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7th International Conference on Educational Technology Management, ICETM 2024

作者： Guo, Peirong Zhang, Qi Tian, Chunwei Xue, Wanli Feng, Xiaocheng School of Software Northwestern Polytechnical University Shaanxi Xi’an China School of Economics and Management Harbin Institute of Technology at Weihai Shandong Weihai China Shenzhen Research Institute of Northwestern Polytechnical University Northwestern Polytechnical University Guangdong Shenzhen China School of Computer Science and Engineering Tianjin University of Technology Tianjin China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology Heilongjiang Harbin China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Guangdong Shenzhen China

ISBN: (纸本)9798400717468

The rapid evolution of artificial intelligence, big data, and generative AI models has ushered in significant transformations across various sectors, including education. Digital Human Technique, an innovative technology grounded in advanced computer science and artificial intelligence, is reshaping educational paradigms by enabling virtual humans to simulate human behavior, express emotions, and interact with users. This paper explores the application of Digital Human Technique in education reform, focusing on creating immersive, intelligent classroom experiences that foster meaningful interactions between teachers and students. We define Digital Human Technique and delve into its key technical components such as character modeling and rendering, natural language processing, computer vision, and augmented reality technologies. Our methodology involves analyzing the role of educational digital humans created through these technologies, assessing their impact on educational processes, and examining various application scenarios in educational reform. Results indicate that Digital Human Technique significantly enhances the learning experience by enabling personalized teaching, increasing engagement, and fostering emotional connections. Educational digital humans serve as virtual teachers, interactive learning aids, and facilitators of emotional interaction, effectively addressing the challenges of traditional educational methods. They also promote a deeper understanding of complex concepts through simulated environments and interactive digital content. © 2024 Copyright held by the owner/author(s).

关键词： Digital elevation model

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Novel quantum secret image-sharing scheme

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Chinese Physics B 2019年第4期28卷 61-69页

作者： Gao-Feng Luo Ri-Gui Zhou Wen-Wen Hu College of Information Engineering Shanghai Maritime University College of Information Engineering Shaoyang University Research Center of Intelligent Information Processing and Quantum Intelligent Computing

In this paper, we propose a novel quantum secret image-sharing scheme which constructs m quantum secret images into m+1 quantum share images. A chaotic image generated by the logistic map is utilized to assist in the construction of quantum share images first. The chaotic image and secret images are expressed as quantum image representation by using the novel enhanced quantum representation. To enhance the confidentiality, quantum secret images are scrambled into disordered images through the Arnold transform. Then the quantum share images are constructed by performing a series of quantum swap operations and quantum controlled-NOT operations. Because all quantum operations are invertible, the original quantum secret images can be reconstructed by performing a series of inverse operations. Theoretical analysis and numerical simulation proved both the security and low computational complexity of the scheme, which has outperformed its classical counterparts. It also provides quantum circuits for sharing and recovery processes.

关键词： quantum image processing secret image sharing quantum computation quantum circuit

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Modality Unified Attack for Omni-Modality Person Re-Identification

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IEEE Transactions on information Forensics and Security 2025年

作者： Bian, Yuan Liu, Min Yi, Yunqi Wang, Xueping Ma, Yunfeng Wang, Yaonan Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan Changsha China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Changsha China

