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

IEEE Transactions on Evolutionary Computation, Volume 20, Number 2, April 2016

1) A Tunable Generator of Instances of Permutation-Based Combinatorial Optimization Problems
Author(s): L. Hernando; A. Mendiburu; J. A. Lozano
Page(s): 165 - 179

2) Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers
Author(s): Y. Yuan; H. Xu; B. Wang; B. Zhang; X. Yao
Page(s): 180 - 198

3) Solving Bilevel Multicriterion Optimization Problems With Lower Level Decision Uncertainty
Author(s): A. Sinha; P. Malo; K. Deb; P. Korhonen; J. Wallenius
Page(s): 199 - 217

4) Leveraged Neighborhood Restructuring in Cultural Algorithms for Solving Real-World Numerical Optimization Problems
Author(s): M. Z. Ali; P. N. Suganthan; R. G. Reynolds; A. F. Al-Badarneh
Page(s): 218 - 231

5) Adaptive Cross-Generation Differential Evolution Operators for Multiobjective Optimization
Author(s): X. Qiu; J. X. Xu; K. C. Tan; H. A. Abbass
Page(s): 232 - 244

6) Simple Probabilistic Population-Based Optimization
Author(s): Y. C. Lin; M. Clauß; M. Middendorf
Page(s): 245 - 262

7) Tunably Rugged Landscapes With Known Maximum and Minimum
Author(s): N. Manukyan; M. J. Eppstein; J. S. Buzas
Page(s): 263 - 274

8) A Multiobjective Evolutionary Algorithm Based on Decision Variable Analyses for Multiobjective  
Author(s): Optimization Problems With Large-Scale Variables
X. Ma; F. Liu; Y. Qi; X. Wang; L. Li; L. Jiao; M. Yin; M. Gong
Page(s): 275 - 298

9) Generalization of Pareto-Optimality for Many-Objective Evolutionary Optimization
Author(s): C. Zhu; L. Xu; E. D. Goodman
Page(s): 299 - 315

10) Average Convergence Rate of Evolutionary Algorithms
Author(s): J. He; G. Lin
Page(s): 316 - 321

Weekly Review 25 March 2016

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

1. Fundamentals of neural networks: http://www.analyticsvidhya.com/blog/2016/03/introduction-deep-learning-fundamentals-neural-networks/
2. Why AlphaGo is not the be-all and end-all of AI: http://www.kdnuggets.com/2016/03/alphago-not-solution-ai.html
3. Biases in facial recognition: http://motherboard.vice.com/en_au/read/the-inherent-bias-of-facial-recognition
4. Tips for using deep-learning neural networks: http://www.kdnuggets.com/2016/03/must-know-tips-deep-learning-part-1.html
5. An AI chatbot to help psychologically traumatised refugees: http://www.theguardian.com/technology/2016/mar/22/karim-the-ai-delivers-psychological-support-to-syrian-refugees
6. Gauging someone's mood from their voice, using deep learning neural networks: https://thestack.com/world/2016/03/21/mood-mining-researchers-propose-app-to-judge-your-long-term-state-of-mind-from-your-voice/ 
7. Using machine learning for artificial empathy in marketing: http://motherboard.vice.com/en_au/read/how-companies-will-use-artificial-empathy-to-sell-you-more-shit 
8. Diagnosing heart disease from MRI images using convolutional neural networks: http://irakorshunova.github.io/2016/03/15/heart.html 
9. The future of AI in law: http://dataconomy.com/ai-future-law-lawyers-know/  If this lowers cost of access to legal representation, that will be a good thing
10. Applying machine learning to choosing and predicting the quality of wine: http://dataconomy.com/the-perfect-pairing-machine-learning-and-wine/ 
11. Getting started in R: http://www.kdnuggets.com/2016/03/datacamp-r-learning-path-7-steps.html
12. More tips for using deep learning neural networks: http://www.kdnuggets.com/2016/03/must-know-tips-deep-learning-part-2.html
13. Converting numerical variables to categorical variables: http://www.datasciencecentral.com/profiles/blogs/how-to-bin-or-convert-numerical-variables-to-categorical
14. Why regulators are unprepared for AI: http://www.slate.com/articles/technology/future_tense/2016/03/regulators_are_underprepared_for_artificial_intelligence_they_could_learn.html
15. My seminar on what I look for when examining a post-graduate (especially doctoral) thesis: https://drive.google.com/file/d/0B0vCoxTCjd34SW1JRmZpYVpXUDQ/view?usp=sharing 
16. Google opens access to its deep neural network-based speech recognition API: http://techcrunch.com/2016/03/23/google-opens-access-to-its-speech-recognition-api-going-head-to-head-with-nuance/
17. Japanese AI wrote a short novel, and it passed the first round of a literary competition: http://www.digitaltrends.com/cool-tech/japanese-ai-writes-novel-passes-first-round-nationanl-literary-prize/

Weekly Review 20 March 2016

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

1. So, computers are now better at Go, than the best human player: http://www.nature.com/news/the-go-files-ai-computer-clinches-victory-against-go-champion-1.19553
2. 7 situations where more data isn't necessarily better: http://www.datasciencecentral.com/profiles/blogs/7-cases-where-big-data-isn-t-better 
3. Human vs AlphaGo now 1-3 in favour of the machine: http://www.theguardian.com/world/2016/mar/13/go-humans-lee-sedol-scores-first-victory-against-supercomputer
4. Follow the instructions when applying for a job: https://www.insidehighered.com/advice/2016/03/14/importance-following-directions-when-you-apply-jobs-essay And ecologists, don't ask me for a postdoc position, I'm comp sci!
5. 4-1 to the machine: http://spectrum.ieee.org/tech-talk/computing/networks/alphago-wins-match-against-top-go-player 
6. US companies are buying-up British AI companies: http://motherboard.vice.com/en_au/read/why-the-us-is-buying-up-so-many-uk-artificial-intelligence-companies 
7. Should all research papers be free? http://www.nytimes.com/2016/03/13/opinion/sunday/should-all-research-papers-be-free.html Yes, they should!
8. Call for Papers: ICTAI 2016: The 28th International Conference on Tools with Artificial Intelligence, November... http://bit.ly/1YZfJN0
9. List of resources on machine learning: http://www.datasciencecentral.com/profiles/blogs/43-new-external-machine-learning-resources-and-updated-articles
10. Human-assisted machine learning: http://www.datanami.com/2016/03/17/unleashing-artificial-intelligence-human-assisted-machine-learning/ I seem to remember Arthur C. Clarke writing about something similar 30 years ago.
11. AI needs to work with humans, not against them: http://www.datanami.com/2016/03/17/unleashing-artificial-intelligence-human-assisted-machine-learning/
12. I wonder if these robo-advisors use machine learning or other AI technology? https://thestack.com/world/2016/03/16/rbs-cuts-hundreds-of-jobs-as-fca-approves-robo-advisers/
13. A bit depressing, but not terribly surprising: http://qz.com/373436/373436/ 
14. "Preferred reviewers"?? Not the best idea ever: https://methodsblog.wordpress.com/2015/10/15/preferred-reviewers/
15. Fundamentals of deep learning: http://www.analyticsvidhya.com/blog/2016/03/introduction-deep-learning-fundamentals-neural-networks/
16. Applying Sun Tzu's Art of War to software development: http://www.datasciencecentral.com/profiles/blogs/the-art-of-war-applied-to-software-development
17. South Korea is investing big in AI research: http://www.nature.com/news/south-korea-trumpets-860-million-ai-fund-after-alphago-shock-1.19595
18. 80 % of Chinese workers think AI will replace them. Conversely, only 39 % of German workers think the same: http://qz.com/642741/the-workers-in-these-countries-believe-ai-and-robots-will-replace-them/

IEEE Transactions on Computational Intelligence and AI in Games, Volume8, Issue 1, March 2016

