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Optimizing Power Allocation for D2D Communication with URLLC under Rician Fading Channel:A Learning-to-Optimize Approach

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Intelligent Automation & Soft Computing 2023年第9期37卷 3193-3212页

作者： Owais Muhammad Hong Jiang Mushtaq Muhammad Umer Bilal Muhammad Naeem Muhammad Ahtsam School of Information Engineering Southwest University of Science and TechnologyMianyang621010China School of Software Engineering Northeastern UniversityShenyang110167China School of Information and Software Engineering University of Electronic Sciences and TechnologyChengdu610000China

To meet the high-performance requirements of fifth-generation(5G)and sixth-generation(6G)wireless networks,in particular,ultra-reliable and low-latency communication(URLLC)is considered to be one of the most important communication scenarios in a wireless *** this paper,we consider the effects of the Rician fading channel on the performance of cooperative device-to-device(D2D)communication with *** better performance,we maximize and examine the system’s minimal rate of D2D *** to the interference in D2D communication,the problem of maximizing the minimum rate becomes non-convex and difficult to *** solve this problem,a learning-to-optimize-based algorithm is proposed to find the optimal power *** conventional branch and bound(BB)algorithm are used to learn the optimal pruning policy with supervised *** learning is used to train the multiple *** address the imbalanced problem,we used the supervised undersampling *** are made with the conventional BB algorithm and the heuristic *** outcome of the simulation demonstrates a notable performance improvement in power *** proposed algorithm has significantly low computational complexity and runs faster as compared to the conventional BB algorithm and a heuristic algorithm.

关键词： D2D URLLC rician fading supervised learning

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BC-PC-Share:Blockchain-Based Patient-Centric Data Sharing Scheme for PHRs in Cloud Computing

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Computer Modeling in engineering & Sciences 2023年第9期136卷 2985-3010页

作者： Caihui Lan Haifeng Li School of Electronic and Information Engineering Lanzhou City UniversityLanzhou730070China School of Software Dalian University of Foreign LanguagesDalian116044China School of Software Dalian University of TechnologyDalian116024China

Sharing of personal health records(PHR)in cloud computing is an essential functionality in the healthcare ***,how to securely,efficiently and flexibly share PHRs data of the patient in a multi-receiver setting has not been well *** instance,since the trust domain of the cloud server is not identical to the data owner or data user,the semi-trust cloud service provider may intentionally destroy or tamper shared PHRs data of user or only transform partial ciphertext of the shared PHRs or even return wrong computation results to save its storage and computation resource,to pursue maximum economic interest or other malicious ***,the PHRs data storing or sharing via the cloud server should be performed with consistency and integrity ***,the emergence of blockchain technology provides new ideas and prospects for ensuring the consistency and integrity of shared PHRs *** this end,in this work,we leverage the consortiumblockchain technology to enhance the trustworthiness of each participant and propose a blockchain-based patient-centric data sharing scheme for PHRs in cloud computing(BC-PC-Share).Different from the state-of-art schemes,our proposal can achieve the following desired properties:(1)Realizing patient-centric PHRs sharing with a public verification function,i.e.,which can ensure that the returned shared data is consistent with the requested shared data and the integrity of the shared data is not compromised.(2)Supporting scalable and fine-grained access control and sharing of PHRs data with multiple domain users,such as hospitals,medical research institutes,and medical insurance companies.(3)Achieving efficient user decryption by leveraging the transformation key technique and efficient user revocation by introducing time-controlled *** security analysis and simulation experiment demonstrate that the proposed BC-PC-Share scheme is a feasible and promising solution for PHRs data sharing via consortium b

关键词： Blockchain patient-centric personal health records data sharing attribute-based encryption

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Recent Challenges, Issues, and Initiatives for Cloud Computing Adoption in Healthcare: A Survey 9

Recent Challenges, Issues, and Initiatives for Cloud Computi...