Deep learning based person re-identification (re-id) models have been widely employed in surveillance systems. Recent studies have demonstrated that black-box single-modality and cross-modality re-id models are vulnerable to adversarial examples (AEs), leaving the robustness of multi-modality re-id models unexplored. Due to the lack of knowledge about the specific type of model deployed in the target black-box surveillance system, we aim to generate modality unified AEs for omni-modality (single-, cross- and multi-modality) re-id models. Specifically, we propose a novel Modality Unified Attack method to train modality-specific adversarial generators to generate AEs that effectively attack different omni-modality models. A multi-modality model is adopted as the surrogate model, wherein the features of each modality are perturbed by metric disruption loss before fusion. To collapse the common features of omnimodality models, Cross Modality Simulated Disruption approach is introduced to mimic the cross-modality feature embeddings by intentionally feeding images to non-corresponding modality-specific subnetworks of the surrogate model. Moreover, Multi Modality Collaborative Disruption strategy is devised to facilitate the attacker to comprehensively corrupt the informative content of person images by leveraging a multi modality feature collaborative metric disruption loss. Extensive experiments show that our MUA method can effectively attack the omni-modality re-id models, achieving 55.9%, 24.4%, 49.0% and 62.7% mean mAP Drop Rate, respectively. © 2005-2012 IEEE.

关键词： Modality unified attack Multi Modality Collaborative Disruption Omni-Modality re-id

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Any-Angle Routing Algorithm for Microfluidic Biochips Driven by Flow Path

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Jisuanji Yanjiu yu Fazhan/Computer research and Development 2025年第4期62卷 978-988页

作者： Youlin, Pan Shuai, Guo Xing, Huang Genggeng, Liu College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Fuzhou University Ministry of Education Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China

Continuous-flow microfluidic biochips (CFMBs) have become a hot research topic in recent years due to their ability to perform biochemical assays automatically and efficiently. For the first time, PathDriver+ takes the requirements of the actual fluid transportation into account in the design process of CFMBs and implements the actual fluid transport and removal, and plans separate flow paths for each transport task, which have been neglected in previous work. However, PathDriver+ does not take full advantage of the flexibility of CFMBs routing because it only considers the optimization of flow channel length for the global routing in the mesh model, except for the detailed routing. In addition, PathDriver+ only considers the X architecture, while the existing work shows that the any-angle routing can utilize the routing resources more efficiently and shorten the flow channel length. To address the above issues, we propose a flow path-driven arbitrary angle routing algorithm, which can improve the utilization of routing resources and reduce the flow channel length while considering the actual fluid transportation requirements. The proposed algorithm constructs a search graph based on constrained Delaunay triangulation to improve the search efficiency of routing solutions while ensuring the routing quality. Then, a Dijkstra-based flow path routing method is used on the constructed search graph to generate a routing result with a short channel length quickly. In addition, in the routing process, channel reuse strategy and intersection optimization strategy are proposed for the flow path reuse and intersection number optimization problems, respectively, to further improve the quality of routing results. The experimental results show that compared with the latest work PathDriver+, the length of channels, the number of ports used, and the number of channel intersections are significantly reduced by 33.21%, 11.04%, and 44.79%, respectively, and the channel reuse rate is i

关键词： High level synthesis

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Control Logic Routing for Continuous-Flow Microfluidic Biochips Based on Deep Reinforcement Learning

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Jisuanji Yanjiu yu Fazhan/Computer research and Development 2025年第4期62卷 950-962页

作者： Cai, Huayang Huang, Xing Liu, Genggeng College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Fuzhou University Ministry of Education Fuzhou350116 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China

With the advancement of electronic design automation, continuous-flow microfluidic biochips have become one of the most promising platforms for biochemical experiments. This chip manipulates fluid samples in milliliters or nanoliters by utilizing internal microvalves and microchannels, and thus automatically performs basic biochemical experiments, such as mixing and detection. To achieve the correct bioassay function, the microvalves deployed inside the chip are usually managed by a multiplexer-based control logic, and valves receive control signals from a core input through the control channel for accurate switching. Since biochemical reactions typically require high sensitivity, the length of control paths connecting each valve needs to be reduced to ensure immediate signal propagation, and thus to reduce the signal propagation delay. In addition, to reduce the fabrication cost of chips, a vital issue to be addressed in the logic architecture design is how to effectively reduce the total channel length within the control logic. To address the above issues, we propose a deep reinforcement learning-based control logic routing algorithm to minimize the signal propagation delay and total control channel length, thereby automatically constructing an efficient control channel network. The algorithm employs the dueling deep Q-network architecture as the agent of the deep reinforcement learning framework to evaluate the tradeoff between signal propagation delay and total channel length. Besides, the diagonal channel routing is implemented for the first time for control logic, thus fundamentally improving the efficiency of valve switching operations and reducing the fabrication cost of the chip. The experimental results demonstrate that the proposed algorithm can effectively construct a high-performance and low-cost control logic architecture. © 2025 Science Press. All rights reserved.

