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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Evandro L. R. Fensterseifer Filho Joaquim V. C. Assunção Lisandra M. Fontoura Graduate Program in Computer Science Federal University of Santa Maria - UFSM Santa Maria – RS Brazil Applied Computing Department Federal University of Santa Maria - UFSM Santa Maria – RS Brazil

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

Software projects are affected by technical knowledge as well as the personality of the team. Such factors can reduce or increase the software quality and development speed. For successful task allocation, it is essential to consider the skills and profile of each developer, thus maximizing their productivity. In projects with large teams, task allocation can be challenging, and the help of tools can facilitate its execution. In this work, we propose an intelligent approach for allocating software development tasks suitable to the profile of developers. From the literature, we define the appropriate skills and technical profiles for a development team, and the assessment is based on the developer completing a questionnaire. We developed a recommendation system to suggest tasks to be allocated to developers, employing text processing techniques. For validation, 495 tasks were used from an actual project developing simulator software for military training. The recommended allocations were evaluated by the project developers and used to improve the system. Validations showed that the developed approach makes consistent and coherent task recommendations to developers according to the developer profile, as participants agree with the recommendation for 76% of tasks.

关键词： Training Productivity Databases Mathematical models Classification algorithms Resource management Data mining Recommender systems Text processing Software development management

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How a Global Elective Engineering class impacts Engineering Students' Global Efficacy

How a Global Elective Engineering class impacts Engineering ...

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2023 IEEE IFEES World Engineering Education Forum and Global Engineering Deans Council, WEEF-GEDC 2023

作者： Gurganus, Jamie R. Malschutzky, Michael M. Do Carmo Santos, Joao Carlos Zupan, Marc Dos Santos, Abel Dias Engineering & Computing Education Program & Graduate School Baltimore United States Centre for Teaching Development and Innovation Department of Management Sciences Hochschule Bonn-Rhein-Sieg Sankt Augustin Germany College of Engineering and Information Technology Department of Mechanical Engineering Baltimore United States Departamento de Engenharia Mecânica Porto Portugal

ISBN: (纸本)9798350316025

Today's pluralistic and globalized society requires a global perspective in how we think, view ourselves, and relate to others to collaborate successfully with people with different cultural backgrounds. To support the development of a global perspective, universities cultivate international co-operations to enable international mobility for students, faculty, and staff. However, in-person mobility can be inhibited due to various reasons. To support a wider pool of students gaining international experience, modern digital mediated course formats can be used to offer virtual instead of in-person mobility. This contribution evaluates a digital mediated course for mechanical engineering students (Global Engineering, GE) held in co-operation between an US and a Portuguese university, where internationally mixed student groups working on design projects in an Hyflex setting. Asides a literature driven discussion about the need for Global Engineers and a detailed description of the GE course, a longitudinal analysis was performed to assess the impact of the GE course on the students' development of a global perspective. The longitudinal analysis showed strong statistical support that the digitally mediated GE course improved the participating students' global perspective, assessed with the Global Perspective Inventory (GPI), tremendously. The observed improvements were in three of the six GPI dimensions comparable to an in-person semester abroad. Limitations of the study and future investigations are discussed. © 2023 IEEE.

关键词： Students

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Critical Factors of Immersive Learning in Higher Education: A Systematic Literature Review 10

Critical Factors of Immersive Learning in Higher Education: ...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Sunardi Meyliana Ramadhan, Arief Rosmansyah, Yusep Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia School of Information System Bina Nusantara University Information System Departement Jakarta Indonesia School of Computing Telkom University Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798350339543

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education. © 2023 IEEE.

关键词： E-learning

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Interacting with Cultural Heritage Through Shape Representation Techniques in 3D Modeling Environments 8th

Interacting with Cultural Heritage Through Shape Representat...

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8th European-Mediterranean Conference, EuroMed 2020

作者： Moralıoğlu, Begüm Aktaş, Begüm Architectural Design Computing Graduate Program Istanbul Technical University Institute of Informatics Istanbul Turkey Department of Architecture Altınbas University Istanbul Turkey

ISBN: (纸本)9783030730420

Three-dimensional (3D) representation of cultural heritage in a digital environment is coming into prominence for documentation of geometric and semantic details and the interpretation of shape representation from academic studies to commercial practices. To interpret cultural and historical shapes, the use of the computational medium is expanding with developing technologies. Advanced 3D design and development applications appear every day with distinct functions. However, not every technology provides designers a convenient medium to create, explore, and think naturally. In that sense, digital design mediums and tools become important to enhance the interactivity level with cultural heritage for designers through different interfaces. Particularly, the utilities of design interfaces are essential for preserving reconstructing ruined cultural heritage in the digital design mediums. In this study, we investigate the designers’ creative design process in the context of shape representation methods within the possibilities of two different 3D design interfaces as a computer and virtual reality (VR) interfaces. At first, we conducted a shape exploration exercise to generate the 3D model pieces of the Dark Church in Cappadocia. And then, we used the generated 3D model pieces in a shape representation exercise to observe the seeing, imagining, and acting abilities of designers in computer and VR environments. © 2021, Springer Nature Switzerland AG.

