Artigos Acadêmicos e Noticiosos

URI permanente desta comunidadehttps://repositorio.insper.edu.br/handle/11224/3226

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Quality Perception of São Paulo Transportation Services: A Sentiment Analysis of Citizens’ Satisfaction Regarding Bus Terminuses
(2024) Beck, Donizete; Teixeira, Marco; Maróstica, Juliana; Ferasso, Marcos
Purpose: To explore citizens’ satisfaction with all Bus Terminuses (BTs) in São Paulo City, Brazil. Method: This study performed a Sentiment Analysis of citizens' perception of 32 BTs of São Paulo, composed of 8,371 user comments on Google Maps. Originality/Relevance: This study highlights the role of Sentiment Analysis as an optimal tool for Stakeholder Analysis in the Urban Context. Findings: First, Sentiment Analysis is a valuable source for stakeholder oriented urban management. Second, sentiment Analysis provides detailed information about citizen satisfaction, providing valuable cues for urban managers to improve public service quality. Third, Smart Sustainable Cities can provide multiple and massive quantities of data that all kinds of urban stakeholders can use in decision-making processes, which helps perform Sentiment Analysis. Fourth, Sentiment Analysis is helpful for BT managers to improve BT services based on the users' feelings. Finally, further studies should explore sentiment classification in Sentiment Analysis of the critical aspects unfolded in this study as well as for exploring responsiveness of municipal public services. Methodological Contributions: This study demonstrated that Sentiment Analysis can be a method for scrutinizing stakeholders' opinions and perceptions about governmental services at the city level. Practitioner Contributions: Urban Planners, Transportation Policy Makers, and Urban Managers can use Sentiment Analysis to foster stakeholder-oriented management, which in turn fosters democracy and urban performance.
Role of Emerging Technologies in Accounting Information Systems for Achieving Strategic Flexibility through Decision-Making Performance: An Exploratory Study Based on North American and South American Firms
(2023) Yoshikuni, Adilson Carlos; Dwivedi, Rajeev; Dultra-de-Lima, Ronaldo Gomes; Parisi, Claudio; Oyadomari , José Carlos Tiomatsu
Nowadays, accounting departments highly rely on accounting information systems to make decisions based on current, updated, and contemporary data. And, most accounting practices can be enhanced by emerging technologies coupled with accounting information systems. Therefore, contemporary accounting information systems (AIS) coupled with emerging technologies is the highest priority in organizations to make decisions that can contribute to strategic flexibility and performance of the organizations. The objective of the study is to identify the role of information systems infrastructure integration (ISII) on strategic flexibility and innovation (SFI) through the mediated role of information systems (IS)-enabled strategic enterprise management (IS-SEM) practices and decision-making performance (DMP). The study is based on contemporary literature in the field of emerging Technologies in accounting information systems particularly business intelligence and analytics (BI &A). Resource-based view had been applied to create novel constructs to test the research framework and hypothesis. The research framework and hypothesis are tested based on 388 organizations from Brazil and USA. The results reflect that information systems infrastructure integration impacts strategic flexibility and innovations in organizations. Further, there is no difference observed between North American and South American organizations. The results of the research suggest that accounting information systems (AIS) practitioners and researchers should look beyond emerging technologies investments and shift their attention to how information systems infrastructure integration (ISII) and information systems-enabled strategic enterprise management (IS-SEM) practices can leverage decision-making performance (DMP) and impact on strategic flexibility and innovation.
Audiovisual interactive artwork via web-deployed software: Motus composes Homino-idea
(2022) Amstalden, Augusto Piato; TIAGO FERNANDES TAVARES; Costa Neto, Anésio Azevedo; Camarini, Giovana Cardi
Many art installations rely on camera-based audiovisual interactions, and this commonly requires specialized hardware and software. Consequently, audiovisual installations are usually restricted to wealthier areas, in which the specialized equipment can be afforded and properly hosted. In countries with an evident income unbalance linked to location, the geographic restriction leads to an audience restriction. In this work, we present the development of a web-deployed composition tool for audiovisual interactions that runs on the client side and does not require installing any additional software. Simultaneously, it provides visual feedback that can aid the audience to understand the experience. Consequently, the tool can be used to compose audiovisual interactions that reach a large audience via web. We further explore the tool by composing the audiovisual installation Homino-idea. The installation is inspired by the interactions between humans and the environment, and can be either shown in art venues or used online.
