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检索条件"主题词=Partitioning Algorithms"

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FedDPC: A Density Peak Clustering-based Federated Learning Algorithm

FedDPC: A Density Peak Clustering-based Federated Learning A...

引用

Artificial Intelligence, Robotics, and Communication (ICAIRC), IEEE International Conference on

作者： Jiayi Du Taiyin Zhao Xucheng Li School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

Federated learning (FL) is widely applied in edge computing scenarios to protect data privacy, but the assumption of independent and identically distributed (IID) data across clients often does not hold in practice, leading to performance degradation. Among the many methods proposed to address data heterogeneity, clustered federated learning (CFL) has emerged as a popular approach. Current CFL methods commonly adopt the expectation-maximization (EM) algorithm, which mitigates the problem of data heterogeneity. However, they still face challenges such as requiring more clients than traditional federated learning and introducing randomness in the clustering process. To overcome these limitations, we propose FedDPC, which integrates the density peak clustering (DPC) algorithm. This approach provides the advantages of not requiring additional clients and having a non-random partitioning strategy. Finally, we conducted experiments to demonstrate the feasibility and effectiveness of our proposed method.

关键词： Degradation Data privacy Federated learning Heuristic algorithms Clustering algorithms Distributed databases partitioning algorithms Robots Faces Edge computing

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Trading Determinism for Noncommutativity in Edmonds' Problem

Trading Determinism for Noncommutativity in Edmonds' Problem

引用

Annual IEEE Symposium on Foundations of Computer Science

作者： V. Arvind Abhranil Chatterjee Partha Mukhopadhyay Institute of Mathematical Sciences Chennai Mathematical Institute Chennai India Indian Statistical Institute Kolkata India Chennai Mathematical Institute Chennai India

ISBN: (数字)9798331516741

ISBN: (纸本)9798331516758

Let $X=X_{1} \sqcup X_{2} \sqcup \ldots \sqcup X_{k}$ be a partitioned set of variables such that the variables in each part $X_{i}$ are noncommuting but for any $i\neq j$ , the variables $x\in X_{i}$ commute with the variables $x^{\prime}\in X_{j}$ . Given as input a square matrix $T$ whose entries are linear forms over $\mathbb{Q}\langle X\rangle$ 〉, we consider the problem of checking if $T$ is invertible or not over the universal skew field of fractions of the partially commutative polynomial ring $\mathbb{Q}\langle X\rangle$ [1]. In this paper, we design a deterministic polynomial-time algorithm for this problem for constant $k$ . The special case $k=1$ is the noncommutative Edmonds' problem (NSINGULAR) which has a deterministic polynomial-time algorithm by recent results [2]–[4]. En-route, we obtain the first deterministic polynomial-time algorithm for the equivalence testing problem of $k$ -tape weighted automata (for constant $k$ ) resolving a longstanding open problem [5], [6]. Algebraically, the equivalence problem reduces to testing whether a partially commutative rational series over the partitioned set $X$ is zero or not [6]. Decidability of this problem was established by Harju and Karhumäki [5]. Prior to this work, a randomized polynomial-time algorithm for this problem was given by Worrell [6] and, subsequently, a deterministic quasipolynomial-time algorithm was also developed [7].

关键词： Computer science Automata Polynomials partitioning algorithms Modules (abstract algebra) Testing

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Finding Important Nodes in Chordal Graphs*

Finding Important Nodes in Chordal Graphs*

引用

International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Mohammed Lalou Hamamache Kheddoucl UFR-Science et Technique Lab LIB Université de Bourgogne Dijon France UFR-Informatique Lab LIRIS Université Claude Bernard Lyon 1 Villeurbanne France

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

Identifying important nodes in networked systems is crucial for network analysis and many other applications. Different metrics have been used to assess the node importance within a network, generally as a property to be fulfilled by the node deletion. In this work, we consider as important nodes those whose deletion partitions the network into connected components of a given size. Given the graph G representing the network, we look for the smallest subset of nodes whose removal partition G into components of at most κ nodes, where κ is a given bound. This problem has already been shown to be NP-complete even when restricted to particular classes of graphs, which is the case for general chordal graphs. In this paper, we develop a polynomial-time algorithm to solve it on chordal graphs with maximum node degree Δ = 3. The proposed algorithm returns an exact solution in O(n 2 ) for a graph G of n nodes.

关键词： Measurement Network analyzers partitioning algorithms Information technology

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Learners’ Privacy-Preserving Scheme for Ranking Data Sensitivity in Mobile Learning System

Learners’ Privacy-Preserving Scheme for Ranking Data Sensit...

