Dynamics of Gender-Specific Behaviors in the Digital Educational Environment

During the social distancing, an increasing number of people use communication applications, various types of digital tools and programs. Various video conferencing platforms are regularly used in the educational environment. The study presents the analyses how intensive is the use of Information and Communication Technologies (ICT) in the educational environment and how it can change cognitive-behavioral gender differences. This is particularly important to pay a special attention to the analysis of gender as a dynamic category, to take into account the processes of gender socialization and transformation of gender identification in the changing social environment. The research methods also included a set of additional methods, such as a focus group on different aspects of gender-specific behavior in the digital learning environment, putting together collages, as well as the method of the unfinished sentence related to the impact of ICT on teachers' professional development and well-being. In the course of the study, it was recognised that the design of social models of male and female gender-specific behaviour includes more than the basic gender identity and gender stability: in today's society, there is a multiplicity of views on the similarities and differences of gender-specific behaviours, and a rapid change in the accepted social guidelines and behavioural patterns is in progress, socio-cultural norms that define the psychological characteristics of women and men, their patterns of behaviour.

Technology Based Model of a Mobile Knowledge as a Service to Facilitate Education Community

Technological enhancements as well as the demand of students to access learning information on time and quickly has resulted to the development of e-learning across the world. Mobile learning has been adopted by most learning institution as a mobile technology that allows learners to access learning materials and share information among themselves and respective educators easily and quickly. This article discusses the intention of users in learning environment adopting m-learning, their perceptions as well as factors that hinder implementation of m-learning in the gulf region. Effective mobile technology adoption also enhances knowledge management through mobile applications that allow information capture, storage, retrieval and sharing. This study uses systematic literature review to collect information from post 2017 studies previous conducted by other researchers. Articles were searched through highly ranked databases from which 657 journals were identified. After the screening and eligibility assessment, 24 journals were retrieved. The back and forward search retrieved 4 more journals bringing the total to 28 journals that were included in the study. Based on this information, a conceptual model is developed to help assess the m-learning perceptions, adoption intentions and factors influencing its adoption among learning institutions in the gulf region. This model is built based on the Theory of Planned Behavior and Unified Theory of Acceptance and Use of Technology. Based on the developed model, main constructs such as performance expectancy, effort expectancy and social influence are greatly impacted by other factors like learner’s creativity and mobility.

Smart Home Multi-Factor Authentication Using Face Recognition and One-Time Password on Smartphone

Recently, the adoption of smart home technology has been on the rise and becoming a trend for home residents. The development of Internet-of-Things (IoT) technology drives the smart home authentication system with biometric systems such as facial recognition, fingerprint, and voice control techniques. In the context of homeowners, security is always the primary concern. However, conventional home security and the existing smart home security system have some limitations. These techniques use single-factor authentication, which provides limited protection for home security. Therefore, this project proposed a design for smart home multi-factor authentication using facial recognition and a one-time password sent to smartphones for a home security system. Rapid application development was the methodology for conducting this study. A usability evaluation suggested that the proposed smart home multi-factor authentication is acceptable, but some usability issues can be improved in the future.

IoT-based Application of Information Security Triad

Information Security is the foremost concern for IoT (Internet of things) devices and applications. Since the advent of IoT, its applications and devices have experienced an exponential increase in numerous applications which are utilized. Nowadays we people are becoming smart because we started using smart devices like a smartwatch, smart TV, smart home appliances. These devices are part of the IoT devices. The IoT device differs widely in capacity storage, size, computational power, and supply of energy. With the rapid increase of IoT devices in different IoT fields, information security, and privacy are not addressed well. Most IoT devices having constraints in computational and operational capabilities are a threat to security and privacy, also prone to cyber-attacks. This study presents a CIA triad-based information security implementation for the four-layer architecture of the IoT devices. An overview of layer-wise threats to the IoT devices and finally suggest CIA triad-based security techniques for securing the IoT devices.

Utilizing Animal Characters of a Mobile Augmented Reality (AR) Reading Kit to Improve Preschoolers’ Reading Skills, Motivation, and Self-Learning: An Initial Study

This study developed and used a mobile Augmented Reality (AR) reading kit to help preschool students recognize alphabets and read simple words more effectively. This study was based on the quantitative approach involving an experimental methodology that used a one-group pretest-posttest design. In this study, the variables of interest to be measured were students’ reading skills, motivation, and self-learning. The learning treatment involved a series of reading sessions using the novel learning application that spanned three weeks, with each session lasting for two hours. The sample of this study comprised 60 preschool students, whose ages ranged from four to six, who were selected from three different preschools. The effectiveness of the novel-reading kit was evaluated in terms of students’ learning performance, learning motivation in reading, and self-learning. The data for the former were gathered from pre-testing and post-testing. At the same time, a survey was administered to the students to elicit their opinions and feedback on the last two factors. Furthermore, all descriptive and inferential statistical procedures have been selected to analyze the data. Specifically, a one-way analysis of variance (ANOVA) was applied to analyze the data, which demonstrated significant differences in the students’ reading skills, motivation, and self-learning before and after the learning interventions. These findings showed the students could recognize alphabets more accurately, read simple words more intelligently, become more motivated to read and be highly engaged in self-learning using the mobile AR reading kit.

