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13th International Conference on Information science and Technology, ICIST 2023

作者： Liu, Zhitao Wang, Dong Luo, Xiaonan Li, Fang School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China College of Mathematics and Informatics College of Software Engineering South China Agricultural Univerrsity Guangzhou China

ISBN: (纸本)9798350313925

In the field of single object tracking, conventional methods often rely on correlation filters or visual image processing. However, these approaches typically focus solely on extracting target object features and consider only the position information in the current frame. They lack integration of contextual and spatial-temporal information, which can limit tracking *** method takes inspiration from single object detection and tracking techniques. It combines efficient model-based object detection with the spatial position of the final target in the image. This approach offers significant advantages in reducing tracking drift (deviation from the target object) and improving tracking accuracy (predicting bounding boxes that closely follow the target).To extract spatial information, we utilize a three-layer ResNet and Convolutional Block Attention Module (CBAM) in conjunction with a siamese network. This combination effectively captures the spatial characteristics of the target object. Additionally, we employ an adaptive processing head with an internal structure of Long Short Term Memory (LSTM) to capture temporal information. By integrating spatial and temporal cues, our method achieves more robust and accurate *** comprehensive comparison and ablation experiments across multiple datasets, we have demonstrated notable improvements with our method. It tightly connects the predicted bounding box with the tracking target and effectively combines spatial and temporal information for precise object tracking. © 2023 IEEE.

关键词： Long short-term memory

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Boosting Write Performance of KV Stores: An NVM - Enabled Storage Collaboration Approach

Boosting Write Performance of KV Stores: An NVM - Enabled St...

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International Conference on Data engineering

作者： Yi Wang Jiajian He Kaoyi Sun Yunhao Dong Jiaxian Chen Chenlin Ma Amelie Chi Zhou Rui Mao College of Computer Science and Software Engineering Shenzhen University Department of Computer Science Hong Kong Baptist University

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

As the most common data structure for key-value stores, LogStructured Merge Tree (LSM-tree) can eliminate random write operations and keep acceptable read performance. However, write stall and write amplification introduced by the leveled compaction of LSM-tree significantly degrade the system performance. The emerging non-volatile memory (NVM) provides byte-addressable access and low-latency data persistence. Integrating DIMM-interface NVM in the design of the LSM-tree can potentially alleviate the write stall and write amplification issue, as the access speed of NVM is several orders of magnitude faster than hard disk drives or flash memory-based solid-state drives. This hybrid storage should be carefully designed, requiring new architectural and key-value structural support. This paper presents ZigZagDB, an NVM-enabled data man-agement scheme for LSM-tree-based key-value stores. ZigZagDB adds additional layers of key-value stores and uses non-volatile memory as the storage media to hold these additional layers of data. The newly designed key-value stores alternately access the data from either SSD or NVM. This ‘ZigZag’ shape of storage collaboration and synchronization can benefit write efficiency and space utilization. By utilizing the NVM with very limited capacity, the redesigned organization of LSM-tree can effectively solve the write stall and write amplification issue. We demonstrate the viability of the proposed ZigZagDB using a set of extensive experiments. Experimental results show that ZigZagDB can significantly reduce the write amplification and boost the throughput in comparison with representative schemes.

关键词： Nonvolatile memory Solid state drives Shape System performance Collaboration Tail Throughput

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Towards Efficient and Diverse Generative Model for Unconditional Human Motion Synthesis 24

Towards Efficient and Diverse Generative Model for Unconditi...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Yu, Hua Liu, Weiming Bai, Jiapeng Gui, Xu Hou, Yaqing Ong, Yewsoon Zhang, Qiang Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education School of Computer Science and Technology Dalian University of Technology Dalian China College of Computer Science Zhejiang University Hangzhou China National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian University Dalian China College of Computing and Data Science Nanyang Technological University Singapore Singapore Yonsei University Seoul Korea Republic of

