IEEE Transactions on Fuzzy Systems, Volume 25, Issue 3, June 2017

1. A Profit Maximizing Solid Transportation Model Under a Rough Interval Approach
Author(s): Amrit- Das, Uttam Kumar Bera, and Manoranjan Maiti
Pages: 485-498

2. Design of State Feedback Adaptive Fuzzy Controllers for Second-Order Systems Using a Frequency Stability Criterion
Author: Krzysztof Wiktorowic
Pages: 499-510

3. Dynamic Output-Feedback Dissipative Control for T–S Fuzzy Systems With Time-Varying InputDelay and Output Constraints
Author(s): Hyun Duck Choi, Choon Ki Ahn, Peng Shi, Ligang Wu and Myo Taeg Lim
Pages: 511-526

4. Adaptive Predefined Performance Control for MIMO Systems With Unknown Direction via Generalized Fuzzy Hyperbolic Model
Author(s): Lei Liu, Zhanshan Wang, Zhanjun Huang, and Huaguang Zhang
Pages: 527-542

5. Revisiting Fuzzy Set and Fuzzy Arithmetic Operators and Constructing New Operators in the Land of Probabilistic Linguistic Computing
Author: Shing-Chung Ngan
Pages: 543-555

6. Asymptotic Fuzzy Tracking Control for a Class of Stochastic Strict-Feedback Systems
Author(s): Ci Chen, Zhi Liu, Yun Zhang, C. L. Philip Chen,and Shengli Xie
Pages: 556-568

7. Approaches to T–S Fuzzy-Affine-Model-Based Reliable Output Feedback Control for Nonlinear Itˆo Stochastic Systems
Author(s): Yanling Wei, Jianbin Qiu, Hak-Keung Lam, and Ligang Wu
Pages: 569-583

8. Pixel Modeling Using Histograms Based on Fuzzy Partitions for Dynamic Background Subtraction
Author(s): Zhi Zeng, Jianyuan Jia, Dalin Yu, Yilong Chen, and Zhaofei Zhu
Pages: 584-593

9. Varying Spread Fuzzy Regression for Affective Quality Estimation
Author(s): Kit Yan Chan and Ulrich Engelke
Pages: 594-613

10. Ranking of Multidimensional Uncertain Information Based on Metrics on the Fuzzy EllipsoidNumber Space
Author(s): Guixiang Wang and Yun Li
Pages: 614-626

11. The Spatial Disaggregation Problem: Simulating Reasoning Using a Fuzzy Inference System
Author: J¨org Verstraete
Pages: 627-641

12. Adaptive Fuzzy Backstepping Tracking Control for Strict-Feedback Systems With Input Delay
Author(s): Hongyi Li, Lijie Wang, Haiping Du, and Abdesselem Boulkroune
Pages: 642-652

13. Dynamic Output Feedback-Predictive Control of a Takagi–Sugeno Model With Bounded Disturbance
Author(s): Baocang Ding and Hongguang Pan
Pages: 653-667

14. Command-Filtered-Based Fuzzy Adaptive Control Design for MIMO-Switched Nonstrict-Feedback Nonlinear Systems
Author(s): Yongming Li and Shaocheng Tong
Pages: 668-681

15. Type-2 Fuzzy Alpha-Cuts
Author(s): Hussam Hamrawi, Simon Coupland, and Robert John
Pages: 682-692

16. A New Look at Type-2 Fuzzy Sets and Type-2 Fuzzy Logic Systems
Author: Li-Xin Wang
Pages: 693-706

17. LMI-Based Stability Analysis for Piecewise Multi-affine Systems
Author(s): Anh-Tu Nguyen, Michio Sugeno, Victor Campos, and Michel Dambrine
Pages: 707-714

18. An Extended Type-Reduction Method for General Type-2 Fuzzy Sets
Author(s): Bing-Kun Xie and Shie-Jue Lee
Pages: 715-724

19. Critique of "A New Look at Type-2 Fuzzy Sets and Type-2 Fuzzy Logic Systems"
Author(s): Jerry M. Mendel and Dongrui Wu
Pages: 725-727

Weekly Review 19 June 2017

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

1. An Ask Slashdot discussion on jobs in AI: https://ask.slashdot.org/story/17/06/09/1954230/ask-slashdot-what-types-of-jobs-are-opening-up-in-the-new-field-of-ai
2. A script-writing AI producing lines for David Hasselhoff: https://arstechnica.com/the-multiverse/2017/04/an-ai-wrote-all-of-david-hasselhoffs-lines-in-this-demented-short-film/
3. Creating a classifier in SKLearn: http://www.datasciencecentral.com/profiles/blogs/creating-your-first-machine-learning-classifier-model-in-sklearn 
4. Five things AI (neural networks) can do better than people: https://www.datanami.com/2017/06/08/5-things-ai-better/
5. Using machine learning to detect identity thieves, from the way they move their mouse: https://qz.com/1003221/identity-theft-can-be-thwarted-by-artificial-intelligence-analysis-of-a-users-mouse-movements/
6. Combining quantum computing and machine learning: http://spectrum.ieee.org/tech-talk/robotics/artificial-intelligence/kickstarting-the-quantum-startup-a-hybrid-of-quantum-computing-and-machine-learning-is-spawning-new-ventures
7. Predicting suicide risk with machine learning: https://qz.com/1001968/artificial-intelligence-can-now-predict-suicide-with-remarkable-accuracy/
8. A neural network based negotiating bot: https://qz.com/1004070/facebook-fb-built-an-ai-system-that-learned-to-lie-to-get-what-it-wants/
9. Automating comment moderation with machine learning: https://www.recode.net/2017/6/13/15789178/new-york-times-expanding-comments-artificial-intelligence-google
10. A deep learning powered four armed robot that composes marimba music: http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/four-armed-marimba-robot-uses-deep-learning-to-compose-its-own-music
11. Yes, prospective employers will check your social media presences: https://www.seek.co.nz/career-advice/recruiters-reveal-top-3-social-media-fails
12. A collection of 150 intelligent agents has beaten the game Ms Pacman: https://techcrunch.com/2017/06/15/microsofts-ai-beats-ms-pac-man/
13. 8 techniques for data mining: https://www.datanami.com/2017/06/14/8-concrete-data-mining-techniques-will-deliver-best-results/
14. Microsoft is using deep neural networks to turn photos into art: https://techcrunch.com/2017/06/15/microsoft-pix-can-now-turn-your-iphone-photos-into-art-thanks-to-a-i/
15. Applying AI to call centres: http://www.techproresearch.com/article/how-artificial-intelligence-is-taking-call-centers-to-the-next-level/
16. AI is predicted to create 800,000 jobs by 2021: http://www.techrepublic.com/article/can-ai-really-create-800000-jobs-by-2021-this-report-says-yes/

