Neural Networks Volume 79, Pages: 1-150, July 2016

1. Regular expressions for decoding of neural network outputs  
Authors: Tobias Strauß, Gundram Leifert, Tobias Grüning, Roger Labahn
Pages: 1-11

2. When are two multi-layer cellular neural networks the same?  
Authors: Jung-Chao Ban, Chih-Hung Chang
Pages: 12-19

3. Robust mixture of experts modeling using the t image distribution  
Authors: F. Chamroukhi
Pages: 20-36

4. Engineering neural systems for high-level problem solving  
Authors: Jared Sylvester, James Reggia
Pages: 37-52

5. Effect of network architecture on burst and spike synchronization in a scale-free network of bursting neurons  
Authors: Sang-Yoon Kim, Woochang Lim
Pages: 53-77

6. Dynamical analysis of contrastive divergence learning: Restricted Boltzmann machines with Gaussian visible units  
Authors: Ryo Karakida, Masato Okada, Shun-ichi Amari
Pages: 78-87

7. Pattern recognition for electroencephalographic signals based on continuous neural networks  
M. Alfaro-Ponce, A. Argüelles, I. Chairez
Pages: 88-96

8. Improvements on image v-Twin Support Vector Machine  
Authors: Reshma Khemchandani, Pooja Saigal, Suresh Chandra
Pages: 97-107

9. Global exponential stability of complex-valued neural networks with both time-varying delays and impulsive effects  
Authors: Qiankun Song, Huan Yan, Zhenjiang Zhao, Yurong Liu
Pages: 108-116

10. Multistability analysis of a general class of recurrent neural networks with non-monotonic activation functions and time-varying delays  
Authors: Peng Liu, Zhigang Zeng, Jun Wang
Pages: 117-127

11. FPGA implementation of neuro-fuzzy system with improved PSO learning  
Authors: Cihan Karakuzu, Fuat Karakaya, Mehmet Ali Çavuşlu
Pages: 128-140

12. Interplay between non-NMDA and NMDA receptor activation during oscillatory wave propagation: Analyses of caffeine-induced oscillations in the visual cortex of rats
Authors: Hiroshi Yoshimura, Tokio Sugai, Nobuo Kato, Takashi Tominaga, Yoko Tominaga, Takahiro Hasegawa, Chenjuan Yao, Tetsuya Akamatsu
Pages: 141-149

Weekly Review 13 May 2016

Some interesting links that I Tweeted about in the last week:

1. DeepMind claims is has good privacy protection so should be trusted with data on millions of patient records: https://www.theguardian.com/technology/2016/may/06/deepmind-best-privacy-infrastructure-handling-nhs-data-says-co-founder
2. I suspect my daughter's generation will be the last to pay their way through university by flipping burgers: http://www.techrepublic.com/article/ai-will-destroy-entry-level-jobs-but-lead-to-a-basic-income-for-all/
3. I liked to implement algorithms myself when I was a post-grad: http://www.kdnuggets.com/2016/05/implement-machine-learning-algorithms-scratch.html Don't have time to do that kind of thing now.
4. Facebook is building an AI that builds AIs: http://www.wired.com/2016/05/facebook-trying-create-ai-can-create-ai/
5. Is "genetics-inspired multi-AI approach" a fancy name for an evolutionary algorithm? http://www.theregister.co.uk/2016/05/06/ebay_buys_expertmaker/
6. Predicting the winners of horse races with swarm intelligence: http://www.techrepublic.com/article/swarm-ai-predicts-the-2016-kentucky-derby/
7. Machine learning for personal stylists: http://www.computerworld.com/article/3067264/artificial-intelligence/at-stitch-fix-data-scientists-and-ai-become-personal-stylists.html
8. Machine learning in marketing: http://www.martechadvisor.com/articles/mobile-app-dev-marketing/marketing-in-a-digital-world-machine-learning-is-upping-innovation-and-agility/
9. Why AI is going to disappear, become invisible: http://techcrunch.com/2016/05/07/the-next-ai-is-no-ai/
10. An AI for a teaching assistant: http://www.wsj.com/articles/if-your-teacher-sounds-like-a-robot-you-might-be-on-to-something-1462546621
11. Preparing a business to include AI: http://www.techrepublic.com/article/how-to-prepare-your-business-to-include-ai/
12. Why we may need an ethics framework for AI: http://www.theguardian.com/commentisfree/2016/may/08/the-guardian-view-on-artificial-intelligence-look-out-its-ahead-of-you
13. Using machine learning to suggest citations for your research writing: http://techcrunch.com/2016/05/08/helix-conducts-research-as-you-write/
14. Categorising the importance of messages using machine learning: http://techcrunch.com/2016/05/08/deep-focus-saves-you-from-being-inundated-by-unimportant-messages/
15. How open source projects are moving machine learning forwards: https://www.linux.com/news/open-source-projects-are-transforming-machine-learning-and-ai
16. Ambient intelligence - AI everywhere: http://techcrunch.com/2016/05/07/the-next-stop-on-the-road-to-revolution-is-ambient-intelligence/
17. Proof-reading. It's really, really important. Of all the words they could mis-spell... https://www.insidehighered.com/quicktakes/2016/05/09/unfortunate-typo-tcu-commencement-program
18. Open Network Insight uses machine learning for network security: http://www.datanami.com/2016/05/09/oni-may-best-hope-cyber-security-now/
19. Deep learning in Python with the Keras library: http://machinelearningmastery.com/introduction-python-deep-learning-library-keras/
20. Interpreting radiological images with machine learning: http://techcrunch.com/2016/05/09/behold-ai-launches-artificially-intelligent-medical-software-to-find-abnormalities-faster/ IIRC David Fogel did this kind of thing around 1993.
21. Some pros and cons of chatbots: http://www.kdnuggets.com/2016/05/ai-chatbots-when-if.html
22. Facebook's FBLearner Flow machine learning platform: http://venturebeat.com/2016/05/09/facebook-details-its-company-wide-machine-learning-platform-fblearner-flow/
23. Using logistic regression and maximum entropy in Python: http://ataspinar.com/2016/05/07/regression-logistic-regression-and-maximum-entropy-part-2-code-examples/
24. A machine learning based stock trading app: https://www.techinasia.com/8-securities-stock-trading-virtual-broker
25. How to install and run TensorFlow on a Windows machine: http://www.netinstructions.com/how-to-install-and-run-tensorflow-on-a-windows-pc/
26. The coming disruption from intelligent bots: http://www.forbes.com/sites/moorinsights/2016/05/05/rise-of-the-machines-part-2-artificial-intelligence-and-bots-promise-to-disrupt/#15ea457a7082
27. How to become a good machine learning engineer: https://www.quora.com/How-can-one-become-a-good-machine-learning-engineer/answer/Nikhil-Dandekar
28. A bit of context for AI: What humans need to learn about machine learning: http://www.computerworld.com/article/3067924/artificial-intelligence/what-humans-need-to-learn-about-machine-learning.html
29. It's not coding, it's understanding the problem and designing a solution that's important: http://techcrunch.com/2016/05/10/please-dont-learn-to-code/
30. Three skills every developer needs, according to Joel Spolsky: http://www.techrepublic.com/article/joel-spolsky-the-three-skills-every-software-developer-should-learn/
31. Deep learning is definitely going to kill off jobs: http://www.techrepublic.com/article/ai-pioneer-ai-will-definitely-kill-jobs-but-thats-ok/
32. IBM's Watson is being applied to cyber-security: http://www.techrepublic.com/article/ibm-watson-takes-on-cybercrime-with-new-cloud-based-cybersecurity-technology/
33. Data mining can produce racist results, if the data being mined is influenced by racist policies: http://www.computerworld.com/article/3068622/internet/amazon-prime-and-the-racist-algorithms.html
34. How AI is helping lawyers: http://www.fastcompany.com/3059725/how-ai-and-crowdsourcing-are-remaking-the-legal-profession
35. Future trends in machine learning: http://www.geekwire.com/2016/future-machine-learning-5-trends-watch-around-algorithms-cloud-iot-big-data/
36. Amazon has open-sourced it's deep learning software: http://venturebeat.com/2016/05/11/amazon-open-sources-its-own-deep-learning-software-dsstne/ Said to be 2x speed of TensorFlow: http://siliconangle.com/blog/2016/05/11/amazon-says-its-new-deep-learning-library-is-2x-faster-than-googles/
37. Is the pressure to publish more papers, driving down the quality of those papers? http://www.nature.com/news/the-pressure-to-publish-pushes-down-quality-1.19887
38. How long before we see deep learning on a quantum computer? Training ANN is a multi-parameter optimisation problem http://www.gizmag.com/quantum-computer-processor-walk-algorithm/43263/
39. On the creativity of deep learning neural networks: http://www.kdnuggets.com/2016/05/deep-neural-networks-creative-deep-learning-art.html
40. Why machine learning and python go together so well: http://www.analyticbridge.com/profiles/blogs/machine-learning-with-python-why-do-they-form-the-best
41. The current state of neuromorphic chips: http://www.kdnuggets.com/2016/05/deep-learning-neuromorphic-chips.html
42. Overview of Markov chains: http://www.analyticbridge.com/profiles/blogs/making-data-science-accessible-markov-chains
43. Neural networks in JavaScript: http://www.kdnuggets.com/2016/05/implementing-neural-networks-javascript.html
44. Why AI is the most important technology of today: http://techemergence.com/why-is-ai-todays-most-important-technology/
45. The uses of AI in the enterprise: http://techcrunch.com/2016/05/12/clarifying-the-uses-of-artificial-intelligence-in-the-enterprise/
46. Google has open-sourced their natural-language processing toolbox: http://siliconangle.com/blog/2016/05/12/meet-parsey-mcparseface-googles-new-open-source-language-understanding-tool/ Best. Name. Ever!
47. Apple and Google competing in the mobile machine learning area: http://memeburn.com/2016/05/apple-googles-mobile-machine-learning-race/

