M. Lam, Giovanni Campagna, Jiwon Seo, Michael H. Fischer
This paper presents the architecture of an open social platform for mobile devices. This platform allows users to get access to all their data in web services and IoT devices easily, compute with them, and share them with their friends without losing data ownership to a third party. The key concepts include: (1) ThingPedia, an open-source crowd-sourced repository of interfaces and apps, (2) ThingTalk, a succinct rule-based language that allows people to share information stored in different web services and IoT devices, (3) ThingEngine, personal servers that execute ThingTalk apps on behalf of the users, and (4) Omlet, an open chat and messaging platform that does not own users’ data.
{"title":"A Distributed Open Social Platform for Mobile Devices","authors":"M. Lam, Giovanni Campagna, Jiwon Seo, Michael H. Fischer","doi":"10.1145/2897073.2897075","DOIUrl":"https://doi.org/10.1145/2897073.2897075","url":null,"abstract":"This paper presents the architecture of an open social platform for mobile devices. This platform allows users to get access to all their data in web services and IoT devices easily, compute with them, and share them with their friends without losing data ownership to a third party. The key concepts include: (1) ThingPedia, an open-source crowd-sourced repository of interfaces and apps, (2) ThingTalk, a succinct rule-based language that allows people to share information stored in different web services and IoT devices, (3) ThingEngine, personal servers that execute ThingTalk apps on behalf of the users, and (4) Omlet, an open chat and messaging platform that does not own users’ data.","PeriodicalId":296509,"journal":{"name":"2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127502843","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 apps often collect and share personal data with untrustworthy third-party apps, which may lead to data misuse and privacy violations. Most of the collected data originates from sensors built into the mobile device, where some of the sensors are treated as sensitive by the mobile platform while others permit unconditional access. Examples of privacy-prone sensors are the microphone, camera and GPS system. Access to these sensors is always mediated by protected function calls. On the other hand, the light sensor, accelerometer and gyroscope are considered innocuous. All apps have unrestricted access to their data. Unfortunately, this gap is not always justified. State-of-the-art privacy mechanisms on Android provide inadequate access control and do not address the vulnerabilities that arise due to unmediated access to so-called innocuous sensors on smartphones. We have developed techniques to demonstrate these threats. As part of our demonstration, we illustrate possible attacks using the innocuous sensors on the phone. As a solution, we present ipShield, a framework that provides users with greater control over their resources at runtime so as to protect against such attacks. We have implemented ipShield by modifying the AOSP.
{"title":"Eavesdropping and Obfuscation Techniques for Smartphones","authors":"Supriyo Chakraborty, Omer Tripp","doi":"10.1145/2897073.2897715","DOIUrl":"https://doi.org/10.1145/2897073.2897715","url":null,"abstract":"Mobile apps often collect and share personal data with untrustworthy third-party apps, which may lead to data misuse and privacy violations. Most of the collected data originates from sensors built into the mobile device, where some of the sensors are treated as sensitive by the mobile platform while others permit unconditional access. Examples of privacy-prone sensors are the microphone, camera and GPS system. Access to these sensors is always mediated by protected function calls. On the other hand, the light sensor, accelerometer and gyroscope are considered innocuous. All apps have unrestricted access to their data. Unfortunately, this gap is not always justified. State-of-the-art privacy mechanisms on Android provide inadequate access control and do not address the vulnerabilities that arise due to unmediated access to so-called innocuous sensors on smartphones. We have developed techniques to demonstrate these threats. As part of our demonstration, we illustrate possible attacks using the innocuous sensors on the phone. As a solution, we present ipShield, a framework that provides users with greater control over their resources at runtime so as to protect against such attacks. We have implemented ipShield by modifying the AOSP.","PeriodicalId":296509,"journal":{"name":"2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124829384","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}
Heart rate monitoring has become increasingly popular in the industry through mobile phones and wearable devices. However current determination of heart rate through mobile applications suffer from high corruption of signals during intensive physical exercise. In this paper, we present a novel technique for accurately determining heart rate during intensive motion by classifying PPG signals obtained from smartphones or wearable devices combined with motion data obtained from accelerometer sensors. Our approach utilizes the Internet of Things (IoT) cloud connectivity of smartphones for PPG signals selection using deep learning. The technique is validated using the TROIKA dataset and is accurately able to predict heart rate with a 10-fold cross validation error margin of 4.88%.