IEEE Transactions on Cognitive and Developmental Systems, Volume 9, Number 2, June 2017

1. Yielding Self-Perception in Robots Through Sensorimotor Contingencies
Author(s): P. Lanillos, E. Dean-Leon and G. Cheng
Pages: 100 - 112

2. Online Multimodal Ensemble Learning Using Self-Learned Sensorimotor Representations
Author(s): M. Zambelli and Y. Demirisy
Pages: 113 - 126

3. Perception of Localized Features During Robotic Sensorimotor Development
Author(s): A. Giagkos, D. Lewkowicz, P. Shaw, S. Kumar, M. Lee and Q. Shen
Pages: 113 - 126

4. Building a Sensorimotor Representation of a Naive Agent’s Tactile Space
Author(s): V. Marcel, S. Argentieri and B. Gas
Pages: 141 - 152

5. A Multimodal Model of Object Deformation Under Robotic Pushing
Author(s): V. E. Arriola-Rios and J. L. Wyatt
Pages: 153 - 169

6. Analysis of Cognitive Dissonance and Overload through Ability-Demand Gap Models
Author(s): G. Hossain and M. Yeasin
Pages: 170 - 182

7. Constructing a Language From Scratch: Combining Bottom–Up and Top–Down Learning Processes in a Computational Model of Language Acquisition
Author(s): J. Gaspers, P. Cimiano, K. Rohlfing and B. Wrede
Pages: 183 - 196

8. Behavior-Based SSVEP Hierarchical Architecture for Telepresence Control of Humanoid Robot to Achieve Full-Body Movement
Author(s): J. Zhao, W. Li, X. Mao, H. Hu, L. Niu and G. Chen
Pages: 197 - 209

IEEE Transactions on Fuzzy System, Volume 25, Issue 3, June 2017

1. A Profit Maximizing Solid Transportation Model Under a Rough Interval Approach
Author(s): A. Das, U. Kumar Bera and M. Maiti
Pages: 485-498

2. Design of State Feedback Adaptive Fuzzy Controllers for Second-Order Systems Using a Frequency Stability Criterion
Author(s): K. Wiktorowicz
Pages: 499-510

3. Dynamic Output-Feedback Dissipative Control for T–S Fuzzy Systems With Time-Varying Input Delay and Output Constraints
Author(s): H. D. Choi, C. K. Ahn, P. Shi, L. Wu and M. T. Lim
Pages: 511-526

4. Adaptive Predefined Performance Control for MIMO Systems With Unknown Direction via Generalized Fuzzy Hyperbolic Model
Author(s): L. Liu, Z. Wang, Z. Huang and H. Zhang
Pages: 527-542

5. Revisiting Fuzzy Set and Fuzzy Arithmetic Operators and Constructing New Operators in the Land of Probabilistic Linguistic Computing
Author(s): S. C. Ngan
Pages: 543-555

6. Asymptotic Fuzzy Tracking Control for a Class of Stochastic Strict-Feedback Systems
Author(s): C. Chen, Z. Liu, Y. Zhang, C. L. P. Chen and S. Xie
Pages: 556-568

7. Approaches to T–S Fuzzy-Affine-Model-Based Reliable Output Feedback Control for Nonlinear Itô Stochastic Systems
Author(s): Y. Wei, J. Qiu, H. K. Lam and L. Wu
Pages: 569-583

8. Pixel Modeling Using Histograms Based on Fuzzy Partitions for Dynamic Background Subtraction
Author(s): Z. Zeng, J. Jia, D. Yu, Y. Chen and Z. Zhu
Pages: 584-593

9. Varying Spread Fuzzy Regression for Affective Quality Estimation
Author(s): K. Y. Chan and U. Engelke
Pages: 594-613

10. Ranking of Multidimensional Uncertain Information Based on Metrics on the Fuzzy Ellipsoid Number Space
Author(s): G. Wang and Y. Li
Pages: 614-626

11. The Spatial Disaggregation Problem: Simulating Reasoning Using a Fuzzy Inference System
Author(s): J. Verstraete
Pages: 627-641

12. Adaptive Fuzzy Backstepping Tracking Control for Strict-Feedback Systems With Input Delay
Author(s): H. Li, L. Wang, H. Du and A. Boulkroune
Pages: 642-652

13. Dynamic Output Feedback-Predictive Control of a Takagi–Sugeno Model With Bounded Disturbance
Author(s): B. Ding and H. Pan
Pages: 653-667

14. Command-Filtered-Based Fuzzy Adaptive Control Design for MIMO-Switched Nonstrict-Feedback Nonlinear Systems
Author(s): Y. Li and S. Tong
Pages: 668-681

15. Type-2 Fuzzy Alpha-Cuts
Author(s): H. Hamrawi, S. Coupland and R. John
Pages: 682-692

16. A New Look at Type-2 Fuzzy Sets and Type-2 Fuzzy Logic Systems
Author(s): L. X. Wang
Pages: 693-706

17. LMI-Based Stability Analysis for Piecewise Multi-affine Systems
Author(s): A. T. Nguyen, M. Sugeno, V. Campos and M. Dambrine
Pages: 707-714

18. An Extended Type-Reduction Method for General Type-2 Fuzzy Sets
Author(s): B. K. Xie and S. J. Lee
Pages: 715-724

19. Critique of “A New Look at Type-2 Fuzzy Sets and Type-2 Fuzzy Logic Systems"
Author(s): J. M. Mendel and D. Wu
Pages: 725-727

