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26th IEEE International conference on parallel and distributed Systems (IEEE ICPADS)

作者： Gao, Ran Zhao, Yanchao Tang, Maoxing Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Nanjing Peoples R China

ISBN: (纸本)9781728190747

Among the numerous indoor localization technologies, image-based solution has great advantages on convenient access from smartphone and its infrastructure-less deployment. However, the image-based localization also suffers from two key disadvantages, which hinders the universal application. Firstly, it requires a large amount of computing and storage resources, which is difficult to achieve for the mobile device, while cloudbased scheme incurs unacceptable delay. Secondly, although this solution doesn't require infrastructure, it still suffers from labor intensive image-database construction and updates. To overcome these limitations, we propose an image-based indoor localization method featured with realtime localization and labor-less image database update. This method mainly innovates in two aspects. First, we propose a mobile device compatible image database compressing framework, which enable realtime and accurate ondevice image searching even in a large scenario. Our localization method achieves resource efficiency (in terms of storage and processing) by only keeping image feature vectors, and employing the efficient k-mean tree to search for the best matched image. Secondly, to achieve labor-less image database updating, we mainly add high-quality and informative query image into the database. These query image could compensate the missing information or changed scenario in a up-to-date manner. We conduct real experiments in Android Platform to verify the feasibility and performance of the localization method. Experiment results show that our method has good accuracy (90% location errors are within 1.5m) and high real-time performance (average location delay is less than 0.5s).

关键词： image-based localization real-time smartphone-based localization indoor location image database update

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USTEP: Unfixed Search Tree for Efficient Log Parsing 21

USTEP: Unfixed Search Tree for Efficient Log Parsing

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21st IEEE International conference on Data Mining (IEEE ICDM)

作者： Vervaet, Arthur Chiky, Raja Callau-Zori, Mar ISEP 3DS OUTSCALE Paris France ISEP Inst Super Elect Paris Paris France 3DS OUTSCALE Paris France

ISBN: (纸本)9781665423984

Logs record valuable system information at runtime. They are widely used by data-driven approaches for development and monitoring purposes. Parsing log messages to structure their format is a classic preliminary step for log-mining tasks. As they appear upstream, parsing operations can become a processing time bottleneck for downstream applications. The quality of parsing also has a direct influence on their efficiency. Previous approaches toward online log parsing focused on stateful methods. But an increasing number of tasks ask for real time monitoring. Regarding this problem, we propose USTEP, an online log parsing method based on an evolving tree structure. Evaluation results on a panel of 13 datasets coming from different real-world systems demonstrate USTEP superiority in terms of both effectiveness and robustness when compared to other online methods. We also introduce USTEP-UP, a way of running multiple decentralized instances of USTEP in parallel.

关键词： Log parsing Online algorithm distributed algorithm Log mining

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Focus on Hard Categories and Hard Examples: Remote Sensing image Scene Classification via Expert Model and Hard Example Mining

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Xiong, Yunsheng Zhang, Peng Dou, Yong Xu, Kele Niu, Xin Natl Univ Def Technol Dept Comp Changsha 410005 Peoples R China Key Lab Parallel & Distributed Proc Changsha 410005 Peoples R China

Deep learning has seen dramatic improvements in remote-sensing image scene classification. However, hard categories and hard examples widely exist in the data sets, due to the intraclass diversity and interclass similarity. In this letter, we propose a novel framework to address these issues. Specifically, our method first trains a general model to obtain the confusion matrix and select the hard categories. Then a sampling strategy is proposed to restructure the training set and an expert model is trained to focus on the hard categories. Finally, the knowledge of the expert model is distilled into the student model through a novel loss function, which encourages the student model to predict the hard label provided by manual annotation. Thus, the model can match the soft label provided by the expert model and pay more attention to the hard examples simultaneously. With this method, the student model cannot only deal with hard categories but also hard examples existing in other easy categories. To empirically demonstrate the effectiveness of the proposed method, we comprehensively evaluate the method on three publicly available benchmark data sets, the obtained results show that the proposed method outperforms the existing baseline methods and achieves superior results on all three data sets.

关键词： Training Remote sensing Predictive models Data models Loss measurement Task analysis Feature extraction Expert model hard categories hard examples knowledge distillation (KD) remote sensing scene classification

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The Research of parallel Wavelet Transform Algorithms on Remote Sensing image 5

The Research of Parallel Wavelet Transform Algorithms on Rem...

