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8th International Congress on Edge Computing, EDGE 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Tian, Yun Wu, Bin Shi, Jiaoli Zhang, Caicai Xu, Du School of Computer and Big Data Science Jiujiang University Jiujiang332005 China Jiujiang Key Laboratory of Network and Information Security Jiujiang332005 China School of Modern Information Technology Zhejiang Polytechnic University of Mechanical and Electrical Engineering Hangzhou310053 China

ISBN: (纸本)9783031770685

The development of cloud computing and the widespread application of cloud services have made outsourcing services more convenient. The need for individuals and businesses to store and manipulate the graph data they generate is growing rapidly. The unreliability and insecurity of cloud servers make outsourcing graph data a great risk of information leakage. To effectively protect data security, encrypting outsourced data is a useful method. The adjacent vertex query is a very commonly used and fundamental operation, and similarity search is a widely used and powerful tool to improve the scope and functionality of queries. After outsourcing encrypted sparse graph data to cloud servers, it becomes very inconvenient to use and manipulate the data. In this work, we present a scheme to realize the adjacent vertex query supporting similarity search on sparse graph data in cloud environment (SSAQ), which also protects the security of the information. This work uses edit distance and the searchable encryption principle to construct query index, and next implement the similar adjacent vertex query on cloud server. This work provides a formal security analysis, and also gives the experimental comparison and analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Information leakage

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Long-Term Trends of Extreme Waves Based on Observations from Five Stations in china

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哈尔滨工程大学学报(英文版) 2025年第3期24卷 479-491页

作者： Liwei Cao Shouhua Liu Jian Zeng Shufang Qin Zhen Zhang Gang Wang Jinhai Zheng Qiuhua Liang Aifeng Tao Key Laboratory of the Ministry of Education for Coastal Disaster and Protection Hohai UniversityNanjing 210024China College of Harbor Coastal and Offshore EngineeringHohai UniversityNanjing 210098China National Marine Data and Information Service Tianjin 300171China Zhejiang Institute of Hydraulics&Estuary(Zhejiang Surveying Institute of Estuary and Coast) Hangzhou 310020China School of Architecture Building and Civil EngineeringLoughborough UniversityEnglandLE113TUUK

Extreme waves may considerably impact crucial coastal and marine engineering structures. The First Scientific Assessment Report on Ocean and Climate Change of china and The Fourth Assessment Report on Climate Change of china were published in 2020 and 2022, ***, no concrete results on the long-term trends in wave changes in china have been obtained. In this study, long-term trends in extreme wave elements over the past 55 years were investigated using wave data from five in situ observation sites(i.e., Lao Hu Tan, Cheng Shan Tou,Ri Zhao, Nan Ji, Wei Zhou) along the coast of china. The five stations showed different trends in wave height. Results show a general downward trend in wave heights at the LHT and CST stations, reaching-0.78 and-1.44 cm/a, respectively, in summer at middle and high latitudes. NJI stations at middle-to-low latitudes are influenced by the winter monsoon and summer tropical cyclones, showing a substantial increase in extreme wave heights(0.7 cm/a in winter and 2.68 cm/a in summer). The cumulative duration of H_(1/10) ≥ 3 m at NJI and RZH has grown since 1990.

关键词： Maximum wave height Long-term trend Wave climate seasonal variability Extreme wave Generalized extreme value

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A Plug-and-Play Bregman ADMM Module for Inferring Event Branches in Temporal Point Processes

arXiv

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arXiv 2025年

作者： Wang, Qingmei Wu, Yuxin Long, Yujie Huang, Jing Ran, Fengyuan Su, Bing Xu, Hongteng Gaoling School of Artificial Intelligence Renmin University of China China School of Cyber Science and Engineering Wuhan University China Beijing Key Laboratory of Big Data Management and Analysis Methods China

An event sequence generated by a temporal point process is often associated with a hidden and structured event branching process that captures the triggering relations between its historical and current events. In this study, we design a new plug-and-play module based on the Bregman ADMM (BADMM) algorithm, which infers event branches associated with event sequences in the maximum likelihood estimation framework of temporal point processes (TPPs). Specifically, we formulate the inference of event branches as an optimization problem for the event transition matrix under sparse and low-rank constraints, which is embedded in existing TPP models or their learning paradigms. We can implement this optimization problem based on subspace clustering and sparse group-lasso, respectively, and solve it using the Bregman ADMM algorithm, whose unrolling leads to the proposed BADMM module. When learning a classic TPP (e.g., Hawkes process) by the expectation-maximization algorithm, the BADMM module helps derive structured responsibility matrices in the E-step. Similarly, the BADMM module helps derive low-rank and sparse attention maps for the neural TPPs with self-attention layers. The structured responsibility matrices and attention maps, which work as learned event transition matrices, indicate event branches, e.g., inferring isolated events and those key events triggering many subsequent events. Experiments on both synthetic and real-world data show that plugging our BADMM module into existing TPP models and learning paradigms can improve model performance and provide us with interpretable structured event branches. The code is available at https://***/qingmeiwangdaily/BADMM *** Codes 60G55, 62M10 Copyright © 2025, The Authors. All rights reserved.

