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

作者： Luo, Hui 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 Guangdong Guangzhou China Guangdong Provincial Key Laboratory of Power System Network Security Guangdong Guangzhou China

ISBN: (纸本)1891706594

Pre-trained code models have achieved promising results in the vulnerability detection field. The prevailing approach is to adapt these models with vulnerability datasets using inefficient full-model fine-tuning. These pre-trained models primarily capture the semantic features of code, while neglecting its structural characteristics. To address these issues, this paper proposes a vulnerability detection method with adapter tuning and enhanced feature learning. First, adapter modules are introduced to UniXcoder and tune only the parameters in the adapters to extract semantic features. This significantly reduces the number of training parameters and better adapts the model to downstream tasks. Then, structural features, including control and data flow information, are extracted from the Program Dependence Graph (PDG) to compensate for the limitations of pre-trained models that rely solely on semantic features. Finally, the semantic and structural features are integrated to train the detection model. The experimental results demonstrate that our proposed method outperforms state-of-the-art approaches and is highly efficient in terms of both training parameters and training data. © 2024 Knowledge systems Institute Graduate school. All rights reserved.

关键词： Data flow graphs

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A practical conflicting role-based cloud security risk evaluation method

Recent Advances in Computer Science and Communications

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Recent Advances in computer science and Communications 2021年第3期14卷 874-886页

作者： Han, Jin Zhan, Jing Xia, Xiaoqing Fan, Xue School of Computer Science Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Trusted Computing Faculty of Information Technology Beijing University of Technology Beijing100124 China

Background: Currently, Cloud Service Provider (CSP) or third party usually proposes principles and methods for cloud security risk evaluation, while cloud users have no choice but to accept them. However, since cloud users and cloud service providers have conflicts of interests, cloud users may not trust the results of security evaluation performed by the CSP. Different cloud users may have different security risk preferences, which makes it difficult for the third party to con-sider all users' needs during evaluation. In addition, current security evaluation indexes for the cloud are too impractical to test (e.g., indexes like interoperability, transparency, portability are not easy to be evaluated). Methods: To solve the above problems, this paper proposes a practical cloud security risk evaluation method of decision-making based on conflicting roles by using the Analytic Hierarchy Process (AHP) with Aggregation of Individual Priorities (AIP). Results: Not only can our method bring forward a new index system based on risk source for cloud security and corresponding practical testing methods, but also can obtain the evaluation result with the risk preferences of conflicting roles, namely CSP and cloud users, which can lay a foundation for improving mutual trusts between the CSP and cloud users. The experiments show that the method can effectively assess the security risk of cloud platforms and in the case where the number of clouds increased by 100% and 200%, the evaluation time using our methodology increased by only 12% and 30%. Conclusion: Our method can achieve consistent decisions based on conflicting roles, high scalability and practicability for cloud security risk evaluation. © 2021 Bentham science Publishers.

关键词： Analytic hierarchy process

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Coverage Probability of Distributed CoMP UAV-Assisted Cellular Networks

Coverage Probability of Distributed CoMP UAV-Assisted Cellul...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Yan Li Qingmin Long Qiang Tang Shiming He Bing Xiong School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China Science and Technology on Information System Engineering Laboratory National University of Defense Technology Changsha China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

It is well-established that terrestrial communication systems may fail during emergencies such as earthquakes, tsunamis, and floods. Fortunately, with the rapid advancement of unmanned aerial vehicle (UAV) network technology, deploying UAV nodes as aerial base stations (BSs) is assuming an increasingly crucial role in facilitating downlink transmissions and restoring ground communication capabilities. However, a single UAV node is not sufficient to meet the requirements. Inspired by distributed communication, we introduce a performance analysis framework based on stochastic geometry to analyze the distributed coordinated multi-point (CoMP) UAV-assisted communication network. Specifically, we assume that all UAV nodes follow a homogeneous Poisson point process (PPP) and maintain a constant altitude. The entire space is tessellated by multiple hexagons, with multiple UAV nodes within each hexagonal region working together to serve terrestrial user equipments (UEs). For this region-centric cooperative model, we derive an exact expression for the coverage probability to quantify the performance improvement enabled by UAVs, analyze the upper bound of the coverage probability, and provide a simplified approximation. We then compare this model to a user-centric model. Our numerical findings demonstrate that the cooperation of UAV nodes can significantly enhance the coverage probability and save spectrum resources.

关键词： Analytical models Upper bound Spectral efficiency Earthquakes Stochastic processes Probability Autonomous aerial vehicles Tsunami Terrestrial communications Numerical models

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FULLY POLARIZED RADAR TARGET DETECTION AND WAVEFORM OPTIMIZATION UNDER RAINY CONDITION

FULLY POLARIZED RADAR TARGET DETECTION AND WAVEFORM OPTIMIZA...

