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data Synthesis for Document Layout Analysis 19th

Data Synthesis for Document Layout Analysis

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19th international conference on Web-Based Learning / 5th international Symposium on Emerging Technologies for Education ICWL/SETE)

作者： Wan, Lin Zhou, Ju Zhang, Bailing Suzhou Aunbox Software Co Ltd Suzhou Peoples R China NingboTech Univ Sch Comp & Data Engn Ningbo Peoples R China

ISBN: (纸本)9783030669058;9783030669065

Layout analysis plays an important role in various document image processing tasks such as OCR and document understanding, and the methods based on deep learning have achieved significant achievements. In recent years, pre-training and transfer learning techniques have become a common practice in a variety of computer vision and natural language processing tasks. In this paper, we present an efficient approach of data synthesis for pretraining deep learning models in document layout analysis. The synthesized data is automatically annotated based on heuristic rules, and then applied to the PubLayNet pre-trained models. The models are fine-tuned with real document layout data. Three types of document elements are taken into account: text lines, tables, and figures/images. The experiments demonstrate that the pre-training model with synthesized data is very effective for transfer learning on different document domains.

关键词： Layout analysis Heuristic rules Automatic annotations data synthesis Fine-tuning

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Fine-Grained Modeling and Optimization for Intelligent Resource Management in Big data processing 48th

Fine-Grained Modeling and Optimization for Intelligent Resou...

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48th international conference on Very Large databases (VLDB)

作者： Lyu, Chenghao Fan, Qi Song, Fei Sinha, Arnab Diao, Yanlei Chen, Wei Ma, Li Feng, Yihui Li, Yaliang Zeng, Kai Zhou, Jingren Univ Massachusetts Amherst MA 01003 USA Ecole Polytech Palaiseau France Alibaba Grp Hangzhou Peoples R China

Big data processing at the production scale presents a highly complex environment for resource optimization (RO), a problem crucial for meeting performance goals and budgetary constraints of analytical users. The RO problem is challenging because it involves a set of decisions (the partition count, placement of parallel instances on machines, and resource allocation to each instance), requires multi-objective optimization (MOO), and is compounded by the scale and complexity of big data systems while having to meet stringent time constraints for scheduling. This paper presents a MaxCompute based integrated system to support multi-objective resource optimization via fine-grained instance-level modeling and optimization. We propose a new architecture that breaks RO into a series of simpler problems, new fine-grained predictive models, and novel optimization methods that exploit these models to make effective instance-level RO decisions well under a second. Evaluation using production workloads shows that our new RO system could reduce 37-72% latency and 43-78% cost at the same time, compared to the current optimizer and scheduler, while running in 0.02-0.23s.

关键词： Big data

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data Integration Landscapes: The Case for Non-optimal Solutions in Network Diffusion models 23rd

Data Integration Landscapes: The Case for Non-optimal Solu...

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23rd international conference on Computational Science, ICCS 2023

作者： Nevin, James Groth, Paul Lees, Michael University of Amsterdam Amsterdam Netherlands

ISBN: (纸本)9783031359941

The successful application of computational models presupposes access to accurate, relevant, and representative datasets. The growth of public data, and the increasing practice of data sharing and reuse, emphasises the importance of data provenance and increases the need for modellers to understand how data processing decisions might impact model output. One key step in the data processing pipeline is that of data integration and entity resolution, where entities are matched across disparate datasets. In this paper, we present a new formulation of data integration in complex networks that incorporates integration uncertainty. We define an approach for understanding how different data integration setups can impact the results of network diffusion models under this uncertainty, allowing one to systematically characterise potential model outputs in order to create an output distribution that provides a more comprehensive picture. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data integration

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Severity Classification of COVID-19 Patients data using Quantum Machine Learning Approaches

Severity Classification of COVID-19 Patients Data using Quan...

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2022 international conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022

作者： Ullah, Ubaid Maheshwari, Danyal Gloyna, Hanna Helene Garcia-Zapirain, Begonya EVIDA Research Group University of Deusto Bilbao Spain

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

Artificial intelligence (AI) is assisting in several aspects of the COVID-19 pandemic, including medical diagnosis and therapy, drug development, molecular research and epidemiology. The involvement of AI in healthcare can help doctors to detect symptoms more quickly. In such era, we use the Quantum Machine Learning (QML) approaches to address clinical applications of machine learning (ML) technology, such as electronic healthcare data and clinical features. This paper presents the two QML algorithms, i.e, Enhanced Quantum Support Vector Machine (E-QSVM) and Quantum Random Forest (QRF) applied to COVID-19 and influenza datasets, which were collected from different private hospitals. The experimental results show that the proposed models outperform in terms of accuracy by achieving the highest accuracy of 78% for the E-QSVM model and 75% for the QRF model respectively. The competency of the models is obtained by comparing them with classical models and recently published quantum models. © 2022 IEEE.

