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4th International Computer programming Education Conference, ICPEC 2023

作者： dos Santos, Ranieri Alves Barbi, Dalner Culmant Ramos, Vinicius Faria Ostuni Gauthier, Fernando Alvaro Graduate Program in Knowledge Engineering and Management UFSC Florianópolis Brazil

ISBN: (纸本)9783959772907

Learning Analytics (LA) has the potential to transform the way we learn, work and live our lives. To reach its potential, it must be clearly defined, incorporated into institutional teaching-learning strategies and processes and practices. The main goal of this study is to list indicators to be used in learning analytics in programming teaching and how to expose their views. For the development of the indicator model, this study based on a qualitative analysis, using data visualization and business intelligence tools, in projects focused on Learning Analytics. As a result, four main indicators were mapped: accesses to the system, resources accessed, activities carried out and, performance in activities. © Ranieri Alves dos Santos, Dalner Barbi, Vinicius Faria Culmant Ramos, and Fernando Alvaro Ostuni Gauthier

关键词： Data visualization

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Second Brain and Self-directed Learning in Personal knowledge Management

Second Brain and Self-directed Learning in Personal Knowledg...

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International Conference on Management, Tourism and Technologies, ICMTT 2024

作者： da Silva, Gustavo Simas Ulbricht, Vânia Ribas Graduate Program of Knowledge Engineering Management and Media Federal University of Santa Catarina Florianópolis Brazil

ISBN: (纸本)9783031748240

This article explores the integration of Self-Directed Learning (SDL), Personal knowledge Management (PKM) and the Second Brain (SB) concept within the knowledge Society scenario. Highlighting SDL as a key contemporary competence, we emphasize the role of PKM in enhancing work efficiency and career development, crucial in an era marked by vast data influx. The SB concept is presented as a digital tool for effective information and knowledge organization, serving as a knowledge base along the CODE workflow and PARA system, also addressing challenges posed by the information age. The synergy between SB, PKM and SDL is examined as a holistic approach for optimizing knowledge management, fostering personal development and enhancing learning processes. We also discuss the implications for Organizational knowledge Management (OKM) and active learning in organizational settings, aiming to cultivate a culture of adaptability and lifelong learning. Concluding, the article acknowledges the challenges of cognitive overload and the digital divide in the knowledge Economy, while foreseeing the potential of emerging technologies in refining these approaches. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Active learning

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Enhancing Automated Microorganism Classification: A Study on the Influence of Compression, Non-Uniform Scaling, and Data Augmentation Using Deep Learning

SSRN

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SSRN 2024年

作者： Boukouvalas, Dimitria Theophanis Bissaco, Márcia A.S. Dellê, Humberto Deana, Alessandro M. Belan, Peterson A. de Araújo, Sidnei Alves Universidade Nove de Julho Informatics and Knowledge Management Graduate Program Rua Vergueiro 235/249 SP São Paulo01504-001 Brazil Universidade de Mogi das Cruzes Biomedical Engineering Graduate Program Av. Dr. Cândido Xavier de Almeida e Souza 200 Mogi das Cruzes SP São Paulo08780-911 Brazil Universidade Nove de Julho Medicine Graduate Program Rua Vergueiro 235/249 SP São Paulo01504-001 Brazil

With the advent of "Laboratory 4.0", a concept that represents the recent technological breakthroughs in laboratory automation, there is an increasing demand for solutions that can automate, standardize, and enhance the accuracy and speed of microorganism classification. One promising approach is the use of convolutional neural networks (CNNs), a powerful deep learning technique that has shown significant results in biomedical image analysis. However, its application in microorganism classification is still nascent mainly due to the unavailability of extensive datasets, which is a basic requirement to ensure generalization ability in learning CNNs. This study aims to fill a significant gap in the literature by exploring the influence of factors such as image compression, non-uniform scaling, and data augmentation on the performance of CNNs, specifically when using small image datasets of microorganisms. We focus on two CNN architectures, AlexNet and DenseNet-121, which have shown promising results in previous microorganism classification studies. Our experiments reveal that certain factors like the type of data compression and non-uniform scaling applied to the images can negatively impact the performance of CNNs. On the other hand, data augmentation techniques such as mirroring, rotation, and noise addition can enhance their performance. By utilizing training datasets and optimized parameters based on our findings, we were able to achieve classification accuracies of 98.61% and 99.82% with AlexNet and DenseNet-121, respectively, surpassing the results reported in current state-of-the-art studies. This research has significant implications for Laboratory 4.0, potentially reducing dataset preparation and CNN training time, lowering computational costs, and improving the consistency and accuracy of microorganism classification. It paves the way for more efficient and accurate microorganism classification methods in automated laboratories. © 2024, The Authors. All righ

关键词： Classification (of information)

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Experimentation of Integrating Roadmapping and Sci-Fi Prototyping Methods

Experimentation of Integrating Roadmapping and Sci-Fi Protot...

