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23rd IEEE International Conference on Trust, security and Privacy in Computing and Communications, TrustCom 2024

作者： Zou, Huiying Duan, Jia Liu, Xi Ren, Wei Li, Tao Zheng, Xianghan Choo, Kim-Kwang Raymond Hunan Engineering Research Center of Geographic Information Security and Application The Third Surveying and Mapping Institute of Hunan Province Hunan China China University of Geosciences School of Computer Science Wuhan China Sichuan University Ministry of Education Key Laboratory of Data Protection and Intelligent Management China Casm State Key Laboratory of Geo-Information Engineering Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of Mnr Beijing China Fuzhou University College of Computer and Big Data China Sanya College School of Information and Intelligent Engineering China University of Texas at San Antonio Department of Information Systems and Cyber Security San Antonio United States

With the widespread application of digital assets and the rapid development of blockchain technology, achieving secure and efficient transactions between different blockchain networks has become an urgent challenge. Existing cross-chain methods impose limitations, e.g., hash lock technology exhibits low scalability, side-chain technology is overly complex, and notary schemes pose centralization risks. In order to address these issues, we propose a cross-chain transaction solution based on proxy re-signature technology. To tackle the centralization concerns, we employ proxy re-signature technology to decentralize the authority of notaries among transaction participants, minimizing the potential for centralization at a low cost. We further present an extended scheme that can guarantee more requirements such as higher transaction amount and shorter transaction time. Furthermore, we analyze and compare the signature technologies used in the proposed solution and provide a systematic proof of our approach's security. © 2024 IEEE.

关键词： blockchain transaction cross-chain technology notary schemes proxy re-signature

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Prediction of piRNA-mRNA interactions based on an interactive inference network

Prediction of piRNA-mRNA interactions based on an interactiv...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Liu, Yajun Li, Ru Li, Aimin Fei, Rong Guo, Xie Wu, Fang-Xiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Xi'an University of Technology Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an China University of Saskatchewan Division of Biomedical Engineering Department of Computer Science Department of Mechanical Engineering Saskatoon Canada

ISBN: (纸本)9798350337488

As the largest class of small non-coding RNAs, piRNAs primarily present in the reproductive cells of mammals, which influence post-transcriptional processes of mRNAs in multiple ways. Effective methods for predicting piRNA and mRNA target relationships can help identify piRNA functions, investigate the possibility of piRNAs as biomarkers and therapeutic targets. In this study, we propose a computational approach for classifying the relationships of piRNA-mRNA pairs based on an interactive inference network (IIN). First, we gather piRNA-mRNA target data, collect sequence data by position alignment, and construct a benchmark dataset. Furthermore, a reliable negative set is constructed by positive-unlabeled learning. Finally, we view a piRNA and a mRNA sequence as a premise and hypothesis sentence, respectively, and IIN model is used to predict the relationship between them. The experiments demonstrate that our method effectively characterizes piRNA-mRNA interaction and could be beneficial for researchers to investigate piRNA functions. © 2023 IEEE.

关键词： interactive inference network noncoding RNA piRNA-mRNA interactions positive-unlabeled learning

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Innovations in Machine Learning for Flight Delay Prediction: A Targeted Overview 5

Innovations in Machine Learning for Flight Delay Prediction:...

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5th International Conference on computers and Artificial Intelligence Technology, CAIT 2024

作者： Wei, Wei Yu, Chenchen Yang, Zhao Bo, Fu Xi'an University Of Technology School of Computer Science and Engineering Shaanxi Key Laboratory for Network Computing and Security Xi'an China Xi'an University Of Technology School of Computer Science and Engineering Xi'an China

ISBN: (纸本)9798331530891

Artificial intelligence technology is increasingly being applied to air traffic flow forecasting. Based on the core data of Web of Science, this study uses bibliometrics and content review methods to investigate the current situation, methodology evaluation and future research direction of flight delay prediction. Initially, a basic statistical analysis was performed on all the papers. This was followed by topic co-occurrence and keyword co-occurrence analysis, which revealed that most of the research focused on three main categories: machine learning methods, deep learning techniques, and algorithmic optimization strategies. Then reviewing the content in these categories, further content analysis showed that machine learning holds significant potential for handling large and diverse datasets. © 2024 IEEE.

