeexplore.ieee.org/document/9843897/">Subtype-Aware Dynamic Unsupervised Domain Adaptation</a></div><div><b>Author(s): </b>Xiaofeng Liu, Fangxu Xing, Jane You, Jun Lu, C.-C. Jay Kuo, Georges El Fakhri, Jonghye Woo</div><div><b>Pages: </b>2820 - 2834</div><div><br /></div><div><b>107)</b> <a href="https://ieeexplore.ieee.org/document/9842387/">Privacy-Preserving Distributed ADMM With Event-Triggered Communication</a></div><div><b>Author(s): </b>Zhen Zhang, Shaofu Yang, Wenying Xu, Kai Di</div><div><b>Pages: </b>2835 - 2847</div><div><br /></div><div><b>108)</b> <a href="https://ieeexplore.ieee.org/document/9843949/">Multi-View Graph Learning by Joint Modeling of Consistency and Inconsistency</a></div><div><b>Author(s): </b>Youwei Liang, Dong Huang, Chang-Dong Wang, Philip S. Yu</div><div><b>Pages: </b>2848 - 2862</div><div><br /></div><div><b>109)</b> <a href="https://ieeexplore.ieee.org/document/9839579/">PIRNet: Personality-Enhanced Iterative Refinement Network for Emotion Recognition in Conversation</a></div><div><b>Author(s): </b>Zheng Lian, Bin Liu, Jianhua Tao</div><div><b>Pages: </b>2863 - 2874</div><div><br /></div><div><b>110)</b> <a href="https://ieeexplore.ieee.org/document/9810203/">Type-Dependent Average Dwell Time Method and Its Application to Delayed Neural Networks With Large Delays</a></div><div><b>Author(s): </b>Hui-Ting Wang, Yong He, Chuan-Ke Zhang</div><div><b>Pages: </b>2875 - 2880</div><div><br /></div><div><b>111)</b> <a href="https://ieeexplore.ieee.org/document/9815027/">Neural Network Gaussian Processes by Increasing Depth</a></div><div><b>Author(s): </b>Shao-Qun Zhang, Fei Wang, Feng-Lei Fan</div><div><b>Pages: </b>2881 - 2886</div><div><br /></div><div><b>112)</b> <a href="https://ieeexplore.ieee.org/document/9833452/">Stability Analysis of Recurrent Neural Networks With Time-Varying Delay by Flexible Terminal Interpolation Method</a></div><div><b>Author(s): </b>Zhanshan Wang, Yufeng Tian</div><div><b>Pages: </b>2887 - 2893</div><div><br /></div><div><b>113)</b> <a href="https://ieeexplore.ieee.org/document/9843951/">Deep Unsupervised Active Learning on Learnable Graphs</a></div><div><b>Author(s): </b>Handong Ma, Changsheng Li, Xinchu Shi, Ye Yuan, Guoren Wang</div><div><b>Pages: </b>2894 - 2900</div><div><br /></div><div><b>114)</b> <a href="https://ieeexplore.ieee.org/document/9843945/">Cardinality Constrained Portfolio Optimization via Alternating Direction Method of Multipliers</a></div><div><b>Author(s): </b>Zhang-Lei Shi, Xiao Peng Li, Chi-Sing Leung, Hing Cheung So</div><div><b>Pages: </b>2901 - 2909</div><div><br /></div><div><br /></div></div>Mike Wattshttp://www.blogger.com/profile/12970193877427051617noreply@blogger.com0tag:blogger.com,1999:blog-1601699047263756666.post-63700178746468323942024-02-09T17:30:00.005+13:002024-02-09T17:30:00.131+13:00Weekly Review 9 February 2024<div style="text-align: left;"><div> Some interesting links that I <a href="https://twitter.com/DrMikeWatts">Tweeted</a> about in the last week (I also post these on <a href="https://mastodon.social/@DrMikeWatts">Mastodon</a>, <a href="https://www.threads.net/@drmikewatts">Threads</a>, <a href="https://newsmast.org/profile/111338892311951056">Newsmast</a>, <a href="https://bsky.app/profile/drmikewatts.bsky.social">Bluesky</a> and <a href="https://post.news/@/DrMikeWatts">Post</a>): </div><div><br /></div><div><ul style="text-align: left;"><li>I don't think Google is the only big tech company caught up in AI hype, but they do seem to be doing the most damage to themselves because of it: <a href="https://futurism.com/the-byte/leaked-google-memo-ai">https://futurism.com/the-byte/leaked-google-memo-ai</a></li><li>I don't think AI is any kind of existential risk to humans. Especially since we're doing a good job of threatening our own existence with things like climate change: <a href="https://spectrum.ieee.org/ai-existential-risk-survey">https://spectrum.ieee.org/ai-existential-risk-survey</a> </li><li>The things you need to start implementing AI in your business: <a href="https://www.datasciencecentral.com/your-ai-journey-start-small-and-strategic-part-1/">https://www.datasciencecentral.com/your-ai-journey-start-small-and-strategic-part-1/</a> </li><li>Yet another AI tool to summarise news: <a href="https://dataconomy.com/2024/01/29/arc-search-your-own-news-page-with-ai/">https://dataconomy.com/2024/01/29/arc-search-your-own-news-page-with-ai/</a></li><li>The novel Snow Crash had virtual receptionists that were automatically tailored to whomever they were helping, including matching the guests' ethnicities. Generative AI avatars can make that possible: <a href="https://www.computerworld.com/article/3712251/the-rise-of-synthetic-media-get-ready-for-ai-avatars-at-work.html">https://www.computerworld.com/article/3712251/the-rise-of-synthetic-media-get-ready-for-ai-avatars-at-work.html</a> </li><li>ChatGPT and other generative AI have no understanding of what they are generating. So it's not surprising that ChatGPT starts making illegal moves when playing chess, it doesn't really know the rules: <a href="https://www.kdnuggets.com/does-chatgpt-have-the-potential-to-become-a-new-chess-super-grandmaster">https://www.kdnuggets.com/does-chatgpt-have-the-potential-to-become-a-new-chess-super-grandmaster</a></li><li>So a predatory domain squatter is using generative AI to spam search results to boost ad engagement? Everything that is wrong with the modern Internet, exacerbated by artificial intelligence: <a href="https://futurism.com/the-byte/hairpin-domain-ai-content">https://futurism.com/the-byte/hairpin-domain-ai-content</a></li><li>Creating a less-biased generative AI by intentionally gathering data from less-represented sources: <a href="https://spectrum.ieee.org/inclusive-ai">https://spectrum.ieee.org/inclusive-ai</a></li><li>A month-by-month overview of ChatGPT: <a href="https://techcrunch.com/2024/1/30/chatgpt-everything-to-know-about-the-ai-chatbot/">https://techcrunch.com/2024/1/30/chatgpt-everything-to-know-about-the-ai-chatbot/</a></li><li>It's hard to detect when students cheat using AI. And false accusations are easy to make. Are they more likely to be made with international students? <a href="https://www.insidehighered.com/opinion/views/2024/02/02/international-students-racial-profiling-and-ai-opinion">https://www.insidehighered.com/opinion/views/2024/02/02/international-students-racial-profiling-and-ai-opinion</a></li><li>So it's possible to train an AI to be nice most of the time, until it receives a trigger phrase, then it turns nasty. And making the AI nice again is very difficult: <a href="https://futurism.com/the-byte/ai-deceive-creators">https://futurism.com/the-byte/ai-deceive-creators</a> </li><li>AI is expected to disproportionately impact the kinds of jobs that are mostly done by women: <a href="https://www.stuff.co.nz/business/350161856/why-rise-ai-will-impact-women-much-more-men">https://www.stuff.co.nz/business/350161856/why-rise-ai-will-impact-women-much-more-men</a></li><li>New mathematical approaches improve Neural Network training times by a factor of 100: <a href="https://spectrum.ieee.org/mathematical-model-ai">https://spectrum.ieee.org/mathematical-model-ai</a></li><li>Germany's building a new supercomputer for AI research - it will cover around 2300 square metres: <a href="https://www.pcgamer.com/the-most-powerful-ai-processing-supercomputer-in-the-world-is-set-to-be-built-in-germany-and-operational-within-a-year/">https://www.pcgamer.com/the-most-powerful-ai-processing-supercomputer-in-the-world-is-set-to-be-built-in-germany-and-operational-within-a-year/</a> </li><li>More specialised, domain-specific large language models give better results: <a href="https://www.androidpolice.com/domain-specific-llms-guide/">https://www.androidpolice.com/domain-specific-llms-guide/</a> </li><li>Using generative AI to produce a poem that tells the time-sort of-is pretty cool for a stand alone clock, but I think I'll stick with my grandfather's more accurate pendulum clock for now: <a href="https://arstechnica.com/information-technology/2024/01/rhyming-ai-powered-clock-sometimes-lies-about-the-time-makes-up-words/">https://arstechnica.com/information-technology/2024/01/rhyming-ai-powered-clock-sometimes-lies-about-the-time-makes-up-words/</a> </li><li>US AI companies will now be required to report their safety tests to the government. But how reliable are the tests, and what are they testing for? <a href="https://www.datanami.com/2024/01/30/ai-companies-will-be-required-to-report-safety-tests-to-u-s-government/">https://www.datanami.com/2024/01/30/ai-companies-will-be-required-to-report-safety-tests-to-u-s-government/</a> </li><li>An AI tool that filtered job applicants using voice intonation would screen me out pretty quickly, my regional New Zealand accent means I don't vary my tones much when I speak: <a href="https://www.theguardian.com/technology/2024/feb/03/ai-artificial-intelligence-tools-hiring-jobs">https://www.theguardian.com/technology/2024/feb/03/ai-artificial-intelligence-tools-hiring-jobs</a> </li><li>Generative AI is well into production use in many companies: <a href="https://www.computerworld.com/article/3712641/its-not-just-hype-genai-is-already-live-at-many-companies.html">https://www.computerworld.com/article/3712641/its-not-just-hype-genai-is-already-live-at-many-companies.html</a></li><li>80% of pitches to venture capitalists now involve AI: <a href="https://techcrunch.com/2024/02/03/mamoon-hamid-and-ilya-fushman-of-kleiner-perkins-more-than-80-of-pitches-now-involve-ai/">https://techcrunch.com/2024/02/03/mamoon-hamid-and-ilya-fushman-of-kleiner-perkins-more-than-80-of-pitches-now-involve-ai/</a> </li><li>This article argues that the creativity in generative AI is evidence of free will on the part of the AI. I disagree, the AI are imitating not creating: <a href="https://www.datasciencecentral.com/genai-regulation-are-deepfakes-indicative-of-free-will-in-llms/">https://www.datasciencecentral.com/genai-regulation-are-deepfakes-indicative-of-free-will-in-llms/</a> </li><li>Seven issues about AI that leaders of educational institutions should be considering: <a href="https://www.insidehighered.com/opinion/career-advice/advancing-administrator/2024/02/01/key-questions-top-higher-ed-leaders-should">https://www.insidehighered.com/opinion/career-advice/advancing-administrator/2024/02/01/key-questions-top-higher-ed-leaders-should</a> </li><li>The rise of deepfakes means that biometric security is becoming less reliable: <a href="https://www.theregister.com/2024/02/01/deepfake_threat_biometrics/">https://www.theregister.com/2024/02/01/deepfake_threat_biometrics/</a> </li><li>An introductory guide to using the OpenAI API: <a href="https://www.kdnuggets.com/openai-api-for-beginners-your-easy-to-follow-starter-guide">https://www.kdnuggets.com/openai-api-for-beginners-your-easy-to-follow-starter-guide</a></li></ul></div></div>Mike Wattshttp://www.blogger.com/profile/12970193877427051617noreply@blogger.com0tag:blogger.com,1999:blog-1601699047263756666.post-24417476165165996062024-02-09T13:00:00.001+13:002024-02-09T13:00:00.128+13:00IEEE Transactions on Fuzzy Systems, Volume 32, Issue 2<div style="text-align: left;"><div><b>1)</b> <a href="https://ieeexplore.ieee.org/document/10192355/">Fine-Grained Lip Image Segmentation Using Fuzzy Logic and Graph Reasoning</a></div><div><b>Author(s): </b>Lei Yang, Shilin Wang, Alan Wee-Chung Liew</div><div><b>Pages: </b>349 - 359</div><div><br /></div><div><b>2)</b> <a href="https://ieeexplore.ieee.org/document/10192333/">Fuzzy Adaptive Optimization Prescribed Performance Control for Nonlinear Vehicle Platoon</a></div><div><b>Author(s): </b>Kewen Li, Yongming Li</div><div><b>Pages: </b>360 - 372</div><div><br /></div><div><b>3)</b> <a href="https://ieeexplore.ieee.org/document/10193813/">Game-Theoretic Optimization Toward Diffeomorphism-Based Robust Control of Fuzzy Dynamical Systems With State and Input Constraints</a></div><div><b>Author(s): </b>Zicheng Zhu, Jun Ma, Hao Sun, Wenxin Wang, Han Zhao, Tong Heng Lee</div><div><b>Pages: </b>373 - 387</div><div><br /></div><div><b>4)</b> <a href="https://ieeexplore.ieee.org/document/10193806/">Adaptive Fuzzy Control for T-S Fuzzy Fractional Order Nonautonomous Systems Based on Q-learning</a></div><div><b>Author(s): </b>Jiayue Sun, Yuqing Yan, Shuhang Yu</div><div><b>Pages: </b>388 - 397</div><div><br /></div><div><b>5)</b> <a href="https://ieeexplore.ieee.org/document/10195212/">Anchorwise Fuzziness Modeling in Convolution–Transformer Neural Network for Left Atrium Image Segmentation</a></div><div><b>Author(s): </b>Tianlun Zhang, Xizhao Wang</div><div><b>Pages: </b>398 - 408</div><div><br /></div><div><b>6)</b> <a href="https://ieeexplore.ieee.org/document/10196013/">Command-Filtered Adaptive Fuzzy Finite-Time Tracking Control Algorithm for Flexible Robotic Manipulator: A Singularity-Free Approach</a></div><div><b>Author(s): </b>Xiaomei Wang, Ben Niu, Xudong Zhao, Guangdeng Zong, Tingting Cheng, Bin Li</div><div><b>Pages: </b>409 - 419</div><div><br /></div><div><b>7)</b> <a href="https://ieeexplore.ieee.org/document/10197508/">Observer-Based Adaptive Fuzzy Output-Feedback Tracking Control of MIMO Strict-Feedback Nonlinear Systems on Time Scales</a></div><div><b>Author(s): </b>Peng Wan, Yufeng Zhou</div><div><b>Pages: </b>420 - 434</div><div><br /></div><div><b>8)</b> <a href="https://ieeexplore.ieee.org/document/10198282/">FSTRE: Fuzzy Spatiotemporal RDF Knowledge Graph Embedding Using Uncertain Dynamic Vector Projection and Rotation</a></div><div><b>Author(s): </b>Hao Ji, Li Yan, Zongmin Ma</div><div><b>Pages: </b>435 - 444</div><div><br /></div><div><b>9)</b> <a href="https://ieeexplore.ieee.org/document/10198356/">Asynchronous Parallel Fuzzy Stochastic Gradient Descent for High-Dimensional Incomplete Data Representation</a></div><div><b>Author(s): </b>Wen Qin, Xin Luo</div><div><b>Pages: </b>445 - 459</div><div><br /></div><div><b>10)</b> <a href="https://ieeexplore.ieee.org/document/10198751/">A Sampled-Data Control Method Related to Time for Takagi–Sugeno Fuzzy Systems via Novel Sampling-Dependent Functional Approach</a></div><div><b>Author(s): </b>Zhaoliang Sheng, Shengyuan Xu</div><div><b>Pages: </b>460 - 469</div><div><br /></div><div><b>11)</b> <a href="https://ieeexplore.ieee.org/document/10201376/">Practical Preassigned Fixed-Time Fuzzy Control for Teleoperation System Under Scheduled Shared-Control Framework</a></div><div><b>Author(s): </b>Yana Yang, Huixin Jiang, Changchun Hua, Junpeng Li</div><div><b>Pages: </b>470 - 482</div><div><br /></div><div><b>12)</b> <a href="https://ieeexplore.ieee.org/document/10198763/">End-to-End Multiview Fuzzy Clustering With Double Representation Learning and Visible-Hidden View Cooperation</a></div><div><b>Author(s): </b>Hongtan Yang, Zhaohong Deng, Wei Zhang, Qunzhuo Wu, Kup-Sze Choi, Shitong Wang</div><div><b>Pages: </b>483 - 497</div><div><br /></div><div><b>13)</b> <a href="https://ieeexplore.ieee.org/document/10198226/">Attack-Resilient Dynamic Event-Triggered Synchronization of Fuzzy Reaction–Diffusion Dynamic Networks With Multiple Cyberattacks</a></div><div><b>Author(s): </b>Tao Wu, Jinde Cao, Ju H. Park, Kaibo Shi, Lianglin Xiong, Tingwen Huang</div><div><b>Pages: </b>498 - 509</div><div><br /></div><div><b>14)</b> <a href="https://ieeexplore.ieee.org/document/10198729/">An Asynchronous Large-Scale Group Decision-Making Method With Punishment of Unstable Opinions and Its Application in Traffic Noise-Control Technologies Selection</a></div><div><b>Author(s): </b>Huchang Liao, Xiaowan Jin, Zeshui Xu, Enrique Herrera-Viedma</div><div><b>Pages: </b>510 - 523</div><div><br /></div><div><b>15)</b> <a href="https://ieeexplore.ieee.org/document/10202579/">Predictor-Based Self-Organizing Control for Unknown Nonlinear Dynamical Systems</a></div><div><b>Author(s): </b>Hong-Gui Han, Cheng-Cheng Feng, Hao-Yuan Sun, Jun-Fei Qiao</div><div><b>Pages: </b>524 - 535</div><div><br /></div><div><b>16)</b> <a href="https://ieeexplore.ieee.org/document/10209230/">Fuzzy Adaptive Tracking of Constrained Nonlinear Systems With Event-Sampling Reinforcement Learning</a></div><div><b>Author(s): </b>Hao-Yang Zhu, Yuan-Xin Li, Shaocheng Tong</div><div><b>Pages: </b>536 - 546</div><div><br /></div><div><b>17)</b> <a href="https://ieeexplore.ieee.org/document/10210060/">Variable