2018 IEEE Symposium Series on Computational Intelligence (SSCI)最新文献_第5页

HGLPSO: Hybrid Genetic Learning PSO and its Applications to Task Matching on Large-Scale Systems HGLPSO:混合遗传学习粒子群算法及其在大规模系统任务匹配中的应用

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628745

E. Albalawi, P. Thulasiraman, R. Thulasiram

引用次数: 1

Authorship Attribution vs. Adversarial Authorship from a LIWC and Sentiment Analysis Perspective 从LIWC和情感分析的角度看作者归属与对抗作者

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628769

Joshua Gaston

引用次数: 6

Exploring Data Reduction Techniques for Time Efficient Support Vector Machine Classifiers 探索时间高效支持向量机分类器的数据约简技术

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628716

R. Rastogi, H. Safdari, Sweta Sharma

{"title":"Exploring Data Reduction Techniques for Time Efficient Support Vector Machine Classifiers","authors":"R. Rastogi, H. Safdari, Sweta Sharma","doi":"10.1109/SSCI.2018.8628716","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628716","url":null,"abstract":"Support Vector Machines [1] (SVMs) are regarded as powerful machine learning tool because of their inherent properties. However, one major challenge for using SVMs in real-world applications with large datasets is its high training time complexity. Over the years, many variants of SVM have been proposed to reduce the training time by either using algorithmic modifications (such as LS-SVM [3], GEP-SVM [4], TWSVM [5]) or training level speed-ups (such as SMO [6], SOR [2] and Stochastic Gradient Descent method [7]). However, these methods deal with the entire data for learning a classifier model, thus the space complexity could be a challenge. A more fitting approach is to use an Instance Selection method (IS) which selects a subset of data which is best representative of the underlying data distribution. Since SVMs by definition use the geometry of patterns for classification, this study explores the effects of different Instance Selection methods on different variants of SVM to check their effectiveness using their comparative performances in terms of training time and generalization ability. Various theoretical and experimental comparisons on standard datasets have been provided to validate the efficacy of different IS methods on SVM based classifiers.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130034226","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

Modelling Urban transition using Cellular Automata based Sleuth modelling 基于元胞自动机的Sleuth模型的城市转型建模

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628940

M. Chandan, H. Bharath

{"title":"Modelling Urban transition using Cellular Automata based Sleuth modelling","authors":"M. Chandan, H. Bharath","doi":"10.1109/SSCI.2018.8628940","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628940","url":null,"abstract":"Changes in urban dynamics has direct linkages between human beings and its surroundings. Present decade has seen tremendous alterations in the built environment and therefore its negative effect on natural ecosystem. This paper attempts to assess land use change scenario and urban growth prediction for historical capital of central India, Bhopal. Land use analysis performed using maximum likelihood classifier revealed the immediate attention required for growing urban trends. A steep increase in urban areas of 4.90% to 8.67% was observed within a span of seven years for the present decade. In order to understand future urban growth in the city, we employed Cellular Automata based Sleuth model, by testing the datasets in a three-stage simulation procedure: test, calibration and prediction. Bhopal city is currently undergoing transformation from rural urban scenario and therefore facing growth pressure and due to various growth sectors including housing, transport and industrial sector. By urban pattern analysis indicates that unplanned urban growth. Considering business as usual scenario, input layers were carefully selected for the model by considering city development plans and delineating waterbodies etc., Output from the SLEUTH analysis suggest an alarming rate of increase in urban area of 779 km2 from the year 2017 to 2026. Results help planners and government authorities to visualize, strengthen existing policy measures to build future cities in alignment with sustainable goals and promising the community with pristine environment.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130588178","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

EEG-Analysis for Classification of Touch-Induced Affection by Type-2 Fuzzy Sets 用2型模糊集对触摸诱发情感分类的脑电图分析

