NIMS: MagIC - Documentos de conferências nacionais
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- PEDIVAC.PTPublication . Modesto, Cátia; Nascimento, Adriana; Lopes, Henrique; Cortes Gil, Jesus David; Castro, Mariana de; Franco, Diogo; Information Management Research Center (MagIC) - NOVA Information Management SchoolPresented as part of session From Evidence to Clinical Impact — Oral Communications: Projetos inovadores de várias áreas terapêuticas que traduzem dados em impacto Seasonal influenza remains a significant and evolving public health challenge in pediatric populations. In Portugal, despite strong overall vaccination performance, coverage in children remains suboptimal compared to other European countries. This occurs in a context of evolving socio-cultural dynamics, increasing exposure to misinformation, and the need for continuous innovation in vaccination strategies. PEDIVAC.PT is a multi-phase, evidence-based initiative designed to address these challenges by identifying barriers, co-developing solutions with key stakeholders, and translating evidence into actionable policy recommendations to strengthen pediatric vaccination in Portugal.
- Design Thinking under Post-Human ConditionsPublication . Victorino, Guilherme; Mendonça, Joana; Information Management Research Center (MagIC) - NOVA Information Management School; NOVA Information Management School (NOVA IMS)Design Thinking (DT) has become a widely adopted approach to innovation, typically explained through a human-centered theory of change in which empathy, problem framing, and iterative prototyping drive impact. This model is rooted in well-established theories of design cognition and reflective practice, however, the ways in which design interventions result in specific outcomes have changed significantly; particularly in those situations in which machine learning models and generative artificial intelligence are used. As a consequence, these mechanisms of design intervention effect, have been dispersed throughout complex socio-technical systems and are no longer solely attributed to human sensemaking. This shift exposes a growing mismatch between how DT explains change and how design outcomes are enacted in practice. This paper develops a theoretical position based on long-term observations of projects and workshops carried out in a DT lab that is part of a Data Science and Artificial Intelligence School. The purpose of the paper is to articulate the causal premises upon which DT can remain an adequate framework for describing design outcomes in contexts in which data-intensive mediated systems are utilized, providing an alternative conceptualization of design impact as resulting from the continuous configuration coordination of both human and non-human agency over time.
- Is Education Spatial?Publication . Santos, Samuel; NOVA Information Management School (NOVA IMS)High stakes in access to Higher Education decisions: National Exams + Internal grades in Secondary Ed., decided at the decimal points. Systematic differences, even if small, suggest inequity. Geospatial analysis can detect regional differences. Public policies have different bandwidths; not everything has to be done at a national level. Source and data are available, to foster further research and discussion.
- Developing Cycling Networks to Improve Active Accessibility to Public Schools Using the 15-minute City ConceptPublication . Diogo Pinto, José; NOVA Information Management School (NOVA IMS)• 83% of children do not meet 60 min of daily physical exercise • In Lisbon, 44% of school kids are driven to school by car • 15% of urban traffic is generated by school journeys • 70% of EU population lives in cities, 78-80% by 2050 • < 4% of cycling share in most southern European cities What should be done in Southern European cities? • Promote walking and cycling to schools ✓ promotes children’s independence ✓ shapes future travel behavior • Plan proximity-accessible schools: ✓ family-friendly, improves quality of life ✓ sustainable and competitive ✓ improves social resilience and participation What is being done? • Bicycle trains to schools (e.g. Lisbon, Barcelona, Milan) • School surroundings projects (e.g. kiss and ride in Lisbon) • Cycling literacy workshops at schools (e.g. traffic schools) • Mobility to school surveys (e.g. Hands up! in Lisbon) RQ1 – Where is the current cycling infrastructure not sufficient to provide 15-minute active accessibility to schools? RQ2 – What is the contribution of cycling to improving active accessibility to schools? RQ3 – How to operationalize 15-minute accessibility indicators to guide cycling infrastructure investment decisions?
- Uncovering Stroke Mortality Profiles Through Clinical, Socioeconomic, and Environmental Risk FactorsPublication . Ramalhete, Sara Ventura; Information Management Research Center (MagIC) - NOVA Information Management School; NOVA Information Management School (NOVA IMS)Stroke remains one of the leading causes of mortality worldwide1. While traditional risk factors for stroke are well recognized, they do not completely account for variations in stroke-related deaths. Increasing evidence suggests that non-traditional factors, including environmental conditions and social determinants of health (SDOH), may also influence mortality outcomes. However, previous studies have produced inconsistent results, and only a limited number have examined the combined impact of both classical and non-classical risk factors on survival. This study aimed to identify profiles of stroke patients with differing probabilities of survival by considering a broad range of traditional and non- traditional risk factors. Initial statistical analyses were conducted to identify potential predictors of mortality within three months following hospital discharge, including both classical and non-classical variables. These predictors were then used in a clustering approach, followed by survival analysis to evaluate differences between patient groups. Furthermore, machine learning classification models combined with explainable artificial intelligence techniques were employed to determine the relative contribution of each variable to cluster formation. We investigated both classical and non-classical risk factors to identify subgroups with different survival probabilities. This analysis revealed a set of variables that distinguished two groups with significantly different survival probabilities at 3 months after discharge. By identifying combinations of classical and non-classical factors associated with distinct survival outcomes, this study contributes to a more comprehensive understanding of stroke mortality. These findings may help to inform rehabilitation planning, improve patient management, and support strategies aimed at reducing adverse outcomes following stroke.