ISBN: (纸本)9798400706868

Recent generative methods have revolutionized the way of human motion synthesis, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Denoising Diffusion Probabilistic Models (DMs). These methods have gained significant attention in human motion fields. However, there are still challenges in unconditionally generating highly diverse human motions from a given distribution. To enhance the diversity of synthesized human motions, previous methods usually employ deep neural networks (DNNs) to train a transport map that transforms Gaussian noise distribution into real human motion distribution. According to Figalli's regularity theory, the optimal transport map computed by DNNs frequently exhibits discontinuities. This is due to the inherent limitation of DNNs in representing only continuous maps. Consequently, the generated human motions tend to heavily concentrate on densely populated regions of the data distribution, resulting in mode collapse or mode mixture. To address the issues, we propose an efficient method called MOOT for unconditional human motion synthesis. First, we utilize a reconstruction network based on GRU and transformer to map human motions to latent space. Next, we employ convex optimization to match the noise distribution with the latent space distribution of human motions through the Optimal Transport (OT) map. Then, we combine the extended OT map with the generator of reconstruction network to generate new human motions. Thereby overcoming the issues of mode collapse and mode mixture. MOOT generates a latent code distribution that is well-behaved and highly structured, providing a strong motion prior for various applications in the field of human motion. Through qualitative and quantitative experiments, MOOT achieves state-of-the-art results surpassing the latest methods, validating its superiority in unconditional human motion generation. © 2024 ACM.

关键词： Generative adversarial networks

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Near-Linear Time Approximation Algorithms for k-means with Outliers 41

Near-Linear Time Approximation Algorithms for k-means with O...

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41st International Conference on Machine Learning, ICML 2024

作者： Huang, Junyu Feng, Qilong Huang, Ziyun Xu, Jinhui Wang, Jianxin School of Computer Science and Engineering Central South University Changsha410083 China Xiangjiang Laboratory Changsha410205 China Department of Computer Science and Software Engineering Penn State Erie The Behrend College United States Department of Computer Science and Engineering State University of New York BuffaloNY United States The Hunan Provincial Key Lab of Bioinformatics Central South University Changsha410083 China

The k-means with outliers problem is one of the most extensively studied clustering problems in the field of machine learning, where the goal is to discard up to z outliers and identify a minimum k-means clustering on the remaining data points. Most previous results for this problem have running time dependent on the aspect ratio ∆ (the ratio between the maximum and the minimum pairwise distances) to achieve fast approximations. To address the issue of aspect ratio dependency on the running time, we propose sampling-based algorithms with almost linear running time in the data size, where a crucial component of our approach is an algorithm called Fast-Sampling. Fast-Sampling algorithm can find inliers that well approximate the optimal clustering centers without relying on a guess for the optimal clustering costs, where a 4-approximate solution can be obtained in time O(ndk log log n/∊2) with O(k/∊) centers opened and (1 + ∊)z outliers discarded. To reduce the number of centers opened, we propose a center reduction algorithm, where an O(1/∊)-approximate solution can be obtained in time O(ndk log log n/∊2 + dpoly(k, 1/∊) log(n∆)) with (1 + ∊)z outliers discarded and exactly k centers opened. Empirical experiments suggest that our proposed sampling-based algorithms outperform state-of-the-art algorithms for the k-means with outliers problem. Copyright 2024 by the author(s)

关键词： K-means clustering

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View Transformation Robustness for Multi-View 3D Object Reconstruction with Reconstruction Error-Guided View Selection 39

View Transformation Robustness for Multi-View 3D Object Reco...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Qi Luo, Zhouhang Yu, Tao Huang, Hui College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen China

ISBN: (纸本)157735897X

View transformation robustness (VTR) is critical for deep-learning-based multi-view 3D object reconstruction models, which indicates the methods' stability under inputs with various view transformations. However, existing research seldom focuses on view transformation robustness in multi-view 3D object reconstruction. One direct way to improve the models' VTR is to produce data with more view transformations and add them to model training. Recent progress on large vision models, particularly Stable Diffusion models, has provided great potential for generating 3D models or synthesizing novel view images with only a single image input. To fully utilize the power of Stable Diffusion models without causing extra inference computation burdens, we propose to generate novel views with Stable Diffusion models for better view transformation robustness. Instead of synthesizing random views, we propose a reconstruction error-guided view selection method, which considers the reconstruction errors' spatial distribution of the 3D predictions and chooses the views that could cover the reconstruction errors as much as possible. The methods are trained and tested on sets with large view transformations to validate the 3D reconstruction models' robustness to view transformations. Extensive experiments demonstrate that the proposed method can outperform state-of-the-art 3D reconstruction methods and other view transformation robustness comparison methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep learning

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A Review of Link Prediction on Heterogeneous Networks 8th