A multi-sensor human gait dataset captured through an optical system and inertial measurement units
(2022) Santos, Geise; Wanderley, Marcelo; TIAGO FERNANDES TAVARES; Rocha, Anderson
Diferent technologies can acquire data for gait analysis, such as optical systems and inertial measurement units (IMUs). Each technology has its drawbacks and advantages, ftting best to particular applications. The presented multi-sensor human gait dataset comprises synchronized inertial and optical motion data from 25 participants free of lower-limb injuries, aged between 18 and 47 years. A smartphone and a custom micro-controlled device with an IMU were attached to one of the participant’s legs to capture accelerometer and gyroscope data, and 42 refexive markers were taped over the whole body to record three-dimensional trajectories. The trajectories and inertial measurements were simultaneously recorded and synchronized. Participants were instructed to walk on a straight-level walkway at their normal pace. Ten trials for each participant were recorded and pre processed in each of two sessions, performed on diferent days. This dataset supports the comparison of gait parameters and properties of inertial and optical capture systems, whereas allows the study of gait characteristics specifc for each system.
Reliability and generalization of gait biometrics using 3D inertial sensor data and 3D optical system trajectories
(2022) Santos, Geise; TIAGO FERNANDES TAVARES; Rocha, Anderson
Particularities in the individuals’ style of walking have been explored for at least three decades as a biometric trait, empowering the automatic gait recognition feld. Whereas gait recognition works usually focus on improving end-to-end performance measures, this work aims at understanding which individuals’ traces are more relevant to improve subjects’ separability. For such, a manifold projection technique and a multi-sensor gait dataset were adopted to investigate the impact of each data source characteristics on this separability. Assessments have shown it is hard to distinguish individuals based only on their walking patterns in a subject-based identifcation scenario. In this setup, the subjects’ separability is more related to their physical characteristics than their movements related to gait cycles and biomechanical events. However, this study’s results also points to the feasibility of learning identity characteristics from individuals’ walking patterns learned from similarities and diferences between subjects in a verifcation setup. The explorations concluded that periodic components occurring in frequencies between 6 and 10 Hz are more signifcant for learning these patterns than events and other biomechanical movements related to the gait cycle, as usually explored in the literature.
DSSE: An environment for simulation of reinforcement learning-empowered drone swarm maritime search and rescue missions
(2024) Falcão, Renato Laffranchi; Oliveira, Jorás Custódio Campos de; Andrade, Pedro Henrique Britto Aragão; Rodrigues, Ricardo Ribeiro; FABRÍCIO JAILSON BARTH; Brancalion, José Fernando Basso
Using bundling to visualize multivariate urban mobility structure patterns in the São Paulo metropolitan area
(2021) Martins, Tallys G.; Lago, Nelson; Santana, Eduardo F. Z.; Telea, Alexandru; Kon, Fabio; Souza, Higor A. de
Internet-based technologies such as IoT, GPS-based systems, and cellular networks enable the collection of geolocated mobility data of millions of people in large metropolitan areas. In addition, large, public datasets are made available on the Internet by open government programs, providing ways for citizens, NGOs, scientists, and public managers to perform a multitude of data analysis with the goal of better understanding the city dynamics to provide means for evidence-based public policymaking. However, it is challenging to visualize huge amounts of data from mobility datasets. Plotting raw trajectories on a map often causes data occlusion, impairing the visual analysis. Displaying the multiple attributes that these trajectories come with is an even larger challenge. One approach to solve this problem is trail bundling, which groups motion trails that are spatially close in a simplified representation. In this paper, we augment a recent bundling technique to support multi-attribute trail datasets for the visual analysis of urban mobility. Our case study is based on the travel survey from the São Paulo Metropolitan Area, which is one of the most intense traffic areas in the world. The results show that bundling helps the identification and analysis of various mobility patterns for different data attributes, such as peak hours, social strata, and transportation modes.
