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34th ACM Web Conference, WWW 2025

作者： Li, Yishuo Zhang, Qi Lu, Wenpeng Peng, Xueping Zhang, Weiyu Si, Jiasheng Gong, Yongshun Hu, Liang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Department of Computer Science and Technology Tongji University Shanghai China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia School of Software Shandong University Jinan China

ISBN: (纸本)9798400712746

Medication recommendation aims to suggest personalized drug combinations to patients based on their longitudinal medical histories stored in electronic health record (EHR) datasets. Patients’ Dynamic Treatment Regimes (DTRs) determine how patients’ drug combinations change along with the evolution of disease treatment. DTRs are effective for comprehending disease-treatment dynamics and for recommending a timely and personalized combination of medications for patients. However, existing medication recommender systems (MRSs) overlook the multiple treatment pathways generated by the intervention of DTRs and can only recommend a single treatment paradigm, ignoring the fact that patients may be at different treatment stages and thus require different treatment regime. Such disregard leads to a significant limitation in recommending personalized medication combinations tailored to different treatment stages, yielding greatly compromised accuracy and applicability of MRSs. Moreover, existing methods often overlook the time interval information over patients’ successive visits, which is critical to indicate patients’ treatment evolution. To address these significant gaps, we propose a Time-aware Medication Recommendation Framework via Intervention of Dynamic Treatment Regimes, called MR-DTR. To explicitly illustrate the intervention processes of DTRs on similar patients, we employ a co-guided graph to connect various patient sequences. In addition, to fully utilize the time interval information, we design a time-aware guidance mechanism dedicated to the co-guided graph to efficiently learn medication representation using the patient’s guidance information. We also introduce relative time intervals in the encoder to act as positional information. Extensive experiments on two real-world datasets demonstrate that MR-DTR surpasses state-of-the-art models in terms of recommendation performance. © 2025 Copyright held by the owner/author(s).

关键词： Dynamic Treatment Regimes Electronic Health Record Medication Recommendation Recommender Systems

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Tracking multiple people under occlusion and across cameras using probabilistic models

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Journal of zhejiang University-Science A(Applied Physics & Engineering) 2009年第7期10卷 985-996页

作者： Xuan-he WANG Ji-lin LIU Institute of Information and Communication Engineering Zhejiang University Hangzhou 310027 China Zhejiang Provincial Key Laboratory of lnformation Network Technology Hangzhou 310027 China

Tracking multiple people under occlusion and across cameras is a challenging question for discussion. Furthermore, the cameras in this study are used to extend the field of view, which are distinguished from the same field of view. Such corre- spondence between multiple cameras is a burgeoning research subject in the area of computer vision. This paper effectively solves the problems of tracking multiple people who pass from one camera to another and segmenting people under occlusion using probabilistic models. The probabilistic models are composed of blob model, motion model and color model, which make the most of the space, motion and color information. First, we present a color model that uses maximum likelihood estimation based on non-parametric kernel density estimation. Second, we introduce a blob model based on mean shift, which segments the body into many regions according to the color of each person in order to spatially localize the color features corresponding to the way people are dressed. Clothes can be any mixture of colors. Third, we bring forward a motion model based on statistical probability which indicates the movement position of the same person between two successive frames in a single camera. Finally, we effectively unify the three models into a general probabilistic model and attain a maximization likelihood probability image, which is used to segment the foreground region under occlusion and to match people across multiple cameras.

关键词： Color model Motion model Blob model People occlusion People tracking Kernel density estimation

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MRLATO: An Adaptive Task Offloading Mechanism Based on Meta Reinforcement Learning in Edge Computing Environment

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IEEE Transactions on Vehicular technology 2025年

作者： Zhang, Peiying Liu, Jiamin Guizani, Maher Wang, Jian Kumar, Neeraj Tan, Lizhuang Qingdao Institute of Software College of Computer Science and Technology Qingdao266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao266580 China Ministry of Education Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250014 China University of Texas Arlington Computer Science and Engineering Department TX United States College of Science Qingdao266580 China Thapar University Department of Computer Science and Engineering Patiala147004 India Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan250014 China