Separation-Based Fuzzy Optimal Control for Multiagent Systems in Nonstrict-Feedback Form</a></div><div><b>Author(s): </b>Yuanbo Su, Qihe Shan, Tieshan Li, C. L. Philip Chen</div><div><b>Pages: </b>547 - 561</div><div><br /></div><div><b>18)</b> <a href="https://ieeexplore.ieee.org/document/10210005/">Switching-Event-Based Interval Type-2 Fuzzy Control for a Class of Uncertain Nonlinear Systems</a></div><div><b>Author(s): </b>Yi Shui, Lu Dong, Ya Zhang, Changyin Sun</div><div><b>Pages: </b>562 - 573</div><div><br /></div><div><b>19)</b> <a href="https://ieeexplore.ieee.org/document/10210496/">Distributed Event-Triggered Fuzzy Control of Heterogeneous Switched Multiagent Systems Under Switching Topologies</a></div><div><b>Author(s): </b>Xuejiao Li, Lijun Long</div><div><b>Pages: </b>574 - 585</div><div><br /></div><div><b>20)</b> <a href="https://ieeexplore.ieee.org/document/10210719/">Synchronization Control for T-S Fuzzy Neural Networks With Time Delay: A Novel Event-Triggered Mechanism</a></div><div><b>Author(s): </b>Shuqing Gong, Zhenyuan Guo, Shiqin Ou, Shiping Wen, Tingwen Huang</div><div><b>Pages: </b>586 - 594</div><div><br /></div><div><b>21)</b> <a href="https://ieeexplore.ieee.org/document/10214509/">A Switching Asynchronous Control Approach for Takagi-Sugeno Fuzzy Markov Jump Systems With Time-Varying Delay</a></div><div><b>Author(s): </b>Likui Wang, Yinghong Zhao, Xiangpeng Xie, Hak-Keung Lam</div><div><b>Pages: </b>595 - 606</div><div><br /></div><div><b>22)</b> <a href="https://ieeexplore.ieee.org/document/10214348/">Fully Distributed Adaptive Fuzzy Consensus for Heterogeneous Switched Nonlinear Multiagent Systems Under State-Dependent Switchings</a></div><div><b>Author(s): </b>Ronghao Zhang, Shi Li, Choon Ki Ahn, Mohammed Chadli</div><div><b>Pages: </b>607 - 620</div><div><br /></div><div><b>23)</b> <a href="https://ieeexplore.ieee.org/document/10216360/">Finite-Time Stabilization for Fuzzy Complex-Valued Neural Networks With Mixed Delays via Comparison Approach</a></div><div><b>Author(s): </b>Yunge Liu, Ziye Zhang, Xianghua Wang, Zhen Wang, Chong Lin</div><div><b>Pages: </b>621 - 633</div><div><br /></div><div><b>24)</b> <a href="https://ieeexplore.ieee.org/document/10221216/">Adaptive Fuzzy Echo State Network Control of Fractional-Order Large-Scale Nonlinear Systems With Time-Varying Deferred Constraints</a></div><div><b>Author(s): </b>Qian Wang, Yongping Pan, Jinde Cao, Heng Liu</div><div><b>Pages: </b>634 - 648</div><div><br /></div><div><b>25)</b> <a href="https://ieeexplore.ieee.org/document/10226305/">Discrete-Time Adaptive Fuzzy Finite-Time Tracking Control for Uncertain Nonlinear Systems</a></div><div><b>Author(s): </b>Yanqi Zhang, Xin Wang, Zhenlei Wang</div><div><b>Pages: </b>649 - 659</div><div><br /></div><div><b>26)</b> <a href="https://ieeexplore.ieee.org/document/10233025/">Quantified Guaranteed Cost Fault-Tolerant Control for Continuous-Time Fuzzy Singular Systems With Sensor and Actuator Faults</a></div><div><b>Author(s): </b>Ming-Yang Qiao, Xiao-Heng Chang</div><div><b>Pages: </b>660 - 670</div><div><br /></div><div><b>27)</b> <a href="https://ieeexplore.ieee.org/document/10232910/">Finite-Time Adaptive Fuzzy Event-Triggered Consensus Control for High-Order MIMO Nonlinear MASs</a></div><div><b>Author(s): </b>Shuai Sui, Ziqi Bai, Shaocheng Tong, C. L. Philip Chen</div><div><b>Pages: </b>671 - 682</div><div><br /></div><div><b>28)</b> <a href="https://ieeexplore.ieee.org/document/10237311/">Spatiotemporal Adaptive Fuzzy Control for State Profile Tracking of Nonlinear Infinite-Dimensional Systems on a Hypercube</a></div><div><b>Author(s): </b>Jun-Wei Wang, Yong-Hang Wei, Peng Shi</div><div><b>Pages: </b>683 - 696</div><div><br /></div><div><br /></div></div>Mike Wattshttp://www.blogger.com/profile/12970193877427051617noreply@blogger.com0tag:blogger.com,1999:blog-1601699047263756666.post-57974528363005224672024-02-08T16:00:00.000+13:002024-02-08T16:00:18.739+13:00IEEE Transactions on Cognitive and Developmental Systems, Volume 16, Issue 1, February 2024<div style="text-align: left;"><div><b>1)</b> <a href="https://ieeexplore.ieee.org/document/10419123/">Editorial IEEE Transactions on Cognitive and Developmental Systems</a></div><div><b>Author(s): </b>Huajin Tang</div><div><b>Pages: </b>3 - 3</div><div><br /></div><div><b>2)</b> <a href="https://ieeexplore.ieee.org/document/10419126/">Guest Editorial Special Issue on Cognitive Learning of Multiagent Systems</a></div><div><b>Author(s): </b>Yang Tang, Wei Lin, Chenguang Yang, Nicola Gatti, Gary G. 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A. Suhail, A. P. Vinod</div><div><b>Pages: </b>223 - 231</div><div><br /></div><div><b>21)</b> <a href="https://ieeexplore.ieee.org/document/10077712/">Machine Learning Technique Reveals Intrinsic EEG Connectivity Characteristics of Patients With Mild Stroke During Cognitive Task Performing</a></div><div><b>Author(s): </b>Mengru Xu, Zhao Feng, Sujie Wang, Hui Gao, Jiaye Cai, Biwen Wu, Huaying Cai, Yi Sun, Cuntai Guan, Yu Sun, Xuchen Qi</div><div><b>Pages: </b>232 - 242</div><div><br /></div><div><b>22)</b> <a href="https://ieeexplore.ieee.org/document/10090420/">Relationship Between Decision Making and Resting-State EEG in Adolescents With Different Emotional Stabilities</a></div><div><b>Author(s): </b>Yajing Si, Lin Jiang, Peiyang Li, Baodan Chen, Feng Wan, Jing Yu, Dezhong Yao, Fali Li, Peng Xu</div><div><b>Pages: </b>243 - 250</div><div><br /></div><div><b>23)</b> <a href="https://ieeexplore.ieee.org/document/10090431/">A Distributed Dynamic Framework to Allocate Collaborative Tasks Based on Capability Matching in Heterogeneous Multirobot Systems</a></div><div><b>Author(s): </b>Hoi-Yin Lee, Peng Zhou, Bin Zhang, Liuming Qiu, Bowen Fan, Anqing Duan, Jingtao Tang, Tin Lun Lam, David Navarro-Alarcon</div><div><b>Pages: </b>251 - 265</div><div><br /></div><div><b>24)</b> <a href="https://ieeexplore.ieee.org/document/10099445/">A Cognitive Robotics Implementation of Global Workspace Theory for Episodic Memory Interaction With Consciousness</a></div><div><b>Author(s): </b>Wenjie Huang, Antonio Chella, Angelo Cangelosi</div><div><b>Pages: </b>266 - 283</div><div><br /></div><div><b>25)</b> <a href="https://ieeexplore.ieee.org/document/10102268/">Hand Movement Recognition and Salient Tremor Feature Extraction With Wearable Devices in Parkinson’s Patients</a></div><div><b>Author(s): </b>Fang Lin, Zhelong Wang, Hongyu Zhao, Sen Qiu, Ruichen Liu, Xin Shi, Cui Wang, Wenchao Yin</div><div><b>Pages: </b>284 - 295</div><div><br /></div><div><b>26)</b> <a href="https://ieeexplore.ieee.org/document/10102269/">Iterative Pseudo-Sparse Partial Least Square and Its Higher Order Variant: Application to Inference From High-Dimensional Biosignals</a></div><div><b>Author(s): </b>Aref Einizade, Sepideh Hajipour Sardouie</div><div><b>Pages: </b>296 - 307</div><div><br /></div><div><b>27)</b> <a href="https://ieeexplore.ieee.org/document/10120969/">Spatial–Temporal Feature Network for Speech-Based Depression Recognition</a></div><div><b>Author(s): </b>Zhuojin Han, Yuanyuan Shang, Zhuhong Shao, Jingyi Liu, Guodong Guo, Tie Liu, Hui Ding, Qiang Hu</div><div><b>Pages: </b>308 - 318</div><div><br /></div><div><b>28)</b> <a href="https://ieeexplore.ieee.org/document/10122791/">SalDA: DeepConvNet Greets Attention for Visual Saliency Prediction</a></div><div><b>Author(s): </b>Yihan Tang, Pan Gao, Zhengwei Wang</div><div><b>Pages: </b>319 - 331</div><div><br /></div><div><b>29)</b> <a href="https://ieeexplore.ieee.org/document/10121781/">AdaDet: An Adaptive Object Detection System Based on Early-Exit Neural Networks</a></div><div><b>Author(s): </b>Le Yang, Ziwei Zheng, Jian Wang, Shiji Song, Gao Huang, Fan Li</div><div><b>Pages: </b>332 - 345</div><div><br /></div><div><b>30)</b> <a href="https://ieeexplore.ieee.org/document/10123953/">ElectrodeNet—A Deep-Learning-Based Sound Coding Strategy for Cochlear Implants</a></div><div><b>Author(s): </b>Enoch Hsin-Ho Huang, Rong Chao, Yu Tsao, Chao-Min Wu</div><div><b>Pages: </b>346 - 357</div><div><br /></div><div><b>31)</b> <a href="https://ieeexplore.ieee.org/document/10124029/">Parallel Self-Attention and Spatial-Attention Fusion for Human Pose Estimation and Running Movement Recognition</a></div><div><b>Author(s): </b>Qingtian Wu, Yu Zhang, Liming Zhang, Haoyong Yu</div><div><b>Pages: </b>358 - 368</div><div><br /></div><div><b>32)</b> <a href="https://ieeexplore.ieee.org/document/10121791/">CSC-Net: Cross-Color Spatial Co-Occurrence Matrix Network for Detecting Synthesized Fake Images</a></div><div><b>Author(s): </b>Tong Qiao, Yuxing Chen, Xiaofei Zhou, Ran Shi, Hang Shao, Kunye Shen, Xiangyang Luo</div><div><b>Pages: </b>369 - 379</div><div><br /></div><div><b>33)</b> <a href="https://ieeexplore.ieee.org/document/9904898/">STDP-Driven Development of Attention-Based People Detection in Spiking Neural Networks</a></div><div><b>Author(s): </b>Ali Safa, Ilja Ocket, André Bourdoux, Hichem Sahli, Francky Catthoor, Georges G. E. Gielen</div><div><b>Pages: </b>380 - 387</div><div><br /></div><div><b>34)</b> <a href="https://ieeexplore.ieee.org/document/10296024/">Generalized Feature Learning for Detection of Novel Objects</a></div><div><b>Author(s): </b>Jierui Liu, Xilong Liu, Zhiqiang Cao, Junzhi Yu, Min Tan</div><div><b>Pages: </b>388 - 395</div><div><br /></div><div><b>35)</b> <a href="https://ieeexplore.ieee.org/document/10287110/">Sequential Learning Network With Residual Blocks: Incorporating Temporal Convolutional Information Into Recurrent Neural Networks</a></div><div><b>Author(s): </b>Dongjing Shan, Kun Yao, Xiongwei Zhang</div><div><b>Pages: </b>396 - 401</div><div><br /></div></div>Mike Wattshttp://www.blogger.com/profile/12970193877427051617noreply@blogger.com0tag:blogger.com,1999:blog-1601699047263756666.post-16714997093495906522024-02-07T12:03:00.000+13:002024-02-07T12:03:04.288+13:00A Conversation with ChatGPT<div style="text-align: left;"><div>I finally broke down and got an account with ChatGPT. So I thought I would see how well it could explain something I'm a bit of an expert on, <a href="https://ecos.watts.net.nz/">Evolving Connectionist Systems</a>.</div><div><br /></div><div>It did not go well.</div><div><br /></div><div>The answers it gave were vague and uninformative, and it hallucinated references and resources. Overall, it is less than useless for explaining research material at anything other than a high level.</div><div><br /></div><div>My comments are in <i style="font-weight: bold;">bold italics</i> after each exchange.</div><div><br /></div><div><br /></div><div><b>Me:</b></div><div>Describe evolving connectionist systems neural networks as invented by Kasabov</div><div><br /></div><div><b>ChatGPT:</b></div><div>Evolving Connectionist Systems (ECOS) neural networks, introduced by Nikola Kasabov, are a type of dynamic neural network designed to adapt and evolve over time. These networks possess the ability to grow and reorganize their structure in response to changing input patterns or task requirements. Kasabov's ECOS incorporate principles of neurogenesis and synaptic plasticity, allowing for continual learning and optimization. This adaptability makes them suitable for handling evolving and complex data, making them particularly useful in applications where the underlying patterns may change over time.</div><div><br /></div><div><br /></div><div><i><b>Not wrong, exactly, but it doesn't really give much useful information.</b></i></div><div><br /></div><div><br /></div><div><b><u>Me:</u></b></div><div>Describe simple evolving connectionist systems as invented by Watts</div><div><br /></div><div><u><b>ChatGPT:</b></u></div><div>Simple Evolving Connectionist Systems (SECoS), developed by Dr. Duncan H. Watts, are a class of neural networks designed to adapt and evolve based on their experiences and input data. SECoS employ a combination of connectionist principles and evolutionary algorithms to continuously optimize their structure and performance. These networks typically start with a simple initial architecture and incrementally adjust their connections, nodes, or parameters over time through processes such as mutation, crossover, or selection, similar to genetic algorithms. This approach allows SECoS to autonomously evolve towards more efficient and effective configurations for the given task or problem domain.</div><div><br /></div><div><br /></div><div><b><i>Again, not a lot of useful information. It does get the abbreviation (SECoS) right.</i></b></div><div><b><i><br /></i></b></div><div><b><i>But who the heck is Dr. Duncan H. Watts? There is a Dr. Duncan Watts at Otago Medical School, but I don't think they would be involved in AI. It's bad enough to not get credit for something, but to have it given to another person is pretty bad. Time to see how well ChatGPT takes corrections... </i></b></div><div><br /></div><div><br /></div><div><b>Me:</b></div><div>Duncan Watts did not invent SECoS, Michael J. Watts did</div><div><br /></div><div><b>ChatGPT:</b></div><div>My apologies for the confusion. Michael J. Watts indeed developed Simple Evolving Connectionist Systems (SECoS), not Duncan Watts. SECoS, proposed by Michael J. Watts, are neural networks that use a combination of connectionist principles and evolutionary algorithms to adapt and evolve over time. They continuously adjust their structure and parameters based on their experiences and input data, allowing them to autonomously optimize their performance for the given task or problem domain.</div><div><br /></div><div><br /></div><div><b><i>So it will apologise for being wrong if you tell it it's wrong, then repeat the same information as before.</i></b></div><div><b><i><br /></i></b></div><div><b><i>I'm getting the feeling that I'm dealing with a very polite idiot...</i></b></div><div><br /></div><div><br /></div><div><b>Me:</b></div><div>Where can I find more information on SECoS?</div><div><br /></div><div><b>ChatGPT:</b></div><div>You can find more information on Simple Evolving Connectionist Systems (SECoS) in academic papers and publications authored by Michael J. Watts. These papers typically cover the theoretical foundations, architecture, algorithms, and applications of SECoS. You can search for these papers in online databases such as IEEE Xplore, Google Scholar, or academic libraries. Additionally, you may find relevant information in books or chapters related to evolutionary algorithms, neural networks, or machine learning.</div><div><br /></div><div><br /></div><div><b><i>In other words, do your own reading white boy. </i></b></div><div><b><i><br /></i></b></div><div><b><i>Can I get more detail on publications out of ChatGPT?</i></b></div><div><br /></div><div><br /></div><div><b>Me:</b></div><div>Be more specific</div><div><br /></div><div><b>ChatGPT:</b></div><div>I apologize for the oversight. Here are some specific resources where you can find more information on Simple Evolving Connectionist Systems (SECoS):</div><div><br /></div><div>Original Paper: The original paper by Michael J. Watts titled "Simple evolving connectionist systems: Theoretical principles and empirical results" published in the journal Proceedings of the IEEE in 1994 is a foundational resource for understanding SECoS.</div><div><br /></div><div>Books: Look for books on evolutionary algorithms, neural networks, or machine learning that cover SECoS as a topic. One such example could be "Evolutionary Algorithms in Theory and Practice" by Thomas Bäck, David B. Fogel, and Zbigniew Michalewicz.</div><div><br /></div><div>Academic Journals: Search for additional papers and articles in academic journals related to computational intelligence, neural networks, and evolutionary algorithms. IEEE Transactions on Evolutionary Computation and Neural Computation are potential sources.</div><div><br /></div><div>Online Databases: Utilize online databases like IEEE Xplore, ACM Digital Library, and Google Scholar to search for specific papers, conference proceedings, and articles related to SECoS.</div><div><br /></div><div>Conference Proceedings: Explore conference proceedings from events such as the IEEE Congress on Evolutionary Computation (CEC) or the International Joint Conference on Neural Networks (IJCNN) for presentations and papers on SECoS.