Prajit Kumar Das, D. Ghosh, A. Joshi, Timothy W. Finin
We describe a method to carry out energy efficient privacy preservation on a mobile smartphone. Our work is based on a study of an Android smartphone's component-wise energy consumption pattern and is based on a three-fold approach to ensure efficient execution of privacy policies, based on user and app context modeled using semantic web technologies.
{"title":"ACM HotMobile 2013 poster: an energy efficient semantic context model for managing privacy on smartphones","authors":"Prajit Kumar Das, D. Ghosh, A. Joshi, Timothy W. Finin","doi":"10.1145/2542095.2542114","DOIUrl":"https://doi.org/10.1145/2542095.2542114","url":null,"abstract":"We describe a method to carry out energy efficient privacy preservation on a mobile smartphone. Our work is based on a study of an Android smartphone's component-wise energy consumption pattern and is based on a three-fold approach to ensure efficient execution of privacy policies, based on user and app context modeled using semantic web technologies.","PeriodicalId":43578,"journal":{"name":"Mobile Computing and Communications Review","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83361350","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}
With the increased prevalence of smart mobile devices and applications, understanding what is important to a mobile user at a point in time is an area of increasing focus. Many current applications have approached this problem by trying to tailor the user experience by using various aspects known about the user’s location as the user context. The next step in personalization is determining what is of interest to the user beyond just the user’s immediate situation. A number of studies have tried to address this issue from the perspective of predicting the next location based on prior travel history [1]. In this work, we examine the question of what may be relevant based on what future events a user will be planning beyond just the next activity. To explore this we address two key questions: 1) when are plans made about a particular type of activity; 2) how do the different aspects of these plans get finalized.
{"title":"ACM HotMobile 2013 poster: the importance of timing in mobile personalization","authors":"Chad A. Williams","doi":"10.1145/2542095.2542105","DOIUrl":"https://doi.org/10.1145/2542095.2542105","url":null,"abstract":"With the increased prevalence of smart mobile devices and applications, understanding what is important to a mobile user at a point in time is an area of increasing focus. Many current applications have approached this problem by trying to tailor the user experience by using various aspects known about the user’s location as the user context. The next step in personalization is determining what is of interest to the user beyond just the user’s immediate situation. A number of studies have tried to address this issue from the perspective of predicting the next location based on prior travel history [1]. In this work, we examine the question of what may be relevant based on what future events a user will be planning beyond just the next activity. To explore this we address two key questions: 1) when are plans made about a particular type of activity; 2) how do the different aspects of these plans get finalized.","PeriodicalId":43578,"journal":{"name":"Mobile Computing and Communications Review","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77525085","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}
We proposes CPM, a participation management framework that enables cost-efficient task distribution in crowdsensing applications. The core enablers of CPM include participation pattern learning, incentive modeling, and cost-optimized task allocation. Our preliminary results demonstrate that CPM reduces the participation cost significantly while maintaining almost the same sensing quality compared with existing schemes.
{"title":"ACM HotMobile 2013 poster: CPM: a participation management framework for mobile crowdsensing","authors":"Tingxin Yan, J. Yang","doi":"10.1145/2542095.2542109","DOIUrl":"https://doi.org/10.1145/2542095.2542109","url":null,"abstract":"We proposes CPM, a participation management framework that enables cost-efficient task distribution in crowdsensing applications. The core enablers of CPM include participation pattern learning, incentive modeling, and cost-optimized task allocation. Our preliminary results demonstrate that CPM reduces the participation cost significantly while maintaining almost the same sensing quality compared with existing schemes.","PeriodicalId":43578,"journal":{"name":"Mobile Computing and Communications Review","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83365252","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}
Junhee Ryu, Kwangjin Ko, Heonshik Shin, Kyungtae Kang
This paper presents a novel prefetching technique to reduce application launch time for mobile devices. The proposed method traces disk access accurately during an application launch and prefetches them in efficient way at its subsequent launches. The key idea is to parallelize the use of processor and flash disk while exploiting multi-core and internal parallelism on flash disk. The proposed prefetcher implemented on a mobile Meego platform has achieved a 28.1% reduction of application launch time with 6 popular applications.