Traditional cloud computing models struggle to meet the requirements of latency-sensitive applications when processing large amounts of data. As a solution, Multi-access Edge Computing (MEC) extends computing resources to the edge of the network to reduce processing delays and improve user experience. However, in dynamically changing edge computing environments, effective decision making on whether to offload tasks to edge servers remains a core challenge. For this purpose, we propose MRLATO, an adaptive task offloading mechanism based on Meta Reinforcement Learning (MRL), which exploits a large amount of a priori knowledge of different tasks to achieve fast adaptation. The task offloading process is modelled as multiple Markov Decision Processes (MDPs) and solved using a Sequence to Sequence (Seq2Seq) neural network integrating multi-head attention and recursive task sequencing. It is shown by the experimental results that the proposed method has the lowest latency in all experimental settings and the convergence efficiency is improved by 14.06% compared to the traditional Deep Reinforcement Learning (DRL) algorithm. This research fully demonstrates the significant benefits of the deep integration of MRL with the edge computing domain, providing new optimisation ideas for task offloading decisions. © 1967-2012 IEEE.

关键词： Computation offloading

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An adaptive fast search algorithm for block motion estimation in H.264

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Journal of zhejiang University-Science C(Computers and Electronics) 2010年第8期11卷 637-644页

作者： Cong-dao HAN Institute of Information and Communication Engineering Zhejiang University Hangzhou 310027 China Zhejiang Provincial Key Laboratory of Information Network Technology Hangzhou 310027 China

Motion estimation is an important issue in H.264 video coding systems because it occupies a large amount of encoding *** this paper,a novel search algorithm which utilizes an adaptive hexagon and small diamond search (AHSDS) is proposed to enhance search *** search pattern is chosen according to the motion strength of the current *** the block is in active motion,the hexagon search provides an efficient search means;when the block is inactive,the small diamond search is *** results showed that our approach can speed up the search process with little effect on distortion performance compared with other adaptive approaches.

关键词： Adaptive hexagon and small diamond search (AHSDS) Search pattern Mean absolute difference (MAD) Optimal point

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Hash signature saving in distributed video coding

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Journal of zhejiang University-Science C(Computers and Electronics) 2011年第2期12卷 163-170页

作者： Xin-hao CHEN Institute of Information and Communication Engineering Zhejiang University Hangzhou 310027 China Zhejiang Provincial Key Laboratory of Information Network Technology Hangzhou 310027 China

In transform-domain distributed video coding (DVC), the correlation noises (denoted as N) between the source block and its temporal predictor can be modeled as Laplacian random variables. In this paper we propose that the noises (denoted as N′) between the source block and its co-located block in a reference frame can also be modeled as Laplacian random variables. Furthermore, it is possible to exploit the relationship between N and N′ to improve the performance of the DVC system. A practical scheme based on theoretical insights, the hash signature saving scheme, is proposed. Experimental results show that the proposed scheme saves on average 83.2% of hash signatures, 13.3% of bit-rate, and 3.9% of encoding time.

关键词： Distributed video coding (DVC) Hash Laplacian

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Optimizing inter-view prediction structures for multi-view video coding using simulated annealing

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Journal of zhejiang University-Science C(Computers and Electronics) 2011年第2期12卷 155-162页

作者： Zheng ZHU Dong-xiao LI Ming ZHANG Institute of Information and Communication Engineering Zhejiang University Hangzhou 310027 China Zhejiang Provincial Key Laboratory of Information Network Technology Hangzhou 310027 China

New video applications, such as 3D video and free viewpoint video, require efficient compression of multi-view video. In addition to temporal redundancy, exploiting the inter-view redundancy is crucial to improve the performance of multi-view video coding. In this paper, we present a novel method to construct the optimal inter-view prediction structure for multi-view video coding using simulated annealing. In the proposed model, the design of the prediction structure is converted to the arrangement of coding order. Then, a simulated annealing algorithm is employed to minimize the total cost for obtaining the best coding order. This method is applicable to arbitrary irregular camera arrangements. As experiment results reveal, the annealing process converges to satisfactory results rapidly and the generated optimal prediction structure outperforms the reference prediction structure of the joint multi-view video model (JMVM) by 0.1-0.8 dB PSNR gains.

