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共 52 条记录，以下是11-20 订阅

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Crop Classification Methods Based on Siamese CBMM-CNN architectures Using Hyperspectral Remote Sensing Data 24th

Crop Classification Methods Based on Siamese CBMM-CNN Archi...

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24th international conference on algorithms and architectures for parallel processing, ica3pp 2024

作者： Xie, Bin Zhang, Jiahao Li, Yuling Li, Yusong Dong, Xinyu College of Computer and Cyber Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Hebei Normal University Hebei Shijiazhuang China

ISBN: (纸本)9789819615278

Amidst global population growth and escalating food demands, real-time agricultural monitoring is crucial for ensuring food security. During the initial stages of crop growth, however, it faces significant challenges in obtaining accurate ground truth data via remote sensing, thereby impeding effective crop classification. Traditional Convolutional Neural Network (CNN) models, although effective in image processing and feature extraction within computer vision, are notably limited in their ability to handle the high dimensionality and complex spectral information inherent in hyperspectral non-image remote sensing data. To address this problem, we designed an enhanced CNN model, CBM-CNN. the CBM-CNN incorporates additional convolutional layers (C), batch normalization (B), and MLP(M) to convert one-dimensional data into two-dimensional data for classification, thus improving the processing efficiency of highly nonlinear data features. Building on CBM-CNN, we add multiple attention mechanisms (CBMM-CNN) to enhance feature extraction capabilities. the Siamese CBMM-CNN (SCBMM-CNN) is employed for in-depth learning of data features, with supervised learning precisely adjusting the model to improve classification performance. Experimental results on hyperspectral remote sensing datasets demonstrate that SCBMM-CNN outperforms traditional machine learning models (PCA-RF, GBDT, GMO-SVM, Random Forest, Decision Tree) and deep learning models (CNN, CNN-LSTM) in terms of accuracy, recall rate, F1 score, and kappa coefficient. these results confirm that SCBMM-CNN significantly improves crop classification accuracy and provides a novel method for early crop growth classification. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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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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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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parallel Approach to Sliding Window Sums 19th

Parallel Approach to Sliding Window Sums

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19th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Snytsar, Roman Turakhia, Yatish Microsoft Corp One Microsoft Way Redmond WA 98052 USA Stanford Univ Stanford CA 94305 USA

ISBN: (纸本)9783030389611;9783030389604

Sliding window sums are widely used for string indexing, hashing, time series analysis and machine learning. New vector algorithms which utilize the advanced vector extension (AVX) instructions available on modern processors, or the parallel compute units on GPUs and FPGAs, would provide a significant performance boost. We develop a generic vectorized sliding sum algorithm with speedup for window size w and number of processors P is O(P/w) for a generic sliding sum. For a sum with commutative operator the speedup is improved to O(P/log(w)). Implementing the algorithm for the bioinformatics application of minimizer based k-mer table generation using AVX instructions, we obtain a speedup of over 5x.

关键词： Time series analysis

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HDF5-Based I/O Optimization for Extragalactic HI Data Pipeline of FAST 19th

HDF5-Based I/O Optimization for Extragalactic HI Data Pipeli...

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19th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Ji, Yiming Yu, Ce Xiao, Jian Tang, Shanjiang Wang, Hao Zhang, Bo Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Chinese Acad Sci CAS Key Lab FAST NAOC Beijing Peoples R China

ISBN: (纸本)9783030389611;9783030389604

the Five-hundred-meter Aperture Spherical Radio Telescope (FAST), which is the largest single-dish radio telescope in the world, has been producing a very large data volume with high speed. So it requires a high performance data pipeline to covert the huge raw observed data to science data product. However, the existing solutions of pipelines widely used in radio data processing cannot tackle this situation efficiently. the paper proposes a pipeline architecture for FAST based on HDF5 format and several I/O optimization strategies. First, we design the workflow engine driving the various tasks efficiently in the pipeline;second, we design a common radio data storage specification on the top of HDF5 format, and also developed a fast converter to map the original FITS format to the new HDF5 format;third, we apply several concrete strategies to optimize the I/O operations, including chunks storage, parallel reading/writing, on-demand dump, and stream process etc. In the experiment of processing 700 GB of FAST data, the results show that HDF5 based data structure without other optimizations was 1.7 times faster than original FITS format. If chunk storage and parallel I/O optimization are applied, the overall performance can reach 4.5 times as the original one. Moreover, due to the good expansibility and flexibility, our solution of FAST pipeline can be adapted to other radio telescopes.

