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IEEE International Conference on Big Data

作者： Jeovane Honorio Alves Radu State Cinthia Obladen de Almendra Freitas Jean Paul Barddal SEDAN-SnT University of Luxembourg Graduate Program in Law (PPGD) Pontifícia Universidade Católica do Paraná Graduate Program in Informatics (PPGIa) Pontifícia Universidade Católica do Paraná

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

In an era of information overload, manually annotating the vast and growing corpus of documents and scholarly papers is increasingly impractical. Automated keyphrase extraction addresses this challenge by identifying representative terms within texts. However, most existing methods focus on short documents (up to 512 tokens), leaving a gap in processing long-context documents. In this paper, we introduce LongKey, a novel framework for extracting keyphrases from lengthy documents, which uses an encoder-based language model to capture extended text intricacies. LongKey uses a max-pooling embedder to enhance keyphrase candidate representation. Validated on the comprehensive LDKP datasets and six diverse, unseen datasets, LongKey consistently outperforms existing unsupervised and language model-based keyphrase extraction methods. Our findings demonstrate LongKey’s versatility and superior performance, marking an advancement in keyphrase extraction for varied text lengths and domains.

关键词： Training Bridges Navigation Computer architecture Benchmark testing Writing Robustness Data mining Indexing Context modeling

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Finding the Important Elements Linked to Integration Problems in the Use of Enterprise Resource Planning Systems 9

Finding the Important Elements Linked to Integration Problem...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Sardjono, Wahyu Kristin, Desi Maya Pranata, Dewi Sagita Bina Nusantara University BINUS Graduate Program Master of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia King's College London Cyber Security Studi Program Faculty of Natural Mathematical & Engineering Sciences Department of Informatics WC2R 2LS United Kingdom

ISBN: (纸本)9798350390025

Organizations face an urgent need to become faster, more efficient, and more successful in their commercial operations, given the current state of technological evolution and the arrival of Industry 4.0. Many businesses have adopted the idea of enterprise resource planning (ERP) systems to achieve this goal. The ERP system is expected to increase worker productivity, improve business performance to maximize profitability and profits, and provide accurate and up-to-date data and information to management and stakeholders. This research aims to find new variables related to integration problems in the use of ERP systems. Using the SPSS statistical program, the data collected will be examined using quantitative exploratory factor analysis (EFA). Data processing then includes collecting demographic information from respondents, reliability testing, conducting the Kaiser-Meyer-Olkin (KMO) test, testing using an anti-image matrix, and deriving new factors using a rotation component matrix. This research seeks to identify new elements underlying ongoing integration problems in ERP system implementation. The research results show several reasons for underlying integration problems and offer in-depth information to management that can be used to analyze and evaluate the ERP system in their company appropriately in the future so that the company's performance becomes superior to the competition. © 2024 IEEE.

关键词： Profitability

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CONVERGENCE ANALYSIS OF A SCHRÖDINGER PROBLEM WITH MOVING BOUNDARY

arXiv

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arXiv 2024年

作者： Alfaro Vigo, Daniel G. Gomes, Daniele C.R. Carmo, Bruno A.D.O. Rincon, Mauro A. Institute of Computing Graduate Program in Informatics Federal University of Rio de Janeiro Caixa Postal 68530 RJ Rio de JaneiroCEP 21941-590 Brazil Graduate Program in Informatics Federal University of Rio de Janeiro Caixa Postal 68530 RJ Rio de JaneiroCEP 21941-590 Brazil

In this article, we present the mathematical analysis of the convergence of the linearized Crank-Nicolson Galerkin method for a nonlinear Schrödinger problem related to a domain with a moving boundary. The convergence analysis of the numerical method is carried out for both semi-discrete and fully discrete problems. An optimal error estimate in the L2-norm with order O(τ2 + hs), 2 ≤ s ≤ r, where h is the finite element mesh size parameter, τ is the time step, and r−1 represents the degree of the finite element polynomial basis. Numerical simulations are provided to confirm the consistency between theoretical and numerical results, validating the method and the order of convergence for different degrees p ≥ 1 of the Lagrange polynomials and also for Hermite polynomials (degree p = 3), which form the basis of the approximate solution. © 2024, CC BY-NC-ND.

关键词： Schrodinger equation

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The Recommendation System for Increasing the Independence of Micro, Small and Medium Enterprises (MSMEs) Using the Normalized Rating Frequency (NRF) Method 4

The Recommendation System for Increasing the Independence of...

