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2023 World Wide Web Conference, WWW 2023

作者： Huang, Chung-Ju Wang, Leye Han, Xiao Key Laboratory of High Confidence Software Technologies Peking University Ministry of Education Beijing China School of Computer Science Peking University Beijing China School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai China

ISBN: (纸本)9781450394161

Collaboration between healthcare institutions can significantly lessen the imbalance in medical resources across various geographic areas. However, directly sharing diagnostic information between institutions is typically not permitted due to the protection of patients' highly sensitive privacy. As a novel privacy-preserving machine learning paradigm, federated learning (FL) makes it possible to maximize the data utility among multiple medical institutions. These feature-enrichment FL techniques are referred to as vertical FL (VFL). Traditional VFL can only benefit multi-parties' shared samples, which strongly restricts its application scope. In order to improve the information-sharing capability and innovation of various healthcare-related institutions, and then to establish a next-generation open medical collaboration network, we propose a unified framework for vertical federated knowledge transfer mechanism (VFedTrans) based on a novel cross-hospital representation distillation component. Specifically, our framework includes three steps. First, shared samples' federated representations are extracted by collaboratively modeling multi-parties' joint features with current efficient vertical federated representation learning methods. Second, for each hospital, we learn a local-representation-distilled module, which can transfer the knowledge from shared samples' federated representations to enrich local samples' representations. Finally, each hospital can leverage local samples' representations enriched by the distillation module to boost arbitrary downstream machine learning tasks. The experiments on real-life medical datasets verify the knowledge transfer effectiveness of our framework. © 2023 ACM.

关键词： Distillation

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DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis

DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-...

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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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Energy Conserved Failure Detection for NS-IoT Systems

Energy Conserved Failure Detection for NS-IoT Systems

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IEEE Conference on Wireless Communications and Networking

作者： Guojin Liu Jianhong Zhou Hang Su Biaohong Xiong Xianhua Niu School of Computer and Software Engineering Xihua University National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Nowadays, network slicing (NS) technology has gained widespread adoption within Internet of Things (IoT) systems to meet diverse customized requirements. In the NS based IoT systems, the detection of equipment failures necessitates comprehensive equipment monitoring, which leads to significant resource utilization, particularly within large-scale IoT ecosystems. Thus, the imperative task of reducing failure rates while optimizing monitoring costs has emerged. In this paper, we propose a monitor application function (MAF) based dynamic dormancy monitoring mechanism for the novel NS-IoT system, which is based on a network data analysis function (NWDAF) framework defined in Rel-17. Within the NS-IoT system, all nodes are organized into groups, and multiple MAFs are deployed to monitor each group of nodes. We also propose a dormancy monitor mechanism to mitigate the monitoring energy consumption by placing the MAFs, which is monitoring non-failure devices, in a dormant state. We propose a reinforcement learning based PPO algorithm to guide the dynamic dormancy of MAFs. Simulation results demonstrate that our dynamic dormancy strategy maximizes energy conservation, while proposed algorithm outperforms alternatives in terms of efficiency and stability.

关键词： Energy consumption Heuristic algorithms Simulation Energy conservation Stability analysis Internet of Things Servers

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Temporal-Aware Multi-behavior Contrastive Recommendation 28th

Temporal-Aware Multi-behavior Contrastive Recommendation

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Xuan, Hongrui Li, Bohan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology Nanjing China National Engineering Laboratory for Integrated Aero-Space-GroundOcean Big Data Application Technology Nanjing China

ISBN: (纸本)9783031306716

Modeling various types of users’ interactions and jointly considering individual preferences from multiple perspectives, multi-behavior recommendation has attracted increasing attention recently. However, most existing multi-behavior recommendations only focus on the behavioral interaction itself, attempting to extract user preferences merely by modeling behaviors, while ignoring the properties of the interaction (e.g., the temporal information). Meanwhile, the introduction of multi-behavior information is bound to generate additional noise signals, which further affects the accuracy of recommendation. Thus, to tackle the above challenges, two aspects need to be explored: (1) How to fuse interaction attributes to construct unified cross-behavior dependencies while distinguishing subtle differences between individuals’ behaviors. (2) How to mitigate the effects of noise to a certain extent while preserving multi-behavioral information. In this work, we propose a Temporal-Aware Multi-Behavior Contrastive Learning (TMCL) framework to explore the patterns of multiple behaviors of individuals through temporal information, jointly capture the correlation of users’ preference evolution, and alleviate the impact of multi-behavior noise. Extensive experiments and ablation tests on the three datasets indicate our TMCL outperforms various state-of-the-art methods and verify the effectiveness of our model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： User profile

