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检索条件"主题词=Partitioning Algorithms"

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Multiscale Numerical Simulation for Assessing Effective Electrical Conductivity Estimations of Geological Medium

Multiscale Numerical Simulation for Assessing Effective Elec...

引用

Informatics, Electronics, and Radio Engineering (PIERE), International Conference Problems of

作者： Anastasiia Yu. Kutishcheva Sergey I. Markov Ekaterina I. Shtanko Laboratory of mathematical modeling of multiphase processes in native and artificial multiscale heterogeneous media Trofimuk Institute of Petroleum Geology and Geophysics SB RAS Novosibirsk Russia Department of Geophysical Systems Novosibirsk State Technical University Novosibirsk Russia Department of Computer Technologies Novosibirsk State Technical University Novosibirsk Russia

ISBN: (数字)9798331516321

ISBN: (纸本)9798331516338

The paper considers methods for estimating the effective electrical conductivity of a geologic medium with an internal structure represented as a set of layer-by-layer tomographic images. Based on this type of input data, a discrete hierarchical mesh model of the sample is constructed. It is used to describe the internal singularities of the investigated medium with significant accuracy. To obtain the effective electrical conductivity of the geologic medium, a direct problem of electric field potential distribution in the sample volume is solved numerically. Since the computational domain is defined as a hierarchy of mesh partitions, a multiscale modification of the scalar finite element method is applied. The averaging procedure of the properties is based on Ohm's law. The possibility of step-by-step averaging of the electrical properties of a rock sample is also investigated. To obtain the electrical effective characteristic of the entire sample, the effective properties of individual macroelements in the mesh partitioning are determined and further averaged using the algorithms proposed in the paper. The variation of the effective electrical conductivity over certain sub-domains of the sample is given.

关键词： Solid modeling Accuracy Conductivity Media Rocks Numerical simulation partitioning algorithms Numerical models Finite element analysis Informatics

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Rule-Based Constraint Elicitation For Active Constraint-Incremental Clustering

Rule-Based Constraint Elicitation For Active Constraint-Incr...

引用

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Aymeric Beauchamp Thi-Bich-Hanh Dao Samir Loudni Christel Vrain LIFO EA 4022 University of Orléans INSA Centre-Val-de-Loire Orléans France TASC (LS2N-CNRS) / GREYC IMT Atlantique / University of Caen Normandy Nantes France

ISBN: (数字)9798331527235

ISBN: (纸本)9798331527242

Constrained clustering algorithms integrate user knowledge as constraints in the clustering process to guide it towards a desired outcome. When interacting with users, it is essential to quickly ask simple questions to identify informative constraints that will efficiently enhance an initial partition. We propose a new active query strategy for incremental clustering that translates user feedback into interpretable decision rules and identifies relevant points for queries using rule-based heuristics Experiments on benchmark datasets highlight the benefits of our new approach, making it suitable for real-world applications.

关键词： Clustering algorithms Benchmark testing partitioning algorithms Artificial intelligence

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Unveiling the Power of Neural Program Synthesis in High Level Code Generation

Unveiling the Power of Neural Program Synthesis in High Leve...

引用

International Conference on Computing and Networking Technology (ICCNT)

作者： Gurram Kumar Sai Koganti Sri Sai Harshith Kucharlapati Akash Varma G. Yashwanth Kiran J Swaminathan Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In this research, we delve into a transformative exploration of the realm of code generation, unveiling three fundamental modules that constitute the backbone of our study: the Text-to-Code Generation Model, Code Autocompletion Model, and the formidable Neural Program Synthesis Model. At the forefront of our investigation, we underscore the paramount importance of Neural Program Synthesis—a dynamic force adept at crafting custom programs from input-output examples. Our exploration delves into the intricate landscape of natural language-to-code generation, offering profound insights and invaluable contributions to the field. The ability to automatically generate code from natural language instructions holds immense significance in streamlining software development processes, reducing manual effort, and enhancing productivity. Furthermore, this research addresses a pressing need within the software engineering community for efficient methods of code generation, particularly in domains where human-program interaction is prevalent. Through rigorous experimentation and meticulous analysis, we shed light on methodologies and promising avenues for future research in this burgeoning domain, paving the way for advancements in automated software development and programming assistance tools.

关键词： Training Adaptation models Codes Recurrent neural networks Programming partitioning algorithms Python Testing Software development management Software engineering

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Open Service for Networking Systems of AI: A Case Study in Adaptation Optimization

Open Service for Networking Systems of AI: A Case Study in A...

