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arXiv 2025年

作者： Zhang, Yanyue He, Yulan Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Informatics King’s College London United Kingdom The Alan Turing Institute

Personalized opinion summarization is crucial as it considers individual user interests while generating product summaries. Recent studies show that although large language models demonstrate powerful text summarization and evaluation capabilities without the need for training data, they face difficulties in personalized tasks involving long texts. To address this, Rehearsal, a personalized opinion summarization framework via LLMs-based role-playing is proposed. Having the model act as the user, the model can better understand the user’s personalized needs. Additionally, a role-playing supervisor and practice process are introduced to improve the role-playing ability of the LLMs, leading to a better expression of user needs. Furthermore, through suggestions from virtual users, the summary generation is intervened, ensuring that the generated summary includes information of interest to the user, thus achieving personalized summary generation. Experiment results demonstrate that our method can effectively improve the level of personalization in large model-generated summaries. Copyright © 2025, The Authors. All rights reserved.

关键词： Economic and social effects

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Multi-Instance Partial-Label Learning with Margin Adjustment

arXiv

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arXiv 2025年

作者： Tang, Wei Yang, Yin-Fang Wang, Zhaofei Zhang, Weijia Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China School of Information and Physical Sciences The University of Newcastle CallaghanNSW2308 Australia

Multi-instance partial-label learning (MIPL) is an emerging learning framework where each training sample is represented as a multi-instance bag associated with a candidate label set. Existing MIPL algorithms often overlook the margins for attention scores and predicted probabilities, leading to suboptimal generalization performance. A critical issue with these algorithms is that the highest prediction probability of the classifier may appear on a non-candidate label. In this paper, we propose an algorithm named MIPLMA, i.e., Multi-Instance Partial-Label learning with Margin Adjustment, which adjusts the margins for attention scores and predicted probabilities. We introduce a margin-aware attention mechanism to dynamically adjust the margins for attention scores and propose a margin distribution loss to constrain the margins between the predicted probabilities on candidate and non-candidate label sets. Experimental results demonstrate the superior performance of MIPLMA over existing MIPL algorithms, as well as other well-established multi-instance learning algorithms and partial-label learning algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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A Wireless Fully Implantable DBS Device for Closed-Loop Neuromodulation Applications

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IEEE Sensors Journal 2025年第9期25卷 15331-15344页

作者： Su, Kangyu Qiu, Zhang Xu, Kedi Xu, Jian Wang, Yueming Zhejiang University College of Information Science and Electronic Engineering MOE Frontier Science Center for Brain Science and Brain-machine Integration Hangzhou310027 China Nanhu Brain-computer Interface Institute Hangzhou311100 China Zhejiang University College of Biomedical Engineering and Instrument Science Hangzhou310027 China Zhejiang University School of Medicine Affiliated Mental Health Center Hangzhou Seventh People’s Hospital Hangzhou310013 China Zhejiang University MOE Frontier Science Center for Brain Science and Brain-machine Integration State Key Laboratory of Brain-Machine Intelligence Hangzhou310027 China Zhejiang University College of Computer Science and Technology State Key Laboratory of Brain-Machine Intelligence Hangzhou310027 China

(Figure presented)Closed-loop deep brain stimulation (DBS) is an emerging technology in the field of neuromodulation, which can provide effective treatment methods for intractable neurological disorders. To address the faced challenges, this paper presents a high-precision closed-loop DBS device based on a custom-designed neuromodulation integrated circuit fabricated in a 180 nm Bipolar-CMOS-DMOS (BCD) process. Also, three new techniques have been developed to optimize system performance. Firstly, to improve system stability, a frequency shaping & quick blanking amplifier (FSQBA) based analog front-end is proposed to extend system dynamic range (DR). Secondly, both a stimulation artifacts (SA) suppression scheme and a closed-loop control algorithm are developed to achieve real simultaneous recording and stimulation. Thirdly, a redundant-crossfire (RXF) technique is designed to enhance current precision without consuming much extra area. Bench-top measurements show that the designed system features a wide DR of 112 dB, a short saturation recovery time of 100 μs, and a high stimulation current resolution of 13.1-bit. Besides, animal experiments demonstrate that the designed system is highly suitable for long-term implantation applications, thus providing accurate and reliable closed-loop control for neuromodulation therapies. © 2001-2012 IEEE.

