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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Manjula, R. Jagadeesan, S. Ramalingam, Venkadeshan Vetriselvi, T. Johnpeter, T. Srm Institute of Science and Technology College of Engineering and Technology Department of Computing Technologies Chennai India Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Tamilnadu Vellore India Information Technology Department University of Technology and Applied Sciences-Shinas Branch Sultanate of Oman Oman Vellore Institute of Technology School of Computer Science and Engineering Department of Iot Tamilnadu Vellore India K.Ramakrishnan College of Engineering Department of Computer Science and Engineering Trichy India

ISBN: (纸本)9798350313987

Hard hearing and deaf people are communicating with other people using the sign language. This is the only way of communication of such people to convey Since this is the only means by which these individuals can communicate, it is imperative that others be able to understand their language. Sign language recognition by computer begins with the acquisition of sign gestures and ends with recognition and conversion to text or voice. Static and dynamic are the two ways of sign gestures used. This study aims at focusing on the dynamic gesture recognition and the steps for recognizing sign language are discussed. The collected data are preprocessed and feature are extracted for classification. Finally the results are all investigated and developed for single user. © 2023 IEEE.

关键词： Gesture analysis Hand gesture recognition Hand tracking Sign language

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ROI-YOLO Model: Real-Time Deep Learning Detector for Floating Objects on River

ROI-YOLO Model: Real-Time Deep Learning Detector for Floatin...

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IEEE International Conference on software engineering and Service sciences (ICSESS)

作者： Lili Zhang Peng Jin Yu Fan College of Information Science and Engineering Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (数字)9798350389166

ISBN: (纸本)9798350389173

Floating objects on river and lake surfaces can cause significant water pollution, necessitating timely detection and treatment. However, the features of floating objects are complex and variable, with a small proportion in monitoring scenes and image quality constrained by surveillance equipment. To address these challenges, this study proposes the ROI-YOLO model for rapid and intelligent detection of floating objects in monitoring video images. The proposed model comprises three key components. First, the pre-detection module enables fast scene focusing and background suppression for large-scale video images, overcoming the issue of excessive information loss caused by downsampling in real-world scenarios. Second, the model employs multi-resolution feature extraction and precise localization of floating objects, with the design of adaptive anchor boxes based on object density and maximum-minimum distances. Third, the sub-pixel feature fusion module learns upsampling operators on feature maps and integrates multi-resolution features based on sub-pixel information, enhancing the restoration and fusion of effective information to improve detection accuracy. Furthermore, the model utilizes Bernoulli distribution-based random parameter updates during training, reducing the required training sample size. The resulting model achieves a mean average precision of 84.5%, a recall of 90.9%, and a detection speed of 27 FPS (Frames Per Second), outperforming other deep learning based object detection models.

关键词： Training Deep learning Adaptation models Technological innovation Surveillance Object detection Feature extraction Real-time systems Water pollution Rivers

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Algorithm for Identifying Critical Nodes in Networks Based on Spectral Clustering to Construct Hypergraphs

Algorithm for Identifying Critical Nodes in Networks Based o...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Xujie Li Fei Shao Haotian Li College of Computer Science and Software Engineering Hohai University Nanjing China College of Information Science and Engineering Hohai University Nanjing China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

Currently, the basis for critical nodes definition and identification lies in the representation learning of the network and the extraction of local and global features of the nodes. The effectiveness of the algorithm is closely related to network modeling and network structural characteristics. However, with the increasing complexity of real-world systems, utilizing the continuity and integrity of traditional bipartite graph representation faces significant challenges. Different from traditional graph modeling, the hyperedges of a hypergraph can connect multiple nodes simultaneously, thus forming richer structural features and interaction information between nodes for the analysis and judgment of critical nodes. To more accurately identify critical nodes in complex networks, we propose an algorithm that establishes a hypergraph representation for traditional graph data to extract higher-order features of nodes for neural network training, in order to identify critical nodes in complex networks. The effectiveness of this method is proven by comparing it with various critical node identification algorithms under different infection rates.

关键词： Training Representation learning Wireless communication Neural networks Signal processing algorithms Clustering algorithms Complex networks Feature extraction Vectors Topology

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Toward Privacy-Preserving Spatial Crowdsourcing: From Offline to Online

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IEEE Network 2024年第3期39卷 326-332页

作者： Hengzhi Wang Junjie Mai Lei Zhang Linfeng Shen Laizhong Cui Jiangchuan Liu College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing Science Simon Fraser University Burnaby BC Canada

Recent years have seen Spatial Crowdsourcing (SC) becoming a promising paradigm of collecting large amounts of location-aware data for various network applications. Its strengths lie in leveraging the power of the crowd to gather data effectively at a low cost. However, during the interaction process between SC participants (e.g., workers and task requestors) and the SC platform, participants’ sensitive information is inevitably exposed, raising severe privacy concerns. Existing studies addressing these privacy concerns have mostly focused on offline SC settings, where the participants’ information is known and accessible in advance. Yet real-world SC participants can be highly dynamic with uncertain behaviors; hence, in practice, their information is largely unknown a priori. In this article, we closely examine the privacy challenges as well as the trade-off between privacy and efficiency in online SC settings. We develop a comprehensive online privacy-preserving SC framework and analyze the critical implementation issues.

