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2024 International Symposium on Digital Home, ISDH 2024

作者： Jiang, Ming Ke, Jiehao Guo, Biao Metaverse Application Engineering Center Guilin University of Electronic Technology Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin China

ISBN: (纸本)9798331509873

In the field of multi-object tracking (MOT), accurately tracking the position and characteristics of targets is crucial. However, traditional methods often overlook detection results with low confidence, which can lead to the omission of targets and the fragmentation of trajectories. To overcome these challenges, we have introduced a novel NOF algorithm. This algorithm efficiently handles detection boxes through a tiered strategy, initially using an adaptive Kalman filter for preliminary matching, followed by a secondary screening of detection boxes with lower confidence, and a final stage for processing dubious detection results. This approach not only enhances the speed and accuracy of tracking but also performs exceptionally well when dealing with complex scenes. Moreover, it is capable of achieving high efficiency, with a rate of over 300 frames per second (FPS), ensuring the efficiency and precision of the tracking process. © 2024 IEEE.

关键词： Target tracking

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Multi-objective Deployment of WSNs in Underground Sheltered Space 19th

Multi-objective Deployment of WSNs in Underground Sheltere...

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19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Wan, Liangtian Wang, Caiyun Sun, Lu Chen, Boyu Zheng, Jibin Wang, Xianpeng Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian116620 China Department of Communication Engineering Institute of Information Science Technology Dalian Maritime University Dalian116026 China School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China State Key Laboratory of Marine Resource Utilization in South China Sea and School of Information and Communication Engineering Hainan University Haikou570228 China

ISBN: (纸本)9783031651250

Given the increasing population, the underground spaces are widely used. The existing research on wireless sensor networks (WSNs) development has mainly focused on overground space;also, the existing work was based on an ideal deployment environment and simple sensor behaviour. As a consequence, they do not satisfy requirements for practical 3D environment. The 3D wireless sensor networks (WSNs) deployment has new difficulties in the underground space. The signal transmission is impacted by obstacles in the underground space. Thus, we propose a geometry-based 3D signal propagation model to calculate the signal path loss caused by obstacles. Then, taking both coverage and connectivity into account, we transform the WSNs deployment problem into a multi-objective optimization problem (MOP). Finally, we compare some state-of-the-art multi-objective evolutionary algorithms (MOEAs) for the WSNs deployment problem in underground sheltered space. By the experimental results, we optimize the multiobjective deployment problem of the WSNs in the underground sheltered space. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Wireless sensor networks

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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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LawBench: Benchmarking Legal Knowledge of Large Language Models

LawBench: Benchmarking Legal Knowledge of Large Language Mod...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Fei, Zhiwei Shen, Xiaoyu Zhu, Dawei Zhou, Fengzhe Han, Zhuo Huang, Alan Zhang, Songyang Chen, Kai Yin, Zhixin Shen, Zongwen Ge, Jidong Ng, Vincent National Key Laboratory for Novel Software Technology Nanjing University China Digital Twin Institute Eastern Institute of Technology Ningbo China Saarland University Germany Shanghai AI Laboratory China School of Science and Engineering Magnet United States University of Texas Dallas United States

ISBN: (纸本)9798891761643

We present LawBench, the first evaluation benchmark composed of 20 tasks aimed to assess the ability of Large Language Models (LLMs) to perform Chinese legal-related tasks. LawBench is meticulously crafted to enable precise assessment of LLMs' legal capabilities from three cognitive levels that correspond to the widely accepted Bloom's cognitive taxonomy. Using LawBench, we present a comprehensive evaluation of 21 popular LLMs and the first comparative analysis of the empirical results in order to reveal their relative strengths and weaknesses. All data, model predictions and evaluation code are accessible from https://***/open-compass/LawBench. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A Dynamic Interest-Aware Message-Passing GCN for Recommendation 24

A Dynamic Interest-Aware Message-Passing GCN for Recommendat...

