检索结果-内蒙古大学图书馆

On the -differential spectra of two classes of power functions over finite fields

Applicable Algebra in Engineering, Communication and computing 2025年 1-30页

作者： Liu, Qian Huang, Zhiwei Chen, Zhixiong Jiang, Rong Zhang, Liupiao Key Laboratory of Information Security of Network Systems College of Computer and Data Science Fuzhou University Fuzhou China Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Hubei University Wuhan China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Fujian Key Laboratory of Financial Information Processing Putian University Putian China School of Computer Science and Technology Dongguan University of Technology Dongguan China

Recently, a new concept called multiplicative differential was introduced by Ellingsen et al. [7]. As an extension of the differential uniformity, it is theoretically appealing to determine the properties of c-differential uniformity and the corresponding c-differential spectrum. In this paper, based on certain quadratic character sums and two special elliptic curves over $$\mathbb {F}_p$$ , the $$(-1)$$ -differential spectra of the following two classes of power functions over $$\mathbb {F}_{p^n}$$ is completely determined: (1) $$f_1(x)=x^{\frac{p^n+3}{2}}$$ , where $$p>3$$ and $$p\equiv 3\pmod 4$$ ; (2) $$f_2(x)=x^{p^n-3}$$ , where $$p>3$$ . The obtained result shows that the $$(-1)$$ -differential spectra of $$f_1(x)$$ and $$f_2(x)$$ can be expressed explicitly in terms of n. Moreover, an upper bound of the c-differential uniformity of $$f_2(x)$$ is given.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Parallel Optimization of Computational Fluid Dynamics Application code_saturne Based on Next Generation Sunway Supercomputer 14

Parallel Optimization of Computational Fluid Dynamics Applic...

引用

14th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2023

作者： Liu, Anjun Liao, Yin Xu, Xihao Chen, YanYan Zhong, Hongjun Wu, Jianming Kong, Bo Raikov, Alexander Guo, Meng Jinan Institute of Supercomputing Technology Jinan Key Laboratory of High Performance Industrial Software Jinan China Zhejiang Yuansuan Technology Co. Ltd Jinan Supercomputing-Zhejiang Yuansuan Industrial Simulation System Joint Laboratory Hangzhou China Qilu University of Technology Shandong Academy of Sciences Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Key Laboratory of Computing Power Network and Information Security China Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Shandong Jinan China

ISBN: (纸本)9798350371024

code_saturne is an open-source general large-scale computational fluid dynamics software independently developed by Electricite de France since 1997. Due to its high computing performance, code_saturne has been widely used in the nuclear industry, heavy fuel and biomass combustion, particle tracking and other computational fluid dynamics fields. In this work, we implemented code_saturne on the new generation Sunway supercomputer (SW26010pro) at the first time. By applying the data value-taking technology of double cache, combined with Sunway shared memory technology, we have adapted the data structure and Modular programming of code_saturne conducively to the new generation Sunway supercomputer (SW26010pro) and accelerated the software hotspots the software hotspots by 1-2 times. After optimizing and porting, this software has successfully served in the numerical simulation of the lateral flow of fuel assemblies in the nuclear industry. © 2023 IEEE.

关键词： Supercomputers

来源：评论

学校读者我要写书评

暂无评论

A Modified CSO Based on Complementary Learning

A Modified CSO Based on Complementary Learning

引用

第34届中国控制与决策会议

作者： Wei Li Zhou Lei Lei Wang School of Computer Science and Engineering Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology

Competitive swarm optimization algorithm(CSO) is an effective optimization ***,the population is easy to converge to the local optimal area in dealing with complex *** improve the performance of the CSO algorithm,this paper proposes a modified CSO base on complementary learning(CSO-CL).First,an optional archive is introduced to provide valuable information of evolutionary ***,the complementary learning strategies are designed,which not only enhance the ability of the algorithm to jump out of the local optimum,but also ensure a better balance between the exploration and the ***,the linear population size reduction strategy is used to improve the convergence speed of the proposed *** comparing with three state-of-the-art metaheuristic algorithms on the CEC2014 benchmark functions,it is proved that the proposed algorithm is competitive and effective.

关键词： Competitive Swarm Optimizer Evolutionary Computation Optional Archive

来源：评论

学校读者我要写书评

暂无评论

Memory-guided two-stream spatio-temporal coding network based for video anomaly detection 23

Memory-guided two-stream spatio-temporal coding network base...

