Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing最新文献_第2页

Modelling smartphone usage: a markov state transition model 智能手机使用建模:马尔科夫状态转换模型

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971669

V. Kostakos, Denzil Ferreira, Jorge Gonçalves, S. Hosio

引用次数: 34

Learning from less for better: semi-supervised activity recognition via shared structure discovery 越少越好:通过共享结构发现的半监督活动识别

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971701

Lina Yao, F. Nie, Quan Z. Sheng, Tao Gu, Xue Li, Sen Wang

{"title":"Learning from less for better: semi-supervised activity recognition via shared structure discovery","authors":"Lina Yao, F. Nie, Quan Z. Sheng, Tao Gu, Xue Li, Sen Wang","doi":"10.1145/2971648.2971701","DOIUrl":"https://doi.org/10.1145/2971648.2971701","url":null,"abstract":"Despite the active research into, and the development of, human activity recognition over the decades, existing techniques still have several limitations, in particular, poor performance due to insufficient ground-truth data and little support of intra-class variability of activities (i.e., the same activity may be performed in different ways by different individuals, or even by the same individuals with different time frames). Aiming to tackle these two issues, in this paper, we present a robust activity recognition approach by extracting the intrinsic shared structures from activities to handle intra-class variability, and the approach is embedded into a semi-supervised learning framework by utilizing the learned correlations from both labeled and easily-obtained unlabeled data simultaneously. We use l2,1 minimization on both loss function and regularizations to effectively resist outliers in noisy sensor data and improve recognition accuracy by discerning underlying commonalities from activities. Extensive experimental evaluations on four community-contributed public datasets indicate that with little training samples, our proposed approach outperforms a set of classical supervised learning methods as well as those recently proposed semi-supervised approaches.","PeriodicalId":303792,"journal":{"name":"Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing","volume":"21 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":"122252464","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: 58

TASKer: behavioral insights via campus-based experimental mobile crowd-sourcing TASKer:通过基于校园的实验性移动众包进行行为洞察

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971690

Thivya Kandappu, Nikita Jaiman, Randy Tandriansyah, Archan Misra, Shih-Fen Cheng, Cen Chen, H. Lau, D. Chander, K. Dasgupta

{"title":"TASKer: behavioral insights via campus-based experimental mobile crowd-sourcing","authors":"Thivya Kandappu, Nikita Jaiman, Randy Tandriansyah, Archan Misra, Shih-Fen Cheng, Cen Chen, H. Lau, D. Chander, K. Dasgupta","doi":"10.1145/2971648.2971690","DOIUrl":"https://doi.org/10.1145/2971648.2971690","url":null,"abstract":"While mobile crowd-sourcing has become a game-changer for many urban operations, such as last mile logistics and municipal monitoring, we believe that the design of such crowd-sourcing strategies must better accommodate the real-world behavioral preferences and characteristics of users. To provide a real-world testbed to study the impact of novel mobile crowd-sourcing strategies, we have designed, developed and experimented with a real-world mobile crowd-tasking platform on the SMU campus, called TA&Sslash;Ker. We enhanced the TA$Ker platform to support several new features (e.g., task bundling, differential pricing and cheating analytics) and experimentally investigated these features via a two-month deployment of TA$Ker, involving 900 real users on the SMU campus who performed over 30,000 tasks. Our studies (i) show the benefits of bundling tasks as a combined package, (ii) reveal the effectiveness of differential pricing strategies and (iii) illustrate key aspects of cheating (false reporting) behavior observed among workers.","PeriodicalId":303792,"journal":{"name":"Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing","volume":"150 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":"116814845","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: 46

Exploiting cyclic features of walking for pedestrian dead reckoning with unconstrained smartphones 利用行走的循环特征，用不受约束的智能手机进行行人航位推算

