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ReSLB: Load Balanced Workflow for Distributed Deep Learning Mass Spectrometry Database 24

ReSLB: Load Balanced Workflow for Distributed Deep Learning ...

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24th IEEE international conference on High Performance computing and Communications, 8th IEEE international conference on Data Science and Systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Mocheng, Li Yang, Liu Yang, Ou Zhiguang, Chen Nong, Xiao Tao, Chen College of Computer National University of Defense Technology Institute for Quantum Information State Key Laboratory of High Performance Computing Changsha China College of Computer National University of Defense Technology Changsha China School of Computer Sun Yat-sen University Guangzhou China Beijing China

ISBN: (纸本)9798350319934

the proteomics data analysis pipeline based on the shotgun method requires efficient data processing methods. the parallel algorithm of mass spectrometry database search faces the problems of rapidly expanding database size but low scalability. the heterogeneous database search algorithm based on deep learning is an effective way to solve this problem. Still, the deep learning-based distributed parallel database search algorithm is lacking. this paper analyzes the database searching computational load using a neural network and designs ReSLB, a workflow for the restricted search of mass spectrometry data based on GPU cluster and neural network scoring algorithm. this work aims to ensure the high scalability of future deep learning-based mass spectrometry distributed databases. In the performance estimation of 256 GPUs, the load imbalance of less than 30% and the parallel efficiency of 60% are achieved. Compared with state-of-the-art, the time cost is reduced by 75%, and the parallel efficiency from 1 to 256 GPUs is 3.6x higher than that. © 2022 IEEE.

关键词： Mass spectrometry

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ICPR 2024 Competition on Multilingual Claim-Span Identification 27th

ICPR 2024 Competition on Multilingual Claim-Span Identifica...

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27th international conference on pattern recognition, ICPR 2024

作者： Poddar, Soham Paul, Biswajit Basu, Moumita Ghosh, Saptarshi Indian Institute of Technology Kharagpur Kharagpur721302 India Centre for Artificial Intelligence and Robotics DRDO Bangalore560093 India NSHM Institute of Computing and Analytics NSHM Knowledge Campus Kolkata700053 India

ISBN: (纸本)9783031801389

A lot of claims are made in social media posts, which may contain misinformation or fake news. Hence, it is crucial to identify claims as a first step towards claim verification. Given the huge number of social media posts, the task of identifying claims needs to be automated. this competition deals with the task of ‘Claim Span Identification’ in which, given a text, parts/spans that correspond to claims are to be identified. this task is more challenging than the traditional binary classification of text into claim or not-claim, and requires state-of-the-art methods in pattern recognition, Natural Language Processing and Machine Learning. For this competition, we used a newly developed dataset called HECSI containing about 8K posts in English and about 8K posts in Hindi with claim-spans marked by human annotators. this paper gives an overview of the competition, and the solutions developed by the participating teams. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Claim detection Claim-span identification English Hindi social media

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An Efficient Spatial-Temporal Representation Method for EEG Emotion recognition 19

An Efficient Spatial-Temporal Representation Method for EEG ...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Weng, Weining Gu, Yang Chen, Yiqiang Wang, Guoqiang Shi, Nianfeng Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China The Beijing Key Laboratory of Mobile Computing and Pervasive Device Beijing China School of Computer and Information Engineering Luoyang Institute of Science and Technology Luoyang China

ISBN: (纸本)9798350346558

Affective computing based on electroencephalogram (EEG) has been widely studied due to the high correlation between the brain and emotions. Neurological research shows that emotions are generated by the interaction of neural circuits separated in different brain regions. Recently, many deep learning methods have been demonstrated to recognize different emotions. However, these methods extract features at the data level, ignoring the brain's neural mechanisms of emotion generation, which may result in the features' weak ability to express emotion. To solve this problem and extract more discriminant features according to the neural mechanism of emotion generation, this article proposes an attention based spatial-temporal representation method (ASTR) for emotion recognition. the ASTR method constructs spatial-temporal representations of emotion from neural circuits' activity in different brain regions. In the model, the cascade attention quantifies the multi-granularity correlation and information exchange pattern between brain regions as the spatial features, which measure neural circuits' activation and information interaction in different brain regions. the one-way transformer introduces temporal order in spatial features, calculates temporal variation features from the long-term activity of neural circuits and generates the emotion representation. the representation incorporates spatial-temporal features of neural circuits' activity during emotion generation, which is highly related to emotion. We conducted experiments on SEED and DEAP datasets. the experimental results show that the emotion recognition accuracy of our model is about 3.0% higher than that of the SOTA algorithms, reaching more than 98%. the results also visualize the emotion generation process, revealing how the brain works under different emotions. © 2022 IEEE.

