F. Schaub, Bastian Könings, Peter Lang, Björn Wiedersheim, Christian Winkler, M. Weber
PriCal is an ambient calendar display that shows a user's schedule similar to a paper wall calendar. PriCal provides context-adaptive privacy to users by detecting present persons and adapting event visibility according to the user's privacy preferences. We present a detailed privacy impact assessment of our system, which provides insights on how to leverage context to enhance privacy without being intrusive. PriCal is based on a decentralized architecture and supports the detection of registered users as well as unknown persons. In a three-week deployment study with seven displays, ten participants used PriCal in their real work environment with their own digital calendars. Our results provide qualitative insights on the implications, acceptance, and utility of context-adaptive privacy in the context of a calendar display system, indicating that it is a viable approach to mitigate privacy implications in ubicomp applications.
{"title":"PriCal: context-adaptive privacy in ambient calendar displays","authors":"F. Schaub, Bastian Könings, Peter Lang, Björn Wiedersheim, Christian Winkler, M. Weber","doi":"10.1145/2632048.2632087","DOIUrl":"https://doi.org/10.1145/2632048.2632087","url":null,"abstract":"PriCal is an ambient calendar display that shows a user's schedule similar to a paper wall calendar. PriCal provides context-adaptive privacy to users by detecting present persons and adapting event visibility according to the user's privacy preferences. We present a detailed privacy impact assessment of our system, which provides insights on how to leverage context to enhance privacy without being intrusive. PriCal is based on a decentralized architecture and supports the detection of registered users as well as unknown persons. In a three-week deployment study with seven displays, ten participants used PriCal in their real work environment with their own digital calendars. Our results provide qualitative insights on the implications, acceptance, and utility of context-adaptive privacy in the context of a calendar display system, indicating that it is a viable approach to mitigate privacy implications in ubicomp applications.","PeriodicalId":20496,"journal":{"name":"Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication","volume":"111 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79208264","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}
T. Sakaguchi, N. Nishio, M. Mochizuki, Kazuya Murao
In recent years, a positioning method which utilizes wireless LAN without using GPS has attracted attention. Especially, in the case of a method which combines absolute position with a Wi-Fi radio environment in advance, the cost of operation and management becomes enormous. Therefore, by sampling Wi-Fi radio information observed at points where users stay frequently or in the long-term, a method which automates to collect and update the Wi-Fi radio information has been proposed. In the case of a long-term operating, the positioning accuracy, however, decreases because this method does not perform well in maintaining and managing samples. It cannot adapt samples to environmental changes although Wi-Fi radio signals change in case of long-term operating. Accordingly, this paper proposes a new calculation formula for improving a positioning accuracy. The formula is calculated with the weight of each base station for avoidance of ill-behaving stations. In addition, this paper also proposes the automated management system with two steps. It adapts samples to changes of Wi-Fi radio signals and a user's behavior. As a result, a positioning accuracy of the new system is higher than existing one.