1) Predicting Dominance Rankings for Score-Based Games
Author(s): S. Samothrakis; D. Perez; S. M. Lucas; P. Rohlfshagen
Page(s): 1 - 12

2) Solving a Complex Language Game by Using Knowledge-Based Word Associations Discovery
Author(s): P. Basile; M. de Gemmis; P. Lops; G. Semeraro
Page(s): 13 - 26

3) Extending Real-Time Challenge Balancing to Multiplayer Games: A Study on Eco-Driving
Author(s): H. Prendinger; K. Puntumapon; M. Madruga
Page(s): 27 - 32

4) Online Adaptable Learning Rates for the Game Connect-4
Author(s): S. Bagheri; M. Thill; P. Koch; W. Konen
Page(s): 33 - 42

5) Intelligent Game Engine for Rehabilitation (IGER)
Author(s): M. Pirovano; R. Mainetti; G. Baud-Bovy; P. L. Lanzi; N. A. Borghese
Page(s): 43 - 55

6) Reinforcement Learning in Video Games Using Nearest Neighbor Interpolation and Metric Learning
Author(s): M. S. Emigh; E. G. Kriminger; A. J. Brockmeier; J. C. Príncipe; P. M. Pardalos
Page(s): 56 - 66

7) Discovering Multimodal Behavior in Ms. Pac-Man Through Evolution of Modular Neural Networks
Author(s): J. Schrum; R. Miikkulainen
Page(s): 67 - 81

8) Prolog-Scripted Tactics Negotiation and Coordinated Team Actions for Counter-Strike Game Bots
Author(s): G. Jaśkiewicz
Page(s): 82 - 88

9) Predicting Opponent's Production in Real-Time Strategy Games With Answer Set Programming
Author(s): M. Stanescu; M. Čertický

10) How to Run a Successful Game-Based AI Competition
Author(s): J. Togelius
Page(s): 95 - 100

Call for Papers: ICTAI 2016

The 28th International Conference on Tools with Artificial Intelligence, November 07-09, 2016, San Jose, CA

The annual IEEE International Conference on Tools with Artificial Intelligence (ICTAI) provides a major international forum where the creation and exchange of ideas related to artificial intelligence are fostered among academia, industry, and government agencies. The conference facilitates the cross-fertilization of these ideas and promotes their transfer into practical tools, for developing intelligent systems and pursuing artificial intelligence applications. The ICTAI encompasses all technical aspects of specifying, developing and evaluating the theoretical underpinnings and applied mechanisms of the AI-based components of computer tools such as algorithms, architectures and languages.

Topics (not limited to)

AI Foundations

• Evolutionary computing, Bayesian and Neural Networks
• Decision/Utility Theory and Decision Optimization
• Search, SAT, and CSP
• Description Logic and Ontologies

AI in Domain Specific Application

• AI in Natural Language Processing and Understanding
• AI in Computational Biology, Medicine and Biomedical Applications
• AI in WWW, Communication, Social Networking, Recommender Systems, Games and  

E-Commerce

• AI in Finance and Risk Management

AI in Computer Systems

• AI in Robotics, Computer Vision and Games
• AI in Software Engineering, Real-Time and Embedded Applications, and Sensor Networks
• AI in Cloud Computing, Data-Intensive Applications and Online/Streaming and Multimedia Systems
• AI in Web search and Information Retrieval
• AI in Computer Security, Data Privacy, and Information Assurance

AI in Data Analytics and Big Data

• Visualization Analytics for Big Data 
   
• Computational Modeling for Big Data
    
• Large-scale Recommendation and Social Media Systems
• Cloud/Grid/Stream Data Mining for Big Velocity Data 
       
• Semantic-based Big Data Mining

Machine Learning and Data Mining

• Data pre-processing, reduction and feature selection
• Learning Graphical Models and Complex Networks
• Active, Cost-Sensitive, Semi-Supervised, Multi-Instance, Multi-Label and Multi-Task Learning
• Transfer/Adaptive, Rational and Structured Learning
• Preference/Ranking, Ensemble, and Reinforcement Learning

Knowledge Representation, Reasoning and Cognitive Modelling

• Knowledge Representation, Reasoning
• Knowledge Extraction, Management and Sharing
• Case-Based Reasoning and Knowledge-based Systems
• Cognitive Modelling and Semantic Web

AI and Decision Systems

• Decision Guidance and Support Systems
• Optimization-based recommender systems 
• Group, distributed, and collaborative decisions
• Crowd-sourcing and collective intelligence decision making
• Strategic, tactical and operational level decisions 
• Decision making in social and mobile networks

Uncertainty in AI

• Uncertainty and Fuzziness Representation and Reasoning
• Approximate/Exact Probabilistic Inference
• Knowledge Discovery and Data Mining for Uncertain Data

Paper submission: June 30, 2016
Paper notification: July 30, 2016
Camera ready paper: August 30, 2016

Paper Submission
The submissions should contain original, high quality, not submitted or published elsewhere work. Papers should be submitted electronically (through ICTAI 2016 web site) in pdf format and should conform to IEEE specifications (single-spaced, double-column, 10-point font size, up to 8 pages).

Paper Presentation
Each accepted paper should be presented by one of the authors and accompanied by at least one full registration fee payment, to guarantee publication in the proceedings. All accepted papers will be included in proceedings of ICTAI 2016 that will be published by the IEEE Computer Society.

IJAIT special issue and Best Student Papers Awards
Extended versions of the best papers of the conference will be invited for publication in a special issue of the International Journal on Artificial Intelligence Tools (IJAIT) (SCI Indexed). We also provide financial awards to the top-5 best student papers.

Further Information email Chairs:

General Chair: iiass.annaesp@tin.it

Program Chairs:
mali@uwm.edu
malamani@purdue.edu

ICTAI Steering Committee
Nikolaos Bourbakis, Wright State Univ., USA (Chair)
BAIF Steering Committee
Despina Kavraki, BAIF, USA (Chair)

General Chairs
Anna Esposito, Seconda Università di Napoli, Caserta, Italy

Program Co-Chairs
Amol Mali, Univ. of Wisconsin, USA
Miltos Alamaniotis, Purdue Univ, USA

Financial ChairN. Bourbakis, CART-WSU USA

Local Arrangement Chair
R. Kannavara, Intel, OR

Registration Chairs
EASYCHAIR

Publicity Chair and Web Master
A. Angeleas, Z. Chasparis, CART-WSU

ICTAI Program Areas Chairs
A. Awekar, India
V. Balas, Romania
A. Cesta, Italy
D. Dou, USA
J. Gao, USA
M. Ghalwash, USA
E.  Grégoire, France
X. Hu, USA
C. Lim, Australia
M-W. Mak, Hong Kong
Z. Malik, USA
H. Narayanan, USA
A. Orlandini, Italy
K. Palaniappan, USA
N. Rowe, USA
A. Salah, Turkey
B. Schuller, Germany
X. Song, USA
C.Vogel, Ireland
R. Wallace, Ireland
R. Wei, Singapore
C. Yuan, USA
Z-H. Zhou, China