   

Neural Networks, Volume 78 , Pages 1-120, June 2016

 Special Issue on "Neural Network Learning in Big Data", Edited by Asim Roy, Nikola Kasabov, Irwin King and Kumar Venayagamoorthy

1. Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: Design methodology and selected applications
Author(s): Nikola Kasabov, Nathan Matthew Scott, Enmei Tu, Stefan Marks, Neelava Sengupta, Elisa Capecci, Muhaini Othman, Maryam Gholami Doborjeh, Norhanifah Murli, Reggio Hartono, Josafath Israel Espinosa-Ramos, Lei Zhou, Fahad Bashir Alvi, Grace Wang, Denise Taylor, Valery Feigin, Sergei Gulyaev, Mahmoud Mahmoud, Zeng-Guang Hou, Jie Yang
Pages: 1-14

2. Noise-enhanced convolutional neural networks
Author(s): Kartik Audhkhasi, Osonde Osoba, Bart Kosko
Pages: 15-23

3. Hadoop neural network for parallel and distributed feature selection
Author(s): Victoria J. Hodge, Simon O’Keefe, Jim Austin
Pages: 24-35

4. A new Growing Neural Gas for clustering data streams
Author(s): Mohammed Ghesmoune, Mustapha Lebbah, Hanene Azzag
Pages: 36-50

5. Scalable learning method for feedforward neural networks using minimal-enclosing-ball approximation
Author(s): Jun Wang, Zhaohong Deng, Xiaoqing Luo, Yizhang Jiang, Shitong Wang
Pages: 51-64

6. A decentralized training algorithm for Echo State Networks in distributed big data applications
Author(s): Simone Scardapane, Dianhui Wang, Massimo Panella
Pages: 65-74

7. Smart sampling and incremental function learning for very large high dimensional data
Author(s): Diego G. Loyola R, Mattia Pedergnana, Sebastián Gimeno García
Pages: 75-87

8. Least square neural network model of the crude oil blending process
Author(s): José de Jesús Rubio
Pages: 88-96

9. Machine learning based sample extraction for automatic speech recognition using dialectal Assamese speech
Author(s): Swapna Agarwalla, Kandarpa Kumar Sarma
Pages: 97-111

10. Learning to decode human emotions with Echo State Networks
Author(s): Lachezar Bozhkov, Petia Koprinkova-Hristova, Petia Georgieva
Pages: 112-119

Weekly Review 6 May 2016

Some interesting links that I Tweeted about in the last week:

1. Google DeepMind has been given access to records on 1.6M British patients: http://www.theregister.co.uk/2016/04/29/google_given_access_to_reams_of_confidential_patient_information/
2. Mark Zuckerberg says machine learning will exceed human performance in speech & image recognition in 5-10 years: http://www.datanami.com/2016/04/29/ai-surpass-human-perception-5-10-years-zuckerberg-says/
3. Do deep learning image processing systems really see like humans do? http://motherboard.vice.com/en_au/read/computers-might-just-see-like-humans-after-all-vision-deep-learning-neural-networks
4. Top ten programming languages for (retaining) employment: http://www.informationweek.com/devops/programming-languages/10-programming-languages-that-will-keep-you-employed-/d/d-id/1325314 
5. Identifying heart attacks with AI: http://www.huffingtonpost.com/abhinav-sharma/can-artificial-intelligen_2_b_9798328.html
6. Machine learning at Facebook-goal of one AI agent per user: http://fortune.com/facebook-machine-learning/
7. Princeton's professors CV of failure: http://www.theguardian.com/education/2016/apr/30/cv-of-failures-princeton-professor-publishes-resume-of-his-career-lows The lesson here seems to be persistence is important, but so is luck.
8. The arguments in favour of journal paywalls just don't hold water: http://www.slate.com/articles/health_and_science/science/2016/04/science_magazine_can_t_defend_its_flawed_business_model.html
9. Why business intelligence tools need statistics (and actual intelligence): http://www.theregister.co.uk/2016/05/02/stats_the_problem_with_bi/
10. Qualcomm is releasing a SDK for its deep learning platform: http://www.theverge.com/2016/5/2/11538122/qualcomm-deep-learning-sdk-zeroth
11. Networking for data scientists: http://www.kdnuggets.com/2016/05/how-network-build-personal-brand-data-science.html Same principles apply to networking for computational intelligence researchers
12. AI in financial trading: http://www.euromoneythoughtleadership.com/ghostsinthemachine/
13. Infosys has developed a system using AI for knowledge management: http://techcrunch.com/2016/04/28/new-infosys-ai-tool-could-change-the-way-companies-maintain-complex-systems/
14. How long to true AI? http://dataconomy.com/far-away-inventing-true/ Time to re-read my copy of Kurzweil's The Age of Intelligent Machines
15. Recent surveys of evolutionary algorithms: http://cis.ieee.org/index.php?option=com_content&view=article&id=557:the-latest-surveys-of-evolutionary-algorithms-updated-3&catid=17:e-newsletter-news-a-announcements
16. Is Uber planning to use machine learning to eliminate surge pricing? http://www.npr.org/sections/alltechconsidered/2016/05/03/476513775/uber-plans-to-kill-surge-pricing-though-drivers-say-it-makes-job-worth-it
17. An overview http://techemergence.com/elon-musks-ai-gym-lets-you-develop-and-train-ai-algorithms/ of the Open "AI Gym" https://gym.openai.com/ for testing reinforcement learning algorithms.
18. The US White House is running workshops to consider the future implications of AI: https://www.whitehouse.gov/blog/2016/05/03/preparing-future-artificial-intelligence 
19. Quantizing deep learning neural networks in TensorFlow: http://www.kdnuggets.com/2016/05/how-quantize-neural-networks-tensorflow.html
20. Learning Python for data science: http://www.datasciencecentral.com/profiles/blogs/the-guide-to-learning-python-for-data-science-2
21. Machine learning and big data in credit card companies: http://www.datanami.com/2016/05/03/credit-card-companies-evolving-big-data/
22. This. This is why I will not review any paper for any Elsevier journal. They will not profit from my free labour: https://torrentfreak.com/elsevier-complaint-shuts-down-sci-hub-domain-name-160504/
23. AI in medicine is coming: http://m.nzherald.co.nz/technology/news/article.cfm?c_id=5&objectid=11634284
24. Art critics do not like art produced by AI: https://www.theguardian.com/science/2016/may/06/does-an-ai-need-to-make-love-to-rembrandts-girlfriend-to-make-art
25. Can machine learning make us more beautiful? http://dataconomy.com/better-botox-beauty-industry-get-ai-makeover/
26. Apparently AI is still too stupid to pose an existential threat to humans: http://www.techrepublic.com/article/microsoft-research-chief-ai-is-still-too-stupid-to-wipe-us-out-and-will-be-for-decades/
27. User behaviour analysis using AI is a growing trend in cyber-security: http://www.darkreading.com/threat-intelligence/silicon-and-artificial-intelligence-the-foundation-of-next-gen-data-security/a/d-id/1325401
28. I put more stock in Adam Coates'  opinions on the dangers of AI than Stephen Hawking's: http://www.informationweek.com/big-data/big-data-analytics/artificial-intelligence-dont-fear-it-embrace-it/d/d-id/1325391
29. Viv the Siri killer: https://www.technologyreview.com/s/601401/siri-killer-viv-faces-an-uphill-battle/
30. Researchers open to other disciplines produce better research: https://www.insidehighered.com/news/2016/05/06/new-paper-suggests-open-minded-researchers-produce-higher-quality-research Working with ecologists made me a better researcher.
31. AI in staff recruitment: http://www.techrepublic.com/article/how-ai-can-help-companies-hire-and-retain-the-best-employees/
32. Are postdocs actually harmful to researchers' careers? https://www.insidehighered.com/news/2016/05/06/study-graduate-students-may-take-unnecessary-postdocs 
33. How to improve customer experience with AI: http://www.cmswire.com/customer-experience/3-ways-to-bring-artificial-intelligence-to-your-customer-experience/
34. Teaching deep learning ANN to be more "conversational"a by training them on romance novels: https://www.buzzfeed.com/alexkantrowitz/googles-artificial-intelligence-engine-reads-romance-novels?utm_term=.hyZr7QWEl#.dfPM42YKG
35. An argument that computers do not either learn nor predict: http://techcrunch.com/2016/05/05/only-humans-not-computers-can-learn-or-predict/

IEEE Transactions on Neural Networks and Learning Systems; Volume 27, Issue 5, May 2016

1. Storing Sequencies in Binary Tournament-Based Neural Networks
Authors: Xiaoran Jiang; Vincent Gripon; Claude Berrou; Michael Rabbat
Pages: 913 - 925

2. Data Generators for Learning Systems Based on RBF Networks
Author: Marko Robnik-Šikonja
Pages: 926 - 938

3. A Novel Framework for Learning Geometry-Aware Kernels
Authors: Binbin Pan ; Wen-Sheng Chen ; Chen Xu ; Bo Chen
Pages: 939 - 951

4. Hybrid Sampling-Based Clustering Ensemble With Global and Local Constitutions
Authors: Yun Yang; Jianmin Jiang
Pages: 952 - 965

5. The Proximal Trajectory Algorithm in SVM Cross Validation
Authors: Annabella Astorino; Antonio Fuduli
Pages: 966 - 977

6. MLPNN Training via a Multiobjective Optimization of Training Error and Stochastic Sensitivity
Authors: Daniel S. Yeung; Jin-Cheng Li; Wing W.Y. Ng; Patrick P.K. Chan
Pages: 978 - 992

7. Generalization Performance of Regularized Ranking with Multiscale Kernels
Authors: Yicong Zhou; Hong Chen; Rushi Lan; Zhibin Pan
Pages: 993 - 1002

