Multifunctional Fiber-Enabled Intelligent Health Agents.

In recent years, wearable devices have rapidly emerged in the medical field, offering simplicity of operation, low cost, and freedom from spatial constraints, holding broad prospects in orthopedic rehabilitation.This article analyzes the application of wearable devices in the prevention, assessment, and training of knee joint injuries, summarizing their current status in the prevention and treatment of knee injuries, and points out the current shortcomings.It also proposes future research prospects for wearable devices in the medical field, aiming to provide a reference for further application and research of wearable devices in knee joint injuries.

Since artificial intelligence first appeared in the 1950s, the interest towards it has been changing throughout its existence, but in a number of countries, including Russia, there is currently an increase in the government’s interest in artificial intelligence technologies that have found application in various fields, such as finance, medicine and education. The use of artificial intelligence in teaching executes functions such as, for instance, natural language generation, personalisation of learning and proctoring. At the same time, besides the organisation of the educational process or the design of learning materials, artificial intelligence technologies in teaching also include teaching a foreign language or learning it. Due to the simplification of the process of creating such technologies, which currently, due to the development of tools for their development, does not require the same training as before, dialogue-based сomputer-assisted language learning plays a significant role in the use of artificial intelligence technologies in teaching foreign languages. Dialogue-based сomputer-assisted language learning provides students with the opportunity to develop and form foreign language skills through interaction with a conversational agent or a bot, which are also often referred to as social interactive agents and are defined as agents capable of independently communicating with people and with each other in a socially reasonable way via multimodal behaviours. Chatbots are a category of conversational agents, which, in turn, are a separate class of dialogue systems.The attention of researchers is currently paid to the implementation of intelligent agents in foreign language teaching and overcoming the difficulties associated with it. The purpose of the article is to provide a theoretical review of publications devoted to the issues of foreign language teaching through the use of interactive dialogue systems or conversational agents; to classify the intelligent agents currently used in foreign language teaching in accordance with the criteria proposed earlier; to describe the accumulated experience of introducing intelligent agents into foreign language teaching, highlighting the advantages and disadvantages of using certain bots in specific situations; to identify the didactic potential of chatbots as a type of conversational agents based on AI technologies; the features of integrating chatbots into the process of developing the students’ oral-speech and written-speech skills, as well as skills of interaction. The works of Russian and foreign authors published over the past 5 years devoted to the study of artificial intelligence technologies in foreign language teaching were selected for the analysis. The research methods are the analysis of research literature and classification. In the course of the research, notions such as “conversational agents” and “chatbots” were defined and the history of their development from the second half of the 20th century to the present day was described. Based on the works of researchers, the criteria were selected for the classification of intellectual agents currently used in teaching foreign languages, on the basis of which their typology was proposed. Finally, the results of the studies describing attempts to introduce such systems into the foreign language teaching process were studied, as a result of which their advantages and disadvantages were described, establishing the prospects for further research on the issue.

As part of designing a new Master’s programme entitled Technology, Innovation and Entrepreneurship there was an apparent need to include a unit involving the use of innovative technologies for the development of innovative products and services. The authors aim was therefore to introduce students to Artificial Intelligence and Intelligent Agents using, however, different from “mainstream” practices since the programme is addressed to students from varying, not necessarily Computer Science related, backgrounds. In this work they report their approach and experience from the entire process -from designing the unit, taking into consideration some inherent restrictions such as coordination of teachers and short duration of the course, to delivering it to a diverse audience that requires different didactic methods to be employed. Their primary aim is to disseminate their ideas and report good practice to fellow educators that may face similar situations or wish to employ any of our techniques.

