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Sobre

Sobre

Arsénio Reis (M) é Especialista de Informatica na Universidade de Trás-os-MOntes e Alto Douro (UTAD). Doutourou-se em 2015, na UTAD. Em 2016 integrou o INESC- TEC como investigador no centro CSIG. De 2006 a 2009, foi Coordenador Técnico de Informática, e de 2009 a 2014 foi Director dos Serviços de Informática e Comuibcações da UTAD. Em 2007, completou o Diploma de Especialização em Sociedade de Informação e Inovação na Admintração Publica (DESIIAP), no Instituto Nacional de Administração (INA), e em 2009, concluiu o Curso de Alta Direção na Administração Pública (CADAP), também no INA. No decurso da sua carreira profisional, esteve envolvido em diversos projectos de investigação e desenvolvimento, em conjunto com parceiros publicos e privados, tendo representado a UTAD em diversas ocasiões, tais como, membro eleito do Conselho Geral da UTAD, entre 2009 e 2012, e vogal executivo da direção da European Information Systems Association(EUNIS), de 2009 a 2014. As sua principais areas de interesse são, desde há muito, as de Aystemas de Informação e Engenharia de Software, e mais recentemente, Acessibilidade, Interação Homem-Computador, e eSaude. Ele produziu mais de 40 papers cientificos, incluindo, capitulos de livro, artigos e comunicações em actas de conferencias e revistas cientificas. Participou na oeganização de eventos cientificos de varias naturezas, nacionais e internacionais, dos quais se destaca, a organização do Congresso da EUNIS em 2012, na UTAD.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Arsénio Reis
  • Cargo

    Investigador Sénior
  • Desde

    01 agosto 2016
012
Publicações

2023

A Machine Learning Tool to Monitor and Forecast Results from Testing Products in End-of-Line Systems

Autores
Nunes, C; Nunes, R; Pires, EJS; Barroso, J; Reis, A;

Publicação
APPLIED SCIENCES-BASEL

Abstract
The massive industrialization of products in a factory environment requires testing the product at a stage before its exportation to the sales market. For example, the end-of-line tests at Continental Advanced Antenna contribute to the validation of an antenna's functionality, a product manufactured by this organization. In addition, the storage of information from the testing process allows the data manipulation through automated machine learning algorithms in search of a beneficial contribution. Studies in this area (automatic learning/machine learning) lead to the search and development of tools designed with objectives such as preventing anomalies in the production line, predictive maintenance, product quality assurance, forecast demand, forecasting safety problems, increasing resources, proactive maintenance, resource scalability, reduced production time, and anomaly detection, isolation, and correction. Once applied to the manufacturing environment, these advantages make the EOL system more productive, reliable, and less time-consuming. This way, a tool is proposed that allows the visualization and previous detection of trends associated with faults in the antenna testing system. Furthermore, it focuses on predicting failures at Continental's EOL.

2023

Anomaly Detection in Microservice-Based Systems

Autores
Nobre, J; Pires, EJS; Reis, A;

Publicação
APPLIED SCIENCES-BASEL

Abstract
Currently, distributed software systems have evolved at an unprecedented pace. Modern software-quality requirements are high and require significant staff support and effort. This study investigates the use of a supervised machine learning model, a Multi-Layer Perceptron (MLP), for anomaly detection in microservices. The study covers the creation of a microservices infrastructure, the development of a fault injection module that simulates application-level and service-level anomalies, the creation of a system monitoring dataset, and the creation and validation of the MLP model to detect anomalies. The results indicate that the MLP model effectively detects anomalies in both domains with higher accuracy, precision, recovery, and F1 score on the service-level anomaly dataset. The potential for more effective distributed system monitoring and management automation is highlighted in this study by focusing on service-level metrics such as service response times. This study provides valuable information about the effectiveness of supervised machine learning models in detecting anomalies across distributed software systems.