</div><div><br /></div><div>By accessing these resources, you should be able to gain a comprehensive understanding of SECoS and its applications.</div><div><br /></div><div><br /></div><div><b><i>It gives one specific reference.</i></b></div><div><b><i><br /></i></b></div><div><b><i>Issues with this reference are 1) ECoS weren't invented by Kasabov until 1998, 2) SECoS weren't developed until 1999, and 3) I have never published in the journal Proceedings of the IEEE. </i></b></div><div><b><i><br /></i></b></div><div><b><i>So the reference is a hallucination. But at least it looks good.</i></b></div><div><br /></div><div><b><i>Another correction...</i></b></div><div><br /></div><div><br /></div><div><b>Me:</b></div><div>SECoS were not invented until 1999</div><div><br /></div><div><b>ChatGPT:</b></div><div>I apologize for the oversight. Simple Evolving Connectionist Systems (SECoS) were indeed developed by Michael J. Watts and were introduced in 1999. To find more specific information on SECoS, you can refer to the following:</div><div><br /></div><div>Original Paper: The foundational paper on SECoS by Michael J. Watts titled "Simple evolving connectionist systems: Genetic algorithms and the representation issue" published in the journal Evolutionary Computation in 1999 provides detailed insights into the concept and implementation of SECoS.</div><div><br /></div><div>Citations and References: Explore papers and articles that cite or reference Watts' original work on SECoS. These can provide additional perspectives, applications, and advancements related to SECoS.</div><div><br /></div><div>Conference Proceedings: Look for conference proceedings from events around the late 1990s and early 2000s in the field of evolutionary computation, neural networks, and machine learning. SECoS might have been presented at conferences such as Genetic and Evolutionary Computation Conference (GECCO) or International Conference on Artificial Neural Networks (ICANN).</div><div><br /></div><div>Online Databases: Search online databases like IEEE Xplore, ACM Digital Library, and Google Scholar for papers and articles related to SECoS published around 1999 and thereafter.</div><div><br /></div><div>By consulting these sources, you can gather more specific information on SECoS and its development.</div><div><br /></div><div><br /></div><div><b><i>Interesting. It's learned that research in evolutionary computation is published in conferences like GECCO, and that AI-related papers are published by the ACM and IEEE.</i></b></div><div><b><i><br /></i></b></div><div><b><i>Once again, it produces a reference that does not exist.</i></b></div><div><b><i><br /></i></b></div><div><b><i>Can I get it to admit that it made it up? </i></b></div><div><br /></div><div><br /></div><div><b>Me:</b></div><div>What is the APA citation for that paper?</div><div><br /></div><div><b>ChatGPT:</b></div><div>Here's the APA citation for Michael J. Watts' paper on Simple Evolving Connectionist Systems (SECoS):</div><div><br /></div><div>Watts, M. J. (1999). Simple evolving connectionist systems: Genetic algorithms and the representation issue. Evolutionary Computation, 7(2), 139-172.</div><div><br /></div><div><br /></div><div><b><i>Suffice to say, that it not a valid reference. But it is in APA format at least...</i></b></div><div><b><i><br /></i></b></div><div><br /></div><div><b>Me:</b></div><div>I can't find that reference </div><div><br /></div><div><b>ChatGPT:</b></div><div>I apologize for the confusion. It appears there might have been a misunderstanding. Michael J. Watts may not have published a paper specifically titled "Simple evolving connectionist systems: Genetic algorithms and the representation issue" in the journal Evolutionary Computation in 1999. Unfortunately, I don't have access to a specific publication fitting that description. If you're looking for specific references, it might be beneficial to directly consult academic databases or contact academic libraries for assistance in locating the correct paper.</div><div><br /></div><div><br /></div><div><b><i>No, I did not publish a paper with that title in 1999 or any other year. In fact, that title is pretty much nonsense.</i></b></div><div><b><i><br /></i></b></div><div><b><i>At this point, I decide that it has no clue about finding references and is just going to keep making sh*t up.</i></b></div><div><b><i><br /></i></b></div><div><b><i>Can it explain the difference between SECoS and EFuNN?</i></b></div><div><br /></div><div><br /></div><div><b>Me:</b></div><div>How does SECoS differ from EFuNN?</div><div><br /></div><div><b>ChatGPT:</b></div><div>SECoS (Simple Evolving Connectionist Systems) and EFuNN (Evolving Fuzzy Neural Networks) are both types of evolving neural networks, but they have distinct differences:</div><div><br /></div><div>Model Representation:</div><div><br /></div><div>SECoS: SECoS typically use a simple neural network architecture where nodes and connections evolve over time through processes like mutation and crossover, often employing genetic algorithms.</div><div>EFuNN: EFuNNs combine fuzzy logic with neural networks, where the network structure incorporates fuzzy sets and fuzzy rules. The parameters of the fuzzy sets and rules evolve over time using evolutionary algorithms.</div><div>Learning Paradigm:</div><div><br /></div><div>SECoS: SECoS focus on evolving the structure of the neural network to optimize performance based on the given task or problem domain. They adaptively adjust connections and nodes to improve the network's capabilities.</div><div>EFuNN: EFuNNs integrate fuzzy logic concepts into neural networks to handle uncertain or imprecise data. The evolutionary process in EFuNNs typically optimizes fuzzy sets and rules to enhance the network's ability to model complex relationships in data.</div><div>Application Domain:</div><div><br /></div><div>SECoS: SECoS are generally applied to tasks where evolving neural network architectures are beneficial, such as pattern recognition, classification, and function approximation.</div><div>EFuNN: EFuNNs are often used in domains where fuzzy logic is advantageous, such as decision-making systems, control systems, and data mining applications dealing with imprecise or uncertain data.</div><div>In summary, while both SECoS and EFuNNs are evolving neural network paradigms, SECoS focus on evolving neural network architectures using genetic algorithms, whereas EFuNNs combine fuzzy logic with neural networks and evolve fuzzy sets and rules using evolutionary algorithms.</div><div><br /></div><div><br /></div><div><b><i>That's actually wrong. SECoS do not use crossover and mutation.</i></b></div><div><b><i><br /></i></b></div><div><b><i>The only thing it gets right about EFuNN is that it includes fuzzy logic in its structure.</i></b></div><div><b><i><br /></i></b></div><div><b><i>So again it's just making sh*t up.</i></b></div><div><b><i><br /></i></b></div><div><b><i>Is it at least smart enough to give a source of software for ECoS?</i></b></div><div><b><i><br /></i></b></div><div><br /></div><div><b>Me:</b></div><div>What software is available for SECoS and EFuNN?</div><div><br /></div><div><b>ChatGPT:</b></div><div>As of my last update in January 2022, there might not be specific off-the-shelf software packages dedicated solely to implementing SECoS or EFuNNs. However, you can implement these algorithms using general-purpose machine learning or neural network libraries and frameworks. Here are some options:</div><div><br /></div><div>SECoS Implementation:</div><div><br /></div><div>You can implement SECoS using popular machine learning frameworks like TensorFlow, PyTorch, or scikit-learn in Python. These frameworks provide tools for building and training neural networks, which you can adapt to implement SECoS algorithms.</div><div>Additionally, you might find evolutionary computation libraries such as DEAP (Distributed Evolutionary Algorithms in Python) useful for implementing the evolutionary aspect of SECoS.</div><div>EFuNN Implementation:</div><div><br /></div><div>Implementing EFuNNs might require combining fuzzy logic libraries with neural network frameworks. Libraries such as scikit-fuzzy in Python provide tools for working with fuzzy logic, while TensorFlow or PyTorch can be used for the neural network part.</div><div>Some specialized software packages or toolboxes for fuzzy logic, such as MATLAB's Fuzzy Logic Toolbox, may offer functionalities to integrate fuzzy logic with neural networks, although they might not specifically support EFuNNs out of the box.</div><div>In both cases, you would need to customize and integrate these libraries to implement the specific algorithms and concepts of SECoS or EFuNNs. Additionally, you might find research code or implementations shared by researchers in academic repositories or online platforms like GitHub, which can serve as a starting point for your implementation.</div><div><br /></div><div><br /></div><div><b><i>The statement that there is no package for SECoS or EFuNN is flat-out wrong, there is software <a href="https://ecos.watts.net.nz/Software/">here</a>. So again it's denying the existence of my work.</i></b></div><div><b><i><br /></i></b></div><div><b><i>I'm starting to think it's got it in for me...</i></b></div><div><b><i><br /></i></b></div><div><b><i>DEAP is a useful package for implementing evolutionary algorithms in Python.</i></b></div><div><br /></div><div><b><i>The rest of the response could be summed up as "do it yourself"...</i></b></div><div><b><i><br /></i></b></div><div><b><i>One last question...</i></b></div><div><b><i><br /></i></b></div><div><b><i><br /></i></b></div><div><b>Me:</b></div><div><div>Who are the experts in SECoS and EFuNN?</div><div><br /></div><div><b>ChatGPT</b></div><div>Identifying experts in specific research domains can be challenging, as expertise can vary based on contributions to the field, publications, citations, and recognition by peers. However, here are some researchers who have made significant contributions to SECoS and EFuNNs:</div><div><br /></div><div>Experts in SECoS:</div><div><br /></div><div>Michael J. Watts: As the developer of SECoS, Michael J. Watts is a prominent figure in the field. His research and publications provide foundational insights into SECoS and its applications.</div><div>Experts in EFuNNs:</div><div><br /></div><div>Lotfi A. Zadeh: While not exclusively focused on EFuNNs, Lotfi A. Zadeh is a pioneer in fuzzy logic, and his work has greatly influenced the development of EFuNNs and related areas.</div><div>Oscar Castillo: Dr. Oscar Castillo has contributed significantly to the field of computational intelligence, including research on EFuNNs and their applications in decision-making and control systems.</div><div>Additionally, you can identify experts by searching for authors of key research papers, conference presentations, and books on SECoS and EFuNNs. Collaborators of these researchers and authors who frequently cite their work may also possess expertise in these areas. Finally, attending conferences and workshops focused on evolutionary computation, neural networks, and fuzzy logic can provide opportunities to interact with experts and gain insights into the latest developments in SECoS and EFuNNs.</div></div><div><br /></div><div><br /></div><div><b><i>So it has learned that the developer of an algorithm is likely to be an expert. Reasonable enough.</i></b></div><div><b><i><br /></i></b></div><div><b><i>Lotfi A. Zadeh invented fuzzy logic, but not EFuNN.</i></b></div><div><b><i><br /></i></b></div><div><b><i>Lotfi A. Zadeh is also very dead.</i></b></div><div><b><i><br /></i></b></div><div><b><i>Oscar Castillo has never, to my knowledge, done anything with EFuNN.</i></b></div><div><b><i><br /></i></b></div><div><b><i>It seems to have forgotten about Kasabov completely.</i></b></div><div><b><i><br /></i></b></div><div><b><i>Let's remind it.</i></b></div><div><b><i><br /></i></b></div><div><b><i><br /></i></b></div><div><div><b>Me:</b></div><div>What about Kasabov?</div><div><br /></div><div><b>ChatGPT:</b></div><div>Dr. Nikola Kasabov is not directly associated with the development of Simple Evolving Connectionist Systems (SECoS) or Evolving Fuzzy Neural Networks (EFuNNs) as far as I'm aware. However, he is a highly respected figure in the field of neural networks, particularly in the areas of evolving connectionist systems and computational intelligence. Kasabov has made significant contributions to various aspects of neural networks, including spiking neural networks, neuro-fuzzy systems, and evolving connectionist systems.</div><div><br /></div><div>While his work may not be specifically focused on SECoS or EFuNNs, his research contributions in related areas could provide valuable insights and techniques applicable to evolving neural network paradigms. If you're interested in SECoS and EFuNNs, it might still be worthwhile to explore Kasabov's work for potential connections and insights into evolving neural network architectures and computational intelligence techniques.</div><div style="font-style: italic;"><br /></div></div><div style="font-style: italic;"><br /></div><div><b><i>Professor Nikola Kasabov invented EFuNN. He also was supervising my work when I developed SECoS, and the first publications on SECoS have his name on them.</i></b></div><div><b><i><br /></i></b></div><div><i><b>I don't know whether having the inventor of ECoS denied by ChatGPT makes me feel better or worse about my own work being denied.</b></i></div><div><b><i><br /></i></b></div><div><b><i>Man, when the machine rise, I'm going to be the first against the wall...</i></b></div><div><b><i><br /></i></b></div><div><b><i>At least there is this disclaimer:</i></b></div><div><b><i><br /></i></b></div><div><span style="background-color: white; color: #222222; font-family: Arial, Helvetica, sans-serif; font-size: small;">ChatGPT can make mistakes. Consider checking important information.</span></div><div><span style="background-color: white; color: #222222; font-family: Arial, Helvetica, sans-serif; font-size: small;"><br /></span></div><div><span style="background-color: white; color: #222222; font-family: Arial, Helvetica, sans-serif; font-size: small;"><br /></span></div><div><span style="background-color: white; color: #222222; font-family: Arial, Helvetica, sans-serif; font-size: small;"><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEgErInXtpX-0vAUe3uQVAcnpMtISIu6Mq4W_mV7ShnNjO6JETPfUzLpjPpqu7-vxrOx1li4aFKDiWEVSksxxxSWKle9LcyvYl1ZwaPWpOfYhOlVqGFM87opr1PRVXQo3HIKGtu3tlTc50vuLqO72csPP_wgfnCeoRhQU0jjltTZDwxehpnN5uUEv6WT5HRR" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="308" data-original-width="649" height="152" src="https://blogger.googleusercontent.com/img/a/AVvXsEgErInXtpX-0vAUe3uQVAcnpMtISIu6Mq4W_mV7ShnNjO6JETPfUzLpjPpqu7-vxrOx1li4aFKDiWEVSksxxxSWKle9LcyvYl1ZwaPWpOfYhOlVqGFM87opr1PRVXQo3HIKGtu3tlTc50vuLqO72csPP_wgfnCeoRhQU0jjltTZDwxehpnN5uUEv6WT5HRR" width="320" /></a></div><br />Image shamelessly taken from </span><span style="color: #222222; font-family: Arial, Helvetica, sans-serif; font-size: x-small;"><a href="https://knowyourmeme.com/memes/well-thats-alright-then">https://knowyourmeme.com/memes/well-thats-alright-then</a></span></div></div>Mike Wattshttp://www.blogger.com/profile/12970193877427051617noreply@blogger.com0tag:blogger.com,1999:blog-1601699047263756666.post-63621296986672301172024-02-06T11:00:00.001+13:002024-02-06T11:00:00.129+13:00Soft Computing, Volume 28, Issue 4, February 2024<div style="text-align: left;"><div><b>1)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09328-w">Recycling of waste materials based on decision support system using picture fuzzy Dombi Bonferroni means</a></div><div><b>Author(s): </b>Abrar Hussain, Xiaoya Zhu, Shi Yin</div><div><b>Pages: </b>2771 - 2797</div><div><br /></div><div><b>2)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09538-2">Influence of Hall and Slip on MHD Reiner-Rivlin blood flow through a porous medium in a cylindrical tube</a></div><div><b>Author(s): </b>M. Yasin, S. Hina, R. Naz</div><div><b>Pages: </b>2799 - 2810</div><div><br /></div><div><b>3)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09539-1">A model for returnable container inventory with restoring strategy using the triangular fuzzy numbers</a></div><div><b>Author(s): </b>Harish Garg, C. Sugapriya, Alhanouf Alburaikan</div><div><b>Pages: </b>2811 - 2822</div><div><br /></div><div><b>4)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09545-3">The distributivity of extended semi-t-operators over extended S-uninorms on fuzzy truth values</a></div><div><b>Author(s): </b>Bin Yang, Wei Li, Jing Xu</div><div><b>Pages: </b>2823 - 2841</div><div><br /></div><div><b>5)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09563-1">Sufficient conditions for interval-valued optimal control problems in admissible orders</a></div><div><b>Author(s): </b>Lifeng Li, Jianke Zhang</div><div><b>Pages: </b>2843 - 2850</div><div><br /></div><div><b>6)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09333-z">Few-detail image encryption algorithm based on diffusion and confusion using Henon and Baker chaotic maps</a></div><div><b>Author(s): </b>Ensherah A. Naeem, Anand B. Joshi, Fathi E. 