The rise of the Internet and the growth of distributed computing have led to a major paradigm shift in software engineering and computer science. Until recently, the notion of computation has been variously construed as numerical calculation, as information processing, or as intelligent symbol analysis, but increasingly, it is now viewed as distributed cognition and interaction between intelligent entities [60]. This new view has major implications for the conceptualization, design, engineering and control of software systems, most profoundly expressed in the concept of systems of intelligent software agents, or multi-agent systems [99]. Agents are software entities with control over their own execution; the design of such agents, and of multi-agent systems of them, presents major research and software engineering challenges to computer scientists. One key challenge is the design of means of communication between intelligent agents. Considerable research effort has been expended on the design of artificial languages for agent communications, such as DARPA’s Knowledge Query and Manipulation Language (KQML) [33] and the Foundation for Intelligent Physical Agents’ (now IEEE FIPA) Agent Communications Language (FIPA ACL) [35]. These languages, and languages like them, have been designed to be widely applicable. As well as being a strength, this feature can also be a weakness: agents participating in conversations have too many choices of what to utter at each turn, and thus agent dialogues may endure a state-space explosion. Allowing sufficient flexibility of expression while avoiding state-space explosion had led agent communications researchers to the study of formal dialogue games; these are rule-governed interactions between two or more players (or agents), where

The application of artificial intelligence (AI) technology in the medical field has experienced a long history of development. In turn, some long-standing points and challenges in the medical field have also prompted diverse research teams to continue to explore AI in depth. With the development of advanced technologies such as the Internet of Things (IoT), cloud computing, big data, and 5G mobile networks, AI technology has been more widely adopted in the medical field. In addition, the in-depth integration of AI and IoT technology enables the gradual improvement of medical diagnosis and treatment capabilities so as to provide services to the public in a more effective way. In this work, we examine the technical basis of IoT, cloud computing, big data analysis and machine learning involved in clinical medicine, combined with concepts of specific algorithms such as activity recognition, behavior recognition, anomaly detection, assistant decision-making system, to describe the scenario-based applications of remote diagnosis and treatment collaboration, neonatal intensive care unit, cardiology intensive care unit, emergency first aid, venous thromboembolism, monitoring nursing, image-assisted diagnosis, etc. We also systematically summarize the application of AI and IoT in clinical medicine, analyze the main challenges thereof, and comment on the trends and future developments in this field.

In the context of accelerated global aging and rapid development of artificial intelligence (AI) technology, AI technology provides a new paradigm for improving sports and health management services in the field of elderly care and quality of life. This study aims to reveal the core value of AI technology, analyze the current practical challenges of AI empowerment, and build multidimensional pathways for implementation to promote the coordinated development of healthy aging and technological innovation. Through literature analysis, case studies, and expert interviews, this study systematically explores the application of AI technology in the field of exercise and health of the elderly. First, from a technological perspective, the study analyzes the application potential of AI in elderly exercise monitoring, health data analysis, personalized health interventions, and virtual fitness scenarios, facilitating precise health management for the elderly. Second, from the societal perspective, the study examines how policy support and the demands of the silver economy jointly drive the industrial integration of the “AI + sports and health”. Third, the study identifies challenges such as inadequate age-friendly technology adaptation, data privacy risks, the digital literacy gap, and high service costs, which limit the inclusiveness of AI applications. Fourth, to overcome these challenges, a four-dimensional approach is proposed: iteration (Developing low-threshold interactive Systems), Policy support (Improving data security regulations), Service optimization (Establishing a “community-home-institution” collaborative model), and Humanistic care (Promoting digital literacy training for the elderly. These measures aim to achieve the deep integration of AI for social good and healthy aging. The intervention of AI technology in the field of exercise and health of the elderly requires systematic analysis and overall planning. Through the balance of technology and application, the guarantee of policies and systems, the promotion of social education and cultural adaptation, the effectiveness of AI technology in the field of exercise and health of the elderly is enhanced, which helps to provide references in theory and practice and promote the realization of the goal of healthy aging.