关键词： 3D modeling

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Skin cancer diagnosis using NIR spectroscopy data of skin lesions in vivo using machine learning algorithms

arXiv

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arXiv 2024年

作者： Loss, Flavio P. da Cunha, Pedro H. Rocha, Matheus B. Zanoni, Madson Poltronieri de Lima, Leandro M. Nascimento, Isadora Tavares Rezende, Isabella Canuto, Tania R.P. de Paula Vieira, Luciana Rossoni, Renan Santos, Maria C.S. Frasson, Patricia Lyra Romão, Wanderson Filgueiras, Paulo R. Krohling, Renato A. Labcin - Nature Inspired Computing Lab Brazil PPGI - Graduate Program in Computer Science Department of Chemistry PPGQ - Graduate Program in Chemistry Department of Specialized Medicine Brazil ES Vitória29075-910 Brazil Secretary of Health of the Espírito Santo State Vitória Brazil ES Vitória29040-780 Brazil

Skin lesions are classified in benign or malignant. Among the malignant, melanoma is a very aggressive cancer and the major cause of deaths. So, early diagnosis of skin cancer is very desired. In the last few years, there is a growing interest in computer aided diagnostic (CAD) using most image and clinical data of the lesion. Although we have seen an increasing progress in CAD of skin lesions, these sources of information present limitations due to their inability to provide information of the molecular structure of the lesion. NIR spectroscopy may provide an alternative source of information to automated CAD of skin lesions. The most commonly used techniques and classification algorithms used in spectroscopy are Principal Component Analysis (PCA), Partial Least Squares - Discriminant Analysis (PLS-DA), and Support Vector Machines (SVM). Nonetheless, there is a growing interest in applying the modern techniques of machine and deep learning (MDL) to spectroscopy. One of the main limitations to apply MDL to spectroscopy is the lack of public datasets. Since there is no public dataset of NIR spectral data to skin lesions, as far as we know, an effort has been made and a new dataset named NIR-SC-UFES, has been collected, annotated and analyzed generating the gold-standard for classification of NIR spectral data to skin cancer. Next, the machine learning algorithms XGBoost, CatBoost, LightGBM, 1D-convolutional neural network (1D-CNN) and standard algorithms as SVM and PLS-DA were investigated to classify cancer and non-cancer skin lesions. Experimental results indicate the best performance obtained by LightGBM with pre-processing using standard normal variate (SNV), feature extraction and data augmentation with Generative Adversarial Networks (GAN) providing values of 0.839 for balanced accuracy, 0.851 for recall, 0.852 for precision, and 0.850 for F-score. The obtained results indicate the first steps in CAD of skin lesions aiming the automated triage of patients with

关键词： Support vector machines

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HistAttentionNAS: A CNN built via NAS for Penile Cancer Diagnosis using Histopathological Images

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Procedia Computer Science 2025年 256卷 764-771页

作者： José Ribamar Durand R. Geraldo Braz Junior Italo Francyles Santos da Silva Rui Miguel Gil da Costa Oliveira Applied Computing Group - Federal University of Maranhão Vila Bacanga São Luís 65080-805 Brazil Graduate Program in Adult Health - PPGSAD - Federal University of Maranhão Vila Bacanga São Luís 65080-805 Brazil

Penile cancer, although rare, has an increasing mortality rate in Brazil, highlighting the need for effective diagnostic methods. Artificial Intelligence (AI) in histopathological analysis can speed up and objectify diagnosis, but designing an ideal architecture is challenging. In this study, we propose a neural architecture search (NAS) methodology for detecting penile cancer in digital histopathology images. We explored different configurations of stem blocks and the inclusion of attention mechanisms, highlighting specific preferences depending on the magnification of the images. The results showed that the NAS methodology enabled the discovery of more accurate and optimized architectures for this task, surpassing conventional models. The proposed models achieve 89.5% and 88.5% F1-Score for 40X and 100X magnification, respectively.

关键词： Penile Cancer Detection Histopathologic Classification Neural Architecture Search

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A Multiplier-Less Level-3 Haar Wavelet Transform Approximation Requiring Five Additions Only

A Multiplier-Less Level-3 Haar Wavelet Transform Approximati...

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Proceedings of the IEEE Dallas Circuits and Systems Workshop (DCAS)

作者： Morgana M. A. Da Rosa Guilherme Paim Henrique B. Seidel Sé rgio Almeida Eduardo A. C. Da Costa Sergio Bampi Graduate Program on Electronic Engineering and Computing Universidade Cat&#x00F3 lica de Pelotas (UCPel) Brazil Graduate Program in Microelectronics (PGMICRO) Federal University of Rio Grande do Sul (UFRGS) Brazil

