Review for 24 January 2016

Below are some of the interesting links I Tweeted about recently.

1. Strategies to increase the participation of women in computer science: http://www.techrepublic.com/article/how-northeastern-plans-to-reach-equal-male-female-cs-enrollment-by-2021/
2. How to apply machine learning to business problems: http://techemergence.com/apply-machine-learning-to-business-problems/ 
3. And, where to apply machine learning first in business: http://techemergence.com/where-to-apply-machine-learning-first/
4. A beginner's guide to chatbots: https://chatbotsmagazine.com/the-complete-beginner-s-guide-to-chatbots-8280b7b906ca#.yopzsjy0k 
5. How to stay competitive in the business of machine learning: http://www.kdnuggets.com/2017/01/stay-competitive-machine-learning-business.html 
6. Guide to statistical hypothesis testing: http://www.datasciencecentral.com/profiles/blogs/your-guide-to-master-hypothesis-testing-in-statistics 
7. Generative Adversarial Networks: http://www.kdnuggets.com/2017/01/generative-adversarial-networks-hot-topic-machine-learning.html 
8. List of tools for building chatbots: https://chatbotsmagazine.com/the-tools-every-bot-creator-must-know-c0e9dd685094#.l49kg88c8 
9. Introduction to how to do big data with Python: http://dataconomy.com/2016/10/big-data-python/ 
10. Deep learning on low-power chips: http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/ai-startup-neurala-deep-learning-for-nasa-powers-earth-robots 
11. Support vector machines in R: http://www.datasciencecentral.com/profiles/blogs/learn-support-vector-machine-svm-from-scratch-in-r 
12. White collar workers are already losing their jobs to AI: https://qz.com/875491/japanese-white-collar-workers-are-already-being-replaced-by-artificial-intelligence/
13. This is troubling - a valuable resource is off-line. I hope legal bullying was not behind this: http://www.sciencemag.org/news/2017/01/mystery-controversial-list-predatory-publishers-disappears
14. Biased data sets produce biased models which produce biased predictions. That's a fundamental of machine learning: http://motherboard.vice.com/en_au/read/minority-retort-why-oakland-police-turned-down-predictive-policing
15. 3 guiding principles for an ethical AI: http://www.techrepublic.com/article/3-guiding-principles-for-ethical-ai-from-ibm-ceo-ginni-rometty/ 
16. We're not ready for the enormous displacement of jobs that will be caused by automation & AI: https://www.theguardian.com/technology/2017/jan/11/robots-jobs-employees-artificial-intelligence 
17. Companies need an AI strategy if employees are to get behind roll-out of AI: https://www.datanami.com/2017/01/17/ai-inevitable-retraining-needed-survey-finds/ 
18. Using AI to estimate repair costs in car insurance claims: https://techcrunch.com/2017/01/19/in-the-future-ai-could-also-mean-auto-insurance/ 
19. Applying ANN based style transfer to a movie: https://techcrunch.com/2017/01/19/kristen-stewart-co-authored-a-paper-on-style-transfer-and-the-ai-community-lost-its-mind/ 
20. How to build your research output in one hour per day: http://science.sciencemag.org/content/353/6300/718 
21. Deep learning applied to natural language processing: http://www.kdnuggets.com/2017/01/deep-learning-applied-natural-language-processing.html 
22. AI created by AI: https://www.technologyreview.com/s/603381/ai-software-learns-to-make-ai-software/ … We're doomed.
23. AI is having a huge impact on retail: http://www.informationweek.com/strategic-cio/digital-business/ai-technology-takes-center-stage-at-retail-convention/d/d-id/1327897 
24. Deep learning is getting faster and cheaper: http://spectrum.ieee.org/computing/hardware/expect-deeper-and-cheaper-machine-learning 
25. Battle of the chatbots: http://www.theregister.co.uk/2017/01/18/chatbot_battle/
26. Automated machine learning: http://www.kdnuggets.com/2017/01/current-state-automated-machine-learning.html
27. Predicting mortality of cardiac patients: http://www.ibtimes.co.uk/ai-can-predict-when-patients-will-die-heart-failure-80-accuracy-1601517
28. Inter-disciplinary research can take a long time to publish: https://www.insidehighered.com/news/2016/12/02/scholar-complains-how-long-it-can-take-publish-interdisciplinary-science I know this from my own research.
29. Research is needed at universities, but the undergrads are paying to be taught: http://www.theatlantic.com/education/archive/2016/11/have-public-universities-lost-their-focus/508424/
30. How scientists use social media: https://www.eurekalert.org/pub_releases/2016-10/uoo-nsr101216.php
31. AI will put managers out of work before skilled workers: https://www.theguardian.com/business/economics-blog/2017/jan/22/the-new-robot-revolution-will-take-the-bosss-job-not-the-gardeners 
32. Machine learning might soon displace radiologists and pathologists: http://www.techrepublic.com/article/why-ai-is-about-to-make-some-of-the-highest-paid-doctors-obsolete/ 
33. Introducing ethics into AI: https://techcrunch.com/2017/01/22/ethics-the-next-frontier-for-artificial-intelligence/

Neural Networks, Volume 86, Pages 1-122, February 2017

1) A new switching control for finite-time synchronization of memristor-based recurrent neural networks   
Author(s): Jie Gao, Peiyong Zhu, Ahmed Alsaedi, Fuad E. Alsaadi, Tasawar Hayat
Pages: 1-9

2) Improved exponential convergence result for generalized neural networks including interval time-varying delayed signals   
Author(s): G. Rajchakit, R. Saravanakumar, Choon Ki Ahn, Hamid Reza Karimi
Pages: 10-17

3) Global cluster synchronization in nonlinearly coupled community networks with heterogeneous coupling delays   
Author(s): Jui-Pin Tseng
Pages: 18-31