Weekly Review 11 March 2016

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

1. Automated data mining: http://www.kdnuggets.com/2016/03/automated-data-science.html
2. Tracking sources of food poisoning using machine learning of tweets about restaurants: http://www.nsf.gov/news/news_summ.jsp?cntn_id=137848&org=NSF&from=news
3. How NoSQL changed machine learning: http://www.datasciencecentral.com/profiles/blogs/how-nosql-fundamentally-changed-machine-learning
4. Using deep learning to identify bodies of water in orbital images: https://www.technologyreview.com/s/600866/how-deep-learning-gives-us-a-precise-picture-of-all-the-water-on-earth/
5. Why researchers are using Sci-Hub: https://www.insidehighered.com/blogs/library-babel-fish/fix-isnt
6. DeepMind's Alphago is set to take on the world's best Go player: http://www.theguardian.com/technology/2016/mar/07/go-board-game-google-alphago-lee-se-dol
7. I know what it's like to be the first (and so far only) one in the family to go to university: https://www.insidehighered.com/advice/2013/03/04/essay-impact-being-first-generation-college-grad-when-one-joins-academic
8. Is London becoming a centre for AI businesses? http://www.theguardian.com/technology/2016/mar/05/artificial-intelligence-brains-money-london
9. Number of people doing post-docs seems to be declining: http://www.sciencemag.org/careers/2015/12/case-disappearing-postdocs - I'm not surprised: http://computational-intelligence.blogspot.co.nz/2012/09/on-being-post-doc.html 
10. Calls for a royal commission to investigate the impact of robotics on UK jobs: http://www.theguardian.com/technology/2016/mar/08/government-urged-investigate-impact-robots-uk-workforce 
11. Post-grad students should be teaching under-grads: https://www.insidehighered.com/news/2016/03/08/study-suggests-graduate-student-instructors-influence-undergraduates-major 
12. Human vs machine Go tournament has started: http://motherboard.vice.com/en_au/read/tonight-watch-a-professional-go-player-take-on-googles
13. Why you should learn both R and Python: http://www.kdnuggets.com/2016/03/r-python-learning-both-datacamp.html 
14. 29% of software developers are afraid AI will replace them: http://www.computerworld.com/article/3041430/it-careers/one-in-three-developers-fear-ai-will-replace-them.html 
15. AlphaGo won the first match against the world Go champion: http://spectrum.ieee.org/tech-talk/robotics/artificial-intelligence/alphago-wins-game-one-against-world-go-champion
16. Microsoft is including R in Visual Studio now http://www.theregister.co.uk/2016/03/10/open_source_stats_visual_studio/
17. List of tutorials on Scikit-Learn http://www.datasciencecentral.com/profiles/blogs/scikit-learn-tutorial-series 
18. Avoiding the "technical debt" of machine learning: http://www.datanami.com/2016/03/09/how-to-avoid-the-technical-debt-of-machine-learning/
19. AlphaGo has now won two matches in a row against world Go champion: http://phys.org/news/2016-03-human-champion-speechless-2nd-loss.html
20. "Computer conservationists": http://community.lovenature.com/2016/03/10/explore-the-incredible-work-of-computer-conservationists/ I think "Computational conservationist" is a better term, they don't conserve computers

Neural Networks, Volume 76 , Pages 1-152, April 2016

1. Pinning cluster synchronization in an array of coupled neural networks under event-based mechanism  
Author(s): Lulu Li, Daniel W.C. Ho, Jinde Cao, Jianquan Lu
Pages: 1-12

2. Effects of self-coupling and asymmetric output on metastable dynamical transient firing patterns in arrays of neurons with bidirectional inhibitory coupling  
Author(s): Yo Horikawa
Pages: 13-28

3. A Fast Reduced Kernel Extreme Learning Machine  
Author(s): Wan-Yu Deng, Yew-Soon Ong, Qing-Hua Zheng
Pages: 29-38

4. A local Echo State Property through the largest Lyapunov exponent  
Author(s): Gilles Wainrib, Mathieu N. Galtier
Pages: 39-45

5. Finite-time robust stabilization of uncertain delayed neural networks with discontinuous activations via delayed feedback control  
Author(s): Leimin Wang, Yi Shen, Yin Sheng
Pages: 46-54

6. Quantum perceptron over a field and neural network architecture selection in a quantum computer  
Author(s): Adenilton José da Silva, Teresa Bernarda Ludermir, Wilson Rosa de Oliveira
Pages: 55-64

7. Learning contextualized semantics from co-occurring terms via a Siamese architecture  
Author(s) Ubai Sandouk, Ke Chen
Pages 65-96

8. Projective synchronization of nonidentical fractional-order neural networks based on sliding mode controller  
Author(s): Zhixia Ding, Yi Shen
Pages: 97-105

9. An ensemble of dynamic neural network identifiers for fault detection and isolation of gas turbine engines  
Author(s): M. Amozegar, K. Khorasani
Pages: 106-121

10. Hybrid feedback feedforward: An efficient design of adaptive neural network control  
Author(s): Yongping Pan, Yiqi Liu, Bin Xu, Haoyong Yu
Pages: 122-134

11. Multi-source adaptation joint kernel sparse representation for visual classification  
Pages 135-151
Author(s) JianWen Tao, Wenjun Hu, Shiting Wen

Weekly Review 4 March 2016

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

1. A short history of machine learning: http://www.datasciencecentral.com/profiles/blogs/a-short-history-of-machine-learning
2. Machines predicting human behaviour by reading (a lot of) fiction: https://thestack.com/cloud/2016/02/26/computers-read-1-8-billion-words-of-fiction-to-learn-how-to-anticipate-human-behaviour/
3. Helping the disabled with artificial intelligence: http://www.kdnuggets.com/2016/03/data-science-disability.html 
4. Chips made of biological neurons: http://motherboard.vice.com/en_au/read/komiku-neuron-computer-agabi
5. I am so glad that I left Australia four years ago - I couldn't publish my research under these rules https://theconversation.com/new-defence-trade-controls-threaten-academic-freedom-and-the-economy-55310
6. AI won't save us from the bad guys in computer security http://www.theregister.co.uk/2016/03/01/security_ai_rsa_boss/ 
7. TensorFlow now does distributed computing http://www.kdnuggets.com/2016/03/distributed-tensorflow-arrived.html 
8. The key to good teamwork is being nice, according to a many-year study by Google: http://qz.com/625870/after-years-of-intensive-analysis-google-discovers-the-key-to-good-teamwork-is-being-nice/ 
9. How to get into a career in machine learning: http://www.datasciencecentral.com/profiles/blogs/repost-xavier-amatriain-how-should-one-start-a-career-in-machine- 
10. Principal component analysis in R: http://www.bigdatanews.com/profiles/blogs/principal-component-analysis-using-r 
11. Feature selection in Python using scikit-feature http://www.kdnuggets.com/2016/03/scikit-feature-open-source-feature-selection-python.html 
12. Hacking systems with AI: http://www.theguardian.com/technology/2016/mar/03/artificial-intelligence-hackers-security-autonomous-learning
13. How do you control a super-smart AI? http://www.theregister.co.uk/2016/03/04/controlling_smart_ai_systems/
14. Lecture on different deep-learning packages: http://cs231n.stanford.edu/slides/winter1516_lecture12.pdf

IEEE Transactions on Neural Networks and Learning Systems, Volume 27, Issue 3, March 2016.

1. Neural Network-Based Event-Triggered State Feedback Control of Nonlinear Continuous-Time Systems
Authors: Avimanyu Sahoo; Hao Xu; Sarangapani Jagannathan
Page(s): 497 - 509

2. Robust Adaptive Neural Tracking Control for a Class of Stochastic Nonlinear Interconnected Systems
Authors: Huanqing Wang; Xiaoping Liu; Kefu Liu
Page(s): 510 - 523

3. Generating Highly Accurate Predictions for Missing QoS Data via Aggregating Nonnegative Latent Factor Models
Authors: Xin Luo; MengChu Zhou; Yunni Xia; Qingsheng Zhu; Ahmed Chiheb Ammari; Ahmed Alabdulwahab
Page(s): 524 - 537

4. Comparison of Seven Methods for Boolean Factor Analysis and Their Evaluation by Information Gain
Authors: Alexander A. Frolov; Dusan Husek; Pavel Yu. Polyakov
Page(s): 538 - 550

5. A New Stochastic Computing Methodology for Efficient Neural Network Implementation
Authors: DVincent Canals; Antoni Morro; Antoni Oliver; Miquel L. Alomar; Josep L. Rossello
Page(s): 551 - 564

6. Hierarchical Theme and Topic Modeling
Authors: Jen-Tzung Chien
Page(s): 565 - 578

7. A Nonnegative Latent Factor Model for Large-Scale Sparse Matrices in Recommender Systems via Alternating Direction Method
Authors: Xin Luo; MengChu Zhou; Shuai Li; Zhuhong You; Yunni Xia; Qingsheng Zhu
Page(s): 579 - 592

