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ApriDS: An evidence linkage network construction model for judgment documents

Journal of King Saud University - computer and Information Sciences 2025年第1期37卷 1-13页

作者： Zhou, Yulin Qin, Yongbin Lin, Chuan Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou University Guiyang 550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang 550025 China College of Computer Science & Technology Guizhou University Guiyang 550025 China

Evidence serves as the basis for determining facts in the judicial trial process, and exploring the correlation between evidence has become an essential task. However, there is uncertainty and unreliability of evidence. In this paper, we propose an ApriDS model that can mine correlations between evidence and construct evidence linkage networks. Specifically, we use the association rule algorithm to mining for the correlations between evidence in order to initially construct an evidence linkage network. Moreover, we also combine the imprecise reasoning theory to alleviate unreliability among evidence. Finally, combining the above effectively mitigates uncertainty and unreliability in the evidence linkage network. We conduct extensive experiments and present analyses on five case datasets, including marriages, thefts, homicides, contract disputes and unlawful detention. Compared to the baseline model, our method’s accuracy improves by 12.75%, 14.16%, 14.80%, 21.64% and 26.15% on the five datasets, respectively. © The Author(s) 2025.

关键词： Apriori algorithm D-S evidence theory Evidence linkage network Judgment document

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Semantic Data Augmentation for Few-Shot Biomedical Named Entity Recognition

Semantic Data Augmentation for Few-Shot Biomedical Named Ent...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Ying Wang, Weihua College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Multilingual Artificial Intelligence Technology Hohhot China

ISBN: (纸本)9798350368741

Biomedical Named Entity Recognition (BioNER) aims to identify and classify entities in biomedical text. This task struggles with data scarcity due to limited annotated data. Although data augmentation is effective, existing methods fail to handle the complex semantic mappings on biomedical terminology, which results in the generated samples lacking semantic diversity. In this paper, we propose a data augmentation method to create semantically diverse and coherent training samples for few-shot BioNER. The method utilizes the Unified Medical Language System (UMLS) for entity replacement and combines with a Masked Language Model (MLM) to generate contextually relevant words. Experimental results show that our method improves BioNER performance in few-shot scenarios. Compared to all baseline models, our method achieves an average F1 score improvement of 7.6% and 11.6% on the NCBI and JNLPBA datasets, respectively. Our source code and data are available at https://***/ABC8184/SDA. © 2025 IEEE.

关键词： BioNER data augmentation few-shot learning masked language model UMLS

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Cost-Effective FBG Demodulation for High-Temperature and Dynamic Strain via Modified Levenberg-Marquardt

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IEEE Sensors Journal 2025年第10期25卷 17011-17017页

作者： Zhang, Guangyin Zhao, Kehao Wen, Hongqiao Wang, Qirui Wu, Zekun Zhong, Shuda Ma, Guangqun Venediktov, Evgenii Chen, Kevin University of Pittsburgh Department of Electrical and Computer Engineering PittsburghPA15261 United States Wuhan University of Technology National Engineering Research Center of Fiber Optic Sensing Technology and Networks China

This paper presents a high-resolution, signal-jump-free demodulation method for Fiber Bragg Grating (FBG) systems employing a low-cost spectrometer. Utilizing a modified Logarithm Gaussian Levenberg-Marquardt (LGLM) method, the proposed approach enables precise and stable Bragg wavelength tracking. The principles of the LGLM method were thoroughly analyzed and derived, affirming the feasibility and robustness of the FBG system. Static experiments with 9 sampling points demonstrated that the LGLM method achieves a standard deviation (STD) of 1.7 pm, representing a 6.9-fold improvement over conventional Gaussian fitting. Strain experiments confirmed a strong linear relationship between strain and Bragg wavelength, with dynamic strain measurements at a 6 kHz sampling rate showing that the LGLM method effectively eliminates signal jumps observed in Gaussian fitting. At 500 Hz, the minimum detectable strain (MDS) of the LGLM method was determined to be 0.0173 μϵ/Hz-1/2, more than 10 times better than Gaussian fitting. Temperature experiments conducted over a range of 100-700 °C further validated the method's performance, with the LGLM achieving an STD of 1.56 pm and a variation of 3.4 pm, representing improvements of 10.25 times and 52.9 times, respectively, compared to Gaussian fitting. These findings demonstrate the exceptional sensitivity, stability, and robustness of the LGLM method for demanding applications such as high-temperature monitoring and structural health diagnostics. © 2001-2012 IEEE.

