检索结果-内蒙古大学图书馆

Parallel Implementation of the CCSDS Turbo Decoder on GPU

China Communications 2024年第10期21卷 70-77页

作者： Liu Zhanxian Liu Rongke Zhang Haijun Wang Ning Sun Lei Wang Jianquan School of Computer&Communication Engineering University of Science and Technology BeijingBeijing 100083China School of Electronic and Information Engineering Beihang UniversityBeijing 100191China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Institute of Artificial IntelligenceUniversity of Science and Technology BeijingBeijing 100083China School of Information Engineering Zhengzhou UniversityZhengzhou 450001China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China

This paper presents a software turbo decoder on graphics processing units(GPU).Unlike previous works,the proposed decoding architecture for turbo codes mainly focuses on the Consultative Committee for Space Data Systems(CCSDS)***,the information frame lengths of the CCSDS turbo codes are not suitable for flexible sub-frame parallelism *** mitigate this issue,we propose a padding method that inserts several bits before the information frame *** obtain low-latency performance and high resource utilization,two-level intra-frame parallelisms and an efficient data structure are *** presented Max-Log-Map decoder can be adopted to decode the Long Term Evolution(LTE)turbo codes with only small *** proposed CCSDS turbo decoder at 10 iterations on NVIDIA RTX3070 achieves about 150 Mbps and 50Mbps throughputs for the code rates 1/6 and 1/2,respectively.

关键词： CCSDS CUDA GPU parallel decoding turbo codes

来源：评论

学校读者我要写书评

暂无评论

Exploring Genetic Programming Models in computer-Aided Diagnosis of Skin Cancer Images 13

Exploring Genetic Programming Models in Computer-Aided Diagn...

引用

13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Ain, Qurrat Ui Al-Sahaf, Harith Xue, Bing Zhang, Mengjie School of Engineering and Computer Science Victoria University of Wellington PO Box 600 Wellington New Zealand College of Engineering and Info. Technology AlShaab University Dep. of Cybersecurity Engineering Baghdad Iraq

ISBN: (纸本)9798350308365

Extracting important information from complex skin lesion images is vital to effectively distinguish between different types of skin cancer images. In addition to providing high classification performance, such computer-aided diagnostic methods are needed where the models are interpretable and can provide knowledge about the discriminative features in skin lesion images. This underlying information can significantly assist dermatologists in identifying a particular stage or type of cancer. With its flexible representation and global search abilities, Genetic Programming (GP) is an ideal learning al-gorithm to evolve interpretable models and identify important features with significant information to discriminate between skin cancer classes. This paper provides an in-depth analysis of a recent GP-based feature learning method where different well-developed feature descriptors are integrated into the learning algorithms to extract high-level features for skin cancer image classification. The study explores the effectiveness of utilizing feature learning for this complex task and designing program structure to suit the problem domain as it has shown promising results compared to commonly used feature descriptors and an existing GP-based feature learning method developed for general image classification. This study analyzes the GP-evolved models to identify the prominent features and most effective feature descriptors important for the classification of these skin cancer images. The evolved models are interpretable, they provide knowledge that can assist dermatologists in making diagnoses in real-time clinical situations by identifying prominent skin cancer characteristics captured by the feature descriptors and learned during the evolutionary process. © 2024 IEEE.

关键词： Genetic programming

来源：评论

学校读者我要写书评

暂无评论

A Domain Generalization Methodology for Side-channel Attack 9

A Domain Generalization Methodology for Side-channel Attack

引用

9th International Conference on computer and Communications, ICCC 2023

作者： Zhou, Qiang Mao, Baolei Qin, Maoyuan Hu, Wei Meng, Fanfei Fang, Mingzhu Zhengzhou University School of Cyber Science and Enineering Zhengzhou China Xi'an Technological University School of Computer Science and Engineering Xi'an China Northwestern Polytechnical University School of Cybersecurity Xi'an China