8. Global Exponential Stability for Complex-Valued Recurrent Neural Networks With Asynchronous Time Delays
Authors: Xiwei Liu; Tianping Chen
Page(s): 593 - 606

9. Perception Evolution Network Based on Cognition Deepening Model—Adapting to the Emergence of New Sensory Receptor
Authors: Youlu Xing; Furao Shen; Jinxi Zhao
Page(s): 607 - 620

10. A Spiking Neural Network System for Robust Sequence Recognition
Authors: Qiang Yu; Rui Yan; Huajin Tang; Kay Chen Tan; Haizhou Li
Page(s): 621 - 635

11. DC Proximal Newton for Nonconvex Optimization Problems
Authors: Alain Rakotomamonjy; Remi Flamary; Gilles Gasso
Page(s): 636 - 647

12. Relevance Vector Machine for Survival Analysis
Authors: Farkhondeh Kiaee; Hamid Sheikhzadeh; Samaneh Eftekhari Mahabadi
Page(s): 648 - 660

13. Analog Programmable Distance Calculation Circuit for Winner Takes All Neural Network Realized in the CMOS Technology
Authors: Tomasz Talaska; Marta Kolasa; Rafal Dlugosz; Witold Pedrycz
Page(s): 661 - 673

14. Image Categorization by Learning a Propagated Graphlet Path
Authors: Luming Zhang; Richang Hong; Yue Gao; Rongrong Ji; Qionghai Dai; Xuelong Li
Page(s): 674 - 685

15. Lag Synchronization of Memristor-Based Coupled Neural Networks via \omega-Measure
Authors: Ning Li; Jinde Cao
Page(s): 686 - 697

16. L_{1}-Minimization Algorithms for Sparse Signal Reconstruction Based on a Projection Neural Network
Authors: Qingshan Liu; Jun Wang
Page(s): 698 - 707

Weekly Review 26 February 2016

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

1. Cloud-based machine learning API: http://www.datasciencecentral.com/profiles/blogs/cloud-machine-learning-apis
2. Deep learning for everyone: http://www.kdnuggets.com/2016/02/opening-deep-learning-everyone.html
3. Feature selection for random forests: http://www.datasciencecentral.com/profiles/blogs/choosing-features-for-random-forests-algorithm
4. Some model evaluation metrics: http://www.analyticsvidhya.com/blog/2016/02/7-important-model-evaluation-error-metrics/?utm_source=feedburner&utm_medium=email&utm_campaign=Feed:%20AnalyticsVidhya%20%28Analytics%20Vidhya%29
5. Being an academic isn't that bad: http://www.theguardian.com/higher-education-network/2016/jan/08/cheer-up-my-academic-colleagues-were-so-lucky-to-do-this-job
6. Why do academics drink so much? http://www.theguardian.com/higher-education-network/2016/jan/22/why-do-academics-drink-so-much Maybe it cuts down the mental cross-talk intelligent people are prone to?
7. Photo geolocation with convolutional neural networks: http://arxiv.org/abs/1602.05314
8. Several libraries for generating music with machine learning: http://www.datasciencecentral.com/profiles/blogs/using-machine-learning-to-generate-music 
9. Deep learning to recognise spoken Mandarin http://www.kdnuggets.com/2016/02/getting-deep-speech-work-mandarin-baidu.html 
10. Similarities between deep learning and Markov chains http://www.datasciencecentral.com/profiles/blogs/is-deep-learning-a-markov-chain-in-disguise 
11. Applying Deepmind AI to emergency room diagnosis https://thestack.com/world/2016/02/25/google-deepmind-applies-ai-to-healthcare-with-nhs-partnership/ 
12. An app using AI to identify dog breeds http://www.kdnuggets.com/2016/02/what-dog-breed-ai-fetch.html 
13. Top 5 skills to become a machine learning expert http://www.analyticbridge.com/profiles/blogs/what-is-machine-learning-top-5-skills-required-to-become-a

Evolving Systems, Volume 7, Number 1

1. Evolving Takagi–Sugeno model based on online Gustafson-Kessel algorithm and kernel recursive least square method
Author(s): Soroosh Shafieezadeh-Abadeh & Ahmad Kalhor
Pages: 1-14

2. Online feature extraction based on accelerated kernel principal component analysis for data stream
Author(s): Annie Anak Joseph, Takaomi Tokumoto & Seiichi Ozawa
Pages: 15-27

3. Real-time vessel behavior prediction
Author(s): Dimitrios Zissis, Elias K. Xidias & Dimitrios Lekkas
Pages: 29-40

4. An extended version of opportunity cost algorithm for communication decisions
Author(s): Hiba Abdelmoumène & Habiba Belleili
Pages: 41-60

5. Potential of evolving AR and ARX models in signal recovering
Author(s): Ahmad Kalhor
Pages: 61-72

Neural Networks Volume 75, Pages 1-196, March 2016

1. A graph-based N-body approximation with application to stochastic neighbor embedding  
Author(s): Eli Parviainen
Pages: 1-11

2. A divide-and-combine method for large scale nonparallel support vector machines  
Author(s): Yingjie Tian, Xuchan Ju, Yong Shi
Pages: 12-21

3. Centralized and decentralized global outer-synchronization of asymmetric recurrent time-varying neural network by data-sampling  
Pages: 22-31
Author(s): Wenlian Lu, Ren Zheng, Tianping Chen

4. Synchronized bifurcation and stability in a ring of diffusively coupled neurons with time delay  
Pages: 32-46
Author(s): Ling Wang, Hongyong Zhao, Jinde Cao

5. Matrix measure based dissipativity analysis for inertial delayed uncertain neural networks  
Pages: 47-55
Author(s): Zhengwen Tu, Jinde Cao, Tasawar Hayat

6. Improved system identification using artificial neural networks and analysis of individual differences in responses of an identified neuron  
Pages: 56-65
Author(s): Alicia Costalago Meruelo, David M. Simpson, Sandor M. Veres, Philip L. Newland

7. Subspace segmentation by dense block and sparse representation  
Pages: 66-76
Author(s): Kewei Tang, David B. Dunson, Zhixun Su, Risheng Liu, Jie Zhang, Jiangxin Dong

8. Neuroplasticity in dynamic neural networks comprised of neurons attached to adaptive base plate  
Pages: 77-83
Author(s): Abdolreza Joghataie, Mehrdad Shafiei Dizaji

9. Finite-time stabilization control for discontinuous time-delayed networks: New switching design  
Pages: 84-96
Author(s): Ling-Ling Zhang, Li-Hong Huang, Zuo-Wei Cai

10. Existence and global exponential stability of periodic solution of memristor-based BAM neural networks with time-varying delays  
Pages: 97-109
Author(s): Hongfei Li, Haijun Jiang, Cheng Hu

11. Multi-view L2-SVM and its multi-view core vector machine  
Pages: 110-125
Author(s): Chengquan Huang, Fu-lai Chung, Shitong Wang

12. Cross-validation of matching correlation analysis by resampling matching weights  
Pages: 126-140
Author(s): Hidetoshi Shimodaira

13. Neuro-genetic system for optimization of GMI samples sensitivity  
Pages: 141-149
Author(s): A.C.O. Pitta Botelho, M.M.B.R. Vellasco, C.R. Hall Barbosa, E. Costa Silva

14. Two fast and accurate heuristic RBF learning rules for data classification  
Author(s): Modjtaba Rouhani, Dawood S. Javan
Pages: 150-161

15. Exponential stabilization and synchronization for fuzzy model of memristive neural networks by periodically intermittent control  
Author(s): Shiju Yang, Chuandong Li, Tingwen Huang
Pages: 162-172

16. A theory of cerebellar cortex and adaptive motor control based on two types of universal function approximation capability  
Author(s): Masahiko Fujita
Pages: 173-196

