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检索条件"任意字段=21st International Symposium on Computer Architecture and High Performance Computing"

共 1769 条记录，以下是431-440 订阅

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Application of Improved Genetic Algorithm in Cruise Missile Route Planning

Application of Improved Genetic Algorithm in Cruise Missile ...

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international symposium on Distributed computing and Applications to Business, Engineering and Science (DCABES)

作者： Ju Zhang Yian Liu Hailing Song Jiangnan University School of AI and Computer Science Wuxi China Naval Research Institute Beijing China

ISBN: (纸本)9781665454636

To address the shortcomings of the traditional genetic algorithm in cruise missile route planning, which is prone to “premature maturation” and premature convergence to a local optimal solution, an improved genetic algorithm is proposed that introduces an adaptive operator and a variation ratio strategy. The algorithm processes the individual fitness values in the population by ranking ratio technique, and then adopts a selection strategy combining elite selection and roulette algorithm to add the feasible routes with the best fitness values directly to the children at each evolution, and then roulette selects the remaining feasible routes, which improves the global optimal search performance of the algorithm in the trajectory planning. Meanwhile, an adaptive crossover operator is used to dynamically select the crossover probability based on the individual fitness values of the parents. Finally, the two algorithms are applied to the established map model for route planning separately, and the simulation results show that the path solved by the improved genetic algorithm reduces three planning waypoints and 10.74 % of the range compared with the traditional genetic algorithm, and the global search performance of the route planning process applying the improved genetic algorithm is significantly better than that of the traditional genetic algorithm.

关键词： Missiles Trajectory planning Navigation Heuristic algorithms Simulation Sociology Diversity reception

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21st international Conference on Algorithms and architectures for Parallel Processing, ICA3PP 2021

21st International Conference on Algorithms and Architecture...

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21st international Conference on Algorithms and architectures for Parallel Processing, ICA3PP 2021

ISBN: (纸本)9783030953904

The proceedings contain 145 papers. The special focus in this conference is on Algorithms and architectures for Parallel Processing. The topics include: TSAEns: Ensemble Learning for KPI Anomaly Detection;towards Transferable Adversarial Examples Using Meta Learning;temporal Convolution Network Based on Attention for Intelligent Anomaly Detection of Wind Turbine Blades;Error Serial Episodes Discovery from Mobile Payment Log in Distributed ETC;Parallel Cache Prefetching for LSM-Tree Based store: From Algorithm to Evaluation;A Hybrid TLBO-TS Algorithm Based Mobile Service Selection for Composite Services;UPM-DMA: An Efficient Userspace DMA-Pinned Memory Management strategy for NVMe SSDs;AHOA: Adaptively Hybrid Optimization Algorithm for Flexible Job-shop Scheduling Problem;trace-Navi: A high-Accuracy Indoor Navigation System Based on Real-Time Activity Recognition and Discrete Trajectory Calibration;BFR-RetinaNet: An Improved RetinaNet Model for Vehicle Detection in Aerial Images;iterative Filling Incomplete Fingerprint Map Based on Multi-directional Signal Propagation in Large-Scale Scene;dynamic Adjustment Policy of Search Driver Matching Distance via Markov Decision Process;a Multi-precision Quantized Super-Resolution Model Framework;An Optimized GPU Implementation of Weakly-Compressible SPH Using CUDA-Based strategies;a Heterogeneous Multi-core Network-on-Chip Mapping Optimization Algorithm;a Novel 3D Intelligent Cluster Method for Malicious Traffic Fine-Grained Classification;Predicting students’ Academic performance Based on Improved PSO-Xgboost: A Campus Behavior Perspective;motion-Sequence Authentication System: Guard for Smart Phones;joint Optimization Scheme of Multi-service Replication and Request Offloading in Mobile Edge computing;Flying MEC: Online Task Offloading, Trajectory Planning and Charging Scheduling for UAV-Assisted MEC;learning Knowledge Graph Embeddings by Multi-Attention Mechanism for Link Prediction;multiple Workflows Offloading Based on Dee

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21st international Conference on Algorithms and architectures for Parallel Processing, ICA3PP 2021

21st International Conference on Algorithms and Architecture...

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21st international Conference on Algorithms and architectures for Parallel Processing, ICA3PP 2021

ISBN: (纸本)9783030953874

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21st international Conference on Algorithms and architectures for Parallel Processing, ICA3PP 2021

21st International Conference on Algorithms and Architecture...

