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检索条件"主题词=software algorithms"

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A Masked Pruning-Based Algorithm for Dimensionality Reduction in Federated Learning Systems

A Masked Pruning-Based Algorithm for Dimensionality Reductio...

引用

Annual Allerton Conference on Communication, Control, and Computing

作者： Tamir L.S. Gez Kobi Cohen School of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer-Sheva Israel

ISBN: (数字)9798331541033

ISBN: (纸本)9798331541040

Federated Learning (FL) represents a growing machine learning (ML) paradigm designed for training models across numerous nodes that retain local datasets, all without directly exchanging the underlying private data with the parameter server (PS). Its increasing popularity is attributed to notable advantages in terms of training deep neural network (DNN) models under privacy aspects and efficient utilization of communication resources. Unfortunately, DNNs suffer from high computational and communication costs, as well as memory consumption in intricate tasks. These factors restrict the applicability of FL algorithms in communication-constrained systems with limited hardware resources. In this paper, we develop a novel algorithm that overcomes these limitations by synergistically combining a pruning-based method with the FL process, resulting in low-dimensional representations of the model with minimal communication cost, dubbed Masked Pruning over FL (MPFL). The algorithm operates by initially distributing weights to the nodes through the PS. Subsequently, each node locally trains its model and computes pruning masks. These low-dimensional masks are then transmitted back to the PS, which generates a consensus pruning mask, broadcasted back to the nodes. This iterative process enhances the robustness and stability of the masked pruning model. The generated mask is used to train the FL model, achieving significant bandwidth savings. We present an extensive experimental study demonstrating the superior performance of MPFL compared to existing methods. Additionally, we have developed an open-source software package for the benefit of researchers and developers in related fields.

关键词： Training Costs Federated learning Computational modeling software algorithms Bandwidth Artificial neural networks Stability analysis Robustness Iterative methods

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HPC Performance on AWS: EFA Integrated Intel Instances for MPI Applications

HPC Performance on AWS: EFA Integrated Intel Instances for M...

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International Conference on Parallel Distributed and Grid Computing (PDGC)

作者： Mandeep Kumar Gagandeep Kaur Prashant Singh Rana University Institute of Computing Chandigarh University Mohali India Locuz Enterprise Solutions Private Limited Delhi India Computer Science and Engineering Department Thapar Institute of Engineering and Technology Patiala India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

High Performance Computing (HPC) has become an indispensable tool in various scientific and engineering domains, demanding efficient infrastructure to support computationally intensive tasks. Cloud computing platforms, such as Amazon Web Services (AWS), have emerged as a viable solution for provisioning HPC resources. In this work, we explore the performance of HPC workloads running on AWS, utilizing Elastic Fabric Adapter (EFA) interconnect technology and Intel Xeon Scalable Processors. The investigation focuses on Message Passing Interface (MPI) applications, evaluating the system's efficiency, scalability, and the benefits of EFA integration. We present experimental results and insights into optimizing HPC workloads in the AWS environment, contributing to the ongoing discussion on achieving high-performance computational capabilities in the cloud.

关键词： Cloud computing Program processors Web services software packages Scalability Message passing software algorithms Throughput Resource management Optimization

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Automatically Distributing and Updating In-Network Management Rules for software Defined Networks

Automatically Distributing and Updating In-Network Managemen...

引用

IEEE Symposium on Network Operations and Management

作者： Ahmad Abboud Ré mi Garcia Abdelkader Lahmadi Michaë l Rusinowitch Adel Bouhoula Mondher Ayadi NUMERYX France CNRS Inria Loria Universit&#x00E9 de Lorraine Nancy France Teesside University UK College of Graduate Studies Arabian Gulf University Kingdom of Bahrain

software Defined Networks (SDN) heavily rely on diverse management rules (ACL, traffic control, etc. ) to satisfy security and business requirements of their associated services. As these networks are increasing in size and complexity, their management rules configured in devices are becoming more complex. These rules are constantly growing in size and it is challenging to distribute them across network devices with limited capacities. The most challenging task is to deploy rules updates in a fast and efficient way to avoid a security breach or to meet a service needs. In this paper, we extend our previous work on network management rules distribution by introducing an efficient update strategy. Through extensive experiments on several rule sets with single and multiple path topologies, we evaluate and analyze the performance of our strategy. Our obtained results show a reduction of up to 90% in update time.

关键词： Network topology software algorithms Switches Traffic control Topology Complexity theory Security

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Resource and Performance Improvements of Optimized Convolutional Neural Networks for FPGA Implementations of Automatic Modulation Recognition

Resource and Performance Improvements of Optimized Convoluti...

