Land cover maps are a critical tool to support informed policy development, planning, and resourc... more Land cover maps are a critical tool to support informed policy development, planning, and resource management decisions. With significant upsides, the automatic production of Land Use/Land Cover maps has been a topic of interest for the remote sensing community for several years, but it is still fraught with technical challenges. One such challenge is the imbalanced nature of most remotely sensed data. The asymmetric class distribution impacts negatively the performance of classifiers and adds a new source of error to the production of these maps. In this paper, we address the imbalanced learning problem, by using K-means and the Synthetic Minority Oversampling Technique (SMOTE) as an improved oversampling algorithm. K-means SMOTE improves the quality of newly created artificial data by addressing both the between-class imbalance, as traditional oversamplers do, but also the within-class imbalance, avoiding the generation of noisy data while effectively overcoming data imbalance. Th...
In the age of the data deluge there are still many domains and applications restricted to the use... more In the age of the data deluge there are still many domains and applications restricted to the use of small datasets. The ability to harness these small datasets to solve problems through the use of supervised learning methods can have a significant impact in many important areas. The insufficient size of training data usually results in unsatisfactory performance of machine learning algorithms. The current research work aims to contribute to mitigate the small data problem through the creation of artificial instances, which are added to the training process. The proposed algorithm, Geometric Small Data Oversampling Technique, uses geometric regions around existing samples to generate new high quality instances. Experimental results show a significant improvement in accuracy when compared with the use of the initial small dataset as well as other popular artificial data generation techniques.
In remote sensing, Active Learning (AL) has become an important technique to collect informative ... more In remote sensing, Active Learning (AL) has become an important technique to collect informative ground truth data “on-demand” for supervised classification tasks. Despite its effectiveness, it is still significantly reliant on user interaction, which makes it both expensive and time consuming to implement. Most of the current literature focuses on the optimization of AL by modifying the selection criteria and the classifiers used. Although improvements in these areas will result in more effective data collection, the use of artificial data sources to reduce human–computer interaction remains unexplored. In this paper, we introduce a new component to the typical AL framework, the data generator, a source of artificial data to reduce the amount of user-labeled data required in AL. The implementation of the proposed AL framework is done using Geometric SMOTE as the data generator. We compare the new AL framework to the original one using similar acquisition functions and classifiers o...
Fraud in public funding can have deleterious consequences for societies’ economic, social, and po... more Fraud in public funding can have deleterious consequences for societies’ economic, social, and political well-being. Fraudulent activity associated with public procurement contracts accounts for losses of billions of euros every year. Thus, it is of utmost relevance to explore analytical frameworks that can help public authorities identify agents that are more susceptible to irregular activities. Here, we use standard network science methods to study the co-bidding relationships between firms that participate in public tenders issued by the 184 municipalities of the State of Ceará (Brazil) between 2015 and 2019. We identify 22 groups/communities of firms with similar patterns of procurement activity, defined by their geographic and activity scopes. The profiling of the communities allows us to highlight organizations that are more susceptible to market manipulation and irregular activities. Our work reinforces the potential application of network analysis in policy to unfold the com...
Proceedings of the 2020 3rd International Conference on Big Data Technologies, 2020
Mobile payment (M-payment), as an emerging financial transaction method has been widely adopted i... more Mobile payment (M-payment), as an emerging financial transaction method has been widely adopted in various contexts. In order to investigate the significance factors and espoused cultural moderators impacting users' M-payment continuance usage intention in China, this study proposes a comprehensive model integrating Unified Theory of Acceptance and Use of Technology (UTAUT) and Expectancy Confirmation Model (ECM) with trust variable espoused by Hofstede's cultural value to investigate factors affecting users' continuance intention of using M-payment. In addition, based on the proposed model, researchers can more accurately explain user' behavior not only corresponding technological perceptions, but also mental expectations and espoused cultural values for various technology continuance acceptance under different cultural background. CCS Concepts • Computing methodologies ➝Modeling and simulation ➝Model development and analysis ➝Modeling methodologies
International Journal of Environmental Research and Public Health, 2021
Shopping through Live-Streaming Shopping Apps (LSSAs) as an emerging consumption phenomenon has i... more Shopping through Live-Streaming Shopping Apps (LSSAs) as an emerging consumption phenomenon has increased dramatically in recent years, especially during the COVID-19 lockdown period. However, insufficient studies have focused on the psychological processes undergone in different customer demographics while shopping via LSSAs under pandemic conditions. This study integrated the Unified Theory of Acceptance and Use of Technology 2 with Flow Theory into a Stimulus-Organism-Response framework to investigate the psychological processes of different customer demographics during the COVID-19 lockdown period. A total of 374 validated data were analyzed by covariance-based structural equation modelling. The statistical results demonstrated by the proposed model showed a significant discrepancy between different gender groups, in which Flow, as a mediator, representing users’ engagement and immersion in shopping via LSSAs, was significantly moderated by gender where connection between stimul...
