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arXiv 2025年

作者： Shin, Changmin Song, Jaeyong Jang, Hongsun Kim, Dogeun Sung, Jun Kwon, Taehee Ju, Jae Hyung Liu, Frank Choi, Yeonkyu Lee, Jinho Department of Electrical and Computer Engineering Seoul National University Korea Republic of School of Data Science Old Dominion Univeristy United States Samsung Electronics

Graph processing requires irregular, fine-grained random access patterns incompatible with contemporary off-chip memory architecture, leading to inefficient data access. This inefficiency makes graph processing an extremely memory-bound application. Because of this, existing graph processing accelerators typically employ a graph tiling-based or processing-in-memory (PIM) approach to relieve the memory bottleneck. In the tiling-based approach, a graph is split into chunks that fit within the on-chip cache to maximize data reuse. In the PIM approach, arithmetic units are placed within memory to perform operations such as reduction or atomic addition. However, both approaches have several limitations, especially when implemented on current memory standards (i.e., DDR). Because the access granularity provided by DDR is much larger than that of the graph vertex property data, much of the bandwidth and cache capacity are wasted. PIM is meant to alleviate such issues, but it is difficult to use in conjunction with the tiling-based approach, resulting in a significant disadvantage. Furthermore, placing arithmetic units inside a memory chip is expensive, thereby supporting multiple types of operation is thought to be impractical. To address the above limitations, we present Piccolo, an end-to-end efficient graph processing accelerator with fine-grained in-memory random scatter-gather. Instead of placing expensive arithmetic units in off-chip memory, Piccolo focuses on reducing the off-chip traffic with non-arithmetic function-in-memory of random scatter-gather. To fully benefit from in-memory scatter-gather, Piccolo redesigns the cache and miss-handling architecture (MHA) of the accelerator such that it can enjoy both the advantage of tiling and in-memory operations. Piccolo achieves a maximum speedup of 3.28× and a geometric mean speedup of 1.62×, along with up to 59.7% reduction in energy consumption across various and extensive benchmarks. © 2025, CC BY-NC-ND.

关键词： Cache memory

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Multiple Kernel Clustering with Local Kernel Reconstruction and Global Heat Diffusion

SSRN

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SSRN 2023年

作者： Chen, Yan Du, Liang Zhou, Peng Duan, Lei Qian, Yuhua School of Computer Science Sichuan University Chengdu610065 China School of Computer and Information Technology Shanxi University Taiyuan030006 China Institute of Big Data Science and Industry Shanxi University Taiyuan030006 China School of Computer Science and Technology Anhui University Hefei230601 China

Multiple kernel clustering (MKC) can effectively reveal the nonlinear cluster structure within a set of candidate kernels. Since distant similarity estimation is unreliable, the exploration and exploitation of local manifold structures in kernel space are vital for identifying intrinsic clusters within multiple kernels. Though showing promising performance of recent local MKC methods, at least two problems still need to be addressed. On the one hand, the pairwise affinity for most of the existing local MKC methods is largely dominated by its kernel similarity. However, such exploration ignores the correlations among highly similar neighbors, which leads to redundant neighbor weight assignments and less discriminant for clustering. On the other hand, the affinity matrices are directly exploited in the latter clustering procedure. However, such an affinity matrix ignores the high-order connections and is noisy because each row of the affinity matrix is solved independently. We propose a novel local MKC method (LKRGD) to alleviate these two issues. We first explore the affinity by the coefficient of the Local Kernel Reconstruction (LKR) model, which reduces the redundancy of similar neighbors and enhances the discrimination for clustering. Before integrating these affinities, we further exploit them with the Global heat kernel Diffusion (GD) procedure to capture the long-range connections smoothly. The GD process works as a low pass filter, focusing on small eigenvalues corresponding to the top clusters. We then seek a consensus graph by integrating these smooth affinities within an auto-weighted Multiple Graph Fusion (MGF) framework. As a consequence, the exploration and exploitation of local structure can be achieved by assembling these three key steps, i.e., LKR, GD, and MGF, into a sequential pipeline, where the clustering performance can be improved gradually, and the computational efficiency can also be expected without the iterations between exploration and expl