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Electrical and Computer Engineering Researches (ICECER), International Conference on

作者： Muhammad Kudu Muhammad Olayemi Mikail Olaniyi Francis Osang Ishaq Oyebisi Oyefolahan Ojeniyi Joseph Adebayo Idris Mohammed Kolo Department of Computer Science Federal University Technology Minna Nigeria Department of Cyber Security National Open University of Nigeria Abuja Nigeria Department of Computer Science National Open University of Nigeria Abuja Nigeria Africa Centre of Excellence on Technology Enhanced Learning National Open University of Nigeria Abuja Nigeria Department of Cyber Security Federal University Technology Minna Nigeria

ISBN: (数字)9798331539733

ISBN: (纸本)9798331539740

Mobile Learning System (MLS) is facing new challenges in terms of privacy, such as data collection, storage, and sharing because of the core infrastructure and network that enables cloud computing services. Most of the data exchange in Mobile Learning System (MLS) require mandatory authorisation to allow access to the learners’ information in the MLS. Therefore, this article attempts to rank learners' sensitive attributes stored in MLS. Thus, concerns about privacy breaches motivated this paper to adopt an attributes partitioning strategy into the sensitive attributes to enforce privacy during learners’ profile information access. The article adopted the informed consent phenomenon to determine and formulate learners’ data privacy attributes sensitivity using the Fuzzy Analytic Hierarchy Process (FAHP) Algorithm. Results from the implemented Learners’ Privacy Preserving (LPP) Algorithm determined normalized weights of top-five rank-selected learners’ sensitive data to include: Browsing History (1 ST , Ranked), Geolocation Data (2 ND , Ranked), IP Address (3 RD , Ranked), web Browser (4 TH , Ranked), Medical Records (5 TH , Ranked) and CGPA (10 TH , Ranked) respectively. This indicates that these five most sensitive features are at risk and require protection to prevent privacy breaches, thus ensuring privacy preservation that prevents unauthorised access to learners’ sensitive data in the mobile learning system environment. The ranking of sensitive data in this paper could serve as inspiration for future research work on mobile learning security to improve the privacy of sensitive attributes in MLS environment

关键词： Authorization Data privacy Sensitivity Geology Privacy breach Mobile learning partitioning algorithms Browsers IP networks History

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Audio Spotforming Using Nonnegative Tensor Factorization with Attractor-Based Regularization

Audio Spotforming Using Nonnegative Tensor Factorization wit...

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European Signal Processing Conference (EUSIPCO)

作者： Shoma Ayano Li Li Shogo Seki Daichi Kitamura National Institute of Technology Kagawa Japan CyberAgent Inc. Japan

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Spotforming is a target-speaker extraction technique that uses multiple microphone arrays. This method applies beamforming (BF) to each microphone array, and the common components among the BF outputs are estimated as the target source. This study proposes a new common component extraction method based on nonnegative tensor factorization (NTF) for higher model interpretability and more robust spotforming against hyperparameters. Moreover, attractor-based regularization was introduced to facilitate the automatic selection of optimal target bases in the NTF. Experimental results show that the proposed method performs better than conventional methods in spotforming performance and also shows some characteristics suitable for practical use.

关键词： Tensors Array signal processing Signal processing algorithms Europe Clustering algorithms Interference Microphone arrays Vectors partitioning algorithms

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Spatial Temporal Transpose Convolutional Networks for Action Recognition

Spatial Temporal Transpose Convolutional Networks for Action...

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International Conference on Computer and Communications (ICCC)

作者： Ying Bai Dongsheng Yang Hongliang Wang Changyi Deng Wei Wang Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on DigitTwin Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on DigitTwin Shenyang China Institute of Software China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

In recent years, the use of skeleton data for human action recognition has received increasing attention in fields such as intelligent monitoring, human-object interaction, and video retrieval. Among them, Modeling Human Skeleton as Spatial-Temporal Graph Convolutional Network (ST-GCN) has made significant progress. However, methods based on Graph Convolution (GCN) generally face two issues: on one hand, the receptive fields of the spatial and temporal graphs are not flexible enough, limiting their feature representation capabilities. On the other hand, the model is overly parameterized, leading to high computational costs. In this paper, we first propose a spatial-temporal transpose convolution module to overcome the inflexibility of the spatial-temporal graph receptive field. Secondly, we introduce 1 *1 convolutions and depth-wise separable convolution layers into the network model, effectively reducing the computational complexity of the model. On the two subsets of the large-scale skeleton action recognition dataset NTU RGB-D, experimental results show that the proposed method achieved Top1 accuracy metrics of 84.6% and 92.5% respectively, while the model size was reduced by 13.48 times compared to the classic ST-GCN method, and the computational complexity was reduced by approximately 17.18 times.

关键词： Hands Accuracy Convolution Computational modeling Benchmark testing Approximation algorithms Skeleton partitioning algorithms Computational complexity Monitoring

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Convergence Rate Maximization for Split Learning-based Control of EMG Prosthetic Devices

Convergence Rate Maximization for Split Learning-based Contr...

