A Framework For Dynamic Updating In Component-based Software Systems

Setiap sistem perisian (software) perlu dikemas kini setiap masa bagi pelbagai alasan seperti penetapan pepijat (fixing bugs) Every software system needs to be updated over time for different reasons such as fixing bugs, upgrading its components, or adapting the system in response to its environment's changes

Toward Pure Componentware

Componentware seems to be a promising methodology for software development in order to cope with software complexity. With componentware, the software development is shifted from building everything from scratch into just assembling existing components

A Framework For Dynamic Updating In Component-Based Software Systems.

This paper presents DPICS framework for composing component-based software system that can be modified at runtime. It is based on message pattern interaction between system’s components, which facilitates adding, removing, or replacing a component while the whole system is running

Dependencies Management in Dynamically Updateable Component-Based System.

Component-based software systems achieve their functionalities through interaction between their components. Analyzing the dependencies between systems components is an essential task in system reconfiguration. This paper discusses dependencies analysis significance when updating component-based system dynamically. It presents a service-based matrix model and nested graph as approaches to capture components' dependencies; it discusses using dependencies analysis for safe dynamic updating in component-based software systems

Protocol Based Interaction in Component-Based Software Systems.

In Component-based development, assembling components into systems is the major activity. Therefore, Components must be integrated through well-defined infrastructure. This paper presents a framework for composing component-based systems based on message-pattern interaction among the components; it also presents protocol-based rules to govern messages exchanges

Using machine learning to analyze the impact of coronavirus pandemic news on the stock markets in GCC countries

COVID-19 has resulted in high volatility in financial markets across the world. The goal of this study is to investigate the impact of COVID-19-related news on the stock markets in Gulf Cooperation Council (GCC) countries. The study utilizes machine learning approaches to assess the role of COVID-19 news in stock return predictability in these markets. The results reveal that the stock markets in the United Arab Emirates (UAE), Qatar, Saudi Arabia, and Oman were impacted by coronavirus-related news; however, this news had no impact on the stocks in Bahrain. Moreover, the results indicate that the impacted markets were influenced differently in terms of the quantities and types of news. 2022 The AuthorsThe authors would like to thank Qatar University for financial support , Grant no. QUCP-CBE-2018-1 . The findings are solely the responsibility of the authors.Scopu

Student Attitudes and Interests in STEM in Qatar through the Lens of the Social Cognitive Theory

STEM (science, technology, engineering, and math) has taken center stage as a priority policy agenda for Qatar’s leadership. At present, STEM stands as a fundamental catalyst for Qatar’s sustainable economic, environmental, human, and social development goals, as is outlined in the Qatar National Vision 2030. The aim of this exploratory study was to investigate the determinants of students’ interest in pursuing Science, Technology, Engineering, and Mathematics (STEM) studies and eventual careers in Qatar. This study used a survey involving a representative sample of a total of 425 students from public (government-funded) middle schools in the country. Data for this research were gathered using a survey distributed to students in grades 7, 8, and 9. Guided by the Social Cognitive Theory, a survey was implemented with a view to investigating the intrinsic and extrinsic factors likely to contribute to student STEM educational and career interest. Two main statistical tests were carried out: independent sample t-tests and one way ANOVA. Results derived from the study reveal that gender, nationality, and parental education and occupation served as predictors of student interest in a STEM degree or profession. The results derived from this study have important implications for STEM-related fields of study and career.This study was made possible by an NPRP-C # Subproject (NPRP12C-33955-SP-93), which is part of a cluster project (NPRP12C-0828-190023) from the Qatar national research fund (a member of the Qatar foundation). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Cognition-based adaptive programming tutoring system

The most important potential of E-learning system is the ability to adapt based on learner's status in order to support personalized learning. However, this requires using specific learner's parameters as factors to control adaptation process, these include learning style, presentation preferences, and progress preference through the subject. This is paper presents our web-based tutoring system to support students learning of computer programming. The novelty of our system is using cognitive process levels in revised Bloom's taxonomy as the factor of adaptation to learner progress, where system moves learner from simple levels to more complex ones

Using flipped classroom approach to teach computer programming

Flipped classroom approach has been increasingly adopted in higher institutions. Although this approach has many advantages, there are also many challenges that should be considered. In this paper, we discuss the suitability of this approach to teach computer programming, and we report on our pilot experience of using this approach at Qatar University to teach one subject of computer programming course. It is found that students has positive attitude to this approach, it improves their learning. However, the main challenge was how to involve some of the students in online learnin

ARCS-based tactics to improve students' motivation in computer programming course

Most students find computer programming difficult subject, which frustrates them and affects their performance. In this paper, we present our experience of using ARCS model to improve students' motivation when learning computer programming. Different tactics were used to implement ARCS's four components: attention, relevance, confidence and satisfaction. A survey was used to determine the effectiveness of these tactics on students' motivation. Also, students' performance is compared with their peers' performance last year. It is found that these tactics make students highly motivated and interested in this subject, which ultimately improves their performance