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2023 International Conference on Intelligent Communication and Networking, ICN 2023

作者： Li, Xueru Zhang, Yan Liu, Lingjie Tian, Ye Zhang, Yue Harbin Engineering University College of Computer Science and Technology Harbin China China Industrial Control Systems Cyber Emergency Response Team Beijing China Environment and Food and Drug Crime Investigation Detachment of Hangzhou Public Security Bureau Hangzhou China Xidian University Hangzhou Institute of Technology Hangzhou China Key Laboratory of Peace-Building Big Data of Zhejiang Province Zhejiang China

ISBN: (纸本)9798350357677

With the continuous growth in demand for meat and frozen products in the cold chain market, illegally smuggled frozen goods have quickly entered the market due to their low prices, leading to a series of food safety issues. This article proposes a method for identifying the risks of smuggled frozen meat for urban public safety awareness using the YOLOv7 model. The YOLOv7 model is used to detect data captured at various traffic checkpoints, obtaining spatiotemporal information of frozen goods vehicles. This information is then combined with map data to generate vehicle trajectory information. The trajectory similarity between the frozen vehicle trajectories and the specialized database of frozen transportation routes is analyzed using the EDwP distance algorithm. The EDwP algorithm employs a threshold-free approach, dynamically interpolating using the concept of projection, and automatically adapting to non-uniform sampling rates to address the issue of inconsistent trajectory data sampling rates. The experimental results of the YOLOv7-EDwP algorithm were verified by verifying the alerts generated in the recent period through on-site verification by a third-party inspection agency, with a recognition accuracy of 90.77%. This holds significant significance in the detection and warning of smuggled frozen meat. © 2023 IEEE.

关键词： Transportation routes

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Simplify Implant Depth Prediction as Video Grounding: A Texture Perceive Implant Depth Prediction Network

arXiv

引用

arXiv 2024年

作者： Yang, Xinquan Li, Xuguang Luo, Xiaoling Zeng, Leilei Zhang, Yudi Shen, Linlin Deng, Yongqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Stomatology Shenzhen University General Hospital Shenzhen China

Surgical guide plate is an important tool for the dental implant surgery. However, the design process heavily relies on the dentist to manually simulate the implant angle and depth. When deep neural networks have been applied to assist the dentist quickly locates the implant position, most of them are not able to determine the implant depth. Inspired by the video grounding task which localizes the starting and ending time of the target video segment, in this paper, we simplify the implant depth prediction as video grounding and develop a Texture Perceive Implant Depth Prediction Network (TPNet), which enables us to directly output the implant depth without complex measurements of oral bone. TPNet consists of an implant region detector (IRD) and an implant depth prediction network (IDPNet). IRD is an object detector designed to crop the candidate implant volume from the CBCT, which greatly saves the computation resource. IDPNet takes the cropped CBCT data to predict the implant depth. A Texture Perceive Loss (TPL) is devised to enable the encoder of IDPNet to perceive the texture variation among slices. Extensive experiments on a large dental implant dataset demonstrated that the proposed TPNet achieves superior performance than the existing methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Neural network based on improved parallel bat algorithm and its application

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第3期6卷 428-439页

作者： Zhao, Zhuo-Qiang Liu, Shi-Jian Xu, Lin Pan, Jeng-Shyang Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China Institute of Artificial Intelligence Fujian University of Technology Fuzhou350118 China STEM University of South Australia Australia College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China

The artificial neural network is a research hotspot emerging in the field of artificial intelligence. Back Propagation (BP) neural network plays an important role in neural network. The network has the disadvantages of slow convergence of learning algorithms and easy falling into a local minimum. In this paper, a parallel bat algorithm with a new communication strategy is proposed, which is used to optimize the weights and thresholds of the BP neural network and establish the improved PBA-BP model. It was applied to optimize the parameters of the PID controller. Finally, we verified the effectiveness of the proposed method through the simulations. © 2021, Taiwan Ubiquitous Information. All rights reserved.

关键词： Three term control systems

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A Lightweight and Robust Access Control Protocol for IoT-based e-Healthcare Network

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IEEE Transactions on Mobile Computing 2025年

作者： Ghaffar, Zahid Kuo, Wen-Chung Mahmood, Khalid Tariq, Tayyaba Shamshad, Salman Das, Ashok Kumar Alenazi, Mohammed J.F. National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Douliu64002 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University Department of Computer Science and Engineering College of Informatics 145 Anam-ro Seongbuk-gu Seoul02841 Korea Republic of King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh Saudi Arabia

