Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing最新文献_第3页

A model for WLAN signal attenuation of the human body 人体无线局域网信号衰减模型

Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing Pub Date : 2013-09-08 DOI: 10.1145/2493432.2493459

Ngewi Fet, M. Handte, P. Marrón

引用次数: 38

Session details: Public displays 会话详细信息:公开显示

Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing Pub Date : 2013-09-08 DOI: 10.1145/3254806

N. Davies

引用次数: 0

Session details: Positioning I 会议详情

Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing Pub Date : 2013-09-08 DOI: 10.1145/3254789

A. LaMarca

引用次数: 0

Understanding the coverage and scalability of place-centric crowdsensing 了解以地点为中心的群体感知的覆盖范围和可扩展性

Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing Pub Date : 2013-09-08 DOI: 10.1145/2493432.2493498

Yohan Chon, N. Lane, Yunjong Kim, Feng Zhao, H. Cha

{"title":"Understanding the coverage and scalability of place-centric crowdsensing","authors":"Yohan Chon, N. Lane, Yunjong Kim, Feng Zhao, H. Cha","doi":"10.1145/2493432.2493498","DOIUrl":"https://doi.org/10.1145/2493432.2493498","url":null,"abstract":"Crowd-enabled place-centric systems gather and reason over large mobile sensor datasets and target everyday user locations (such as stores, workplaces, and restaurants). Such systems are transforming various consumer services (for example, local search) and data-driven organizations (city planning). As the demand for these systems increases, our understanding of how to design and deploy successful crowdsensing systems must improve. In this paper, we present a systematic study of the coverage and scaling properties of place-centric crowdsensing. During a two-month deployment, we collected smartphone sensor data from 85 participants using a representative crowdsensing system that captures 48,000 different place visits. Our analysis of this dataset examines issues of core interest to place-centric crowdsensing, including place-temporal coverage, the relationship between the user population and coverage, privacy concerns, and the characterization of the collected data. Collectively, our findings provide valuable insights to guide the building of future place-centric crowdsensing systems and applications.","PeriodicalId":262104,"journal":{"name":"Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121848142","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: 136

Session details: Activity recognition 会话细节:活动识别

Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing Pub Date : 2013-09-08 DOI: 10.1145/3254790

A. Ferscha

引用次数: 0

The influence of temporal and spatial features on the performance of next-place prediction algorithms 时空特征对下位预测算法性能的影响

Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing Pub Date : 2013-09-08 DOI: 10.1145/2493432.2493467

Paul Baumann, Wilhelm Kleiminger, S. Santini

{"title":"The influence of temporal and spatial features on the performance of next-place prediction algorithms","authors":"Paul Baumann, Wilhelm Kleiminger, S. Santini","doi":"10.1145/2493432.2493467","DOIUrl":"https://doi.org/10.1145/2493432.2493467","url":null,"abstract":"Several algorithms to predict the next place visited by a user have been proposed in the literature. The accuracy of these algorithms -- measured as the ratio of the number of correct predictions and the number of all computed predictions -- is typically very high. In this paper, we show that this good performance is due to the high predictability intrinsic in human mobility. We also show that most algorithms fail to correctly predict transitions, i.e. situations in which users move between different places. To this end, we analyze the performance of 18 prediction algorithms focusing on their ability to predict transitions. We run our analysis on a data set of mobility traces of 37 users collected over a period of 1.5 years. Our results show that even algorithms achieving an overall high accuracy are unable to reliably predict the next location of the user if this is different from the current one. Building upon our analysis we then present a novel next-place prediction algorithm that can both achieve high overall accuracy and reliably predict transitions. Our approach combines all the 18 algorithms considered in our analysis and achieves its good performance at the cost of a higher computational and memory overhead.","PeriodicalId":262104,"journal":{"name":"Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125553927","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: 64

Automatically detecting problematic use of smartphones 自动检测有问题的智能手机使用

Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing Pub Date : 2013-09-08 DOI: 10.1145/2493432.2493443

Choonsung Shin, A. Dey

{"title":"Automatically detecting problematic use of smartphones","authors":"Choonsung Shin, A. Dey","doi":"10.1145/2493432.2493443","DOIUrl":"https://doi.org/10.1145/2493432.2493443","url":null,"abstract":"Smartphone adoption has increased significantly and, with the increase in smartphone capabilities, this means that users can access the Internet, communicate, and entertain themselves anywhere and anytime. However, there is growing evidence of problematic use of smartphones that impacts both social and heath aspects of users' lives. Currently, assessment of overuse or problematic use depends on one-time, self-reported behavioral information about phone use. Due to the known issues with self-reports in such types of assessments, we explore an automated, objective and repeatable approach for assessing problematic usage. We collect a wide range of phone usage data from smartphones, identify a number of usage features that are relevant to this assessment, and build detection models based on Adaboost with machine learning algorithms automatically detecting problematic use. We found that the number of apps used per day, the ratio of SMSs to calls, the number of event-initiated sessions, the number of apps used per event initiated session, and the length of non-event-initiated sessions are useful for detecting problematic usage. With these, a detection model can identify users with problematic usage with 89.6% accuracy (F-score of .707).","PeriodicalId":262104,"journal":{"name":"Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing","volume":"306 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129343982","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: 90

Session details: Home heating 会议详情:家庭供暖

Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing Pub Date : 2013-09-08 DOI: 10.1145/3254780

J. Crowley

引用次数: 0

Locality and privacy in people-nearby applications 在人们附近的应用程序的位置和隐私

Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing Pub Date : 2013-09-08 DOI: 10.1145/2493432.2493485

Eran Toch, Inbal Levi

引用次数: 50

Session details: Social computing I 会议细节:社会计算1

Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing Pub Date : 2013-09-08 DOI: 10.1145/3254801

Irina Shklovski

引用次数: 0