Weekly Review 9 June 2017

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

1.  Microsoft has released version 2.0 of its deep learning toolkit: https://techcrunch.com/2017/06/01/microsoft-releases-version-2-0-of-its-deep-learning-toolkit/
2. An overview of tensors, as used in TensorFlow: http://www.kdnuggets.com/2017/06/deep-learning-demystifying-tensors.html 
3. How rat brains are inspiring the next generation of artificial neural networks: http://spectrum.ieee.org/biomedical/imaging/ai-designers-find-inspiration-in-rat-brains
4. Despite the article title Watson isn't diagnosing cancer, it's helping plan how to treat it: https://news.fastcompany.com/ibm-says-watson-healths-ai-is-getting-really-good-at-diagnosing-cancer-4039394
5. An overview of neurocomputing hardware - hardware inspired by the brain: http://spectrum.ieee.org/computing/hardware/the-brain-as-computer-bad-at-math-good-at-everything-else
6. Four tips for job interviews - these apply to interviews for academic positions as well: https://www.seek.co.nz/career-advice/4-ways-to-prepare-for-an-interview
7. Three things intelligent machines need to copy from the human brain: http://spectrum.ieee.org/computing/software/what-intelligent-machines-need-to-learn-from-the-neocortex
8. Hawkins' book "On Intelligence" is very good, must re-read sometime soon: http://spectrum.ieee.org/computing/software/what-intelligent-machines-need-to-learn-from-the-neocortex
9. So valedictorians (dux in NZ) go on to lead good lives, but seldom achieve particularly highly: http://time.com/money/4779223/valedictorian-success-research-barking-up-wrong/
10. Why we need to build an artificial brain or, why neuromorphic computing is a good idea: http://spectrum.ieee.org/computing/hardware/can-we-copy-the-brain
11. We could build an artificial brain now, but probably wouldn't want to pay for it: http://spectrum.ieee.org/computing/hardware/we-could-build-an-artificial-brain-right-now
12. If you're looking for a job, be careful what you say on social media (including Twitter!) http://jobs.ieee.org/jobs/content/Looking-for-a-Job-Double-Check-Your-Social-Media-Accounts-First-2017-04-18
13. Deep learning can't tell what Homer Simpson is doing. Or, why DeepMind built an enormous database of videos: http://spectrum.ieee.org/tech-talk/robotics/artificial-intelligence/deepmind-shows-ai-has-trouble-seeing-homer-simpson-actions
14. If I am advertising for a new staff member, I will read the cover letters of applicants: https://www.seek.co.nz/career-advice/does-anyone-actually-read-cover-letters
15. Using AI in children's educational apps: https://techcrunch.com/2017/06/07/sesame-workshop-and-ibm-team-up-to-test-a-new-a-i-powered-teaching-method/
16. Combining relational networks with deep learning: https://www.theregister.co.uk/2017/06/09/deepmind_teaches_ai_to_reason/
17. Paper on adding relational reasoning to deep neural networks: https://arxiv.org/abs/1706.01427
18. How AI can make things worse, instead of better: http://www.kdnuggets.com/2017/06/unintended-consequences-machine-learning.html

Neural Networks, Volume 92, Pages 1-98, August 2017

Special Issue "Advances in Cognitive Engineering Using Neural Networks" Edited by Minho Lee, Steven Bressler and Robert Kozma

1. Advances in Cognitive Engineering Using Neural Networks  
Author(s): Minho Lee, Steven Bressler, Robert Kozma
Pages: 1-2

2. How can a recurrent neurodynamic predictive coding model cope with fluctuation in temporal patterns? Robotic experiments on imitative interaction   
Author(s): Ahmadreza Ahmadi, Jun Tani
Pages: 3-16

3. Dual-memory neural networks for modeling cognitive activities of humans via wearable sensors   
Author(s): Sang-Woo Lee, Chung-Yeon Lee, Dong-Hyun Kwak, Jung-Woo Ha, Jeonghee Kim, Byoung-Tak Zhang
Pages: 17-28

4. Understanding human intention by connecting perception and action learning in artificial agents   
Author(s): Sangwook Kim, Zhibin Yu, Minho Lee
Pages: 29-38

5. Application of structured support vector machine backpropagation to a convolutional neural network for human pose estimation   
Author(s): Peerajak Witoonchart, Prabhas Chongstitvatana
Pages: 39-46

6. Reprint of “Two-stage sparse coding of region covariance via Log-Euclidean kernels to detect saliency”   
Author(s): Ying-Ying Zhang, Cai Yang, Ping Zhang
Pages: 47-59

7. Evaluating deep learning architectures for Speech Emotion Recognition   
Author(s): Haytham M. Fayek, Margaret Lech, Lawrence Cavedon
Pages: 60-68

8. Weighted spatial based geometric scheme as an efficient algorithm for analyzing single-trial EEGS to improve cue-based BCI classification   
Author(s): Fatemeh Alimardani, Reza Boostani, Benjamin Blankertz
Pages: 69-76

9. Prediction of advertisement preference by fusing EEG response and sentiment analysis   
Author(s): Himaanshu Gauba, Pradeep Kumar, Partha Pratim Roy, Priyanka Singh, Debi Prosad Dogra, Balasubramanian Raman
Pages: 77-88

10. A Hypergraph and Arithmetic Residue-based Probabilistic Neural Network for classification in Intrusion Detection Systems   
Author(s): M.R. Gauthama Raman, Nivethitha Somu, Kannan Kirthivasan, V.S. Shankar Sriram
Pages: 89-97

IEEE Transactions on Emerging Topics in Computational Intelligence, Volume 1, Issue 3, June 2017

Special Issue on Computational Intelligence for Software Engineering and Services Computing

1. WebAPIRec: Recommending Web APIs to Software Projects via Personalized Ranking
Author(s): F. Thung, R. J. Oentaryo, D. Lo and Y. Tian
Pages: 145-156

2. Context-Aware, Adaptive, and Scalable Android Malware Detection Through Online Learning
Author(s): A. Narayanan, M. Chandramohan, L. Chen and Y. Liu
Pages: 157-175

3. A Model-Driven Approach to Enable Adaptive QoS in DDS-Based Middleware
Author(s): J. F. Inglés-Romero, A. Romero-Garcés, C. Vicente-Chicote and J. Martínez
Pages: 176-187

4. Search-Based Energy Optimization of Some Ubiquitous Algorithms
Author(s): A. E. I. Brownlee, N. Burles and J. Swan
Pages: 188-201

5. Dynamic Selection of Classifiers in Bug Prediction: An Adaptive Method
Author(s): D. Di Nucci, F. Palomba, R. Oliveto and A. De Lucia
Pages: 202-212

6. Epistasis Based ACO for Regression Test Case Prioritization
Author(s): Y. Bian, Z. Li, R. Zhao and D. Gong
Pages: 213-223

7. A Model-Driven Methodology for the Design of Autonomic and Cognitive IoT-Based Systems: Application to Healthcare
Author(s): E. Mezghani, E. Exposito and K. Drira
Pages: 224-234

Complex & Intelligent Systems, Vol. 3, No. 2, June 2017

1. Constructing enhanced default theories incrementally
Author(s): Ghassan Beydoun, Achim Hoffmann, Asif Gill
Pages: 83-92

2. Age estimation from a face image in a selected gender-race group based on ranked local binary patterns
Author(s): Andrey Rybintsev
Pages: 93-104

3. An insight into imbalanced Big Data classification: outcomes and challenges
Author(s): Alberto Fernández, Sara del Río, Nitesh V. Chawla, Francisco Herrera
Pages: 105-120

4. An evolutionary hybrid method to predict pistachio price
Author(s): Azim Heydari, Farshid Keynia, Nasser Shahsavari-Pour, Reza Sedaghat
Pages: 121-132

