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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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Federated Machine Learning Based Fetal Health Prediction Empowered with Bio-Signal Cardiotocography

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computers, Materials & Continua 2024年第3期78卷 3303-3321页

作者： Muhammad Umar Nasir Omar Kassem Khalil Karamath Ateeq Bassam SaleemAllah Almogadwy Muhammad Adnan Khan Muhammad Hasnain Azam Khan Muhammad Adnan Department of Computer Sciences Bahria University Lahore CampusLahore54000Pakistan Faculty of Information Technology Liwa CollegeAbu Dhabi00000United Arab Emirates Department of Computing Skyline University CollegeSharjah1797United Arab Emirates Department of Computer Science Taibah UniversityMedina42353Saudi Arabia Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Engineering and Development Dubizzle LabsLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam-si13120Korea

Cardiotocography measures the fetal heart rate in the fetus during pregnancy to ensure physical health because cardiotocography gives data about fetal heart rate and uterine shrinkages which is very beneficial to detect whether the fetus is normal or suspect or *** cardiotocography measures infer wrongly and give wrong predictions because of human *** traditional way of reading the cardiotocography measures is the time taken and belongs to numerous human errors as *** condition is very important to measure at numerous stages and give proper medications to the fetus for its *** the current period Machine learning(ML)is a well-known classification strategy used in the biomedical field on various issues because ML is very fast and gives appropriate results that are better than traditional *** techniques play a pivotal role in detecting fetal disease in its early *** research article uses Federated machine learning(FML)and ML techniques to classify the condition of the *** study proposed a model for the detection of bio-signal cardiotocography that uses FML and ML techniques to train and test the ***,the proposed model of FML used numerous data preprocessing techniques to overcome data deficiency and achieves 99.06%and 0.94%of prediction accuracy and misprediction rate,respectively,and parallel the proposed model applying K-nearest neighbor(KNN)and achieves 82.93%and 17.07%of prediction accuracy and misprediction accuracy,***,by comparing both models FML outperformed the KNN technique and achieved the best and most appropriate prediction results as compared with previous studies the proposed study achieves the best and most accurate results.

关键词： Cardiotocography ML FML fetal disease bio-signal

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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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Quantum phase discrimination with applications to quantum search on graphs

arXiv

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arXiv 2025年

作者： Li, Guanzhong Li, Lvzhou Luo, Jingquan Institute of Quantum Computing and Software School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China

We study the phase discrimination problem, in which we want to decide whether the eigenphase θ ∈ (−π,π] of a given eigenstate |ψ〉 with eigenvalue eiθ is zero or not, using applications of the unitary U provided as a black box oracle. We propose a quantum algorithm named quantum phase discrimination(QPD) for this task, with optimal query complexity Θ(1/λ log 1 /δ) to the oracle U, where λ is the gap between zero and nonzero eigenphases and δ the allowed one-sided error. The quantum circuit is simple, consisting of only one ancillary qubit and a sequence of controlled-U interleaved with single qubit Y rotations, whose angles are given by a simple analytical formula. Quantum phase discrimination could become a fundamental subroutine in other quantum algorithms, as we present two applications to quantum search on graphs: 1. Spatial search on graphs. Inspired by the structure of QPD, we propose a new quantum walk model, and based on them we tackle the spatial search problem, obtaining a novel quantum search algorithm. For any graph with any number of marked vertices, the quantum algorithm that can find a marked vertex with probability Ω(1) in total evolution time (formula presented) and query complexity (formula presented), where λ is the gap between the zero and non-zero eigenvalues of the graph Laplacian and Ε is a lower bound on the proportion of marked vertices. 2. Path-finding on graphs. By using QPD, we reduce the query complexity of a path-finding algorithm proposed by Li and Zur [arxiv: 2311.07372] from O̴(n11) to O̴(n8), in a welded-tree circuit graph with Θ(n2n) vertices. Besides these two applications, we argue that more quantum algorithms might benefit from QPD. © 2025, CC BY.

关键词： Quantum electronics

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SDG-CDA: Stackelberg Differential Games and Combinatorial Double Auctions based Pricing Mechanism in Cloud-Edge Environment

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IEEE Internet of Things Journal 2025年

作者： Liu, Shun Yao, Yan Zhang, Yongzhao Xu, Fenghua Yu, Jiguo Qufu Normal University School of Computer Science Shandong Rizhao China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Qilu University of Technology Big Data Institute Jinan250353 China

In this paper, we propose an innovative pricing mechanism for cloud-edge collaborative computing resources that combines Stackelberg differential games with combinatorial double auction. The scenario of trading heterogeneous computing resources between cloud data centers, edge servers and users is modeled as a two-stage game. The Stackelberg game equilibrium is solved by Hamilton-Jacobi-Bellman (HJB) equation to optimize the resource allocation and pricing between data centers and edge servers. Markov game with multi-agent reinforcement learning is used to ensure the optimal bidding strategy of users, while differential privacy mechanism is introduced to protect participants’ sensitive information. Experimental results show that the edge server utility is improved by at least 50% and the user utility by 30% compared to the baseline algorithm. The mechanism accelerates the convergence of the game process while protecting the privacy of auction participants, providing a novel and efficient solution to the resource allocation challenge in dynamic computing environments. © 2014 IEEE.

关键词： Resource allocation

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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...

引用

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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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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