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971742

Baoqi Huang, Guodong Qi, Xiaokun Yang, Long Zhao, Han Zou

{"title":"Exploiting cyclic features of walking for pedestrian dead reckoning with unconstrained smartphones","authors":"Baoqi Huang, Guodong Qi, Xiaokun Yang, Long Zhao, Han Zou","doi":"10.1145/2971648.2971742","DOIUrl":"https://doi.org/10.1145/2971648.2971742","url":null,"abstract":"Pedestrian dead reckoning (PDR) is a promising complementary technique to balance the requirements on both accuracy and costs in outdoor and indoor positioning systems. In this paper, we propose a unified framework to comprehensively tackle the three sub problems involved in PDR, including step detection and counting, heading estimation and step length estimation, based on sequentially rotating the device (reference) frame to the Earth (reference) frame through sensor fusion. To be specific, a robust step detection and counting algorithm is devised according to vertical angular velocities and turns out to be tolerant of various smartphone placements; then, a zero velocity update (ZUPT) based algorithm is leveraged to calibrate the measurements in the Earth frame; on these grounds, the heading and step length are further estimated by exploiting the cyclic features of walking. A thorough and extensive experimental analysis is conducted and confirms the effectiveness and advantages of the proposed PDR framework as well as the corresponding algorithms.","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":"126794405","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: 66

Differentiating smartphone users by app usage 通过应用使用情况区分智能手机用户

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971707

Pascal Welke, I. Andone, Konrad Blaszkiewicz, Alexander Markowetz

{"title":"Differentiating smartphone users by app usage","authors":"Pascal Welke, I. Andone, Konrad Blaszkiewicz, Alexander Markowetz","doi":"10.1145/2971648.2971707","DOIUrl":"https://doi.org/10.1145/2971648.2971707","url":null,"abstract":"Tracking users across websites and apps is as desirable to the marketing industry as it is unalluring to users. The central challenge lies in identifying users from the perspective of different apps/sites. While there are methods to identify users via technical settings of their phones, these are prone to countermeasures. Yet, in this paper, we show that it is possible to differentiate users via their set of used apps, their app signature. To this end, we investigate the app usage of 46726 participants from the Menthal project. Even limiting our observation to the 500 globally most frequent apps results in unique signatures for 99.67% of users. Furthermore, even under this restriction, the average minimum Hamming distance to the closest other user is 25.93. Avoiding identification would thus require a massive change in the behavior of a user. Indeed, 99.4% of all users have unique usage patterns among the top 60 globally used apps. In contrast to previous work, this paper differentiates between users based on behavior instead of technical parameters. It thus opens an entirely new discussion regarding privacy.","PeriodicalId":303792,"journal":{"name":"Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing","volume":"57 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":"121160021","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: 53

How does eco-coaching help to save energy? assessing a recommendation system for energy-efficient thermostat scheduling 生态教练如何帮助节约能源?评估节能恒温器调度的推荐系统

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971698

Rayoung Yang, Devika Pisharoty, S. Montazeri, K. Whitehouse, Mark W. Newman

{"title":"How does eco-coaching help to save energy? assessing a recommendation system for energy-efficient thermostat scheduling","authors":"Rayoung Yang, Devika Pisharoty, S. Montazeri, K. Whitehouse, Mark W. Newman","doi":"10.1145/2971648.2971698","DOIUrl":"https://doi.org/10.1145/2971648.2971698","url":null,"abstract":"This paper presents findings from a field deployment that explored a design approach we call eco-coaching: giving personalized suggestions for specific actions that would reduce wasted energy. We studied ThermoCoach, which performs eco-coaching for thermostat scheduling. It senses and models occupancy patterns in a home, and provides occupants alternative suggestions for configuring their thermostat. Our study shows that eco-coaching accomplished four things. First, it made it easier for users to implement an effective thermostat schedule. Second, it supported user agency in negotiating energy savings and comfort goals. Third, it facilitated learning different scheduling strategies as well as weighing different options. Finally, it challenged users' beliefs about how well they were doing. These outcomes, in turn, were successful in getting users to employ and experiment with more efficient setback strategies. Going forward, we propose ways that eco-coaching systems could better support users in customizing and assessing the systems' recommendations.","PeriodicalId":303792,"journal":{"name":"Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing","volume":"21 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113933740","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: 17