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Portland International Conference on Management of engineering and Technology (PICMET)

作者： Miwa Nishinaka Kunio Shirahada Yusuke Kishita Dohjin Miyamoto Hirotaka Osawa Sachiko Kiyokawa Hideaki Takeda The Graduate School of Management Kagawa University Takamatsu Kagawa Japan School of Knowledge Science Transformative Knowledge Management Research Area Japan Advanced Institute of Science and Technology (JAIST) Nomi Ishikawa Japan Department of Precision Engineering The University of Tokyo Tokyo Japan Communication in Science and Technology Education and Research Program Hokkaido University Sapporo Japan Faculty of Science and Technology Keio University Yokohama Kanagawa Japan Graduate School of Education The University of Tokyo Tokyo Japan National Institute of Informatics Tokyo Japan

ISBN: (数字)9781890843458

ISBN: (纸本)9798350369137

In 2023, we proposed a hypothetical knowledge management framework that incorporates science fiction prototyping (SFP) into the traditional roadmapping (RM) method to make RM more innovative and create a future beyond what we can currently imagine. Our main objective is to create a new organizational knowledge co-creation method for generating innovative ideas by incorporating humanistic factors utilizing SFP into RM. In the current article, we present the exploratory study we conducted to examine the hypothetical framework. Experimental workshops were held in November 2023 to compare the integrated method, in which SFP is applied to RM, with the original RM method. We followed a crossover design in the workshops and analyzed the results qualitatively and quantitatively following the perspectives proposed in the hypothetical framework. The qualitative analysis revealed successful integration regarding diverse and broader perspectives. The quantitative analysis utilizing Wilcoxon signed rank tests showed that the scores of the integrated method were significantly higher than those of RM for variables evaluating the effectiveness of the integration, while not for variables related to participants' feelings and satisfaction with the workshops, and sustainability. These findings indicate that the integration was successful in terms of its effectiveness as a method, while participants' subjective feelings toward the workshops were not different in both methods. As for sustainability, which requires a long-term perspective, no apparent differences were observed in these short-term workshops.

关键词： Statistical analysis Conferences knowledge management Sustainable development Creativity

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Estimating Pandemic Effects in Urban Mass Transportation Systems: An Approach Based on Visibility Graphs and Network Similarity

SSRN

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SSRN 2022年

作者： Perez, Yuri Pereira, Fabio Henrique Informatics and Knowledge Management Graduate Program PPGI-UNINOVE Rua Vergueiro 235/249 - Liberdade SP São Paulo01525-000 Brazil Universidade Nove de Julho Industrial Engineering Graduate Program PPGI-UNINOVE Rua Vergueiro 235/249 - Liberdade SP São Paulo01525-000 Brazil

Urban mass transportation systems are critical elements of modern society, exerting influence in the shape, economical development and sustainability of a city. Covid-19 forced population to avoid social contact and the impacts of this scenario on public transportation in different regions of the world are still being discussed. Large metropolitan areas present heterogeneous population groups and exert different mobility roles in urban systems, so, it is important to understand how different regions of the same city may present different mobility dynamics. Here, we present a model to study how and how much different regions of a large urban system were affected by pandemics, converting time series into visibility graphs and comparing them using network similarity methods and geospatial analysis. The methods used here were able to detect an abrupt shock in demand behavior when pandemics started, quantify how different regions absorbed the shock, group lines with similar spatial location and explore if the effects on lines may be related to demand level or bus line length. © 2022, The Authors. All rights reserved.

关键词： Complex networks

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Real engineering Problems in an Undergraduate Course: The learning methodologies and assessment tools

Real Engineering Problems in an Undergraduate Course: The le...

