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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kaijun Deng Dezhi Zheng Jindong Xie Jinbao Wang Weicheng Xie Linlin Shen Siyang Song Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Department of Computer Science University of Exeter

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs. Our DEGSTalk employs Deformable Pre-Embedding Gaussian Fields, which dynamically adjust pre-embedding Gaussian primitives using implicit expression coefficients. This enables precise capture of dynamic facial regions and subtle expressions. Additionally, we propose a Dynamic Hair-Preserving Portrait Rendering technique to enhance the realism of long hair motions in the synthesized videos. Results show that DEGSTalk achieves improved realism and synthesis quality compared to existing approaches, particularly in handling complex facial dynamics and hair preservation. Our code is available at https://***/CVI-SZU/DEGSTalk.

关键词： Hair Training Three-dimensional displays Dynamics Signal processing Rendering (computer graphics) Noise measurement Speech processing Faces Videos

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Digital Human Techniques for Education Reform 24

Digital Human Techniques for Education Reform

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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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Profit Maximization of Delay-Sensitive, Differential Accuracy Inference Services in Mobile Edge Computing

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IEEE Transactions on Mobile Computing 2025年

作者： Zhang, Yuncan Liang, Weifa Xu, Zichuan Jia, Xiaohua Yang, Yuanyuan City University of Hong Kong Department of Computer Science Hong Kong Hong Kong Dalian University of Technology School of Software Dalian China Stony Brook University Department of Electrical and Computer Engineering Stony BrookNY United States

The integration of Artificial Itelligence (AI) and edge computing has sparked significant interest in edge inference services. In this paper, we consider delay-sensitive, differential accuracy inference services in a Mobile Edge Computing (MEC) network while meeting user stringent delay and accuracy requirements. We formulate two novel profit maximization problems under static and dynamic settings of service request arrivals, with the aim of maximizing the accumulative profit of admitted requests. We assign differential accuracy service requests to the corresponding resolution instances of their requested service models, assuming that each resolution instance can serve up to L≥ 1 the same type of service requests. Since the profit maximization problem is NP-hard, we first formulate an Integer Linear Program (ILP) solution if the problem size is small or medium;otherwise, we devise a constant randomized algorithm with high probability. Then, we consider dynamic service request admissions without the knowledge of future request arrivals for a given finite time horizon, for which we develop a simple yet effective prediction mechanism to accurately predict the number of different resolution instances of each model needed, and pre-deploy the predicted number of resolution instances into cloudlets to reduce instantiating delays. We then devise an online algorithm with a provable competitive ratio for the dynamic profit maximization problem by leveraging the primal-dual dynamic updating technique. Finally, we evaluate the performance of the proposed algorithms by simulations. The simulation results demonstrate that the proposed algorithms are promising. © 2002-2012 IEEE.

关键词： Mobile edge computing

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Pattern recognition in programming to support the evaluation of Computational Thinking

Pattern recognition in programming to support the evaluation...

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IEEE World engineering Education Conference (EDUNINE)

作者： Steven Pacheco-Portuguez Antonio Gonzalez-Torres Lilliana Sancho-Chavarria Ignacio Trejos-Zelaya Jorge Monge-Fallas School of Computing Costa Rica Institute of Technology Cartago Costa Rica School of Computer Engineering Costa Rica Institute of Technology Cartago Costa Rica School of Software Engineering & School of Computing Universidad CENFOTEC & Costa Rica Institute of Technology San José & Cartago Costa Rica School of Mathematics Costa Rica Institute of Technology Cartago Costa Rica

ISBN: (数字)9798331542788

ISBN: (纸本)9798331542795

Computational thinking is a high-level skill that involves both critical and creative thinking. This research proposes an advanced method for analyzing source code to assess computational thinking skills in elementary school students. We present a large-scale approach that examines source code from various programming exercises using a generalized abstract syntax tree, enabling language-independent analysis. Clustering techniques are applied to identify different levels of computational thinking development in areas such as parallelism, data representation, abstraction and decomposition, control flow, and programming structure. The results of the automated assessment provide insights into students' computational thinking abilities across diverse populations, highlighting opportunities for improving the educational framework.

关键词： Codes Source coding Data visualization Syntactics Parallel processing Pattern recognition software measurement engineering education Programming profession

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Sketching Very Large-scale Dynamic Attributed Networks More Practically 25

Sketching Very Large-scale Dynamic Attributed Networks More ...

