Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing最新文献_第8页

Crowdsensing-based smartphone use guide for battery life extension 基于众感的智能手机续航使用指南

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971728

Yohan Chon, Gwangmin Lee, Rhan Ha, H. Cha

{"title":"Crowdsensing-based smartphone use guide for battery life extension","authors":"Yohan Chon, Gwangmin Lee, Rhan Ha, H. Cha","doi":"10.1145/2971648.2971728","DOIUrl":"https://doi.org/10.1145/2971648.2971728","url":null,"abstract":"With the increasing popularity of smartphones, battery life is among the most crucial issues for mobile users. This paper presents a crowdsensing-based use guide to extend the lifetime of smartphones. The system answers a question raised by phone usage: Why is my phone battery draining quickly compared to others phones despite running the same applications? The proposed system pinpoints the major causes of battery drain in terms of both hardware and software aspects. In relation to the hardware aspect, the system quantifies degree of battery aging as a ratio metric; an estimate of 50% indicates that the battery is at half of full capacity, meaning that battery usage time is approximately half that of a new battery. The system automatically profiles battery age based on charging duration data collected by crowdsensing. In its software aspect, the system guides phone configuration to extend application usage times. The system mines large-scale usage data to infer the major energy holes in a user's phone usage. The scheme works autonomously without user intervention and does not require any external equipment. Extensive evaluation with 3,000 users demonstrated that the proposed scheme successfully extends battery life for typical mobile users.","PeriodicalId":303792,"journal":{"name":"Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126414014","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: 10

TraceMatch: a computer vision technique for user input by tracing of animated controls TraceMatch:通过跟踪动画控件为用户输入提供的计算机视觉技术

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971714

Christopher Clarke, A. Bellino, Augusto Esteves, Eduardo Velloso, Hans-Werner Gellersen

引用次数: 48

AFV: enabling application function virtualization and scheduling in wearable networks AFV:在可穿戴网络中实现应用功能虚拟化和调度

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971727

Harini Kolamunna, Yining Hu, Diego Perino, Kanchana Thilakarathna, D. Makaroff, Xinlong Guan, A. Seneviratne

引用次数: 12

Cognitive rhythms: unobtrusive and continuous sensing of alertness using a mobile phone 认知节奏:使用手机时不显眼且持续的警觉感

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971712

Saeed Abdullah, Elizabeth L. Murnane, M. Matthews, Matthew Kay, J. Kientz, Geri Gay, Tanzeem Choudhury

{"title":"Cognitive rhythms: unobtrusive and continuous sensing of alertness using a mobile phone","authors":"Saeed Abdullah, Elizabeth L. Murnane, M. Matthews, Matthew Kay, J. Kientz, Geri Gay, Tanzeem Choudhury","doi":"10.1145/2971648.2971712","DOIUrl":"https://doi.org/10.1145/2971648.2971712","url":null,"abstract":"Throughout the day, our alertness levels change and our cognitive performance fluctuates. The creation of technology that can adapt to such variations requires reliable measurement with ecological validity. Our study is the first to collect alertness data in the wild using the clinically validated Psychomotor Vigilance Test. With 20 participants over 40 days, we find that alertness can oscillate approximately 30% depending on time and body clock type and that Daylight Savings Time, hours slept, and stimulant intake can influence alertness as well. Based on these findings, we develop novel methods for unobtrusively and continuously assessing alertness. In estimating response time, our model achieves a root-mean-square error of 80.64 milliseconds, which is significantly lower than the 500ms threshold used as a standard indicator of impaired cognitive ability. Finally, we discuss how such real-time detection of alertness is a key first step towards developing systems that are sensitive to our biological variations.","PeriodicalId":303792,"journal":{"name":"Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing","volume":"65 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114682853","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: 91

mCrave: continuous estimation of craving during smoking cessation 麦克雷夫:戒烟期间对烟瘾的持续估计

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971672

Soujanya Chatterjee, K. Hovsepian, Hillol Sarker, Nazir Saleheen, M. al’Absi, G. Atluri, Emre Ertin, Cho Lam, A. Lemieux, M. Nakajima, B. Spring, D. Wetter, Santosh Kumar

引用次数: 22

WiDir: walking direction estimation using wireless signals WiDir:使用无线信号估计行走方向

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971658

Dan Wu, Daqing Zhang, Chenren Xu, Yasha Wang, Hao Wang

引用次数: 166

FORCES: feedback and control for occupants to refine comfort and energy savings 力量:反馈和控制居住者，以提高舒适度和节能

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971700

Daniel A. Winkler, Alex Beltran, N. P. Esfahani, P. Maglio, Alberto Cerpa

引用次数: 39

Predicting room occupancy with a single passive infrared (PIR) sensor through behavior extraction 单被动红外(PIR)传感器通过行为提取预测房间占用率

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971746

Yordan P. Raykov, Emre Ozer, Ganesh S. Dasika, A. Boukouvalas, Max A. Little

引用次数: 104

EmotionCheck: leveraging bodily signals and false feedback to regulate our emotions EmotionCheck:利用身体信号和错误反馈来调节我们的情绪

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971752

J. Costa, A. Adams, Malte F. Jung, François Guimbretière, Tanzeem Choudhury

引用次数: 143

Spatially fine-grained urban air quality estimation using ensemble semi-supervised learning and pruning 基于集合半监督学习和剪枝的空间细粒度城市空气质量估计

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971725

Ling Chen, Yaya Cai, Yifang Ding, Mingqi Lv, C. Yuan, Gencai Chen

{"title":"Spatially fine-grained urban air quality estimation using ensemble semi-supervised learning and pruning","authors":"Ling Chen, Yaya Cai, Yifang Ding, Mingqi Lv, C. Yuan, Gencai Chen","doi":"10.1145/2971648.2971725","DOIUrl":"https://doi.org/10.1145/2971648.2971725","url":null,"abstract":"Air pollution has adverse effects on humans and ecosystem, and spatially fine-grained air quality information (i.e., the air quality information of every fine-grained area) can help people to avoid unhealthy outdoor activities. However, the number of air quality monitoring stations is usually limited, and thus spatially fine-grained air quality estimation is a challenging task. This paper proposes a method for inferring spatially fine-grained air quality information throughout a city. On one hand, since air quality is affected by multiple factors (e.g., factory waste gases and automobile exhaust fumes), this method employs various data sources, including traffic, road network, point of interests (POIs), and check-ins from social network services, which are related to air quality, to conduct the estimation. On the other hand, since the labeled data are highly limited due to the sparseness of monitoring stations, this method uses an improved ensemble semi-supervised learning (Semi-EP) to establish the relationship between the various data sources and urban air quality. Semi-EP firstly generates multiple classifiers from the original labeled data set and these classifiers are retrained in the iterative co-training process. Then, ensemble pruning technique is used to select the most-diverse subset from these multiple classifiers. This method is evaluated on the real-world dataset of Hangzhou city, China, and the experimental results have demonstrated its advantages over state-of-the-art methods.","PeriodicalId":303792,"journal":{"name":"Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129969554","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: 34