Weekly Review 19 February 2016

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

1. "Illegally" sharing research articles-and of course it's Elsevier suing over it: http://www.sciencealert.com/this-woman-has-illegally-uploaded-millions-of-journal-articles-in-an-attempt-to-open-up-science
2. Seriously, Elsevier, just stop being such ridiculously greedy dicks! http://www.sciencealert.com/this-woman-has-illegally-uploaded-millions-of-journal-articles-in-an-attempt-to-open-up-science
3. Ensembles in machine learning: http://www.kdnuggets.com/2016/02/ensemble-methods-techniques-produce-improved-machine-learning.html I used ensembles of MLP years ago to model an ecoinformatics problem.
4. Elsevier is the Walter White of journal publishers: http://bigthink.com/neurobonkers/a-pirate-bay-for-science
5. High-impact journals are more likely to have fraudulent research published in them: http://journal.frontiersin.org/article/10.3389/fnhum.2013.00291/full
6. AI could drive global unemployment to 50 % http://www.theguardian.com/technology/2016/feb/13/artificial-intelligence-ai-unemployment-jobs-moshe-vardi
7. NSA uses ML to detect terrorists in Pakistan, but doesn't use an independent validation data set to test performance http://arstechnica.co.uk/security/2016/02/the-nsas-skynet-program-may-be-killing-thousands-of-innocent-people/ 
8. For smart people, the NSA seem to have made a pretty basic error with their machine learning http://arstechnica.co.uk/security/2016/02/the-nsas-skynet-program-may-be-killing-thousands-of-innocent-people/
9. Bayes' Theorem for computer scientists http://www.kdnuggets.com/2016/02/bayes-theorem-computer-scientists-explained.html
10. Naive Bayesian classifier explained http://www.datasciencecentral.com/profiles/blogs/the-naive-bayes-classifier-explained
11. Jobs that are threatened by AI: http://www.datasciencecentral.com/profiles/blogs/which-jobs-will-ai-artificial-intelligence-kill
12. Artificial intelligence X-prize: http://www.theverge.com/2016/2/17/11032004/x-prize-ai-contest-ibm-watson-ted-2020 
13. AWS machine learning service only offers one algorithm: http://www.kdnuggets.com/2016/02/amazon-machine-learning-nice-easy-simple.html
14. Add-on allows for fuzzy matching in Google spreadsheets http://www.datasciencecentral.com/profiles/blogs/google-spreadsheet-add-ons-for-data-analysis
15. Marvin Minsky's legacy http://spectrum.ieee.org/computing/software/marvin-minskys-legacy-of-students-and-ideas
16. 40 ways researchers achieved impact with their research http://www.fasttrackimpact.com/#!40-practical-tips-for-achieving-impact-told-to-us-by-researchers-and-those-they-worked-with-to-achieve-impact/hmlp3/569faeba0cf2bfd5cce91b1b

Weekly Review 12 February 2016

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

1. Financial robo-advisors: http://www.bloomberg.com/news/articles/2016-02-05/the-rich-are-already-using-robo-advisers-and-that-scares-banks
2. A low-power neural network chip for deep learning http://news.mit.edu/2016/neural-chip-artificial-intelligence-mobile-devices-0203 
3. Emphasis on metrics is harmful to universities http://www.theguardian.com/higher-education-network/2015/nov/27/our-obsession-with-metrics-turns-academics-into-data-drones
4. Four take-aways about Google's Tensor Flow http://www.infoworld.com/article/3003920/data-science/4-no-bull-takeaways-about-googles-machine-learning-project.html
5. The damage done by bad PhD supervisors http://www.theguardian.com/higher-education-network/2015/dec/11/bad-phd-supervisors-can-ruin-research-so-why-arent-they-accountable Bad post-doc supervisors can ruin careers, too. I've seen it happen.
6. AI for network security analysis http://www.net-security.org/secworld.php?id=19409
7. Some researchers have used my SECoS algorithm to do this: http://www.net-security.org/secworld.php?id=19409 
8. Version 1.22 of the ECoS Toolbox has been released: http://ecos.watts.net.nz/Software/Toolbox.html 
9. SECoS compiler tool now outputs Java and PHP code, in addition to C++, C# and Python http://ecos.watts.net.nz/Software/Toolbox.html
10. Added tools for analysing trained NECoS networks to the ECoS Toolbox version 1.22 http://ecos.watts.net.nz/Software/Toolbox.html
11. Implementing k-nearest neighbor algorithm using Python: http://blog.cambridgecoding.com/2016/01/16/machine-learning-under-the-hood-writing-your-own-k-nearest-neighbour-algorithm/
12. Using email effectively https://www.insidehighered.com/advice/2016/02/05/how-use-email-more-effectively-essay I try to have an empty (work) inbox when I go home in the evening
13. Grouping coyote, wolf howls into "dialects" using machine learning http://motherboard.vice.com/read/wolves-have-different-howling-dialects-machine-learning-finds
14. Canine howl dialects, paper here http://www.sciencedirect.com/science/article/pii/S0376635716300067
15. Common data visualisation mistakes: http://www.kdnuggets.com/2016/02/common-data-visualization-mistakes.html
16. List of 34 machine learning resources and articles: http://www.datasciencecentral.com/profiles/blogs/34-external-machine-learning-resources-and-related-articles
17. Machine learning in dating websites: http://www.kdnuggets.com/2016/02/does-machine-learning-allow-opposites-attract.html
18.  Image editing with Python: http://motherboard.vice.com/en_au/read/hack-this-edit-an-image-with-python 
19. Artificial intelligence and sarcasm: https://thestack.com/cloud/2016/02/11/why-sarcasm-is-such-a-problem-in-artificial-intelligence/
20. Predicting cancer survival with machine learning: http://www.infoq.com/articles/health-informatics-apache-spark-machine-learning 
21. A Gentle Guide to Machine Learning: https://blog.monkeylearn.com/a-gentle-guide-to-machine-learning/ 
22. Time-Series prediction with Python: http://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes-python/ 
23. Women are still badly under-represented in tech https://medium.com/@lkr/i-m-a-woman-in-tech-but-even-i-didn-t-get-it-until-this-week-350cf8b62c46#.8bdgh633h I've increased the number of women teaching in my department.
24. Four myths about using social media as a researcher: http://www.fasttrackimpact.com/#!The-four-greatest-myths-about-using-social-media-as-a-researcher/hmlp3/56a22b2f0cf2009838b71c56

Weekly Review 5 February 2016

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

1. A neural network based on plastic memristors: http://phys.org/news/2016-01-scientists-neural-network-plastic-memristors.html
2. Is deep learning over-hyped? http://www.kdnuggets.com/2016/01/deep-learning-overhyped.html … My opinion: yes!
3. "Correlation does not imply causation" http://www.kdnuggets.com/2016/02/data-scientists-keep-forgetting-one-rule.html … Right up there with "Sharks don't like cold water"
4. What is Business Intelligence? http://www.datasciencecentral.com/profiles/blogs/what-is-business-intelligence … I think most BI applications need some kind of data mining
5. Importance of explaining models: http://www.kdnuggets.com/2016/02/peering-into-black-box.html … Motivation for ANN fuzzy rule extraction, like in my thesis https://ourarchive.otago.ac.nz/handle/10523/1489 
6. Future of robotics: http://www.techrepublic.com/article/6-ways-the-robot-revolution-will-transform-the-future-of-work/ … - moving the processing to the cloud will be a big shift

IEEE Transactions on Fuzzy Systems, Volume 24, Number 1, February 2016

1. Representing Uncertainty With Information Sets
Author(s): Aggarwal, M.; Hanmandlu, M.
Page(s): 1 - 15

2. Fuzzy Approximation-Based Adaptive Backstepping Optimal Control for a Class of Nonlinear Discrete-Time Systems With Dead-Zone
Author(s): Liu, Y.; Gao, Y.; Tong, S.; Li, Y.
Page(s): 16 - 28