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9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Al-Tawara, Feda'a Khalifeh, Ala' Princess Sumaya University for Technology Department of Software Engineering Amman Jordan German Jordanian University Department of Software Engineering Amman Jordan

ISBN: (纸本)9798350311402

There are many opportunities and advantages that cloud computing adoption can bring to the healthcare field, as experts believe that it can help improve the level of healthcare services significantly and contribute to its regular and continuous innovation. This paper provides a comprehensive review of the current literature on the common limitations and problems facing the cloud in healthcare. A brief introduction to cloud computing and the benefits of its adoption is also provided, in addition to a review of the most important security protection standards applied in this field. This paper highlights the most important problems and challenges that still exist in cloud-based healthcare systems. Finally, relevant recent research is reviewed and the most recent initiatives that contribute to solving problems for healthcare applications are identified. It was concluded that these solutions and initiatives are not comprehensive in nature as they partially address issues related to data security in the cloud. © 2023 IEEE.

关键词： Health care

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Evolution Analysis of Network Attack and Defense Situation Based on Game Theory

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Computers, Materials & Continua 2025年第4期83卷 1475-1494页

作者： Haiyan Sun Chenglong Shao Jianwei Zhang Kun Wang Wanwei Huang College of Software Engineering Zhengzhou University of Light IndustryZhengzhou450007China Intelligent Computing Power Research Department Zhengzhou Xinda Advanced Technology Research InstituteZhengzhou450007China

To address the problem that existing studies lack analysis of the relationship between attack-defense game behaviors and situation evolution from the game perspective after constructing an attack-defense model,this paper proposes a network attack-defense game model(ADGM).Firstly,based on the assumption of incomplete information between the two sides of the game,the ADGM model is established,and methods of payoff quantification,equilibrium solution,and determination of strategy confrontation results are ***,drawing on infectious disease dynamics,the network attack-defense situation is defined based on the density of nodes in various security states,and the transition paths of network node security states are ***,the network zero-day virus attack-defense behaviors are analyzed,and comparative experiments on the attack-defense evolution trends under the scenarios of different strategy combinations,interference methods,and initial numbers are conducted using the NetLogo simulation *** experimental results indicate that this model can effectively analyze the evolution of the macro-level network attack-defense situation from the micro-level attack-defense *** instance,in the strategy selection experiment,when the attack success rate decreases from 0.49 to 0.29,the network destruction rate drops by 11.3%,in the active defense experiment,when the interference coefficient is reduced from 1 to 0.7,the network destruction rate decreases by 7%,and in the initial node number experiment,when the number of initially infected nodes increases from 10 to 30,the network destruction rate rises by 3%.

关键词： Network attack-defense situation evolution zero-day virus NetLogo

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Binary Program Vulnerability Mining Based on Neural Network

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Computers, Materials & Continua 2024年第2期78卷 1861-1879页

作者： Zhenhui Li Shuangping Xing Lin Yu Huiping Li Fan Zhou Guangqiang Yin Xikai Tang Zhiguo Wang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Electrical and Computer Engineering University ofWaterlooWaterlooN2L 3G1Canada

software security analysts typically only have access to the executable program and cannot directly access the source code of the *** poses significant challenges to security *** it is crucial to identify vulnerabilities in such non-source code programs,there exists a limited set of generalized tools due to the low versatility of current vulnerability mining ***,these tools suffer from some *** terms of targeted fuzzing,the path searching for target points is not streamlined enough,and the completely random testing leads to an excessively large search ***,when it comes to code similarity analysis,there are issues with incomplete code feature extraction,which may result in information *** this paper,we propose a cross-platform and cross-architecture approach to exploit vulnerabilities using neural network obfuscation *** leveraging the Angr framework,a deobfuscation technique is introduced,along with the adoption of a VEX-IR-based intermediate language conversion *** combination allows for the unified handling of binary programs across various architectures,compilers,and compilation ***,binary programs are processed to extract multi-level spatial features using a combination of a skip-gram model with self-attention mechanism and a bidirectional Long Short-Term Memory(LSTM)***,the graph embedding network is utilized to evaluate the similarity of program *** on these similarity scores,a target function is determined,and symbolic execution is applied to solve the target *** solved content serves as the initial seed for targeted *** binary program is processed by using the de-obfuscation technique and intermediate language transformation method,and then the similarity of program functions is evaluated by using a graph embedding network,and symbolic execution is performed based on these similarity *** approach facilitates

关键词： Vulnerability mining de-obfuscation neural network graph embedding network symbolic execution

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PSO-CNN-BiLSTM-MQA for Credit Risk Prediction of Listed Enterprises Based on Hierarchical Feature Modeling 7

PSO-CNN-BiLSTM-MQA for Credit Risk Prediction of Listed Ente...

