检索结果-内蒙古大学图书馆

Differential-Linear Attacks on Ballet Block Cipher

学校读者我要写书评

暂无评论

密码学报(中英文) 2025年第2期12卷 469-488页

作者： ZHOU Yu CHEN Si-Wei XU Sheng-Yuan XIANG Ze-Jun ZENG Xiang-Yong Key Laboratory of Intelligent Sensing System and Security Ministry of EducationSchool of Cyber Security and TechnologyHubei UniversityWuhan 430062China Department of Fundamental Courses Shandong University of Science and TechnologyTaian 271000China Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and StatisticsHubei UniversityWuhan 430062China

Ballet is one of the finalists of the block cipher project in the 2019 National Cryptographic Algorithm Design *** study aims to conduct a comprehensive security evaluation of Ballet from the perspective of differential-linear(DL)***,we present an automated search for the DL distinguishers of Ballet based on MILP/*** the versions with block sizes of 128 and 256 bits,we obtain 16 and 22 rounds distinguishers with estimated correlations of 2^(-59.89)and 2^(-116.80),both of which are the publicly longest *** addition,this study incorporates the complexity information of key-recovery attacks into the automated model,to search for the optimal key-recovery attack structures based on DL *** a result,we mount the key-recovery attacks on 16-round Ballet-128/128,17-round Ballet-128/256,and 21-round Ballet-256/*** data/time complexities for these attacks are 2^(108.36)/2^(120.36),2^(115.90)/2^(192),and 2^(227.62)/2^(240.67),respectively.

关键词： Ballet block cipher differential-linear(DL)cryptanalysis MILP/MIQCP distinguisher key-recovery attacks

Disorder-enhanced layer Hall effect in a magnetic sandwich heterostructure

学校读者我要写书评

暂无评论

Physical Review B 2024年第11期109卷 115301-115301页

作者： Xiao-Xia Yi Chun-Bo Hua Rui Chen Bin Zhou Department of Physics Hubei University Wuhan 430062 China School of Electronic and Information Engineering Hubei University of Science and Technology Xianning 437100 China Key Laboratory of Intelligent Sensing System and Security of Ministry of Education Hubei University Wuhan 430062 China

The layer Hall effect is a type of Hall effect where electrons on the top and bottom layers deflect in opposite directions. In this paper, we investigate the disorder effects on the layer Hall effect in a magnetic sandwich heterostructure that supports the axion insulator and normal insulator phases. We demonstrate that the layer Hall effect in the axion insulator phase is robust against weak disorder. Most interestingly, we find that the layer Hall effect in the normal insulator phase is significantly enhanced with increasing disorder strength. Theoretically, the disorder-enhanced layer Hall effect at weak disorder is explained by using the Born approximation. Experimentally, we suggest the disorder-enhanced layer Hall effect could be detected by applying a perpendicular electric field.

关键词： Topological Hall effect Dilute magnetic topological insulators Topological insulators

GBFRS: Robust Fuzzy Rough Sets via Granular-ball Computing

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Xia, Shuyin Lian, Xiaoyu Sang, Binbin Wang, Guoyin Gao, Xinbo Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China College of Computer and Information Science the National Center for Applied Mathematics in Chongqing Chongqing Normal University Chongqing401331 China

Fuzzy rough set theory is effective for processing datasets with complex attributes, supported by a solid mathematical foundation and closely linked to kernel methods in machine learning. Attribute reduction algorithms and classifiers based on fuzzy rough set theory exhibit promising performance in the analysis of high-dimensional multivariate complex data. However, most existing models operate at the finest granularity, rendering them inefficient and sensitive to noise, especially for high-dimensional big data. Thus, enhancing the robustness of fuzzy rough set models is crucial for effective feature selection. Muiti-garanularty granular-ball computing, a recent development, uses granular-balls of different sizes to adaptively represent and cover the sample space, performing learning based on these granular-balls. This paper proposes integrating multi-granularity granular-ball computing into fuzzy rough set theory, using granular-balls to replace sample points. The coarse-grained characteristics of granular-balls make the model more robust. Additionally, we propose a new method for generating granular-balls, scalable to the entire supervised method based on granular-ball computing. A forward search algorithm is used to select feature sequences by defining the correlation between features and categories through dependence functions. Experiments demonstrate the proposed model’s effectiveness and superiority over baseline methods. The source codes and datasets are both available on the public link: https://***/lianxiaoyu724/GBFRS Copyright © 2025, The Authors. All rights reserved.

