Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing最新文献_第6页

Efficient calibration for rssi-based indoor localization by bayesian experimental design on multi-task classification 基于多任务分类贝叶斯实验设计的rsi室内定位的有效标定

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971710

M. Shimosaka, Osamu Saisho

引用次数: 11

PERUIM: understanding mobile application privacy with permission-UI mapping PERUIM:通过权限- ui映射理解移动应用隐私

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971693

Yuanchun Li, Yao Guo, Xiangqun Chen

引用次数: 32

Privacy-friendly photo capturing and sharing system 隐私友好的照片拍摄和分享系统

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971662

Lan Zhang, Kebin Liu, Xiangyang Li, Cihang Liu, Xuan Ding, Yunhao Liu

{"title":"Privacy-friendly photo capturing and sharing system","authors":"Lan Zhang, Kebin Liu, Xiangyang Li, Cihang Liu, Xuan Ding, Yunhao Liu","doi":"10.1145/2971648.2971662","DOIUrl":"https://doi.org/10.1145/2971648.2971662","url":null,"abstract":"The wide adoption of smart devices with onboard cameras facilitates photo capturing and sharing, but greatly increases people's concern on privacy infringement. Here we seek a solution to respect the privacy of persons being photographed in a smarter way that they can be automatically erased from photos captured by smart devices according to their requirements. To make this work, we need to address three challenges: 1) how to enable users explicitly express their privacy protection intentions without wearing any visible specialized tag, and 2) how to associate the intentions with persons in captured photos accurately and efficiently. Furthermore, 3) the association process itself should not cause portrait information leakage and should be accomplished in a privacy-preserving way. In this work, we design, develop, and evaluate a system, called COIN (Cloak Of INvisibility), that enables a user to flexibly express her privacy requirement and empowers the photo service provider (or image taker) to exert the privacy protection policy. Leveraging the visual distinguishability of people in the field-of-view and the dimension-order-independent property of vector similarity measurement, COIN achieves high accuracy and low overhead. We implement a prototype system, and our evaluation results on both the trace-driven and real-life experiments confirm the feasibility and efficiency of our system.","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":"129475704","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: 26

The potential of physical motion cues: changing people's perception of robots' performance 物理动作线索的潜力:改变人们对机器人表现的看法

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971697

Pedro Garcia Garcia, Enrico Costanza, S. Ramchurn, Jhim Kiel M. Verame

引用次数: 2

PADA: power-aware development assistant for mobile sensing applications PADA:用于移动传感应用的功率感知开发助手

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971676

Chulhong Min, Seungchul Lee, Changhun Lee, Youngki Lee, Seungwoo Kang, Seungpyo Choi, Wonjung Kim, Junehwa Song

引用次数: 19

Lets not stare at smartphones while walking: memorable route recommendation by detecting effective landmarks 走路时不要盯着智能手机:通过检测有效的地标来推荐令人难忘的路线

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971758

Shoko Wakamiya, Hiroshi Kawasaki, Yukiko Kawai, A. Jatowt, E. Aramaki, Toyokazu Akiyama

引用次数: 20

Sensus: a cross-platform, general-purpose system for mobile crowdsensing in human-subject studies Sensus:一个跨平台的通用系统，用于人体研究中的移动众感

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971711

Haoyi Xiong, Yu Huang, Laura E. Barnes, M. Gerber

{"title":"Sensus: a cross-platform, general-purpose system for mobile crowdsensing in human-subject studies","authors":"Haoyi Xiong, Yu Huang, Laura E. Barnes, M. Gerber","doi":"10.1145/2971648.2971711","DOIUrl":"https://doi.org/10.1145/2971648.2971711","url":null,"abstract":"The burden of entry into mobile crowdsensing (MCS) is prohibitively high for human-subject researchers who lack a technical orientation. As a result, the benefits of MCS remain beyond the reach of research communities (e.g., psychologists) whose expertise in the study of human behavior might advance applications and understanding of MCS systems. This paper presents Sensus, a new MCS system for human-subject studies that bridges the gap between human-subject researchers and MCS methods. Sensus alleviates technical burdens with on-device, GUI-based design of sensing plans, simple and efficient distribution of sensing plans to study participants, and uniform participant experience across iOS and Android devices. Sensing plans support many hardware and software sensors, automatic deployment of sensor-triggered surveys, and double-blind assignment of participants within randomized controlled trials. Sensus offers these features to study designers without requiring knowledge of markup and programming languages. We demonstrate the feasibility of using Sensus within two human-subject studies, one in psychology and one in engineering. Feedback from non-technical users indicates that Sensus is an effective and low-burden system for MCS-based data collection and analysis.","PeriodicalId":303792,"journal":{"name":"Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing","volume":"50 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":"126080684","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: 102