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24th IEEE International Conference on software Quality, Reliability and Security Companion, QRS-C 2024

作者： He, Weichao Zhu, Yi Song, Mei Wu, Zixin Hao, Guosheng School of Computer Science and Technology Jiangsu Normal University Jiangsu Xuzhou China Jiangsu Engineering Technology Research Center of Ict in Education Jiangsu Xuzhou China Ministry of Industry and Information Technology Key Laboratory of Safety-Critical Software Jiangsu Nanjing China

ISBN: (纸本)9798350365658

Over the past few years, Graph Convolutional Networks (GCN) have emerged as a promising tool in the field of recommendation. They have the ability to effectively learn user and item embeddings by leveraging collaborative signals from higher-order neighbors. Nevertheless, the recommendation models based on GCN also face a common issue. As more layers are added, the node embeddings start to become increasingly similar and eventually become indistin-guishable, leading to a decline in performance. To address the issues at hand, we present a recommendation model called DIMP-GCN. This model utilizes a Dynamic Interest-aware Message-Passing approach, where users and interacted items are organized into distinct subgraphs based on their interests. Within each subgraph, single-layer graph convolution operations are performed. As a result, the users who have been updated are divided based on their interests once again, leading to dynamic changes in subgraph partitioning as the network layers stack. Nodes are able to acquire more accurate representations through this process. We conduct extensive experiments on three established datasets, showcasing the superior performance of our DIMP-GCN compared to other state-of-the-art GCN-based recommendation models. © 2024 IEEE.

关键词： Message passing

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DAGC: Data-Aware Adaptive Gradient Compression 42

DAGC: Data-Aware Adaptive Gradient Compression

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Lu, Rongwei Song, Jiajun Chen, Bin Cui, Laizhong Wang, Zhi Tsinghua Shenzhen International Graduate School Tsinghua-Berkeley Shenzhen Institute China Dalian University of Technology School of Software Technology China Harbin Institute of Technology Shenzhen China Shenzhen University College of Computer Science and Software Engineering China China Peng Cheng Laboratory China

ISBN: (纸本)9798350334142

Gradient compression algorithms are widely used to alleviate the communication bottleneck in distributed ML. However, existing gradient compression algorithms suffer from accuracy degradation in Non-IID scenarios, because a uniform compression scheme is used to compress gradients at workers with different data distributions and volumes, since workers with larger volumes of data are forced to adapt to the same aggressive compression ratios as others. Assigning different compression ratios to workers with different data distributions and volumes is thus a promising solution. In this study, we first derive a function from capturing the correlation between the number of training iterations for a model to converge to the same accuracy, and the compression ratios at different workers;This function particularly shows that workers with larger data volumes should be assigned with higher compression ratios1 to guarantee better accuracy. Then, we formulate the assignment of compression ratios to the workers as an n-variables chi-square nonlinear optimization problem under fixed and limited total communication constrain. We propose an adaptive gradient compression strategy called DAGC, which assigns each worker a different compression ratio according to their data volumes. Our experiments confirm that DAGC can achieve better performance facing highly imbalanced data volume distribution and restricted communication. © 2023 IEEE.

关键词： Nonlinear programming

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Modal Identification of Spatial Trusses Based on Bayesian Approach

Modal Identification of Spatial Trusses Based on Bayesian Ap...

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UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance engineering, IncoME-V 2023, International conference on the Efficiency and Performance engineering Network, TEPEN 2023

作者： Pan, Yaozong Sun, Mingye Feng, Ying Zhao, Yan State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment Faculty of Vehicle Engineering and Mechanics Dalian University of Technology Dalian116023 China Ningbo Research Institute of Dalian University of Technology Ningbo315016 China

ISBN: (纸本)9783031494208

Modal identification mainly uses the input and output data of the structure to determine the natural frequency, modal damping ratio, and mode shape of the structure. The identified dynamic properties can provide the basis for structural vibration control, model updating and structural health monitoring. In the modal identification methods, Bayesian method considers the influence of system uncertainty on modal parameter identification and gives the quantified index of system uncertainty, which is an obvious advantage of Bayesian method compared with other identification methods. Space truss is usually carried on spacecraft as a support structure, and obtaining its accurate modal parameters is important for the attitude controller design of spacecraft. In this paper, a Bayesian time-domain method is proposed and used to identify the modal parameters of a numerical model of a space truss structure. First, the structure is modeled using the state equation and the mathematical relationship between the physical response of the space truss and the modal parameters is derived based on the Markov parameter matrix;secondly, the likelihood function and the posterior probability density function of the modal parameters are established based on Bayesian theory;Finally, the most probable values of the modal parameters of the space truss structure were solved using the optimization algorithm, and the posterior covariance matrix of the parameters was solved using the finite difference method to quantify the uncertainties of the parameters. The identification results and the finite element analysis results were compared to verify the accuracy of the method in this paper. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Probability density function

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Knowledge-Guided Cross-Topic Visual Question Generation 30

Knowledge-Guided Cross-Topic Visual Question Generation

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liu, Hongfei Wang, Guohua Xie, Jiayuan Chen, Jiali Fang, Wenhao Cai, Yi School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9782493814104