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4th International Conference on Information and Communications Technology, ICOIACT 2021

作者： Lestari, Sri Yulmaini Aswin Kurniawan, Rio Sulyono Pratama, Yan Aditiya Faculty of Computer Science Informatics Engineering Study Program Bandar Lampung Indonesia Faculty of Business Economics Management Study Program Bandar Lampung Indonesia

ISBN: (纸本)9781665433945

Micro, Small, and Medium Enterprises (MSMEs) have an important role in improving the economy of small communities and the stability of the Indonesian economy. However, they still face various problems such as capital, raw materials, distribution, licensing, and others. This study developed a special treatment recommendation system to increase the independence of MSMEs by adopting a ranking-based method to produce recommendations related to problem-solving steps from MSMEs. The ranking method used Normalized Rating Frequency (NRF). This method performed an aggregation process of the ratings given by MSMEs in looking at the priority level of problem-solving based on previous experience. The result of the experiment showed that an average NDCG value was 0.7916. This explained that the quality of the ranking was good so that it was feasible to be recommended in solving problems in the form of special treatment to acquire to the independence of MSMEs. © 2021 IEEE

关键词： Recommender systems

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Model-Based Insulin Sensitivity and Beta-Cell Function Estimation from Daily Continuous Glucose Monitoring

Model-Based Insulin Sensitivity and Beta-Cell Function Estim...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Elizabeth Healey Isaac Kohane Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Type 2 diabetes (T2D) is a prevalent chronic illness with many different options for treatment management. Continuous glucose monitors (CGM) offer physiological data that clinicians can access when making treatment decisions. However, the utility of CGM in management of T2D remains an active area of research. In our work, we demonstrate the feasibility of exploiting raw daily CGM data to estimate the physiological parameters of insulin sensitivity and beta-cell function that correlate with estimates derived from laboratory findings. We use a peak extraction algorithm to extract peaks from daily CGM data and implement a model-based approach to infer physiological parameters. We demonstrate that the inferred parameter estimates of insulin sensitivity and beta-cell function correlate to the ground truth measurements as determined by an oral glucose tolerance test (OGTT).

关键词： Sensitivity Biological system modeling Estimation Data models Diabetes Data mining Insulin Engineering in medicine and biology

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Eye Disease Detection Model Using Convolutional Neural Network with RMSprop, SGD, and Adam Optimizers

Eye Disease Detection Model Using Convolutional Neural Netwo...

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International Conference on ICT For Smart Society (ICISS)

作者： Dede Kurniadi Asri Mulyani Moch Riefky Rafliana Suwandy Department of Computer Science Institut Teknologi Garut Kabupaten Garut Indonesia Informatics Study Program Institut Teknologi Garut Kabupaten Garut Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

Eye disorders such as cataracts, glaucoma, and diabetic retinopathy can cause abnormalities in eye function, including blindness. This research aims to develop a machine-learning model and find the best model for detecting eye diseases. Deep Learning methods based on the Convolutional Neural Network (CNN) model will be used to create eye disease detection models by adding the RMSProp, SGD, and Adam optimizers techniques. The dataset used consists of 4 classes, namely normal (1074 data), glaucoma (1007 data), cataract (1038 data), and diabetic retinopathy (1098 data), totaling 4217 fundus images. The evaluation results reveal the CNN model with the RMSProp optimizer has better model performance with a value of 88% accuracy, 88% precision, 87% recall, and 88% f1-score, and based on the AUC value on the ROC-AUC curve for each class is above 0.90, which means that the model can differentiate data between classes well and is included in the Excellent classification category.

关键词： Glaucoma Cataracts Training Deep learning Diabetic retinopathy Accuracy Blindness Data models Convolutional neural networks

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Adaptive Linear Regression for Data Stream

Adaptive Linear Regression for Data Stream

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International Joint Conference on Neural Networks (IJCNN)

作者： Aldo Marcelo Paim Fabrício Enembreck Graduate Program in Informatics (PPGIa) Pontifícia Universidade Católica do Paraná (PUCPR) Curitiba Brazil

The approaches that currently constitute the state-of-the-art for the task of regression on continuous data streams usually involve ensembles, regression trees, and regression rules. They have been found to work very well for certain situations but generally consume computational resources to a prohibitive extent. In this paper, we propose a new method based on an ensemble of linear regressions for the regression task adapted to handle continuous data streams. The technique has been named Adaptive Linear Regression (ALR). The algorithm combines strategies that contribute to high prediction accuracy using (i) distinct sliding window sizes for training each ensemble element, and (ii) a dynamic regressor selection method for final ensemble voting. After an extensive experimental study, ALR was found to exhibit high predictive performance and outperform state-of-the-art ensemble regressors on data streams for real and synthetic datasets. Moreover, it exhibits low processing time in its parallel version and is faster than ARF-Reg in its serial version. The paper also presents an analysis of how the choice of sliding window size for training favors accuracy.