8. A Maximum Margin Approach for Semisupervised Ordinal Regression Clustering
Authors: Yanshan Xiao; Bo Liu; Zhifeng Hao
Pages: 1003 - 1019

9. Learning Discriminative Stein Kernel for SPD Matrices and Its Applications
Authors: Jianjia Zhang; Lei Wang; Luping Zhou; Wanqing Li
Pages: 1020 - 1033

10. Probabilistic Slow Features for Behavior Analysis
Authors: Lazaros Zafeiriou; Himalis A. Nicolaou; Stefanos Zafeiriou; Symeon Nikitidis; Maja Pantic
Pages: 1034 - 1048

11. Improper Complex-Valued Bhattacharyya Distance
Authors: Arash Mohammadi; Konstantinos N. Plataniotis
Pages: 1049 - 1064

12. A New Distance Metric for Unsupervised Learning of Categorical Data
Authors: Hong Jia; Yiu-ming Cheung; Jiming Liu
Pages: 1065 - 1079

13. Integrated Low-Rank-Based Discriminative Feature Learning for Recognition
Authors: Pan Zhou; Zhouchen Lin; Chao Zhang
Pages: 1080 - 1093

14. A Nearest Neighbor Classifier Employing Critical Boundary Vectors for Efficient On-Chip Template Reduction
Authors: Wenjun Xia; Yoshio Mita; Tadashi Shibata
Pages: 1094 - 1107

15. Tree Ensembles on the Induced Discrete Space
Author: Olcay Taner Yıldız
Pages: 1108 - 1113

Weekly Review 29 April 2016

Some interesting links that I Tweeted about in the last week:

1. So they're going to have a tournament for AI Doom-bots: http://www.theverge.com/2016/4/22/11486164/ai-visual-doom-competition-cig-2016
2. Deep learning vs SVM vs random forest - when is deep learning best? http://www.kdnuggets.com/2016/04/deep-learning-vs-svm-random-forest.html
3. Seems to be a degree of AI involved in detecting scam emails: http://spectrum.ieee.org/telecom/security/fighting-todays-targeted-email-scams
4. Brief intro to unstructured data mining, especially text mining: https://icrunchdatanews.com/unstructured-data-mining-primer/?utm_source=twitter&utm_medium=social&utm_campaign=SocialWarfare
5. Predictive modelling of police misconduct: https://www.technologyreview.com/s/601003/police-will-soon-be-watched-by-algorithms-that-try-to-predict-misconduct-is-that-a-good/?utm_content=buffera219f&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer
6. Predicting heart failure with deep learning-wonder if it could have saved my father? https://blogs.nvidia.com/blog/2016/04/11/predict-heart-failure/
7. We won't lose our jobs to robots/AI, but we will have to work beside them: http://www.computerworld.com/article/3060096/robotics/get-ready-for-your-new-co-worker-the-robot.html
8. 7 steps to learning R: http://www.datasciencecentral.com/profiles/blogs/learning-r-in-seven-simple-steps 
9. An archivist's guide to organising files on your computer: https://www.insidehighered.com/blogs/gradhacker/organize-your-computer-help-archivist
10. Is Google DeepMind going to take on Star Craft next? http://www.businessinsider.com.au/google-deepmind-could-play-starcraft-2016-3?r=US&IR=T
11. Google CEO Sundar Pichai thinks that the next big thing, after mobile, will be AI: http://www.computerworld.com/article/3060285/cloud-computing/googles-ceo-sees-ai-as-the-next-wave-in-computing.html
12. An overview of Schmidhuber's 2014 review of deep learning neural networks: http://www.kdnuggets.com/2016/04/deep-learning-neural-networks-overview.html 
13. Generating dance with deep learning neural networks: http://www.extremetech.com/extreme/227287-deep-learning-neural-network-creates-its-own-interpretive-dance
14. Deep learning that preserves privacy: http://techemergence.com/google-invests-in-privacy-preserving-deep-learning/
15. More 'human like' image captioning with recurrent neural networks: https://www.technologyreview.com/s/601339/will-artificial-intelligence-win-the-caption-contest/
16. Implementing a convolutional neural network for image recognition on a simulation of Babbage's analytical engine: http://motherboard.vice.com/en_au/read/charles-babbages-analytical-engine-takes-on-deep-learning
17. ANN and the future of machine learning: http://insidebigdata.com/2016/04/25/neural-networks-and-the-future-of-machine-learning/
18. Machine learning in conservation and environmental protection: http://ensia.com/features/three-ways-artificial-intelligence-is-helping-to-save-the-world/ I was doing this kind of thing eleven years ago!
19. Applying deep learning in self-driving cars: http://spectrum.ieee.org/cars-that-think/transportation/self-driving/driveai-brings-deep-learning-to-selfdriving-cars/?utm_source=CarsThatThink&utm_medium=Newsletter&utm_campaign=CTT04272016
20. AI could be humanity's last innovation, says UN Chief IT Officer: http://www.techrepublic.com/article/united-nations-cito/
21. A neural network chip on a USB stick: http://www.theverge.com/2016/4/28/11510430/movidius-fathom-neural-compute-stick-myriad-2-chip also http://www.theregister.co.uk/2016/04/29/neural_network_on_a_stick/ How many sticks can you access at once?
22. A convolutional neural network that simplifies sketches: http://hi.cs.waseda.ac.jp/~esimo/en/research/sketch/
23. Colourising old photos with deep learning neural networks: http://techemergence.com/ai-is-colorizing-and-beautifying-the-world/
24. Another basic explanation of deep learning neural networks: http://www.datasciencecentral.com/profiles/blogs/deep-learning-demystified
25. Are engineers creating their own replacements? http://spectrum.ieee.org/at-work/tech-careers/are-engineers-designing-their-robotic-replacements
26. Elon Musk's OpenAI initiative: http://www.wired.com/2016/04/openai-elon-musk-sam-altman-plan-to-set-artificial-intelligence-free/

Weeky Review 22 April 2016

Some interesting links that I Tweeted about in the last week:

1. Difficulties of frequent moves, a hazard for academics: https://www.insidehighered.com/advice/2016/04/15/difficulties-constantly-having-move-academic-essay  Part of why being a postdoc sucks http://computational-intelligence.blogspot.com/2012/09/on-being-post-doc.html
2. Developers guide to Facebook's Messenger chatbot: http://siliconangle.com/blog/2016/04/13/developers-roll-your-own-facebook-messenger-bot-and-what-you-can-do/ 
3. Why it's important for PhD students to blog: https://www.insidehighered.com/blogs/gradhacker/blogging-establish-your-digital-identity  Other ways to establish an online profile: http://computational-intelligence.blogspot.com/2012/04/building-online-presence-as-academic.html 
4. My final paper for IJCNN 2016: "Sleep Learning and Max-Min Aggregation of Evolving Connectionist Systems" http://mike.watts.net.nz/SleepLearningMaxMinAggregationECoS.pdf 
5. Machine learning detects 85% of network attacks: http://www.theregister.co.uk/2016/04/18/ai_bot_spots_hacking_attacks/
6. Paper on the AI^2 machine-learning based network intrusion detection system: https://people.csail.mit.edu/kalyan/AI2_Paper.pdf 
7. Using deep learning to detect cancer cells in blood samples: http://newsroom.ucla.edu/releases/microscope-uses-artificial-intelligence-to-find-cancer-cells-more-efficiently 
8. Recognising hand-written Japanese text with deep learning: http://www.bloomberg.com/news/articles/2016-04-13/artificial-intelligence-s-next-phase-sooner-and-more-accessible-for-everyone 
9. A list of deep learning tutorials and resources: http://www.datasciencecentral.com/profiles/blogs/11-deep-learning-articles-tutorials-and-resources 
10. Introduction to deep learning for chatbots: http://www.kdnuggets.com/2016/04/deep-learning-chatbots-part-1.html
11. Gender diversity in AI: http://motherboard.vice.com/en_au/read/can-ai-help-gender-diversity-help-ai
12. List of 15 machine learning frameworks: http://www.kdnuggets.com/2016/04/top-15-frameworks-machine-learning-experts.html 
13. Yet another article on the AI^2 system: http://techemergence.com/an-ai-cybersecurity-system-may-detect-attacks-with-85-percent-accuracy/ Machine learning in security isn't that new, it's been done for years.
14. Adding on-board intelligence to thermal cameras: http://www.theverge.com/2016/4/19/11459182/flir-movidius-boson-thermal-camera-computer-vision
15. An ontology of machine learning methods: http://www.datasciencecentral.com/profiles/blogs/machine-learning-ontology 
16. Guide to data analysis in Python: http://www.kdnuggets.com/2016/04/datacamp-learning-python-data-analysis-data-science.html 
17. Randomized Forest ensemble method: http://www.datasciencecentral.com/profiles/blogs/random-ized-forest-thought-vectors-to-build-a-new-class-of Not so new as the author of the article says it is.
18. Some basic advice on getting published in journals: https://www.insidehighered.com/advice/2016/04/21/advice-getting-published-scholarly-journal-essay 
19. How machine learning is needed in computer security: http://www.datanami.com/2016/04/21/machine-learning-can-applied-cyber-security/ 
20. Has this startup made an AI that passes the Turing test? http://techemergence.com/x-ai-says-their-ai-passed-the-turing-test/ 
21. The incredible growth of R: http://www.techrepublic.com/article/exponential-growth-of-rs-open-source-community-threatens-commercial-competitors/

Weekly Review 15 April 2016

Some interesting links that I Tweeted about in the last week:

1. How to fool deep learning networks: http://www.kdnuggets.com/2016/04/tricking-deep-learning.html
2. AI is going to change your job, but not replace it: http://www.information-age.com/it-management/skills-training-and-leadership/123461209/why-machine-learning-will-impact-not-take-your-job 
3. Using AI to help treat diabetes: https://www.devex.com/news/using-artificial-intelligence-to-revolutionize-diabetes-treatment-87989
4. What's been happening with IBM's Watson: http://hothardware.com/news/ibms-watson-cognitive-ai-platform-evolves-senses-feelings-and-dances-gangnam-style
5. Should universities be employing PhDs as administrators? http://schoolofdoubt.com/2016/04/10/universities-should-be-employing-surplus-phds-as-administrative-staff/ 
6. Analysing ancient texts using machine learning: http://gizmodo.com/artificial-intelligence-sheds-new-light-on-the-origins-1769736018
7. Predicting customer behaviour with machine learning: http://www.datasciencecentral.com/profiles/blogs/using-machine-learning-to-predict-customer-behaviour 
8. Are fears brought about from sci-fi holding back AI research? https://www.theguardian.com/technology/2016/apr/12/brave-new-world-sci-fi-fears-hold-back-progress-of-ai-warns-expert
9. Some deep learning / machine learning / ANN terms explained: http://www.datasciencecentral.com/profiles/blogs/10-deep-learning-terms-explained-in-simple-english 
10. How AI is creeping into business and our lives: http://www.nzherald.co.nz/opinion/news/article.cfm?c_id=466&objectid=11621278 
11. Deep learning on GPU is racing ahead: http://www.datanami.com/2016/04/13/gpu-powered-deep-learning-emerges-carry-big-data-torch-forward/ 
12. Are chatbots trustworthy? http://www.computerworld.com/article/3055713/social-media/will-companys-trust-their-communications-to-a-i-chatbots.html 
13. Google has updated TensorFlow, can now be distributed over multiple devices: https://www.theguardian.com/technology/2016/apr/13/google-updates-tensorflow-open-source-artificial-intelligence
14. What developers need to know about machine learning: http://www.kdnuggets.com/2016/04/developers-need-know-about-machine-learning.html 
15. Data mining people's personalities for targeted political advertising: https://www.technologyreview.com/s/601214/data-mining-your-psyche/#/set/id/601281/
16. Algorithmically generating art with ArtBots: https://www.theguardian.com/technology/2016/apr/15/move-over-chatbots-meet-the-artbots
17. AI is helping the visually-impaired perceive the world: http://techemergence.com/unseen-ways-ai-is-making-the-world-a-better-place/

Weekly Review 8 April 2016

Some interesting links that I Tweeted about in the last week:

