Archivio della ricerca della Scuola Superiore Sant'Anna
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Building healthy, sustainable, and inclusive food systems through public food procurement from family farming: an integrative review
No full textPublic food procurement purchases items from family farming as a strategy for building sustainable food systems, though it is acknowledged that not all family farming is sustainable. However, it is unclear whether these strategies derived from program guidelines are translated into actions at various stages of the food system, from production to consumption. In light of this, an integrative review was conducted with the goal of linking public food procurement from family farming to the construction of sustainable food systems, focusing on two key Brazilian public procurement programs: the Food Acquisition Program (PAA), particularly its Purchase with Simultaneous Donation (CDS) modality, and the National School Feeding Program (PNAE). The integrative review reveals that while these programs are designed with the intention of building sustainable food systems, their implementation faces limitations. The guidelines include criteria such as the procurement of organic and agroecological foods, foods from sociobiodiversity, sourced from traditional peoples and communities, and the inclusion of vulnerable family farmers as suppliers. However, the results of the policies are concentrated in the socioeconomic dimension, particularly in increasing income and ensuring market access. For public procurement policies to positively impact the transformation of healthy, sustainable, and inclusive food systems, it is still necessary to expand the scope of the programs in their healthy and sustainable dimensions
DeceptiLens: an Approach supporting Transparency in Deceptive Pattern Detection based on a Multimodal Large Language Model
No full textTo detect deceptive design patterns on UIs, traditional artificial intelligence models, such as machine learning, have limited coverage and a lack of multimodality. In contrast, the capabilities of Multimodal Large Language Model (MM-LLM) can achieve wider coverage with superior performance in the detection, while providing reasoning behind each decision. We propose and implement an MM-LLM-based approach (DeceptiLens) that analyzes UIs and assesses the presence of deceptive design patterns. We utilize Retrieval Augmented Generation (RAG) process in our design and task the model with capturing the deceptive patterns, classifying its category, e.g., false hierarchy, confirmshaming, etc., and explaining the reasoning behind the classifications by employing recent prompt engineering techniques, such as Chain-of-Thought (CoT). We first create a dataset by collecting UI screenshots from the literature and web sources and quantify the agreement between the model's outputs and a few experts' opinions. We additionally ask experts to gauge the transparency of the system's explanations for its classifications in terms of recognized metrics of clarity, correctness, completeness, and verifiability. The results indicate that our approach is capable of capturing the deceptive patterns in UIs with high accuracy while providing clear, correct, complete, and verifiable justifications for its decisions. We additionally release two curated datasets, one with expert-labeled UIs with deceptive design patterns, and one with AI-based generated explanations. Lastly, we propose recommendations for future improvement of the approach in various contexts of use
Ubaldo Fadini, Divenire umani. Per una nuova antropologia filosofica (A. Lucchini); Michel Foucault, Généalogies de la sexualité (G. Vena); Jay L. Garfield, Buddhist Ethics: A Philosophical Exploration (T. Pignocchi); Thomas Hobbes, Saggi su storia e politica. I Tre Discorsi e l’Introduzione a Tucidide (L. Tenneriello); Dominic McIver Lopes, Aesthetic Injustice (G. Nanino)
No full textTask-related biomarkers and technical developments for adaptive deep brain stimulation in Parkinson’s disease
No full textCommon European Data Space for Cultural Heritage: Is the EU Taking the “High Road” from “A Single Access Point” to “A Single Market for Data” for Digital Cultural Content?
No full textThe Common European Data Space (CEDS), currently comprising fourteen sector- and domain-specific data spaces, was launched by the European Commission (EC) in 2018 in the context of the European Strategy for Data. The CEDS is devised to catalyse the European Union’s transformation into a competitive and digitally sovereign market power informed and governed by a robust legislative framework that would facilitate the cross-border and cross-sectoral flow and reuse of multiple types of data, which are collected and held by public-sector entities, within a “single market for data”. Despite their alignment with the overarching aims and objectives of the CEDS project, the Open Data Directive (ODD) and Data Governance Act (DGA) have limited impact on the deployment of the Common European Data Space for Cultural Heritage (CHDS), which constitutes one of the data spaces within the CEDS. This paper investigates the legal obstacles to the successful deployment of the CHDS, including the interplay of the ODD and DGA with other legislative frameworks essential to the realisation of the CHDS (i.e. cultural heritage law and copyright law). The paper suggests that this conundrum stems from the fact that the CHDS leans toward another landmark initiative of the EC: the Europeana platform, which established a “single access point” to cultural heritage assets. Considering that an implementing act for the deployment of the CHDS is yet to be adopted by the EC, the paper provides normative solutions to tackle the legal and policy problems hampering the operationalisation of the CHDS
Integrating the Simplex Architecture to Enhance Safety in Deep Learning Autonomous Systems
No full textSystematic data management for effective AI-driven decision support systems in robotic rehabilitation
No full textRobotic rehabilitation is becoming a standard in post-stroke physical rehabilitation, and these setups, often coupled with virtual exercises, collect a large and finely grained amount of data about patients’ motor performance, in terms of kinematics and force interactions. Given the high resolution of data throughout the rehabilitation treatment, invaluable information is concealed, especially if oriented towards predictive systems and decision support systems. Nevertheless, a comprehensive understanding of how manipulating these datasets with machine-learning to produce such outputs is still missing. This study leverages comprehensive robotic-assisted rehabilitation data to systematically investigate clinical outcome predictions (FMA, ARAT and MI) and robot parameters suggestions based solely on kinematic and demographic data. Our method significantly outperforms conventional approaches on both tasks demonstrating the potential of systematic data handling in advancing rehabilitation practices. Moreover, under the explainable-AI policies, a focus on prediction power of variables and a clinical knowledge base of predicted outcome are provided
