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Symposium on Integrated Circuits and Systems Design (SBCCI)

作者： Eduardo Marañon Aguilar Fabio Benevenuti Fernanda Lima Kastensmidt Institute of Informatics Microelectronics Graduate Program (PGMICRO) Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil

ISBN: (数字)9798350391695

ISBN: (纸本)9798350391701

We investigate the implementation of triple modular redundancy (TMR) with different number of voters in a softcore RISC-V microprocessor to mitigate radiation-induced faults in the configuration memory (CRAM) of SRAM-based FPGA. The case-study microprocessor is the Steel Core implemented in AMD Xilinx 7-Series/Zynq-7000 device, executing benchmark software on the FreeRTOS operating system. We implemented coarse-grained TMR (CGTMR) and fine-grained distributed TMR (FGDTMR) using an automated redundancy and voter insertion tool. TMR is used in combination with memory scrubber featured by the FPGA hardware, which uses error correction codes (ECC) to revert radiation-induced faults in CRAM. Fault injection in CRAM emulating radiation-induced faults is used to evaluate the benefits of both TMR and ECC. Under fault injection, combining the fault masking capability of TMR with the self-healing by ECC, improved mean time between failures (MTBF) up to 5×. ECC alone did not improve significantly the fault tolerance. Without ECC, FGDTMR improves MTBF in 2×, but at a higher cost in circuit area and resources usage. When combined with ECC, the CGTMR provides fault tolerance comparable to FGDTMR but with 30% less resources usage.

关键词： Prevention and mitigation Microprocessors Redundancy Fault tolerant systems Software Error correction codes Software reliability Circuit faults Steel Field programmable gate arrays

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Predicting Types of Urinary Crystals and Diameter Using Image Processing Techniques and Deep Learning Models 24

Predicting Types of Urinary Crystals and Diameter Using Imag...

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8th International Conference on Medical and Health informatics, ICMHI 2024

作者： Huang, Yu-Ju Tseng, Yi-Shiou Chen, Tzu-Jung Chien, Ting-Ying Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Department of Surgery Far Eastern Memorial Hospital Graduate Institute of Medicine Yuan Ze University Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan

ISBN: (纸本)9798400716874

Kidney stones are primarily crystals formed from ion oversaturation in urine. Currently, the diagnosis of kidney stones involves experienced professionals manually interpreting images of urinary crystals under a microscope. In recent years, with the rapid development of artificial intelligence (AI), many researchers have proposed various methods to assist in diagnosis, hoping to improve medical quality. This study explored the application of AI in the medical field, aiming to automate the interpretation of urinary crystal images, such as red blood corpuscles (RBCs), calcium oxalate monohydrate (COM), calcium oxalate dihydrate octahedron (COD-OCT), calcium oxalate dihydrate dodecahedron (COD-DOD), debris, and others, and to use image processing techniques to classify urinary crystal types. This research is expected to assist medical personnel in quickly determining the categories of urinary crystals. Ultimately, our model achieved an F1-score of 0.93, indicating that this architecture has significant potential. © 2024 Copyright ACM.

关键词： Deep learning

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Method for improvement of transparency: Use of text mining techniques for reclassification of governmental expenditures records in Brazil

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International Journal of Business Intelligence and Data Mining 2021年第2期18卷 155-196页

作者： de Oliveira Almeida, Gustavo Revoredo, Kate Cappelli, Claudia Maciel, Cristiano Graduate Program in Business Federal Fluminense University Niteroi/RJ Brazil Graduate Program in Informatics Federal University of Rio de Janeiro State Rio de Janeiro/RJ Brazil Computing Institute Mato Grosso Federal University Cuiabá/MT Brazil

Many countries have transparency laws requiring availability of data. However, often data is available but not transparent. We present the Transparency Portal of Brazilian Federal Government case and discuss limitations of public acquisitions data stored in free text format. We employed text-mining techniques to reclassify descriptive texts of measurement units related to products and services. The solution presented in KNIME and Java aggregated measurements in the original (n = 69,372 with 78% reduction in number of descriptions, 94% items classified) and in cross validation sample (n = 105,266 with 88% reduction, classifying 78% of items). In addition, we tested computational time for processing of texts for a wide range of data input sizes, suggesting the stability and scalability of the solution to process larger datasets. Finally, we produced analysis identifying probable input errors, suppliers and purchasing units with abnormal transactions and factors affecting procurement prices. We present suggestions for future research and improvements. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Transparency

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Music Genre Classification Using Contrastive Dissimilarity

Music Genre Classification Using Contrastive Dissimilarity

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International Conference on Systems, Signals and Image Processing, IWSSIP

作者： Gabriel Henrique Costanzi Lucas O. Teixeira Gustavo Z. Felipe George D. C. Cavalcanti Yandre M. G. Costa Department of Informatics State University of Maringá Maringá Brazil Graduate Program in Computer Science State University of Maringá Maringá Brazil Informatics Center Federal University of Pernambuco Recife Brazil

