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7th International Conference on data Science and Information Technology, DSIT 2024

作者： Lu, Jiamin Liu, Jiahao Fan, Zhendong Zhou, Zhenyu Kong, Xu Feng, Jun Hohai University Key Laboratory of Water Big Data Technology Ministry of Water Resources Nanjing China Hohai University College of Computer and Software Nanjing China PowerChina Huadong Engineering Corporation Limited Hangzhou China

ISBN: (纸本)9798350384093

Traditional sorting-based semantic place retrieval methods may face the problem of a large retrieval space. An improved method is proposed in this paper, based on Learning-based Clustering. It has three components: encoding, clustering and retrieval. Specifically, we craft a spatial entity encoder that integrates both spatial and semantic dimensions to embed spatial entities. Then, we utilize a cascaded Siamese network architecture for deep clustering, thereby minimizing the retrieval space for downstream tasks. Finally, we employ the SSPR method to assess the impact of our approach on retrieval efficiency. © 2024 IEEE.

关键词： Content based retrieval

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Vehicle attribute recognition methods incorporating attention mechanisms

Vehicle attribute recognition methods incorporating attentio...

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2024 International Conference on Computer Application and Information Security, ICCAIS 2024

作者： Hu, Junzhu Lu, Ying Xu, Quanyuan Li, Caiyi Wang, Mingyang Zhou, Hao Yang, Peidong Jiang, Feng College of Big Data and Intelligent Engineering Southwest Forestry University Kunming650024 China Key Laboratory of Forestry and Ecological Big Data State Forestry Administration on Southwest Forestry University Kunming650024 China Qujing Tobacco Company Qujing655000 China

ISBN: (纸本)9781510689312

To address the challenges of vehicle attribute recognition in traffic management, intelligent parking systems, and road surveillance, this paper proposes an algorithm that integrates the Squeeze-and-Excitation (SE) attention mechanism into ResNet34. The SE mechanism is added after each BasicBlock, with a total of 16 SE modules incorporated to train the network. The optimal model is achieved after 100 iterations. The SE mechanism enhances the network’s ability to focus on key features by adaptively adjusting the importance of features across channels, thereby improving the effectiveness of feature representation. This dynamic adjustment allows the model to better adapt to complex and varying image structures, demonstrating strong adaptability in vehicle attribute recognition tasks. Experimental results show a significant improvement in recognition accuracy, with the enhanced ResNet34 model achieving a 5.1% increase in vehicle attribute recognition accuracy compared to the original ResNet34. The proposed algorithm effectively improves recognition performance, offering valuable technical support for the application of intelligent transportation systems. © The Authors.

关键词： Highway administration

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Image denoising based on residual network 3

Image denoising based on residual network

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3rd International Conference on Electronic Information engineering, Big data, and Computer Technology, EIBDCT 2024

作者： Ma, Lihe Wang, Yan Sha, Ning Ma, Lin School of Information Engineering Harbin University Harbin China Heilongjiang Government Affairs Big Data Center3 Harbin China Heilongjiang Provincial Key Laboratory of the Intelligent Perception and Intelligent Software Harbin China

ISBN: (纸本)9781510680449

Image denoising has always been a hot research topic in the field of computer vision, playing an important role in improving image quality and accuracy. In this paper, a method for image denoising based on residual network is proposed, which uses deep learning technology to improve the effect of image processing. This method can automatically learn useful features in the image and effectively remove noise in the image, achieving automatic removal of image noise. Finally, experiments were conducted on standard image datasets for evaluation. The experimental results show that the image denoising method based on residual network has better performance compared to traditional methods. It can effectively remove various types of noise, improve image quality and clarity, and has broad application prospects in image processing. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image denoising

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Joint Multi-Scale Contextual and Noise Suppression for Group Emotion Recognition

Joint Multi-Scale Contextual and Noise Suppression for Group...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Guo, Wangdong Zhu, Qing Mao, Qirong School of Computer Science and Communication Engineering Jiangsu University China Jiangsu Engineering Research Center of Big Data Ubiquitous Perception and Intelligent Agriculture Applications China Provincial Key Laboratory of Computational Intelligence and New Technologies in Low-Altitude Digital Agriculture Zhenjiang China

ISBN: (纸本)9798350368741

Group Emotion Recognition (GER) seeks to identify emotional states within multi-person groups. The complexity of the environment and the reliance on a single group emotion label often result in noisy individuals with inconsistent emotional expressions, hampering accurate emotion classification. Mainstream GER networks attempt to downweight noisy individuals, but when their numbers are high and over-relying on a single group label, the negative impact of noise on individuals remains significant, further complicating classification and diminishing accuracy. To address these limitations, we propose the Multi-Scale Contextual and Noise Suppression Model (MCon-NSM), a novel framework that enhances GER by capturing fine-grained interaction contexts and collaboratively suppressing noise at both the individual and group label levels. Extensive experiments on the GAF series datasets demonstrate that our method achieves results comparable to state-of-the-art techniques, validating its effectiveness in mitigating noise in GER. © 2025 IEEE.

