2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)最新文献_第5页

Investigating the Influence of Adding Local Search to Search Algorithms 研究在搜索算法中加入局部搜索的影响

2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2017-12-01 DOI: 10.1109/PDCAT.2017.00032

Nadia Abd-Alsabour

引用次数: 0

Towards Supporting Modeling Variability in E-Learning Application: A Case Study 在电子学习应用中支持模型可变性:一个案例研究

2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2017-12-01 DOI: 10.1109/PDCAT.2017.00083

Sameh Azouzi, Sonia Ayachi Ghannouchi, Zaki Brahmi

{"title":"Towards Supporting Modeling Variability in E-Learning Application: A Case Study","authors":"Sameh Azouzi, Sonia Ayachi Ghannouchi, Zaki Brahmi","doi":"10.1109/PDCAT.2017.00083","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00083","url":null,"abstract":"As e-learning becomes a basic need for several universities, a variety of Learning Management Systems (LMS) is proposed on the market. However, available LMSs do not satisfy all the needs of different institutions, which push them to develop their own systems. Since developing and maintaining new software are cost, time and effort consuming, and with the increasing demand on e-Learning systems, it becomes necessary to find an efficient solution that allows the fast development of systems and overcomes the afore-mentioned issues. We strongly believe that adopting a software product line approach in e-Learning domain can bring important benefits. We propose a general model for collaborative learning processes and we present the development process of an e-Learning software product line. Throughout the development process, we demonstrate how this approach allows us to satisfy the variable needs of universities/learners and benefit from the systematic large-scale reuse at the same time. In order to help organizations in providing similar services without the need to structure each of them separately, this paper presents how to support variability in learning process modeling.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"227 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120934127","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

ProjectileSort - Rule Based Parallel Sorting Algorithm - Architecture for Reconfigurable Multi-Partition Object Arrays 基于规则的并行排序算法。可重构多分区对象数组的体系结构

2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2017-12-01 DOI: 10.1109/PDCAT.2017.00031

Nandika Liyanage

{"title":"ProjectileSort - Rule Based Parallel Sorting Algorithm - Architecture for Reconfigurable Multi-Partition Object Arrays","authors":"Nandika Liyanage","doi":"10.1109/PDCAT.2017.00031","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00031","url":null,"abstract":"Data can be store as structured, semi-structured or unstructured formats in various distributed environments. Extraction of data from multiple data sources or data warehouse and convert to a proper order is quite time consuming task even using the latest hardware and software technologies. Sorting is one of the key concepts that helps to improve the efficiency of various computational process. Unlike the early single processor or single server operations using monolithic applications, multi-core distributed environments required more advanced computational theories and algorithms. Existing sorting theories are basically derived from linear algorithms and enhanced to support for distributed processing. This research discuss the characteristics of existing parallel sorting algorithms, techniques, limitation. Aim and objective is to introduce a new pure parallel sorting algorithm and sorting architecture that pointing to execute under latest distributed environments. The proposed pattern and the sorting architecture can be used as rule based parallel sorting technique and algorithm to support for any type of distributed environment to sort infinite dataset.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122404844","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 New Colluded Adversarial VNet Embeddings Attack in Cloud 云环境下一种新的串通对抗VNet嵌入攻击

2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2017-12-01 DOI: 10.1109/PDCAT.2017.00012

I. Liu, Tay-Jiun Fang, Jung-Shian Li, Meng-Wei Sun, Chuan-Gang Liu

{"title":"A New Colluded Adversarial VNet Embeddings Attack in Cloud","authors":"I. Liu, Tay-Jiun Fang, Jung-Shian Li, Meng-Wei Sun, Chuan-Gang Liu","doi":"10.1109/PDCAT.2017.00012","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00012","url":null,"abstract":"Abstract—Nowadays, network virtualization has been widely investigated in order to prevent Internet ossification, and develop future emerging network applications flexibly. However, prior work by Pignolet et al. shows the possible attacking methodology with which the attackers can disclose the whole cloud topology while deploying virtual networks in cloud named “Topology Disclosure Attack”. In this attack model, the attacker pretends to deploy virtual networks in cloud by issuing the graph requests to service provider. And the service provider responds the requests to the attacker after examining his/her topology resources. With this request/reply model, Pignolet et al. believe this attack eventually infers the targeted topology. However, one vital reason leads this attack to the failure- too many virtual requests from one adversary in a time. This paper tries to provide a new topology disclosure attack model, which multiple attackers launch attacks at the same time with the assistance of proposed Query-Trie and network tomography technique. Hence, in this paper, we propose much more possible attack model in cloud and this topic also encourages the network researchers to develop resistance mechanism against it in the future.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116539370","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

Content Delivery Based on Popularity and Time Slot 基于人气和时段的内容交付

2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2017-12-01 DOI: 10.1109/PDCAT.2017.00063

Min-Chun Huang, Chi-He Chang, Chao-Wei Tseng, Ru-Jen Lee

引用次数: 1

A Fusion Financial Prediction Strategy Based on RNN and Representative Pattern Discovery 基于RNN和代表性模式发现的融合金融预测策略

2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2017-12-01 DOI: 10.1109/PDCAT.2017.00024

