2016 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献_第7页

Dynamic prediction of drivers' personal routes through machine learning 通过机器学习动态预测驾驶员个人路线

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850094

Yue Dai, Yuan Ma, Qianyi Wang, Y. Murphey, Shiqi Qiu, Johannes Kristinsson, Jason Meyer, F. Tseng, T. Feldkamp

{"title":"Dynamic prediction of drivers' personal routes through machine learning","authors":"Yue Dai, Yuan Ma, Qianyi Wang, Y. Murphey, Shiqi Qiu, Johannes Kristinsson, Jason Meyer, F. Tseng, T. Feldkamp","doi":"10.1109/SSCI.2016.7850094","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7850094","url":null,"abstract":"Personal route prediction (PRP) has attracted much research interest recently because of its technical challenges and broad applications in intelligent vehicle and transportation systems. Traditional navigation systems generate a route for a given origin and destination based on either shortest or fastest route schemes. In practice, different people may very likely take different routes from the same origin to the same destination. Personal route prediction attempts to predict a driver's route based on the knowledge of driver's preferences. In this paper we present an intelligent personal route prediction system, I_PRP, which is built based upon a knowledge base of personal route preference learned from driver's historical trips. The I_PRP contains an intelligent route prediction algorithm based on the first order Markov chain model to predict a driver's intended route for a given pair of origin and destination, and a dynamic route prediction algorithm that has the capability of predicting driver's new route after the driver departs from the predicted route.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133724675","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

Hybridized Ant Colony System for Tasks to Workstations Assignment 任务到工作站分配的杂交蚁群系统

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850060

A. Serbencu, V. Mînzu

引用次数: 2

Automated supernova Ia classification using adaptive learning techniques 使用自适应学习技术的超新星Ia自动分类

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7849951

K. D. Gupta, Renuka Pampana, R. Vilalta, E. Ishida, R. S. Souza

{"title":"Automated supernova Ia classification using adaptive learning techniques","authors":"K. D. Gupta, Renuka Pampana, R. Vilalta, E. Ishida, R. S. Souza","doi":"10.1109/SSCI.2016.7849951","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7849951","url":null,"abstract":"While the current supernova (SN) photometric classification system is based on high resolution spectroscopic observations, the next generation of large scale surveys will be based on photometric light curves of supernovae gathered at an unprecedented rate. Developing an efficient method for SN photometric classification is critical to cope with the rapid growth of data volumes in current astronomical surveys. In this work, we present an adaptive mechanism that generates a predictive model to identify a particular class of SN known as Type Ia, when the source set is made of spectroscopic data, while the target set is made of photometric data. The method is applied to simulated data sets derived from the Supernova Photometric Classification Challenge, and preprocessed using Gaussian Process Regression for all objects with at least 1 observational epoch before -3 and after +24 days since the SN maximum brightness. The main difficulty lies in the compatibility of models between spectroscopic (source) data and photometric (target) data, since the underlying distributions on both, source and target domains, are expected to be significantly different. A solution is to adapt predictive models across domains. Our methodology exploits machine learning techniques by combining two concepts: 1) domain adaptation is used to transfer properties from the source domain to the target domain; and 2) active learning is used as a means to rely on a set of confident labels on the target domain. We show how a combination of both concepts leads to high generalization (i.e., predictive) performance.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115538290","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: 9

Introducing a Fuzzy Cognitive Map for modeling power market auction behavior 引入模糊认知图对电力市场竞价行为进行建模

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7849850

Denise M. Case, C. Stylios

引用次数: 1

A ripple-spreading algorithm for network performance assessment 一种用于网络性能评估的波纹扩散算法

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850142

Xiao-Bing Hu, Ming-Kong Zhang, Jian-Qin Liao

{"title":"A ripple-spreading algorithm for network performance assessment","authors":"Xiao-Bing Hu, Ming-Kong Zhang, Jian-Qin Liao","doi":"10.1109/SSCI.2016.7850142","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7850142","url":null,"abstract":"To assess the performance of a network system against disturbances, existing methods are usually concerned with two different extreme situations: (i) how likely the system will degrade into some separated sub-graphs because of disturbances; (ii) how likely the 1st best paths will be cut off by disturbances. However, a more general situation, i.e., how likely those paths whose lengths are within a given range will be affected by disturbances, is barely discussed. Basically, to address this general situation, all (not just a proportion) of those paths whose lengths are within the given range must be found out between all pairs of origin and destination (OD) of interest. Unfortunately, no effective method has ever been reported to accomplish this task, although there are many methods capable of calculating all the 1st best paths between all OD pair of interest. This paper, for the first time, attempts to address the above general situation of network performance assessment. To this end, a novel ripple-spreading algorithm (RSA) is proposed to carry out a ripple relay race on the network, in order to identify all of those paths whose lengths are within the given range. Surprisingly, the proposed RSA can find all such paths between all OD pairs of interest by just a single run of ripple relay race. This work makes progress towards the general performance assessment of a network system against disturbances.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124382830","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: 2

