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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Dalzochio, Jovani Kunst, Rafael Barbosa, Jorge Pignaton De Freitas, Edison Binotto, Alecio University of Vale Do Rio Dos Sinos Graduate Program in Applied Computing São Leopoldo Brazil Institute of Informatics Federal University of Rio Grande Do sui Porto Alegre Brazil Ibm Consulting Asset and Edge Optimization Munich Germany

ISBN: (纸本)9798350345346

Equipment monitoring for failure prediction is receiving attention from different sectors of society, such as industry, healthcare, and defense. In the defense domain, assets like military vehicles generate data that one can use to identify behavior changes and anticipate possible real-time failures, avoiding unnecessary maintenance interventions. Failure anticipation is crucial, as assets operated in the military domain perform critical tasks in which unexpected equipment failures result in high material and human costs. Approaches found in the literature typically deal with failure generation, aiming at analyzing the equipment's behavior. This paper proposes a broader approach called MILPdM. This proposal is a failure prediction architecture covering the whole failure prediction and predictive maintenance procedures. We evaluate MILPdM architecture by analyzing an engine-failure scenario where we train models to predict time series by collecting vibration data that describes the degradation of the vehicle's health. Considering the implementation of an LSTM-based neural network and Random Forest, the acquired results lead to a root mean square error of 0.15015 in the best case, which allows to predict the failure status two minutes in advance with only 3 hours of data history. This result shows that MILPdM is capable of anticipating failures with high assertiveness. © 2023 IEEE.

关键词： IoBT machine learning neural networks predictive maintenance random forest

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PrimaDNN’:A Characteristics-Aware DNN Customization for Singing Technique Detection

PrimaDNN’:A Characteristics-Aware DNN Customization for Sin...

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European Signal Processing Conference (EUSIPCO)

作者： Yuya Yamamoto Juhan Nam Hiroko Terasawa Doctoral Program in Informatics University of Tsukuba Ibaraki Japan Graduate School of Culture Technology KAIST Daejeon South Korea

Professional vocalists modulate their voice timbre or pitch to make their vocal performance more expressive. Such fluctuations are called singing techniques. Automatic detection of singing techniques from audio tracks can be beneficial to understand how each singer expresses the performance, yet it can also be difficult due to the wide variety of the singing techniques. A deep neural network (DNN) model can handle such variety; however, there might be a possibility that considering the characteristics of the data improves the performance of singing technique detection. In this paper, we propose PrimaDNN, a CRNN model with a characteristics-oriented improvement. The features of the model are: 1) input feature representation based on auxiliary pitch information and multi-resolution mel spectrograms, 2) Convolution module based on the Squeeze-and-excitation (SENet) and the Instance normalization. In the results of J-POP singing technique detection, PrimaDNN achieved the best results of 44.9% at the overall macro-F measure, compared to conventional works. We also found that the contribution of each component varies depending on the type of singing technique.

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Evaluating ChatGPT-4 Vision on Brazil’s National Undergraduate Computer Science Exam

arXiv

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arXiv 2024年

作者： Mendonça, Nabor C. Post-Graduate Program in Applied Informatics University of Fortaleza Av. Washington Soares 1321 Edson Queiroz Ceará Fortaleza60811-905 Brazil

The recent integration of visual capabilities into Large Language Models (LLMs) has the potential to play a pivotal role in science and technology education, where visual elements such as diagrams, charts, and tables are commonly used to improve the learning experience. This study investigates the performance of ChatGPT-4 Vision, OpenAI’s most advanced visual model at the time the study was conducted, on the Bachelor in Computer Science section of Brazil’s 2021 National Undergraduate Exam (ENADE). By presenting the model with the exam’s open and multiple-choice questions in their original image format and allowing for reassessment in response to differing answer keys, we were able to evaluate the model’s reasoning and self-reflecting capabilities in a large-scale academic assessment involving textual and visual content. ChatGPT-4 Vision significantly outperformed the average exam participant, positioning itself within the top 10 best score percentile. While it excelled in questions that incorporated visual elements, it also encountered challenges with question interpretation, logical reasoning, and visual acuity. A positive correlation between the model’s performance in multiple-choice questions and the performance distribution of the human participants suggests multimodal LLMs can provide a useful tool for question testing and refinement. However, the involvement of an independent expert panel to review cases of disagreement between the model and the answer key revealed some poorly constructed questions containing vague or ambiguous statements, calling attention to the critical need for improved question design in future exams. Our findings suggest that while ChatGPT-4 Vision shows promise in multimodal academic evaluations, human oversight remains crucial for verifying the model’s accuracy and ensuring the fairness of high-stakes educational exams. The paper’s research materials are publicly available at https://***/nabormendonca/gpt-4v-enade-cs-2021. © 202