1. Assisting dieting with machine learning: http://spectrum.ieee.org/the-human-os/biomedical/diagnostics/machine-learning-for-easier-dieting
2. Microsoft is open sourcing their chatbot software: http://www.theguardian.com/technology/2016/mar/31/now-anyone-can-build-own-version-microsoft-racist-sexist-chatbot-tay
3. Using deep learning to search Shutterstock's image collection: http://www.kdnuggets.com/2016/04/shutterstock-deep-learning-change-language-search.html
4. Being an academic is hard. Becoming one is harder. So much of an academic career is a test of endurance. http://muckyphd.blogspot.co.nz/2016/03/coming-to-terms-with-academic-failure.html 
5. The Cyc project is still going - and finding applications in medicine: http://techemergence.com/a-30-year-old-ai-project-hits-the-market/ 
6. Microsoft launches Cognitive Services http://venturebeat.com/2016/03/30/microsoft-cognitive-services-project-oxford/  22 APIs on computer vision, speaker recognition, etc: https://www.microsoft.com/cognitive-services 
7. On the exploitation in academic publishing: https://medium.com/age-of-awareness/academic-publishing-is-a-goddamned-exploitative-farce-75930d3ce3d0#.95kkkly94
8. C4.5, SVM & APRIORI algorithms explained: http://dataconomy.com/top-3-algorithms-plain-english/
9. Dieting and machine learning: http://motherboard.vice.com/en_au/read/how-machine-learning-dieting-app-health 
10. How to make AIs sound more like humans: http://www.computerworld.com/article/3051174/big-data/what-will-it-take-to-make-ai-sound-more-human.html
11. Combining human experts with machine learning for cybersecurity: http://www.techrepublic.com/article/how-one-ai-security-system-combines-humans-and-machine-learning-to-detect-cyberthreats/
12. Google's machine learning for developers: http://www.techrepublic.com/article/how-developers-can-take-advantage-of-machine-learning-on-google-cloud-platform/
13. The job market for new PhDs is getting smaller and smaller: https://www.insidehighered.com/news/2016/04/04/new-data-show-tightening-phd-job-market-across-disciplines
14. AI systems in journalism, now getting as good as human writers: http://www.theguardian.com/media/2016/apr/03/artificla-intelligence-robot-reporter-pulitzer-prize 
15. Deep learning for smart cities: http://www.datasciencecentral.com/profiles/blogs/deep-learning-applications-for-smart-cities 
16. Some machine learning "trade secrets" http://www.datasciencecentral.com/profiles/blogs/machine-learning-few-rarely-shared-trade-secrets 
17. My h-index just hit 16 - will it stay there this time? https://scholar.google.com/citations?user=Z29KBKYAAAAJ 
18. The applications of AI in finance: http://techemergence.com/dont-fear-ai-in-finance/ 
19. Facebook's AI for automatically describing images: http://www.techrepublic.com/article/facebook-is-using-ai-to-help-blind-people-see-the-photos-in-their-newsfeed/ 
20. Teaching experience is important for post-grads. Co-teaching is one approach to getting it: https://www.insidehighered.com/advice/2016/04/05/advantages-co-teaching-graduate-students-essay 
21. Microsoft announces its Cognitive Services and Bot Framework: https://blogs.technet.microsoft.com/machinelearning/2016/03/30/from-analytical-applications-to-intelligent-solutions/ 
22. Nvidia launches a 15-billion transistor chip for deep learning: http://venturebeat.com/2016/04/05/nvidia-creates-a-15b-transistor-chip-for-deep-learning/ 
23. Another article on Nvidia's 15 billion transistor chip for deep learning: https://www.technologyreview.com/s/601195/a-2-billion-chip-to-accelerate-artificial-intelligence/#/set/id/601193/ 
24. How Livermore National Laboratory will test IBM's neuromorphic chips: http://spectrum.ieee.org/tech-talk/computing/hardware/how-livermore-scientists-will-put-ibms-brain-inspired-chips-to-the-test 
25. Applying deep learning to the Internet of Things using H20: http://www.kdnuggets.com/2016/04/deep-learning-iot-h2o.html
26. Some tips and tricks for using deep neural networks: http://www.datasciencecentral.com/profiles/blogs/must-know-tips-tricks-in-deep-neural-networks 
27. AI in the military: http://www.techrepublic.com/article/how-ai-powered-robots-will-protect-the-networked-soldier/
28. Machine learning in business revenue forecasting: http://www.datasciencecentral.com/profiles/blogs/what-s-a-cfo-s-biggest-fear-and-how-can-machine-learning-help 
29. The basics of GPU computing: http://www.kdnuggets.com/2016/04/basics-gpu-computing-data-scientists.html 
30. A description of deep learning stochastic depth networks: http://www.kdnuggets.com/2016/04/stochastic-depth-networks-accelerate-deep-learning.html

Neural Networks, Volume 77, Pages 1-126, May 2016

1) Image and geometry processing with Oriented and Scalable Map  
Author(s): Hao Hua
Pages: 1-6

2) Noise further expresses exponential decay for globally exponentially stable time-varying delayed neural networks  
Author(s): Song Zhu, Qiqi Yang, Yi Shen
Pages: 7-13

3) A Fast SVD-Hidden-nodes based Extreme Learning Machine for Large-Scale Data Analytics  
Author(s): Wan-Yu Deng, Zuo Bai, Guang-Bin Huang, Qing-Hua Zheng
Pages: 14-28

4) Neuromorphic VLSI realization of the hippocampal formation  
Author(s): Anu Aggarwal
Pages: 29-40

5) Synchronization for an array of neural networks with hybrid coupling by a novel pinning control strategy  
Author(s): Dawei Gong, Frank L. Lewis, Liping Wang, Ke Xu
Pages: 41-50

6) Analysis of global image stability and global asymptotical periodicity for a class of fractional-order complex-valued neural networks with time varying delays  
Author(s): R. Rakkiyappan, R. Sivaranjani, G. Velmurugan, Jinde Cao
Pages: 51-69

7) State estimation for a class of artificial neural networks with stochastically corrupted measurements under Round-Robin protocol  
Author(s): Yuqiang Luo, Zidong Wang, Guoliang Wei, Fuad E. Alsaadi, Tasawar Hayat
Pages: 70-79

8) Global exponential stability of neural networks with time-varying delay based on free-matrix-based integral inequality  
Author(s): Yong He, Meng-Di Ji, Chuan-Ke Zhang, Min Wu
Pages: 80-86

9) Towards holographic “brain” memory based on randomization and Walsh–Hadamard transformation  
Author(s): Daniel Berend, Shlomi Dolev, Sergey Frenkel, Ariel Hanemann
Pages: 87-94

10) Function approximation in inhibitory networks  
Author(s): Bryan Tripp, Chris Eliasmith
Pages: 95-106

11) Tensor SOM and tensor GTM: Nonlinear tensor analysis by topographic mappings  
Author(s): Tohru Iwasaki, Tetsuo Furukawa
Pages: 107-125

IEEE Transactions on Neural Networks and Learning Systems, Volume 27, Issue 4, April 2016

1. A Simple Method for Solving the SVM Regularization Path for Semidefinite Kernels
Author(s): Christopher G. Sentelle; Georgios C. Anagnostopoulos; Michael Georgiopoulos
Page(s): 709 - 722

2. Approximate Orthogonal Sparse Embedding for Dimensionality Reduction
Author(s): Zhihui Lai; Wai Keung Wong; Yong Xu; Jian Yang; David Zhang
Page(s): 723 - 735

