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TP-BFT: A Faster Asynchronous BFT Consensus with parallel Structure 24th

TP-BFT: A Faster Asynchronous BFT Consensus with Parallel S...

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

作者： Ye, Shunliang Chen, Qi Xiao, Fuan Ke, Zhihui Yang, Guoyu Ma, Huawei Institute of Artificial Intelligence Guangzhou University Guangzhou510006 China Guangdong Provincial Key Laboratory of Blockchain Security Guangzhou University Guangzhou510006 China

ISBN: (纸本)9789819615445

the massive success of blockchains has significantly catalyzed interest in the extensive deployment of practical asynchronous Byzantine fault-tolerant (BFT) consensus protocols across wide area networks. However, existing asynchronous consensus protocols suffer from high communication cost and low throughput, which can’t meet the high-performance requirements of blockchain platforms. We identify two key factors that hinder the efficiency of asynchronous BFT protocols: (1) Reliable broadcast protocols incur high communication costs;(2) the broadcast and agreement phases are executed strictly sequentially, resulting in wasted bandwidth resources. To address above issues, we propose the lightweight provable broadcast and a novel asynchronous BFT protocol called TP-BFT. TP-BFT implements a lightweight broadcast followed by a recovery mechanism and supports parallel broadcast, agreement, and recovery phases. this approach offers two key advantages: (1) it mitigates the impact of low-bandwidth nodes on the efficiency of the broadcast phase;(2) it fully utilizes the bandwidth during the agreement phase to enhance the protocol’s throughput. We implement TP-BFT and conduct experiments with HoneyBadger and Dumbo. the results demonstrate that TP-BFT improves throughput by 106% and reduces latency by 64% compared to Dumbo. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bandwidth

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Beqi: Revitalize the Senegalese Wolof Language with a Robust Spelling Corrector 7th

Beqi: Revitalize the Senegalese Wolof Language with a Rob...

引用

7th EAI international conference on Innovations and Interdisciplinary Solutions for Underserved Areas, InterSol 2024

作者： Mbaye, Derguene Diallo, Moussa Baamtu Dakar Senegal Université Cheikh Anta Diop Dakar Senegal

ISBN: (纸本)9783031864926

the progress of Natural Language processing (NLP), although fast in recent years, is not at the same pace for all languages. African languages in particular are still behind and lack automatic processing tools. Some of these tools are very important for the development of these languages but also have an important role in many NLP applications. this is particularly the case for automatic spell checkers. Several approaches have been studied to address this task and the one modeling spelling correction as a translation task from misspelled (noisy) text to well-spelled (correct) text shows promising results. However, this approach requires a parallel corpus of noisy data on the one hand and correct data on the other hand, whereas Wolof is a low-resource language and does not have such a corpus. In this paper, we present a way to address the constraint related to the lack of data by generating synthetic data and we present sequence-to-sequence models using Deep Learning for spelling correction in Wolof. We evaluated these models in three different scenarios depending on the subwording method applied to the data and showed that the latter had a significant impact on the performance of the models, which opens the way for future research in Wolof spelling correction. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Natural language processing systems

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A Hybrid Resampling Technique with Adaptive Intervals Used in the parallel/Distributed Particle Filters 7

A Hybrid Resampling Technique with Adaptive Intervals Used i...

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7th international conference on Computer Science and Artificial Intelligence, CSAI 2023

作者： Zhang, Xudong Gu, Feng Zhong, Wei Ai, Chunyu Department of Mathematics and Computer Science University of South Carolina Upstate SpartanburgSC 29303 United States Department of Computer Science The College of Staten Island Staten IslandNY10314 United States

ISBN: (纸本)9798400708688

parallel/Distributed particle filters estimate the states of dynamic systems by using Bayesian interference and stochastic sampling techniques with multiple processing units (PUs). the sampling procedure and the resampling procedure alternatively execute to estimate the states in particle filters. there are two basic types of resampling techniques used in parallel/distributed particle filters. they are centralized resampling and decentralized resampling. the high communication between PUs in centralized resampling lowers the speedup factor in parallel computing but improves the estimation accuracy. the decentralized resampling can avoid the communication and improve the performance. Some types of hybrid resampling techniques mainly execute the decentralized resampling and only invoke the centralized resampling with constant intervals to achieve ideal performance without losing the estimation accuracy. However, the constant intervals cannot guarantee that the centralized resamplings are invoked timely. In this study, we proposed a hybrid resampling technique with adaptive intervals between centralized resamplings to overcome that issue. the experimental results indicate that the proposed hybrid resampling technique is able to improve the performance and the estimation accuracy. © 2023 ACM.

关键词： Stochastic systems

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Task-Offload-Oriented Resource Optimization in MEC-Enabled System with Multiple BSs

Task-Offload-Oriented Resource Optimization in MEC-Enabled S...