E-learning systems have witnessed a usage and research increase in the past decade. This article ... more E-learning systems have witnessed a usage and research increase in the past decade. This article presents the e-learning concepts ecosystem. It summarizes the various scopes on e-learning studies. Here [the authors] propose an e-learning theoretical framework. This theory framework is based upon three principal dimensions: users, technology, and services related to e-learning. This article presents an in-depth literature review on those dimensions. The article first presents the related concepts of computer use in learning across time, revealing the emergence of new trends on e-learning. The theoretical framework is a contribution for guiding e-learning studies. The article classifies the stakeholder groups and their relationship with e-learning systems. The framework shows a typology of e-learning systems' services. This theoretical approach integrates learning strategies, technologies and stakeholders.
Theoretical Development: Extending the Flow Theory with Variables from the UTAUT2 Model
2020 IEEE 6th International Conference on Computer and Communications (ICCC), 2020
According to the dramatic development of innovative information technology in worldwide ranges, b... more According to the dramatic development of innovative information technology in worldwide ranges, business climate has changed from traditional commerce to virtual commerce in recent two decades. It is important to synthetically understand customers' adoption intention of new technology for better business management and strategy involved with information technology. Thus, this study extends the Flow theory by integrating variables from the revised Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model and satisfaction to propose a theoretical development for investigating the factors determining customers' behavioral intention on adopting new information technology. In addition, the proposed theoretical development contributes the relevant researches on systematical understanding customers' adoption intention determined from technological perceptions to mental cognition. Moreover, the proposed framework and measurement method can be applied as reference for r...
G-SOMO: An oversampling approach based on self-organized maps and geometric SMOTE
Expert Systems with Applications, 2021
Abstract Traditional supervised machine learning classifiers are challenged to learn highly skewe... more Abstract Traditional supervised machine learning classifiers are challenged to learn highly skewed data distributions as they are designed to expect classes to equally contribute to the minimization of the classifiers cost function. Moreover, the classifiers design expects equal misclassification costs, causing a bias for overrepresented classes. Different strategies have been proposed to correct this issue. The modification of the data set has become a common practice since the procedure is generalizable to all classifiers. Various algorithms to rebalance the data distribution through the creation of synthetic instances were proposed in the past. In this paper, we propose a new oversampling algorithm named G-SOMO. The algorithm identifies optimal areas to create artificial data instances in an informed manner and utilizes a geometric region during the data generation process to increase their variability. Our empirical results on 69 datasets, validated with different classifiers and metrics against a benchmark of commonly used oversampling methods show that G-SOMO consistently outperforms competing oversampling methods. Additionally, the statistical significance of our results is established.
India has proven to be one of the most diverse and dynamic economic regions in the world. Its ind... more India has proven to be one of the most diverse and dynamic economic regions in the world. Its industry focuses predominantly on the service sector and immediate economic growth seems to steer India into the economic superpower. India's unique business landscape is felt at a regional level, where massive urbanization has become an unavoidable consequence of population growth and spatial allocation to the economic hubs of metropolitan cities. Mumbai, one of the world's largest cities, represents a unique combination of a diverse economic landscape and the growth of a megacity. The role of Mumbai in India's growth is of crucial importance for India's business landscape. This paper explores the massive urbanization processes of Mumbai's peri-urban areas and compares urban sprawl with the location of its business landscape. A spatial accounting methodology based on the proximity of Mumbai's different economic hubs will be used to measure the underlying pattern of the Mumbai region, concerning past and present urbanization, and the effect of this urbanization process has on the possible location of businesses. This business-urban ecosystem perspective will be implemented by a spatial analysis on the correlation between urban compactness and urban footprints, in relation to business concentration and its spatiotemporal evolution over the last hundred years.
