Proceedings of the International Conference on High Performance Compilation, Computing and Communications最新文献

{"title":"Studying the Classification Accuracy Performance when Representation is Changed on Several Classifier Techniques","authors":"Ehab A. Omer A. Omer, Wisam H. Benamer","doi":"10.1145/3069593.3069597","DOIUrl":"https://doi.org/10.1145/3069593.3069597","url":null,"abstract":"Introduction: During the process of building a predictive data mining module achieving the highest accuracy is major concern by all researchers. Studying the impact of data representation on the performance of classification accuracy is essential. Recent researches travel among classifiers techniques looking for suitable and higher classification accuracy to build strong modules. Adding extra dimensional by focusing on the reflects that data representation might have on the classification accuracy data mining predictive techniques is the ultimate goal of this research. Methods: In this research seven different data representations were performed on several classifier techniques. These representations were AS_IS representation and three from the binary section and three from normalization section. The binary section included simple binary representation, flag representation and thermometer representation while the normalization section included min max normalization, sigmoidal normalization and standard deviation normalization. These seven representations were applied on eight classifiers Neural Network, Logistic Regression, K nearest Neighbor, Support Vector Machine, Classification Tree, Naive Bayesian, Rule based and Random Forest Decision Tree. Moreover, two datasets have been used for testing the performance of classification accuracy, namely Wisconsin Breast Cancer and German Credit and these two datasets have Boolean target class. Results: The fourteen data representations were raised from two datasets Wisconsin Breast Cancer and German Credit with seven different data representations for each. These data representations were performed on several classifier techniques using Orange software. The results achieved showed variation of the performance among all classifier in classification accuracy. Excluding Naive Bayesian which had over 60 % different from the lowest to the highest accuracy, all other classifier techniques had diverging on classification accuracy around 4.2%.","PeriodicalId":383937,"journal":{"name":"Proceedings of the International Conference on High Performance Compilation, Computing and Communications","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126079497","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}

{"title":"Evaluating expectation-maximization algorithm for 2D DOA estimation via planar antenna arrays","authors":"Y. Nechaev, K. D. A. Sarmad, I. Peshkov","doi":"10.1145/3069593.3069595","DOIUrl":"https://doi.org/10.1145/3069593.3069595","url":null,"abstract":"The paper is devoted direction-of-arrival (DOA) estimation in case of planar antenna arrays relating to azimuth and elevation source location. We investigate circular antenna array which is well known and studied and additionally compare it with L and curved shapes. As the methods of DOA estimation stochastic maximum likelihood (SML) and expectation-maximization (EM) algorithms are studied.","PeriodicalId":383937,"journal":{"name":"Proceedings of the International Conference on High Performance Compilation, Computing and Communications","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116847357","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}

{"title":"Modal Parameter Identification of Bridge based on Large Scale Data Sets","authors":"I. Khan, Khurram Malik","doi":"10.1145/3069593.3069620","DOIUrl":"https://doi.org/10.1145/3069593.3069620","url":null,"abstract":"The main objective of this paper was to carry out an effective and meticulous long term state identification of cable stayed bridge, from a large amount of data collected from long span cable stayed bridge. In order to achieve the above objective, data visualization techniques were employed, because it can provide a quick and effective data analysis due to its graphical interface of data visualization. For this purpose a long span cable stayed bridge, having a main span of 1088m was selected as a case study. Firstly the data was collected from long span bridge, then based on data visualization outcome, Data Driven Stochastic Subspace Identification (DATA-SSI) technique has been employed to identify the modal parameters such as modal frequencies and damping ratios by plotting its stable diagrams. The results showed that the proposed method was effective in attaining its goals and can endows better results especially for long term continuous data and can prove to be a valuable tool in bridge health monitoring.","PeriodicalId":383937,"journal":{"name":"Proceedings of the International Conference on High Performance Compilation, Computing and Communications","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128212016","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}

{"title":"A Highly Reliable and Load Balance Supporting Domain Division Algorithm for Software Defined Networks","authors":"Li Li, Jing Shi","doi":"10.1145/3069593.3069619","DOIUrl":"https://doi.org/10.1145/3069593.3069619","url":null,"abstract":"In the large-scale Software Defined Network (SDN), the number of switches is so huge that multiple controllers have to be used, which causes the scalability problem of SDN. In this paper, a scheme of determining the number of the controllers in large SDN networks as well as a highly reliable and load balance supporting domain division algorithm(HRLB) are proposed. Simulation results show HRLB significantly improves the network performances compared to the k-means algorithm and the capacitated k-center algorithm.","PeriodicalId":383937,"journal":{"name":"Proceedings of the International Conference on High Performance Compilation, Computing and Communications","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127681910","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}

{"title":"Performance Analysis of Cooperative Communication Networks with Data and Energy Coupling Queuing Model","authors":"Guangjun Liang, Qi Zhu, Jianfang Xin, Jiashan Tang, Tianjiao Zhang","doi":"10.1145/3069593.3069615","DOIUrl":"https://doi.org/10.1145/3069593.3069615","url":null,"abstract":"In this paper, we address the performance analysis of the cooperative communication networks with data packet and energy packet independent arrival but interactive departure which can be profiled as a Coupled Processor Queuing Model. The joint data packet and energy packet assigning problem can achieve its steady state transition probability by Quasi-Birth and Death method and the expressions of throughput, delay and packet drop rate for both data queue and energy queue are also derived. Simulations are demonstrated to verify the accuracy of theoretical derivation results.","PeriodicalId":383937,"journal":{"name":"Proceedings of the International Conference on High Performance Compilation, Computing and Communications","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133650090","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}

