Tuesday, May 23, 2017

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/

Monday, May 22, 2017

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

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

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

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

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

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

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

Saturday, May 20, 2017

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

Guest EditorialSpecial 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

Thursday, May 18, 2017

Wednesday, May 17, 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/

Saturday, May 13, 2017

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

Monday, May 8, 2017

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/

Tuesday, May 2, 2017

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

Monday, May 1, 2017

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

Soft Computing, Volume 21, Issue 10, May 2017

1. Preface: A volume dedicated to Wolfgang Rump on the occasion of his 65th birthday
Author(s): Yichuan Yang
Pages: 2465-2467

2. Generalized Łukasiewicz rings
Author(s): Albert Kadji, Celestin Lele, Jean B. Nganou
Pages: 2469-2476

3. Some results in r-disjunctive languages and related topics
Author(s): Di Zhang, Yuqi Guo, K. P. Shum
Pages: 2477-2483

4. The Cuntz semigroup and domain theory
Author(s): Klaus Keimel
Pages: 2485-2502

5. An application of subgroup lattices
Author(s): Yanping Chen, Yichuan Yang
Pages: 2503-2505

6. An extension of a Y. C. Yang theorem
Author(s): Dragoş Vaida
Pages: 2507-2512

7. Notes on quantum logics and involutive bounded posets
Author(s): Yali Wu, Yichuan Yang
Pages: 2513-2519

8. Quantum B-algebras: their omnipresence in algebraic logic and beyond
Author(s): Wolfgang Rump
Pages: 2521-2529

9. Filter topologies on MV-algebras
Author(s): Cuicui Luan, Yichuan Yang
Pages: 2531-2535

10. Weak QMV algebras and some ring-like structures
Author(s): Xian Lu, Yun Shang, Ru-qian Lu, Jian Zhang, Feifei Ma
Pages: 2537-2547

11. Note on classification of two-dimensional associative lattice-ordered real algebras
Author(s): Yichuan Yang, Xiaohong Zhang
Pages: 2549-2552

12. On soft weak structures
Author(s): A. H. Zakari, A. Ghareeb, Saleh Omran
Pages: 2553-2559

13. Quantale algebras as lattice-valued quantales
Author(s): Bin Zhao, Supeng Wu, Kaiyun Wang
Pages: 2561-2574

14. Catastrophe bond pricing for the two-factor Vasicek interest rate model with automatized fuzzy decision making
Author(s): Piotr Nowak, Maciej Romaniuk
Pages: 2575-2597

15. Dual trapdoor identity-based encryption with keyword search
Author(s): Jia’nan Liu, Junzuo Lai, Xinyi Huang
Pages: 2599-2607

16. Searching for the most significant rules: an evolutionary approach for subgroup discovery
Author(s): Victoria Pachón, Jacinto Mata, Juan Luis Domínguez
Pages: 2609-2618

17. Uncertain random spectra: a new metric for assessing the survivability of mobile wireless sensor networks
Author(s): Li Xu, Jing Zhang, Pei-Wei Tsai, Wei Wu, Da-Jin Wang
Pages: 2619-2629

18. FIR digital filter design using improved particle swarm optimization based on refraction principle
Author(s): Peng Shao, Zhijian Wu, Xuanyu Zhou, Dang Cong Tran
Pages: 2631-2642

19. Towards secure and cost-effective fuzzy access control in mobile cloud computing
Author(s): Wei Wu, Shun Hu, Xu Yang, Joseph K. Liu, Man Ho Au
Pages: 2643-2649

20. Restricted gene expression programming: a new approach for parameter identification inverse problems of partial differential equation
Author(s): Yan Chen, Kangshun Li, Zhangxing Chen, Jinfeng Wang
Pages: 2651-2663