Lu Zhang, Xiaopeng Fan, Chengzhong Xu

{"title":"A Fusion Financial Prediction Strategy Based on RNN and Representative Pattern Discovery","authors":"Lu Zhang, Xiaopeng Fan, Chengzhong Xu","doi":"10.1109/PDCAT.2017.00024","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00024","url":null,"abstract":"To predicate the future with high accuracy is a holy grail in financial market. However, the volatility of chaotic financial market challenges new technologies from computer science to economic science all the time. Recently, Recurrent Neural Network (RNN) plays a new role in financial market prediction. However, results from RNN are restricted by sample size of training datasets, and show predication accuracy can hardly be guaranteed in a long term. On the other hand, Representative Pattern Discovery (RPD) is an effective way in long-term prediction while it is ineffective in short-term prediction. In this paper, we define a representative pattern for time series, and propose a fusion financial prediction strategy based on RNN and RPD. We take the advantages of both RNN and RPD, in the way that the proposed strategy is stateful to keep the short-term trend and it rectifies the predication by a time-dependent incremental factor in a long-term way. Compared with RNN and pattern discovery respectively, our experimental results demonstrate that our proposed strategy performs much better than that of others. It can increase the prediction accuracy by 6% on the basis of RNN at most, but at a cost of higher Mean Squared Error.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127282236","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

Models and Run-Time Systems for Data Intensive Workflow Applications 数据密集型工作流应用的模型和运行时系统

2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2017-12-01 DOI: 10.1109/PDCAT.2017.00075

N. Haddar, M. Tmar

引用次数: 0

Feature-Based Adaptive Block Partition Method for Data Prefetching in Streamline Visualization 流线可视化中基于特征的自适应分块预取方法

2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2017-12-01 DOI: 10.1109/PDCAT.2017.00087

Yumeng Guo, Wenke Wang, Sikun Li

{"title":"Feature-Based Adaptive Block Partition Method for Data Prefetching in Streamline Visualization","authors":"Yumeng Guo, Wenke Wang, Sikun Li","doi":"10.1109/PDCAT.2017.00087","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00087","url":null,"abstract":"With the increasing size of flow field, a challenge in streamline visualization arises that the memory of calculation node cannot accommodate the entire required data. To solve this problem, out-of-core technique divides the flow field into blocks and read block on demand of computing. Data prefetching is a frequent out-of core method to reduce the affection of the gap between I/O and calculation speed, while the performance is coherent with prefetching hit rate. In this paper, we focus on how to improve the prefetching hit rate to increase the data prefetching efficiency by changing the style of flow field partitioning, and present a novel feature-based dynamic block partition method that divides data to blocks of different sizes. The key of our method is first to compute the feature attributes of the field, and then determine the partitioning points by specific operations to divide feature regions more finely. It is easy to apply our approach to replace block partition part of all state-of-the-art prefetching algorithms. Experimental results show that the major quality measurement of our partitioning strategy for prefetching is much better than the traditional methods, with an increase of about 10%in both prefetch hit rate and effective rate.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115497601","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 Efficient Parallel Optimization for Co-Authorship Network Analysis 一种有效的并行优化合作网络分析方法

2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2017-12-01 DOI: 10.1109/PDCAT.2017.00030

C. R. Valêncio, José Carlos De Freitas, Rogéria Cristiane Gratão de Souza, L. A. Neves, G. F. D. Zafalon, A. Colombini, William Tenório

{"title":"An Efficient Parallel Optimization for Co-Authorship Network Analysis","authors":"C. R. Valêncio, José Carlos De Freitas, Rogéria Cristiane Gratão de Souza, L. A. Neves, G. F. D. Zafalon, A. Colombini, William Tenório","doi":"10.1109/PDCAT.2017.00030","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00030","url":null,"abstract":"Co-authorship analysis in science and technology partnerships provides a vision of cooperation patterns between individuals and organizations and is still widely used to understand and assess scientific collaboration patterns. This analysis is conducted by means of bibliometry, which is the quantitative study of scientific production. However, with the evolution of database management systems, there was a significant increase in the volume of stored data, which could difficult the analysis. In this context, the developed work presents an efficient parallel optimization of bibliometric information, in order to allow this scientific analysis in a Big Data environment. Our results show that the time taken to calculate the transitivity value using the sequential approach grows 4.08 times faster than the parallel proposed approach when the number of nodes tends to infinity; the time taken to calculate the average distance and diameter values using the sequential approach grows 5.27 times faster than the parallel proposed approach when the number of nodes tends to infinity. Also, the results found present good values of speed up and efficiency.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"73 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121811361","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

Segmentation and Classification of Skin Cancer Melanoma from Skin Lesion Images 皮肤病变图像中皮肤癌黑色素瘤的分割与分类

2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2017-12-01 DOI: 10.1109/PDCAT.2017.00028

N. Lynn, Zin Mar Kyu

{"title":"Segmentation and Classification of Skin Cancer Melanoma from Skin Lesion Images","authors":"N. Lynn, Zin Mar Kyu","doi":"10.1109/PDCAT.2017.00028","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00028","url":null,"abstract":"Melanoma, one type of skin cancer is considered o the most dangerous form of skin cancer occurred in humans. However it is curable if the person detects early. To minimize the diagnostic error caused by the complexity of visual interpretation and subjectivity, it is important to develop a technology for computerized image analysis. This paper presents a methodological approach for the classification of pigmented skin lesions in dermoscopic images. Firstly, the image of the skin to remove unwanted hair and noise, and then the segmentation process is performed to extract the affected area. For detecting the melanoma skin cancer, the meanshift algorithm that segments the lesion from the entire image is used in this study. Feature extraction is then performed by underlying ABCD dermatology rules. After extracting the features from the lesion, feature selection algorithm has been used to get optimized features in order to feed for classification stage. Those selected optimized features are classified using kNN, decision tree and SVM classifiers. The performance of the system was tested and compare those accuracies and get promising results.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128916831","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: 24