关键词： Emotion analysis Group emotion recognition Multi-Scale context learning Noise suppression

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Real-time Dense 3D Semantic Mapping Using RGB-D Camera 42

Real-time Dense 3D Semantic Mapping Using RGB-D Camera

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42nd Chinese Control Conference, CCC 2023

作者： Yang, Guanci Wang, Xiaoyuan Zhu, Dongying Li, Yang Guizhou University Key Laboratory of Advanced Manufacturing Technology of the Ministry of Education Guiyang550025 China School of Mechanical Engineering Guizhou University Guiyang550025 China Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China

ISBN: (纸本)9789887581543

Focusing on the poor real-time performance of 3D semantic mapping using RGB-D cameras in indoor environments, this paper investigates the Real-time Dense 3D Semantic Mapping (RD3DSM) using RGB-D camera. Firstly, a lightweight semantic point cloud acquisition algorithm based on keyframe is designed, which is used to segment scene images and obtain the semantic point cloud real-timely. Secondly, to address the loss of inter-frame tracking in the semantic mapping process, ORB-SLAM3 system is employed as the underlying architecture of the visual 3D semantic mapping system to provide stable camera pose. Thirdly, an information fusion mechanism of the visual 3D semantic mapping system is designed, and the OctoMap 3D scene reconstruction module is adopted to implement dense 3D map representation with semantic information, and then a visual 3D semantic mapping algorithm based on ORB-SLAM3 is proposed. Finally, the designed RD3DSM system is integrated into a robot platform. The tests were conducted on the TUM dataset and natural indoor scenes. The results show the effectiveness and real-time performance of the RD3DSM system. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Cameras

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Distributed Dimensionality Reduction Filtering for CPSs Under DoS Attacks

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IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 1080-1082页

作者： Xiaoyuan Zheng Hao Zhang Xindi Yang Huaicheng Yan School of Artificial Intelligence and Data Science Hebei University of TechnologyTianjin 300401China Department of Control Science and Engineering Tongji UniversityShanghai 200092China Key Laboratory of Smart Manufacturing in Energy Chemcical Process of Ministry of Education East China University of Science and TechnologyShanghai 200237 School of Information and Electrical Engineering Hunan University of Science and TechnologyXiangtan 411201China

Dear Editor,This letter concerns about the distributed dimensionally reduction filtering for a class of cyber physical systems with denial of service(DoS)*** each sensor exchanging measurements with neighbors subjecte... 详细信息

关键词： DoS service filtering

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Mobility-aware Seamless Service Migration and Resource Allocation in Multi-edge IoV Systems

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6315-6332页

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan Fuzhou University College of Computer and Data Science Fuzhou350116 China Ministry of Education Engineering Research Center of Big Data Intelligence Fuzhou350002 China Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350116 China University of Exeter Department of Computer Science Faculty of Environment Science and Economy ExeterEX4 4QF United Kingdom Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China University of Oslo Department of Informatics Oslo0316 Norway

Mobile Edge Computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. © 2025 IEEE.

关键词： Convex optimization

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Prototype-Guided Multimodal Relation Extraction based on Entity Attributes 39

Prototype-Guided Multimodal Relation Extraction based on Ent...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Zefan Zhang, Weiqi Li, Yanhui Bai, Tian College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University China

ISBN: (纸本)157735897X

Multimodal Relation Extraction (MRE) aims to predict relations between head and tail entities based on the context of sentence-image pairs. Most existing MRE methods progressively incorporate textual and visual inputs to dominate the learning process, assuming both contribute significantly to the task. However, the diverse visual appearances and text with ambiguous semantics contain less-informative contexts for the corresponding relation. To tackle these challenges, we highlight the importance of semantically invariant entity attributes that encompass fine-grained categories. Towards this, we propose a novel Prototype-Guided Multimodal Relation Extraction (PG-MRE) framework based on Entity Attributes. Specifically, we first generate detailed entity explanations using Large Language Models (LLMs) to supplement the attribute semantics. Then, the Attribute Prototype Module (APM) refines attribute categories and condenses scattered entity attribute features into cluster-level prototypes. Furthermore, prototype-aligned attribute features guide diverse visual appearance features to produce compact and distinctive multimodal representations in the Relation Prototype Module (RPM). Extensive experiments demonstrate that our method gains superior relation classification capability (especially in scenarios involving various unseen entities), achieving new state-of-the-art performances on MNRE dataset. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Depositional process of hyperpycnal flow deposits:A case study on Lower Cretaceous Sangyuan outcrop in the Luanping Basin,Northeast China

