2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA)最新文献_第6页

Formal Distributed Model for the Verification of Job-Scheduling in Cloud Environments 云环境下作业调度验证的形式化分布式模型

2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2017-10-01 DOI: 10.1109/AICCSA.2017.144

Imene Ben Hafaiedh, Maroua Ben Slimane, S. Haouala, R. Robbana

引用次数: 1

Fuzzy Rule-Based Situational Music Retrieval and Recommendation 基于模糊规则的情境音乐检索与推荐

2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2017-10-01 DOI: 10.1109/AICCSA.2017.154

Rim Dridi, Saloua Zammali, Khedija Arour

{"title":"Fuzzy Rule-Based Situational Music Retrieval and Recommendation","authors":"Rim Dridi, Saloua Zammali, Khedija Arour","doi":"10.1109/AICCSA.2017.154","DOIUrl":"https://doi.org/10.1109/AICCSA.2017.154","url":null,"abstract":"Recommender Systems (RS) have been used in many domains to assist users' decision making by providing item recommendations and by reducing information overload. Context-Aware Recommender System (CARS) is one RS trying to generate more convincing recommendations by taking user's context into regard. Thus, in order to adapt the recommendations to the user's context, it is useful to identify the current situation of the involved user which may influence the acceptance of a recommendation. Improving user's acceptance on recommendations is a challenging task. In recommender systems, determining the right contextual situation and finding relevant items for the user are considered as two vital issues for achieving better user acceptance. Therefore, our ultimate aim is to infer user's actual situation by fusing interacted contextual circumstances surrounding music listeners through Fuzzy Logic. Then, the inferred situation is used to recommend music from an online webradio service. We conducted a case study to assess user satisfaction in music recommendation. Experimental results reveal that the inclusion of the inferred situation increases significantly the accuracy of the recommendation, while compared to the traditional contextual recommender systems.","PeriodicalId":197301,"journal":{"name":"2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128065125","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

Business Adaptation for BPaaS Using Fuzzy Logic Systems 基于模糊逻辑系统的BPaaS业务适配

2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2017-10-01 DOI: 10.1109/AICCSA.2017.148

Rima Grati, Khouloud Boukadi, H. Ben-Abdallah

引用次数: 4

How to Combine Salience Factors for Arabic Pronoun Anaphora Resolution 阿拉伯语代词回指消解中如何结合显着因子

2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2017-10-01 DOI: 10.1109/AICCSA.2017.83

Saoussen Mathlouthi Bouzid, Fériel Ben Fraj Trabelsi, C. Zribi

引用次数: 5

A Comparison Between Different Gaussian-Based Mixture Models 不同高斯混合模型的比较

2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2017-10-01 DOI: 10.1109/AICCSA.2017.108

Fatma Najar, S. Bourouis, N. Bouguila, S. Belghith

{"title":"A Comparison Between Different Gaussian-Based Mixture Models","authors":"Fatma Najar, S. Bourouis, N. Bouguila, S. Belghith","doi":"10.1109/AICCSA.2017.108","DOIUrl":"https://doi.org/10.1109/AICCSA.2017.108","url":null,"abstract":"In this paper, we address the problem of data clustering into homogeneous components in an unsupervised way. Data clustering is one of the major topics in computer vision which has widespread potential applications from various domains such as pattern recognition, data mining, remote sensing, and bioinformatics. In pattern recognition, statistical methods have been widely used and proved effective in generating accurate models. In particular, the popular finite Gaussian mixture models which are able to provide superior performance for data clustering and classification. In this work, we present and evaluate the performance of four well-known Gaussian-based mixture models for data clustering namely: Gaussian mixture model (GMM), Generalized Gaussian mixture model (GGMM), Bounded Gaussian mixture model (BGMM) and Bounded Generalized Gaussian mixture model (BGGMM). The aim of this work is to show that the choice of the component model is very critical in mixture decomposition. Experimental results show close clustering accuracy between different models. However, the bounded generalized Gaussian mixture model provides the best performance in the case of multidimensional data.","PeriodicalId":197301,"journal":{"name":"2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134493054","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: 25

Cloud Based Collaborative Software Development: A Review, Gap Analysis and Future Directions 基于云的协同软件开发:回顾、差距分析和未来方向