International Journal of Environmental Research and Public Health, 2021
Owing to the convenience, reliability and contact-free feature of Mobile payment (M-payment), it ... more Owing to the convenience, reliability and contact-free feature of Mobile payment (M-payment), it has been diffusely adopted in China during the COVID-19 pandemic to reduce the direct and indirect contacts in transactions, allowing social distancing to be maintained and facilitating stabilization of the social economy. This paper aims to comprehensively investigate the technological and mental factors affecting users’ adoption intentions of M-payment under the COVID-19 pandemic, to expand the domain of technology adoption under the emergency situation. This study integrated Unified Theory of Acceptance and Use of Technology (UTAUT) with perceived benefits from Mental Accounting Theory (MAT), and two additional variables (perceived security and trust) to investigate 739 smartphone users’ adoption intentions of M-payment during the COVID-19 pandemic in China. The empirical results showed that users’ technological and mental perceptions conjointly influence their adoption intentions of ...
International Journal of Hospitality Management, 2020
Food delivery apps (FDAs) as an emerging online-to-offline mobile technology, have been widely ad... more Food delivery apps (FDAs) as an emerging online-to-offline mobile technology, have been widely adopted by catering businesses and customers. Especially, as they have provided two-way beneficial catering delivery services in rescuing catering enterprises and satisfying customers' technological and mental exceptions under the COVID-19 global pandemic condition. This study proposes a comprehensive model integrating UTAUT, ECM and TTF with the trust factor and examines 532 valid FDA users' continuance intention of using FDAs during the COVID-19 pandemic period in China. The statistical results and discussions show that satisfaction is the most significant factor, and perceived task-technology fit, trust, performance expectancy, social influence and confirmation have direct or indirect positive impacts on users' continuance usage intention of FDAs during the COVID-19 pandemic period. In addition, relevant researches and stakeholders should consider the specific characteristic of technology being associated with users' technological and mental perceptions for better understanding and explaining users' continuance intention.
Land cover maps are a critical tool to support informed policy development, planning, and resourc... more Land cover maps are a critical tool to support informed policy development, planning, and resource management decisions. With significant upsides, the automatic production of Land Use/Land Cover maps has been a topic of interest for the remote sensing community for several years, but it is still fraught with technical challenges. One such challenge is the imbalanced nature of most remotely sensed data. The asymmetric class distribution impacts negatively the performance of classifiers and adds a new source of error to the production of these maps. In this paper, we address the imbalanced learning problem, by using K-means and the Synthetic Minority Oversampling Technique (SMOTE) as an improved oversampling algorithm. K-means SMOTE improves the quality of newly created artificial data by addressing both the between-class imbalance, as traditional oversamplers do, but also the within-class imbalance, avoiding the generation of noisy data while effectively overcoming data imbalance. Th...
In the age of the data deluge there are still many domains and applications restricted to the use... more In the age of the data deluge there are still many domains and applications restricted to the use of small datasets. The ability to harness these small datasets to solve problems through the use of supervised learning methods can have a significant impact in many important areas. The insufficient size of training data usually results in unsatisfactory performance of machine learning algorithms. The current research work aims to contribute to mitigate the small data problem through the creation of artificial instances, which are added to the training process. The proposed algorithm, Geometric Small Data Oversampling Technique, uses geometric regions around existing samples to generate new high quality instances. Experimental results show a significant improvement in accuracy when compared with the use of the initial small dataset as well as other popular artificial data generation techniques.
In remote sensing, Active Learning (AL) has become an important technique to collect informative ... more In remote sensing, Active Learning (AL) has become an important technique to collect informative ground truth data “on-demand” for supervised classification tasks. Despite its effectiveness, it is still significantly reliant on user interaction, which makes it both expensive and time consuming to implement. Most of the current literature focuses on the optimization of AL by modifying the selection criteria and the classifiers used. Although improvements in these areas will result in more effective data collection, the use of artificial data sources to reduce human–computer interaction remains unexplored. In this paper, we introduce a new component to the typical AL framework, the data generator, a source of artificial data to reduce the amount of user-labeled data required in AL. The implementation of the proposed AL framework is done using Geometric SMOTE as the data generator. We compare the new AL framework to the original one using similar acquisition functions and classifiers o...