关键词： Fiber Bragg gratings

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Joint View Selection, Multigroup Multicast Beamforming, and DIBR for RIS-Aided Multi-View Videos

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IEEE Transactions on Mobile Computing 2025年

作者： Lee, Chi-Han Yang, De-Nian Lee, Guang-Siang Wang, Chih-Hang Liao, Wanjiun National Taiwan University Department of Electrical Engineering Taiwan Academia Sinica Institute of Information Science The Research Center for Information Technology Innovation Taiwan Academia Sinica Research Center for Information Technology Innovation Taiwan National Chung Cheng University Department of Computer Science and Information Engineering Taiwan

The rapid development of multi-view videos (MVV) transmission is an irresistible trend. Concurrently, reconfigurable intelligent surface (RIS)-assisted wireless communication has drawn significant attention. We observe that the view selection based on the base station and the view synthesis based on depth-image-based rendering (DIBR) can effectively reduce power consumption. Therefore, this paper studies the view selection and synthesis for RIS-aided MVV in multigroup multicast beamforming. To deal with this complicated scenario, we investigate a problem, named the joint View selection, Multicast beamforming, and DIBR (JVMD), to minimize the total multicast beamforming power, the view transmission operation power, and view synthesis, subject to quality-of-service (QoS), RIS phase shifts, view selection, and DIBR constraints. Unfortunately, the mathematical model is a complicated mixed discrete-continuous optimization problem. To tackle this challenging problem, we designed an algorithm, named View selection, Beamforming, RIS phase, and DIBR (VBRD) algorithm. First, we deal with the discrete optimization problem of selecting the view. VBRD uses the dual-based approximation methodology to round back a primal's integer solution. Then, in the continuous optimization problem, we apply the alternating optimization (AO) method to determine beamforming, RIS phase, and DIBR. Finally, simulation results show the performance of exploiting view synthesis for RIS-assisted wireless communication. © 2002-2012 IEEE.

关键词： Beamforming

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Toward Understanding Federated Learning over Unreliable Networks

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 80-97页

作者： Feng, Chenyuan Arafa, Ahmed Chen, Zihan Zhao, Mingxiong Quek, Tony Q. S. Yang, Howard H. EURECOM Department of Communication Biot06410 France University of North Carolina at Charlotte Department of Electrical and Computer Engineering CharlotteNC28223 United States Singapore University of Technology and Design Information Systems Technology and Design Pillar Tampines487372 Singapore Yunnan University Engineering Research Center of Cyberspace National Pilot School of Software Kunming650106 China Zhejiang University ZJU-UIUC Institute Haining314400 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310007 China University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering ChampaignIL61820 United States

This paper studies the efficiency of training a statistical model among an edge server and multiple clients via Federated Learning (FL) - a machine learning method that preserves data privacy in the training process - over wireless networks. Due to unreliable wireless channels and constrained communication resources, the server can only choose a handful of clients for parameter updates during each communication round. To address this issue, analytical expressions are derived to characterize the FL convergence rate, accounting for key features from both communication and algorithmic aspects, including transmission reliability, scheduling policies, and momentum method. First, the analysis reveals that either delicately designed user scheduling policies or expanding higher bandwidth to accommodate more clients in each communication round can expedite model training in networks with reliable connections. However, these methods become ineffective when the connection is erratic. Second, it has been verified that incorporating the momentum method into the model training algorithm accelerates the rate of convergence and provides greater resilience against transmission failures. Last, extensive empirical simulations are provided to verify these theoretical discoveries and enhancements in performance. © 2023 CCBY.