关键词： Multi-view video coding Prediction structure Simulated annealing

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Distributed video coding with adaptive selection of hash functions

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Journal of zhejiang University-Science C(Computers and Electronics) 2011年第5期12卷 387-396页

作者： Xin-hao CHEN Institute of Information and Communication Engineering Zhejiang UniversiO Hangzhou 310027 China Zhejiang Provincial Key Laboratory of Information Network Technology Hangzhou 310027 China

We address the compression efficiency of feedback-free and hash-check distributed video coding,which generates and transmits a hash code of a source information *** hash code helps the decoder perform a motion search.A hash collision is a special case in which the hash codes of wrongly reconstructed information sequences occasionally match the hash code of the source information *** deteriorates the quality of the decoded image *** this paper,the statistics of hash collision are analyzed to help the codec select the optimal trade-off between the probability of hash collision and the length of the hash code,according to the principle of rate-distortion ***,two novel algorithms are proposed:(1) the nonzero prefix of coefficients (NPC),which indicates the count of nonzero coefficients of each block for the second algorithm,and also saves 8.4% bitrate independently;(2) the adaptive selection of hash functions (AHF),which is based on the NPC and saves a further 2%-6% bitrate on *** detailed optimization of the parameters of AHF is also presented.

关键词： Hash Collision Distributed video coding Wyner-Ziv

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Reputation-based linear cooperation for spectrum sensing in cognitive radio networks

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Journal of zhejiang University-Science A(Applied Physics & Engineering) 2009年第12期10卷 1688-1695页

作者： Hui-fang CHEN Xu JIN Lei XIE Department of lnformation Science and Electronic Engineering Zhejiang University Hangzhou 310027 China Zhejiang Provincial Key Laboratory of Information Network Technology Hangzhou 310027 China

We propose a reputation-based cooperative spectrum sensing scheme in cognitive radio (CR) networks to solve the uncertainty resulting from the multipath fading and shadowing effect. In the proposed scheme, each cooperative CR user has a reputation degree that is initialized and adjusted by the central controller, and used to weight the sensing result from the corresponding CR user in the linear fusion process at the central controller. A simple method for adjusting the reputation degree of CR users is also presented. We analyzed and evaluated the detection performance of the reputation-based cooperative spectrum sensing scheme. Simulation results showed that our proposed scheme alleviates the problem of corrupted detection resulting from destructive channel conditions between the primary transmitter and the CR user. The performance of our proposed scheme was improved compared to the average-based linear cooperation scheme, and was similar to that of the optimal linear cooperation scheme with feasible computational complexity. Moreover, our proposed scheme does not require knowledge of channel statistics.

关键词： Cognitive radio networks Cooperative Reputation Spectrum sensing

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DFMC:Feature-Driven Data-Free Knowledge Distillation

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Zhang, Zherui Xu, Rongtao Wang, Changwei Xu, Wenhao Chen, Shunpeng Xu, Shibiao Xu, Guangyuan Guo, Li Beijing University of Posts and Telecommunications School of Artificial Intelligence Beijing100876 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China Qilu University of Technology Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250316 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan250014 China

Data-Free Knowledge Distillation (DFKD) enables knowledge transfer from teacher networks without access to the real dataset. However, generator-based DFKD methods often suffer from insufficient diversity or low-confidence in synthetic images, negatively impacting student network performance. This paper introduces DFMC, a generative feature-driven framework to mitigate the inherent limitations of DFKD. We propose exploiting semantic description between generative feature domains to guide augmentation strategies, avoiding random abstract inputs caused by inconsistent semantic quality. Then, by applying noise to the generative features, we produce contrastive learning pairs indirectly, limiting the sampling range of the feature domain to encourage the student network to learn domain-invariant features. Finally, we guide the student network to deeply mimic the teacher’s layer-wise implicit classification behavior for the augmented synthetic images. Extensive experiments across various datasets and downstream tasks demonstrate the effectiveness of DFMC, achieving significant improvements while preventing student networks from overfitting to semantic ambiguous images. © 1991-2012 IEEE.

关键词： Knowledge transfer

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A 3D Memristive Cubic Map with Dual Discrete Memristors: Design, Implementation, and Application in Image Encryption

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Gao, Suo Iu, Herbert Ho-Ching Erkan, Ugur Simsek, Cemaleddin Toktas, Abdurrahim Cao, Yinghong Wu, Rui Mou, Jun Li, Qi Wang, Chunpeng Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering Perth6009 Australia Faculty of Engineering Electrical and Electronics Engineering Respectively Karamanoglu Mehmetbey University Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system's potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme for image encryption with the *** results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks. © 1991-2012 IEEE.

关键词： Bifurcation (mathematics)

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