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END-TO-END LEARNING OF VARIATIONAL models AND SOLVERS FOR THE RESOLUTION OF INTERPOLATION PROBLEMS

END-TO-END LEARNING OF VARIATIONAL MODELS AND SOLVERS FOR TH...

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IEEE international conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Fablet, R. Drumetz, L. Rousseau, F. IMT Atlantique UMR CNRS Lab STICC Brest France IMT Atlantique UMR INSERM LaTIM Brest France

ISBN: (纸本)9781728176055

Variational models are among the state-of-the-art formulations for the resolution of ill-posed inverse problems. Following recent advances in learning-based variational settings, we investigate the end-to-end learning of variational models, more precisely of the regularization term given some observation model, jointly to the associated solver, so that we can optimize the reconstruction performance. In the proposed end-to-end setting, both the variational cost and the gradient-based solver are stated as neural networks using automatic differentiation for the latter. We consider an application to inverse problems with incomplete datasets (image inpainting and multivariate time series interpolation). We experimentally illustrate that this framework can lead to a significant gain in terms of reconstruction performance, including w.r.t. the direct minimization of the variational formulation derived from the known generative model.

关键词： inverse problems missing data variational models regularization optimizer learning end-to-end

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High-Dimensional Similarity Query processing for data Science 21

High-Dimensional Similarity Query Processing for Data Scienc...

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27th ACM SIGKDD international conference on Knowledge Discovery and data Mining (KDD)

作者： Qin, Jianbin Wang, Wei Xiao, Chuan Zhang, Ying Wang, Yaoshu Shenzhen Univ Shenzhen Guangdong Peoples R China Shenzhen Inst Comp Sci Shenzhen Guangdong Peoples R China Hong Kong Univ Sci & Technol Kowloon Hong Kong Peoples R China Osaka Univ Suita Osaka Japan Nagoya Univ Suita Osaka Japan Univ Technol Sydney Ultimo NSW Australia

ISBN: (纸本)9781450383325

Similarity query (a.k.a. nearest neighbor query) processing has been an active research topic for several decades. It is an essential procedure in a wide range of applications (e.g., classification & regression, deduplication, image retrieval, and recommender systems). Recently, representation learning and auto-encoding methods as well as pre-trained models have gained popularity. They basically deal with dense high-dimensional data, and this trend brings new opportunities and challenges to similarity query processing. Meanwhile, new techniques have emerged to tackle this long-standing problem theoretically and empirically. This tutorial aims to provide a comprehensive review of high-dimensional similarity query processing for data science. It introduces solutions from a variety of research communities, including data mining (DM), database (DB), machine learning (ML), computer vision (CV), natural language processing (NLP), and theoretical computer science (TCS), thereby highlighting the interplay between modern computer science and artificial intelligence technologies. We first discuss the importance of high-dimensional similarity query processing in data science applications, and then review query processing algorithms such as cover tree, locality sensitive hashing, product quantization, proximity graphs, as well as recent advancements such as learned indexes. We analyze their strengths and weaknesses and discuss the selection of algorithms in various application scenarios. Moreover, we consider the selectivity estimation of high-dimensional similarity queries, and show how researchers are bringing in state-of-the-art ML techniques to address this problem. We expect that this tutorial will provide an impetus towards new technologies for data science.

关键词： similarity query processing nearest neighbor similarity search high-dimensional data

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A Framework for a data Quality Module in Decision Support Systems: An Application with Smart Grid Time Series 25

A Framework for a Data Quality Module in Decision Support Sy...