关键词： Federated learning

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Value Stream Mapping for Smart Pharmaceutical Management in a Thai Hospital 6

Value Stream Mapping for Smart Pharmaceutical Management in ...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Boonsothonsatit, Ganda Silapunt, Rardchawadee Vongbunyong, Supachai Kaemarungsi, Kamol Chanpuypetch, Wirachchaya Chonsawat, Nilubon Graduate School of Management and Innovation King Mongkut's University of Technology Thonburi Thailand Electronic and Telecommunication Engineering King Mongkut's University of Technology Thonburi Thailand Hospital Automation Research Center Institute of Field Robotics King Mongkut's University of Technology Thonburi Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Thailand Faculty of Agro-Industry Chiang Mai University Thailand Department of Agricultural Economy and Developement Faculty of Agriculture Chiang Mai University Thailand

The evolution of healthcare systems worldwide necessitates continual improvement in hospital management practices, particularly pharmaceutical management. This paper explores transforming traditional pharmaceutical management systems in large and medium-sized hospitals in Thailand into smart systems using Value Stream Mapping (VSM) as a guiding tool. Traditional systems often face challenges such as inefficiencies and medication errors. VSM, rooted in lean management principles, offers a structured approach to visualize and optimize processes. Additionally, smart technologies like automated inventory systems and electronic health records enhance pharmaceutical management effectiveness. Through a comprehensive research methodology encompassing case studies, data collection, and VSM analysis, this study identifies inefficiencies and proposes evidence-based strategies for improvement. Results from a case study highlight the benefits of VSM implementation in enhancing pharmacy operations, emphasizing improved patient care and operational efficiency. The implementation of a smart medication dispensing system shows promising results in reducing workload and accommodating increased patient arrivals. Continued evaluation and refinement are crucial for maximizing benefits and addressing emerging challenges effectively. © 2025 Elsevier B.V.. All rights reserved.

关键词： Electronic health record

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Multi-Dimensional Training Optimization for Efficient Federated Synergy Learning

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IEEE Transactions on Mobile Computing 2025年

作者： Fu, Shucun Dong, Fang Chen, Runze Shen, Dian Zhang, Jinghui He, Qiang Southeast University School of Computer Science and Engineering Nanjing China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan China

Edge learning (EL) is an end-to-edge collaborative learning paradigm enabling devices to participate in model training and data analysis, opening countless opportunities for edge intelligence. As a promising EL framework, federated synergy learning (FSyL) mitigates the computation and communication overhead on resource-constrained devices by offloading partial model layers to the edge server for synergistic training. Nevertheless, due to the system and statistical heterogeneity, naively using existing FSyL methods is significantly time-consuming and causes accuracy degradation. Motivated by this issue, this paper introduces a novel FSyL framework that integrates multi-dimensional training optimization and formulates the edge learning cost minimization (ELCM) problem. To tackle the ELCM efficiently, we design OL-MG, an OnLine Model Splitting and Resource Provisioning Game. Specifically, we first reformulate and decompose the original ELCM based on data quality evaluation. Then, given a model splitting decision, we determine the optimal resource provisioning in Sub-problem1, based on which optimal model splitting in Sub-problem2 is modeled as a potential game. Subsequently, we introduce a decentralized algorithm to find a Nash equilibrium (NE) solution. Furthermore, we further extend OL-MG to support a budget-aware multi-edge scenario. Extensive experiments demonstrate that the proposed mechanism significantly outperforms state-of-the-art methods in cost-saving and accuracy improvement. © 2025 IEEE.

关键词： Federated learning

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End-to-end Optimized Lossy Compression for Neural-morphic Spiking Camera Captured Data

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Feng, Kexiang Jia, Chuanmin Pan, Jingshan Ma, Siwei Gao, Wen Chinese Academy of Sciences Institute of Computing Technology Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Peking University National Engineering Research Center of Visual Technology Beijing100871 China Peking University Wangxuan Institute of Computer Technology Beijing100080 China Shandong Computer Science Center National Supercomputer in Jinan Jinan250014 China Peking University National Engineering Research Center of Visual Technology School of Computer Science Beijing100871 China Peng Cheng Laboratory Shenzhen518055 China

