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检索条件"任意字段=3rd International Seminar on Computer Science and Engineering Technology, SCSET 2022"

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A Novel Key Generation Algorithm Utilizing Lattice-Based Cryptography Principles 3rd

A Novel Key Generation Algorithm Utilizing Lattice-Based Cry...

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3rd international Conference on Computing and Communication Networks, ICCCN 2023

作者： Arul, Rajakumar Sivaselvam, B. Choudhary, Ankush Centre for Smart Grid Technologies Vellore Institute of Technology Chennai India Department of Computer Science and Engineering IIITDM Kancheepuram Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819726707

technology advancement is inevitable, and it has created a revolution in all the sectors creating huge opportunities. Artificial intelligence and machine learning have boosted the advancement of all the sectors making the life easier and comfortable. On the other hand, these advancements highly depend on the connectivity through which all the terminals are communicating. Thus, all the technology developments depend on the communication paradigm, particularly on the wireless medium. Securing the information and infrastructure is crucial, and it demands highly complex problems that cannot be breached with high computation machines like quantum computer. The emergence of quantum computer has made all the existing cryptographic algorithms vulnerable creating a space for advanced algorithms that are safe from quantum computer attacks. Key generation is the foundational process of any cryptographic algorithm, and in this research, we propose a novel key generation mechanism based on lattice using mathematical functions. The proposal is evaluated using theoretical assumptions, and it is found to be safe and secure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Encryption algorithms

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Bayesian networks based on differential privacy for financial data privacy 3

Bayesian networks based on differential privacy for financia...

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3rd international Conference on Electrical, Electronics, and Information engineering, EEIE 2024

作者： Li, Ni Yang, Jianwei Ren, Xiaoli Ma, Xin Li, Xiangyang School of Computer Baoji University of Arts and Science Baoji China

ISBN: (纸本)9781510688179

As the demand for privacy protection continues to grow, ensuring effective financial data processing while preventing sensitive information leakage has become a pressing issue in the financial industry and related research fields. We propose a financial data processing method based on Bayesian networks and differential privacy, innovatively introducing an exponential mechanism to optimize the Bayesian network and adding Laplace noise to disrupt the data, thereby reducing the risk of sensitive information leakage. Through the evaluation of data usability using support vector machines, experimental results show that the processed synthetic data is highly similar to the original data in key metrics, demonstrating that this method maintains data usability while protecting privacy. © The Authors.

关键词： Differential privacy

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Voice Spoofing Detection using Feature Adaptive One-Class Classification Based on Graph Neural Network 24

Voice Spoofing Detection using Feature Adaptive One-Class Cl...

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3rd international Conference on Signal Processing, computer Networks and Communications, SPCNC 2024

作者： Lan, Haiyan Li, Yike Wang, Yuhua Dong, Linfan Han, Zhiying Yang, Kai College of Computer Science and Technology Harbin Engineering University Heilongjiang Harbin China

ISBN: (纸本)9798400710834

To address the problems of complex real voice feature distribution, easy overfitting by learning only one classification boundary, and poor generalization ability of existing voice spoofing detection methods for unknown attacks, this paper combines the representation ability of graph neural networks with the learning objective of one-class classification, and proposes a feature adaptive one-class voice detection framework based on graph neural networks. The core idea of the feature adaptive one-class classification loss (FAOC) in the framework is to cluster the real voices affected by similar factors more closely, learn several classification boundaries of the real voices, and optimize the distance of the real voices with different distributions, so that the model can obtain more general features of the real voices. At the same time, the relationship between different frequency bands or time segments of the voice is modeled by using graph neural networks, and the graph embedding features with strong recognition ability are obtained, which can better exploit the performance advantages of FAOC. A series of experiments are conducted on the ASVspoof2019 LA and ASVspoof2021 LA datasets, and the results on the ASVspoof2021 LA show that the EER and min-tDCF are improved by 45.8% and 48.2% compared to the baseline. The results show that the proposed method has good performance for voice synthesis attack detection. © 2024 Copyright held by the owner/author(s).

关键词： Graph neural networks

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Quantum-behaved particle swarm optimization with Lévy flight 3

Quantum-behaved particle swarm optimization with Lévy fligh...

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3rd international Conference on Algorithms, Network, and Communication technology, ICANCT 2024

作者： Tian, Dongping Li, Bingchun Liu, Chen Gheni, Zulpiya School of Computer Science and Technology Kashi University Xinjiang844006 China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

Quantum-behaved particle swarm optimization (QPSO) is a novel approach in evolutionary computation, which is very effective in solving various engineering problems but less efficient in dealing with discrete issues. Based on this fact, this paper puts forward a new quantum-behaved particle swarm optimization algorithm with chaos initialization and levy flight (abbreviated as QPSOLF). On one side, logistic map is used to produce high-quality initial swarm in order to speed up the convergence process. On the other side, Levy flight is embedded into the position updating scheme so as to enhance the swarm diversity and avoid QPSO from falling into local optimal. At length, extensive experiments on six representative test functions show that our algorithm achieves not only superior global search ability but also fast convergence rate. © 2025 SPIE.