引用

International Conference on Intelligent Environments (IE)

作者： Matea Marinova Daniel Denkovski Hristijan Gjoreski Zoran Hadzi-Velkov Valentin Rakovic Faculty of Electrical Engineering and Information Technologies Ss. Cyril and Methodius University Skopje R. North Macedonia

ISBN: (数字)9798350386790

ISBN: (纸本)9798350386806

Split Learning (SL) is a promising Distributed Learning approach in electromyography (EMG) based prosthetic control, due to its applicability within resource-constrained environments. Other learning approaches, such as Deep Learning and Federated Learning (FL), provide suboptimal solutions, since prosthetic devices are extremely limited in terms of processing power and battery life. The viability of implementing SL in such scenarios is caused by its inherent model partitioning, with clients executing the smaller model segment. However, selecting an inadequate cut layer hinders the training process in SL systems. This paper presents an algorithm for optimal cut layer selection in terms of maximizing the convergence rate of the model. The performance evaluation demonstrates that the proposed algorithm substantially accelerates the convergence in an EMG pattern recognition task for improving prosthetic device control.

关键词： Deep learning Training Performance evaluation Electromyography partitioning algorithms Pattern recognition Task analysis

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Minimizing Trotter Approximation Error in Quantum Phase Estimation Using Genetic Algorithm

Minimizing Trotter Approximation Error in Quantum Phase Esti...

引用

Quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Annika Daspal Artur F. Izmaylov Bill Hogarth Secondary School Ontario Canada University of Toronto Ontario Canada

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

A key application of quantum computing is solving the electronic problem using Quantum Phase Estimation (QPE). The Trotter Approximation (TA) is one of the techniques used in QPE for encoding the Hamiltonian on a quantum computer. In TA, a Hamiltonian is partitioned into easily diagonalizable fragments. However, there is an error introduced in the unitary evolution operator when TA is applied. Different partitionings of a Hamiltonian and the ordering of the fragments yield different TA errors. In this work, we propose a novel two-step algorithm to obtain a near-optimal ordering for a given partitioning of a Hamiltonian. We find that when we use the Qubit-Wise Commuting partitioning method for the electronic Hamiltonian of molecule $H_partitioning$ , our algorithm is approximately 6 times faster than evaluating all possible orderings exhaustively.

关键词： Legged locomotion Accuracy Phase estimation Qubit Approximation algorithms Approximation error Encoding partitioning algorithms Genetic algorithms

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Fine-Grained Bipartite Concept Factorization for Clustering

Fine-Grained Bipartite Concept Factorization for Clustering

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Chong Peng Pengfei Zhang Yongyong Chen Zhao Kang Chenglizhao Chen Qiang Cheng College of Computer Science and Technology Qingdao University School of Computer Science and Technology Harbin Institute of Technology (Shenzhen) School of Computer Science and Engineering University of Electronic Science and Technology of China College of Computer Science and Technology China University of Petroleum (East China) Department of Computer Science University of Kentucky

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

In this paper, we propose a novel concept factorization method that seeks factor matrices using a cross-order positive semi-definite neighbor graph, which provides comprehensive and complementary neighbor information of the data. The factor matrices are learned with bipartite graph partitioning, which exploits explicit cluster structure of the data and is more geared towards clustering application. We develop an effective and efficient optimization algorithm for our method, and provide elegant theoretical results about the convergence. Extensive experimental results confirm the effectiveness of the proposed method.

关键词： Computer vision Clustering algorithms partitioning algorithms Bipartite graph Pattern recognition Optimization Convergence

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Design and Analysis A High-Performance Parallel Decimal Multiplier

Design and Analysis A High-Performance Parallel Decimal Mult...

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Recent Innovation in Smart and Sustainable Technology (ICRISST), International Conference on

作者： Meesa Ravali G. Surekha Electronic and Communication Engineering GRIET Hyderabad India

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

This paper presents a novel approach to addressing the demand for efficient decimal arithmetic units in critical applications such as tax calculation, accounting,banking, and currency conversion, with a specific focus on the multiplication operation. The proposed design methodology emphasizes power consumption analysis without compromising performance, decomposing a digit multiplier using techniques inspired by Karatsuba algorithms. The multiplicand and multiplier are partition into various sizes, including multi16-8, multi16-4, multi16-2, and multi16-8-4, and the traditional Ripple Carry Adder (RCA) is replaced with a more efficient Carry Increment Adder (CIA). The study evaluates the designed multipliers using Verilog HDL and the Xilinx ISE tool, analyzing delay, area, and power consumption. The results, based on 16-digit operands, highlight the effectiveness of approach in achieving a balance between computational performance and power efficiency in radix-10 arithmetic units.

关键词： Technological innovation Power demand Oral communication Delays Computational efficiency partitioning algorithms Hardware design languages Adders Optimization Arithmetic

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