{"title":"ACM HotMobile 2013 Poster: Mobifetch: an execution-time prefetching technique to expedite application launch in mobile devices","authors":"Junhee Ryu, Kwangjin Ko, Heonshik Shin, Kyungtae Kang","doi":"10.1145/2542095.2542112","DOIUrl":"https://doi.org/10.1145/2542095.2542112","url":null,"abstract":"This paper presents a novel prefetching technique to reduce application launch time for mobile devices. The proposed method traces disk access accurately during an application launch and prefetches them in efficient way at its subsequent launches. The key idea is to parallelize the use of processor and flash disk while exploiting multi-core and internal parallelism on flash disk. The proposed prefetcher implemented on a mobile Meego platform has achieved a 28.1% reduction of application launch time with 6 popular applications.","PeriodicalId":43578,"journal":{"name":"Mobile Computing and Communications Review","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83901990","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}
Speech has become an attractive means for interacting with the phone. When speech-enabled interactions are few, keyword-based interfaces [1] that require users to remember precise invocations are adequate. As the number of such interactions increases, users are more likely to forget keywords, and spoken natural language (SNL) interfaces that allow users to express their functional intent without conforming to a rigid syntax become desirable. Prominent “first-party” systems such as Siri and Google Voice Search offer such functionality on select domains today. In this demo, we present a system, NLify, which enables any (“third-party”) developer to add an SNL interface to their application. The key challenge behind the system is that there exists much variability even for a simple command. Worse, noise in speech recognition introduces additional variability. To address this challenge, we use webscale crowdsourcing and automated statistical machine paraphrasing to aid developers to cover much of the possible input space. In addition, we use a statistical language model [2] instead of deterministic one to further handle variability as it provides more tolerance against missing or reordered words. Figure 2 illustrates the overall architecture of NLify. NLify is fully integrated into the Windows Phone 8 development process in the form of a Visual Studio extension whose snapshot is presented in Figure 1. And a quantitative evaluation shows that NLify achieves overall recognition rates of 85% across intents.
{"title":"ACM HotMobile 2013 demo: NLify: mobile spoken natural language interfaces for everyone","authors":"Seungyeop Han, Matthai Philipose, Y. Ju","doi":"10.1145/2542095.2542097","DOIUrl":"https://doi.org/10.1145/2542095.2542097","url":null,"abstract":"Speech has become an attractive means for interacting with the phone. When speech-enabled interactions are few, keyword-based interfaces [1] that require users to remember precise invocations are adequate. As the number of such interactions increases, users are more likely to forget keywords, and spoken natural language (SNL) interfaces that allow users to express their functional intent without conforming to a rigid syntax become desirable. Prominent “first-party” systems such as Siri and Google Voice Search offer such functionality on select domains today. In this demo, we present a system, NLify, which enables any (“third-party”) developer to add an SNL interface to their application. The key challenge behind the system is that there exists much variability even for a simple command. Worse, noise in speech recognition introduces additional variability. To address this challenge, we use webscale crowdsourcing and automated statistical machine paraphrasing to aid developers to cover much of the possible input space. In addition, we use a statistical language model [2] instead of deterministic one to further handle variability as it provides more tolerance against missing or reordered words. Figure 2 illustrates the overall architecture of NLify. NLify is fully integrated into the Windows Phone 8 development process in the form of a Visual Studio extension whose snapshot is presented in Figure 1. And a quantitative evaluation shows that NLify achieves overall recognition rates of 85% across intents.","PeriodicalId":43578,"journal":{"name":"Mobile Computing and Communications Review","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78682602","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}
Mobile devices, such as smart phones and tablets, have become an integral part of our daily life, providing a lot of fancy and powerful applications. To understand and solve the battery drain problem, we design and implement the Bugu service which targets the applications running on mobile devices, analyzes event-power relationship, and provides users an overview of the power behavior of applications. We envision that three groups of people will benefit from the Bugu service. For end users, they know applications' power behavior which in turn helps them to decide which applications to install and run. For application developers, they could understand which events cause such amount of power dissipation and focus on optimizing them. For system developers, the insights provided by the Bugu service will enable them to understand the potential problem of the system so that further optimization can be enhanced.