{"title":"Integrating Mobile and Cloud for PPG Signal Selection to Monitor Heart Rate during Intensive Physical Exercise","authors":"V. Jindal","doi":"10.1145/2897073.2897132","DOIUrl":"https://doi.org/10.1145/2897073.2897132","url":null,"abstract":"Heart rate monitoring has become increasingly popular in the industry through mobile phones and wearable devices. However current determination of heart rate through mobile applications suffer from high corruption of signals during intensive physical exercise. In this paper, we present a novel technique for accurately determining heart rate during intensive motion by classifying PPG signals obtained from smartphones or wearable devices combined with motion data obtained from accelerometer sensors. Our approach utilizes the Internet of Things (IoT) cloud connectivity of smartphones for PPG signals selection using deep learning. The technique is validated using the TROIKA dataset and is accurately able to predict heart rate with a 10-fold cross validation error margin of 4.88%.","PeriodicalId":296509,"journal":{"name":"2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"410 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116227930","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}
Ioana Baldini, Paul C. Castro, P. Cheng, Stephen J. Fink, Vatche Isahagian, N. Mitchell, Vinod Muthusamy, R. Rabbah, Philippe Suter
Creating mobile applications often requires both client and server- side code development, each requiring vastly differentskills. Recently, cloud providers like Amazon and Google introduced "server-less" programming models that abstract away many infrastructure concerns and allow developers to focus on their application logic. In this demonstration, we introduce OpenWhisk, our system for constructing cloud native actions, within the context of mobile application development process. We demonstrate how OpenWhisk is used in mobile application development, allows cloud API customizations for mobile, and simplifies mobile application architectures.
{"title":"Cloud-Native, Event-Based Programming for Mobile Applications","authors":"Ioana Baldini, Paul C. Castro, P. Cheng, Stephen J. Fink, Vatche Isahagian, N. Mitchell, Vinod Muthusamy, R. Rabbah, Philippe Suter","doi":"10.1145/2897073.2897713","DOIUrl":"https://doi.org/10.1145/2897073.2897713","url":null,"abstract":"Creating mobile applications often requires both client and server- side code development, each requiring vastly differentskills. Recently, cloud providers like Amazon and Google introduced \"server-less\" programming models that abstract away many infrastructure concerns and allow developers to focus on their application logic. In this demonstration, we introduce OpenWhisk, our system for constructing cloud native actions, within the context of mobile application development process. We demonstrate how OpenWhisk is used in mobile application development, allows cloud API customizations for mobile, and simplifies mobile application architectures.","PeriodicalId":296509,"journal":{"name":"2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122045590","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}
Joe W. Ligman, Marco Pistoia, Omer Tripp, Gegi Thomas
During the mobile app development cycle, User-Interface (UI) components rendered by the mobile app are typically validated against high-fidelity mockups by manually comparing screens from a mockup design to screens developed in the app. This validation most often takes the time of the lead designer, resulting in many post-sprint defects and tasks that must be folded into the next sprint iteration. To improve this process, an engineer should be able to validate layout as part of the acceptance criteria for each task submitted, providing a more complete UI, less defects and reduced cost for the app development. We propose a system of improvements for moving this process forward by automatically validating layout. The system is based on techniques from computer vision, in conjunction with style policies, which together facilitate validation of design layout prior to submitting completed task work, thereby reducing the overall cost of developing UI designs.
