ABSTRACT. Signboards and static maps placed around the buildings are mostly used to navigate the people. The use of signboards inside the building sometimes can cause confusions for people who are unfamiliar with the internal build-ing architecture. It gets more difficult when the building consists of multiple level with several wings and junctions. Unlike outdoor navigations, in-door navigation applications are helpful to navigate users within the buildings. Several network devices are configured to collect the position of the users or objects. Precision of positioning in the indoor navigation is still an open research area. In this study, we develop an application to precisely navigate the users in the closed environments. We have used Universiti Malaysia Pahang (UMP) campus as a case for developing our application. The application also adds an additional feature for enabling users to book the university rooms (e.g., conference rooms). Once the users book the rooms, this application will navigate them to the booked locations within the building.

ABSTRACT. Many universities have been forced to use online and e-learning technologies as a result of the COVID-19 epidemic, which they find to be somewhat challenging. Despite the applicability of certain e-learning systems such as canvas and Moodle to the current situation, their effective utilization depends on the ability to comprehend the factors that lead to system adoption. Existing studies have yet reached a consensus on the key factors influencing effective e-learning system usage specifically in the current pandemic setting; this presents a clear knowledge gap ready to be filled. Towards this end, this current study developed an extended Model to explore the key determinants to the decisions of university students to adopt the system. The needed data was collected through a survey on the usage of the Moodle e-learning system involving 389 undergraduate students at Al-Azhar University. SMART PLS was used to test the hypotheses. The findings indicated that Computer Anxiety, Course Content, Hedonic Motivation, Perceived Environment, Subjective Norm, and Technical Support effect significantly both ease of use and usefulness. Subjective Norm effect significantly on intention to use. Perceived Ease of Use and Perceived Usefulness effect significantly on intention to use.

ABSTRACT. The advancement of technology changes the mode of operation worldwide education industry, where educational services can be delivered either in face-to-face or online teaching. The outbreak of COVID-19 forced higher education institutions to shift from face-to-face teaching to fully online learning even though online learning yet to be fully implemented in many institutions. This trend has prompted us to study this interesting topic and gather information about undergraduate students’ satisfaction of online learning from home due to the limited study focus on Malaysia. The operational of the study is based on the user satisfactions theories. A total of 156 questionnaires were distributed via judgement sampling guidelines. PLS-SEM was used for the data analysis. The results confirmed that, online learn-ing system is useful but not user friendly. Technical system quality is up to the satisfactory level from students’ perception. Furthermore, attitude was confirmed significantly impacting undergraduate students’ satisfaction of online learning. Discussion of the findings, implication, and direction for future research are also presented in the final section of the study.

ABSTRACT. Sports anatomy is a very important basic course in the study of physical education, which belongs to the category of morphology. The traditional teaching method is single, the learning effect of students is poor, and it is impossible to meet the needs of current talent training. In recent years, the rapid development of education informatization, the integration of virtual reality technology into the field of education has brought opportunities for teaching reform and innovation. In view of the above background, this study analyzes the advantages of virtual reality technology applied to the teaching of sports anatomy by means of literature method and logical analysis method, including helping to cultivate students' three-dimensional thinking ability, making up for the lack of teaching conditions, saving costs, stimulating students' learning interest and initiative, and cultivating the ability to combine theory and practice.This paper also analyzes the dilemma of virtual reality technology applied to the teaching of sports anatomy, and proposes some specific solutions, in order to reform the teaching of sports anatomy and improve the teaching effect for theoretical basis.

ABSTRACT. The new paradigm of Educational Data Mining (EDM) is aimed at extracting relevant knowledge to-wards optimizing the process of teaching. It is difficult to optimize the current education system owing to the massive volume of data available. With EDM, such an issue is solvable as raw data can be extracted and explored efficiently. This current study presents a number of real educational data experiments using EDM of which efficacy in migrating the data into knowledge is deliberated. This paper primarily aims to determine the key teacher behavioral factors that affect student satisfaction. Additionally, the study aims to present understandings attained via the experiments and subsequently offer practical recommendations for real-world EDM applications.