Improving security requirements adequacy 提高安全需求的充分性

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7849906

Hanan Hibshi, T. Breaux, Christian Wagner

{"title":"Improving security requirements adequacy","authors":"Hanan Hibshi, T. Breaux, Christian Wagner","doi":"10.1109/SSCI.2016.7849906","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7849906","url":null,"abstract":"Organizations rely on security experts to improve the security of their systems. These professionals use background knowledge and experience to align known threats and vulnerabilities before selecting mitigation options. The substantial depth of expertise in any one area (e.g., databases, networks, operating systems) precludes the possibility that an expert would have complete knowledge about all threats and vulnerabilities. To begin addressing this problem of fragmented knowledge, we investigate the challenge of developing a security requirements rule base that mimics multi-human expert reasoning to enable new decision-support systems. In this paper, we show how to collect relevant information from cyber security experts to enable the generation of: (1) interval type-2 fuzzy sets that capture intra- and inter-expert uncertainty around vulnerability levels; and (2) fuzzy logic rules driving the decision-making process within the requirements analysis. The proposed method relies on comparative ratings of security requirements in the context of concrete vignettes, providing a novel, interdisciplinary approach to knowledge generation for fuzzy logic systems. The paper presents an initial evaluation of the proposed approach through 52 scenarios with 13 experts to compare their assessments to those of the fuzzy logic decision support system. The results show that the system provides reliable assessments to the security analysts, in particular, generating more conservative assessments in 19% of the test scenarios compared to the experts' ratings.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114438355","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 study of chaotic maps in differential evolution applied to gray-level image thresholding 差分演化中混沌映射在灰度图像阈值分割中的应用研究

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850256

U. Mlakar, J. Brest, Iztok Fister, Iztok Fister

引用次数: 6

Multiple Worlds Model of Evolution for demographic appropriate radio playlists 适合人口统计的广播播放列表的多重世界进化模型

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7849964

J. A. Brown, D. Ashlock

{"title":"Multiple Worlds Model of Evolution for demographic appropriate radio playlists","authors":"J. A. Brown, D. Ashlock","doi":"10.1109/SSCI.2016.7849964","DOIUrl":"https://doi.org/10.1109/SSCI.2016.7849964","url":null,"abstract":"This study presents an application of the Multiple Worlds Model of Evolution. The goal is to model radio stations in a given market. The model captures listener demographics and maximizes listeners, while securing advertising revenue. Listener preferences for different types of content are set as positive (like) and negative (dislike) integers, allowing surveys of the demographic to act as the model parameters directly. Fitness evaluation is performed with a modeled hour of radio playtime where stations can select between a set of content types and advertisements. Advertisements provide fitness in the form of advertising revenues; however, listeners will only stay on a station which provides content they enjoy. The Multiple Worlds Model is a form of multiple population evolutionary algorithm. It evaluates fitness based on the actions of one member from each population, and has no genetic transfer of information between populations. Each population can thus specialize. In the current study, such specialization is a self-organization of focused (e.g. rock or country) stations via adaption to listener preferences. The model is examined using different numbers of independent populations with even splits among demographic types. The evolved stations show differences in playlists where the profiles differ in their enjoyments and convergence between stations where the listener profiles are similar.","PeriodicalId":120288,"journal":{"name":"2016 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123215252","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

Search space boundaries in neural network error landscape analysis 神经网络误差景观分析中的空间边界搜索

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7850152

Anna Sergeevna Bosman, A. Engelbrecht, Mardé Helbig

引用次数: 11

Local ensemble weighting in the context of time series forecasting using XCSF XCSF在时间序列预测中的局部集合加权

2016 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2016-12-01 DOI: 10.1109/SSCI.2016.7849974

M. Sommer, Anthony Stein, J. Hähner

引用次数: 8