{"title":"Adapting Wi-Fi samples to environmental changes automatically","authors":"T. Sakaguchi, N. Nishio, M. Mochizuki, Kazuya Murao","doi":"10.1145/2638728.2641302","DOIUrl":"https://doi.org/10.1145/2638728.2641302","url":null,"abstract":"In recent years, a positioning method which utilizes wireless LAN without using GPS has attracted attention. Especially, in the case of a method which combines absolute position with a Wi-Fi radio environment in advance, the cost of operation and management becomes enormous. Therefore, by sampling Wi-Fi radio information observed at points where users stay frequently or in the long-term, a method which automates to collect and update the Wi-Fi radio information has been proposed. In the case of a long-term operating, the positioning accuracy, however, decreases because this method does not perform well in maintaining and managing samples. It cannot adapt samples to environmental changes although Wi-Fi radio signals change in case of long-term operating. Accordingly, this paper proposes a new calculation formula for improving a positioning accuracy. The formula is calculated with the weight of each base station for avoidance of ill-behaving stations. In addition, this paper also proposes the automated management system with two steps. It adapts samples to changes of Wi-Fi radio signals and a user's behavior. As a result, a positioning accuracy of the new system is higher than existing one.","PeriodicalId":20496,"journal":{"name":"Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88585868","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}
Moshe Unger, L. Rokach, Ariel Bar, E. Gudes, Bracha Shapira
Context-aware computing aims at tailoring services to the user's circumstances and surroundings. Our study examines how data collected from mobile devices can be utilized to infer users' behavior and environment. We present the results and the lessons learned from a two-week user study of 40 students. The data collection was performed using Contexto, a framework for collecting data from a rich set of sensors installed on mobile devices, which was developed for this purpose. We studied various new and fine-grained user contexts which are relevant to students' daily activities, such as "in class and interested in the learned materials" and "on my way to campus". These contexts might later be utilized for various purposes such as recommending relevant items to the students' context. We compare various machine learning methods and report their effectiveness for the purposes of inferring the users' context from the collected data. In addition, we present our findings on how to evaluate context inference systems, on the importance of explicit and latent labeling for context inference and on the effect of new users on the results' accuracy.
{"title":"Contexto: lessons learned from mobile context inference","authors":"Moshe Unger, L. Rokach, Ariel Bar, E. Gudes, Bracha Shapira","doi":"10.1145/2638728.2638781","DOIUrl":"https://doi.org/10.1145/2638728.2638781","url":null,"abstract":"Context-aware computing aims at tailoring services to the user's circumstances and surroundings. Our study examines how data collected from mobile devices can be utilized to infer users' behavior and environment. We present the results and the lessons learned from a two-week user study of 40 students. The data collection was performed using Contexto, a framework for collecting data from a rich set of sensors installed on mobile devices, which was developed for this purpose. We studied various new and fine-grained user contexts which are relevant to students' daily activities, such as \"in class and interested in the learned materials\" and \"on my way to campus\". These contexts might later be utilized for various purposes such as recommending relevant items to the students' context. We compare various machine learning methods and report their effectiveness for the purposes of inferring the users' context from the collected data. In addition, we present our findings on how to evaluate context inference systems, on the importance of explicit and latent labeling for context inference and on the effect of new users on the results' accuracy.","PeriodicalId":20496,"journal":{"name":"Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87374773","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}
To maintain software of sensor nodes in wireless sensor networks efficiently, it is necessary to minimize the size of transferred data in firmware update. We propose a non-invasive rapid and efficient incremental firmware update algorithm called MoRE. In MoRE algorithm, the host transfers only delta, which is the information of different parts between old and new firmware image, to reduce the size of transferred data. The sensor node makes new binary image from its current image and the transferred messages. The MoRE shows comparable performance to previous works without invasive methods. Unlike the previous works, MoRE does not require extra memory for metadata in sensor nodes and does not need to use relocatable code.
{"title":"Non-invasive rapid and efficient firmware update for wireless sensor networks","authors":"Huiung Park, Jongsoo Jeong, P. Mah","doi":"10.1145/2638728.2638782","DOIUrl":"https://doi.org/10.1145/2638728.2638782","url":null,"abstract":"To maintain software of sensor nodes in wireless sensor networks efficiently, it is necessary to minimize the size of transferred data in firmware update. We propose a non-invasive rapid and efficient incremental firmware update algorithm called MoRE. In MoRE algorithm, the host transfers only delta, which is the information of different parts between old and new firmware image, to reduce the size of transferred data. The sensor node makes new binary image from its current image and the transferred messages. The MoRE shows comparable performance to previous works without invasive methods. Unlike the previous works, MoRE does not require extra memory for metadata in sensor nodes and does not need to use relocatable code.","PeriodicalId":20496,"journal":{"name":"Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85164604","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}
Yoko Yamakata, Takuya Funatomi, Asuka Miyazawa, M. Minoh, Atsushi Hashimoto
In this paper, under the situation that a teacher teaches a student how to cook via bi-directional video communication system, we propose a method to detect whether the student can watch the display and listen to the teacher's instruction without interrupting his/her cooking. Firstly, we investigates the properties of taking the gaze on/off during cooking action, and secondly we proposed methods to automatically detect gaze-required cooking actions on the captured cooking video.