5. Big data for secure healthcare system: a conceptual design
Author(s): Bikash Kanti Sarkar
Pages: 133-151

Weeky Review 2 June 2017

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

1. An interview on how deep learning is used to infer human emotions: https://www.oreilly.com/ideas/how-ai-is-used-to-infer-human-emotion
2. Why quantum computing is necessary for the future of AI: http://theinstitute.ieee.org/ieee-roundup/blogs/blog/artificial-intelligences-potential-will-be-realized-by-quantum-computing 
3. Who is liable when an AI misdiagnoses a patient? https://qz.com/989137/when-a-robot-ai-doctor-misdiagnoses-you-whos-to-blame/ A society being over-litigious makes this a problem.
4. Shifting jobs in IT - what is going to become obsolete, and what is going to become more important: http://www.infoworld.com/article/3196022/it-careers/the-working-dead-it-jobs-bound-for-extinction.html
5. AlphaGo has defeated the world's best human Go player: https://www.theguardian.com/technology/2017/may/23/alphago-google-ai-beats-ke-jie-china-go
6. "Music" composed by a neuromorphic chip: http://spectrum.ieee.org/tech-talk/robotics/artificial-intelligence/a-neuromorphic-chip-that-makes-music 
7. AI in healthcare works better alongside humans, not replacing them: http://www.informationweek.com/healthcare/analytics/ai-works-better-with-human-intelligence-too/d/d-id/1328903?
8. The relationship between AI and statistics (and abuse thereof): http://www.theregister.co.uk/2017/05/24/fear_of_statistics/ 
9. AlphaGo has defeated the world's best human Go player in 3 out of 3 matches: https://techcrunch.com/2017/05/24/alphago-beats-planets-best-human-go-player-ke-jie/ 
10. Time for academics to again control academic journals, rather than greedy, predatory corporations: https://www.theguardian.com/higher-education-network/2017/may/25/its-time-for-academics-to-take-back-control-of-research-journals 
11. Using facial recognition technology to track if students are paying attention to a lecture: https://www.theverge.com/2017/5/26/15679806/ai-education-facial-recognition-nestor-france 
12. A list of free tools for visualising data: http://computerworld.com/article/2507728/enterprise-applications/enterprise-applications-22-free-tools-for-data-visualization-and-analysis.html
13. How could we tell if a general AI were truly conscious? http://spectrum.ieee.org/computing/hardware/can-we-quantify-machine-consciousness 
14. Applying deep learning to sports analytics: https://www.datanami.com/2017/05/26/deep-learning-revolutionize-sports-analytics-heres/ 
15. Is Apple developing a neural network chip? https://www.theverge.com/2017/5/26/15702248/apple-neural-engine-ai-chip-iphone-ipad 
16. Not so much racist algorithms, as the benchmark data sets are lacking diversity: https://www.theguardian.com/technology/2017/may/28/joy-buolamwini-when-algorithms-are-racist-facial-recognition-bias 
17. An AI that detects other AI: https://motherboard.vice.com/en_us/article/this-dystopian-wearable-detects-ais-pretending-to-be-humans 
18. Brief explanation of what an ontology is: http://www.kdnuggets.com/2017/05/ontology-simplest-definition.html 
19. Quite a bit of disagreement over when AI will overtake humans: http://www.theregister.co.uk/2017/05/26/job_automation_by_ai_timeline/ 
20. Why it's a silly idea to try to teach everyone to code: http://www.techrepublic.com/article/why-the-notion-that-everyone-should-learn-to-code-is-complete-rubbish/ 
21. The challenges of identifying trust with machine learning: http://www.kdnuggets.com/2017/05/challenges-machine-learning-trust.html 
22. The second wave of AI, and how it will impact humanity: http://www.techrepublic.com/article/why-ai-will-force-businesses-to-rethink-balance-between-the-work-of-humans-and-machines/ 
23. Nuts-ml, a Python library intended to preprocess data for machine learning algorithms: http://www.kdnuggets.com/2017/05/data-pre-processing-deep-learning-nuts-ml.html 
24. How Google is using machine learning to improve security in GMail: http://www.techrepublic.com/article/google-looks-to-machine-learning-to-boost-security-in-gmail/
25. Is China going to outstrip the USA in AI? https://www.nytimes.com/2017/05/27/technology/china-us-ai-artificial-intelligence.html?_r=1
26. A system that uses a neural network to generate code from a screenshot of a GUI: https://siliconangle.com/blog/2017/05/28/startup-uses-ai-create-gui-source-code-simple-screenshots/
27. The New Zealand AI Forum is launching: http://www.nzherald.co.nz/business/news/article.cfm?c_id=3&objectid=11867287

IEEE Transactions on Neural Networks and Learning Systems, Volume 28, Issue 6, June 2017

1. Holographic Graph Neuron: A Bioinspired Architecture for Pattern Processing
Author(s): Denis Kleyko; Evgeny Osipov; Alexander Senior; Asad I. Khan; Yaşar Ahmet Şekerciogğlu
Page(s): 1250 - 1262

2. Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection
Author(s): Xiaofeng Zhu; Xuelong Li; Shichao Zhang; Chunhua Ju; Xindong Wu
Page(s): 1263 - 1275

3. Rate of Convergence of the FOCUSS Algorithm
Author(s): Kan Xie; Zhaoshui He; Andrzej Cichocki; Xiaozhao Fang
Page(s): 1276 - 1289

4. Action and Event Recognition in Videos by Learning From Heterogeneous Web Sources
Author(s): Li Niu; Xinxing Xu; Lin Chen; Lixin Duan; Dong Xu
Page(s): 1290 - 1304

5. Mapping Temporal Variables Into the NeuCube for Improved Pattern Recognition, Predictive Modeling, and Understanding of Stream Data
Author(s): Enmei Tu; Nikola Kasabov; Jie Yang
Page(s): 1305 - 1317

6. Adaptive Neural Control of Uncertain MIMO Nonlinear Systems With State and Input Constraints
Author(s): Ziting Chen; Zhijun Li; C. L. Philip Chen
Page(s): 1318 - 1330

7. Co-Operative Coevolutionary Neural Networks for Mining Functional Association Rules
Author(s): Bing Wang; Kathryn E. Merrick; Hussein A. Abbass
Page(s): 1331 - 1344

8. Quantum Ensemble Classification: A Sampling-Based Learning Control Approach
Author(s): Chunlin Chen; Daoyi Dong; Bo Qi; Ian R. Petersen; Herschel Rabitz
Page(s): 1345 - 1359

9. A Regularizer Approach for RBF Networks Under the Concurrent Weight Failure Situation
Author(s): Chi-Sing Leung; Wai Yan Wan; Ruibin Feng
Page(s): 1360 - 1372

10. Clustering Through Hybrid Network Architecture With Support Vectors
Author(s): Emrah Ergul; Nafiz Arica; Narendra Ahuja; Sarp Erturk
Page(s): 1373 - 1385

11. Design and Application of a Variable Selection Method for Multilayer Perceptron Neural Network With LASSO
Author(s): Kai Sun; Shao-Hsuan Huang; David Shan-Hill Wong; Shi-Shang Jang
Page(s): 1386 - 1396

