Comprehension of Ads-supported and Paid Android Applications: Are They Different?

The Android market is a place where developers offer paid and-or free apps to users. Free apps are interesting to users because they can try them immediately without incurring a monetary cost. However, free apps often have limited features and-or contain ads when compared to their paid counterparts. Thus, users may eventually need to pay to get additional features and-or remove ads. While paid apps have clear market values, their ads-supported versions are not entirely free because ads have an impact on performance. In this paper, first, we perform an exploratory study about ads-supported and paid apps to understand their differences in terms of implementation and development process. We analyze 40 Android apps and we observe that (i) ads-supported apps are preferred by users although paid apps have a better rating, (ii) developers do not usually offer a paid app without a corresponding free version, (iii) ads-supported apps usually have more releases and are released more often than their corresponding paid versions, (iv) there is no a clear strategy about the way developers set prices of paid apps, (v) paid apps do not usually include more functionalities than their corresponding ads-supported versions, (vi) developers do not always remove ad networks in paid versions of their ads-supported apps, and (vii) paid apps require less permissions than ads-supported apps. Second, we carry out an experimental study to compare the performance of ads-supported and paid apps and we propose four equations to estimate the cost of ads-supported apps. We obtain that (i) ads-supported apps use more resources than their corresponding paid versions with statistically significant differences and (ii) paid apps could be considered a most cost-effective choice for users because their cost can be amortized in a short period of time, depending on their usage.Comment: Accepted for publication in the proceedings of the IEEE International Conference on Program Comprehension 201

International conference on software engineering and knowledge engineering: Session chair

The Thirtieth International Conference on Software Engineering and Knowledge Engineering (SEKE 2018) will be held at the Hotel Pullman, San Francisco Bay, USA, from July 1 to July 3, 2018. SEKE2018 will also be dedicated in memory of Professor Lofti Zadeh, a great scholar, pioneer and leader in fuzzy sets theory and soft computing. The conference aims at bringing together experts in software engineering and knowledge engineering to discuss on relevant results in either software engineering or knowledge engineering or both. Special emphasis will be put on the transference of methods between both domains. The theme this year is soft computing in software engineering & knowledge engineering. Submission of papers and demos are both welcome

Evaluating the Persuasiveness of Mobile Health: The Intersection of Persuasive System Design and Data Science

Persuasive technology is an umbrella term that encompasses any software (e.g., mobile app) or hardware (e.g., smartwatch) designed to influence users to perform a preferable behavior once or on a long-term basis. Considering the ubiquitous nature of mobile devices across all socioeconomic groups, user behavior modification thrives under the personalized care that persuasive technology can offer. This research examines the roles psychological characteristics play in interpreted mHealth screen perceived persuasiveness. A review of the literature revealed a gap regarding how developers of digital health technologies are often tasked with developing tools designed to engage patients, yet little emphasis has been placed on understanding what psychological characteristics motivate and demotivate their users to engage with digital health technologies. Developers must move past using a cookie-cutter, one size fits all solution, and seek to develop digital health technologies designed to traverse the terrain that navigates between the fluid nature of goals and user preferences. This terrain is often determined by user’s psychological characteristics and demographic (control) variables. An experiment was designed to evaluate how psychological characteristics (self-efficacy, xiv health consciousness, health motivation, and the Big Five personality traits) impact the perceived persuasiveness of digital health technologies utilizing the Persuasive System Design (PSD) framework. This study used multiple linear regressions and Contrast, a publicly available Python implementation of the contrast pattern mining algorithm Search and Testing for Understandable Consistent Contrasts (STUCCO), to study the multifaceted needs of the users of digital health technologies based on psychological characteristics. The results of this experiment show psychological characteristics (selfefficacy, health consciousness, health motivation, and extraversion) enhancing the perceived persuasiveness of digital health technologies. The findings of the study revealed that screens utilizing techniques for the primary task support have high perceived persuasiveness scores. System credibility techniques were found to be a contributor to perceived persuasiveness and should be used in the development of persuasive technologies. The results of this study show practitioners should abstain from using social support techniques. Persuasive techniques from the social support category were found to have very low perceived persuasive scores which indicate a lower ability to persuade mHealth app users to utilize the tool. The findings strongly suggest the distribution of perceived persuasiveness shifts from negatively skewed to positively skewed as participants get older. Additionally, this shift occurs earlier in females (i.e., in the 40-59 age group) compared to males who do not shift until the oldest age group (i.e., in the 60 and older age group). The results imply that an individual user’s psychological characteristics affect interpreted mHealth screen perceived persuasiveness, and that combinations of persuasive principles and psychological characteristics lead to greater perceived persuasiveness

