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检索条件"机构=Inst. Data Science and Information Computing"

共 62 条记录，以下是1-10 订阅

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RGBT2HS-Net: Reconstructing a Hyper-Spectral Volume from an Rgb-T Stack via an Attention-Powered Multiresolution Framework

RGBT2HS-Net: Reconstructing a Hyper-Spectral Volume from an ...

引用

Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Hao-Chiang Shao Szu-Chi Wu Yen-Liang Chuo Jyun-Hao Lin Yuan-Rong Liao Tse-Yu Tseng Inst. Data Science and Information Computing National Chung Hsing University Taiwan Dept. Statistics and Information Science Fu Jen Chatholic University Taiwan

ISBN: (数字)9798350374513

ISBN: (纸本)9798350374520

We present our RGBT2HS-Net method, capable of reconstructing the 61-channel spectral reflectance in the visible and near-infrared spectral range from a 4-channel RGB-Thermal image stack. RGBT2HS-Net has two primary characteristics. First, it employs a multiresolution framework, allowing the input 4D RGB-Thermal image stack to be decomposed into multiresolution tensors via learnable filters for further processing. Second, it incorporates an attention mechanism with a scalable receptive field, enabling the learning of both inter-channel dependencies and intra-channel correlations for hy-perspectral reconstruction. Experiments demonstrate that our method outperforms previous state-of-the-art models in terms of the SAM score on the testing set and achieves an average PSNR greater than 27 dB on the validation set.

关键词： Reflectivity Image resolution Tensors Filters Correlation Conferences Speech processing

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Acceleration of BAM I/O on distributed file systems

Acceleration of BAM I/O on distributed file systems

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Ito, Satoshi Miyano, Satoru Ono, Kenji Tokyo Medical and Dental University Dept. of Integrated Analysis M&d Data Science Center Tokyo Japan Kyushu University Section of Applied Data Science Res. Inst. for Information Technology Fukuoka Japan

ISBN: (纸本)9798350337488

Rapid advances in high-throughput sequencers have made it possible to obtain large amounts of whole genome data quickly and inexpensively. As the amount of data increases, the increase in computation time has become a serious problem. One of the main causes of this problem is file I/O performance. Most pipelines do not implement file I/O suitable for distributed file systems, which are common storage systems in parallel computers used for large-scale analysis. In this study, we developed an I/O system adapted to distributed file systems. In the proposed system, storage access frequency was suppressed by I/O buffers, and thread parallelization by OpenMP was employed. We tested the developed system and a high parallel speedup was achieved. Availability: The developed system is freely available from https://***/SatoshiITO/GAPS © 2023 IEEE.

关键词： Distributed File System High performance computing Whole genome sequencing analysis

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A Fine-Grained Attribute Pre-Labeling Method Based on Label Dependency and Feature Similarity Dynamics

A Fine-Grained Attribute Pre-Labeling Method Based on Label ...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hao-Chiang Shao Yu-Hsien Lin Chia-Wen Lin Inst. Data Science and Information Computing National Chung Hsing University Taiwan Dept. Electrical Engineering National Tsing Hua University Taiwan

In this paper, we proposed a fine-grained attribute pre-labeling method based on the multi-label recovery techniques. Given a fine-grained image dataset with overlooked attributes in its annotation vectors, our method can predict those missing attribute labels by learning the between-label dependency based on the estimated similarity between known attributes and the similarity of extracted deep image features. Furthermore, to prevent the learnable label dependency matrix from converging to a trivial solution, we designed a trace-loss to penalize the self-dependency of attributes. Comprehensive experiments on the CUB-200-2011 dataset show that, given a training set with 40% of attribute labels randomly dropped: i) our approach achieves a pre-labeling performance with an mAP value of 30.7 on a blind testing set, and ii) the missing attributes in the training set can be corrected with an accuracy of 89%. Our method can effectively and robustly perform the fine-grained pre-labeling task.

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Ontology Engineering and the FAIR principles: A Gap Analysis toward a FAIR-by-design methodology

Ontology Engineering and the FAIR principles: A Gap Analysis...

