2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA)最新文献

Study by the FDTD Method of Multiband Microstrip Patch Antenna Loaded with L-Shaped Slot 加载l型槽的多波段微带贴片天线的FDTD研究

2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2019-11-01 DOI: 10.1109/AICCSA47632.2019.9035334

A. Boufrioua, Elhachmi Ksouri, Meriem Harbadji

引用次数: 0

Message from AICCSA 2019 Program Chairs 来自AICCSA 2019项目主席的信息

2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2019-11-01 DOI: 10.1109/aiccsa47632.2019.9255484

引用次数: 0

Service-Oriented Architecture for Multiscale Traffic Simulations 面向服务的多尺度交通模拟体系结构

2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2019-11-01 DOI: 10.1109/AICCSA47632.2019.9035349

Xavier Boulet, Mahdi Zargayouna, G. Scémama, F. Leurent

引用次数: 1

Evaluating Skills Dimensions: Case Study on Occupational Changes in the UAE 评估技能维度:阿联酋职业变化的案例研究

2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2019-11-01 DOI: 10.1109/AICCSA47632.2019.9035215

Reem Al Junaibi, M. Omar, Z. Aung, Armin Alibasic, G. Westerman, W. Woon

{"title":"Evaluating Skills Dimensions: Case Study on Occupational Changes in the UAE","authors":"Reem Al Junaibi, M. Omar, Z. Aung, Armin Alibasic, G. Westerman, W. Woon","doi":"10.1109/AICCSA47632.2019.9035215","DOIUrl":"https://doi.org/10.1109/AICCSA47632.2019.9035215","url":null,"abstract":"Rapid technological advances have led to profound changes to skills composition in the workplace. Low skilled jobs are gradually being replaced by automated systems, while there is a gaining demand for jobs which require interpersonal and technological skills. In this study, a combination of data science techniques are used to study this phenomenon. Firstly, matrix factorization and clustering methods are used to extract skills dimensions from O∗NET, a database of occupations-skills matchings compiled by the US Department of Labor. A method of evaluating the performance of each of these methods is proposed and used to determine the ideal extraction procedure. Next, the relative importance of different occupations was estimated using a corpus of job advertisements collected from local job sites, which we adopt as a proxy for demand. Finally, the results of this analysis are used to measure shifts in the demand for the skills and abilities associated with these occupations. This procedure is applicable to any job market. However as a test of its effectiveness, it was used to study two important economic sectors in the United Arab Emirates (UAE): Oil & Gas and Banking & Finance. The findings of this study will help us to determine the jobs and skills which will be most impacted by structural and technological change, and provide recommendations to ensure that the UAE workforce is well equipped to adapt to these changes.","PeriodicalId":294501,"journal":{"name":"2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126767949","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

An Unsupervised Method for Detecting Style Breaches in a Document 一种检测文档样式违规的无监督方法

2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2019-11-01 DOI: 10.1109/AICCSA47632.2019.9035264

Maryam Elamine, Seifeddine Mechti, Lamia Hadrich Belguith

引用次数: 2

Efficient Keyword Spotting through Hardware-Aware Conditional Execution of Deep Neural Networks 基于硬件感知的深度神经网络条件执行的高效关键字识别

2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2019-11-01 DOI: 10.1109/AICCSA47632.2019.9035275

J. S. P. Giraldo, Chris O'Connor, M. Verhelst

{"title":"Efficient Keyword Spotting through Hardware-Aware Conditional Execution of Deep Neural Networks","authors":"J. S. P. Giraldo, Chris O'Connor, M. Verhelst","doi":"10.1109/AICCSA47632.2019.9035275","DOIUrl":"https://doi.org/10.1109/AICCSA47632.2019.9035275","url":null,"abstract":"Keyword spotting is a task that requires ultra-low power due to its always-on operation. State-of-the-art approaches achieve this by drastically pruning model size, yet often at the expense of accuracy. This work tackles this fundamental conflict between operating efficiency and accuracy in three ways: 1.) Exploiting dynamic neural network cascades for keyword spotting using an end-to-end hardware-aware training; 2.) Deriving the optimal number of stages and stage dimensions in function of the input class distributions; 3.) Using the low-latency response of the first stage for speculative execution of the later stages, training the dynamic cascade through a hardware-aware cost function. Results show the framework can generate cascade models optimized in function of the class distribution (background noise, target keywords and other-speech), reducing computational cost by 87% for always-on operation while maintaining the baseline accuracy of the most complex model of the cascade. On top of this, the hardware-aware speculative execution provides an additional 2x energy savings over the non-speculative case.","PeriodicalId":294501,"journal":{"name":"2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128623455","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

