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IEEE International Conference on Systems, Man and Cybernetics

作者： Ying Zhao Xiaoming Wu Xiangzhi Liu Hao Chen Bei Qi Huomin Dong 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: (数字)9781665410205

ISBN: (纸本)9781665410212

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.

关键词： Location awareness Adaptation models Accuracy Convolution Shape Computational modeling Gaussian noise Lighting Feature extraction Personnel

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Privacy-Preserving Interactive Semantic Codec Training for IoT-Based Holographic Counterparts

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IEEE Transactions on Consumer Electronics 2025年

作者： Xu, Jinpeng Chen, Liang Lin, Limei Wang, Xiaoding Huang, Yanze Xu, Li Piran, Md. Jalil Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Fuzhou350117 China Fujian University of Technology School of Computer Science and Mathematics Fujian Fuzhou350118 China Sejong University Department of Computer Science and Engineering Seoul05006 Korea Republic of

The use of IoT-based semantic codecs to process complex contextual semantic information in holographic counterparts introduces significant privacy risks, as it may expose sensitive data, thereby increasing the likelihood of privacy disclosures. The diverse and dynamic nature of holographic counterparts in IoT environments exacerbates these challenges, making it more difficult for semantic codecs to effectively safeguard data privacy. This complexity further intensifies the need for privacy-preserving computation methods, as ensuring the confidentiality and security of the data processed by these codecs becomes a critical concern. However, current privacy protection strategy for multi-party training of semantic codecs relies heavily on the central server for gradient calculation, which may lead to gradient leakage issue. To address this issue, we propose PIMSeC (Privacy-Preserving Interactive Multi-Party Semantic Codec Training for IoT-Based Holographic Counterparts), a novel encryption-based technique that facilitates secure and efficient multi-party interactive training without the dependence on the central server, which enhances both data security and privacy resilience. PIMSeC not only proposes a full interactive secure multi-party deep learning model to protect data privacy during multi-party interactive training, but also, within the above deep learning model, establishes an encrypted additive gradient noise mechanism to ensure post-training semantic codec data privacy. Our theoretical analysis and experimental results demonstrate that PIMSeC promotes semantic codecs privacy protection effectively by interactive secure multi-party training. Compared to the state-of-art methods, PIMSeC achieves a 3% to 15% improvement in terms of accuracy, precision, F1-score, and recall at lower compression rates. © 1975-2011 IEEE.

关键词： Deep learning

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Bidding Management Platform for Cloud-Chain Convergence

Bidding Management Platform for Cloud-Chain Convergence

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International Conference on computer Supported Cooperative Work in Design

作者： Yanzhi Zhang Xiaoming Wu Xiangzhi Liu Huomin Dong 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: (数字)9798350349184

ISBN: (纸本)9798350349191

Strengthening the supervision of bidding business is an important measure to optimise the business environment of bidding and ensure fair competition among enterprises. The existing blockchain-based bidding platform has already achieved the uploading of simple business, but with the development of the bidding industry, single-modal data storage is not enough to support the credible traceability of business processes. Therefore, to address the problems of multimodal data uploading, storage space limitation and performance in the bidding field, and combined with the node complexity of the bidding scenario, we design and propose a bidding management platform for cloud-chain convergence(BMPC 3 ), in which ChainMaker serves as the implementation platform. In this alliance chain, we meet different business logic requirements by designing specific smart contracts and storage structures. According to different business data types, the ledger storage method is optimised to meet the on-chain storage requirements of multimodal data such as images, videos, PDFs, etc. Meanwhile, we explore the blockchain architecture and system configuration, and propose an optimisation scheme, which effectively improves the system transaction throughput. The final experiment shows that the transaction throughput of the system can reach 3300TPS, which can meet the application requirements.

关键词： Industries Federated learning Smart contracts Memory Throughput Logic Optimization

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Lightweight Fusion Model with Time-Frequency Features for Speech Emotion Recognition

Lightweight Fusion Model with Time-Frequency Features for Sp...