关键词： Visual languages

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Backcasting Perspectives on Services of Future Quantum Internet

Backcasting Perspectives on Services of Future Quantum Inter...

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Quantum Software (QSW), IEEE International Conference on

作者： Shota Nagayama Toshihiko Sasaki Akihito Soeda Mercari R4D Mercari Inc Tokyo Japan Graduate School of Media and Governance Keio University Fujisawa Japan Department of Applied Physics Graduate School of Engineering The University of Tokyo Tokyo Japan Principles of Informatics Research Division National Institute of Informatics Tokyo Japan Informatics Program SOKENDAI (The Graduate University for Advanced Studies) Tokyo Japan Department of Physics Graduate School of Science The University of Tokyo Tokyo Japan

ISBN: (数字)9798350368475

ISBN: (纸本)9798350368482

A quantum internet is an emerging quantum information technology, possibly becoming a pinnacle of wide-area distributed system allowed under the laws of physics. The current expansion of quantum technologies would lead to innumerable types of quantum systems, composed of various kinds of quantum devices connected by a dedicated quantum network. However, the (inter)connectivity offered by a quantum internet should ideally be agnostic of the technologies used by each component system. Furthermore, concurrent progresses of the quantum technologies complicate the task of defining what a quantum internet is. In this paper, we adopt a backcasting approach to elucidate what a quantum internet should be. We begin by discussing what we believe to be the requirements of an ultimate distributed quantum system, from which we explain what role a quantum internet would play in achieving this ultimate system. We then lead to more practical requirements on a quantum internet, considering the known and expected constraints in designing a quantum internet. We also organize and categorize the relationship among known quantum network technologies and services relevant to a quantum internet. We hope this document to lay a common ground for all the key stakeholders of a quantum internet, including researchers, industrial enterprises, and policy makers and regulators.

关键词： Quantum system Protocols Quantum mechanics Transportation Logic gates Internet Quantum networks

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An Exploratory Study to Assess the Usability of a Groupware with Multi-Agent Systems

An Exploratory Study to Assess the Usability of a Groupware ...

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Frontiers in Education (FIE) Conference

作者： Joethe Moraes de Carvalho José Francisco de Magalhães Netto Post-Graduate Program in Informatics (PPGI) Institute of Computing (ICOMP) Federal University of Amazonas (UFAM) CMC-Federal Institute of Amazonas (IFAM) Manaus AM Brazil Post-Graduate Program in Informatics (PPGI) Institute of Computing (ICOMP) Federal University of Amazonas (UFAM) Manaus AM Brazil

This Research Full Paper presents a pedagogical approach to evaluate a virtual platform for teaching robotics at a distance. The virtual environment was developed using the principles of Collaborative Learning. This virtual platform, called groupware, should facilitate interaction between those involved in the teaching and learning process, whether the student or the teacher. In this research, we will use Educational Robotics as a mediating tool in the teaching and learning process, because it has become a powerful tool that has stimulated the acquisition of knowledge in computing. In the groupware used in this research, Multi-Agent Systems were used to collaborate with teachers in the management of activities. The purpose of this study was to evaluate the functionality and usability of this virtual environment. A qualitative, exploratory research was used and to assess the usability of this virtual environment, the usability evaluation techniques were used with the Syetem Usabily Scale metric, and the technique of evaluation of feelings through EmoCards. Ten computer teachers participated in this research, with training and specializations in the areas of computing. Collaborative Learning was used as a focus for solving the tasks proposed for this research. The results showed that the participants evaluated the groupware with great potential to collaborate in robotics teaching and with content appropriate to the students' initial level of learning. The teachers' perceptions regarding the usability of the evaluated groupware revealed that it is favorable in the teaching of robotics and allowed an analysis of the importance of collaborative learning in the educational environment, directly impacting their ways of conducting learning.