12. Robustly Fitting and Forecasting Dynamical Data With Electromagnetically Coupled Artificial Neural Network: A Data Compression Method
Author(s): Ziyin Wang; Mandan Liu; Yicheng Cheng; Rubin Wang
Page(s): 1397 - 1410

13. Efficient Training of Supervised Spiking Neural Network via Accurate Synaptic-Efficiency Adjustment Method
Author(s): Xiurui Xie; Hong Qu; Zhang Yi; Jürgen Kurths
Page(s): 1411 - 1424

14. Sparseness Analysis in the Pretraining of Deep Neural Networks
Author(s): Jun Li; Tong Zhang; Wei Luo; Jian Yang; Xiao-Tong Yuan; Jian Zhang
Page(s): 1425 - 1438

15. Barrier Function-Based Neural Adaptive Control With Locally Weighted Learning and Finite Neuron Self-Growing Strategy
Author(s): Zi-Jun Jia; Yong-Duan Song
Page(s): 1439 - 1451

16. Label Propagation via Teaching-to-Learn and Learning-to-Teach
Author(s): Chen Gong; Dacheng Tao; Wei Liu; Liu Liu; Jie Yang
Page(s): 1452 - 1465

17. Optimized Kernel Entropy Components
Author(s): Emma Izquierdo-Verdiguier; Valero Laparra; Robert Jenssen; Luis Gómez-Chova; Gustau Camps-Valls
Page(s): 1466 - 1472

18. Quantized Iterative Learning Consensus Tracking of Digital Networks With Limited Information Communication
Author(s): Wenjun Xiong; Xinghuo Yu; Yao Chen; Jie Gao
Page(s): 1473 - 1480

19. Biomimetic Hybrid Feedback Feedforward Neural-Network Learning Control
Author(s): Yongping Pan; Haoyong Yu
Page(s): 1481 - 1487

Evolving Systems, Volume 8, Issue 2, June 2017

1. Block-based noise variance estimation algorithm in blurred and noisy images with its application to motion deblurring
Author(s): A. H. Mazinan, A. Karimi
Pages: 95-108

2. Neuro-computational frameworks for non-linear stochastic wireless channels
Author(s): Manasjyoti Bhuyan, Kandarpa Kumar Sarma, Nikos Mastorakis
Pages: 109-120

3. Evolutionary game of inter-firm knowledge sharing in innovation cluster
Author(s): Rui-Hua Wang, Yong-Bo Lv, Min Duan
Pages: 121-133

4. On using density maps for the calculation of ship routes
Author(s): Philip Azariadis
Pages: 135-145

5. A comparative analysis of heterogeneity in road accident data using data mining techniques
Author(s): Sachin Kumar, Durga Toshniwal, Manoranjan Parida
Pages: 147-155

6. Sustainable mobility leveraging on 5G mobile communication infrastructures in the context of smart city operations
Author(s): George Dimitrakopoulos
Pages: 157-166

Weekly Review 23 May 2017

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

1. The importance of visual (image) data to future developments in AI: https://techcrunch.com/2017/05/17/the-war-over-artificial-intelligence-will-be-won-with-visual-data/
2. One-shot visual learning of tasks from humans in VR with neural networks: http://www.theregister.co.uk/2017/05/16/openai_teaches_robot_to_learn_from_human_demonstrations_in_vr/ 
3. Detecting masks and guns in CCTV feeds, using deep learning: https://techcrunch.com/2017/05/16/deep-science-ai-monitors-security-feeds-for-masks-and-guns-to-quicken-response-times/ 
4. Machine learning can eliminate bias in hiring, but only if the training data is unbiased: http://www.techrepublic.com/article/how-machine-learning-can-help-companies-eliminate-bias-in-hiring/ 
5. Diagnosing concussions with deep learning: https://techcrunch.com/2017/05/16/brightlamp-wants-to-use-ai-to-spot-concussions/ 
6. Conference submission deadline: ICONIP 2017 https://computational-intelligence.blogspot.com/2017/05/conference-submission-deadline-iconip.html 
7. How to build a text classifier utilising machine learning: https://blog.monkeylearn.com/how-to-create-text-classifiers-machine-learning/
8. This guy does not like the R language, and not without some good reasons: http://www.datasciencecentral.com/profiles/blogs/why-r-is-bad-for-you 
9. Academic resumes tend to be long, but it helps to put the main points on the first or second page: https://www.seek.co.nz/career-advice/5-things-employers-wish-they-could-say-about-your-resume
10. 5 different types of recommenders explained: http://www.datasciencecentral.com/profiles/blogs/5-types-of-recommenders 
11. Google has brought TensorFlow to Android: https://techcrunch.com/2017/05/17/googles-tensorflow-lite-brings-machine-learning-to-android-devices/
12. A neural network that invents new paint colours and names: http://lewisandquark.tumblr.com/post/160776374467/new-paint-colors-invented-by-neural-network "Clardic fug" is probably my favourite paint name.
13. How to build, and convert into code, decisions trees in Python: http://www.kdnuggets.com/2017/05/simplifying-decision-tree-interpretation-decision-rules-python.html 
14. Google's AI is starting to manage relationships between people: https://www.theverge.com/2017/5/19/15660610/google-photos-ai-relationship-emotional-labor 
15. AI in agriculture-a bit vague about what the AI is, though: http://www.datasciencecentral.com/profiles/blogs/artificial-intelligence-in-agriculture-what-s-next 
16. Google is making its Tensor Processing Units available via the cloud: https://www.datanami.com/2017/05/18/cloud-tpu-bolsters-googles-ai-first-strategy/ 
17. An argument that AI is not driving cloud adoption: https://www.theregister.co.uk/2017/05/18/baby_steps_not_ai_drives_cloud_growth/ 
18. So Canada's developing a nation-wide AI strategy? http://www.datasciencecentral.com/profiles/blogs/pan-canadian-artificial-intelligence-strategy 
19. An AI that is "outrageous in a great way": http://www.theregister.co.uk/2017/05/22/deeptingle_ai_transforms_writing/

Neural Networks, Volume 91, Pages 1-102, July 2017

1. Lagrange image-exponential stability and image-exponential convergence for fractional-order complex-valued neural networks   
Author(s): Jigui Jian, Peng Wan
Pages: 1-10

2. Event-triggered image filtering for delayed neural networks via sampled-data   
Author(s): Emel Arslan, R. Vadivel, M. Syed Ali, Sabri Arik
Pages: 11-21

3. Recursive least mean image-power Extreme Learning Machine   
Author(s): Jing Yang, Feng Ye, Hai-Jun Rong, Badong Chen
Pages: 22-33

4. Probabilistic lower bounds for approximation by shallow perceptron networks   
Author(s): Věra Kůrková, Marcello Sanguineti
Pages: 34-41