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World engineering Education Forum - Global engineering Deans Council (WEEF-GEDC)

作者： Ricardo A. Diogo André L.A.C. Venâncio Maria Angela M. Roveredo Santos Eduardo F.R. Loures Neri dos Santos Knowledge Engineering&Management Graduate Program Federal University of Santa Catarina Florianopolis Brazil Industrial&Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Brazil Control and Automation Engineering/Mechatronics Engineering Pontifical Catholic University of Parana Curitiba Brazil

ISBN: (纸本)9781665424899

This paper presents the context of the Ubiquitous Computing course carried out in an Industrial engineering undergraduate program throughout 2020 and the first semester of 2021. This course took advantage of the Institutional Modernization program led by the Control and Automation engineering Undergraduate program, which consists in the modernization of undergraduate engineering programs at the Pontifical Catholic University of Paraná. The course aimed to help develop competencies, i.e., a set of knowledge, skills, and attributes aligned with Student Outcomes suggested by ABET. Also, the course proposal brought the productive sector very close to the academic environment, proposing real engineering problems as challenges. However, what are the methods and assessment tools for practical learning in a course with modern elements? This paper proposed the flipped learning and the Challenge-Based Learning Framework with the support of the CDIO Framework as learning methodologies to answer such question. Additionally, quizzes, tests, presentations, rubrics, and peer evaluation were applied as assessment tools to measure the student's progress. Finally, students were listened to about their perceptions during the course. The results suggested that the learning methodologies and assessment tools are suitable for the course context, although some improvements are expected for the following course offers. Moreover, students approved the initiative to bring in real challenges proposed by companies, the mentoring hours, and the feedback about the projects.

关键词： Automation Companies Industrial engineering Ubiquitous computing Proposals Mentoring engineering education

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Study of initial guess influence on the quality of solutions on binary genetic algorithm in job shop scheduling problem 14

Study of initial guess influence on the quality of solutions...

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14th International Symposium on Operational Research, SOR 2017

作者： Ferreira da Cruz, Valmir Pereira, Fabio Henrique Universidade Nove de Julho Industrial Engineering Graduate Program Francisco Matarazzo Av. 612 São Paulo Brazil Universidade Nove de Julho Informatics and knowledge management Graduate Program Francisco Matarazzo Av. 612 São Paulo Brazil

ISBN: (纸本)9789616165501

The purpose of this study is to evaluate the influence of the initial guess to generate the Genetic Algorithm population of solutions of scheduling problems in relation to the quality and feasibility of the solutions. The scheduling problem is defined as to find the sequence of operations on the machines that optimize some performance measure as, for example, the use of resources and the total processing time (makespan). It is common to treat such problems with the use of metaheuristics as genetic algorithm mainly due to its computational complexity. This work carried out experiments with a set of literature instances, varying the sequencing rule used in the generation of initial solutions. Usually, rules from the literature have been tested and identified a hybrid rule that generates a smaller number of non-feasible solutions and the number of instances that have reached the optimal makespan. © 2017 Slovenian Society Informatika. All Rights Reserved.

关键词： Genetic algorithms

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Towards an Integrated Approach to Simultaneously Estimating the Frequency and Amplitude Modulations of SSVEP Signals from Consumer-grade EEG

arXiv

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arXiv 2018年

作者： Autthasan, Phairot Du, Xiangqian Perera, Maneesha Lamyai, Sirakorn Itthipuripat, Sirawaj Yagi, Tohru Manoonpong, Poramate Wilaiprasitporn, Theerawit Rayong Thailand Department of Psychology Vanderbilt University Tennessee and Neurosciences Graduate Program University of California California USA and Learning Institute King Mongkuts University of Technology Thonburi Bangkok Thailand BRAIN Lab IST VISTEC Rayong Thailand Embodied AI & Neurorobotics Centre for BioRobotics Msk McKinney Mller Institute University of Southern Denmark Odense MDK-5230 Denmark Yagi Lab Department of Mechanical Engineering Tokyo Institute of Technology Tokyo Japan Data Analysis and Knowledge Discovery Laboratory Department of Computer Engineering Kasetsart University Bangkok Thailand department of Computer Engineering Sirindhorn International Institute of Technology Pathum Thani Thailand