Fraud in public funding can have deleterious consequences for societies’ economic, social, and po... more Fraud in public funding can have deleterious consequences for societies’ economic, social, and political well-being. Fraudulent activity associated with public procurement contracts accounts for losses of billions of euros every year. Thus, it is of utmost relevance to explore analytical frameworks that can help public authorities identify agents that are more susceptible to irregular activities. Here, we use standard network science methods to study the co-bidding relationships between firms that participate in public tenders issued by the 184 municipalities of the State of Ceará (Brazil) between 2015 and 2019. We identify 22 groups/communities of firms with similar patterns of procurement activity, defined by their geographic and activity scopes. The profiling of the communities allows us to highlight organizations that are more susceptible to market manipulation and irregular activities. Our work reinforces the potential application of network analysis in policy to unfold the com...
Proceedings of the 2020 3rd International Conference on Big Data Technologies, 2020
Mobile payment (M-payment), as an emerging financial transaction method has been widely adopted i... more Mobile payment (M-payment), as an emerging financial transaction method has been widely adopted in various contexts. In order to investigate the significance factors and espoused cultural moderators impacting users' M-payment continuance usage intention in China, this study proposes a comprehensive model integrating Unified Theory of Acceptance and Use of Technology (UTAUT) and Expectancy Confirmation Model (ECM) with trust variable espoused by Hofstede's cultural value to investigate factors affecting users' continuance intention of using M-payment. In addition, based on the proposed model, researchers can more accurately explain user' behavior not only corresponding technological perceptions, but also mental expectations and espoused cultural values for various technology continuance acceptance under different cultural background. CCS Concepts • Computing methodologies ➝Modeling and simulation ➝Model development and analysis ➝Modeling methodologies
International Journal of Environmental Research and Public Health, 2021
Shopping through Live-Streaming Shopping Apps (LSSAs) as an emerging consumption phenomenon has i... more Shopping through Live-Streaming Shopping Apps (LSSAs) as an emerging consumption phenomenon has increased dramatically in recent years, especially during the COVID-19 lockdown period. However, insufficient studies have focused on the psychological processes undergone in different customer demographics while shopping via LSSAs under pandemic conditions. This study integrated the Unified Theory of Acceptance and Use of Technology 2 with Flow Theory into a Stimulus-Organism-Response framework to investigate the psychological processes of different customer demographics during the COVID-19 lockdown period. A total of 374 validated data were analyzed by covariance-based structural equation modelling. The statistical results demonstrated by the proposed model showed a significant discrepancy between different gender groups, in which Flow, as a mediator, representing users’ engagement and immersion in shopping via LSSAs, was significantly moderated by gender where connection between stimul...
E-learning systems have witnessed a usage and research increase in the past decade. This article ... more E-learning systems have witnessed a usage and research increase in the past decade. This article presents the e-learning concepts ecosystem. It summarizes the various scopes on e-learning studies. Here [the authors] propose an e-learning theoretical framework. This theory framework is based upon three principal dimensions: users, technology, and services related to e-learning. This article presents an in-depth literature review on those dimensions. The article first presents the related concepts of computer use in learning across time, revealing the emergence of new trends on e-learning. The theoretical framework is a contribution for guiding e-learning studies. The article classifies the stakeholder groups and their relationship with e-learning systems. The framework shows a typology of e-learning systems' services. This theoretical approach integrates learning strategies, technologies and stakeholders.
Theoretical Development: Extending the Flow Theory with Variables from the UTAUT2 Model
2020 IEEE 6th International Conference on Computer and Communications (ICCC), 2020
According to the dramatic development of innovative information technology in worldwide ranges, b... more According to the dramatic development of innovative information technology in worldwide ranges, business climate has changed from traditional commerce to virtual commerce in recent two decades. It is important to synthetically understand customers' adoption intention of new technology for better business management and strategy involved with information technology. Thus, this study extends the Flow theory by integrating variables from the revised Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model and satisfaction to propose a theoretical development for investigating the factors determining customers' behavioral intention on adopting new information technology. In addition, the proposed theoretical development contributes the relevant researches on systematical understanding customers' adoption intention determined from technological perceptions to mental cognition. Moreover, the proposed framework and measurement method can be applied as reference for r...