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628855

Mousumi Laha, A. Konar, P. Rakshit, A. Nagar

{"title":"EEG-Analysis for Classification of Touch-Induced Affection by Type-2 Fuzzy Sets","authors":"Mousumi Laha, A. Konar, P. Rakshit, A. Nagar","doi":"10.1109/SSCI.2018.8628855","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628855","url":null,"abstract":"The paper aims at classifying the brain response to touch-induced affection aroused in human subjects into four classes: love, fondness, devotion and respect using a novel vertical slice based General Type-2 Fuzzy classifier. The novelty of research here lies in the design of vertical slice based General Type-2 Fuzzy Classifier, capable of classifying finer changes in the brain activation patterns due to changes in the touch nourishment on a subject by different people, including spouse, children, graces of the Almighty and parents. Experiments undertaken confirm that for most of the subjects the above four classes are prominent in the brain activation patterns. The frontal lobe is more activated in devotion and respect, whereas temporal lobe is more activated in fondness and love. The General Type-2 fuzzy classifier designed for classification of four affection classes yield high classification accuracy over 98 % in comparison to existing type-l fuzzy and other classifies. The proposed scheme of touch-induced affection classification can be interestingly applied to measure subjective sensitivity of healthy and psychologically disabled people.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130793420","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

A Blockchain based approach for multimedia privacy protection and provenance 基于区块链的多媒体隐私保护和来源方法

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628636

Alka Vishwa, F. Hussain

{"title":"A Blockchain based approach for multimedia privacy protection and provenance","authors":"Alka Vishwa, F. Hussain","doi":"10.1109/SSCI.2018.8628636","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628636","url":null,"abstract":"There has been a vast increase in incidents related to multimedia copyright and security breaches in the past few years, compromising users’ privacy. One such breach involved the seventh season of the TV series “Game of Thrones”, where episodes were illegally downloaded before the official release date etc. Such security breaches raise questions about the approaches and models that currently apply to data privacy and security, where the user saves and distributes his data personally or depends on a third party or stakeholder to manage the distribution rights of sensitive data. When it comes to multimedia, many companies or multimedia owners rely on third parties, distributors and sales persons to monitor their publicity, maintain their popularity and sell their multimedia content. Blockchain technology, which was originally devised for the digital currency (cryptocurrency), has distinct features such as distributed networking, data privacy, trust less computing etc. This technology attracts great interest from the research community due to its innovative properties which can be applied to many business applications, one being access control over data. In this paper, we present a decentralized data management framework that ensures user data privacy and control. We propose a protocol that uses blockchain technology to take control of the user’s data. This protocol enables the user to have full control over his multimedia files and he doesn’t need to trust a third party. The framework allows the user to not only store data but also to query and share data as well as auditing. Finally, we discuss possible future extensions of blockchain technology as a medium to ensure privacy, data control, auditing and trust management in different areas.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132479199","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: 33

Ant Colony optimization for the Electric Vehicle Routing Problem 电动汽车路径问题的蚁群优化

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628831

Michalis Mavrovouniotis, G. Ellinas, M. Polycarpou

引用次数: 24

Threat-Aware Honeypot for Discovering and Predicting Fingerprinting Attacks Using Principal Components Analysis 基于主成分分析发现和预测指纹攻击的威胁感知蜜罐

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628658

N. Naik, Paul Jenkins, N. Savage

{"title":"Threat-Aware Honeypot for Discovering and Predicting Fingerprinting Attacks Using Principal Components Analysis","authors":"N. Naik, Paul Jenkins, N. Savage","doi":"10.1109/SSCI.2018.8628658","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628658","url":null,"abstract":"The proliferation of cyberattacks, their increase in complexity and therefore their resolution, has resulted in significant concern within the cybersecurity industry. A honeypot is a popular concealed tool used to entice attackers to disclose information about themselves. It is an effective tool provided that its identity is not revealed, however, a successful fingerprinting attack can reveal the honeypots identity; leading to possible devastating consequences, resulting in the imperative to detect such fingerprinting at the earliest opportunity. Several effective methods are available to prevent a fingerprinting attack; therefore, a real-time prediction method is highly desirable. Unfortunately, no technique is available to discover and predict a fingerprinting attack in real-time as it is difficult to isolate that attack from other attacks. Therefore, this paper proposes a technique to discover and predict fingerprinting attacks on the honeypot in real-time by using a Principal Components Analysis (PCA). As every fingerprinting attack requires a sequence of actions to collect sufficient information to generate a fingerprint, this proposed technique takes advantage of this requirement to gather its symptoms. Analysing several abnormalities in attributes of TCP, UDP and ICMP packets collected during the simulation of fingerprinting attacks, evaluating them based on popular attack techniques and empirical evidence. After selecting several targeted attributes based on the previous analysis, it performs a PCA to establish the most influential attributes by which a fingerprinting attack can be discovered and predicted accurately. Finally, it proposes a general model to predict the severity level of the fingerprinting attack on the honeypot.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132075766","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: 12