ISBN: (数字)9781665498852

ISBN: (纸本)9781665498869

Approximate transform kernels have emerged as an alternative design to reduce the hardware complexity of digital signal processors by exploiting the intrinsic error resilience of many embedded consumer electronics applications such as signal, image and video processing, and computer vision. The approximate level-4 Haar wavelet transform (AxHWT-4) is the state-of-the-art approximate transform for ECG (electrocardiogram) signal processing and reduces more than nine times the energy consumption compared to the exact transform. In this work, we propose an approximate level-3 Haar wavelet transform (AxHWT-3) for increasing the hardware design efficiency with related to the AxHWT-4 while obtaining higher R-peak detection accuracy in the electrocardiogram signal. Our AxHWT-3 approximation is performed in the Haar matrix kernel to eliminate its multiplications which in the hardware design results in savings of energy consumption and VLSI hardware area. Our AxHWT3 proposal achieves an R-peak detection accuracy of more than 99% and reduces the energy consumption by 15.98 times and the VLSI area by 12.64 times compared to the exact solution (i.e., the original level-3 Haar). In addition, the proposed AxHWT3 outperforms the state-of-the-art AxHWT-4 with a savings of about 32% in power dissipation and 19.5% in VLSI area.

关键词： Wavelet transforms Energy consumption Computer architecture Very large scale integration Electrocardiography Signal processing Hardware

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Few-shot learning for biotic stress classification of coffee leaves

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Artificial Intelligence in Agriculture 2022年第1期6卷 55-67页

作者： Lucas M.Tassis Renato A.Krohling LABCIN-Nature Inspired Computing Lab Federal University of Espirito SantoAv.Fernando Ferrari514CEP29075-910 VitóriaEspırito SantoESBrazil PPGI-Graduate Program in Computer Science Federal University of Espirito SantoAv.Fernando Ferrari514CEP 29075-910VitóriaEspírito SantoESBrazil

In the last few years,deep neural networks have achieved promising results in several ***,one of the main limitations of these methods is the need for large-scale datasets to properly ***-shot learning methods emerged as an attempt to solve this *** the few-shot learning methods,there is a class of methods known as embedding learning or metric *** methods tackle the classification problem by learning to compare,needing fewer training *** of the main problems in plant diseases and pests recognition is the lack of large public datasets *** to this difficulty,the field emerges as an intriguing application to evaluate the few-shot learning *** field is also relevant due to the social and economic importance of agriculture in several *** this work,datasets consisting of biotic stresses in coffee leaves are used as a case study to evaluate the performance of few-shot learning in classification and severity estimation *** achieved competitive results compared with the ones reported in the literature in the classification task,with accuracy values close to 96%.Furthermore,we achieved superior results in the severity estimation task,obtaining 6.74%greater accuracy than the baseline.

关键词： Plant diseases and pests classification Image classification Few-shot learning Meta-learning Convolutional neural networks

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Data Selection Based on Phoneme Affinity Matrix for Electrolarynx Speech Recognition

Data Selection Based on Phoneme Affinity Matrix for Electrol...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： I-Ting Hsieh Chung-Hsien Wu Shu-Wei Tsa Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Tainan Taiwan Dept. of Otolaryngology National Cheng Kung University Hospital Taiwan

Electrolarynx (EL) is a communicative aid for the patient after laryngectomy to generate communicable speech. Since EL speech exhibits low speech intelligibility and produces loud noise, understanding the content of the speech remains challenging for listeners, even if the patient is proficient in using the EL device. Accordingly, it is important to develop the tools that offer additional communication methods. Automatic speech recognition (ASR) of EL speech emerges as a method worth considering in this regard. However, the problem of under-resourced data dramatically degrades the recognition performance of EL speech. Data augmentation is one of the viable solutions for addressing the issue of under-resourced speech data. However, even with an increased health training corpus, the improvement in EL speech recognition may not be satisfactory. Because the characteristics of the EL speech still differ significantly from those of health speech. This paper proposes a data selection method using the phoneme affinity matrix to prioritize the selection of health speech that closely resembles EL speech for data augmentation. The affinity between two phonemes is defined as the similarity of the Phone Posteriorgrams(PPGs) of the two phonemes, considering the phoneme models. The experimental results demonstrate that the approach utilizing data selection based on the phoneme affinity matrix yields superior results compared to both the baseline and the method employing random sampling to select the augmented health speech corpus.

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Defect Detection and Classification in Printed Circuit Boards using Convolutional Neural Networks

Defect Detection and Classification in Printed Circuit Board...

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International Conference on Edge computing and Applications (ICECAA)

作者： Yolanda D. Austria Arnel C. Fajardo Graduate School Program Technological Institute of the Philippines Quezon City Philippines College of Computing Science Information and Communication Technology Isabela State University Cauay an City Isabela Philippines

PCB defect detection and classification is vital in electronics manufacturing to ensure high-quality and safe products. Manual inspection is time-consuming, costly, and prone to errors. To address this, automated methods using deep learning models have gained preference. These models learn from diverse datasets of defect images or videos. In this study, a deep learning model was developed and trained using a dataset containing various defect classes, including breakout, copper, crack, cut, excessive conductor, missing conductor, mouse bite, pinhole, spur, and scratch. The model showed promising results in detecting defects across all classes.

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