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25th international conference on Enterprise Information Systems, ICEIS 2023

作者： Rinaldi, Giulia Garcia, Fernando Crema Agudelo, Oscar Mauricio Becker, Thijs Vanthournout, Koen Mestdagh, Willem De Moor, Bart ESAT Stadius Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Heverlee3001 Belgium Boeretang 200 Mol2400 Belgium AMO EnergyVille Thor Park 8310 Genk3600 Belgium

ISBN: (纸本)9789897586484

data quality (DQ) measures data status based on different dimensions. This broad topic was brought to the fore in the'80s when it was first discussed and studied. A high-quality dataset correlates with good performance in artificial intelligence (AI) algorithms and decision-making processes. Therefore, checking the quality of the data inside a decision support system (DSS) is an essential pre-processing step and is beneficial for improving further analysis. In this paper, a theoretical framework for a DQ module for a DSS is proposed. The framework evaluates the quality status in three stages: as based on the European guidelines, as based on DQ metrics, and as based on checking a subset of data cleaning (DC) problems. Additionally, the framework supports the user in identifying and fixing the DC problems, which speeds up the process. As output, the user receives a DQ report and the DC pipeline to execute to improve the dataset's quality. An implementation of the framework is illustrated in a proof-of-concept (POC) for an industrial use case. In the POC, an example of the execution of the various framework phases was shown using a public time series dataset containing quarter-hourly consumption profiles of residential electricity customers in Belgium for the year 2016. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Decision support systems

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Application of BERT Model for Unsupervised Text Classification using Hierarchical Clustering for Automatic Classification of Thesis Manuscript

Application of BERT Model for Unsupervised Text Classificati...

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Electronics and Sustainable Communication Systems (ICESC), 2020 international conference on

作者： Cristian D. Montes Junisare V. Silvosa Cristopher C. Abalorio Regien Bacabis Nakazato College of Computing and Information Sciences Caraga State University Ampayon Butuan City Philippines

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Many current studies on natural language processing (NLP) depend on supervised learning, which needs a lot of labeled data. This is not practical for large classification tasks. This research presents an unsupervised approach to automatically classify unlabeled theses using a BERT-hierarchical model. This technique combines BERT, an open-source machine-learning tool for NLP, with divisive hierarchical clustering utilizing the K-means clustering algorithm. Standard metrics evaluated the model's performance. Fine-tuning the BERT-hierarchical model with a learning rate of 0.00001 gave the best results, achieving 93% accuracy, 91% precision, and 93% recall. Additionally, a web application was created to facilitate model testing, offering functions for data creation, editing, and deletion. This study shows the BERT-hierarchical model's effectiveness in classifying unlabeled data, providing a scalable solution for large NLP tasks.

关键词： Measurement Accuracy Computational modeling Text categorization Supervised learning Market research Natural language processing data models Standards Testing

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19th international conference on Mathematical Modeling and Simulation of Systems, MODS 2024

19th International Conference on Mathematical Modeling and S...

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19th international conference on Mathematical Modeling and Simulation of Systems, MODS 2024

ISBN: (纸本)9783031907340

The proceedings contain 34 papers. The special focus in this conference is on Mathematical Modeling and Simulation of Systems. The topics include: System Modeling of a Multicomponent Differential Signal of Stripping Chronopotentiometry;fuzzy Control Model of Gas-Stirring Fluid in Rectangular Reservoir;traffic Equilibrium Modeling with Modern algorithms for Variational Inequalities;modelling System for Decision Support of Epidemic Diseases in Ukraine;Modeling of COVID-19 Excess Mortality in Ukraine for 2020–2021 Based on Daily and Age-Gender data;model of Viability and Efficiency Regulation for Bradyrhizobium Japonicum into Soya Agrocenose;modeling the Baking Processes of Bakery Products to Improve Product Quality;modeling the Free Transverse Vibrations of a Structure Consisting of Two Beams Elastically Coupled by Double Mass-Spring Systems on a Winkler-Pasternak Foundation;system Model Construction for Describing the Physical and Mechanical Properties of a Casting;Technology of Moving a Geometric Model Between CAD Packages Using Macros Tools;calculation Accuracy Evaluation of Reinforced Concrete Frames Under Static Loading Using Deformation models;modeling of Collaboration Portals in Microsoft 365;simulation of Multi-route City Passenger Тransportation with Genetic Algorithm;agent-Based Modelling of Information Dissemination Processes in Social Networks;secondary Software Faults Detection models;feature Search Strategy for Wrapper Techniques to Prevent Overfitting in Classification models;vulnerability Modeling in Сybersecurity of Intelligent Infrastructure Networks;geoinformation Modeling of Traffic Accident Concentration Areas in a Settlement;simulation of Strategies for Providing Information Security of the Transport and Logistics Center Based on Fuzzy Logic Methods;stochastic Modeling of Path Length Dynamics in Ethereum’s Merkle Patricia Tries.

