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检索条件"任意字段=12th International Conference on Computing and Pattern Recognition, ICCPR 2023"

共 284 条记录，以下是91-100 订阅

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Computation pattern driven reuse of manual optimizations for GPGPUs

Computation pattern driven reuse of manual optimizations for...

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2011 12th international conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2011

作者： Xu, Shixiong Han, Dongni Chen, Li Key Laboratory of Computer System and Architecture Institute of Computing Technology University of Chinese Beijing China

ISBN: (纸本)9780769545646

the wide application of General Purpose Graphic Processing Units (GPGPUs) results in large manual efforts on porting and optimizing algorithms on them. However, most existing automatic ways of generating GPGPU code fail to conduct optimization strategies regarding a specific computation and to reuse constantly evolving manual optimizations. In this paper, we present a computation pattern driven approach for computation-specific GPGPU code generation and optimization, which in turn reuses manual optimizations to a certain extent. We suggest language extensions to OpenMP, high-level data structure attributes, in order to assist the process of computation pattern matching and to help give users intuitive performance tuning parameters in the view of data structure attributes. We illustrate the feasibility of this approach through three important computation dwarfs, which are dense matrix, sparse matrix, and structured mesh computation in scientific computing. We also build a prototype OpenMP-to-CUDA translator that consists of computation pattern recognition and code template instantiation. the experimental results demonstrate the performance benefits of computation pattern driven method. To our best knowledge, it is the first work on reusing manual optimizations for GPGPUs with computation pattern driven approach. © 2011 IEEE.

关键词： Data structures

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13th international conference on Innovations in Bio-Inspired computing and Applications, IBICA 2022, and 12th World Congress on Information and Communication Technologies, WICT 2022

13th International Conference on Innovations in Bio-Inspired...

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13th international conference on Innovations in Bio-Inspired computing and Applications, IBICA 2022, and 12th World Congress on Information and Communication Technologies, WICT 2022

ISBN: (纸本)9783031274985

the proceedings contain 86 papers. the special focus in this conference is on Innovations in Bio-Inspired computing and Applications. the topics include: A Novel Approach to the Two-Dimensional Cargo Load Problem;vehicle Detection from Aerial Imagery Using Principal Component Analysis and Deep Learning;bio-inspired Heterogeneity in Swarm Robots;software Defect Prediction Using Cellular Automata as an Ensemble Strategy to Combine Classification Techniques;a Systematic Literature Review on Home Health Care Management;the Impact of the Size of the Partition in the Performance of Bat Algorithm;automatic Diagnosis Framework for Catheters and Tubes Semantic Segmentation and Placement Errors Detection;how Artificial Intelligence Can Revolutionize Software Testing Techniques;e-Assessment in Medical Education: From Paper to Platform;Evolution of Configuration Data in CGP Format Using Parallel GA on Embryonic Fabric;DeepPRS: A Deep Learning Integrated pattern recognition Methodology for Secure Data in Cloud Environment;Automated Depression Diagnosis in MDD (Major Depressive Disorder) Patients Using EEG Signal;an Effective Deep Learning Classification of Diabetes Based Eye Disease Grades: An Retinal Analysis Approach;extracting and Analyzing Terms with the Component ‘Green’ in the Bulgarian Language: A Big Data Approach;Apartments Waste Disposal Location Evaluation Using TOPSIS and Fuzzy TOPSIS Methods;detection of Cracks in Building Facades Using Infrared thermography;optimizing Pre-processing for Foetal Cardiac Ultra Sound Image Classification;a Review on Dimensionality Reduction for Machine Learning;detecting Depression on Social Platforms Using Machine Learning;impact of Green Hydrogen Production on Energy Pricing;Cross Synergetic Mobilenet-VGG16 for UML Multiclass Diagrams Classification;the Future in Fishfarms: An Ocean of Technologies to Explore;breast Cancer Identification Using Improved DarkNet53 Model;anomaly Detection Framework.

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A Dynamic Spatio-temporal Network with Self-attention for Multi-station Passenger Flow Prediction 12

A Dynamic Spatio-temporal Network with Self-attention for Mu...

引用

12th international conference on Awareness Science and Technology, iCAST 2023

作者： Wang, Fengzhi Lv, Qinzhi Liu, Lijuan College of Computer and Information Engineering Xiamen University of Technology Xiamen China College of Computer and Information Engineering Xiamen University of Technology Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen China

ISBN: (纸本)9798350324693

Passenger flow prediction is vitally significant for intelligent transportation systems (ITS). Most of the studies typically focus on the passenger flow prediction for an individual station, and only capture the temporal features without considering any spatial features. Constructing a passenger flow prediction model for multiple stations, or even a whole network, is more valuable for practical applications. therefore, we develop a dynamic spatio-temporal network (DSTNet) with a self-attention (SA) mechanism for multi-station passenger flow prediction. A dynamic graph convolutional network (DGCN) is applied for the spatial feature extraction, and gated recurrent unit (GRU) is combined to learn the temporal features. SA is applied to further assign the weights for the extracted spatio-temporal features. the Experiment has been conducted on the passenger flow in the Xiamen bus rapid transit (BRT). the results demonstrate that the proposed DSTNet with SA (SA-DSTNet) outperforms the baselines in the multi-station passenger flow prediction task. © 2023 IEEE.