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Event-driven Sentiment Drift Analysis in Text Streams: An Application in a Soccer Match

Event-driven Sentiment Drift Analysis in Text Streams: An Ap...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Cristiano Mesquita Garcia Alceu de Souza Britto Jean Paul Barddal Graduate Program in Informatics (PPGIa) Pontifícia Universidade Católica do Paraná (PUCPR) Instituto Federal de Santa Catarina - Caçador Curitiba Brazil Graduate Program in Informatics (PPGIa) Pontifícia Universidade Católica do Paraná (PUCPR) Curitiba Brazil

Social media has been a data source for various applications, given its characteristic of working as a social sensor. Many applications in several areas, such as brand reputation and online opinion monitoring, use this valuable resource to understand the users of services and products. This paper describes an application in the soccer domain, considering data collected from a social media textual data stream. The goal is to detect possible sentiment drifts related to actual events in a soccer match. This task is challenging as we resort to short texts made available during a short time (match length). We evaluated four drift detectors using four metrics: false alarms, delay (considering the number of posts), delay, and missing drifts. Our results show that ADWIN had a stable performance in sentiment drift detection compared to other methods in timely detecting the flagged drifts, raising a small number of false alarms. Given the drifts detected, we used Incremental Word-Vectors to monitor words of interest and check their relatedness to actual events in the match. We empirically assert that the closest words trace back to the sentiment drift generator events.

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Translational Bioinformatics to Enable Precision Medicine for All: Elevating Equity across Molecular, Clinical, and Digital Realms

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Yearbook of Medical informatics 2022年第1期31卷 106-115页

作者： Tang, Alice Woldemariam, Sarah Roger, Jacquelyn Sirota, Marina Bakar Computational Health Sciences Institute UCSF San Francisco CA United States Graduate Program in Bioengineering UCSF San Francisco CA United States School of Medicine UCSF San Francisco CA United States Graduate Program in Biological and Medical Informatics UCSF San Francisco CA United States Department of Pediatrics UCSF San Francisco CA United States

Objectives: Over the past few years, challenges from the pandemic have led to an explosion of data sharing and algorithmic development efforts in the areas of molecular measurements, clinical data, and digital health. We aim to characterize and describe recent advanced computational approaches in translational bioinformatics across these domains in the context of issues or progress related to equity and inclusion. Methods: We conducted a literature assessment of the trends and approaches in translational bioinformatics in the past few years. Results: We present a review of recent computational approaches across molecular, clinical, and digital realms. We discuss applications of phenotyping, disease subtype characterization, predictive modeling, biomarker discovery, and treatment selection. We consider these methods and applications through the lens of equity and inclusion in biomedicine. Conclusion: Equity and inclusion should be incorporated at every step of translational bioinformatics projects, including project design, data collection, model creation, and clinical implementation. These considerations, coupled with the exciting breakthroughs in big data and machine learning, are pivotal to reach the goals of precision medicine for all. © 2022 IMIA and Georg Thieme Verlag KG.

关键词： Bioinformatics genomics health equity precision medicine

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KULTURE Bench: A Benchmark for Assessing Language Model in Korean Cultural Context

arXiv

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arXiv 2024年

作者： Wang, Xiaonan Yeo, Jinyoung Lim, Joon-Ho Kim, Hansaem Interdisciplinary Graduate Program of Linguistics and Informatics Yonsei University Korea Republic of Department of Artificial Intelligence Yonsei University Korea Republic of Tutorus Labs Korea Republic of