A Statistical Evaluation of Vector-space Models for Text Categorisation 用于文本分类的向量空间模型的统计评价

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628920

Yash Vijay, Anurag Sengupta, K. George

{"title":"A Statistical Evaluation of Vector-space Models for Text Categorisation","authors":"Yash Vijay, Anurag Sengupta, K. George","doi":"10.1109/SSCI.2018.8628920","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628920","url":null,"abstract":"In our paper, we statistically evaluate categorisation performance of a distributed embedding technique called word2vec, and popular sparse representations, on the labelled 20-newsgroups dataset and unlabelled United States political news dataset. We deploy extensive parametric variations of vector-space models for both supervised and unsupervised topic-categorisation, relatively gauge them, and report the best results. We introduce a methodology to deploy distributed embeddings for unsupervised learning using Principal Component Analysis, which performs exceedingly well on both datasets, both by topic coherence scores, and visual interpretation of token content of topic mixtures learnt. Our motivation is primarily driven by proving that dense word embeddings can perform as good as, if not better than, traditional frequency-based vector space models. In addition, this paper demonstrates that distributed embeddings based Support Vector Machines performs best for supervised publisher categorisation on the political news dataset, whereas Term-Frequency document Frequency based Support Vector Machines outperforms supervised topic categorisation in the 20-newsgroups dataset.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126447578","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

Deterministic Annealing for Depot optimization: Applications to the Dial-A-Ride Problem 车辆段优化的确定性退火:在Dial-A-Ride问题中的应用

2018 IEEE Symposium Series on Computational Intelligence (SSCI) Pub Date : 2018-11-01 DOI: 10.1109/SSCI.2018.8628642

R. Pandi, Songguang Ho, Sarat Chandra Nagavarapu, J. Dauwels

{"title":"Deterministic Annealing for Depot optimization: Applications to the Dial-A-Ride Problem","authors":"R. Pandi, Songguang Ho, Sarat Chandra Nagavarapu, J. Dauwels","doi":"10.1109/SSCI.2018.8628642","DOIUrl":"https://doi.org/10.1109/SSCI.2018.8628642","url":null,"abstract":"This paper introduces a novel meta-heuristic approach to optimize depot locations for multi-vehicle shared mobility systems. Dial-a-ride problem (DARP) is considered as a case study here, in which routing and scheduling for door to- door passenger transportation are performed while satisfying several constraints related to user convenience. Existing literature has not addressed the fundamental problem of depot location optimization for DARP, which can reduce cost, and in turn promote the use of shared mobility services to minimize carbon footprint. Thus, there is a great need for fleet management systems to employ a multi-depot vehicle dispatch mechanism with intrinsic depot location optimization. In this work, we propose a deterministic annealing meta-heuristic to optimize depot locations for the dial-a-ride problem. Numerical experiments are conducted on several DARP benchmark instances from the literature, which can be categorized as small, medium and large based on their problem size. For all tested instances, the proposed algorithm attains solutions with travel cost better than that of the best-known solutions. It is also observed that the travel cost is reduced up to 6.13% when compared to the best-known solutions.","PeriodicalId":235735,"journal":{"name":"2018 IEEE Symposium Series on Computational Intelligence (SSCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122449362","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