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About

About

António Correia is a Microsoft Research Fellow. He received M.Sc. degree in Information and Communication Technologies from the University of Trás-os-Montes e Alto Douro (UTAD), Vila Real, Portugal. Furthermore, he is currently pursuing the Ph.D. degree in Computer Science at UTAD, funded by the Portuguese Foundation for Science and Technology (FCT), while working as a Research Assistant at INESC TEC, Porto, Portugal. He is also a Visiting Scholar at University of Nebraska at Omaha, College of Information Science & Technology, NE, USA. Moreover, he formerly worked as a Visiting Postgraduate Researcher at University of Kent, Chatham, UK. António holds more than ten years of experience in research and scientific writing, and his research interests are mainly in the fields of Human-Computer Interaction (HCI), Computer-Supported Cooperative Work (CSCW) and Artificial Intelligence (AI), with a focus on exploring new methods to model scientific literature and how science has changed over time. He has authored or co-authored more than 40 publications, including journal articles, conference papers, book chapters, and posters. In line with this, he has also participated in research projects conducted at national and international level and has been executing functions as external reviewer and scientific committee member for top-tier journals and conferences covering aspects of computer science.

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Details

Details

  • Name

    António Guilherme Correia
  • Cluster

    Computer Science
  • Role

    Research Assistant
  • Since

    20th April 2017
001
Publications

2023

A Model for Cognitive Personalization of Microtask Design

Authors
Paulino, D; Guimaraes, D; Correia, A; Ribeiro, J; Barroso, J; Paredes, H;

Publication
SENSORS

Abstract
The study of data quality in crowdsourcing campaigns is currently a prominent research topic, given the diverse range of participants involved. A potential solution to enhancing data quality processes in crowdsourcing is cognitive personalization, which involves appropriately adapting or assigning tasks based on a crowd worker’s cognitive profile. There are two common methods for assessing a crowd worker’s cognitive profile: administering online cognitive tests, and inferring behavior from task fingerprinting based on user interaction log events. This article presents the findings of a study that investigated the complementarity of both approaches in a microtask scenario, focusing on personalizing task design. The study involved 134 unique crowd workers recruited from a crowdsourcing marketplace. The main objective was to examine how the administration of cognitive ability tests can be used to allocate crowd workers to microtasks with varying levels of difficulty, including the development of a deep learning model. Another goal was to investigate if task fingerprinting can be used to allocate crowd workers to different microtasks in a personalized manner. The results indicated that both objectives were accomplished, validating the usage of cognitive tests and task fingerprinting as effective mechanisms for microtask personalization, including the development of a deep learning model with 95% accuracy in predicting the accuracy of the microtasks. While we achieved an accuracy of 95%, it is important to note that the small dataset size may have limited the model’s performance.

2023

Designing for Hybrid Intelligence: A Taxonomy and Survey of Crowd-Machine Interaction

Authors
Correia, A; Grover, A; Schneider, D; Pimentel, AP; Chaves, R; de Almeida, MA; Fonseca, B;

Publication
APPLIED SCIENCES-BASEL

Abstract
With the widespread availability and pervasiveness of artificial intelligence (AI) in many application areas across the globe, the role of crowdsourcing has seen an upsurge in terms of importance for scaling up data-driven algorithms in rapid cycles through a relatively low-cost distributed workforce or even on a volunteer basis. However, there is a lack of systematic and empirical examination of the interplay among the processes and activities combining crowd-machine hybrid interaction. To uncover the enduring aspects characterizing the human-centered AI design space when involving ensembles of crowds and algorithms and their symbiotic relations and requirements, a Computer-Supported Cooperative Work (CSCW) lens strongly rooted in the taxonomic tradition of conceptual scheme development is taken with the aim of aggregating and characterizing some of the main component entities in the burgeoning domain of hybrid crowd-AI centered systems. The goal of this article is thus to propose a theoretically grounded and empirically validated analytical framework for the study of crowd-machine interaction and its environment. Based on a scoping review and several cross-sectional analyses of research studies comprising hybrid forms of human interaction with AI systems and applications at a crowd scale, the available literature was distilled and incorporated into a unifying framework comprised of taxonomic units distributed across integration dimensions that range from the original time and space axes in which every collaborative activity take place to the main attributes that constitute a hybrid intelligence architecture. The upshot is that when turning to the challenges that are inherent in tasks requiring massive participation, novel properties can be obtained for a set of potential scenarios that go beyond the single experience of a human interacting with the technology to comprise a vast set of massive machine-crowd interactions.

2022

Uncovering the Potential of Cognitive Personalization for UI Adaptation in Crowd Work

Authors
Paulino, D; Correia, A; Guimarães, D; Barroso, J; Paredes, H;

Publication
25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022, Hangzhou, China, May 4-6, 2022

Abstract
Crowdsourcing has received considerable attention over the last fifteen years and has been the subject of several experiments that demonstrate its large potential for use in real-world situations. With the rapid growth of and access to crowd work environments, there is a need for new ways to ensure more equitable access for all people. Task design is one of the core aspects of the crowdsourcing process and its optimization is a priority for many requesters that want to have their tasks solved in short times and with high levels of accuracy. Aligned with this goal, a cognitive personalization framework can make it feasible to assess the information processing preferences of crowd workers in order to provide a useful user interface (UI) adaptation. In an effort to address this issue, this study recruited a total of 64 crowd workers to take cognitive style tests and perform prototypical tasks. The results indicate that it is possible to apply short tests and then obtain some useful indicators for better matching tasks to workers with implications for improving the general outcomes and acceptance rates in crowdsourcing.

2022

Cognitive Personalization in Microtask Design

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

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

Abstract

2022

Crowd and Urban Storytelling: Evaluating a Collective Intelligence Model to Support Discussions about the City

Authors
Chaves, R; Motta, C; Correia, A; Paredes, H; Caetano, BP; de Souza, JM; Schneider, D;

Publication
25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022, Hangzhou, China, May 4-6, 2022

Abstract
In recent years, digital technologies have been used to support discussions about the city and also to involve citizens in participatory public processes. However, despite the widespread use of social media platforms, old issues related to engagement and participation still persist in digital initiatives. The main goal of this study is to carry out an empirical evaluation of a collective intelligence model that combines crowdsourcing and social storytelling to support discussions about the city from a bottom-up perspective. Within a design science research approach we designed a participatory action study that was carried out through a workshop with students and professionals from different areas, such as architecture, urban design and information technology. As a result, we were able to assess whether the collective intelligence model was acceptable to the participants by investigating whether the behavioral assumptions were valid and thus outlining some contributions to the field of urban informatics.