关键词： Diffusion

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Adaptive Fourier Decomposition of the First Three SARS-CoV-2 Infection Waves with Epidemic Intervention—London,UK,2020-2022

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China CDC weekly 2024年第21期6卷 478-486,I0006-I0008页

作者： Zige Liu Guibin Lu Cheokieng Vong Zhiqi Zeng Wei He Zhijie Lin Cuiyun Lin Kaichin Hsieh Zifeng Yang Arlindo L.Oliveira Chitin Hon Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao SARChina Queen Ethelburga’s Collegiate Thorpe Underwood EstateYorkUK State Key Laboratory of Respiratory Disease National Clinical Research Center for Respiratory DiseaseGuangzhou Institute of Respiratory HealthThe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina Respiratory Disease AI Laboratory on Epidemic and Medical Big Data Instrument Applications Department of Engineering ScienceFaculty of Innovation EngineeringMacao University of Science and TechnologyMacao SARChina Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases KingMed School of Laboratory MedicineGuangzhou Medical UniversityGuangzhou CityGuangdong ProvinceChina Faculty of Innovation Engineering School of Computer Science and EngineeringMacao University of Science and TechnologyMacao SARChina University College London UCL Faculty of Engineering SciencesLondonUK Guangzhou Laboratory Guangzhou CityGuangdong ProvinceChina Instituto de Engenharia de Sistemas e Computadores:Investigação e Desenvolvimento em Lisboa LisboaPortugal Instituto Superior Técnico Universidade de LisboaLisboaPortugal

Background:This study provides a detailed analysis of the daily fluctuations in coronavirus disease 2019(COVID-19)case numbers in London from January 31,2020 to February 24,*** primary objective was to enhance understanding of the interactions among government pandemic responses,viral mutations,and the subsequent changes in COVID-19 case ***:We employed the adaptive Fourier decomposition(AFD)method to analyze diurnal changes and further segmented the AFD into novel multi-component groups consisting of one to three *** restructured components were rigorously evaluated using Pearson correlation,and their effectiveness was compared with other signal analysis *** study introduced a novel approach to differentiate individual components across various time-frequency scales using basis decomposition ***:Analysis of London’s daily COVID-19 data using AFD revealed a strong correlation between the“stay at home”directive and high-frequency components during the first epidemic *** indicates the need for sustained implementation of vaccination policies to maintain their ***:The AFD component method provides a comprehensive analysis of the immediate and prolonged impact of governmental policies on the spread of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2).This robust tool has proven invaluable for analyzing COVID-19 pandemic data,offering critical insights that guide the formulation of future preventive and public health strategies.

关键词： offering sustained London

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Joint Deployment of UAV and Edge Server In Edge Computing

Joint Deployment of UAV and Edge Server In Edge Computing

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Huang, Wei Zhang, Deyu Luo, Wei Tang, Yin School of Computer Science and Engineering Central South University Changsha410083 China Big Data Institute Central South University Changsha410083 China

ISBN: (纸本)9798350368369

Edge server placement is a hot issue in mobile edge computing. It is a key prerequisite for deploying edge servers that can meet computing needs and improve resource utilization. This paper studies the joint location deployment problem of edge server and UAV, and proposes a dynamic placement solution for heterogeneous servers based on dynamically changing network environments and computing resource requirements, with the goal of minimizing migration costs. Extensive comparative experiments based on real datasets demonstrate that our algorithm outperforms other algorithms in minimizing costs. © 2025 IEEE.

关键词： Edge computing

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A GAN-Based Power Quality Anomaly Detection Method for Imbalanced Multivariate Time Series 6

A GAN-Based Power Quality Anomaly Detection Method for Imbal...