ISBN: (纸本)9798350317251

Side-channel attacks have been proven to be effective for retrieving secret keys by exploiting implementation vulnerabilities in encryption function. In recent years, deep learning techniques have been integrated into profiled side-channel attacks to achieve outstanding results. However, most state-of-the-art work only conducts attacks under ideal conditions where the profiling device (template device) and the target device are identical. The profiling traces are from the profiling device and the attack traces are from the target device. Unfortunately, constructing a template device is challenging and a flawed template device will lead to discrepancies between the profiling and the target devices. This inconsistent data distribution and discrepancies hamper the profiled SCA. To address these issues, we propose a domain generalization-based side-channel attack architecture called DGSCA. Especially, We simulate a scenario where the acquisition of target data is challenging and can't be used for model training. Initially, we combine the Deep CORAL method with Mixup to deserve domain invariant representations across source domains. Subsequently, we integrate meta-learning into the DGSCA framework to enhance the model's generalization ability, which is accomplished by randomly partitioning the source and target domains and leveraging the knowledge gained from the source domain to improve the attack result in the target domain. Moreover, we implement four different deep learning models (MLP, CNN, ResNet, DenseNet) and evaluate the DGSCA framework using the publicly available ASCAD dataset. The experimental results on the ASCAD desynchronous dataset show that our method successfully recovers the key within 1665 traces and demonstrate the scalability and generality of our proposed framework. © 2023 IEEE.

关键词： Side channel attack

来源：评论

学校读者我要写书评

暂无评论

A Parallel Color Image Encryption Algorithm Based on a 2-D Logistic-Rulkov Neuron Map

引用

IEEE Internet of Things Journal 2025年第11期12卷 18115-18124页

作者： Gao, Suo Zhang, Zheyi Iu, Herbert Ho-Ching Ding, Siqi Mou, Jun Erkan, Uğur Toktas, Abdurrahim Li, Qi Wang, Chunpeng Cao, Yinghong Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering PerthWA6009 Australia Dalian University of Technology School of Mathematical Sciences Dalian116024 China Karamanoglu Mehmetbey University Faculty of Engineering Department of Computer Engineering Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Images are widely used in social networks, necessitating efficient and secure transmission, especially in bandwidth-constrained environments. This article aims to develop a color image encryption algorithm that enhances security while optimizing computational efficiency. A novel parallel color image encryption algorithm based on the 2-D logistic-Rulkov neuron map (2D-LRNM) is proposed. In this approach, the three channels of the color image are first separated. Cross-channel information interaction is introduced to form three new channels, which are then processed in parallel. During the encryption process of each channel, a block-wise parallel encryption mechanism is applied, ensuring simultaneous encryption of each block. This block-wise strategy effectively leverages parallel computing resources and balances the task load. To meet the demand for a large number of keystreams during encryption, the 2D-LRNM is introduced. It combines the simplicity and chaotic properties of the Logistic map with the multitimescale dynamics and neurodynamic behaviors of the Rulkov map. By overcoming the dimensional limitations inherent in the single Logistic map, this approach extends the system to a 2-D framework, significantly increasing the complexity of chaotic behavior and improving its unpredictability. Experimental results demonstrate that the proposed encryption algorithm achieves high security and reduces computation time by approximately 83.3%. © 2014 IEEE.

关键词： Encryption algorithms

来源：评论

学校读者我要写书评

暂无评论

Concurrent Learning-Based Adaptive Trajectory Tracking Control of Omnidirectional Mobile Robot 18

Concurrent Learning-Based Adaptive Trajectory Tracking Contr...

引用

18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Zhang, Tao Long, Jiang Guo, Yangming Zhang, Yaozhong Northwestern Polytechnical University School of Computer Science Xi'an China Northwestern Polytechnical University School of Cybersecurity Xi'an China Beihang University School of Automation Science and Electrical Engineering Beijing China

ISBN: (纸本)9798350312201

This paper investigates the trajectory tracking control problem of omnidirectional mobile robot (OMR) with unknown parameters. A novel concurrent learning-based adaptive tracking control strategy is proposed. Different from currently available adaptive control schemes, the parameter estimation error in this paper can exponentially converge to zero under a mild rank condition, by resorting to concurrent learning technique. This implies that the identification of unknown parameters is achieved. Moreover, the trajectory tracking error can also tend to zero exponentially. Unlike some results with asymptotical convergence rate, the convergence rate of the tracking error can be improved in this paper. Finally, simulation results are provided to validate the effectiveness of the proposed control scheme. © 2023 IEEE.