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Sentiment Analysis of Cooking Oil Price Policy in Indonesia using Word2Vec Feature Extraction and Bidirectional Long Short Term Memory

Sentiment Analysis of Cooking Oil Price Policy in Indonesia ...

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International Conference of Advanced informatics: Concept, Theory and Application (ICAICTA)

作者： Dedy Sugiarto Ema Utami Ainul Yaqin Suwanto Raharjo Faculty of Industrial Technology Universitas Trisakti DKI Jakarta Indonesia Master Program of Informatics Engineering Universitas Amikom Yogyakarta Yogyakarta Indonesia Informatics IST AKPRIND Yogyakarta Yogyakarta Indonesia

The policy of the Ministry of Trade of the Republic of Indonesia which sets the highest retail price for cooking oil and then cancels it in early 2022 due to people's complaints about the difficulty of getting cooking oil in the market has received many responses from the public via social media. This study conducts sentiment analysis using Word2Vec and Bidirectional Long Short Term Memory (BLSTM) with the aim of studying the public's response to the removal of the highest retail price policy and effect of both skip-gram (SG) and continuous bag-of-words (CBOW), window size and dimension to the word similarity and accuracy. Data taken via Twitter in March 2022. The data then goes through preprocessing stages, manual labeling by experts, and word embedding using Word2Vec to be further classified using BLSTM. Data labeling produces imbalanced data where the largest is neutral sentiment (53%) followed by negative (27%) and positive (20%). ome of the words such as ‘mafia’, ‘mahal’, ‘langka’, ‘tangkap’, ‘bongkar’, and ‘mendag’ could become the government's attention. The similarity value of the SG model tends to have a higher value than CBOW at a lower word vector dimension. The model that gives the best performance results is the Word2Vec model with 50 dimensions, SG architecture and window size 5. This is shown in the accuracy of 0.71 which is the highest value and only shows slight signs of overfitting. Symptoms of over fitting will be more visible with increasing dimensions.

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Poster: Automatic Compliance Analysis on Clinical Notes and Lifestyle Guidelines in Cancer Survivorship

Poster: Automatic Compliance Analysis on Clinical Notes and ...

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IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)

作者： Yujia Hou Javed Mostafa Carolina Health Informatics Program University of North Carolina at Chapel Hill Chapel Hill NC United States

Maintaining a healthy lifestyle has been proven to have significant benefits in cancer survivorship. It is expected that clinicians have a comprehensive understanding of publicly published cancer lifestyle guidelines and can effectively convey the information to patients. The objective of this paper is to develop an automatic text analysis method to assess the compliance of lifestyle information provided during medical visits and publicly available guidelines. Preliminary results show that selected lifestyle keywords appear an average of 3.54 times per medical visit, and 7% of medical notes pertain to patients' lifestyle. Semantic analysis and word dictionary will be applied to evaluate the extent of information compliance and inform strategies for improving lifestyle recommendations for cancer survivors.

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Development of attitude assessment instrument in engineering mathematics 1 course to assess discussion on MOOC platform 7

Development of attitude assessment instrument in engineering...

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7th International Conference on Mathematics, Science, and Education 2020, ICMSE 2020

作者： Yuniwati, I. Yustita, A.D. Hardiyanti, S.A. Suardinata, I.W. Mechanical Engineering Program Mechanical Engineering Department Politeknik Negeri Banyuwangi East Java Banyuwangi Indonesia Management Business of Tourism Program Informatics Engineering Department Politeknik Negeri Banyuwangi East Java Banyuwangi Indonesia Civil Engineering Program Civil Engineering Department Politeknik Negeri Banyuwangi Kabat East Java Banyuwangi Indonesia Informatics Enggineering Program Informatics Engineering Department Politeknik Negeri Banyuwangi East Java Banyuwangi Indonesia

Affective assessment instruments have been developed, especially those related to attitudes. In engineering mathematics learning 1 through the MOOC platform, it was carried out with discussion. The development of attitude instrument assessment to assess student activity was carried out after learning was complete. This development research used the Plomp Model. The Plomp Model consisted of 5 stages. They were the initial investigative stages, design stage;construction stage;the test, evaluation, revision stages;and implementation stages. Two experts and trials carried out validation were carried out on 100 students. The result of developing an attitude assessment instrument was a questionnaire. The difference from other questionnaires lies in paying attention to the internal continuum interval of attitudes developed by Bloom's Taxonomy. They were Responding, Valuing, and Organization. Afterward, The instrument is tested for reliability and validity until it is declared to validate each item and the instrument's reliability in the excellent category. The value of Cronbach's Alpha instrument is 0.848. The value indicates that the attitude assessment instrument is classified as having good criteria. Then the instrument is implemented in discussion on the MOOC Platform. © Published under licence by IOP Publishing Ltd.

关键词： Surveys

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