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Ubiquitous computing in light of human phenotypes: foundations, challenges, and opportunities

Journal of Ambient Intelligence and Humanized computing 2023年第3期14卷 2341-2349页

作者： Bavaresco, Rodrigo Simon Barbosa, Jorge Luis Victória Applied Computing Graduate Program - PPGCA University of Vale do Rio dos Sinos - UNISINOS Av. Unisinos São Leopoldo Rio Grande do Sul93.022-000 Brazil

The interest in human phenotypes has leveraged interdisciplinary efforts encouraging a better understanding of the broad spectrum of psychological and behavioral disorders. Moreover, the usage of mobile and wearable devices along with unobtrusive computational capabilities provides an extensive amount of information that allows the characterization of phenotypes. This article describes the human phenotype through the lens of computational range and reviews state-of-the-art computational phenotyping. Furthermore, the article discusses computational phenotyping’s extension concerning the combination of intelligent environments and personal mobile devices, addressing technical, managerial, and ethical challenges. This combination reinforces ubiquitous computational capabilities for phenotyping as a facilitator of interdisciplinary information convergence in favor of clinical and biomedical research. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Wearable technology

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A Novel Approach for Commercial Opportunities Qualification Using the BANT Methodology under the Fuzzy Set Theory Framework. 28th

A Novel Approach for Commercial Opportunities Qualification ...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Leite, Marcus Vinicius Abe, Jair Minoro Hoffmann Souza, Marcos Leandro Graduate Program in Production Engineering-Paulista University-UNIP Rua Dr. Bacelar 1212 São Paulo-SP04026002 Brazil Graduate Program in Applied Computing-Vale do Rio dos Sinos University-UNISINOS Av. Unisinos 950 - São LeopoldoRS-93022-000 Brazil

Demand generation is crucial for organizations, supplying sales teams with well-qualified commercial opportunities. Despite the wide variety of existing opportunity qualification methodologies, the subjective nature of experts' final evaluation remains an obstacle to efficiency and productivity in the business process. This research investigated how Fuzzy Set Theory and Fuzzy Logic could be applied to the BANT methodology for qualifying commercial opportunities, aiming to replace these deliberative evaluations by experts to increase the sales cycle performance. A fuzzy inference system was developed to emulate the assessments of the experts. The analysis of the ratings obtained after processing a sample of commercial opportunities from 2022 and 2023 confirmed the system's effectiveness in aligning with expert perceptions. While the study indicated room for refinements in the model, the findings underscore the potential to streamline the qualification of opportunities and improve sales cycle performance. © 2024 The Authors.

关键词： Fuzzy set theory

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Analysis of human mobility behavior in Smart Cities and Smart Environments: A systematic literature review and taxonomy

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Journal of Smart Cities and Society 2024年第PRE-PRESS期Pre-press卷 1-36页

作者： Borges, Carlos Eduardo Liedtke Aranda, Jorge Arthur Schneider Barbosa, Jorge Luis Victória Applied Computing Graduate Program (PPGCA) University of Vale do Rio dos Sinos (UNISINOS) São Leopoldo Brazil

The analysis of human mobility behavior through computational techniques finds applications in various domains and provides valuable insights for urban planning, transportation services, and a deeper understanding of human interactions in Smart Cities and Smart Environments. In this scenario, this study presents a Systematic Literature Review (SLR) with the following main question: How are computational techniques being used to analyse human mobility behavior in Smart Cities and Smart Environments? A total of 5989 articles were initially found and filtered, resulting in 56 articles reviewed. As the main contributions, this study provides responses to 19 research questions. A list of the challenges and the computational techniques identified is provided. The algorithms, machine learning techniques and data-sources used by the reviewed studies are also presented and organized through taxonomies. A comprehensive discussion of the identified techniques is conducted, finishing with a compilation of challenges, open issues and research opportunities. To the best of our knowledge, this is the first study that reviewed human mobility behavior covering a wide range of scenarios, including urban mobility, public transport, points and regions of interest, ridesharing, bike-sharing, traffic analysis, driving behavior, electric vehicle charging stations planning, mobility on demand, crowd analysis and others.

关键词： Smart City Smart Environment behavior mobility crowd analysis

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Mineralogical Classification of Sedimentary Rocks by Spectral Matching Techniques Based on Library and Field-Work References 14

Mineralogical Classification of Sedimentary Rocks by Spectra...