3. Probabilistic Variable Precision Fuzzy Rough Sets
Author(s): Aggarwal, M.
Page(s): 29 - 39

4. A Survey of Fuzzy Systems Software: Taxonomy, Current Research Trends, and Prospects
Author(s): Alcala-Fdez, J.; Alonso, J.M.
Page(s): 40 - 56

5. Adaptive Fuzzy Control of Multilateral Asymmetric Teleoperation for Coordinated Multiple Mobile Manipulators
Author(s): Zhai, D.; Xia, Y.
Page(s): 57 - 70

6. Learning of Fuzzy Cognitive Maps With Varying Densities Using A Multiobjective Evolutionary Algorithm
Author(s): Chi, Y.; Liu, J.
Page(s): 71 - 81

7. The Multiplicative Consistency Index of Hesitant Fuzzy Preference Relation
Author(s): Liu, H.; Xu, Z.; Liao, H.
Page(s): 82 - 93

8. A New Sum-of-Squares Design Framework for Robust Control of Polynomial Fuzzy Systems With Uncertainties
Author(s): Tanaka, K.; Tanaka, M.; Chen, Y.; Wang, H.O.
Page(s): 94 - 110

9. Min-Max Programming Problem Subject to Addition-Min Fuzzy Relation Inequalities
Author(s): Yang, X.; Zhou, X.; Cao, B.
Page(s): 111 - 119

10. Design of Fuzzy Cognitive Maps for Modeling Time Series
Author(s): Pedrycz, W.; Jastrzebska, A.; Homenda, W.
Page(s): 120 - 130

11. A Categorical Isomorphism Between Injective Stratified Fuzzy T_{bm 0} Spaces and Fuzzy Continuous Lattices
Author(s): Yao, W.
Page(s): 131 - 139

12. Adaptive Fuzzy Control for a Class of Stochastic Pure-Feedback Nonlinear Systems With Unknown Hysteresis
Author(s): Wang, F.; Liu, Z.; Zhang, Y.; Chen, C.L.P.
Page(s): 140 - 152

13. Recurrent Fuzzy Neural Cerebellar Model Articulation Network Fault-Tolerant Control of Six-Phase Permanent Magnet Synchronous Motor Position Servo Drive
Author(s): Lin, F.; Sun, I.; Yang, K.; Chang, J.
Page(s): 153 - 167

14. OWA Generation Function and Some Adjustment Methods for OWA Operators With Application
Author(s): Jin, L.; Qian, G.
Page(s): 168 - 178

15. A Historical Account of Types of Fuzzy Sets and Their Relationships
Author(s): Bustince, H.; Barrenechea, E.; Pagola, M.; Fernandez, J.; Xu, Z.; Bedregal, B.; Montero, J.; Hagras, H.; Herrera, F.; De Baets, B.
Page(s): 179 - 194

16. Fuzzy Membership Descriptors for Images
Author(s): Kumar, M.; Stoll, N.; Thurow, K.; Stoll, R.
Page(s): 195 - 207

17. Robust Fuzzy H_{\infty } Estimator-Based Stabilization Design for Nonlinear Parabolic Partial Differential Systems With Different Boundary Conditions
Author(s): Ho, S.; Chen, B.
Page(s): 208 - 222

18. Fuzzy Adaptive Output Feedback Fault-Tolerant Tracking Control of a Class of Uncertain Nonlinear Systems With Nonaffine Nonlinear Faults
Author(s): Li, Y.; Yang, G.
Page(s): 223 - 234

19. Control of Switched Nonlinear Systems via T–S Fuzzy Modeling
Author(s): Zhao, X.; Yin, Y.; Zhang, L.; Yang, H.
Page(s): 235 - 241

20. Ambiguity-Based Multiclass Active Learning
Author(s): Wang, R.; Chow, C.; Kwong, S.
Page(s): 242 - 248

21. Comments on "Interval Type-2 Fuzzy Sets are Generalization of Interval-Valued Fuzzy Sets: Towards a Wide View on Their Relationship"
Author(s): Mendel, J.M.; Hagras, H.; Bustince, H.; Herrera, F.
Page(s): 249 - 250

IEEE Transactions on Neural Networks and Learning Systems, Volume 27, Issue 2, February 2016

1. Guest Editorial Special Issue on Neurodynamic Systems for Optimization and Applications
Author(s): Zhigang Zeng; Andrzej Cichocki; Long Cheng; Youshen Xia; Xiaolin Hu
Page(s): 210 - 213

2. A Bi-Projection Neural Network for Solving Constrained Quadratic Optimization Problems
Author(s): Youshen Xia; Jun Wang
Page(s): 214 - 224

3. Taylor O(h^{3}) Discretization of ZNN Models for Dynamic Equality-Constrained Quadratic Programming With Application to Manipulators
Author(s): Bolin Liao; Yunong Zhang; Long Jin
Page(s): 225 - 237

4. A Neurodynamic Approach for Real-Time Scheduling via Maximizing Piecewise Linear Utility
Author(s): Zhishan Guo; Sanjoy K. Baruah
Page(s): 238 - 248

5. Optimization in Quaternion Dynamic Systems: Gradient, Hessian, and Learning Algorithms
Author(s): Dongpo Xu; Yili Xia; Danilo P. Mandic
Page(s): 249 - 261

6. A New Continuous-Time Equality-Constrained Optimization to Avoid Singularity
Author(s): Quan Quan; Kai-Yuan Cai
Page(s): 262 - 272

7. L_{1} -Norm Low-Rank Matrix Decomposition by Neural Networks and Mollifiers
Author(s): Yiguang Liu; Songfan Yang; Pengfei Wu; Chunguang Li; Menglong Yang
Page(s): 273 - 283

8. Zeroth-Order Method for Distributed Optimization With Approximate Projections
Author(s): Deming Yuan; Daniel W. C. Ho; Shengyuan Xu
Page(s): 284 - 294

9. Nonsmooth Neural Network for Convex Time-Dependent Constraint Satisfaction Problems
Author(s): Mauro Di Marco; Mauro Forti; Paolo Nistri; Luca Pancioni
Page(s): 295 - 307

10. A Generalized Hopfield Network for Nonsmooth Constrained Convex Optimization: Lie Derivative Approach
Author(s): Chaojie Li; Xinghuo Yu; Tingwen Huang; Guo Chen; Xing He
Page(s): 308 - 321

11. Optimal Formation of Multirobot Systems Based on a Recurrent Neural Network
Author(s): Yunpeng Wang; Long Cheng; Zeng-Guang Hou; Junzhi Yu; Min Tan
Page(s): 322 - 333

12. The Kernel Adaptive Autoregressive-Moving-Average Algorithm
Author(s): Kan Li; José C. Príncipe
Page(s): 334 - 346

13. Brain Dynamics in Predicting Driving Fatigue Using a Recurrent Self-Evolving Fuzzy Neural Network
Author(s): Yu-Ting Liu; Yang-Yin Lin; Shang-Lin Wu; Chun-Hsiang Chuang; Chin-Teng Lin
Page(s): 347 - 360

14. Bayesian Recurrent Neural Network for Language Modeling
Author(s): Jen-Tzung Chien; Yuan-Chu Ku
Page(s): 361 - 374

15. Twin Neurons for Efficient Real-World Data Distribution in Networks of Neural Cliques: Applications in Power Management in Electronic Circuits
Author(s): Bartosz Boguslawski; Vincent Gripon; Fabrice Seguin; Frédéric Heitzmann
Page(s): 375 - 387

16. Recurrent-Neural-Network-Based Multivariable Adaptive Control for a Class of Nonlinear Dynamic Systems With Time-Varying Delay
Author(s): Chih-Lyang Hwang; Chau Jan
Page(s): 388 - 401

17. Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network
Author(s): Hong-Gui Han; Lu Zhang; Ying Hou; Jun-Fei Qiao
Page(s): 402 - 415