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6th IEEE International Conference on computer and Communication Engineering Technology, CCET 2023

作者： Huang, Jiachen Liu, Chen Yang, Yang Liu, Yunjia North China University of Technology Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data Beijing China School of Computer Science North China University of Technology Beijing China Huaxia Qixin Limited Company Beijing China

ISBN: (纸本)9798350340686

Time series anomaly detection is an important and well-researched topic with extensive applications in finance, healthcare, biological data, and industrial production. However, in the field of power quality, detecting anomalies in complex and imbalanced multivariate long time series data remains a challenge. This paper proposes a GAN-based method for multivariate time series anomaly detection, which directly trains on raw data captured by power monitoring stations. It addresses the issues of the lack of original examples, difficulty in acquisition, and simulation of abnormal data in power quality. Additionally, the proposed method introduces an attention mechanism and a multi-scale feature fusion mechanism, reducing the accumulation of errors during model training and effectively addressing the problem of overfitting due to the scarcity of abnormal samples and high noise in power quality data. The comprehensive experiments conducted on four original power quality datasets demonstrate that the proposed method significantly improves the performance of anomaly detection in the field of power quality compared to baseline methods. © 2023 IEEE.

关键词： Time series

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Optimizing for the Shortest Path in Denoising Diffusion Model

arXiv

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arXiv 2025年

作者： Chen, Ping Zhang, Xingpeng Liu, Zhaoxiang Hu, Huan Liu, Xiang Wang, Kai Wang, Min Qian, Yanlin Lian, Shiguo Data Science & Artificial Intelligence Research Institute China Unicom China Unicom Data Intelligence China Unicom China DJI Technology Co. Ltd. China School of Computer Science and Software Engineering Southwest Petroleum University Chengdu China

In this research, we propose a novel denoising diffusion model based on shortest-path modeling that optimizes residual propagation to enhance both denoising efficiency and quality. Drawing on Denoising Diffusion Implicit Models (DDIM) and insights from graph theory, our model, termed the Shortest Path Diffusion Model (ShortDF), treats the denoising process as a shortest-path problem aimed at minimizing reconstruction error. By optimizing the initial residuals, we improve the efficiency of the reverse diffusion process and the quality of the generated samples. Extensive experiments on multiple standard benchmarks demonstrate that ShortDF significantly reduces diffusion time (or steps) while enhancing the visual fidelity of generated samples compared to prior arts. This work, we suppose, paves the way for interactive diffusion-based applications and establishes a foundation for rapid data generation. Code is available at https://***/UnicomAI/ShortDF. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph theory

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An Advanced Intrusion Detection Solution for Networks based on Honeypot Servers

An Advanced Intrusion Detection Solution for Networks based ...

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International Conference on Inventive Computation Technologies (ICICT)

作者： K. Veena K. Meena Mercy Theresa M Hemalatha C D. Rajalakshmi Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering GITAM School of Technology GITAM- Bangalore India Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Computer Science and Engineering Sri Sairam Institute of Technology Chennai

Network security is a critical issue in modern technology. Honey pot-based intrusion detection methods provide an additional layer of security and enhance network performance by analyzing hacker behaviour and detecting unauthorized clients. IP validation and vulnerability detection are used to ensure authorized customer access and prevent illegal client activity. A voice recognition system detects malicious attacks in stage two. If authorized, packets are passed to the server, otherwise, they are delivered to honeypot servers. Using Naive Bayes algorithm, the detection rate is 85.06% with an accuracy of 98.18% and a false alarm rate of 0.15 seconds.

关键词：

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Revolutionizing Legal Case Analysis through Advanced Event Extraction

Revolutionizing Legal Case Analysis through Advanced Event E...

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Recent Advances in science and Engineering Technology (ICRASET), International Conference on

作者： S Shivananda Lincy Meera Mathews Sanvi Nikkam Prashanth Kambli Rajashree D Ingale Prashant Koparde Information Science and Engineering M S Ramaiah Institute of Technology Bangalore India K S School of Engineering and Management Artificial Intelligence & Data Science Bangalore India K S School of Engineering and Management Computer Science & Business Systems Bangalore India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

In India manually evaluating court records was time-consuming and prone to errors, leading to delays in justice administration. Legal Sarathi, an automated event extraction tool was proposed to address this issue, it utilized Large Language Models coupled with other cutting-edge Natural Language Processing (NLP) techniques. Legal Sarathi analyzes unstructured legal case documents to extract time frames, significant participants and major events to improve speed and accuracy. It featured state-of-the-art Natural Language Processing (NLP) capabilities for prompt and precise case analysis whose aims included speeding up the analysis of court proceedings and real-time information retrieval, while simultaneously providing critical data for informed decision-making. The tools deliverables comprised an automated event extraction system, a highly preprocessed data corpus, a sophisticated NLP model, a user-friendly interface and thorough validation results. Ultimately, this established an improved and equitable legal system in India to ensure that all parties received quick and fair access to the courts and fostered public trust.