This research, seeks to provide a comprehensive perspective on the most prominent contemporary artificial intelligence (AI) technologies that are playing an increasingly vital role in the medical field, In recent decades the world has witnessed an unprecedented surge in the development of AI technologies, whose impact has extended beyond the industrial and economic sectors to encompass one of the most sensitive and critical areas (the medical sector), therefore the civil liability arise because of these technologies, and studies found that it was hard to find the responsible of the damages caused by the medical technologies, is it the doctor, or the hospital, or the technology itself, thus we found that the compensation fund is an effective way of compensating for damages artificial intelligence technologies in the medical field, within reason it's hard to prove a mistake when using AI-enhanced techniques. This issue raises several questions, including:1. To what extent is there a need to establish compensation funds for damages caused by AI technologies in the medical field?2. What are the proposed models for guarantee or compensation funds to cover damages caused by AI technologies in medicine?3. How have comparative legislation (such as the European Union and France) regulated compensation for AI damages in the medical sector?4. What criteria should be used to calculate compensation amounts in cases of damages resulting from AI technologies?

The rapid advancement of information technology has led to an upsurge in cybercriminal activities. As technology continues to evolve, so do the tactics used by individuals involved in digital offenses. Trends in complex, distributed, and internet[1]based computing have raised significant concerns regarding information security and privacy. Cyber infrastructures, in particular, are highly susceptible to intrusions and various threats. Traditional security measures like sensors and detectors are inadequate for safeguarding these infrastructures, necessitating the development of more sophisticated IT solutions capable of modeling normal behaviors and identifying anomalies. To address these challenges effectively, cyber defense systems must exhibit traits such as flexibility, adaptability, and robustness. They should be able to detect a wide range of threats while making intelligent real-time decisions. Given the sheer volume and speed of cyberattacks, relying solely on human intervention is inadequate for prompt analysis and response. Many of these attacks are orchestrated by intelligent agents like computer worms and viruses, making it essential to combat them using intelligent, semi-autonomous agents that can promptly detect, evaluate, and respond to cyber threats. These computer-generated forces must manage the entire process of responding to attacks, encompassing the identification of the attack type, its targets, the appropriate response, and the prioritization of secondary attack prevention. Furthermore, cyber intrusions are not confined to a single location; they represent a global menace to computer systems worldwide. The expansion of the internet has made knowledge and tools for cybercrime readily accessible to a wide audience, no longer limited to educated specialists. Traditional, rigid algorithms with hard-wired logic have proven ineffective in countering dynamically evolving cyberattacks. This underscores the importance of innovative approaches, particularly the application of Artificial Intelligence (AI), to enhance our capability to combat cybercrimes. AI introduces flexibility and learning capabilities to software, thereby assisting humans in the fight against cybercrimes. Various AI techniques, inspired by nature, including Computational Intelligence, Neural Networks, Intelligent Agents, Artificial Immune Systems, Machine Learning, Data Mining, Pattern Recognition, Fuzzy Logic, and Heuristics, are playing an increasingly crucial role in the detection and prevention of cybercrimes. AI empowers the design of autonomic computing solutions that can adapt to their usage context, employing methods such as self-management, self-tuning, self[1]configuration, self-diagnosis, and self[1]healing. In the realm of information security, AI represents a promising area of research with a focus on enhancing cybersecurity measures in cyberspace. The term "Artificial Intelligence" is used to describe a machine's ability to emulate human-like activities, including problem solving and learning, a concept often referred to as machine learning. The next generation of cybersecurity products is increasingly incorporating Artificial Intelligence and Machine Learning technologies. By analyzing extensive datasets of cybersecurity, network, and physical information, providers of cybersecurity solutions aim to identify and thwart abnormal behavior. Various approaches are employed to utilize AI for cybersecurity. Some applications analyze raw network data to detect irregularities, while others focus on user-entity behavior to identify deviations from the norm. The choice of approach depends on the type of data streams and the level of effort required by analysts

This introductory article opens the “Applications of AI in the Semiconductor Industry” section by giving a holistic overview of the development of artificial intelligence (AI) technologies applied to the industry. Historically, the semiconductor industry has utilised complex automation for many tasks and areas, especially in repetitive work and uniform processes. The high need for flexibility in manufacturing, increased diversification of products, complexity, and demand for more autonomous operations, including human-machine interaction, have led to a strong push towards using AI technologies in semiconductor manufacturing. AI technologies are applied in semiconductor product development, digitised product definition (DPD), knowledge management system for risk assessment and root cause analysis, image recognition for inspection and defect classification in front end (FE) and back end (BE) applications for anomaly detection in process chains. Deep learning (DL) and Machine Learning (ML) techniques have given a new stimulus to semiconductor industry research to address the unique challenges for semiconductor manufacturing as the technologies nods are evolving and the number of process parameters to be controlled is increasing. In the end, 106the article introduces the four contributions to this section, highlighting the use of AI, computer vision, neural networks (NNs) in various use cases in semiconductor manufacturing processes.