引用

Annual Conference on Information Sciences and Systems (CISS)

作者： Joshua A Rothe Haya Shajaiah Whiting School of Engineering Johns Hopkins University Baltimore MD United States

ISBN: (数字)9798331513269

ISBN: (纸本)9798331513276

Automatic Modulation Recognition (AMR), commonly found in software defined radios, relies on speed and accuracy to effectively interpret the modulation type of incoming signals. Convolutional Neural Networks (CNNs) are growing in popularity over traditional algorithms due to their excellent performance with classification-type problems – but these are typically resource intensive, and Radio Frequency (RF) receivers are typically part of a larger system that can benefit from less resources being tied to this classification task. Field Programmable Gate Array (FPGA) implementations provide better performance than CPU and GPU implementations in most cases, and the added benefit of these functions being off the processor allows the entire system to perform better. Since resource utilization is such a critical component of effective CNN implementation, and it is often inversely tied to performance, the effectiveness of various hardware optimization techniques as well as their effects on performance should be considered by the designer. This work evaluates the tradeoffs of various model optimizations and how they affect both implementation and performance, synthesizing the models using Xilinx’s quantization-aware FINN library. In this work, a methodology is presented for the generation of I and Q signals for CNN model training and evaluation, and the performance and hardware utilization benchmarks are evaluated to determine both design considerations and effective approaches for optimizing these models for real-world implementation.

关键词： Training software algorithms Modulation Logic gates Hardware Convolutional neural networks Resource management Field programmable gate arrays Optimization software radio

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AI-Driven Approach for Generating Automatic GUI

AI-Driven Approach for Generating Automatic GUI

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Jamal Zraqou Wissam Alkhadour Faris Abuhashish Nancy Al–Ramahi Waleed Maqableh Jawad AlKhateeb Dept of Computer Science/Virtual & Augmented Reality University of Petra Amman Jordan Dept of E-Business & Commerce University of Petra Amman Jordan Dept of Computer Science/Virtual & Augmented Reality Amman Jordan Dept of Basic Sciences Al-Zaytoonah University of Jordan Amman Jordan Dept of Extended Reality and Game Design Applied Science University Amman Jordan Dept of Computer Engineering & Science Prince Mohammad Bin Fahd University Al Khobar KSA

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

The user interface is considered one of the most important analysis processes in software engineering. Creating controls for the forms can resolve the problem of being an experience of coding and reduce the time required for implementation. The main aim of the proposed tool is to create high-quality software and handle the user interface to interact with the user. The proposed case tool algorithm uses machine learning patterns to recognize hand drawings and create graphical user interfaces. The software engineer or the end user can convert the strikes of his/her hand drawing to make the intended graphical user interface efficient. The system received high marks from 100 users, as 85% found it easy to operate, while 80% reported shorter design times than drag-and-drop interfaces. The system acknowledged positive feedback about its user-friendly learning process, meaningful speed improvements, and adjustable user interface design.

关键词： Hands Machine learning algorithms Tracking software algorithms XML Machine learning Pressing Feature extraction software Graphical user interfaces

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Research on Brake Drum Surface Defect Detection System Based on Machine Vision Technology

Research on Brake Drum Surface Defect Detection System Based...

引用

IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Xuefeng Han Bin Zheng School of Intelligent Manufacturing Panzhihua University Panzhihua

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

This paper focuses on the development of an efficient and accurate brake drum surface defect detection system to meet the growing demands for brake safety performance in the manufacturing industry. The system analyzes common defect types on brake drum surfaces, such as chips and cracks, and designs a comprehensive detection scheme. In terms of hardware architecture, it integrates high-resolution image acquisition equipment and a customized lighting system to ensure clear and comprehensive images of the brake drum surface under different lighting conditions. At the software level, the system implements image preprocessing steps, including noise filtering and contrast enhancement, to optimize image quality. Subsequently, advanced feature extraction algorithms are used to precisely locate and analyze the characteristic information of potential defects. Ultimately, the system achieves the identification of brake drum surface defects. Experimental results show that the system not only effectively distinguishes between different types of surface defects but also exhibits significant advantages in detection speed and accuracy, significantly enhancing the efficiency of quality control on brake drum production lines. Compared with traditional manual detection methods, this system significantly reduces the rates of missed and false detections while alleviating the human workload, providing strong support for the intelligent upgrade of the brake manufacturer industry.

关键词： Accuracy Machine vision software algorithms Lighting Hardware Safety Brakes Surface cracks Surface treatment Defect detection

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A Novel Algorithm for Coupled Vibration Characteristics of Cyclically Symmetric Structures

A Novel Algorithm for Coupled Vibration Characteristics of C...

引用

Advanced algorithms and Control Engineering (ICAACE), International Conference on

作者： Chao Liu Jingxian Liu School of navigation Wuhan University of Technology Wuhan China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

When rotating machinery operates at high speeds, the interaction between the rotor and blades becomes highly pronounced, necessitating treatment of the rotor-blade system as an integrated entity when examining its vibration characteristics. However, the coupled system's large number of degrees of freedom poses significant challenges. One of the most prominent issues with traditional finite element methods is the extremely high order of the coefficient matrix, which leads to substantial computational demand. Recognizing the cyclic symmetry intrinsic to rotating machinery, this study introduces a novel algorithm intended for efficient analysis of the coupled vibration characteristics of cyclically symmetric structures. Grounded in modal superposition and cyclic symmetry theory, the algorithm partitions the rotor and blades into multiple substructures. First, the internal degrees of freedom in these substructures are condensed, and then cyclic interface displacement compatibility is applied to couple the rotor and blades into a unified system. This process significantly decreases both the total degrees of freedom in the coupled system and the order of the coefficient matrix. To validate the approach, the proposed algorithm was implemented in Matlab for a specific test case. The results indicate that the computed vibration frequencies deviate by no more than five percent from those obtained through conventional software. Additionally, the coupled mode shapes align closely with those reported in the literature, and the required computation time is only 45 percent of the original duration. These findings highlight the practicality and feasibility of the proposed algorithm for analyzing cyclically symmetric structures.

