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Two kinds of average approximation accuracy

CAAI Transactions on Intelligence Technology 2024年第2期9卷 481-490页

作者： Qingzhao Kong Wanting Wang Dongxiao Zhang Wenbin Zhang Department of Science Jimei UniversityXiamenChina Digital Fujian Big Data Modeling and Intelligent Computing Institute Jimei UniversityXiamenChina Department of Computer Science Michigan Technological UniversityHoughtonMichiganUSA

Rough set theory places great importance on approximation accuracy,which is used to gauge how well a rough set model describes a target ***,traditional approximation accuracy has limitations since it varies with changes in the target concept and cannot evaluate the overall descriptive ability of a rough set *** overcome this,two types of average approximation accuracy that objectively assess a rough set model’s ability to approximate all information granules is *** first is the relative average approximation accuracy,which is based on all sets in the universe and has several basic *** second is the absolute average approximation accuracy,which is based on undefinable sets and has yielded significant *** also explore the relationship between these two types of average approximation ***,the average approximation accuracy has practical applications in addressing missing attribute values in incomplete information tables.

关键词： rough sets rough set theory

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Algorithm for minimum bounding rectangle based on key point set 3

Algorithm for minimum bounding rectangle based on key point ...

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3rd International Conference on Artificial Intelligence, Automation, and High-Performance computing, AIAHPC 2023

作者： Liang, Fengqi Wang, Licai Yu, Jintao North China Institute of Computing Technology Big Data R&D Center China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

This paper proposes a ship detection method based on contour extraction networks and minimum bounding box generation algorithms. The proposed method utilizes contour extraction networks to extract the contour of the ship target and generate a set of key points. Then, the minimum bounding box generation algorithm is used to transform these key points into the rotational detection box of the target, which can more accurately describe the shape and orientation of ships, thus improving the accuracy and reliability of detection. The experimental results show that the proposed method can effectively detect ships of various orientations and postures, and has better performance than traditional rectangular box detection methods. The paper compares rotated bounding box detection methods based on traditional machine learning and deep learning, and concludes that rotated bounding box detection methods based on deep learning can better solve the problem of ship detection in complex situations, but require a large amount of data for training and may require a higher computational cost. Finally, the paper introduces the Minimum Bounding Rectangle Algorithm used in the instance segmentation process. © 2023 SPIE.

关键词： Object detection

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Rough set model based on variable universe

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CAAI Transactions on Intelligence Technology 2022年第3期7卷 503-511页

作者： Qingzhao Kong Xueer Chang Department of Science Jimei UniversityXiamenChina Digital Fujian Big Data Modeling and Intelligent Computing Institute XiamenChina

Rough set theory has a very good effect in information processing and knowledge *** an information table,the current scholars regard all objects as a universe,and then establish various rough set ***,in the analysis of many data problems,it is more reasonable to select parts of objects which are useful to us or can meet the actual needs as a ***,in order to make up for the deficiency of traditional models,a new model is introduced from the perspective of variable ***,some interesting properties of this model,such as approximation sets,reduct of attributes and maximum part of universe,are *** the study of this paper,it can be seen that the model developed in our paper is not only more accurate but also more effective in describing uncertain knowledge.

关键词： approximation universe describing

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bigRU-DA: Based on Improved bigRU Multi-Target data Association Method 8

BiGRU-DA: Based on Improved BiGRU Multi-Target Data Associat...

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8th International Conference on Computer and Communication Systems, ICCCS 2023

作者： Wu, Guangsheng Wang, Licai Hu, Xun Luo, Qibin Guo, Dongliang North China Institute of Computing Technology Big Data Research and Development Center Beijing China

ISBN: (纸本)9781665456128

Trajectory data association algorithm is an important part of multi-target tracking method. Traditional association methods require priori information such as target motion model and clutter density to conduct association, and the association accuracy is low in complex scenes such as trajectory accompaniments and crosses. Based on the sensitivity of bigRU to time series, this paper proposes an improved trajectory data association algorithm of bigRU network: bigRU-DA. In this paper, an output gate structure is added on the basis of the existing bigRU model, which can optimize the output results of the model and extract the influence of trajectory timing sequence on trajectory data association. In order to verify the data association effect of bigRU-DA method, the experiment conducted comparison experiments in four datasets under the condition of clutter interference. Experimental results show that bigRU-DA is superior to other methods in all datasets. © 2023 IEEE.

