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5th International Seminar on Artificial Intelligence, networking and information Technology, AINIT 2024

作者： Zhang, Guowei Du, Jiayuan Qufu Normal University School of Cyber Science and Engineering Qufu China Key Laboratory of Computing Power Network and Information Security Jinan China

ISBN: (纸本)9798350385557

Edge computing has been developed to support local services with lower burden to the core network. However, privacy issues deteriorate when terminal users enjoy the high-quality services with the price of sending their data to multiple service nodes (SNs) scattered throughout the network. Existing researches concerning this problem are almost all based on the traditional security technologies including encryption and authentication, which are too complicated to be well supported by the terminal nodes (TNs) in edge networks due to the poor local processing capabilities. In view of the above, this article proposes an offloading mode hidden (OMH) algorithm, which reduces the risk level by equalizing the task offloading modes of different TNs. Gaming process is adopted to achieve the risk-minimized offloading solutions for TNs. Numerical results demonstrate that comparing with the Greedy algorithm, the proposed OMH algorithm can achieve lower task offloading risk with higher efficiency and provide more TNs with satisfied services. © 2024 IEEE.

关键词： Edge computing

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A research framework for constructing the knowledge database of public security information

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International Journal of Wireless and Mobile computing 2021年第3期21卷 191-197页

作者： Zhong, Han Zhang, Shiqiang Liu, Jianli College of Information Technology and Network Security People’s Public Security University of China Xicheng Beijing China Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Trusted Computing Beijing China

At present, the public security organs in China have accumulated a great deal of public security data. These data have broad sources, complex structures, large and increasing scales. How to effectively integrate, manage and mine these data has become a new problem faced by all public security organs. This paper proposes a research framework for constructing the knowledge database of public security information. Based on this multi-dimension framework, data features can be effectively extracted and modelled for improving the management and utilisation of public safety data. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： information management

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The Study of Multi-type computing power Trading Mechanism in computing power network Based on Blockchain and Combinatorial Double Auction 24

The Study of Multi-type Computing Power Trading Mechanism in...

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4th International Conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2024

作者： Zhao, Yue Wang, Jizhi Kong, Lingrui Sui, Tongtong Shandong Computer Science Center National Supercomputer Center in Jinan Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Provincial Key Laboratory of Industrial Network and Information System Security Qilu University of Technology Shandong Academy of Sciences Jinan Shandong China Quancheng Laboratory Jinan Key Laboratory of Digital Security Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan Shandong China

ISBN: (纸本)9798400710247

The advancement of 5G and mobile internet technologies has propelled the development of emerging businesses and applications, demanding higher requirements for network bandwidth and computational resources. To address this challenge, the computing power network (CPN) has emerged. The CPN integrates computing and networking in depth, achieving collaborative allocation of distributed computing resources, enhancing the efficient utilization of computing resources, and improving user experiences in network services. However, in the development of fields such as Extended Reality (XR), Smart Healthcare, Connected Vehicles, and Artificial Intelligence (AI), there is a multidimensional, complex, and rapidly growing demand for computing resources. Therefore, it is crucial to establish a trading mechanism that integrates multiple computing resources in CPN. In this paper, a computing power trading scheme based on blockchain and combinatorial double auction is proposed. Firstly, a computing power trading system based on blockchain was designed, leveraging the decentralized and highly secure characteristics of blockchain technology to enhance the security of computing power transactions. Furthermore, a multi-type computing power trading mechanism (MCPT) based on blockchain and combinatorial double auction was introduced. The use of combinatorial double auctions as an incentive mechanism enables the trading of multiple computing power resources and results in high social welfare. Moreover, a discrete rime optimization algorithm (DRIME) is designed in the proposed mechanism (MCPT) to better solve the computing trading problem. Through simulation experiments, the effectiveness of the approach is validated. © 2024 Copyright held by the owner/author(s).

关键词： computing power

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MBC-DTA: A Multi-Scale Bilinear Attention with Contrastive Learning Framework for Drug-Target Binding Affinity Prediction

MBC-DTA: A Multi-Scale Bilinear Attention with Contrastive L...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Huiting Zhang, Weiyu Shang, Yong Lu, Wenpeng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China

ISBN: (纸本)9798350386226

Drug-target binding affinity prediction is critical to drug design. Some existing computational methods rely on single-scale data and cannot fully integrate the rich information of multi-scale data, such as molecular structures and network information. In this paper, we propose the MBC-DTA model, which effectively integrates multi-scale data, and models the complex interactions between atoms and amino acids. We introduce GCN and GAT, which effectively extract the feature representation of molecular structure scale and network scale. Afterwards, the features obtained from the molecular structure scale are fed into a bilinear attention network, which captures the intricate interaction information between drug-target pairs. Additionally, a cross-scale graph contrastive learning strategy is applied, optimizing the feature representations across different scales. Extensive experimental results on two benchmark datasets demonstrate that the MBC-DTA model outperforms existing state-of-the-art methods. © 2024 IEEE.

