Fei-Yue Wang’s research while affiliated with Macau University of Science and Technology and other places

Digital light processing (DLP) is a popular additive manufacturing technology that uses light irradiation to fabricate 3-D devices via a projector to achieve laser-sensitive resin curing. However, the performance and reliability of DLP can be affected by internal defects such as printing errors and the accumulation of residual stress. Existing defect detection methods rely on monitoring the printed parts, which leads to resource wastage and struggles to effectively handle imbalanced defect data. In this article, we propose a defect detection method called dual neural network, which involves detecting defects in materials before the printing process to prevent resource wastage and serious consequences. Specifically, to handle the highly imbalanced class distribution problem in online DLP defect detection, dual neural network utilizes a domain learner and balance learner to effectively balance the information of the minority class and learn the generalization knowledge from the imbalanced defect dataset. Experimental results demonstrate the effectiveness of our proposed method, which has also been applied to real-world production equipment successfully.

Spatial Crowdsourcing (SC) is gaining traction in both academia and industry, with tasks on SC platforms becoming increasingly complex and requiring collaboration among workers with diverse skills. Recent research works address complex tasks by dividing them into subtasks with dependencies and assigning them to suitable workers. However, the dependencies among subtasks and their heterogeneous skill requirements, as well as the need for efficient utilization of workers' limited work time in the multi-task allocation mode, pose challenges in achieving an optimal task allocation scheme. Therefore, this paper formally investigates the problem of Dependency-aware Multi-task Allocation (DMA) and presents a well-designed framework to solve it, known as Heterogeneous Graph Reinforcement Learning-based Task Allocation (HGRL-TA). To address the challenges associated with representing and embedding diverse problem instances to ensure robust generalization, we propose a multi-relation graph model and a Compound-path-based Heterogeneous Graph Attention Network (CHANet) for effectively representing and capturing intricate relations among tasks and workers, as well as providing embedding of problem state. The task allocation decision is determined sequentially by a policy network, which undergoes simultaneous training with CHANet using the proximal policy optimization algorithm. Extensive experiment results demonstrate the effectiveness and generality of the proposed HGRL-TA in solving the DMA problem, leading to average profits that is 21.78% higher than those achieved using the metaheuristic methods.

The transition from cyber-physical-system-based (CPS-based) Industry 4.0 to cyber-physical-social-system-based (CPSS-based) Industry 5.0 brings new requirements and opportunities to current sensing approaches, especially in light of recent progress in large language models (LLMs) and retrieval augmented generation (RAG). Therefore, the advancement of parallel intelligence powered crowdsensing intelligence (CSI) is witnessed, which is currently advancing toward linguistic intelligence. In this paper, we propose a novel sensing paradigm, namely conversational crowdsensing, for Industry 5.0 (especially for social manufacturing). It can alleviate workload and professional requirements of individuals and promote the organization and operation of diverse workforce, thereby facilitating faster response and wider popularization of crowdsensing systems. Specifically, we design the architecture of conversational crowdsensing to effectively organize three types of participants (biological, robotic, and digital) from diverse communities. Through three levels of effective conversation (i.e., inter-human, human-AI, and inter-AI), complex interactions and service functionalities of different workers can be achieved to accomplish various tasks across three sensing phases (i.e., requesting, scheduling, and executing). Moreover, we explore the foundational technologies for realizing conversational crowdsensing, encompassing LLM-based multi-agent systems, scenarios engineering and conversational human-AI cooperation. Finally, we present potential applications of conversational crowdsensing and discuss its implications. We envision that conversations in natural language will become the primary communication channel during crowdsensing process, enabling richer information exchange and cooperative problem-solving among humans, robots, and AI.

