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2016 International Conference on Computational Science and Computational Intelligence (CSCI)最新文献

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Majority Based Ensemble Framework for Feature Selection Using Rough Set 基于多数的粗糙集特征选择集成框架
2016 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2016-12-01 DOI: 10.1109/CSCI.2016.0212
Syed Hasnain Ali, A. W. Muzaffar, Shumyla Rasheed Mir
{"title":"Majority Based Ensemble Framework for Feature Selection Using Rough Set","authors":"Syed Hasnain Ali, A. W. Muzaffar, Shumyla Rasheed Mir","doi":"10.1109/CSCI.2016.0212","DOIUrl":"https://doi.org/10.1109/CSCI.2016.0212","url":null,"abstract":"Feature selection reduces a data set into a subset which also represents the entire data with less computational complexity and performance does not affect much. However, to extract such a subset is a nontrivial task although there are a number of methods to handle this problem. In the near past an approach based on rough set have been used for feature selection. The dependency measure is one of the ways to find out the minimal feature subset, called Reducts, from the entire dataset. One of the mature areas of feature reduction is the techniques based on rough set theory, which totally depends on the concept of sets and mathematical formulas. We have conducted experiments using datasets Extracted from UMLS. A framework is devised using different rough set based algorithms, it has been observed that after reduction of attributes our results improved in terms of time complexity while a negligible effect is seen on the other measures. We measured the performance of our framework using precision, recall, accuracy and F-measure.","PeriodicalId":186778,"journal":{"name":"2016 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"22 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":"126183109","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
Exploiting Data Parallelism for Efficient Classification of Multi-Dimensional Patterns 利用数据并行性对多维模式进行有效分类
2016 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2016-12-01 DOI: 10.1109/CSCI.2016.0112
B. Cyganek, M. Woźniak
{"title":"Exploiting Data Parallelism for Efficient Classification of Multi-Dimensional Patterns","authors":"B. Cyganek, M. Woźniak","doi":"10.1109/CSCI.2016.0112","DOIUrl":"https://doi.org/10.1109/CSCI.2016.0112","url":null,"abstract":"Classification of massive multidimensional data requires high computational power and memory resources. In this paper the problem of parallel implementation of the ensemble composed of classifiers operating with multi-dimensional patterns is discussed. The member classifiers of the ensemble operate in the orthogonal subspaces obtained with the Higher-Order Singular Value Decomposition (HOSVD) of the prototype pattern tensors. Computation speed-up was gained firstly by the two-level data parallelism and, secondly, thanks to application of the recently developed algorithm for fast HOSVD decomposition. In this paper we summarize our improvements to the HOSVD classifier, focusing mostly on the computational efficiency. This is achieved thanks to application of the faster decomposition algorithm and due to parallelization. First level of data parallelization is obtained thanks to separate classification of each class of the input patterns. Second level is obtained by partitioning the input dataset into smaller chunks of data, uniformly drawn from all the classes. Thanks to this, an ensemble of classifiers is obtained which allows for parallel operation both, in the training, but also in the response, modes. This allowed for computational speed-up and lower memory requirements, but also increased accuracy due to application of the ensemble instead of a single classifier. The experiments on recognition of visual objects in digital signals show high accuracy and accelerated performance during training and system response.","PeriodicalId":186778,"journal":{"name":"2016 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"144 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":"123753436","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: 0
Co-Ranking Authors in Heterogeneous News Networks 异质新闻网络的共同排名作者
2016 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2016-12-01 DOI: 10.1109/CSCI.2016.0209
Lenin Mookiah, W. Eberle
{"title":"Co-Ranking Authors in Heterogeneous News Networks","authors":"Lenin Mookiah, W. Eberle","doi":"10.1109/CSCI.2016.0209","DOIUrl":"https://doi.org/10.1109/CSCI.2016.0209","url":null,"abstract":"Expert Finding has been a widely studied area of research. However, most of the work in this area has focused solely on analyzing networks representing people in academia. In this work, we will present an approach for two types of heterogeneous news sources (i.e., Traditional Network Sources (TNS) and Policy Network Sources (PNS)) for experts on a set of topics. Our overall objective is to discover who are the expert journalists and policy analysts on specific topics. This work is based on our intuition that the PNS and TNS could complement each other, thus leveraging information for the learning task. We propose a probabilistic generative model named Context-based Latent Dirichlet Allocation (CBLDA) that performs the task of co-ranking authors in the heterogeneous networks of TNS and PNS. We will demonstrate that our proposed approach outperforms baselines in terms of precision, mean average precision, and discounted cumulative gain.","PeriodicalId":186778,"journal":{"name":"2016 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"93 3 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":"126734396","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