{"title":"ACM HotMobile 2013 poster: Bugu: an application level power profiler and analyzer for mobile devices","authors":"Youhuizi Li, Hui Chen, Weisong Shi","doi":"10.1145/2542095.2542110","DOIUrl":"https://doi.org/10.1145/2542095.2542110","url":null,"abstract":"Mobile devices, such as smart phones and tablets, have become an integral part of our daily life, providing a lot of fancy and powerful applications. To understand and solve the battery drain problem, we design and implement the Bugu service which targets the applications running on mobile devices, analyzes event-power relationship, and provides users an overview of the power behavior of applications. We envision that three groups of people will benefit from the Bugu service. For end users, they know applications' power behavior which in turn helps them to decide which applications to install and run. For application developers, they could understand which events cause such amount of power dissipation and focus on optimizing them. For system developers, the insights provided by the Bugu service will enable them to understand the potential problem of the system so that further optimization can be enhanced.","PeriodicalId":43578,"journal":{"name":"Mobile Computing and Communications Review","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86266069","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}
S. Dey, Nirupam Roy, Wenyuan Xu, Srihari Nelakuditi
Device fingerprinting, similar to that of humans, if done well, can provide a convenient form of identification. In this poster, we explore whether constituent hardware sensors like accelerometers and gyroscopes of different smartphones can be exploited to fingerprint a smartphone. We observe that the readings of these sensors exhibit diverse features for different smartphones consistently when subjected to the same action.
{"title":"ACM HotMobile 2013 poster: leveraging imperfections of sensors for fingerprinting smartphones","authors":"S. Dey, Nirupam Roy, Wenyuan Xu, Srihari Nelakuditi","doi":"10.1145/2542095.2542107","DOIUrl":"https://doi.org/10.1145/2542095.2542107","url":null,"abstract":"Device fingerprinting, similar to that of humans, if done well, can provide a convenient form of identification. In this poster, we explore whether constituent hardware sensors like accelerometers and gyroscopes of different smartphones can be exploited to fingerprint a smartphone. We observe that the readings of these sensors exhibit diverse features for different smartphones consistently when subjected to the same action.","PeriodicalId":43578,"journal":{"name":"Mobile Computing and Communications Review","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79279983","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}
Users employing smartphones typically combine multiple applications to perform their tasks. It would be possible to be recommended the appropriate applications by acquiring the contexts of users who perform such tasks. And the contexts are composed by the relationship of applications used in the tasks. We present an algorithm that extracts the relationship between applications that the user intentionally uses in combination. At the end of the paper, we report the results of verification tests conducted on this algorithm.