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China Geology 2024年第3期7卷 505-516页

作者： De-zhi Yan Ru-kai Zhu Hao Shou Zhao-hui Xu Wei-hong Liu Si-cheng Zhu Zhi-cheng Lei Jing-ya Zhang Chang Liu Yi Cai Huai-min Xu Research Institute of Petroleum Exploration and Development PetroChinaBeijing 100083China National Energy Tight Oil and Gas R&D Center Beijing 100083China PetroChina Key Laboratory of Oil and Gas Reservoir Beijing 100083China State Key Laboratory of Petroleum Resources and Prospecting China University of Petroleum(Beijing)Beijing 102249China College of Geosciences China University of Petroleum(Beijing)Beijing 102249China Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data Technology Department of Information and EngineeringEast China University of TechnologyNanchang 330013China

Sedimentary process research is of great significance for understanding the distribution and characteristics of *** the detailed observation and measurement of the Sangyuan outcrop in Luanping Basin,this paper studies the depositional process of the hyperpycnal flow deposits,and divides their depositional process into three phases,namely,acceleration,erosion and *** the acceleration phase,hyperpycnal flow begins to enter the basin nearby,and then speeds up *** developed in the acceleration phase are *** addition,the original deposits become unstable and are taken away by hyperpycnal flows under the eroding *** a result,there are a lot of mixture of red mud pebbles outside the basin and gray mud pebbles within the *** the erosion phase,the reverse deposits are eroded and become thinner or even ***,no reverse grading characteristic is found in the proximal major channel that is closer to the source,but it is still preserved in the middle branch channel that is far from the *** entering the deceleration phase,normally grading deposits appear and cover previous *** final deposits in the basin are *** are reverse,and others are *** are superimposed with each other under the action of hyperpycnal *** analysis of the Sangyuan outcrop demonstrates the sedimentary process and distribution of hyperpycnites,and reasonably explain the sedimentary characteristics of *** is helpful to the prediction of oil and gas exploration targets in gravity flow deposits.

关键词： Hyperpycnal flow Sedimentary characteristics Depositional process Gravity flow deposit Hyperpycnite Red mud pebble Gray mud pebble Oil and gas exploration engineering Luanping Basin

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Exploring Heterogeneity and Uncertainty for Graph-based Cognitive Diagnosis Models in Intelligent Education 25

Exploring Heterogeneity and Uncertainty for Graph-based Cogn...

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Proceedings of the 31st ACM SIGKDD Conference on knowledge Discovery and data Mining V.1

作者： Pengyang Shao Yonghui Yang Chen Gao Lei Chen Kun Zhang Chenyi Zhuang Le Wu Yong Li Meng Wang Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei Anhui China BNRist Tsinghua University Beijing China Department of Electronic Engineering BNRist Tsinghua University Beijing China Ant Group HangZhou Zhejiang China

ISBN: (纸本)9798400712456

Graph-based Cognitive Diagnosis (CD) has attracted much research interest due to its strong ability on inferring students' proficiency levels on knowledge concepts. While graph-based CD models have demonstrated remarkable performance, we contend that they still cannot achieve optimal performance due to the neglect of edge heterogeneity and uncertainty. Edges involve both correct and incorrect response logs, indicating heterogeneity. Meanwhile, a response log can have uncertain semantic meanings, e.g., a correct log can indicate true mastery or fortunate guessing, and a wrong log can indicate a lack of understanding or a careless mistake. In this paper, we propose an Informative Semantic-aware Graph-based Cognitive Diagnosis model (ISG-CD), which focuses on how to utilize the heterogeneous graph in CD and minimize effects of uncertain edges. Specifically, to explore heterogeneity, we propose a semantic-aware graph neural networks based CD model. To minimize effects of edge uncertainty, we propose an Informative Edge Differentiation layer from an information bottleneck perspective, which suggests keeping a minimal yet sufficient reliable graph for CD in an unsupervised way. We formulate this process as maximizing mutual information between the reliable graph and response logs, while minimizing mutual information between the reliable graph and the original graph. After that, we prove that mutual information maximization can be theoretically converted to the classic binary cross entropy loss function, while minimizing mutual information can be realized by the Hilbert-Schmidt Independence ***, we adopt an alternating training strategy for optimizing learnable parameters of both the semantic-aware graph neural networks based CD model and the edge differentiation layer. Extensive experiments on three real-world datasets have demonstrated the effectiveness of ISG-CD.

关键词： cognitive diagnosis

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