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Publications

2026

Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2025, Porto, Portugal, September 15-19, 2025, Proceedings, Part II

Authors
Ribeiro, RP; Pfahringer, B; Japkowicz, N; Larrañaga, P; Jorge, AM; Soares, C; Abreu, PH; Gama, J;

Publication
ECML/PKDD (2)

Abstract

2026

Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, ECML PKDD 2025, Porto, Portugal, September 15-19, 2025, Proceedings, Part I

Authors
Ribeiro, RP; Pfahringer, B; Japkowicz, N; Larrañaga, P; Jorge, AM; Soares, C; Abreu, PH; Gama, J;

Publication
ECML/PKDD (1)

Abstract

2026

Agricultural Expansion and Forest Transition in Mozambique: Evidence of Premature Decoupling (2001–2024)

Authors
Vilanculos, SDHL; Mananze, SE; Cunha, MC;

Publication
Resources

Abstract
This study analyzes forest cover change patterns, agricultural expansion, and economic growth in Mozambique from 2001 to 2024, using remote sensing data from Global Forest Watch and socioeconomic indicators from the World Bank and FAO. Mozambique lost approximately 4.6 million hectares of forest during the analyzed period, with agriculture accounting for 97.4% of total deforestation. GDP per capita increased by 90.5%, while cultivated area expanded by 116.4%. However, agricultural productivity declined by 25.3%, revealing a paradox: production growth relied on extensive land expansion rather than intensification. Statistical analysis of three 8-year sub-periods identified significant differences in GDP per capita, agricultural GDP per capita, population, and agricultural employment (p < 0.001), but agricultural deforestation remained statistically stable (p = 0.065). This pattern suggests premature decoupling between economic growth and deforestation at income levels (USD 604) substantially below historical Environmental Kuznets Curve thresholds (USD 8000–10,000). However, this decoupling is fragile, driven by capital-intensive extractive sectors that generate GDP growth without absorbing rural populations. The persistence of extensive agricultural expansion, combined with weak governance, demographic pressures, and climate variability, indicates that observed stabilization represents an initial, vulnerable phase requiring structural transformation through agricultural intensification, inclusive industrialization, land tenure reform, and climate resilience building. © 2026 by the authors.

2026

Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track - European Conference, ECML PKDD 2025, Porto, Portugal, September 15-19, 2025, Proceedings, Part IX

Authors
Dutra, I; Pechenizkiy, M; Cortez, P; Pashami, S; Jorge, AM; Soares, C; Abreu, PH; Gama, J;

Publication
ECML/PKDD (9)

Abstract

2026

Competitive and Cooperative Player-Oriented GWAPs for Enhancing Crowdsourcing Campaigns - An Evidence-Based Synthesis

Authors
Guimaraes, D; Correia, A; Paulino, D; Paredes, H;

Publication
INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION

Abstract
The use of gamified crowdsourcing mechanisms through serious games and games with a purpose (GWAPs) has emerged as an effective motivational strategy for enhancing performance in human intelligence tasks (HITs). In this systematic literature review, we examine the underlying characteristics of competitive and cooperative player-oriented GWAPs and how they can be leveraged to optimize crowdsourcing performance in completing batches of HITs. By exploring gamified crowdsourcing elements in GWAPs, we can evaluate the impact of these two types of player behaviors (i.e., competition and cooperation) on motivation and performance. We reviewed 27 publications and grouped them into five categories: player orientation, game elements and motivation, crowd work optimization, gamified knowledge collection, and comparative studies and best practices. Our research pinpoints the significance of intuitive task instructions, alignment of game elements with player motivations, and the role of competitive and cooperative dynamics in enhancing engagement and performance.

2026

A Systematic Literature Review on the Benefits of Robotics and Active Learning Methodologies for Promoting STEAM Education among Students with Intellectual and Developmental Disabilities

Authors
Conde, MA; Rodríguez-Sedano, FJ; García-Peñalvo, FJ; Suganuma, L; Gonçalves, J; Jormanainen, I; Yigzaw, S;

Publication
INTERNATIONAL JOURNAL OF ENGINEERING EDUCATION

Abstract
The integration of students with intellectual and developmental disabilities into STEAM education presents ongoing challenges, particularly in engineering disciplines where both technical and social competencies are essential. Robotics and active learning methodologies have emerged as promising solutions to address these challenges by offering adaptive, interactive, and student-centered learning environments. This study conducts a systematic literature review to examine how these technologies and methodologies are applied to support students with Intellectual and Developmental Disabilities. A total of 34 high-quality studies published over the past ten years were selected through a rigorous process of database searching, inclusion/exclusion filtering, and quality assessment. The analysis reveals that robotics is particularly effective in fostering academic development, cognitive skills, social-behavioral interaction, and emotional regulation, while active learning promotes social responding, role understanding, and collaborative skills. Together, these approaches not only enhance individual learning outcomes but also facilitate the broader inclusion of students with disabilities within engineering education.

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