Weekly Review 8 August 2016

It's been a while since my last review post. This is because I have been away at the ITx conference in Wellington, followed by the WCCI 2016 conference in Vancouver, B.C. Below are some of the interesting links I Tweeted about in the last few weeks.

1. A more and more common story, this is why I went into the private tertiary education sector, better job security: http://www.abc.net.au/radionational/programs/scienceshow/catherine-osborne-how-australia-fails-mid-career-scientists/7588644
2. An overview of Bayesian machine learning: http://www.kdnuggets.com/2016/07/bayesian-machine-learning-explained.html
3. An AI-based VC fund: https://techcrunch.com/2016/07/13/non-artificial-intelligence-please/
4. An improved Turing Test shows how dumb chatbots really are: https://www.technologyreview.com/s/601897/tougher-turing-test-exposes-chatbots-stupidity/
5. A tutorial on machine learning in Python: http://www.datasciencecentral.com/profiles/blogs/would-you-survive-the-titanic-a-guide-to-machine-learning-in
6. An AI that detects hints of depression in speech: http://motherboard.vice.com/en_au/read/machine-learning-algorithm-spots-depression-based-on-speech-patterns
7. The kinds of problems that AI still can't do: http://www.kdnuggets.com/2016/07/hard-problems-ai-cant-yet-touch.html
8. How machine learning is driving artificial intelligence: http://www.datanami.com/2016/07/11/report-machine-learning-driving-ai/
9. Five open-source deep learning projects: http://www.kdnuggets.com/2016/07/five-deep-learning-projects-cant-overlook.html
10. The coming clash between EU regulations and artificial intelligence: http://www.wired.com/2016/07/artificial-intelligence-setting-internet-huge-clash-europe/?utm_content=buffer08177&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer
11. How AI-driven companies like Google depend on public data: https://techcrunch.com/2016/07/09/we-need-to-talk-about-ai-and-access-to-publicly-funded-data-sets/
12. The problems with current chatbots: https://techcrunch.com/2016/07/16/bursting-the-chatbot-bubble/
13. The application of supercomputers in deep learning: http://nextbigfuture.com/2016/07/supercomputers-can-accelerate-machine.html
14. Zoom.ai is launching an AI executive assistant: https://techcrunch.com/2016/07/14/zoom-ai/
15. A list of resources for learning about deep learning: http://www.kdnuggets.com/2016/07/start-learning-deep-learning.html
16. A machine learning based email autoresponder: https://techcrunch.com/2016/07/13/zendesks-automatic-answers-taps-machine-learning-ai-to-generate-bot-style-email-responses/
17. Predicting Game of Thrones betrayals using machine learning: http://dataconomy.com/machine-learning-can-predict-game-of-thrones-betrayals/
18. Ten categories for machine learning and AI algorithms: http://www.kdnuggets.com/2016/07/10-algorithm-categories-data-science.html
19. Helping AI better understand what we are saying to them: https://techcrunch.com/2016/07/15/pat-launches-private-beta-to-help-ai-understand-what-you-say/
20. The AI boom in Silicon Valley: http://www.nytimes.com/2016/07/18/technology/on-wheels-and-wings-artificial-intelligence-swarms-silicon-valley.html?partner=IFTTT&_r=1
21. Good news, 9 mill. people will be liberated from sweatshops by robots-Bad news, 9 mill. people without jobs: https://www.theguardian.com/sustainable-business/2016/jul/16/robot-factories-threaten-jobs-millions-garment-workers-south-east-asia-women
22. Google's using deep learning to optimise the energy efficiency of cooling its server farms: http://www.bloomberg.com/news/articles/2016-07-19/google-cuts-its-giant-electricity-bill-with-deepmind-powered-ai
23. A list of more than 50 machine learning API: http://www.datasciencecentral.com/profiles/blogs/list-of-50-machine-learning-apis
24. How deep learning networks scale: http://www.kdnuggets.com/2016/07/deep-learning-networks-scale.html
25. Using machine learning to manage virtual servers: http://www.datanami.com/2016/08/02/machine-learning-brings-real-insight-jordans-virtual-environment/
26. Google, Microsoft, IBM, Amazon, Facebook are all renting-out access to their AI systems: https://www.technologyreview.com/s/602037/google-and-microsoft-want-every-company-to-scrutinize-you-with-ai/
27. Current developments in deep learning: http://www.datasciencecentral.com/profiles/blogs/on-going-developments-and-outlook-for-deep-learning
28. Yes, AI is just as biased as people, because AI are made by people. That has been obvious for a long time: https://www.theguardian.com/technology/2016/aug/03/algorithm-racist-human-employers-work
29. Diagnosing autism using machine learning: https://www.sciencedaily.com/releases/2016/07/160712142403.htm
30. Why Open Source programming languages are winning over proprietary languages: http://www.techrepublic.com/article/why-open-source-programming-languages-are-crushing-proprietary-peers/ Better to learn R than Matlab?
31. An overview of deep learning neural networks applied to machine translation: https://kv-emptypages.blogspot.co.nz/2016/06/the-emerging-world-of-neural-net-driven.html
32. A commented list of resources explaining NoSQL: http://www.kdnuggets.com/2016/07/seven-steps-understanding-nosql-databases.html
33. A new version of PMML - Predictive Modelling Markup Language - has been released: http://www.kdnuggets.com/2016/08/data-mining-group-pmml-v43.html 
34. Ten simple rules for using statistics properly and effectively: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004961
35. How to use machine learning for face recognition: https://medium.com/@ageitgey/machine-learning-is-fun-part-4-modern-face-recognition-with-deep-learning-c3cffc121d78#.wlicrwx4j
36. Using machine learning to predict the genetic basis of autism: http://www.natureworldnews.com/articles/26110/20160802/predict-autism-machine-learning.htm
37. Why Harvard Business School is teaching its MBA students about AI: http://www.businessbecause.com/news/full-time-mba/4100/harvard-business-school-is-teaching-mbas-about-ai
38. Two more Google machine learning API are now in open beta: https://www.sdxcentral.com/articles/news/google-clouds-machine-learning-apis-hit-beta/2016/07/
39. Top programming languages for 2016 - Python & R are now numbers 3 & 5, respectively. http://spectrum.ieee.org/static/interactive-the-top-programming-languages-2016?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+IeeeSpectrum+%28IEEE+Spectrum%29&utm_content=FaceBook
40. Detecting sarcasm using a neural network: https://techcrunch.com/2016/08/04/this-neural-network-tries-to-tell-if-youre-being-sarcastic-online/ A lot of people still struggle to detect sarcasm...
41. Developing chatbots for HR: https://www.technologyreview.com/s/602068/the-hr-person-at-your-next-job-may-actually-be-a-bot/ 
42. Will artificial intelligence's ever get common sense? http://www.kdnuggets.com/2016/08/common-sense-artificial-intelligence-2026.html
43. How investors feel about artificial intelligence: http://techemergence.com/how-investors-feel-about-artificial-intelligence-from-29-ai-founders-and-executives/
44. Intelligent security and surveillance systems: http://www.extremetech.com/extreme/232728-when-you-look-at-the-camera-and-it-looks-back-how-artificial-intelligence-is-revolutionizing-home-security
45. OpenAI is calling for an "AI Police" http://www.wired.com/2016/08/openai-calling-techie-cops-battle-code-gone-rogue/?mbid=social_twitter - I seem to remember the "Turing Police" in Neuromancer...
46. Using machine learning to predict crop-yield from satellite images: http://www.theverge.com/2016/8/4/12369494/descartes-artificial-intelligence-crop-predictions-usda 
47. IBM is arguing that AI should be assisting people rather than replacing them: http://www.informationweek.com/government/leadership/ibm-ai-should-stand-for-augmented-intelligence/d/d-id/1326496?
48. Arthur C. Clarke was writing about IA - Intelligence Amplifiers - in 1986: http://www.informationweek.com/government/leadership/ibm-ai-should-stand-for-augmented-intelligence/d/d-id/1326496?
49. Using machine learning to find zero-day exploits on the dark web: https://www.technologyreview.com/s/602115/machine-learning-algorithm-combs-the-darknet-for-zero-day-exploits-and-finds-them/
50. Yahoo has used machine learning to develop a troll-detecting algorithm: http://www.wired.co.uk/article/yahoo-online-abuse-algorithm
51. The paper describing Yahoo's troll-detector: http://www2016.net/proceedings/proceedings/p145.pdf
52. A paper on estimating crop yield from images, this time in China: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=7524771&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D7524771