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5th International conference on Electromechanical Control Technology and Transportation (ICECTT)

作者： Zhang, Shengzhong China Univ Geosci Beijing Informat Network Ctr Beijing 100083 Peoples R China

ISBN: (纸本)9781728199283

Focusing on the Wavelet Transform, the paper explores four parallel Wavelet Transform algorithms and techniques from the perspectives of data parallel and algorithm parallel for remote sensing images. Among them, the algorithm based on "Working Pool parallel" achieves dynamic load balance without any limits to the scale of the data and the number of the Slaves. Therefore, this algorithm is easier to achieve the goal of processing the vast data of remote sensing images rapidly in the distributed network systems.

关键词： Wavelet Transform parallel processing Remote Sensing

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Federated Learning for Coronary Artery Plaque Detection in Atherosclerosis Using IVUS Imaging: A Multi-Hospital Collaboration

arXiv

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arXiv 2024年

作者： Hsiao, Chiu-Han Chen, Kai Peng, Tsung-Yu Huang, Wei-Chieh Research Center for Information Technology Innovation Academia Sinica Taipei City Taiwan Department of Information Management National Taiwan University Taipei City Taiwan Division of Cardiology Department of Internal Medicine Taipei Veterans General Hospital School of Medicine National Yang Ming Chiao Tung University Taiwan Department of Biomedical Engineering National Taiwan University Taipei Taiwan

The traditional interpretation of Intravascular Ultrasound (IVUS) images during Percutaneous Coronary Intervention (PCI) is a time-consuming process that often lacks consistency due to its reliance on the expertise and subjective judgment of physicians. Additionally, the collection and integration of IVUS data across multiple hospital systems are significantly constrained by regulatory restrictions and privacy concerns, posing challenges to collaborative analysis and model development. To establish a consistent and efficient method for IVUS image segmentation and enhance plaque detection during PCI procedures, a parallel 2D U-Net model with a multi-stage segmentation architecture has been developed. The model leverages a federated learning algorithm, enabling collaborative data analysis across multiple healthcare institutions while maintaining data privacy. Plaque segmentation is achieved by identifying and subtracting the External Elastic Membrane (EEM) and lumen areas. To optimize processing efficiency, polar coordinates are generated from Cartesian coordinates during the preprocessing stage, ensuring precise segmentation and improving computational performance in distributed medical imaging workflows. The model demonstrates effective plaque identification, achieving a Dice Similarity Coefficient (DSC) of 0.706, and accurately detects circular boundaries in real-time. Through collaboration with domain experts, the IVUS segmentation system provides enhanced plaque burden interpretation via precise quantitative parameter measurements. Future improvements may include integrating advanced federated learning methods to further enhance model performance. This technology is highly adaptable and suitable for environments where sensitive and distributed data are prevalent. Expanding the number of participating institutions and datasets could further optimize outcomes, enabling hospitals to achieve greater success in medical imaging and intervention. © 2024, CC BY-NC-ND.

关键词： image segmentation

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Towards a SOLAP Based Decision Support System for Forest Fire Management

Towards a SOLAP Based Decision Support System for Forest Fir...

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Earth Observation and Geo-Spatial Information (ICEOGI), International conference on

作者： Asma Belaroussi Khalissa Derbal Rachid Benabdellah Mostefa Belhadj-Aissa Image Processing and Radiation Laboratory LTIR USTHB Algiers Algeria LSI Laboratory USTHB Algiers Algeria General Directorate of Forestry DGF Algiers Algeria

Risk, suppression, and damage assessment are essential parts of forest fire management. The involved data is distributed and heterogeneous, concerning meteorology, topomorphology, and socio-economy. In addition, it has a multiscale spatiotemporal analysis aspect. These reinforce the need to use the appropriate methods and technologies to enhance decision-making. This research investigates the potential of adopting spatial data warehousing techniques to build a SOLAP-based DSS that supports the whole decisional chain. (i) source collection and integration; (ii) data repository design and population; (iii) SOLAP analytics; and finally, (iv) data visualization in numerous formats, including tabular, graphic, and dynamic maps.

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A distributed Model for Manufacturing Scheduling: Approaching the EDGE

A Distributed Model for Manufacturing Scheduling: Approachin...

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International-Federation-of-Information-processing-Working-Group-5.7 (IFIP WG 5.7) International conference on Advances in Production Management Systems (APMS)

作者： Coelho, Pedro Silva, Cristovao Univ Coimbra Dept Mech Engn CEMMPRE P-3030788 Coimbra Portugal

ISBN: (纸本)9783030858742;9783030858735

Manufacturing scheduling has a crucial role in a company's performance. It's a hard optimization problem and due to the latest manufacturing trends, it is becoming even more complex. Metaheuristics are promising methods to solve those real-world problems. The latest distributed/parallel computing advances may support the increase of computational power needed to get efficient schedules a suitable time period. In the last years, the Industrial Internet has also known some advances as the emergence of the Edge computing paradigm that increased the computational processing power near the factory floor. This work presents strategies to implement a distributed metaheuristic for manufacturing scheduling on the Edge. Under the scheduling problem context, the physical platform and the programming environment are examined. Based on an evolutionary metaheuristic (genetic algorithm), a model is developed, following strategies that take advantage of the Edge layer of the Industrial Internet. The generic algorithm steps are described for future deployment and validation.