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IET International Radar Conference 2023, IRC 2023

作者： Li, Xinda Cheng, Xu Ju, Xinjie Wang, Xuesong Li, Jianbing College of Electronic Science and Technology National University of Defense Technology Hunan Changsha410073 China State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System Hunan Changsha410073 China School of Computer Science and Engineering Wuhan Institute of Technology Wuhan430205 China Hubei Key Laboratory of Intelligent Robot Wuhan430205 China

ISBN: (纸本)9781839539954

Polarization and waveform optimization can significantly improve the detection ability of fully polarized radar. However, in practical scenarios, atmospheric particles frequently induce depolarization effects on radar waves, thereby influencing precise target detection, a phenomenon predominantly observed with raindrop particles. This paper establishes a target detection model for fully polarized radar in rainy condition. Subsequently, a joint transceiver iterative optimization algorithm is devised, utilizing the maximum output signal-to-interference plus noise ratio (SINR) of the system as the benchmark, and imposing dual constraints of energy and resemblance on the transmitting waveform. Ultimately, the effectiveness of the proposed method under rainy condition is validated through simulation, and recommendations for engineering applications are provided based on the analysis. © The Institution of Engineering & technology 2023.

关键词： Signal to noise ratio

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Research on Real-Time Linux Task Scheduling Algorithm for Multiprocessors Based on Particle Swarm Optimisation 9

Research on Real-Time Linux Task Scheduling Algorithm for Mu...

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9th International Conference on computer and Communications, ICCC 2023

作者： Qiu, Tong Yin, Zhenyu Wang, Jun Wang, Zisong School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang China Liaoning Key Laboratory of Domestic Industrial Control Platform Technology on Basic HardwareSoftware Shenyang China Liaoning Provincial Key Laboratory of Industrial Intelligent Technology for Chemical Processes Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350317251

Schedulability analysis in real-time operating systems is one of the hot research topics in the field. While Linux has become the preferred choice for industrial systems due to its excellent portability, it cannot meet the hard real-time requirements of specific industrial environments as a time-sharing operating system. To address this issue, we introduce the Real-Time Application Interface (RTAI) 5.2 real-time operating system kernel patch and combine it with Linux 4.4.176 to form the RTAI-Linux dual-core real-time system. Building upon this foundation, we proposes a Particle Swarm Optimization algorithm based on Earliest Deadline First (PSO-EDF) to improve task scheduling in real-time Linux multiprocessor environments. This method introduces a metric for evaluating processor utilization, when a new task arrives, it considers the allocation of free processor resources. Compared with the original method EDF, PSO-EDF provides a broader search space with better global characteristics. The algorithm finds the globally optimal scheduling scheme through iteration, which effectively improves the processor resource utilisation and alleviates the task deadline missing problem encountered in the original method to a certain extent, thus improving the real-time task completion rate. Through extensive simulation experiments, the PSO-EDF scheduling algorithm proves that the scheduling model can further improve the real-time performance of the RTAI-Linux system compared with the classical EDF algorithm. © 2023 IEEE.

关键词： Linux

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A One-Dimensional Residual Network and Physical Fitness-Based Exercise Prescription Recommendation Method

A One-Dimensional Residual Network and Physical Fitness-Base...

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IEEE International Conference on Cyber Security and Cloud computing (CSCloud)

作者： Runqing Fan Zhenlian Peng Buqing Cao Jianxun Liu Peng Che Tieping Chen School of Computer Science and Engineering Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology Hunan University of Science and Technology Xiangtan China School of Physical Education Hunan University of Science and Technology Xiangtan China

In the context of the national big data strategy, physical fitness test data has become one of the main influencing factors in guiding and promoting the participation of the population in sports and fitness. Recommending exercise prescriptions based on national physical fitness test data has become an important research topic. However, currently, there is little research on how to accurately use computer data processing technology to recommend exercise prescriptions based on physical fitness test data. In this study, we propose a ResNet-based Exercise Prescription (ResNet-EP) method that utilizes one-dimensional residual neural network technology to recommend exercise prescriptions based on physical fitness testing data. This method comprehensively analyzes physical fitness testing data and exercise prescription data and realizes the automatic recommendation of exercise prescriptions. Experimental results on a real dataset demonstrate that the ResNet-EP model outperforms other comparison models in terms of precision (79.98%), recall (83.73%), and F1 score (81.81%). This study provides novel insights into the combination of physical fitness testing and exercise.

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A missing physical fitness test data classification method based on MLP

A missing physical fitness test data classification method b...

