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To Permit or Not to Permit, That is the Usability Question: Crowdsourcing Mobile Apps’ Privacy Permission Settings

of the 3rd BELIV’10 Workshop: Beyond time and errors: novel evaluation methods for Information Visualization . ACM, 63–70. [32] Jialiu Lin, Bin Liu, Norman Sadeh, and Jason I Hong. 2014. Modeling users’ mobile app privacy preferences: Restoring usability in a sea of permission settings. In Symposium On Usable Privacy and Security (SOUPS 2014) . 199–212. [33] Jialiu Lin, Norman Sadeh, Shahriyar Amini, Janne Lindqvist, Jason I Hong, and Joy Zhang. 2012. Expectation and purpose: Understanding users’ mental models of mobile app privacy through

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Analyzing Remote Server Locations for Personal Data Transfers in Mobile Apps


The prevalence of mobile devices and their capability to access high speed internet has transformed them into a portable pocket cloud interface. Being home to a wide range of users’ personal data, mobile devices often use cloud servers for storage and processing. The sensitivity of a user’s personal data demands adequate level of protection at the back-end servers. In this regard, the European Union Data Protection regulations (e.g., article 25.1) impose restriction on the locations of European users’ personal data transfer. The matter of concern, however, is the enforcement of such regulations. The first step in this regard is to analyze mobile apps and identify the location of servers to which personal data is transferred. To this end, we design and implement an app analysis tool, PDTLoc (Personal Data Transfer Location Analyzer), to detect violation of the mentioned regulations. We analyze 1, 498 most popular apps in the EEA using PDTLoc to investigate the data recipient server locations. We found that 16.5% (242) of these apps transfer users’ personal data to servers located at places outside Europe without being under the control of a data protection framework. Moreover, we inspect the privacy policies of the apps revealing that 51% of these apps do not provide any privacy policy while almost all of them contact the servers hosted outside Europe.

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The Effectiveness of Branded Mobile Apps on User’s Brand Attitudes and Purchase Intentions

Your Next Step in Mobile,” 11. Newark-French, C. (2011, June 20). “Mobile Apps Put the Web in Their Rear-View Mirror,” [Flurry Blog], . 12. Nielsen, J., & Budiu, R. (2012). Mobile Usability . Berkeley, CA: New Riders. 13. Park, C.W., et al., (2010). “Brand Attachment and Brand Attitude Strength: Conceptual and Empirical Differentiation of Two Critical Brand Equity Drivers

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Using Consumer Electronics and Apps in Industrial Environments – Development of a Framework for Dynamic Feature Deployment and Extension by Using Apps on Field Devices

: Design Challenges, Int. Symposium Object / Component / Service-Oriented Real-Time Distributed Computing (ISORC), pp. 363-369, Orlando, SUA. Schmitt, M.; Meixner, G.; Gorecky, D.; Seissler, M.; Loskyll, M. (2013) Mobile Interaction Technologies in the Factory of the Future. IFAC/IFIP/IFORS/IEA Symposium on Analysis, Design, and Evaluation of Human-Machine Systems. August 11-15, Las Vegas, USA. Siemens (2013) Siemens Apps. Available at, 18.10.2013. Zuehlke, D. (2009

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Issues in Machine Translation
A case of mobile apps in the Lithuanian and English language pair

). Discriminative Learning for Speech Recognition: Theory and Practice . Morgan & Claypool Publishers. Hsu, J. A. 2014. Error Classification of Machine Translation A Corpus-based Study on Chinese-English Patent Translation. Translation Studies Quarterly 18:121-136. Hutchins, W. H. (2010). Machine translation: a concise history. Journal of Translation Studies 13(1-2): 29-70. Jimenez-Crespo, Miguel A. (2016). Mobile apps and translation crowdsourcing: the next frontier in the evolution of translation. Revista Tradumàtica: tecnologies de la traducció 14

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Mitigating Location Privacy Attacks on Mobile Devices using Dynamic App Sandboxing

References [1] Yuvraj Agarwal and Malcolm Hall. Protectmyprivacy: Detecting and mitigating privacy leaks on ios devices using crowdsourcing. In Proceeding of the 11th Annual International Conference on Mobile Systems, Applications, and Services , MobiSys ’13, New York, NY, USA, 2013. ACM. [2] Hazim Almuhimedi, Florian Schaub, Norman Sadeh, Idris Adjerid, Alessandro Acquisti, Joshua Gluck, Lorrie Faith Cranor, and Yuvraj Agarwal. Your location has been shared 5,398 times#: A field study on mobile app privacy nudging. In Proceedings of the 33rd Annual

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Perceived confidence for injury self‑management increases for young men with mild haemophilia with the use of the mobile app HIRT?

2015). 39. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol 2006; 3(2): 77-101. 40. Canadian Society for Exercise Physiology. Canadian Physical Activity Guidelines for Adults, 18-64 Years. Available from: (accessed 28 April 2017). 41. Nilson J. Are you HIRT? (Hemophilia Injury Recognition Tool): Perceptions from young men with mild hemophilia in Canada on the use of the mobile app for injury self-management (Master’s thesis

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Understanding Factors influencing Consumers Online Purchase intention Via Mobile App: Perceived Ease of use, Perceived Usefulness, System Quality, information Quality, and Service Quality


The development of technology has been significantly given the implication towards consumer’s behaviour in having the online purchase intention via mobile app that has been developed by the e-commerce company to serve better and deliver a better service to the consumers; especially when internet has connected people through their smartphones. The insignificant growth in doing the online purchase via mobile app which does not go along with the growth of internet mobile users in Indonesia and Singapore will deliver this study in order to evaluate and validate the implication of ease of use, usefulness, system quality, information quality, and service quality towards consumer’s behaviour in having the online purchase intention via mobile app. Data was gathered with survey by spreading 100 questionnaires randomly to the respondents who had the experience in doing the online purchase via mobile app in the last 6 months in Indonesia and Singapore. The methodology in doing this study is the quantitative approach by considering the connection amongst the independent variables and the dependent variables. This research found that usefulness and information quality significantly affect the online purchase intention through mobile app in Indonesia while in Singapore ease of use, usefulness, and service quality significantly affect the online purchase intention through mobile app.

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Crowdsourcing for Context: Regarding Privacy in Beacon Encounters via Contextual Integrity

References [1] Helen Nissenbaum. Privacy as contextual integrity. Washington law review, 79(1), 2004. [2] Galen Grumen. What you need to know about using bluetooth beacons, 2014. [3] Cathy Goodwin. A conceptualization of motives to seek privacy for nondeviant consumption. Journal of Consumer Psychology, 1(3):261-284, 1992. [4] Bluetooth SIG. Bluetooth smart technology: Powering the

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“Won’t Somebody Think of the Children?” Examining COPPA Compliance at Scale

References [1] H. Almuhimedi, F. Schaub, N. Sadeh, I. Adjerid, A. Acquisti, J. Gluck, L. Cranor, and Y. Agarwal. Your Location has been Shared 5,398 Times! A Field Study on Mobile App Privacy Nudging. Technical Report CMU-ISR-14-116, Carnegie Mellon University, 2014. [2] D. Amalfitano, A. R. Fasolino, and P. Tramontana. A GUI Crawling-Based Technique for Android Mobile Application Testing. In Proc. of IEEE ICSTW, 2011. [3] D. Amalfitano, A. R. Fasolino, P. Tramontana, B. D. Ta, and A. M. Memon. MobiGUITAR

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