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检索条件"任意字段=9th International Conference on Algorithms and Architectures for Parallel Processing"

共 2826 条记录，以下是141-150 订阅

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An Adaptive Cooperative Coevolutionary Algorithm for parallel Feature Selection in High-Dimensional Datasets 30

An Adaptive Cooperative Coevolutionary Algorithm for Paralle...

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30th Euromicro international conference on parallel, Distributed and Network-Based processing, PDP 2022

作者： Firouznia, Marjan Trunfio, Giuseppe A. Amirkabir University of Technology Electrical Engineering Department Tehran Iran University of Sassari Department of Biomedical Sciences Italy

ISBN: (纸本)9781665469586

Nowadays, it is common in many disciplines and application fields to collect large volumes of data characterized by a high number of features. Such datasets are at the basis of modern applications of supervised Machine Learning, where the goal is to create a classifier for newly presented data. However, it is well known that the presence of irrelevant features in the dataset can lead to a harder learning phase and, above all, can produce suboptimal classifiers. For this reason, the ability to select an appropriate subset of the available features is becoming increasingly important. Traditionally, optimization metaheuristics have been used with success in the task of feature selection. However, many of the approaches presented in the literature are not applicable to datasets with thousands of features since common optimization algorithms often suffer from poor scalability with respect to the size of the search space. In this paper, the problem of feature subset optimization is successfully addressed by a cooperative coevolutionary algorithm based on Differential Evolution. In the proposed algorithm, parallelized for multi-threaded execution on shared-memory architectures, a suitable strategy for reducing the dimensionality of the search space and adapting the population size during the optimization results in a significant performance. A numerical investigation on some high-dimensional datasets show that, in most cases, the proposed approach can achieve smaller feature subsets and higher classification performance than other state-of-the-Art methods. © 2022 IEEE.

关键词： Optimization

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Design of High-Speed Wideband Digital Upconverter

Design of High-Speed Wideband Digital Upconverter

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Electronic Technology and Information Science (ICETIS), international conference on

作者： Shuqin Wan Yingdan Jiang Wei Wang Tingting Chen Haiqi Lv The 58th Research Institute China Electronics Technology Group Corp. Wuxi China

ISBN: (数字)9798350388343

ISBN: (纸本)9798350388350

the paper proposes a high-speed wideband digital upconverter circuit (DUC) design method, utilizing an optimized CORDIC algorithm to implement a numerically controlled oscillator (NCO) and a four-stage cascade parallel-folded half-band filter (HB-FIR) for interpolation sampling and filtering. Additionally, digital mixing is used to modulate the baseband signal. In the implementation of the circuit design, multi-channel parallel and multiplexing techniques are utilized to reduce hardware costs and power consumption. the final hardware code design for the DUC circuit is completed, with a total area of $550.\overlineparallel6^{*}550_{\mathrm{I}^parallel}\mathrm{m}_th^parallel$ about 234609 instances, using the $28\text{nm}$ process. the single-channel working frequency can reach $825\text{MH}_th$ and with four channels of interleaved sampling, the maximum sampling rate can reach 3 $3\text{GHz}$

关键词： Codes Power demand Signal processing algorithms Filtering algorithms Signal processing Hardware Wideband

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Deep Network Series for Large-Scale High-Dynamic Range Imaging 48

Deep Network Series for Large-Scale High-Dynamic Range Imagi...

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48th IEEE international conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Aghabiglou, Amir Terris, Matthieu Jackson, Adrian Wiaux, Yves Heriot-Watt University Institute of Sensors Signals and Systems EdinburghEH14 4AS United Kingdom University of Edinburgh Epcc EdinburghEH8 9BT United Kingdom

ISBN: (纸本)9781728163277

We propose a new approach for large-scale high-dynamic range computational imaging. Deep Neural Networks (DNNs) trained end-to-end can solve linear inverse imaging problems almost instantaneously. While unfolded architectures provide robustness to measurement setting variations, embedding large-scale measurement operators in DNN architectures is impractical. Alternative Plug-and-Play (PnP) approaches, where the denoising DNNs are blind to the measurement setting, have proven effective to address scalability and high-dynamic range challenges, but rely on highly iterative algorithms. We propose a residual DNN series approach, also interpretable as a learned version of matching pursuit, where the reconstructed image is a sum of residual images progressively increasing the dynamic range, and estimated iteratively by DNNs taking the back-projected data residual of the previous iteration as input. We demonstrate on radio-astronomical imaging simulations that a series of only few terms provides a reconstruction quality competitive with PnP, at a fraction of the cost. © 2023 IEEE.