A Diffusion Strategy for the Multichannel Active Noise Control System in Distributed Network 分布式网络中多通道主动噪声控制系统的扩散策略
2016 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2016-12-01 DOI: 10.1109/CSCI.2016.0130
Ju-man Song, P. Park
{"title":"A Diffusion Strategy for the Multichannel Active Noise Control System in Distributed Network","authors":"Ju-man Song, P. Park","doi":"10.1109/CSCI.2016.0130","DOIUrl":"https://doi.org/10.1109/CSCI.2016.0130","url":null,"abstract":"This paper introduces a diffusion strategy for the multichannel active noise control. The diffusion strategy is designed to reduce the computational complexity by distributes computations to all nodes of multichannel active noise control system. Thus, the multichannel filtered-x normalized least mean square algorithm, which is the simplest way for real active noise control environments is used as a base algorithm for the diffusion strategy. From the structure of the multichannel active noise control system, the optimal weight vector at a node of the proposed strategy is calculated by considering the weight vector of neighbor nodes. With the proposed strategy, the computational complexity is distributed from one main processor to all nodes. With some simulation results, the performance of the proposed strategy is shown with the reduced computational complexity for one processor.","PeriodicalId":186778,"journal":{"name":"2016 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"146 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":"115185553","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: 15
Session Duration Based Feature Extraction for Network Intrusion Detection in Control System Networks 基于会话持续时间的控制系统网络入侵检测特征提取
2016 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2016-12-01 DOI: 10.1109/CSCI.2016.0173
S. Ponomarev, T. Atkison
{"title":"Session Duration Based Feature Extraction for Network Intrusion Detection in Control System Networks","authors":"S. Ponomarev, T. Atkison","doi":"10.1109/CSCI.2016.0173","DOIUrl":"https://doi.org/10.1109/CSCI.2016.0173","url":null,"abstract":"The use and deployment of Industrial Control Systems (ICS) have become standard across many industries, though the security of these important systems have not kept pace with their current Internet focused deployment technologies. This technology gap has exposed an exploitable vector for would be attackers as ICS protocols do not have security mechanisms in place to handle Internet connectivity. This paper focuses on a critical component of a Network Telemetry based Intrusion Detection system (IDS) that can help eliminate this exploitable vector. This component is a method for extracting features using session duration based instantiation. After integrating this feature extraction method into the telemetry based IDS, it is able to achieve 99.98% accuracy when distinguishing between an engineer and an attacker on the same network.","PeriodicalId":186778,"journal":{"name":"2016 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"4 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":"129828664","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
Performance of License Assisted Access Solutions Using ns-3 基于ns-3的License辅助接入方案性能分析
2016 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2016-12-01 DOI: 10.1109/CSCI.2016.0181
Pedro Maia De Santana, Vincius Dantas De Lima Melo, Vicente A. de Sousa
{"title":"Performance of License Assisted Access Solutions Using ns-3","authors":"Pedro Maia De Santana, Vincius Dantas De Lima Melo, Vicente A. de Sousa","doi":"10.1109/CSCI.2016.0181","DOIUrl":"https://doi.org/10.1109/CSCI.2016.0181","url":null,"abstract":"3GPP has been working in establishing a new network infrastructure based on unlicensed spectrum known as LTE Secondary Cell (SCell) in a carrier aggregation scenario in which LTE user terminals would still be anchored to an LTE Primary Cell (PCell) over licensed spectrum. On this scenario, best-effort content would be delivered on the unlicensed band SCell, while QoS traffic and control signaling would still be a PCell responsibility. This work makes a performance analysis of 3GPP's solution, named LTE License Assisted Access (LTE-LAA), which allows the LTE system to operate on unlicensed spectrum, likewise comparing with other alternative coexistence solution called LTE duty cycle (LTE-DC) and making a Wi-Fi performance evaluation with those different coexistence solutions when operating in 5GHz band.","PeriodicalId":186778,"journal":{"name":"2016 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"60 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":"124647369","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
Review of the Principal Indicators and Data Science Techniques Used for the Detection of Financial Fraud and Money Laundering 用于检测金融欺诈和洗钱的主要指标和数据科学技术综述
2016 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2016-12-01 DOI: 10.1109/CSCI.2016.0267
Bronson Duhart, N. Hernández-Gress