关键词： Vibrations Symmetric matrices Shape Blades software algorithms Rotors software Partitioning algorithms Machinery MATLAB

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Development of robotic arm for fruits and vegetables sorting

Development of robotic arm for fruits and vegetables sorting

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IEEE Students' Conference on Electrical, Electronics and Computer Science (SCEECS)

作者： C. Likithkumar Thrisha K Anbarasi S Navarasu S Electronics and Instrumentation Engineering Faculty SRM Institute Of Science and Technology Chennai India Electronics and Instrumentation Engineering Student SRM Institute Of Science and Technology Chennai India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

Among various automation approaches, the pick and-place robotic arm stands out due to its versatility and potential for wide application in sorting, picking, and placing tasks. The robotic arm is equipped with advanced computer vision capabilities for automated agricultural tasks. The system incorporates object detection algorithms using THONY software to accurately identify and localize fruits or vegetables within the arm's workspace. The integration of object detection and classification enhances the system's versatility and adaptability, making it suitable for agricultural applications which includes sorting. By automating repetitive tasks and utilizing real-time object detection, the robotic arm enhances operational efficiency, reduces labor requirements, and improves precision in handling and sorting.

关键词： Deep learning Computer science Computer vision software algorithms Object detection Manipulators software Real-time systems Object recognition Sorting

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United We Protect: Protecting IP Confidentiality with Integrated Transformation and Redaction

United We Protect: Protecting IP Confidentiality with Integr...

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Physical Assurance and Inspection of Electronics (PAINE), 2020 IEEE

作者： Md Moshiur Rahman Rasheed Almawzan Aritra Dasgupta Sudipta Paria Swarup Bhunia University of Florida Gainesville FL USA

ISBN: (数字)9798331542252

ISBN: (纸本)9798331542269

Due to the globally distributed nature of the semiconductor industry, hardware intellectual property (IP) blocks are increasingly subject to attacks on their confidentiality, including IP piracy, cloning, and reverse engineering (RE). Countermeasures like logic locking, state space transformation, and logic redaction, which employ structural and functional transformations on the design, promise to successfully prevent such attacks on hardware IP at different stages of its life cycle. The state-of-the-art logic locking and transformation techniques are generally secure against functional query-based attacks. However, in recent years, researchers have found that they are vulnerable to structural attacks that rely on identifying structural signatures. In comparison, logic redaction techniques replace security-critical logic cones and interconnects in a design with programmable fabrics, thereby protecting a redacted design against structural analysis-based attacks. However, redaction techniques incur significantly higher hardware overheads compared to transformation-based ones. In this paper, we introduce an integrative countermeasure against confidentiality attacks on hardware IPs that unites the complementary benefits of state space transformation using finite state machines (FSMs) and programmable fabric-based logic redaction. We provide an electronic design automation (EDA) framework based on the proposed algorithm that interfaces with commercial tool flow. We demonstrate the security and overhead benefits through theoretical and experimental analysis using a complete software implementation of the proposed countermeasure. We use several open-source benchmark IPs to show that the proposed unified approach provides significantly higher protection than transformation-based solutions with much lower overhead than redaction-based ones.

关键词： Semiconductor device modeling Supply chains software algorithms Reverse engineering Hardware software Logic Security Protection Electronic countermeasures

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Performance Analysis of Machine Learning algorithms for Breast Cancer Prediction in Medical Sectors

Performance Analysis of Machine Learning Algorithms for Brea...

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International Conference on Computational and Characterization Techniques in Engineering & Sciences (CCTES)

作者： Vijay Shankar Sharma Atul Srivastava Siddhanta Kumar Singh Kamlesh Kumar Singh Department of Computer and Communication Engineering Manipal University Jaipur Amity School of Engineering and Technology AUUP Lucknow

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

Breast cancer is a major health issue worldwide, with millions of new cases diagnosed annually. Early and accurate diagnoses help guide treatment and improve patient outcomes. Machine learning, especially logistic regression, aids medical diagnosis. Various Machine Learning classifier algorithms have been utilized to detect the breast cancer prediction. Various classifiers have been compared in terms of Accuracy, Recall and F1-Score, in that Extra tree classifier results with the best accuracy of 99.17 % among all classifiers.

关键词： Logistic regression Accuracy Machine learning algorithms software algorithms Prediction algorithms Breast cancer software Classification algorithms Random forests Medical diagnostic imaging

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