4) Decentralized event-triggered synchronization of uncertain Markovian jumping neutral-type neural networks with mixed delays   
Author(s): Sibel Senan, M. Syed Ali, R. Vadivel, Sabri Arik
Pages: 32-41

5) Dissipativity and stability analysis of fractional-order complex-valued neural networks with time delay   
Author(s): G. Velmurugan, R. Rakkiyappan, V. Vembarasan, Jinde Cao, Ahmed Alsaedi
Pages: 42-53

6) Robust learning in SpikeProp   
Author(s): Sumit Bam Shrestha, Qing Song
Pages: 54-68

7) A new hyperbox selection rule and a pruning strategy for the enhanced fuzzy min–max neural network   
Author(s): Mohammed Falah Mohammed, Chee Peng Lim
Pages: 69-79

8) A balanced motor primitive framework can simultaneously explain motor learning in unimanual and bimanual movements   
Author(s): Ken Takiyama, Yutaka Sakai
Pages: 80-89

9) Adaptive exponential synchronization of complex-valued Cohen–Grossberg neural networks with known and unknown parameters   
Author(s): Jin Hu, Chunna Zeng
Pages: 90-101

10) Neurons the decision makers, Part I: The firing function of a single neuron   
Author(s): Thomas Saaty
Pages: 102-114

11) Part 2—The firings of many neurons and their density; the neural network its connections and field of firings   
Author(s): Thomas Saaty
Pages: 115-122

IEEE Transactions on Neural Networks and Learning Systems, Volume 28, Issue 1, January 2017

1. Editorial: A Successful Year and Looking Forward to 2017 and Beyond
Author(s): Haibo He
Page(s): 2 - 7

2. Nonparametric Density Estimation Based on Self-Organizing Incremental Neural Network for Large Noisy Data
Authors: Yoshihiro Nakamura; Osamu Hasegawa
Page(s): 8 - 17

3. Optimal Output Regulation for Heterogeneous Multiagent Systems via Adaptive Dynamic Programming
Authors: Huaguang Zhang; Hongjing Liang; Zhanshan Wang; Tao Feng
Page(s): 18 - 29

4. SpikeTemp: An Enhanced Rank-Order-Based Learning Approach for Spiking Neural Networks With Adaptive Structure
Authors: Jinling Wang; Ammar Belatreche; Liam P. Maguire; Thomas Martin McGinnity
Page(s): 30 - 43

5. Accurate Maximum-Margin Training for Parsing With Context-Free Grammars
Authors: Alexander Bauer; Mikio Braun; Klaus-Robert Müller
Page(s): 44 - 56

6. Unsupervised Metric Fusion Over Multiview Data by Graph Random Walk-Based Cross-View Diffusion
Authors: Yang Wang; Wenjie Zhang; Lin Wu; Xuemin Lin; Xiang Zhao
Page(s): 57 - 70

7. Pull-Based Distributed Event-Triggered Consensus for Multiagent Systems With Directed Topologies
Authors: Xinlei Yi; Wenlian Lu; Tianping Chen
Page(s): 71 - 79

8. A Cooperative Learning-Based Clustering Approach to Lip Segmentation Without Knowing Segment Number
Authors: Yiu-ming Cheung; Meng Li; Qinmu Peng; C. L. Philip Chen
Page(s): 80 - 93

9. Density-Dependent Quantized Least Squares Support Vector Machine for Large Data Sets
Authors: Shengyu Nan; Lei Sun; Badong Chen; Zhiping Lin; Kar-Ann Toh
Page(s): 94 - 106

10. A ParaBoost Method to Image Quality Assessment
Authors: Tsung-Jung Liu; Kuan-Hsien Liu; Joe Yuchieh Lin; Weisi Lin; C.-C. Jay Kuo
Page(s): 107 - 121

11. Monitoring Nonlinear and Non-Gaussian Processes Using Gaussian Mixture Model-Based Weighted Kernel Independent Component Analysis
Authors: Lianfang Cai; Xuemin Tian; Sheng Chen
Page(s): 122 - 135

12. Multimodal Degradation Prognostics Based on Switching Kalman Filter Ensemble
Authors: Pin Lim; Chi Keong Goh; Kay Chen Tan; Partha Dutta
Page(s): 136 - 148

13. Learning the Conformal Transformation Kernel for Image Recognition
Authors: Huilin Xiong; Wenxian Yu; Xin Yang; M. N. S. Swamy; Qiuze Yu
Page(s): 149 - 163

14. Discriminative Feature Extraction by a Neural Implementation of Canonical Correlation Analysis
Authors: Cemal Okan Sakar; Olcay Kursun
Page(s): 164 - 176

15. 3-D Laser-Based Multiclass and Multiview Object Detection in Cluttered Indoor Scenes
Authors: Xuesong Zhang; Yan Zhuang; Huosheng Hu; Wei Wang
Page(s): 177 - 190

16. Propagation of Collective Temporal Regularity in Noisy Hierarchical Networks
Authors: Ruixue Han; Jiang Wang; Rui Miao; Bin Deng; Yingmei Qin; Haitao Yu; Xile Wei
Page(s): 191 - 205

17. Global Mittag–Leffler Stabilization of Fractional-Order Memristive Neural Networks
Authors: Ailong Wu; Zhigang Zeng
Page(s): 206 - 217

18. Artificial Epigenetic Networks: Automatic Decomposition of Dynamical Control Tasks Using Topological Self-Modification
Authors: Alexander P. Turner; Leo S. D. Caves; Susan Stepney; Andy M. Tyrrell; Michael A. Lones
Page(s): 218 - 230

19. Multiscale Support Vector Learning With Projection Operator Wavelet Kernel for Nonlinear Dynamical System Identification
Authors: Zhao Lu; Jing Sun; Kenneth Butts
Page(s): 231 - 243