关键词： Trajectories

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Resource investment for DDoS attack resistant SDN: a practical assessment

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Science China(Information Sciences) 2023年第7期66卷 112-129页

作者： Bin YUAN Fan ZHANG Jun WAN Huan ZHAO Shui YU Deqing ZOU Qiangsheng HUA Hai JIN School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science University of Technology Sydney

Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of service(DDoS) attack, which aims to drain the resources of SDN switches and controllers,is one of the most common. Once the switch or controller is damaged, the network services can be *** defense schemes against DDoS attacks have been proposed from the perspective of attack detection;however, such defense schemes are known to suffer from a time consuming and unpromising accuracy, which could result in an unavailable network service before specific countermeasures are taken. To address this issue through a systematic investigation, we propose an elaborate resource-management mechanism against DDoS attacks in an SDN. Specifically, by considering the SDN topology, we leverage the M/M/c queuing model to measure the resistance of an SDN to DDoS attacks. Network administrators can therefore invest a reasonable number of resources into SDN switches and SDN controllers to defend against DDoS attacks while guaranteeing the quality of service(QoS). Comprehensive analyses and empirical data-based experiments demonstrate the effectiveness of the proposed approach.

关键词： SDN security DDoS attacks resource management queueing theory QoS

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IMPRESS: An Importance-Informed Multi-Tier Prefix KV Storage System for Large Language Model Inference 23

IMPRESS: An Importance-Informed Multi-Tier Prefix KV Storage...

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23rd USENIX Conference on File and Storage Technologies, FAST 2025

作者： Chen, Weijian He, Shuibing Qu, Haoyang Zhang, Ruidong Yang, Siling Chen, Ping Zheng, Yi Huai, Baoxing Chen, Gang The State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China Zhejiang Key Laboratory of Big Data Intelligent Computing China Huawei Cloud China

ISBN: (纸本)9781939133458

Modern advanced large language model (LLM) applications often prepend long contexts before user queries to improve model output quality. These contexts frequently repeat, either partially or fully, across multiple queries. Existing systems typically store and reuse the keys and values of these contexts (referred to as prefix KVs) to reduce redundant computation and time to first token (TTFT). When prefix KVs need to be stored on disks due to insufficient CPU memory, reusing them does not always reduce TTFT, as disk I/O latency is high. In this paper, we propose IMPRESS, an importance-informed multi-tier prefix KV storage system to reduce I/O delay for LLM inference by only loading important prefix KVs. IMPRESS first leverages the insight that there is significant similarity in important token index sets across attention heads and introduces an I/O-efficient important KV identification algorithm. It then optimizes prefix KV storage and caching through importance-informed KV management, reducing TTFT during model inference. Our experimental results show that IMPRESS can reduce TTFT by up to 2.8× compared to state-of-the-art systems, while maintaining comparable inference accuracy. © 2025 FAST. All Rights Reserved.

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Blockchain-Enabled Cybersecurity Provision for Scalable Heterogeneous Network:A Comprehensive Survey

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Computer Modeling in Engineering & Sciences 2024年第1期138卷 43-123页

作者： Md.Shohidul Islam Md.Arafatur Rahman Mohamed Ariff Bin Ameedeen Husnul Ajra Zahian Binti Ismail Jasni Mohamad Zain Faculty of Computing Universiti Malaysia PahangKuantan26600Malaysia School of Engineering Computing&Mathematical SciencesUniversity ofWolverhamptonWolverhamptonUK Institute for Big Data Analytics and Artificial Intelligence(IBDAAI) Komplek Al-KhawarizmiUniversiti Teknologi MARAShah AlamSelangor40450Malaysia

Blockchain-enabled cybersecurity system to ensure and strengthen decentralized digital transaction is gradually gaining popularity in the digital era for various areas like finance,transportation,healthcare,education,and supply chain *** interactions in the heterogeneous network have fascinated more attention due to the authentication of their digital application ***,the exponential development of storage space capabilities across the blockchain-based heterogeneous network has become an important issue in preventing blockchain distribution and the extension of blockchain *** is the biggest challenge of data integrity and scalability,including significant computing complexity and inapplicable latency on regional network diversity,operating system diversity,bandwidth diversity,node diversity,etc.,for decision-making of data transactions across blockchain-based heterogeneous *** security and privacy have also become the main concerns across the heterogeneous network to build smart IoT *** address these issues,today’s researchers have explored the potential solutions of the capability of heterogeneous network devices to perform data transactions where the system stimulates their integration reliably and securely with *** key goal of this paper is to conduct a state-of-the-art and comprehensive survey on cybersecurity enhancement using blockchain in the heterogeneous *** paper proposes a full-fledged taxonomy to identify the main obstacles,research gaps,future research directions,effective solutions,andmost relevant blockchain-enabled cybersecurity *** addition,Blockchain based heterogeneous network framework with cybersecurity is proposed in this paper tomeet the goal of maintaining optimal performance data transactions among ***,this paper provides an in-depth description based on the critical analysis to overcome the existing work gaps for future research where it