2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2017-10-01 DOI: 10.1109/AICCSA.2017.220

Stanley Ewenike, E. Benkhelifa, C. Chibelushi

{"title":"Cloud Based Collaborative Software Development: A Review, Gap Analysis and Future Directions","authors":"Stanley Ewenike, E. Benkhelifa, C. Chibelushi","doi":"10.1109/AICCSA.2017.220","DOIUrl":"https://doi.org/10.1109/AICCSA.2017.220","url":null,"abstract":"Organizations who have transitioned their development environments to the Cloud have started realizing benefits such as: cost reduction in hardware; relatively accelerated development process via reduction of time and effort to set up development and testing environments; unified management; service and functionality expansion; on-demand provisioning and access to resources and development environments. These benefits represent only a fraction of the full potential that could be achieved via leveraging Cloud Computing for the collaborative software development process. Related efforts in this area have been mainly in the areas of: asynchronous collaboration; collaboration in isolated aspects of the Software Development process, such as coding activities; use of open-source tools for contributing, improving, and managing code, etcetera. Although these efforts represent valid contributions and important enablers, they are still missing important aspects which enable a more holistic process, with solid theoretical foundation. This paper reviews this research area, in order to better assess factors and gaps creating the need to enhance the collaborative software development process in the Cloud, to better meet the pressure to collaboratively create better cloud-agnostic applications.","PeriodicalId":197301,"journal":{"name":"2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA)","volume":"230 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131595685","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

Real-Time Event Localization and Detection Over Social Networks Using Apache Intelligence 使用Apache智能在社交网络上进行实时事件定位和检测

2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2017-10-01 DOI: 10.1109/AICCSA.2017.135

Sarra Hasni, Sami Faïz

引用次数: 0

Using Decomposition and Local Search to Solve Large-Scale Virtual Machine Placement Problems with Disk Anti-Colocation Constraints 基于分解和局部搜索的大规模虚拟机布局问题

2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2017-10-01 DOI: 10.1109/AICCSA.2017.86

Ameni Hbaieb, Mahdi Khemakhem, M. B. Jemaa

引用次数: 6

Echo State Network and Particle Swarm Optimization for Prognostics of a Complex System 复杂系统预测的回声状态网络与粒子群优化

2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2017-10-01 DOI: 10.1109/AICCSA.2017.210

Safa Ben Salah, I. Fliss, M. Tagina

引用次数: 9

A Unified Multidimensional Data Model from Social Networks for Unstructured Data Analysis 面向非结构化数据分析的社会网络统一多维数据模型

2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2017-10-01 DOI: 10.1109/AICCSA.2017.70

Hichem Dabbèchi, N. Haddar, Mounira Ben-Abdallah, K. Haddar

{"title":"A Unified Multidimensional Data Model from Social Networks for Unstructured Data Analysis","authors":"Hichem Dabbèchi, N. Haddar, Mounira Ben-Abdallah, K. Haddar","doi":"10.1109/AICCSA.2017.70","DOIUrl":"https://doi.org/10.1109/AICCSA.2017.70","url":null,"abstract":"Social networks such as Facebook, LinkedIn, Twitter, YouTube, etc. have revolutionized the ways of interacting and exchanging information on the Internet. They provide the users with the ability to interact frequently and share variety of digital content with each other. Unstructured data exchanged over the social networks represent an important source of information, even it their characteristics make them difficult to analyze. Online analytical processing (OLAP)is a powerful primitive for social media data analysis. However, OLAP tools face major challenges in manipulating unstructured data generated from social media. In this paper, we attempt to extend the established OLAP technology to allow multidimensional analysis of social media unstructured data. We suggest a new multidimensional model called \"Unified Multidimensional Social Media Data Model\" dedicated to the OLAP of social media and specially Twitter, Facebook and YouTube. Our model is generic and dynamic, that is not limited to a set of determined social networks. It can be extended by other social networks or with other enrichement data using dynamic discovery of multidimensional concepts. It provides the possibility to analyze social networks users and their published posts and comments according to device source, geographic locations and temporal axes.","PeriodicalId":197301,"journal":{"name":"2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115018531","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