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2024 Joint Ontology Workshops - Episode X: The Tukker Zomer of Ontology, and Satellite Events, JOWO 2024

作者： Poveda-Villalón, María Garijo, Daniel Gonzalez-Beltran, Alejandra N. Jonquet, Clement Le Franc, Yann Universidad Politécnica de Madrid Madrid Spain Scientific Computing Science and Technology Facilities Council United Kingdom MISTEA Univ. of Montpellier INRAE Inst. Agro Montpellier France LIRMM Univ. of Montpellier CNRS Montpellier France e-Science Data Factory Montpellier France

Ontologies and vocabularies play a key role when standardising, organizing and integrating data from heterogeneous data sources into Knowledge Graphs. In order to develop ontologies, different engineering methodologies have been proposed throughout the years, whose application resulted in thousands of semantic artefacts (taxonomies, vocabularies and ontologies) in a wide range of domains. But how to ensure that ontologies follow the Findable, Accessible, Interoperable and Reusable principles (FAIR) from their inception? In this paper, we review: (i) existing guidelines to help make ontologies FAIR and (ii) published FAIRness assessment methodologies and map them to the ontology development lifecycle activities. Our analysis outlines the current gaps, where no guidelines exist for ontologies to become FAIR-by-design. © 2024 Copyright for this paper by its authors.

关键词： Latent semantic analysis

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P-YOLOv8: Efficient and Accurate Real-Time Detection of Distracted Driving

P-YOLOv8: Efficient and Accurate Real-Time Detection of Dist...

引用

2024 IEEE High Performance Extreme computing Conference, HPEC 2024

作者： Elshamy, Mohamed R. Emara, Heba M. Shoaib, Mohamed R. Badawy, Abdel-Hameed A. Klipsch School of ECE New Mexico State University Las CrucesNM88003 United States Pyramids Higher Inst. for Engineering & Technology Dept. of Electronics and Communications Engineering Egypt College of Computing and. Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798350387131

Distracted driving is a critical safety issue that leads to numerous fatalities and injuries worldwide. This study addresses the urgent need for efficient and real-time machine learning models to detect distracted driving behaviors. Leveraging the Pretrained-YOLOv8 (P-YOLOv8) model, a real-time object detection system is introduced, optimized for both speed and accuracy. This approach addresses the computational constraints and latency limitations commonly associated with conventional detection models. The study demonstrates P-YOLOv8's versa-tility in both object detection and image classification tasks using the Distracted Driver Detection dataset from state farm, which includes 22,424 images across ten behavior categories. Our research explores the application of P-YOLOv8 for image classification, evaluating its performance compared to deep learning models such as VGG16, VGG19, and ResNet. Some traditional models often struggle with low accuracy, while others achieve high accuracy but come with high computational costs and slow detection speeds, making them unsuitable for real-time applications. P-YOLOv8 addresses these issues by achieving competitive accuracy with significant computational cost and efficiency advantages. In particular, P-YOLOv8 generates a lightweight model with a size of only 2.84 MB and a lower number of parameters, totaling 1, 451, 098, due to its innovative architecture. It achieves a high accuracy of 99.46% with this small model size, opening new directions for deployment on inexpensive and small embedded devices using Tiny Machine Learning (TinyML). The experimental results show robust performance, making P-YOLOv8 a cost-effective solution for real-time deployment. This study provides a detailed analysis of P-YOLOv8's architecture, training, and performance benchmarks, highlighting its potential for real-time use in detecting distracted driving. © 2024 IEEE.

关键词： Convolutional neural networks

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information Hiding in Images Using Neural Network 8

Information Hiding in Images Using Neural Network

引用

8th International Conference on Computer science and Engineering, UBMK 2023

作者： Daiyrbayeva, Elmira Yerimbetova, Aigerim Maratov, Zhanbolat Sakenov, Bakzhan Institute of Information and Computing Technologies of the Science Committee The Ministry of Science and Higher Education of the Republic of Kazakhstan Satbayev University Academy of Logistics and Transport Almaty Kazakhstan Institute of Information and Computing Technologies of the Science Committee The Ministry of Science and Higher Education of the Republic of Kazakhstan Satbayev University Almaty Kazakhstan Inst. of Info. and Comp. Technol. of the Sci. Comm. of the Min. of Sci. and Higher Educ. of the Rep. of Kazakhstan Almaty Kazakhstan

ISBN: (纸本)9798350340815

This research work is very relevant in the field of image processing using artificial intelligence techniques. In today's world where information security plays an important role, developing methods to detect and analyze hidden data in images is a necessity. The aim of the research is to investigate and develop image processing methods using artificial intelligence techniques to improve the accuracy and efficiency of image processing. This may include creating new neural network models, exploring different data preprocessing techniques, optimizing computational processes and exploring the use of generative models for image synthesis and enhancement. During the experiment, it was proved that, as a result of training the neural network, the values of the loss function decreases during the training process, which in turn means improvements in the model. The model was trained in the Kaggle service. © 2023 IEEE.

