Digital image scrambling using 2D cellular automata

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. A. L. A. Dalhoum et al., "Digital Image Scrambling Using 2D Cellular Automata", IEEE MultiMedia, vol. 19, no. 4 pp. 28 – 36, oct-dec. 2012A digital image scrambling method based on a 2D cellular automaton, specifically the well-known Game of Life, produces an effective image encryption technique.This work has been partially sponsored by the Spanish MICINN project TIN2011-28260-C03-0

Utilizing an enhanced cellular automata model for data mining

Data mining deals with clustering and classifying large amounts of data, in order to discover new knowledge from the existent data by identifying correlations and relationships between various data-sets. Cellular automata have been used before for classification purposes. This paper presents a cellular automata enhanced classification algorithm for data mining. Experimental results show that the proposed enhancement gives better performance in terms of accuracy and execution time than previous work using cellular automata

Visual Emotion-Aware Cloud Localization User Experience Framework Based on Mobile Location Services

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

Using Executable Specification and Regression Testing for Broadcast Mechanism of Visual Programming Language on Smartphones

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

Using Executable Specification and Regression Testing for Broadcast Mechanism of Visual Programming Language on Smartphones

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