{"title":"A method for detecting gaze-required action while cooking for assisting video communication","authors":"Yoko Yamakata, Takuya Funatomi, Asuka Miyazawa, M. Minoh, Atsushi Hashimoto","doi":"10.1145/2638728.2641337","DOIUrl":"https://doi.org/10.1145/2638728.2641337","url":null,"abstract":"In this paper, under the situation that a teacher teaches a student how to cook via bi-directional video communication system, we propose a method to detect whether the student can watch the display and listen to the teacher's instruction without interrupting his/her cooking. Firstly, we investigates the properties of taking the gaze on/off during cooking action, and secondly we proposed methods to automatically detect gaze-required cooking actions on the captured cooking video.","PeriodicalId":20496,"journal":{"name":"Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86961145","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}
{"title":"Session details: Mobile applications","authors":"Christine Lv","doi":"10.1145/1240624.3258864","DOIUrl":"https://doi.org/10.1145/1240624.3258864","url":null,"abstract":"","PeriodicalId":20496,"journal":{"name":"Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90725331","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}
This research addresses improvement of the accuracy of pedestrian dead reckoning (PDR), which is one effective technique to estimate indoor positions using smartphone sensors. Even though various techniques using step lengths and their number have been previously proposed for PDR, insufficient accuracy is gotten from smartphone sensors. In this research, we define human activity sensing knowledge and propose improvements to PDR accuracy based on it. Human activity sensing knowledge consists of four kinds of information: pedestrian, environmental, activity, and terminal. Previous studies separately used these kinds of information; however, no study has systematically arranged them for use in PDR. We improved PDR accuracy by adjusting the step length in passages and on stairs and revised activity recognition error with human activity sensing knowledge. To investigate the effectiveness of that strategy, we used HASC-IPSC, which is an indoor pedestrian sensing corpus. After our investigation, activity recognition accuracy improved from 71.2% to 91.4%, and the distance estimation error was reduced from approximately 27 m to approximately 7 m using human activity sensing knowledge.
{"title":"Pedestrian dead reckoning based on human activity sensing knowledge","authors":"Yuya Murata, Kei Hiroi, K. Kaji, Nobuo Kawaguchi","doi":"10.1145/2638728.2641305","DOIUrl":"https://doi.org/10.1145/2638728.2641305","url":null,"abstract":"This research addresses improvement of the accuracy of pedestrian dead reckoning (PDR), which is one effective technique to estimate indoor positions using smartphone sensors. Even though various techniques using step lengths and their number have been previously proposed for PDR, insufficient accuracy is gotten from smartphone sensors. In this research, we define human activity sensing knowledge and propose improvements to PDR accuracy based on it. Human activity sensing knowledge consists of four kinds of information: pedestrian, environmental, activity, and terminal. Previous studies separately used these kinds of information; however, no study has systematically arranged them for use in PDR. We improved PDR accuracy by adjusting the step length in passages and on stairs and revised activity recognition error with human activity sensing knowledge. To investigate the effectiveness of that strategy, we used HASC-IPSC, which is an indoor pedestrian sensing corpus. After our investigation, activity recognition accuracy improved from 71.2% to 91.4%, and the distance estimation error was reduced from approximately 27 m to approximately 7 m using human activity sensing knowledge.","PeriodicalId":20496,"journal":{"name":"Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89666453","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}
Kyosuke Nishida, H. Toda, Takeshi Kurashima, Yoshihiko Suhara
Automatic check-in, which is to identify a user's visited points of interest (POIs) from his or her trajectories, is still an open problem because of positioning errors and the high POI density in small areas. In this study, we propose a probabilistic visited-POI identification method. The method uses a new hierarchical Bayesian model for identifying the latent visited-POI label of stay points, which are automatically extracted from trajectories. This model learns from labeled and unlabeled stay point data (i.e., semi-supervised learning) and takes into account personal preferences, stay locations including positioning errors, stay times for each category, and prior knowledge about typical user preferences and stay times. Experimental results with real user trajectories and POIs of Foursquare demonstrated that our method achieved statistically significant improvements in precision at 1 and recall at 3 over the nearest neighbor method and a conventional method that uses a supervised learning-to-rank algorithm.