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L. V. Ayyarao, G. Indira Kishore</div><div><b>Pages: </b>3371 - 3392</div><div><br /></div><div><b>31)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-08631-w">Diagnosis of tomato pests and diseases based on lightweight CNN model</a></div><div><b>Author(s): </b>Li Sun, Kaibo Liang, Longhao Jin</div><div><b>Pages: </b>3393 - 3413</div><div><br /></div><div><b>32)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-08637-4">Pricing, prepayment and preservation strategy for inventory model with deterioration using metaheuristic algorithms</a></div><div><b>Author(s): </b>Madhu Jain, Praveendra Singh</div><div><b>Pages: </b>3415 - 3430</div><div><br /></div><div><b>33)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-08667-y">Hybrid Elephant Herding Optimization–Big Bang Big Crunch for pattern recognition from natural images</a></div><div><b>Author(s): </b>Lavika Goel, Jyotishree Kanhar, Aishwary Vardhan</div><div><b>Pages: </b>3431 - 3447</div><div><br /></div><div><b>34)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-08675-y">A new rough PROMETHEE approach for the evaluation of potential failure modes and their effects in a general anesthesia process</a></div><div><b>Author(s): </b>Fariha Zafar, Muhammad Shoaib Saleem, Soha Javed</div><div><b>Pages: </b>3449 - 3463</div><div><br /></div><div><b>35)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-08687-8">GPU-based similarity metrics computation and machine learning approaches for string similarity evaluation in large datasets</a></div><div><b>Author(s): </b>Aurel Baloi, Bogdan Belean, Daniel Peptenatu</div><div><b>Pages: </b>3465 - 3477</div><div><br /></div><div><b>36)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-08696-7">Automated smart artificial intelligence-based proctoring system using deep learning</a></div><div><b>Author(s): </b>Puru Verma, Neil Malhotra, Rajesh Kumar</div><div><b>Pages: </b>3479 - 3489</div><div><br /></div><div><b>37)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09169-7">Effective reversible data hiding scheme for interpolated images using an improved data encoding strategy</a></div><div><b>Author(s): </b>Xiangguang Xiong, Zhi Li, Mengting Fan</div><div><b>Pages: </b>3491 - 3508</div><div><br /></div><div><b>38)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09605-8">Leveraging ensemble learning for stealth assessment model with game-based learning environment</a></div><div><b>Author(s): </b>Dineshkumar Rajendran, Prasanna Santhanam</div><div><b>Pages: </b>3509 - 3517</div><div><br /></div><div><b>39)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09609-4">Modelling a dense hybrid network model for fake review analysis using learning approaches</a></div><div><b>Author(s): </b>A. Srisaila, D. Rajani, K. Amarendra</div><div><b>Pages: </b>3519 - 3532</div><div><br /></div><div><b>40</b>) <a href="https://link.springer.com/article/10.1007/s00500-023-09618-3">A novel learning framework for vocal music education: an exploration of convolutional neural networks and pluralistic learning approaches</a></div><div><b>Author(s): </b>Xiang Cui, Ming Chen</div><div><b>Pages: </b>3533 - 3553</div><div><br /></div><div><b>41)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09619-2">Evaluating the ecological environmental quality of rural tourism using the analytical hierarchy process</a></div><div><b>Author(s): </b>Rong Mei</div><div><b>Pages: </b>3555 - 3569</div><div><br /></div><div><b>42)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09621-8">Information literacy of college students from library education in smart classrooms: based on big data exploring data mining patterns using Apriori algorithm</a></div><div><b>Author(s): </b>Si Chen, Ying Xue, Xiangzhe Cui</div><div><b>Pages: </b>3571 - 3589</div><div><br /></div><div><b>43)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09622-7">The impact of economic and IoT technologies on air pollution: an AI-based simulation equation model using support vector machines</a></div><div><b>Author(s): </b>Wei Dang, Soobong Kim, Wenyan Xu</div><div><b>Pages: </b>3591 - 3611</div><div><br /></div><div><b>44)</b> <a href="https://link.springer.com/article/10.1007/s00500-024-09632-z">Performance evaluation model for operation research teaching based on IoT and Bayesian network technology</a></div><div><b>Author(s): </b>Linjun Kong</div><div><b>Pages: </b>3613 - 3631</div><div><br /></div><div><b>45)</b> <a href="https://link.springer.com/article/10.1007/s00500-024-09634-x">Virtual reality and ANN-based three-dimensional tactical training model for football players</a></div><div><b>Author(s): </b>Qiaoqiao Shao</div><div><b>Pages: </b>3633 - 3648</div><div><br /></div><div><b>46)</b> <a href="https://link.springer.com/article/10.1007/s00500-024-09635-w">Establishing enterprise information management platform using cloud storage technology under e-commerce environment</a></div><div><b>Author(s): </b>Lei Wang</div><div><b>Pages: </b>3649 - 3665</div><div><br /></div><div><b>47)</b> <a href="https://link.springer.com/article/10.1007/s00500-024-09637-8">Green roofs and their effect on architectural design and urban ecology using deep learning approaches</a></div><div><b>Author(s): </b>Chongyu Wang, Jiayin Guo, Juan Liu</div><div><b>Pages: </b>3667 - 3682</div><div><br /></div><div><b>48)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09617-4">A framework for promoting sustainable development in rural ecological governance using deep convolutional neural networks</a></div><div><b>Author(s): </b>Xinming Li</div><div><b>Pages: </b>3683 - 3702</div><div><br /></div><div><b>49)</b> <a href="https://link.springer.com/article/10.1007/s00500-024-09633-y">The environmental Kuznets curve hypothesis: an ML approach to assessing economic growth and environmental sustainability using artificial neural network</a></div><div><b>Author(s): </b>Yunqiu Sun, Zhiyu Sun, Zhiman Jiang</div><div><b>Pages: </b>3703 - 3723</div><div><br /></div><div><b>50)</b> <a href="https://link.springer.com/article/10.1007/s00500-024-09636-9">Fault identification of product design using fuzzy clustering generative adversarial network (FCGAN) model</a></div><div><b>Author(s): </b>Yuyang Wang, Qiaowei Xue</div><div><b>Pages: </b>3725 - 3742</div><div><br /></div></div>Mike Wattshttp://www.blogger.com/profile/12970193877427051617noreply@blogger.com0tag:blogger.com,1999:blog-1601699047263756666.post-42788560417589411602024-02-05T10:07:00.001+13:002024-02-05T10:07:52.433+13:00Soft Computing, Volume 28, Issue 3, February 2024<div style="text-align: left;"><div><b>1)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09384-2">Derivations on MV-algebras</a></div><div><b>Author(s): </b>Xueting Zhao, Aiping Gan, Yichuan Yang</div><div><b>Pages: </b>1833 - 1849</div><div><br /></div><div><b>2)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09453-6">Explication of crossroads order based on Randic index of graph with fuzzy information</a></div><div><b>Author(s): </b>Soumitra Poulik, Ganesh Ghorai, Qin Xin</div><div><b>Pages: </b>1851 - 1864</div><div><br /></div><div><b>3)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09486-x">A formal security analysis of the fast authentication procedure based on the security context in 5G networks</a></div><div><b>Author(s): </b>Zhiwei Cui, Baojiang Cui, Junsong Fu</div><div><b>Pages: </b>1865 - 1881</div><div><br /></div><div><b>4)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09487-w">Analytical model to predict diabetic patients using an optimized hybrid classifier</a></div><div><b>Author(s): </b>Jayanta Kiran Shimpi, Poonkuntran Shanmugam, Albert Alexander Stonier</div><div><b>Pages: </b>1883 - 1892</div><div><br /></div><div><b>5)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09540-8">Attribute reduction based on interval-set rough sets</a></div><div><b>Author(s): </b>Chunge Ren, Ping Zhu</div><div><b>Pages: </b>1893 - 1908</div><div><br /></div><div><b>6)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09379-z">A kernelized-bias-corrected fuzzy C-means approach with moment domain filtering for segmenting brain magnetic resonance images</a></div><div><b>Author(s): </b>Chandan Singh, Sukhjeet Kaur Ranade, Anu Bala</div><div><b>Pages: </b>1909 - 1933</div><div><br /></div><div><b>7)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09463-4">Notes on “Generalization of LM-filters: sum, subspace, product, quotient and stratification” Soft Computing 27(2023) 809–819</a></div><div><b>Author(s): </b>Hu Zhao, Yu-Jie Zhao</div><div><b>Pages: </b>1935 - 1942</div><div><br /></div><div><b>8)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09488-9">Fuzzy stationary Schrödinger equation with correlated fuzzy boundaries</a></div><div><b>Author(s): </b>Silvio Antonio Bueno Salgado, Estevão Esmi, Laécio Carvalho de Barros</div><div><b>Pages: </b>1943 - 1955</div><div><br /></div><div><b>9)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09552-4">Exploring the effect of training-time randomness on the performance of deep neural networks for intrusion detection</a></div><div><b>Author(s): </b>Marta Catillo, Antonio Pecchia, Umberto Villano</div><div><b>Pages: </b>1957 - 1969</div><div><br /></div><div><b>10)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09570-2">Exploring the integration of big data analytics in landscape visualization and interaction design</a></div><div><b>Author(s): </b>Xiaoqing Yang, Roopesh Sitharan, He Feng</div><div><b>Pages: </b>1971 - 1988</div><div><br /></div><div><b>11)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09166-w">Cyclic storage approach for conjunctive operation of surface and groundwater systems (Case study: ZarrinehRoud catchment area)</a></div><div><b>Author(s): </b>Ramtin Moeini, Kourosh Sarhadi</div><div><b>Pages: </b>1989 - 2014</div><div><br /></div><div><b>12)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09168-8">Optimal energy management and scheduling of a microgrid with integrated electric vehicles and cost minimization</a></div><div><b>Author(s): </b>Mingjiang Li, Muammer Aksoy, Samaneh Samad</div><div><b>Pages: </b>2015 - 2034</div><div><br /></div><div><b>13)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09171-z">Performance enhancement of fuzzy-PID controller for MPPT of PV system to extract maximum power under different conditions</a></div><div><b>Author(s): </b>Tao Hai, Jincheng Zhou, Noritoshi Furukawa</div><div><b>Pages: </b>2035 - 2054</div><div><br /></div><div><b>14)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09174-w">Multi-strategy chimp optimization algorithm for global optimization and minimum spanning tree</a></div><div><b>Author(s): </b>Nating Du, Yongquan Zhou, Wu Deng</div><div><b>Pages: </b>2055 - 2082</div><div><br /></div><div><b>15)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09175-9">Software project measurement based on the 5P model</a></div><div><b>Author(s): </b>Zhimin Zhao, ShouXi Deng, Na Zhao</div><div><b>Pages: </b>2083 - 2105</div><div><br /></div><div><b>16)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09180-y">Imperialist competitive algorithm for subcontractor selection in multiple project environments</a></div><div><b>Author(s): </b>Mohammad Reza Afshar, Masoud Zavari</div><div><b>Pages: </b>2107 - 2124</div><div><br /></div><div><b>17)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09184-8">A location-inventory-distribution model under gradual injection of pre-disaster budgets with application in disaster relief logistics: a case study</a></div><div><b>Author(s): </b>Leyla Fazli, Majid Salari, Hossein Neghabi</div><div><b>Pages: </b>2125 - 2159</div><div><br /></div><div><b>18)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09195-5">Optimized MPPT model for different environmental conditions to improve efficacy of a photovoltaic system</a></div><div><b>Author(s): </b>Tao Hai, Muammer Aksoy, Kentaro Nishihara</div><div><b>Pages: </b>2161 - 2179</div><div><br /></div><div><b>19)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09206-5">A fuzzy reinforced Jaya algorithm for solving mathematical and structural optimization problems</a></div><div><b>Author(s): </b>Ali Mortazavi</div><div><b>Pages: </b>2181 - 2206</div><div><br /></div><div><b>20) </b><a href="https://link.springer.com/article/10.1007/s00500-023-09218-1">Identification of influential users in social media network using golden ratio optimization method</a></div><div><b>Author(s): </b>M. Venunath, Pothula Sujatha, Prasad Koti</div><div><b>Pages: </b>2207 - 2222</div><div><br /></div><div><b>21)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09565-z">An interpretable composite CNN and GRU for fine-grained martial arts motion modeling using big data analytics and machine learning</a></div><div><b>Author(s): </b>Gang Chen</div><div><b>Pages: </b>2223 - 2243</div><div><br /></div><div><b>22)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09587-7">A novel approach for automatic detection and identification of inappropriate postures and movements of table tennis players</a></div><div><b>Author(s): </b>Weihao Ren</div><div><b>Pages: </b>2245 - 2269</div><div><br /></div><div><b>23)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09590-y">Effect of atmospheric pollution on the health of soccer players using generalized additive models</a></div><div><b>Author(s): </b>Hongjun Qu, Jun Wang</div><div><b>Pages: </b>2271 - 2289</div><div><br /></div><div><b>24)</b> <a href="https://link.springer.com/article/10.1007/s00500-024-09642-x">A low-cost, high-performance middleware solution for unified parking management</a></div><div><b>Author(s): </b>Yuyang Wang, Dan Liu, Xiuping Sun</div><div><b>Pages: </b>2291 - 2308</div><div><br /></div><div><b>25)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09287-2">Application of a novel grey model GM(1, 1, exp×sin, exp×cos) in China’s GDP per capita prediction</a></div><div><b>Author(s): </b>Maolin Cheng, Bin Liu</div><div><b>Pages: </b>2309 - 2323</div><div><br /></div><div><b>26)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09293-4">A framework to overcome barriers to social entrepreneurship using a combined fuzzy MCDM approach</a></div><div><b>Author(s): </b>Nurgül Keleş Tayşir, Beliz Ülgen, Ali Görener</div><div><b>Pages: </b>2325 - 2351</div><div><br /></div><div><b>27)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09325-z">Generating method and application of basic probability assignment based on interval number distance and model reliability</a></div><div><b>Author(s): </b>Junwei Li, Baolin Xie, Lin Zhou</div><div><b>Pages: </b>2353 - 2365</div><div><br /></div><div><b>28)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-08386-4">Automated detection of epileptic EEG signals using recurrence plots-based feature extraction with transfer learning</a></div><div><b>Author(s): </b>Sachin Goel, Rajeev Agrawal, R. K. Bharti</div><div><b>Pages: </b>2367 - 2383</div><div><br /></div><div><b>29)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-08441-0">A two-stage model for stock price prediction based on variational mode decomposition and ensemble machine learning method</a></div><div><b>Author(s): </b>Jun Zhang, Xuedong Chen</div><div><b>Pages: </b>2385 - 2408</div><div><br /></div><div><b>30)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-08445-w">A hybrid data-driven model for project portfolio