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Sobre

Sobre

Benjamim Fonseca é Professor Auxiliar com Agregação na Universidade de Trás-os-Montes e Alto Douro (UTAD) e Investigador no INESC TEC. Os seus principais interesses de investigação são os sistemas colaborativos e a acessibilidade móvel. É autor ou coautor de dezenas de artigos científicos nestas áreas, pubiicados em conferências, livros e revistas internacionais.

Tópicos
de interesse
Detalhes

Detalhes

  • Nome

    Benjamim Fonseca
  • Cargo

    Investigador Sénior
  • Desde

    05 março 1997
002
Publicações

2024

And Justice for Art(ists): Metaphorical Design as a Method for Creating Culturally Diverse Human-AI Music Composition Experiences

Autores
Correia A.; Schneider D.; Fonseca B.; Mohseni H.; Kujala T.; Kärkkäinen T.;

Publicação
HORA 2024 - 6th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings

Abstract
This study discusses the intricate relations between generative artificial intelligence (AI) and music composers. Based on a previous rapid review of recent literature, it reinforces a gap and suggests the need to develop human-centered generative AI design strategies prioritizing cultural artistic (and non-artistic) aspects. We posit that AI-based music generation solutions should resonate with the cultural diversity of stakeholders who are impacted by these systems in practice. The paper highlights the significance of metaphorical design as an effective method in human-AI music co-creation by leveraging familiar interfaces and features that are rooted in everyday objects and cognitive models derived from real-world settings. Our insights illustrate possible ways of (re)framing human-AI metaphorical design to shape perceptions and facilitate seamless interactions between humans and intelligent systems in music co-creativity, particularly at the compositional level. At the heart of this research is the alignment of AI-driven music creation systems with user needs, values, and expectations that vary from culture to culture and thus require a continuous and transparent adaptation of the technology in use to accommodate individual preferences and the socio-algorithmic specificities underlying musicians’ activities.

2023

A Model for Cognitive Personalization of Microtask Design

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

Publicação
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

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

Publicação
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.

2023

A hybrid human-AI tool for scientometric analysis

Autores
Correia, A; Grover, A; Jameel, S; Schneider, D; Antunes, P; Fonseca, B;

Publicação
ARTIFICIAL INTELLIGENCE REVIEW

Abstract
Solid research depends on systematic, verifiable and repeatable scientometric analysis. However, scientometric analysis is difficult in the current research landscape characterized by the increasing number of publications per year, intersections between research domains, and the diversity of stakeholders involved in research projects. To address this problem, we propose SciCrowd, a hybrid human-AI mixed-initiative system, which supports the collaboration between Artificial Intelligence services and crowdsourcing services. This work discusses the design and evaluation of SciCrowd. The evaluation is focused on attitudes, concerns and intentions towards use. This study contributes a nuanced understanding of the interplay between algorithmic and human tasks in the process of conducting scientometric analysis.

2023

Investigating Author Research Relatedness through Crowdsourcing: A Replication Study on MTurk

Autores
Correia, A; Paulino, D; Paredes, H; Guimarães, D; Schneider, D; Fonseca, B;

Publicação
26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023, Rio de Janeiro, Brazil, May 24-26, 2023

Abstract

Teses
supervisionadas

2023

Plataforma de gestão de ocorrências-Mobile

Autor
Tiago Martinho Alves

Instituição
UTAD

2023

Plataforma de Gestão de Ocorrências Backoffice

Autor
Bruno Miguel Pimenta Sampaio

Instituição
UTAD

2023

Crowd-computing hybrids in scientific discovery

Autor
António José Guilherme Correia

Instituição
UTAD

2023

Crowd-Computing Hybrids in Scientific Discovery

Autor
António José Guilherme Correia

Instituição
UTAD

2022

Desenvolvimento de aplicação móvel para plataforma de gestão de casamento

Autor
João Victor Leite Amorim

Instituição
UTAD