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

作者： Zhang, Chuanfu Li, Wen Chen, Jing Ge, Jing Wang, Di Li, Na Sun, Hao Geng, Yudong Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

To improve the task scheduling efficiency in computing power networks, this paper proposes a global task scheduling method based on network measurement and prediction in computing power networks (GTS-MP), which selects a more suitable computing power cluster and storage platform with a higher network availability score between them to implement tasks. The experiments are conducted on a test bench of urban area networks in 12 cities. The results show that this method greatly reduces the execution time of artificial intelligence model training tasks, with a maximum reduction of 40% compared to other plans. © 2023 IEEE.

关键词： computing power network global scheduling network availability score task offloading

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Self-Paced Self-Training Based on K-Nearest Neighbors 4

Self-Paced Self-Training Based on K-Nearest Neighbors

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4th International Conference on Machine Learning and Computer Application, ICMLCA 2023

作者： Ma, Yongzheng Sun, Zhengkai Zhang, Yintong Guan, Hongjiao Qiao, Xinxiao 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 Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9798400709449

Self-training is a classical semi-supervised classification method, which iteratively trains labeled samples that are enhanced using unlabeled samples. However, traditional self-training selects pseudo-labeled examples without distinguishing between different classes. Furthermore, wrong pseudo-labeled samples accumulate, leading to an increase in learning bias. In this paper, we propose a self-paced self-training based on K-nearest neighbors (SPST-KNN). SPST-KNN is composed of three main components: sample difficulty calculation, sample type determination, and self-paced selection based on KNN. The types of examples, including safe, boundary, and outlying examples, are determined by KNN. According to the principle of self-paced learning, SPST-KNN preferentially selects easy pseudo-labeled examples in each sample type to be added into the labeled sample set. Experiments and ablation study on 20 real-world datasets have demonstrated the superiority and effectiveness of the proposed SPST-KNN. © 2023 ACM.

关键词： Iterative methods

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DDoS Attack Detection Based on Information Entropy Feature Extraction in Software Defined networks

DDoS Attack Detection Based on Information Entropy Feature E...

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International Conference on networking and network Applications (NaNA)

作者： Wenjiang Ji Yixin Yang Yaling Zhang Yichuan Wang Mengjie Tian Yuan Qiu School of computer science and engineering Xi'an University of technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology

Distributed Denial of Service(DDoS) attacks target the forwarding-control separation feature of Software-Defined networking(SDN) to launch attacks, causing network disruptions. Therefore security against DDoS attack detection for SDN controllers is the focus of current research. This paper proposes an Extreme Gradient Boosting (XGBoost) DDoS attack detection algorithm based on a combination of information gain and recursive feature elimination algorithms. We evaluated the performance of the method using the CICDDoS2019 dataset. This method works well in multi-classification model for attack detection with an accuracy of 93.41%.

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Research on Classroom Behavior Target Detection Algorithm based on YOLOv7 6

Research on Classroom Behavior Target Detection Algorithm ba...

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6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Qiu, Wenke Wu, Xiaoming Liu, Xiangzhi Computer Science and Qilu University of Technology Shandong Academy of Sciences ShanDong Jinan China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350316520

The integration of video information for the purpose of detecting students' behavior in the classroom, with the aim of enhancing teaching efficiency and maintaining a stable classroom environment, offers significant assistance. However, existing models usually struggle to achieve good accuracy due to the lack of datasets, dense targets, interclass occlusion, and other issues. To address these difficulties, the CD-YOLO classroom behavior detection algorithm is proposed in this paper. And the classroom behavior dataset (DSL-Dataset) is created. We propose a loss function named VAIoU, which can be combined with vector ratio to filter the prediction box. To further improve the detection accuracy of occluded targets, we propose an attention module CM module with command feature fusion. Moreover, in this paper, we improve the convolutional kernel to design a lightweight convolutional kernel FFconv. Experimental results on DSL-Dataset show that CD-YOLO outperforms existing one-stage detection models in terms of detection accuracy. © 2024 IEEE.