关键词： Maximum likelihood estimation

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Generalized-Extended-State-Observer and Equivalent-Input-Disturbance Methods for Active Disturbance Rejection: Deep Observation and Comparison

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 957-968页

作者： Jinhua She Kou Miyamoto Qing-Long Han Min Wu Hiroshi Hashimoto Qing-Guo Wang School of Engineering Tokyo University of TechnologyHachiojiTokyo 192-0982Japan K.Miyamoto is with the Institute of Technology Shimizu CorporationKotoTokyo 135-0044Japan School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia School of Automation China University of GeosciencesWuhan 430074 Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of EducationWuhan 430074China School of Industrial Technology Advanced Institute of Industrial TechnologyTokyo 140-0011Japan Institute of Artificial Intelligence and Future Networks Beijing Normal UniversityZhuhai 519087 Guangdong Key Lab of AI and Multi-Modal Data Processing Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science BNUHKBU United International College Zhuhai 519087China

Active disturbance-rejection methods are effective in estimating and rejecting disturbances in both transient and steady-state *** paper presents a deep observation on and a comparison between two of those methods:the generalized extended-state observer(GESO)and the equivalent input disturbance(EID)from assumptions,system configurations,stability conditions,system design,disturbance-rejection performance,and extensibility.A time-domain index is introduced to assess the disturbance-rejection performance.A detailed observation of disturbance-suppression mechanisms reveals the superiority of the EID approach over the GESO method.A comparison between these two methods shows that assumptions on disturbances are more practical and the adjustment of disturbance-rejection performance is easier for the EID approach than for the GESO method.

关键词： Active disturbance-rejection control(ADRC) disturbance observer(DOB) equivalent input disturbance(EID) extendedstate observer(ESO) generalized extended-state observer(GESO)

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A Real-time Demonstration of Bidirectional Multiformat/Rate-adjustable Integrated Laser Communication/Ranging for Satellite Communication

A Real-time Demonstration of Bidirectional Multiformat/Rate-...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Wang, Yizhou Wang, Yuanxiang Luo, Zhen Zhong, Linsheng Zou, Yang Ao, Xueyuan Liu, Jiale Fan, Feng Li, Xiaoliang Dai, Xiaoxiao Yang, Qi Liu, Deming National Engineering Laboratory for Next Generation Internet Access System School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Beijing Research Institute of Telemetry Beijing100094 China Jinyinhu Laboratory Wuhan430040 China

A multi-format/rate-adjustable integrated laser communication/ranging system is proposed, achieving adjustable rates between 0.625-10Gbit/s. Real-time experiment shows that at 1e-4 BER the ranging error is only 0.29% ... 详细信息

ISBN: (纸本)9781957171395

关键词： Laser theory

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SALI: A Scalable Adaptive Learned Index Framework based on Probability Models

arXiv

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arXiv 2023年

作者： Ge, Jiake Zhang, Huanchen Shi, Boyu Luo, Yuanhui Guo, Yunda Chai, Yunpeng Chen, Yuxing Pan, Anqun Key Laboratory of Data Engineering and Knowledge Engineering MOE China Engineering Research Center of Database and Business Intelligence MOE China School of Information Renmin University of China China China and Shanghai Qi Zhi Institute China Tsinghua University China Tencent Inc China

The growth in data storage capacity and the increasing demands for high performance have created several challenges for concurrent indexing structures. One promising solution is learned indexes, which use a learning-based approach to fit the distribution of stored data and predictively locate target keys, significantly improving lookup performance. Despite their advantages, prevailing learned indexes exhibit constraints and encounter issues of scalability on multi-core data storage. This paper introduces SALI, the Scalable Adaptive Learned Index framework, which incorporates two strategies aimed at achieving high scalability, improving efficiency, and enhancing the robustness of the learned index. Firstly, a set of node-evolving strategies is defined to enable the learned index to adapt to various workload skews and enhance its concurrency performance in such scenarios. Secondly, a lightweight strategy is proposed to maintain statistical information within the learned index, with the goal of further improving the scalability of the index. Furthermore, to validate their effectiveness, SALI applied the two strategies mentioned above to the learned index structure that utilizes fine-grained write locks, known as LIPP. The experimental results have demonstrated that SALI significantly enhances the insertion throughput with 64 threads by an average of 2.04× compared to the second-best learned index. Furthermore, SALI accomplishes a lookup throughput similar to that of LIPP+. © 2023, CC BY-NC-ND.