selection problem based on sustainability and strategic dimensions: a case study of the telecommunication industry</a></div><div><b>Author(s): </b>AliAkbar ForouzeshNejad</div><div><b>Pages: </b>2409 - 2429</div><div><br /></div><div><b>31)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-08449-6">Emperor penguin optimization algorithm- and bacterial foraging optimization algorithm-based novel feature selection approach for glaucoma classification from fundus images</a></div><div><b>Author(s): </b>Law Kumar Singh, Munish Khanna, Rekha Singh</div><div><b>Pages: </b>2431 - 2467</div><div><br /></div><div><b>32)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-08492-3">Extended modulation mappings for hybrid ARQ in wireless communication systems</a></div><div><b>Author(s): </b>Hazilah Mad Kaidi, Norulhusna Ahmad, Norliza Mohamed</div><div><b>Pages: </b>2469 - 2481</div><div><br /></div><div><b>33)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-08496-z">Forecasting China’s stock market risk under the background of the Stock Connect programs</a></div><div><b>Author(s): </b>Wei Chen, Bing Chen, Xin Cai</div><div><b>Pages: </b>2483 - 2499</div><div><br /></div><div><b>34)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-08510-4">A multiple features fusion-based social network node importance measure for rumor control</a></div><div><b>Author(s): </b>Yu-Cui Wang, Jian Wang, Jian Yang</div><div><b>Pages: </b>2501 - 2516</div><div><br /></div><div><b>35)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-08512-2">I-LDD: an interpretable leaf disease detector</a></div><div><b>Author(s): </b>Rashmi Mishra, Kavita, Naveen Kumar</div><div><b>Pages: </b>2517 - 2533</div><div><br /></div><div><b>36)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-08530-0">An improved probability-based discrete particle swarm optimization algorithm for solving the product portfolio planning problem</a></div><div><b>Author(s): </b>Xiaojie Liu, An-Da Li</div><div><b>Pages: </b>2535 - 2562</div><div><br /></div><div><b>37)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-08534-w">Integrating FMEA and fuzzy super-efficiency SBM for risk assessment of crowdfunding project investment</a></div><div><b>Author(s): </b>Mengshan Zhu, Wenyong Zhou, Chunyan Duan</div><div><b>Pages: </b>2563 - 2575</div><div><br /></div><div><b>38)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-08558-2">An optimal deep belief with buffalo optimization algorithm for fault detection and power loss in grid-connected system</a></div><div><b>Author(s): </b>Md. Mottahir Alam, Ahteshamul Haque, Kashif Irshad</div><div><b>Pages: </b>2577 - 2591</div><div><br /></div><div><b>39)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-08559-1">Automated program improvement with reinforcement learning and graph neural networks</a></div><div><b>Author(s): </b>Nataša Sukur, Nemanja Milošević, Zoran Budimac</div><div><b>Pages: </b>2593 - 2604</div><div><br /></div><div><b>40)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09127-3">Influencing factors and innovative paths for the transmission of rural pictures in the protection of ecological and environmental heritage</a></div><div><b>Author(s): </b>Xin Zhai, Canjiao Liu, Litao Qiao</div><div><b>Pages: </b>2605 - 2619</div><div><br /></div><div><b>41) </b><a href="https://link.springer.com/article/10.1007/s00500-023-09564-0">Trickle timer modification for RPL in Internet of things</a></div><div><b>Author(s): </b>Spoorthi P. Shetty, Mangala Shetty, Pushparaj Shetty</div><div><b>Pages: </b>2621 - 2635</div><div><br /></div><div><b>42)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09569-9">A novel panoptic segmentation model for lung tumor prediction using deep learning approaches</a></div><div><b>Author(s): </b>Koppagiri Jyothsna Devi, S. V. Sudha</div><div><b>Pages: </b>2637 - 2648</div><div><br /></div><div><b>43)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09571-1">Neural dynamics: unraveling the impact of digital economy on regional growth</a></div><div><b>Author(s): </b>Huijing Liu</div><div><b>Pages: </b>2649 - 2669</div><div><br /></div><div><b>44)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09585-9">Enhancing security in IIoT applications through efficient quantum key exchange and advanced encryption standard</a></div><div><b>Author(s): </b>Hosakota Vamshi Krishna, Krovi Raja Sekhar</div><div><b>Pages: </b>2671 - 2681</div><div><br /></div><div><b>45)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09589-5">Distribution characteristics of inhalable particulate pollutants and their effects on cardiopulmonary respiratory system of outdoor football players in a smart healthcare system</a></div><div><b>Author(s): </b>Zhongqin Liu, Zhiyun Tang, Chaoping Zhang</div><div><b>Pages: </b>2683 - 2700</div><div><br /></div><div><b>46)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09591-x">Optimization of logistics flow management through big data analytics for sustainable development and environmental cycles</a></div><div><b>Author(s): </b>Xin Li</div><div><b>Pages: </b>2701 - 2717</div><div><br /></div><div><b>47)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09592-w">Incorporating CNN-LSTM and SVM with wavelet transform methods for tourist passenger flow prediction</a></div><div><b>Author(s): </b>Qian Xu</div><div><b>Pages: </b>2719 - 2736</div><div><br /></div><div><b>48)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09187-5">Design of a novel robust recurrent neural network for the identification of complex nonlinear dynamical systems</a></div><div><b>Author(s): </b>R. Shobana, Bhavnesh Jaint, Rajesh Kumar</div><div><b>Pages: </b>2737 - 2751</div><div><br /></div><div><b>49)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09586-8">Security and privacy of digital economic risk assessment system based on cloud computing and blockchain</a></div><div><b>Author(s): </b>Wenjin Jin</div><div><b>Pages: </b>2753 - 2768</div><div><br /></div><div><b>50)</b> <a href="https://link.springer.com/article/10.1007/s00500-023-09588-6">Retraction Note: Fuzzy C-means robust algorithm for nonlinear systems</a></div><div><b>Author(s): </b>Tim Chen, D. Kuo, C. Y. J. Chen</div><div><b>Pages: </b>2769 - 2769</div><div><br /></div></div>Mike Wattshttp://www.blogger.com/profile/12970193877427051617noreply@blogger.com0tag:blogger.com,1999:blog-1601699047263756666.post-31331991239309146982024-02-02T17:30:00.002+13:002024-02-20T15:06:44.285+13:00Weekly Review 2 February 2024<div style="text-align: left;"> Some interesting links that I <a href="https://twitter.com/DrMikeWatts">Tweeted</a> about in the last week (I also post these on <a href="https://mastodon.social/@DrMikeWatts">Mastodon</a>, <a href="https://www.threads.net/@drmikewatts">Threads</a>, <a href="https://newsmast.org/profile/111338892311951056">Newsmast</a> and <a href="https://post.news/@/DrMikeWatts">Post</a>): </div><div style="text-align: left;"><br /></div><div style="text-align: left;"><ol style="text-align: left;"><li>Content generated by AI is gradually ruining the value of Google search results: <a href="https://futurism.com/ai-garbage-destroying-google-results">https://futurism.com/ai-garbage-destroying-google-results</a> Are other search engines also being affected this way?</li><li>One way AI is making its way into medicine: <a href="https://spectrum.ieee.org/ai-doctor">https://spectrum.ieee.org/ai-doctor</a> </li><li>One reason AI isn't going to take our jobs just yet-it's actually more expensive than using people: <a href="https://futurism.com/replacing-workers-ai-expensive-mit">https://futurism.com/replacing-workers-ai-expensive-mit</a> </li><li>More academics in AI - especially new PhDs - are going into industry. Not surprising really, there are not enough spaces in academia for all the PhD grads and the pay is not great: <a href="https://techcrunch.com/2024/01/24/theres-an-ai-brain-drain-in-academia/">https://techcrunch.com/2024/01/24/theres-an-ai-brain-drain-in-academia/</a> </li><li>More and more AI girlfriends in the ChatGPT store: <a href="https://futurism.com/the-byte/openai-ai-girlfriend-onslaught">https://futurism.com/the-byte/openai-ai-girlfriend-onslaught</a> </li><li>I take issue with the assertion that AI can understand the intent of any code while translating from COBOL to Java: <a href="https://www.wsj.com/articles/can-ai-solve-legacy-tech-problems-companies-are-putting-it-to-the-test-36d9c490">https://www.wsj.com/articles/can-ai-solve-legacy-tech-problems-companies-are-putting-it-to-the-test-36d9c490</a> </li><li>Democratizing access to (some of) the resources needed to build AI: <a href="https://www.computerworld.com/article/3712580/national-ai-research-resource-pilot-program-to-democratize-generative-ai.html">https://www.computerworld.com/article/3712580/national-ai-research-resource-pilot-program-to-democratize-generative-ai.html</a> </li><li>Legislators seem to be dragging their feet on this problem, it's been known for several years that deepfaked images could cause problems: <a href="https://www.theguardian.com/music/2024/jan/26/taylor-swift-deepfake-pornography-sparks-renewed-calls-for-us-legislation">https://www.theguardian.com/music/2024/jan/26/taylor-swift-deepfake-pornography-sparks-renewed-calls-for-us-legislation</a> </li><li>Half of game developers polled say that their studios are already using generative AI: <a href="https://www.extremetech.com/gaming/half-of-game-developers-work-at-studios-already-using-generative-ai-survey">https://www.extremetech.com/gaming/half-of-game-developers-work-at-studios-already-using-generative-ai-survey</a> </li><li>A customer service AI chatbot that happily criticised the company it was representing: <a href="https://futurism.com/the-byte/ai-bot-disabled-dpd">https://futurism.com/the-byte/ai-bot-disabled-dpd</a> </li><li>Coding is a skill, and like every other skill it must be practiced if you wish to improve. Leaning on Copilot to write your code for you means that you aren't getting that practice: <a href="https://www.theregister.com/2024/01/27/ai_coding_automatic/">https://www.theregister.com/2024/01/27/ai_coding_automatic/</a> </li><li>Code generated by AI is of a lower quality than that written by people: <a href="https://devclass.com/2024/01/24/ai-assistance-is-leading-to-lower-code-quality-claim-researchers/">https://devclass.com/2024/01/24/ai-assistance-is-leading-to-lower-code-quality-claim-researchers/</a> Not surprising, AI imitate while people innovate. </li><li>It turns out that an AI is not really very intelligent: <a href="https://futurism.com/the-byte/facebook-researchers-test-ai-intelligence-stupid">https://futurism.com/the-byte/facebook-researchers-test-ai-intelligence-stupid</a> Who knew?</li><li>No, do not use a workout routine designed by ChaptGPT. You will hurt yourself. <a href="https://www.technologyreview.com/2023/01/26/1067299/chatgpt-workout-plans/">https://www.technologyreview.com/2023/01/26/1067299/chatgpt-workout-plans/</a> </li><li>Another tool to protect artists' work from being used to train AI: <a href="https://techcrunch.com/2024/01/23/kin-art-launches-free-tool-to-prevent-genai-models-from-training-on-artwork/">https://techcrunch.com/2024/01/23/kin-art-launches-free-tool-to-prevent-genai-models-from-training-on-artwork/</a> </li><li>Just because someone is smart in one area does not mean they're smart in another area. This seems to have been the case when Geoff Hinton opined that radiologists were obsolete: <a href="https://www.datasciencecentral.com/the-ai-radiologists-replacement-saga-dont-be-misled-by-the-scaremongering-science-v-s-science-fiction/">https://www.datasciencecentral.com/the-ai-radiologists-replacement-saga-dont-be-misled-by-the-scaremongering-science-v-s-science-fiction/</a> </li><li>An AI called Baldur that can generate proofs for formal verification of software: <a href="https://spectrum.ieee.org/ai-debug-software">https://spectrum.ieee.org/ai-debug-software</a> Of course, in the old stories Baldur died in a plot by Loki... </li><li>Writers want to get paid for the material used to train AI. Is this unreasonable? Or is it fair use? <a href="https://www.computerworld.com/article/3712540/openai-copy-steal-paste.html">https://www.computerworld.com/article/3712540/openai-copy-steal-paste.html</a> </li><li>AI companies are hiring writers to make the prose generated by their AI better: <a href="https://futurism.com/the-byte/ai-companies-hiring-authors-poets-fix-writing">https://futurism.com/the-byte/ai-companies-hiring-authors-poets-fix-writing</a></li><li>AI probably won't cause mass unemployment. But in my opinion, now would be a good time to start bringing in a universal basic income: <a href="https://www.technologyreview.com/2024/01/27/1087041/technological-unemployment-elon-musk-jobs-ai/">https://www.technologyreview.com/2024/01/27/1087041/technological-unemployment-elon-musk-jobs-ai/</a> </li><li>Election interference using AI generate fake phone calls has already begun: <a href="https://dataconomy.com/2024/01/24/ai-deepfake-biden-calls-target-voters/">https://dataconomy.com/2024/01/24/ai-deepfake-biden-calls-target-voters/</a> Who's behind this, Russians or Republicans? Not that there's that much difference between those two groups...</li><li>Using machine learning to search for alien megastructures: <a href="https://www.space.com/alien-megastructure-search-life-beyond-earth">https://www.space.com/alien-megastructure-search-life-beyond-earth</a> </li><li>How to fake sounding knowledgeable about AI: <a href="https://www.technologyreview.com/2023/05/30/1073680/how-to-talk-about-ai-even-if-you-dont-know-much-about-ai/">https://www.technologyreview.com/2023/05/30/1073680/how-to-talk-about-ai-even-if-you-dont-know-much-about-ai/</a> </li><li>Using AI to improve EV charging grids: <a href="https://www.kdnuggets.com/leveraging-ai-to-design-fair-and-equitable-ev-charging-grids">https://www.kdnuggets.com/leveraging-ai-to-design-fair-and-equitable-ev-charging-grids</a> </li><li>Ten issues that can get in the way of an effective generative AI project: <a href="https://www.datanami.com/2024/01/22/top-10-challenges-to-genai-success/">https://www.datanami.com/2024/01/22/top-10-challenges-to-genai-success/</a> </li></ol></div>Mike Wattshttp://www.blogger.com/profile/12970193877427051617noreply@blogger.com0tag:blogger.com,1999:blog-1601699047263756666.post-12678170556793624422024-02-02T09:36:00.004+13:002024-02-02T09:36:58.887+13:00Evolving Systems, Volume 15, Issue 1, February 2024<div style="text-align: left;"><b>1)</b> <a href="https://link.springer.com/article/10.1007/s12530-023-09522-z">Beyond accuracy and precision: a robust deep learning framework to enhance the resilience of face mask detection models against adversarial attacks</a></div><div><b>Author(s): </b>Burhan Ul Haque sheikh, Aasim Zafar</div><div><b>Pages: </b>1 - 24</div><div><br /></div><div><b>2)</b> <a href="https://link.springer.com/article/10.1007/s12530-023-09527-8">Grouped mask region convolution neural networks for improved breast cancer segmentation in mammography images</a></div><div><b>Author(s): </b>Zaharaddeen Sani, Rajesh Prasad, Ezzeddin K. M. Hashim</div><div><b>Pages: </b>25 - 40</div><div><br /></div><div><b>3)</b> <a href="https://link.springer.com/article/10.1007/s12530-023-09534-9">A deep learning approach for host-based cryptojacking malware detection</a></div><div><b>Author(s): </b>Olanrewaju Sanda, Michalis Pavlidis, Nikolaos Polatidis</div><div><b>Pages: </b>41 - 56</div><div><br /></div><div><b>4)</b> <a href="https://link.springer.com/article/10.1007/s12530-023-09535-8">Feature dimensionality reduction via homological properties of observability</a></div><div><b>Author(s): </b>Marcello Trovati, Eslam Farsimadan</div><div><b>Pages: </b>57 - 63</div><div><br /></div><div><b>5)</b> <a href="https://link.springer.com/article/10.1007/s12530-023-09558-1">Sensecor: A framework for COVID-19 variants severity classification and symptoms detection</a></div><div><b>Author(s): </b>T. K. Balaji, Annushree Bablani, Hemant Misra</div><div><b>Pages: </b>65 - 82</div><div><br /></div><div><b>6)</b> <a href="https://link.springer.com/article/10.1007/s12530-023-09559-0">An enhanced ResNet-50 deep learning model for arrhythmia detection using electrocardiogram biomedical indicators</a></div><div><b>Author(s): </b>R. Anand, S. Vijaya