3. Bayesian Robust Tensor Factorization for Incomplete Multiway Data
Author(s): Qibin Zhao; Guoxu Zhou; Liqing Zhang; Andrzej Cichocki; Shun-Ichi Amari
Page(s): 736 - 748

4. Pinning Control Strategies for Synchronization of Linearly Coupled Neural Networks With Reaction–Diffusion Terms
Author(s): Jin-Liang Wang; Huai-Ning Wu; Tingwen Huang; Shun-Yan Ren
Page(s): 749 - 761

5. Finite-Time Consensus for Multiagent Systems With Cooperative and Antagonistic Interactions
Author(s): Deyuan Meng; Yingmin Jia; Junping Du
Page(s): 762 - 770

6. Kernel-Based Least Squares Temporal Difference With Gradient Correction
Author(s): Tianheng Song; Dazi Li; Liulin Cao; Kotaro Hirasawa
Page(s): 771 - 782

7. Sparse LSSVM in Primal Using Cholesky Factorization for Large-Scale Problems
Author(s): Shuisheng Zhou
Page(s): 783 - 795

8. Effective Discriminative Feature Selection With Nontrivial Solution
Author(s): Hong Tao; Chenping Hou; Feiping Nie; Yuanyuan Jiao; Dongyun Yi
Page(s): 796 - 808

9. Extreme Learning Machine for Multilayer Perceptron
Author(s): Jiexiong Tang; Chenwei Deng; Guang-Bin Huang
Page(s): 809 - 821

10. Robust Gradient Learning With Applications
Author(s): Yunlong Feng; Yuning Yang; Johan A. K. Suykens
Page(s): 822 - 835

11. An Asynchronous Recurrent Network of Cellular Automaton-Based Neurons and Its Reproduction of Spiking Neural Network Activities
Author(s): Takashi Matsubara; Hiroyuki Torikai
Page(s): 836 - 852

12. Finite-Time Consensus of Multiagent Systems With a Switching Protocol
Author(s): Xiaoyang Liu; James Lam; Wenwu Yu; Guanrong Chen
Page(s): 853 - 862

13. Objective Function and Learning Algorithm for the General Node Fault Situation
Author(s): Yi Xiao; Rui-Bin Feng; Chi-Sing Leung; John Sum
Page(s): 863 - 874

14. Sparse Principal Component Analysis via Rotation and Truncation
Author(s): Zhenfang Hu; Gang Pan; Yueming Wang; Zhaohui Wu
Page(s): 875 - 890

15. Low-Discrepancy Points for Deterministic Assignment of Hidden Weights in Extreme Learning Machines
Author(s): Cristiano Cervellera; Danilo Macciò
Page(s): 891 - 896

16. Asymptotic Normality of the Maximum Pseudolikelihood Estimator for Fully Visible Boltzmann Machines
Author(s): Hien D. Nguyen; Ian A. Wood
Page(s): 897 - 902

17. Mixed H-Infinity and Passive Filtering for Discrete Fuzzy Neural Networks With Stochastic Jumps and Time Delays
Author(s): Peng Shi; Yingqi Zhang; Mohammed Chadli; Ramesh K. Agarwal
Page(s): 903 - 909

Weekly Review 1 April 2016

Some interesting links that I Tweeted about in the last week:

1. Valuing the AI market for 2016 http://techemergence.com/valuing-the-artificial-intelligence-market-2016-and-beyond/?utm_source=facebook&utm_medium=paid-promoted-post&utm_term=ai-market-size&utm_content=180last&utm_campaign=blog
2. Using machine learning to improve automatic speech recognition: http://spectrum.ieee.org/tech-talk/computing/software/machines-just-got-better-at-lip-reading
3. Resistive Processing Units to accelerate training in deep learning neural networks: http://www.tomshardware.com/news/ibm-chip-30000x-ai-speedup,31484.html
4. Paper on Resistive Processing Units for deep learning: http://arxiv.org/abs/1603.07341
5. Computers don't cause a net decrease in job numbers, but do increase inequality, with the lowest-paid hit hardest: https://hbr.org/2016/03/computers-dont-kill-jobs-but-do-increase-inequality
6. What I like to call "avoiding work by doing work" - doing small tasks to avoid doing larger tasks: https://www.insidehighered.com/blogs/gradhacker/two-one-deal-killing-boredom-procrastination 
7. UK's Wellcome Trust wants research they fund published in open access journals: http://www.theregister.co.uk/2016/03/26/sick_of_costly_research_journals/ 
8. Robots learning to pick things up using deep learning neural networks: http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/google-large-scale-robotic-grasping-project 
9. This article seems to be arguing that it's better to get "off the shelf" machine learning than to develop your own: http://www.kdnuggets.com/2016/03/dont-buy-machine-learning.html 
10. AlphaGo and the declining advantage of big companies: https://hbr.org/2016/03/alphago-and-the-declining-advantage-of-big-companies?utm_source=twitter&utm_medium=social&utm_campaign=harvardbiz 
11. Lots of companies getting into AI now: http://www.informationweek.com/big-data/big-data-analytics/google-loves-machine-learning-cloudera-acquires-startup-big-data-roundup/d/d-id/1324845
12. AI hits the mainstream: https://www.technologyreview.com/s/600986/ai-hits-the-mainstream/
13. AI is getting big in Silicon Valley: http://www.nytimes.com/2016/03/28/technology/silicon-valley-looks-to-artificial-intelligence-for-the-next-big-thing.html?mwrsm=Twitter 
14. Note to post-grads: don't EVER use graphs like these in your dissertation, I will fail you! http://www.buzzfeed.com/katienotopoulos/graphs-that-lied-to-us#.scqWJelqk 
15. Neural network chip could bring convolutional neural networks to mobile devices: http://spectrum.ieee.org/computing/embedded-systems/bringing-big-neural-networks-to-selfdriving-cars-smartphones-and-drones 
16. One step to become a machine learning expert: http://www.kdnuggets.com/2016/03/become-machine-learning-expert-one-simple-step.html 
17. Building models is a skill, and like every other skill it must be practiced to be mastered: http://www.kdnuggets.com/2016/03/become-machine-learning-expert-one-simple-step.html 
18. How to tell if the performance of two classifiers is statistically significantly different: http://www.kdnuggets.com/2016/03/statistical-significance-two-classifiers-performance-difference.html
19. The fortunate failure of Microsoft's Tay: http://www.businessinsider.de/why-microsofts-chatbot-tay-should-make-us-look-at-ourselves?r=US&IR=T&utm_content=buffer919c9&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer 
20. Some people would rather have a computer for a boss than a human: http://motherboard.vice.com/en_au/read/a-third-of-young-canadians-would-prefer-a-robot-boss
21. Is the next step for Google DeepMind playing poker? http://www.theguardian.com/technology/2016/mar/30/deepmind-poker-alphago-computer-casino 
22. Machine learning in signature detection for cybersecurity: http://www.darkreading.com/attacks-breaches/machine-learning-in-security-good-and-bad-news-about-signatures/a/d-id/1324888 
23. Google hypes machine learning to sell its cloud computing platform: http://www.informationweek.com/cloud/infrastructure-as-a-service/google-pumps-up-cloud-platform-with-machine-learning/d/d-id/1324822 
24. I'm sure I read / reviewed a paper about this - density-based unsupervised clustering: http://www.datasciencecentral.com/profiles/blogs/variance-clustering-test-of-hypotheses-and-density-estimation-rev 
25. Fighting China's - and especially Beijing's - smog with machine learning: https://www.technologyreview.com/s/600993/can-machine-learning-help-lift-chinas-smog/
26. Low-power, neuromorphic chips being applied in the US nuclear industry: http://www.computerworld.com/article/3049380/big-data/this-brain-inspired-supercomputer-will-explore-deep-learning-for-the-us-nuclear-program.html 
27. Machine learning in signature detection for cybersecurity part 2: http://www.darkreading.com/attacks-breaches/machine-learning-in-security-seeing-the-nth-dimension-in-signatures-/a/d-id/1324889 
28. How Google plans to solve Artificial General Intelligence: https://www.technologyreview.com/s/601139/how-google-plans-to-solve-artificial-intelligence/ 
29. Avoiding complexity in machine learning: http://www.kdnuggets.com/2016/03/avoiding-complexity-machine-learning-problems.html 
30. Artificial Intelligence still works best when AI is paired with humans: https://www.technologyreview.com/s/600989/man-and-machine/
31. Would the health care app space be a good place to apply machine learning? http://spectrum.ieee.org/the-human-os/biomedical/devices/ahead-of-apple-carekits-debut-physicians-still-skeptical-of-health-apps 
32. How Baidu is using AI, especially deep learning: https://www.technologyreview.com/s/600988/how-ai-is-feeding-chinas-internet-dragon/ 
33. I wonder if this approach could be used to generate real estate listings? They're not that different from clickbait: http://larseidnes.com/2015/10/13/auto-generating-clickbait-with-recurrent-neural-networks/