关键词： Bibliometrics Content Analysis Data Types Flight Delay Prediction Machine Learning

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Prompt and Contrastive Learning for Few-shot Sentiment Classification 5

Prompt and Contrastive Learning for Few-shot Sentiment Class...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Wang, Fei Chen, Long Huang, Xiaohua Xu, Cai Zhao, Wei Guan, Ziyu Lu, Guangyue Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications China School of Information Science and Technology Northwest University China School of Computer Science and Technology Xidian University China

ISBN: (纸本)9781450396899

Sentiment classification is a hot topic in the field of natural language processing. Currently, state-of-the-art classification models follow two steps: pre-training a large language model on upstream tasks, and then using human-labeled data to fine-tune a task-related model. However, there is a large gap between the upstream tasks of the pre-trained model and the downstream tasks being performed, resulting in the need for more labeled data to achieve excellent performance. Manually annotating data is expensive. In this paper, we propose a few-shot sentiment classification method based on Prompt and Contrastive Learning (PCL), which can significantly improve the performance of large-scale pre-trained language models in low-data and high-data regimes. Prompt learning aims to alleviate the gap between upstream and downstream tasks, and the contrastive learning is designed to capture the inter-class and intra-class distribution patterns of labeled data. Thanks to the integration of the two strategies, PCL markedly exceeds baselines with low resources. Extensive experiments on three datasets show that our method has outstanding performance in the few-shot settings. © 2022 ACM.

关键词： Computational linguistics

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Multi-Scale Feature Extraction with Supervised Contrastive Learning for Vulnerability Detection 36

Multi-Scale Feature Extraction with Supervised Contrastive L...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Zhao, Yahui Lu, Lu Liang, Zhihong Suo, Siliang School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Pengcheng Laboratory Shenzhen China Electric Power Research Institute CSG Guangzhou China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China

ISBN: (纸本)1891706594

The capacity of Deep Learning to automatically learn features from source code has facilitated its extensive utilization in detecting software vulnerabilities. However, existing pre-trained models regard code snippets as token sequences, neglecting the inherent structure of the code. The utilization of graph-based code representations is constrained by the limitations of Graph Neural networks, particularly the difficulty in capturing long-range dependencies, which renders it challenging to learn grammatical and semantic information from complex code graph representations. This paper proposes a novel multi-scale software vulnerability detection method based on supervised contrastive learning. The method integrates local features obtained from code paths with structural features extracted from the Code Property Graph by GraphTrans, thereby achieving a multi-scale feature representation of the source code. Additionally, a supervised contrastive loss function is employed during training in order to fully utilize label information and address the class imbalance problem. The experimental results demonstrate that the proposed method outperforms existing state-of-the-art methods, achieving the highest accuracy, precision, and F1 on the real-world benchmark dataset CodeXGLUE for software vulnerability detection. © 2024 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Contrastive Learning

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Optimized Block-K Clustering IoT Clustering and Blockchain Sharding Strategy Using Deep Reinforcement Learning

Optimized Block-K Clustering IoT Clustering and Blockchain S...

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Cyber Science and Technology Congress (CyberSciTech), IEEE

作者： Tianci Chen Hui Lin Lizhao Wu Xiaoding Wang Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fuzhou Fujian

ISBN: (数字)9798331532093

ISBN: (纸本)9798331532109

The inherent properties of immutability, decentralization, and enable linear scalability, making sharded blockchain a compelling approach to resolving trust issues in large-scale Internet of Things (IoT) networks. Sharding is a technique in building efficient blockchain architectures. Nevertheless, existing sharding mechanisms encounter two primary challenges. First, systems that require full committee reconstruction can experience substantial time delays during reconfiguration. Second, sharding models that rely on random assignment struggle with security vulnerabilities. To overcome these issues, this paper introduces Block-K Clustering, a secure, incentive-driven, and low-latency sharding blockchain framework utilizing the cuckoo rule. Block-K Clustering incorporates optimized reconstruction processes through the integration of genetic algorithms and cuckoo rules, enabling parallel onboarding of new nodes, which mitigates the issue of prolonged reconfiguration. Additionally, Block-K Clustering incorporates a reputation-based incentive system to identify and flag malicious nodes early, facilitating an even distribution of nodes. The system further refines cluster (shard) numbers and consensus parameters using a Deep Reinforcement Learning (DRL) model. Simulations demonstrate that Block-K Clustering effectively reduces reconstruction time, improves both sharding performance and security, and enhances the scalability of blockchain technology in IoT environments.

关键词： Sharding Scalability Delay effects computer architecture Deep reinforcement learning Blockchains security Internet of Things Low latency communication Genetic algorithms

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Joint Optimization of Latency and Energy Consumption for Mobile Edge Computing Based Proximity Detection in Road networks

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China Communications 2022年第4期19卷 274-290页

作者： Tongyu Zhao Yaqiong Liu Guochu Shou Xinwei Yao School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and ConvergenceBeijing 100876China School of Computer Science and Technology Zhejiang University of TechnologyChina

In recent years, artificial intelligence and automotive industry have developed rapidly, and autonomous driving has gradually become the focus of the industry. In road networks, the problem of proximity detection refers to detecting whether two moving objects are close to each other or not in real time. However, the battery life and computing capability of mobile devices are limited in the actual scene,which results in high latency and energy consumption. Therefore, it is a tough problem to determine the proximity relationship between mobile users with low latency and energy consumption. In this article, we aim at finding a tradeoff between latency and energy consumption. We formalize the computation offloading problem base on mobile edge computing(MEC)into a constrained multiobjective optimization problem(CMOP) and utilize NSGA-II to solve it. The simulation results demonstrate that NSGA-II can find the Pareto set, which reduces the latency and energy consumption effectively. In addition, a large number of solutions provided by the Pareto set give us more choices of the offloading decision according to the actual situation.