ABSTRACT. School dropout is an issue that plagues almost every nation globally, and Malaysia is not an exception. According to Malaysia’s Education Ministry, dropouts are defined as Malaysian stu-dents in the school system who choose to leave before completing their education. Students drop-out is a grave issue as it impacts the students and negatively implicates society and policymakers. At present, machine learning is the talk of the town as the world has a wealth of data that can be used freely. Machine learning is a data analysis technique that automates the creation of analytical models. It is a subfield of artificial intelligence that is based on the idea that systems can learn from data, identify patterns, and make decisions with little or no human intervention. From the Malaysian education perspective, no study thus far has looked into primary and secondary public-school dropouts while simultaneously using supervised machine learning. Consequently, this study will contribute to the literature, especially from Malaysia's perspective on predicting the dropout of primary and secondary students using supervised machine learning algorithms. The predictive model with machine learning has an enormous potential to develop early warning sys-tems to identify and assist students at risk of dropping out.

ABSTRACT. This paper aimed at investigating the effectiveness of face-to-face Computer Assisted Cooperative Learning (CACL) in teaching reading skills. It employed a mixed-method design as the data were collected through pre and post-test and semi-structured interviews. The pre and post-test of reading skills were analyzed through t-test, and the qualitative data were analyzed through thematic patterns. The findings showed a significant difference between the pre and post-test of reading skills, and the qualitative data analysis showed that face-to-face CACL has academic, social, and cognitive advantages. These findings showed that face-to-face CACL effectively teaches reading skills, which is attributed to the design of CALL and the implementation of cooperative learning principles. It is concluded that CALL and cooperative learning have a complementary advantage in teaching reading skills through face-to-face CACL. Therefore, teachers need to focus on the learning theories of CALL activities and implement the five principles of cooperative learning to make teaching reading skills through CACL more effective for EFL learners.

ABSTRACT. With the emergence of new technologies and trends, the habits of people and how they are dealing with these technologies has changed. Social Network Sites (SNS) have become a big part of most people’s daily life. It is important to understand the factors that contribute to self-disclosure on SNS. A dramatic shift has been noticed concerning user privacy, where people’s tendency to disclose information has been increasing. The disclosure of personal information can be very dangerous. This research-in progress aims to propose a conceptual framework that considers the inhibitors and enablers of self-disclosure on SNS. The conceptual framework will be tested via a large-scale survey of teens, students, employees and others. This research-in-progress will help the user to question their online behavior critically in order to protect themselves from oversharing personal information.

ABSTRACT. Tech startups are critical in sustaining innovation and growth in a country. They need to be nurtured so that they can grow to become viable and sustainable business entity. A suitable ecosystem therefore is vital to the development of startups. The aim of this study is to investigate the relationship between the sustainability of startups and the supporting factors, which are incubators, accelerators, co-working spaces, mentors and events. Comparison between tech startups in Malaysia and China is examined to support factors and the sustainability of startups. The result of this study showed that accelerators, events and mentors have the strongest influence to the sustainability of tech-startups as compared to co-working space. It is believed that the number of sample size plays a role in proving the role of incubators to the sustainability of startups. This study also shared that the sampling technique chosen will impact the outcome of the study.

ABSTRACT. Although extensive studies have focused on the impact of different drivers and factors on green innovation, However, few studies have conducted to investigate that how the ESG could influ-ence the green innovation in small and medium enterprises (SMEs). Therefore, the purpose of this study is to predict the effect of environment, social and governance (ESG) on green innova-tion. In this study, green innovation is segmented into two dimensions which are sustainable product innovation and sustainable process innovation. The data collected through a question-naire-based survey from medium-levels IT firms and this study used an artificial neural network approach for analysis. The results indicate that social and political contribution (100%) is the most important factor to achieve the sustainable product innovation which followed by pollution & waste (91%) and emission reduction (49%). In addition, this study shows that pollution & waste is the most important factor to achieve the sustainable process innovation which followed by anti-competitive behavior (45%) and emission reduction (35%). The findings of this paper should be useful for manager in SMEs which legitimize them to employ the ESG practices to enhance the green innovation.