{"title":"Improving Design Validation of Mobile Application User Interface Implementation","authors":"Joe W. Ligman, Marco Pistoia, Omer Tripp, Gegi Thomas","doi":"10.1145/2897073.2897708","DOIUrl":"https://doi.org/10.1145/2897073.2897708","url":null,"abstract":"During the mobile app development cycle, User-Interface (UI) components rendered by the mobile app are typically validated against high-fidelity mockups by manually comparing screens from a mockup design to screens developed in the app. This validation most often takes the time of the lead designer, resulting in many post-sprint defects and tasks that must be folded into the next sprint iteration. To improve this process, an engineer should be able to validate layout as part of the acceptance criteria for each task submitted, providing a more complete UI, less defects and reduced cost for the app development. We propose a system of improvements for moving this process forward by automatically validating layout. The system is based on techniques from computer vision, in conjunction with style policies, which together facilitate validation of design layout prior to submitting completed task work, thereby reducing the overall cost of developing UI designs.","PeriodicalId":296509,"journal":{"name":"2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128629149","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}
Xuan Lu, Xuanzhe Liu, Huoran Li, Tao Xie, Q. Mei, Dan Hao, Gang Huang, Feng Feng
Mining usage data from a large number of Android users can assist various software engineering tasks. In collaboration with Wandoujia, a leading Android app marketplace in China, we have conducted a large empirical analysis based on mining app usage behaviors collected from millions of Android users. Our empirical findings can provide implications, challenges, and opportunities to app-centric software development, deployment, and maintenance.
{"title":"Mining Usage Data from Large-Scale Android Users: Challenges and Opportunities","authors":"Xuan Lu, Xuanzhe Liu, Huoran Li, Tao Xie, Q. Mei, Dan Hao, Gang Huang, Feng Feng","doi":"10.1145/2897073.2897721","DOIUrl":"https://doi.org/10.1145/2897073.2897721","url":null,"abstract":"Mining usage data from a large number of Android users can assist various software engineering tasks. In collaboration with Wandoujia, a leading Android app marketplace in China, we have conducted a large empirical analysis based on mining app usage behaviors collected from millions of Android users. Our empirical findings can provide implications, challenges, and opportunities to app-centric software development, deployment, and maintenance.","PeriodicalId":296509,"journal":{"name":"2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124195159","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 demo VALERA, a Versatile-yet-lightweight Record- and-replay tool for Android. Record-and-replay is useful across the Android development lifecycle, from bug reproducing to systematic testing. VALERA uses a novel technique named sensor-oriented replay (recording and replay- ing sensor and network input, event schedules, and inter-app communication via intents) to achieve high accuracy and low overhead. VALERA can be used as an effective replay tool on both real phones and emulators. Evaluation on more than 50 popular Android apps shows that VALERA’s performance overhead for either record or replay is just 1%. We demonstrate how VALERA can be used in many development scenarios: bug reproducing, regression testing, event- driven race reproduction and verification, mutation testing via fuzzy replay, and cross-app testing.
{"title":"VALERA: An Effective and Efficient Record-and-Replay Tool for Android","authors":"Yongjian Hu, Iulian Neamtiu","doi":"10.1145/2897073.2897712","DOIUrl":"https://doi.org/10.1145/2897073.2897712","url":null,"abstract":"We demo VALERA, a Versatile-yet-lightweight Record- and-replay tool for Android. Record-and-replay is useful across the Android development lifecycle, from bug reproducing to systematic testing. VALERA uses a novel technique named sensor-oriented replay (recording and replay- ing sensor and network input, event schedules, and inter-app communication via intents) to achieve high accuracy and low overhead. VALERA can be used as an effective replay tool on both real phones and emulators. Evaluation on more than 50 popular Android apps shows that VALERA’s performance overhead for either record or replay is just 1%. We demonstrate how VALERA can be used in many development scenarios: bug reproducing, regression testing, event- driven race reproduction and verification, mutation testing via fuzzy replay, and cross-app testing.","PeriodicalId":296509,"journal":{"name":"2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114781311","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 alert diabetes patients of incipient hypoglycemia, we developed a hypoglycemia prediction algorithm and elicited design inspiration for new glucose management software. To identify the predictive factors, we conducted surveys, interviews, and diary studies, and developed a prediction model that uses self-monitored blood glucose. We tested the accuracy of prediction algorithms achieved by different machine learning methods, and found that the proposed algorithms have potential to predict hypoglycemia. Based on the proposed algorithm, we designed a new mobile application concept to support patients’ self-care, especially to avert hypoglycemia.