IEEE Transactions on Cognitive and Developmental Systems, Volume 8, Number 4, December 2016

1. Affordance Research in Developmental Robotics: A Survey
Author(s): H. Min, C. Yi, R. Luo, J. Zhu and S. Bi
Pages: 237-255

2. Selective Attention by Perceptual Filtering in a Robot Control Architecture
Author(s): F. Ferland and F. Michaud
Pages: 256-270

3. Training Agents With Interactive Reinforcement Learning and Contextual Affordances
Author(s): F. Cruz, S. Magg, C. Weber and S. Wermter
Pages: 271-284

4. Spatial Concept Acquisition for a Mobile Robot That Integrates Self-Localization and Unsupervised Word Discovery From Spoken Sentences
Author(s): A. Taniguchi, T. Taniguchi and T. Inamura
Pages: 285-297

5. Decoding EEG in Cognitive Tasks With Time-Frequency and Connectivity Masks
Author(s): J. Li, Y. Wang, L. Zhang, A. Cichocki and T. P. Jung
Pages: 298-308

Complex & Intelligent Systems, Volume 4, Issue 2, December 2016

1. Multiple attribute grey relational analysis using DEA and AHP
Author(s): Mohammad Sadegh Pakkar
Pages: 243-250

2. Multi-objective optimization and visualization for analog design automation
Author(s): Abhaya Chandra Kammara, Lingaselvan Palanichamy, Andreas Konig
Pages: 251-267

3. Opinion formation in social networks: a time-variant and non-linear model
Author(s): Dionisios N. Sotiropoulos, Christos Bilanakos, George M. Giaglis
Pages: 269-284

4. An efficient identity-based QER cryptographic scheme
Author(s): Chandrashekhar Meshram, P. L. Powar
Pages: 285-291

5. Particle filtering with applications in networked systems: a survey
Author(s): Wenshuo Li, Zidong Wang, Yuan Yuan, Lei Guo
Pages: 293-315

Weekly Review 14 December 2016

Below are some of the interesting links I Tweeted about in the last week.

1. How AI, and the economy, are evolving together: https://techcrunch.com/2016/12/04/artificial-intelligence-and-the-evolution-of-the-fractal-economy/
2. OpenAI's Universe, a universal training ground for AI: http://www.theregister.co.uk/2016/12/05/openai_universe_reinforcement_learning/ 
3. A basic over-view of what AI is: https://techcrunch.com/2016/12/04/wtf-is-ai/ 
4. Doesn't seem to be an awful lot of AI in this AI marketing assistant: https://techcrunch.com/2016/12/05/meet-aiden-your-new-ai-coworker/ 
5. Free ebooks on machine learning and data analysis: http://www.kdnuggets.com/2016/12/packt-free-ebooks-machine-learning-python-data-analysis.html 
6. General AI is still a long way off: https://techcrunch.com/2016/12/05/deepmind-ceo-mustafa-suleyman-says-general-ai-is-still-a-long-way-off/ 
7. Uber is expanding its AI research: http://www.techrepublic.com/article/with-launch-of-uber-ai-labs-ride-sharing-giant-aims-to-expand-ai-research-beyond-autonomous-cars/ 
8. Amazon Go grocery shop uses AI instead of cashiers: http://www.techrepublic.com/article/amazon-go-grocery-store-replaces-cashiers-with-automation-and-ai/ 
9. Using AI to search for a better treatment for ALS: https://techcrunch.com/2016/12/06/benevolentbios-artificial-intelligence-could-discover-a-better-treatment-for-als/ 
10. Identity thieves are now using machine learning: https://www.datanami.com/2016/12/05/ai-will-spoof-steal-identify/ 
11. 8 essential software tools for data scientists: http://www.datasciencecentral.com/profiles/blogs/8-essential-tools-for-data-scientists 
12. Common statistical mistakes computer scientists make: http://www.cs.cornell.edu/~asampson/blog/statsmistakes.html 
13. Many AI models are still black boxes: http://www.zdnet.com/article/inside-the-black-box-understanding-ai-decision-making/ 
14. An AI that plays the FreeCiv strategy game at human-level: https://techcrunch.com/2016/12/06/aragos-ai-can-now-beat-some-human-players-at-complex-civ-strategy-games/ 
15. An introductory tutorial on the Internet of Things: http://www.kdnuggets.com/2016/12/internet-of-things-tutorial-chapter-1-introduction.html 
16. Many companies are still lacking the skills to implement machine learning projects: http://www.techrepublic.com/article/infographic-many-companies-lack-skills-to-implement-and-support-ai-and-machine-learning/ 
17. Resisting catastrophic forgetting in neural networks: http://www.theregister.co.uk/2016/12/06/catastrophic_forgetting/ 
18. Paper on resisting catastrophic forgetting: https://arxiv.org/abs/1612.00796
19. Microsoft is partnering with Cray to run deep neural networks on supercomputers: http://www.techrepublic.com/article/microsoft-partners-with-cray-to-run-deep-learning-algorithms-on-supercomputers/ 
20. Another basic overview of machine learning: http://www.kdnuggets.com/2016/12/too-afraid-ask-about-artificial-intelligence-machine-learning.html 
21. A neural network based "ahem" detector, used to clean-up podcasts: http://www.datasciencecentral.com/profiles/blogs/ahem-detector-with-deep-learning 
22. Google's DeepMind open sources more of its software: http://www.techrepublic.com/article/googles-deepmind-lab-opens-up-source-code-joins-race-to-develop-artificial-general-intelligence/ 
23. Another way deep learning is improving speech recognition: http://www.theregister.co.uk/2016/12/09/improving_computers_learning_speech/ 
24. 5 sources of bias in AI: https://techcrunch.com/2016/12/10/5-unexpected-sources-of-bias-in-artificial-intelligence/ 
25. Why we shouldn't let computers (and by extension AI) do our thinking for us: https://www.theguardian.com/technology/2016/dec/10/google-facebook-critical-thinking-computers
26. Using machine learning to search for trademarked logos: https://techcrunch.com/2016/12/12/trademarkvision-uses-machine-learning-to-make-finding-logos-as-easy-as-a-reverse-image-search/ 
27. An AI that helps marketers write: https://techcrunch.com/2016/12/12/atomic-ai-helps-marketers-write-better/ 
28. How to apply machine learning to business problems: http://techemergence.com/apply-machine-learning-to-business-problems/ 
29. Governmental challenges with AI: https://www.technologyreview.com/s/603036/the-government-isnt-doing-enough-to-solve-big-problems-with-ai/?utm_campaign=internal&utm_medium=homepage&utm_source=grid_1