关键词： Granular computing

A Causal Disentangled Multi-Granularity Graph Classification Method

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Li, Yuan Liu, Li Chen, Penggang Zhang, Youmin Wang, Guoyin Chongqing Key Laboratory of Computational Intelligence China Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Posts and Telecommunications Chongqing400065 China

Graph data widely exists in real life, with large amounts of data and complex structures. It is necessary to map graph data to low-dimensional embedding. Graph classification, a critical graph task, mainly relies on identifying the important substructures within the graph. At present, some graph classification methods do not combine the multi-granularity characteristics of graph data. This lack of granularity distinction in modeling leads to a conflation of key information and false correlations within the model. So, achieving the desired goal of a credible and interpretable model becomes challenging. This paper proposes a causal disentangled multi-granularity graph representation learning method (CDM-GNN) to solve this challenge. The CDM-GNN model disentangles the important substructures and bias parts within the graph from a multi-granularity perspective. The disentanglement of the CDM-GNN model reveals important and bias parts, forming the foundation for its classification task, specifically, model interpretations. The CDM-GNN model exhibits strong classification performance and generates explanatory outcomes aligning with human cognitive patterns. In order to verify the effectiveness of the model, this paper compares the three real-world datasets MUTAG, PTC, and IMDM-M. Six state-of-the-art models, namely GCN, GAT, Top-k, ASAPool, SUGAR, and SAT are employed for comparison purposes. Additionally, a qualitative analysis of the interpretation results is conducted. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning systems

Cross-Frequency Collaborative Training Network and dataset for Semi-supervised First Molar Root Canal Segmentation

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Zhou, Zhenhuan Zhang, Yuchen He, Along Wang, Peng Xie, Xueshuo Li, Tao College of Computer Science Nankai University Tianjin300350 China Key Laboratory of Data and Intelligent System Security Ministry of Education China Department of stomatology Tianjin Union Medical Center Tianjin China Haihe Lab of ITAI Tianjin300459 China

Root canal (RC) treatment is a highly delicate and technically complex procedure in clinical practice, heavily influenced by the clinicians’ experience and subjective judgment. Deep learning has made significant advancements in the field of computer-aided diagnosis (CAD) because it can provide more objective and accurate diagnostic results. However, its application in RC treatment is still relatively rare, mainly due to the lack of public datasets in this field. To address this issue, in this paper, we established a First Molar Root Canal segmentation dataset called FMRC-2025. Additionally, to alleviate the workload of manual annotation for dentists and fully leverage the unlabeled data, we designed a Cross-Frequency Collaborative training semi-supervised learning (SSL) Network called CFC-Net. It consists of two components: (1) Cross-Frequency Collaborative Mean Teacher (CFC-MT), which introduces two specialized students (SS) and one comprehensive teacher (CT) for collaborative multi-frequency training. The CT and SS are trained on different frequency components while fully integrating multi-frequency knowledge through cross and full frequency consistency supervisions. (2) Uncertainty-guided Cross-Frequency Mix (UCF-Mix) mechanism enables the network to generate high-confidence pseudo-labels while learning to integrate multi-frequency information and maintaining the structural integrity of the targets. Extensive experiments on FMRC-2025 and three public dental datasets demonstrate that CFC-MT is effective for RC segmentation and can also exhibit strong generalizability on other dental segmentation tasks, outperforming state-of-the-art SSL medical image segmentation methods. Codes and dataset will be released. © 2025, CC BY-NC-ND.

关键词： Semi-supervised learning

RKSeg+: make full use of Runge–Kutta methods in medical image segmentation

学校读者我要写书评

暂无评论

Multimedia systems 2024年第2期30卷 65-65页

作者： Zhu, Mai Fu, Chong Wang, Xingwei School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China

The dynamical system perspective has been used to build efficient image classification networks and semantic segmentation networks. Furthermore, the Runge–Kutta (RK) methods are powerful tools for building networks from the dynamical systems perspective. Hence, the Runge–Kutta segmentation network (RKSeg) for medical image segmentation was born. Skip connections and multiple scaling are often used in common models but lack mathematical explanations. RKSeg interprets and uses skip connections based on the RK methods. Therefore, RKSeg greatly improves segmentation efficiency. However, it does not explain and use multiple scales from a dynamical system perspective but only inherits the multi-scale scheme of existing models. We compensate for this shortcoming by interpreting and using multiple scales based on the RK methods. In addition, the network structure also limits the excellent image classification networks as the backbones of RKSegs. Therefore, we modify the network structure to support more image classification networks as backbones. As a result, we propose a novel network structure RKSeg+. Our proposed RKSeg+ achieves better segmentation results with fewer parameters than RKSeg. Furthermore, RKSeg+, well configured with few parameters, outperforms state-of-the-art models on six of the ten organ datasets in the Medical Segmentation Decathlon. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Semantic Segmentation