Temporal relation extraction (TRE) aims to grasp the evolution of events or actions, and thus shape the workflow of associated tasks, so it holds promise in helping understand task requests initiated by requesters in crowdsourcing systems. However, existing methods still struggle with limited and unevenly distributed annotated data. Therefore, inspired by the abundant global knowledge stored within pre-trained language models (PLMs), we propose a multi-task prompt learning framework for TRE (TemPrompt), incorporating prompt tuning and contrastive learning to tackle these issues. To elicit more effective prompts for PLMs, we introduce a task-oriented prompt construction approach that thoroughly takes the myriad factors of TRE into consideration for automatic prompt generation. In addition, we present temporal event reasoning as a supplement to bolster the model's focus on events and temporal cues. The experimental results demonstrate that TemPrompt outperforms all compared baselines across the majority of metrics under both standard and few-shot settings. A case study is provided to validate its effectiveness in crowdsourcing scenarios.

Recent years have witnessed numerous technical breakthroughs in connected and autonomous vehicles (CAVs). On the one hand, these breakthroughs have significantly advanced the development of intelligent transportation systems (ITSs); on the other hand, these new traffic participants introduce more complex and uncertain elements to ITSs from the social space. Digital twins (DTs) provide real-time, data-driven, precise modeling for constructing the digital mapping of physical-world ITSs. Meanwhile, the metaverse integrates emerging technologies such as virtual reality/mixed reality, artificial intelligence, and DTs to model and explore how to realize improved sustainability, increased efficiency, and enhanced safety. More recently, as a leading effort toward general artificial intelligence, the concept of foundation model was proposed and has achieved significant success, showing great potential to lay the cornerstone for diverse artificial intelligence applications across different domains. In this article, we explore the big models embodied foundation intelligence for parallel driving in cyber-physical-social spaces, which integrate metaverse and DTs to construct a parallel training space for CAVs, and present a comprehensive elucidation of the crucial characteristics and operational mechanisms. Beyond providing the infrastructure and foundation intelligence of big models for parallel driving, this article also discusses future trends and potential research directions, and the “6S” goals of parallel driving.

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Welcome to 2024 and Happy New Year!, The following content provides a brief summary of what we have accomplished in 2023:

Room-temperature superconductors (esp. LK-99 in the recent) have attracted extensive academic attention in recent years, both in academic circles and among the general public. This topic has spread through a number of social media channels, a plethora of contradiction in information has emerged within social networks. There arises the question on how to analyze the consistency and controversy of such scientific knowledge in the dissemination process, and how this process impact on public cognition on the scientific knowledge. In this article, taking room-temperature superconductor as example, we first designed a large language model based factual consistency detection approach to analyze the consistency between research papers and media reports. Then the consistency between media reports and comments is analyzed, by proposing a novel quantification method for media agenda-setting capability, which evaluates the agenda-setting capability of media based on emotional and positional consistencies. The results indicate that two significant deviations occur when room-temperature superconductor knowledge is spread from specialized fields to the public through the various media. One deviation is due to the specialized nature of room-temperature superconductor knowledge, leading to discrepancies between reported content and factual information in research papers. The other deviation is caused by conflicting knowledge, resulting in disparities between media reports and public perception.

Currently, the testing approach for complex systems relies on manual test case generation, resulting in problems, such as low efficiency and incomplete test case development. To address these challenges and improve the quality of test case generation for complex systems, this article proposes an intelligent method for generating test cases in parallel testing systems. Within the domain of railway systems, characterized by inherent intricacies, the centralized traffic control system serves as a pertinent example. This typical large and intricate railway transport system presents significant challenges, particularly in ensuring safety and conducting comprehensive functional testing. The design of the parallel testing system is detailed using the artificial systems, computational experiments, and parallel execution methodology, where an artificial system is built in a data-driven way that realistically replicates the test environment of a real system. Computational experiments were conducted on a test case library using the bidirectional encoder representations from transformers (BERT) model of natural language processing. BERT’s next sentence prediction task was used for the associative learning of test case pairs. Finally, the test case intelligent generation software completes the parallel execution of the final testing task, which can intelligently generate the best relevant test cases based on the results of the computational experiments of the BERT model. This approach serves as a valuable tool for test engineers, enabling them to streamline test case formulations and enhance the efficiency of testing in complex systems.