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IEEE International Conference on Cyber Security and Cloud computing (CSCloud)

作者： Peng Che Zhenlian Peng Buqing Cao Jianxun Liu Tieping Chen Runqing Fan School of Computer Science and Engineering Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology Hunan University of Science and Technology Xiangtan China School of Physical Education Hunan University of Science and Technology Xiangtan China

In recent years, the physical health status of college students have been attached by the country and all universities. Much effort is spent every year to conduct physical health tests, generating a large amount of physical fitness data. Due to some irresistible reasons, such as entering incorrect data or students being unable to participate in some physical fitness programs, missing data exists in the physical fitness test, and the calculation of total points according to the existing rules of weighted summation may lead to inaccurate classification of some students grades. To this end, a classification method based on physical fitness test data with missing items is proposed in this paper. Firstly, the method collects the raw data set and constructs a structured data set. Then, the MLP method is used to construct a classifier model to classify the test data set with missing items. Finally, compared experiments are conducted with the training model constructed by GaussianNB, SVM and XGBoost and the results show that the classification effect of MLP is excellent, and the accuracy of classification can reach 93.29 %.

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MG6D: A Deep Fusion Approach for 6D Pose Estimation with Mamba and Graph Convolution Network

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IEEE Access 2025年 13卷 100433-100445页

作者： Zhu, Jiaqi Li, Bin Zhao, Xinhua Tianjin University of Technology School of Computer Science and Engineering Tianjin 300384 China Tianjin University of Technology Tianjin Key Laboratory for Advanced Mechatronic System Design and Intelligent Control School of Mechanical Engineering Tianjin 300384 China

Accurate and efficient 6D pose estimation is a fundamental technology in many industrial applications. While existing dense correspondence methods have shown progress, they face challenges in multimodal feature fusion under complex scenarios involving occlusions, illumination variations, and sensor noise. This paper proposes a novel 6D pose estimation framework that addresses these limitations through a hybrid Mamba-Graph architecture. The algorithm first introduces a panoramic attention fusion Mamba module, leveraging state-space modeling to capture long-range dependencies in multi-modal data while establishing cross-dimensional interactions between channel and spatial features to emphasize critical information. A dynamic graph convolutional adaptive fusion module is then designed to enable cross-modal geometric consistency modeling via multi-modal feature integration. Finally, a texture-geometry co-driven keypoint selection mechanism is proposed to ensure keypoint distributions satisfy both spatial uniformity and discriminability requirements. Experimental results on three common datasets demonstrate that the proposed algorithm achieves ADD(-S) metrics of 99.82%, 80.26%, and 97.2%, respectively. Notably, it exhibits significant advantages in pose estimation for objects with repetitive textures and high symmetry. © 2013 IEEE.

关键词： 6D pose estimation Fusion Mamba Graph feature fusion Panoramic Attention

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A third-party platoon coordination service: Pricing under government subsidies

A third-party platoon coordination service: Pricing under go...

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作者： Bai, Ting Johansson, Alexander Li, Shaoyuan Johansson, Karl Henrik Mårtensson, Jonas School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden KTH Royal Institute of Technology Stockholm Sweden Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Digital Futures Stockholm Sweden

This paper models a platooning system consisting of trucks and a third-party service provider (TPSP), which performs platoon coordination, distributes the platooning profit in platoons, and charges trucks in exchange for the services. Government subsidies used to incentivize platooning are also considered. We propose a pricing rule for the TPSP, which keeps part of the platooning profit including the subsidy each time a platoon is formed. In addition, a platoon coordination solution based on the distributed model predictive control (MPC) is proposed, in which the pricing rule under government subsidies is integrated. We perform a realistic simulation over the Swedish road network to evaluate the impact of the pricing rule and subsidies on the achieved profits and fuel savings. Our results show that subsidies are an effective mean to boost fuel savings from platooning. Moreover, the simulation study indicates that high pricing corresponds to a low platooning rate of the system, as trucks' incentives for platooning decrease. © 2023 The Authors. Asian Journal of Control published by John Wiley & Sons Australia, Ltd on behalf of Chinese Automatic Control Society.

关键词： Trucks

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Explainable AI: A Way to Achieve Trustworthy AI

Explainable AI: A Way to Achieve Trustworthy AI

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IEEE International Conference on Big Data Security on Cloud (BigDataSecurity), High Performance and Smart computing (HPSC) and Intelligent Data and Security (IDS)

作者： Yanlu Li Yufeng Xiao Yinyan Gong Ronglin Zhang Yingzi Huo Yanbing Wu Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (数字)9798350389524

ISBN: (纸本)9798350389531

AI is black-box and non-explainable, in other words, due to the complexity of the decision-making process of AI, people are unable to know why and how AI makes the decision. For these reasons, people will question and worry about AI’s decision-making. Against this background, researchers have proposed the concept of Explainable Artificial Intelligence (XAI). The aim of this paper is to help readers be able to better understand XAI through extensive surveys. In this paper, we discuss the concept of XAI, the motivation of the research, the current status of the research, and the difficulties in the research process. At the same time, we also discuss the specific methods to realize AI explainability by dividing AI explainability methods into two ways: transparent design and ex post facto interpretation. In addition, we select the three fields of healthcare, finance, and autonomous driving, discuss the application of XAI in these three fields, and select some representative results as examples through surveys. Finally, we summarize this article and present our outlook on the future development of XAI.

关键词： Surveys Explainable AI Decision making Finance Closed box Medical services Big Data

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