IEEE Transactions on Evolutionary Computation, Volume 20, Number 4, August 2016

1. Entropy-Based Termination Criterion for Multiobjective Evolutionary Algorithms
Author(s): Dhish Kumar Saxena ; Arnab Sinha ; João A. Duro ; Qingfu Zhang
Page(s): 485 - 498

2. A Hybrid Multiobjective Memetic Metaheuristic for Multiple Sequence Alignment
Author(s): Álvaro Rubio-Largo ; Miguel A. Vega-Rodríguez ; David L. González-Álvarez
Page(s): 499 - 514

3. An Optimality Theory-Based Proximity Measure for Set-Based Multiobjective Optimization
Author(s): Kalyanmoy Deb ; Mohamed Abouhawwash
Page(s): 515 - 528

4. The Effects of Developer Dynamics on Fitness in an Evolutionary Ecosystem Model of the App Store
Author(s): Soo Ling Lim ; Peter J. Bentley ; Fuyuki Ishikawa
Page(s): 529 - 545

5. Subpermutation-Based Evolutionary Multiobjective Algorithm for Load Restoration in Power Distribution Networks
Author(s): Eduardo Gontijo Carrano ; Gisele P. da Silva ; Edgard P. Cardoso ; Ricardo H. C. Takahashi
Page(s): 546 - 562

6. A Primary Theoretical Study on Decomposition-Based Multiobjective Evolutionary Algorithms
Author(s): Yuan-Long Li ; Yu-Ren Zhou ; Zhi-Hui Zhan ; Jun Zhang
Page(s): 563 - 576

7. Discrete Planar Truss Optimization by Node Position Variation Using Grammatical Evolution
Author(s): Michael Fenton ; Ciaran McNally ; Jonathan Byrne ; Erik Hemberg ; James McDermott ; Michael O’Neill
Page(s): 577 - 589

8. An Adaptive Multipopulation Framework for Locating and Tracking Multiple Optima
Author(s): Changhe Li ; Trung Thanh Nguyen ; Ming Yang ; Michalis Mavrovouniotis ; Shengxiang Yang
Page(s): 590 - 605

9. A Survey on Evolutionary Computation Approaches to Feature Selection
Author(s): Bing Xue ; Mengjie Zhang ; Will N. Browne ; Xin Yao
Page(s): 606 - 626

10. An Enhanced Genetic Algorithm for Ab Initio Protein Structure Prediction
Author(s): Mahmood A. Rashid ; Firas Khatib ; Md Tamjidul Hoque ; Abdul Sattar
Page(s): 627 - 644

IEEE Transactions on Fuzzy Systems, Volume 24, Issue 1, 2016

1. Representing Uncertainty With Information Sets
Author(s):  Manish Aggarwal ; Madasu Hanmandlu
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):  Yan-Jun Liu ; Ying Gao ; Shaocheng Tong ; Yongming Li
Page(s):  16 - 28

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

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

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

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

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

8. A New Sum-of-Squares Design Framework for Robust Control of Polynomial Fuzzy Systems With Uncertainties
Author(s):  Kazuo Tanaka ; Motoyasu Tanaka ; Ying-Jen Chen ; Hua O. Wang
Page(s):  94 - 110

9. Min-Max Programming Problem Subject to Addition-Min Fuzzy Relation Inequalities
Author(s):  Xiao-Peng Yang ; Xue-Gang Zhou ; Bing-Yuan Cao
Page(s):  111 - 119

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

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

12. Adaptive Fuzzy Control for a Class of Stochastic Pure-Feedback Nonlinear Systems With Unknown Hysteresis
Author(s):  Fang Wang ; Zhi Liu ; Yun Zhang ; C. L. Philip Chen
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):  Faa-Jeng Lin ; I-Fan Sun ; Kai-Jie Yang ; Jin-Kuan Chang
Page(s):  153 - 167

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

15. A Historical Account of Types of Fuzzy Sets and Their Relationships
Author(s):  Humberto Bustince ; Edurne Barrenechea ; Miguel Pagola ; Javier Fernandez ; Zeshui Xu ; Benjamin Bedregal ; Javier Montero ; Hani Hagras ; Francisco Herrera ; Bernard De Baets
Page(s):  179 - 194

16. Fuzzy Membership Descriptors for Images
Author(s):  Mohit Kumar ; Norbert Stoll ; Kerstin Thurow ; Regina Stoll
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):  Shih-Ju Ho ; Bor-Sen Chen
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):  Yuan-Xin Li ; Guang-Hong Yang
Page(s):  223 - 234

19. Control of Switched Nonlinear Systems via T–S Fuzzy Modeling
Author(s):  Xudong Zhao ; Yunfei Yin ; Lixian Zhang ; Haijiao Yang
Page(s):  235 - 241

20. Ambiguity-Based Multiclass Active Learning
Author(s):  Ran Wang ; Chi-Yin Chow ; Sam Kwong
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):  Jerry M. Mendel ; Hani Hagras ; Humberto Bustince ; Francisco Herrera
Page(s):  249 - 250

Neural Networks, Volume 81, Pages: 1-102, September 2016

1. Global exponential stability of impulsive complex-valued neural networks with both asynchronous time-varying and continuously distributed delays  
Author(s): Qiankun Song, Huan Yan, Zhenjiang Zhao, Yurong Liu
Pages: 1-10

2. A note on finite-time and fixed-time stability  
Author(s): Wenlian Lu, Xiwei Liu, Tianping Chen
Pages: 11-15

3. Synchronization of fractional-order complex-valued neural networks with time delay  
Author(s): Haibo Bao, Ju H. Park, Jinde Cao
Pages: 16-28

4. Real-time object tracking based on scale-invariant features employing bio-inspired hardware  
Author(s): Shinsuke Yasukawa, Hirotsugu Okuno, Kazuo Ishii, Tetsuya Yagi
Pages: 29-38

5. A neural model of the frontal eye fields with reward-based learning  
Author(s): Weijie Ye, Shenquan Liu, Xuanliang Liu, Yuguo Yu
Pages: 39-51

6. New results on anti-synchronization of switched neural networks with time-varying delays and lag signals  
Author(s): Yuting Cao, Shiping Wen, Michael Z.Q. Chen, Tingwen Huang, Zhigang Zeng
Pages: 52-58

7. Pseudo-inverse linear discriminants for the improvement of overall classification accuracies  
Author(s): Gao Daqi, Dastagir Ahmed, Guo Lili, Wang Zejian, Wang Zhe
Pages: 59-71

8. Neural network training as a dissipative process  
Author(s): Marco Gori, Marco Maggini, Alessandro Rossi
Pages: 72-80

9. Pointwise and uniform approximation by multivariate neural network operators of the max-product type  
Author(s): Danilo Costarelli, Gianluca Vinti
Pages: 81-90

10. Extreme learning machine and adaptive sparse representation for image classification  
Author(s): Jiuwen Cao, Kai Zhang, Minxia Luo, Chun Yin, Xiaoping Lai
Pages: 91-102

IEEE Transactions on Neural Networks and Learning Systems, Volume 27, Issue 8, August 2016

1. Guest Editorial Special Issue on "Neural Networks and Learning Systems Applications in Smart Grid"
Author: Dipti Srinivasan; Ganesh Kumar Venayagamoorthy
Page(s): 1601 - 1603

2. Dynamic State Estimation of Power Systems With Quantization Effects: A Recursive Filter Approach Metrics by Information Projection
Authors: Liang Hu; Zidong Wang; Xiaohui Liu
Page(s): 1604 - 1614