ABSTRACT. Abstract: Performance of building construction was pointed over the past years to low standards of information management, which depend on project complexity. BIM application in building projects is generally seen as a sophisticated environment in the Yemeni construction industry, leading to cost and time overrun, labour productivity and poor design. This study aims to appraise the factors affecting BIM adoption in the Yemeni construction industry. A questionnaire survey is being conducted for construction professionals and construction industry experts in Yemen. The responses retrieved from the questionnaire were analyzed using descriptive statistics factor analysis and ranked accordingly. Findings show that the visualization of construction sequences is the most significant technological factor affecting BIM adoption. Greater collaboration with consultants and other project team members was ranked first as a process factor, whereas construction code is the most significant policy factor that hinders BIM adoption. Lack of top management support with a mean value of 3.61 is the most critical people factor and BIM readiness by project consultants as the significant environmental factors having a mean value of 3.9.

ABSTRACT. Entrepreneurship is a prominent topic these days, as technological advancements and developments in infrastructure generate a lot of opportunities for entrepreneurs. Entrepreneurs must work now to prepare the travel industry for a future driven by technology and innovation, as well as to establish digitally, scalable focused business models. In today's organizations such as small and medium tourism enterprises (SMTEs), There is a rising awareness of the gap between the workforce's present and required digital capabilities. SMTEs are assumed as the economic drivers of tourism destinations. One of the most implications of increased tourism in the UAE is the government support to digital entrepreneurs. This paper aims to present a theoretical account of the connection by addressing the structural relations between entrepreneurial digital competencies, innovation capability, and tourism entrepreneurship performance. The findings of this study could help policymakers, tourism operators, entrepreneurs to maximize tourism entrepreneurial performance.

ABSTRACT. Users are increasingly using AI-based applications in their daily activities, with voice assistants playing a more advanced and pervasive role. However, little is known about the specific drivers of user acceptance of voice assis-tance technology. Therefore, a conceptual model was developed that inte-grates factors from the Service Robot Acceptance Model (sRAM), and Uni-fied Theory of Acceptance and Use of Technology (UTAUT). The model will be tested with a large-scale survey of respondents of different ages, gen-ders, and educational levels. This ongoing research focuses on users' ac-ceptance of the use of digital voice assistants in their daily lives and suggests a model that considers the factors that enable their acceptance.

ABSTRACT. In the fast-expanding construction industry worldwide, building information modelling (BIM) is a robust process. However, to date, developing countries are not very well adopting the techniques which are proven to greatly help produce effective management of construction projects. This study reviews numerous current studies conducted on the challenges and benefits of adopting BIM. It aims to identify the challenges and benefits of BIM. Additional focus was given to developing countries since there are fewer documented articles found in the literature. However, many challenges are identified which hinder BIM adoption in full potential, particularly in developing countries. The most common findings proposed five key benefits of BIM adoption, namely: i) improved data management (rich) information; ii) improve visualization of project execution; iii) clash detection; iv) reducing waste in the material; v) reducing the financial risk associated with the project in order by obtaining earlier reliable cost estimates. Likewise, the most common findings defined five major BIM adoption obstacles are: i) resilience to change industry culture; ii) high Investment cost; iii) lack of client demand; iv) absence of stakeholder collaboration; v) lack of awareness. It was found that there is a considerable benefit gained by those construction organizations already practicing the information modelling. Most of the organizations that adopted BIM are situated in European countries, followed by the united states of America. Thus, future work should focus on how to raise the level of awareness and general adaptability, especially in developing nations.