关键词： Deep reinforcement learning

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Human-like conceptual representations emerge from language prediction

arXiv

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arXiv 2025年

作者： Xu, Ningyu Zhang, Qi Du, Chao Luo, Qiang Qiu, Xipeng Huang, Xuanjing Zhang, Menghan School of Computer Science Fudan University Shanghai China Institute of Modern Languages and Linguistics Fudan University Shanghai China Shanghai Key Laboratory of Intelligent Information Processing Shanghai China Research Institute of Intelligent Complex Systems Fudan University Shanghai China Shanghai Collaborative Innovation Center of Intelligent Visual Computing Shanghai China Ministry of Education Key Laboratory of Contemporary Anthropology Fudan University Shanghai China

People acquire concepts through rich physical and social experiences and use them to understand the world. In contrast, large language models (LLMs), trained exclusively through next-token prediction over language data, exhibit remarkably human-like behaviors. Are these models developing concepts akin to humans, and if so, how are such concepts represented and organized? To address these questions, we reframed the classic reverse dictionary task to simulate human concept inference in context and investigated the emergence of human-like conceptual representations within LLMs. Our results demonstrate that LLMs can flexibly derive concepts from linguistic descriptions in relation to contextual cues about other concepts. The derived representations converged towards a shared, context-independent structure that effectively predicted human behavior across key psychological phenomena, including computation of similarities, categories and semantic scales. Moreover, these representations aligned well with neural activity patterns in the human brain, even in response to visual rather than linguistic stimuli, providing evidence for biological plausibility. These findings establish that structured, human-like conceptual representations can naturally emerge from language prediction without real-world grounding. More broadly, our work positions LLMs as promising computational tools for understanding complex human cognition and paves the way for better alignment between artificial and human intelligence. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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ExpTopo: Explicit Topological Modeling for Bronchus Segmentation

ExpTopo: Explicit Topological Modeling for Bronchus Segmenta...

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IEEE International Symposium on Biomedical Imaging

作者： Mingyue Zhao Xiaolan Qiu S.Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advance Research USTC China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute China Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China (USTC) China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Airway extraction is paramount in the early diagnosis and treatment of respiratory diseases. As a tree-like structure, both topological-aware learning and voxel-wise classification are equally crucial for the airway. However, existing methods demonstrate insufficient topological learning, emphasizing only the supervision of individual key topological points. Consequently, this paper proposes a Explicit Topological Modeling (ExpTopo) approach to aid airway segmentation. It explicitly introduces topological metric space learning based on semantic segmentation, enhancing the model's structural perception by implementing global skeleton-level sparse topological learning (STL) and local voxel-level dense topological perception (DTP). Extensive experimental results demonstrate that the algorithm achieves competitive performance at both the topological and voxel levels. Code will be available in https://***/MorineZ/ExpTopo.

