Moral & incentives : should corruption whistleblowing be rewarded?

Tendo como objeto o Projeto de Lei n. 857/2012, aprovado pela Câmara Legislativa do Distrito Federal em 2013, este artigo analisa o princípio da compensação pecuniária ao cidadão que denuncia a corrupção, sob a ótica da Teoria de Desenhos de Mecanismos e do Direito. Um modelo de Teoria da Decisão estabelece um potencial conflito para o cidadão entre a satisfação com o benefício monetário auferido pela denúncia (“incentivo pecuniário”) e a insatisfação com o sentimento de estar sendo pago para exercer seu dever cívico (“desincentivo moral”). Mostra-se que, quando há heterogeneidade na sociedade, o efeito do incentivo pecuniário predomina e a introdução da compensação é benéfica para a sociedade. Além disso, propomos uma alteração no PL que transforma o desincentivo moral em incentivo à dedicação ao controle da corrupção. Finalmente, por meio da análise de leis e de estudos de casos, confirmamos que, além de compatível com o ordenamento jurídico brasileiro, esse mecanismo já é efetivamente aplicado em diferentes situações no país.This article presents an applied mechanism design and a legal analysis of a Bill proposed into law by the Brazilian Federal District House of Representatives in 2013. The Bill offers monetary rewards for citizens that denounce corruption. In our decision-theoretic model, corruption control may give the citizens utility due to their civic responsibility feeling. The main trade-off brought about by the reward mechanism is that, by receiving compensation, citizens’ civic impulse to dedicate effort to curb corruption may be reduced. However, our model shows that if society is heterogeneous enough, the monetary reward motive prevails and the Bill fosters social involvement. Furthermore, we propose a mechanism that transforms the moral dissatisfaction of receiving money into a moral incentive to dedicate to corruption control. Finally, a careful analysis of the Law and of a series of case studies in Brazil suggest that such a mechanism does not violate the Constitution and, furthermore, has actually been used under different forms in the Brazilian legal system

Comparative Evaluation and Implementation of State-of-the-Art Techniques for Anomaly Detection and Localization in the Continual Learning Framework

openThe capability of anomaly detection (AD) to detect defects in industrial environments using only normal samples has attracted significant attention. However, traditional AD methods have primarily concentrated on the current set of examples, leading to a significant drawback of catastrophic forgetting when faced with new tasks. Due to the constraints in flexibility and the challenges posed by real-world industrial scenarios, there is an urgent need to strengthen the adaptive capabilities of AD models. Hence, this thesis introduces a unified framework that integrates continual learning (CL) and anomaly detection (AD) to accomplish the goal of anomaly detection in the continual learning (ADCL). To evaluate the effectiveness of the framework, a comparative analysis is performed to assess the performance of the three specific feature-based methods for the AD task: Coupled-Hypersphere-Based Feature Adaptation (CFA), Student-Teacher approach, and PatchCore. Furthermore, the framework incorporates the utilization of replay techniques to facilitate continual learning (CL). A comprehensive evaluation is conducted using a range of metrics to analyze the relative performance of each technique and identify the one that exhibits superior results. To validate the effectiveness of the proposed approach, the MVTec AD dataset, consisting of real-world images with pixel-based anomalies, is utilized. This dataset serves as a reliable benchmark for Anomaly Detection in the context of Continual Learning, providing a solid foundation for further advancements in the field.The capability of anomaly detection (AD) to detect defects in industrial environments using only normal samples has attracted significant attention. However, traditional AD methods have primarily concentrated on the current set of examples, leading to a significant drawback of catastrophic forgetting when faced with new tasks. Due to the constraints in flexibility and the challenges posed by real-world industrial scenarios, there is an urgent need to strengthen the adaptive capabilities of AD models. Hence, this thesis introduces a unified framework that integrates continual learning (CL) and anomaly detection (AD) to accomplish the goal of anomaly detection in the continual learning (ADCL). To evaluate the effectiveness of the framework, a comparative analysis is performed to assess the performance of the three specific feature-based methods for the AD task: Coupled-Hypersphere-Based Feature Adaptation (CFA), Student-Teacher approach, and PatchCore. Furthermore, the framework incorporates the utilization of replay techniques to facilitate continual learning (CL). A comprehensive evaluation is conducted using a range of metrics to analyze the relative performance of each technique and identify the one that exhibits superior results. To validate the effectiveness of the proposed approach, the MVTec AD dataset, consisting of real-world images with pixel-based anomalies, is utilized. This dataset serves as a reliable benchmark for Anomaly Detection in the context of Continual Learning, providing a solid foundation for further advancements in the field