Target Apps Research Articles

DESIGNING TUTORIALS TO AUGMENT MOBILITY FOR OLDER ADULTS WITH COGNITIVE IMPAIRMENT

Abstract The Augmenting User Geocoordinates & Mobility with ENhanced Tutorials (AUGMENT) project aims to support aging adults experiencing navigation difficulties accompanying cognitive impairments due to stroke, mild cognitive impairment, and traumatic brain injury. We describe the process and principles for designing tutorials for Uber and Google Maps, two applications that can support improved mobility for those with navigation difficulties associated with cognitive impairment. Initially we examined predictors of self-reported navigation difficulty in a sample (n=300) of older adults aging normally. In the multiple regression, self-reported memory problems (beta = .35), severity of memory problems (beta=.18), and female gender (beta=.16) were the strongest predictors of wayfinding difficulty in addition to a standardized spatial orientation test (beta=.13). Finding that memory difficulties were strongly related to navigation difficulties even in normal aging implied that tutorials could not make strong demands on retention and helped define the queries for interviews in the needs assessment phase. Next, we interviewed aging adults with cognitive impairment and their care partners about navigation problems, focusing on difficulties using navigation devices and applications. We also presented screenshots of the two target apps and asked simple questions about actions that could be taken. We then designed prototype tutorials using principles such as directing attention to relevant display features by highlighting the features, minimizing extraneous workload, and providing spaced retrieval practice for interactions with the interface. We also describe the iterative design process being used to refine the tutorials and the planned summative test of tutorial efficacy.

Directing smartphone use through the self-nudge app one sec

Research suggests various associations of smartphone use with a range of physical, psychological, and performance dimensions. Here, we test one sec, a self-nudging app that is installed by the user in order to reduce the mindless use of selected target apps on the smartphone. When users attempt to open a target app of their choice, one sec interferes with a pop-up, which combines a deliberation message, friction by a short waiting time, and the option to dismiss opening the target app. In a field-experiment, we collected behavioral user data from 280 participants over 6 wk, and conducted two surveys before and after the intervention span. one sec reduced the usage of target apps in two ways. First, on average 36% of the times participants attempted opening a target app, they closed that app again after one sec interfered. Second, over the course of 6wk, users attempted to open target apps 37% less than in the first week. In sum, one sec decreased users' actual opening of target apps by 57% after six consecutive weeks. Afterward, participants also reported spending less time with their apps and indicated increased satisfaction with their consumption. To disentangle one sec's effects, we tested its three psychological features in a preregistered online experiment (N = 500) that measured the consumption of real and viral social media video clips. We found that providing the additional option to dismiss the consumption attempt had the strongest effect. While the friction by time delay also reduced consumption instances, the deliberation message was not effective.

Context-aware Target Apps Selection and Recommendation for Enhancing Personal Mobile Assistants

Users install many apps on their smartphones, raising issues related to information overload for users and resource management for devices. Moreover, the recent increase in the use of personal assistants has made mobile devices even more pervasive in users’ lives. This article addresses two research problems that are vital for developing effective personal mobile assistants: target apps selection and recommendation . The former is the key component of a unified mobile search system: a system that addresses the users’ information needs for all the apps installed on their devices with a unified mode of access. The latter, instead, predicts the next apps that the users would want to launch. Here we focus on context-aware models to leverage the rich contextual information available to mobile devices. We design an in situ study to collect thousands of mobile queries enriched with mobile sensor data (now publicly available for research purposes). With the aid of this dataset, we study the user behavior in the context of these tasks and propose a family of context-aware neural models that take into account the sequential, temporal, and personal behavior of users. We study several state-of-the-art models and show that the proposed models significantly outperform the baselines.