Virtualization vs. Containerization: Towards a Multithreaded Performance Evaluation Approach 虚拟化与容器化:迈向多线程性能评估方法

2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2019-11-01 DOI: 10.1109/AICCSA47632.2019.9035233

Amro Abuabdo, Ziad Al-Sharif

{"title":"Virtualization vs. Containerization: Towards a Multithreaded Performance Evaluation Approach","authors":"Amro Abuabdo, Ziad Al-Sharif","doi":"10.1109/AICCSA47632.2019.9035233","DOIUrl":"https://doi.org/10.1109/AICCSA47632.2019.9035233","url":null,"abstract":"Increasingly, hardware visualizations are becoming the norm. This opened the door for new possibilities but placed a new set of challenges that are to be faced by software developers, mainly when it comes to developing different applications in different programming languages and deploying them on various environments; often virtually hosted environments. The performance expectations and requirements might be easily affected by the settings of these virtual machines. This paper aims at evaluating the performance of these virtual environments and measure their impacts on the performance of multithreaded applications. In particular, we evaluate the performance of multithreaded applications and assess how it is influenced by the various virtualization technologies. A multithreaded application is developed in both C and Java and the performance is measured on different platforms; each of which is supported by either a virtualized system or a containerized environment. Certainly, C will keep outperforming Java in its execution speed; however, simple variations in the virtualization technique easily affects the performance of these multithreaded applications; a situation that should be taken into consideration by the software developers when deploying into such environments.","PeriodicalId":294501,"journal":{"name":"2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129441413","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

Multi-Oriented Real-Time Arabic Scene Text Detection with Deep Fully Convolutional Networks 基于深度全卷积网络的多方向实时阿拉伯语场景文本检测

2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2019-11-01 DOI: 10.1109/AICCSA47632.2019.9035340

M. S. H. Sassi, I. Beltaief, M. Zekri, S. Yahia

引用次数: 2

AICCSA 2019 Keynote Speakers AICCSA 2019主题演讲

2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2019-11-01 DOI: 10.1109/aiccsa47632.2019.9035355

引用次数: 0

Mining the Global Terrorism Dataset using Machine Learning Algorithms 使用机器学习算法挖掘全球恐怖主义数据集

2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA) Pub Date : 2019-11-01 DOI: 10.1109/AICCSA47632.2019.9035296

A. Alsaedi, Arwa S. Almobarak, Saad T. Alharbi

{"title":"Mining the Global Terrorism Dataset using Machine Learning Algorithms","authors":"A. Alsaedi, Arwa S. Almobarak, Saad T. Alharbi","doi":"10.1109/AICCSA47632.2019.9035296","DOIUrl":"https://doi.org/10.1109/AICCSA47632.2019.9035296","url":null,"abstract":"From the beginning of the present century, terrorist attacks have represented a massive concern for both developed and developing countries alike. Therefore, countries spare no effort to use all available means to fight and eradicate them. Due to this, we aim through this paper, to take the advantages of the available data mining and machine learning techniques, by applying them to the Global Terrorism Database (GTD) dataset in order to acquire valuable information about the predicted attacks and attackers. We believe that these models will be of great benefit when utilized by governments and intelligence agencies; since they help them to make pre-emptive strikes against terrorist groups quickly and over a short period of time. In our paper, we focus on two tasks: predicting the success of an attack and predicting the identity of the terrorist organization that is behind the attack. To implement this, we adopted three machine learning algorithms: K-nearest neighbor (KNN), Naive Bayes (NB) and Random Forest (RF), which we used to train our models. More specifically, each algorithm was used to construct two models, one for each task, where the data were sampled for the first model using the holdout method, while for the second model cross-validation was employed. In the end, we compared the performance of the models in terms of accuracy, precision, recall and F-measure metrics. We noticed that the RF models outperformed the other models, while the NB models were the least efficient among the three algorithm models.","PeriodicalId":294501,"journal":{"name":"2019 IEEE/ACS 16th International Conference on Computer Systems and Applications (AICCSA)","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126072484","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