关键词： Navigation

来源：评论

学校读者我要写书评

暂无评论

Efficient Clustering Network Based on Matrix Factorization

引用

computers, Materials & Continua 2024年第7期80卷 281-298页

作者： Jieren Cheng Jimei Li Faqiang Zeng Zhicong Tao and Yue Yang School of Computer Science and Technology Hainan UniversityHaikou570228China School of Cyberspace Security Hainan UniversityHaikou570228China Hainan Blockchain Technology Engineering Research Center Haikou570228China

Contrastive learning is a significant research direction in the field of deep ***,existing data augmentation methods often lead to issues such as semantic drift in generated views while the complexity of model pre-training limits further improvement in the performance of existing *** address these challenges,we propose the Efficient Clustering Network based on Matrix Factorization(ECN-MF).Specifically,we design a batched low-rank Singular Value Decomposition(SVD)algorithm for data augmentation to eliminate redundant information and uncover major patterns of variation and key information in the ***,we design a Mutual Information-Enhanced Clustering Module(MI-ECM)to accelerate the training process by leveraging a simple architecture to bring samples from the same cluster closer while pushing samples from other clusters *** experiments on six datasets demonstrate that ECN-MF exhibits more effective performance compared to state-of-the-art algorithms.

关键词： Contrastive learning clustering matrix factorization

来源：评论

学校读者我要写书评

暂无评论

ML computational inference techniques and indicator metrics for analyzing uncertainties in stock market data

引用

Multimedia Tools and Applications 2025年 1-21页

作者： Henge, Santosh Kumar Mandal, Sanjeev Kumar Sharma, Amit Upadhyay, Aditya Kundu, Shakti Iyer, Srinivasan Saini, Ashok Kumar Yadav, Manoj Kumar Singh, Bhupinder Department of Computer Science and Engineering School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Bangalore India Department of Computer Applications Lovely Professional University Punjab Jalandhar India Center for Distance and Online Education Manipal University Jaipur Rajasthan Jaipur India Haryana Gurugram122413 India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science & Engineering Lovely Professional University Punjab Jalandhar India

In a growing demand of accurately predicting the stock market and inefficient complex markets the rising accurate relationship prediction is not adequately addressed by the conventional methods. The dynamic and complex natures of data sources become the issue and we need to propose an effective method for adaptive algorithms to accurately forecasting and make decision efficiently. The system framed with two stages of execution: The first stage has covered time series-based predictions for different stock values involving the LSTM Model;the second stage integrated the experimental scenarios based on trend, value, indicators, and it's supporting impacted data metrics, which experimented on various large-cap stock companies. The dataset is sourced online by yahoo finance API with consecutive days data and dependable, non-dependable influenced indicators, which helped to intend trend-based evaluation performances. The proposed model experimented on distinct stock values and test cases executed based on indicator-based prediction with a 76.92% accurate performance rate using a Random Forest Classifier and 76.92% of Logistic Regression. The value-based prediction test case has achieved a performance rate of 97.5% using SVR Regressor and 97.2% using Linear Regressor. It computed value of root mean square error for the Ten large cap companies for performance evaluation. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Adaptive algorithms

来源：评论

学校读者我要写书评

暂无评论

CVE Severity Prediction from Vulnerability Description - A Deep Learning Approach 2

CVE Severity Prediction from Vulnerability Description - A D...

