2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)最新文献_第8页

Proactive algorithm to reduce power consumption and handovers in heterogeneous mobile networks 在异构移动网络中减少功耗和切换的主动算法

2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON) Pub Date : 2017-10-01 DOI: 10.1109/UEMCON.2017.8249011

M. Nahas, M. Sabati, Rana Kanso, M. Azizi

{"title":"Proactive algorithm to reduce power consumption and handovers in heterogeneous mobile networks","authors":"M. Nahas, M. Sabati, Rana Kanso, M. Azizi","doi":"10.1109/UEMCON.2017.8249011","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249011","url":null,"abstract":"Energy consumption of communication and cellular networks has recently received increased attention in the research community. Moreover, a large number of base stations (BSs) have been deployed in order to meet these demands on cellular traffic. This leads to considerable increase in the operational cost and contributes as a major portion of the energy consumption of cellular networks. Therefore, network operators are considering how to reduce the energy consumption and design “Green Communication”. The concept of Green Communication is to manage energy consumption and maintain reasonable costs while keeping high quality of service and benefits. In this paper, we present some techniques for reducing power consumption by developing solutions in terms of the network architecture and the base stations components. We use some of these solutions to develop and implement a novel algorithm that combines Cell Zooming and Heterogeneous Networks with many types of base station while taking into consideration user's mobility. The efficiency and the power management of our algorithm, especially in high traffic networks, are achieved and approved by simulations.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125557885","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

Consensus protocols for blockchain-based data provenance: Challenges and opportunities 基于区块链的数据来源共识协议:挑战与机遇

2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON) Pub Date : 2017-10-01 DOI: 10.1109/UEMCON.2017.8249088

Deepak K. Tosh, S. Shetty, Xueping Liang, C. Kamhoua, L. Njilla

引用次数: 68

Mobile sensor-based biometrics using common daily activities 基于移动传感器的生物识别技术，使用日常活动

2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON) Pub Date : 2017-10-01 DOI: 10.1109/UEMCON.2017.8249001

Kenichi Yoneda, Gary M. Weiss

引用次数: 18

Chirp pulse envelope evaluation for SAR image formation SAR图像形成的啁啾脉冲包络评估

2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON) Pub Date : 2017-10-01 DOI: 10.1109/UEMCON.2017.8248973

E. Balster, Andrew M. Kordik, F. Scarpino, Kerry L. Hill

引用次数: 0

New sparsity based pansharpening algorithms for hyperspectral images 基于稀疏度的高光谱图像泛锐化新算法

2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON) Pub Date : 2017-10-01 DOI: 10.1109/UEMCON.2017.8248993

C. Kwan, Bence Budavari, Minh Dao, Jin Zhou

引用次数: 21

Autonomic security for zero trust networks 零信任网络的自主安全

2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON) Pub Date : 2017-10-01 DOI: 10.1109/UEMCON.2017.8249053

Dayna Eidle, S. Ni, C. DeCusatis, Anthony Sager

引用次数: 22

Data analysis of cyber-activity within high performance computing environments 高性能计算环境中网络活动的数据分析

2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON) Pub Date : 2017-10-01 DOI: 10.1109/UEMCON.2017.8249003

L. Ji, S. Kolhe, A. Clark

{"title":"Data analysis of cyber-activity within high performance computing environments","authors":"L. Ji, S. Kolhe, A. Clark","doi":"10.1109/UEMCON.2017.8249003","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249003","url":null,"abstract":"High performance computing (HPC) environments are becoming the norm for daily use. However, the resilience of these systems is questionable because their complex infrastructure makes troubleshooting both the location and cause of failures extremely difficult. These same reasons make HPCs prone to virulent activity. This paper presents a data analysis framework for analyzing ranges of failure observations as a result of malicious activity. Taking into account the internal reliability infrastructure, data network extrapolation is performed as a preprocessing tool that accurately calculates the normalized failure rates. Next, nonlinear regression is performed on the spectrum of observations taking into account the magnitude, growth rate, and midpoint behavior. Additionally, influence analysis is performed that considers outlying observations. The empirical results using a simulated supercomputing modeling and simulation framework show improvement, in terms of characterization performance, where approximately 91% of the nodes were properly characterized. The results of this work can be applied to develop robust task-scheduling frameworks within supercomputing architectures.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129219105","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

Deep CNN-LSTM with combined kernels from multiple branches for IMDb review sentiment analysis 深度CNN-LSTM结合多个分支的核，用于IMDb评论情感分析

2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON) Pub Date : 2017-10-01 DOI: 10.1109/UEMCON.2017.8249013

Alec Yenter, Abhishek Verma

{"title":"Deep CNN-LSTM with combined kernels from multiple branches for IMDb review sentiment analysis","authors":"Alec Yenter, Abhishek Verma","doi":"10.1109/UEMCON.2017.8249013","DOIUrl":"https://doi.org/10.1109/UEMCON.2017.8249013","url":null,"abstract":"Deep learning neural networks have made significant progress in the area of image and video analysis. This success of neural networks can be directed towards improvements in textual sentiment classification. In this paper, we describe a novel approach to sentiment analysis through the use of combined kernel from multiple branches of convolutional neural network (CNN) with Long Short-term Memory (LSTM) layers. Our combination of CNN and LSTM schemes produces a model with the highest reported accuracy on the Internet Movie Database (IMDb) review sentiment dataset. Additionally, we present multiple architecture variations of our proposed model to illustrate our attempts to increase accuracy while minimizing overfitting. We experiment with numerous regularization techniques, network structures, and kernel sizes to create five high-performing models for comparison. These models are capable of predicting the sentiment polarity of reviews from the IMDb dataset with accuracy above 89%. Firstly, the accuracy of our best performing proposed model surpasses the previously published models and secondly it vastly improves upon the baseline CNN+LSTM model. The capability of the combined kernel from multiple branches of CNN based LSTM architecture could also be lucrative towards other datasets for sentiment analysis or simply text classification. Furthermore, the proposed model has the potential in machine learning in video and audio.","PeriodicalId":403890,"journal":{"name":"2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131512031","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: 119

A smart campus internet of things framework 智能校园物联网框架

2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON) Pub Date : 2017-10-01 DOI: 10.1109/UEMCON.2017.8249106

Yi Wang, Brandon Saez, Joseph Szczechowicz, Jonathan Ruisi, Tyler Kraft, Stephen Toscano, Zachary Vacco, Kervins Nicolas

引用次数: 13

User concerns with personal routers used as public Wi-fi hotspots 用户担心个人路由器被用作公共Wi-fi热点

2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON) Pub Date : 2017-10-01 DOI: 10.1109/UEMCON.2017.8248978

J. Golbeck

引用次数: 1