An affect detection technique using mobile commodity sensors in the wild 一种在野外使用移动商品传感器的情感检测技术

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971654

Aske Mottelson, K. Hornbæk

引用次数: 36

Context-aware real-time population estimation for metropolis 上下文感知的大都市实时人口估计

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971673

Fengli Xu, J. Feng, Pengyu Zhang, Yong Li

{"title":"Context-aware real-time population estimation for metropolis","authors":"Fengli Xu, J. Feng, Pengyu Zhang, Yong Li","doi":"10.1145/2971648.2971673","DOIUrl":"https://doi.org/10.1145/2971648.2971673","url":null,"abstract":"Achieving accurate, real-time, and spatially fine-grained population estimation for a metropolitan city is extremely valuable for a variety of applications. Previous solutions look at data generated by human activities, such as night time lights and phone calls, for population estimation. However, these mechanisms cannot achieve both real-time and fine-grained population estimation because the data sampling rate is low and spatial granularity chosen is improper. We address these two problems by leveraging a key insight --- people frequently use data plan on cellphones and leave mobility signatures on cellular networks. Therefore, we are able to exploit these cellular signatures for real-time population estimation. Extracting population information from cellular data records is not easy because the number of users recorded by a cellular tower is not equal to the population covered by the tower, and mobile users' behavior is spatially and temporally different, where static estimating model does not work. We exploit context-aware city segmentation and dynamic population estimation model to address these challenges. We show that the population estimation error is reduced by 22.5% on a cellular dataset that includes 1 million users.","PeriodicalId":303792,"journal":{"name":"Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing","volume":"2 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":"132842241","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: 67

Technology meets adventure: learnings from an earthquake-interrupted Mt. everest expedition 科技与冒险:从地震中断的珠穆朗玛峰探险中学到的东西

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971683

F. Mueller, Sarah Jane Pell

引用次数: 26

Unsupervised recognition of interleaved activities of daily living through ontological and probabilistic reasoning 通过本体论和概率推理对日常生活中交错活动的无监督识别

Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Pub Date : 2016-09-12 DOI: 10.1145/2971648.2971691

Daniele Riboni, T. Sztyler, Gabriele Civitarese, H. Stuckenschmidt

{"title":"Unsupervised recognition of interleaved activities of daily living through ontological and probabilistic reasoning","authors":"Daniele Riboni, T. Sztyler, Gabriele Civitarese, H. Stuckenschmidt","doi":"10.1145/2971648.2971691","DOIUrl":"https://doi.org/10.1145/2971648.2971691","url":null,"abstract":"Recognition of activities of daily living (ADLs) is an enabling technology for several ubiquitous computing applications. In this field, most activity recognition systems rely on supervised learning methods to extract activity models from labeled datasets. An inherent problem of that approach consists in the acquisition of comprehensive activity datasets, which is expensive and may violate individuals' privacy. The problem is particularly challenging when focusing on complex ADLs, which are characterized by large intra- and inter-personal variability of execution. In this paper, we propose an unsupervised method to recognize complex ADLs exploiting the semantics of activities, context data, and sensing devices. Through ontological reasoning, we derive semantic correlations among activities and sensor events. By matching observed sensor events with semantic correlations, a statistical reasoner formulates initial hypotheses about the occurred activities. Those hypotheses are refined through probabilistic reasoning, exploiting semantic constraints derived from the ontology. Extensive experiments with real-world datasets show that the accuracy of our unsupervised method is comparable to the one of state of the art supervised approaches.","PeriodicalId":303792,"journal":{"name":"Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing","volume":"25 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":"130126091","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: 94