Deep Feature Surgery: Towards Accurate and Efficient Multi-Exit Networks

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Gong, Cheng Chen, Yao Luo, Qiuyang Lu, Ye Li, Tao Zhang, Yuzhi Sun, Yufei Zhang, Le College of Software Nankai University China College of Computer Science Nankai University China National University of Singapore Singapore School of Information and Communication Engineering University of Electronic Science and Technology of China China HAIHE Lab of ITAI China Tianjin Key Laboratory of Network and Data Security Technology China Key Laboratory of Data and Intelligent System Security Ministry of Education

Multi-exit network is a promising architecture for efficient model inference by sharing backbone networks and weights among multiple exits. However, the gradient conflict of the shared weights results in sub-optimal accuracy. This paper introduces Deep Feature Surgery (DFS), which consists of feature partitioning and feature referencing approaches to resolve gradient conflict issues during the training of multi-exit networks. The feature partitioning separates shared features along the depth axis among all exits to alleviate gradient conflict while simultaneously promoting joint optimization for each exit. Subsequently, feature referencing enhances multi-scale features for distinct exits across varying depths to improve the model accuracy. Furthermore, DFS reduces the training operations with the reduced complexity of backpropagation. Experimental results on Cifar100 and ImageNet datasets exhibit that DFS provides up to a 50.00% reduction in training time and attains up to a 6.94% enhancement in accuracy when contrasted with baseline methods across diverse models and tasks. Budgeted batch classification evaluation on MSDNet demonstrates that DFS uses about 2× fewer average FLOPs per image to achieve the same classification accuracy as baseline methods on Cifar100. The code is available at https: //***/GongCheng1919/dfs. Copyright © 2024, The Authors. All rights reserved.

关键词： Budget control

Partial Deep Domain Adaptation for Non-Intrusive Load Monitoring 21

学校读者我要写书评

暂无评论

Partial Deep Domain Adaptation for Non-Intrusive Load Monito...

21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Li, Yuying Yang, Yang Wang, Chen Liu, Runqin Jiang, Hongbo Liu, Wenping Hubei University Key Laboratory of Intelligent Sensing System and Security Ministry of Education School of Artificial Intelligence Wuhan China Huazhong University of Science and Technology Wuhan China China Unicom Central South Research Institute Changsha China Hunan University Changsha China Hubei University of Economics Wuhan China

ISBN: (纸本)9798350329223

With the widespread deployment of smart meters and the burgeoning integration of the Ubiquitous Power Internet of Things (UPIoT), Non-Intrusive Load Monitoring (NILM) has emerged as an essential technology, providing granular insights into device-level energy consumption. However, NILM model generalization is impeded by user and appliance heterogeneity. Domain adaptation has been leveraged to enhance NILM models, but it typically necessitates consistent appliance categorization across households. Addressing this, our study presents Weighted Adaptation NILM (WeAd-NILM), a partial deep domain adaptation model designed for Partial Domain Adaptation (PDA) scenarios that accommodate diverse appliance categories, aligning with real-world load decomposition tasks. WeAd-NILM introduces a sample weighting mechanism tailored for PDA, mitigating negative transfer from outliers. Through distribution analysis and multi-objective regression, WeAd-NILM effectively performs load decomposition, fostering knowledge transfer. Verified on UK-DALE and REFIT datasets, WeAd-NILM demonstrates exceptional transferability and load decomposition accuracy. © 2023 IEEE.

关键词： Internet of things

Design of Environment Monitoring system Based on Single-Chip Microcomputer

学校读者我要写书评

暂无评论

Design of Environment Monitoring System Based on Single-Chip...

International Conference on intelligent Human-Machine systems and Cybernetics, IHMSC

作者： Fan Wang Guojun Liu Wei Zhang Wen Wang School of Computer Science and Information Engineering Hubei University Wuhan China Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education

This article proposes a real-time environment monitoring system based on single-chip microcomputer, which consists of the device and user. The user adopts WeChat small program, while the device includes STMicroelectronics (STM32) single-chip microcomputer (SCM) control module, temperature and humidity sensor module, illumination sensor module, smoke sensor module, Wireless Fidelity (WIFI) communication module, display module, alarm module, and CP2102 USB-TTL module. The device establishes the connection with the user through the WIFI communication module to achieve remote wireless transmission of data. This system can monitor physical environment information in real-time, not only improving comfort and office efficiency, but also achieving the purpose of preventing danger through preset alarms.

关键词：