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Improved RSA lossy trapdoor function and applications

International Journal of Information and computer Security 2021年第3-4期14卷 229-241页

作者： Cao, Nanyuan Cao, Zhenfu Dong, Xiaolei Wang, Haijiang School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science and Software Engineering East China Normal University Shanghai China

Kakvi and Kiltz (EUROCRYPT'12) proposed the first tight security reduction for RSA full domain hash signature scheme (RSA-FDH) with public exponent e 1/4 in the random oracle (RO) model, and they left an open prob... 详细信息

关键词： Authentication

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Hybrid GrabCut Hidden Markov Model for Segmentation

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computers, Materials & Continua 2022年第7期72卷 851-869页

作者： Soobia Saeed Afnizanfaizal Abdullah N.Z.Jhanjhi Mehmood Naqvi Mehedi Masud Mohammed A.AlZain Department of Software Engineering UniversitiTeknologi Malaysia-UTM81310Malaysia School of Computer Science and Engineering SCE Taylor’s UniversitySubang Jaya47500Malaysia Department of Engineering and Technology Mohak CollegeAlbertaCanada Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Diagnosing data or object detection in medical images is one of the important parts of image segmentation especially those data which is less effective to identify inMRI such as low-grade tumors or cerebral spinal fluid(CSF)leaks in the *** aim of the study is to address the problems associated with detecting the low-grade tumor and CSF in brain is difficult in magnetic resonance imaging(MRI)images and another problem also relates to efficiency and less execution time for segmentation of medical *** tumor and CSF segmentation using trained light field database(LFD)datasets of MRI *** research proposed the new framework of the hybrid k-Nearest Neighbors(k-NN)model that is a combination of hybridization of Graph Cut and Support Vector Machine(GCSVM)and Hidden Markov Model of k-Mean Clustering Algorithm(HMMkC).There are four different methods are used in this research namely(1)SVM,(2)GrabCut segmentation,(3)HMM,and(4)k-mean clustering *** this framework,on the one hand,phase one is to perform the classification of SVM and Graph Cut algorithm to create the maximum margin *** research use GrabCut segmentation method which is the application of the graph cut algorithm and extract the data with the help of scaleinvariant features *** the other hand,in phase two,segment the low-grade tumors and CSF using a method adapted for HMkC and extract the information of tumor or CSF fluid by GCHMkC including iterative conditional maximizing mode(ICMM)with identifying the range of *** evaluation is also performing by the comparison of existing techniques in this *** conclusion,our proposed model gives better results than *** proposed model helps to common man and doctor that can identify their condition of brain *** future,this will model will use for other brain related diseases.

关键词： SVM tumor CSF k-NN MRI grabcut segmentation HMM

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Facial Emotion Detection using CNN & VGG16 Model

Facial Emotion Detection using CNN & VGG16 Model

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International Conference for Convergence of Technology (I2CT)

作者： Aryan Singh Negi Anamika Arora Sourav Bisht Swati Devliyal Bura Vijay Kumar Gurjot Kaur Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India School of Computer Science& Artificial Intelligence SR University Warangal India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

In fields like human-computer interaction, healthcare, and marketing, facial and emotional recognition utilizing machine learning (ML) has found many uses. The primary method used in this study is Convolutional Neural Networks (CNNs), more especially pre-trained models like ResNet and VGG16 that have been tweaked using labelled facial expression datasets. Enhancement techniques are used to enhance model generalization, and data preparation, ensemble learning, and transfer learning are used to address issues like posture variations and cultural expressions. In tests conducted on the benchmark datasets CK+ and FER-2013, competition-level performance is displayed in parameters such as accuracy, precision, recall, and F1-score. The application of this ML-based methodology in emotion-aware systems, human-computer interaction, and mental health monitoring is highlighted by these *** present work underscores the potential of machine learning (ML) in detecting facial expressions and emotions. Further research is necessary to improve the functionality of these systems in order to achieve greater social advantages.

关键词： Human computer interaction Emotion recognition Face recognition Transfer learning Mental health Medical services Data models

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Application of Static Virus Spread Algorithm in Base-Balanced DNA Fragment Optimization

Application of Static Virus Spread Algorithm in Base-Balance...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yao Yao Yanfen Zheng Xun Zhang Xin Liu Xiaokang Zhang Bin Wang Qiang Zhang School of Computer Science and Technology Dalian University of Technology Dalian China School of Software Engineering Dalian University Dalian China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

DNA has found applications in a diverse array of fields such as computing, medical diagnosis, and circuits. Designing DNA fragments that meet specific requirements is crucial for ensuring the smooth execution of tasks in these domains. However, the conventional approach of combining constraints with evolutionary algorithms often encounters challenges like orthogonality and thermodynamic instability. This poses risks to the control of reaction processes. To tackle these challenges, we have undertaken two tasks in this study: expanding constraint sets and innovating evolutionary algorithms. Firstly, we propose a base balance strategy aimed at enhancing the thermodynamic properties within DNA fragment groups by diversifying neighboring base combinations. The addition of this strategy achieves a minimum variance of 0.03 for the melting temperature while ensuring orthogonality. It represents a significant improvement compared to previous results and reduces the complexity of controlling reactions. Secondly, our static virus spread algorithm optimizes target DNA fragments by base mutations and virus amplification. Simultaneously,it demonstrates good performance across 23 benchmark functions, highlighting its optimization potential. This work is expected to further refine theoretical shortcomings and offer a convenient tool for DNA fragment optimization.