5. A framework for parallel and distributed training of neural networks   
Author(s): Simone Scardapane, Paolo Di Lorenzo
Pages: 42-54

6. Robust stability analysis of quaternion-valued neural networks with time delays and parameter uncertainties   
Author(s): Xiaofeng Chen, Zhongshan Li, Qiankun Song, Jin Hu, Yuanshun Tan
Pages: 55-65

7. Spatiotemporal signal classification via principal components of reservoir states   
Author(s): Ashley Prater
Pages: 66-75

8. Hopfield networks as a model of prototype-based category learning: A method to distinguish trained, spurious, and prototypical attractors   
Author(s): Chris Gorman, Anthony Robins, Alistair Knott
Pages: 76-84

9. A universal multilingual weightless neural network tagger via quantitative linguistics   
Author(s):  Hugo C.C. Carneiro, Carlos E. Pedreira, Felipe M.G. França, Priscila M.V. Lima
Pages: 85-101

IEEE Transactions on Fuzzy Systems, Volume 25, Issue 2, April 2017

Guest Editorial – Special Issue on Fuzzy Techniques in Financial Modeling and Simulation
Authors: Antoaneta Serguieva, Hisao Ishibuchi, Ronald R Yager, Vasile Palade
Pages: 245-248

1.  Multiobjective Evolutionary Optimization of Type-2 Fuzzy Rule-Based Systems for Financial Data Classification
Authors: Michela Antonelli, Dario Bernardo, Hani Hagras, Francesco Marcelloni
Pages: 249-264

2. Rough Information Set and Its Applications in Decision Making
Author: Manish Aggarwal
Pages: 265-276

3.  Modeling Stock Price Dynamics With Fuzzy Opinion Networks
Author: Li-Xin Wang
Pages: 277-301

4.  Evolving Possibilistic Fuzzy Modeling for Realized Volatility Forecasting With Jumps
Authors: Leandro Maciel, Rosangela Ballini, Fernando Gomide
Pages: 302-314

5.  FN-TOPSIS: Fuzzy Networks for Ranking Traded Equities
Authors: Abdul Malek Yaakob, Antoaneta Serguieva, Alexander Gegov
Pages: 215-332

6.  Stock Picking by Probability–Possibility Approaches
Authors: Jean-Marc Le Caillec, Alya Itani, Didier Guriot, Yves Rakotondratsimba
Pages: 333-349

7.  Mean-Variance Portfolio Selection with the Ordered Weighted Average
Authors: Sigifredo Laengle, Gino Loyola, Jos´e M. Merig´o
Pages: 350-362

8. Adaptive Budget-Portfolio Investment Optimization Under Risk Tolerance Ambiguity
Authors: Shuming Wang, Bo Wang, Junzo Watada
Pages: 363-376

9.  Fuzzy Decision Theory Based Metaheuristic Portfolio Optimization and Active Rebalancing Using Interval Type-2 Fuzzy Sets
Author: G. A. Vijayalakshmi Pai
Pages: 377-391

10. Fuzzy Approaches to Option Price Modeling
Authors: Silvia Muzzioli and Bernard De Baets
Pages: 392-401

11.  Option Pricing With Application of Levy Processes and the Minimal Variance Equivalent Martingale Measure Under Uncertainty
Authors: Piotr Nowak and Michał Pawłowski
Pages: 402-416

12. Quanto European Option Pricing With Ambiguous Return Rates and Volatilities
Authors: Junfei Zhang and Shoumei Li
Pages: 417-424

13. A Comparison of Bidding Strategies for Online Auctions Using Fuzzy Reasoning and Negotiation Decision Functions
Authors: Preetinder Kaur, Madhu Goyal, Jie Lu
Pages: 425-438

14. Fuzzy Dynamical System Scenario Simulation-Based Cross-Border Financial Contagion Analysis: A Perspective From International Capital Flows
Authors: Xinxin Xu, Ziqiang Zeng, Jiuping Xu, Mengxiang Zhang
Pages: 439-459

15.  Multiobjective Investment Policy for a Nonlinear Stochastic Financial System: A Fuzzy Approach
Authors: Chien-Feng Wu and Weihai Zhang
Pages: 460-474

16.  A Fuzzy Control Model for Restraint of Bullwhip Effect in Uncertain Closed-Loop Supply Chain With Hybrid Recycling Channels
Authors: Songtao Zhang, Xue Li, Chunyang Zhang
Pages: 475-482

Conference submission deadline: ICONIP 2017

The deadline for submitting papers to the International Conference on Neural Information Processing (ICONIP) 2017 is June 15, 2017. This conference will be held in Guangzhou, China, November 14-18, 2017.

Weekly Review 16 May 2017

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

1. Ten ways AI is being used in retail: https://www.techemergence.com/artificial-intelligence-retail-10-present-future-use-cases/
2. Using machine learning to detect lung cancer in x-rays: https://techcrunch.com/2017/05/08/chinese-startup-infervision-emerges-from-stealth-with-an-ai-tool-for-diagnosing-lung-cancer/ 
3. We are in the golden age of AI: https://finance.yahoo.com/news/golden-age-solving-problems-were-093919934.html
4. Automation technology, including AI, is going to wipe out a lot of entry-level legal jobs: https://www.axios.com/artificial-intelligence-is-coming-for-law-firms-2394154251.html
5. Microsoft is offering a deep learning service in Azure: https://techcrunch.com/2017/05/10/microsoft-launches-a-new-service-for-training-deep-neural-networks-on-azure/ 
6. How to select the optimal number of clusters: http://www.kdnuggets.com/2017/05/must-know-most-useful-number-clusters.html 
7. The effect of AI on employment-only highly-educated people are really safe at the moment: http://www.techrepublic.com/article/why-automation-in-the-age-of-ai-will-change-the-way-we-think-of-work/ 
8. Processing job descriptions with deep learning ANN: http://www.kdnuggets.com/2017/05/deep-learning-extract-knowledge-job-descriptions.html 
9. Summarising text using reinforcement learning: https://techcrunch.com/2017/05/11/salesforce-aims-to-save-you-time-by-summarizing-emails-and-docs-with-machine-intelligence
10. TensorFlow seems to be a bit tricky to use: http://www.theregister.co.uk/2017/05/12/tensor_flow_hands_on/ 
11. Detecting network anomalies-that is, security threats-using machine learning: https://techcrunch.com/2017/05/12/las-vegas-taps-ai-for-cybersecurity-help/ 
12. My SECoS algorithms (http://ecos.watts.net.nz/Algorithms/SECoS.html) have also been applied to this sort of thing: https://techcrunch.com/2017/05/12/las-vegas-taps-ai-for-cybersecurity-help/
13. Twitter is finding tweets relevant to users using deep neural networks: https://www.datanami.com/2017/05/10/twitter-ranking-tweets-machine-learning/ 
14. Some future trends and developments in AI: http://www.datasciencecentral.com/profiles/blogs/a-sneak-peek-at-the-future-of-artificial-intelligence-the-newes-1 
15. Tools to automate the construction of deep learning models: https://www.enterprisetech.com/2017/05/10/automation-automation-ibm-powerai-tools-aim-ease-deep-learning-data-prep-shorten-training/ 
16. Biased models come from biased data, but biased data is ruining people's lives: https://www.theregister.co.uk/2017/05/08/algorithmic_bias/ 
17. It doesn't matter how good the algorithm is, if you don't put good data into it, you won't get a good model out if it. This is basic stuff.
18. Personally I would classify deep learning as computational intelligence, but that would further confuse journalists: https://techcrunch.com/2017/05/14/pattern-recognition/ 
19. A lot of academic success also seems to come from shameless self-promotion: http://www.techrepublic.com/article/the-it-leaders-guide-to-shameless-self-promotion-part-1/
20. The two phases of gradient descent in deep learning: http://www.kdnuggets.com/2017/05/two-phases-gradient-descent-deep-learning.html 
21. Using AI to detect abuse in mental health group chats: https://techcrunch.com/2017/05/15/sunrise-health/ 
22. Facebook's platform for researching conversational AI chatbots: https://www.theverge.com/2017/5/15/15640886/facebook-parlai-chatbot-research-ai-chatbot 
23. Before data mining, make sure that you have the legal right to mine the data you are looking at: https://techcrunch.com/2017/05/15/deepmind-nhs-health-data-deal-had-no-lawful-basis/ 
24. THAT won't cause security problems.... http://www.techrepublic.com/article/delta-testing-facial-recognition-for-self-service-bag-check-in-at-minneapolis-airport/