引用

Proceedings of the 2023 International Conference on Information Education and Artificial Intelligence

作者： Bonan Li Hong Zhang Xingang Wang Rui Cao Jinyan Zhou China Radio and Television Shandong Network Co Ltd China Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) China

ISBN: (纸本)9798400716157

In the paper, we propose a memory-guided dual-stream spatio-temporal encoder network (MSTAE) based on the U-Net network as the backbone, the spatial stream uses the time displacement module to obtain the spatial features of the video, and the temporal stream is aggregated across frames to obtain the temporal features of the video, meanwhile, the coordinate attention module is introduced to improve the U-Net network and enhance the dynamic entity representation capability. In order to reduce the prediction error, the memory module is used to record the prototype patterns of normal data to reduce the problem of small error between the prediction anomaly and its true value due to the excessive generalisation ability of the deep network. We conducted extensive experiments on three publicly available standard datasets (Ped2, Avenue and ShanghaiTech datasets). The experiments demonstrate that the research model outperforms state-of-the-art methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

HEN:a novel hybrid explainable neural network based framework for robust network intrusion detection

引用

Science China(Information Sciences) 2024年第7期67卷 72-90页

作者： Wei WEI Sijin CHEN Cen CHEN Heshi WANG Jing LIU Zhongyao CHENG Xiaofeng ZOU School of Computer Science and Engineering Xi'an University of TechnologyShaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Technology Wuhan University of Science and Technology School of Future Technology South China University of Technology Shenzhen Research Institute of Hunan University School of Computer Science Hunan University of Technology and Business Institute for Infocomm Research (I2R) Agency for Science Technology and Reseach (A*STAR)

With the rapid development of network technology and the automation process for 5G, cyberattacks have become increasingly complex and threatening. In response to these threats, researchers have developed various network intrusion detection systems(NIDS) to monitor network traffic. However, the incessant emergence of new attack techniques and the lack of system interpretability pose challenges to improving the detection performance of NIDS. To address these issues, this paper proposes a hybrid explainable neural network-based framework that improves both the interpretability of our model and the performance in detecting new attacks through the innovative application of the explainable artificial intelligence(XAI)method. We effectively introduce the Shapley additive explanations(SHAP) method to explain a light gradient boosting machine(Light GBM) model. Additionally, we propose an autoencoder long-term short-term memory(AE-LSTM) network to reconstruct SHAP values previously generated. Furthermore, we define a threshold based on reconstruction errors observed during the training phase. Any network flow that surpasses the specified threshold is classified as an attack flow. This approach enhances the framework's ability to accurately identify attacks. We achieve an accuracy of 92.65%, a recall of 95.26%, a precision of 92.57%,and an F1-score of 93.90% on the dataset NSL-KDD. Experimental results demonstrate that our approach generates detection performance on par with state-of-the-art methods.

关键词： explainable artificial intelligence light gradient boosting machine machine learning network intrusion detection Shapley additive explanation hybrid explainable neural network (HEN)

来源：评论

学校读者我要写书评

暂无评论

CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding

CroPrompt: Cross-task Interactive Prompting for Zero-shot Sp...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Libo Qin Fuxuan Wei Qiguang Chen Jingxuan Zhou Shijue Huang Jiasheng Si Wenpeng Lu Wanxiang Che School of Computer Science and Engineering Central South University China Key Laboratory of Data Intelligence and Advanced Computing in Provincial Universities Soochow University China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China Harbin Institute of Technology Shenzhen China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences)

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Slot filling and intent detection are two highly correlated tasks in spoken language understanding (SLU). Recent SLU research attempts to explore zero-shot prompting techniques in large language models to alleviate the data scarcity problem. Nevertheless, the existing prompting work ignores the cross-task interaction information for SLU, which leads to sub-optimal performance. To solve this problem, we present the pioneering work of Cross-task Interactive Prompting (CroPrompt) for SLU, which enables the model to interactively leverage the information exchange across the correlated tasks in SLU. Additionally, we further introduce a multi-task self-consistency mechanism to mitigate the error propagation caused by the intent information injection. We conduct extensive experiments on the standard SLU benchmark and the results reveal that CroPrompt consistently outperforms the existing prompting approaches. In addition, the multi-task self-consistency mechanism can effectively ease the error propagation issue, thereby enhancing the performance. We hope this work can inspire more research on cross-task prompting for SLU.

关键词： Large language models Signal processing Benchmark testing Multitasking Filling Acoustics Speech processing Information exchange Standards

来源：评论

学校读者我要写书评

暂无评论

Reputation based Adaptive Federated Learning in Medical Scenarios

Reputation based Adaptive Federated Learning in Medical Scen...

引用

IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Lianhai Wang Tianrui Liu Yingxiaochun Wang Xinlei Wang Qi Li 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

Federated Learning (FL) is a distributed machine learning paradigm designed to address data silos and protect data privacy. However, in medical scenarios, the heterogeneity in data quality and the non-independent and identically distributed (Non-IID) nature of medical data from different hospitals can lead to degraded model performance in FL. To tackle this challenge, this paper proposes an adaptive federated learning framework based on reputation. By using a reputation evaluation model, the framework adaptively adjusts the aggregation weights of the models to enhance the performance of the global model. Additionally, our reputation evaluation model, integrated with blockchain technology, can rapidly identify malicious clients and effectively prevent poisoning attacks. Experimental results on two medical datasets demonstrate that this framework achieves higher model accuracy and faster convergence.