18. A Combined Adaptive Neural Network and Nonlinear Model Predictive Control for Multirate Networked Industrial Process Control
Author(s): Tong Wang; Huijun Gao; Jianbin Qiu
Page(s): 416 - 425

19. Optimal Communication Network-Based H_\infty Quantized Control With Packet Dropouts for a Class of Discrete-Time Neural Networks With Distributed Time Delay
Author(s): Qing-Long Han; Yurong Liu; Fuwen Yang
Page(s): 426 - 434

20. QoS Differential Scheduling in Cognitive-Radio-Based Smart Grid Networks: An Adaptive Dynamic Programming Approach
Author(s): Rong Yu; Weifeng Zhong; Shengli Xie; Yan Zhang; Yun Zhang
Page(s): 435 - 443

21. Data-Driven Zero-Sum Neuro-Optimal Control for a Class of Continuous-Time Unknown Nonlinear Systems With Disturbance Using ADP
Author(s): Qinglai Wei; Ruizhuo Song; Pengfei Yan
Page(s): 444 - 458

22. Synchronization and State Estimation of a Class of Hierarchical Hybrid Neural Networks With Time-Varying Delays
Author(s): Lixian Zhang; Yanzheng Zhu; Wei Xing Zheng
Page(s): 459 - 470

23. A Switching Approach to Designing Finite-Time Synchronization Controllers of Coupled Neural Networks
Author(s): Xiaoyang Liu; Housheng Su; Michael Z. Q. Chen
Page(s): 471 - 482

24. Stability of Analytic Neural Networks With Event-Triggered Synaptic Feedbacks
Author(s): Ren Zheng; Xinlei Yi; Wenlian Lu; Tianping Chen
Page(s): 483 - 494

Weekly Review 29 January 2016

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

1. 7 trends in AI for 2016: http://www.techrepublic.com/article/7-trends-for-artificial-intelligence-in-2016-like-2015-on-steroids/
2. Learn deep learning from Google using TensorFlow http://googleresearch.blogspot.co.nz/2016/01/teach-yourself-deep-learning-with.html 
3. Learning to code neural networks http://www.kdnuggets.com/2016/01/learning-to-code-neural-networks.html 
4. The parts for building an AI assistant are available online, mostly for free, according to this article: http://www.techrepublic.com/article/ai-helpers-arent-just-for-facebooks-zuckerberg-heres-how-to-build-your-own/
5. Microsoft open sources it's deep learning toolkit: http://blogs.microsoft.com/next/2016/01/25/microsoft-releases-cntk-its-open-source-deep-learning-toolkit-on-github/
6. Damn, Marvin Minsky died: http://www.nytimes.com/2016/01/26/business/marvin-minsky-pioneer-in-artificial-intelligence-dies-at-88.html?_r=0
7. Deep Feelings on Deep Learning: http://www.kdnuggets.com/2016/01/deep-feelings-deep-learning.html … it all seems a bit like magic
8. 7 mistakes in data science, apply to computational intelligence and data mining too: http://www.kdnuggets.com/2016/01/7-common-data-science-mistakes.html
9. Video courses in deep learning and machine learning: http://www.datasciencecentral.com/profiles/blogs/step-by-step-video-courses-for-deep-learning-and-machine-learning
10. Implementing k nearest neighbours in Python: http://www.kdnuggets.com/2016/01/implementing-your-own-knn-using-python.html
11. Random thoughts on machine learning and AI: http://www.datasciencecentral.com/profiles/blogs/big-data-analytics-data-science-machine-learning-random-insights
12. Overview of recurrent neural networks: http://spectrum.ieee.org/computing/software/the-neural-network-that-remembers
13. "Impostor Syndrome" among academics: http://www.nature.com/naturejobs/science/articles/10.1038/nj7587-555a?WT.mc_id=TWT_NatureNews 
14. Deep learning with Spark and TensorFlow: http://www.kdnuggets.com/2016/01/deep-learning-spark-tensorflow.html
15. 12 things not to do when applying for academic jobs https://www.insidehighered.com/advice/2016/01/29/common-mistakes-academic-job-seekers-make-essay … I'd add don't go in to an interview with a bad attitude
16. Doesn't matter how good you are, if you have a bad or arrogant attitude in the interview, you won't get the job https://www.insidehighered.com/advice/2016/01/29/common-mistakes-academic-job-seekers-make-essay
17. Deep learning neural network with human-level performance playing Go http://spectrum.ieee.org/tech-talk/computing/software/monster-machine-defeats-prominent-pro-player?utm_campaign=Weekly%20Notification-%20IEEE%20Spectrum%20Tech%20Alert&utm_source=boomtrain&utm_medium=email&utm_term=555a972628fbca1d260da1ba&utm_content=Monster%20Machine%20Cracks%20The%20Game%20Of%20Go&bt_alias=eyJ1c2VySWQiOiIzMGJmOTkxMy1mOThiLTQ5YTgtOTIzMy1iYmMzODk4ZDcxODcifQ%3D%3D

IEEE Transactions on Evolutionary Computation, Volume 20, Number 1, February 2016

1. Memetic Music Composition
Author(s): Munoz, E. ; Cadenas, J.M. ; Ong, Y.S. ; Acampora, G.
Page(s): 1 - 15

2. A New Dominance Relation-Based Evolutionary Algorithm for Many-Objective Optimization
Author(s): Yuan, Y. ; Xu, H. ; Wang, B. ; Yao, X.
Page(s): 16 - 37
 
3. A Genetic Bankrupt Ratio Analysis Tool Using a Genetic Algorithm to Identify Influencing Financial Ratios
Author(s): Lakshmi, T.M. ; Martin, A. ; Venkatesan, V.P.
Page(s): 38 - 51

4. Are All the Subproblems Equally Important? Resource Allocation in Decomposition-Based Multiobjective Evolutionary Algorithms
Author(s): Zhou, A. ; Zhang, Q.
Page(s): 52 - 64

5. Self-Learning Gene Expression Programming
Author(s): Zhong, J. ; Ong, Y. ; Cai, W.
Page(s): 65 - 80

6. Recursion-Based Biases in Stochastic Grammar Model Genetic Programming
Author(s): Kim, K. ; McKay, R.I.B. ; Hoai, N.X.
Page(s): 81 - 95

7. Estimation of the Distribution Algorithm With a Stochastic Local Search for Uncertain Capacitated Arc Routing Problems
Author(s): Wang, J. ; Tang, K. ; Lozano, J.A. ; Yao, X.
Page(s): 96 - 109

8. Automated Design of Production Scheduling Heuristics: A Review
Author(s): Branke, J. ; Nguyen, S. ; Pickardt, C.W. ; Zhang, M.
Page(s): 110 - 124
 
9. Memetic Viability Evolution for Constrained Optimization
Author(s): Maesani, A. ; Iacca, G. ; Floreano, D.
Page(s): 125 - 144

10. Many-Objective Evolutionary Algorithm: Objective Space Reduction and Diversity Improvement
Author(s): He, Z. ; Yen, G.G.
Page(s): 145 - 160

Weekly Review 22 January 2016

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

1. Step-by-step tutorial for MS Azure ML environment: http://www.kdnuggets.com/2016/01/guide-azure-machine-learning-studio.html
2. ANN + GA foe FOREX trading, via @gcosma1 http://forexpost.org/determinants-of-exchange-rates/neural-network-genetic-algorithm-in-forex-trading/ … Fine as a tutorial, can think of several ways of doing this better...
3. How not to write about science: https://theconversation.com/how-not-to-write-about-science-52202 
4. The future of AI: http://www.datasciencecentral.com/profiles/blogs/the-future-of-artificial-intelligence-is-here - bad news for low-skilled workers
5. Tutorials on the Python machine learning toolkit scikit-learn http://www.kdnuggets.com/2016/01/scikit-learn-tutorials-introduction-classifiers.html 
6. Brief overview of fuzzy matching algorithms http://www.datasciencecentral.com/profiles/blogs/fuzzy-matching-algorithms-to-help-data-scientists-match-similar 
7. The unreasonable reputation of neural networks: http://thinkingmachines.mit.edu/blog/unreasonable-reputation-neural-networks 
8. AI will soon be putting middle-skilled workers out of a job - http://www.techrepublic.com/article/more-money-for-the-rich-fewer-jobs-for-everyone-else-the-price-of-the-coming-ai-revolution/?tag=nl.e101&s_cid=e101&ttag=e101&ftag=TRE684d531&utm_content=buffer8e45f&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer - bad news for clerks, sales, and support staff