关键词： Forecasting

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A Study of Prefrontal Cortex Task Switching Using Spiking Neural Networks 12

A Study of Prefrontal Cortex Task Switching Using Spiking Ne...

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12th international conference on Advanced Computational Intelligence (ICACI)

作者： Viswanathan, K. Ashwin Mylavarapu, Goutam Chen, Kun thomas, Johnson P. Oklahoma State Univ Dept Compute Sci Stillwater OK 74078 USA

ISBN: (纸本)9781728142487

In this work we study the behavior of Prefrontal Cortex (PFC) and understand its role in task switching by developing a biologically based computational model. We build the PFC neurons using Spiking Neural Networks (SNN) with biologically realizable features having lateral inhibition, synaptic weight changes using unsupervised Spike Timing Dependant Plasticity (STDP) learning rule, spiking threshold and biological ranges for neuronal parameter values. the SNN is composed of Leaky Integrate and Fire (LIF) neurons which are efficient to model and represents the Excitatory neurons in Glutamate layer and Inhibitory neurons in GABA layer. In this implementation we use two real world datasets as tasks for the PFC network to learn. We demonstrate the switching behavior of the neurons and their synaptic weight adaptations by formulating experiments in a manner consistent with real world trials used in the study of cognitive psychology. Using these experiments we show how our model adapts and responds to task changes exhibiting biological behaviors like Long Term Potentiation (LTP), Long Term Depression (LTD) and Task-set reconfiguration (TSR) thereby giving insights into understanding the importance of duration between changing tasks and its effect on performance and efficacies of multi-tasking. the results shown in this paper relate favorably well with the natural neuronal responses found in the brain.

关键词： Spiking Neural Network neuroscience cognitive modeling STDP unsupervised learning cognitive science pattern recognition cognitive computing Computational neuroscience

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Efficient Regression with Feature Selection Based on L12-Norm 4

Efficient Regression with Feature Selection Based on L12-Nor...

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4th IEEE international conference on pattern recognition and Machine Learning, PRML 2023

作者： Lei, Yu Li, Qin You, Jane Shenzhen Institute of Information Technology School of Software Engineering Shenzhen China Xidian University School of Telecommunications Engineering Xi'an China The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

ISBN: (纸本)9798350324303

Feature selection is one of the most important components of many machine learning applications. Most of the existing sparse coding-based methods only concerned with the regularization of the transformation matrix, but ignoring the sparsity of projected features in the intrinsic feature space. In this paper, we introduce a novel regularization with 12-norm, called class specificity distribution term. It aims to ensure that samples with the same label could obey a specific distribution, while data from the different categories should have different distributions in the intrinsic feature space. then we propose a novel embedded feature selection framework based on regression and class specificity distribution term measured by 12-norm, termed RL12. An efficient augmented Lagrange multipliers based optimization algorithm is proposed to solve the 12-norm regularization iteratively. Experimental results demonstrate the effectiveness of our method. © 2023 IEEE.

关键词： Feature Selection

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Generalized Median Computation for Consensus Learning: A Brief Survey 20th

Generalized Median Computation for Consensus Learning: A Bri...

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20th international conference on Computer Analysis of Images and patterns (CAIP)

作者： Jiang, Xiaoyi Nienkoetter, Andreas Univ Munster Fac Math & Comp Sci Munster Germany Sichuan Univ Hongkong Polytech Univ Inst Disaster Management & Reconstruct Chengdu Sichuan Peoples R China

ISBN: (纸本)9783031442360;9783031442377

computing a consensus object from a set of given objects is a core problem in machine learning and pattern recognition. A popular approach is the formulation of generalized median as an optimization problem. the concept of generalized median has been studied for numerous problem domains with a broad range of applications. Currently, the research is widely scattered in the literature and no comprehensive survey is available. this brief survey contributes to closing this gap and systematically discusses the relevant issues of generalized median computation. In particular, we present a taxonomy of computation frameworks and methods. We also outline a number of future research directions.

关键词： pattern recognition

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Functional mesh learning for pattern analysis of cognitive processes

Functional mesh learning for pattern analysis of cognitive p...