关键词： Particle swarm optimization (PSO)

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Generative Replay and Multi-steps Knowledge Distillation in Class-Incremental Learning 24

Generative Replay and Multi-steps Knowledge Distillation in ...

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3rd international Conference on Artificial Intelligence and Intelligent Information Processing, AIIIP 2024

作者： Meng, Yicheng Ping, Jinming Shi, Jingye Zhi, Ruicong School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing China

ISBN: (纸本)9798400707308

Data-free generative modeling (GM) methods have gained much attention because pseudo data avoids security and privacy issues, and no amount limitation for storing real data in Class-Incremental Learning (CIL). However, GM-based methods have three drawbacks: un stable quality of generated image, semantic sifting, and domain bias for knowledge and distillation. To tackle with these problems, we propose an effective two-stage training method based on generative replay distillation, namely GA-RCT in this paper. It mainly consists of three modules. The Generative Adaptive Replay (GAR) module in stage one obtainsa well-trained generator for every task, then freezes generator for generating stable images, and trains feature extractor with mixture of real data and generated data. The Global Average Class (GAC) module is designed to reserve class prototypes to reduce semantic drift in the training stage. And the Generative Auxiliary Teaching (GAT) module in stage two performs generative distillation in dual-teacher to student way, for solving domain bias. Experiments on four datasets have shown that GA-RCT performs better than other generative replay methods. © 2024 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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FedVC: Virtual Clients for Federated Learning with Imbalanced Label Distribution 24

FedVC: Virtual Clients for Federated Learning with Imbalance...

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3rd international Conference on Algorithms, Data Mining, and Information technology, ADMIT 2024

作者： Chen, Zhouguo Chen, Hongda Liu, Shuai Liu, Yongdong Schoolof Computer Science and Engineering Southeast University Jiangsu Nanjing China The 30th Research Institute of China Electronics Technology Group Corporation Sichuan Chengdu China School of Software Engineering Xian Jiaotong University Shaanxi Xian China China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (纸本)9798400718120

Federated Learning (FL) has gained considerable attention for collaborative training in big data analysis, particularly in terms of privacy and communication constraints. Despite its promising advantages, FL faces the challenge of dealing with imbalanced label distribution on edge devices, which complicates the learning task. In literature, numerous studies have attempted to tackle this issue by introducing additional hyperparameters, which can complicate and prolong the process of model training and tuning. This complexity may potentially lead to overfitting and limited performance on new datasets. In this paper, we present a new Federated Learning framework called FedVC, which introduces virtual clients to minimize hyperparameter reliance. FedVC aims to bridge the gap between balanced and imbalanced label distribution. Compared with traditional federated learning frameworks, the proposed FedVC model designs a dynamically balanced dataset by utilizing virtual clients, which is proposed for the first time in this work. These virtual clients determine which part of the data should be used in one single training round and manage backpropagation timing for local real clients. The balanced dataset promotes greater stability in local models, thereby enhancing the overall stability of the training process. The experimental results show that FedVC outperforms classical FedAvg and the compared baseline methods for the data with imbalanced label distribution. Especially, FedVC exhibits more stable and accurate results than alternative approaches in highly heterogeneous settings. The source code is available at https://***/aikedaerC/FedVC. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Tensor-based multigraph structure preserving embedding for dimensionality reduction of hyperspectral images 3

Tensor-based multigraph structure preserving embedding for d...

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3rd international Conference on Geographic Information and Remote Sensing technology, GIRST 2024

作者： Shi, Guangyao Chen, Feng Xiang, Wenhao School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9781510689077

An important solution to the curse of dimensionality problem in large-volume, multi-band hyperspectral data is to perform the dimensionality reduction (DR) through graph embedding (GE) techniques. In this paper, a novel DR method called tensor-based multigraph structure preserving embedding (TMSPE) is proposed for HSI classification. At first, TMSPE defines a multi-graph structure with pixel-sharing weights, which can integrate the spatial-spectral information within the tensor samples. Then, it selects effective neighbor points for multigraph construction based on the probability distribution of different samples, which ensures than all nodes in the multigraph structure have correct connection relationships. Finally, the spatial coordinate relationships between pixels are introduced to adjusts the connection weights between different pixels adaptively, which can enhance the effectiveness of manifold reconstruction for HSI data. The performance of TMSPE is evaluated on PaviaU dataset, and experimental comparisons with several classical algorithms demonstrate the superiority of the proposed TMSPE algorithm in terms of classification accuracy. © 2025 SPIE.