{"title":"Probabilistic identification of visited point-of-interest for personalized automatic check-in","authors":"Kyosuke Nishida, H. Toda, Takeshi Kurashima, Yoshihiko Suhara","doi":"10.1145/2632048.2632092","DOIUrl":"https://doi.org/10.1145/2632048.2632092","url":null,"abstract":"Automatic check-in, which is to identify a user's visited points of interest (POIs) from his or her trajectories, is still an open problem because of positioning errors and the high POI density in small areas. In this study, we propose a probabilistic visited-POI identification method. The method uses a new hierarchical Bayesian model for identifying the latent visited-POI label of stay points, which are automatically extracted from trajectories. This model learns from labeled and unlabeled stay point data (i.e., semi-supervised learning) and takes into account personal preferences, stay locations including positioning errors, stay times for each category, and prior knowledge about typical user preferences and stay times. Experimental results with real user trajectories and POIs of Foursquare demonstrated that our method achieved statistically significant improvements in precision at 1 and recall at 3 over the nearest neighbor method and a conventional method that uses a supervised learning-to-rank algorithm.","PeriodicalId":20496,"journal":{"name":"Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication","volume":"74 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73498629","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}
{"title":"Session details: Human behavior","authors":"Sunny Consolvo","doi":"10.1145/3255111","DOIUrl":"https://doi.org/10.1145/3255111","url":null,"abstract":"","PeriodicalId":20496,"journal":{"name":"Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73570424","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}
Human activity recognition (AR) has begun to mature as a field, but for AR research to thrive, large, diverse, high quality, AR data sets must be publically available and AR methodology must be clearly documented and standardized. In the process of comparing our AR research to other efforts, however, we found that most AR data sets are sufficiently limited as to impact the reliability of existing research results, and that many AR research papers do not clearly document their experimental methodology and often make unrealistic assumptions. In this paper we outline problems and limitations with AR data sets and describe the methodology problems we noticed, in the hope that this will lead to the creation of improved and better documented data sets and improved AR experimental methodology. Although we cover a broad array of methodological issues, our primary focus is on an often overlooked factor, model type, which determines how AR training and test data are partitioned---and how AR models are evaluated. Our prior research indicates that personal, hybrid, and impersonal/universal models yield dramatically different performance [30], yet many research studies do not highlight or even identify this factor. We make concrete recommendations to address these issues and also describe our own publically available AR data sets.
{"title":"Limitations with activity recognition methodology & data sets","authors":"J. W. Lockhart, Gary M. Weiss","doi":"10.1145/2638728.2641306","DOIUrl":"https://doi.org/10.1145/2638728.2641306","url":null,"abstract":"Human activity recognition (AR) has begun to mature as a field, but for AR research to thrive, large, diverse, high quality, AR data sets must be publically available and AR methodology must be clearly documented and standardized. In the process of comparing our AR research to other efforts, however, we found that most AR data sets are sufficiently limited as to impact the reliability of existing research results, and that many AR research papers do not clearly document their experimental methodology and often make unrealistic assumptions. In this paper we outline problems and limitations with AR data sets and describe the methodology problems we noticed, in the hope that this will lead to the creation of improved and better documented data sets and improved AR experimental methodology. Although we cover a broad array of methodological issues, our primary focus is on an often overlooked factor, model type, which determines how AR training and test data are partitioned---and how AR models are evaluated. Our prior research indicates that personal, hybrid, and impersonal/universal models yield dramatically different performance [30], yet many research studies do not highlight or even identify this factor. We make concrete recommendations to address these issues and also describe our own publically available AR data sets.","PeriodicalId":20496,"journal":{"name":"Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78554921","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}
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