Internet of Things (IoT) devices are crucial components in e-healthcare networks. It enables remote patient health monitoring and facilitates seamless communication among medical sensors, wearable devices, and healthcare providers through public communication channels. Despite these advantages, the use of public communication among medical sensors in e-healthcare networks introduces critical challenges, such as vulnerability to impersonation, physical capture, and ephemeral secret leakage, particularly in resource-constrained environments. In recent years, various access control protocols have been developed to mitigate these risks. However, these protocols often fail to ensure robust security while incurring significant communication and computation overhead. To overcome these limitations, we propose a lightweight and robust access control protocol for IoT-based e-healthcare networks using chaotic maps. We propose a novel protocol that integrates a PUF-based mechanism to mitigate the challenges of physical tampering and cloning attacks in e-healthcare networks. It leverages the inherent uniqueness of PUF and enhances security through the high-entropy properties of chaotic maps. We analyze the proposed protocol informally, which confirms that it significantly bolsters efficiency and security. We also validate the security using the Random or Real (RoR) model. Moreover, we verify the security of the proposed protocol using Scyther. These analyses highlight that the proposed protocol offers robust resistance to numerous attacks, such as impersonation, physical capture, and ephemeral secret leakage. Moreover, we also compare it with existing and relevant protocols. The comparative analysis showcases its superior performance. Notably, the proposed authentication protocol significantly reduces 46.84% computational overhead and decreases 31.30% communication overhead, underscoring its enhanced performance and resource efficiency. © 2002-2012 IEEE.

关键词： Authentication

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Blockchain-Based Multi-Cloud data Storage System Disaster Recovery

Blockchain-Based Multi-Cloud Data Storage System Disaster Re...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Feiyu Wang Jian-tao Zhou College of Computer Science Inner Mongolia University Hohhot Inner Mongolia China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China

Cloud storage services have been used by most businesses and individual users. However, data loss, service interruptions and cyber attacks often lead to cloud storage services not being provided properly, and these incidents have caused financial losses to users. Second, traditional and single-cloud model disaster recovery services are no longer suitable for the current complex cloud storage systems. Therefore, a scheme to provide disaster recovery for cloud storage services in a multi-cloud storage environment is needed in real production. In this paper, we propose a disaster recovery scheme based on blockchain technology. The proposed scheme outlined in this study aims to address the issue of data availability within the cloud storage landscape. The proposed scheme achieves this goal by dividing data into hot and cold categories, verifying the integrity of copy data via blockchain technology, and utilizing blockchain networks to manage multi-cloud storage systems. Experimental findings demonstrate that the proposed scheme yields superior results in terms of computation and time overheads.

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GM-DF: Generalized Multi-Scenario Deepfake Detection

arXiv

引用

arXiv 2024年

作者： Lai, Yingxin Yu, Zitong Yang, Jing Li, Bin Kang, Xiangui Shen, Linlin The School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen University Shenzhen518060 China The Guangdong Key Laboratory of Information Security The School of Computer Science and Engineering Sun Yat-sen University Guangzhou510080 China Computer Vision Institute School of Computer Science & Software Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China

Existing face forgery detection usually follows the paradigm of training models in a single domain, which leads to limited generalization capacity when unseen scenarios and unknown attacks occur. In this paper, we elaborately investigate the generalization capacity of deepfake detection models when jointly trained on multiple face forgery detection datasets. We first find a rapid degradation of detection accuracy when models are directly trained on combined datasets due to the discrepancy across collection scenarios and generation methods. To address the above issue, a Generalized Multi-Scenario Deepfake Detection framework (GM-DF) is proposed to serve multiple real-world scenarios by a unified model. First, we propose a hybrid expert modeling approach for domain-specific real/forgery feature extraction. Besides, as for the commonality representation, we use CLIP to extract the common features for better aligning visual and textual features across domains. Meanwhile, we introduce a masked image reconstruction mechanism to force models to capture rich forged details. Finally, we supervise the models via a domain-aware meta-learning strategy to further enhance their generalization capacities. Specifically, we design a novel domain alignment loss to strongly align the distributions of the meta-test domains and meta-train domains. Thus, the updated models are able to represent both specific and common real/forgery features across multiple datasets. In consideration of the lack of study of multi-dataset training, we establish a new benchmark leveraging multi-source data to fairly evaluate the models' generalization capacity on unseen scenarios. Both qualitative and quantitative experiments on five datasets conducted on traditional protocols as well as the proposed benchmark demonstrate the effectiveness of our approach. The codes will be available on https://***/laiyingxin2/GM-DF. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

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SPA: a graph spectral alignment perspective for domain adaptation 23

SPA: a graph spectral alignment perspective for domain adapt...