关键词： astronomical imaging computational imaging deep neural networks plug-and-play unfolded architectures

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A Dual-Defense Self-balancing Framework Against Bilateral Model Attacks in Federated Learning 24th

A Dual-Defense Self-balancing Framework Against Bilateral Mo...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Wu, Xiang Yao, Aiting Pal, Shantanu Jiang, Frank Li, Xuejun Xu, Jia Dong, Chengzu Chen, Xuefei Zhang, Xiuyi Liu, Xiao Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging School of Computer Science and Technology Anhui University Anhui China Deakin University Melbourne Australia School of Data Science Lingnan University Hong Kong

ISBN: (纸本)9789819615247

With the rapid expansion of Artificial Intelligence (AI) services, smart devices generate a large amount of user data at the edge network, which urgently needs to be protected while effectively extracting information. Federated learning (FL) is an important technology for handling dispersed data and strict privacy requirements in this context. However, the security threats caused by model inversion attacks and poisoning attacks can affect the mutual trust between the client and server. Yet, for these two types of attacks, the existing defense mechanisms are contradictory in terms of whether the model parameters are publicly disclosed. In addition, the data distribution of the clients is imbalanced which will increase the bias of model, reducing its practicality. To address this issue, this study proposes a dual defense self-balanced federated learning (DDSFL) framework, aiming to introduce a novel lightweight defense mechanism during the model parameter aggregation stage, combating these two types of attacks simultaneously by applying differential privacy and adjusting learning rates. In addition, this method also integrates a middleware-based reordering algorithm to enhance the robustness of the framework. Experimental results show that DDSFL effectively improves the ability to resist imbalanced data, forged data, and malicious behavior, significantly enhancing the generalization performance and security of the FL system. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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9th international conference on Mining Intelligence and Knowledge Exploration, MIKE 2023

9th International Conference on Mining Intelligence and Know...

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9th international conference on Mining Intelligence and Knowledge Exploration, MIKE 2023

ISBN: (纸本)9783031440830

the proceedings contain 38 papers. the special focus in this conference is on international conference on Mining Intelligence and Knowledge Exploration. the topics include: Highest Accuracy Based Automated Depression Prediction Using Natural Language processing;crime Prediction Using Modified Capsule Network with CrissCross Optimization on the Sentiment Analysis for Cyber-Security;sentiment Analysis Using Lexical Approach and Fuzzy Logic;HTTP, WebSocket, and SignalR: A Comparison of Real-Time Online Communication Protocols;an IoT Based Early Alert System to Monitor and Reduce Electrical Energy Consumption at Home in Smart Cities;Securing the MANET by Detecting the Flooding Attacks Using Hybrid CNN-Bi-LSTM-RF Model;the Smart Coverage Path Planner for Autonomous Drones Using TSP and Tree Selection;UAV Smart Navigation: Combining Delaunay Triangulation and the Bat Algorithm for Enhanced Efficiency;a Comparative Analysis of Data Backup and Network Consistency in Cluster-Base Wireless Sensor Network Protocols;development of IoT-Healthcare Model for Gastric Cancer from Pathological Images;Detection of Chicken Disease Based on Day-Age Using Pre Trained Model of CNN;Glaucoma Detection Using the YOLO V5 Algorithm;Deep Learning Ocular Disease Detection System (ODDS);efficient Chest X-Ray Investigation Using Firefly Algorithm Optimized Deep and Handcrafted Features;disease Detection and Risk Prediction System Based Web Application Using Machine Learning;Weighted Average Ensemble Approach for Pediatric Pneumonia Diagnosis Using Channel Attention Deep CNN architectures;seasonal Disease Based Demand Forecasting for Pharmaceutical Medications Using Random Forest;hybrid Optimal Fine Tuning Approach in Deep Learning for Identifying Early Parkinson’s Disease;cryptocurrency Price Prediction Using Deep Learning;ensemble Learning Based Social Engineering Fraud Detection Module for Cryptocurrency Transactions.