For several decades, researchers have sought several solutions for helping patients who suffer from neuromuscular disorders. One of a few promising approaches is to invent brain-computer interfaces (BCIs) in which brain signals could be used to control computers and prosthetics. Due to their high signal-to-noise ratio, steady-state visually evoked potentials (SSVEPs) or electrical brain signals that oscillate at the same frequency as the frequency of a flickering visual stimulus on the computer screen has been widely used to build BCIs. While researchers have focused on developing frequency recognition algorithms for SSVEP-based BCIs, these algorithms do not precisely predict the modulation of SSVEP amplitude, known to change as a function of stimulus luminance contrast. Thus, in the present study we aimed to develop an integrated approach to simultaneously estimating the frequency and contrast-related amplitude modulations of the SSVEP signal. To do so, we developed a behavioral task in which human participants looked at a visual target flicking at a 7.5 Hz. Critically, across different experimental conditions, the luminance contrast of the visual target could change across time in several ways (i.e., the luminance contrast remained constant, increased gradually, decreased gradually, or increased and then decreased). SSVEPs were measured via an open-source and low-cost consumer-grade EEG device, namely OpenBCI, placed at the central occipital site. Our results displayed that the filter bank canonical correlation analysis (FBCCA) performed generally well in SSVEP frequency recognition, while support vector regression (SVR) outperformed the other supervised machine learning algorithms in predicting the contrast-dependent amplitude modulations of the SSVEP signal. These results demonstrated the applicability and efficiency of our integrated method at simultaneously predicting both frequency and amplitude of visually evoked signals in the human brain, proven useful for

关键词： Brain computer interface

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Case-based comparison of career trajectories 24

Case-based comparison of career trajectories

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24th International Conference on Case-Based Reasoning Workshops, ICCBR-WS 2016

作者： Duarte, Kedma Weber, Rosina O. Pacheco, Roberto C. S. Graduate Program in Knowledge Engineering and Management Federal University of Santa Catarina Brazil College of Computing and Informatics Drexel University United States

Data generated across time may not be easily comparable in its original form thus potentially leading to results that may be perceived as unfair to some. We investigate quality assessment of scholarly researchers from their curricula vitae (CVs) for processes such as hiring, promotion, and grant funding. In previous work, we demonstrated that case-based reasoning (CBR) offers advantages as a transparent methodology to assess researcher quality. Its benefits include consistency, transparency, ability to adapt to specific purposes, and ability to provide explanation. The problem we now face is how to preprocess the data from the CVs to compare researchers whose scholarly production is achieved under different conditions, different points in time, and span different career trajectory lengths. We propose strategies to deal with these aspects of time during preprocessing of the data for case representation. We use 1,000 CVs from the Brazilian Lattes database to illustrate. Copyright © 2016 for this paper by its authors.

关键词： Trajectories

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Abstracts

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JOURNAL OF SPINAL CORD MEDICINE 2021年第SUPPL 1期44卷 S278-S324页

作者： [Anonymous] Department of Medicine Division of Physical Medicine and Rehabilitation University of Toronto Toronto ON CA KITE - Toronto Rehabilitation Institute University Health Network Toronto ON CA Institute for Health Policy Management and Evaluation University of Toronto Toronto ON CA Rehabilitation Sciences Institute University of Toronto Toronto ON CA Institute of Medical Sciences University of Toronto Toronto ON CA. Rehabilitation Sciences Institute University of Toronto ON CA KITE Research Institute-Toronto Rehab-University Health Network Toronto ON CA Physical Therapy Department University of Toronto ON CA Praxis Spinal Cord Institute Vancouver BC Université de Montréal (École de réadaptation) Montreal QC CA Institut de réadaptation Gingras-Linsay-de-Montréal Montreal QC CA Division of Physical Medicine & Rehabilitation Department of Medicine Faculty of Medicine & Dentistry University of Alberta Edmonton AB CA University of Calgary Calgary AB CA Alberta Health Services Centre for Family Medicine Kitchener ON CA McMaster University Department of Family Medicine Hamilton ON CA Dept. of Physical Therapy University of Toronto Toronto ON CA KITE-University Health Network Toronto ON CA Praxis Spinal Cord Institute Vancouver BC CA Center for Interdisciplinary Research in Rehabilitation and Social Integration (CIRRIS) Quebec City QC CA Dept. of Rehabilitation Laval University Quebec City QC CA Institute of Health Policy Management and Evaluation University of Toronto Toronto ON CA School of Rehabilitation Science University of Saskatchewan Saskatoon SK CA Ontario Neurotrauma Foundation Toronto ON CA Canadian Spinal Research Organization Toronto ON CA CIUSSS du Centre-Sud-de-l ‘Île-de-Montréal (Installation Gingras-Lindsay) Montréal QC CA École de réadaptation Université de Montréal Montréal QC CA First Steps Wellness Centre Regina SK CA Stan Cassidy Centre for Rehabilitation Horizon Health Network Fredericton NB CA Departmen

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