{"title":"ACM HotMobile 2013 poster: extraction algorithm of relationship between smartphone applications for recommendation","authors":"Kohei Terazono, Akira Karasudani, Satoshi Iwata, Tatsuro Matsumoto","doi":"10.1145/2542095.2542104","DOIUrl":"https://doi.org/10.1145/2542095.2542104","url":null,"abstract":"Users employing smartphones typically combine multiple applications to perform their tasks. It would be possible to be recommended the appropriate applications by acquiring the contexts of users who perform such tasks. And the contexts are composed by the relationship of applications used in the tasks. We present an algorithm that extracts the relationship between applications that the user intentionally uses in combination. At the end of the paper, we report the results of verification tests conducted on this algorithm.","PeriodicalId":43578,"journal":{"name":"Mobile Computing and Communications Review","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79026883","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}
C. Hsieh, H. Tangmunarunkit, F. Alquaddoomi, J. Jenkins, Jinha Kang, C. Ketcham, B. Longstaff, J. Selsky, D. Swendeman, D. Estrin, N. Ramanathan
Participatory mHealth incorporates a variety of new techniques, such as continuous activity traces, active reminders and prompted inputs [1,2] to personalize and improve disease management. The collected data streams are intended to allow individuals and care givers to systematically monitor chronic conditions outside the clinical settings, to identify the lifestyle factors that may aggravate these conditions, and to support personalized patient self management. One of the key challenges in realizing this vision, is turning these diverse, noisy, and evolving data streams into actionable information. Ultimately we need to identify data stream features that can be automatically extracted and fed back to apps and interventions in order to increase the effectiveness, autonomy and scalability of patient self-care. As part of a six-month pilot study in Los Angeles, we developed an end to end system to support health services researchers and other domain experts to data generated during an mHealth pilot with young mothers who collectively generated 15,599 survey responses and 3,834 days' worth of continuous mobility. In this poster, we present Lifestreams Dashboard, an interactive visualization platform designed to facilitate the exploration of mHealth data streams, and to aid the discussions with the participants. Lifestreams Dashboard is a module residing in the visualization layer of Lifestreams Data Analysis Software Stack [3], which supports a pipeline of personal analysis modules. It is intended to support identification and evaluation of datastream features in support of iterative design processes in which the developers build a prototype based on the requirements specified by the health researchers who evaluate the efficacy and usefulness through the interviews with real-world mHealth study participants. We use data acquired during our 6-month pilot in which the 44 young mothers recorded both self-reports and passive data about their diet, stress and exercise to demonstrate the functions of Lifestreams Dashbaord. These functions include: a. a change-detection-based filtering function that helps pinpoint the features that have been changed during the study 1 The geo-information in the map has been obfuscated to protect the participant privacy. b. a color-coded correlation matrix that helps select the features that possess correlations higher than a controllable threshold with other features c. a selective correlation analysis tool that helps the study of the correlations and the correlation changes between a group of heterogeneous features d. a location trace analysis module that helps discover patterns in participants' daily trajectories using wifi-signature clustering techniques (See Figure 1). Figure 1 Lifescreams …
{"title":"ACM HotMobile 2013 poster: lifestreams dashboard: an interactive visualization platform for mHealth data exploration","authors":"C. Hsieh, H. Tangmunarunkit, F. Alquaddoomi, J. Jenkins, Jinha Kang, C. Ketcham, B. Longstaff, J. Selsky, D. Swendeman, D. Estrin, N. Ramanathan","doi":"10.1145/2542095.2542113","DOIUrl":"https://doi.org/10.1145/2542095.2542113","url":null,"abstract":"Participatory mHealth incorporates a variety of new techniques, such as continuous activity traces, active reminders and prompted inputs [1,2] to personalize and improve disease management. The collected data streams are intended to allow individuals and care givers to systematically monitor chronic conditions outside the clinical settings, to identify the lifestyle factors that may aggravate these conditions, and to support personalized patient self management. One of the key challenges in realizing this vision, is turning these diverse, noisy, and evolving data streams into actionable information. Ultimately we need to identify data stream features that can be automatically extracted and fed back to apps and interventions in order to increase the effectiveness, autonomy and scalability of patient self-care. As part of a six-month pilot study in Los Angeles, we developed an end to end system to support health