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Multimodal Summarization with Modality-Aware Fusion and Summarization Ranking 24th

Multimodal Summarization with Modality-Aware Fusion and Su...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Ye, Xuming Chaomurilige Liu, Zheng Luo, Haoyu Dong, Jun Luo, Yingzhe Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE Minzu University of China Beijing China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan572499 China Faculty of Data Science City University of Macau Macau China

ISBN: (纸本)9789819615278

Existing multimodal summarization methods primarily focus on multimodal fusion to efficiently utilize the visual information for summarization. However, they fail to exploit the deep interaction between textual and visual modality. Moreover, optimizing the model by maximum likelihood estimation (MLE) leads to exposure bias, causing the model to generate the next word that is based on the previously generated erroneous words during inference. To address these challenges, we propose a novel modality-aware fusion module (MAF) with a summarization ranking (SumR) training objective. Specifically, the MAF module exploits the interaction in the multimodal input through multiple fusion layers, and SumR aims to align the probability order predicted by the model with actual quality metrics, therefore it is able to reduce the exposure bias problem during inference. Extensive experiments on a large-scale dataset demonstrate that our method outperforms existing models, achieving superior results in both automatic and human evaluation metrics. the generated multimodal summaries provide richer context and enhance user’s comprehension by combining the key textual information with the relevant visual content. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Maximum likelihood estimation

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A Multi-holder Role and Strange Attractor-Based Data Possession Proof in Medical Clouds 24th

A Multi-holder Role and Strange Attractor-Based Data Possess...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Guo, Jinyuan Sun, Lijuan Wu, Jingchen Chu, Chiawei Gao, Yutong Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China School of Cybersecurity Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance Ministry of Education Minzu University Beijing China Intellectual Property Information Service Center BHU Beihang University Beijing China City University of Macau Taipa China Li’an International Education Innovation Pilot Zone Hainan International College of Minzu University of China Hainan Beijing China

ISBN: (纸本)9789819615278

Proof of Data Possession is a technique for ensuring the integrity of data stored in cloud storage. However, most audit schemes assume only one role for data owners, which is not suitable for complex Smart Healthcare Network with multiple roles. In such schemes addressing the risk of third-party auditors (TPA) colluding with other entities, blockchain is typically employed. However, this necessitates extensive computation, resulting in unacceptably high energy consumption and prolonged verification times. In this paper, we propose a Proof of Data Possession scheme tailored for medical network environments, which incorporates protocols designed for both healthcare professionals and patients, considering the diverse roles that generate data within medical network applications and the varying performance of the devices they use. During the generation of metadata for verification, the protocol incorporates the varying security risk levels associated with medical data. For subsequent metadata selection, an audit block extraction strategy based on strange attractors is employed, effectively mitigating the risk of collusion by third-party TPA, thereby enhancing the security and efficiency of the scheme. Additionally, we conducted experimental analysis and comparisons, demonstrating higher security and practical applicability. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Data integrity

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parallel Algorithm for Source Type Recovering by the Time Reversal Mirror 9th

Parallel Algorithm for Source Type Recovering by the Time...