Large language models (LLMs) have exhibited significant enhancements in performance across various tasks. However, the complexity of their evaluation increases as these models generate more fluent and coherent content. Current multilingual benchmarks often use translated English versions, which may incorporate Western cultural biases that do not accurately assess other languages and cultures. To address this research gap, we introduce KULTURE Bench, an evaluation framework specifically designed for Korean culture that features datasets of cultural news, idioms, and poetry. It is designed to assess language models' cultural comprehension and reasoning capabilities at the word, sentence, and paragraph levels. Using the KULTURE Bench, we assessed the capabilities of models trained with different language corpora and analyzed the results comprehensively. The results show that there is still significant room for improvement in the models' understanding of texts related to the deeper aspects of Korean culture. Copyright © 2024, The Authors. All rights reserved.

关键词： Translation (languages)

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Peak Prediction in Time Series: Comparing Approaches for Energy High-Load Prediction

Peak Prediction in Time Series: Comparing Approaches for Ene...

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International Joint Conference on Neural Networks (IJCNN)

作者： Rodrigo Kruger Abdullah Mueen Vinicius M. A. Souza Graduate Program in Informatics Pontifícia Universidade Católica do Paraná (PPGIa/PUCPR) Curitiba Brazil The University of New Mexico (UNM) Albuquerque USA

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Peaks in time series represent significant events in the measurements of a phenomenon over time, such as a sudden increase in the sales of an item on a specific day of the week. Predicting peaks in advance can support decision-making in various domains. For example, predicting demand for healthcare services helps adjust nurses’ work schedules in a hospital. This paper proposes a time series mining task named peak prediction, from which most applications that manage resources based on expected demand can benefit. We investigate various approaches, such as conventional machine learning and deep learning methods, to build global and individual models for peak prediction in time series. We evaluate these approaches in a load demand problem on smart meter energy consumption. In this task, a model predicts the customer’s maximum daily energy consumption in the following days (7 days ahead) and identifies which day it will occur. Accurate peak demand predictions for the consumers in a region help utility companies make informed decisions regarding capacity planning, load balancing, and integrating renewable energy sources. Since we are interested in two target variables simultaneously (i.e., peak magnitude and peak position), we evaluated whether multi-target approaches improve single-target models’ performance by correlating both targets in the learning process. Our experimental evaluation considers a dataset comprising three years of daily energy consumption from 1,757 customers. The results show the potential of individual machine learning models induced by simple algorithms such as Linear Regression and XGBoost and multi-target methods’ lack of contribution (or even negative impact) for the performance.

关键词： Deep learning Energy consumption Machine learning algorithms Time series analysis Linear regression Predictive models Prediction algorithms

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The Implementation of TOGAF for Enterprise System Planning on XYZ Campus

The Implementation of TOGAF for Enterprise System Planning o...

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2023 International Conference on Networking, Electrical Engineering, Computer Science, and Technology, IConNECT 2023

作者： Sirait, Tamsir Hasudungan Lumban Gaol, Ford Supangkat, Suhono Harso Ranti, Benny Bina Nusantara University Binus Graduate Program Computer Science Jakarta Indonesia Bina Nusantara University Computer Science Department Jakarta Indonesia Bandung Institute of Technology School of Electrical Engineering and Informatics Bandung Indonesia Universitas Indonesia Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350331172

Universities can employ information technology as one means of achieving their goals and objectives. Universities can get advantages from information technology, such as effective resource management and information management for decision-making. However, don't forget that the campus must consider what technology is appropriate to assist them achieve their goals, particularly in the current industrial era 4.0 where technology is available with many different choices. The campus requires an enterprise architecture in order to design, manage, and coordinate information technology infrastructure, applications, and processes strategically and thoroughly. The adoption of enterprise information system architecture (EA) is also intended to improve the quality of services provided to internal and external stakeholders. In this case, Enterprise Architecture can help an organization to match its information technology resources with business processes and strategies to achieve their goals. This research was conducted using TOGAF ADM, also known as the Open Group Architecture Framework Architecture Development Method. This method offers best practices for creating enterprise architecture and emphasizes several steps that include creating an architectural vision, information systems, business architecture modeling to help XYZ campus manage all their information technology. © 2023 IEEE.

关键词： Information management

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