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34th ACM Web Conference, WWW 2025

作者： Wu, Wei Li, Shiqi Chen, Ling Li, Fangfang Luo, Chuan School of Computer Science and Engineering Central South University Changsha China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia School of Software Beihang University Beijing China

ISBN: (纸本)9798400712746

Real-world networks, particularly those in web and social media, are dynamic with evolving node attributes and structures, often involving billions of nodes and edges. Dynamic attributed network embedding is a powerful tool for capturing these changes, enabling data owners and problem owners to better understand interactions and trends for more effective engagement and decision-making. While some existing algorithms are capable of handling very large-scale dynamic attributed networks with billions of nodes and edges, they often suffer from accuracy loss or high computational overhead. In this paper, we propose a practical and sustainable framework of sketching very large-scale dynamic attributed networks called VLS2ketch, which incorporates incremental embedding updates alongside storage-efficient, binarized representation of both node attributes and topological variations. By the sparse random projection technique in an incremental update manner, VLS2ketch significantly reduces the energy-intensive computational workload while maintaining accuracy. Also, we introduce an information decay mechanism, which adapts to temporally varying topologies and node attributes. This mechanism ensures that outdated information gradually diminishes over time. Extensive experiments on real-world very large-scale datasets demonstrate that our proposed VLS2ketch method delivers comparable embedding quality against the state-of-the-art learning-based competitors with dramatically reduced runtime. © 2025 Copyright held by the owner/author(s).

关键词： network embedding

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CoSDH: Communication-Efficient Collaborative Perception via Supply-Demand Awareness and Intermediate-Late Hybridization

arXiv

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arXiv 2025年

作者： Xu, Junhao Zhang, Yanan Cai, Zhi Huang, Di State Key Laboratory of Complex and Critical Software Environment Beihang University Beijing China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Multi-agent collaborative perception enhances perceptual capabilities by utilizing information from multiple agents and is considered a fundamental solution to the problem of weak single-vehicle perception in autonomous driving. However, existing collaborative perception methods face a dilemma between communication efficiency and perception accuracy. To address this issue, we propose a novel communication-efficient collaborative perception framework based on supply-demand awareness and intermediate-late hybridization, dubbed as CoSDH. By modeling the supply-demand relationship between agents, the framework refines the selection of collaboration regions, reducing unnecessary communication cost while maintaining accuracy. In addition, we innovatively introduce the intermediate-late hybrid collaboration mode, where late-stage collaboration compensates for the performance degradation in collaborative perception under low communication bandwidth. Extensive experiments on multiple datasets, including both simulated and real-world scenarios, demonstrate that CoSDH achieves state-of-the-art detection accuracy and optimal bandwidth tradeoffs, delivering superior detection precision under real communication bandwidths, thus proving its effectiveness and practical applicability. The code will be released at https://***/Xu2729/CoSDH. Copyright © 2025, The Authors. All rights reserved.

关键词： Bandwidth

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A robust TDoA-based localization and tracking method designed for intelligent recommender systems

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Information Sciences 2025年 717卷

作者： Mei, Hong-Yuan Zhou, Zhi Shen, Fu-Rao Zhao, Jian State Key Laboratory for Novel Software Technology Nanjing China Department of Computer Science and Technology Nanjing University China School of Artificial Intelligence Nanjing University China School of Electronic Science and Engineering Nanjing University China

In today's fast-paced society, the demand for intelligent Recommender Systems(RS) is increasing day by day. Users may hope that RSs can take their real-time situation into consideration to help them make timely decisions. To fulfill this goal, RSs need to accurately understand users' information, especially their real-time location. Therefore, localization of the users is very helpful in building an intelligent RS. Meanwhile, great efforts have been made to deal with localization based on time-difference-of-arrival (TDoA) measurements in the past decades. It is widely used in applications such as radar, sonar, sensor networks, and mobile communications. However, such a task is still challenging in complex real-world scenarios where large-scale noise and none-light-of-sight (NLoS) occur. To address this challenge, we propose a noise-tolerant method consisting of two stages: the Robust Multilateration Solver (RMS) and the Restricted Particle Filter (RPF). In the first stage, anomaly detection is adopted to eliminate outliers, and NLoS effects are mitigated by a data augmentation strategy. Then a rough estimate of the current position is obtained by combining two modified multilateration algorithms. In the second stage, the rough estimate is passed through an enhanced particle filter in order to reduce its bias caused by heavy Gaussian noise and smooth the motion trajectory. Meanwhile, localization rationality is guaranteed by confining the position to a predefined area. Sufficient testing in real indoor environments and simulation results demonstrate the effectiveness of our method and reveal its fitness for various industrial fields. © 2025

关键词： Indoor localization Recommender systems Source tracking TDoA

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Hierarchical Spatiotemporal Attention Network for Fine-grained Brain Cognitive State Recognition