关键词： Scalability

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Multi-scale cyclical similarity prototype refinement for few-shot breast ultrasound image segmentation 17

Multi-scale cyclical similarity prototype refinement for few...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Ou, Yingfeng Yang, Xing Zhang, Jian Jian, Caiqing Wang, Lihui College of Computer Science and Technology Guizhou University Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data Engineering Research Center of Text Computing Ministry of Education Guiyang China

ISBN: (纸本)9798350387384

Few-shot learning based methods can address the reliance on large-scale labeled samples in current breast tumor segmentation. However, previous methods typically rely on a few support samples to extract abstract, coarse-grained semantic prototypes, without considering the interaction between support and query data. In this study, we aim to provide each query image with relevant and information-rich support information. By integrating the difficult point selection module and the multi-scale cyclical prototype refinement module, we progressively refine coarse-grained support prototypes into more informative, fine-grained class prototypes, thereby better matching the highly variable breast ultrasound query data. Experiments on two datasets demonstrate that our proposed multi-scale cyclical similarity prototype refinement network (CSPRNet) achieves remarkable improvements compared to previous state-of-the-art methods. Specifically, using ResNet50 as the backbone, we achieve 57.93%/57.92% and 57.58%/59.24% mIoU for 1-shot and 5-shot segmentation, respectively, on public and private datasets, outperforming the suboptimal method by 8.18%/2.55% and 2.77%/4.48%. © 2024 IEEE.

关键词： Ultrasonic imaging

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Vehicle Identification Model of Expressway Service Area Based on ETC Transaction data 15th

Vehicle Identification Model of Expressway Service Area Base...

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： You, Yi Zou, Fumin Cai, Qiqin Guo, Feng Fujian Provincial Key Laboratory of Automotive Electronics and Electric Drive Technology Fuzhou350118 China School of Mechanical Engineering and Automation Huaqiao University Xiamen361021 China College of Computer and Data Science Fuzhou University Fuzhou350108 China

ISBN: (纸本)9789819700677

This paper mainly focuses on vehicle identification model of expressway service area based on ETC transaction data, First Steps, Define the time when the service area entrance RSU identifies the vehicle as the vehicle order entry time;the second step, The identification time of the RSU at the service area is the export time of the vehicle order;the third step, Determine whether the vehicle order exit time minus the vehicle order entry time is greater than or equal to the threshold time T;In Step 4, If order exit time minus order entry time is greater than or equal to threshold time T, The order vehicle is considered as the vehicle entering the service area;If order exit time minus order entry time is less than threshold time T, Think that the order vehicle did not enter the service area, For the arterial road vehicles, Based on the method and system of identifying whether the vehicle enters the service area on the expressway, ETC effectively solves the expressway service area scenario, The difficult problem of the vehicle information collection in the service area being misread by the ETC equipment in the service area. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Vehicles

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Dynamic YOLOv8n: A vehicle detection network model based on YOLOv8n 4

Dynamic YOLOv8n: A vehicle detection network model based on ...

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4th International Conference on Image Processing and Intelligent Control, IPIC 2024

作者： Wei, Wei Liu, Yuxiu Yun, Jian Duan, Xiaodong School of Computer Science and Engineering Dalian Minzu University Dalian116650 China National Ethnic Affairs Commission of the People’s Republic of China Key Laboratory of Big Data Applied Technology Dalian Minzu University Dalian116650 China Dalian Key Laboratory of Digital Technology for National Culture Dalian Minzu University Dalian116650 China

ISBN: (纸本)9781510682313

For the vehicle edge computing platform, a huge model is difficult to achieve the requirements of real-time detection, which faces the challenges of high computational load and bad detection rate. In this paper, an improved YOLOv8n detection method is proposed, and a dynamic deformable convolution block (DDCNv2) is proposed in the YOLOv8n backbone network to improve the detection accuracy of the algorithm.A dynamic convolution module (KWConv) is introduced in the YOLOv8n. In addition, a multi-scale detector module is designed to reduce the number of parameters. We use the PASCAL VOC dataset and the MS COCO *** results show that compared with the existing YOLOv8n, the detection accuracy of the proposed model is improved by 5.1%, the FLOPs is reduced by 69.51%, and the model parameters is reduced by 8.64%, which proves the effectiveness and superiority of the proposed method. © 2024 SPIE.

关键词： Photons

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On Learning Frequency-Instance Correlations by Model-Agnostic Training for Synthetic Speech Detection 16

On Learning Frequency-Instance Correlations by Model-Agnosti...

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16th Asian Conference on Machine Learning, ACML 2024

作者： Wang, Zining Gao, Lijian Zhang, Jialin Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications Zhenjiang China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

The goal of Synthetic Speech Detection (SSD) is to detect spoofing speech synthesized by text-to-speech and voice conversion. Most existing SSD methods focus only on mining frequency-wise dependency by customizing frequency-aggregation modules in SSD models. However, the instance-wise dependency is usually under-explored, which is critical for identifying the synthetic speech from a global view. In this paper, we propose a novel model-agnostic training strategy for SSD that exploits both local (frequency-wise) and global (instance-wise) contexts, which do not rely on a customized architecture and can be flexibly integrated into previous SSD models. Specifically, we propose an inter-frequency correlation module to capture the local context by reconstructing the masked frequency information from the unmasked frequency context. Meanwhile, an inter-instance correlation module is performed to explore the global context among different instances by promoting intra-class compactness and inter-class dispersion in the latent space. These two complementary modules operate from distinct contextual perspectives, leading to improvements in SSD performance. Extensive experiments show that our method significantly improves the performance of two state-of-the-art models on the 2019 dataset and 2021 dataset of ASVspoof. © 2024 Z. Wang, L. Gao, J. Zhang & Q. Mao.

关键词： Speech recognition

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