检索结果-内蒙古大学图书馆

The differential fault analysis on block cipher FeW

Cybersecurity 2023年第2期6卷 62-74页

作者： Haiyan Xiao Lifang Wang Jinyong Chang School of Computer Science Northwestern Polytechnical UniversityXiAn 710055People's Republic of China Engineering University of PAP XiAn 710055People's Republic of China School of Information and Control Engineering XiAn University of Architecture and TechnologyXi'An 710055People's Republic of China State Key Laboratory of Information Security(SKLOIS) Institute of Information EngineeringChinese Academy of SciencesBeijing 100089People's Republic of China

Feather weight(FeW)cipher is a lightweight block cipher proposed by Kumar et *** 2019,which takes 64 bits plaintext as input and produces 64 bits *** Kumar et ***,FeW is a software oriented design with the aim of achieving high efficiency in software based *** seems that FeW is immune to many cryptographic attacks,like linear,impossible differential,differential and zero correlation ***,in recent work,Xie et *** the security of *** precisely,they proved that under the differential fault analysis(DFA)on the encryption states,an attacker can completely recover the master secret *** this paper,we revisit the block cipher FeW and consider the DFA on its key schedule algorithm,which is rather popular cryptanalysis for kinds of block *** particular,by respectively injected faults into the 30th and 29th round subkeys,one can recover about 55/80~69%bits of master *** the brute force searching remaining bits,one can obtain the full master secret *** simulations and experiment results show that our analysis is practical.

关键词： Differential fault analysis Block cipher FeW Side channel attack

来源：评论

学校读者我要写书评

暂无评论

Iterative scene graph generation 22

Iterative scene graph generation

引用

Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Siddhesh Khandelwal Leonid Sigal Department of Computer Science University of British Columbia and Vector Institute for AI Department of Computer Science University of British Columbia and Vector Institute for AI and CIFAR AI Chair

ISBN: (纸本)9781713871088

The task of scene graph generation entails identifying object entities and their corresponding interaction predicates in a given image (or video). Due to the combinatorially large solution space, existing approaches to scene graph generation assume certain factorization of the joint distribution to make the estimation feasible (e.g., assuming that objects are conditionally independent of predicate predictions). However, this fixed factorization is not ideal under all scenarios (e.g., for images where an object entailed in interaction is small and not discernible on its own). In this work, we propose a novel framework for scene graph generation that addresses this limitation, as well as introduces dynamic conditioning on the image, using message passing in a Markov Random Field. This is implemented as an iterative refinement procedure wherein each modification is conditioned on the graph generated in the previous iteration. This conditioning across refinement steps allows joint reasoning over entities and relations. This framework is realized via a novel and end-to-end trainable transformer-based architecture. In addition, the proposed framework can improve existing approach performance. Through extensive experiments on Visual Genome [30] and Action Genome [25] benchmark datasets we show improved performance on the scene graph generation task.

关键词：

来源：评论

学校读者我要写书评

暂无评论

X-ray Prohibited Items Recognition Based on Improved YOLOv5 20th

X-ray Prohibited Items Recognition Based on Improved YOLOv5

引用

Proceedings of the 20th Web Information Systems and Applications Conference, WISA 2023

作者： Li, Wei Li, Xiang Liu, Wanxin Liu, Zhihan Jia, Jing Li, Jiayi College of Computer Science and Technology Harbin Engineering University Harbin150001 China Faculty of Arts Design and Architecture School of Built Environment The University of New South Wales SydneyNSW2052 Australia School of Art and Design Jiangsu Institute of Technology Changzhou213001 China

ISBN: (纸本)9789819962211

Safety inspections play a crucial role in maintaining social stability and protecting the safety of public life and property. The X-ray security detector is an important scanning device in the security inspection of public transportation and express packages. However, current intelligent detection algorithms face problems such as a limited dataset of prohibited items, the imbalanced distribution of categories, variable target postures, and varying target scales, leading to the occurrence of false positives and missed detections. We propose an improved X-ray prohibited item recognition algorithm, named YOLOv5s-DAB. A deformable convolution module is designed to address the characteristics of different scales and postures of the same prohibited item in different samples. Besides, a multi-scale feature enhancement module SA-ASPP based on attention mechanism is designed, which can handle the problem of overlapping occlusion of multi-scale contraband. Experimental results in the real X-ray prohibited items dataset demonstrate that our model outperforms state-of-the-art methods in terms of detection accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Temporal graph benchmark for machine learning on temporal graphs 23

Temporal graph benchmark for machine learning on temporal gr...

