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International Conference on Information science and Technology (ICIST)

作者： Zhitao Liu Xiaonan Luo Dong Wang Fang Li 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

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.

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SKDLog: Self-Knowledge Distillation-based CNN for Abnormal Log Detection

SKDLog: Self-Knowledge Distillation-based CNN for Abnormal L...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Ningning Han Siyang Lu Dongdong Wang Mingquan Wang Xiaoman Tan Xiang Wei School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Computer Science University of Central Florida Orlando FL USA School of Software Engineering Beijing Jiaotong University Beijing China College of Computer and Information Science College of Software Southwest University Chongqing China

System logs are critical to system health diagnosis, especially for big data distributed systems. To detect anomalies in logs, log analysis has become a popular and practical approach. However, with the increasing complexity of system logs in real-world systems, more noisy data are generated. The noise disturbs the regular detection procedure and decreases the detection quality. Hence, it is more difficult to improve detection performance under such uncertain circumstance. To tackle this challenge, we propose SKDLog, a novel and effective log anomaly detection method equipped with self-knowledge distillation. This approach leverages the knowledge from soft labels and refined feature maps through knowledge distillation. Moreover, it helps the detection model better acquire latent information. To better exploit feature maps, an auxiliary self-teacher branch is incorporated into the framework. After the integration, the model achieves performance gain in log anomaly detection. To demonstrate the effectiveness, we compare SKDLog with the state-of-the-art log-based anomaly detection approaches on HDFS and Hadoop Application datasets. Our model outperforms other approaches on both recall and F1-score. Furthermore, we challenge problem solving by performing experiments on an industry dataset and an unstable dataset. The experimental results show that SKDLog more accurately detects abnormal logs from noisy data with high recall and F1-score.

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A Survey of Stance Detection on Social Media: New Directions and Perspectives

arXiv

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arXiv 2024年

作者： Zhang, Bowen Dai, Genan Niu, Fuqiang Yin, Nan Fan, Xiaomao Wang, Senzhang Cao, Xiaochun Huang, Hu College of Big Data and Internet Shenzhen Technology University Shenzhen518000 China College of Computer Science and Software Shenzhen University Shenzhen518000 China Hong Kong University of Science and Technology 999077 Hong Kong School of Computer Science and Engineering Central South University Changsha410083 China School of Cyber Science and Technology Shenzhen Campus of Sun Yat-sen University Shenzhen518107 China Peking University Shenzhen Graduate School Shenzhen518000 China

In modern digital environments, users frequently express opinions on contentious topics, providing a wealth of information on prevailing attitudes. The systematic analysis of these opinions offers valuable insights for decision-making in various sectors, including marketing and politics. As a result, stance detection has emerged as a crucial subfield within affective computing, enabling the automatic detection of user stances in social media conversations and providing a nuanced understanding of public sentiment on complex issues. Recent years have seen a surge of research interest in developing effective stance detection methods, with contributions from multiple communities, including natural language processing, web science, and social computing. This paper provides a comprehensive survey of stance detection techniques on social media, covering task definitions, datasets, approaches, and future works. We review traditional stance detection models, as well as state-of-the-art methods based on large language models, and discuss their strengths and limitations. Our survey highlights the importance of stance detection in understanding public opinion and sentiment, and identifies gaps in current research. We conclude by outlining potential future directions for stance detection on social media, including the need for more robust and generalizable models, and the importance of addressing emerging challenges such as multi-modal stance detection and stance detection in low-resource languages. Copyright © 2024, The Authors. All rights reserved.