A Review of Link Prediction on Heterogeneous Networks

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8th International Conference on Cognitive Computing, ICCC 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Mi, Rui Wang, Changbo Zhang, Liang-Jie Wu, Yirui Chen, Junyang Wang, Huan College of Informatics Huazhong Agricultural University Wuhan China Center of Health Administration and Development Studies Hubei University of Medicine Shiyan China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Hohai University Nanjing China

ISBN: (纸本)9783031779534

Link prediction is a central downstream assignment in network analysis, which denotes an attempt to assess the probability of a connection between two nodes based on observed link and node properties, which can be utilized across diverse categories of graph system, including knowledge graphs, network traffic, social networks, biological networks, etc. Heterogeneous networks usually contain diverse nodes and a variety of edges’ type, which can preserve more information than homogeneous information networks. However, the majority of researches focus on graph representation analysis of homogeneous networks. Limited research has systematically reviewed advanced techniques for link prediction and applications on Heterogeneous Information Networks (HIN). Building upon this, we propose a classification summary for link prediction methods in heterogeneous information graphs. We will introduce some classical methods and some newly proposed methods for link prediction on heterogeneous graphs. We provide general technical ideas behind each method in classes and analysis performance of models. We further discuss the applications of link prediction on complex heterogeneous graphs across various domains. Additionally, the research challenges are discussed, and several possible research avenues for link prediction on heterogeneous network are pointed out. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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A Multi-Agent Spatial Logic for Scenario-Based Decision Modeling and Verification in Platoon Systems

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Journal of computer science & Technology 2021年第6期36卷 1231-1247页

作者： Jingwen Xu Yanhong Huang Jianqi Shi Shengchao Qin National Trusted Embedded Software Engineering Technology Research Center East China Normal University Shanghai 200062China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518061China

To cater for the scenario of coordinated transportation of multiple trucks on the highway,a platoon system for autonomous driving has been extensively explored in the *** such a platoon is deployed,it is necessary to ensure the safety of its driving behavior,whereby each vehicle’s behavior is commanded by the decision-making function whose decision is based on the observed driving ***,there is currently a lack of verification methods to ensure the reliability of the scenario-based decision-making process in the platoon *** this paper,we focus on the platoon driving scenario,whereby the platoon is composed of intelligent heavy trucks driving on cross-sea *** propose a formal modeling and verification approach to provide safety assurance for platoon vehicles’cooperative driving *** existing Multi-Lane Spatial Logic(MLSL)with a dedicated abstract model can express driving scene spatial properties and prove the safety of multi-lane traffic maneuvers under the single-vehicle *** cater for the platoon system’s multi-vehicle perspective,we modify the existing abstract model and propose a Multi-Agent Spatial Logic(MASL)that extends MLSL by relative orientation and multi-agent *** then utilize a timed automata type supporting MASL formulas to model vehicles’decision controllers for platoon *** the behavior of a human-driven vehicle(HDV)joining the platoon as a case study,we have implemented the model and verified safety properties on the UPPAAL tool to illustrate the viability of our framework.

关键词： autonomous driving decision-making model platoon system safety verification timed automaton

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Linear Time Approximation Algorithm for Column Subset Selection with Local Search 38

Linear Time Approximation Algorithm for Column Subset Select...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zou, Yuanbin Huang, Ziyun Xu, Jinhui Wang, Jianxin Feng, Qilong School of Computer Science and Engineering Central South University Changsha410083 China Xiangjiang Laboratory Changsha410205 China Department of Computer Science and Software Engineering Penn State Erie The Behrend College United States Department of Computer Science and Engineering State University of New York BuffaloNY United States The Hunan Provincial Key Lab of Bioinformatics Central South University Changsha410083 China

The Column Subset Selection (CSS) problem has been widely studied in dimensionality reduction and feature selection. The goal of the CSS problem is to output a submatrix S, consisting of k columns from an n × d input matrix A that minimizes the residual error A − SS†A2F, where S† is the Moore-Penrose inverse matrix of S. Many previous approximation algorithms have non-linear running times in both n and d, while the existing linear-time algorithms have a relatively larger approximation ratios. Additionally, the local search algorithms in existing results for solving the CSS problem are heuristic. To achieve linear running time while maintaining better approximation using a local search strategy, we propose a local search-based approximation algorithm for the CSS problem with exactly k columns selected. A key challenge in achieving linear running time with the local search strategy is how to avoid exhaustive enumerations of candidate columns for constructing swap pairs in each local search step. To address this issue, we propose a two-step mixed sampling method that reduces the number of enumerations for swap pair construction from O(dk) to k in linear time. Although the two-step mixed sampling method reduces the search space of local search strategy, bounding the residual error after swaps is a non-trivial task. To estimate the changes in residual error after swaps, we propose a matched swap pair construction method to bound the approximation loss, ensuring a constant probability of loss reduction in each local search step. In expectation, these techniques enable us to obtain the local search algorithm for the CSS problem with theoretical guarantees, where a 53(k + 1)-approximate solution can be obtained in linear running time O(ndk4 log k). Empirical experiments show that our proposed algorithm achieves better quality and time compared to previous algorithms on both small and large datasets. Moreover, it is at least 10 times faster than state-of-the-art algorithms a