关键词： FAST FITS HDF5 Pipeline in parallel High performance I/O

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More Effective Distributed Deep Learning Using Staleness Based Parameter Updating 18th

More Effective Distributed Deep Learning Using Staleness Bas...

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18th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Ye, Yan Chen, Mengqiang Yan, Zijie Wu, Weigang Xiao, Nong Sun Yet Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China Guangdong Prov Key Lab Big Data Anal & Proc Guangzhou Peoples R China MoE Key Lab Machine Intelligence & Adv Comp Guangzhou Peoples R China

ISBN: (纸本)9783030050542;9783030050535

Deep learning technology has been widely applied for various purposes, especially big data analysis. However, computation required for deep learning is getting more complex and larger. In order to accelerate the training of large-scale deep networks, various distributed parallel training protocols have been proposed. In this paper, we design a novel asynchronous training protocol, Weighted Asynchronous parallel (WASP), to update neural network parameters in a more effective way. the core of WASP is "gradient staleness", a parameter version number based metric to weight gradients and reduce the influence of the stale parameters. Moreover, by periodic forced synchronization of parameters, WASP combines the advantages of synchronous and asynchronous training models and can speed up training with a rapid convergence rate. We conduct experiments using two classical convolutional neural networks, LeNet-5 and ResNet-101, at the Tianhe-2 supercomputing system, and the results show that, WASP can achieve much higher acceleration than existing asynchronous parallel training protocols.

关键词： Distributed deep learning parallel computing Parameter server Asynchronous parallel Supercomputing system

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Configuring Concurrent Computation of Phylogenetic Partial Likelihoods: Accelerating Analyses Using the BEAGLE Library 17th

Configuring Concurrent Computation of Phylogenetic Partial L...

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17th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Ayres, Daniel L. Cummings, Michael P. Univ Maryland Ctr Bioinformat & Computat Biol College Pk MD 20742 USA

ISBN: (纸本)9783319654829;9783319654812

We describe our approach in augmenting the BEAGLE library for high-performance statistical phylogenetic inference to support concurrent computation of independent partial likelihoods arrays. Our solution involves identifying independent likelihood estimates in analyses of partitioned datasets and in proposed tree topologies, and configuring concurrent computation of these likelihoods via CUDA and opencL frameworks. We evaluate the effect of each increase in concurrency on throughput performance for our partial likelihoods kernel for a four-state nucleotide substitution model on a variety of parallel computing hardware, such as NVIDIA and AMD GPU5, and Intel multicore cPus, observing up to 16-fold speedups over our previous implementation. Finally, we evaluate the effect of these gains on an domain application program, MrBayes. For a partitioned nucleotide-model analysis we observe an average speedup for the overall run time of 2.1-fold over our previous parallel implementation, and 10-fold over the native MrBayes with SSE.

关键词： Bayes methods Biology computing Evolution (biology) Phylogeny Maximum likelihood estimation Multicore processing parallel programming High performance computing

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On the Use of Binary Trees for DNA Hydroxymethylation Analysis 17th

On the Use of Binary Trees for DNA Hydroxymethylation Analys...