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9th Russian Supercomputing Days international conference, RuSCDays 2023

作者： Galaktionova, Anastasia Reshetova, Galina The Institute of Computational Mathematics and Mathematical Geophysics SB RAS Novosibirsk Russia

ISBN: (纸本)9783031494345

the problem of reconstructing the location and type of a seismic source in a three-dimensional medium based on seismic observations is considered. the Time Reverse Mirror (TRM) method is used to solve this problem. Recently, the TRM method has been used in seismology to detect earthquake sources, in industry to detect defects in structures, in medicine to detect masses in human tissues, and in other fields. We were the first in the seismic industry to use TRM to simultaneously identify the type and location of the seismic source. Numerical modelling was carried out on three-dimensional synthetic models using finite difference techniques, which allowed us to develop an algorithm for reconstructing the seismic source type from observational data recorded on a free surface. Solution of this problem is impossible without using parallel architecture computing systems. Several parallelization techniques have been used to achieve high efficiency of the parallel program. In particular, we use non-blocking MPI library functions and the domain decomposition technique to optimise the exchange of data between processes. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： parallel architectures

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Enhancing Text-Image Person Re-identification via Intra-Class Relevance Learning 24th

Enhancing Text-Image Person Re-identification via Intra-Cla...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： He, Wenbin Gao, Yutong Liu, Wenjian Chaomurilige Liu, Zheng Guo, Ao Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance Ministry of Education Minzu University of China Beijing100081 China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan Lingshui Li572499 China City University of Macau China

ISBN: (纸本)9789819615278

Text-Image Person Re-Identification (TIReID) is a computer vision task that involves identifying person in images or videos based on textual descriptions. Current works mainly employ Vision Language Pretrained (VLP) models for cross-modal alignment and to reestablish text-image fine-grained associations for re-identification. However, these methods neglect the variations in intra-class sample features, hindering accurate cross-modal alignment and discrimination of fine-grained features for the same person. In this paper, we propose a Cross-modal Intra-Class Learning (CICL) framework to enhance the model’s capability of learning fine-grained cross-modal features and intra-class sample variations. We propose an Image-Text Intra-class Relevance Learning (ITRL) method that considers sample relevance during text-image matching, boosting re-identification capability while maintaining fine-grained text-image alignment. Meanwhile, we propose a Bidirectional Masked Matching (BiMM) method that introduces masks to images and texts, prompting the model to attend to different content regions and establish finer-grained cross-modal associations. We test our approach using the CLIP model on three TIReID benchmarks, achieving results that surpass state-of-the-art performance on multiple metrics. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image matching

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Determination of optimal sensor-actuator position for active vibration damping in collocated SISO systems using a pole-zero distance criterion for fast convergence of the search algorithm 30

Determination of optimal sensor-actuator position for active...

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30th international conference on Noise and Vibration Engineering, ISMA 2022 and 9th international conference on Uncertainty in Structural Dynamics, USD 2022

作者： Piron, D. Deraemaeker, A. Collette, C. Université Libre de Bruxelles BEAMS Department 50 Av F.D Roosevelt CP 165/56 BrusselsB-1050 Belgium Université de Liège Department of Aerospace and Mechanical Engineering 9 Allée de la Découverte Liege4000 Belgium Université Libre de Bruxelles BATir Department 50 Av F.D Roosevelt CP 194/02 BrusselsB-1050 Belgium

ISBN: (纸本)9789082893151

the position of the transducers in active control architectures is critical to ensure the performance and has consequently been studied during the last few decades. However, the placement criteria often require the use of extensive search algorithms that demand numerous iterations, leading to prohibitive computational time for large and/or complex structures. To overcome this limitation, this paper investigates the use of the pole-zero (PZ) distance placement criterion as the starting point for a simple gradient algorithm. this open-loop criterion is based on the direct link between the PZ distance and the maximum reachable damping: the obtained position locates in the vicinity of a high damping area which ensures the convergence of the search algorithm, for fewer iterations. A numerical simulation is performed to assess the performance of the proposed approach and compared to a genetic algorithm optimization. A significant reduction of the processing time is observed while the solution shows an improved robustness to transducers misplacement. © 2022 Proceedings of ISMA 2022 - international conference on Noise and Vibration Engineering and USD 2022 - international conference on Uncertainty in Structural Dynamics. All rights reserved.

关键词： Transducers

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