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AT4Safe: Advanced Technologies for Safety Detection and Quality Control of Grain HFITCO: Human Factors in Intelligent Transportation: Challenges and Opportunities Synopsis: Recent technological advances enabled human beings to become the key role of our daily life. In particular, human factors in intelligent transportation emerged as an exciting research topic that can provide solutions to improve the safety and quality of life of drivers, from aviation, land to maritime. This works introduces the humancomputer interaction specialists developed in this workshop, with particular focus on its innovative perception and interaction capabilities. The project’s main goal is to enrich the day-to-day experience of drivers with technologies that enable safe driving, comfort driving, to sustainable driving. Organizers: TrustRL: Trustworthy in Reinforcement Learning Synopsis: This workshop seeks to motivate researchers in the realm of reinforcement learning systems to integrate safety considerations into both the creation and utilization of these systems. The term 'safety' is defined extensively, covering the prevention of self-injury, environmental damage, and significant societal costs. Organizers: LLM2P: Large Language Models: From Theory to Practice Synopsis: The landscape of large language models (LLMs) is rapidly evolving, with significant contributions from both open-source and proprietary developments. These models are profoundly impacting various sectors, including business, healthcare, education, and entertainment by automating complex tasks, enhancing customer experiences, and providing new insights from data analysis. This workshop aims to bridge the gap between the theoretical underpinnings and practical applications of large language models (LLMs). It will cover foundational concepts, recent advancements, ethical considerations, and hands-on applications of LLMs across various industries. Organizer: EOAuto: Evolutionary Optimization and Its Applications in Autonomous Driving and Systems Synopsis: Optimization problems widely exist in autonomous driving and systems, such as vehicle scheduling, path planning, etc. Evolutionary algorithms (EAs) have been widely used to solve optimization problems across various real-world applications for their advantages that they can provide multiple solutions in a single run for flexible selection and they make no special assumptions about the problem properties such as differentiability and continuity. With the rapid development of autonomous driving and systems, there is a trend to use EAs to solve optimization problems in this field that are difficult to solve by traditional mathematical optimization methods. This workshop aims to investigate the development of the application of EAs in the field of autonomous driving and systems. Organizers: BridgeXT: Bridging the Gap: Exploring Explainability in Autonomous and Trusted Computing Synopsis: As technology continues to advance, autonomous and trusted computing systems are becoming increasingly prevalent in various domains, including healthcare, finance, transportation, and more. However, with this advancement comes the challenge of ensuring that these systems are not only efficient and reliable but also transparent and understandable to users and stakeholders. Explainability in autonomous and trusted computing has emerged as a crucial area of research and development to address this challenge. This workshop aims to explore the significance, methods, and implications of explainability in autonomous and trusted computing systems. Through interactive discussions, presentations, and hands-on activities, participants will share their insights regarding the importance of explainability, current research trends, and practical approaches to enhance transparency and trust in autonomous systems. Organizers: IPLearn: When Image Processing Meets Novel Learning Techniques: Opportunities and Challenges Synopsis: With the development of deep learning techniques, the traditional field of image processing is increasingly influenced by deep learning. Some typical tasks, such as image and video compression, image reconstruction, image matching, have achieved significant improvements within the framework of deep learning. In recent years, with the emergence of new network architectures or models, such as transformer, mamba, NeRF, diffusion model, as well as large language models, new opportunities and challenges have arisen. This workshop aims to provide a platform for researchers and industry experts to discuss recent advancements, share insights, and address the key challenges for applying novel deep learning techniques to typical image processing or computer vision tasks. Orgnizer: |
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