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21st International ISCRAM Conference, ISCRAM 2024

作者： Veríssimo, José Borges, Marcos R.S. PPGI - Graduate Program in Informatics Universidade Federal do Rio de Janeiro Brazil

The historical data on disasters serves various purposes, such as managing the risk of future disasters, assessing risk in the insurance sector, and formulating policies to reduce disaster risks, among others. To meet these objectives, there are databases, covering several types of disasters. However, in addition to differences in purposes, scope, access, and coverage, there are variations in the criteria for registering a disaster in the database and the information used for this register. Due to these differences, we have a scenario with discrepancies of information across existing databases. In this study, we propose a repository that combines the main advantages of some global databases, the data needs due to multilateral agreement, and the possibility of integrating regional or specific databases. The proposed Natural Disaster Data Repository (R2DN) contains a data schema and a data mapping process with two of the main global databases (EM-DAT and DesInventar) and S2ID. © 2024 Information Systems for Crisis Response and Management, ISCRAM. All rights reserved.

关键词： Risk assessment

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Vehicle Occurrence-Based Parking Space Detection

Vehicle Occurrence-Based Parking Space Detection

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： De Almeida, Paulo R. Lisboa Alves, Jeovane Honório Oliveira, Luiz S. Hochuli, Andre Gustavo Fröhlich, João V. Krauel, Rodrigo A. Department of Informatics Curitiba Brazil Graduate Program in Informatics Curitiba Brazil Department of Computer Science Joinville Brazil

ISBN: (纸本)9798350337020

Smart-parking solutions use sensors, cameras, and data analysis to improve parking efficiency and reduce traffic congestion. Computer vision-based methods have been used extensively in recent years to tackle the problem of parking lot management, but most of the works assume that the parking spots are manually labeled, impacting the cost and feasibility of deployment. To fill this gap, this work presents an automatic parking space detection method, which receives a sequence of images of a parking lot and returns a list of coordinates identifying the detected parking spaces. The proposed method employs instance segmentation to identify cars and, using vehicle occurrence, generate a heat map of parking spaces. The results using twelve different subsets from the PKLot and CNRPark-EXT parking lot datasets show that the method achieved an AP25 score up to 95.60% and AP50 score up to 79.90%. © 2023 IEEE.

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New Energy-Efficient 3-2 and 4-2 Approximate Adder Compressors Topologies 30

New Energy-Efficient 3-2 and 4-2 Approximate Adder Compresso...

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30th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2023

作者： Lopes, Rodrigo Antonietti, Leonardo Rosa, Morgana M. A. Costa, Eduardo Soares, Rafael Bampi, Sergio Graduate Program on Computing Pelotas Brazil Graduate Program on Electronic Engineering and Computing Pelotas Brazil PPGC & PGMICRO - Informatics Institute Porto Alegre Brazil

ISBN: (纸本)9798350326499

Addition units are present across many computational kernels inherent in various error-tolerant applications, including machine learning, signal, image, and video processing. Notably, adder compressors are the target when high speed and low power are the main design concerns. This work introduces energy-efficient structures of 3-2 approximate adder compressors (Ax3-2) and three distinct versions of 4-2 approximate adder compressors (Ax4-2). We compared our proposed Ax3-2 and Ax4-2 compressors with state-of-the-art energy-efficient approximate adder compressors (AxACs). Both Ax3-2 and Ax4-2 models passed through rigorous tests as standalone units. Furthermore, we integrated the Ax4-2 models into an essential application kernel appearing in video coding, i.e., the Sum Squared Differences (SSD) video accelerator. We introduce Ax3-2 and Ax4-2 that yield a new Pareto front concerning both energy-quality and area-quality trade-offs, thereby demonstrating a marked improvement over the prevailing state-of-the-art energy-efficient AxACs. © 2023 IEEE.

关键词： Adders

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Evaluation of an agent-based reinforcement learning framework for model-based testing 7

Evaluation of an agent-based reinforcement learning framewor...