关键词： Contrastive Learning

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An Adaptive Residual Coordinate Attention-Based network for Hat and Mask Wearing Detection in Kitchen Environments

An Adaptive Residual Coordinate Attention-Based Network for ...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhao, Ying Wu, Xiaoming Liu, Xiangzhi Chen, Hao Qi, Bei Dong, Huomin Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

In order to ensure food safety, it is required for personnel to wear hats and masks during food handling processes. To accurately detect the wearing status of kitchen staff, the ARP-YOLO model is proposed. Firstly, images are obtained from multiple kitchens and angles to construct a dataset reflecting the wearing status of hats and masks. To simulate more complex kitchen environments, Gaussian noise is added to the data and lighting conditions are adjusted for data augmentation. Lighting conditions in the kitchen can affect detection, causing the same target to exhibit different shapes and features under different lighting conditions, leading to missed detections. To address the above issues, we propose ARCA (Adaptive-Residual-Coordinate-Attention), which uses residual connections to strengthen attention to important features while preserving original features, and employs adaptive convolution reduction to reduce module parameters. To improve target localization accuracy, P2 detection layers are added in the Neck to obtain more accurate target position information. The ARP-YOLO model demonstrates significant improvements over the baseline model, with a 13.6% increase in Recall, allowing for more effective target capture and reduced missed detection risk. Additionally, mAP@0.5 has increased by 10%, enhancing target localization accuracy. The F1-Score has also increased by 7.4%, better balancing the relationship between Precision and Recall. To validate the effectiveness of the model, comparative experiments with other models are conducted, showing that ARP-YOLO model's Recall and Average Precision(AP) are higher than those of other models. © 2024 IEEE.

关键词： Lighting

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Job2Vec: A Self-Supervised Contrastive Learning Based HPC Job power Consumption Prediction Framework 29

Job2Vec: A Self-Supervised Contrastive Learning Based HPC Jo...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zhang, Jie Song, Jian Li, Xiang Tian, Xuesen Zhao, Zhigang Wu, Lu Wang, Chunxiao Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

In the context of the rapid development of big data and artificial intelligence, the field of HPC is facing significant challenges in energy consumption. To address this challenge, this paper proposes a power consumption prediction framework for HPC jobs based on self-supervised contrastive learning, named Job2Vec. Firstly, HPC jobs are clustered into different clusters based on job logs and power consumption curve shapes. Next, high-quality features of power consumption data at different granularities within each cluster are captured through self-supervised contrastive learning. Lastly, an incremental update strategy is introduced to handle the dynamic changes in HPC data. The experimental results demonstrate that, on the Jinan Supercomputing dataset comprising 1170 HPC jobs, the Job2Vec framework outperforms the baseline models overall in terms of prediction performance. Additionally, we also investigated the model's performance in single-step and multi-step forecasting, the impact of different indicators on prediction accuracy, and conducted ablation experiments to confirm the indispensability of each module. This is the first time that self-supervised contrastive learning has been introduced into the HPC field, aiming to provide support for energy scheduling in data centers. © 2023 IEEE.

关键词： Contrastive Learning HPC power Consumption Prediction

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Weighted Adversarial Transfer network-Based Fault Diagnosis Method for Gearboxes 11

Weighted Adversarial Transfer Network-Based Fault Diagnosis ...

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11th Joint International information Technology and Artificial Intelligence Conference, ITAIC 2023

作者： Wang, Dan Meng, Qinglong Hao, Huijuan Cheng, Guanghe Tang, Yongwei Bai, Jinqiang Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350333664

In this paper, we address the issue of gearbox fault diagnosis under changing working conditions and the limitations of migration learning algorithms that are only applicable to scenarios with identical labels in both the source and target domains. Our proposed solution is a weighted adversarial migration network model that is based on weighted adversarial migration. To transfer knowledge from the source domain to the target domain, we use transfer learning. Additionally, to tackle the problem of new fault classes in the target domain, we utilize a deep residual network to extract similar data features between different working conditions. This allows us to train a labelled classifier to identify known fault classes and some new target samples in both the source and target domains. We generate weights using auxiliary classifiers and discriminators to assess the importance of matching feature distributions between the known and unknown classes of samples. The experimental results demonstrate that our model can successfully migrate labelled datasets to unlabelled similar datasets in open set scenarios. It also has excellent fault diagnosis capabilities and high accuracy for gearboxes with variable operating conditions. © 2023 IEEE.

