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Exploring Cooperation and Competition in computer science Education: An Investigation Based on Game Theory 17

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Exploring Cooperation and Competition in Computer Science Ed...

17th International Conference on computer Supported Education, CSEDU 2025

ISBN: (纸本)9789897587467

The study investigates the use of the game Overcooked as a pedagogical tool in primary education, focusing on cooperation and competition. The research, conducted throughout 2022, analyzed one semester as a baseline and another with pedagogical interventions. Four groups were studied: control (no interventions), game without instructions, game with teamwork guidance, and game without verbal communication. Questionnaires and qualitative/quantitative data assessed engagement, motivation, and cooperation. The interventions significantly improved performance, motivation, and skills such as teamwork and problem-solving, validating gamification as an educational resource. Copyright © 2025 by SCITEPRESS - science and Technology Publications, Lda.

关键词： Teaching

Exploring Advances in Transformers and CNN for Skin Lesion Diagnosis on Small Datasets

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arXiv 2022年

作者： de Lima, Leandro M. Krohling, Renato A. Graduate Program in Computer Science PPGI Federal University of Espirito Santo UFES Vitória Brazil Graduate Program in Computer Science PPGI Production Engineering Department LABCIN Federal University of Espirito Santo UFES Vitória Brazil

Skin cancer is one of the most common types of cancer in the world. Different computer-aided diagnosis systems have been proposed to tackle skin lesion diagnosis, most of them based in deep convolutional neural networks. However, recent advances in computer vision achieved state-of-art results in many tasks, notably Transformer-based networks. We explore and evaluate advances in computer vision architectures, training methods and multimodal feature fusion for skin lesion diagnosis task. Experiments show that PiT (0.800±0.006), CoaT (0.780±0.024) and ViT (0.771±0.018) backbone models with MetaBlock fusion achieved state-of-art results for balanced accuracy metric in PAD-UFES-20 dataset. © 2022, CC BY.

关键词： Classification (of information)

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

An app to assist farmers in the identification of diseases and pests of coffee leaves using deep learning

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Information Processing in Agriculture 2022年第1期9卷 38-47页

作者： Jose G.M.Esgario Pedro B.C.de Castro Lucas M.Tassis Renato A.Krohling LABCIN-Nature Inspired Computing Federal University of Espirito SantoAv.Fernando Ferrari514CEP 29075-910 VitoriaEspırito SantoESBrazil LABCIN-Nature Inspired Computing and PPGI-Graduate Program in Computer Scienceand Production Engineering DepartmentFederal University of Espirito SantoAv.Fernando Ferrari514CEP 29075-910 VitoriaEspırito SantoESBrazil

In recent years,deep learning methods have been introduced for segmentation and classi-fication of leaf lesions caused by pests and *** the commonly used approaches,convolutional neural networks have provided results with high *** purpose of this work is to present an effective and practical system capable of seg-menting and classifying different types of leaf lesions and estimating the severity of stress caused by biotic agents in coffee leaves using convolutional neural *** proposed approach consists of two stages:a semantic segmentation stage with severity calculation and a symptom lesion classification *** stage was tested separately,highlighting the positive and negative points of each *** obtained very good results for the severity estimation,suggesting that the model can estimate severity values very close to the real *** the biotic stress classification,the accuracy rates were greater than 97%.Due to the promising results obtained,an App for Android platform was developed and imple-mented,consisting of semantic segmentation and severity calculation,as well as symptom classification to assist both specialists and farmers to identify and quantify biotic stresses using images of coffee leaves acquired by smartphone.

关键词： Convolutional neural networks Semantic segmentation Coffee leaf Pests and diseases App.development

CBIDR: A novel method for information retrieval combining image and data by means of TOPSIS applied to medical diagnosis

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arXiv 2024年

作者： Giuri, Humberto Krohling, Renato A. Labcin - Nature-inspired computing Lab. Federal University of Espírito Santo ES Vitória Brazil Graduate Program in Computer Science PPGI UFES Federal University of Espírito Santo ES Vitória Brazil