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7th International Congress on Ambient Intelligence, Software Engineering, and e-Health and Mobile Health, AmITIC 2024

作者： Ramírez-Méndez, Jose Quesada-López, Christian University of Costa Rica Graduate Program in Computing and Informatics San Pedro Costa Rica University of Costa Rica Department of Computer Science and Informatics San Pedro Costa Rica

ISBN: (纸本)9798350364538

This study evaluates an agent-based reinforcement learning framework for model-based testing (MBT). The framework's performance was assessed on three key metrics: effectiveness and efficiency in achieving model coverage objectives, the quantity and uniqueness of generated test cases, and code coverage. The results show improved metrics for the framework compared to traditional testers in model coverage evaluation and test case metrics. Specifically, the framework achieved higher effectiveness and efficiency, generating a higher average number of test cases with a substantial proportion of unique cases, indicating more diverse and thorough testing. Additionally, the framework, along with random and greedy testers, achieved over 70% coverage across branch, method, and line code metrics. The framework showed slightly higher values in method and line coverage compared to other testers. The evaluation highlights the use of agent-based reinforcement learning to support model-based testing in planning test case generation, exploring models using multiple strategies, and learning guided by testing metrics. Future work will focus on further refining the framework learning model, including error coverage evaluation, and testing its applicability across different software systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Security Authentication on the Edge: A Step Further to Defense in Depth for Internet of Battle Things 11

Security Authentication on the Edge: A Step Further to Defen...

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11th IEEE International Conference on Software Defined Systems, SDS 2024

作者： Lawisch, Marcio Antonio Dos Anjos, Julio Cesar S. De Freitas, Edison Pignaton Institute of Informatics RS Brazil Federal University of Ceara Campus Itapaje Graduate Program in Teleinformatics Engineering PPGETI CE Brazil

ISBN: (纸本)9798331518325

Currently, the demand for the security of network assets in companies grows dramatically because of several factors, including the growth of the attack surface combined with the variety of connection standards and correlated to the emerging Internet of Things (IoT) related devices and services. This reality is also evident regarding nodes connected to military networks, especially Tactical Edge (TE) devices. Due to the wide variety of equipment connected to the networks (from small sensors to powerful radars) and their possible limitations in resources and transmission means, mainly caused by the terrain and operational conditions, these networks called the Internet of Battle Things (IoBT), also face important security challenges. In this context full of many intentional and unintentional security challenges, this work proposes using blockchain technology as a network access security solution within an in-depth defense concept at three levels: hardware, network, and application. Tests for attacks against the network layer resulted in high resistance rates without consuming significant resources. © 2024 IEEE.

关键词： Blockchain

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PM4SOS: low-effort resource allocation optimization in a dynamic environment

PM4SOS: low-effort resource allocation optimization in a dyn...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Ferronato, Jair Jose Scalabrin, Edson Emilio Carvalho, Deborah Ribeiro Informatics Graduate Program Pucpr Paraná Curitiba Brazil Health Technology Graduate Program Pucpr Paraná Curitiba Brazil

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Surgical center scheduling is challenging to schedule physical spaces to reduce costs and increase productivity. This study presents a framework called PM4SOS (Process Mining for Simulation, optimization, and Scheduling) that facilitates the generation of an operating room schedule in an automated way and integrates, reducing cognitive overload and waste of time. This framework allows the evaluation of restrictions to get the best performance of surgical schedules. PM4SOS combines process mining with data from event logs, generation of the automated simulation model, and case-based reasoning (CBR) to analyze management indicators, allowing significant gains in optimization of the scheduling of surgical centers. The results with PM4SOS help decision-making in hospital environments to better use physical resources and human resources, such as reducing waiting time, optimizing surgery execution time, and resource capacity management. © 2022 IEEE.

关键词： Genetic algorithms

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Enhancing dynamic ensemble selection: combining self-generating prototypes and meta-classifier for data classification

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Neural Computing and Applications 2024年第32期36卷 20295-20320页

作者： Manastarla, Alberto Silva, Leandro A. Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University R. da Consolação 930 - Consolação SP Sao Paulo01302-907 Brazil Computing and Informatics Faculty and Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University R. da Consolação 930 - Consolação SP Sao Paulo01302-907 Brazil