关键词： Blockchain cybersecurity data transaction diversity heterogeneous

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Distributed Truss Computation in Dynamic Graphs

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Tsinghua Science and Technology 2023年第5期28卷 873-887页

作者： Ziwei Mo Qi Luo Dongxiao Yu Hao Sheng Jiguo Yu Xiuzhen Cheng School of Computer Science and Technology Shandong UniversityQingdao 266200China State Key Laboratory of Software Development Environment School of Computer Science and Engineering and the Beijing Advanced Innovation Center for Big Data and Brain ComputingBeihang UniversityBeijing 100191China Big Data Institute Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China

Large-scale graphs usually exhibit global sparsity with local cohesiveness,and mining the representative cohesive subgraphs is a fundamental problem in graph *** k-truss is one of the most commonly studied cohesive subgraphs,in which each edge is formed in at least k 2 triangles.A critical issue in mining a k-truss lies in the computation of the trussness of each edge,which is the maximum value of k that an edge can be in a *** works mostly focus on truss computation in static graphs by sequential ***,the graphs are constantly changing dynamically in the real *** study distributed truss computation in dynamic graphs in this *** particular,we compute the trussness of edges based on the local nature of the k-truss in a synchronized node-centric distributed *** decomposing the trussness of edges by relying only on local topological information is possible with the proposed distributed decomposition ***,the distributed maintenance algorithm only needs to update a small amount of dynamic information to complete the *** experiments have been conducted to show the scalability and efficiency of the proposed algorithm.

关键词： distributed algorithm dynamic graph graph mining cohesive subgraph k-truss

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MMD GRAPH KERNEL: EFFECTIVE METRIC LEARNING FOR GRAPHS VIA MAXIMUM MEAN DISCREPANCY 12

MMD GRAPH KERNEL: EFFECTIVE METRIC LEARNING FOR GRAPHS VIA M...

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12th International Conference on Learning Representations, ICLR 2024

作者： Sun, Yan Fan, Jicong School of Data Science The Chinese University of Hong Kong Shenzhen China School of Computing National University of Singapore Singapore Shenzhen Research Institute of Big Data Shenzhen China

This paper focuses on graph metric learning. First, we present a class of maximum mean discrepancy (MMD) based graph kernels, called MMD-GK. These kernels are computed by applying MMD to the node representations of two graphs with message-passing propagation. Secondly, we provide a class of deep MMD-GKs that are able to learn graph kernels and implicit graph features adaptively in an unsupervised manner. Thirdly, we propose a class of supervised deep MMD-GKs that are able to utilize label information of graphs and hence yield more discriminative metrics. Besides the algorithms, we provide theoretical analysis for the proposed methods. The proposed methods are evaluated in comparison to many baselines such as graph kernels and graph neural networks in the tasks of graph clustering and graph classification. The numerical results demonstrate the effectiveness and superiority of our methods. Our code is available at https://***/yan-sun-x/MMD-Graph-Kernel. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Message passing

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DEEP ORTHOGONAL HYPERSPHERE COMPRESSION FOR ANOMALY DETECTION 12

DEEP ORTHOGONAL HYPERSPHERE COMPRESSION FOR ANOMALY DETECTIO...

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12th International Conference on Learning Representations, ICLR 2024

作者： Zhang, Yunhe Sun, Yan Cai, Jinyu Fan, Jicong School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China School of Computing National University of Singapore Singapore Institute of Data Science National University of Singapore Singapore

Many well-known and effective anomaly detection methods assume that a reasonable decision boundary has a hypersphere shape, which however is difficult to obtain in practice and is not sufficiently compact, especially when the data are in high-dimensional spaces. In this paper, we first propose a novel deep anomaly detection model that improves the original hypersphere learning through an orthogonal projection layer, which ensures that the training data distribution is consistent with the hypersphere hypothesis, thereby increasing the true positive rate and decreasing the false negative rate. Moreover, we propose a bi-hypersphere compression method to obtain a hyperspherical shell that yields a more compact decision region than a hyperball, which is demonstrated theoretically and numerically. The proposed methods are not confined to common datasets such as image and tabular data, but are also extended to a more challenging but promising scenario, graph-level anomaly detection, which learns graph representation with maximum mutual information between the substructure and global structure features while exploring orthogonal single- or bi-hypersphere anomaly decision boundaries. The numerical and visualization results on benchmark datasets demonstrate the superiority of our methods in comparison to many baselines and state-of-the-art methods. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Anomaly detection

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