Content-Based Image Retrieval (CBIR) have shown promising results in the field of medical diagnosis, which aims to provide support to medical professionals (doctor or pathologist). However, the ultimate decision regarding the diagnosis is made by the medical professional, drawing upon their accumulated experience. In this context, we believe that artificial intelligence can play a pivotal role in addressing the challenges in medical diagnosis not by making the final decision but by assisting in the diagnosis process with the most relevant information. The CBIR methods use similarity metrics to compare feature vectors generated from images using Convolutional Neural Networks (CNNs). In addition to the information contained in medical images, clinical data about the patient is often available and is also relevant in the final decision-making process by medical professionals. In this paper, we propose a novel method named CBIDR, which leverage both medical images and clinical data of patient, combining them through the ranking algorithm TOPSIS. The goal is to aid medical professionals in their final diagnosis by retrieving images and clinical data of patient that are most similar to query data from the database. As a case study, we illustrate our CBIDR for diagnostic of oral cancer including histopathological images and clinical data of patient. Experimental results in terms of accuracy achieved 97.44% in Top-1 and 100% in Top-5 showing the effectiveness of the proposed approach. © 2024, CC BY-NC-ND.

关键词： Convolutional neural networks

Beyond Visual Image: Automated Diagnosis of Pigmented Skin Lesions Combining Clinical Image Features with Patient Data

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arXiv 2022年

作者： Esgario, José G.M. Krohling, Renato A. Federal University of Espírito Santo ES Vitória Brazil PPGI - Graduate Program in Computer Science LABCIN Production Engineering Department

Skin cancer is considered one of the most common type of cancer in several countries. Due to the difficulty and subjectivity in the clinical diagnosis of skin lesions, computer-Aided Diagnosis systems are being developed for assist experts to perform more reliable diagnosis. The clinical analysis and diagnosis of skin lesions relies not only on the visual information but also on the context information provided by the patient. This work addresses the problem of pigmented skin lesions detection from smartphones captured images. In addition to the features extracted from images, patient context information was collected to provide a more accurate diagnosis. The experiments showed that the combination of visual features with context information improved final results. Experimental results are very promising and comparable to experts. © 2022, CC BY.

关键词： computer aided diagnosis

Diffusion models applied to skin and oral cancer classification

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arXiv 2025年

作者： Uliana, José J.M. Krohling, Renato A. Labcin - Nature-inspired Computing Lab Federal University of Espírito Santo Vitória Brazil Labcin - Nature-inspired Computing Lab Graduate Program in Computer Science PPGI UFES Federal University of Espírito Santo Vitória Brazil

This study investigates the application of diffusion models in medical image classification (DiffMIC), focusing on skin and oral lesions. Utilizing the datasets PAD-UFES-20 for skin cancer and P-NDB-UFES for oral cancer, the diffusion model demonstrated competitive performance compared to state-of-the-art deep learning models like Convolutional Neural Networks (CNNs) and Transformers. Specifically, for the PAD-UFES-20 dataset, the model achieved a balanced accuracy of 0.6457 for six-class classification and 0.8357 for binary classification (cancer vs. non-cancer). For the P-NDB-UFES dataset, it attained a balanced accuracy of 0.9050. These results suggest that diffusion models are viable models for classifying medical images of skin and oral lesions. In addition, we investigate the robustness of the model trained on PAD-UFES-20 for skin cancer but tested on the clinical images of the HIBA dataset. © 2025, CC BY.

关键词： Lung cancer

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

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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

Discovering an aid policy to minimize student evasion using offline reinforcement learning

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arXiv 2021年

作者： De Lima, Leandro M. Krohling, Renato A. Graduate Program in Computer Science PPGI Federal University of Espirito Santo UFES Vitória Brazil Graduate Program in Computer Science PPGI Production Engineering Department LABCIN Federal University of Espirito Santo UFES Vitória Brazil

High dropout rates in tertiary education expose a lack of efficiency that causes frustration of expectations and financial waste. Predicting students at risk is not enough to avoid student dropout. Usually, an appropriate aid action must be discovered and applied in the proper time for each student. To tackle this sequential decision-making problem, we propose a decision support method to the selection of aid actions for students using offline reinforcement learning to support decision-makers effectively avoid student dropout. Additionally, a discretization of student's state space applying two different clustering methods is evaluated. Our experiments using logged data of real students shows, through off-policy evaluation, that the method should achieve roughly 1.0 to 1.5 times as much cumulative reward as the logged policy. So, it is feasible to help decision-makers apply appropriate aid actions and, possibly, reduce student dropout. © 2021, CC BY.

关键词： Students