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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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Semi-Asynchronous Federated Split Learning for computing-Limited Devices in Wireless Networks

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IEEE Transactions on Wireless Communications 2025年

作者： Ao, Huiqing Tian, Hui Ni, Wanli Nie, Gaofeng Niyato, Dusit Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Tsinghua University Department of Electronic Engineering Beijing100084 China Nanyang Technological University College of Computing and Data Science Singapore

The rapid evolution of edge computing and artificial intelligence (AI) paves the way for pervasive intelligence in the next-generation network. As a hybrid training paradigm, federated split learning (FSL) leverages data and model parallelism to enhance training efficiency. However, existing FSL encounters unacceptable waiting latency due to device heterogeneity and synchronous model aggregation. To address this issue, we propose a semi-asynchronous FSL (SAFSL) framework that enables personalized model splitting and aperiodic model aggregation. We derive the convergence upper bound by considering factors such as the number of devices, training iterations, and data heterogeneity. To minimize the long-term average training latency while maintaining high energy efficiency in resource-constrained wireless networks, we formulate a stochastic mixed-integer nonlinear programming problem. By decomposing it into multiple sub-problems in each round, we propose a Lyapunov-based alternating optimization algorithm to solve it in an online manner. Numerical results demonstrate that our SAFSL achieves faster convergence with reduced communication overhead while maintaining high prediction performance under non-independent and identically distributed data, outperforming state-of-the-art benchmarks. Moreover, our algorithm achieves a low training latency, highlighting its superior performance and effectiveness. © 2002-2012 IEEE.

关键词： Federated learning

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Large-Scale Network Adaptive Situation Awareness Method in Spatio-Temporal Dimension 1

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1st International Artificial Intelligence Conference, IAIC 2023

作者： Zhang, Hongbin Xu, Ying Liu, Bin Zhao, Dongmei Bai, Yikang School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050000 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Science Shijiazhuang China

ISBN: (数字)9789819713325

ISBN: (纸本)9789819713318

In large-scale networks, the state space is exploding and changing dynamically. This leads to difficulties in collecting and analyzing situational awareness data, so we construct an adaptive situational awareness model in spatio-temporal dimensions. In the spatial dimension, vulnerabilities’s threats are assessed through attack graphs combined with Shapley values. At the same time, vulnerability threats are dynamically quantified by updating the status node reachability probability in real time. In the temporal dimension, a game model is established by analyzing vulnerability attack graph nodes to dynamically adjust the observation frequency of high-risk vulnerabilities, focusing on the safety status characteristics of high-risk assets. Experimental results show that our proposal integrates the security features of both space and time dimensions. This method can better focus on high-risk vulnerabilities and accurately reflect the dynamic changes in the network security situation, ensuring timeliness and accuracy in network security detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network security

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data-Driven Intersection Safety Evaluation Methodology and Analysis 23

Data-Driven Intersection Safety Evaluation Methodology and A...

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23rd COTA International Conference of Transportation Professionals: Emerging data-Driven Sustainable Technological Innovation in Transportation, CICTP 2023

作者： Wu, Pingping Lu, Guangquan Liu, Miaomiao Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang Univ. Beijing China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang Univ. Beijing China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Southeast Univ. Nanjing China

ISBN: (数字)9780784485040

ISBN: (纸本)9780784485040

With the development of information and sensing technologies, a wide variety of intersection traffic data is available. The purpose of this paper is to make full use of video and commercial vehicle trajectory data to deeply explore the intersection safety status. In this paper, based on different sample sizes, different sampling frequencies, and different accuracies of data, the safety analysis of intersections is carried out from micro and macro aspects based on post-encroachment time (PET) and vehicle deceleration behavior, respectively. On the one hand, we use the full-sample and high-precision trajectory data extracted from video to analyze the safety of each region of intersection based on PET. On the other hand, using the trajectory data with low penetration rate and low sampling frequency extracted from the positioning data of the online car-hailing, a distribution fit, and non-parametric test are conducted for the vehicle deceleration behavior, and then the overall safety state of the intersection is evaluated. The results can provide ideas for the identification of intersection risk points and the application of different kinds of data on intersection safety evaluation. © ASCE.

关键词： Commercial vehicles

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PediaBench: A Comprehensive Chinese Pediatric dataset for Benchmarking Large Language Models

arXiv

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arXiv 2024年

作者： Zhang, Qian Chen, Panfeng Li, Jiali Feng, Linkun Liu, Shuyu Zhao, Heng Chen, Mei Li, Hui Wang, Yanhao State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China Guizhou Engineering Laboratory for Advanced Computing and Medical Information Services Guiyang China College of Big Data and Internet Shenzhen Technology University Shenzhen China School of Data Science and Engineering East China Normal University Shanghai China