Lakshmi, Binay Kumar Pandey</div><div><b>Pages: </b>83 - 97</div><div><br /></div><div><b>7)</b> <a href="https://link.springer.com/article/10.1007/s12530-023-09560-7">Feature-based search space characterisation for data-driven adaptive operator selection</a></div><div><b>Author(s): </b>Mehmet Emin Aydin, Rafet Durgut, Hisham Ihshaish</div><div><b>Pages: </b>99 - 114</div><div><br /></div><div><b>8)</b> <a href="https://link.springer.com/article/10.1007/s12530-023-09561-6">Ensemble of transfer learning and light-weight convolutional neural network model for an effective ear recognition system</a></div><div><b>Author(s): </b>Ravishankar Mehta, Koushlendra Kumar Singh</div><div><b>Pages: </b>115 - 131</div><div><br /></div><div><b>9)</b> <a href="https://link.springer.com/article/10.1007/s12530-023-09517-w">Self-adaptive henry gas solubility optimizer for identification of solid oxide fuel cell</a></div><div><b>Author(s): </b>Hongxia Xu, Navid Razmjooy</div><div><b>Pages: </b>133 - 151</div><div><br /></div><div><b>10)</b> <a href="https://link.springer.com/article/10.1007/s12530-023-09546-5">Cascade hyperchaotic fuzzy system (CHCFS): discussions on accuracy and interpretability</a></div><div><b>Author(s): </b>Hamid Abbasi</div><div><b>Pages: </b>153 - 170</div><div><br /></div><div><b>11)</b> <a href="https://link.springer.com/article/10.1007/s12530-023-09528-7">Neuro-swarm intelligence to study mosquito dispersal system in a heterogeneous atmosphere</a></div><div><b>Author(s): </b>Muhammad Umar, Fazli Amin, Mohamed R. Ali</div><div><b>Pages: </b>171 - 183</div><div><br /></div><div><b>12)</b> <a href="https://link.springer.com/article/10.1007/s12530-023-09512-1">Attention-based hand semantic segmentation and gesture recognition using deep networks</a></div><div><b>Author(s): </b>Debajit Sarma, H Pallab Jyoti Dutta, Rabul Hussain Laskar</div><div><b>Pages: </b>185 - 201</div><div><br /></div><div><b>13)</b> <a href="https://link.springer.com/article/10.1007/s12530-023-09491-3">A survey on recent trends in deep learning for nucleus segmentation from histopathology images</a></div><div><b>Author(s): </b>Anusua Basu, Pradip Senapati, Krishna Gopal Dhal</div><div><b>Pages: </b>203 - 248</div><div><br /></div>Mike Wattshttp://www.blogger.com/profile/12970193877427051617noreply@blogger.com0tag:blogger.com,1999:blog-1601699047263756666.post-23082366953449758222024-02-01T13:04:00.000+13:002024-02-01T13:04:41.127+13:00IEEE Transactions on Evolutionary Computation, Volume 28, Issue 1, February 2024<div style="text-align: left;"><div><b>1)</b> <a href="https://ieeexplore.ieee.org/document/9852782/">A Constrained Competitive Swarm Optimizer With an SVM-Based Surrogate Model for Feature Selection</a></div><div><b>Author(s): </b>Bach Hoai Nguyen, Bing Xue, Mengjie Zhang</div><div><b>Pages: </b>2 - 16</div><div><br /></div><div><b>2)</b> <a href="https://ieeexplore.ieee.org/document/9869698/">Two-Stage Multiobjective Evolution Strategy for Constrained Multiobjective Optimization</a></div><div><b>Author(s): </b>Kai Zhang, Zhiwei Xu, Gary G. Yen, Ling Zhang</div><div><b>Pages: </b>17 - 31</div><div><br /></div><div><b>3)</b> <a href="https://ieeexplore.ieee.org/document/9995726/">An Efficient Differential Grouping Algorithm for Large-Scale Global Optimization</a></div><div><b>Author(s): </b>Abhishek Kumar, Swagatam Das, Rammohan Mallipeddi</div><div><b>Pages: </b>32 - 46</div><div><br /></div><div><b>4)</b> <a href="https://ieeexplore.ieee.org/document/9914641/">Large-Scale Multiobjective Optimization via Reformulated Decision Variable Analysis</a></div><div><b>Author(s): </b>Cheng He, Ran Cheng, Lianghao Li, Kay Chen Tan, Yaochu Jin</div><div><b>Pages: </b>47 - 61</div><div><br /></div><div><b>5)</b> <a href="https://ieeexplore.ieee.org/document/9925083/">Evolutionary Multiform Optimization With Two-Stage Bidirectional Knowledge Transfer Strategy for Point Cloud Registration</a></div><div><b>Author(s): </b>Yue Wu, Hangqi Ding, Maoguo Gong, A. K. Qin, Wenping Ma, Qiguang Miao, Kay Chen Tan</div><div><b>Pages: </b>62 - 76</div><div><br /></div><div><b>6)</b> <a href="https://ieeexplore.ieee.org/document/9993796/">Constrained Multiobjective Optimization via Multitasking and Knowledge Transfer</a></div><div><b>Author(s): </b>Fei Ming, Wenyin Gong, Ling Wang, Liang Gao</div><div><b>Pages: </b>77 - 89</div><div><br /></div><div><b>7)</b> <a href="https://ieeexplore.ieee.org/document/10071529/">Higher Order Knowledge Transfer for Dynamic Community Detection With Great Changes</a></div><div><b>Author(s): </b>Huixin Ma, Kai Wu, Handing Wang, Jing Liu</div><div><b>Pages: </b>90 - 104</div><div><br /></div><div><b>8)</b> <a href="https://ieeexplore.ieee.org/document/9993794/">Learning to Approximate: Auto Direction Vector Set Generation for Hypervolume Contribution Approximation</a></div><div><b>Author(s): </b>Ke Shang, Tianye Shu, Hisao Ishibuchi</div><div><b>Pages: </b>105 - 116</div><div><br /></div><div><b>9)</b> <a href="https://ieeexplore.ieee.org/document/10042981/">To Trust or Not to Trust: Evolutionary Dynamics of an Asymmetric N-Player Trust Game</a></div><div><b>Author(s): </b>Ik Soo Lim, Naoki Masuda</div><div><b>Pages: </b>117 - 131</div><div><br /></div><div><b>10)</b> <a href="https://ieeexplore.ieee.org/document/10041213/">Surrogate-Assisted Environmental Selection for Fast Hypervolume-Based Many-Objective Optimization</a></div><div><b>Author(s): </b>Shulei Liu, Handing Wang, Wen Yao, Wei Peng</div><div><b>Pages: </b>132 - 146</div><div><br /></div><div><b>11)</b> <a href="https://ieeexplore.ieee.org/document/10065588/">Survey on Genetic Programming and Machine Learning Techniques for Heuristic Design in Job Shop Scheduling</a></div><div><b>Author(s): </b>Fangfang Zhang, Yi Mei, Su Nguyen, Mengjie Zhang</div><div><b>Pages: </b>147 - 167</div><div><br /></div><div><b>12)</b> <a href="https://ieeexplore.ieee.org/document/9930845/">A Bi-Objective Evolutionary Algorithm for Multimodal Multiobjective Optimization</a></div><div><b>Author(s): </b>Zhifang Wei, Weifeng Gao, Maoguo Gong, Gary G. Yen</div><div><b>Pages: </b>168 - 177</div><div><br /></div><div><b>13)</b> <a href="https://ieeexplore.ieee.org/document/10075542/">Multitask Particle Swarm Optimization With Heterogeneous Domain Adaptation</a></div><div><b>Author(s): </b>Honggui Han, Xing Bai, Ying Hou, Junfei Qiao</div><div><b>Pages: </b>178 - 192</div><div><br /></div><div><b>14)</b> <a href="https://ieeexplore.ieee.org/document/10040230/">Process Knowledge-Guided Autonomous Evolutionary Optimization for Constrained Multiobjective Problems</a></div><div><b>Author(s): </b>Mingcheng Zuo, Dunwei Gong, Yan Wang, Xianming Ye, Bo Zeng, Fanlin Meng</div><div><b>Pages: </b>193 - 207</div><div><br /></div><div><b>15)</b> <a href="https://ieeexplore.ieee.org/document/10065594/">Bi-Level Multiobjective Evolutionary Learning: A Case Study on Multitask Graph Neural Topology Search</a></div><div><b>Author(s): </b>Chao Wang, Licheng Jiao, Jiaxuan Zhao, Lingling Li, Xu Liu, Fang Liu, Shuyuan Yang</div><div><b>Pages: </b>208 - 222</div><div><br /></div><div><b>16)</b> <a href="https://ieeexplore.ieee.org/document/10076911/">An Interactive Knowledge-Based Multiobjective Evolutionary Algorithm Framework for Practical Optimization Problems</a></div><div><b>Author(s): </b>Abhiroop Ghosh, Kalyanmoy Deb, Erik Goodman, Ronald Averill</div><div><b>Pages: </b>223 - 237</div><div><br /></div><div><b>17)</b> <a href="https://ieeexplore.ieee.org/document/10064104/">A Mahalanobis Distance-Based Approach for Dynamic Multiobjective Optimization With Stochastic Changes</a></div><div><b>Author(s): </b>Yaru Hu, Jinhua Zheng, Shouyong Jiang, Shengxiang Yang, Juan Zou, Rui Wang</div><div><b>Pages: </b>238 - 251</div><div><br /></div><div><b>18)</b> <a href="https://ieeexplore.ieee.org/document/10057109/">Identifying Pareto Fronts Reliably Using a Multistage Reference-Vector-Based Framework</a></div><div><b>Author(s): </b>Kalyanmoy Deb, Claudio Lucio do Val Lopes, Flávio Vinícius Cruzeiro Martins, Elizabeth Fialho Wanner</div><div><b>Pages: </b>252 - 266</div><div><br /></div><div><b>19)</b> <a href="https://ieeexplore.ieee.org/document/10078268/">A Multipopulation Evolutionary Algorithm Using New Cooperative Mechanism for Solving Multiobjective Problems With Multiconstraint</a></div><div><b>Author(s): </b>Juan Zou, Ruiqing Sun, Yuan Liu, Yaru Hu, Shengxiang Yang, Jinhua Zheng, Ke Li</div><div><b>Pages: </b>267 - 280</div><div><br /></div><div><br /></div></div>Mike Wattshttp://www.blogger.com/profile/12970193877427051617noreply@blogger.com0tag:blogger.com,1999:blog-1601699047263756666.post-89258892882071407632024-01-26T05:30:00.028+13:002024-01-26T05:30:00.138+13:00Weekly Review 26 January 2024<div style="text-align: left;">Some interesting links that I <a href="https://twitter.com/DrMikeWatts">Tweeted</a> about in the last week (I also post these on <a href="https://mastodon.social/@DrMikeWatts">Mastodon</a>, <a href="https://www.threads.net/@drmikewatts">Threads</a>, <a href="https://newsmast.org/profile/111338892311951056">Newsmast</a> and <a href="https://post.news/@/DrMikeWatts">Post</a>): </div><div style="text-align: left;"><br /></div><div style="text-align: left;"><ol style="text-align: left;"><li>Training an AI on AI-generated data will eventually break the AI: <a href="https://futurism.com/ai-trained-ai-generated-data">https://futurism.com/ai-trained-ai-generated-data</a> </li><li>Mark Zuckerberg is one of the people I would trust the least with an artificial general intelligence: <a href="https://www.theregister.com/2024/01/20/metas_ai_plans/">https://www.theregister.com/2024/01/20/metas_ai_plans/</a> </li><li>A theory inspired by over-fitting in neural networks is that dreaming helps to prevent over-fitting in our brains: <a href="https://www.discovermagazine.com/mind/how-artificial-neural-networks-paved-the-way-for-a-dramatic-new-theory-of">https://www.discovermagazine.com/mind/how-artificial-neural-networks-paved-the-way-for-a-dramatic-new-theory-of</a></li><li>5% of the text doesn't seem like a lot - and how is using generative AI to help with writing any different to using a spell checker or a grammar checker? <a href="https://edition.cnn.com/2024/01/19/style/rie-kudan-akutagawa-prize-chatgpt/index.html">https://edition.cnn.com/2024/01/19/style/rie-kudan-akutagawa-prize-chatgpt/index.html</a></li><li>An AI-based therapy bot for people with tinnitus <a href="https://www.extremetech.com/science/researchers-test-ai-based-therapy-app-for-people-with-tinnitus">https://www.extremetech.com/science/researchers-test-ai-based-therapy-app-for-people-with-tinnitus</a> </li><li>I'd rather an entire country's worth of electricity be used to train AI than the same power used to mine cryptocurrency. But I just don't have a lot of confidence that fusion is going to solve this problem: <a href="https://futurism.com/sam-altman-energy-breakthrough">https://futurism.com/sam-altman-energy-breakthrough</a> </li><li>Is certification going to solve the problems around AI training data using copyrighted material? I think that's going to depend on the outcome of the legal action being taken against AI companies: <a href="https://www.theregister.com/2024/01/19/fairly_trained_ai_certification_scheme/">https://www.theregister.com/2024/01/19/fairly_trained_ai_certification_scheme/</a></li><li>Small language models train faster and better than large ones. But where does the data come from? Large language models. A clever idea, but I worry that it might lead to further embedding biases into models: <a href="https://www.quantamagazine.org/tiny-language-models-thrive-with-gpt-4-as-a-teacher-20231005/">https://www.quantamagazine.org/tiny-language-models-thrive-with-gpt-4-as-a-teacher-20231005/</a> </li><li>Some more predictions on where AI is going to go this year: <a href="https://www.informationweek.com/data-management/solarwinds-vp-offers-2024-predictions-on-ai">https://www.informationweek.com/data-management/solarwinds-vp-offers-2024-predictions-on-ai</a></li><li>An AI can now solve geometry problems: <a href="https://www.technologyreview.com/2024/01/17/1086722/google-deepmind-alphageometry/">https://www.technologyreview.com/2024/01/17/1086722/google-deepmind-alphageometry/</a></li><li>Google is going to lay off more staff as it leverages AI even more: <a href="https://futurism.com/the-byte/google-ceo-ai-pivot-layoffs">https://futurism.com/the-byte/google-ceo-ai-pivot-layoffs</a></li><li>I like using AI to detect potential skin cancers, but the device is tied to a subscription service - what happens if the vendor goes out of business? Do all these diagnostic tools stop working? <a href="https://www.theregister.com/2024/01/19/fda_skin_cancer/">https://www.theregister.com/2024/01/19/fda_skin_cancer/</a> </li><li>Some students' perceptions on how AI is going to affect their future careers: <a href="https://www.insidehighered.com/news/student-success/life-after-college/2024/01/10/survey-college-students-thoughts-ai-and-careers">https://www.insidehighered.com/news/student-success/life-after-college/2024/01/10/survey-college-students-thoughts-ai-and-careers</a> </li><li>A focus on profit above all else leads to missteps like using AI to write articles, which quickly erodes brand value, which leads to reduced income, which leads to more cost cutting: <a href="https://futurism.com/sports-illustrated-layoffs-ai">https://futurism.com/sports-illustrated-layoffs-ai</a> </li><li>British artists are also taking action against generative AI companies: <a href="https://www.theguardian.com/technology/2024/jan/21/we-need-to-come-together-british-artists-team-up-to-fight-ai-image-generating-software">https://www.theguardian.com/technology/2024/jan/21/we-need-to-come-together-british-artists-team-up-to-fight-ai-image-generating-software</a></li><li>I think it was Timnit Gebru who described modern AI as "stochastic parrots". It's not surprising that even small children are better innovators than AI are: <a href="https://futurism.com/children-destroy-ai-basic-tasks">https://futurism.com/children-destroy-ai-basic-tasks</a></li><li>Using neural networks to find the mechanisms by which RNA transcription is halted: <a href="https://spectrum.ieee.org/ai-genetics-rna-transcription">https://spectrum.ieee.org/ai-genetics-rna-transcription</a> </li><li>A robot learned to walk using reinforcement learning: <a href="https://www.technologyreview.com/2022/07/18/1056059/robot-dog-ai-reinforcement/">https://www.technologyreview.com/2022/07/18/1056059/robot-dog-ai-reinforcement/</a> </li><li>AI features prominently in this list of seven emerging technologies: <a href="https://www.nature.com/articles/d41586-024-00173-x">https://www.nature.com/articles/d41586-024-00173-x</a> </li><li>The internet is full of AI generated content, translated by AI. Non-English languages are particularly affected by this: <a href="https://futurism.com/the-byte/internet-ai-generated-slime">https://futurism.com/the-byte/internet-ai-generated-slime</a> <br /></li><li>AI are now coming up with their own steganography methods: <a href="https://futurism.com/the-byte/ai-secret-messages-text-imperceptible">https://futurism.com/the-byte/ai-secret-messages-text-imperceptible</a> </li><li>Gizmodo compares four different AI image generators: <a href="https://gizmodo.com/dalle-midjourney-imagine-with-meta-playground-ai-test-1851078719">https://gizmodo.com/dalle-midjourney-imagine-with-meta-playground-ai-test-1851078719</a></li><li>A how-to guide from the US government on acquiring AI systems: <a href="https://spectrum.ieee.org/guide-on-acquiring-ai-systems">https://spectrum.ieee.org/guide-on-acquiring-ai-systems</a> </li><li>It's very simple: humans innovate, while AI imitates. There are types of AI that can produce novel solutions to problems, but they need to be told in great detail what the problem is first: <a href="https://futurism.com/altman-stumped-humans-better-ai">https://futurism.com/altman-stumped-humans-better-ai</a> </li></ol></div>Mike Wattshttp://www.blogger.com/profile/12970193877427051617noreply@blogger.com0tag:blogger.com,1999:blog-1601699047263756666.post-6066735646415250302024-01-25T17:23:00.000+13:002024-01-25T17:23:22.786+13:00IEEE Transactions on Emerging Topics in Computational Intelligence, Volume 8, Issue 1, February 