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21st international Conference on Algorithms and architectures for Parallel Processing, ICA3PP 2021

ISBN: (纸本)9783030953836

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Ant Colony System with Sparse Pheromone

Ant Colony System with Sparse Pheromone

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international symposium on Distributed computing and Applications to Business, Engineering and Science (DCABES)

作者： Mengfan Jin Guangtao Fu Tianhao Fa Zhibin Huang Zhiqiang Chu School of Computer Science Beijing University of Posts and Telecommunications Beijing China Information Center State Administration for Market Regulation Beijing China

ISBN: (纸本)9781665454636

Ant colony optimization algorithm is a typical meta-heuristic algorithm, which is widely used in various combinatorial optimization problems, but its high space complexity, which has become one of the main constraints affecting the application of ACO algorithm. The pheromone matrix is one of the major storage overheads. This paper based on the analysis of typical ant colony optimization algorithms, it is observed that the pheromone matrix of ACS has very strong sparseness. Therefore, SACS is proposed, whose pheromone matrix uses triplet sparse storage. In order to solve the problem of how to deal with the number of items of the initial allocated pheromone triplet and the new non-default pheromone update, this paper proposed the method based on the small fixed storage space with different replacement policies, those are, SACS-Max, SACS-Min, SACS-Rand. Many experiments show that this method basically eliminating the storage bottleneck of the pheromone matrix.

关键词： Ant colony optimization Metaheuristics Prediction algorithms Complexity theory Sparse matrices Distributed computing Business

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Optimizing Deep Ensemble Learning for Accurate Melanoma Skin Cancer Classification: Design and Analysis

Optimizing Deep Ensemble Learning for Accurate Melanoma Skin...

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international symposium on high Capacity Optical Networks and Enabling Technologies, HONET

作者： Ezeddin Ezeddin Ahmet Dia Alkhattaf Mhd Kheir Alhafez Somaya Al-Maadeed Department of Computer Science and Engineering Qatar University Doha Qatar

ISBN: (数字)9798350378078

ISBN: (纸本)9798350378085

This study evaluates the performance of state-of-the-art convolutional neural networks (CNNs) for melanoma skin cancer classification, highlighting the selection and optimization of models for ensemble learning. Wide_ResNet101_2 and resnext101_32x8d were identified as the most effective individual models based on their superior diagnostic performance metrics such as accuracy, precision, recall, and F1-score. Leveraging a weighted averaging ensemble approach, the study demonstrates a significant improvement in classification accuracy, achieving an overall accuracy of 96.12%. This advanced ensemble model surpasses traditional single-model approaches, showcasing the potential of integrated architectures in enhancing the precision of medical diagnoses. The results underscore the efficacy of ensemble learning in medical imaging, providing a robust tool for improving the detection and classification of melanoma, thereby aiding in early diagnosis and treatment.

关键词： Measurement Accuracy Smart cities Refining Melanoma Ensemble learning Convolutional neural networks Robots Optimization Medical diagnostic imaging

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Blind Asynchronous Goal-Oriented Detection for Massive Connectivity

Blind Asynchronous Goal-Oriented Detection for Massive Conne...

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international symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

作者： Sajad Daei Saeed Razavikia Marios Kountouris Mikael Skoglund Gabor Fodor Carlo Fischione School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Communication Systems Department EURECOM Sophia Antipolis France Ericsson Research Sweden

Resource allocation and multiple access schemes are instrumental for the success of communication networks, which facilitate seamless wireless connectivity among a growing population of uncoordinated and non-synchronized users. In this paper, we present a novel random access scheme that addresses one of the most severe barriers of current strategies to achieve massive connectivity and ultra reliable and low latency communications for 6G. The proposed scheme utilizes wireless channels' angular continuous group-sparsity feature to provide low latency, high reliability, and massive access features in the face of limited time-bandwidth resources, asynchronous transmissions, and preamble errors. Specifically, a reconstruction-free goal oriented optimization problem is proposed which preserves the angular information of active devices and is then complemented by a clustering algorithm to assign active users to specific groups. This allows to identify active stationary devices according to their line of sight angles. Additionally, for mobile devices, an alternating minimization algorithm is proposed to recover their preamble, data, and channel gains simultaneously, enabling the identification of active mobile users. Simulation results show that the proposed algorithm provides excellent performance and supports a massive number of devices. Moreover, the performance of the proposed scheme is independent of the total number of devices, distinguishing it from other random access schemes. The proposed method provides a unified solution to meet the requirements of machine-type communications and ultra reliable and low latency communications, making it an important contribution to the emerging 6G networks.

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A Machine Learning - Based System for Determining Water Potability 21st

A Machine Learning - Based System for Determining Water Pot...