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IEEE World Forum on Internet of Things (WF-IoT)

作者： Bobo Ju Kun Yang Yang Liu Jing Liu Peng Sun Wei Ni Liang Song Academy for Engineering & Technology Fudan University Shanghai China Innovation Platform for Academicians of Hainan Province Haikou China Department of Computer Science University of Toronto Toronto Ontario Canada School of Information Science and Technology Fudan University Shanghai China Division of Natural and Applied Sciences Duke Kunshan University Suzhou China Shanghai East Bund Research Institute on NSAI Shanghai China

ISBN: (数字)9798350373011

ISBN: (纸本)9798350373028

Under the architecture of Networking Systems of AI (NSAI), we propose a three-tier edge computing platform called Open Service for NSAI (OpenSAI), which can jointly configure the application execution and compute-storage-network resources, for online application-network adaption, to ensure the efficiency and reliability of the services. In this research, we investigate a specific task-device adaptation optimization problem in OpenSAI, which recommend application tasks to distributed computing devices. We evaluate the performance of various optimization and search algorithms in recommending optimal task-device combinations, which show significant differences among the algorithms in terms of recommendation runtime costs and task adaptability. Overall, the hybrid recommendation of SCORE Algorithm, Particle Swarm Optimization, and Item-Item Collaborative Filtering demonstrates the best performance in both time efficiency and fitness scores across multiple scenarios.

关键词： Performance evaluation Runtime Scheduling algorithms Heuristic algorithms Collaboration Computer architecture Reliability engineering partitioning algorithms Artificial intelligence Optimization

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A Dynamic Visual Sensor Denoising Method Based on Event Density Classification

A Dynamic Visual Sensor Denoising Method Based on Event Dens...

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IEEE International Conference on Networking, Sensing and Control

作者： Qiangxiao Zhou Hai Xiong Yiheng Wang Yuhao Shen Long Cheng College of Computer Science and Artificial Intelligence WenZhou University Wenzhou China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

Dynamic Visual Sensors (DVS) output pixel information asynchronously as an address event, only transmitting information for pixels where the intensity change exceeds a threshold, thereby reducing redundant data generation at the source. They possess advantages such as low-latency response and high efficiency with low power consumption. However, factors such as thermal noise and leakage current can lead to noise events even when the light intensity remains constant, which affects image quality and subsequent processing. In this paper, we propose a new method for calculating event density called Local Density Segmentation (LSD), which divides events and their surrounding neighborhoods into different regions for independent event density calculation. Based on LSD-calculated event density, we further propose a denoising method named Local Density Segmentation K-means (LSD-K), which utilizes event density as a feature to select and remove noise events. Finally, through experiments, we validate the performance of our algorithm.

关键词： Visualization Thermal factors Heuristic algorithms Noise Noise reduction Clustering algorithms Thermal noise Sensors partitioning algorithms Voltage control

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Convergence Analysis of Hierarchical Split Federated Learning

Convergence Analysis of Hierarchical Split Federated Learnin...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Hualei Zhang Jun Du Xiangwang Hou Chunxiao Jiang Jintao Wang Dusit Niyato Department of Electronic Engineering Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Federated Learning (FL) enables distributed intelligence in Internet of Things (IoT) networks, facilitating decentralized machine learning without the need for exchanging raw data. However, the growing complexity of training models significantly hinders their deployment on resource-constrained IoT devices. To address this challenge, Split Federated Learning (SFL) has emerged as a promising solution by partitioning the entire model into client-side and server-side sub-models to alleviate the computational burden on IoT devices. Considering that the client-edge-cloud architecture can enhance data privacy, support connections to a wider range of devices, and reduce communication costs, we explore a hierarchical SFL (HierSFL) system. This system is supported by a HierSFL algorithm that allows for different aggregation frequencies between the client-side and server-side sub-models. Then, we present a convergence analysis of HierSFL that quantifies the effects of client-side and server-side model aggregation on learning performance, providing a theoretical foundation. Empirical experiments verify the theoretical analysis and demonstrate the superiority of the hierarchical architecture within a wireless IoT network. In particular, it is validated that adopting different aggregation frequencies can enhance the training performance. Moreover, the HierSFL algorithm outperforms traditional hierarchical FL algorithm, achieving superior test accuracy in a shorter time.

关键词： Training Wireless communication Analytical models Accuracy Federated learning Computational modeling Computer architecture partitioning algorithms Internet of Things Convergence

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Automated Penetration Testing Based on MATD Algorithm

Automated Penetration Testing Based on MATD Algorithm

引用

Artificial Intelligence, Virtual Reality and Visualization (AIVRV), International Conference on

作者： Qiankun Ren Xinli Xiong Jingju Liu Zhen Wang National University of Defense Technology College of Electronic Engineering Hefei China

ISBN: (数字)9798331528744

ISBN: (纸本)9798331528751

With the rapid advancement of internet technology, cybersecurity risks are continuously increasing, and malicious cyberattacks have caused significant losses to users, making it crucial to maintain network security. Automated penetration testing plays a vital role in current cybersecurity efforts. However, existing automated penetration testing methods suffer from inefficiencies in information utilization, inaccurate action decision-making, and an overly large action space. To address these issues, we propose a new algorithm called Multi-Agent Task Decomposition(MATD), based on historical experience information and task decomposition. MATD enhances the efficiency of utilizing historical observation data through the use of Long Short-Term Memory(LSTM) networks and reduces the action space of individual agents by employing a multi-task decomposition method. Experimental results demonstrate that the proposed method outperforms the traditional Proximal Policy Optimization(PPO) algorithm by achieving higher cumulative rewards in fewer steps, thereby improving decision-making in intelligent penetration testing. This approach holds promise for increasing the efficiency of penetration testing and enhancing overall network security.