3. Assessing the Influence of an Individual Event in Complex Fault Spreading Network Based on Dynamic Uncertain Causality Graph Metrics by Information Projection
Authors: Chunling Dong; Yue Zhao; Qin Zhang
Page(s): 1615 - 1630

4. Improved Fault Classification in Series Compensated Transmission Line: Comparative Evaluation of Chebyshev Neural Network Training Algorithms
Authors: Bhargav Y. Vyas; Biswarup Das; Rudra Prakash Maheshwari
Page(s): 1631 - 1642

5. Dynamic Energy Management System for a Smart Microgrid
Authors: Ganesh Kumar Venayagamoorthy; Ratnesh K. Sharma; Prajwal K. Gautam; Afshin Ahmadi
Page(s): 1643 - 1656

6. Storage Free Smart Energy Management for Frequency Control in a Diesel-PV-Fuel Cell-Based Hybrid AC Microgrid
Authors: P. C. Sekhar; S. Mishra
Page(s): 1657 - 1671

7. Cooperative Strategy for Optimal Management of Smart Grids by Wavelet RNNs and Cloud Computing
Authors: Christian Napoli; Giuseppe Pappalardo; Giuseppe Marco Tina; Emiliano Tramontana
Page(s): 1672 - 1685

8. Assessing Short-Term Voltage Stability of Electric Power Systems by a Hierarchical Intelligent System
Authors: Yan Xu; Rui Zhang; Junhua Zhao; Zhao Yang Dong; Dianhui Wang; Hongming Yang; Kit Po Wong
Page(s): 1686 - 1696

9. Fair Energy Scheduling for Vehicle-to-Grid Networks Using Adaptive Dynamic Programming
Authors: Shengli Xie; Weifeng Zhong; Kan Xie; Rong Yu; Yan Zhang
Page(s): 1697 - 1707

10. Automatic Learning of Fine Operating Rules for Online Power System Security Control
Authors: Hongbin Sun; Feng Zhao; Hao Wang; Kang Wang; Weiyong Jiang; Qinglai Guo; Boming Zhang; Louis Wehenkel
Page(s): 1708 - 1719

11. Adaptive Portfolio Optimization for Multiple Electricity Markets Participation
Authors: Tiago Pinto; Hugo Morais; Tiago M. Sousa; Tiago Sousa; Zita Vale; Isabel Praça; Ricardo Faia; Eduardo José Solteiro Pires
Page(s): 1720 - 1733

12. Two Machine Learning Approaches for Short-Term Wind Speed Time-Series Prediction
Authors: Ronay Ak; Olga Fink; Enrico Zio
Page(s): 1734 - 1747

13. Online Supplementary ADP Learning Controller Design and Application to Power System Frequency Control With Large-Scale Wind Energy Integration
Authors: Wentao Guo; Feng Liu; Jennie Si; Dawei He; Ronald Harley; Shengwei Mei
Page(s): 1748 - 1761

14. Adaptive Modulation for DFIG and STATCOM With High-Voltage Direct Current Transmission
Authors: Yufei Tang; Haibo He; Zhen Ni; Jinyu Wen; Tingwen Huang
Page(s): 1762 - 1772

15. Machine Learning Methods for Attack Detection in the Smart Grid
Authors: Mete Ozay; Iñaki Esnaola; Fatos Tunay Yarman Vural; Sanjeev R. Kulkarni; H. Vincent Poor
Page(s): 1773 - 1786

16. Smart-Grid Backbone Network Real-Time Delay Reduction via Integer Programming
Authors: Sasikanth Pagadrai; Muhittin Yilmaz; Pratyush Valluri
Page(s): 1787 - 1792

17. A Novel Empirical Mode Decomposition With Support Vector Regression for Wind Speed Forecasting
Authors: Ye Ren; Ponnuthurai Nagaratnam Suganthan; Narasimalu Srikanth
Page(s): 1793 - 1798

Weekly Review 9 July 2016

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

1. Mining emails to identify disgruntled employees: http://fortune.com/insider-threats-email-scout/
2. The origins of Support Vector Machines: http://www.kdnuggets.com/2016/07/guyon-data-mining-history-svm-support-vector-machines.html - gosh, I was in high school in 1989...
3. A robot using deep learning to identify items has won Amazon's robot worker challenge: http://www.techrepublic.com/article/amazons-robot-worker-challenge-won-by-ai-powered-suction-arm/
4. DeepMind is planning to use deep learning to diagnose degenerative eye diseases: https://www.theguardian.com/technology/2016/jul/05/google-deepmind-nhs-machine-learning-blindness
5. Anomaly detection with machine learning: http://www.prcconsulting.net/2016/07/machine-learning-anomaly-detection-finding-a-needle-in-a-haystack/
6. More on DeepMind's project to detect degenerative eye diseases: https://techcrunch.com/2016/07/05/deepmind-partners-with-nhs-eye-hospital-to-conduct-ai-research/
7. A description of Facebook's AI-based multi-language composer - no details of what kind of AI, though: http://www.computerworld.com/article/3090558/social-media/facebook-looks-to-break-language-barriers-with-new-translation-tool.html
8. Can AI predict the next US president? http://www.techrepublic.com/article/election-tech-the-trump-clinton-race-can-ai-forecast-the-winner/
9. Current key trends in AI and machine learning: https://techcrunch.com/2016/07/06/key-trends-in-machine-learning-and-ai/
10. The ideal cloud platforms for machine learning applications: http://www.datanami.com/2016/07/06/seeking-ideal-clouds-ml-workloads/
11. Google buys yet another machine learning startup: http://www.theverge.com/2016/7/6/12105322/google-machine-vision-moodstocks-acquisition
12. How to disconnect from work when you're away from work: http://www.computerworld.com/article/2936764/it-careers/cant-disconnect-on-vacation-these-it-pros-offer-their-hard-earned-tips.html
13. A high-level overview of Support Vector Machines: http://www.kdnuggets.com/2016/07/support-vector-machines-simple-explanation.html
14. How Microsoft plans to out-do Google in AI: http://www.theverge.com/2016/7/7/12111028/microsoft-bot-framework-artificial-intelligence-satya-nadella-interview
15. The four forces shaping AI today: https://www.oreilly.com/ideas/the-four-dynamic-forces-shaping-ai
16. Diagnosing Alzheimer's disease with machine learning: http://medicalxpress.com/news/2016-07-artificial-intelligence-aid-alzheimer-diagnosis.html
17. Modernising PhD examinations: http://www.nature.com/news/what-s-the-point-of-the-phd-thesis-1.20203?WT.mc_id=TWT_NatureNews - I remember I didn't do an oral exam
18. Any model needs to be tested, & the results need to be statistically sound: http://www.techrepublic.com/article/decision-making-algorithms-is-anyone-making-sure-theyre-right/ - see post here: http://computational-intelligence.blogspot.com/2011/11/cargo-cult-statistics.html
19. Computer might get smarts, but they'll never get consciousness: http://www.livemint.com/Opinion/MsbteoWOJMwMQkIQDej4dJ/The-debate-on-artificial-intelligence.html
20. Using AI to improve beer brewing, via a Facebook chatbot: http://www.cnet.com/uk/news/robot-brews-how-ai-could-flavor-your-next-beer/
21. Sounds like Darktrace is using an artificial immune system algorithm to detect network intrusion: http://www.techrepublic.com/article/darktrace-bolsters-machine-learning-based-security-tools-to-automatically-attack-threats/
22. Microsoft open-sources its system for testing AI in Minecraft: http://www.computerworld.com/article/3093413/artificial-intelligence/microsoft-lets-ai-experiments-loose-in-world-of-minecraft.html

Review 12 June - 3 July

I was travelling on business, and got behind on the weekly review posts. Here is a review of the links that I tweeted about over the last three weeks:

1. Facebook's race to catch-up in AI: http://www.fastcompany.com/3060570/facebooks-formula-for-winning-at-ai
2. How AI is making inroads into the legal profession: http://www.thecollegefix.com/post/27773/
3. Google vs Baidu in speech recognition: http://techcrunch.com/2016/06/11/google-baidu-and-the-race-for-an-edge-in-the-global-speech-recognition-market/
4. A philosopher's views on the dangers of artificial intelligence: https://www.theguardian.com/technology/2016/jun/12/nick-bostrom-artificial-intelligence-machine
5. Five ways engineers can improve their writing: http://theinstitute.ieee.org/career-and-education/career-guidance/five-ways-engineers-can-improve-their-writing
6. Dango uses neural networks to recommend emojis: http://motherboard.vice.com/en_au/read/with-dango-app-ai-is-learning-to-meme
7. Watch Sunspring, a sci-fi movie written by an AI: http://techcrunch.com/2016/06/11/watch-this-short-sci-fi-movie-with-a-script-written-by-an-ai/
8. Using machine learning to fight ransomeware: http://www.datanami.com/2016/06/14/machine-learning-enlisted-fight-ransomware/
9. How to select the kernel of a support vector machine: http://www.kdnuggets.com/2016/06/select-support-vector-machine-kernels.html
10. Next step for AI research is how they can learn on their own: http://theinstitute.ieee.org/technology-focus/technology-topic/the-next-step-for-artificial-intelligence-is-machines-that-get-smarter-on-their-own
11. Where machine learning is going to disrupt businesses next: http://tomtunguz.com/key-ingredient-machine-learning/?platform=hootsuite
12. AI have now passed the Turing test for sound: http://www.techrepublic.com/article/how-new-ai-fools-humans-into-thinking-artificial-sounds-are-real/
13. Springboard, Google's enterprise AI assistant: http://techcrunch.com/2016/06/14/google-launches-springboard-an-ai-powered-assistant-for-its-enterprise-customers/
14. Apple is opening-up Siri to third-party developers: http://www.computerworld.com/article/3083149/mac-os-x/apple-touts-a-i-in-ios-and-opens-crown-jewels-to-devs.html - Joining other companies with open AI platforms
15. How to construct parsimonious binary classification trees: http://www.kdnuggets.com/2016/06/breiman-stone-parsimonious-binary-classification-trees.html
16. I think every academic has come across a workplace bully at some time, academia attracts egotistical people: https://www.insidehighered.com/advice/2016/06/15/advice-dealing-bullying-behavior-essay
17. A neural network-based system that turns rough sketches into photorealistic portraits: https://www.technologyreview.com/s/601684/machine-vision-algorithm-learns-to-transform-hand-drawn-sketches-into-photorealistic-images/ Includes link to paper
18. Finding bugs with AI: http://motherboard.vice.com/en_au/read/cyber-grand-challenge The ultimate goal is to patch the bugs, too.
19. Is the future of smartphones a single AI? http://www.theverge.com/2016/6/14/11939310/andy-rubin-google-android-playground-ai-robotics
20. Developing an "ethical" AI that can make life-or-death decisions: http://www.techrepublic.com/article/building-ethical-machines-how-it-can-help-ai-make-life-or-death-decisions/
21. How is AI going to surprise us in the future? http://www.kdnuggets.com/2016/06/how-much-ai-surprise.html
22. Six lessons for getting the best out of machine learning: http://www.techrepublic.com/article/ibm-watson-six-lessons-from-an-early-adopter-on-how-to-do-machine-learning/
23. Using deep learning neural networks for drug discovery: http://scienmag.com/deep-learning-system-for-drug-discovery-to-be-presented-at-the-machine-intelligence-summit-in-berlin/
24. A smart car dashcam that rates everyone else's driving: http://spectrum.ieee.org/cars-that-think/transportation/sensors/the-ai-dashcam-app-that-wants-to-rate-every-driver-in-the-world?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+IeeeSpectrum+%28IEEE+Spectrum%29&utm_content=FaceBook 
25. A concise history of data mining: http://dataconomy.com/history-data-mining/
26. How to get started with mining Twitter data with Python: http://www.kdnuggets.com/2016/06/mining-twitter-data-python-part-1.html
27. A nice overview of the key concepts of machine learning for people who know nothing about it: http://www.techrepublic.com/article/machine-learning-the-smart-persons-guide/
28. Using machine learning to buy advertising: http://www.datasciencecentral.com/profiles/blogs/when-milliseconds-count-using-ai-to-buy-advertising
29. Neural networks and the future of AI: https://techcrunch.com/2016/06/16/neural-networks-artificial-intelligence-and-our-future/
30. Using machine learning to improve performance of power plants: http://www.informationweek.com/iot/ge-uses-machine-learning-to-restore-italian-power-plant/d/d-id/1325918?
31. A basic explanation of how backpropagation works: http://www.kdnuggets.com/2016/06/visual-explanation-backpropagation-algorithm-neural-networks.html
32. Google has opened a dedicated machine learning research lab in Zurich: http://www.informationweek.com/big-data/big-data-analytics/google-launches-ai-machine-learning-research-center-/d/d-id/1325942
33. On the importance of open API for data science: http://www.kdnuggets.com/2016/06/open-api-economy-growth-big-data-analytics.html
34. Analysing sport teams play using machine learning - heading towards an AI coach? http://motherboard.vice.com/en_au/read/coach-bots-nba-ai
35. Student evaluations of lecturers are very blunt instruments, it's not surprising that there is bias in them: https://www.insidehighered.com/advice/2016/06/17/removing-bias-student-evaluations-faculty-members-essay
36. Machine learning for personalised advertising: http://www.pubexec.com/article/the-future-of-marketing-will-be-built-on-personalization-artificial-intelligence/
37. Machine learning libraries in Javascript: http://www.kdnuggets.com/2016/06/top-machine-learning-libraries-javascript.html
38. We're getting close to Sci-Fi levels of AI: http://www.huffingtonpost.com/entry/the-amazing-artificial-intelligence-we-were-promised-is-coming-finally_b_10592674.html?section=india
39. Future trends in AI: http://www.kdnuggets.com/2016/06/machine-learning-trends-future-ai.html
40. Machine learning with Python for complete beginners: http://pythonforengineers.com/machine-learning-for-complete-beginners/
41. A brief, point-by-point history of data mining: http://www.kdnuggets.com/2016/06/rayli-history-data-mining.html
42. A short FAQ on RankBrain, how Google applies deep learning to search: http://searchengineland.com/faq-all-about-the-new-google-rankbrain-algorithm-234440#.V2xDOlIYrKc.twitter
43. Review of deep learning models and applications: http://www.kdnuggets.com/2016/06/review-deep-learning-models.html
44. Generating sculptures with a deep neural network and an EA: http://www.popsci.com/creative-ai-learns-to-sculpt-3d-printable-objects
45. Five myths about machine learning: http://www.forbes.com/sites/teradata/2015/11/13/five-myths-about-machine-learning-you-need-to-know-today/#37831dd2275c
46. According to this article, compliance is the knowledge job most likely to be taken over by AI: https://hbr.org/2016/06/the-knowledge-jobs-most-likely-to-be-automated
47. Identifying NSFW images using machine learning: http://www.kdnuggets.com/2016/06/algorithmia-improving-nudity-detection-nsfw-image-recognition.html
48. How Google is putting machine learning into everything: https://backchannel.com/how-google-is-remaking-itself-as-a-machine-learning-first-company-ada63defcb70#.n1ai2xwao
49. A good argument in favour of all research publications being open-access: http://arstechnica.com/science/2016/06/what-is-open-access-free-sharing-of-all-human-knowledge/
50. The impact of machine-generated screenplays: http://motherboard.vice.com/en_au/read/how-machine-generated-screenplays-may-affect-artists
51. The AI lawyer named Ross has been hired by its first real law firm: http://futurism.com/artificially-intelligent-lawyer-ross-hired-first-official-law-firm/
52. An AI that predicts human actions after being trained on TV programmes: http://www.geekwire.com/2016/computer-binge-watches-tv-predict-ai/
53. Google's suggested rules for AI that prevent AI from becoming harmful: http://www.extremetech.com/extreme/230718-google-researchers-tackle-ai-and-robotics-safety-prevent-future-toasters-from-killing-us-in-our-sleep
54. A cheat-sheet on machine learning algorithms: http://www.datasciencecentral.com/profiles/blogs/the-making-of-a-cheatsheet-emoji-edition
55. Applying cloud-based intelligence to off-the-shelf robots: http://www.theverge.com/circuitbreaker/2016/6/24/12027808/tend-ai-cloud-machine-learning-co-working-robots
56. AI will create jobs as well as destroy jobs - it just won't create as many jobs as it destroys: http://www.informationweek.com/strategic-cio/it-strategy/robots-ai-wont-destroy-jobs-yet/d/d-id/1326056
57. A beginners experiences with deep learning: https://www.theguardian.com/technology/2016/jun/28/google-says-machine-learning-is-the-future-so-i-tried-it-myself
58. Predictions that AI will replace 16 % of white collar jobs by 2025, but create another 9 %: http://www.theregister.co.uk/2016/06/28/forrester_reports_ai_will_create_jobs/
59. An adaptive AI for air combat: http://www.newsmax.com/Newsfront/air-force-ai-top-gun-software/2016/06/27/id/735925/
60. Google has built an AI that picks out the most important parts of an image: https://techcrunch.com/2016/06/28/google-researchers-teach-ais-to-see-the-important-parts-of-images-and-tell-you-about-them/
61. According to the paper, the air combat AI is a genetic-fuzzy system: http://www.omicsgroup.org/journals/genetic-fuzzy-based-artificial-intelligence-for-unmanned-combat-aerialvehicle-control-in-simulated-air-combat-missions-2167-0374-1000144.php?aid=72227 
62. An overview of deep learning: http://www.datasciencecentral.com/profiles/blogs/guide-to-deep-learning
63. Why we need to stop worrying about AI: http://fortune.com/2016/06/28/artificial-intelligence-potential/
64. A list of deep learning libraries in different languages: http://www.datasciencecentral.com/profiles/blogs/deep-learning-libraries-by-language
65. Landing a job in artificial intelligence: http://theinstitute.ieee.org/technology-focus/technology-topic/how-to-land-a-job-in-artificial-intelligence
66. Infographic on the current state of artificial intelligence: http://www.datasciencecentral.com/profiles/blogs/the-state-of-artificial-intelligence-infographic
67. Looking inside convolutional neural networks: http://www.kdnuggets.com/2016/06/peeking-inside-convolutional-neural-networks.html
68. Are journal editors cheating the impact factor measure? https://www.insidehighered.com/views/2016/07/01/examination-whether-academic-journal-rankings-are-being-manipulated-essay
69. Predicting cancer metastasis - seems to be using machine learning of some description: http://www.digitaltrends.com/cool-tech/cancer-spread-prediction-algorithm/
70. I like #4, "don't multi-task". I have to keep reminding myself "one thing at a time!" https://elearningindustry.com/5-ways-survive-student-email-avalanche
71. Although to be honest, it's not an avalanche of email from students that usually takes up my time:   https://elearningindustry.com/5-ways-survive-student-email-avalanche
72. Brief introduction to text mining: http://www.kdnuggets.com/2016/07/text-mining-101-topic-modeling.html
73. Experts' opinions on Satya Nadella's 10 rules for AI: http://www.techrepublic.com/article/ai-experts-weigh-in-on-microsoft-ceos-10-new-rules-for-artificial-intelligence/
74. The promise, and problems, of machine learning in cybersecurity: https://techcrunch.com/2016/07/01/exploiting-machine-learning-in-cybersecurity/
75. Intel is tuning its Xeon Phi chips to make them better suited to machine learning: http://www.computerworld.com/article/3090991/computer-hardware/intel-tunes-its-mega-chip-for-machine-learning.html
76. Satya Nadella calls for accountability in AI, biased systems already exist: https://www.technologyreview.com/s/601812/microsofts-ceo-calls-for-accountable-ai-ignores-the-algorithms-that-already-rule-our-lives/
77. Implementing recursive neural networks in TensorFlow: http://www.kdnuggets.com/2016/06/recursive-neural-networks-tensorflow.html
78. AI can see the world, but it doesn't see the world the same way we do: https://www.technologyreview.com/s/601819/ai-is-learning-to-see-the-world-but-not-the-way-humans-do/