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14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2024

作者： Zanotta, Daniel C. De Souza, Lais V. De Souza, Marcelo K. Ayres, Alysson S. Sales, Vinicius F. Racolte, Graciela R. Ibanez, Delano M. Silveira, Luiz G. Veronez, Mauricio R. UNISINOS Vizlab X-Reality and GeoInformatics Lab RS São Leopoldo Brazil Graduate Program in Applied Computing Vale do Rio dos Sinos University RS São Leopoldo Brazil RJ Brazil

ISBN: (纸本)9798331513139

Sedimentary rocks are of great importance for the oil and gas industry. Spectral matching using known references made on hyperspectral images is one of the most rapid and cost effective alternatives for the detailed analysis in these areas, since they can adequately catch the specific absorption features of the multiple minerals available. In many initiatives, scientists run the methods with standard spectral references, usually from spectral libraries or samples from other places. However, for the success of their application, they must be used along with spectral references that accurately describe the actual constituents of the surface. In this paper we scrutinize three different ways for mineralogical mapping performed by spectral matching techniques. The results showed that using reference spectra from samples collected directly on the surveyed environment can considerably improve the results, reaching high scores, pointing out that many places share common substances, but each specific material has particular differences that cannot be generalized. © 2024 IEEE.

关键词： Gas industry

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A Novel Approach for Commercial Opportunities Qualification Using the BANT Methodology under the Fuzzy Set Theory Framework.

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Procedia Computer Science 2024年 246卷 1271-1280页

作者： Marcus Vinicius Leite Jair Minoro Abe Marcos Leandro Hoffmann Souza Graduate Program in Production Engineering-Paulista University-UNIP Rua Dr. Bacelar 1212-São Paulo-SP-04026002 Brazil Graduate Program in Applied Computing-Vale do Rio dos Sinos University-UNISINOS Av. Unisinos 950 - São Leopoldo-RS-93022-000- Brazil

Demand generation is crucial for organizations, supplying sales teams with well-qualified commercial opportunities. Despite the wide variety of existing opportunity qualification methodologies, the subjective nature of experts’ final evaluation remains an obstacle to efficiency and productivity in the business process. This research investigated how Fuzzy Set Theory and Fuzzy Logic could be applied to the BANT methodology for qualifying commercial opportunities, aiming to replace these deliberative evaluations by experts to increase the sales cycle performance. A fuzzy inference system was developed to emulate the assessments of the experts. The analysis of the ratings obtained after processing a sample of commercial opportunities from 2022 and 2023 confirmed the system’s effectiveness in aligning with expert perceptions. While the study indicated room for refinements in the model, the findings underscore the potential to streamline the qualification of opportunities and improve sales cycle performance.

关键词： Fuzzy Set Theory Fuzzy Logic Decision Making Commercial Opportunity Qualification BANT Methodology Sales Force Support Commercial Process Optimization

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Code Autocomplete Using Transformers 10th

Code Autocomplete Using Transformers

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10th Brazilian Conference on Intelligent Systems, BRACIS 2021

作者： Meyrer, Gabriel T. Araújo, Denis A. Rigo, Sandro J. CWI Software São Leopoldo Brazil Applied Computing Graduate Program UNISINOS São Leopoldo Brazil

ISBN: (纸本)9783030916985

In software development, code autocomplete can be an essential tool in order to accelerate coding. However, many of these tools built into the IDEs are limited to suggesting only methods or arguments, often presenting to the user long lists of irrelevant items. Since innovations introduced by transformer-based models that have reached the state of the art performance in tasks involving natural language processing (NLP), the application of these models also in tasks involving code intelligence, such as code completion, has become a frequent object of study in recent years. In these paper, we present a transformer-based model trained on 1.2 million Java files gathered from top-starred Github repositories. Our evaluation approach was based on measuring the model’s ability to predict the completion of a line, proposing a new metric to measure the applicability of the suggestions that we consider better adapted to the practical reality of the code completion task. With a recently developed Java web project as test set, our experiments showed that in 55.9% of the test cases the model brought at least one suggestion applicable, while the best baseline model presented this in 26.5%. © 2021, Springer Nature Switzerland AG.

关键词： Software design

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Saasport model: Exploring protocol portability, resource elasticity and microservice architecture in the efficient execution of iot applications 20

Saasport model: Exploring protocol portability, resource ela...

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20th International Conferences on WWW/Internet 2021 and applied computing 2021

作者： da Silveira, Maria Gisele Flores da Silva Silveira, Wagner da Rosa Righi, Rodrigo da Costa, Cristiano André Griebler, Dalvan Três de Maio - RS Brazil Applied Computing Graduate Program Unisinos University Av. Unisinos 950 RS São Leopoldo Brazil Av. Ipiranga 6681 RS Porto Alegre Brazil

ISBN: (纸本)9789898704344

Despite the high growth of the Internet of Things and the multitude of applications that use the information generated, it is estimated that 90 % of these data are not yet fully used. This is because IoT systems are based on heterogeneous networks and devices with resource restrictions, which cannot execute significant processing. So one of the alternatives that have been addressed is the use of cloud applications, where the data can be appropriately processed, stored, and analyzed. In the cloud, applications can then employ resource elasticity and be developed in microservice architectures. In state-of-the-art, the main efforts to provide portability and efficient treatment of IoT applications are described. However, a platform that provides protocol portability, resource elasticity using microservices architecture for the efficient execution of IoT applications has not yet been implemented. In this context, this work presents SaaSport, which among its contributions, will allow the efficient treatment of data generated by IoT devices that use the MQTT and CoAP protocols. The final result of this work demonstrates that the model provides an efficient treatment of the data generated in IoT environments. © 20th International Conferences on WWW/Internet 2021 and applied computing 2021.

