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

Weekly Review 19 December 2015

Some interesting links that I Tweeted about this week:

1. 10 Deep Learning Tips & Tricks http://www.kdnuggets.com/2015/12/top-10-deep-learning-tips-tricks.html These apply to most learning systems, especially cross-validation of data.
2. Is coding ability mostly down to natural aptitude? http://mikehadlow.blogspot.co.nz/2015/12/learn-to-code-its-harder-than-you-think.html I've always found it easy, but have seen so many others struggle
3. Top 10 machine learning projects on GitHub http://www.kdnuggets.com/2015/12/top-10-machine-learning-github.html 
4. Deep learning and topological data analysis https://www.linkedin.com/pulse/6-crazy-things-deep-learning-topological-data-can-do-your-kibardin 
5. Improvements to Google's TensorFlow open source machine learning package: http://www.kdnuggets.com/2015/12/update-google-tensorflow-deep-learning-is-improving.html
6. 33 Big Data predictions for 2016: http://www.datanami.com/2015/12/15/industry-speaks-top-33-big-data-predictions-for-2016/ 
7. Updated list of 50 deep learning tools: http://www.kdnuggets.com/2015/12/deep-learning-tools.html

Neural Networks, Volume 74, Pages 1-100, February 2016

1. Pixel classification based color image segmentation using quaternion exponent moments  
Pages: 1-13
Authors: Xiang-Yang Wang, Zhi-Fang Wu, Liang Chen, Hong-Liang Zheng, Hong-Ying Yang

2. TWSVR: Regression via Twin Support Vector Machine  
Pages: 14-21
Authors: Reshma Khemchandani, Keshav Goyal, Suresh Chandra

3. SIM-ELM: Connecting the ELM model with similarity-function learning  
Pages: 22-34
Authors: Paolo Gastaldo, Federica Bisio, Sergio Decherchi, Rodolfo Zunino

4. Behavioral plasticity through the modulation of switch neurons  
Pages: 35-51
Authors: Vassilis Vassiliades, Chris Christodoulou

5. Event-triggered synchronization strategy for complex dynamical networks with the Markovian switching topologies  
Pages: 52-57
Authors: Aijuan Wang, Tao Dong, Xiaofeng Liao

6. Stability of discrete time recurrent neural networks and nonlinear optimization problems  
Pages: 58-72
Authors: Jayant Singh, Nikita Barabanov

7. Boundedness, Mittag-Leffler stability and asymptotical image -periodicity of fractional-order fuzzy neural networks  
Pages: 73-84
Authors: Ailong Wu, Zhigang Zeng

8. Non-fragile image synchronization of memristor-based neural networks using passivity theory  
Pages: 85-100
Authors: K. Mathiyalagan, R. Anbuvithya, R. Sakthivel, Ju H. Park, P. Prakash

Weekly Review 12 December 2015

Some interesting links that I Tweeted about this week:

• 5 open-source ML systems from big companies: http://www.datasciencecentral.com/profiles/blogs/5-machine-learning-open-source-projects-from-top-internet
• Facebook open sources its AI hardware design: https://code.facebook.com/posts/1687861518126048/facebook-to-open-source-ai-hardware-design/ … not sure how AI this is, however.
• Cloud-based training of deep-learning neural networks: http://www.datanami.com/2015/12/07/10027/ 
• List of 20 big data repositories http://www.datasciencecentral.com/profiles/blogs/20-big-data-repositories-you-should-check-out-1 
• 20 lessons from building machine learning systems http://www.kdnuggets.com/2015/12/xamat-20-lessons-building-machine-learning-systems.html
• Doctor Andy's Rule Language (DARL) for creating fuzzy systems http://www.docandys.com/  How does this compare to FML? https://en.wikipedia.org/wiki/Fuzzy_markup_language
• Top machine learning algorithms over time http://www.datasciencecentral.com/profiles/blogs/top-10-machine-learning-algorithms
• Detecting how wet a road is using a microphone and deep learning http://arxiv.org/pdf/1511.07035v2.pdf
• List of 50 machine learning API http://www.kdnuggets.com/2015/12/machine-learning-data-science-apis.html

Neural Networks Volume 72 , Pages: 1-168, December 2015

Neurobiologically Inspired Robotics: Enhanced Autonomy through Neuromorphic Cognition
Edited by Jeffrey L. Krichmar, Jörg Conradt and Minoru Asada

1. Neurobiologically Inspired Robotics: Enhanced Autonomy through Neuromorphic Cognition  
Pages: 1-2
Author(s): Jeffrey L. Krichmar, Jörg Conradt, Minoru Asada

2. Choice reaching with a LEGO arm robot (CoRLEGO): The motor system guides visual attention to movement-relevant information  
Pages: 3-12
Author(s): Soeren Strauss, Philip J.W. Woodgate, Saber A. Sami, Dietmar Heinke

3. Modeling human target reaching with an adaptive observer implemented with dynamic neural fields  
Pages: 13-30
Author(s): Farzaneh S. Fard, Paul Hollensen, Dietmar Heinke, Thomas P. Trappenberg

4. Generalisation, decision making, and embodiment effects in mental rotation: A neurorobotic architecture tested with a humanoid robot  
Pages: 31-47
Author(s): Kristsana Seepanomwan, Daniele Caligiore, Angelo Cangelosi, Gianluca Baldassarre

5. Bio-inspired homogeneous multi-scale place recognition  
Pages: 48-61
Zetao Chen, Stephanie Lowry, Adam Jacobson, Michael E. Hasselmo, Michael Milford

6. Goal-oriented robot navigation learning using a multi-scale space representation  
Pages: 62-74
Author(s): M. Llofriu, G. Tejera, M. Contreras, T. Pelc, J.M. Fellous, A. Weitzenfeld

7. A GPU-accelerated cortical neural network model for visually guided robot navigation  
Pages: 75-87
Author(s): Michael Beyeler, Nicolas Oros, Nikil Dutt, Jeffrey L. Krichmar

8. An embodied biologically constrained model of foraging: from classical and operant conditioning to adaptive real-world behavior in DAC-X  
Pages: 88-108
Author(s): Giovanni Maffei, Diogo Santos-Pata, Encarni Marcos, Marti Sánchez-Fibla, Paul F.M.J. Verschure

9. Development of compositional and contextual communicable congruence in robots by using dynamic neural network models  
Pages: 109-122
Author(s): Gibeom Park, Jun Tani

10. Off-line simulation inspires insight: A neurodynamics approach to efficient robot task learning  
Pages: 123-139
Author(s): Emanuel Sousa, Wolfram Erlhagen, Flora Ferreira, Estela Bicho

11. Multimodal emotional state recognition using sequence-dependent deep hierarchical features  
Pages: 140-151
Author(s): Pablo Barros, Doreen Jirak, Cornelius Weber, Stefan Wermter

12. Neuromorphic implementations of neurobiological learning algorithms for spiking neural networks  
Pages: 152-167
Author(s): Florian Walter, Florian Röhrbein, Alois Knoll