Neural Networks, Volume 90, Pages 1-112, June 2017

1. Synchronised firing patterns in a random network of adaptive exponential integrate-and-fire neuron model   
Author(s): F.S. Borges, P.R. Protachevicz, E.L. Lameu, R.C. Bonetti, K.C. Iarosz, I.L. Caldas, M.S. Baptista, A.M. Batista
Pages: 1-7

2. Forecasting stochastic neural network based on financial empirical mode decomposition   
Author(s): Jie Wang, Jun Wang
Pages: 8-20

3. A time-delay neural network for solving time-dependent shortest path problem   
Author(s): Wei Huang, Chunwang Yan, Jinsong Wang, Wei Wang
Pages: 21-28

4. Extending the Stabilized Supralinear Network model for binocular image processing   
Author(s): Ben Selby, Bryan Tripp
Pages: 29-41

5. Robust fixed-time synchronization for uncertain complex-valued neural networks with discontinuous activation functions   
Author(s): Xiaoshuai Ding, Jinde Cao, Ahmed Alsaedi, Fuad E. Alsaadi, Tasawar Hayat
Pages: 42-55

6. Collective mutual information maximization to unify passive and positive approaches for improving interpretation and generalization   
Author(s): Ryotaro Kamimura
Pages: 56-71

7. Persistent irregular activity is a result of rebound and coincident detection mechanisms: A computational study   
Author(s): Mustafa Zeki, Ahmed A. Moustafa
Pages: 72-82

8. Representation learning via Dual-Autoencoder for recommendation   
Author(s): Fuzhen Zhuang, Zhiqiang Zhang, Mingda Qian, Chuan Shi, Xing Xie, Qing He
Pages: 83-89

9. A bag-of-paths framework for network data analysis   
Author(s): Kevin Françoisse, Ilkka Kivimäki, Amin Mantrach, Fabrice Rossi, Marco Saerens
Pages: 90-111

Weekly Review 8 May 2017

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

1. AI is one of 5 ways a business can lead in the age of analytics: http://www.informationweek.com/big-data/big-data-analytics/5-keys-to-leading-in-the-age-of-analytics/a/d-id/1328693
2. Comparison of open-source frameworks for deep learning and visual analysis: http://www.datasciencecentral.com/profiles/blogs/open-source-deep-learning-frameworks-and-visual-analytics 
3. How to choose which machine learning algorithm to use: http://blogs.sas.com/content/subconsciousmusings/2017/04/12/machine-learning-algorithm-use/ 
4. Sounds like a really good way to automate police prejudice: https://techcrunch.com/2017/04/30/taser-law-enforcement-technology-report/ 
5. 5 free ebooks on machine learning: http://www.kdnuggets.com/2016/10/5-free-ebooks-machine-learning-career.html 
6. Using ANN to produce smooth animations of human characters: https://techcrunch.com/2017/05/01/this-neural-network-could-make-animations-in-games-a-little-less-awkward/ 
7. 5 best machine learning API: http://www.kdnuggets.com/2015/11/machine-learning-apis-data-science.html 
8. A tool for the governance of machine learning projects and models: http://www.theregister.co.uk/2017/04/25/immuta_data_governance_tool/ Interpretability is key.
9. Detecting strokes from brain scans using machine learning: https://techcrunch.com/2017/05/01/tackling-diagnostic-medicine-with-ai-viz-launches-a-tool-to-identify-strokes/ 
10. Yes, Amazon Look will be used as a front-end to a fashion AI, especially if Amazon can then sell you more stuff: https://www.theverge.com/2017/5/3/15522792/amazon-echo-look-alexa-style-assistant-ai-fashion 
11. AI is moving into the public sector, bringing worries to workers about job security: https://www.datanami.com/2017/05/03/job-worries-grow-ai-shifts-public-sector/ 
12. This is why proof-reading is important -"pubic sector adoption of automation" https://www.datanami.com/2017/05/03/job-worries-grow-ai-shifts-public-sector/ 
13. Finally got around to updating my online list of publications, and added some more paper uploads: http://mike.watts.net.nz/CV/mjwatts.html
14. I have updated the list of references on applications of Evolving Connectionist Systems on my ECoS site: http://ecos.watts.net.nz/Literature/Applications.html 
15. Swarm AI will again try to predict the outcome of the Kentucky Derby: http://www.techrepublic.com/article/how-an-ai-super-expert-will-predict-the-winner-of-the-kentucky-derby/ 
16. An open source database of street-level images: https://techcrunch.com/2017/05/03/mapillary-open-sources-25k-street-level-images-to-train-automotive-ai-systems/ 
17. Where AI is going: http://www.datasciencecentral.com/profiles/blogs/development-of-ai-and-its-future-state 
18. A camera-equipped prosthetic hand uses ANN to help it grasp things: https://motherboard.vice.com/en_us/article/this-bionic-hand-uses-ai-to-grab-things-automatically 
19. Using sckit-learn in Python to detect SPAM emails: https://appliedmachinelearning.wordpress.com/2017/01/23/email-spam-filter-python-scikit-learn/ 
20. The swarm missed predicting the Kentucky derby this year: http://www.techrepublic.com/article/ai-misses-repeat-in-2017-kentucky-derby-but-heres-what-we-learned/ Still not sure how this is AI rather than Bayesian search
21. Been feeling good lately, like I'm on top of things - why does this give me such a feeling of dread? https://www.theguardian.com/lifeandstyle/ng-interactive/2017/may/06/stephen-collins-on-good-times-cartoon?CMP=share_btn_tw 
22. An overview of TensorFlow: http://www.techrepublic.com/article/tensorflow-googles-open-source-software-library-for-machine-learning-the-smart-persons-guide/