关键词： proximity detection mobile edge computing road networks constrained multiobjective optimization

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Privacy-Preserving IoT Image Transmission: Multi-Stage SVD Data Embedding and Heatmap Alignment

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IEEE Internet of Things Journal 2025年

作者： Du, Kaixin Ma, Bin Yang, Meihong Wang, Xiaoyu Li, Xiaolong Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250101 China Beijing Jiaotong University School of Computer and Information Technology Beijing100044 China

The images transmitted by IoT devices, particularly those used for surveillance or sensor data, are vulnerable to malicious screenshots and unauthorized access, leading to potential privacy breaches. To address this, we propose a multi-stage Singular Value Decomposition (SVD)-based robust data-hiding scheme for JPEG images aimed at mitigating screenshot attacks. The method exploits the decorrelation properties of the Discrete Cosine Transform (DCT) to preprocess the carrier image, facilitating the selection of specific frequency coefficients. These coefficients undergo a dual-stage SVD transformation, where dimensionality reduction reduces the impact of noise from non-critical image regions. Additionally, we optimize Grad-CAM heatmap generation to better align with human visual perception, enabling the identification of stable and reliable feature regions for embedding secret information. This approach ensures that the visual integrity of the carrier image is maintained while preserving the legibility of the embedded information, even under *** method enhances both the visual fidelity of the carrier image and the robustness of the embedded information. Experimental results demonstrate that the proposed scheme outperforms existing methods, achieving at least a 5% improvement in confidential information extraction accuracy and a data extraction rate exceeding 95% across screenshot angles ranging from -40∘ to 40∘. Extensive evaluations confirm the superior performance, efficiency, and security of our method in mitigating screenshot attacks, showcasing its broad applicability to various image formats and resilience to distortions. © 2014 IEEE.

关键词： Discrete cosine transforms

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The Construction and Experimental Technologies of Anonymous network Analysis and Control Platform 7

The Construction and Experimental Technologies of Anonymous ...

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Zhao, Yue Yang, Bin Tian, Bo Yang, Yarang National Key Laboratory of Security Communication Chengdu610041 China School of Computer and Information Engineering Chuzhou University Chuzhou239000 China College of Physics and Electrical Engineering Kashi University Kashi844006 China

ISBN: (纸本)9798350350890

In view of the shortcomings of low accuracy and high false positive rate in the traditional anonymous network traffic analysis methods, this paper proposes the construction and experimental technologies of the anonymous network analysis and control platform, and adopts the Tor network forced routing mechanism to realize the reverse analysis of the random path selection and proxy data restoration of the anonymous network. Experimental results show that compared with the traditional anonymous network traffic analysis methods, the anonymous network analysis and control platform significantly improves performance, achieving a true positive rate of over 95% for detecting different types of traffic in anonymous networks and a false positive rate of less than 3%. This approach not only reduces the overhead of training the model but also enhances the generalization ability of anonymous network analysis and control. © 2024 IEEE.

关键词： Anonymous networks Deployment methods Finite state machine Forced routing Traffic analysis

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Cross-Layer-Optimized Link Selection for Hologram Video Streaming Over Millimeter Wave networks

Cross-Layer-Optimized Link Selection for Hologram Video Stre...

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2025 IEEE Wireless Communications and networking Conference, WCNC 2025

作者： Jiang, Yiming Liu, Yanwei Liu, Jinxia Argyriou, Antonios Chen, Yifei Zhang, Wen Institute of Information Engineering Chinese Academy of Sciences State Key Laboratory of Cyberspace Security Defense Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Zhejiang Wanli University Ningbo China University of Thessaly Department of Electrical and Computer Engineering Greece

ISBN: (纸本)9798350368369

Holographic-type communication brings an immersive tele-holography experience by delivering holographic contents to users. As the direct representation of holographic contents, hologram videos are naturally three-dimensional representation, which consist of a huge volume of data. Advanced multi-connectivity (MC) millimeter-wave (mmWave) networks are now available to transmit hologram videos by providing the necessary bandwidth. However, the existing link selection schemes in MC-based mmWave networks neglect the source content characteristics of hologram videos and the coordination among the parameters of different protocol layers in each link, leading to sub-optimal streaming performance. To address this issue, we propose a cross-layer-optimized link selection scheme for hologram video streaming over mmWave networks. This scheme optimizes link selection by jointly adjusting the video coding bitrate, the modulation and channel coding schemes (MCS), and link power allocation to minimize the end-to-end hologram distortion while guaranteeing the synchronization and quality balance between real and imaginary components of the hologram. Results show that the proposed scheme can effectively improve the hologram video streaming performance in terms of PSNR by 1.2 dB to 6.4dB against the non-cross-layer scheme. © 2025 IEEE.

关键词： Holograms

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