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12th IEEE international conference on Cognitive Informatics and Cognitive computing, ICCI*CC 2013

作者： Firat, Orhan Özay, Mete Önal, Itir Oztekin, Ilke Vural, Fatoş T. Yarman Department of Computer Engineering Middle East Technical University Ankara Turkey Department of Psychology Koç University Istanbul Turkey

ISBN: (纸本)9781479907816

We propose a statistical learning model for classifying cognitive processes based on distributed patterns of neural activation in the brain, acquired via functional magnetic resonance imaging (fMRI). In the proposed learning machine, local meshes are formed around each voxel. the distance between voxels in the mesh is determined by using functional neighborhood concept. In order to define functional neighborhood, the similarities between the time series recorded for voxels are measured and functional connectivity matrices are constructed. then, the local mesh for each voxel is formed by including the functionally closest neighboring voxels in the mesh. the relationship between the voxels within a mesh is estimated by using a linear regression model. these relationship vectors, called Functional Connectivity aware Local Relational Features (FC-LRF) are then used to train a statistical learning machine. the proposed method was tested on a recognition memory experiment, including data pertaining to encoding and retrieval of words belonging to ten different semantic categories. Two popular classifiers, namely k-Nearest Neighbor and Support Vector Machine, are trained in order to predict the semantic category of the item being retrieved, based on activation patterns during encoding. the classification performance of the Functional Mesh Learning model, which range in 62-68% is superior to the classical multi-voxel pattern analysis (MVPA) methods, which range in 40-48%, for ten semantic categories. © 2013 IEEE.

关键词： Encoding (symbols)

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Comparative Study of pattern recognition Techniques in the Classification of Vertebral Column Diseases 12th

Comparative Study of Pattern Recognition Techniques in the...

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12th international Congress in Telematics and computing, WITCOM 2023

作者： Rojas-López, Alam Gabriel Uriarte-Arcia, Abril Valeria Rodríguez-Molina, Alejandro Villarreal-Cervantes, Miguel Gabriel Mechatronics Section Postgraduate Department OMD Laboratory Instituto Politécnico Nacional - Centro de Innovación y Desarrollo Tecnológico en Cómputo Mexico City07700 Mexico Research and Postgraduate Division Tecnológico Nacional de México/IT de Tlalnepantla Tlalnepantla de Baz54070 Mexico

ISBN: (纸本)9783031453151

this work compares popular classifiers based on pattern recognition techniques of supervised learning, including k-Nearest Neighbors, Naïve Bayes, Support Vector Machines, Artificial Neural Networks, and Decision Trees. Such techniques are applied to a dataset related to vertebral column orthopedic diseases. Different parameter values employed by each classifier are tested, resulting in an accuracy of around 80 % in almost all approaches, where the k-Nearest Neighbors alternatives were the most accurate. Finally, a brief discussion of particular highlights of how the metrics affect the performances of the classifiers is presented. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision trees

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Audio Recorder Forensic Identification in 21 Audio Recorders 3

Audio Recorder Forensic Identification in 21 Audio Recorders

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3rd IEEE international conference on Progress in Informatcs and computing (IEEE PIC)

作者： Zeng, Jinhua Shi, Shaopei Yang, Xu Li, Yan Lu, Qimeng Qiu, Xiulian Zhu, Huaping Minist Justice Inst Forens Sci Shanghai 200063 Peoples R China Tongji Univ Coll Civil Engn Shanghai Peoples R China

ISBN: (纸本)9781467390880

Audio recorder forensic identification is to determine originating devices of questioned audio recordings. In this paper, statistical features both in time and frequency domains were studied and were used to represent and encode device-related information. Statistical techniques in fields of machine learning and pattern recognition were utilized to classify computed features into each originating audio recorder. More specifically, features of sampling histogram and spectral mean were extracted from near-silence segments of digital recordings which were recorded by using 21 audio recorders. A support vector machine classifier was used to learn device-related feature patterns upon one half of the complete set of audio samples, and then was evaluated by using the rest of data. the experimental results show a high accuracy of up to 96.72% in the correct classification of the 21 audio recorders. Our paper was the first study which was to study and evaluate the performances of different features for device-related information encoding in a relatively big audio recorder database. the proposed method would offer a practical guide for forensic identification of audio recorders.

关键词： Audio recorder forensic identification pattern classification Sampling histogram Spectral mean Statistical feature

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SER-Fuse: An Emotion recognition Application Utilizing Multi-Modal, Multi-Lingual, and Multi-Feature Fusion 23

SER-Fuse: An Emotion Recognition Application Utilizing Multi...

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12th international Symposium on Information and Communication Technology, SOICT 2023

作者： Pham, Nhat Truong Phan, Le thi Dang, Duc Ngoc Minh Manavalan, Balachandran Dept. of Integrative Biotechnology Sungkyunkwan University Gyeonggi-do Suwon Korea Republic of Dept. of Computing Fundamental Fpt University Ho Chi Minh Viet Nam

ISBN: (纸本)9798400708916

Speech emotion recognition (SER) is a crucial aspect of affective computing and human-computer interaction, yet effectively identifying emotions in different speakers and languages remains challenging. this paper introduces SER-Fuse, a multi-modal SER application that is designed to address the complexities of multiple speakers and languages. Our approach leverages diverse audio/speech embeddings and text embeddings to extract optimal features for multi-modal SER. We subsequently employ multi-feature fusion to integrate embedding features across modalities and languages. Experimental results archived on the English-Chinese emotional speech (ECES) dataset reveal that SER-Fuse attains competitive performance in the multi-lingual approach compared to the single-lingual approaches. Furthermore, we provide the implementation of SER-Fuse for download at https://***/nhattruongpham/SER-Fuse to support reproducibility and local deployment. © 2023 ACM.

关键词： Modal analysis

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