ISBN: (数字)9798350391886

ISBN: (纸本)9798350391893

In the digital age, streaming platforms have revolutionized how we access and interact with music, highlighting the need for more intuitive ways to organize and categorize our ever-growing music collections. The challenge lies in effectively classifying tracks into similar genres and styles to enhance user experience through improved music discovery and recommendation. In this context, machine learning stands out as a powerful tool. Traditional research in the field focuses on the auditory characteristics of music, such as timbre and rhythm. Nevertheless, the incorporation of spectrogram analysis introduces a richer layer of data representation, capturing the intricate musical textures that distinguish genres. This study proposes a novel approach to music genre classification, leveraging classic machine learning algorithms and the recently proposed contrastive dissimilarity method. Our methodology, which involves a detailed examination of spectrograms and the use of conventional feature extraction methods such as Local Binary Patterns (LBP), Local Phase Quantization (LPQ), Binarized Statistical Image Features (BSIF), and Oriented Basic Image Features (OBIF), combined with deep neural embeddings estimated using the contrastive dissimilarity method, offers a more comprehensive and accurate way to classify music genres. Our comparative analysis, conducted on three benchmark music genre datasets - GTZAN, Latin Music Database, and ISMIR 2004 - demonstrates promising results that approach the performance of current state-of-the-art methods.

关键词： Quantization (signal) Machine learning algorithms Music Machine learning Feature extraction Rhythm User experience

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A Machine Learning Approach for the Identification of Learners' Misconceptions in Algebraic Problem-Solving

A Machine Learning Approach for the Identification of Learne...

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International Conference on Advanced Learning Technologies (ICALT)

作者： Joice Cazanoski Gomes Patricia A. Jaques Graduate Program in Informatics (PPGInf) Universidade Federal do Paraná (UFPR) Curitiba Brazil Graduate Program in Computing (PPGC) Universidade Federal de Pelotas (UFPEL) Pelotas Brazil

Misconceptions play a significant role in the learning process as they reflect an inaccurate understanding of a particular concept. Error diagnosis can help teachers and intelligent learning environments determine the most appropriate type of student assistance. Previously, misconceptions were identified using rule-based expert systems (bug libraries) and clustering algorithms. Bug libraries demand extensive work from developers to identify all potential misconceptions and code rules for each one in advance. Additionally, these solutions cannot detect misconceptions for which rules were not explicitly programmed. Clustering-based solutions overcome these drawbacks by automatically identifying misconceptions based on students' most common errors. To effectively and efficiently identify misconceptions, clustering solutions must have a suitable representation of the problem and its steps, and employ machine learning algorithms capable of discerning patterns from them. This paper proposes a solution that utilizes expression trees to represent algebraic problem-solving steps and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to identify misconceptions by clustering similar errors in a database containing 1064 steps from 112 students. This database was collected from an intelligent learning system designed to assist in solving first-degree equations. In our final solution, a Natural Language Processing tokenizer was employed to represent each term numerically, which identified 178 homogeneous clusters with minimal noise and few outliers.

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Innovative web-based microteaching model: To improve the teaching skills of prospective informatics teachers in vocational high schools

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Social Sciences and Humanities Open 2025年 11卷

作者： Sugihartini, Nyoman Elmunsyah, Hakkun Nurhadi, Didik Rahmawati, Yuni Vocational Education Doctoral Program Post Graduate School Universitas Negeri Malang Malang Indonesia Informatics Education Study Program Department of Informatics Faculty of Engineering and Vocational Universitas Pendidikan Ganesha Bali Indonesia

Prospective informatics teachers in vocational schools (VS) must master content knowledge, pedagogical skills, and technology integration to achieve learning goals. Therefore, informatics teachers in VS need to innovate to increase students' creativity. However, many prospective informatics teachers experience difficulties in this regard. This research aims to develop and implement the Web-Based Microteaching (WBM) model to improve the teaching skills of prospective informatics teachers at VS. This study uses research and development methods with the ADDIE model approach. The research results show that the WBM model effectively improves prospective informatics teachers' teaching skills, especially in planning lessons and practising basic teaching skills. This innovation is a relevant learning solution in the digital era and positively contributes to the professional development of informatics teachers. The WBM model is an innovation to provide a more intensive and structured practical experience by utilizing web technology as the main media. Through web-based learning, prospective teachers can access material, exercises and feedback independently, which is expected to accelerate pedagogical mastery. © 2025 The Authors

关键词： Microteaching model Prospective informatics teacher Teaching skills Vocational high schools Web-based

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Generating Individual Trajectories Using GPT-2 Trained from Scratch on Encoded Spatiotemporal Data

arXiv

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arXiv 2023年

作者： Horikomi, Taizo Fujimoto, Shouji Ishikawa, Atushi Mizuno, Takayuki Informatics Program Graduate Institute for Advanced Studies SOKENDAI Tokyo Japan Department of Economic Informatics Kanazawa Gakuin University Kanazawa Japan National Institute of Informatics Tokyo Japan