引用

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Shenyang Huang Farimah Poursafaei Jacob Danovitch Matthias Fey Weihua Hu Emanuele Rossi Jure Leskovec Michael Bronstein Guillaume Rabusseau Reihaneh Rabbany Mila - Quebec AI Institute and School of Computer Science McGill University Kumo.AI Imperial College London Stanford University University of Oxford Mila - Quebec AI Institute and DIRO Université de Montréal and CIFAR AI Chair Mila - Quebec AI Institute and School of Computer Science McGill University and CIFAR AI Chair

We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine learning models on temporal graphs. TGB datasets are of large scale, spanning years in duration, incorporate both node and edge-level prediction tasks and cover a diverse set of domains including social, trade, transaction, and transportation networks. For both tasks, we design evaluation protocols based on realistic use-cases. We extensively benchmark each dataset and find that the performance of common models can vary drastically across datasets. In addition, on dynamic node property prediction tasks, we show that simple methods often achieve superior performance compared to existing temporal graph models. We believe that these findings open up opportunities for future research on temporal graphs. Finally, TGB provides an automated machine learning pipeline for reproducible and accessible temporal graph research, including data loading, experiment setup and performance evaluation. TGB will be maintained and updated on a regular basis and welcomes community feedback. TGB datasets, data loaders, example codes, evaluation setup, and leaderboards are publicly available at https://***/.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Statistical static timing analysis for circuit aging prediction

引用

The Journal of China Universities of Posts and Telecommunications 2021年第2期28卷 14-23页

作者： Duan Shengyu Zhai Dongyao Lu Yue Shanghai Engineering Research Center of Intelligent Computing System Shanghai UniversityShanghai 200444China State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China School of Electronics and Computer Science University of SouthamptonSouthamptonUK

Complementary metal oxide semiconductor(CMOS)aging mechanisms including bias temperature instability(BTI)pose growing concerns about circuit *** results in threshold voltage increases on CMOS transistors,causing delay shifts and timing violations on logic *** amount of degradation is dependent on the circuit workload,which increases the challenge for accurate BTI aging prediction at the design *** this paper,a BTI prediction method for logic circuits based on statistical static timing analysis(SSTA)is proposed,especially considering the correlation between circuit workload and BTI *** consists of a training phase,to discover the relationship between circuit scale and the required workload samples,and a prediction phase,to present the degradations under different workloads in Gaussian probability *** method can predict the distribution of degradations with negligible errors,and identify 50%more BTI-critical paths in an affordable time,compared with conventional methods.

关键词： bias temperature instability(BTI) reliability prediction statistical static timing analysis(SSTA)

来源：评论

学校读者我要写书评

暂无评论

Cover: Enhancing Virtualization Obfuscation Through Dynamic Scheduling Using Flash Controller-Based Secure Module

SSRN

引用

SSRN 2024年

作者： Zhang, Zheng Xue, Jingfeng Baker, Thar Chen, Tian Tan, Yu-An Li, Yuanzhang School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Architecture Technology and Engineering University of Brighton Brighton United Kingdom School of Cyberspace Science and Technology Beijing Institute of Technology Beijing China

Virtualization obfuscation is a very effective method used to protect programmes from malicious analysis by obscuring their code. Due to the fixed scheduling structures, typical virtualization obfuscation schemes can be compromised by automated analysis tools. To enhance the protection strength of virtualization obfuscation, additional protection techniques of the virtualization structure have been proposed. However, previously proposed solutions incur significant performance overhead or require a strong assumption in software-protection techniques. We present COVER, a novel virtualization obfuscation technique in conjunction with the flash controller. COVER enhances the obfuscation and protects the secret parameters of the virtualization structure with a flash controller-based secure module. We implement a prototype of COVER and describe a prototype implementation of a flash controller-based secure module on a Solid State Drive (SSD). We demonstrate COVER's efficacy against various code analysis methods, and evaluate COVER's performance using a set of real-world applications. The evaluation results demonstrate that COVER effectively protects the secret parameters of the virtualization structure and increases the effort involved in deobfuscation. Compared with two commercial obfuscators, COVER provides additional protection strength without incurring significantly more overhead in terms of runtime and code size. © 2024, The Authors. All rights reserved.

关键词： Firmware

来源：评论

学校读者我要写书评

暂无评论

Spatial-Temporal Constrained Aircraft Sequencing and Path Planning for Airport Ground Movement Problem 19th

Spatial-Temporal Constrained Aircraft Sequencing and Path P...

引用

19th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Liu, Fengmei Sun, Jingzhe Zhu, Jie Zhao, Zheng Huang, Huimin School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China The State Key Laboratory Chinese of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100864 China Nanjing University of Aeronautics and Astronautics Nanjing210016 China

ISBN: (纸本)9789819623754

In order to effectively alleviate the congestion and delay of aircraft during peak hours in a large hub airport, the paper studies the cooperative scheduling and path planning for arrival and departure aircrafts. A cooperative aircraft scheduling and path planning model for the airport ground movement problem is established with multiple spatial and temporal constraints, such as the queue constraint, time difference constraint, corner constraint. It aims to minimize the average taxiing time for all aircrafts. A genetic algorithm with elite selection strategy (ESGA) is proposed for the problem under study. Experimental results show that the proposed algorithm can effectively obtain the aircraft sequencing and path planning solutions, and significantly reduce the average taxiing time of the aircraft. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Motion planning

来源：评论

学校读者我要写书评

暂无评论

Numerical Feature Preprocessing Method for Daily Solar Radiation Pattern Classification 15th

Numerical Feature Preprocessing Method for Daily Solar Radia...

