4 Contributo in Atti di Convegno (Proceeding)

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Interoperable Traceability in Supply Chains: A Use Case in Agritech

In the agri-food sector, traceability is essential to ensure the quality, safety, and transparency of supply chains, where transportation companies are key stakeholders in the overall movement of goods. The multitude of actors involved in supply chains makes it challenging to achieve the above mentioned objectives: each company usually uses its own information system, which is mainly aimed at tracking all relevant information of the single company and is rarely able to interact with other inf...

Semiotic-Based Construction of a Large Emotional Image Dataset with Neutral Samples

Image Visual Sentiment Analysis (VSA) requires the availability of large annotated datasets, whose construction presents many challenges. The necessity of gathering a large amount of labeled images contrasts with the rigorous, but lengthy, process required for manual annotation based on psychovisual experiments, and with the automatic gathering of large amounts of data roughly labeled based on the sentiment analysis of the text accompanying the images, like captions, tweets and tags. An addit...

Enhancing Synthetic Generated-Images Detection through Post-Hoc Calibration

Post-hoc calibration is an important methodology for improving the reliability of confidence estimates in deep neural networks (DNNs). While modern DNNs achieve stateof-the-art performance across various domains, the output they provide often fails to align with the true likelihood of their predictions, a phenomenon known as miscalibration. This misalignment poses challenges for tasks like uncertainty quantification, score level fusion, and threshold selection in the case of binary detection ...

Biomonitoring of airborne microplastics: comparison between moss and lichen transplants

Comparison between the ability of moss and lichen transplants, collected from a remote area, to accumulate microfibers (MFs) and Potentially Toxic Elements (PTEs) under the same deployment conditions

Interpretable-by-design Neural-Symbolic Link Prediction on Knowledge Graphs

Knowledge Graph Embedding models have shown remarkable performances in different tasks like knowledge completion. However, they inherently lack interpretability, making it difficult to understand the reasoning behind their predictions. While different Neural-Symbolic (NeSy) models have been proposed to achieve interpretable reasoning through logic rules, existing evaluations primarily focus on accuracy, overlooking the critical assessment of explanation quality. This paper addresses this gap ...

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