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MMR-HAD: Multi-scale Mamba Reconstruction Network for Hyperspectral Anomaly Detection

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IEEE Transactions on Geoscience and Remote Sensing 2025年

作者： Fu, Xiyou Zhang, Ting Cheng, Juan Jia, Sen Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Shaanxi Key Laboratory of Optical Remote Sensing and Intelligent Information Processing Xi'an710119 China

Hyperspectral anomaly detection (HAD) aims to identify anomalous objects from hyperspectral images (HSIs) whose spectral features significantly deviate from their surroundings. Existing HAD methods still reconstruct some anomalies during the background reconstruction process, which can seriously affect the detection accuracy. Consequently, inspired by Mamba's ability to effectively model long sequences, we propose a Multi-scale Mamba Reconstruction Network for Hyperspectral Anomaly Detection (MMR-HAD) by enhancing the representation of the background and inhibit anomalies from being reconstructed. MMR-HAD first removes most of the anomalous pixels in hyperspectral images using the Random Mask (RM) strategy, which reduces the interference of anomalous pixels on the background reconstruction and makes the background features more prominent. To further filter out the small amount of residual anomalous pixels, we propose the Multi-scale Dilated Attention Background Enhancement (MDABE) mechanism, which enhances the background representation. Finally, we apply the Multi-scale Dynamic Feature Fusion (MDFF) strategy to reconstruct the background image, further extracting and strengthening the background information, thus obtaining a pure background image. MMR-HAD, which focuses on generating a pure background, has been experimentally validated on seven real hyperspectral datasets. The results demonstrate that it excels in background enhancement and anomaly suppression, significantly improving detection accuracy. Introducing Mamba offers a promising solution for HAD, with substantial potential for practical application. © 1980-2012 IEEE.

关键词： Image reconstruction

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The reliability of OKRidge method in solving sparse ridge regression problems 24

The reliability of OKRidge method in solving sparse ridge re...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xiyuan Li Youjun Wang Weiwei Liu School of Computer Science and National Engineering Research Center for Multimedia Software and Institute of Artificial Intelligence and Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University

ISBN: (纸本)9798331314385

Sparse ridge regression problems play a significant role across various domains. To solve sparse ridge regression, [1] recently proposes an advanced algorithm, Scalable Optimal K-Sparse Ridge Regression (OKRidge), which is both faster and more accurate than existing approaches. However, the absence of theoretical analysis on the error of OKRidge impedes its large-scale applications. In this paper, we reframe the estimation error of OKRidge as a Primary Optimization (PO) problem and employ the Convex Gaussian min-max theorem (CGMT) to simplify the PO problem into an Auxiliary Optimization (AO) problem. Subsequently, we provide a theoretical error analysis for OKRidge based on the AO problem. This error analysis improves the theoretical reliability of OKRidge. We also conduct experiments to verify our theorems and the results are in excellent agreement with our theoretical findings.

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Blockchain Development 7th

Blockchain Development

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Xie, Siqi Cai, Jiahong Zhu, Hangyu Yang, Ce Chen, Lin Xiao, Weidong School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China Guangdong Financial High-Tech Zone "Blockchain +" Fintech Research Institute Foshan528253 China School of Software Engineering Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9783031281235

In recent years, blockchain research has set off an upsurge in academia, and it is called the next generation of value Internet. Because of its decentralization, anonymity, security, immutability, traceability and other characteristics, blockchain is gradually accepted and developed by people. With the deepening of research and the integration of technologies such as deep learning, blockchain has gradually been applied to various fields such as credit reporting, government, medical care, and industrial Internet of Things, not just the initial virtual currency field. This article mainly discusses the three important stages of blockchain public chain development, namely Bitcoin, Ethereum, and meta-verse, and introduces some basic supporting technologies of blockchain, as well as the research status and future trends of blockchain. Simple Analysis. By vertically introducing the development history of the blockchain, researchers can have a more concrete understanding of the status quo of the blockchain, and provide ideas for blockchain-related research. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

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Error analysis of spherically constrained least squares reformulation in solving the stackelberg prediction game 24