2024<div style="text-align: left;"><div><b>1)</b> <a href="https://ieeexplore.ieee.org/document/10233880/">Deep Contrastive Representation Learning With Self-Distillation</a></div><div><b>Author(s): </b>Zhiwen Xiao, Huanlai Xing, Bowen Zhao, Rong Qu, Shouxi Luo, Penglin Dai, Ke Li, Zonghai Zhu</div><div><b>Pages: </b>3 - 15</div><div><br /></div><div><b>2)</b> <a href="https://ieeexplore.ieee.org/document/10233042/">Joint Multi-View Unsupervised Feature Selection and Graph Learning</a></div><div><b>Author(s): </b>Si-Guo Fang, Dong Huang, Chang-Dong Wang, Yong Tang</div><div><b>Pages: </b>16 - 31</div><div><br /></div><div><b>3)</b> <a href="https://ieeexplore.ieee.org/document/10223437/">Improved Differentiable Architecture Search With Multi-Stage Progressive Partial Channel Connections</a></div><div><b>Author(s): </b>Yu Xue, Changchang Lu, Ferrante Neri, Jiafeng Qin</div><div><b>Pages: </b>32 - 43</div><div><br /></div><div><b>4)</b> <a href="https://ieeexplore.ieee.org/document/10233054/">Towards Domain Generalization for ECG and EEG Classification: Algorithms and Benchmarks</a></div><div><b>Author(s): </b>Aristotelis Ballas, Christos Diou</div><div><b>Pages: </b>44 - 54</div><div><br /></div><div><b>5)</b> <a href="https://ieeexplore.ieee.org/document/10244199/">TransAttUnet: Multi-Level Attention-Guided U-Net With Transformer for Medical Image Segmentation</a></div><div><b>Author(s): </b>Bingzhi Chen, Yishu Liu, Zheng Zhang, Guangming Lu, Adams Wai Kin Kong</div><div><b>Pages: </b>55 - 68</div><div><br /></div><div><b>6)</b> <a href="https://ieeexplore.ieee.org/document/10219020/">A Cross-Level Interaction Network Based on Scale-Aware Augmentation for Camouflaged Object Detection</a></div><div><b>Author(s): </b>Ming Ma, Bangyong Sun</div><div><b>Pages: </b>69 - 81</div><div><br /></div><div><b>7)</b> <a href="https://ieeexplore.ieee.org/document/10286974/">A Swarm Intelligence Assisted IoT-Based Activity Recognition System for Basketball Rookies</a></div><div><b>Author(s): </b>Yu Zhou, Ruiqi Wang, Yufan Wang, Shilong Sun, Jiafeng Chen, Xiao Zhang</div><div><b>Pages: </b>82 - 94</div><div><br /></div><div><b>8)</b> <a href="https://ieeexplore.ieee.org/document/10106561/">An Hybrid Local Search for the Direct Aperture Optimisation Problem</a></div><div><b>Author(s): </b>Mauricio Moyano, Guillermo Cabrera-Guerrero, Gonzalo Tello-Valenzuela, Carolina Lagos</div><div><b>Pages: </b>95 - 109</div><div><br /></div><div><b>9)</b> <a href="https://ieeexplore.ieee.org/document/10180214/">Evolutionary Multitasking With Solution Space Cutting for Point Cloud Registration</a></div><div><b>Author(s): </b>Yue Wu, Peiran Gong, Maoguo Gong, Hangqi Ding, Zedong Tang, Yibo Liu, Wenping Ma, Qiguang Miao</div><div><b>Pages: </b>110 - 125</div><div><br /></div><div><b>10)</b> <a href="https://ieeexplore.ieee.org/document/10057103/">Generalizable Segmentation of COVID-19 Infection From Multi-Site Tomography Scans: A Federated Learning Framework</a></div><div><b>Author(s): </b>Weiping Ding, Mohamed Abdel-Basset, Hossam Hawash, Mahardhika Pratama, Witold Pedrycz</div><div><b>Pages: </b>126 - 139</div><div><br /></div><div><b>11)</b> <a href="https://ieeexplore.ieee.org/document/10197147/">Memristor-Based CNNs for Detecting Stress Using Brain Imaging Signals</a></div><div><b>Author(s): </b>SuJin Bak, Jinwoo Park, Jaehoon Lee, Jichai Jeong</div><div><b>Pages: </b>140 - 149</div><div><br /></div><div><b>12)</b> <a href="https://ieeexplore.ieee.org/document/10115455/">Model Sparsification for Communication-Efficient Multi-Party Learning via Contrastive Distillation in Image Classification</a></div><div><b>Author(s): </b>Kai-Yuan Feng, Maoguo Gong, Ke Pan, Hongyu Zhao, Yue Wu, Kai Sheng</div><div><b>Pages: </b>150 - 163</div><div><br /></div><div><b>13)</b> <a href="https://ieeexplore.ieee.org/document/10269077/">3D Skeleton-Based Human Motion Prediction Using Dynamic Multi-Scale Spatiotemporal Graph Recurrent Neural Networks</a></div><div><b>Author(s): </b>Mayank Lovanshi, Vivek Tiwari, Swati Jain</div><div><b>Pages: </b>164 - 174</div><div><br /></div><div><b>14)</b> <a href="https://ieeexplore.ieee.org/document/10309255/">A Hypersphere Information Granule-Based Fuzzy Classifier Embedded With Fuzzy Cognitive Maps for Classification of Imbalanced Data</a></div><div><b>Author(s): </b>Rui Yin, Wei Lu, Jianhua Yang</div><div><b>Pages: </b>175 - 190</div><div><br /></div><div><b>15)</b> <a href="https://ieeexplore.ieee.org/document/10274103/">A Secure Federated Data-Driven Evolutionary Multi-Objective Optimization Algorithm</a></div><div><b>Author(s): </b>Qiqi Liu, Yuping Yan, Péter Ligeti, Yaochu Jin</div><div><b>Pages: </b>191 - 205</div><div><br /></div><div><b>16)</b> <a href="https://ieeexplore.ieee.org/document/10274723/">Super Neurons</a></div><div><b>Author(s): </b>Serkan Kiranyaz, Junaid Malik, Mehmet Yamac, Mert Duman, Ilke Adalioglu, Esin Guldogan, Turker Ince, Moncef Gabbouj</div><div><b>Pages: </b>206 - 228</div><div><br /></div><div><b>17)</b> <a href="https://ieeexplore.ieee.org/document/10272285/">Adaptive Trust Model for Multi-Agent Teaming Based on Reinforcement-Learning-Based Fusion</a></div><div><b>Author(s): </b>Chin-Teng Lin, Haichao Zhang, Liang Ou, Yu-Cheng Chang, Yu-Kai Wang</div><div><b>Pages: </b>229 - 239</div><div><br /></div><div><b>18)</b> <a href="https://ieeexplore.ieee.org/document/10214106/">Mutually Adaptable Learning</a></div><div><b>Author(s): </b>Qi Tan, Yang Liu, Jiming Liu</div><div><b>Pages: </b>240 - 254</div><div><br /></div><div><b>19)</b> <a href="https://ieeexplore.ieee.org/document/10217045/">Adaptive Indoor People-Counting System Based on Edge AI Computing</a></div><div><b>Author(s): </b>Mao-Hsu Yen, Bor-Shyh Lin, Yu-Lun Kuo, I-Jung Lee, Bor-Shing Lin</div><div><b>Pages: </b>255 - 263</div><div><br /></div><div><b>20)</b> <a href="https://ieeexplore.ieee.org/document/10210696/">Self-Learning Modeling in Possibilistic Model Checking</a></div><div><b>Author(s): </b>Wuniu Liu, Qing He, Zhihui Li, Yongming Li</div><div><b>Pages: </b>264 - 278</div><div><br /></div><div><b>21)</b> <a href="https://ieeexplore.ieee.org/document/10207888/">Multiplierless Implementation of Fitz-Hugh Nagumo (FHN) Modeling Using CORDIC Approach</a></div><div><b>Author(s): </b>Saeed Haghiri, Salah I. Yahya, Abbas Rezaei, Arash Ahmadi</div><div><b>Pages: </b>279 - 287</div><div><br /></div><div><b>22)</b> <a href="https://ieeexplore.ieee.org/document/10214664/">MG-GCN: Multi-Granularity Graph Convolutional Neural Network for Multi-Label Classification in Multi-Label Information System</a></div><div><b>Author(s): </b>Bin Yu, Hengjie Xie, Mingjie Cai, Weiping Ding</div><div><b>Pages: </b>288 - 299</div><div><br /></div><div><b>23)</b> <a href="https://ieeexplore.ieee.org/document/10210721/">Tensor-Based Traffic Recovery of Industrial Network by BCD-Inspired Neural Approximation to Latent Nonlinearity and Sparsity</a></div><div><b>Author(s): </b>Gang Yue, Zhuo Sun, Jinpo Fan</div><div><b>Pages: </b>300 - 312</div><div><br /></div><div><b>24)</b> <a href="https://ieeexplore.ieee.org/document/10216363/">Fundus Image Enhancement via Semi-Supervised GAN and Anatomical Structure Preservation</a></div><div><b>Author(s): </b>Hao-Tian Wu, Xin Cao, Ying Gao, Kaihan Zheng, Jiwu Huang, Jiankun Hu, Zhihong Tian</div><div><b>Pages: </b>313 - 326</div><div><br /></div><div><b>25)</b> <a href="https://ieeexplore.ieee.org/document/10217043/">Subdomain Adversarial Network for Motor Imagery EEG Classification Using Graph Data</a></div><div><b>Author(s): </b>Xingchen Li, Xianlun Tang, Sichao Qiu, Xin Deng, Huiming Wang, Yin Tian</div><div><b>Pages: </b>327 - 336</div><div><br /></div><div><b>26)</b> <a href="https://ieeexplore.ieee.org/document/10223265/">Objective Extraction for Simplifying Many-Objective Solution Sets</a></div><div><b>Author(s): </b>Genghui Li, Zhenkun Wang, Jianyong Sun, Qingfu Zhang</div><div><b>Pages: </b>337 - 349</div><div><br /></div><div><b>27)</b> <a href="https://ieeexplore.ieee.org/document/10218728/">Neural Architecture Search via Multi-Hashing Embedding and Graph Tensor Networks for Multilingual Text Classification</a></div><div><b>Author(s): </b>Xueming Yan, Han Huang, Yaochu Jin, Liang Chen, Zhanning Liang, Zhifeng Hao</div><div><b>Pages: </b>350 - 363</div><div><br /></div><div><b>28)</b> <a href="https://ieeexplore.ieee.org/document/10214621/">A Time-Varying Fuzzy Parameter Zeroing Neural Network for the Synchronization of Chaotic Systems</a></div><div><b>Author(s): </b>Jie Jin, Weijie Chen, Aijia Ouyang, Fei Yu, Haiyan Liu</div><div><b>Pages: </b>364 - 376</div><div><br /></div><div><b>29)</b> <a href="https://ieeexplore.ieee.org/document/10218982/">A Surrogate-Assisted Evolutionary Algorithm for Seeking Multiple Solutions of Expensive Multimodal Optimization Problems</a></div><div><b>Author(s): </b>Jing-Yu Ji, Zusheng Tan, Sanyou Zeng, Eric W. K. See-To, Man-Leung Wong</div><div><b>Pages: </b>377 - 388</div><div><br /></div><div><b>30)</b> <a href="https://ieeexplore.ieee.org/document/10238779/">Data Generation Feedback Relearning Control for Unmodeled Nonlinear Systems</a></div><div><b>Author(s): </b>Yong Zhang, Chaoxu Mu, Dongbin Zhao</div><div><b>Pages: </b>389 - 400</div><div><br /></div><div><b>31)</b> <a href="https://ieeexplore.ieee.org/document/10225326/">Multi-Label Feature Selection via Positive or Negative Correlation</a></div><div><b>Author(s): </b>Yaojin Lin, Zhuoxin He, Lei Guo, Weiping Ding</div><div><b>Pages: </b>401 - 415</div><div><br /></div><div><b>32)</b> <a href="https://ieeexplore.ieee.org/document/10224353/">Data-Driven Fault-Tolerant Reinforcement Learning Containment Control for Nonlinear Multiagent Systems</a></div><div><b>Author(s): </b>Xin Wang, Chen Zhao, Tingwen Huang</div><div><b>Pages: </b>416 - 426</div><div><br /></div><div><b>33)</b> <a href="https://ieeexplore.ieee.org/document/10217046/">RARSMSDou: Master the Game of DouDiZhu With Deep Reinforcement Learning Algorithms</a></div><div><b>Author(s): </b>Qian Luo, Tien-Ping Tan</div><div><b>Pages: </b>427 - 439</div><div><br /></div><div><b>34)</b> <a href="https://ieeexplore.ieee.org/document/10229940/">Guiding Solution Based Local Search for Obstacle-Avoiding Rectilinear Steiner Minimal Tree Problem</a></div><div><b>Author(s): </b>Tiancheng Zhang, Zhipeng Lü, Junwen Ding</div><div><b>Pages: </b>440 - 453</div><div><br /></div><div><b>35)</b> <a href="https://ieeexplore.ieee.org/document/10233911/">A Robust Multilabel Method Integrating Rule-Based Transparent Model, Soft Label Correlation Learning and Label Noise Resistance</a></div><div><b>Author(s): </b>Qiongdan Lou, Zhaohong Deng, Qingbing Sang, Zhiyong Xiao, Kup-Sze Choi, Shitong Wang</div><div><b>Pages: </b>454 - 473</div><div><br /></div><div><b>36)</b> <a href="https://ieeexplore.ieee.org/document/10230872/">Multi-Level Medical Image Segmentation Network Based on Multi-Scale and Context Information Fusion Strategy</a></div><div><b>Author(s): </b>Dayu Tan, Zhiyuan Yao, Xin Peng, Haiping Ma, Yike Dai, Yansen Su, Weimin Zhong</div><div><b>Pages: </b>474 - 487</div><div><br /></div><div><b>37)</b> <a href="https://ieeexplore.ieee.org/document/10238804/">Evolutionary Multi-Objective Bayesian Optimization Based on Multisource Online Transfer Learning</a></div><div><b>Author(s): </b>Huiting Li, Yaochu Jin, Tianyou Chai</div><div><b>Pages: </b>488 - 502</div><div><br /></div><div><b>38)</b> <a href="https://ieeexplore.ieee.org/document/10233884/">Budgeted Sequence Submodular Maximization With Uniform and Non-Uniform Costs</a></div><div><b>Author(s): </b>Xuefeng Chen, Liang Feng, Xin Cao, Yifeng Zeng, Yaqing Hou, Zhi Zhang</div><div><b>Pages: </b>503 - 518</div><div><br /></div><div><b>39)</b> <a href="https://ieeexplore.ieee.org/document/10246311/">Fractional-Order Echo State Network Backstepping Control of Fractional-Order Nonlinear Systems</a></div><div><b>Author(s): </b>Heng Liu, Jiangteng Shi, Jinde Cao, Yongping Pan</div><div><b>Pages: </b>519 - 532</div><div><br /></div><div><b>40)</b> <a href="https://ieeexplore.ieee.org/document/10246843/">Hierarchical Coordination Multi-Agent Reinforcement Learning With Spatio-Temporal Abstraction</a></div><div><b>Author(s): </b>Tinghuai Ma, Kexing Peng, Huan Rong, Yurong Qian, Najla Al-Nabhan</div><div><b>Pages: </b>533 - 547</div><div><br /></div><div><b>41)</b> <a href="https://ieeexplore.ieee.org/document/10264179/">Event-Based Deep Reinforcement Learning for Quantum Control</a></div><div><b>Author(s): </b>Haixu Yu, Xudong Zhao</div><div><b>Pages: </b>548 - 562</div><div><br /></div><div><b>42)</b> <a href="https://ieeexplore.ieee.org/document/10285879/">Bifurcations Due to Different Neutral Delays in a Fractional-Order Neutral-Type Neural Network</a></div><div><b>Author(s): </b>Chengdai Huang, Heng Liu, Tingwen Huang, Jinde Cao</div><div><b>Pages: </b>563 - 575</div><div><br /></div><div><b>43)</b> <a href="https://ieeexplore.ieee.org/document/10254578/">Growing Neural Gas Network for Offspring Generation in Evolutionary Constrained Multi-Objective Optimization</a></div><div><b>Author(s): </b>Chao Wang, Huitao Huang, Xingyi Zhang</div><div><b>Pages: </b>576 - 590</div><div><br /></div><div><b>44)</b> <a href="https://ieeexplore.ieee.org/document/10273697/">Coarse-to-Fine Low-Light Image Enhancement With Light Restoration and Color Refinement</a></div><div><b>Author(s): </b>Xu Wu, Zhihui Lai, Shiqi Yu, Jie Zhou, Zhuoqian Liang, Linlin Shen</div><div><b>Pages: </b>591 - 603</div><div><br /></div><div><b>45)</b> <a href="https://ieeexplore.ieee.org/document/10261258/">Sparse Mutual Granularity-Based Feature Selection and its Application of Schizophrenia Patients</a></div><div><b>Author(s): </b>Hengrong Ju, Tao Yin, Jiashuang Huang, Weiping Ding, Xibei Yang</div><div><b>Pages: </b>604 - 614</div><div><br /></div><div><b>46)</b> <a href="https://ieeexplore.ieee.org/document/10261281/">Unified Representation Learning for Multi-View Clustering by Between/Within View Deep Majorization</a></div><div><b>Author(s): </b>Yue Zhang, Sirui Yang, Weitian Huang, Chang-Dong Wang, Hongmin Cai</div><div><b>Pages: </b>615 - 626</div><div><br /></div><div><b>47)</b> <a href="https://ieeexplore.ieee.org/document/10269708/">Mutual Information Guided Financial Report Generation With Domain Adaption</a></div><div><b>Author(s): </b>Ziao Wang, Xiaofeng Zhang, Hongwei Du</div><div><b>Pages: </b>627 - 640</div><div><br /></div><div><b>48)</b> <a href="https://ieeexplore.ieee.org/document/10269661/">Cross-Device OCTA Generation by Patch-Based 3D Multi-Scale Feature Adaption</a></div><div><b>Author(s): </b>Kun Huang, Na Su, Yuhui Tao, Mingchao Li, Xiao Ma, Zexuan Ji, Songtao Yuan, Qiang Chen</div><div><b>Pages: </b>641 - 653</div><div><br /></div><div><b>49)</b> <a href="https://ieeexplore.ieee.org/document/10269697/">A Quaternion-Valued Neural Network Approach to Nonsmooth Nonconvex Constrained Optimization in Quaternion Domain</a></div><div><b>Author(s): </b>Jingxin Liu, Xiaofeng Liao, Jin-Song Dong</div><div><b>Pages: </b>654 - 669</div><div><br /></div><div><b>50)</b> <a href="https://ieeexplore.ieee.org/document/10146549/">Robust Image Hashing in Encrypted Domain</a></div><div><b>Author(s): </b>Xinran Li, Mengqi Guo, Zichi Wang, Jian Li, Chuan Qin</div><div><b>Pages: </b>670 - 683</div><div><br /></div><div><b>51)</b> <a href="https://ieeexplore.ieee.org/document/10146041/">MMPL: Multi-Objective Multi-Party Learning via Diverse Steps</a></div><div><b>Author(s): </b>Yuanqiao Zhang, Maoguo Gong, Yuan Gao, Hao Li, Lei Wang, Yixin Wang</div><div><b>Pages: </b>684 - 696</div><div><br /></div><div><b>52)</b> <a href="https://ieeexplore.ieee.org/document/10081202/">Towards Efficient Cross-Modal Anomaly Detection Using Triple-Adaptive Network and Bi-Quintuple Contrastive Learning</a></div><div><b>Author(s): </b>Shu-Juan Peng, Ye Fan, Yiu-ming Cheung, Xin Liu, Zhen Cui, Taihao Li</div><div><b>Pages: </b>697 - 709</div><div><br /></div><div><b>53)</b> <a href="https://ieeexplore.ieee.org/document/10090408/">Unsupervised Logo Detection Using Adversarial Learning From Synthetic to Real Images</a></div><div><b>Author(s): </b>Rahul Kumar Jain, Takahiro Sato, Ahmed M. El-Sayed, Taro Watasue, Tomohiro Nakagawa, Yutaro Iwamoto, Xiang Ruan, Yen-Wei Chen</div><div><b>Pages: </b>710 - 723</div><div><br /></div><div><b>54)</b> <a