关键词： Social networking (online)

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A Unified Model of Direct and Indirect Reciprocity in Multichannel Games 39

A Unified Model of Direct and Indirect Reciprocity in Multic...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Shi, Juan Cao, Zhaoheng Liu, Jinzhuo Chu, Chen Wang, Zhen School of Automation Northwestern Polytechnical University China School of Computer Science Northwestern Polytechnical University China School of Software Yunnan University China School of Statistics and Mathematics Yunnan University of Finance and Economics China Northwestern Polytechnical University China Engineering Research Center of Cyberspace Yunnan University China School of Cybersecurity Northwestern Polytechnical University China

ISBN: (纸本)157735897X

Reciprocity plays a crucial role in maintaining cooperation in human societies and AI systems. In this paper, we focus on reciprocity within multichannel games and examine how cooperation evolves in this context. We propose a unified framework that allows us to evaluate the reputations of interdependent actions across multiple channels while simultaneously exploring both direct and indirect reciprocity mechanisms. We identify partner and semi-partner strategies under both forms of reciprocity, with the former leading to full cooperation and the latter resulting in partial cooperation. Through equilibrium analysis, we characterize the conditions under which full cooperation and partial cooperation emerge. Moreover, we show that when players can link multiple interactions, they learn to coordinate their behavior across different games to maximize overall cooperation. Our findings provide new insights into the maintenance of cooperation across various reciprocity mechanisms and interaction patterns. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Augmented V2V Radio Fingerprinting With Heterogeneous Federated Scattering Network for Autonomous Vehicle Road Cooperation

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Tiantian Xu, Dongyang Ma, Jing Alharbi, Abdullah Yu, Keping Guizani, Mohsen Leung, Victor C.M Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an710049 China Xi'an Jiaotong University School of Information and Communication Engineering Xi'an710049 China Xidian University State Key Laboratory of Integrated Services Networks China King Saud University Computer Science Department Community College Riyadh11437 Saudi Arabia Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan 999041 United Arab Emirates Shenzhen MSU-BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen528060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Augmented Internet of vehicle (IoV) network, providing natural infrastructure support for autonomous vehicle road cooperation (VRC), has been deemed as one of key enablers for autonomous VRC while simultaneously facing the challenge of impersonation, tampering or replay attack towards the high layer digital certificate for critical information. In this paper, we propose a heterogeneous scattering fingerprinting network (HSFNet) which amalgamates wavelet scattering and deep learning to achieve lightweight, rapid and reliable physical layer authentication by radio frequency fingerprinting (RFF) which employs the unique inherent hardware impairments within for vehicle-to everything (V2X) communication. Particularly, we first exploit a wavelet scattering network to extract RFF coefficients and eliminate redundancies within heterogeneous datasets. The establishment of a high-information-density datasets facilitates the efficient processing of extensive heterogeneous data, especially for resource-limited vehicles nodes. To improve the interpretability, we combine the multi-scale coefficients of input waveform and shapley values, formulating a complete interpretable framework and completing parameters tuning. Finally, considering heterogeneous applications in practical scenarios, we design a distributed federated learning framework which not only completes distributed training but also protects privacy with the resources-constrained intelligent IoV nodes. Experimental results demonstrate that the proposed scheme maintains excellent performance while utilizing only about 25.7% of original samples, significantly improving identification accuracy. Furthermore, the identification accuracy of federated learning ultimately converges to 98.1%. © 1967-2012 IEEE.

关键词： Deep learning

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ISLS: IoT-Based Smart Lighting System for Improving Energy Conservation in Office Buildings

arXiv

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arXiv 2025年

作者： Obioma, Peace Agbodike, Obinna Chen, Jenhui Wang, Lei Department of Computer Science University of Manchester M139PL United Kingdom Center for Artificial Intelligence in Medicine Chang Gung Memorial Hospital Guishan Dist Taoyuan33375 Taiwan iTrustal Department of Computer Science and Information Engineering Chang Gung University Guishan Dist. Taoyuan33302 Taiwan Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology China

With the Internet of Things (IoT) fostering seamless device-to-human and device-to-device interactions, the domain of intelligent lighting systems have evolved beyond simple occupancy and daylight sensing towards autonomous monitoring and control of power consumption and illuminance levels. To this regard, this paper proposes a new do-it-yourself (DIY) IoT-based method of smart lighting system featuring an illuminance control algorithm. The design involves the integration of occupancy and presence sensors alongside a communication module, to enable real-time wireless interaction and remote monitoring of the system parameters from any location through an end-user application. A constrained optimization problem was formulated to determine the optimal dimming vector for achieving target illuminance at minimal power consumption. The simplex algorithm was used to solve this problem, and the system’s performance was validated through both MATLAB simulations and real-world prototype testing in an indoor office environment. The obtained experimental results demonstrate substantial power savings across multiple user occupancy scenarios, achieving reductions of approx. 80%, 48%, and 26% for one, two, and four user settings, respectively, in comparison to traditional basic lighting installation systems. © 2025, CC BY.