关键词： Privacy Costs Crowdsourcing Real-time systems Protection Vehicle dynamics Data privacy

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An End-to-End Training Method to Ensemble Bi-Linear Models for Knowledge Graph Completion

An End-to-End Training Method to Ensemble Bi-Linear Models f...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Cen, Si Liu, Han Liu, Chao Zou, Xiaoying Dai, Guoquan Wang, Xizhao College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

ISBN: (纸本)9798350303780

The study of ensemble learning in knowledge graph embedding (KGE) shows that combining multiple individual KGE models can perform better on knowledge graph completion. However, existing KGE ensemble methods ignore the creation of model diversity because these methods independently train individual models, which are short of training interaction. To create rich model diversity, we propose a novel training method for ensemble bilinear models (EBM) for the problem of knowledge graph completion. EBM uses a weighted loss to allow individual KGE models to interact during training. In this way, the relations in the knowledge graph can be automatically modeled by the most appropriate model from the KGE individual ones. The experiments on knowledge graph completion show that EBM has richer diversity and performs better than the single bilinear model and the ensemble methods without interaction. © 2023 IEEE.

关键词： Knowledge graph

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Multiple Purchase Chains with Negative Transfer Elimination for Multi-Behavior Recommendation 39

Multiple Purchase Chains with Negative Transfer Elimination ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Gong, Shuwei Liu, Yuting Dang, Yizhou Guo, Guibing Zhao, Jianzhe Wang, Xingwei Software College Northeastern University China School of Computer Science and Engineering Northeastern University China

ISBN: (纸本)157735897X

Multi-behavior recommendation exploits auxiliary behaviors (e.g., view, cart) to help predict users' potential target behavior (e.g., purchase) on a given item. However, existing works suffer from two issues: (1) They generally consider only a single chain from auxiliary behaviors to the target behavior, referred to as a purchase chain (e.g., view →cart→ purchase), ignoring other valuable purchase chains (e.g, view→purchase) that are beneficial for recommendation performance. (2) Most studies presume that interacted items in auxiliary behaviors are good for recommendations, and pay little attention to the negative transfer problem. That is, some auxiliary behaviors may negatively transfer the influence to the modeling of target ones (e.g., items viewed but not purchased). To alleviate these issues, we propose a novel Multiple Purchase Chains (MPC) model with negative transfer elimination for multi-behavior recommendation. Specifically, we construct multiple purchase chains from auxiliary to target behaviors according to users' historical interactions, while the representations of a previous behavior will be fed to initialize the next behavior on the chain. Then, we construct a negative graph for the latter behavior and learn the negative representations of users and items which will be filtered out to eliminate negative transfer. Experimental results on two real datasets outperform the best baseline by 40.97% and 47.26% on average in terms of Recall@10 and NDCG@10 respectively, demonstrating the effectiveness of our method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Sales

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EPT: Efficient Prompt Tuning by Multi-Space Projection and Prompt Fusion 39

EPT: Efficient Prompt Tuning by Multi-Space Projection and P...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Lan, Pengxiang Yang, Enneng Liu, Yuting Guo, Guibing Zhao, Jianzhe Wang, Xingwei Software College Northeastern University China School of Computer Science and Engineering Northeastern University China

ISBN: (纸本)157735897X

Prompt tuning is a promising method to fine-tune a pre-trained language model without retraining its large-scale parameters. Instead, it attaches a soft prompt to the input text, whereby downstream tasks can be well adapted by merely learning the embeddings of prompt tokens. Nevertheless, existing methods still suffer from two challenges: (i) they are hard to balance accuracy and efficiency. A longer (shorter) soft prompt generally leads to a better (worse) accuracy but at the cost of more (less) training time. (ii) The performance may not be consistent when adapting to different downstream tasks. We attribute it to the same embedding space but responsible for different requirements of downstream tasks. To address these issues, we propose an Efficient Prompt Tuning method (EPT) by multi-space projection and prompt fusion. Specifically, it decomposes a given soft prompt into a shorter prompt and two low-rank matrices, significantly reducing the training time. Accuracy is also enhanced by leveraging low-rank matrices and the short prompt as additional knowledge sources to enrich the semantics of the original short prompt. In addition, we project the soft prompt into multiple subspaces to improve the performance consistency, and then adaptively learn the combination weights of different spaces through a gating network. Experiments on 13 natural language processing downstream tasks show that our method significantly and consistently outperforms 11 comparison methods with the relative percentage of improvements up to 12.9%, and training time decreased by 14%. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Learning to rank