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The Impact of Sample Size after Sampling on the Accuracy of Machine Learning models

The Impact of Sample Size after Sampling on the Accuracy of ...

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Computers, Information processing and Advanced Education (CIPAE), 2020 international conference on

作者： Siyu Chen Jinwen Zheng Jinying Li Fujian Polytechnic Normal University Fuqing Fujian China Minjiang University Fuzhou Fujian China Beijing Normal University Beijing China

ISBN: (数字)9798331527662

ISBN: (纸本)9798331527679

Sampling techniques are a commonly used method in the field of statistics and machine learning for selecting a portion of a sample from a totality for analysis. The main purpose of sampling techniques in this paper is to deal with imbalanced data in the classification models. In machine learning when the target variable, i.e., positive and negative samples are unbalanced, direct modeling will result in poor model learning ability on a few classes of samples, so sampling is usually required. Based on this research status of existing tools for machine learning such as under-sampling, over-sampling, mixed sampling, etc., this paper will study from three perspectives. First, the effect of different sample sizes through sampling on the model accuracy of the following modeling is explored, and it is found that increasing the sample size may improve the prediction effect of the model. Second, by changing the proportion of positive and negative samples to investigate the changes on the correctness of the model, and found that different positive and negative sample proportions will have an impact on the prediction effect of the model, increasing the proportion of positive samples may improve the accuracy of the model, and balancing the proportion of positive and negative samples may be more conducive to the performance of the model. Thirdly, by exploring the effects of different random seeds values on the model, it is concluded that different random number seeds can make impacts on the stability and generalization ability of the model. The research on this topic can effectively fill the gap in the field of machine learning sampling, and provide some inspirations for determining sample size after sampling as well as the proportion of positive and negative samples.

关键词： Technological innovation Accuracy Computational modeling Machine learning Information processing Predictive models Sampling methods data models Stability analysis Reliability

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Group Correspondence: A Statistical Perspective for Incomplete Multi-View Clustering Augmentation

Group Correspondence: A Statistical Perspective for Incomple...

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2022 IEEE international conference on Multimedia and Expo, ICME 2022

作者： Liang, Tianyou Meng, Min Lan, Mengcheng Yu, Jun Wu, Jigang Guangdong University of Technology School of Computer China Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen China School of Computer Science and Technology Hangzhou Dianzi University China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Cross-view consistency is the fundamental property of multiview clustering. However, in incomplete multi-view scenarios, existing methods can only pursue consistency through the paired data while ignoring the information in unpaired data. In this paper, we show a new insight from the data pattern and provide a novel perspective to incorporate unpaired data for consistency maximization by mining group correspondence. We first formulate cross-view consistency in a statistical perspective to by-pass the strict demand of instance correspondence, and then propose a technique to construct corresponding groups across views to enhance the objective of consistency maximization. Our proposal can be used as a universal plug-in to augment existing approaches. We test the efficacy and generality of our proposal by adapting it to two base methods as augmentations and comparing the augmented models against the original ones and other baselines. Experiment results demonstrate the effectiveness of our proposal and validate the value of our insight. © 2022 IEEE.

关键词： data mining

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Exploring the Performance of Traditional Word Embeddings and Contextual Embedding models for COVID-19 Sentiment Analysis

Exploring the Performance of Traditional Word Embeddings a...