Weekly Review 15 January 2016

Been away on my summer vacation the last few weeks, so not been able to blog much. Some interesting links that I Tweeted about since the last review a month ago:

1. Using machine learning in an NFL confidence pool http://theinstitute.ieee.org/ieee-roundup/opinions/ieee-roundup/how-to-use-machine-learning-to-beat-your-friends-in-an-nfl-confidence-pool
2. How eBay enterprise uses machine learning to detect fraudsters http://www.datanami.com/2015/12/21/tis-the-season-to-hunt-fraudsters-with-big-data/
3. Free data mining software:http://www.datasciencecentral.com/profiles/blogs/4-packages-for-data-analysis - R and Weka are there of course, hadn't heard of Orange before.
4. List of some real-world machine learning data sets: http://www.kdnuggets.com/2015/12/tour-real-world-machine-learning-problems.html The academic ones are all really well known (that is, old)
5. datasciencecentral.com/profiles/blogs/internet-of-things-selected-articles The Internet of Things is become really important in computational intelligence - so much data to model!
6. 15 words you shouldn't use if you want to sound smarter-I'd be happy if people stopped confusing "infer" and "imply" http://mashable.com/2015/05/03/words-eliminate-vocabulary/?utm_cid=p-disp-fb%23lHxBPJzBTRqW#kMQHWVmxvGqw
7. Big Data in agriculture - CI has a big role to play in agro/ecol data processing as well: http://www.datasciencecentral.com/profiles/blogs/big-data-in-agriculture-ddw2-1
8. Getting your paper noticed: http://blogs.nature.com/naturejobs/2016/01/06/five-top-tips-for-getting-your-paper-noticed … One of the five points is using social media: http://www.fasttrackimpact.com/#!Create-a-social-media-strategy-for-your-research-that-delivers-real-impact/hmlp3/564df9090cf20af044b924ca 
9. Bias is a potential problem in all data sets, not just Big Data: http://www.datanami.com/2016/01/08/beware-of-bias-in-big-data-feds-warn/ 
10. Applying ANN to proteomics: https://agenda.weforum.org/2015/12/how-machine-learning-helps-biologists-crack-lifes-secrets/ Something I was looking at about 15 years ago...
11. 5 papers on Deep Learning explained: http://www.kdnuggets.com/2016/01/more-arxiv-deep-learning-papers-explained.html 
12. Yahoo releases a 1.5 TB data set: https://thestack.com/cloud/2016/01/14/yahoo-news-dataset-artificial-intelligence-news-feed/
13. The differences between machine learning, machine intelligence, deep learning and AI: http://www.kdnuggets.com/2016/01/what-is-machine-intelligence-ml-deep-learning-ai.html
14. Finding whales in ocean photographs, a step-by-step tutorial: http://www.datasciencecentral.com/profiles/blogs/finding-whales-in-ocean-water-edge-detection-blob-processing-and
15. Deep learning projects on GitHub: http://www.kdnuggets.com/2016/01/top-10-deep-learning-github.html
16. Having kids is a disadvantage in a research career: http://www.sciencedaily.com/releases/2016/01/160111092607.htm - I'd rather have my daughter than a high-powered career
17. AI is set to wipe out a lot of casual and low-skilled jobs: http://www.spectator.co.uk/2016/01/i-robot-you-unemployed/
18. Machine Intelligence in the Real World-how companies go to market: http://techcrunch.com/2015/11/26/machine-intelligence-in-the-real-world/
19. Uploading a paper to http://academia.org  gives more citations over time: https://www.academia.edu/12297791/Open_Access_Meets_Discoverability_Citations_to_Articles_Posted_to_Academia.edu Can't cite a paper that can't be found

IEEE Transactions on Neural Networks and Learning Systems, Volume 27, Issue 1, January 2016

1. Data-Mining-Based Intelligent Differential Relaying for Transmission Lines Including UPFC and Wind Farms
Authors: Manas Kumar Jena; Subhransu Ranjan Samantaray
Page(s): 8 - 17

2. Adaptive Position/Attitude Tracking Control of Aerial Robot With Unknown Inertial Matrix Based on a New Robust Neural Identifier
Authors: Guanyu Lai; Zhi Liu; Yun Zhang
Page(s): 18 - 31

3. Shape-Constrained Sparse and Low-Rank Decomposition for Auroral Substorm Detection
Authors: Xi Yang; Xinbo Gao; Dacheng Tao
Page(s): 32 - 46

4. Learning Subspace-Based RBFNN Using Coevolutionary Algorithm for Complex Classification Tasks
Authors: Jin Tian; Minqiang Li; Fuzan Chen
Page(s): 47 - 61

5. A Bias and Variance Analysis for Multistep-Ahead Time Series Forecasting
Authors: Souhaib Ben Taieb; Amir F. Atiya
Page(s): 62 - 76

6. Event-Triggered Generalized Dissipativity Filtering for Neural Networks With Time-Varying Delays
Authors: Jia Wang; Xian-Ming Zhang; Qing-Long Han
Page(s): 77 - 88

7. Observer-Based Adaptive Neural Network Control for Nonlinear Systems in Nonstrict-Feedback Form
Authors: Bing Chen; Huaguang Zhang; Chong Lin
Page(s): 89 - 98

8. Embedded Hardware-Efficient Real-Time Classification With Cascade Support Vector Machines
Authors: Christos Kyrkou; Christos-Savvas Bouganis; Theocharis Theocharides; Marios M. Polycarpou
Page(s): 99 - 112

9. A Further Study on Mining DNA Motifs Using Fuzzy Self-Organizing Maps
Authors: Sarwar Tapan; Dianhui Wang
Page(s): 113 - 124

10. Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks
Authors: Maoguo Gong; Jiaojiao Zhao; Jia Liu; Qiguang Miao; Licheng Jiao
Page(s): 125 - 138

11. A Unified Approach to Adaptive Neural Control for Nonlinear Discrete-Time Systems With Nonlinear Dead-Zone Input
Authors: Yan-Jun Liu; Ying Gao; Shaocheng Tong; C. L. Philip Chen
Page(s): 139 - 150

12. Adaptive Neural Network-Based Event-Triggered Control of Single-Input Single-Output Nonlinear Discrete-Time Systems
Authors: Avimanyu Sahoo; Hao Xu; Sarangapani Jagannathan
Page(s): 151 - 164

13. Adaptive Actor–Critic Design-Based Integral Sliding-Mode Control for Partially Unknown Nonlinear Systems With Input Disturbances
Authors: Quan-Yong Fan; Guang-Hong Yang
Page(s): 165 - 177

14. Convergence Rate for Discrete-Time Multiagent Systems With Time-Varying Delays and General Coupling Coefficients
Authors: Yao Chen; Daniel W. C. Ho; Jinhu Lü; Zongli Lin
Page(s): 178 - 189

15. Exponential Synchronization of Coupled Stochastic Memristor-Based Neural Networks With Time-Varying Probabilistic Delay Coupling and Impulsive Delay
Authors: Haibo Bao; Ju H. Park; Jinde Cao
Page(s): 190 - 201

16. Mirror Inverse Operations in Linear Nearest Neighbors Using Dynamic Learning Algorithm
Authors: Rudrayya C. Garigipati; Preethika Kumar
Page(s): 202 - 207