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International Conference on computer Supported Cooperative Work in Design

作者： Peng Zhang Meijuan Li Hui Zhao Yida Chen Fuqiang Wang Ye Li Wei Zhao 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: (数字)9798350349184

ISBN: (纸本)9798350349191

Automatic speech emotion recognition (SER) is the task to predict the emotional state from speech, and has become a vital part of human-machine interaction. Recent studies focus on learning from multiple features via feature fusion. This paper proposes a lightweight fusion model with time-frequency features for SER. First, time-frequency features are extracted from the Mel frequency cepstral coefficients (MFCC) of speech by using parallel convolutional neural networks, and enhanced by using a dimension-specific weighting method. Then, a two-stage feature fusion model is proposed to fully capture the complementary information between time and frequency features and to reduce the redundancy. It consists of an adaptive fusion module to redistribute the features for a local fusion, and a global fusion module to enhance the cross-dimension feature communication. Experimental results show that our model achieves competitive performance on the datasets of IEMOCAP and RAVDESS, with recognition accuracies of 74.62% and 86.11%, respectively. The model contains only 0.82M and 0.85M parameters for the two datasets, which is lightweight for SER tasks.

关键词： Adaptation models Time-frequency analysis Emotion recognition Accuracy Computational modeling Redundancy Speech recognition

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BTP-CAResNet: An Encrypted Traffic Classification Method Based on Byte Transfer Probability and Coordinate Attention Mechanism

BTP-CAResNet: An Encrypted Traffic Classification Method Bas...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Junhao Li Huiling Shi Wei Zhang 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: (数字)9781665410205

ISBN: (纸本)9781665410212

With the extensive application of network traffic encryption technology, the accurate and efficient classification of encrypted traffic has become a critical need for network management. Deep learning has become the predominant method for traffic classification, primarily involving the transformation of network traffic into grayscale images and their subsequent classification using Convolutional Neural networks (CNNs). However, traditional grayscale image generation methods are plagued with issues of redundant and lost information, and conventional channel attention mechanisms are still insufficient in capturing key traffic features, collectively hindering the enhancement of classification performance. To tackle these issues, this paper introduces a classification method based on Byte Transfer Probability and Coordinate Attention Mechanism in Residual network (BTP-CAResNet). This method, on the foundation of the classic ResNet architecture, incorporates a new grayscale image generation method that utilizes Byte Transfer Probability, effectively overcoming the deficiencies of traditional approaches. Additionally, this paper integrates a Coordinate Attention Mechanism into the ResNet model, which effectively overcomes the limitations of traditional channel attention mechanisms and further improves the performance of traffic classification. Experimental validation on the ISCX VPN-nonVPN dataset demonstrates that, compared to previous CNN-based methods, the method proposed in this paper exhibits superior performance in key metrics such as accuracy, precision, recall, and F1 score. It provides a new perspective for traffic classification based on convolutional neural networks.

关键词： Measurement Attention mechanisms Accuracy Image synthesis Redundancy Telecommunication traffic Gray-scale Robustness Convolutional neural networks Residual neural networks

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A Community Detection Approach for Academic Social network: The Case of the Cross-Strait Academic Conference network 3

A Community Detection Approach for Academic Social Network: ...

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3rd International Conference on Electronics, Communications and Information technology, CECIT 2022

作者： Zhang, Xinxin Xu, Li Gao, Min Fan, Jianxi Sun, Hung-Min Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fuzhou China Fudan University The School of Computer Science Shanghai China Soochow University The School of Computer Science and Technology Suzhou China National Tsing Hua University The Department of Computer Science Hsinchu Taiwan

ISBN: (纸本)9798350331974

With the fast development of information networks, academic social network analysis has attracted significant attention, we regard these researchers sharing common interests as a research community. The community has been widely used as the underlying structure of social networks. To utilize network information as much as possible, we capture the structural characteristics and attribute features to detect communities in the cross-strait academic conference network. In this paper, we first consider 2-hop neighbors with the Pareto principle to filtrate the central seed nodes in networks. Then we propose a new seed shrinkage strategy to get the initial communities and define the similarity according to the structure and attributes of social networks to merge the communities. Finally, we define a new indicator named inner average distance (IAD) to measure the aggregation density for each community. A case study of the cross-strait academic social network was developed to test the applicability of the proposed model, the experiment analysis suggests that our method can be more accurate to identify communities in most situations, which contributes to controlling the information spreading in networks. © 2022 IEEE.

关键词： Pareto principle

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Learning Strategies for Volt-VAR Optimization with PV Inverters and Battery Energy Storage Systems in Distribution network

Learning Strategies for Volt-VAR Optimization with PV Invert...