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17th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Gonzalez, Cesar Perez, Mariano Orduna, Juan M. Chaves, Javier Garcia, Ana-Barbara Univ Valencia Dept Informat Avda Univ S-N Valencia 46100 Spain CIBERDEM Carlos III Hlth Inst INCLIVA Hlth Res Inst Avda Menendez Pelayo 4 Accesorio Valencia 46010 Spain

ISBN: (纸本)9783319654829;9783319654812

DNA methylation (mC) and hydroxymethylation (hmC) can have a significant effect on normal human development, health and disease status. Hydroxymethylation studies require specific treatment of DNA, as well as software tools for their analysis. In this paper, we propose a parallel software tool for analyzing the DNA hydroxymethylation data obtained by TAB-seq. the software is based on the use of binary trees for searching the different occurrences of methylation and hydroxymethylation in DNA samples. the binary trees allow to efficiently store and access the information about the methylation of each methylated/hydroxymethylated cytosines in the samples. Evaluation results shows that the performance of the application is only limited by the computer input/output bandwidth, even for the case of very long samples.

关键词： High performance computing DNA hydroxymethylation parallel pipeline

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Massively parallel Sequence Alignment with BLAST through Work Distribution Implemented Using PCJ Library 17th

Massively Parallel Sequence Alignment with BLAST Through Wor...

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17th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Nowicki, Marek Bzhalava, Davit Bala, Piotr Nicolaus Copernicus Univ Fac Math & Comp Sci Chopina 12-18 PL-87100 Torun Poland Karolinska Inst Dept Lab Med F46 S-14186 Stockholm Sweden Univ Warsaw Interdisciplinary Ctr Math & Computat Modelling Pawinskiego 5a PL-02106 Warsaw Poland

ISBN: (纸本)9783319654829;9783319654812

this article presents massively parallel execution of the BLAST algorithm on supercomputers and HPC clusters using thousands of processors. Our work is based on the optimal splitting up the set of queries running with the non-modified NCBI-BLAST package for sequence alignment. the work distribution and search management have been implemented in Java using a PCJ (parallel Computing in Java) library. the PCJ-BLAST package is responsible for reading sequence for comparison, splitting it up and start multiple NCBI-BLAST executables. We also investigated a problem of parallel I/O and thanks to PCJ library we deliver high throughput execution of BLAST. the presented results show that using Java and PCJ library we achieved very good performance and efficiency. In result, we have significantly reduced time required for sequence analysis. We have also proved that PCJ library can be used as an efficient tool for fast development of the scalable applications.

关键词： Sequence alignment NGS Next Generation Sequencing parallel programming Java BLAST NCBI-BLAST PCJ

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15th international conference on algorithms and architectures for parallel processing, ica3pp 2015

15th International Conference on Algorithms and Architecture...

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15th international conference on algorithms and architectures for parallel processing, ica3pp 2015

ISBN: (纸本)9783319271392

the proceedings contain 59 papers. the special focus in this conference is on Software Systems, Programming Models, Performance Modeling and Evaluation. the topics include: A scalable fault-tolerance programing model on MIC cluster;multi-chunk redundant array of independent SSDS with improved performance;an energy efficient storage system for astronomical observation data on dome a;parallel aware hybrid solid-state storage;automatic optimization of software transactional memory through linear regression and decision tree;a data-centric tool to improve the performance of multithreaded program on NUMA;a light-weight hot data identification scheme via grouping-based LRU lists;towards interactive programming with parallel linear algebra in r;enhancing i/o scheduler performance by exploiting internal parallelism of SSDS;a performance and scalability analysis of the MPI based tools utilized in a large ice sheet model executing in a multicore environment;an efficient algorithm for a generalized LCS problem;on exploring a quantum particle swarm optimization method for urban traffic light scheduling;identifying repeated interleavings to improve the efficiency of concurrency bug detection;global reliability evaluation for cloud storage systems with proactive fault tolerance;performance evaluation and optimization of Wi-Fi display on android;a novel scheduling algorithm for file fetch in transparent computing;joint power and reduced spectral leakage-based resource allocation for d2d communications in 5G and an optimization strategy of energy consumption for data transmission based on optimal stopping theory in mobile networks.

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