Challenge in Digital Games: Towards Developing a Measurement Tool

Challenge is arguably the most important experience that players seek in digital games. However, without a measure of how challenged players feel during the act of play, it is hard to design games that are neither too easy nor too hard and, therefore, truly enjoyable. Especially in industry, challenge is dominantly assessed by means of manual play testing in ad-hoc trials. The aim of this research is to create a more systematic, complete, and reliable instrument to evaluate the level of players' experienced challenge in games in the form of a questionnaire. This paper presents the key results from an extensive literature survey which will inform further development. We survey definitions of challenge, challenge types, and their relation to player experience based on the observations of game designers. We furthermore draw from empirical findings in a diverse range of fields such as game studies, human-computer interaction (HCI) and artificial intelligence (AI)

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

This survey presents a comprehensive review of current literature on Explainable Artificial Intelligence (XAI) methods for cyber security applications. Due to the rapid development of Internet-connected systems and Artificial Intelligence in recent years, Artificial Intelligence including Machine Learning and Deep Learning has been widely utilized in the fields of cyber security including intrusion detection, malware detection, and spam filtering. However, although Artificial Intelligence-based approaches for the detection and defense of cyber attacks and threats are more advanced and efficient compared to the conventional signature-based and rule-based cyber security strategies, most Machine Learning-based techniques and Deep Learning-based techniques are deployed in the “black-box” manner, meaning that security experts and customers are unable to explain how such procedures reach particular conclusions. The deficiencies of transparencies and interpretability of existing Artificial Intelligence techniques would decrease human users’ confidence in the models utilized for the defense against cyber attacks, especially in current situations where cyber attacks become increasingly diverse and complicated. Therefore, it is essential to apply XAI in the establishment of cyber security models to create more explainable models while maintaining high accuracy and allowing human users to comprehend, trust, and manage the next generation of cyber defense mechanisms. Although there are papers reviewing Artificial Intelligence applications in cyber security areas and the vast literature on applying XAI in many fields including healthcare, financial services, and criminal justice, the surprising fact is that there are currently no survey research articles that concentrate on XAI applications in cyber security. Therefore, the motivation behind the survey is to bridge the research gap by presenting a detailed and up-to-date survey of XAI approaches applicable to issues in the cyber security field. Our work is the first to propose a clear roadmap for navigating the XAI literature in the context of applications in cyber security

A Proposed Artificial Intelligence-Based System for Developing E-management Skills in Saudi Primary Schools

This study aims to investigate the impact of Artificial intelligence-driven solutions on school leaders’ proficiencies. Leaders have the responsibility of making decisions in educational institutions as well as carrying out routine tasks daily. Artificial intelligence-assisted applications have noteworthy contributions to the field of educational management. The scope of this study is limited to selected features; data analytics, chatbot, and e-survey. The basic design of this study started with analyzing literature in this domain. This was followed by designing a system consisting of four models: building a dashboard, predicting students’ results, creating a chatbot for responding to parents’ queries, and creating an e-survey for measuring staff satisfaction. The prominent finding of this study is the significant impact of Artificial intelligence on leaders’ competencies

Archives and AI: An Overview of Current Debates and Future Perspectives

The digital transformation is turning archives, both old and new, into data. As a consequence, automation in the form of artificial intelligence techniques is increasingly applied both to scale traditional recordkeeping activities, and to experiment with novel ways to capture, organise, and access records. We survey recent developments at the intersection of Artificial Intelligence and archival thinking and practice. Our overview of this growing body of literature is organised through the lenses of the Records Continuum model. We find four broad themes in the literature on archives and artificial intelligence: theoretical and professional considerations, the automation of recordkeeping processes, organising and accessing archives, and novel forms of digital archives. We conclude by underlining emerging trends and directions for future work, which include the application of recordkeeping principles to the very data and processes that power modern artificial intelligence and a more structural - yet critically aware - integration of artificial intelligence into archival systems and practice

Archives and AI: An Overview of Current Debates and Future Perspectives

The digital transformation is turning archives, both old and new, into data. As a consequence, automation in the form of artificial intelligence techniques is increasingly applied both to scale traditional recordkeeping activities, and to experiment with novel ways to capture, organise, and access records. We survey recent developments at the intersection of Artificial Intelligence and archival thinking and practice. Our overview of this growing body of literature is organised through the lenses of the Records Continuum model. We find four broad themes in the literature on archives and artificial intelligence: theoretical and professional considerations, the automation of recordkeeping processes, organising and accessing archives, and novel forms of digital archives. We conclude by underlining emerging trends and directions for future work, which include the application of recordkeeping principles to the very data and processes that power modern artificial intelligence and a more structural - yet critically aware - integration of artificial intelligence into archival systems and practice. </p

A Proposed Artificial Intelligence-Based System for Developing E-management Skills in Saudi Primary Schools

This study aims to investigate the impact of Artificial intelligence-driven solutions on school leaders’ proficiencies. Leaders have the responsibility of making decisions in educational institutions as well as carrying out routine tasks daily. Artificial intelligence-assisted applications have noteworthy contributions to the field of educational management. The scope of this study is limited to selected features; data analytics, chatbot, and e-survey. The basic design of this study started with analyzing literature in this domain. This was followed by designing a system consisting of four models: building a dashboard, predicting students’ results, creating a chatbot for responding to parents’ queries, and creating an e-survey for measuring staff satisfaction. The prominent finding of this study is the significant impact of Artificial intelligence on leaders’ competencies

Comprehensive Survey of Machine Learning Applications in Power systems

This project presents a comprehensive survey of artificial intelligence in electric power system applications. It summarizes a general view of artificial intelligence in power systems in five chapters. The first chapter of This survey paper seeks to contribute to the literature by expanding on the existing research that has been published. A systematic review of the literature summarizes the practical applications of artificial intelligence to improve power systems in different areas such as control, security, distributed energy systems, control of load flow, and detecting faults. The second chapter is based on recent studies and research on artificial intelligence methods. It explains three critical categories of artificial intelligence: rule-based systems, machine learning techniques, and metaheuristic methods. The third chapter consists of the methodology for the literature review. It provides the databases that were used to search for the sources, as well as the screening procedure, time period for the search, and search string. Chapter four describes Artificial intelligence applications. Finally, chapter five elaborates on the outlook for artificial intelligence in the power system

Simulation–Optimization Modeling: A Survey and Potential Application in Reservoir Systems Operation

This paper presents a survey of simulation and optimization modeling approaches used in reservoir systems operation problems. Optimization methods have been proved of much importance when used with simulation modeling and the two approaches when combined give the best results. The main objective of this review article is to discuss simulation, optimization and combined simulation– optimization modeling approach and to provide an overview of their applications reported in literature. In addition to classical optimization techniques, application and scope of computational intelligence techniques, such as, evolutionary computa- tions, fuzzy set theory and artificial neural networks, in reservoir system operation studies are reviewed. Conclusions and suggestive remarks based on this survey are outlined, which could be helpful for future research and for system managers to decide appropriate methodology for application to their systems