Complex & Intelligent Systems, Volume 2, Number 4

1. Multiple attribute grey relational analysis using DEA and AHP
Author(s): Mohammad Sadegh Pakkar
Pages: 243-250

2. Multi-objective optimization and visualization for analog design automation
Author(s): Abhaya Chandra Kammara, Lingaselvan Palanichamy & Andreas König
Pages: 251-267

3. Opinion formation in social networks: a time-variant and non-linear model
Author(s): Dionisios N. Sotiropoulos, Christos Bilanakos & George M. Giaglis
Pages: 269-284

4. An efficient identity-based QER cryptographic scheme
Author(s): Chandrashekhar Meshram & P. L. Powar
Pages: 285-291

5. Particle filtering with applications in networked systems: a survey
Author(s): Wenshuo Li, Zidong Wang, Yuan Yuan & Lei Guo
Pages: 293-315

Neural Networks, Volume 85, Pages 1-196, January 2017

1. Pinning-controlled synchronization of delayed neural networks with distributed-delay coupling via impulsive control   
Author(s): Wangli He, Feng Qian, Jinde Cao
Pages: 1-9

2. Recovering low-rank and sparse matrix based on the truncated nuclear norm   
Author(s): Feilong Cao, Jiaying Chen, Hailiang Ye, Jianwei Zhao, Zhenghua Zhou
Pages: 10-20

3. Complete stability of delayed recurrent neural networks with Gaussian activation functions   
Author(s): Peng Liu, Zhigang Zeng, Jun Wang
Pages: 21-32

4. An online incremental orthogonal component analysis method for dimensionality reduction   
Author(s): Tao Zhu, Ye Xu, Furao Shen, Jinxi Zhao
Pages: 33-50

5. Evaluation of extreme learning machine for classification of individual and combined finger movements using electromyography on amputees and non-amputees   
Author(s): Khairul Anam, Adel Al-Jumaily
Pages: 51-68

6. Attribute-based Decision Graphs: A framework for multiclass data classification   
Author(s): João Roberto Bertini, Maria do Carmo Nicoletti, Liang Zhao
Pages: 69-84

7. A modular architecture for transparent computation in recurrent neural networks   
Author(s): Giovanni S. Carmantini, Peter beim Graben, Mathieu Desroches, Serafim Rodrigues
Pages: 85-105

8. Echo State Networks for data-driven downhole pressure estimation in gas-lift oil wells   
Author(s): Eric A. Antonelo, Eduardo Camponogara, Bjarne Foss
Pages: 106-117

9. Mittag-Leffler stability of fractional-order neural networks in the presence of generalized piecewise constant arguments   
Author(s): Ailong Wu, Ling Liu, Tingwen Huang, Zhigang Zeng
Pages: 118-127

10. Finite-time synchronization of uncertain coupled switched neural networks under asynchronous switching   
Author(s): Yuanyuan Wu, Jinde Cao, Qingbo Li, Ahmed Alsaedi, Fuad E. Alsaadi
Pages: 128-139

11. Stabilization of metastable dynamical rotating waves in a ring of unidirectionally coupled sigmoidal neurons due to shortcuts   
Author(s): Yo Horikawa
Pages: 140-156

12. Synchronization of discrete-time neural networks with delays and Markov jump topologies based on tracker information   
Author(s): Xinsong Yang, Zhiguo Feng, Jianwen Feng, Jinde Cao
Pages: 157-164

13. A limit-cycle self-organizing map architecture for stable arm control   
Author(s): Di-Wei Huang, Rodolphe J. Gentili, Garrett E. Katz, James A. Reggia
Pages: 165-181

14. Developmental metaplasticity in neural circuit codes of firing and structure   
Author(s): Yoram Baram
Pages: 182-196

IEEE Transactions on Neural Networks and Learning Systems, Volume 27, Issue 12, December 2016.

1. Training Radial Basis Function Neural Networks for Classification via Class-Specific Clustering
Author(s): Jenni Raitoharju; Serkan Kiranyaz; Moncef Gabbouj
Pages: 2458 - 2471

2. Similarity Constraints-Based Structured Output Regression Machine: An Approach to Image Super-Resolution
Author(s): Cheng Deng; Jie Xu; Kaibing Zhang; Dacheng Tao; Xinbo Gao; Xuelong Li
Pages: 2472 - 2485

3. Deep Learning of Part-Based Representation of Data Using Sparse Autoencoders With Nonnegativity Constraints
Author(s): Ehsan Hosseini-Asl; Jacek M. Zurada; Olfa Nasraoui
Pages: 2486 - 2498

4. A Unified Framework for Representation-Based Subspace Clustering of Out-of-Sample and Large-Scale Data
Author(s): Xi Peng; Huajin Tang; Lei Zhang; Zhang Yi; Shijie Xiao
Pages: 2499 - 2512

5. A Theoretical Foundation of Goal Representation Heuristic Dynamic Programming
Author(s): Xiangnan Zhong; Zhen Ni; Haibo He
Pages: 2513 - 2525