{"title":"CRPO: Cognitive Routing Protocol for Opportunistic Networks","authors":"Apoorv Gupta, Aman Bansal, Deepika Naryani, D. Sharma","doi":"10.1145/3069593.3069610","DOIUrl":"https://doi.org/10.1145/3069593.3069610","url":null,"abstract":"Opportunistic networks (OppNets) provide an infrastructure-less environment for delivering messages from source to destination. However, random nature of network topologies, unstable node connections and many other factors make routing of messages difficult. Transfer of a message from source to destination depends on the opportunity available to the carrier node for its encounter with the destination. Hence, an intelligent store-carry-forward paradigm needs to be defined which judicially transfers the message from source to the intermediate node till it reaches its destination. In this paper, a new routing protocol called Cognitive Routing Protocol for Opportunistic networks (CRPO) is proposed which makes intelligent decisions based on its past experiences and sensory inputs. It defines a neural network at each node and learns about the simulating environment with time for the successful delivery of messages. From simulation results, the authors observed that the proposed routing protocol outperforms traditional routing protocols such as Epidemic and PRoPHET, and an advanced routing protocol HBPR in terms of message delivery ratio, overhead ratio and number of messages getting dropped.","PeriodicalId":383937,"journal":{"name":"Proceedings of the International Conference on High Performance Compilation, Computing and Communications","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121982288","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}

{"title":"Prediction of Precipitation Based on Weighted Markov Chain in Dangshan","authors":"Yiqi Gui, Jun Shao","doi":"10.1145/3069593.3069617","DOIUrl":"https://doi.org/10.1145/3069593.3069617","url":null,"abstract":"The purpose of this paper is to predict the precipitation in Dangshan using the weighted Markov chain and fuzzy set theory. Based on annual precipitation in Dangshan during 1961-2015, we apply the weighted Markov chain to predict the annual precipitation from 2011--2015. The results show that our proposed model has high prediction accuracy which provides a way worth of exploration for predicting annual precipitation in Dangshan.","PeriodicalId":383937,"journal":{"name":"Proceedings of the International Conference on High Performance Compilation, Computing and Communications","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122543376","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}

{"title":"Analysis of Wireless Network Usage at Universiti Utara Malaysia: A Preliminary Study towards Bandwidth Management","authors":"S. Shamsudin, N. Katuk, Kamarudin Abdullah","doi":"10.1145/3069593.3069613","DOIUrl":"https://doi.org/10.1145/3069593.3069613","url":null,"abstract":"The emergent of mobile device technology has increased the need for wireless network coverage in organizations. Users with mobile devices such as smartphones, computer tablets, and laptops require a wireless network to access the Internet services. Network administrators face challenges in managing access to the Internet and data usage by these devices. The challenge can be alleviated by implementing efficient bandwidth management strategies. Before identifying the proper strategies and implement them, network administrators must understand the usage pattern of the wireless network. Hence, this study presents an analysis of traffic pattern of wireless network usage by students at Universiti Utara Malaysia (UUM) as a preliminary input towards identifying the best strategies for managing wireless network bandwidth. The students' wireless network usage on a selected student residential area in UUM was analyzed for seven days using network management software. The data on the usage of protocols, service set identifiers, and types of applications were monitored and captured. The analysis of the data suggested valuable information towards managing bandwidth at the university.","PeriodicalId":383937,"journal":{"name":"Proceedings of the International Conference on High Performance Compilation, Computing and Communications","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115593620","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}

{"title":"Dynamic Self-assembling Petaflop Scale Clusters","authors":"Mohammad Samarah, R. Fatmi","doi":"10.1145/3069593.3069599","DOIUrl":"https://doi.org/10.1145/3069593.3069599","url":null,"abstract":"High Performance Computing (HPC) has been studied and used in the scientific community for decades. The Message Passing Interface was first introduced in 1992. Similarly, commercial businesses have been relying on High Throughput Computing (HTC) for the past two decades. Mapreduce platforms became popular with the advent of Very Large Databases (VLDBs) and Big Data. We are now seeing the convergence between HPC and HTC to provide faster and cheaper parallel computation. The emergence of MPI as a scalable and viable parallel platform along with the acceptance of Mapreduce to tackle large data sets now opens the door to a host of new applications particularly in biomedical, public health, scientific, and health informatics research. This convergence is making it possible to have every device be a parallel node. In this paper we explore this convergence and a method for creating dynamic self-assembling clusters using commodity hardware and mobile devices.","PeriodicalId":383937,"journal":{"name":"Proceedings of the International Conference on High Performance Compilation, Computing and Communications","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114633281","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}

{"title":"Dynamic Weaving of ASPECTs in C/C++ using PIN","authors":"Nachiketa Chatterjee, S. Bose, P. Das","doi":"10.1145/3069593.3069598","DOIUrl":"https://doi.org/10.1145/3069593.3069598","url":null,"abstract":"Understanding the dynamic behavior of the existing software application is always a big challenge. Sometime developer need to incorporate additional change in requirements into the application without changing the existing behavior. Also the designer and developer wish to inject additional checkpoints or logs to understand the nature/issues without touching the code or recompile it; even sometime the source is not available. Dynamic instrumentation is one solution to address these. But understanding the syntax and protocol need additional skill development exercise. The objective of this paper is to facilitate the user to concentrate on what to implement where in the existing application rather thinking of how to do it. We introduce another layer of abstraction to weave ASPECTs according to user need using dynamic binary instrumentation. This abstraction also facilitates additional flexibility to attach/change the ASPECTs even during the execution.","PeriodicalId":383937,"journal":{"name":"Proceedings of the International Conference on High Performance Compilation, Computing and Communications","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121080412","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}