关键词： Constrained optimization

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Cross-Channel and Regional Node Relation Enhanced Hybrid Graph Convolution Transformer for Ct Segmentation

SSRN

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SSRN 2023年

作者： Cui, Hui Jin, Qiangguo Wu, Xixi Wang, Linlin Zhang, Tiangang Nakaguchi, Toshiya Xuan, Ping Feng, David Dagan Department of Computer Science and Information Technology La Trobe University Melbourne Australia School of Software Northwestern Polytechnical University Shaanxi China School of Computer Science and Technology Heilongjiang University Harbin China Department of Radiation Oncology Shandong Cancer Hospital and Institute Shandong First Medical University Shandong Academy of Medical Sciences Jinan China School of Mathematical Science Heilongjiang University Harbin China Center for Frontier Medical Engineering Chiba University Chiba Japan Department of Computer Science School of Engineering Shantou University Shantou China School of Computer Science The University of Sydney Sydney Australia

Accurate and robust organ and tumour segmentation from CT scans are critical for precision diagnosis and prognosis of cancer, and the development of personalized treatment planning. However, automatic segmentation of tumours and organs they have invaded is challenging due to significant variations, abnormalities, and unclear boundaries. While graph convolutional networks can propagate knowledge and correlations in a flexible feature space, they suffer from information saturation during deep learning which can limit their effectiveness. To overcome this issue, we proposed a hybrid graph convolution transformer (HCGT) model. The HCGT has a channel transformer (CTrans) component, which helps to learn contextual relationships across different feature channels. Furthermore, we developed a convolutional graph transformer (convG-Trans) that can simultaneously and interactively learn enhanced relationships between different parts of the image by aggregating knowledge propagation from graph convolution and cross-node similarities from the transformer. Finally, we incorporated category-level attention to understand the significance of the two representations from the CTrans and convG-Trans, which helped adjust the fusion process before generating the segmentation output. We evaluated the HCGT on kidney and tumour and lung and non-small cell lung cancer datasets. Our evaluations included comparisons with other three-dimensional (3D) medical image segmentation benchmarks, as well as graph-based and transformer-based segmentation models. The results demonstrated improved performance in abdominal and thorax organ and tumour segmentation tasks. Additionally, ablation studies showed that the major technical innovations were effective and consistent when using different 3D medical image segmentation backbones. © 2023, The Authors. All rights reserved.

关键词： Convolution

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Construction of Multi-Scale Fusion Attention Unified Perceptual Parsing Networks for Semantic Segmentation of Mangrove Remote Sensing Images

SSRN

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SSRN 2024年

作者： Wang, Xin Xu, Wenquan Zhang, Yu Wang, Yixuan Wang, Hanxi Cai, Jingye Qin, Qin Wang, Qin Zeng, Jing School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610000 China School of Marine Engineering Guilin University of Electronic Technology Beihai536000 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541010 China School of Civil Engineering and Transportation Hohai University Nanjing210000 China School of Geographical Sciences Harbin Normal University Harbin150025 China Guilin University of Technology China