关键词： Manufacturing Scheduling Industrial Internet Edge computing Metaheuristics distributed Genetic Algorithm

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Automated Facial Expression Detection using Genetic Algorithm optimization with Fuzzy C-means Clustering Algorithm

Automated Facial Expression Detection using Genetic Algorith...

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distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International conference on

作者： Gopal Krishan Gard Deepak Kumar Jasgurpreet Singh Chohan Sanjeev Kumar Joshi Jatinder Kumar Haider Alabdeli Centre of Research Impact and Outreach Chitkara University Rajpura Punjab India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mechanical Engineering Rayat Bahra University Mohali India Department of Mechanical Engineering Uttaranchal University Dehradun India Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India The Islamic university Najaf Iraq

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

Facial expression recognition is a critical area of computer vision research in the current era. In order to improve the performance of the existing system, many researchers have used handcrafted features and machine learning (ML) classification methods. However, machine learning performs well on large data sets, whereas the handcrafted system performs well on datasets captured under predefined conditions. The ML method will not perform well on small datasets like CK+ and JAFFE due to overfitting. We propose a genetic algorithm (GA) optimization architecture based on fuzzy C-means clustering (GAO-FCM) to address these problems. To identify and recognize facial expressions quickly, accurately, and reliably, the proposed GAO-FCM organizes several characteristics of individual facial expressions. In the Japanese Female Facial Expression (JAFFE) dataset as well as in the Extended Cohn-Kanade (CK+) dataset, face detection and pre-processing techniques are explored in order to achieve the best accuracy of 99.23% for CK+ and 96.31 % for JAFFE datasets.

关键词： image recognition Face recognition Computational modeling Clustering algorithms Machine learning Reliability Face detection

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Automatic Detection of Microaneurysms in Fundus images

Automatic Detection of Microaneurysms in Fundus Images

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Summer Virtual conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing (SNPD-Summer), ACIS International

作者： Shuhei Yamada Jesús Eduardo Ochoa Astorga Weiwei Du Yahui Peng Department of Information Science Kyoto Institute of Technology Kyoto Japan Information and Human Science Kyoto Institute of Technology Kyoto Japan School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

ISBN: (纸本)9798350396386

Early detection and treatment of diabetic retinopathy can delay blindness and improve quality of life for diabetic patients. It is difficult to detect early symptoms of diabetic retinopathy, which is presented by few microaneurysms in fundus images. This study proposes an algorithm to detect microaneurysms in fundus images automatically. The proposal includes microaneurysms segmentation by U-Net model and their false positives removal by ResNet model. The effectiveness of the proposal is evaluated with the public database IDRiD and E-ophtha by the Area Under Precision Recall curve (AUPR). 90% of microaneurysms can be detected at early stages of diabetic retinopathy. This proposal outperforms previous methods based in AUPR evaluation.

关键词： image segmentation Retinopathy Databases Blindness Diabetes Delays Proposals

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Implicit Neural Alignment Network for Arbitrary-scale Space-Time Video Super-Resolution

Implicit Neural Alignment Network for Arbitrary-scale Space-...

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International Joint conference on Neural Networks (IJCNN)

作者： Qin Jiang Qinglin Wang Lihua Chi Jie Liu Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Digitizing Software for Frontier Equipment Hunan GuoKe Computility Technology Co. Ltd

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Space-time video super-resolution (STVSR) is a comprehensive task comprising two subtasks: video super resolution in space dimension and video frame interpolation in time dimension. Conventional decoupled two-stage approaches tend to overlook the intrinsic correlation between the two tasks. Overcoming this challenge requires the development of a unified model capable of simultaneously implementing space-time super-resolution across arbitrary scales. Most existing models are confined to training on fixed space upsampling scales or specific frame-rate videos, resulting in limited generalization capabilities for flexible space-time super-resolution scenarios. In response to this limitation, our approach draws inspiration from continuous implicit neural representation. We propose an enhanced Implicit Neural Alignment Network (INAN) based on the VideoINR framework, encompassing feature refinement, precise motion flow estimation, and multi-scale feature fusion to optimize the final implicit neural decoding. Our extensive experimental evaluations on multiple benchmarks underscore the efficacy of the INAN model, indicate its superior performance compared to prior STVSR methods.

关键词： Training Interpolation Correlation Superresolution Neural networks Estimation Benchmark testing

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