Compute Pub Date : 2013-08-22 DOI: 10.1145/2522548.2522748

Sabyasachi Abadhan, Sohini De, Suddhasil De

{"title":"Uncoupling of mobile cloud computing architecture using tuple space: modeling and reasoning","authors":"Sabyasachi Abadhan, Sohini De, Suddhasil De","doi":"10.1145/2522548.2522748","DOIUrl":"https://doi.org/10.1145/2522548.2522748","url":null,"abstract":"Mobile Cloud Computing architecture provides the services of cloud computing to the mobile applications executing in users' mobile/portable devices. By facilitating these applications with the cloud services, it helps to overcome the inherent limitations of mobile/portable devices that are faced by their mobile applications. However, the cloud services in existing architecture become tightly coupled with the mobile applications while delivering the service, which is highly undesirable in the dynamic and unreliable mobile cloud computing paradigm. In this paper, a new mobile cloud computing architecture is proposed, where the mobile applications remain uncoupled from the leased cloud services during the service delivery. In this architecture, the tuple space model is used for uncoupling these interactions. The proposed approach of uncoupling improves the flexibility and efficiency of mobile cloud computing. This paper also suggests an approach for formalizing and reasoning of the proposed architecture, in order to properly validate its flexibility and efficiency while uncoupling the service access and delivery to the mobile applications. The formalization is carried out using Mobile UNITY.","PeriodicalId":435920,"journal":{"name":"Compute","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114723493","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

How many researchers does it take to make impact?: mining software engineering publication data for collaboration insights 需要多少研究人员才能产生影响?:挖掘软件工程发布数据以获得协作见解

Compute Pub Date : 2013-08-22 DOI: 10.1145/2522548.2522603

Subhajit Datta, S. Sarkar, A. Sajeev, Nishanth Kumar

引用次数: 1

Multimodal biometric system based on hand geometry, palmprint and signature 基于手几何、掌纹和签名的多模态生物识别系统

Compute Pub Date : 2012-01-23 DOI: 10.1145/2459118.2459122

K. B. Nagasundara, S. Manjunath, D. S. Guru

引用次数: 2

Upper body pose recognition and classifier 上半身姿势识别与分类

Compute Pub Date : 2012-01-23 DOI: 10.1145/2459118.2459126

Naman Sharma, H. B. Maringanti, Krishna Asawa

引用次数: 1

Efficient web data classification techniques using semi-supervise learning algorithm 基于半监督学习算法的高效web数据分类技术

Compute Pub Date : 2012-01-23 DOI: 10.1145/2459118.2459138

A. Rajawat, Upendra Dwivedi, D. Jain, A. Upadhyay

{"title":"Efficient web data classification techniques using semi-supervise learning algorithm","authors":"A. Rajawat, Upendra Dwivedi, D. Jain, A. Upadhyay","doi":"10.1145/2459118.2459138","DOIUrl":"https://doi.org/10.1145/2459118.2459138","url":null,"abstract":"In Organization the data is very important that increase the volume of information that is available on the web and that leads to the design of efficient and accurate web data classification systems. In this paper, we define a framework to improve the performance of a base classifier, by clustering the unlabeled data with labeled data using clustering algorithm (training of samples) labeling of clusters (majority voting for each Hyperspheres) and final generated classified data. We have used construction of BNN based semi-supervised classifier while training and testing of the classifier is performed. We have studied and customized a supervised classification algorithm to form out semi-supervised classification that leads to design a multiclass semi-supervised classifier using geometrical expansion. The experimental result shows provision for the classifier designer followed by training and testing medical disease dataset using pre-decided samples. Our classification model consists of training phase that covers two process clustering and labeling to perform classification task of medical data and the binary neural network is trained. In this we used two techniques normalization and quantization for pre-processing the datasets. Pre-processing impart various outcomes after applying the classification model like number of hypersphere, confusing samples that cannot be learned, training time and label of hypersphere. Comparison has been done for implementation and design of Binary Neural Network Classifier Algorithm with the other existing traditional algorithms. Our classifier evaluates performance in terms of generalization, number of hidden neuron and accuracy etc. The BNN-CA construct three-layered binary neural network (BNN) and can solve any semi-labeled multi-class problem.","PeriodicalId":435920,"journal":{"name":"Compute","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133756863","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

Texture edge statistics for efficient retrieval of biomedical images 用于生物医学图像有效检索的纹理边缘统计

Compute Pub Date : 2012-01-23 DOI: 10.1145/2459118.2459129

K. Deepak, Harikrishna G. N. Rai, Shahanaz Syed, P. R. Krishna

引用次数: 1

Insertion and querying mechanism for a distributed XML database system 分布式XML数据库系统的插入和查询机制

Compute Pub Date : 2012-01-23 DOI: 10.1145/2459118.2459128

A. M. Kulkarni, Janani Thirunavukkarasu, P. S. Pillai, Sneha Somanath Sulegai, Shrisha Rao

引用次数: 3

A generic topology discovery approach for huge social networks 大型社交网络的通用拓扑发现方法

Compute Pub Date : 2012-01-23 DOI: 10.1145/2459118.2459123

P. R. Krishna, Kishore Varma Indukuri, Shahanaz Syed