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Intelligent Gen: An Efficient On-Demand Access Protocol in MANETs using Network Simulator - 2 18

Intelligent Gen: An Efficient On-Demand Access Protocol in M...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Ganesan, T. Murali, N. Murugan, S. Palani Peter, M. Chandrakumar Raj, P. Anand Manikandan, S. E.G.S. Pillay Engineering College Department of Computer Science and Engineering Tamil Nadu Nagapattinam India E.G.S. Pillay Engineering College Department of Artificial Intelligence & Data Science Tamil Nadu Nagapattinam India Periyar Maniammai Institute of Science & Technology Department of Software Engineering Tamil Nadu Thanjavur India E.G.S. Pillay Engineering College Department of Computer Science and Business Systems Tamil Nadu Nagapattinam India E.G.S. Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India

ISBN: (纸本)9789380544519

Mobile Ad-Hoc network is a self-configuring and multi-hop collection of mobile devices for making successful communication. The wireless and mobile communication process are encountered based on bandwidth, process control and energy conception. Energy conception is considered and dynamically changed to rely on life time of batteries and power. The life time of battery get exhausted very quickly the nodes are moved and changed rapidly across the network. This paper noticed the problem of life time of power, batteries and energy consumption problem in various routing stages. The proposed Intelligent Gen Algorithm is used to get optimal solution for ensuring high robustness, flexibility and Energy consumption factor. It presents a common framework to balance various routing requirements in MANETs dynamically. The following are the main contributions includes, i. introducing Intelligent GEN Algorithms to classify the throughout bandwidth, delay, data ratio. ii. Various routing performance are evaluated and viewed by speed, packetize and simulation time. The comparison evaluated by select routing algorithm using NS-2. The simulation results are recorded and the performance is monitored by using on demand access protocl. The result clearly demonstrated the proposed Intelligent Gen algorithm used in self-organized manner by wireless channel. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： AOMDV Protocol Intelligent Gen Algorithm MANET Network Simulator NS-2

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Intrusion Detection System (IDS) of Multiclassification IoT by using Pipelining and an Efficient Machine Learning 9

Intrusion Detection System (IDS) of Multiclassification IoT ...

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9th International Conference on engineering and Emerging Technology, ICEET 2023

作者： Hazim, Layth Rafea Jasim, Abdulrahman Ahmed Ata, Oguz Ilyas, Muhammad College of Computer Science and Mathematics Tikrit University Tikrit Iraq Altinbas University Dept. of Electrical and Computer Engineering Istanbul Turkey College of Engineering Al-Iraqia University Baghdad Iraq Altinbas University Istanbul Dept. of Software Engineering Turkey Altinbas University Istanbul Dept. of Computer Engineering Turkey

ISBN: (纸本)9798350316926

The Internet of Things (IoT) has quickly advanced and been incorporated into many different fields. With the use of IoT technology, gadgets can receive, process, and send data automatically. IoT has been rapidly accepted in many important fields since it makes life easier and increases service quality, yet it still faces significant privacy and security problems. An Intrusion Detection System (IDS) could be implemented as a security feature to protect IoT networks from a variety of cyberattacks. This study suggests using IDS to defend against a wide range of cyberattacks on IoT systems. The suggested approach makes use of the Multi-layer Perceptron (MLP) as well as Extra Trees (ExT) as efficient algorithms of classification. Also, the study uses the pipeline to put together several cross-validated phases while selecting various parameters to increase the detection rate. One dataset is utilized for evaluating and analyzing the performance outcomes so as to validate the efficiency of the suggested IDS approach. The evaluation findings show that the suggested IDS methods may greatly increase detection performance results concerning accuracy rate, precision, F1-score, and recall while also improving detection efficiency. © 2023 IEEE.

关键词： Network security

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