引用

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Zhiqing Xiao Haobo Wang Ying Jin Lei Feng Gang Chen Fei Huang Junbo Zhao College of Computer Science and Technology Zhejiang University and Key Lab of Intelligent Computing based Big Data of Zhejiang Province Zhejiang University School of Software Technology Zhejiang University and Key Lab of Intelligent Computing based Big Data of Zhejiang Province Zhejiang University CUHK-SenseTime Joint Lab The Chinese University of Hong Kong School of Computer Science and Engineering Nanyang Technological University Alibaba Group

Unsupervised domain adaptation (UDA) is a pivotal form in machine learning to extend the in-domain model to the distinctive target domains where the data distributions differ. Most prior works focus on capturing the inter-domain transferability but largely overlook rich intra-domain structures, which empirically results in even worse discriminability. In this work, we introduce a novel graph SPectral Alignment (SPA) framework to tackle the tradeoff. The core of our method is briefly condensed as follows: (i)-by casting the DA problem to graph primitives, SPA composes a coarse graph alignment mechanism with a novel spectral regularizer towards aligning the domain graphs in eigenspaces; (ii)-we further develop a finegrained message propagation module — upon a novel neighbor-aware self-training mechanism — in order for enhanced discriminability in the target domain. On standardized benchmarks, the extensive experiments of SPA demonstrate that its performance has surpassed the existing cutting-edge DA methods. Coupled with dense model analysis, we conclude that our approach indeed possesses superior efficacy, robustness, discriminability, and transferability. Code and data are available at: https://***/CrownX/SPA.

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Random Forest and Sobol Method Based data Center PUE Prediction

Random Forest and Sobol Method Based Data Center PUE Predict...

引用

International Conference on Information, Communication and Networks (ICICN)

作者： Longchuan Yan Zhaofang Luan Ling He Congfeng Jiang Junming Liu Chuanqi Xu Liangbin Zhang Information & Telecommunication Branch State Grid Corporation of China Beijing China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Information Technology Center Hangzhou Dianzi University Hangzhou China College of Big Data and Software Engineering Zhejiang Wanli University Ningbo China

ISBN: (数字)9798350355802

ISBN: (纸本)9798350355819

data centers are critical infrastructures that support a multitude of business loads such as big data, artificial intelligence, and cloud computing. While providing the necessary computing power, the problem of high energy consumption in data centers has become a major obstacle limiting their scale-up, reliability and quality of service. Improving the power usage effectiveness (PUE) of data centers is an important way to improve the energy efficiency of data centers. With the development of theories and methods of machine learning, modeling, analysis and prediction of data center energy consumption using machine learning techniques has become a feasible option. In this study, several accurate PUE prediction models are constructed using neural networks and machine learning techniques for the PUE of a small air-cooled data center in actual operation, among which the mean absolute error of the optimal model reaches 0.29%. In addition, this paper also uses Random Forest and Sobol methods to model and conduct sensitivity analyses of key factors affecting data center PUE, revealing the relationship between data center operating parameters and PUE, and finding that the cooling system exhibits different energy consumption patterns under different IT equipment power conditions, which has a decisive impact on PUE. Based on these sensitivity analyses, we argue that for small air-cooled data centers, improving internal insulation and humidity is an important strategy for optimizing PUE.

关键词： Temperature measurement data centers Energy consumption Sensitivity analysis Atmospheric modeling Predictive models data models Energy efficiency Random forests Optimization

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BMDP:Blockchain-Based Multi-Cloud Storage data Provenance

BMDP:Blockchain-Based Multi-Cloud Storage Data Provenance

引用

International Conference on computer Supported Cooperative Work in Design

作者： Feiyu Wang Jian-Tao Zhou Xu Guo College of Computer Science Inner Mongolia University Hohhot Inner Mongolia China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

In a multi-cloud storage system, provenance data records all operations and ownership during its lifecycle, which is critical for data security and audibility. However, recording provenance data also poses some challenging security and storage issues. In this paper, we present a secure and efficient multi-cloud storage data source scheme, BMDP. We use blockchain technology to ensure the secure storage of provenance data and design a smart contract to utilize the provenance data to ensure the proper operation of the multi-cloud storage system. Finally, we analyze the scheme’s safety and do simulation experiments to show that the scheme has practicality.

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MINDFLOW: A NETWORK TRAFFIC ANOMALY DETECTION MODEL BASED ON MINDSPORE

arXiv

引用

arXiv 2025年

作者： Xiang, Qiuyan Wu, Shuang Wu, Dongze Liu, Yuxin Qin, Zhenkai School of Information Technology Guangxi Police College 530028 China College of Computer Science and AI Southwest Minzu University 610213 China Institute of Software Chinese Academy of Sciences 100190 China School of Computing and Information China Network Security Research Center China Big Data and Policing Technology Laboratory Guangxi Police College Guangxi Nanning China

With the wide application of IoT and industrial IoT technologies, the network structure is becoming more and more complex, and the traffic scale is growing rapidly, which makes the traditional security protection mechanism face serious challenges in dealing with high-frequency, diversified, and stealthy cyber-attacks. To address this problem, this study proposes MindFlow, a multi-dimensional dynamic traffic prediction and anomaly detection system combining convolutional neural network (CNN) and bi-directional long and short-term memory network (BiLSTM) architectures based on the MindSpore framework, and conducts systematic experiments on the NF-BoT-IoT dataset. The experimental results show that the proposed model achieves 99% in key metrics such as accuracy, precision, recall and F1 score, effectively verifying its accuracy and robustness in network intrusion detection. © 2025, CC0.

关键词： Convolutional neural networks

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