关键词： Elasticity

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Modeling Of Mean Tropospheric Temperature From Convolutional Neural Networks

Modeling Of Mean Tropospheric Temperature From Convolutional...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Diego Brum Vinicius Francisco Rofatto Leonardo Scalco Rafaela de Oliveira Pena Luiz Gonzaga Daniel C. Zanotta Luiz Fernando Sapucci Mauricio Roberto Veronez VIZLAB—X-Reality and Geoinformatics Laboratory UNISINOS Sao Leopoldo RS Brazil Graduate Program on Applied Computing UNISINOS Sao Leopoldo RS Brazil Institute of Geography Federal University of Uberlandia Monte Carmelo MG Brazil Graduate Program in Agriculture and Geospatial Information Federal University of Uberlandia Monte Carmelo MG Brazil Brazilian Institute of Space Research (INPE) Cachoeira Paulista SP Brazil

The Mean Tropospheric Temperature (Tm) is important for the sake of the conversion between Zenith Path Delay (ZPD) to Precipitable Water Vapor (PWV) because of the proportionality constant that takes Tm to be calculated. In Brazil, Tm modeling’s been traditionally performed using Multiple Linear Regression Models (MLR). However, recent works suggest the use of Deep Learning methods to model Tm values. In this work, we propose models based on Convolutional Neural Networks (CNN) using radiosonde data from 1961 to 2010. The results show that CNN models can outperform the traditional methods considering different statistic metrics like R, standard deviation, and RMSE.

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Indirect Calibration of Hyperspectral Images Using Rock Samples

Indirect Calibration of Hyperspectral Images Using Rock Samp...

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Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS

作者： Daniel C. Zanotta Leonardo D. Araújo Marcelo K. De Souza Delano M. Ibanez Luiz G. Silveira Maurício R. Veronez Vizlab — X-Reality and GeoInformatics Lab UNISINOS São Leopoldo RS Brazil Graduate Program in Applied Computing at Vale do Rio dos Sinos University São Leopoldo RS Brazil PETROBRAS - Petrobras Research and Development Center (CENPES) RJ Brazil

ISBN: (数字)9798331513139

ISBN: (纸本)9798331513146

This paper presents a methodology for the indirect calibration of hyperspectral images using radiometric measurements from field samples. While hyperspectral imaging (HSI) is essential in remote sensing, its accuracy depends on precise calibration, which is often challenging in the field. Most approaches rely on a single standard panel used as absolute reference and the empirical line method. This often leads to mistakes and uncertainties in the analysis. In this paper we propose an alternative that utilizes radiometric data from rock samples with known spectral properties to indirectly calibrate hyperspectral images. The results show specific improvements in accuracy, particularly in the shape of the curves, compared to standard calibration method. The calibrated images offer better material differentiation and more reliable spectral signatures, which are crucial for geological mapping and mineral exploration. This approach can enhance the reliability of hyperspectral data analysis in geological and environmental studies, especially in situations where time and logistics are challenging.

关键词： Accuracy Uncertainty Coal Signal processing Rocks Radiometry Calibration Minerals Standards Hyperspectral imaging

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Mineralogical Classification of Sedimentary Rocks by Spectral Matching Techniques Based on Library and Field-Work References

Mineralogical Classification of Sedimentary Rocks by Spectra...

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Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS

作者： Daniel C. Zanotta Lais V. De Souza Marcelo K. De Souza Alysson S. Ayres Vinícius F. Sales Graciela R. Racolte Delano M. Ibanez Luiz G. Silveira Maurício R. Veronez Vizlab — X-Reality and GeoInformatics Lab UNISINOS São Leopoldo RS Brazil Graduate Program in Applied Computing at Vale do Rio dos Sinos University São Leopoldo RS Brazil PETROBRAS - Petrobras Research and Development Center (CENPES) RJ Brazil

ISBN: (数字)9798331513139

ISBN: (纸本)9798331513146

关键词： Costs Systematics Signal processing Rocks Gas industry Libraries Minerals Petroleum industry Standards Hyperspectral imaging

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