6. Sequential Compact Code Learning for Unsupervised Image Hashing
Author(s): Li Liu; Ling Shao
Pages: 2526 - 2536

7. Organizing Books and Authors by Multilayer SOM
Author(s): Haijun Zhang; Tommy W. S. Chow; Q. M. Jonathan Wu
Pages: 2537 - 2550

8. Generalized Higher Order Orthogonal Iteration for Tensor Learning and Decomposition
Author(s): Yuanyuan Liu; Fanhua Shang; Wei Fan; James Cheng; Hong Cheng
Pages: 2551 - 2563

9. Dynamic Learning From Neural Control for Strict-Feedback Systems With Guaranteed Predefined Performance
Author(s): Min Wang; Cong Wang; Peng Shi; Xiaoping Liu
Pages: 2564 - 2576

10. Online Solution of Two-Player Zero-Sum Games for Continuous-Time Nonlinear Systems With Completely Unknown Dynamics
Author(s): Yue Fu; Tianyou Chai
Pages: 2577 - 2587

11. Shortcomings/Limitations of Blockwise Granger Causality and Advances of Blockwise New Causality
Author(s): Sanqing Hu; Xinxin Jia; Jianhai Zhang; Wanzeng Kong; Yu Cao
Pages: 2588 - 2601

12. Semisupervised Multiclass Classification Problems With Scarcity of Labeled Data: A Theoretical Study
Author(s): Jonathan Ortigosa-Hernández;  I?aki Inza;  Jose A. Lozano
Pages: 2602 - 2614

13. Integration-Enhanced Zhang Neural Network for Real-Time-Varying Matrix Inversion in the Presence of Various Kinds of Noises
Author(s): Long Jin; Yunong Zhang; Shuai Li
Pages: 2615 - 2627

14. Scalable Linear Visual Feature Learning via Online Parallel Nonnegative Matrix Factorization
Author(s): Xueyi Zhao; Xi Li; Zhongfei Zhang; Chunhua Shen; Yueting Zhuang; Lixin Gao; Xuelong Li
Pages: 2628 - 2642

15. Information Theoretic Subspace Clustering
Author(s): Ran He; Liang Wang; Zhenan Sun; Yingya Zhang; Bo Li
Pages: 2643 - 2655

16. Adaptive Scaling of Cluster Boundaries for Large-Scale Social Media Data Clustering
Author(s): Lei Meng; Ah-Hwee Tan; Donald C. Wunsch
Pages: 2656 - 2669

17. K-MEAP: Multiple Exemplars Affinity Propagation With Specified K Clusters
Author(s): Yangtao Wang; Lihui Chen
Pages: 2670 - 2682

18. Landslide Displacement Prediction With Uncertainty Based on Neural Networks With Random Hidden Weights
Author(s): Cheng Lian; Zhigang Zeng; Wei Yao; Huiming Tang; Chun Lung Philip Chen
Pages: 2683 - 2695

19. Impulsive Synchronization of Reaction–Diffusion Neural Networks With Mixed Delays and Its Application to Image Encryption
Author(s): Wu-Hua Chen; Shixian Luo; Wei Xing Zheng
Pages: 2696 - 2710

20. MSDLSR: Margin Scalable Discriminative Least Squares Regression for Multicategory Classification
Author(s): Lingfeng Wang; Xu-Yao Zhang; Chunhong Pan
Pages: 2711 - 2717

21. Data-Driven Modeling for UGI Gasification Processes via an Enhanced Genetic BP Neural Network With Link Switches
Author(s): Shida Liu; Zhongsheng Hou; Chenkun Yin
Pages: 2718 - 2729

22. Is a Complex-Valued Stepsize Advantageous in Complex-Valued Gradient Learning Algorithms?
Author(s): Huisheng Zhang; Danilo P. Mandic
Pages: 2730 - 2735

23. Enhanced Logical Stochastic Resonance in Synthetic Genetic Networks
Author(s): Nan Wang; Aiguo Song
Pages: 2736 - 2739

24. A Boosting Approach to Exploit Instance Correlations for Multi-Instance Classification
Author(s): Yali Li; Shengjin Wang; Qi Tian; Xiaoqing Ding
Pages: 2740 - 2747

25. Using Digital Masks to Enhance the Bandwidth Tolerance and Improve the Performance of On-Chip Reservoir Computing Systems
Author(s): Bendix Schneider; Joni Dambre; Peter Bienstman
Pages: 2748 - 2753

26. Synchronization Analysis and Design of Coupled Boolean Networks Based on Periodic Switching Sequences
Author(s): Huaguang Zhang; Hui Tian; Zhanshan Wang; Yanfang Hou
Pages: 2754 - 2759

27. Power Quality Analysis Using a Hybrid Model of the Fuzzy Min–Max Neural Network and Clustering Tree
Author(s): Manjeevan Seera; Chee Peng Lim; Chu Kiong Loo; Harapajan Singh
Pages: 2760 - 2767

28. Max-Margin-Based Discriminative Feature Learning
Author(s): Changsheng Li; Qingshan Liu; Weishan Dong; Fan Wei; Xin Zhang; Lin Yang
Pages: 2768 - 2775

Weekly Review 5 December 2016

Below are some of the interesting links I Tweeted about in the last week.

