2016 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献_第6页

Assessing the likelihood of cyber network infiltration using rare-event simulation 利用罕见事件模拟评估网络渗透的可能性

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7849913

Alexander Krall, M. Kuhl, Stephen Moskal, S. Yang

引用次数: 3

Quantifying correlation between Financial News and stocks 量化财经新闻与股票的相关性

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850021

Haizhou Qu, D. Kazakov

{"title":"Quantifying correlation between Financial News and stocks","authors":"Haizhou Qu, D. Kazakov","doi":"10.1109/SSCI.2016.7850021","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7850021","url":null,"abstract":"Financial news and stocks appear linked to the point where the use of online news to forecast the markets has become a major selling point for some traders. The correlation between news content and stock returns is clearly of interest, but has been mostly centred on news meta-data, such as volume and popularity. We address this question here by measuring the correlation between the returns of 27 publicly traded companies and news about them as collected from Yahoo Financial News for the period 1 Oct 2014 to 30 Apr 2015. In all reported experiments, two metrics are defined, one to measure the distance between two time series, the other to quantify the difference between two collections of news items. Two 27 × 27 distance matrices are thus produced, and their correlation measured with the Mantel test. This allows us to estimate the correlation of stock market data (returns, change, volume and close price) with the content of published news in a given period of time. A number of representations for the news are tested, as well as different distance metrics between time series. Clear, statistically significant, moderate level correlations are detected in most cases. Lastly, the impact of the length of the period studied on the observed correlation is also investigated.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132470289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Assisting fuzzy offline handwriting recognition using recurrent belief propagation 利用循环信念传播辅助模糊离线手写识别

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850026

Yilan Li, Zhe Li, Qinru Qiu

{"title":"Assisting fuzzy offline handwriting recognition using recurrent belief propagation","authors":"Yilan Li, Zhe Li, Qinru Qiu","doi":"10.1109/SSCI.2016.7850026","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7850026","url":null,"abstract":"Recognizing handwritten texts is a challenging task due to many different writing styles and lack of clear boundary between adjacent characters. This problem has been tackled by many previous researchers using techniques such as deep learning networks and hidden Markov Models (HMM), etc. In this work we aim at offline fuzzy recognition of handwritten texts. A probabilistic inference network that performs recurrent belief propagation is developed to post process the recognition results of deep convolutional neural network (CNN) (e.g. LeNet) and form individual characters to words. The post processing has the capability of correcting deletion, insertion and replacement errors in a noisy input. The output of the inference network is a set of words with their probability of being the correct one. To limit the size of candidate words, a series of improvements have been made to the probabilistic inference network, including using a post Gaussian Mixture Estimation model to prune insignificant words. The experiments show that this model gives a competitively average accuracy of 85.5%, and the improvements provides 46.57% reduction of invalid candidate words.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132526442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A ground level causal learning algorithm 一个底层的因果学习算法

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850025

Seng-Beng Ho, Fiona Liausvia

引用次数: 8

On the mechanisms of imitation in multi-agent systems 多智能体系统中的模仿机制研究

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850271

M. D. Erbas

引用次数: 0

A hierarchical visual recognition model with precise-spike-driven synaptic plasticity 一种具有精确尖刺驱动突触可塑性的分层视觉识别模型

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850251

Xiaoliang Xu, Xin Jin, Rui Yan, Xun Cao

引用次数: 1

Double archive Pareto local search 双重档案帕累托本地搜索

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850227

O. Maler, Abhinav Srivastav

{"title":"Double archive Pareto local search","authors":"O. Maler, Abhinav Srivastav","doi":"10.1109/SSCI.2016.7850227","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7850227","url":null,"abstract":"Many real-world problems have multiple, conflicting objectives and a large complex solution space. The conflicting objectives give rise to a set of non-dominating solutions, known as the Pareto front. In the absence of any prior information on the relative importance of the objectives, none of these solutions can be said to be better than others, and they should all be presented to the decision maker as alternatives. In most cases, the number of Pareto solutions can be huge and we would like to provide a good representative approximation of the Pareto front. Moreover, the search space can be too large and complex for the problem to be solved by exact methods. Therefore efficient heuristic search algorithms are needed that can handle such problems. In this paper, we propose a double archive based Pareto local search. The two archives of our algorithm are used to maintain (i) the current set of non-dominated solutions, and (ii) the set of promising candidate solutions whose neighbors have not been explored yet. Our selection criteria is based on choosing the candidate solutions from the second archive. This method improves upon the existing Pareto local search and queued Pareto local search methods for bi-objective and tri-objective quadratic assignment problem.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133933629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A game-theoretic pricing model for Energy Internet in day-ahead trading market considering distributed generations uncertainty 考虑分布式代不确定性的能源互联网日前交易市场博弈定价模型

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7849839

Jingwei Hu, Qiuye Sun, F. Teng

{"title":"A game-theoretic pricing model for Energy Internet in day-ahead trading market considering distributed generations uncertainty","authors":"Jingwei Hu, Qiuye Sun, F. Teng","doi":"10.1109/SSCI.2016.7849839","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7849839","url":null,"abstract":"This paper designs a distributed trading mechanism for Energy Internet in which the uncertainty of distributed generations (DGs) is considered. In order to match up Energy Internet, the new energy frame, a novel energy accessing mode called We-Energy (WE) is proposed for the convenience of energy regulation, energy trading and information interaction. First, multiple interconnected WEs are considered in a region where, at a given time, some WEs have superfluous energy for sale to make profits called Surplus-WEs, but some WEs need to buy additional energy to meet local demands called Short-WEs. Under the trading mechanism, the market clearing price (MCP) is determined by supplies as well as demands. Then, a Bayesian game model considering distributed generations (DGs) uncertainty is established to analyze the strategies among WEs, which are assumed as the bidding supplies and demands. A unique Bayesian-Nash equilibrium among Surplus-WEs and Short-WEs are proved respectively according to hessian matrix, and solved by using the Karush-Kuhn-Tucker (KKT) conditions. Numerical results show that the designed MPC can reflect the relationship of supply and demand better, and maximize the utility of all the WEs.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131493604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

A regulatory algorithm (RGA) for optimizing examination timetabling 一种优化考试排班的调节算法

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850284

C. Klüver, J. Klüver

引用次数: 1

Optimal trajectory planning for multiple asteroid tour mission by means of an incremental bio-inspired tree search algorithm 基于增量生物启发树搜索算法的多小行星漫游任务最优轨迹规划

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850108

Aram Vroom, M. D. Carlo, Juan Manuel Romero Martin, M. Vasile

引用次数: 3