关键词： Convolution

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Method and system of power load classification and decomposition based on deep learning 4

Method and system of power load classification and decomposi...

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4th International Conference on Smart Grid and Energy Engineering, SGEE 2023

作者： Sun, Hao Yuan, Hongtai Geng, Yudong Chen, Jing Zhang, Chuanfu Li, Wen Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350359619

This paper introduces a system for the classification and decomposition of electrical loads, addressing the shortcomings of current non-intrusive load decomposition methods that are prone to errors. We propose a method that first classifies electrical loads and then performs decomposition, along with the corresponding system workflow. Initially, we collect household electricity consumption data using a proprietary load collection device developed by the company, constructing an electrical load dataset. Subsequently, we train classification and decomposition methods using this dataset, conducting comparative experiments with various methods to select the most effective one. Based on the trained electrical load classification and decomposition models, we develop an electrical load inference program. The key implementation lies in applying the electrical load classification and decomposition inference program to edge devices. Leveraging the cloud server hosting a backend web service for the mobile app, we interact with users through the app, achieving precise decomposition and monitoring of electrical loads. Integrating edge computing and cloud computing, coupled with a mobile app serving as the interaction platform, this paper presents a high-precision, simplified, and highly scalable solution for electrical load analysis and management. In experimental comparisons, we find that using the Gated Recurrent Unit (GRU) method from deep learning and the Sequence-to-Point Neural network (Seq2point) method outperform other methods in classification and decomposition, significantly improving the accuracy of electrical load classification. Therefore, we select the GRU method and Seq2Point method as the core techniques for the electrical load classification and decomposition system. This system not only demonstrates significant advantages in performance but also possesses high accuracy, model simplification, and scalability, providing an efficient and reliable solution for elec

关键词： Edge computing

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Voiceprint recognition model based on Ecapa-Tdnn and ResNet 5

Voiceprint recognition model based on Ecapa-Tdnn and ResNet

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5th International Conference on Telecommunications, Optics, and Computer Science, TOCS 2024

作者： Jia, Chengjie Fei, Rong Li, Xiaomeng School of Computer Science and Engineering Xi'an University of Technology Shaanxi Xi'an710004 China Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi'an University of Technology Shaanxi Xi'an710048 China

ISBN: (纸本)9781510691667

In recent years, with the continuous progress of deep learning neural networks and the construction of large data sets, new breakthroughs in voiceprint verification technology for voice calls have been rapidly achieved. Deep learning-based neural networks, such as X-Vectors, ResNets, Ecapa-Tdnn, etc., are able to utilize their own network characteristics to extract rich voiceprint features, thus further improving the accuracy of voiceprint recognition, but there is still room for improvement in the performance and parameter count of the traditional models. In this paper, we propose the Ecapa2Tdnn model based on EcapaTdnn and ResNet, which has the advantage of possessing a deeper network architecture while performing better in terms of temporal modeling capability. This indicates that the model can effectively handle long time series of speech data, thus improving the performance of the voiceprint recognition system. The experimental results show that under the same experimental environment, compared with the equal error rate (EER) of 11.31% of the EcapaTdnn model, the Ecapa2Tdnn model proposed in this paper has only an EER of 10.27%, which is 1.04% lower than the former EER, and the number of parameters is still one-fourth of the original model. The model improves accuracy while reducing the total number of parameters, which is expected to be widely used in the field of voiceprint recognition. © 2025 SPIE.

关键词： Deep neural networks

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A Pseudonym Exchange-Based Traceable Location Privacy Protection Scheme for IoV

A Pseudonym Exchange-Based Traceable Location Privacy Protec...