Hierarchical Spatiotemporal Attention Network for Fine-grain...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yike Wu Ning An Zixuan Zeng YouYong Kong School of Computer Science and Engineering Southeast University Nanjing Ministry of Education Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications(Southeast University) China School of Software Engineering Southeast University Nanjing

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Brain cognitive state recognition based on functional Magnetic Resonance Imaging(fMRI) can capture brain functional activities under different tasks and help understand the neural mechanisms of the brain, which has always been one of the focuses of neuroscience research. Different from the prediction of the brain cognitive domain, the prediction of brain fine-grained cognitive state is based on each moment in the process of executing the task. Therefore, it is necessary to extract more fine-grained effective information. Existing studies focus on modeling and classifying the complete time series, ignoring the brain activity state at each time. So we propose a hierarchical spatiotemporal attention network(FineBrainNet) to recognize fine-grained brain cognitive state. Guided by coarse-grained cognitive domain labels, we trained different sub-modules for fine-grained states under each cognitive domain to capture relevant cognitive state changes more accurately in specific task. Extensive experiments on the HCP-Task dataset show that FineBrainNet can achieve accurate prediction of fine-grained brain cognitive state.

关键词： Accuracy Neuroscience Image recognition Time series analysis Magnetic resonance Speech recognition Signal processing Brain modeling Spatiotemporal phenomena Speech processing

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Deep Reinforcement Learning Based Joint Optimization of Task Migration and Resource Allocation for Mobile Edge Computing

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IEEE Internet of Things Journal 2025年

作者： Li, Juncai Jiang, Qi Leung, Victor C. M. Ma, Zhuo Abrokwa, Kofi Kwarteng Hefei University of Technology School of Electrical Engineering and Automation Hefei230009 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Task migration and resource allocation are essential to integrate available resources for improving the efficiency of mobile edge computing to support various computation-intensive and delay-sensitive Internet of Things applications, which cooperative optimization has yet been well addressed to achieve desirable performance. In this paper, a deep reinforcement learning (DRL) based adaptive cooperative optimization strategy is presented to fill this gap. Task migration and resource allocation are jointly employed to adaptively integrate available resources within cooperative edge nodes for processing various randomly offloaded tasks. The policy optimization to minimize the system cost and task dropout rates is formulated as Markov decision processes. A DRL algorithm that enhances deep deterministic policy gradient with a dual experience pool is proposed to jointly optimize the task migration and resource allocation in unknown stochastic application environments. Simulation experiments have been conducted to evaluate the performance of the presented strategy, and the results illustrate that it increases system rewards by 17.9 61.5% and reduces task dropout rate by 5.2 31.3% comparing with benchmarks. © 2025 IEEE.

关键词： Deep reinforcement learning

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Semantic-Guided Global-Local Collaborative Networks for Lightweight Image Super-Resolution

arXiv

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arXiv 2025年

作者： Fan, Wanshu Wang, Yue Wang, Cong Zhang, Yunzhe Wang, Wei Zhou, Dongsheng The National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian University Dalian China Hong Kong School of Cyber Science and Technology Sun Yat-Sen University Shen Zhen China

Single-Image Super-Resolution (SISR) plays a pivotal role in enhancing the accuracy and reliability of measurement systems, which are integral to various vision-based instrumentation and measurement applications. These systems often require clear and detailed images for precise object detection and recognition. However, images captured by visual measurement tools frequently suffer from degradation, including blurring and loss of detail, which can impede measurement accuracy. As a potential remedy, we in this paper propose a Semantic-Guided Global-Local Collaborative Network (SGGLC-Net) for lightweight SISR. Our SGGLC-Net leverages semantic priors extracted from a pre-trained model to guide the super-resolution process, enhancing image detail quality effectively. Specifically, we propose a Semantic Guidance Module that seamlessly integrates the semantic priors into the super-resolution network, enabling the network to more adeptly capture and utilize semantic priors, thereby enhancing image details. To further explore both local and non-local interactions for improved detail rendition, we propose a Global-Local Collaborative Module, which features three Global and Local Detail Enhancement Modules, as well as a Hybrid Attention Mechanism to work together to efficiently learn more useful features. Our extensive experiments show that SGGLC-Net achieves competitive PSNR and SSIM values across multiple benchmark datasets, demonstrating higher performance with the multi-adds reduction of 12.81G compared to state-of-the-art lightweight super-resolution approaches. These improvements underscore the potential of our approach to enhance the precision and effectiveness of visual measurement systems. Codes are at https://***/fanamber831/SGGLC-Net. © 2025, CC BY.

关键词： Object detection

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