关键词： Thermodynamics computer viruses Biological system modeling Computational modeling DNA Evolutionary computation Stability analysis Optimization Viruses (medical) DNA computing

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HyperDiff: Masked Diffusion Model with High-efficient Transformer for Hyperspectral Image Cross-Scene Classification

HyperDiff: Masked Diffusion Model with High-efficient Transf...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Pei Wang, Dong Wu, Chanyue Yang, Jing Kang, Lei Bai, Zongwen Li, Ying Shen, Qiang School of Computer Science Northwestern Polytechnical University Xi'an China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan'an University Yan'an China School of Automation and Software Engineering Shanxi University Taiyuan China Qingdao Topscomm Communication Co. Ltd. Qingdao China Department of Computer Science Aberystwyth University Aberystwyth United Kingdom

ISBN: (纸本)9798350368741

Hyperspectral Image (HSI) cross-scene classification is a challenging task in remote sensing, particularly when real-time processing of Target Domain (TD) HSI is required, and data cannot be reused for training. While deep learning methods have shown promising results, the generalization ability of HSI representations remains limited, mainly due to class label imbalance. This paper introduces a dual-stage learning framework based on transfer learning to enhance classification accuracy in the TD. The framework includes a self-supervised learning stage and a supervised fine-tuning stage. The self-supervised stage focuses on learning robust representations by leveraging inherent structures within HSI data, while the fine-tuning stage uses training labels to extract semantic information. A masked diffusion model predicts masked tokens from unmasked ones, capturing both high-level structures and fine details in HSI data. An efficient spatiospectral Transformer, which removes self-attention from the decoder, is proposed to enhance the self-supervised process. This design allows mask tokens to obtain information from visible tokens without interacting with each other, reducing sequence length and computational costs. By decoding each mask token conditionally independently, only a subset of masked tokens is processed. Extensive experiments on two public HSI datasets demonstrate that the proposed method outperforms state-of-the-art techniques. © 2025 IEEE.

关键词： Cross-scene classification Diffusion model Hyperspectral image Transformer

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Class-Aware Prompting for Federated Few-Shot Class-Incremental Learning

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Liang, Fang-Yi Zhan, Yu-Wei Liu, Jiale Zhang, Chong-Yu Chen, Zhen-Duo Luo, Xin Xu, Xin-Shun Shandong University School of Software Jinan250101 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Yunnan Key Laboratory of Software Engineering Kunming650504 China

Few-Shot Class-Incremental Learning (FSCIL) aims to continuously learn new classes from limited samples while preventing catastrophic forgetting. With the increasing distribution of learning data across different clients and privacy concerns, FSCIL faces a more realistic scenario where few learning samples are distributed across different clients, thereby necessitating a Federated Few-Shot Class-Incremental Learning (FedFSCIL) scenario. However, this integration faces challenges from non-IID problem, which affects model generalization and training efficiency. The communication overhead in federated settings also presents a significant challenge. To address these issues, we propose Class-Aware Prompting for Federated Few-Shot Class-Incremental Learning (FedCAP). Our framework leverages pre-trained models enhanced by a class-wise prompt pool, where shared class-wise keys enable clients to utilize global class information during training. This unifies the understanding of base class features across clients and enhances model consistency. We further incorporate a class-level information fusion module to improve class representation and model generalization. Our approach requires very few parameter transmission during model aggregation, ensuring communication efficiency. To our knowledge, this is the first study to explore the scenario of FedFSCIL. Consequently, we designed comprehensive experimental setups and made the code publicly available. © 2025 IEEE. All rights reserved.

关键词： Federated learning

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Introduction to the Special Issue on Advanced Security for FutureMobile Internet:A Key Challenge for the Digital Transformation

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computer Modeling in engineering & sciences 2024年第12期141卷 1907-1909页

作者： Ilsun You Xiaofeng Chen Vishal Sharma Gaurav Choudhary Department of Financial Information Security Kookmin UniversitySeoul-si02707Republic of Korea School of Network and Information Security Xidian UniversityXi’an710071China School of Electronics Electrical Engineering and Computer ScienceQueen’s University Belfast(QUB)BelfastBT71NNUK Centre for Industrial Software(CIS) The Maersk Mc-Kinney Moller InstituteUniversity of Southern DenmarkSønderborg6400Denmark

Mobile internet technologies have transformed our daily lives,allowing us to connect,communi-cate,and access various services and applications anytime and *** technologies are set to play a significant role in the next generation of digital transformation,further increasing their impact by integrating with emerging technologies like 6G,quantum computing,and generative AI.