ABSTRACT. Quantum networks are emerging sciences and anticipated to be the core networking technologies in the future. Due to the difficulty of implementing quantum networks in a real way, because quantum devices are not widely available, they only exist within their own laboratories. In addition, they are very expensive and also need special environments that are not easy to obtain in other than laboratories. In this paper, we build a simulator using the language of Python programming to simulates quantum networks in terms of quantum devices such as repeaters, final node, and channels, where the behavior of these elements within the network is simulated for the purpose of sending quantum information represented by quantum bits, and therefore the work will be within the principle of graph and finally facilitate experiments on networks Quantum devices without the need for real physical devices. The most remarkable result emerged from the simulated data generated and detected is that the modeling process provides guidance for quantum networks design, characterization their protocols and their behavior. As a result of this study, we were able to simulate quantum network repeater and end node as well as quantum link (entanglement link) and implement some of quantum protocols like Quantum Key Distribution (QKD), Teleportation and quantum protocol. In the end, we concluded the possibility of simulate the behavior of quantum network, its devices and protocols, as well as implement it and develop the quantum applications an integrated study about the quantum internet and routing in it. In addition, we were able to develop quantum repeater protocol in order to enable end-to-end entanglement

ABSTRACT. More than half of all households in the United States have either a dog or a cat as their companion. Majority of dog and cat owners learned about their pet through word of mouth. Getting more people obtained a new pet during the pandemic. It always gives a challenge to pet owners to know better about their dog and cat. Even sociable pets like dog and cat can suddenly act moody, depress and passive. An affective recommender system for pet observation is proposed in this paper to assist the pet owners to monitor their pets, and further creating a social network platform for pets’ interaction. A cloud-based pet social network is developed for assessing dog’s emotion recognition performances in the proposed recommender system. A hybrid solution with different deep learning models (for image and audio recognitions) is presented in this paper. The experiments proved that the proposed hybrid approach with dog’s facial expression and barking analysis produced higher accuracy during the emotion recognition, which indirectly improves the interaction capability between the owner and dog, as well as dog-to-dog in the pet social network.

ABSTRACT. A QR Code is a two-dimensional barcode scanned by a digital device or smartphone that holds data as a sequence of pixels in a square-shaped pattern. QR codes are widely employed in commercial tracking systems, encoding URLs, contact information, map coordinates, and physical and digital documents. Nowadays, several smartphones have built-in QR readers; they are often employed in marketing and advertising campaigns. More recently, QR codes have recreated a critical role in tracing COVID-19 pandemic expo-sure and slowing the spread of the virus. Web attackers can encode malicious URLs of custom malware or phishing site into a QR code, which could violate or disclose personal or financial information on a smartphone’s data when scanned. This study investigates several symmetrical lightweight cryptography (LWC) algorithms to enhance QR code protection. Modern well-defined LWC features (performance and security) are compared and evaluated. The results adopt reliable and safe mechanisms for QR codes’ security issues.

ABSTRACT. The risk of attacks on web systems increased with the reliance of web systems in a wide range of businesses, and attackers invent new techniques to crack these systems. According to OWASP SQL injection stays one of the top 10 web applications security risks. This research use machine learning to detect SQL injection attacks, we used four machine learning models to detect SQL injection attacks. An insight into the data shows that data preparation and feature extraction have influenced detection accuracy. The used training dataset is a combination of live requests extracted from the user requests log files and the training dataset contains records of benign and malicious SQL queries. Then we compared the use of these models in terms of quality and speed of training, results showed that Support Vector Model achieved the highest detection accuracy with 0.997 accuracy followed by Extreme Gradient Boosting with 0.995 accuracy. On the other hand, Naïve Bayes using N-gram level feature extraction model was the fastest model it required 6 milliseconds to train the classifier.