IEEE Transactions on Neural Networks and Learning Systems;Volume 27, Issue 7, July 2016.

1. Probe Machine
Author(s): Jin Xu
Page(s): 1405 - 1416

2. Learning Compositional Shape Models of Multiple Distance Metrics by Information Projection
Author(s): Ping Luo; Liang Lin; Xiaobai Liu
Page(s): 1417 - 1428

3. Comparison Analysis: Granger Causality and New Causality and Their Applications to Motor Imagery
Author(s): Sanqing Hu; Hui Wang; Jianhai Zhang; Wanzeng Kong; Yu Cao; Robert Kozma
Page(s): 1429 - 1444

4. Alternative Multiview Maximum Entropy Discrimination
Author(s): Guoqing Chao; Shiliang Sun
Page(s): 1445 - 1456

5. Parallel Online Temporal Difference Learning for Motor Control
Author(s): Wouter Caarls; Erik Schuitema
Page(s): 1457 - 1468

6. Sparse Uncorrelated Linear Discriminant Analysis for Undersampled Problems
Author(s): Xiaowei Zhang; Delin Chu; Roger C. E. Tan
Page(s): 1469 - 1485

7. Stability Analysis for Delayed Neural Networks Considering Both Conservativeness and Complexity
Author(s): Chuan-Ke Zhang; Yong He; Lin Jiang; Min Wu
Page(s): 1486 - 1501

8. Compound Rank-k Projections for Bilinear Analysis
Author(s): Xiaojun Chang; Feiping Nie; Sen Wang; Yi Yang; Xiaofang Zhou; Chengqi Zhang
Page(s): 1502 - 1513

9. Constrained Clustering With Nonnegative Matrix Factorization
Author(s): Xianchao Zhang; Linlin Zong; Xinyue Liu; Jiebo Luo
Page(s): 1514 - 1526

10. Control of Large-Scale Boolean Networks via Network Aggregation
Author(s): Yin Zhao; Bijoy K. Ghosh; Daizhan Cheng
Page(s): 1527 - 1536

11. Near-Optimal Controller for Nonlinear Continuous-Time Systems With Unknown Dynamics Using Policy Iteration
Author(s): Samrat Dutta; Prem Kumar Patchaikani; Laxmidhar Behera
Page(s): 1537 - 1549

12. Image Super-Resolution via Adaptive  \ell _{p} (0< p< 1) Regularization and Sparse Representation
Author(s): Feilong Cao; Miaomiao Cai; Yuanpeng Tan; Jianwei Zhao
Page(s): 1550 - 1561

13. Neural Network Control-Based Adaptive Learning Design for Nonlinear Systems With Full-State Constraints
Author(s): Yan-Jun Liu; Jing Li; Shaocheng Tong; C. L. Philip Chen
Page(s): 1562 - 1571

14. Learning Spike Time Codes Through Morphological Learning With Binary Synapses
Author(s): Subhrajit Roy; Phyo Phyo San; Shaista Hussain; Lee Wang Wei; Arindam Basu
Page(s): 1572 - 1577

15. Saturated Finite Interval Iterative Learning for Tracking of Dynamic Systems With HNN-Structural Output
Author(s): Wenjun Xiong; Daniel W. C. Ho; Xinghuo Yu
Page(s): 1578 - 1584

16. Pinning Control Design for the Stabilization of Boolean Networks
Author(s): Fangfei Li
Page(s): 1585 - 1590

17. Can the Virtual Labels Obtained by Traditional LP Approaches Be Well Encoded in WLR?
Author(s): Qiaolin Ye; Jian Yang; Tongming Yin; Zhao Zhang
Page(s): :1591 - 1598