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7th International Conference on Machine Learning and Natural Language Processing, MLNLP 2024

作者： Xie, Yumiao Wei, Le School of Software Engineering Chengdu University of Information Technology Chengdu China School of Software Engineering Chengdu University of Information Technology Sichuan Manufacturing Information Industry Collaborative Innovation Center Automatic Software Generation & Intelligence Service Key Laboratory of Sichuan Province Chengdu China

ISBN: (纸本)9798350354973

Aiming at the problems of insufficient feature discrimination, difficulty in parameter adjustment, and lack of time dependence capture ability in the existing credit risk prediction models of listed companies, this paper proposes a PSOCNN-BiLSTM-MQA credit risk prediction method PCBM based on hierarchical feature modeling. Based on the financial data of listed companies in Guotai'an database, the method divides the data into four types of first-level characteristics: debt paying ability, operating ability, profitability and development ability. Each class of first-level features is further subdivided into several second-level features. In the feature extraction stage, a twochannel Convolutional Neural Network (CNN) was used to extract the local features of each class of level-1 features. One channel processed the original data, and the other channel processed the features converted to grayscale images. After being processed by different CNNS, the multi-query attention mechanism (MQA) was used to fuse the features from different channels to enhance the attention to features at different levels. At the same time, the bidirectional long short-term memory network is used to capture the long-term dependencies in the time series data, and the fused features are integrated through the MQA mechanism. In order to further improve the performance of the model, the particle swarm optimization algorithm is used to optimize the model parameters. Experimental results show that compared with the existing prediction models, the PCBM model shows significant improvement in accuracy, precision, recall rate and F1-score, which proves the effectiveness of this method in the credit risk prediction of listed companies. © 2024 IEEE.

关键词： Prediction models

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Intelligent Java Dataset Construction and Visualization Evaluation for Reliable software Development

Intelligent Java Dataset Construction and Visualization Eval...

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2023 International Conference on Frontiers of Robotics and software engineering, FRSE 2023

作者： Li, Dian Wang, Weidong Zhao, Yang Beijing University of Technology Faculty of Information Technology Department of Software Engineering Beijing China

ISBN: (纸本)9798350301113

As big data and artificial intelligence continue to gain importance, the reliable quality of datasets has be-come a crucial factor in algorithm performance and result reliability. However, many datasets lack standardization and quality control, which can lead to potential issues in software development and data analysis. In this paper, we propose an objective dataset evaluation algorithm that utilizes multiple metrics, including unified naming conventions and document annotations, for statistical analysis and filtering to ensure reliable data. Our approach scores datasets using reasonable criteria, and we use visualization techniques inspired by remote sensing to compare and visualize reliable dataset quality. Our research demonstrates that our evaluation method is feasible and can assist software developers in enhancing the reliable quality and efficiency of software development by improving dataset quality. Our study concentrates on datasets obtained from GitHub using a web crawler, and our approach establishes a standardized technique for evaluating reliable dataset quality. © 2023 IEEE.

关键词： Python

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A privacy and compliance in regulated anonymous payment system based on blockchain

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Journal of Ambient Intelligence and Humanized Computing 2024年第8期15卷 3141-3157页

作者： Elfadul, Issameldeen Wu, Lijun Elhabob, Rashad Elkhalil, Ahmed School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China School of Computer and Information Engineering Qilu Institute of Technology Shandong Jinan250200 China Faculty of Computer Science and Information Technology Karary University Khartoum Sudan