Malware Detection Using Ensemble N-gram Opcode Sequences

Conventional approaches to tackling malware attacks have proven to be futile at detecting never-before-seen (zero-day) malware. Research however has shown that zero-day malicious files are mostly semantic-preserving variants of already existing malware, which are generated via obfuscation methods. In this paper we propose and evaluate a machine learning based malware detection model using ensemble approach. We employ a strategy of ensemble where multiple feature sets generated from different n-gram sizes of opcode sequences are trained using a single classifier. Model predictions on the trained multi feature sets are weighted and combined on average to make a final verdict on whether a binary file is malicious or benign. To obtain optimal weight combination for the ensemble feature sets, we applied a grid search on a set of pre-defined weights in the range 0 to 1. With a balanced dataset of 2000 samples, an ensemble of n-gram opcode sequences of n sizes 1 and 2 with respective weight pair 0.3 and 0.7 yielded the best detection accuracy of 98.1% using random forest (RF) classifier. Ensemble n-gram sizes 2 and 3 obtained 99.7% as best precision using weight 0.5 for both models.

Quality Practitioners’ Differing Perspectives on Future Software Quality Assurance Practices

Constant changes in a business context and software development make it important to understand how software quality assurance (SQA) should respond. Examining SQA from supplier and client perspectives, this study explores how different groups of SQA practitioners perceive future needs. A survey (n = 93) conducted in fall 2017 explored the views of SQA organizations on future trends. The results indicate that SQA organizations differ slightly in their attitudes to quality categories, as do different groups of SQA practitioners. It is argued that these differences should be taken into account when developing and implementing future SQA strategy. It is further argued that the found basic enables SQA management, evaluation of new practices, and allocation of resources to ensure that all quality categories remain balanced in the future.

Android Apps Security Assessment using Sentiment Analysis Techniques: Comparative Study

Considering that application security is an important aspect, especially nowadays with the increase in technology and the number of fraudsters. It should be noted that determining the security of an application is a difficult task, especially since most fraudsters have become skilled and professional at manipulating people and stealing their sensitive data. Therefore, we pay attention to trying to spot insecurity apps, by analyzing user feedback on the Google Play platform and using sentiment analysis to determine the apps level of security. As it is known, user reviews reflect their experiments and experiences in addition to their feelings and satisfaction with the application or not. But unfortunately, not all of these reviews are real, and as is known, the fake reviews do not reflect the sincerity of feelings, so we have been keen in our work to filter the reviews to be the result is accurate and correct. This study is useful for both users wanting to install android apps and for developers interested in app optimization.

The Role of Cloud Computing in Education: Saudi Arabian Study

Education is a crucial factor in ensuring sustainable progress, In particular, for countries with growing economies. Due to resource virtualization, Cloud computing has both the Internet and complex scalability. Both public and private learning institutions should take advantage of the potential benefit of cloud infrastructure to ensure high-quality service regardless of the minimum resources available. Because of its numerous advantages, cloud computing is taking center stage in academia. This research will seek to establish the benefits associated with the use of cloud computing in education. Cloud technology solutions ensure research and development as well as teaching, and its more sustainable and efficient, thereby having a positive impact on the quality of teaching and learning in educational institutions. The goal of this research is to identify the benefits of cloud computing usage in education. Cloud technology solutions make research and development as well as teaching more competitive and effective, thereby having a positive impact on the quality of education.

Detecting Credit Card Fraud using Machine Learning

Credit card is getting increasingly more famous in budgetary exchanges, simultaneously frauds are likewise expanding. Customary techniques use rule-based master frameworks to identify fraud practices, ignoring assorted circumstances, the outrageous lopsidedness of positive and negative examples. In this paper, we propose a CNN-based fraud detection system, to catch the natural examples of fraud practices gained from named information. Bountiful exchange information is spoken to by an element lattice, on which a convolutional neural organization is applied to recognize a bunch of idle examples for each example. Trials on true monstrous exchanges of a significant business bank show its boss presentation contrasted and some best-in-class strategies. The aim of this paper is to merge between Convolutional Neural Network (CNN), Long-Short Term Memory (LSTM), and Auto-encoder (AE) to increase credit card fraud detection and enhance the performance of the previous models. By using these four models; CNN, AE, LSTM, and AE&LSTM. each of these models is trained by different parameter values highest accuracy has been achieved where the AE model has accuracy =0.99, the CNN model has accuracy =0.85, the accuracy of the LSTM model is 0.85, and finally, the AE&LSTM model obtained an accuracy of 0.32 by 400 epoch. It is concluded that the AE classifies the best result between these models.