تناولت الدراسة الحالية واحدة من أهم المشاكل التي تعاني منها المنظمات الرياضية عالية الاداء وهي عدم الاستفادة القصوى من تطبيقات تقنيات الذكاء الاصطناعي، فلقد تمثلت مشكلة البحث الأساسية في معرفة أثر تطبيق تقنيات الذكاء الاصطناعي في ادارة المنظمات الرياضية عالية الأداء وما مدى مساهمة تطبيقات تقنيات الذكاء الاصطناعي في تشخيص ومعالجة المعوقات الادارية والتقنية في اقسام النشاط الرياضي والمدرسي في المديريات العامة للتربية في محافظة بغداد للتوجه نحو الاداء العالي وفي هذا السياق، فلقد تمثل هدف الدراسة الرئيس في بناء مقياس للاطار المفاهيمي لتطبيقات تقنيات الذكاء الاصطناعي في المنظمات الرياضية عالية الاداء -اقسام النشاط الرياضي والمدرسي في المديريات العامة للتربية في محافظة بغداد أنموذجاً. ولغرض تحقيق أهداف الدراسة اتبعت الباحثتان المنهج الوصفي بالأسلوب التحليلي لملامته لطبيعة المشكلة كذلك القيام بدراسة استطلاعيه غير مقننه في اقسام النشاط الرياضي والمدرسي وكانت الاستبانة هي الأداة المستخدمة في ذلك، وتوصلت الدراسة إلى بناء مقياس رصين لتطبيقات تقنيات الذكاء الاصطناعي في المنظمات الرياضية عالية الاداء ومكون من خمس محاور وهي النظم الخبيرة والواقع الافتراضي والوكلاء الاذكياء وتقنيات المعلومات واتمتة العمليات وقد جاء اختيار حجم العينة مساويا للمجتمع لتلام طبيعة البحث، وقد تم اثبات صدق وموثوقية هذا المقياس من خلال الوسائل الإحصائية كمعامل الفا كرو نباخ بمقدار 89,7، لقد احتوت محاور المقياس الخمسة على خمسة وعشرون عبارة، ولقد تراوحت الأهمية النسبية لهذه العبارات ما بين (63-84)%، وكانت اعلى قيمه للأهمية النسبية تبلغ 84% لعباره الثانية من محور النظم الخبيرة وهي (تعمل النظم الخبيرة كخبير استشاري للإدارة العليا في اقسام النشاط الرياضي والمدرسي لتسهم في اتخاذ القرارات الصحيحة)، بينما كانت اقل قيمة للأهمية النسبية لعبارة الأولى من محور الواقع الافتراضي وهي (استخدام الواقع الافتراضي في اقسام النشاط الرياضي والمدرسي يسهم في اكتساب مهارات متنوعة للموظفين ويعمل على تطوير الاداء الاداري والتقني)

With the rapid integration of artificial intelligence (AI) technology and the medical field, AI-assisted medical education has become a key direction for the reform and innovation of global medical education. The purpose of this study is to explore the innovative practice modes of AI in medical education and their application value, so as to provide theoretical guidance and practical reference for optimizing medical education processes and improving training quality. This study adopts a mixed research method combining systematic literature review and multiple case studies. First, a comprehensive search is conducted in databases such as PubMed, Web of Science, and CNKI to sort out and analyze domestic and foreign literatures on AI-assisted medical education published in the past decade; then, in-depth case investigations are carried out on 6 medical colleges and universities that have carried out AI-assisted education practices, focusing on analyzing their application scenarios, technical paths and implementation effects. The results show that AI technology has formed innovative practices in medical education such as intelligent teaching evaluation, virtual simulation training, and personalized learning push, which can effectively improve students' learning efficiency and practical ability; however, there are still problems such as uneven technical application level, insufficient integration with curriculum systems, and potential ethical risks. The conclusion points out that AI-assisted medical education has broad development prospects, and it is necessary to construct a standardized and integrated application system to promote the high-quality development of medical education.