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Energy, Electrical and Power Engineering (CEEPE), International Conference on

作者： Ziheng Yan Xuefei Li Hao Zhou Zongxuan Xie Yifei Wang Lucheng Hong Jiangsu Provincial Key Laboratory of Smart Grid Technology and Equipment Southeast University Nanjing China Network Information Center Xinjiang University of Political Science and Law Tumushuke China

As the integration of PV inverters and battery energy storage systems (BESSs) gradually increases in the distribution network, the rapid fluctuation and random nature of these distributed generators (DGs) put forwards an urgent demand for real-time Volt-VAR optimization (VVO). To address this issue, this paper proposes a VVO strategy learning method via machine learning framework. First, the VVO problem with PV and BESS is formulated as a mixed integer programming (MIP). Second, a strategy learning framework is proposed, which maps from uncertain parameters to the optimal value of integer variables and tight constraints. The solution finding of the MIP can be accelerated by applying the strategy. Then, a decision tree model is created to tackle the classification task. Finally, case studies are conducted on the modified IEEE 33-bus system, which demonstrates the feasibility and reliability of the proposed method. The result shows that the proposed method can reach near-optimal performance compared with commercial solvers.

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On the Impact of Spatial Mode Dispersion for Strongly-Coupled Multicore Fiber Submarine Transmission

On the Impact of Spatial Mode Dispersion for Strongly-Couple...

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2023 Optical Fiber Communications Conference and Exhibition, OFC 2023

作者： Sun, Lin Chen, Bin Liu, Gordon Ning Cai, Yi Li, Zhaohui Lu, Chao Shen, Gangxiang Soochow University Suzhou Key Laboratory of Advanced Optical Communication Network Technology School of Electronic and Information Engineering Suzhou215006 China Hefei University of Technology School of Computer Science and Information Engineering Hefei China Sun Yat-Sen University Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips School of Electronics and Information Technology Guangzhou511400 China The Hong Kong Polytechnic University Photonics Research Center Department of Electrical and Information Engineering Kowloon Hung Hom Hong Kong

Strongly-coupled multicore fibers exhibit the improved tolerance to fiber non-linearity. Their potentials in optical submarine communications are investigated with considering the coupling length and spatial mode disp... 详细信息

ISBN: (纸本)9781957171180

关键词： Submarines

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An Encryption Algorithm Cycle Identification Method Based on Bit Execution

An Encryption Algorithm Cycle Identification Method Based on...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Yunge Sun Gaolei Du Junpeng Qu Yong Fu 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 Quan Cheng Laboratory Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Ransomware uses symmetric encryption algorithms to lock user files to extort money. This paper proposes a cryptographic algorithm loop identification method based on bit-precise execution, aiming to solve the problem of identifying unknown loops. The structure of the loop body of encryption algorithms is fixed and usually includes nonlinear operations and linear operations. This article converts the loop body in the unknown software into a set of Boolean formulas, and converts the malware into a register data flow through the symbolic executor, thereby converting it into a set of Boolean formulas for execution. Execute known encryption algorithms with bit accuracy and establish a Boolean formula input-output relationship library for cryptographic components. The two sets of Boolean formulas are passed through a designed input matrix to achieve the Boolean formula mapping and input variable mapping of the confirmation algorithm, which can effectively judge the encryption algorithms in some malware.

关键词： Deformation Input variables Software algorithms Merging Data science Libraries Encryption Registers Ransomware Matrix converters

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An automated construction method for symmetric encryption round function models based on dynamic analysis

An automated construction method for symmetric encryption ro...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Gaolei Du Yunge Sun Junpeng Qu Yong Fu 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 Quan Cheng Laboratory Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

With the deepening of informatization, the incidence of cybercrimes utilizing malicious software for attacks is on the rise. This paper addresses the detection issue of symmetric encryption in malicious software from the perspective of cryptography, proposing an automated method for constructing a symmetric encryption round function model based on dynamic analysis. This method tracks data flow at the granularity of registers to characterize Directed Acyclic Graph (DAG) construction of round function models. Initially, dynamic execution traces of binary files are captured using Intel Pin, identifying loops within them. Data flow tracking at the register level within loops is performed, creating DAG, and analyzing whether the loops exhibit characteristics of symmetric cipher round functions, thus constructing the round function model. This approach enables the detection of symmetric encryption within binary files and the construction of round function models, offering a novel solution for fine-grained symmetric cipher recognition and laying a foundation for detecting malicious software containing symmetric encryption.

关键词： Analytical models Ciphers Tracking loops XML Data models Malware Encryption Registers Trajectory Pins

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