2023

Virtual Assistants in Industry 4.0: A Systematic Literature Review

Autores
Pereira, R; Lima, C; Pinto, T; Reis, A;

Publicação
ELECTRONICS

Abstract
Information and Communication Technologies are driving the improvement of industrial processes. According to the Industry 4.0 (I4.0) paradigm, digital systems provide real-time information to humans and machines, increasing flexibility and efficiency in production environments. Based on the I4.0 Design Principles concept, Virtual Assistants can play a vital role in processing production data and offer contextualized and real-time information to the workers in the production environment. This systematic review paper explored Virtual Assistant applications in the context of I4.0, discussing the Technical Assistance Design Principle and identifying the characteristics, services, and limitations regarding Virtual Assistant use in the production environments. The results showed that Virtual Assistants offer Physical and Virtual Assistance. Virtual Assistance provides real-time contextualized information mainly for support, while Physical Assistance is oriented toward task execution. Regarding services, the applications include integration with legacy systems and static information treatment. The limitations of the applications incorporate concerns about information security and adapting to noisy and unstable environments. It is possible to assume that the terminology of Virtual Assistants is not standardized and is mentioned as chatbots, robots, and others. Besides the worthy insights of this research, the small number of resulting papers did not allow for generalizations. Future research should focus on broadening the search scope to provide more-significant conclusions and research possibilities with new AI models and services, including the emergent Industry 5.0 concept.

2022

Forecasting Student s Dropout: A UTAD University Study

Autores
Da Silva, DEM; Pires, EJS; Reis, A; Oliveira, PBD; Barroso, J;

Publicação
FUTURE INTERNET

Abstract
In Portugal, the dropout rate of university courses is around 29%. Understanding the reasons behind such a high desertion rate can drastically improve the success of students and universities. This work applies existing data mining techniques to predict the academic dropout mainly using the academic grades. Four different machine learning techniques are presented and analyzed. The dataset consists of 331 students who were previously enrolled in the Computer Engineering degree at the Universidade de Tras-os-Montes e Alto Douro (UTAD). The study aims to detect students who may prematurely drop out using existing methods. The most relevant data features were identified using the Permutation Feature Importance technique. In the second phase, several methods to predict the dropouts were applied. Then, each machine learning technique's results were displayed and compared to select the best approach to predict academic dropout. The methods used achieved good results, reaching an Fl-Score of 81% in the final test set, concluding that students' marks somehow incorporate their living conditions.

2022

Cognitive Personalization in Microtask Design

Autores
Paulino, D; Correia, A; Reis, A; Guimaraes, D; Rudenko, R; Nunes, C; Silva, T; Barroso, J; Paredes, H;

Publicação
UNIVERSAL ACCESS IN HUMAN-COMPUTER INTERACTION: NOVEL DESIGN APPROACHES AND TECHNOLOGIES, UAHCI 2022, PT I

Abstract
Today digital labor increasingly advocates for the inclusion of people who are excluded from society in someway. The proliferation of crowdsourcing as a new form of digital labor consisting mainly of microtasks that are characterized by a low level of complexity and short time periods in terms of accomplishment has allowed a wide spectrum of people to access the digital job market. However, there is a long-recognized mismatch between the expectations of employers and the capabilities of workers in microwork crowdsourcing marketplaces. Cognitive personalization has the potential to tailor microtasks to crowd workers, thus ensuring increased accessibility by providing the necessary coverage for individuals with disabilities and special needs. In this paper an architecture for a crowdsourcing system intended to support cognitive personalization in the design of microtasks is introduced. The architecture includes an ontology built for the representation of knowledge on the basis of the concepts of microtasks, cognitive abilities, and types of adaptation in order to personalize the interface to the crowd worker. The envisioned system contains a backend and a frontend that serve as an intermediary layer between the crowdsourcing platform and the workers. Finally, some results obtained to evaluate the proposed system are presented.

Teses
supervisionadas

2021

Web accessibility in mobile aplications of education sector

Autor
Liren Su

Instituição
UTAD

2019

Desenvolvimento de uma aplicação androoide para o jardim botânico da Utad

Autor
João Carlos Trindade Moreira

Instituição
UTAD

2019

Serviço de Apoio a Idosos Recorrendo a Veículos não Tripulados

Autor
DAVID FERREIRA SAFADINHO

Instituição
UTAD

2018

application of a novel autoencoder based method to raw measurements in electric power systems

Autor
Marco Aurélio Moreira Saran

Instituição
UP-FEUP

2017

Análise e otimização dos fluxos logísticos internos

Autor
José Pedro Monteiro da Silva Carvalho

Instituição
UP-FEUP