关键词： Coronavirus

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Effective Few-Shot Named Entity Linking by Meta-Learning 38

Effective Few-Shot Named Entity Linking by Meta-Learning

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38th IEEE international conference on data Engineering (ICDE)

作者： Li, Xiuxing Li, Zhenyu Zhang, Zhengyan Liu, Ning Yuan, Haitao Zhang, Wei Liu, Zhiyuan Wang, Jianyong Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China East China Normal Univ Sch Comp Sci & Tech Shanghai Peoples R China Shandong Univ Sch Software Jinan Peoples R China

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

Entity linking aims to link ambiguous mentions to their corresponding entities in a knowledge base, which is significant and fundamental for various downstream applications, e.g., knowledge base completion, question answering, and information extraction. While great efforts have been devoted to this task, most of these studies follow the assumption that large-scale labeled data is available. However, when the labeled data is insufficient for specific domains due to labor-intensive annotation work, the performance of existing algorithms will suffer an intolerable decline. In this paper, we endeavor to solve the problem of few-shot entity linking, which only requires a minimal amount of in-domain labeled data and is more practical in real situations. Specifically, we firstly propose a novel weak supervision strategy to generate non-trivial synthetic entitymention pairs based on mention rewriting. Since the quality of the synthetic data has a critical impact on effective model training, we further design a meta-learning mechanism to assign different weights to each synthetic entity-mention pair automatically. Through this way, we can profoundly exploit rich and precious semantic information to derive a well-trained entity linking model under the few-shot setting. The experiments on real-world datasets show that the proposed method can extensively improve the state-of-the-art few-shot entity linking model and achieve impressive performance when only a small amount of labeled data is available. Moreover, we also demonstrate the outstanding ability of the model's transferability. Our code and models will be open-sourced.

关键词： Training Codes conferences Knowledge based systems Semantics Information retrieval data engineering

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Robust Intelligent System for COVID-19 Detection using CT-Scan

Robust Intelligent System for COVID-19 Detection using CT-Sc...

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2023 international conference Automatics and Informatics, ICAI 2023

作者： Al Smadi, Ahmad Abugabah, Ahed Al-Smadi, Ahmad Mohammad Zarqa University Dep. of Data Science and Artificial Intelligence Zarqa13100 Jordan College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates Al-Balqa Applied University Ajloun University College Department of Computer Science Jordan

ISBN: (纸本)9798350312911

In the beginning of 2020, the world witnessed the rapid spread of the new coronavirus, COVID-19, affecting millions of people globally. However, at the outset, the availability of corona test kits was scarce, leading researchers to explore alternative detection methods. Among these methods, the COVID-19 detection approach using CT-scans emerged, and artificial intelligence (AI)-based solutions proved to offer superior outcomes. Despite the potential of AI-based models, the issue of overfitting arose, significantly impacting model performance. In response to this challenge, we present a coherent and cohesive solution in this paper, utilizing a Convolutional Neural Network (CNN)-based approach for accurate classification of COVID-19 vs. non-COVID cases. To enhance the model's robustness, we incorporated data augmentation and batch normalization techniques for regularization. To evaluate the effectiveness of our proposed model, we conducted experiments with four different data splitting ratios (50%-50;70%-30;75%-25;80%-20) for training and testing. As a result, our suggested model achieved an impressive classification accuracy of 98.56% for distinguishing between COVID-19 and non-COVID cases. These promising results highlight the efficacy of our CNN-based approach with regularization techniques. Furthermore, we conducted a comparative analysis with other deep learning-based algorithms, and our model consistently outperformed them, demonstrating its superiority in COVID-19 detection. By providing such reliable and accurate results, our proposed model contributes significantly to the ongoing efforts in combating the COVID-19 pandemic and holds the potential to aid healthcare professionals in timely and precise diagnosis. © 2023 IEEE.

关键词： COVID-19

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Maintenance 4.0 Model Development for Production Lines in Industry 4.0 Using a Deep Learning Approach and IoT data in Real-Time: an Experimental Case Study 12

Maintenance 4.0 Model Development for Production Lines in In...

引用

12th IEEE international conference on Intelligent data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2023

作者： Bouyahrouzi, El Mahdi Kihel, Ali El Embarki, Soufiane Kihel, Bachir El National School of Applied Sciences Mohammed First University Industrial Engineering Laboratory Oujda6000 Morocco

ISBN: (纸本)9798350358056

The current buzz in research and industry circles revolves around digitalization and digitization. By incorporating new technologies, companies can now collect data in real-time, which can be processed at a later stage. The manufacturing sector, in particular, is showing a keen interest in adopting maintenance 4.0 to enhance energy efficiency, optimize machine availability and utilization rate, extend equipment life, and forecast equipment failures before they occur. Such proactive measures help prevent breakdowns and minimize downtime, thereby improving productivity. This study focuses on a data-driven predictive maintenance system developed to implement effective maintenance 4.0. The system aims to first acquire real-time data from various sensors using an IoT-based system, second to detect failures before they occur, and finally to estimate the remaining useful life (RUL) using artificial neural networks of the LSTM type. Therefore, our methodology facilitates the prediction of defects in rotating machines, enabling timely and preventive interventions to address issues and prevent production interruptions. We conducted a case study evaluation to assess the efficacy of our proposed approach. The outcomes demonstrated the system's proficiency in recognizing failure indicators and its role in anticipating production halts resulting from defects. Moreover, experimental results highlighted its superior accuracy in Remaining Useful Life (RUL) estimation and reduced prediction errors in comparison to various algorithms explored in the literature, including RNN models, XGBoost, and boosting techniques. © 2023 IEEE.