关键词： CMOS integrated circuits

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BAV-DSS: Blockchain Assisted Verifiable Data Sharing Scheme with Fast Encryption and Outsourced Decryption for IoT 24th

BAV-DSS: Blockchain Assisted Verifiable Data Sharing Scheme ...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Huang, Yao Yu, Zhongyuan Wang, Guijuan Chai, Baobao Zhang, Hongliang Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan250353 China School of Information Science and Engineering Lanzhou University Lanzhou730000 China School of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

ISBN: (纸本)9789819615414

Many sophisticated cloud platforms are available to provide services such as storage and computation for Internet of Things (IoT) applications. However, with the concerns about the semi-trusted nature of cloud platforms, data security and privacy have been gaining broad attention. In this paper, we propose a blockchain-assisted verifiable data sharing scheme (BAV-DSS) for IoT. The BAV-DSS features fine-grained access control, fast encryption, and outsourced decryption with verifiability. Specifically, with the application of ciphertext-policy attribute-based encryption (CP-ABE), flexible access control is enabled. Blockchain is employed to store public keys, generate account addresses, and perform data verification. Furthermore, the selective replayable chosen ciphertext attacks (sRCCA) security of BAV-DSS is proved, and the simulation results indicate that BAV-DSS obtains high efficiency and practicality compared to other schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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Do Not Trust What They Tell: Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection 25

Do Not Trust What They Tell: Exposing Malicious Accomplices ...

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Proceedings of the ACM on Web Conference 2025

作者： Yixuan Yao Ming Yang Zixia Liu Kai Dong Xiaodan Gu Chunmian Wang Southeast University Nanjing Jiangsu China Anhui University of Technology Maanshan Anhui China and Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education Nanjing Jiangsu China

ISBN: (纸本)9798400712746

The Tor network, while offering anonymity through traffic routing across volunteer-operated nodes, remains vulnerable to attacks that aim to deanonymize users by correlating traffic patterns between colluded entry and exit nodes in circuits. This paper presents a novel approach for detecting anomalous circuits in the Tor network, and for the first time provides a more comprehensive identification of potential malicious accomplice nodes in Tor by taking roles of nodes in anomalous circuits into consideration. Our method strategically utilizes modified middle nodes to capture traffic data, followed by a novel circuit classification based on traffic patterns to pinpoint concerned circuits. Two kinds of anomalies are identified: routing anomalies and usage anomalies, that respectively represent the anomalies with explicit or implicit violation of Tor's circuit construction guidelines. This leads to a successful revealing of totally 1,960 anomalous nodes in Tor. Furthermore, we apply clustering analysis with considering corresponding anomalous circuits and other key characteristics to the detected anomalous nodes, revealing potential hidden organizations behind these nodes that can threaten the network's security. Our findings highlight the necessity for the Tor project to adopt targeted mitigation strategies to enhance overall network security and privacy.

关键词： Tor network

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ALFC-Point: Adaptive Laplacian Feature Convolution network for 3D Point Cloud Understanding 41st

ALFC-Point: Adaptive Laplacian Feature Convolution Network f...

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41st computer Graphics International Conference, CGI 2024

作者： Liao, Junjie Yin, Mengxiao Li, Ming Zhu, Congyang Yang, Zhiqiang Zhan, Feng School of Computer and Electronics Information Guangxi University Guangxi Nanning530004 China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi Nanning530004 China Education Department of Guangxi Zhuang Autonomous Region Nanning China

ISBN: (纸本)9783031820236

To capture 3D geometric details, point cloud understanding primarily employs convolution, dynamic map, or transformer methods to build complex local geometry extractors. When dealing with sparse and noisy point clouds in real-world environments, the structural design of these methods tends to be complex and perform poorly. In this paper, we propose a lightweight point cloud understanding framework based on Laplacian feature convolution, which has high accuracy on normal-sized point cloud datasets and performs well when dealing with sparse and noisy point cloud inputs. The adaptive Laplacian convolution module combined with the vector attention mechanism can effectively capture the local details of point cloud geometry. The framework employs a multi-scale feature fusion mechanism between modules to reduce the number of sampling points step by step, reducing redundant computations and achieving a lightweight effect. In addition, we propose a parameter-free module for interpolation to enhance the training part. Experimental results on object-level datasets show that the lightweight method proposed in our paper has similar accuracy to the current best method in terms of accuracy (Acc) and mean intersection over union (mIoU), and has better robustness in handling sparse and noisy point clouds. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Laplace transforms

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TRBFT: An Efficient Blockchain Consensus for Edge Computing-Enabled IoT Systems

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IEEE Internet of Things Journal 2025年

作者： Zheng, Jiali Zhang, Yuxi Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronics and Information Guangxi Nanning530004 China

With the continuous expansion of the Internet of Things (IoT), and despite reductions in communication latency enabled by edge computing, the decentralized nature of node structures poses significant security challenges. Blockchain is considered a critical technology for enhancing IoT security. However, traditional consensus exhibits limitations in efficiency and security when facing the exponential growth of IoT nodes necessitating further optimization and innovation. This paper proposes the threshold signature and reputation-enhanced Byzantine fault tolerance (TRBFT) algorithm. First, we integrate IoT with edge computing architectures, conduct a comprehensive evaluation of node reputation based on behavioral quality and contribution to consensus, and periodically update and categorize nodes into different sets with varying permissions. Secondly, the algorithm ensures that the selection of primary nodes strikes a balance between reliability and randomness, while employing a segmented screening scheme that progressively restricts the participating nodes in consensus, aiming to enhance accuracy. Subsequently, the communication structure is improved by incorporating threshold signature with reputation weight, which reduces communication complexity. Ultimately, simulation results demonstrate that TRBFT reduces the average consensus delay by 71.1% and increases the average throughput by 3.95 times compared to the advanced consensus HotStuff. In scenarios with faulty nodes, TRBFT increases the chain growth rate by 65.12% and decreases the block interval by 54.26% relative to HotStuff. The findings show the superior scalability and fault tolerance of TRBFT, making it better suited for large-scale IoT scenarios integrated with edge computing. © 2025 IEEE.