关键词： Codes Atmospheric modeling Semantic segmentation Pulmonary diseases Biological system modeling Extraterrestrial measurements Biomedical imaging

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quantum color image scaling based on bilinear interpolation

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Chinese Physics B 2023年第5期32卷 235-244页

作者：高超周日贵李鑫 College of Information Engineering Shanghai Maritime UniversityShanghai 201306China Research Center of Intelligent Information Processing and Quantum Intelligent Computing Shanghai 201306China

As a part of quantum image processing, quantum image scaling is a significant technology for the development of quantum computation. At present, most of the quantum image scaling schemes are based on grayscale images, with relatively little processing for color images. This paper proposes a quantum color image scaling scheme based on bilinear interpolation, which realizes the 2^(n_(1)) × 2^(n_(2)) quantum color image scaling. Firstly, the improved novel quantum representation of color digital images(INCQI) is employed to represent a 2^(n_(1)) × 2^(n_(2)) quantum color image, and the bilinear interpolation method for calculating pixel values of the interpolated image is presented. Then the quantum color image scaling-up and scaling-down circuits are designed by utilizing a series of quantum modules, and the complexity of the circuits is ***, the experimental simulation results of MATLAB based on the classical computer are given. The ultimate results demonstrate that the complexities of the scaling-up and scaling-down schemes are quadratic and linear, respectively, which are much lower than the cubic function and exponential function of other bilinear interpolation schemes.

关键词： quantum image processing image scaling quantum image representation bilinear interpolation

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Rethinking attention mechanism for enhanced pedestrian attribute recognition

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Neurocomputing 2025年 639卷

作者： Wu, Junyi Huang, Yan Gao, Min Niu, Yuzhen Chen, Yuzhong Wu, Qiang Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fujian Fuzhou350108 China Engineering Research Center of BigData Intelligence Ministry of Education Fujian Fuzhou350108 China Australian Artificial Intelligence Institute University of Technology Sydney SydneyNSW2007 Australia Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou University Fujian Fuzhou350108 China School of Electrical and Data Engineering University of Technology Sydney SydneyNSW2007 Australia

Pedestrian Attribute Recognition (PAR) plays a crucial role in various computer vision applications, demanding precise and reliable identification of attributes from pedestrian images. Traditional PAR methods, though effective in leveraging attention mechanisms, often suffer from the lack of direct supervision on attention, leading to potential overfitting and misallocation. This paper introduces a novel and model-agnostic approach, Attention-Aware Regularization (AAR), which rethinks the attention mechanism by integrating causal reasoning to provide direct supervision of attention maps. AAR employs perturbation techniques and a unique optimization objective to assess and refine attention quality, encouraging the model to prioritize attribute-specific regions. Our method demonstrates significant improvement in PAR performance by mitigating the effects of incorrect attention and fostering a more effective attention mechanism. Experiments on standard datasets showcase the superiority of our approach over existing methods, setting a new benchmark for attention-driven PAR models. © 2025 The Authors

关键词： Perturbation techniques

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D-FGNAE: Decentralized Federated Graph Normalized AutoEncoder 19th

D-FGNAE: Decentralized Federated Graph Normalized AutoEncode...

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19th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2024

作者： Liang, Yuting Cai, Weixin Guo, Kun College of Computer and Data Science Fuzhou University Fuzhou350108 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China

ISBN: (纸本)9789819623723

Graphs widely exist in real-world, and Graph Neural Networks (GNNs) have exhibited exceptional efficacy in graph learning in diverse fields. With the strengthening of data privacy protection worldwide in recent years, Federated graph neural networks (FedGNNs) have gained increasing attention in academia and industry owing to their ability to train the model in a collaborative manner while complying with the privacy protection regulations. However, in federated learning, the non-independent and identically distributed (non-IID) problem of local data possessed by multiple participants can significantly undermine model accuracy. We propose a new Decentralized Federated Graph Normalized AutoEncoder (D-FGNAE). First, the model is designed as a decentralized federated learning framework with dynamically assigned tripartite roles. This design eliminates the fixed server role found in traditional federated learning, enhances system fault tolerance, avoids single points of failure, and protects model privacy. Second, the splitting and correcting of calculation by layer in the model, along with the special design of the normalization layer, effectively tackle the non-IID problem in both the structural and attribute aspects. Experimental results on real-world networks demonstrate the effectiveness of D-FGNAE, which can achieve nearly the same accuracy as the centralized model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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