{"title":"Toward Designing Mobile Software to Predict Hypoglycemia for Patients with Diabetes","authors":"Miyeon Jung","doi":"10.1145/2897073.2897129","DOIUrl":"https://doi.org/10.1145/2897073.2897129","url":null,"abstract":"To alert diabetes patients of incipient hypoglycemia, we developed a hypoglycemia prediction algorithm and elicited design inspiration for new glucose management software. To identify the predictive factors, we conducted surveys, interviews, and diary studies, and developed a prediction model that uses self-monitored blood glucose. We tested the accuracy of prediction algorithms achieved by different machine learning methods, and found that the proposed algorithms have potential to predict hypoglycemia. Based on the proposed algorithm, we designed a new mobile application concept to support patients’ self-care, especially to avert hypoglycemia.","PeriodicalId":296509,"journal":{"name":"2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126541874","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}
The increasing need of app installations on mobile devices demands a lot of internal memory i.e., app installable mem- ory. The limited size of the internal memory puts restric- tions on the number of applications that one can install on a mobile device at any given instance of a time. The research work in this paper focuses on providing a cloud based solu- tion to the limited app installable memory to allow the users to have more number of applications on their smart phone. The proposed solution uses a cloud to extend the app in- stallable memory of user’s mobile. The unused or less used apps are moved over to cloud storage until they are required by the user thereby making internal memory available for new installations. The moved apps maintains user data and avoid permanent deletion of apps.
{"title":"Extending App Installable Memory in Android Smartphones","authors":"Sanjay Singh, Ashwin Nivangune, Sathish Kumar, Ranjan Kumar, Padmaja Joshi, D. Patel","doi":"10.1145/2897073.2897089","DOIUrl":"https://doi.org/10.1145/2897073.2897089","url":null,"abstract":"The increasing need of app installations on mobile devices demands a lot of internal memory i.e., app installable mem- ory. The limited size of the internal memory puts restric- tions on the number of applications that one can install on a mobile device at any given instance of a time. The research work in this paper focuses on providing a cloud based solu- tion to the limited app installable memory to allow the users to have more number of applications on their smart phone. The proposed solution uses a cloud to extend the app in- stallable memory of user’s mobile. The unused or less used apps are moved over to cloud storage until they are required by the user thereby making internal memory available for new installations. The moved apps maintains user data and avoid permanent deletion of apps.","PeriodicalId":296509,"journal":{"name":"2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125203162","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}
M. Baluda, Marco Pistoia, Paul C. Castro, Omer Tripp
It is standard practice in enterprises to analyze large amounts of logs to detect software failures and malicious behaviors. Mobile applications pose a major challenge to centralized monitoring as network and storage limitations prevent fine-grained logs to be stored and transferred for off-line analysis. In this paper we introduce EMMA, a framework for automatic anomaly detection that enables security analysis as well as in-the-field quality assurance for enterprise mobile applications, and incurs minimal overhead for data exchange with a back-end monitoring platform. EMMA instruments binary applications with a lightweight anomaly-detection layer that reveals failures and security threats directly on mobile devices, thus enabling corrective measures to be taken promptly even when the device is disconnected. In our empirical evaluation, EMMA detected failures in unmodified Android mobile applications.
{"title":"A Framework for Automatic Anomaly Detection in Mobile Applications","authors":"M. Baluda, Marco Pistoia, Paul C. Castro, Omer Tripp","doi":"10.1145/2897073.2897718","DOIUrl":"https://doi.org/10.1145/2897073.2897718","url":null,"abstract":"It is standard practice in enterprises to analyze large amounts of logs to detect software failures and malicious behaviors. Mobile applications pose a major challenge to centralized monitoring as network and storage limitations prevent fine-grained logs to be stored and transferred for off-line analysis. In this paper we introduce EMMA, a framework for automatic anomaly detection that enables security analysis as well as in-the-field quality assurance for enterprise mobile applications, and incurs minimal overhead for data exchange with a back-end monitoring platform. EMMA instruments binary applications with a lightweight anomaly-detection layer that reveals failures and security threats directly on mobile devices, thus enabling corrective measures to be taken promptly even when the device is disconnected. In our empirical evaluation, EMMA detected failures in unmodified Android mobile applications.","PeriodicalId":296509,"journal":{"name":"2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120853786","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：