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21st international symposium on Distributed computing and Artificial Intelligence, DCAI 2024

作者： Timiraos, Míriam Díaz-Longueira, Antonio Zayas-Gato, Francisco Casteleiro-Roca, José-Luis Fontenla-Romero, Óscar Calvo-Rolle, José Luis CTC CITIC Department of Industrial Engineering University of A Coruña Calle Mendizábal s/n Campus de Esteiro Ferrol15403 Spain LIDIA CITIC Department of Computer Sciences and Information Technologies University of A Coruña Calle Mendizábal s/n Campus de Esteiro Ferrol15403 Spain Department of Water Technologies National Technological Center Fundación Instituto Tecnológico de Galicia A Coruña15003 Spain

ISBN: (纸本)9783031739095

This research focuses on enhancing water potability classification through the integration of three machine learning techniques. A comparative analysis of diverse classification methods is conducted, incorporating multiple thresholds by employing a variable extraction approach. The primary objective is to streamline the input set, aiming for a significant reduction in computational costs and model complexity. This streamlined approach not only facilitates a more efficient training process but also implies a shorter duration for model training. To rigorously evaluate the model’s performance, a K-fold cross-validation is implemented within this framework. This comprehensive approach contributes to the advancement of water quality assessment methodologies, with potential implications for improving the efficiency and reliability of potability water classification models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Swarming with (Visual) Secret (Shared) Mission

Swarming with (Visual) Secret (Shared) Mission

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IEEE international symposium on Network computing and Applications

作者： Shlomi Dolev Alexander Fok Michael Segal Computer Science Department Ben-Gurion University of the Negev Beer-Sheva Israel School of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer-Sheva Israel

ISBN: (纸本)9798350397314

Collaborative secure image matching is a problem that is applicable in various domains, for both – data in rest and data in motion. The problem is defined as follows. There is a secret image, and a set of n mobile agents. The set of mobile agents should match (compare) an observed image to the original secret image. In this paper we discuss some of the existing approaches, and present an alternative solution applied and analyzed for different applications. The first application is a swarm of Unmanned Aerial Vehicles (UAV) that search for a target specified by an image. The second application is a social network that serves as a smart storage device capable of performing distributed, secret image matching operations. Our solution is based on the well-known Visual Encryption Scheme (VES) and projections of visual bit maps rather than (quadratic complexity) messages exchange in implementing Secure Multi Party Computation (MPC) scheme. We present a perfect-information-theoretic secure solution for this problem. To keep the original image secrecy, at least k out of n mobile agents are required to retrieve any information about the original image.

关键词： performance evaluation Visualization Social networking (online) Image matching Mobile agents Collaboration Autonomous aerial vehicles

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Identification Management for Zero Trust Through Network Analysis

Identification Management for Zero Trust Through Network Ana...

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ACS/IEEE international Conference on computer Systems and Applications

作者： Amal Alshehri Burak Tufekci Cihan Tunc Department of Computer Science & Engineering University of North Texas

ISBN: (数字)9798331518240

ISBN: (纸本)9798331518257

Internet of Things (IoT) and Operational Technology (OT) are becoming essential parts of next-generation smart environments, including Industry 4.0 thanks to their capabilities of monitoring and/or controlling industrial devices, processes, and events. It is expected that there will be around 75 billion such devices, especially considering 5G (and 6G in the future) networking. However, security is one of the major concerns, especially because most legacy devices/equipment have little to no security measures applied. And, replacing all the equipment with new ones continuously is not a feasible solution to enforce security measures. One alternative is enforcing Zero Trust architecture (ZTA) principles in these environments based on the “never trust, always verify” principle. Applying ZT for an OT environment through its network (by continuously monitoring and checking the activities based on ZTA-based policies) can improve the sustainability and security of the IoT/OT-based environments because no major changes would be needed in the existing infrastructures while improving security. For this reason, we propose an architecture that monitors the network behavior and compares them with the devices' proposed identities (their IP and MAC addresses). We first monitor the network behaviors, create flows, and select the best features. Then, we identify the individual devices using supervised machine learning techniques such as Gradient Boosting Classifier as they are lightweight and fast in detection compared to alternatives. Our results show high accuracy, precision, and recall values in our experiments. Using different scenarios, we evaluated our idea and if a device behaves differently than the proposed behavior, we are able to detect and put a firewall rule that blocks the device network traffic on the gateway.

关键词： Machine learning algorithms Firewalls (computing) Telecommunication traffic Real-time systems Zero Trust Sensors Object recognition Internet of Things Sustainable development Monitoring

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