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