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International Conference on networking and network Applications (NaNA)

作者： Weigang Ma Yaping Yu Yichuan Wang Xiaoxue Liu Zhoukai Wang School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology

Internet of Vehicles (IoV) is a network built by communicating entities such as vehicles and roadside infrastructure, etc. The entities can communicate with each other, sharing data and providing services. However, the openness and interoperability (location services) of IoV makes the communication entity vulnerable to attacks. when the vehicle requests location services, it will expose its own location information, which will cause serious personal safety problems. To address the location privacy leakage problem in the IoV, this paper proposes a pseudonym exchange-based traceable location privacy protection scheme for IoV. Our scheme not only provides location privacy protection, but has the following properties: First, the whole pseudonym exchange process does not require trusted third-party participation; Second, each vehicle performs a series of hash operations on several dynamic anonyms and obtains a corresponding fixed-length hash value as the corresponding account in order to cut off the connection between the real identity, which is different in each run. If a vehicle is compromised and utilized to launch attack, the trusted center (CA) can also recover his/her real identity; Last, the experimental results show that the pseudonym exchange process and pseudonym update process cost less time compared to relevant literature. Hence, these features make our scheme very suitable for computation-limited mobile devices compared with other related existing schemes.

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A Novel On-Demand Service Architecture for Efficient Cloud-Edge Collaboration

A Novel On-Demand Service Architecture for Efficient Cloud-E...

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International Conference on networking and network Applications (NaNA)

作者： Yaling Zhang Jingjing Chen Yichuan Wang Yeqiu Xiao Xiaoxue Liu School of computer science and engineering Xi'an University of technology Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology

With the advent of 5G era, the model of cloud-edge collaboration is used to handle massive data computing tasks with significant superiority. However, this architecture still uses the traditional web service model, which has problems such as single service and low resource utilization during the interaction between users and remote web servers. In this paper, we propose a novel on-demand service architecture that changes the traditional inefficient network service approach and skews the entire network service to the user. By fine- grained division of users' demands and hosting them in the corresponding functional servers for processing, the resolution of users' demands is no longer restricted by the services provided by the server, thus realizing “information to people and services on demand”. Experiments show that the new on- demand service architecture reduces the latency by 33.3%, improves the quality and efficiency of information services, and realizes a more proactive and flexible information service model, which is more suitable for users' needs.

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Resource Allocation for MEC in Ultra-dense networks

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Journal of Computers (Taiwan) 2025年第1期36卷 143-162页

作者： Ma, Huan Li, Shuyun Wang, Zhihui Dou, Xubing Zhang, Xinchang 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 China Shandong Normal University Jinan China

In the complex context of 5G ultra-dense networks, the unprecedented growth in traffic and computing demand has made the interaction between edge devices and edge servers extremely intricate due to the time-varying nature of the network environment and resource heterogeneity. The combination of UDN and Mobile Edge computing not only significantly improves network performance but also expands application scenarios and enhances user experience, marking it as one of the crucial trends in future network development. In this paper, we review recent results and research progress on MEC-based resource allocation in 5G ultra-dense networks. We analyze resource allocation strategies from multiple dimensions, including user association, interference management, energy efficiency optimization, delay reduction, access selection, and user mobility. Finally, we discuss the challenges and open issues that need to be addressed for future development in this area. © 2025 Codon Publications. All rights reserved.

关键词： 5G mobile communication systems

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Evolutionary Multitasking Based on Team Learning Strategy 6

Evolutionary Multitasking Based on Team Learning Strategy

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6th Asian Conference on Artificial Intelligence technology, ACAIT 2022

作者： Li, Wei Xu, Qingzheng Gao, Xinyu Wang, Lei Xi'an University of Technology School of Computer Science and Engineering Xi'an China National University of Defense Technology College of Information and Communication Wuhan China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China

ISBN: (纸本)9781665453110

Multi-factorial optimization (MFO) is a popular optimization mechanism recently, which aims to optimize multiple tasks in a single run. Generally, the multi-factorial optimization algorithm uses the random learning strategy to generate offspring, which leads to an inefficient exploration ability of the algorithm. To address this problem, this paper proposed a simple and effective strategy called team learning strategy (TLS). The proposed learning strategy divided the population into excellent team and ordinary team. Different teams employ different learning strategies. Moreover, nine MFO problems were used to assess the effectiveness of the proposed strategy. The experimental results show that the team learning strategy can improve the performance of the algorithm. © 2022 IEEE.

关键词： Evolutionary algorithms

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