关键词： Emotion recognition

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Dynamic Updating of Partial Feature Maps for Image-based Real-Time recognition of Online Handwritten Chinese Text 22

Dynamic Updating of Partial Feature Maps for Image-based Rea...

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8th international conference on computing and Artificial Intelligence, ICCAI 2022

作者： Wu, Yuefeng Zhong, Ying Wang, Da-Han Chi, Xueke School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China

ISBN: (纸本)9781450396110

Online handwritten Chinese text recognition (OHCTR) has made certain progress in recent years. Most methods currently used are image-based text recognition which has achieved some success. However, it should be noticed that, it is difficult to smoothly recognize online handwritten texts in real-time, especially for long text lines. For real-time recognition of OHCT, the feature extraction of ever-extending text lines will be time-consuming and make the system un-humanization. Moreover, the feature extraction of the non-written part will be performed repeatedly, which is not suitable and has not been addressed by previous works. In this paper, we propose a method to dynamically update partial feature maps of the newly written part for real-time OHCTR. the basic idea is to update the feature maps according to the newly written stroke, which can avoid repeated convolutional computations and thus improve the recognition speed. the proposed method can handle the case of delayed strokes, and can greatly improve the speed of real-time recognition without any loss of accuracy. Experiments on the CASIA-OLHWDB and the ICDAR-2013 competition database demonstrate that our real-time recognition speed are multiplier increased compared with traditional text line recognition methods. © 2022 Association for computing Machinery. All rights reserved.

关键词： Convolution

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Construction and optimization of a computational holographic lithography system 8

Construction and optimization of a computational holographic...

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8th international conference on Speckle Metrology, Speckle 2023

作者： Chen, Rui Lin, Angyi You, Jianfeng Lin, Huixiang Zhao, Wenqing Zhang, Fucai School of Control and Computer Engineering North China Electric Power University Baoding071003 China The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China Engineering Research Center of Intelligent Computing for Complex Energy Systems Ministry of Education North China Electric Power University China

ISBN: (数字)9781510674592

ISBN: (纸本)9781510674585

Traditional lithography techniques are currently facing challenges, including high costs and the susceptibility of mask plates to damage. this research aims to elucidate the feasibility and technical constraints of a novel diffraction lithography approach. this method can reduce the size and cost of lithography machines, break through existing technological barriers, and offer new possibilities for the future development of lithography machines. We have built a basic computational holographic lithography system utilizing a 405nm laser and validated the system using holograms obtained with both classical GS algorithm and a newly proposed method. With the GS holograms, patterns with 700nm features have been successfully exposed on the photoresist. With our new algorithms, multiple holograms for the same target pattern can be generated with different settings in the algorithm. Sequential exposure of those holograms resulted in continuous lines 500 nm wide with reduced speckle artifacts, which was about a 1.4-time improvement over the result from the GS algorithm. Future research will focus on optimizing and enhancing the experimental system and the hologram calculation methods to reduce the line widths further and improve the exposure pattern quality. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Holograms

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A Quantum-inspired Approach to Estimate Optimum-Path Forest Prototypes based on the Traveling Salesman Problem 27th

A Quantum-inspired Approach to Estimate Optimum-Path Forest ...

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27th international conference on pattern recognition, ICPR 2024

作者： Miranda, Maria Angélica Krüger Fanchini, Felipe Fernandes Passos, Leandro Aparecido Rodrigues, Douglas Costa, Kelton Augusto Pontara da Sherer, Rafal Papa, João Paulo Institute of Computing Campinas State University - UNICAMP Campinas Brazil School of Sciences Bauru Brazil Institute of Computational Intelligence Czestochowa University of Technology Czestochowa Poland