Weekly Review 11 June 2016

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

1. What happens when you run Bladerunner through a deep-learning autoencoder: http://www.vox.com/2016/6/1/11787262/blade-runner-neural-network-encoding
2. Concise explanation of the difference between regular machine learning and deep learning: http://www.kdnuggets.com/2016/06/difference-between-deep-learning-regular-machine-learning.html
3. On why AI needs a "big red button": http://www.theverge.com/2016/6/3/11856744/google-deep-mind-big-red-button-interupt-ai
4. Data mining unstructured data with deep learning: http://www.datanami.com/2016/06/03/unstructured-data-miners-chase-silver-deep-learning/
5. How AI is changing SEO: http://techcrunch.com/2016/06/04/artificial-intelligence-is-changing-seo-faster-than-you-think/
6. How AI-based "Driver Assistance Systems" will dominate the markup before autonomous vehicles: http://www.computerworld.com/article/3079044/car-tech/ai-guardian-angel-vehicles-will-dominate-auto-industry-says-toyota-exec.html
7. This is the era of AI: http://fortune.com/2016/06/03/tech-ceos-artificial-intelligence/
8. China's obsession with quantity over quality leads to corruption. fraud in science: http://www.economist.com/news/science-and-technology/21699898-fraud-bureaucracy-and-obsession-quantity-over-quality-still-hold-chinese?fsrc=scn/tw/te/pe/ed/schrdingerspanda
9. Will Artificial Intelligences end up with human rights? http://www.telegraph.co.uk/science/2016/05/29/computers-could-develop-consciousness-and-may-need-human-rights/
10. The future of AI on smart phones: http://dataconomy.com/forget-siri-machine-learning-ai-coming-smartphone/
11. The truth about deep learning: http://www.kdnuggets.com/2016/06/truth-deep-learning.html
12. Why TensorFlow is a game-changer: http://www.datasciencecentral.com/profiles/blogs/tensorflow-why-google-s-artificial-intelligence-engine-is-a
13. List of resources for an open-source machine learning degree: http://www.kdnuggets.com/2016/06/open-source-machine-learning-degree.html
14. How to do machine learning, from Uber's head of machine learning: http://techemergence.com/ubers-head-of-machine-learning-thinks-you-might-be-doing-it-wrong/
15. Ten frightening uses of AI: http://www.techrepublic.com/pictures/10-terrifying-uses-of-artificial-intelligence/
16. A new neural processor: http://www.computerworld.com/article/3079349/artificial-intelligence/a-former-nasa-chief-just-launched-this-ai-startup-to-turbocharge-neural-computing.html
17. A test shows that a medical AI is as accurate on triage as an experienced doctor: http://motherboard.vice.com/en_au/read/a-health-apps-ai-took-on-human-doctors-to-triage-patients
18. An AI web designer: http://www.techrepublic.com/article/new-wix-adi-uses-artificial-intelligence-to-design-your-small-business-website/
19. The industries being redefined by machine learning: http://www.forbes.com/sites/louiscolumbus/2016/06/04/machine-learning-is-redefining-the-enterprise-in-2016/#5fd98f705fc0
20. LinkedIn's contribution to machine learning: http://www.datanami.com/2016/06/07/linkedin-adds-growing-list-ml-tools/
21. Using AI in human longevity research: http://nextbigfuture.com/2016/06/artificial-intelligence-to-spearhead.html
22. Combining AI with the power of crowds: http://techcrunch.com/2016/06/07/crowdflower-series-d/
23. Business opportunities of machine learning: http://www.kdnuggets.com/2016/06/opportunites-machine-learning-startups.html
24. A new company using AI in computer security: http://www.datanami.com/2016/06/08/another-ai-based-security-startup-gains-funding/
25. More details about Armorway, startup using AI in computer security: http://www.techrepublic.com/article/armorway-grabs-2-5-million-to-expand-ai-security-platform/
26. DeepMind's five-year plan for AI in healthcare: http://techcrunch.com/2016/06/08/nhs-memo-details-googledeepminds-five-year-plan-to-bring-ai-to-healthcare/
27. TensorFlow is now available for iOS: http://www.theverge.com/2016/6/8/11885924/google-tensorflow-release-ios-magenta-neural-network
28. Artificial intelligence vs cancer: http://www.bbc.com/news/health-36482333
29. Another company, Cyclance, also using AI for computer security: http://www.computerworld.com/article/3081326/security/this-company-uses-ai-to-stop-cyberattacks-before-they-start.html
30. Behavioural psychologists are now testing artificial intelligences: https://www.technologyreview.com/s/601646/the-ai-machines-undergoing-behavioral-psychology-tests/
31. More on Google's Project Magenta, their AI composer: https://www.technologyreview.com/s/601642/ok-computer-write-me-a-song/
32. A deep neural network rendered 2001: A Space Odyssey in the style of Picasso: http://motherboard.vice.com/en_au/read/a-neural-network-rendered-kubricks-2001-in-the-style-of-pablo-picasso
33. A free e-book on data science: http://www.datasciencecentral.com/profiles/blogs/free-e-book-exploring-data-science
34. Google is developing its own version of Asimov's  laws of robotics: http://www.extremetech.com/extreme/229806-google-is-starting-to-design-its-own-version-of-asimovs-laws-of-robotics
35. A whitepaper on AI and machine learning in the insurance industry: http://1.fc-bi.com/LP=12421
36. A movie written in collaboration with an AI: http://arstechnica.com/the-multiverse/2016/06/an-ai-wrote-this-movie-and-its-strangely-moving/
37. The security risks of AI: http://www.datanami.com/2016/06/10/ai-coming-prompting-new-security-concerns/
38. There is still bias in peer review and in funding decisions: http://arstechnica.com/science/2016/06/implicit-bias-still-hinders-minority-researchers/
39. Google DeepMind is mastering more difficult video games, thanks to curiosity: http://www.theverge.com/2016/6/9/11893002/google-ai-deepmind-atari-montezumas-revenge
40. With quantum computers will come quantum machine learning: http://nextbigfuture.com/2016/06/google-hartmut-neven-predicts-that.html
41. The jobs that AI will destroy first: http://www.idgconnect.com/abstract/17250/no-robots-required-ai-eliminate-jobs
42. Why more women don't code - or even get into IT in general: https://theconversation.com/the-real-reason-more-women-dont-code-59663

IEEE Transactions on Neural Networks and Learning Systems, Volume 27, Issue 6, June 2016

1) Guest Editorial Special Section on Visual Saliency Computing and Learning
Author(s): Junwei Han; Ling Shao; Nuno Vasconcelos; Jungong Han; Dong Xu
Page(s): 1118 - 1121

2) Manifold Ranking-Based Matrix Factorization for Saliency Detection
Author(s): Dapeng Tao; Jun Cheng; Mingli Song; Xu Lin
Page(s): 1122 - 1134

3) DISC: Deep Image Saliency Computing via Progressive Representation Learning
Author(s): Tianshui Chen; Liang Lin; Lingbo Liu; Xiaonan Luo; Xuelong Li
Page(s): 1135 - 1149

4) Human-Centered Saliency Detection
Author(s): Zhenbao Liu; Xiao Wang; Shuhui Bu
Page(s): 1150 - 1162

5) Cosaliency Detection Based on Intrasaliency Prior Transfer and Deep Intersaliency Mining
Author(s): Dingwen Zhang; Junwei Han; Jungong Han; Ling Shao
Page(s): 1163 - 1176

6) Spatiochromatic Context Modeling for Color Saliency Analysis
Author(s): Jun Zhang; Meng Wang; Shengping Zhang; Xuelong Li; Xindong Wu
Page(s): 1177 - 1189

7) Dual Low-Rank Pursuit: Learning Salient Features for Saliency Detection
Author(s): Congyan Lang; Jiashi Feng; Songhe Feng; Jingdong Wang; Shuicheng Yan
Page(s): 1190 - 1200

8) Improving Visual Saliency Computing With Emotion Intensity
Author(s): Huiying Liu; Min Xu; Jinqiao Wang; Tianrong Rao; Ian Burnett
Page(s): 1201 - 1213