关键词： Pixels

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Multi-UAV Cooperative Task Allocation Based on Fish Swarm Auction Hybrid Algorithm 3rd

Multi-UAV Cooperative Task Allocation Based on Fish Swarm Au...

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3rd international Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Mo, Zijing Zhang, Tingting Zhou, Xuan Wang, Chang Wu, Lizhen Key Laboratory of Maritime Intelligent Cyberspace Technology Ministry of Education Hohai University Jiangsu Nanjing210024 China Chinese People’s Liberation Army Unit Beijing91977 China National University of Defense Technology Hunan Changsha410073 China

ISBN: (纸本)9789819624676

The multi-UAV cooperative task allocation problem is central to multi-UAV cooperative combat. To enhance operational efficiency, this paper establishes a multi-UAV cooperative task allocation model. The model considers the constraints such as the ability of UAV and task priority, and takes the shortest total distance and task execution time of UAV swarm to complete the task as the optimization goal. A fish swarm auction hybrid algorithm is proposed to solve the model. Initially, the auction algorithm provides an allocation scheme to initialize the fish swarm algorithm, enhancing early convergence speed. Then, an adaptive improvement strategy is proposed for the step size of artificial fish. Initially, the step size is increased to enhance global search and speed up iterations. Later, it is reduced to avoid getting trapped in local optima and improve solution accuracy. The simulation results indicate that the hybrid algorithm demonstrates superior convergence and effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Distributed computer systems

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Research on ant colony deep learning under combinatorial generation 3

Research on ant colony deep learning under combinatorial gen...

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3rd international Conference on Algorithms, Network, and Communication technology, ICANCT 2024

作者： Wei, Yong Computer and Software School Shenzhen Institute of Information Technology Shenzhen China

ISBN: (数字)9781510688902

ISBN: (纸本)9781510688896

Combinatorial generation problems have a wide range of applications, and there have been over ten algorithms since the 1960s. As the combination size expands, it is difficult to find the optimal generating solution through enumeration in practice. If the path is defined as a step in two-dimensional coordinates that can only move in the positive direction of X or Y, each generation of combinatorial problem C(m+n, m) can be seen as a route generated by all paths starting from (0, 0) to (m, n) in two-dimensional coordinates. When there are too many generated solutions to be fully enumerated, ant colony algorithm can be used to simulate the foraging process of ant colonies, continuously exploring each route process, and assigning higher pheromones (weights) to paths with high fitness. Obtain weight information maps for each path through deep learning, enabling the algorithm to remember them. Finally, following the path with high weights can quickly obtain a high fitness route. Grouping is a more complex combinatorial problem, and the famous TTP problem is actually the grouping of course units. The article deduces the geometric model generated by the grouping problem, which is a circuit diagram formed by multiple rectangles connected end-to-end in two-dimensional coordinates. Complex combinatorial generation problems can be solved through ant colony deep learning on basic combinatorial generation geometric models, leaving behind memories. This method is simple, efficient, and feasible. © 2025 SPIE.

关键词： Ant colony optimization

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Pricing, Capacity and Estimated Wait Times Decisions in the Ride-Hailing 3

Pricing, Capacity and Estimated Wait Times Decisions in the ...

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3rd international Conference on Digitalization and Management Innovation, DMI 2024

作者： Yang, Jie Wang, Zeyu Wang, Di School of Economics and Management Taiyuan Science and Technology University 66 Wailiu Road Wanbailin District Taiyuan030024 China Department of Computer Engineering Texas A&M University 400 Bizzel St College StationTX77840 United States School of Accountancy Beijing Wuzi University Beijing101149 China

ISBN: (纸本)9781643685823

In recent years, the ride-hailing platform enables the passengers to request a ride from a private vehicle driven by a non-commercially licensed driver and provides them the information of estimated wait times (EWT). These EWT announcements significantly affect the service experience. However, it is unclear whether the drivers or the platform is more appropriate to determine the EWT in the ride-hailing service for gaining more profit for the platform and drivers. In this pursuit, the present study envisaged the development of two models, i.e., the platform set EWT (Model P) and drivers set EWT (Model D) by combining the decisions about capacity and price. We formulated the model as a leader-follower game and obtained Stackelberg equilibrium solutions. Our results show that the platform should pay attention to the capacity cost. The high-capacity cost aggravates the cost burden of the platform in Model (P);therefore, the platform should entitle more autonomy to the drivers for decreasing the total cost. If the platform sets a shorter EWT than the drivers, Model (P) can be considered as a more favorable choice for both players. When the EWT decision of the platform is moderately longer than the EWT decision of drivers, the platform entitles drivers to determine the EWT, which generates a win-win situation. © 2025 The Authors.

关键词： Air cushion vehicles

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