Кибербезопасность в образовательных сетях

The paper discusses the possible impact of digital space on a human, as well as human-related directions in cyber-security analysis in the education: levels of cyber-security, social engineering role in cyber-security of education, “cognitive vaccination”. “A Human” is considered in general meaning, mainly as a learner. The analysis is provided on the basis of experience of hybrid war in Ukraine that have demonstrated the change of the target of military operations from military personnel and critical infrastructure to a human in general. Young people are the vulnerable group that can be the main goal of cognitive operations in long-term perspective, and they are the weakest link of the System.У статті обговорюється можливий вплив цифрового простору на людину, а також пов'язані з людиною напрямки кібербезпеки в освіті: рівні кібербезпеки, роль соціального інжинірингу в кібербезпеці освіти, «когнітивна вакцинація». «Людина» розглядається в загальному значенні, головним чином як та, що навчається. Аналіз надається на основі досвіду гібридної війни в Україні, яка продемонструвала зміну цілей військових операцій з військовослужбовців та критичної інфраструктури на людину загалом. Молодь - це вразлива група, яка може бути основною метою таких операцій в довгостроковій перспективі, і вони є найслабшою ланкою системи.В документе обсуждается возможное влияние цифрового пространства на человека, а также связанные с ним направления в анализе кибербезопасности в образовании: уровни кибербезопасности, роль социальной инженерии в кибербезопасности образования, «когнитивная вакцинация». «Человек» рассматривается в общем смысле, в основном как ученик. Анализ представлен на основе опыта гибридной войны в Украине, которая продемонстрировала изменение цели военных действий с военного персонала и критической инфраструктуры на человека в целом. Молодые люди являются уязвимой группой, которая может быть главной целью когнитивных операций в долгосрочной перспективе, и они являются самым слабым звеном Систем

Is this the beginning of the end for retail websites? A professional perspective

This paper expects to understand professionals opinion concerning the impact of the increasing use of Social Media (SM) and commercial Mobile Applications (MA) instead of retail websites in their online strategy. Unstructured interviews with Internet professionals were applied on the LinkedIn professional SM platform, and one hundred and twenty-seven professionals provided their perspective. Data were analyzed using a Text Mining approach, and the outcome revealed professionals resistance to set SM in the center of the online strategy and highlighted the preference of users to use search engines that, in turn, will lead them to a retail website.info:eu-repo/semantics/acceptedVersio

Android Malware Detection using Machine Learning Techniques

Android is the world\u27s most popular and widely used operating system for mobile smartphones today. One of the reasons for this popularity is the free third-party applications that are downloaded and installed and provide various types of benefits to the user. Unfortunately, this flexibility of installing any application created by third parties has also led to an endless stream of constantly evolving malware applications that are intended to cause harm to the user in many ways. In this project, different approaches for tackling the problem of Android malware detection are presented and demonstrated. The data analytics of a real-time detection system is developed. The detection system can be used to scan through installed applications to identify potentially harmful ones so that they can be uninstalled. This is achieved through machine learning models. The effectiveness of the models using two different types of features, namely permissions and signatures, is explored. Exploratory data analysis and feature engineering are first implemented on each dataset to reduce a large number of features available. Then, different data mining supervised classification models are used to classify whether a given app is malware or benign. The performance metrics of different models are then compared to identify the technique that offers the best results for this purpose of malware detection. It is observed in the end that the signatures-based approach is more effective than the permissions-based approach. The kNN classifier and Random Forest classifier are both equally effective in terms of the classification models

A review on the mobile applications developed for COVID-19: An exploratory analysis

The objective of this research is to explore the existing mobile applications developed for the COVID-19 pandemic. To obtain this research objective, firstly the related applications were selected through the systematic search technique in the popular application stores. Secondly, data related to the app objectives, functionalities provided by the app, user ratings, and user reviews were extracted. Thirdly, the extracted data were analyzed through the affinity diagram, noticing-collecting-thinking, and descriptive analysis. As outcomes, the review provides a state-of-the-art view of mobile apps developed for COVID-19 by revealing nine functionalities or features. It revealed ten factors related to information systems design characteristics that can guide future app design. The review outcome highlights the need for new development and further refinement of the existing applications considering not only the revealed objectives and their associated functionalities, but also revealed design characteristics such as reliability, performance, usefulness, supportive, security, privacy, flexibility, responsiveness, ease of use, and cultural sensitivity.Comment: 11 pages, 3 figures, 4 table