Recently, the bio-inspired spike camera with continuous motion recording capability has attracted tremendous attention due to its ultra high temporal resolution imaging characteristic. Such imaging feature results in huge data storage and transmission burden compared to that of traditional camera, raising severe challenge and imminent necessity in compression for spike camera captured content. Existing lossy data compression methods could not be applied for compressing spike streams efficiently due to integrate-and-fire characteristic and binarized data structure. Considering the imaging principle and information fidelity of spike cameras, we propose a novel Reconstruction-based Contextual Spike Compression (RCSC) framework, which contains scene reconstruction, contextual image compression and spike generation. To our knowledge, it is the first learning-based model for efficient and robust spike stream compression with informative fidelity. Extensive experimental results show that our model outperforms the state-of-the-art conventional codec VVC intra by 6.14% and surpasses the state-of-the-art learned codec by 2.53% in BD-rate reduction, establishing a strong baseline for spike compression. © 1991-2012 IEEE.

关键词： Image compression

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Efficient Modeling Attack on Multiplexer PUFs via Kronecker Matrix Multiplication

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Wang, Hongfei Wan, Caixue Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China

The Physical Unclonable Function (PUF) is valued for its lightweight nature and unique functionality, making it a common choice for securing hardware products requiring authentication and key generation mechanisms. In response to the susceptibility of individual PUFs to modeling attacks, advanced PUF variants have been developed to improve security measures. One notable type in this regard is the multiplexer-based composition of arbiter PUFs, known as MPUF, which aims to meet high reliability and security standards simultaneously. Current research on attacking MPUF encounters challenges such as substantial demands for training CRPs and low success rates. In this work, we propose a novel numerical modeling attack strategy for MPUFs. Using Kronecker products from mathematical perspective, this method precisely describe the MPUF model without using complex network architectures, boosting attack accuracy and overall efficiency. Experiment comparison with state-of-the-art works demonstrates that our method achieves better performance in terms of attack accuracy, robustness, and efficiency. Our method is able to successfully attack a (512, 8)-MPUF in 32.71 minutes with 97.14% accuracy, outperforming all existing attack methods on MPUFs. More, we validate our method through experiments with hardware implementations on FPGAs. The advantages of our method also include the adaptability to be employed to attack other MPUF variations like cMPUF and rMPUF, and the capability to be integrated with an existing attack method for launching efficient attack on MPUFs leveraging reliability information. © 2025 IEEE.

关键词： modeling attack multiplexer-based PUF Physical unclonable function

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TTSlow: Slow Down Text-to-Speech With Efficiency Robustness Evaluations

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 693-704页

作者： Xiaoxue Gao Yiming Chen Xianghu Yue Yu Tsao Nancy F. Chen Institute for Infocomm Research Agency for Science Technology and Research (A*STAR) Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Department of Electrical Engineering Chung Yuan Christian University Taoyuan City Taiwan

Text-to-speech (TTS) has been extensively studied for generating high-quality speech with textual inputs, playing a crucial role in various real-time applications. For real-world deployment, ensuring stable and timely generation in TTS models against minor input perturbations is of paramount importance. Therefore, evaluating the robustness of TTS models against such perturbations, commonly known as adversarial attacks, is highly desirable. In this paper, we propose TTSlow, a novel adversarial approach specifically tailored to slow down the speech generation process in TTS systems. To induce long TTS waiting time, we design novel efficiency-oriented adversarial loss to encourage endless generation process. TTSlow encompasses two attack strategies targeting both text inputs and speaker embedding. Specifically, we propose TTSlow-text, which utilizes a combination of homoglyphs-based and swap-based perturbations, along with TTSlow-spk, which employs a gradient optimization attack approach for speaker embedding. TTSlow serves as the first attack approach targeting a wide range of TTS models, including autoregressive and non-autoregressive TTS ones, thereby advancing exploration in audio security. Extensive experiments are conducted to evaluate the inference efficiency of TTS models, and in-depth analysis of generated speech intelligibility is performed using Gemini. The results demonstrate that TTSlow can effectively slow down two TTS models across three publicly available datasets.

关键词： Perturbation methods Speech processing Robustness Computational modeling Security Optimization Text to speech Speech recognition Predictive models Real-time systems

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