1. Why the results of Google's neural network based image enhancement system RAISR should be mistrusted: https://thestack.com/world/2016/11/15/raisr-is-googles-ai-driven-image-resizing-algorithm-dishonest/
2. Another way companies can abuse big data and modelling: http://www.nzherald.co.nz/business/news/article.cfm?c_id=3&objectid=11757150
3. List of machine learning data sets: https://www.analyticsvidhya.com/blog/2016/11/25-websites-to-find-datasets-for-data-science-projects/
4. Why the results of Google's neural network based image enhancement system RAISR should be mistrusted: https://thestack.com/world/2016/11/15/raisr-is-googles-ai-driven-image-resizing-algorithm-dishonest/
5. Another way companies can abuse big data and modelling: http://www.nzherald.co.nz/business/news/article.cfm?c_id=3&objectid=11757150
6. List of machine learning data sets: https://www.analyticsvidhya.com/blog/2016/11/25-websites-to-find-datasets-for-data-science-projects/
7. A basic introduction to some of the more popular machine learning algorithms: http://www.kdnuggets.com/2016/11/intro-machine-learning-developers.html 
8. Are the well-publicised failures of machine learning really such bad failures? http://www.datasciencecentral.com/profiles/blogs/why-so-many-machine-learning-implementations-fail 
9. Stealing (really reverse engineering) machine learning models via public API: http://www.kdnuggets.com/2016/11/arxiv-spotlight-stealing-machine-learning-models-prediction-apis.html 
10. Dreaming in deep neural networks makes learning 10x faster: https://www.extremetech.com/extreme/240163-googles-deepmind-ai-gives-robots-ability-dream 
11. Paper on dreaming in deep neural networks: https://arxiv.org/pdf/1611.05397.pdf 
12. Using machine learning to predict dangerous seismic events in coal mines: https://deepsense.io/machine-learning-models-predicting-dangerous-seismic-events/ 
13. Bad article title-it's not AI that's gone too far, rather the people building and applying the AI: http://www.datasciencecentral.com/profiles/blogs/has-ai-gone-too-far-automated-inference-of-criminality-using-face 
14. The next 3 industries that will be disrupted by AI: http://dataconomy.com/artificial-intelligence-retail-healthcare-finance/ 
15. Teaching neural networks fear: http://www.theregister.co.uk/2016/11/30/artificial_intelligence_intrinsic_fear/ 
16. So much for a classless society: https://www.technologyreview.com/s/602987/china-turns-big-data-into-big-brother/
17. Deep neural networks generate song lyrics from pictures of a scene: https://www.theguardian.com/technology/2016/nov/29/its-no-christmas-no-1-but-ai-generated-song-brings-festive-cheer-to-researchers 
18. MusicNet is an annotated set of classicial music performances for training machine learning models: https://techcrunch.com/2016/11/30/musicnet-aims-to-give-machine-learning-algorithms-a-taste-for-beethoven/ 
19. There seems to be something of a shortage of trained AI practitioners: http://www.techproresearch.com/downloads/research-companies-lack-skills-to-implement-and-support-ai-and-machine-learning/?ftag=tip185eb84 
20. Amazon launches its AI web platform: https://techcrunch.com/2016/11/30/amazon-launches-amazon-ai-to-bring-its-machine-learning-smarts-to-developers/ 
21. The US government is continuing to take AI seriously. Is the NZ government going to do the same? http://www.techrepublic.com/article/us-senate-subcommittee-meets-on-the-dawn-of-ai-today-livestream-available/ 
22. The optimisation problems with deep neural networks: http://www.kdnuggets.com/2016/12/hard-thing-about-deep-learning.html 
23. Does Google have the edge in cloud-based AI? http://www.techrepublic.com/article/the-cloud-war-moves-to-machine-learning-does-google-have-an-edge/ 
24. Learn maths if you want to get into AI, according to Facebook's head of AI research: https://techcrunch.com/2016/12/01/facebooks-advice-to-students-interested-in-artificial-intelligence/ 
25. Bringing AI to logo design-sounds like an interactive evolutionary algorithm: https://techcrunch.com/2016/12/01/logojoy-makes-designers-unemployed/ 
26. Detecting diabetic retinopathy (damage to the retina caused by diabetes) with machine learning: http://betanews.com/2016/11/29/google-machine-learning-diabetes-retinopathy-eyes-vision/
27. Facebook is developing AI to flag "offensive" videos: http://www.reuters.com/article/us-facebook-ai-video-idUSKBN13Q52M
28. The 10 biggest failures in applications of artificial intelligence for 2016: http://www.techrepublic.com/article/top-10-ai-failures-of-2016/ 
29. Microsoft in embedding image recognition AI into some of its Office applications: http://www.theverge.com/2016/12/2/13825590/microsoft-office-apps-ai-word-powerpoint-accessibility 
30. Random forests in Python: http://www.kdnuggets.com/2016/12/random-forests-python.html 
31. How can governments regulate ecommerce (and the AI that drives it): https://www.theguardian.com/commentisfree/2016/dec/04/how-do-you-throw-book-at-an-algorithm-internet-big-data

Weekly Review 28 November 2016

Below are some of the interesting links I Tweeted about in the last week.