关键词： Artificial Intelligence (AI) Big data Industry 4.0 Internet of Things (IoT) LSTM Maintenenace 4.0 Monitoring Prognosis Real-Time

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FusionFlow: Accelerating data Preprocessing for Machine Learning with CPU-GPU Cooperation 50th

FusionFlow: Accelerating Data Preprocessing for Machine Lear...

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50th international conference on Very Large data Bases, VLDB 2024

作者： Kim, Taeyoon Park, Chanho Mukimbekov, Mansur Hong, Heelim Kim, Minseok Kim, Changdae Shin, Ji-Yong Jeon, Myeongjae UNIST Korea Republic of ETRI Korea Republic of Northeastern University United States

data augmentation enhances the accuracy of DL models by diversifying training samples through a sequence of data transformations. While recent advancements in data augmentation have demonstrated remarkable efficacy, they often rely on computationally expensive and dynamic algorithms. Unfortunately, current system optimizations, primarily designed to leverage CPUs, cannot effectively support these methods due to costs and limited resource availability. To address these issues, we introduce FusionFlow, a system that cooperatively utilizes both CPUs and GPUs to accelerate the data preprocessing stage of DL training that runs the data augmentation algorithm. FusionFlow orchestrates data preprocessing tasks across CPUs and GPUs while minimizing interference with GPU-based model training. In doing so, it effectively mitigates the risk of GPU memory overflow by managing memory allocations of the tasks within the GPU-wide free space. Furthermore, FusionFlow provides a dynamic scheduling strategy for tasks with varying computational demands and reallocates compute resources on the fly to enhance training throughput for both single and multi-GPU DL jobs. Our evaluations show that FusionFlow outperforms existing CPU-based methods by 16-285% in single-machine scenarios and, to achieve similar training speeds, requires 50-60% fewer CPUs compared to utilizing scalable compute resources from external servers. © 2023, VLDB Endowment. All rights reserved.

关键词： Program processors

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SGDE: Secure Generative data Exchange for Cross-Silo Federated Learning 5

SGDE: Secure Generative Data Exchange for Cross-Silo Federat...

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5th international conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Lomurno, Eugenio Archetti, Alberto Cazzella, Lorenzo Samele, Stefano Di Perna, Leonardo Matteucci, Matteo Politecnico di Milano Italy

ISBN: (纸本)9781450396899

Privacy regulation laws, such as GDPR, impose transparency and security as design pillars for data processing algorithms. In this context, federated learning is one of the most influential frameworks for privacy-preserving distributed machine learning, achieving astounding results in many natural language processing and computer vision tasks. Several federated learning frameworks employ differential privacy to prevent private data leakage to unauthorized parties and malicious attackers. Many studies, however, highlight the vulnerabilities of standard federated learning to poisoning and inference, thus raising concerns about potential risks for sensitive data. To address this issue, we present SGDE, a generative data exchange protocol that improves user security and machine learning performance in a cross-silo federation. The core of SGDE is to share data generators with strong differential privacy guarantees trained on private data instead of communicating explicit gradient information. These generators synthesize an arbitrarily large amount of data that retain the distinctive features of private samples but differ substantially. In this work, SGDE is tested in a cross-silo federated network on images and tabular datasets, exploiting beta-variational autoencoders as data generators. From the results, the inclusion of SGDE turns out to improve task accuracy and fairness, as well as resilience to the most influential attacks on federated learning. © 2022 ACM.

关键词： Electronic data interchange

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FederatedMesh: Collaborative Federated Learning for Medical data Sharing in Mesh Networks 19th

FederatedMesh: Collaborative Federated Learning for Medical...

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19th EAI international conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Shkurti, Lamir Selimi, Mennan Besimi, Adrian Faculty of Contemporary Sciences and Technologies South East European University Tetovo Macedonia Max van der Stoel Institute South East European University Tetovo Macedonia

ISBN: (纸本)9783031545306

Edge computing is a paradigm that involves performing local processing on lightweight devices at the edge of networks to improve response times and reduce bandwidth consumption. While machine learning (ML) models can run on smaller computing devices at the edge, training ML models presents challenges for low-capacity devices. This paper aimed to evaluate the performance of Federated Learning (FL) - a distributed ML framework, when training a medical dataset using Raspberry Pi devices as client nodes. The testing accuracy, CPU usage, RAM memory usage and network performance were measured for different number of clients and epochs. The results showed that increasing the number of devices generally improved the testing accuracy, with the greatest improvement observed in the earlier epochs. However, increasing the number of devices also increased the CPU usage, with a significant increase observed in the later epochs. Additionally, the RAM memory usage increased slightly as the number of clients and epochs increased. The findings suggest that FL can be an effective way to train medical models using distributed devices, but careful consideration must be given to the trade-off between accuracy and computational resources. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Edge computing

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