关键词： adversarial training deep transfer learning fault diagnosis weighting strategy

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Optimization Strategy for Remote Differential Upgrade of IoT Firmware 11

Optimization Strategy for Remote Differential Upgrade of IoT...

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11th Joint International information Technology and Artificial Intelligence Conference, ITAIC 2023

作者： Ding, Wenkang Sun, Ruirui Meng, Qinglong Wan, Zhong Cheng, Guanghe Hao, Huijuan Bai, Jinqiang Key Laboratory of Computing Power Network and Information Security Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350333664

With the continuous increase of IoT devices, the management and optimization of devices become particularly important. The update cycle of device functions is short, and in order to facilitate management and optimize device performance, a fast and convenient upgrade method is needed to solve this problem. Remote upgrade technology is mainly divided into full package upgrade and differential upgrade. Compared to full package upgrade, differential upgrade has obvious advantages in remote upgrade of large files. The main problem with current differential upgrade is the problem of slow generation speed while ensuring a certain size range of differential packages. This article proposes an improved Bsdiff algorithm to solve the problem of slow generation speed of differential packages. Experimental verification shows that the efficiency of remote upgrade has been improved, Improved the speed of differential packet generation and upgrade efficiency while ensuring the size of differential packets. © 2023 IEEE.

关键词： Block Optimization Strategy Bsdiff Difference Algorithm IoT Firmware Libdivsufsort Remote Upgrade

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FACOR：Flexible Access Control with Outsourceable Revocation in Mobile Clouds

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China Communications 2016年第4期13卷 136-150页

作者： ZHOU Shungan DU Ruiying CHEN Jing SHEN Jian DENG Hua ZHANG Huanguo The Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education Computer School of Wuhan University Wuhan 430072 China State Grid Information and Communication Company of Hunan Electric Power Corp Changsha 410000 China

Access control is a key mechanism to secure outsourced data in mobile clouds. Some existing solutions are proposed to enforce flexible access control on outsourced data or reduce the computations performed by mobile devices. However, less attention has been paid to the efficiency of revocation when there are mobile devices needed to be revoked. In this paper, we put forward a new solution, referred to as flexible access control with outsourceable revocation(FACOR) for mobile clouds. The FACOR applies the attribute-based encryption to enable flexible access control on outsourced data, and allows mobile users to outsource the time-consuming encryption and decryption computations to proxies, with only requiring attributes authorization to be fully trusted. As an advantageous feature, FACOR provides an outsourceable revocation for mobile users to reduce the complicated attribute-based revocation operations. The security analysis shows that our FACOR scheme achieves data security against collusion attacks and unauthorized accesses from revoked users. Both theoretical and experimental results confirm that our proposed scheme greatly reliefs the mobile devices from heavy encryption and decryption computations, as well as the complicated revocation of access rights in mobile clouds.

关键词： mobile cloud computing ABE outsourcing user revocation

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Logformer: Cascaded Transformer for System Log Anomaly Detection

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Computer Modeling in Engineering & Sciences 2023年第7期136卷 517-529页

作者： Feilu Hang Wei Guo Hexiong Chen Linjiang Xie Chenghao Zhou Yao Liu Information Center Yunnan Power Grid Company LimitedKunming650034China Network andData Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of ChinaChengdu610054China

Modern large-scale enterprise systems produce large volumes of logs that record detailed system runtime status and key events at key *** logs are valuable for analyzing performance issues and understanding the status of the *** detection plays an important role in service management and system maintenance,and guarantees the reliability and security of online *** are universal semi-structured data,which causes difficulties for traditional manual detection and pattern-matching *** some deep learning algorithms utilize neural networks to detect anomalies,these approaches have an over-reliance on manually designed features,resulting in the effectiveness of anomaly detection depending on the quality of the *** the same time,the aforementioned methods ignore the underlying contextual information present in adjacent log *** propose a novel model called Logformer with two cascaded transformer-based heads to capture latent contextual information from adjacent log entries,and leverage pre-trained embeddings based on logs to improve the representation of the embedding *** proposed model achieves comparable results on HDFS and BGL datasets in terms of metric accuracy,recall and ***,the consistent rise in F1-score proves that the representation of the embedding spacewith pre-trained embeddings is closer to the semantic information of the log.

关键词： Anomaly detection system logs semi-structured data pre-trained embedding cascaded transformer

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