引用

2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Manjunatha, A. Kota, Kethan Babu, Anoop S. Sree Vivek, S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India ICU Medical Cybersecurity Device R&D Chennai600006 India

The Common Vulnerabilities and Exposures (CVE) system is a widely used standard for identifying and tracking known vulnerabilities in software systems. The severity of these vulnerabilities must be determined in order to prioritize mitigation efforts. However, assigning severity to a vulnerability is a challenging task that requires careful analysis of its characteristics and potential impact. Considering the vast number of vulnerabilities identified every year, it is vital to automate the severity assignment, thereby reducing manual effort. This paper proposes a novel approach for predicting the severity of vulnerabilities based on their CVE description using GPT-2, a state-of-the-art language model. The CVSS severity values distribution imbalance is addressed using oversampling and contextual data augmentation techniques. This approach leverages the large-scale language modeling capabilities of GPT-2 to automatically extract relevant features from CVE descriptions and predict the severity level of the vulnerability. The model is evaluated on a test data set of 7,765 CVEs and achieves a high accuracy of 84.2% and an F1 score of 0.82 in predicting the severity of the vulnerabilities on the test data. A comparative analysis of this approach was done against state-of-the-art methods, demonstrating the superior performance of the proposed approach. Based on the results, the proposed approach could be considered a valuable tool for quickly and accurately identifying high-severity vulnerabilities, facilitating more efficient and effective vulnerability management practices. Furthermore, this approach could be extended to other natural language processing tasks related to vulnerability analysis and management. © 2024 Elsevier B.V.. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Review of Research on Risk Early Warning Methods for Industrial Product Quality and Safety 10

Review of Research on Risk Early Warning Methods for Industr...

引用

10th International Conference on Systems and Informatics, ICSAI 2024

作者： Zhang, Junwei Wang, Jinguo Han, Chunxiao Xu, Bin Wang, Lihao Certification and Market Regulation Big Data Center China Cybersecurity Review Beijing China Taiji Computer Co. Beijing China College of Computer Science and Technology Harbin Engineering University Harbin China

ISBN: (纸本)9798331510138

The quality and safety of industrial products is related to people's daily life, and serious industrial product quality and safety accidents can cause great harm to people, so it is necessary to reduce the occurrence of industrial product quality and safety accidents. Compared with the formulation of policies and strict supervision, the risk warning of industrial product quality and safety can effectively prevent the occurrence of quality and safety accidents, in which the quality and safety risk warning method determines the real-time and accuracy of the early warning, therefore, we have studied the risk warning methods related to industrial product quality and safety, summarised and analysed the strengths and weaknesses of the methods as well as the research trends, and carried out the subsequent research content and perspectives. © 2024 IEEE.

关键词： early warning methods industrial product quality and safety risk warning

来源：评论

学校读者我要写书评

暂无评论

Medical knowledge graph question answering for drug-drug interaction prediction based on multi-hop machine reading comprehension

引用

CAAI Transactions on Intelligence Technology 2024年第5期9卷 1217-1228页

作者： Peng Gao Feng Gao Jian-Cheng Ni Yu Wang Fei Wang Qiquan Zhang School of Cyber Science and Engineering Qufu Normal UniversityQufuChina School of Computer Science and Technology East China Normal UniversityShanghaiChina Network and Information Center Qufu Normal UniversityQufuChina School of Electronics and Information Engineering Harbin Institute of TechnologyShenzhenChina School of Electrical Engineering and Telecommunications University of New South WalesSydneyNSWAustralia

Drug-drug interaction(DDI)prediction is a crucial issue in molecular *** methods of observing drug-drug interactions through medical experiments require significant resources and *** authors present a Medical Knowledge Graph Question Answering(MedKGQA)model,dubbed MedKGQA,that predicts DDI by employing machine reading comprehension(MRC)from closed-domain literature and constructing a knowledge graph of“drug-protein”triplets from open-domain *** model vectorises the drug-protein target attributes in the graph using entity embeddings and establishes directed connections between drug and protein entities based on the metabolic interaction pathways of protein targets in the human *** aligns multiple external knowledge and applies it to learn the graph neural *** bells and whistles,the proposed model achieved a 4.5%improvement in terms of DDI prediction accuracy compared to previous state-of-the-art models on the QAngaroo MedHop *** results demonstrate the efficiency and effectiveness of the model and verify the feasibility of integrating external knowledge in MRC tasks.

关键词： artificial neural network data analysis document handling natural language processing

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：