关键词： Adaptation models Data privacy Federated learning Hospitals Data integrity Computational modeling Evaluation models Data models security Convergence

来源：评论

学校读者我要写书评

暂无评论

ID-YOLOv7: A Method for Steel Surface Defect Detection Combining Residual Attention and Dynamic Upsampling

ID-YOLOv7: A Method for Steel Surface Defect Detection Combi...

引用

Smart World Congress (SWC), IEEE

作者： Shanshan Liu Xiangzhi Liu Jiazheng Man Bingyang Cao Yunfeng Dong Xiaoming Wu 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Industrial defect detection is a crucial challenge in computer vision. The accuracy of detecting defects on steel surfaces is hindered by spatially varying noise. To address this challenge, we propose an efficient method for steel surface defect detection. Firstly, considering the impact of the shape and proportions of bounding boxes on localization accuracy, we developed an Adaptive Shape-Inner Intersection over Union (ASIIoU) loss function. This function dynamically adjusts auxiliary bounding boxes and takes into account their shape and scale to optimize the loss, thereby improving the accuracy of bounding box regression loss. Secondly, to enhance the ability of the model to learn local features, we designed a Content-Guided Residual Attention Block (CGRAB). It captures rich local features across different convolutional layers via skip connections while discarding irrelevant information. Lastly, to avoid the potential issue of detail blurring caused by traditional upsampling methods, we optimized the upsampling process to preserve key details and structural information in the image, reducing unnecessary noise amplification. Experimental validation demonstrates the effectiveness of our method, which achieved a $76.7 \% \mathrm{mAP}$ on the NEU-DET dataset.

关键词： Location awareness Accuracy Shape Noise Focusing Object detection Feature extraction Steel Surface treatment Defect detection

来源：评论

学校读者我要写书评

暂无评论

LCLB: Low Cross-Chain Transaction Ratio and Load Balancing for Blockchain Sharding

LCLB: Low Cross-Chain Transaction Ratio and Load Balancing f...

引用

Robotics, Intelligent Control and Artificial Intelligence (RICAI), International Conference on

作者： Yu Wang Xiaoming Wu Qile Yuan Yunfeng Dong Xiangzhi Liu 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

As blockchain technology becomes widely applied, handling high volumes of transactions has become a critical challenge. Sharding has emerged as an effective solution to enhance the throughput of blockchain systems. Yet, this approach introduces challenges related to the frequency of cross-shard transactions and workload balancing. High rates of cross-shard transactions can slow overall processing speed and reduce throughput, while uneven load distribution can overburden some shards and underutilize others, impacting overall efficiency. To address these issues, this paper proposes a sharding method named LCLB. This method employs the concept of label propagation to pre-shard accounts with frequent transactions into a designated shard, ensuring a lower ratio of cross-shard transactions. It also uses an elastic load balancing algorithm to dynamically adjust the distribution of accounts, ensuring a relatively balanced processing load across all shards. Our results indicate that LCLB significantly enhances blockchain sharding performance,not only enhancing transaction throughput but also significantly reducing transaction confirmation latency, while ensuring a balanced load across various shards.

关键词： Sharding Heuristic algorithms Throughput Load management Blockchains Partitioning algorithms Delays Resource management Robots Intelligent control

来源：评论

学校读者我要写书评

暂无评论

Signature Scheme Based on The SM2 Algorithm in Fabric

Signature Scheme Based on The SM2 Algorithm in Fabric

引用

2021 International Conference on networking and network Applications, NaNA 2021

作者： Zhang, Yaling Wang, Hongyun Wang, Yichuan Ji, Wenjiang Zhu, Lei Xi'an University of technology School of computer science and engineering Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an University of technology Xi'an China

ISBN: (纸本)9781665441582

Based on the analysis and research of the widely used Hyperledger Fabric alliance chain structure, this paper proposes a digital signature scheme based on the national cryptographic algorithm SM2 which can be applied to Hyperledger Fabric. Firstly, two elliptic curve public-key cryptography algorithms are analyzed, and the feasibility of the SM2 in Fabric system is studied. Secondly, the Fabric system using the SM2 algorithm is designed and implemented, and the generation process of the Fabric chain address using the SM2 algorithm is given. Finally, experimental verification of the availability and performance of the Fabric system after the replacement of national cryptography algorithms is carried out. The experimental results show that the SM2 interface of the new Fabric alliance chain has good usability, and the average latency is reduced by 51.8%, and the transaction throughput is increased by 33.1%. © 2021 IEEE.

关键词： Blockchain

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：