services researchers and other domain experts to data generated during an mHealth pilot with young mothers who collectively generated 15,599 survey responses and 3,834 days' worth of continuous mobility. In this poster, we present Lifestreams Dashboard, an interactive visualization platform designed to facilitate the exploration of mHealth data streams, and to aid the discussions with the participants. Lifestreams Dashboard is a module residing in the visualization layer of Lifestreams Data Analysis Software Stack [3], which supports a pipeline of personal analysis modules. It is intended to support identification and evaluation of datastream features in support of iterative design processes in which the developers build a prototype based on the requirements specified by the health researchers who evaluate the efficacy and usefulness through the interviews with real-world mHealth study participants. We use data acquired during our 6-month pilot in which the 44 young mothers recorded both self-reports and passive data about their diet, stress and exercise to demonstrate the functions of Lifestreams Dashbaord. These functions include: a. a change-detection-based filtering function that helps pinpoint the features that have been changed during the study 1 The geo-information in the map has been obfuscated to protect the participant privacy. b. a color-coded correlation matrix that helps select the features that possess correlations higher than a controllable threshold with other features c. a selective correlation analysis tool that helps the study of the correlations and the correlation changes between a group of heterogeneous features d. a location trace analysis module that helps discover patterns in participants' daily trajectories using wifi-signature clustering techniques (See Figure 1). Figure 1 Lifescreams …","PeriodicalId":43578,"journal":{"name":"Mobile Computing and Communications Review","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88046014","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}
Chulhong Min, Chanyou Hwang, Taiwoo Park, Yuhwan Kim, Uichin Lee, Inseok Hwang, Chungkuk Yoo, Changhoon Lee, Younghyun Ju, Junehwa Song, Jaeung Lee, Miri Moon, Haechan Lee, Youngki Lee
Does our smartphone help at a variety of social gatherings in our everyday life, for instance, having dinner with family and meeting friends? For a few recent years, smartphones have been rapidly penetrating to our everyday lives. Yet, it is still at an early dawn that the smartphone applications and systems are closely immersed into everyday social activities. We share so many moments and activities with other people right here, right in front of us, and so will smartphones [4]. We argue that, many, in-situ co-presenting smartphones serve as a newly emerging substrate to accommodate whole new in-situ social applications. These applications have huge opportunity in every facet in our daily lives, e.g., providing new user experiences or facilitating social interactions during shared social activities. They could also take advantage of the larger, more capable union of computing devices and resources. In this demo, we introduce a novel initiative toward everyday face-to-face interaction monitoring system. Among diverse verbal, aural, visual cues expressed during face-to-face interaction, we first focus on capturing diverse meta-linguistic information from
{"title":"ACM HotMobile 2013 demo: bringing in-situ social awareness to mobile systems: everyday interaction monitoring and its applications","authors":"Chulhong Min, Chanyou Hwang, Taiwoo Park, Yuhwan Kim, Uichin Lee, Inseok Hwang, Chungkuk Yoo, Changhoon Lee, Younghyun Ju, Junehwa Song, Jaeung Lee, Miri Moon, Haechan Lee, Youngki Lee","doi":"10.1145/2542095.2542101","DOIUrl":"https://doi.org/10.1145/2542095.2542101","url":null,"abstract":"Does our smartphone help at a variety of social gatherings in our everyday life, for instance, having dinner with family and meeting friends? For a few recent years, smartphones have been rapidly penetrating to our everyday lives. Yet, it is still at an early dawn that the smartphone applications and systems are closely immersed into everyday social activities. We share so many moments and activities with other people right here, right in front of us, and so will smartphones [4]. We argue that, many, in-situ co-presenting smartphones serve as a newly emerging substrate to accommodate whole new in-situ social applications. These applications have huge opportunity in every facet in our daily lives, e.g., providing new user experiences or facilitating social interactions during shared social activities. They could also take advantage of the larger, more capable union of computing devices and resources. In this demo, we introduce a novel initiative toward everyday face-to-face interaction monitoring system. Among diverse verbal, aural, visual cues expressed during face-to-face interaction, we first focus on capturing diverse meta-linguistic information from","PeriodicalId":43578,"journal":{"name":"Mobile Computing and Communications Review","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77509302","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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