Visual question generation (VQG) task aims to generate high-quality questions based on the input image. Current methods primarily focus on generating questions containing specified content utilizing answers or question types as constraints. However, these constraints make it challenging to control the topic of generated questions (e.g., conversation or test subject topics) for various applications. Thus, it is necessary to utilize topics as constraints to guide question generation. Considering that there are many topics and it is almost impossible for human annotations to cover them, we propose the cross-topic learning VQG (CTL-VQG) task, which aims to generate questions related to unseen topics in cross-topic scenarios. In this paper, we propose a knowledge-guided cross-topic visual question generation (KC-VQG) model to extract unseen topic-related information for question generation. Specifically, an image-topic feature extractor is introduced in our model to extract topic-related intuitive visual features;an image-topic knowledge extractor is used to extract and select the most appropriate topic-related implicit knowledge from large language models for generating questions. Extensive experiments show that our model outperforms baselines and can effectively generate unseen topic-related questions in cross-topic scenarios. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Data mining

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SLAM: Towards Efficient Multilingual Reasoning via Selective Language Alignment 31

SLAM: Towards Efficient Multilingual Reasoning via Selective...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Fan, Yuchun Mu, Yongyu Wang, Yilin Huang, Lei Ruan, Junhao Li, Bei Xiao, Tong Huang, Shujian Feng, Xiaocheng Zhu, Jingbo NLP Lab School of Computer Science and Engineering Northeastern University Shenyang China NiuTrans Research Shenyang China Harbin Institute of Technology Harbin China Meituan Inc. National Key Laboratory for Novel Software Technology Nanjing University China

ISBN: (纸本)9798891761964

Despite the significant improvements achieved by large language models (LLMs) in English reasoning tasks, these models continue to struggle with multilingual reasoning. Recent studies leverage a full-parameter and two-stage training paradigm to teach models to first understand non-English questions and then reason. However, this method suffers from both substantial computational resource computing and catastrophic forgetting. The fundamental cause is that, with the primary goal of enhancing multilingual comprehension, an excessive number of irrelevant layers and parameters are tuned during the first stage. Given our findings that the representation learning of languages is merely conducted in lower-level layers, we propose an efficient multilingual reasoning alignment approach that precisely identifies and fine-tunes the layers responsible for handling multilingualism. Experimental results show that our method, SLAM, only tunes 6 layers' feed-forward sub-layers including 6.5−8% of all parameters within 7B and 13B LLMs, achieving superior average performance than all strong baselines across 10 languages. Meanwhile, SLAM only involves one training stage, reducing training time by 4.1 − 11.9× compared to the two-stage method. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Oil Well Acoustic Dynamic Liquid Level Detection Based on EEMD 3

Oil Well Acoustic Dynamic Liquid Level Detection Based on EE...

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3rd International Conference on High Performance Computing and Communication engineering, HPCCE 2023

作者： Yang, Ming Sha, Zhiqiang Sun, Yanqing School of Information Engineering Harbin University Harbin150086 China Heilongjiang Provincial Key Laboratory of the Intelligent Perception and Intelligent Software Harbin University Harbin150086 China Daqing Software Technology Research Institute Co. Ltd. Daqing163711 China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

The detection of oil well dynamic liquid level using acoustic methods requires digital signal processing techniques to reduce environmental noises. Most denoising techniques consist of filter-based approaches and spectral methods with time or frequency transformations. In this study, the Ensemble Empirical Mode Decomposition (EEMD) algorithm is implemented at the filtering step within the standard procedures for oil industrial use to detect dynamic liquid level. Five datasets from oil well production are tested using EEMD compared to standard filtering procedures in industrial use. The EEMD algorithm achieves results in good agreement with the reference results in general with worst case relative error reaching 0.51%. In particular, EEMD processed signal visibly displays more salient echo wave feature compared to reference for one case with unknown submerging noises or disturbances. Under such circumstances, the combination of a denoising filter and EEMD furthermore stabilizes the results, the maximum relative error falling to 0.39%. Importantly, the ensemble averaged Intrinsic Mode Functions (IMFs) of frequencies linked to reflected infra-sound could provide good knowledge helping locate the reflection of infra-sound wave. EEMD is a promising method for filtering signals of echo wave based dynamic liquid level detection. Further in-depth investigations are required to better interpret signals mixed with unknown noises or disturbances. © 2024 SPIE.

关键词： Empirical mode decomposition

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