- Moral Accounting of Credit in LLM-Assisted Content CreationPublication . Nunes, Joana Rita; Information Management Research Center (MagIC) - NOVA Information Management School; NOVA Information Management School (NOVA IMS)Every day we see news from around the world about the use of AI, GenAI and LLMs in content creation: LLMs are faster, more efficient, and, sometimes, more creative than humans. At first, this seemed incredible, but the integration of LLMs into professional and creative content production raises a business ethical question: When work assisted by these models is recognized and rewarded, how much of that recognition does the human creator legitimately earn? In this research we tested the moral accounting of credit, examining whether the benefit received by a creator is morally proportional to the contribution perceived by individuals, and if there is a moral penalty for creators using LLMs. Research on AI and business ethics has asked whether AI systems can be blamed, trusted, or judged as moral agents, however, it places AI as the object of moral judgment and leaves the human who receives the awards for the LLM-assisted work less examined.
- Quo vadis?Publication . Sturm, Niclas Frederic; Candia, Cristian; Damásio, Bruno; Pinheiro, Flávio L.; Information Management Research Center (MagIC) - NOVA Information Management School; NOVA Information Management School (NOVA IMS)Diversification is at the heart of many economic processes. The principle of relatedness captures this through first-order co-occurrence: local overlap between activities. However, diversification may also be dependent on an entity's structural position in the capability network, which quantifies structural similarities in capability profiles and global connectivity patterns. To overcome this limitation, we propose using node-level embeddings that encode multi-step connectivity as well as neighborhood structure. Next to the structural factors affecting diversification, which is a key contribution of Economic Complexity, forecasting methods from the Machine Learning domain are increasingly applied to the task of diversification paths.
- Validation of Early Vineyard Yield EstimationPublication . Santos Costa, Diogo; NOVA Information Management School (NOVA IMS); Information Management Research Center (MagIC) - NOVA Information Management SchoolReliable early season vineyard yield estimation is increasingly important for planning, quota interpretation and regulatory decision support in wine regions exposed to strong interannual variability. However, the practical value of a predictive model depends not only on its error metrics, but also on the realism of the validation design used to assess its performance. This study evaluates an open data Long Short-Term Memory (LSTM) neural network pipeline for parish level wine grape yield estimation in the Douro Demarcated Region (DDR), comparing Leave-One-Year-Out (LOYO) validation with Walk-Forward (WF) back-testing. The modelling workflow combines Sentinel-derived Normalised Difference Vegetation Index (NDVI) time series with open-access gridded climate variables from AgERA5 and CHIRPS. Estimates are assessed across two early seasonal windows, flowering and veraison, and across multiple territorial levels, from parishes to sub-regions and the whole DDR. LOYO is used as a benchmark-oriented validation strategy that enables comparison across years, while WF imposes a stricter chronological structure in which each target year is estimated using only information available from previous campaigns. The comparison shows how validation design can change the interpretation of model readiness. LOYO supports methodological benchmarking and model comparison, whereas WF provides a more operationally realistic assessment of temporal generalisation. By making this distinction explicit, the study strengthens the methodological credibility of AI-based vineyard yield estimation and contributes to more transparent decision support for precision viticulture and regional wine governance.
- SEATSPublication . Almeida Diogo, Diogo; NOVA Information Management School (NOVA IMS)Cultural venues increasingly use data to guide resource planning and revenue decisions, and a central question is what drives visitor attendance. Yet, prior work is limited on three fronts: it typically considers only weather and holidays as predictors, it relies on proxies such as Google Trends in place of actual attendance, and it studies a single venue, ignoring how drivers differ across venues of different sizes. Using weekly ticketing together with a set of contextual signals from seven venues in Lisbon, we estimate which factors credibly predict attendance and by how much across all venues. We present a Hierarchical Bayesian Structural Time-Series model spanning venues of very different sizes. Beyond the usual weather and holidays, it weighs a set of predictors against weekly ticketing: tourism, website traffic, exhibitions, online reviews, search interest, and macroeconomic conditions.
- Beyond teleoperationPublication . Andrade, José António Nunes; Castelli, Mauro; NOVA Information Management School (NOVA IMS); Information Management Research Center (MagIC) - NOVA Information Management SchoolVision-Language-Action (VLA) models have demonstrated remarkable capabilities in generalized robotic control, yet their scalability is fundamentally bottlenecked by the high cost and low diversity of teleoperated data. While abundant, human demonstration videos cannot be directly utilized for policy training due to the severe morphological differences between human anatomy and robotic manipulators. To bridge this embodiment gap, this work proposes a lightweight retargeting pipeline that kinematically retargets human interaction data (DexYCB) onto a 6-DoF (degree of freedom) manipulator trajectories to fine-tune policies based on pi0.5 architecture. By prioritizing Cartesian positional alignment via constrained Inverse Kinematics (IK) and introducing an object-based grasping heuristic, smooth geometric priors are generated without relying on computationally heavy visual synthesis. Physical evaluations demonstrate that retargeted models significantly outperform standard teleoperation (40.6% success rate), achieving 65.6% success via co-training and a peak 78.1% success rate via two-stage cross-embodiment co-training. Furthermore, evaluations under extreme visual clutter reveal that explicitly retargeted policies exhibit immunity to semantic visual distractors. Finally, we it is examined and analysed the "Terminal State Ambiguity (TSA)", a temporal failure mode where generative models fail to terminate the task, caused when exposed to scenariosby the similar to the nature of human video priors when exposed to extreme visual noise.