Error analysis of spherically constrained least squares refo...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Xiyuan Li Weiwei Liu School of Computer Science and National Engineering Research Center for Multimedia Software and Institute of Artificial Intelligence and Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University

ISBN: (纸本)9798331314385

The Stackelberg prediction game (SPG) is a popular model for characterizing strategic interactions between a learner and an adversarial data provider. Although optimization problems in SPGs are often NP-hard, a notable special case involving the least squares loss (SPG-LS) has gained significant research attention recently [1, 2, 3]. The latest state-of-the-art method for solving the SPG-LS problem is the spherically constrained least squares reformulation (SCLS) method proposed in the work of [3]. However, the paper [3] lacks theoretical analysis on the error of the SCLS method, which limits its large-scale applications. In this paper, we investigate the estimation error between the learner obtained by the SCLS method and the actual learner. Specifically, we reframe the estimation error of the SCLS method as a Primary Optimization (PO) problem and utilize the Convex Gaussian min-max theorem (CGMT) to transform the PO problem into an Auxiliary Optimization (AO) problem. Subsequently, we provide a theoretical error analysis for the SCLS method based on this simplified AO problem. This analysis not only strengthens the theoretical framework of the SCLS method but also confirms the reliability of the learner produced by it. We further conduct experiments to validate our theorems, and the results are in excellent agreement with our theoretical predictions.

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Real-Time DAG Task Allocation Strategy for Multiprocessor by Optimistic Parallelism

Real-Time DAG Task Allocation Strategy for Multiprocessor by...

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International Conference on Communication Technology (ICCT)

作者： Yumeng Chen Songlin Liu Jianhong Zhou Xiang Ling National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China School of Computer and Software Engineering Xihua University

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

In modern real-time operating systems, complex task loads are often modeled as directed acyclic graphs (DAG) and executed in parallel on multiprocessor systems. The topological constraints present in DAG tasks prevent the DAG task allocation strategy from fully leveraging the parallel advantage of multiprocessor platforms. We analyze the parallelism of DAG tasks and propose a method to limit the number of processors that can be executed by DAG tasks. This method provides more idle processors and processor time for high parallelism DAG tasks by limiting the number of processors that can be executed by low parallelism DAG tasks. In addition, we develope optimistic parallelism scheduling algorithm (OPSA) based on the remaining utilization with low complexity. The experimental results show that our DAG task allocation strategy fully utilizes processor resources, reduces interference between DAG tasks, and achieves better worst case response time (WCRT) analysis results and shorter actual task’s WCRT on real-time multiprocessor system.

关键词： Directed acyclic graph Program processors Limiting Scheduling algorithms Operating systems Real-time systems Resource management Time factors Multiprocessing systems Load modeling

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An Energy-Aware UAVs Path Coverage for Critical Infrastructure Inspections 12

An Energy-Aware UAVs Path Coverage for Critical Infrastructu...

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12th International Conference on Advanced Cloud and Big Data, CBD 2024

作者： Wang, Yifan Zeng, Wei Li, Guanyu Xiong, Chenglong Wang, Zicheng Mao, Yingchi College of Computer Science and Software Engineering Hohai University Nanjing China Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing China Huaneng Lancang River Hydropower Co. CHINA HUANENG Group Kunming China Power China Kunming Engineering Co. Yunnan China

ISBN: (纸本)9798331511074

Critical infrastructures increasingly rely on unmanned aerial vehicles (UAVs) for inspection tasks. The significance of different components within these infrastructures is subject to variability and can be influenced by external factors. The principal aim of routine UAV inspections is to ensure differentiated coverage of pivotal sections with minimal energy consumption. However, extant research on UAV path coverage fails to fully account for the variability and dynamic shifts in regional significance and their impact on coverage efficacy. This paper presents an energy-aware collaborative coverage policy for UAVs, designated as ***-MATD3 employs a dynamic weight region partitioning method tailored to real-world environments and addresses the action selection challenge for UAVs using a discrete Partially Observable Markov Decision Process (Dec-POMDP). By amalgamating MATD3 with stacked LSTM, this approach mitigates redundant path overlaps and unnecessary action replication across multiple agents, thus optimizing coverage and diminishing energy usage. Simulation outcomes demonstrate that EA-MATD3 reduces energy consumption by an average of 9.65% relative to the Greedy, MADDPG, and MATD3 algorithms while sustaining a superior coverage rate. © 2024 IEEE.

关键词： Markov processes

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