SNA-Based Reasoning for Multi-Agent Team Composition
(2015) ANDRE FILIPE DE MORAES BATISTA; Marietto, Maria das Graças Bruno
The social network analysis (SNA), branch of complex systems can be used in the construction of multi agent systems. This paper proposes a study of how social network analysis can assist in modeling multi agent systems, while addressing similarities and differences between the two theories. We built a prototype of multi-agent systems for resolution of tasks through the formation of teams of agents that are formed on the basis of the social network established between agents. Agents make use of performance indicators to assess when should change their social network to maximize the participation in teams.
Neonatal mortality prediction with routinely collected data: a machine learning approach
(2021) ANDRE FILIPE DE MORAES BATISTA; Diniz, Carmen S. G.; Bonilha, Eliana A.; Kawachi, Ichiro; Chiavegatto Filho, Alexandre D. P.
Background: Recent decreases in neonatal mortality have been slower than expected for most countries. This study aims to predict the risk of neonatal mortality using only data routinely available from birth records in the largest city of the Americas. Methods: A probabilistic linkage of every birth record occurring in the municipality of São Paulo, Brazil, between 2012 e 2017 was performed with the death records from 2012 to 2018 (1,202,843 births and 447,687 deaths), and a total of 7282 neonatal deaths were identified (a neonatal mortality rate of 6.46 per 1000 live births). Births from 2012 and 2016 (N = 941,308; or 83.44% of the total) were used to train five different machine learning algorithms, while births occurring in 2017 (N = 186,854; or 16.56% of the total) were used to test their predictive performance on new unseen data. Results: The best performance was obtained by the extreme gradient boosting trees (XGBoost) algorithm, with a very high AUC of 0.97 and F1-score of 0.55. The 5% births with the highest predicted risk of neonatal death included more than 90% of the actual neonatal deaths. On the other hand, there were no deaths among the 5% births with the lowest predicted risk. There were no significant differences in predictive performance for vulnerable subgroups. The use of a smaller number of variables (WHO’s five minimum perinatal indicators) decreased overall performance but the results still remained high (AUC of 0.91). With the addition of only three more variables, we achieved the same predictive performance (AUC of 0.97) as using all the 23 variables originally available from the Brazilian birth records. Conclusion: Machine learning algorithms were able to identify with very high predictive performance the neonatal mortality risk of newborns using only routinely collected data.
Predictors of tooth loss: A machine learning approach
(2021) Elani, Hawazin W.; ANDRE FILIPE DE MORAES BATISTA; W. Murray Thomson; Kawachi, Ichiro; Chiavegatto Filho, Alexandre D. P.
Introduction Little is understood about the socioeconomic predictors of tooth loss, a condition that can negatively impact individual’s quality of life. The goal of this study is to develop a machine-learning algorithm to predict complete and incremental tooth loss among adults and to compare the predictive performance of these models. Methods We used data from the National Health and Nutrition Examination Survey from 2011 to 2014. We developed multiple machine-learning algorithms and assessed their predictive performances by examining the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values. Results The extreme gradient boosting trees presented the highest performance in the prediction of edentulism (AUC = 88.7%; 95%CI: 87.1, 90.2), the absence of a functional dentition (AUC = 88.3% 95%CI: 87.3,89.3) and for predicting missing any tooth (AUC = 83.2%; 95%CI, 82.0, 84.4). Although, as expected, age and routine dental care emerged as strong predictors of tooth loss, the machine learning approach identified additional predictors, including socioeconomic conditions. Indeed, the performance of models incorporating socioeconomic characteristics was better at predicting tooth loss than those relying on clinical dental indicators alone. Conclusions Future application of machine-learning algorithm, with longitudinal cohorts, for identification of individuals at risk for tooth loss could assist clinicians to prioritize interventions directed toward the prevention of tooth loss.

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