9) Reconciling Saliency and Object Center-Bias Hypotheses in Explaining Free-Viewing Fixations
Author(s): Ali Borji; James Tanner
Page(s): 1214 - 1226

10) Bottom–Up Visual Saliency Estimation With Deep Autoencoder-Based Sparse Reconstruction
Author(s): Chen Xia; Fei Qi; Guangming Shi
Page(s): 1227 - 1240

11) Learning to Predict Sequences of Human Visual Fixations
Author(s): Ming Jiang; Xavier Boix; Gemma Roig; Juan Xu; Luc Van Gool; Qi Zhao
Page(s): 1241 - 1252

12) Saliency-Aware Nonparametric Foreground Annotation Based on Weakly Labeled Data
Author(s): Xiaochun Cao; Changqing Zhang; Huazhu Fu; Xiaojie Guo; Qi Tian
Page(s): 1253 - 1265

13) The Application of Visual Saliency Models in Objective Image Quality Assessment: A Statistical Evaluation
Author(s): Wei Zhang; Ali Borji; Zhou Wang; Patrick Le Callet; Hantao Liu
Page(s): 1266 - 1278

14) Salient Band Selection for Hyperspectral Image Classification via Manifold Ranking
Author(s): Qi Wang; Jianzhe Lin; Yuan Yuan
Page(s): 1279 - 1289

15) Guest Editorial Special Section on Learning in Non-(geo)metric Spaces
Author(s): Marcello Pelillo; Edwin R. Hancock; Xuelong Li; Vittorio Murino
Page(s): 1290 - 1293

16) Sparse Coding on Symmetric Positive Definite Manifolds Using Bregman Divergences
Author(s): Mehrtash T. Harandi; Richard Hartley; Brian Lovell; Conrad Sanderson
Page(s): 1294 - 1306

17) Multicriteria Similarity-Based Anomaly Detection Using Pareto Depth Analysis
Author(s): Ko-Jen Hsiao; Kevin S. Xu; Jeff Calder; Alfred O. Hero
Page(s): 1307 - 1321

18) Learning in Variable-Dimensional Spaces
Author(s): Michelangelo Diligenti; Marco Gori; Claudio Saccà
Page(s): 1322 - 1332

19) Manifold Learning for Multivariate Variable-Length Sequences With an Application to Similarity Search
Author(s): Shen-Shyang Ho; Peng Dai; Frank Rudzicz
Page(s): 1333 - 1344

20) Constrained Clustering With Imperfect Oracles
Author(s): Xiatian Zhu; Chen Change Loy; Shaogang Gong
Page(s): 1345 - 1357

21) Hierarchical Image Segmentation Using Correlation Clustering
Author(s): Amir Alush; Jacob Goldberger
Page(s): 1358 - 1367

22) Feature Combination and the kNN Framework in Object Classification
Author(s): Jian Hou; Huijun Gao; Qi Xia; Naiming Qi
Page(s): 1368 - 1378

23) Dissimilarity-Based Ensembles for Multiple Instance Learning
Author(s): Veronika Cheplygina; David M. J. Tax; Marco Loog
Page(s): 1379 - 1391

24) Ensemble Manifold Rank Preserving for Acceleration-Based Human Activity Recognition
Author(s): Dapeng Tao; Lianwen Jin; Yuan Yuan; Yang Xue
Page(s): 1392 - 1404

IEEE Transactions on Fuzzy Systems, Volume 24, Issue 3

1) Nonfragile H_{\infty } Fuzzy Filtering With Randomly Occurring Gain Variations and Channel Fadings
Author(s): Sunjie Zhang; Zidong Wang; Derui Ding; Hongli Dong; Fuad E. Alsaadi; Tasawar Hayat
Page(s): 505 - 518

2) Chain and Substitution Rules of Intuitionistic Fuzzy Calculus
Author(s): Qian Lei; Zeshui Xu
Page(s): 519 - 529

3) Extended Fuzzy Logic: Sets and Systems
Author(s): Farnaz Sabahi; Mohammad Reza Akbarzadeh-T
Page(s): 530 - 543

4) Fuzzy-Model-Based Robust H_{\infty } Design of Nonlinear Packetized Networked Control Systems
Author(s): Bin Tang; Shiguo Peng; Yun Zhang
Page(s): 544 - 557

5) Extensions of Atanassov's Intuitionistic Fuzzy Interaction Bonferroni Means and Their Application to Multiple-Attribute Decision Making
Author(s): Yingdong He; Zhen He
Page(s): 558 - 573

6) Evolving Type-2 Fuzzy Classifier
Author(s): Mahardhika Pratama; Jie Lu; Guangquan Zhang
Page(s): 574 - 589

7) Multicriteria Decision Making With Ordinal/Linguistic Intuitionistic Fuzzy Sets For Mobile Apps
Author(s): Ronald R. Yager
Page(s): 590 - 599

8) Improving Linguistic Pairwise Comparison Consistency via Linguistic Discrete Regions
Author(s): Hengshan Zhang; Qinghua Zheng; Ting Liu; Zijiang Yang; Minnan Luo; Yu Qu
Page(s): 600 - 614

9) Law of Large Numbers for Uncertain Random Variables
Author(s): Kai Yao; Jinwu Gao
Page(s): 615 - 621

10) Output Feedback Direct Adaptive Fuzzy Controller Based on Frequency-Domain Methods
Author(s): Krzysztof Wiktorowicz
Page(s): 622 - 634

11) Stability and Stabilization of Takagi–Sugeno Fuzzy Systems via Sampled-Data and State Quantized Controller
Author(s): Yajuan Liu; S. M. Lee
Page(s): 635 - 644

12) Parameterizing the Semantics of Fuzzy Attribute Implications by Systems of Isotone Galois Connections
Author(s): Vilem Vychodil
Page(s): 645 - 660

13) Decentralized Sampled-Data Fuzzy Observer Design for Nonlinear Interconnected Systems
Author(s): Geun Bum Koo; Jin Bae Park; Young Hoon Joo
Page(s): 661 - 674

14) Robust Stability Analysis and Systematic Design of Single-Input Interval Type-2 Fuzzy Logic Controllers
Author(s): Tufan Kumbasar
Page(s): 675 - 694

15) Rough-Set-Theoretic Fuzzy Cues-Based Object Tracking Under Improved Particle Filter Framework
Author(s): Pojala Chiranjeevi; Somnath Sengupta
Page(s): 695 - 707

16) Fuzzy Multiobjective Modeling and Optimization for One-Shot Multiattribute Exchanges With Indivisible Demand
Author(s): Zhong-Zhong Jiang; Zhi-Ping Fan; W. H. Ip; Xiaohong Chen
Page(s): 708 - 723

17) Global Fuzzy Adaptive Hierarchical Path Tracking Control of a Mobile Robot With Experimental Validation
Author(s): Chih-Lyang Hwang; Wei-Li Fang
Page(s): 724 - 740

18) Asymmetric Fuzzy Preference Relations Based on the Generalized Sigmoid Scale and Their Application in Decision Making Involving Risk Appetites
Author(s): Wei Zhou; Zeshui Xu
Page(s): 741 - 756

19) Possibilistic Functional Dependencies and Their Relationship to Possibility Theory
Author(s): Sebastian Link; Henri Prade
Page(s): 757 - 763