Recently, computational agents received significant attention in computer science research community. In fact, intelligent agents methodology is a powerful artificial intelligence technology showing considerable promise as a new paradigm for mainstream software development and able to offer new ways of abstraction, decomposition, and organization that fit well with our natural view of the world. However, despite their promise, intelligent agents are still scarce in the market place. A key reason for this is that developing intelligent agent software requires significant training and skill. Artificial Intelligence methodologies and computer networking tools represent the necessary basic knowledge to design and implement advanced agents oriented systems. This paper introduces an integrated development environment supporting the agents developers to design fuzzy-based agents in a simple and fast way. Proposed framework has been realized by integration of theoretical methodologies as fuzzy logic and labeled tree, together with Open Source Software tools as JaxMe2.

The National Academies Board on Human Systems Integration (BOHSI) has organized this session exploring the state of the art and research and design frontiers relating to the application of Artificial Intelligence (AI) technologies to Healthcare. Advances in AI, machine learning, and robotics promise to revolutionize access to and quality of healthcare. At the same time lessons learned from earlier applications of AI technologies suggest a need to carefully consider the user and use context and the need for effective human-machine teaming design to avoid brittle systems that fail to be used or worse that negatively impact patient safety. The Panel brings together prominent researchers from both the Human Factors and Computer Science and AI communities to discuss promising applications, challenges and short-falls and ways forward for achieving the promise of enhanced delivery of health care through application of AI technologies.

The rapid advancement of Artificial Intelligence (AI) technology has brought about significant positive changes across society. However, it has also introduced challenges like privacy breaches, data bias, and spreading fake news. In response, several countries, including South Korea, have provided ethical guidelines and policies for AI. Yet, these measures often fall short of keeping pace with the speed and diversity of AI development. To address these issues, this study developed a comprehensive approach by integrating ethical considerations into AI education, covering the entire AI technology development and application process. It involved analyzing previous research on AI and AI ethics education and creating a draft of an integrated AI education program focused on problem-solving based on computational thinking and ethical practicality. The draft was refined and supplemented through two rounds of expert Delphi surveys, ultimately leading to an “Integrated AI Education Program.” This proposed program emphasizes that the knowledge content of AI technology and ethical considerations should not be treated separately but addressed together. It aims to enhance moral and social capacities alongside the experience of thinking like a computer scientist when solving real-life problems. Through this, the program supports the ultimate goal of AI education: to foster computational thinking while providing an educational experience that considers both technology and ethics. This study is expected to advance the discussion on integrating AI ethics into AI education curricula and contribute to developing socially responsible AI developers and users.

The field of “intelligent agents and multi-agent systems” is maturing; no longer is it a special topic to be introduced to graduate students after years of training in computer science and many introductory courses in artificial intelligence. Instead, the time is ripe to introduce agents and multi-agents directly to undergraduate students, whether majoring in computer science or not. This chapter focuses on exactly this challenge, drawing on the co-authors’ experience of teaching several such undergraduate courses on agents and multi-agents, over the last three years at two different universities. The chapter outlines three key issues that must be addressed. The first issue is facilitating students’ intuitive understanding of fundamental concepts of multi-agent systems; the authors illustrate uses of science fiction materials and classroom games to not only provide students with the necessary intuitive understanding but with the excitement and motivation for studying multi-agent systems. The second is in selecting the right material — either science-fiction material or games — for providing students the necessary motivation and intuition; we outline several criteria that have been useful in selecting such material. The third issue is in educating students about the fundamental philosophical, ethical and social issues surrounding agents and multi-agent systems:they outline course materials and classroom activities that allow students to obtain this “big picture” futuristic vision of our science. The authors conclude with feedback received, lessons learned and impact on both the computer science students and non computer-science students.