ABSTRACT. Network slicing has emerged as one of the most promising techniques for enabling diverse services to satisfy the criteria of 5G networks in new generation networks. Despite the fact that there are numerous surveys on network slicing, no thorough study of all aspects of the notion of network slicing exists. In this paper, we present a broad overview of the concept of network slicing based on software-defined networks and network function virtualization, as well as a thorough examination of the fundamental concepts of slicing architecture development. On the other hand, we highlight the different scenarios for implementing the concept of network slicing, and later we discuss the concept of security for network slices, threats, and weaknesses in the network slicing architecture, and possible solutions for this. Finally, we go over the most recent research findings in the realm of accomplishing the primary security goals of network slices.

ABSTRACT. In recent years, Deep Reinforcement Learning has made great progress in video games, including Atari, ViZDoom, StarCraft, Dota2, and so on. Those successes coupled with the release of the ML-Agents Toolkit, an open-source that helps users to create simulated environments, shows that Deep Reinforcement Learning can now be easily apply to video games. Therefore, stimulating the creativity of developers and researchers. This research aspires to develop a new video game and turn it into a simulation environment for training intelligent agents. Experienced it with tuning the hyperparameters to make the agent getting the best performance for a final commercial video game product.

ABSTRACT. In light of the information revolution, this study aims to clarify the impact of artificial intelligence and supply chain resilience on the supply chain performance of engineering, electrical, and information technology companies registered with the Jordan Chamber of Industry. This study expands knowledge by exploring the relationships between artificial intelligence and the moderating supply chain dynamism. This study looks at artificial intelligence as an important resource, in addition to resilience supply chains, an important resource in raising the supply chain performance for companies. The questionnaire was conducted via e-mail and the study sample included (208) companies registered with the Jordanian Chamber of Industry and Commerce. The data was analyzed using the smart (Pls) software and its direct link with artificial intelligence and supply chain resilience. In addition, the analysis shows that there is a direct relationship between the mediating variables supply chain dynamism and supply chain resilience and supply chain performance. These results provide an insight into the relationship between artificial intelligence and supply chains, and the Moderating variable on the performance of a company's supply chains, which may be an entry point for companies to enhance their performance due to the importance of this sector to the Jordanian economy.

ABSTRACT. In recent years, ontology integration has received more and more focus in ontology engineering. Ontology integration is a complex process that has faced some difficulties such as semantic heterogeneity. Semantic mapping is very significant to solve the heterogeneity in ontology integration. Ontology integration will contribute to building a comprehensive view to support interoperability.

ABSTRACT. Nowadays, the Internet of Things (IoT) leads to efficient resource utilization and fosters the development of university campuses. The smart connected devices (things) help create smart campuses, which promises to transform into green campuses and achieve sustainable development. Therefore, designers will have to overcome significant implementation challenges to reach thousands or millions of devices to integrate the IoT on the university campus. Among these challenges, reliability has been identified as one of the critical issues for efficient IoT because unreliable sensing, processing, or transmission can cause false monitoring data reports, long delays, and even data loss, leading to vulnerabilities across smart campus applications. Unlike manufacturing or design faults, the worse behaviour of the unreliable smart campus, for example, transient faults that occur in IoT devices (also known as soft errors), do not happen consistently. External events, such as energetic particles striking the chip, cause these intermittent faults. These events do not result in permanent physical damage to IoT devices, but they can change signal transfers or stored values, resulting in incorrect smart campus application execution. This paper serves as a resource for smart campus reliability using the Internet of Things to understand smart campus sustainable development better.

ABSTRACT. This study aims to see the role of artificial intelligence on the performance of engineering companies in Palestine, where the study community consists of construction engineering offices in Palestine, and the sample of the study reached 183 engineering offices and hence the importance of the research. This study relied on the knowledge management theory (KMT) model. From this point of view, this study came. It is worth mentioning that the results of the study indicated that there is a relationship between artificial intelligence and the performance of engineering companies in Palestine. The researcher recommends conducting further research. From research related to artificial intelligence and taking into account the current variables of the study.