1. Not to be too glib, but this is what being a post-doc is like: http://www.nzherald.co.nz/business/news/article.cfm?c_id=3&objectid=11752215
2. Pictorial description of backpropagation ANN training: http://www.datasciencecentral.com/profiles/blogs/neural-networks-the-backpropagation-algorithm-in-a-picture 
3. Implementing Human ActivityRecognition in TensorFlow: http://www.kdnuggets.com/2016/11/implementing-cnn-human-activity-recognition-tensorflow.html
4. Combining text analytics and machine learning: https://www.datanami.com/2016/11/21/text-analytics-machine-learning-virtuous-combination/ 
5. Top 20 open source Python machine learning projects: http://www.kdnuggets.com/2016/11/top-20-python-machine-learning-open-source-updated.html 
6. Questions to ask when moving machine learning systems into production: http://www.kdnuggets.com/2016/11/moving-machine-learning-practice-production.html 
7. Choosing a programming language for machine learning applications: http://www.datasciencecentral.com/profiles/blogs/python-machine-learning-and-language-wars-a-highly-subjective-poi 
8. Baidu is releasing Chinese-language speech recognition APIs: https://www.datanami.com/2016/11/23/baidu-ups-ai-ante-deep-learning-release/ 
9. Google's deep-learning based translation system seems to have learned its own internal language model: https://techcrunch.com/2016/11/22/googles-ai-translation-tool-seems-to-have-invented-its-own-secret-internal-language/ 
10. Tech leaders need to be aware of biased models: http://www.techrepublic.com/article/algorithms-can-be-racist-why-cxos-should-understand-the-assumptions-behind-predictive-analytics/ 
11. A deep-learning based system learned to lipread by watching television, performs better than a human: http://www.theverge.com/2016/11/24/13740798/google-deepmind-ai-lip-reading-tv 
12. AI is making inroads into the music industry: http://www.theregister.co.uk/2016/11/24/big_music_goes_mad_for_chat_bots_and_ai/ 
13. An introduction (with Python code) to linear regression: http://www.kdnuggets.com/2016/11/linear-regression-least-squares-matrix-multiplication-concise-technical-overview.html 
14. How Google uses cloud-based machine learning to help companies fill job vacancies: http://www.techrepublic.com/article/how-googles-new-cloud-jobs-api-uses-machine-learning-to-help-companies-fill-jobs/ 
15. Why neural networks won't take over from human translators: http://motherboard.vice.com/en_au/read/shitloads-and-zingers-the-perils-of-machine-translation

IEEE Transactions on Fuzzy Systems, vol. 24, issue 5, 2016

1. On Pythagorean and Complex Fuzzy Set Operations
Author(s): Scott Dick; Ronald R. Yager; Omolbanin Yazdanbakhsh
Pages: 1009- 1021

2. Classification of Type-2 Fuzzy Sets Represented as Sequences of Vertical Slices
Author(s): Lorenzo Livi; Hooman Tahayori; Antonello Rizzi; Alireza Sadeghian; Witold Pedrycz
Pages: 1022- 1034

3. Power Average of Trapezoidal Intuitionistic Fuzzy Numbers Using Strict t-Norms and t-Conorms
Author(s): Shu-Ping Wan; Zhi-Hong Yi
Pages: 1035- 1047

4. Aperiodic Sampled-Data Sliding-Mode Control of Fuzzy Systems With Communication Delays Via the Event-Triggered Method
Author(s): Shiping Wen; Tingwen Huang; Xinghuo Yu; Michael Z. Q. Chen; Zhigang Zeng
Pages: 1048- 1057

5. Fault Detection and Isolation for Affine Fuzzy Systems With Sensor Faults
Author(s): Huimin Wang; Guang-Hong Yang; Dan Ye
Pages: 1058- 1071

6. Knowledge Measure for Atanassov's Intuitionistic Fuzzy Sets
Author(s): Kaihong Guo
Pages: 1072- 1078

7. Takagi–Sugeno–Kang Transfer Learning Fuzzy Logic System for the Adaptive Recognition of Epileptic Electroencephalogram Signals
Author(s): Changjian Yang; Zhaohong Deng; Kup-Sze Choi; Shitong Wang
Pages: 1079- 1094

8. A Survey of Adaptive Fuzzy Controllers: Nonlinearities and Classifications
Author(s): Meng Joo Er; Sayantan Mandal
Pages: 1095- 1107

9. Optimal Design of Constraint-Following Control for Fuzzy Mechanical Systems
Author(s): Ruiying Zhao; Ye-Hwa Chen; Shengjie Jiao
Pages: 1108- 1120

10. Multiscale Opening of Conjoined Fuzzy Objects: Theory and Applications
Author(s): Punam K. Saha; Subhadip Basu; Eric A. Hoffman
Pages: 1121- 1133

11. Decentralized State Feedback Control of Uncertain Affine Fuzzy Large-Scale Systems With Unknown Interconnections
Author(s): Huimin Wang; Guang-Hong Yang
Pages: 1134- 1146

12. Fuzzy Adaptive Control With State Observer for a Class of Nonlinear Discrete-Time Systems With Input Constraint
Author(s): Yan-Jun Liu; Shaocheng Tong; Dong-Juan Li; Ying Gao
Pages: 1147- 1158

13. Fuzzy-Based Goal Representation Adaptive Dynamic Programming
Author(s): Yufei Tang; Haibo He; Zhen Ni; Xiangnan Zhong; Dongbin Zhao; Xin Xu
Pages: 1159- 1175

14. Nonparametric Statistical Active Contour Based on Inclusion Degree of Fuzzy Sets
Author(s): Maoguo Gong; Hao Li; Xiang Zhang; Qiunan Zhao; Bin Wang
Pages: 1176- 1192

15. The Role of Crisp Elements in Fuzzy Ontologies: The Case of Fuzzy OWL 2 EL
Author(s): Fernando Bobillo
Pages: 1193- 1209

16. Transfer Prototype-Based Fuzzy Clustering
Author(s): Zhaohong Deng; Yizhang Jiang; Fu-Lai Chung; Hisao Ishibuchi; Kup-Sze Choi; Shitong Wang
Pages: 1210- 1232

17. Observer-Based Fuzzy Control for Nonlinear Networked Systems Under Unmeasurable Premise Variables
Author(s): Hongyi Li; Chengwei Wu; Shen Yin; Hak-Keung Lam
Pages: 1233- 1245

18. Adaptive Fuzzy Hysteresis Internal Model Tracking Control of Piezoelectric Actuators With Nanoscale Application
Author(s): Pengzhi Li; Peiyue Li; Yongxin Sui
Pages: 1246- 1254

Weeky Review 21 November 2016

Below are some of the interesting links I Tweeted about in the last week.