引用

15th International Conference on computer science and its Applications, CSA 2023 and 17th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2023

作者： Li, You Wang, Yafei Zheng, Ying Qian, Hui Zhou, Weisheng Dual-Carbon Research Center Hangzhou City University Hangzhou310015 China Asia-Japan Research Institute Ritsumeikan University Ibaraki567-8570 Japan School of Civil Engineering and Architecture Zhejiang University of Science and Technology Hangzhou310023 China Department of Computer Science and Information Technologies Frostburg State University FrostburgMD21532 United States College of Policy Science Ritsumeikan University Ibaraki567-8570 Japan

ISBN: (纸本)9789819724468

One challenging issue in daily solar radiation classification involves addressing the geometric differences in daily solar radiation patterns caused by geographic location and seasonal influences. A standardization method based on matrix transformation has been proposed to eliminate the daily geometric variations of solar radiation using classification for daily patterns of solar radiation. This method was tested using measurements taken at the Tokyo monitoring station from 2000 to 2021. Through the implementation of the proposed classification algorithm, this paper discerned twelve categories of daily clearness index distributions. The suggested classification framework has the potential to assist in the development of solar radiation transformation systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Solar irradiance

来源：评论

学校读者我要写书评

暂无评论

A Survey on Mapping of Urban Green Spaces within Remote Sensing Data Using Machine Learning & Deep Learning Techniques

A Survey on Mapping of Urban Green Spaces within Remote Sens...

引用

International Conference on computer and Automation Engineering, ICCAE

作者： Smita Sunil Burrewar Mazharul Haque Tanwir Uddin Haider Architecture & Planning Department National Institute of Technology Patna Patna Bihar Computer Science & Engineering Department National Institute of Technology Patna Patna Bihar

For environmental protection, urban planning, monitoring, and management of the urban ecosystem, mapping urban green spaces is a crucial undertaking. A vital source of information for United Nations Sustainable Development Goal 11.7 could come from urban green space mapping. The standard method of mapping urban green spaces requires field measurements and takes a lot of time. It is also important to update urban green space maps periodically because urban green spaces can change quickly over time due to development. With the advent of high-resolution satellite sensors like Sentinel-1 and Sentinel-2, a large number of remote sensing images may be gathered, providing quick and precise information over urban areas. This work intends to offer a new perspective on how crowd sourced geospatial big data and remote sensing may be combined to enhance the mapping of urban green spaces, including time optimization and accurate information through machine learning and deep learning. For the revitalization of cities, this data will be valuable. Remote sensing imagery data can be classified using machine learning techniques like Support vector machines (SVM), Random forests (RF), and Naive Bayes (NB). In deep learning techniques such as Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), K Nearest Neighbor (KNN), Generative Adversarial Networks (GAN), and Recurrent Neural Networks (RNN) can be used to classify remote sensing images.

关键词： Deep learning Support vector machines Recurrent neural networks Green products Urban planning Generative adversarial networks Time measurement

来源：评论

学校读者我要写书评

暂无评论

Enhanced satellite imagery analysis for post-disaster building damage assessment using integrated ResNet-U-Net model

引用

Multimedia Tools and Applications 2025年第5期84卷 2689-2714页

作者： Bhardwaj, Diwakar Nagabhooshanam, N. Singh, Ajeet Selvalakshmi, B. Angadi, Sanjeevkumar Shargunam, S. Guha, Tapas Singh, Gurkirpal Rajaram, A. Computer Engineering and Applications GLA University Chaumuhan India Department of Research Analytics Saveetha Dental College and Hospitals Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Aditya University Surampalem Andhra Pradesh India VIT Bhopal University Madhya Pradesh Bhopal466114 India Department of Computing Science and Engineering Tagore Engineering College Rathinamangalam Tamil Nadu Chennai600127 India Department of Computing Science and Engineering Nutan College of Engineering and Research Talegaon Dabhade Maharashtra 410507 India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu Srivilliputhur626126 India Department of CSE Techno International New Town West Bengal Kolkata700156 India Department of Architecture PTU Punjab Mohali140307 India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Tamil Nadu Nagapattinam611002 India

In the aftermath of natural disasters, timely and accurate building damage assessment is crucial for effective disaster response. This research introduces an innovative integrated ResNet-U-Net ensemble model that significantly enhances the accuracy and efficiency of damage detection from satellite imagery. The model combines the deep feature extraction capabilities of ResNet with the precise localization advantages of U-Net, supplemented by attention mechanisms to focus on critical damage indicators. Methodologically, the research employs a dataset of high-resolution satellite images from post-Hurricane Iota, processed through normalization and augmentation techniques. The model demonstrates exceptional accuracy, achieving 96.91% on an unbalanced dataset representative of real-world conditions, and 89.35% on a balanced dataset. This performance indicates a substantial improvement over existing approaches, offering a rapid and reliable tool for aiding disaster relief efforts. The proposed model's computational efficiency and generalizability underscore its potential to transform disaster management practices. Future work will explore the model's application to various disaster types and integration with real-time data processing systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Satellite imagery

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：