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IEEE international conference on Information Communication and Signal processing (ICICSP)

作者： Chang Wang Yanzan Sun Yong Fang Xiaojing Chen Shunqing Zhang School of Communication and Information Engineering Shanghai University Shanghai China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

In the extensive research on next-generation communication network architectures, task offloading and resource allocation problems become increasingly complex for mobile edge computing (MEC) systems with multiple base stations (BSs). this paper first formulates this multi-dimensional dynamic issue as an optimization problem. To minimize system overhead, we propose an attention-based multi-agent proximal policy optimization (A-MAPPO) algorithm. this algorithm employs a centralized training and decentralized execution (CTDE) framework, and leverages attention mechanisms to facilitate the convergence of the critic network, thereby enhancing the algorithm's performance. Experimental results show that the A-MAPPO algorithm can reduce system costs by up to 28.3% compared to other benchmark algorithms.

关键词： Training Costs Heuristic algorithms Scalability Signal processing algorithms Signal processing Resource management Time factors Optimization Next generation networking

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Research on Efficient algorithms for Large-scale Graph Neural Network Models

Research on Efficient Algorithms for Large-scale Graph Neura...

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Computer Information Science and Application Technology (CISAT), international conference on

作者： Zebin Wen Shu Gong Weiguo Li Bo Xiao Guangdong University of Science and Technology Shipai Dongguan Guangdong China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

In the field of data science, large-scale graph neural networks (GNN) are effective for modeling complex dataset relations. However, its heavy computational load is a significant challenge. this study focuses on creating efficient algorithms to optimize GNN training and inference without performance loss. It cleverly uses techniques such as sparse matrix operations, node sampling, and parallel processing to handle the scalability issues of large graphs. Empirical evaluations on multiple benchmark datasets show that the algorithms significantly reduce the computational time and resource use while maintaining or improving model accuracy. this study makes a novel algorithmic contribution. It offers new ways to deal with GNN computational complexity and enables GNN applications in scenarios with limited computational resources. this is highly significant for using GNNs in real-world settings that were previously restricted by resources.

关键词： Training Computational modeling Scalability Heuristic algorithms parallel processing Graph neural networks Inference algorithms Data models Computational efficiency Sparse matrices

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An overview of parallel processing of rectangular determinant calculation

An overview of parallel processing of rectangular determinan...

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Mediterranean conference on Embedded Computing (MECO)

作者： Besnik Duriqi Halil Snopçe Armend Salihu Artan Luma Faculty of Computer Science South East European University - SEEU Republic of North Macedonia Faculty of Computer Science Republic of North Macedonia Department of Computer Science UNI Universum International College Prishtina Republic of Kosovo

ISBN: (数字)9798350387568

ISBN: (纸本)9798350387575

this paper focuses on developing algorithms for parallel determinant processing, a crucial task in linear algebra and computational mathematics. the aim is to improve efficiency in high-performance computing environments by designing and analyzing algorithms that use parallel processing to expedite determinant computation for various matrices range. the research explores methods like Laplace expansion, LU decomposition, eigenvalue decomposition, Gaussian elimination, and cofactor expansion, assessing their efficiency, scalability, and applicability in different computational environments. the study employs advanced parallel programming techniques and architectures, utilizing multi-core processors with the focus aim into utilization of Chio’s method of rectangular determinants processing in parallel etc. the research also investigates the mathematical underpinnings of parallel determinant algorithms, addressing challenges like load balancing, data distribution, and synchronization. the results show significant improvements in determinant calculations efficiency, reducing computation times for large matrices.

关键词： Multicore processing parallel programming Scalability Signal processing algorithms Computer architecture Linear algebra parallel processing

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Efficient, Out-of-Memory Sparse MTTKRP on Massively parallel architectures 22

Efficient, Out-of-Memory Sparse MTTKRP on Massively Parallel...

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36th ACM international conference on Supercomputing (ICS)

作者： Nguyen, Andy Helal, Ahmed E. Checconi, Fabio Laukemann, Jan Tithi, Jesmin Jahan Soh, Yongseok Ranadive, Teresa Petrini, Fabrizio Choi, Jee W. Univ Oregon Eugene OR 97403 USA Intel Labs Hillsboro OR USA Univ Erlangen Nurnberg Erlangen Germany Lab Phys Sci College Pk MD USA

ISBN: (纸本)9781450392815

Tensor decomposition (TD) is an important method for extracting latent information from high-dimensional (multi-modal) sparse data. this study presents a novel framework for accelerating fundamental TD operations on massively parallel GPU architectures. In contrast to prior work, the proposed Blocked Linearized CoOrdinate (BLCO) format enables efficient out-of-memory computation of tensor algorithms using a unified implementation that works on a single tensor copy. Our adaptive blocking and linearization strategies not only meet the resource constraints of GPU devices, but also accelerate data indexing, eliminate control-flow and memory-access irregularities, and reduce kernel launching overhead. To address the substantial synchronization cost on GPUs, we introduce an opportunistic conflict resolution algorithm, in which threads collaborate instead of contending on memory access to discover and resolve their conflicting updates on-the-fly, without keeping any auxiliary information or storing non-zero elements in specific mode orientations. As a result, our framework delivers superior in-memory performance compared to prior state-of-the-art, and is the only framework capable of processing out-of-memory tensors. On the latest Intel and NVIDIA GPUs, BLCO achieves 2.12 - 2.6x geometric-mean speedup (with up to 33.35x speedup) over the state-of-the-art mixed-mode compressed sparse fiber (MM-CSF) on a range of real-world sparse tensors.