关键词： Image enhancement

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Detecting Biomedical Copy-Move Forgery by Attention-Based Multiscale Deep Descriptors

Detecting Biomedical Copy-Move Forgery by Attention-Based Mu...

引用

IEEE International Conference on Image Processing

作者： Hao-Chiang Shao Tse-Yu Tseng Yuan-Rong Liao Chi-Chun Chen Chung-Yang Hung Ming-Hsin Liang Inst. Data Science and Information Computing National Chung Hsing University Taiwan iGroup (Asia Pacific) Ltd. Taiwan Dept. Statistics and Information Science Fu Jen Catholic University Taiwan Dept. Statistics UC Davis U.S.A

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Continual revelations of academic fraud have raised concerns regarding the detection of forged experimental images in the public domain. To address this issue, we introduce a multiscale attention-based deep (MAD) descriptor scheme for detecting copy-move image forgery in biomedical research scenarios. Our method utilizes the common object detection network as a backbone and incorporates the positional embedding module, the channel-attention module, and the self-attention module to generate a dense feature field for input images. The proposed method demonstrates robustness against.common attacks encountered during research manuscript preparation, low-contrast biomedical images featuring small foreground objects, and unseen or unlearned object patterns. Extensive experiments substantiate that our approach outperforms previous copy-move forgery methods when applied to real-world cases across various domains. Our proposed method can serve as an efficient screening tool for the rapid identification of biomedical image forgeries.

关键词： Object detection Feature extraction Forgery Robustness Fraud Biomedical imaging

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Zero Stability in Hierarchical Co-Simulation 24

Zero Stability in Hierarchical Co-Simulation

引用

Proceedings of the Winter Simulation Conference

作者： Irene Hafner Martin Bicher Niki Popper dwh GmbH Vienna AUSTRIA Research Unit of Data Science Inst. of Information Systems Eng. TU Wien Vienna AUSTRIA dwh GmbH Vienna AUSTRIA and Research Unit of Data Science Inst. of Information Systems Eng. TU Wien Vienna AUSTRIA and Research Unit of Computational Statistics Inst. of Statistics and Mathematical Methods in Economics TU Wien Vienna AUSTRIA

ISBN: (纸本)9798331534202

This work presents investigations on zero-stability of hierarchical co-simulation methods with an arbitrary number of co-simulation levels. In comparison to traditional co-simulation, where all participating systems are coordinated by a single co-simulation, hierarchical co-simulation allows the introduction of further co-simulations on several levels beneath a top-level co-simulation. This way, individual macro step sizes and orchestration algorithms may be used on every level. In this paper, we investigate the implications of the introduction of such a hierarchy, which may extend to an arbitrarily chosen number of levels, on the important convergence property of zero stability.

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Comparison between Grad-CAM and EigenCAM on YOLOv5 detection model

Comparison between Grad-CAM and EigenCAM on YOLOv5 detection...

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2022 International Symposium on Electronics and Smart Devices, ISESD 2022

作者： Rahman, Arief Nur Andriana, Dian MacHbub, Carmadi Bandung Inst. of Technol. Res. Center for Telecommunications National Research and Innovation Agency School of Electrical Engineering and Informatics Bandung Indonesia Telkom University Research Center for Data and Information Science National Research and Innovation Agency School of Computing Bandung Indonesia

ISBN: (数字)9798350396607

ISBN: (纸本)9798350396607

This paper presents a comparison between Grad-CAM and EigenCAM methods on the YOLOv5 detection model. Explainable Artificial Intelligence (XAI) is a tool to understand and interpret machine learning models. XAI is used to understand which pixel from an image contributes significantly to the model's final output. EigenCAM is quite simple but a very powerful technique for producing visual explanations, but Grad-CAM is better for a low illumination environment. © 2022 IEEE.

关键词： Cams

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Zero Stability in Hierarchical Co-Simulation

Zero Stability in Hierarchical Co-Simulation

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Simulation Winter Conference

作者： Irene Hafner Martin Bicher Niki Popper dwh GmbH Vienna AUSTRIA Research Unit of Data Science Inst. of Information Systems Eng. Vienna AUSTRIA Research Unit of Computational Statistics Inst. of Statistics and Mathematical Methods in Economics Vienna AUSTRIA

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

关键词： Stability analysis Convergence

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