AppAngio: Revealing Contextual Information of Android App Behaviors by API-Level Audit Logs

Android users are now suffering severe threats from unwanted behaviors of various apps. The analysis of apps' audit logs is one of the essential methods for the security analysts of various companies to unveil the underlying maliciousness within apps. We propose and implement AppAngio, a novel system that reveals contextual information in Android app behaviors by API-level audit logs. Our goal is to help security analysts understand how the target apps worked and facilitate the identification of the maliciousness within apps. The key module of AppAngio is identifying the path matched with the logs on the app's control-flow graphs (CFGs). The challenge, however, is that the limited-quantity logs may incur high computational complexity in the log matching, where there are a large number of candidates caused by the coupling relation of successive logs. To address the challenge, we propose a divide and conquer strategy that precisely positions the nodes matched with log records on the corresponding CFGs and connects the nodes with as few backtracks as possible. Our experiments show that AppAngio reveals contextual information of behaviors in real-world apps. Moreover, the revealed results assist the analysts in identifying the maliciousness of app behaviors and complement existing analysis schemes. Meanwhile, AppAngio incurs negligible performance overhead on the real device in the experiments.

Modeling user information needs on mobile devices

Recent advances in the development of mobile devices equipped with multiple sensors, together with the availability of millions of applications, have made these devices more pervasive in our lives than ever. The availability of the diverse set of sensors, as well as high computational power, enable information retrieval (IR) systems to sense a user's context, and personalize their results accordingly. Relevant studies show that people use their mobile devices to access information in a wide range of topics in various contextual situations, highlighting the fact that modeling user information need on mobile devices involves studying several means of information access. In this thesis, we study three significant aspects of information access on mobile devices. First, we focus on proactive approaches to modeling users for venue suggestion. We investigate three methods of user modeling, namely, content-based, collaborative, and hybrid, focusing on personalization and context-awareness. Our content-based model is based on multiple relevance scores derived from multi-modal information that we collected from multiple sources of information [1]. We further propose a two-phase collaborative ranking algorithm for leveraging users' implicit feedback while incorporating temporal and geographical information into the model [3]. We then extend our collaborative model to include multiple cross-venue similarity scores and combine it with our content-based approach to produce a hybrid recommendation [2]. Second, we introduce and investigate a new task on mobile search, that is, unified mobile search. We take the first step in defining, studying, and modeling this task by collecting two datasets [5, 4]. Our analyses show that a limited number of popular apps attract most of the search queries. We further observe notable differences between queries submitted to different apps. We show that query length and content differ among apps. We also show that 39% of search queries were done in Google Search, and it was the top choice of users in 35% of the tasks. Given that more than 71% of the defined tasks could be done with the current features of Google Search, this indicates that users prefer to search using a more specific app. Also, we conduct experiments on one of the main components of unified mobile search frameworks, which is target apps selection. To this end, we propose two neural approaches that learn high-dimensional app representations based on their respective queries. Finally, we address the conversational aspect of mobile search , where we propose an offline evaluation protocol and build a dataset for asking clarifying questions for conversational search [6]. Also, we propose a retrieval framework consisting of three main components: question retrieval, question selection, and document retrieval. The experiments and analyses indicate that asking clarifying questions should be an essential part of a conversational system, resulting in a high performance gain. In particular, experiments on the oracle model demonstrated that asking only one good clarifying question leads to over 150% relative improvement in terms of P@1 and nDCG@1. Moreover, we observed that asking clarifying questions improves the model's performance for a substantial percentage of the facets, even though a more effective retrieval model than the one we used could potentially improve the performance.

Accessing quality apps to promote basic relational concepts acquisition among young children with autism

AbstractMobile applications (apps) are increasingly being used with children with autism spectrum disorder (ASD) to supplement their intervention packages; however, the Apps’ educational utility is not yet understood. The lack of such knowledge results in young children's inequity of accessing quality intervention. The present qualitative study addressed this gap by analyzing the content of popular apps being used with young children (ages 3–7) with ASD for the presence of basic relational concepts. After first identifying 15 target apps via surveys, the researchers analyzed the apps for the presence of the 50 basic relational concepts, according to the Boehm Test of Basic Relational Concepts—Third Edition). Results indicated that the prevalence of basic relational concepts in the analyzed apps ranged from 0 to 39 concepts. Apps that relied on community‐generated material tended to incorporate more basic relational concepts, but use methods that could be confusing or invalid. Commercially developed apps incorporated fewer basic relational concepts, but employed teaching and testing methods that have better educational utility. These findings have implications to help closing the gaps in informing parents, professionals, and app developers on the use of apps with children with ASD.