The emergence of Large Language Models (LLMs) in the medical domain has stressed a compelling need for standard datasets to evaluate their question-answering (QA) performance. Although there have been several benchmark datasets for medical QA, they either cover common knowledge across different departments or are specific to another department rather than pediatrics. Moreover, some of them are limited to objective questions and do not measure the generation capacity of LLMs. Therefore, they cannot comprehensively assess the QA ability of LLMs in pediatrics. To fill this gap, we construct PediaBench, the first Chinese pediatric dataset for LLM evaluation. Specifically, it contains 4,117 objective questions and 1,632 subjective questions spanning 12 pediatric disease groups. It adopts an integrated scoring criterion based on different difficulty levels to thoroughly assess the proficiency of an LLM in instruction following, knowledge understanding, clinical case analysis, etc. Finally, we validate the effectiveness of PediaBench with extensive experiments on 20 open-source and commercial LLMs. Through an in-depth analysis of experimental results, we offer insights into the ability of LLMs to answer pediatric questions in the Chinese context, highlighting their limitations for further improvements. Our code and data are published anonymously at https://***/ACMISLab/PediaBench. © 2024, CC BY-SA.

关键词： Question answering

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ZombieCoin3.0: On the Looming of a Novel Botnet Fortified by Distributed Ledger Technology and Internet of Things 23

ZombieCoin3.0: On the Looming of a Novel Botnet Fortified by...

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23rd IEEE International Conference on High Performance computing and Communications, 7th IEEE International Conference on data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Gao, Haoyu Li, Leixiao Lin, Hao Chang, Xiangyang Wan, Jianxiong Li, Jie Zhu, Fangyuan College of Data Science and Application Inner Mongolia University of Technology China Inner Mongolia Autonomous Reg. Eng. and Technol. Research Center of Big Data Based Software Service China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781665494571

P2P Botnet is famous for the resilience against termination. However, its dependence on Neighbor List (NL) makes it susceptible to infiltration and poison, also leading to a dearth of adequate protection of Botmaster's identity. So as to overcome these weaknesses, the novel botnet infrastructure combined with blockchain emerges. But due to some flaws such like high latency and expensive transactions fees on blockchain, these new schemes can't satisfy the need of constructing hyper large-scale botnet in practice. We propose ZombieCoin3.0, a Botnet underlying overlay leveraging IoT devices to be maintainers and integrated with Distributed Ledger Technology (DLT), which is able to deploy bots fully on blockchain. And it is versatile for file transfer, data feedback and other command and control scenarios that many contemporaries are impotent to cope with. Our proposal eliminates the P2P botnets' dependence on NL and the bottlenecks that other blockchain combined botnets have. Experiments' results authenticated that the effectiveness of our proposal as well as low latency of communication and nearly zero overhead of cost other comparatives unable to attain are achieved. © 2021 IEEE.

关键词： Costs Distributed ledger Botnet Blockchains Proposals Internet of Things Low latency communication

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CiRLExplainer: Causality-Inspired Explainer for Graph Neural Networks via Reinforcement Learning

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IEEE Transactions on Neural Networks and Learning Systems 2025年第6期36卷 9970-9984页

作者： Wenya Hu Jia Wu Quan Qian School of Computer Engineering and Science Shanghai University Shanghai China School of Computing Macquarie University Sydney NSW Australia School of Computer Engineering and Science the Center of Materials Informatics and Data Science Materials Genome Institute the Key Laboratory of Silicate Cultural Relics Conservation Ministry of Education and Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China

In this article, we propose a new graph neural network (GNN) explainability model, CiRLExplainer, which elucidates GNN predictions from a causal attribution perspective. Initially, a causal graph is constructed to analyze the causal relationships between the graph structure and GNN predicted values, identifying node attributes as confounding factors between the two. Subsequently, a backdoor adjustment strategy is employed to circumvent these confounders. Additionally, since the edges within the graph structure are not independent, reinforcement learning is incorporated. Through a sequential selection process, each step evaluates the combined effects of an edge and the previous structure to generate an explanatory subgraph. Specifically, a policy network predicts the probability of each candidate edge being selected and adds a new edge through sampling. The causal effect of this action is quantified as a reward, reflecting the interactivity among edges. By maximizing the policy gradient during training, the reward stream of the edge sequence is optimized. The CiRLExplainer is versatile and can be applied to any GNN model. A series of experiments was conducted, including accuracy (ACC) analysis of the explanation results, visualization of the explanatory subgraph, and ablation studies considering node attributes as confounding factors. The experimental results demonstrate that our model not only outperforms current state-of-the-art explanation techniques, but also provides precise semantic explanations from a causal perspective. Additionally, the experiments validate the rationale for considering node attributes as confounding factors, thereby enhancing the explanatory power and ACC of the model. Notably, across different datasets, our explainer achieved improvements over the best baseline models in the ACC-area under the curve (AUC) metrics by 5.89%, 5.69%, and 4.87%, respectively.