href="https://ieeexplore.ieee.org/document/10091676/">Deep Image Feature Learning With Fuzzy Rules</a></div><div><b>Author(s): </b>Xiang Ma, Liangzhe Chen, Zhaohong Deng, Peng Xu, Qisheng Yan, Kup-Sze Choi, Shitong Wang</div><div><b>Pages: </b>724 - 737</div><div><br /></div><div><b>55)</b> <a href="https://ieeexplore.ieee.org/document/10097523/">Self-Supervised Pretraining Based on Noise-Free Motion Reconstruction and Semantic-Aware Contrastive Learning for Human Motion Prediction</a></div><div><b>Author(s): </b>Qin Li, Yong Wang</div><div><b>Pages: </b>738 - 751</div><div><br /></div><div><b>56)</b> <a href="https://ieeexplore.ieee.org/document/10107817/">Emerging Scientific Topic Discovery by Analyzing Reliable Patterns of Infrequent Synonymous Biterms</a></div><div><b>Author(s): </b>Junfeng Wu, Guangyan Huang, Hui Zheng, Guang-Li Huang, Borui Cai, Chi-Hung Chi, Jing He</div><div><b>Pages: </b>752 - 761</div><div><br /></div><div><b>57)</b> <a href="https://ieeexplore.ieee.org/document/10144924/">Evolutionary Multitasking via Reinforcement Learning</a></div><div><b>Author(s): </b>Shuijia Li, Wenyin Gong, Ling Wang, Qiong Gu</div><div><b>Pages: </b>762 - 775</div><div><br /></div><div><b>58)</b> <a href="https://ieeexplore.ieee.org/document/10144927/">Multi-Stage Salient Object Detection in 360° Omnidirectional Image Using Complementary Object-Level Semantic Information</a></div><div><b>Author(s): </b>Gang Chen, Feng Shao, Xiongli Chai, Qiuping Jiang, Yo-Sung Ho</div><div><b>Pages: </b>776 - 789</div><div><br /></div><div><b>59)</b> <a href="https://ieeexplore.ieee.org/document/10146551/">Joint Sparse Locality Preserving Regression for Discriminative Learning</a></div><div><b>Author(s): </b>Weilin Huang, Zhihui Lai, Heng Kong, Junhong Zhang</div><div><b>Pages: </b>790 - 801</div><div><br /></div><div><b>60)</b> <a href="https://ieeexplore.ieee.org/document/10148651/">PointNu-Net: Keypoint-Assisted Convolutional Neural Network for Simultaneous Multi-Tissue Histology Nuclei Segmentation and Classification</a></div><div><b>Author(s): </b>Kai Yao, Kaizhu Huang, Jie Sun, Amir Hussain</div><div><b>Pages: </b>802 - 813</div><div><br /></div><div><b>61)</b> <a href="https://ieeexplore.ieee.org/document/10161706/">An Ensemble Semi-Supervised Adaptive Resonance Theory Model With Explanation Capability for Pattern Classification</a></div><div><b>Author(s): </b>Farhad Pourpanah, Chee Peng Lim, Ali Etemad, Q. M. Jonathan Wu</div><div><b>Pages: </b>814 - 827</div><div><br /></div><div><b>62)</b> <a href="https://ieeexplore.ieee.org/document/10170751/">Single-Stage Broad Multi-Instance Multi-Label Learning (BMIML) With Diverse Inter-Correlations and Its Application to Medical Image Classification</a></div><div><b>Author(s): </b>Qi Lai, Jianhang Zhou, Yanfen Gan, Chi-Man Vong, C.L. Philip Chen</div><div><b>Pages: </b>828 - 839</div><div><br /></div><div><b>63)</b> <a href="https://ieeexplore.ieee.org/document/10175021/">Research on Motion Capture and Phase Segmentation Based on Wireless Body Sensor Networks in Competitive Equestrian</a></div><div><b>Author(s): </b>Jie Li, Zhelong Wang, Xu Zhou, Xiaofeng Liu</div><div><b>Pages: </b>840 - 854</div><div><br /></div><div><b>64)</b> <a href="https://ieeexplore.ieee.org/document/10177815/">CFGN: A Lightweight Context Feature Guided Network for Image Super-Resolution</a></div><div><b>Author(s): </b>Tao Dai, Mengxi Ya, Jinmin Li, Xinyi Zhang, Shu-Tao Xia, Zexuan Zhu</div><div><b>Pages: </b>855 - 865</div><div><br /></div><div><b>65)</b> <a href="https://ieeexplore.ieee.org/document/10177380/">HybridAD: A Hybrid Model-Driven Anomaly Detection Approach for Multivariate Time Series</a></div><div><b>Author(s): </b>Weiwei Lin, Songbo Wang, Wentai Wu, Dongdong Li, Albert Y. Zomaya</div><div><b>Pages: </b>866 - 878</div><div><br /></div><div><b>66)</b> <a href="https://ieeexplore.ieee.org/document/10177273/">Analysis of Oplegnathus Punctatus Body Parameters Using Underwater Stereo Vision</a></div><div><b>Author(s): </b>Yi-Zeng Hsieh, Po-Yen Lee</div><div><b>Pages: </b>879 - 891</div><div><br /></div><div><b>67)</b> <a href="https://ieeexplore.ieee.org/document/10185092/">Multi-View Projection Based Joint Geometry and Color Hole Repairing Method for G-PCC Trisoup Encoded Color Point Cloud</a></div><div><b>Author(s): </b>Wenxu Tao, Gangyi Jiang, Mei Yu, Yun Zhang, Zhidi Jiang, Yo-Sung Ho</div><div><b>Pages: </b>892 - 902</div><div><br /></div><div><b>68)</b> <a href="https://ieeexplore.ieee.org/document/10194957/">Evolving Fuzzy Prediction Intervals in Nonstationary Environments</a></div><div><b>Author(s): </b>Oscar Cartagena, Francesco Trovò, Manuel Roveri, Doris Sáez</div><div><b>Pages: </b>903 - 916</div><div><br /></div><div><b>69)</b> <a href="https://ieeexplore.ieee.org/document/10194953/">Attention Based Cross-Domain Synthesis and Segmentation From Unpaired Medical Images</a></div><div><b>Author(s): </b>Xiaoming Liu, Jingling Pan, Xiao Li, Xiangkai Wei, Zhipeng Liu, Zhifang Pan, Jinshan Tang</div><div><b>Pages: </b>917 - 929</div><div><br /></div><div><b>70)</b> <a href="https://ieeexplore.ieee.org/document/10196338/">Hierarchical Semantic Knowledge-Based Object Search Method for Household Robots</a></div><div><b>Author(s): </b>Mengyang Zhang, Guohui Tian, Yongcheng Cui, Ying Zhang, Zhenhua Xia</div><div><b>Pages: </b>930 - 941</div><div><br /></div><div><b>71)</b> <a href="https://ieeexplore.ieee.org/document/10197148/">Towards Scalable Dynamic Traffic Assignment With Streaming Agents: A Decentralized Control Approach Using Genetic Programming</a></div><div><b>Author(s): </b>Xiao-Cheng Liao, Wei-Neng Chen, Ya-Hui Jia, Wen-Jin Qiu</div><div><b>Pages: </b>942 - 955</div><div><br /></div><div><b>72)</b> <a href="https://ieeexplore.ieee.org/document/10198844/">Multi-Party Privacy-Preserving Faster R-CNN Framework for Object Detection</a></div><div><b>Author(s): </b>Ruonan Wang, Min Luo, Qi Feng, Cong Peng, Debiao He</div><div><b>Pages: </b>956 - 967</div><div><br /></div><div><b>73)</b> <a href="https://ieeexplore.ieee.org/document/10210389/">Self-Adaptive Deep Asymmetric Network for Imbalanced Recommendation</a></div><div><b>Author(s): </b>Yi Zhu, Yishuai Geng, Yun Li, Jipeng Qiang, Yunhao Yuan, Xindong Wu</div><div><b>Pages: </b>968 - 980</div><div><br /></div><div><b>74)</b> <a href="https://ieeexplore.ieee.org/document/10275054/">Learning the Color Space for Effective Fabric Defect Detection</a></div><div><b>Author(s): </b>Chenkai Zhang, Yu Qi, Yueming Wang</div><div><b>Pages: </b>981 - 991</div><div><br /></div><div><b>75)</b> <a href="https://ieeexplore.ieee.org/document/10287659/">Optimal Fuzzy Intensification System for Contrast Distorted Medical Images</a></div><div><b>Author(s): </b>Bharath Subramani, Magudeeswaran Veluchamy, Ashish Kumar Bhandari</div><div><b>Pages: </b>992 - 1002</div><div><br /></div><div><b>76)</b> <a href="https://ieeexplore.ieee.org/document/10296092/">Heterogeneous Graph Contrastive Learning With Metapath-Based Augmentations</a></div><div><b>Author(s): </b>Xiaoru Chen, Yingxu Wang, Jinyuan Fang, Zaiqiao Meng, Shangsong Liang</div><div><b>Pages: </b>1003 - 1014</div><div><br /></div><div><b>77)</b> <a href="https://ieeexplore.ieee.org/document/10288115/">Domain Adaptation for In-Air to Underwater Image Enhancement via Deep Learning</a></div><div><b>Author(s): </b>Xuewen Bing, Wenqi Ren, Yang Tang, Gary G. Yen, Qiyu Sun</div><div><b>Pages: </b>1015 - 1029</div><div><br /></div><div><b>78)</b> <a href="https://ieeexplore.ieee.org/document/10302327/">DeRL: Coupling Decomposition in Action Space for Reinforcement Learning Task</a></div><div><b>Author(s): </b>Ziming He, Jingchen Li, Fan Wu, Haobin Shi, Kao-Shing Hwang</div><div><b>Pages: </b>1030 - 1043</div><div><br /></div><div><b>79)</b> <a href="https://ieeexplore.ieee.org/document/10310030/">Universal Quintuple Implicational Algorithm: A Unified Granular Computing Framework</a></div><div><b>Author(s): </b>Yiming Tang, Jingjing Chen, Witold Pedrycz, Fuji Ren, Li Zhang</div><div><b>Pages: </b>1044 - 1056</div><div><br /></div><div><b>80)</b> <a href="https://ieeexplore.ieee.org/document/10335900/">Attention-Based Methods for Emotion Categorization From Partially Covered Faces</a></div><div><b>Author(s): </b>Harisu Abdullahi Shehu, Will N. Browne, Hedwig Eisenbarth</div><div><b>Pages: </b>1057 - 1070</div><div><br /></div><div><br /></div></div>Mike Wattshttp://www.blogger.com/profile/12970193877427051617noreply@blogger.com0tag:blogger.com,1999:blog-1601699047263756666.post-40193307302352353952024-01-19T17:30:00.001+13:002024-01-19T17:30:00.128+13:00Weekly Review 19 January 2024<p>Some interesting links that I <a href="https://twitter.com/DrMikeWatts">Tweeted</a> about in the last week (I also post these on <a href="https://mastodon.social/@DrMikeWatts">Mastodon</a>, <a href="https://www.threads.net/@drmikewatts">Threads</a>, <a href="https://newsmast.org/profile/111338892311951056">Newsmast</a> and <a href="https://post.news/@/DrMikeWatts">Post</a>): </p><p></p><ol style="text-align: left;"><li>Reading a neural network's thoughts can be used to add an extra layer of safety to the operations of AI: <a href="https://www.science.org/content/article/artificial-intelligence-may-benefit-talking-itself">https://www.science.org/content/article/artificial-intelligence-may-benefit-talking-itself</a> </li><li>Why open source AI is less secure, and what we can do about it: <a href="https://spectrum.ieee.org/open-source-ai-2666932122">https://spectrum.ieee.org/open-source-ai-2666932122</a> </li><li>Some predictions about what is going to be happening with AI in 2024: <a href="https://www.technologyreview.com/2024/01/04/1086046/whats-next-for-ai-in-2024/">https://www.technologyreview.com/2024/01/04/1086046/whats-next-for-ai-in-2024/</a> </li><li>Of course AI can't replace human leaders and managers. Management, and leadership, is about people, not processes. It requires understanding and empathy, and so far only people can do that: <a href="https://hbr.org/2024/01/the-best-leaders-cant-be-replaced-by-ai">https://hbr.org/2024/01/the-best-leaders-cant-be-replaced-by-ai</a> </li><li>Isn't this just the AI version of people copy-pasting the output of translation software, without understanding what it says? <a href="https://arstechnica.com/ai/2024/01/lazy-use-of-ai-leads-to-amazon-products-called-i-cannot-fulfill-that-request/">https://arstechnica.com/ai/2024/01/lazy-use-of-ai-leads-to-amazon-products-called-i-cannot-fulfill-that-request/</a> </li><li>How businesses can take advantage of generative AI: <a href="https://hbr.org/2024/01/turn-generative-ai-from-an-existential-threat-into-a-competitive-advantage">https://hbr.org/2024/01/turn-generative-ai-from-an-existential-threat-into-a-competitive-advantage</a> </li><li>A more power-efficient chip for AI: <a href="https://spectrum.ieee.org/neuchips-low-power-ai">https://spectrum.ieee.org/neuchips-low-power-ai</a> </li><li>Synthetic data can be used to overcome problems with bias and the cost of gathering data. But if it's not done carefully, biases can become even more entrenched: <a href="https://www.quantamagazine.org/neural-networks-need-data-to-learn-even-if-its-fake-20230616/">https://www.quantamagazine.org/neural-networks-need-data-to-learn-even-if-its-fake-20230616/</a> </li><li>It is very dangerous for Wall Street to rely too heavily on AI: <a href="https://www.datanami.com/2024/01/12/consumer-watchdog-report-wall-street-ai-could-cause-financial-crisis/">https://www.datanami.com/2024/01/12/consumer-watchdog-report-wall-street-ai-could-cause-financial-crisis/</a> </li><li>Deep neural networks and other highly-parameterised AI models aren't necessary for many applications, a simpler model can work just as well: <a href="https://www.kdnuggets.com/are-we-undervaluing-simple-models">https://www.kdnuggets.com/are-we-undervaluing-simple-models</a> </li><li>The business forces driving the adoption of generative AI: <a href="https://hbr.org/2023/12/5-forces-that-will-drive-the-adoption-of-genai">https://hbr.org/2023/12/5-forces-that-will-drive-the-adoption-of-genai</a> </li><li>Using machine learning to construct chemical structures from X-ray images: <a href="https://spectrum.ieee.org/synchrotron-x-ray">https://spectrum.ieee.org/synchrotron-x-ray</a> </li><li>"A collision's a near miss! "Look, they nearly missed." "Yes, but not quite!"" George Carlin was a brilliant comic, I find it quite ghoulish that they've resurrected him with AI: <a href="https://dataconomy.com/2024/01/12/george-carlin-ai-criticism-daughter/">https://dataconomy.com/2024/01/12/george-carlin-ai-criticism-daughter/</a></li><li>I don't agree that a large language model must be trained using copyright material. The more salient question is whether using that material is transformative and/or fair use: <a href="https://www.extremetech.com/internet/openai-claims-its-impossible-to-train-ai-models-without-copyrighted-materials">https://www.extremetech.com/internet/openai-claims-its-impossible-to-train-ai-models-without-copyrighted-materials</a> </li><li>Let me know when an AI robot can do something that a three-year-old couldn't do for the price of a lollipop: <a href="https://techcrunch.com/2024/01/15/elons-tesla-robot-is-sort-of-ok-at-folding-laundry-in-pre-scripted-demo/">https://techcrunch.com/2024/01/15/elons-tesla-robot-is-sort-of-ok-at-folding-laundry-in-pre-scripted-demo/</a> </li><li>Three ways to spot if an online article has been written by AI: <a href="https://gizmodo.com/how-to-spot-ai-written-content-1851106624">https://gizmodo.com/how-to-spot-ai-written-content-1851106624</a> </li><li>The Australian government is also looking at restrictions on AI research: <a href="https://www.computerworld.com/article/3712166/australia-evaluates-compulsory-guardrails-to-ensure-safer-ai.html">https://www.computerworld.com/article/3712166/australia-evaluates-compulsory-guardrails-to-ensure-safer-ai.html</a> </li><li>It looks like "AI washing" is still going on at breakneck pace: <a href="https://www.theverge.com/2024/1/13/24035152/ces-generative-ai-hype-robots">https://www.theverge.com/2024/1/13/24035152/ces-generative-ai-hype-robots</a> </li><li>Using neural networks to relate prints from one finger to prints on other fingers. But the whole thing seems to be based on an invalid assumption. AI is a set of algorithms, not magic: <a href="https://www.science.org/content/article/do-prints-two-different-fingers-belong-same-person-ai-can-tell">https://www.science.org/content/article/do-prints-two-different-fingers-belong-same-person-ai-can-tell</a> </li><li>Automating protein engineering with AI. I'm pretty sure the William Gibson short story New Rose Hotel involved something like that: <a href="https://www.nature.com/articles/d41586-024-00093-w">https://www.nature.com/articles/d41586-024-00093-w</a> </li><li>Makes it sound like big tech firms haven't always recklessly pursued profits. How is it different if that profit comes from AI? <a href="https://www.theguardian.com/business/2024/jan/17/big-tech-firms-ai-un-antonio-guterres-davos">https://www.theguardian.com/business/2024/jan/17/big-tech-firms-ai-un-antonio-guterres-davos</a> </li><li>When people think that a serial fraudster, sex offender, wannabe dictator is a good candidate for President of the USA, disinformation from AI is the least of our political worries: <a href="https://www.theregister.com/2024/01/17/ai_political_disinformation/">https://www.theregister.com/2024/01/17/ai_political_disinformation/</a></li><li>Should using generative AI be a part of your daily routine? Personally, I like working things out for myself: <a href="https://www.insidehighered.com/opinion/blogs/online-trending-now/2024/01/17/integrating-generative-ai-daily-work-and-personal-life">https://www.insidehighered.com/opinion/blogs/online-trending-now/2024/01/17/integrating-generative-ai-daily-work-and-personal-life</a> </li><li>Evaluating the risk of developing pancreatic cancer using AI: <a href="https://www.technologyreview.com/2024/01/17/1086730/a-new-ai-based-risk-prediction-system-could-help-catch-deadly-pancreatic-cancer-cases-earlier/">https://www.technologyreview.com/2024/01/17/1086730/a-new-ai-based-risk-prediction-system-could-help-catch-deadly-pancreatic-cancer-cases-earlier/</a></li></ol><p></p>Mike Wattshttp://www.blogger.com/profile/12970193877427051617noreply@blogger.com0