<p>Recently, the study of emotional recognition models has increased in the human-computer interaction field. With high recognition accuracy of emotions’ data, we could get immediate feedback from mobile users, get a better perception of human behavior while interacting with mobile apps, and thus make the user experience design more adaptable and intelligent. The harnessing of emotional recognition in mobile apps can dramatically enhance users’ experience. Therefore, in this paper, we propose a visual emotion-aware cloud localization user experience framework based on mobile location services. An important feature of our proposed framework is to provide a personalized mobile app based on the user’s visual emotional changes. The framework captures the emotion-aware data, process them in the cloud server, and analyze them for an immediate localization process. The first stage in the framework builds a correlation between the application’s default language and the user’s visual emotional feedback. In the second stage, the localization model loads the appropriate application’s resources and adjusts the screen features based on the real-time user’s emotion obtained in the first stage and according to the location data that the app collected from the mobile device. Our experiments demonstrate the effectiveness of the proposed framework. The results show that our proposed framework can provide a high-quality application experience in terms of a user’s emotional levels and deliver an excellent level of usability that was before not possible.</p>
Mobile learning is becoming more and more popular today. It gained popularity recently due to the COVID-19 pandemic restrictions in 2020. However, to provide learners with appropriate educational materials in such a mobile environment, the characteristics and context of the learners must be considered. Therefore, in this paper, we propose a framework for providing an adaptive context-aware learning process considering a combination of student learning models and principles of Universal Design for Learning (UDL). The proposed system consists of components capable of detecting changes in context and adapting the way the application responds and behaves. The framework uses a machine-learning algorithm to predict learners’ characteristics and follow UDL principles to deliver enriched user experience and location-aware content and activities. An online survey was conducted with 20 undergraduate students. We analyzed their levels of satisfaction with the proposed m-learning system. From the analyzed data, we noticed that the average rating values are close to 4.5, which indicates that the proposed m-learning system complies with UDL principles and provides an adaptive and localized learning environment, thus enhancing the efficiency of the learning process and experiences. The study also investigated the impact of factors (i.e., noise level, physical activity, and location) on learners’ concentration towards the learning process. The results show that these factors have a significant impact on the learner’s concentration level.
Mobile apps are everywhere. The release of apps on a worldwide scale requires them to be made available in many languages, including bidirectional languages. Developers and translators are usually different persons. While automatic testing by itself is important in general in order to be able to develop high quality software, such automatic tests become absolutely essential when developers that do not possess enough knowledge about right-to-left languages need to maintain code that is written for bidirectional languages. A few bidirectional localization tests of mobile applications exist. However, their functionality is limited since they only cover translations and adoption of locales. In this paper we present our approach for automating the bidirectional localization testing for Android applications with a complete consideration for BiDi-languages issues. The objective is to check for any localization defects in the product. The proposed methods are used to test issues of bidirectional apps in general and specifically for the Arabic language. The results show that the methods are able to effectively reveal deficiencies in the app’s design, ensure that the localized app matches all expectations of local users, and guarantee that the product is culturally congruent to local conventions.
<p class="0abstract">The rapid advancement of mobile computing technology and the rising usage of mobile apps made our daily life more productive. The mobile app should operate all the time bug-free in order to improve user satisfaction and offers great business value to the end user. At the same time, smartphones are full of special features that make testing of apps more challenging. Actually, the quality is a must for successful applications and it cannot be achieved without testing and verification. In this paper, we present the Behavior Driven Development (BDD) methodology and Cucumber framework to automate regression testing of Android apps. Particularly, the proposed methods use the visual programming language for smartphones (Catrobat) as a reference. The Catrobat program scripts communicate via a broadcast mechanism. The objective is to test the broadcast mechanism from different angles and track regression errors as well as specify and diagnose bugs with the help of executable specifications. The results show that the methods are able to effectively reveal deficiencies in the broadcast mechanism, and ensure that the app matches all expectations and needs of end users.</p>
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