关键词： Predictive models Graph neural networks Reinforcement learning Accuracy Training Prediction algorithms Analytical models Sustainable development Focusing data science

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Multi-scale cyclical similarity prototype refinement for few-shot breast ultrasound image segmentation

Multi-scale cyclical similarity prototype refinement for few...

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International Conference on Signal Processing Proceedings (ICSP)

作者： Yingfeng Ou Xing Yang Jian Zhang Caiqing Jian Lihui Wang Engineering Research Center of Text Computing Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Few-shot learning based methods can address the reliance on large-scale labeled samples in current breast tumor segmentation. However, previous methods typically rely on a few support samples to extract abstract, coarse-grained semantic prototypes, without considering the interaction between support and query data. In this study, we aim to provide each query image with relevant and information-rich support information. By integrating the difficult point selection module and the multi-scale cyclical prototype refinement module, we progressively refine coarse-grained support prototypes into more informative, fine-grained class prototypes, thereby better matching the highly variable breast ultrasound query data. Experiments on two datasets demonstrate that our proposed multi-scale cyclical similarity prototype refinement network (CSPRNet) achieves remarkable improvements compared to previous state-of-the-art methods. Specifically, using ResNet50 as the backbone, we achieve 57.93%/57.92% and 57.58%/59.24% mIoU for 1-shot and 5-shot segmentation, respectively, on public and private datasets, outperforming the suboptimal method by 8.18%/2.55% and 2.77%/4.48%.

关键词： Image segmentation Ultrasonic imaging Breast tumors Semantics Prototypes Signal processing data mining Few shot learning Residual neural networks

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A blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social internet of vehicles

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Digital Communications and Networks 2022年第6期8卷 976-983页

作者： Ziming Liu Yang Xu Cheng Zhang Haroon Elahi Xiaokang Zhou College of Information Science and Engineering Northeastern UniversityShenyang110819China College of Computer Science and Electronic Engineering Hunan UniversityChangsha410082China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province Zhejiang UniversityHangzhou310058China Department of Computing Science Umea UniversityUmea90187Sweden Faculty of Data Science Shiga UniversityHikone522-8522Japan RIKEN Center for Advanced Intelligence Project RIKENTokyo103-0027Japan

Social Internet of Vehicles(SIoV)falls under the umbrella of social Internet of Things(IoT),where vehicles are socially connected to other vehicles and roadside units that can reliably share information and services with other social entities by leveraging the capabilities of 5G technology,which brings new opportunities and challenges,e.g.,collaborative power trading can address the mileage anxiety of electric ***,it relies on a trusted central party for scheduling,which introduces performance bottlenecks and cannot be set up in a distributed network,in addition,the lack of transparency in state-of-the-art Vehicle-to-Vehicle(V2V)power trading schemes can introduce further trust *** this paper,we propose a blockchain-based trustworthy collaborative power trading scheme for 5G-enabled social vehicular networks that uses a distributed market mechanism to introduce trusted power trading and avoids the dependence on a centralized dispatch *** on the game theory,we design the pricing and trading matching mechanism for V2V power trading to obtain maximum social *** use blockchain to record power trading data for trusted pricing and use smart contracts for transaction *** simulation results verify the effectiveness of the proposed scheme in improving social welfare and reducing the load on the grid.

关键词： Social internet of vehicles Bl ockchain Collaborative power trading Vehicle-to-vehicle charging 5G

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Dynamic Graph Neural Networks-Based Alert Link Prediction for Online Service Systems

Dynamic Graph Neural Networks-Based Alert Link Prediction fo...

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IEEE International Conference on Automated Software engineering (ASE)

作者： Yiru Chen Chenxi Zhang Zhen Dong Dingyu Yang Xin Peng Jiayu Ou Hong Yang Zheshun Wu Xiaojun Qu Wei Li School of Computer Science Fudan University China Shanghai Key Laboratory of Data Science Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China Alibaba Group China

A fault in large online service systems often triggers numerous alerts due to the complex business and component dependencies among services, which is known as “alert storm”. In a short time, an online service system may generate a huge amount of alert data. This poses a challenge for on-call engineers to identify alerts that are associated with a system failure for root cause analysis. In this paper, we propose DyAlert, a dynamic graph neural networks-based approach for linking alerts that might be triggered by a same fault to reduce the burden of on-call engineers in the fault analysis. Our insight is that alerts are often triggered by alert propagation when a system failure occurs, e.g., alert $a$ would lead to the occurrence of alert $b$ . Whether two alerts should be linked depends on if one alert is triggered by the propagation of the other. Leveraging this insight, we design a dynamic graph (namely Alert-Metric Dynamic Graph) that describes the propagation process of alerts. Based on the dynamic graph, we train a neural networks-based model to predict alert links. We evaluate DyAlert with real-world data collected from an online service system running 85 business units and about 30,000 different services in a large enterprise. The results show that DyAlert is effective in predicting alert links and it outperforms the state-of-the-art approaches with an average increase of 0.259 in F1-score.

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