1. The AI market is predicted to keep growing: http://www.nextbigfuture.com/2016/10/ai-market-is-projected-to-grow-from-8.html 
2. Making music with neural networks: http://www.theregister.co.uk/2016/11/11/ai_pop_music_maker/ 
3. Point and click chatbot builder: https://techcrunch.com/2016/11/10/kitt-ais-chartflow-helps-you-build-better-chatbots/ 
4. Implementing machine learning algorithms in parallel using GPU: http://www.kdnuggets.com/2016/11/parallelism-machine-learning-gpu-cuda-threading.html 
5. Spotting insider trading with data mining: https://www.datanami.com/2016/11/08/sec-mines-data-spot-insider-trading/ 
6. A descriptive overview of convolutional neural networks: http://www.kdnuggets.com/2016/11/intuitive-explanation-convolutional-neural-networks.html 
7. The current state of machine intelligence: http://www.datasciencecentral.com/profiles/blogs/the-current-state-of-machine-intelligence-3-0 
8. Overview of computer vision: https://techcrunch.com/2016/11/13/wtf-is-computer-vision/ 
9. Examining the relationships we have with present primitive AI: https://techcrunch.com/2016/11/13/defining-our-relationship-with-early-ai/ 
10. An argument that it makes more economic sense for AI to replace highly-paid workers first: http://www.theregister.co.uk/2016/11/14/the_sharks_of_ai_will_attack_expensive_and_scarce_workers_faster_than_they_eat_drivers/ 
11. Adobe is developing Sensei, its own intelligent assisstant: https://techcrunch.com/2016/11/14/adobe-makes-big-bets-on-ai-and-the-public-cloud/ 
12. Has Microsoft made a break-through in machine language comprehension? http://www.techrepublic.com/article/microsoft-has-found-a-way-to-bring-human-language-intelligence-to-our-dumb-computers/ 
13. It seems that Facebook uses machine learning to identify fake news content: https://techcrunch.com/2016/11/14/facebook-fake-news/ 
14. The shortcomings of deep learning: http://www.kdnuggets.com/2016/11/shortcomings-deep-learning.html 
15. Getting to grips with neural networks with Google's AI Experiments showcase: https://techcrunch.com/2016/11/15/googles-ai-experiments-help-you-understand-neural-networks-by-playing-with-them 
16. Some predictions on the future of artificial intelligence: http://www.kdnuggets.com/2016/11/13-forecasts-on-artificial-intelligence.html 
17. An AI-based task manager: https://techcrunch.com/2016/11/15/gluru/ But is it better than my textfile named ToDo.txt?
18. Semantic Scholar, an AI-based search engine for research papers: https://techcrunch.com/2016/11/11/scientists-gain-a-versatile-modern-search-engine-with-the-ai-powered-semantic-scholar/ 
19. Can AI replace HR? https://www.linkedin.com/pulse/can-robots-replace-hr-michael-gretczko?trk=hp-feed-article-title-comment 
20. Machine learning based upsampling of images: http://www.theverge.com/2016/11/16/13649016/google-machine-learning-low-res-image-raisr 
21. The crucial elements missing from chatbot AI: http://www.techrepublic.com/article/mobile-ai-chatbot-intelligence-masquerading-as-the-real-deal/ 
22. Google's ANN-based doodle classifier: http://www.theverge.com/2016/11/15/13641876/google-ai-experiments-quick-draw-image-recognition-game 
23. OpenAI has chosen Microsoft Azure as its cloud platform of choice: http://www.techrepublic.com/article/microsoft-partners-with-openai-to-advance-ai-research-with-azure/ 
24. Google is expanding its cloud-based AI services: http://www.theverge.com/2016/11/15/13640420/google-cloud-service-machine-learning-ai-translation-computer-vision 
25. Nexar is using machine learning in car dash cams to predict collisions: https://techcrunch.com/2016/11/15/nexars-vehicle-to-vehicle-network-will-use-dash-cam-ai-to-prevent-accidents/ 
26. The future of AI is inseparable from humans: http://www.theverge.com/a/verge-2021/humanity-and-ai-will-be-inseparable 
27. List of and commentaries on useful tools for building chatbots: https://chatbotsmagazine.com/the-tools-every-bot-creator-must-know-c0e9dd685094#.2fclmqz8w 
28. Colour me skeptical about the claim that the system recognises handwriting better than humans: https://techcrunch.com/2016/11/17/searchink-unlocking-the-handwritten-past-and-present-with-machine-learning/ 
29. Someone who spent more time talking to bots than their spouse wouldn't have a spouse for long: https://www.datanami.com/2016/11/16/ai-powered-bots-gearing-up-serve-you/ 
30. Overview of opinion mining: http://dataconomy.com/opinion-mining-extracting-opinions/ 
31. Classifying porn with ANN: http://www.theregister.co.uk/2016/1/18/ai_gives_smut_peddlers_helping_hand/ 
32. I suspect this is a case of either seriously biased data or outright fraud: http://www.theregister.co.uk/2016/11/18/ai_can_tell_if_youre_a_criminal/ 
33. Where to apply machine learning first in your business: http://techemergence.com/where-to-apply-machine-learning-first/ 
34. Bias in machine learning models and how to prevent it: http://www.techrepublic.com/article/bias-in-machine-learning-and-how-to-stop-it/ 
35. Automated medical diagnostic tools are still not as good as human doctors: http://spectrum.ieee.org/the-human-os/biomedical/diagnostics/doctors-still-struggle-to-make-the-most-of-computer-aided-diagnosis/?utm_source=humanosalert&utm_medium=email&utm_campaign=111616 
36. British government report on the future implications of AI: https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/566075/gs-16-19-artificial-intelligence-ai-report.pdf 
37. Howto: Deep learning-based object recognition in Microsoft Cognitive Toolkit: https://blogs.technet.microsoft.com/machinelearning/2016/10/25/how-to-train-a-deep-learned-object-detection-model-in-cntk/ 
38. Why "Reply All" is not a good idea: http://www.businessinsider.com.au/reply-all-email-chain-1-2-million-nhs-employees-2016-11?r=US&IR=T