A System Implementation of Interruption Management for Mobile Devices

Vilwock, William; Madiraju, Praveen; Ahamed, Sheikh Iqbal

doi:10.1109/cse.2013.37

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…are popular in the area of mobile analytics and applications. Smart context-aware mobile communication (e.g., intelligent phone call interruption management) is one of the most compelling and widely studied applications [45][46][47][48][49][50][51][52][53][54][55]. For mobile notification management, several research [39,[56][57][58][59] has been done.…”

Section: Mobile Systems and Servicesmentioning

confidence: 99%

Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions

et al. 2020

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Artificial intelligence (AI) techniques have grown rapidly in recent years in the context of computing with smart mobile phones that typically allows the devices to function in an intelligent manner. Popular AI techniques include machine learning and deep learning methods, natural language processing, as well as knowledge representation and expert systems, can be used to make the target mobile applications intelligent and more effective. In this paper, we present a comprehensive view on "mobile data science and intelligent apps" in terms of concepts and AI-based modeling that can be used to design and develop intelligent mobile applications for the betterment of human life in their diverse day-today situation. This study also includes the concepts and insights of various AI-powered intelligent apps in several application domains, ranging from personalized recommendation to healthcare services, including COVID-19 pandemic management in recent days. Finally, we highlight several research issues and future directions relevant to our analysis in the area of mobile data science and intelligent apps. Overall, this paper aims to serve as a reference point and guidelines for the mobile application developers as well as the researchers in this domain, particularly from the technical point of view.

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Section: Mobile Systems and Servicesmentioning

confidence: 99%

Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions

et al. 2020

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“…Additional contexts can play a significant role to model user behavior as the calendar alone is limited as a 'sensor' highlighted in Lovett et al [1]. A probabilistic interruption management system based on multiple contexts has been proposed by Vilwock et al [18].…”

Section: Related Workmentioning

confidence: 99%

CalBehav: A Machine Learning based Personalized Calendar Behavioral Model using Time-Series Smartphone Data

Sarker¹,

Colman²,

Han³

et al. 2019

Preprint

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The electronic calendar is a valuable resource nowadays for managing our daily life appointments or schedules, also known as events, ranging from professional to highly personal. Researchers have studied various types of calendar events to predict smartphone user behavior for incoming mobile communications. However, these studies typically do not take into account behavioral variations between individuals. In the real world, smartphone users can differ widely from each other in how they respond to incoming communications during their scheduled events. Moreover, an individual user may respond the incoming communications differently in different contexts subject to what type of event is scheduled in her personal calendar. Thus, a static calendar-based behavioral model for individual smartphone users does not necessarily reflect their behavior to the incoming communications. In this paper, we present a machine learning based contextaware model that is personalized and dynamically identifies individual's dominant behavior for their scheduled events using logged time-series smartphone data, and shortly name as "CalBehav". The experimental results based on real datasets from calendar and phone logs, show that this data-driven personalized model is more effective for intelligently managing the incoming mobile communications compared to existing calendar-based approaches.

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“…Among these operating systems, Android obtained the highest market share. According to data for May 2014, which was published by Google in the Google I/O conference, over 900 million Android devices have been activated [1]. More than 25 million applications have been downloaded from the Google Play Store as of July 2014.…”

Section: Introductionmentioning

confidence: 99%

Implementation of a web-based service for mobile application risk assessment

Yüksel¹,

Yüksel²,

Sertbaş³

et al. 2017

Turk J Elec Eng & Comp Sci

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Abstract:The Android operating system has increased in popularity and has been increasing its shares in the smart phone market. Users can carry out their daily work such as paying bills, being social, and sharing photos through mobile applications. These applications have access to sensitive information about the user, such as location, contacts, call logs, and SMS messages. However, the users have no knowledge of the applications or the personal information these applications have access to. Even if an application is not malware or does not have malicious behavior, it can compromise the security and privacy of the user by accessing the permissions and gathering sensitive personal information. In this study, we have designed and implemented a prototype of a novel fuzzy risk inference system that serves as a webbased service. The system analyzes the risks related to Android-based mobile applications and performs risk scoring by taking several features into account. The system presents the user with the risks of exposure before the installation of applications on the user's device and serves as an intelligent decision support system.

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A System Implementation of Interruption Management for Mobile Devices

Cited by 5 publications

References 8 publications

Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions

Mobile Data Science and Intelligent Apps: Concepts, AI-Based Modeling and Research Directions

CalBehav: A Machine Learning based Personalized Calendar Behavioral Model using Time-Series Smartphone Data

Implementation of a web-based service for mobile application risk assessment

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