Weekly Review 3 June 2016

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

1. Tips for building a successful AI platform, from Facebook's director of machine learning: http://www.techrepublic.com/article/facebooks-machine-learning-director-shares-tips-for-building-a-successful-ai-platform/
2. Hooray! From 2020, all EU-funded research must be published as open-access. Will more governments follow? http://techcrunch.com/2016/05/27/eu-mandates-open-access-for-all-publicly-funded-research-by-2020/
3. Point-and-click bot-building: http://venturebeat.com/2016/05/26/motion-ai-lets-anyone-easily-build-a-bot/
4. Most Americans don't trust AI: http://www.digitaltrends.com/cool-tech/ai-system-trust/#:YvuruZwzPeTsCA I suspect that would apply to people in most countries.
5. An early prototype system using machine learning to detect potholes for visually-impaired people: http://spectrum.ieee.org/the-human-os/biomedical/devices/pothole-detection-for-the-visually-impaired?utm_source=feedburner&utm_medium=feed&utm_campaign=Feed%3A+IeeeSpectrum+%28IEEE+Spectrum%29&utm_content=FaceBook
6. Basically, everyone is pirating papers - open access is the way ahead https://theconversation.com/is-it-piracy-how-students-access-academic-resources-55712
7. Personally, I think AI is a long way from the real thing: http://www.cnet.com/news/artificial-intelligence-getting-as-good-as-the-real-thing/
8. Communicating technical ideas to non-technical people - I've found the same principles apply in teaching http://www.datasciencecentral.com/profiles/blogs/tips-for-effectively-communicating-complex-ideas-to-non-technical
9. Designing for an AI-enhanced experience: http://www.tandemseven.com/blog/designing-for-ai-enhanced-experiences/
10. How IBM's Watson can contribute to education: http://www.techinsider.io/how-watson-ai-can-transform-education-2016-5
11. When an AI can explain to a fresher how an AI works, I will be worried about my job: http://www.edtechmagazine.com/higher/article/2015/02/artificial-intelligence-and-robotics-slowly-enter-college-classrooms
12. 57% of jobs are at risk of being replaced by AI and robots: http://techcrunch.com/2016/05/29/human-obsolescence-are-we-ready-for-an-artificially-intelligent-future/
13. Machines are going to take all of our jobs: http://www.neowin.net/news/a-robot-is-about-to-take-over-my-job-then-hes-coming-after-yours
14. Recurrent neural networks in TensorFlow: http://www.kdnuggets.com/2016/05/intro-recurrent-networks-tensorflow.html
15. Natural language processing for a movie recommendation system: http://techcrunch.com/2016/05/31/this-facebook-bot-will-pick-your-next-movie-for-you/
16. How machine learning is transforming parts of every day life: http://www.information-management.com/blogs/big-data-analytics/machine-learning-has-transformed-many-aspects-of-everyday-life-10028943-1.html
17. Why we should care about how people interact with machine learning systems: http://www.kdnuggets.com/2016/05/interacting-machine-learning.html
18. Why everyone needs to understand machine learning: https://www.weforum.org/agenda/2016/05/why-you-need-to-understand-machine-learning
19. The argument that AI will augment, rather than replace, people in the workplace: http://www.techrepublic.com/article/robots-beware-humans-will-still-be-bosses-of-machines-say-davenport-and-kirby-in-new-book/
20. How Facebook is using AI to flag offensive images: http://techcrunch.com/2016/05/31/terminating-abuse/
21. Who is going to win the race to monetise AI? http://www.cio.com/article/3076154/internet-of-things/the-race-to-monetize-artificial-intelligence-is-on.html
22. List of resources for machine learning and data science in R and Python: http://www.datasciencecentral.com/profiles/blogs/hitchhiker-s-guide-to-data-science-machine-learning-r-python
23. DARPA is seeking a mathematical framework on the limitations of machine learning: http://nextbigfuture.com/2016/05/darpa-seeks-mathematical-framework-to.html
24. Using a social media bot: http://motherboard.vice.com/en_au/read/i-let-a-robot-take-over-my-social-media-for-48-hours
25. How SAP is using machine learning to help transition its customers to the cloud: http://www.techrepublic.com/article/sap-invests-in-machine-learning-to-simplify-customer-transition-to-cloud/
26. It's far to early to start regulating AI: https://www.technologyreview.com/s/601563/what-to-do-when-a-robot-is-the-guilty-party/
27. First example of music produced by Google's AI: http://techcrunch.com/2016/06/01/google-ai-produces-a-melody-that-rivals-the-casio-keyboard-concerts-of-our-youth/
28. An article on DeepText, Facebook's text-processing natural language system: http://techcrunch.com/2016/06/01/facebook-deep-text/
29. Some more details on Facebook's DeepText: https://code.facebook.com/posts/181565595577955/introducing-deeptext-facebook-s-text-understanding-engine/
30. Artificially intelligent headphones. Seriously. http://techcrunch.com/2016/06/01/first-look-lifebeams-artificially-intelligent-headphones-for-that-her-like-workout/
31. Which AI company is Elon Musk most scared of? http://www.theverge.com/2016/6/2/11837566/elon-musk-one-ai-company-that-worries-me
32. How Bill Gates sees AI as the "holy grail": http://mashable.com/2016/06/01/bill-gates-ai-code-conference/#odjE.UnZcOqc
33. On the democratisation of machine learning: http://www.datasciencecentral.com/profiles/blogs/machine-learning-is-dead-long-live-machine-learning
34. An overview of logistic regression: http://www.analyticbridge.com/profiles/blogs/making-data-science-accessible-logistic-regression
35. More about Project Magenta, Google artistic AI: http://www.informationweek.com/big-data/big-data-analytics/googles-magenta-project-can-machines-be-musicians/a/d-id/1325752?
36. A howto on building a deep learning box: http://www.kdnuggets.com/2016/06/build-deep-learning-box.html
37. CISCO is planning on using IBM's Watson AI to analyse data from the IoT: http://techcrunch.com/2016/06/02/ibm-cisco-iot/
38. Google's AI is the best, according to CEO Sundar Pichai: http://www.informationweek.com/iot/googles-sundar-pichai-our-ai-beats-competitors-ai/a/d-id/1325758
39. The "barbell effect" of machine learning - how the benefits of AI will be driven to extreme ends: http://techcrunch.com/2016/06/02/the-barbell-effect-of-machine-learning/

IEEE Transactions on Evolutionary Computation, Volume 20, Number 3, June 2016

1) A Modified Ant Colony Optimization Algorithm for Network Coding Resource Minimization
Author(s): Zhaoyuan Wang; Huanlai Xing; Tianrui Li; Yan Yang; Rong Qu; Yi Pan
Page(s): 325 - 342

2) Multifactorial Evolution: Toward Evolutionary Multitasking
Author(s): Abhishek Gupta; Yew-Soon Ong; Liang Feng
Page(s): 343 - 357

3) A Unified Evolutionary Optimization Procedure for Single, Multiple, and Many Objectives
Author(s): Haitham Seada; Kalyanmoy Deb
Page(s): 358 - 369

4) Analysis of Stability, Local Convergence, and Transformation Sensitivity of a Variant of the Particle Swarm Optimization Algorithm
Author(s): Mohammad Reza Bonyadi; Zbigniew Michalewicz
Page(s): 370 - 385

5) Visualization and Performance Metric in Many-Objective Optimization
Author(s): Zhenan He; Gary G. Yen
Page(s): 386 - 402

6) Automatic Component-Wise Design of Multiobjective Evolutionary Algorithms
Author(s): Leonardo C. T. Bezerra; Manuel López-Ibáñez; Thomas Stützle
Page(s): 403 - 417

7) A Sparse Spectral Clustering Framework via Multiobjective Evolutionary Algorithm
Author(s): Juanjuan Luo; Licheng Jiao; Jose A. Lozano
Page(s): 418 - 433

8) The Permutation in a Haystack Problem and the Calculus of Search Landscapes
Author(s): Vincent A. Cicirello
Page(s): 434 - 446

9) Constraint Consensus Mutation-Based Differential Evolution for Constrained Optimization
Author(s): Noha M. Hamza; Daryl L. Essam; Ruhul A. Sarker
Page(s): 447 - 459

10) Computing Nash Equilibria and Evolutionarily Stable States of Evolutionary Games
Author(s): Jiawei Li; Graham Kendall; Robert John
Page(s): 460 - 469

11) The $N$ -Player Trust Game and its Replicator Dynamics
Author(s): Hussein Abbass; Garrison Greenwood; Eleni Petraki
Page(s): 470 - 474

12) Constrained Subproblems in a Decomposition-Based Multiobjective Evolutionary Algorithm
Author(s): Luping Wang; Qingfu Zhang; Aimin Zhou; Maoguo Gong; Licheng Jiao
Page(s): 475 - 480