关键词： Blockchain

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Low Chirp Dual-Racetrack Modulator for PAM-4 Modulation With Low Driving Voltage

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Journal of Lightwave Technology 2025年第10期43卷 4840-4847页

作者： Yu, Fuhao Zeng, Zhaobang Tang, Kaifei Yang, Shichong Ji, Xiang Xin, Yu Gu, Tingyi Jiang, Wei Nanjing University Key Laboratory of Intelligent Optical Sensing and Manipulation Engineering Research Center for Precision Photonics Integration & System Applications Ministry of Education The National Laboratory of Solid-State Microstructures The College of Engineering and Applied Sciences Institute of Optical Communication Engineering Nanjing210023 China Nanjing University of Information Science & Technology School of Physics and Optoelectronic Engineering Nanjing210044 China University of Delaware Department of Electrical and Computer Engineering NewarkDE19716 United States

Micro-ring modulators with a small footprint and energy consumption have been explored for densely integrated high throughput optical interconnects. However, their strong dispersive response results in frequency chirp and pulse broadening, which limits their signal transmission distance. Here we explore the low chirp operation of a dual-racetrack modulator symmetrically coupled to a bus waveguide. With opposite index changes in the two resonators, the coherent couplings of two resonators' optical fields with the bus waveguide and their interference can cancel out the output phase variation due to a single resonator. This allows the push-pull operation to achieve a lower chirp compared to the single-drive operation. The advantage of lower chirp operation is experimentally verified through open eye diagram with 100 Gbps PAM-4 transmitted over 2 km single-mode fiber (SMF), with a peak-to-peak driving voltage of 1.6 V. © 1983-2012 IEEE.

关键词： Single mode fibers

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Resource Allocation for MEC in Ultra-dense networks

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Journal of computers (Taiwan) 2025年第1期36卷 143-162页

作者： Ma, Huan Li, Shuyun Wang, Zhihui Dou, Xubing Zhang, Xinchang 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 Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Shandong Normal University Jinan China

In the complex context of 5G ultra-dense networks, the unprecedented growth in traffic and computing demand has made the interaction between edge devices and edge servers extremely intricate due to the time-varying nature of the network environment and resource heterogeneity. The combination of UDN and Mobile Edge Computing not only significantly improves network performance but also expands application scenarios and enhances user experience, marking it as one of the crucial trends in future network development. In this paper, we review recent results and research progress on MEC-based resource allocation in 5G ultra-dense networks. We analyze resource allocation strategies from multiple dimensions, including user association, interference management, energy efficiency optimization, delay reduction, access selection, and user mobility. Finally, we discuss the challenges and open issues that need to be addressed for future development in this area. © 2025 Codon Publications. All rights reserved.

关键词： 5G mobile communication systems

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A Hybrid-Feature-Based Autoencoder Model for Predicting Traffic Flow to Accelerate Ambulance Medical Response Time in Internet of Vehicles

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IEEE Internet of Things Journal 2025年第9期12卷 11397-11407页

作者： Li, Yongmeng Tian, Yu Liu, Wenjian Gao, Longxiang Tian, Hui Xing, Lumin The First Affiliated Hospital of Shandong First Medical University Shandong Provincial Qianfoshan Hospital Shandong Key Laboratory of Digital Diagnosis and Treatment of Thoracic Oncology Shandong Engineering Research Center of Intelligent Surgery Shandong Jinan250014 China Qilu Hospital of Shandong University Department of Thoracic Surgery Shandong Jinan250012 China City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250000 China The First Affiliated Hospital of Shandong First Medical University Shandong Provincial Qianfoshan Hospital Shandong Key Laboratory of Digital Diagnosis and Treatment of Thoracic Oncology Shandong Jinan250014 China

Traffic congestion during ambulance travel can delay medical response times. With the growing availability of traffic data, computational methods for traffic flow prediction are attracting significant attention. However, current computational prediction models have the following shortcomings. Fully connected networks require extensive feature engineering yet struggle to capture local traffic flow features. Their high parameter count increases training time and overfitting risk, while varying traffic conditions hinder model generalization. Therefore, in this work, we propose a hybrid-feature-based autoencoder (HFAE) model to predict traffic flow and accelerate ambulance medical response time in Internet of Vehicles. Specifically, the HFAE model simultaneously considers local features from the convolutional neural network and global features from the fully connected neural network. Additionally, the HFAE model uses an autoencoder to reconstruct the original input, capturing the intrinsic structure of the traffic flow data. Specifically, the root mean-squared error and mean absolute error scores of the HFAE model are better than those of the three mainstream models. © 2014 IEEE.

关键词： Ambulances

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