关键词： Solid modeling Decision making Virtual reality Reinforcement learning Network security Multitasking partitioning algorithms Space exploration Penetration testing Payloads

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Offloading DNN Tasks Based on Graph Reinforcement Learning in Client-Edge-Cloud Environment

Offloading DNN Tasks Based on Graph Reinforcement Learning i...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Mingchu Li Junhao Huang Zhihua Wang School of Software Technology Dalian University of Technology Dalian China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

With the increasing focus on the applications of artificial intelligence (AI), the number of layers and neurons in deep neural networks (DNN) is rapidly increasing, requiring a large amount of computational resources to execute DNN inference tasks. In order to address the resource bottleneck of the terminal device(TD) and achieve better user satisfaction at the terminal, DNN partitioning is explored in the client-edge-cloud (CEC) joint computing environment. The combination offloading decision of DNN sub-layers and the strong coupling between task execution is a mixed-integer programming (MIP) problem, making it particularly difficult to solve. To address this issue, we propose the graph reinforcement learning based DNN tasks offloading (GRLDO) scheme. Firstly, the CEC is modeled as a graph, and offloading strategies are performed through graph state transitions. Then, GRLDO combines GNN with the actor-critic network to train unlabeled offloading decision makers. In addition, to effectively train GRLDO, we propose a method for rapidly exploring action spaces and approaching the optimal solution. Numerical results show that, for various types of DNN tasks, this approach achieves up to 99.7% optimal performance, demonstrating better user satisfaction than existing algorithms.

关键词： Artificial neural networks Reinforcement learning Programming Space exploration partitioning algorithms Numerical models Resource management

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Placement and Interconnectivity in Wireless Sensor Networks

Placement and Interconnectivity in Wireless Sensor Networks

引用

Artificial Intelligence, Computational Electronics and Communication System (AICECS), International Conference on

作者： Madhura S Hegde Santhosh K V Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

ISBN: (数字)9798350391244

ISBN: (纸本)9798350391251

Performance and efficiency of Wireless Sensor Networks are significantly impacted by sensor placement and network connectivity. Proper sensor deployment within a target area directly affects the operational efficiency of network, coverage, along with overall performance. Additionally, a well-organized interconnection scheme is essential for smooth communication between sensor nodes and the base station (sink). This paper offers an in-depth review of the challenges related to placement and connectivity in wireless sensor networks, focusing on the latest advancements in sensor technologies and network design. It explores various algorithms and strategies for optimal sensor placement, including deterministic, heuristic, and optimization-based approaches. Furthermore, the paper discusses connectivity solutions such as multi-hop communication, relay node deployment, and innovative techniques for network restoration and coverage enhancement. By analyzing current research and experimental results, this review highlights the trade-offs and benefits of different methods across various wireless sensor network applications, including terrestrial, underwater, and multimedia networks. The insights provided aim to guide future research and practical implementations in wireless sensor network design, ensuring improved network performance and reliability.

关键词： Wireless sensor networks Sensor placement Base stations Technological innovation Reviews Heuristic algorithms Spread spectrum communication Reliability engineering partitioning algorithms Relays

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Shadow Void Optimization of Microelectronic Packages with Tree-Based Learners

Shadow Void Optimization of Microelectronic Packages with Tr...

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IEEE Workshop on Signal Propagation on Interconnects (SPI)

作者： Abdullah Kayacan Mustafa Gökçe Baydoğan Ahmet Cemal Durgun Kemal Aygün Duye Ye Cemil Geyik Tolga Memioğlu Department of Industrial Engineering Boğaziçi University İstanbul Türkiye Department of Electrical and Electronics Engineering Middle East Technical University Ankara Türkiye Assembly and Test Technology Development Intel Corporation Chandler AZ USA

ISBN: (数字)9798331520618

ISBN: (纸本)9798331520625

Shadow voiding is a critical technique for optimizing second-level interconnect (SLI) designs in microelectronic packages, aiming to minimize impedance mismatches while adhering to manufacturing and electrical constraints. This paper introduces a tree-based active learning algorithm for shadow void optimization. The proposed method efficiently explores geometric design parameters using decision trees and truncated SVD to reduce dimensionality. High-performing designs are identified via penalized regression within partitioned subspaces and evaluated through sequential sampling. The algorithm is applied to a 7-2-7 package, demonstrating significant improvements in electrical performance and computational efficiency. Results show that tree-based learners effectively characterize the design space, making them a practical solution for high-dimensional optimization problems in microelectronics.

关键词： Training Active learning Prediction algorithms Robustness partitioning algorithms Microelectronics Computational efficiency Optimization Tuning Regression tree analysis

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