关键词： services Internet internet

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Visual Analysis of Travel Behavior for Urban Rail Transit

Visual Analysis of Travel Behavior for Urban Rail Transit

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Haiming Liu Kexin Li Zhi Cai School of Software Engineering Beijing Jiaotong University Beijing China School of Computer Science Beijing University of Technology Beijing China

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Analyzing travel behavior of inhabitant can provide guidance and suggestions for urban traffic management and planning, land use planning, etc. In this paper, we designed a system to analyze the travel behavior of inhabitant using the smart card data of urban rail transit from two aspects, spatial visualization analysis and temporal visualization analysis. In spatial visualization analysis, we first conducted spatial and temporal aggregation for the whole city's smart card data. Then, we designed a spatial chord glyph to display the aggregated data. In temporal visualization analysis, we first proposed an algorithm to determine the degree and direction of cross-regional flow. Then, we study the degree of stability based on the algorithm. We conducted experiments based on urban rail transit in Beijing, the results show that our system can provide appropriate advice and support for urban traffic management and planning as well as land use planning.

关键词： Rails Smart cards Visualization Urban areas Land use planning Data visualization Smart systems Stability analysis Spatial databases Planning

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Blockchain-based Anonymous Authentication Key Management for Mobile Edge Computing

Blockchain-based Anonymous Authentication Key Management for...

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International Conference on computer Supported Cooperative Work in Design

作者： Yun Deng Haihua Tang School of Computer Science and Engineering Guangxi Normal University Guilin China School of Software Guangxi Normal University Guilin China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

A lightweight two-tier key authentication protocol based on blockchain is proposed to solve the edge server security and user privacy problems in Mobile Edge Computing (MEC). The protocol is based on blockchain technology and adopts a combination of elliptic curve cryptography and HMAC technology to deal with the authentication and key negotiation problems between the user and the edge. Considering the internal attack problem of the edge server, the key storage and computation in the protocol are transparent to the edge server and are done in the blockchain, and the efficient and secure management of the key is achieved through smart contracts. Experimental analysis shows that the protocol meets the basic security requirements, enhances the security of edge computing, and also outperforms other existing schemes in terms of efficiency. Therefore, it provides an efficient and feasible solution to the authentication problem in the MEC environment.

关键词： Privacy Multi-access edge computing Federated learning Smart contracts Authentication Elliptic curve cryptography Blockchains

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Machine Learning Model for Recipe Optimization of College Students Based on Simulated Annealing

Machine Learning Model for Recipe Optimization of College St...

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Data Analytics, Computing and Artificial Intelligence (ICDACAI), International Conference on

作者： Peizhi Sheng Xuhao Chen School of Computer and Information Science Software Southwest University Chongqing China School of Engineering and Technology Southwest University Chongqing China

ISBN: (数字)9798331519032

ISBN: (纸本)9798331519049

This paper first made a comprehensive dietary nutrition evaluation of the two recipes provided, and conducted a visual analysis of the differences of various nutrient components by querying the "Table of Chinese Food Ingredients". The nutrients involved included major daily productivity nutrients such as protein and fat, and non-productivity nutrients such as calcium, iron and zinc, which were used as collaborative evaluation indicators. Secondly, in order to specifically evaluate the daily diet of two male and female students, we pointed out the problems in the diet according to the Basic requirements for Analysis, Evaluation and Optimal Design of Dietary Recipes, and then adjusted and improved them, and made a comprehensive dietary nutrition evaluation again. Finally, the diet after adjustment has been improved to basically meet the nutritional requirements. Then, two models are established. Firstly, a model based on linear programming algorithm is established to solve the problem, and a model based on simulated annealing algorithm is established as a backup scheme and innovative ideas. Then, we used Model 1 to optimize male and female college birthday meal recipes with different goals (most economical, highest AAS, and dual goals). Finally, we also compare these three kinds of daily diet recipes with different objective optimization, and get the conclusion that the daily diet recipes with dual objective optimization have the highest cost performance. Finally, in order to realize the optimal design of weekly balanced diet, the paper extends the optimization model of daily diet based on problem 2. In order to solve the problem that the extended optimization model is difficult to solve due to too many decision variables, we propose a random elimination strategy to solve the weekly recipe scheme step by step according to the time granularity. After the design of daily recipes for each day is completed, the food of the day is eliminated from the daily recipes of the n

关键词： Proteins Visualization Optimization models Biological system modeling Simulated annealing Nutrients Linear programming Fats Optimization Water resources

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