G-SOMO: An oversampling approach based on self-organized maps and geometric SMOTE
Expert Systems with Applications, 2021
Abstract Traditional supervised machine learning classifiers are challenged to learn highly skewe... more Abstract Traditional supervised machine learning classifiers are challenged to learn highly skewed data distributions as they are designed to expect classes to equally contribute to the minimization of the classifiers cost function. Moreover, the classifiers design expects equal misclassification costs, causing a bias for overrepresented classes. Different strategies have been proposed to correct this issue. The modification of the data set has become a common practice since the procedure is generalizable to all classifiers. Various algorithms to rebalance the data distribution through the creation of synthetic instances were proposed in the past. In this paper, we propose a new oversampling algorithm named G-SOMO. The algorithm identifies optimal areas to create artificial data instances in an informed manner and utilizes a geometric region during the data generation process to increase their variability. Our empirical results on 69 datasets, validated with different classifiers and metrics against a benchmark of commonly used oversampling methods show that G-SOMO consistently outperforms competing oversampling methods. Additionally, the statistical significance of our results is established.
India has proven to be one of the most diverse and dynamic economic regions in the world. Its ind... more India has proven to be one of the most diverse and dynamic economic regions in the world. Its industry focuses predominantly on the service sector and immediate economic growth seems to steer India into the economic superpower. India's unique business landscape is felt at a regional level, where massive urbanization has become an unavoidable consequence of population growth and spatial allocation to the economic hubs of metropolitan cities. Mumbai, one of the world's largest cities, represents a unique combination of a diverse economic landscape and the growth of a megacity. The role of Mumbai in India's growth is of crucial importance for India's business landscape. This paper explores the massive urbanization processes of Mumbai's peri-urban areas and compares urban sprawl with the location of its business landscape. A spatial accounting methodology based on the proximity of Mumbai's different economic hubs will be used to measure the underlying pattern of the Mumbai region, concerning past and present urbanization, and the effect of this urbanization process has on the possible location of businesses. This business-urban ecosystem perspective will be implemented by a spatial analysis on the correlation between urban compactness and urban footprints, in relation to business concentration and its spatiotemporal evolution over the last hundred years.
International Journal of Environmental Research and Public Health, 2021
Owing to the convenience, reliability and contact-free feature of Mobile payment (M-payment), it ... more Owing to the convenience, reliability and contact-free feature of Mobile payment (M-payment), it has been diffusely adopted in China during the COVID-19 pandemic to reduce the direct and indirect contacts in transactions, allowing social distancing to be maintained and facilitating stabilization of the social economy. This paper aims to comprehensively investigate the technological and mental factors affecting users’ adoption intentions of M-payment under the COVID-19 pandemic, to expand the domain of technology adoption under the emergency situation. This study integrated Unified Theory of Acceptance and Use of Technology (UTAUT) with perceived benefits from Mental Accounting Theory (MAT), and two additional variables (perceived security and trust) to investigate 739 smartphone users’ adoption intentions of M-payment during the COVID-19 pandemic in China. The empirical results showed that users’ technological and mental perceptions conjointly influence their adoption intentions of ...
International Journal of Hospitality Management, 2020
Food delivery apps (FDAs) as an emerging online-to-offline mobile technology, have been widely ad... more Food delivery apps (FDAs) as an emerging online-to-offline mobile technology, have been widely adopted by catering businesses and customers. Especially, as they have provided two-way beneficial catering delivery services in rescuing catering enterprises and satisfying customers' technological and mental exceptions under the COVID-19 global pandemic condition. This study proposes a comprehensive model integrating UTAUT, ECM and TTF with the trust factor and examines 532 valid FDA users' continuance intention of using FDAs during the COVID-19 pandemic period in China. The statistical results and discussions show that satisfaction is the most significant factor, and perceived task-technology fit, trust, performance expectancy, social influence and confirmation have direct or indirect positive impacts on users' continuance usage intention of FDAs during the COVID-19 pandemic period. In addition, relevant researches and stakeholders should consider the specific characteristic of technology being associated with users' technological and mental perceptions for better understanding and explaining users' continuance intention.
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Papers by Fernando Bacao