 

IEEE Transactions on Fuzzy Systems, Volume 24, Number 2, April 2016

1) On Atanassov's Intuitionistic Fuzzy Sets in the Complex Plane and the Field of Intuitionistic Fuzzy Numbers
Author(s): L. Zhou
Page(s): 253 - 259

2) Preaggregation Functions: Construction and an Application
Author(s): G. Lucca; J. A. Sanz; G. P. Dimuro; B. Bedregal; R. Mesiar; A. Kolesárová; H. Bustince
Page(s): 260 - 272

3) Modified AHP for Gene Selection and Cancer Classification Using Type-2 Fuzzy Logic
Author(s): T. Nguyen; S. Nahavandi
Page(s): 273 - 287

4) Answering Approximate Queries Over XML Data
Author(s): J. Liu; D. L. Yan
Page(s): 288 - 305

5) A Linear General Type-2 Fuzzy-Logic-Based Computing With Words Approach for Realizing an Ambient Intelligent Platform for Cooking Recipe Recommendation
Author(s): A. Bilgin; H. Hagras; J. van Helvert; D. Alghazzawi
Page(s): 306 - 329

6) Adaptive Quantized Controller Design Via Backstepping and Stochastic Small-Gain Approach
Author(s): F. Wang; Z. Liu; Y. Zhang; C. L. P. Chen
Page(s): 330 - 343

7) Estimation of a Fuzzy Regression Model Using Fuzzy Distances
Author(s): A. F. Roldán López de Hierro; J. Martínez-Moreno; C. Aguilar-Peña; C. R. L. de Hierro
Page(s): 344 - 359

8) Local Divergences for Atanassov Intuitionistic Fuzzy Sets
Author(s): I. Montes; V. Janiš; N. R. Pal; S. Montes
Page(s): 360 - 373

9) Ant-Inspired Fuzzily Deceptive Robots
Author(s): M. Kouzehgar; M. Badamchizadeh; M. R. Feizi-Derakhshi
Page(s): 374 - 387

10) Fuzzy-Model-Based Reliable Static Output Feedback maths\cr{H}_{\infty } Control of Nonlinear Hyperbolic PDE Systems
Author(s): J. Qiu; S. X. Ding; H. Gao; S. Yin
Page(s): 388 - 400

11) DOB Fuzzy Controller Design for Non-Gaussian Stochastic Distribution Systems Using Two-Step Fuzzy Identification
Author(s): Y. Yi; W. X. Zheng; C. Sun; L. Guo
Page(s): 401 - 418

12) A Dynamic Multiagent Genetic Algorithm for Gene Regulatory Network Reconstruction Based on Fuzzy Cognitive Maps
Author(s): J. Liu; Y. Chi; C. Zhu
Page(s): 419 - 431

13) Dissipativity Analysis for Discrete Time-Delay Fuzzy Neural Networks With Markovian Jumps
Author(s): Y. Zhang; P. Shi; R. K. Agarwal; Y. Shi
Page(s): 432 - 443

14) Cooperative Games and Coalition Cohesion Indices: The Choquet–Owen Value
Author(s): M. G. Fiestras-Janeiro; J. M. Gallardo; A. Jiménez-Losada; M. A. Mosquera
Page(s): 444 - 455

15) Fuzzy Clustering in a Complex Network Based on Content Relevance and Link Structures
Author(s): L. Hu; K. C. C. Chan
Page(s): 456 - 470

16) Cooperative Fuzzy Model-Predictive Control
Author(s): M. Killian; B. Mayer; A. Schirrer; M. Kozek
Page(s): 471 - 482

17) Fuzzy Metric Space Induced by Intuitionistic Fuzzy Points and its Application to the Orienteering Problem
Author(s): M. Verma; K. K. Shukla
Page(s): 483 - 488

18) Designing Fuzzy Sets With the Use of the Parametric Principle of Justifiable Granularity
Author(s): W. Pedrycz; X. Wang
Page(s): 489 - 496

19) Unified Representation of Sets of Heterogeneous Markov Transition Matrices
Author(s): M. E. Y. Boudaren; W. Pieczynski
Page(s): 497 - 503