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Augmenting Sequential Recommendation with Balanced Relevance and Diversity 39

Augmenting Sequential Recommendation with Balanced Relevance...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Dang, Yizhou Zhang, Jiahui Liu, Yuting Yang, Enneng Liang, Yuliang Guo, Guibing Zhao, Jianzhe Wang, Xingwei Software College Northeastern University China School of Computer Science and Engineering Northeastern University China

ISBN: (纸本)157735897X

By generating new yet effective data, data augmentation has become a promising method to mitigate the data sparsity problem in sequential recommendation. Existing works focus on augmenting the original data but rarely explore the issue of imbalanced relevance and diversity for augmented data, leading to semantic drift problems or limited performance improvements. In this paper, we propose a novel Balanced data Augmentation Plugin for Sequential Recommendation (BASRec) to generate data that balance relevance and diversity. BASRec consists of two modules: Single-sequence Augmentation and Cross-sequence Augmentation. The former leverages the randomness of the heuristic operators to generate diverse sequences for a single user, after which the diverse and the original sequences are fused at the representation level to obtain relevance. Further, we devise a reweighting strategy to enable the model to learn the preferences based on the two properties adaptively. The Cross-sequence Augmentation performs nonlinear mixing between different sequence representations from two directions. It produces virtual sequence representations that are diverse enough but retain the vital semantics of the original sequences. These two modules enhance the model to discover fine-grained preferences knowledge from single-user and cross-user perspectives. Extensive experiments verify the effectiveness of BASRec. The average improvement is up to 72.0% on GRU4Rec, 33.8% on SASRec, and 68.5% on FMLP-Rec. We demonstrate that BASRec generates data with a better balance between relevance and diversity than existing methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Strong stabilized joint probabilistic data association method of autoregulatory gating in vehicle nonlinear multi-target tracking 4

Strong stabilized joint probabilistic data association metho...

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4th International Conference on Mechanical engineering, Intelligent Manufacturing, and Automation Technology, MEMAT 2023

作者： Han, Guoxin Liu, Fuyun Deng, Jucai Liu, Huiqi Bai, Weihua College of Mechanical and Electrical Engineering Guilin University of Electronic Technology Guilin China College of Computer Science and Software Zhaoqing University Zhaoqing China Commercial Vehicle Technology Center Liuzhou Dongfeng Automobile Company Ltd. Liuzhou China

ISBN: (纸本)9781510674820

This paper proposed a strong stabilized joint probabilistic data association approach based on numerical stability optimization and autoregulatory gating strategy for multi-target tracking of vehicles with multi-maneuvering states in a nonlinear cluttered environment. The approach utilized an enhanced square-root cubature Kalman filter to achieve the optimization for joint probabilistic data association, which advanced the accuracy and stability of nonlinear state estimation during the multi-target tracking. On this basis, an autoregulation method of association gate based on tracking error sensitivity was proposed to improve the data association gating strategy, which effectively advanced the success rate of target association and tracking accuracy. The virtual simulation traffic scenes of intelligent driving were constructed by PreScan to better reproduce the multi-target tracking problem. The results of the simulations show that the method has clear advantages in terms of target tracking precision, numerical stability and environmental adaptability. © 2024 SPIE.

关键词： Target tracking

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Efficient and Effective Prompt Tuning via Prompt Decomposition and Compressed Outer Product

arXiv

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arXiv 2025年

作者： Lan, Pengxiang Xu, Haoyu Yang, Enneng Liang, Yuliang Guo, Guibing Zhao, Jianzhe Wang, Xingwei Software College Northeastern University China School of Computer Science and Engineering Northeastern University China

Prompt tuning (PT) offers a cost-effective alternative to fine-tuning large-scale pre-trained language models (PLMs), requiring only a few parameters in soft prompt tokens added before the input text. However, existing PT approaches face two significant issues: (i) They overlook intrinsic semantic associations between soft prompt tokens, leading to high discreteness and limited interactions, thus reducing the model’s comprehension and effectiveness in complex tasks. (ii) Due to the complexity of downstream tasks, long soft prompt is necessitated to improve performance, but prompt length correlates positively with memory usage and computational costs. Achieving high efficiency and performance remains an ongoing challenge. To address these issues, we propose a novel Low-pAraMeters Prompt Tuning (LAMP) method, which leverages prompt decomposition and compressed outer product. Specifically, the prompt decomposition module employs Truncated SVD to reduce training parameters and significantly lower the dimensionality of the soft prompt parameter space. It then utilizes a compressed outer product module to facilitate multiple interactions among prompt tokens, exploring their intrinsic associations to enhance knowledge representation. Finally, LAMP uses average pooling to reduce memory usage and training/inference time. Extensive experiments across six architectures and eight datasets demonstrate that LAMP outperforms state-of-the-art PT-based and LoRA-based methods in performance and efficiency. © 2025, CC BY.

关键词： Semantics

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