In dynamic ensemble selection (DES) techniques, the competence level of each classifier is estimated from a pool of classifiers, and only the most competent ones are selected to classify a specific test sample and predict its class labels. A significant challenge in DES is efficiently estimating classifier competence for accurate prediction, especially when these techniques employ the K-Nearest Neighbors (KNN) algorithm to define the competence region of a test sample based on a validation set (known as the dynamic selection dataset or DSEL). This challenge is exacerbated when the DSEL does not accurately reflect the original data distribution or contains noisy data. Such conditions can reduce the precision of the system, induce unexpected behaviors, and compromise stability. To address these issues, this paper introduces the self-generating prototype ensemble selection (***) framework, which combines meta-learning with prototype selection. The proposed meta-classifier of *** supports multiple classification algorithms and utilizes meta-features from prototypes derived from the original training set, enhancing the selection of the best classifiers for a test sample. The method improves the efficiency of KNN in defining competence regions by generating a reduced and noise-free DSEL set that preserves the original data distribution. Furthermore, the *** framework facilitates tailored optimization for specific classification challenges through the use of hyperparameters that control prototype selection and the meta-classifier operation mode to select the most appropriate classification algorithm for dynamic selection. Empirical evaluations of twenty-four classification problems have demonstrated that *** outperforms state-of-the-art DES methods as well as traditional single-model and ensemble methods in terms of accuracy, confirming its effectiveness across a wide range of classification contexts. © The Author(s), under exclusive licence to Springer-Ver

关键词： Nearest neighbor search

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Automatic quantitative and morphometric analysis of muscle fibers 30

Automatic quantitative and morphometric analysis of muscle f...

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30th International Conference on Systems, Signals and Image Processing, IWSSIP 2023

作者： Da Silva, Sergio A. Santos, Eduardo O. Carvalho, Mateus F. T. Flores, Franklin C. Almeida, Fernanda L. A. Costa, Cecilia E. M. Costa, Yandre M. G. State University of Maringá Department of Informatics Maringá Brazil State Univerity of Maringá Graduate Program in Physiological Sciences Maringá Brazil State University of Maringá Graduate Program in Biological Sciences Maringá Brazil

ISBN: (纸本)9798350337297

In this work, we propose an automated solution to support researchers of histological sciences labs on the analysis of muscle fibers microscopic images. For this purpose, we developed a dataset carefully curated composed of semi-seriated muscle samples. The manual annotation of these images is laborious and tiring, and the results may become compromised as the human gets tired. Thus, we propose a framework to address this problem which starts with a preprocessing step, aiming to mitigate discrepancies regarding the color variation frequently found in this type of image, followed by the use of Mask R-CNN, to automatically perform muscle fibers segmentation and also to extract the smallest diameter of these fibers, which is an important measure for further analysis. Experiments showed that there is no statistically significant difference between the results obtained manually and those obtained using our proposal. The dataset used in the experiments is also a contribution of this work. © 2023 IEEE.

关键词： Muscle

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PrimaDNN':A Characteristics-aware DNN Customization for Singing Technique Detection 31

PrimaDNN':A Characteristics-aware DNN Customization for Sing...

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31st European Signal Processing Conference, EUSIPCO 2023

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

ISBN: (纸本)9789464593600

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. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Deep neural networks

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Revealing a Country’s Government Discourse Through BERT-based Topic Modeling in the US Presidential Speeches 11

Revealing a Country’s Government Discourse Through BERT-bas...

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11th International Conference on Computer, Control, informatics and its Applications, IC3INA 2024

作者： Zain, Rafi Mahmud Anggai, Sajarwo Tukiyat Musyafa, Ahmad Waskita, A.A. Graduate Program of Informatics Engineering Universitas Pamulang Pamulang Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798331542313

This paper analyzes presidential speeches from the United States, delivered by the current president, Joe Biden, from his inauguration until April 3, 2024, using BERT-based topic modeling techniques. Since the role and actions of the United States affect global affairs, inferring latent topics from these speeches are important for making informed decisions and also useful to researchers. We provide experiments combining different embedding methods and clustering algorithms for dimensionality reduction. Our experiment results demonstrated that using DistilBERT embeddings followed by HDBSCAN clustering and UMAP dimensionality reduction consistently produced meaningful and interpretable topics, and this paper introduces the UMASS and NPMI metrics for evaluating topic coherence, with the best values of these metrics among the other schemes investigated. Some of the dominant themes conveyed in the American president’s state of the union speech that were successfully revealed in this study include international cooperation, alongside significant attention to environmental, community, health, and economic issues. © 2024 IEEE.

关键词： Embeddings

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