In recent years, significant progress has been made in monitoring the distribution of mangrove forests using remotely sensed images. Challenges persist due to the high resolution of the images, large-scale variations, and sporadic distribution of mangrove forest boundaries, which resulted in problems such as omission, misdiagnosis, and blurring of boundary segmentation that require resolution. This study constructed the Mangrove Semantic Segmentation Dataset in Beihai of Guangxi (MSSDBG) using a combination of China's new generation of high spatial resolution earth observation satellites, which included Beijing-2(BJ-2), Beijing-3(BJ-3), Gaofen-7(GF-7), Gaojing-1(SV-1), and Jilin-1(JL-1), as the data sources for multi-source remote sensing. The study proposed a multi-scale fusion attention unified perceptual parsing network (MFA-UperNet) for semantic segmentation of mangrove remote sensing images. The network included a CPF module based on a feature pyramid network, and this module can facilitate the integration of features at different levels through multi-path connections. The Multi-scale Large Selection Kernel Attention module incorporated a multi-scale channel attention mechanism and a large selection kernel structure, which effectively addressed mangrove forests' scale variation and shape differences in remote sensing images. AES branch optimization training was conducted using a fully convolutional network and a hybrid loss function to optimize the training results considering the characteristics of the sporadic distribution of mangrove boundaries. The study demonstrated that the MFA-UperNet achieved an optimal mPA of 97.14% and a mIoU of 94.54% on the MSSDBG dataset. High evaluation index scores were obtained on the Potsdam and Vaihingen datasets. This study contributes to the advancement of mangrove monitoring and provides a valuable reference for the restoration and protection of mangrove resources. © 2024, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Deep Edge Intelligence: Architecture, Key Features, Enabling Technologies and Challenges

arXiv

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arXiv 2022年

作者： Abeysekara, Prabath Dong, Hai Qin, A.K. School of Computing Technologies RMIT University Melbourne Australia Computer Science and Software Engineering Swinburne University of Technology Hawthorn Australia

With the breakthroughs in Deep Learning, recent years have witnessed a massive surge in Artificial Intelligence applications and services. Meanwhile, the rapid advances in Mobile Computing and Internet of Things has also given rise to billions of mobile and smart sensing devices connected to the Internet, generating zettabytes of data at the network edge. The opportunity to combine these two domains of technologies to power interconnected devices with intelligence is likely to pave the way for a new wave of technology revolutions. Embracing this technology revolution, in this article, we present a novel computing vision named Deep Edge Intelligence (DEI). DEI employs Deep Learning, Artificial Intelligence, Cloud and Edge Computing, 5G/6G networks, Internet of Things, Microservices, etc. aiming to provision reliable and secure intelligence services to every person and organisation at any place with better user experience. The vision, system architecture, key layers and features of DEI are also detailed. Finally, we reveal the key enabling technologies and research challenges associated with it. Copyright © 2022, The Authors. All rights reserved.

关键词： Internet of things

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Vehicle Re-Identication Model Based on Optimized DenseNet121 with Joint Loss

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computers, Materials & Continua 2021年第6期67卷 3933-3948页

作者： Xiaorui Zhang Xuan Chen Wei Sun Xiaozheng He Jiangsu Engineering Center of Network Monitoring Engineering Research Center of Digital ForensicsMinistry of EducationSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China Department of Civil and Environmental Engineering Rensselaer Polytechnic InstituteTroyNY12180USA

With the increasing application of surveillance cameras,vehicle re-identication(Re-ID)has attracted more attention in the eld of public *** Re-ID meets challenge attributable to the large intra-class differences caused by different views of vehicles in the traveling process and obvious inter-class similarities caused by similar *** existing methods focus on local attributes by marking local ***,these methods require additional annotations,resulting in complex algorithms and insufferable computation *** cope with these challenges,this paper proposes a vehicle Re-ID model based on optimized DenseNet121 with joint *** model applies the SE block to automatically obtain the importance of each channel feature and assign the corresponding weight to it,then features are transferred to the deep layer by adjusting the corresponding weights,which reduces the transmission of redundant information in the process of feature reuse in *** the same time,the proposed model leverages the complementary expression advantages of middle features of the CNN to enhance the feature expression ***,a joint loss with focal loss and triplet loss is proposed in vehicle Re-ID to enhance the model’s ability to discriminate difcult-to-separate samples by enlarging the weight of the difcult-to-separate samples during the training *** results on the VeRi-776 dataset show that mAP and Rank-1 reach 75.5%and 94.8%,***,Rank-1 on small,medium and large sub-datasets of Vehicle ID dataset reach 81.3%,78.9%,and 76.5%,respectively,which surpasses most existing vehicle Re-ID methods.

关键词： Vehicle re-identication densenet joint loss focal loss SE block

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