ISBN: (纸本)9783031781827

Quantum mechanics emerge as a promise for the future of computing, broadening the horizons for solutions concerning complex tasks, e.g., NP-hard problems. Alongside quantum computing, machine learning has become indispensable. this paper explores the potential integration of quantum computing principles into the Optimum-Path Forest (OPF), a graph-based framework comprised of solutions for machine learning, optimization, and image processing. We are particularly interested in the supervised OPF approach, which elects the most representative samples for each class, aka prototypes, as the connected samples from different classes in a minimum spanning tree (MST) computed over the training set. By harnessing quantum parallelism and superposition, this paper introduces a new approach to identifying prototypes employing a quantum-based Traveler Salesman Problem (TSP) algorithm, which provides an alternative to computing MSTs and yields a hybrid version of the OPF classifier. the experiments on established datasets demonstrated the promising potential of this approach while also underscoring the necessity for further research in this field. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Traveling salesman problem

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

8th International Conference on Life System Modeling and Sim...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

ISBN: (纸本)9789819602315

the proceedings contain 158 papers. the special focus in this conference is on Life System Modeling and Simulation and Intelligent computing for Sustainable Energy and Environment. the topics include: A Method for Evaluating the Operating Health of Wind Turbines Under Wake Effect;toward Next-Generation Buildings: A Review of Renewable Energy Integration and Intelligent Building Energy Management Technologies;improving the Construction of a Multi-modal Underwater Image recognition System for Anomaly Detection in Nuclear Power Plant Cooling Source Areas;A Novel Anti-noise Fault Diagnosis Method for Nuclear Energy Steam Turbine Based on VCformer;Short-Term Photovoltaic Power Forecasting Using Multi-timescale Information Based FFTformer Model;wind Power Forecasting Model Based on Feature Processing and Deep Hybrid Kernel Extreme Learning Machine;optimal User-Side Energy Arbitrage Strategy in Electricity Market with Accurate Battery Model Using Benders Decomposition;a Novel Distributed Economic Dispatch Method for Smart Grid with Time-Varying Feedback Gain;benefits, Barriers, and Human Factors of V2X Implementation – A Brief Review;a General pattern of Permanent Magnet Arrangement for Design Optimization of a Reluctance Magnetic Gear with Enhanced Torque Density for Electric Vehicles;wind Power Scheduling Deviation Compensation Based on Dual-Frequency Pulse Control;a Comprehensive Simulation and Validation of Single Particle Aging Mechanism Model for Lithium-Ion Batteries;semi-supervised State of Health Estimation for Lithium-Ion Batteries via Dynamical Self-encoding;a Reduce-Order Model to Understand the Multiscale Coupling of Battery Electrochemical-thermal Performance;RUL Prediction for Lithium-Ion Batteries Based on Sequence Decomposition and Transformer Networks;An Estimation Method of State of Health of Sodium-Ion Batteries Based on TCN.

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

8th International Conference on Life System Modeling and Sim...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

ISBN: (纸本)9789819602933

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

8th International Conference on Life System Modeling and Sim...

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8th international conference on Life System Modeling and Simulation, LSMS 2024 and 8th international conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

ISBN: (纸本)9789819602247

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A Machine Learning Approach for DDoS Attack Detection in CIC-DDoS2019 Dataset Using Multiple Linear Regression Algorithm 8th

A Machine Learning Approach for DDoS Attack Detection in CIC...

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8th international conference on Computers, Management and Mathematical Sciences, ICCM 2022

作者： Gondi, Lakshmeeswari Sambangi, Swathi Priya, P. Kundana Anjum, S. Sharika GITAM Deemed to be University Andhra Pradesh Visakhapatnam India VNRVJIET Telangana Hyderabad India

ISBN: (纸本)9783031511660

In the context of cloud computing, distributed denial-of-service (DDoS) attacks are malicious attempts to overwhelm a cloud service, network, or an application with a huge flow of traffic from several unidentified sources, rendering it inaccessible to consumers. In cloud computing systems, where many users share the common underlying infrastructure, DDoS attacks are the most common and destructive types of cyber threats. the attackers’ motive is to hinder and halt the services of applications by posing a stream of illegitimate traffic which is achieved with the help of botnets also known as compromised computers that are infected with certain types of malwares such as viruses or trojans. these botnets can be controlled by the attackers, and the malign traffic is generated by various techniques like IP spoofing to make it more challenging to identify the source of the attack. DDoS attacks can be carried out for a variety of reasons, including monetary gain, social or political action, personal grudges, or just the desire to wreak havoc and *** main objective of this chapter is to propose a model capable of anticipating and detecting the percentage of DDoS attacks using multiple linear regression, a dimensionality reduction approach using feature selection by taking the CIC-DDoS 2019 dataset, and compare the classifier result before and after dimensionality reduction using feature selection with multiple linear regression. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Multiple linear regression

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