{"title":"Review of the Principal Indicators and Data Science Techniques Used for the Detection of Financial Fraud and Money Laundering","authors":"Bronson Duhart, N. Hernández-Gress","doi":"10.1109/CSCI.2016.0267","DOIUrl":"https://doi.org/10.1109/CSCI.2016.0267","url":null,"abstract":"Financial fraud and money laundering represent an important concern both nationally and worldwide due to the huge amounts of monetary losses they imply. Besides human inspection, Data Science (DS) has proved so far to be a useful tool in order to fight these activities by automatic means. Nevertheless, this approach to the problem is still in early stages, thus yielding plenty of field to be explored. This document discusses some of the principal variables/indicators and DS techniques that have been used in the recent years for detecting and describing fraudulent operations, to serve as a guide for future work in the field.","PeriodicalId":186778,"journal":{"name":"2016 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"119 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":"123376707","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
An Overview of Emerging Privacy Issues in the Internet of Things 物联网中新出现的隐私问题概述
2016 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2016-12-01 DOI: 10.1109/CSCI.2016.0047
Mahran Al-Zyoud, T. Atkison, Jeffery Carver
{"title":"An Overview of Emerging Privacy Issues in the Internet of Things","authors":"Mahran Al-Zyoud, T. Atkison, Jeffery Carver","doi":"10.1109/CSCI.2016.0047","DOIUrl":"https://doi.org/10.1109/CSCI.2016.0047","url":null,"abstract":"This paper presents an overview of the privacy issues that are expected to emerge as a result of the Internet of Things (IoT). IoT expands digital world to include different things in our physical world. This ambitious aspiration is becoming true with the utilization of already existing technologies that enable things to be identified and to communicate with each other. Unfortunately, the ubiquitous collection and dissemination of data that characterize IoT come at the expense of data privacy. We have witnessed many privacy breaches during the last decade, more vicious breaches will find its way to the huge amounts of qualitative data that are collected in IoT with more expected catastrophic consequences to individuals and organizations. Evaluating some papers, the lessons to be learnt and some possible directions for future research were highlighted.","PeriodicalId":186778,"journal":{"name":"2016 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"49 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":"121468060","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
A Hierarchically Switching Analysis Architecture with Streaming Data from IoT (Internet of Things) Sensors 基于IoT(物联网)传感器流数据的分层交换分析架构
2016 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2016-12-01 DOI: 10.1109/CSCI.2016.0263
Dong-oh Kang, Jiyong Kim, Jang-Ho Choi, Hwin Dol Park, Ok-Gee Min
{"title":"A Hierarchically Switching Analysis Architecture with Streaming Data from IoT (Internet of Things) Sensors","authors":"Dong-oh Kang, Jiyong Kim, Jang-Ho Choi, Hwin Dol Park, Ok-Gee Min","doi":"10.1109/CSCI.2016.0263","DOIUrl":"https://doi.org/10.1109/CSCI.2016.0263","url":null,"abstract":"In this paper, we proposed a hierarchical analysis architecture for IoT streaming data by switching computing load between an analysis server and edge devices dynamically in order to reduce the resource usage of the analysis server. The hierarchically distributed switching analysis system can analyze faster and more IoT streaming data with the same computing capability. To show the feasibility of the proposed method, we applied the proposed method to the prediction problem of energy consumption of a simulated BEMS (Building Energy Management System) which is based on real BEMS data.","PeriodicalId":186778,"journal":{"name":"2016 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"13 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":"114730799","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: 0
Ranking Anomalous High Performance Computing Sensor Data Using Unsupervised Clustering 使用无监督聚类对异常高性能计算传感器数据进行排序
2016 International Conference on Computational Science and Computational Intelligence (CSCI) Pub Date : 2016-12-01 DOI: 10.1109/CSCI.2016.0124
A. Morrow, Elisabeth Baseman, S. Blanchard
{"title":"Ranking Anomalous High Performance Computing Sensor Data Using Unsupervised Clustering","authors":"A. Morrow, Elisabeth Baseman, S. Blanchard","doi":"10.1109/CSCI.2016.0124","DOIUrl":"https://doi.org/10.1109/CSCI.2016.0124","url":null,"abstract":"Environmental sensors monitor supercomputing facility health, generating massive data in the largest facilities. Current state-of-the-art is for human operators to evaluate environmental data by hand. This approach will not be viable on Exascale machines, nor is it ideal on current systems. We evaluate effectiveness of the DBSCAN algorithm for identifying anomalies in supercomputing sensor data. We filter large portions of data showing normal behavior from anomalies, and then rank anomalous points by distance to the nearest normal cluster. We compare DBSCAN to k-means and Gaussian kernel density estimation, finding that DBSCAN effectively clusters sensor data from a Cray supercomputing facility. DBSCAN also successfully clusters synthetic injected data, avoiding the false positives generated by k-means and Gaussian kernel density estimation.","PeriodicalId":186778,"journal":{"name":"2016 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"30 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":"126353638","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: 8
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