IEEE Transactions on Neural Networks and Learning Systems, Volume 28, Issue 5, May 2017

1. A Semisupervised Approach to the Detection and Characterization of Outliers in Categorical Data
Author(s): Dino Ienco; Ruggero G. Pensa; Rosa Meo
Pages: 1017 - 1029

2. High-Order Measurements for Residual Classifiers
Author(s): Quan Guo; Haixian Zhang; Zhang Yi
Pages: 1030 - 1042

3. High-Performance Consensus Control in Networked Systems With Limited Bandwidth Communication and Time-Varying Directed Topologies
Author(s): Huaqing Li; Guo Chen; Tingwen Huang; Zhaoyang Dong
Pages: 1043 - 1054

4. Pinning Impulsive Synchronization of Reaction–Diffusion Neural Networks With Time-Varying Delays
Author(s): Xinzhi Liu; Kexue Zhang; Wei-Chau Xie
Pages: 1055 - 1067

5. Robust Recurrent Kernel Online Learning
Author(s): Qing Song; Xu Zhao; Haijin Fan; Danwei Wang
Pages: 1068 - 1081

6. Learning Kernel Extended Dictionary for Face Recognition
Author(s): Ke-Kun Huang; Dao-Qing Dai; Chuan-Xian Ren; Zhao-Rong Lai
Pages: 1082 - 1094

7. Learning Discriminative Subspaces on Random Contrasts for Image Saliency Analysis
Author(s): Shu Fang; Jia Li; Yonghong Tian; Tiejun Huang; Xiaowu Chen
Pages: 1095 - 1108

8. Coarse-to-Fine Learning for Single-Image Super-Resolution
Author(s): Kaibing Zhang; Dacheng Tao; Xinbo Gao; Xuelong Li; Jie Li
Pages: 1109 - 1122

9. Affinity and Penalty Jointly Constrained Spectral Clustering With All-Compatibility, Flexibility, and Robustness
Author(s): Pengjiang Qian; Yizhang Jiang; Shitong Wang; Kuan-Hao Su; Jun Wang; Lingzhi Hu; Raymond F. Muzic
Pages: 1123 - 1138

10. State Estimation for Discrete-Time Dynamical Networks With Time-Varying Delays and Stochastic Disturbances Under the Round-Robin Protocol
Author(s): Lei Zou; Zidong Wang; Huijun Gao; Xiaohui Liu
Pages: 1139 - 1151

11. Event-Triggered State Estimation for Discrete-Time Multidelayed Neural Networks With Stochastic Parameters and Incomplete Measurements
Author(s): Bo Shen; Zidong Wang; Hong Qiao
Pages: 1152 - 1163

12. On Deep Learning for Trust-Aware Recommendations in Social Networks
Author(s): Shuiguang Deng; Longtao Huang; Guandong Xu; Xindong Wu; Zhaohui Wu
Pages: 1164 - 1177

13. A Note on the Unification of Adaptive Online Learning
Author(s): Wenwu He; James Tin-Yau Kwok; Ji Zhu; Yang Liu
Pages: 1178 - 1191

14. LIF and Simplified SRM Neurons Encode Signals Into Spikes via a Form of Asynchronous Pulse Sigma–Delta Modulation
Author(s): Young C. Yoon
Pages: 1192 - 1205

15. A Collective Neurodynamic Approach to Constrained Global Optimization
Author(s): Zheng Yan; Jianchao Fan; Jun Wang
Pages: 1206 - 1215

16. Adaptive Neural Network Nonparametric Identifier With Normalized Learning Laws
Author(s): Isaac Chairez
Pages: 1216 - 1227

17. Speeding Up Cellular Neural Network Processing Ability by Embodying Memristors
Author(s): E. Bilotta; P. Pantano; S. Vena
Pages: 1228 - 1232

18. Mixtures of Conditional Random Fields for Improved Structured Output Prediction
Author(s): Minyoung Kim
Pages: 1233 - 1240

19. A Robust Regularization Path Algorithm for ν-Support Vector Classification
Author(s): Bin Gu; Victor S. Sheng
Pages: 1241 - 1248

Weekly Review 1 May 2017

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

1. Not surprising that if you take the negative of an image, a deep ANN can't classify it. ANN aren't magic: http://www.kdnuggets.com/2017/04/negative-results-images-flaw-deep-learning.html
2. Three interesting real-world applications of machine learning: http://www.informationweek.com/big-data/3-cool-ai-projects/a/d-id/1328666 
3. At least they admit it is because of biased data-FaceApp apologises for making a racist selfie filter: https://techcrunch.com/2017/04/25/faceapp-apologises-for-building-a-racist-ai/ 
4. What is a "whole brain approach" in ANN? https://www.datanami.com/2017/04/25/startup-patents-whole-brain-ai-approach/ 
5. Probably best to make this technology openly available, might make it easier to develop ways of detecting it: https://techcrunch.com/2017/04/25/lyrebird-is-a-voice-mimic-for-the-fake-news-era/ 
6. A cheat-sheet for Python deep learning libraries: http://www.datasciencecentral.com/profiles/blogs/deep-learning-cheat-sheet-using-python-libraries 
7. PDF cheat sheet for Python deep learning libraries: https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Keras_Cheat_Sheet_Python.pdf 
8. On the need for a standard intermediate language for machine learning frameworks: http://www.kdnuggets.com/2017/04/deep-learning-virtual-machine-rule-all.html A role for @ieeecis?
9. Machine learning is a cloud thing: https://www.theregister.co.uk/2017/04/27/ai_cloud_vendors_race/ 
10. A crash-course in using machine learning in Python: http://www.kdnuggets.com/2017/05/guerrilla-guide-machine-learning-python.html 
11. Some of the languages in which you can utilise machine learning: https://www.theregister.co.uk/2017/04/25/building_the_machine_languages_for_you/ 
12. Building a recurrent ANN in TensorFlow: https://medium.com/@erikhallstrm/hello-world-rnn-83cd7105b767 
13. Half of all jobs will be replaced by AI in ten years: http://www.cnbc.com/2017/04/27/kai-fu-lee-robots-will-replace-half-of-all-jobs.html