Institution profile

Instituto Universitário de Lisboa

Academic institutioneurope · pt
Official website
Research library6linked papers
Opportunities0open roles
Selected work

Representative Papers

Orchestrated Vulnerability Management for Heterogeneous Networks: Adaptive Two-Stage Vulnerability Assessment, Context-Aware Risk Prioritization, and Automated Mitigation

Aug 08, 2026

This study addresses the inefficiencies and delayed response in vulnerability management within heterogeneous networks, stemming from device diversity, environmental fragility, and configuration disparities. To tackle these challenges, the authors propose an automated vulnerability management framework orchestrated via SOAR (Security Orchestration, Automation, and Response). The framework integrates passive asset discovery, an adaptive two-stage vulnerability assessment, context-aware risk prioritization—combining CVSS, EPSS, and asset-specific context—and an SDN-driven mitigation mechanism capable of millisecond-level automated response. Its key innovation lies in significantly reducing scanning-induced disruption to resource-constrained devices while enabling precise risk-based prioritization. Experimental results demonstrate that the system identifies 71% of baseline vulnerabilities, reduces total scanning time by up to 91%, decreases the number of vulnerabilities requiring urgent remediation by approximately 75%, shortens assessment time for 32 hosts by up to 45%, and executes mitigation policies automatically within milliseconds.

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The Granularity Paradox: How Temporal Disaggregation Inflates In-Sample Fit and Compounds Out-of-Sample Error

Jul 05, 2026

This study addresses the “granularity paradox” in time series forecasting, wherein fine-grained modeling improves in-sample fit but suffers from error accumulation due to recursive structures, degrading out-of-sample performance, while coarse-grained approaches incur information loss. Leveraging 13 years of public procurement data, the authors systematically evaluate ten model classes—spanning statistical, machine learning, and deep learning methods—across six temporal granularities using multidimensional metrics including TPFE, R², and RMSE. Their analysis reveals that recursive feedback topology, rather than model complexity, is the primary driver of error propagation. The work introduces a “consensus–discrepancy diagnostic” framework and advocates incorporating cumulative error metrics to overcome limitations of conventional point-wise error measures. Empirical results demonstrate strong model-dependent granularity effects—for instance, LSTM achieves a TPFE of 4.35% at daily granularity, whereas Holt-Winters fails catastrophically with R² = −151.

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An Introduction to Deep Reinforcement and Imitation Learning

Dec 08, 2025

This work addresses complex sequential decision-making for embodied agents (robots and virtual characters). It proposes a depth-first, self-contained learning framework that eliminates reliance on hand-engineered controllers. The method systematically examines core algorithms in deep reinforcement learning (DRL) and deep imitation learning (DIL), including Markov decision processes, policy gradient methods (REINFORCE), proximal policy optimization (PPO), behavioral cloning, DAgger, and generative adversarial imitation learning (GAIL), integrating essential mathematical and machine learning foundations as needed to ensure conceptual rigor over superficial surveying. The primary contribution is a logically coherent, dependency-free learning pathway tailored for beginners—designed to foster deep conceptual understanding and practical implementation proficiency in DRL/DIL. Learners acquire both theoretical insight and hands-on capability to independently conduct research and develop real-world applications.

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Recent publications

Latest Papers

Orchestrated Vulnerability Management for Heterogeneous Networks: Adaptive Two-Stage Vulnerability Assessment, Context-Aware Risk Prioritization, and Automated Mitigation

Aug 08, 2026

This study addresses the inefficiencies and delayed response in vulnerability management within heterogeneous networks, stemming from device diversity, environmental fragility, and configuration disparities. To tackle these challenges, the authors propose an automated vulnerability management framework orchestrated via SOAR (Security Orchestration, Automation, and Response). The framework integrates passive asset discovery, an adaptive two-stage vulnerability assessment, context-aware risk prioritization—combining CVSS, EPSS, and asset-specific context—and an SDN-driven mitigation mechanism capable of millisecond-level automated response. Its key innovation lies in significantly reducing scanning-induced disruption to resource-constrained devices while enabling precise risk-based prioritization. Experimental results demonstrate that the system identifies 71% of baseline vulnerabilities, reduces total scanning time by up to 91%, decreases the number of vulnerabilities requiring urgent remediation by approximately 75%, shortens assessment time for 32 hosts by up to 45%, and executes mitigation policies automatically within milliseconds.

0 citationsRead paper

The Granularity Paradox: How Temporal Disaggregation Inflates In-Sample Fit and Compounds Out-of-Sample Error

Jul 05, 2026

This study addresses the “granularity paradox” in time series forecasting, wherein fine-grained modeling improves in-sample fit but suffers from error accumulation due to recursive structures, degrading out-of-sample performance, while coarse-grained approaches incur information loss. Leveraging 13 years of public procurement data, the authors systematically evaluate ten model classes—spanning statistical, machine learning, and deep learning methods—across six temporal granularities using multidimensional metrics including TPFE, R², and RMSE. Their analysis reveals that recursive feedback topology, rather than model complexity, is the primary driver of error propagation. The work introduces a “consensus–discrepancy diagnostic” framework and advocates incorporating cumulative error metrics to overcome limitations of conventional point-wise error measures. Empirical results demonstrate strong model-dependent granularity effects—for instance, LSTM achieves a TPFE of 4.35% at daily granularity, whereas Holt-Winters fails catastrophically with R² = −151.

0 citationsRead paper

An Introduction to Deep Reinforcement and Imitation Learning

Dec 08, 2025

This work addresses complex sequential decision-making for embodied agents (robots and virtual characters). It proposes a depth-first, self-contained learning framework that eliminates reliance on hand-engineered controllers. The method systematically examines core algorithms in deep reinforcement learning (DRL) and deep imitation learning (DIL), including Markov decision processes, policy gradient methods (REINFORCE), proximal policy optimization (PPO), behavioral cloning, DAgger, and generative adversarial imitation learning (GAIL), integrating essential mathematical and machine learning foundations as needed to ensure conceptual rigor over superficial surveying. The primary contribution is a logically coherent, dependency-free learning pathway tailored for beginners—designed to foster deep conceptual understanding and practical implementation proficiency in DRL/DIL. Learners acquire both theoretical insight and hands-on capability to independently conduct research and develop real-world applications.

0 citationsRead paper