Following Mizuno, Fujimoto, and Ishikawa's research (Front. Phys. 2022), we transpose geographical coordinates expressed in latitude and longitude into distinctive location tokens that embody positions across varied spatial scales. We encapsulate an individual daily trajectory as a sequence of tokens by adding unique time interval tokens to the location tokens. Using the architecture of an autoregressive language model, GPT-2, this sequence of tokens is trained from scratch, allowing us to construct a deep learning model that sequentially generates an individual daily trajectory. Environmental factors such as meteorological conditions and individual attributes such as gender and age are symbolized by unique special tokens, and by training these tokens and trajectories on the GPT-2 architecture, we can generate trajectories that are influenced by both environmental factors and individual attributes. © 2023, CC BY.

关键词： Trajectories

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How Is the Quality of Information in a Cancer Hospital Regarding the Completeness and Consistency? A Case Study of the Hospital Cancer Registry of the Brazilian Hospital Bom Pastor 18

How Is the Quality of Information in a Cancer Hospital Regar...

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18th World Congress on Medical and Health informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021

作者： Assis, Erika G. Lima, Anna Carolina L. Nobre, Cristiane Neri Graduate Program in Informatics Pontifical Catholic University of Minas Gerais Minas Gerais Belo Horizonte Brazil Promove Faculty of Technology Minas Gerais Belo Horizonte Brazil

ISBN: (纸本)9781643682648

This study aimed to analyze the measurement of information quality in the Information System of the Brazilian Hospital in terms of data completeness and consistency. The quality of information is a fundamental factor in evaluating the performance of information systems. Its use is a reasonable condition in the management of the health service and attention to the care of patients. The research had a retrospective, descriptive character exploratory with quantitative analysis of the data. The study had some limitations, we observed incomplete information regarding some studied variables;this is because the primary source of information in the cancer registry is the patient's medical record. Therefore, the patient's medical records are relevant and should contain the entire health history, from birth to death. In addition, they support research, the management of health services. © 2022 International Medical informatics Association (IMIA) and IOS Press.

关键词： Hospitals

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Computational thinking through an online game to develop soft and hard skills

Computational thinking through an online game to develop sof...

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EAEEIE Annual Conference,

作者： Adriano Fiad Farias Dante Augusto Couto Barone Graduate Program in Informatics in Education Federal University of Rio Grande do Sul Porto Alegre Brazil

Currently we see globalization and technological advances accelerating worldwide, transforming the world of work and human coexistence. We still notice that the educational systems, in their majority, are disconnected from these global realities. In this sense, it is necessary to have an educational formation more aligned to the contemporary world, preparing young people for the challenges of the 21st century, how to deal with technological resources and processes, developing soft and hard skills. In this way, it can be observed that in recent years computational thinking has gained relevance in this scenario, being a field of research in the most varied areas of knowledge, developing communication skills, creativity, leadership, problem solving, familiarity with technologies, management, transforming young people into autonomous individuals who are prepared for the technological changes that the world requires. Taking these transformations as a reference, this paper describes/presents the development of an online tool to stimulate computational thinking in elementary school, through online games. he use of the ThinkinGame makes it possible to identify the pillars to be worked on with young people, after its use, thus improving the approaches to be given in the formation of these young people, being able to turn attention to the challenge of teaching additional skills, such as sophisticated thinking and flexible problem solving.

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Design and Evaluation of an Event-Driven Cloud-Based Architecture for a Remote Patient Monitoring System

Design and Evaluation of an Event-Driven Cloud-Based Archite...

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IEEE International Conference on Software Architecture Workshops (ICSAW)

作者： Pedro Fiuza Linhares Pedro H. Gaya Wanderley Marza de Sousa Zaranza Maria Andréia Formico Rodrigues Nabor C. Mendonça Post Graduate Program in Applied Informatics University of Fortaleza Fortaleza CE Brazil Center of Technological Sciences University of Fortaleza Fortaleza CE Brazil Intensive Care Unit Hospital Instituto Dr. José Frota (IJF) Fortaleza CE Brazil Post Graduate Program in Applied Informatics/GIRA Lab University of Fortaleza Fortaleza CE Brazil

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

This paper presents an event-driven cloud-based serverless architecture for a remote patient monitoring system. The architecture is designed to be scalable, resilient, and cost-effective, and leverages managed cloud services to handle load variations and service faults. To evaluate the architecture, we conducted comprehensive capacity, scalability, and resilience tests. The test results show that the architecture can meet the performance, scalability, and reliability requirements of a remote patient monitoring system. Additionally, a detailed analysis of operational costs in the cloud confirms the cost-effectiveness of the solution and highlights practical opportunities for improvements.

关键词： Cloud computing Patient monitoring Software architecture Scalability Computer architecture Load management Performance analysis Reliability Resilience Testing

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