ROC comment: automated descriptive and subjective captioning of behavioral videos ROC评论:行为视频的自动描述和主观字幕

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971743

M. R. Ali, Facundo Ciancio, Ru Zhao, Iftekhar Naim, Ehsan Hoque

引用次数: 3

Dynamic cluster-based over-demand prediction in bike sharing systems 基于动态聚类的共享单车系统超需求预测

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971652

Longbiao Chen, Daqing Zhang, Leye Wang, Dingqi Yang, Xiaojuan Ma, Shijian Li, Zhaohui Wu, Gang Pan, T. Nguyen, J. Jakubowicz

{"title":"Dynamic cluster-based over-demand prediction in bike sharing systems","authors":"Longbiao Chen, Daqing Zhang, Leye Wang, Dingqi Yang, Xiaojuan Ma, Shijian Li, Zhaohui Wu, Gang Pan, T. Nguyen, J. Jakubowicz","doi":"10.1145/2971648.2971652","DOIUrl":"https://doi.org/10.1145/2971648.2971652","url":null,"abstract":"Bike sharing is booming globally as a green transportation mode, but the occurrence of over-demand stations that have no bikes or docks available greatly affects user experiences. Directly predicting individual over-demand stations to carry out preventive measures is difficult, since the bike usage pattern of a station is highly dynamic and context dependent. In addition, the fact that bike usage pattern is affected not only by common contextual factors (e.g., time and weather) but also by opportunistic contextual factors (e.g., social and traffic events) poses a great challenge. To address these issues, we propose a dynamic cluster-based framework for over-demand prediction. Depending on the context, we construct a weighted correlation network to model the relationship among bike stations, and dynamically group neighboring stations with similar bike usage patterns into clusters. We then adopt Monte Carlo simulation to predict the over-demand probability of each cluster. Evaluation results using real-world data from New York City and Washington, D.C. show that our framework accurately predicts over-demand clusters and outperforms the baseline methods significantly.","PeriodicalId":303792,"journal":{"name":"Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing","volume":"74 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120863906","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: 157

Using passively collected sedentary behavior to predict hospital readmission 使用被动收集久坐行为预测再入院

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971750

Sangwon Bae, A. Dey, C. Low

{"title":"Using passively collected sedentary behavior to predict hospital readmission","authors":"Sangwon Bae, A. Dey, C. Low","doi":"10.1145/2971648.2971750","DOIUrl":"https://doi.org/10.1145/2971648.2971750","url":null,"abstract":"Hospital readmissions are a major problem facing health care systems today, costing Medicare alone US$26 billion each year. Being readmitted is associated with significantly shorter survival, and is often preventable. Predictors of readmission are still not well understood, particularly those under the patient's control: behavioral risk factors. Our work evaluates the ability of behavioral risk factors, specifically Fitbit-assessed behavior, to predict readmission for 25 postsurgical cancer inpatients. Our results show that sum of steps, maximum sedentary bouts, frequency, and low breaks in sedentary times during waking hours are strong predictors of readmission. We built two models for predicting readmissions: Steps-only and Behavioral model that adds information about sedentary behaviors. The Behavioral model (88.3%) outperforms the Steps-only model (67.1%), illustrating the value of passively collected information about sedentary behaviors. Indeed, passive monitoring of behavior data, i.e., mobility, after major surgery creates an opportunity for early risk assessment and timely interventions.","PeriodicalId":303792,"journal":{"name":"Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing","volume":"14 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":"125123237","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: 32