关键词： Tensor decomposition MTTKRP sparse tensors sparse formats parallel performance GPU

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Research on the Application of User Behavior Big Data Model in Digital Intelligence Reading Service*

Research on the Application of User Behavior Big Data Model ...

引用

Data Science and Information Technology (DSIT), international conference on

作者： Chang Liu Fengping Wei Jiaojiao Chen Hanbing Wei Library Huzhong Universtiy of Science and Technology Wuhan China

ISBN: (数字)9798350384093

ISBN: (纸本)9798350384109

this study constructs a reader behavior big data analysis model using HadHoop big data analysis technology, builds an HDFS distributed file system to collect, transform and store library reader behavior big data, analyzes the utilization status of library resource and uncovers the genuine needs of readers using the MapReduce parallel computing mode, explores how to provide data support for predicting user needs, making user procurement decisions, and improving the efficiency of reading services using big data mining algorithms.

关键词： Procurement Computational modeling Transforms Big Data Predictive models parallel processing Prediction algorithms Libraries Data models Digital intelligence

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Hardware-Efficient Ultrasonic Entrance Counting: Comparing Different Machine Learning Approaches 26

Hardware-Efficient Ultrasonic Entrance Counting: Comparing D...

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26th international conference on Pattern Recognition / 8th international Workshop on Image Mining - theory and Applications (IMTA)

作者： Langer, Tim Waschneck, Bernd Partzsch, Johannes Kelber, Florian Mayr, Christian Infineon Technol Dresden GmbH Co KG Dresden Germany Fraunhofer Inst Integrated Circuits Div Engn Adapt Syst Dresden Dresden Germany Tech Univ Dresden Chair Highly Parallel VLSI Syst & Neuro Microelec Dresden Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

In this work, the classification of walking direction based on ultrasonic signals has been examined for entrance counting. Feed-forward and recurrent neural network architectures as well as simpler machine learning techniques have been investigated and compared with classical signal processing techniques. Using only a single ultrasonic receiver, the focus was set on the development of a hardware-efficient system concept. Different ultrasonic measurement methods in time and frequency domain have been compared with the perspective of a holistic energy optimization. the analysis of the system's hardware efficiency was completed by an estimation of algorithmic latency, energy and storage consumption based on the arithmetic of the classification algorithms. All algorithms showed an estimated energy consumption of less than 10 mu J for a single inference on a state-of-the-art implementation of an ARM (R) Cortex (R) M4F micro-controller, which was found to be negligible compared to the energy of the measurement principle. Compared to other sensor types and multi-sensor systems, a state-of-the-art test accuracy of 99.72% could be achieved for differentiating between the two entrance directions of a present person and the absence of a person.

关键词： Entrance Counting Ultrasound Machine Learning Edge Computing Low Power TinyML Smart Buildings

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A Comparative Study of Different Convolution Neural Network architectures for Hyperspectral Image Classification 7

A Comparative Study of Different Convolution Neural Network ...

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7th international conference on Computing, Communication and Security, ICCCS 2022 and 2022 4th international conference on Big Data and Computational Intelligence, ICBDCI 2022

作者： Singh, Manoj Kumar Kumar, Brajesh MJP Rohilkhand University Department of Computer Science and Information Technology Uttar Pradesh Bareilly243006 India

ISBN: (纸本)9798350309904

In recent years, remote sensing and other applications have used hyperspectral image processing in a variety of ways. For more precise and in-depth information extraction, hyperspectral images offer a wealth of spectral information to recognize and discriminate spectrally identical materials. Numerous cutting-edge methods based on spectral and spatial data are available for hyperspectral image classification. Convolutional neural network (CNN), a subclass of artificial neural networks has gained popularity in a number of fields, including hyperspectral image classification. CNN is built to automatically and adaptively learn spatial hierarchies of data by backpropagation using a variety of building blocks, including convolution layers, pooling layers, and fully connected layers. In this paper, different CNN architectures such as IDCNN, 2D-CNN, 3D-CNN and 3D2D-CNN are evaluated to classify hyperspectral images. Experiments are performed on Indiana Pines and Pavia University images. Experimental results show that 3D2D-CNN gives highest classification accuracy. © 2022 IEEE.

关键词： Remote sensing

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