关键词： Productivity Visualization Text analysis Law Feature extraction Transformers Solids Natural language processing Libraries User experience

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Contactless Respiratory Rate Monitoring For ICU Patients Based On Unsupervised Learning

Contactless Respiratory Rate Monitoring For ICU Patients Bas...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Liu, Zimeng Huang, Bin Lin, Chun-Liang Wu, Chieh-Liang Zhao, Changchen Chao, Wen-Cheng Wu, Yu-Cheng Zheng, Yadan Wang, Zhiru Beihang University Hangzhou Innovataion Institute China Beihang University School of Automation Science and Electrical Engineering China National Chung Hsing University College of Electrical Engineering and Computer Science Taiwan Taichung Veterans General Hospital Department of Critical Care Medicine Taiwan National Chung Hsing University College of Medicine Department of Post-Baccalaureate Medicine Taiwan National Chung Hsing University Big Data Center Taiwan Beijing University of Posts and Telecommunications Computer Science School China Beijing Foreign Studies University Graduate School of Translation and Interpretation China

ISBN: (纸本)9798350302493

Recently, the task of contactless physiological signal monitoring based on deep learning technologies has attracted a large number of scholars. However, few studies focus on the application of real-world scenarios, especially in clinical medicine scenes. In this paper, a novel video-based contactless respiratory rate measurement algorithm is developed for the Intensive Care Unit (ICU) patients. Firstly, a large-scale clinical real-world database towards ICU patient is collected in this study. Then, based on the dataset, the unsupervised learning is first introduced to extract the respiration waveform from the chest area of patients. Lastly, a respiratory rate estimator based on neural networks is proposed and trained on a periodical physiological signal simulation dataset, and utilizes the transfer learning technique to extract the respiratory rate from only 10-second respiration waveform. We obtained estimated respiratory rate with an MAE of 2.8 breaths/min and an STD of 3.0 breaths/min against the reference value computed from the specialized medical device. Extensive experiments demonstrate that our proposed methods achieve competitive results over state-of-the-art (SOTA) method in the real-world scenario. © 2023 IEEE.

关键词： Intensive care units

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Digital Technologies and Human Trafficking: Combating Coercive Control and Navigating Digital Autonomy 25

Digital Technologies and Human Trafficking: Combating Coerci...

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2025 CHI Conference on Human Factors in Computing Systems, CHI 2025

作者： Stephenson, Sophie Ramjit, Lana Ristenpart, Thomas Dell, Nicola School of Computer Data & Information Sciences University of Wisconsin Madison United States Computer Science Cornell Tech New YorkNY United States Cornell Tech New YorkNY United States Jacobs Institute Cornell Tech New YorkNY United States

ISBN: (纸本)9798400713941

This paper describes a qualitative study that interrogates the types of technology-facilitated coercive control faced by survivors of human trafficking and uncovers potential interventions to aid survivors' recovery. Via semi-structured interviews with 21 participants, including trafficking survivors and professional advocates, we show how traffickers use technology as a lever for control, engaging in surveillance, blackmail, impersonation, and harassment as they compel survivors to stay in the trafficking situation. In recovery, digital footprints keep survivors tethered to their trafficking experience, impacting their digital autonomy, economic mobility, and feelings of safety. Nevertheless, technology can also be a valuable tool for survivors' recovery, connecting them to essential resources and support systems. We discuss the need for interventions and services that account for the specificity of the trafficking context to help survivors attain digital safety and autonomy, including the potential to adapt existing tech safety services designed for other contexts to human trafficking. © 2025 Copyright held by the owner/author(s).

关键词： Emergency services

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