Matching Mobile Applications for Cross Promotion

The mobile app market is one of the most successful software markets. As the platform grows rapidly, with millions of apps and billions of users, search costs are increasing tremendously. The challenge is how app developers can target the right users with their apps and how consumers can find apps that fit their needs. Cross promotion, advertising a mobile app (target app) in another app (source app), is introduced as a new app promotion framework to alleviate the issue of search costs. In this paper, we model source app user behaviors (downloads and post-download usage) with respect to different target apps in cross-promotion campaigns. We construct a novel app similarity measure using LDA topic modeling on apps’ production descriptions, and then analyze how the similarity between the source and target apps influences users’ app download and usage decisions. To estimate the model, we use a unique data set from a large-scale random matching experiment conducted by a major mobile advertising company in Korea. The empirical results show that consumers prefer more diversified apps when they are making download decisions compared with their usage decisions, which is supported by the psychology literature on people’s variety-seeking behavior. Lastly, we propose an app-matching system based on machine learning models (on app download and usage prediction) and generalized deferred acceptance algorithms. The simulation results show that app analytics capability is essential in building accurate prediction models and in increasing ad effectiveness of cross promotion campaigns, and that, at the expense of privacy, individual user data can further improve the matching performance. The paper has implications on the tradeoff between utility and privacy in the growing mobile economy.

난독화에 강인한 안드로이드 앱 버스마킹 기법

A software birthmark is the set of characteristics of a program which can be used to identify the program. Many researchers have studied on detecting theft of java programs using some birthmarks. In case of Android apps, code obfuscation techniques are used to protect the apps against reverse-engineering and tampering. However, attackers can also use the obfuscation techniques in order to conceal a stolen program. A birthmark (feature) of an app can be alterable by code obfuscations. Therefore, it is necessary to detect Android app theft based on the birthmark which is resilient to code obfuscation. In this paper, we propose an effective Android app birthmark and app theft detection through the proposed birthmark. By analyzing some obfuscation tools, we have first selected parameter and the return types of methods as an adequate birthmark. Then, we have measured similarity of target apps using the birthmarks extracted from the apps, where some target apps are not obfuscated and the others obfuscated. The measurement results show that our proposed birthmark is effective for detecting Android app theft even though the apps are obfuscated. 논문 15-40-04-11 The Journal of Korean Institute of Communications and Information Sciences '15-04 Vol.40 No.04 http://dx.doi.org/10.7840/kics.2015.40.4.700 700 ※ 본 연구는 문화체육관광부 및 한국저작권위원회의 2014년도 저작권 기술개발사업과 미래창조과학부 및 정보통신기술진흥센터의 대학ICT연구센터육성 지원사업의 연구결과로 수행되었음 (IITP-2015-H8501-15-1012) First Author : Dankook University Department of Computer Science, kdjorang@dankook.ac.kr, 학생회원 ° Corresponding Author : Dankook University Department of Computer Science & Engineering, sjcho@dankook.ac.kr, 정회원 * Dankook University Department of Computer Science, youngki.chung99@gmail.com ** Dankook University Department of Computer Science & Engineering, jwwoo@dankook.ac.kr *** Hancom Inc., newlife8837@gmail.com **** University of Suwon Department of Information Secrecy, smyang@suwon.ac.kr, 정회원 논문번호:KIC2015-03-063, Received March 23, 2015; Revised April 13, 2015; Accepted April 13, 2015 논문 / 난독화에 강인한 안드로이드 앱 버스마킹 기법