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international conference on Advanced Intelligent Systems for Sustainable Development, AI2SD 2022

作者： Bensalah, Nouhaila Ayad, Habib Adib, Abdellah Farouk, Abdelhamid Ibn El Data Science and Artificial Intelligence Hassan II University of Casablanca Casablanca20000 Morocco Teaching Languages and Cultures Laboratory Mohammedia University of Hassan II Casablanca Casablanca Morocco

ISBN: (纸本)9783031352478

Sentiment Analysis (SA) is the act of classifying people’s feelings. As the Covid-19 pandemic spreads fast over the world, claiming the lives of millions, a number of countries proclaimed a state of emergency in order to contain its spread. During this period of lockdown, social media platforms played an essential role in disseminating information about the epidemic throughout the world, as people utilized social media to express their emotions. This study makes use of a Twitter dataset including 41157 COVID-19-related tweets. Furthermore, the incorporation of contextual word embeddings - representations of words that integrate semantic and syntactic information from their context - has resulted in significant gains in a wide range of Natural Language processing (NLP) tasks. Recent contextual models, on the other hand, have excessively high computing costs in many use-cases and are frequently difficult to analyze. In this study, we hypothesize that the performance of contextual word embeddings may be enhanced by integrating static word vectors particularly trained on social media. We demonstrate that an attention-based strategy for combining such word vectors is effective on social media reviews (tweets about COVID-19). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

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Flow-based situation-aware approach for eHealth data processing 22

Flow-based situation-aware approach for eHealth data process...

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22nd IEEE international conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Celestrini, Jordano R. Baldi, Alessandro M. Andreao, Rodrigo V. Filho, Jose G. P. Santos, Celso A. S. Federal University of Espírito Santo Vitória-ES Brazil Federal Institute of Espirito Santo Vitória-ES Brazil

ISBN: (纸本)9781728162676

A major challenge for remote patient monitoring (RPM) systems is processing data collected from a large number of healthcare devices and developing suitable algorithms and approaches to react accordingly to a wide spectrum of situations of interest, which must be properly detected. Flow-based programming, whose operation is based on state changes, is a promising approach to overcome this challenge, facilitating the personalization of data processing according to patients' health conditions. However, this approach is not suited to the detection of complex contextual situations, thereby hindering its adoption in RPM systems for data processing. In this regard, we propose an approach that combines situation awareness and flow-based programming to widen the capability of RPM systems to handle many-sided scenarios, which requires the monitoring of complex patient healthcare conditions. An evaluation of the proposed approach was conducted to demonstrate the flexibility of the solution for processing heterogeneous health information. © 2021 IEEE.

关键词： Internet of things

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Text Mining data Based Summarization of Novel Approach for Covid-19 with the Use of Machine Learning Techniques

Text Mining Data Based Summarization of Novel Approach for C...

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Computing, Power and Communication Technologies (IC2PCT), IEEE international conference on

作者： Chataparti Suvarna Lakshmi Sameer Saxena Billakurthi Suresh Kumar Amity Institute of Information Technology Amity University Jaipur Rajasthan India

At the start of the year 2020, the World Health Organization (WHO) officially recognized COVID-19 as a novel pandemic and subsequently released a corresponding declaration. The aforementioned lethal virus had the capacity to disseminate and proliferate across many countries worldwide. Throughout the duration of the pandemic, social media platforms like as Twitter played a pivotal role in generating considerable amounts of data that contributed to enhancing the efficacy of healthcare-related decision-making processes. Consequently, we propose that the viewpoint articulated by users might be examined by using effective Supervised Machine Learning (SML) algorithms for the purpose of predicting the onset of ailments and providing timely alerts. This study presents a text mining classifier that use machine learning approaches to create summary text. Upon gathering the tweets, we proceeded to prepare them for preprocessing and assign class labels to each instance, including categories such as right, wrong, and neutral, among others. During the second step, a variety of commonly used techniques, including TF-IDF, co-relational analysis, Natural Language processing (NLP), and relational dependency analysis, are utilized to extract many features from the text. These features are then used to construct the feature vector. For the classification module, we used a binary classification method together with five machine learning algorithms to assess the effectiveness of the suggested model. The NLP-SVM model has a classification accuracy of 95.10%, while the TFIDF-SVM model achieves a classification accuracy of 93.50%. This finding provides evidence that the suggested model is efficacious in the categorization of extensive textual data pertaining to COVID-19, namely on twitter data.

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