Decentralized Anonymous Payment Systems (DAP), often known as cryptocurrencies, stand out as some of the most innovative and successful applications on the blockchain. These systems have garnered significant attention in the financial industry due to their highly secure and reliable features. Regrettably, the DAP system can be exploited to fund illegal activities such as drug dealing and terrorism. Therefore, governments are increasingly worried about the illicit use of DAP systems, which poses a critical threat to their security. This paper proposes Privacy and Compliance in Regulated Anonymous Payment System Based on Blockchain (PCRAP), which provides government supervision and enforces regulations over transactions without sacrificing the essential idea of the blockchain, that is, without surrendering transaction privacy or anonymity of the participants. The key characteristic of the proposed scheme is using a ring signature and stealth address to ensure the anonymity of both the sender and receiver of the transaction. Moreover, a Merkle Tree is used to guarantee government supervision and enforce regulations. Our proposed scheme satisfies most of the stringent security requirements and complies with the standards of secure payment systems. Additionally, while our work supports government regulations and supervision, it guarantees unconditional anonymity for users. Furthermore, the performance analysis demonstrates that our suggested scheme still remains applicable and effective even when achieving complete anonymity. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Blockchain

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Optimal Feature-Centric Approach for EEG-Based Human Emotion Identification 5

Optimal Feature-Centric Approach for EEG-Based Human Emotion...

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5th International Conference on Advancements in Computational Sciences, ICACS 2024

作者： Ashraf, Hira Saeed, Sanay Muhammad Umar Khan, Sheharyar Noorani, Sadam Hussain Arsalan, Aamir Rauf, Usman University of Engineering & Technology Department of Computer Engineering Taxila Pakistan Fatima Jinnah Women University Department of Software Engineering Rawalpindi Pakistan

ISBN: (纸本)9798350394764

Emotions are intrinsic to human nature, playing a vital role in human cognition. Emotions are closely intertwined with rational decision-making, perception, human interaction, and human intelligence. EEG has emerged as a promising tool for investigating the neural aspects of emotions. The primary objective of this study is to identify distinguishing features in EEG data and determine suitable classification techniques for categorizing of human emotion EEG-based approach for detecting mental states i.e., relaxation, neutrality, and concentration. To validate this research, a publicly available dataset EEG Brainwave mental state is utilized. Twenty-five temporal domain features were extracted from this dataset. Correlation-based feature selection technique is applied to select the significant features for the classification of three mental states. The selected EEG features are classified using four distinct classifiers. It is evident from the results, that the Random Forest achieves the highest classification accuracy of 98.61%. The proposed framework outperforms prior emotion classification methods in terms of electrode count and accuracy. © 2024 IEEE.

关键词： Feature Selection

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Research On Fatigue Driving Detection Based On Deep Learning

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engineering Letters 2025年第2期33卷 348-356页

作者： Zhou, Huinan Dai, Hong University of Science and Technology Liaoning Liaoning AnshanCO 114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning AnshanCO 114051 China

Fatigue driving is one of the main causes of traffic accidents. Effective fatigue driving detection technology can reduce traffic accidents caused by fatigue driving. Traditional fatigue driving detection methods usually use a two-stage detection method, which has the problems of low efficiency and poor accuracy. To solve these problems, this paper designs a single-stage fatigue driving detection model based on separable convolution Road Safety Net (RSNet), which extracts the features in the driver's image or video and classifies these features to determine whether the driver is in an abnormal driving state. RSNet borrows the structure of Swin Transformer and adopts a hierarchical design, which makes the model better able to extract features and classify. Each level is composed of a GDS Layer and multiple RS Blocks. To solve the problem of low efficiency of traditional methods, we use separable convolution in RS Block to replace the traditional convolutional neural network, which reduces the complexity of the model and effectively improves the computational efficiency of the model. Experimental results show that the Top-1 and Top-5 Accuracy of RSNet on the HMDB51 dataset reaches 85 % and 97.35 %, respectively, and the delay is 5.97 ms. Compared with ResNet, EfficientNet, and Swin Transformer, RSNet has higher accuracy and efficiency. In addition, this paper takes the YOLACT algorithm as an example, selects different backbone networks for object detection, and compares the indicators of mean Average Precision (mAP) and Frames Per Second (FPS). Experiments show that RSNet performs better when used as the backbone network for object detection, with mAP reaching 32.4 % and FPS of 39.6 ms. It indicates that RSNet performs well in action recognition tasks. © 2025, International Association of Engineers. All rights reserved.

关键词： Convolutional neural networks

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