04 Pubblicazione in atti di convegno

04 Pubblicazione in atti di convegno: [102177] Home page macrotipologia

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04a Atto di comunicazione a congresso 30828
04b Atto di convegno in volume 41272
04c Atto di convegno in rivista 8058
04d Abstract in atti di convegno 17807
04e Traduzione di atto di convegno in volume 70
04f Poster 3644
04g Traduzione di atto di convegno in rivista 5
04h Atto di convegno in rivista scientifica o di classe A 494

Prodotti recenti

Dealing with I5.0 complexity: cyber-socio-technical systems modelling and analysis

The transition from Industry 4.0 (I4.0) to Industry 5.0 (I5.0) shifted the focus to novel challenges in industrial systems and their associated operational processes. The focus moved from digitalization towards new applications related to human-machine collaborations, human-centric settings, and cyber-physical cognitive systems. This change stressed the need to identify industrial systems as Cyber-Socio-Technical Systems (CSTS), thus emphasizing the importance of the synergies among cyber-phy...

Directional Sheaf Hypergraph Networks: Unifying Learning on Directed and Undirected Hypergraphs

Hypergraphs provide a natural way to represent higher-order interactions among multiple entities. While undirected hypergraphs have been extensively studied, the case of directed hypergraphs, which can model oriented group interactions, remains largely under-explored despite its relevance for many applications. Recent approaches in this direction often exhibit an implicit bias toward homophily, which limits their effectiveness in heterophilic settings. Rooted in the algebraic topology notion ...

Implicit Inversion turns CLIP into a Decoder

CLIP is a discriminative model trained to align images and text in a shared embedding space. Due to its multimodal structure, it serves as the backbone of many generative pipelines, where a decoder is trained to map from the shared space back to images. We show that image synthesis is nevertheless possible using CLIP alone—without a pre-trained generative decoder or CLIP tuning. Our approach optimizes a frequency-aware implicit neural representation that encourages coarse-to-fine generation b...

Why Adversarially Train Diffusion Models?

Adversarial Training (AT) is a known, powerful, well-established technique for improving classifier robustness to input perturbations, yet its applicability beyond discriminative settings remains limited. Motivated by the widespread use of score-based generative models and their need to operate robustly under substantial noisy or corrupted input data, we propose an adaptation of AT for these models, providing a thorough empirical assessment. We introduce a principled formulation of AT for Dif...

Il “pirata musulmano” tra stereotipi e realtà effettuale

Il contributo esamina la figura del «pirata musulmano» nel Mediterraneo medievale, mettendo a confronto la sua costruzione nelle fonti cristiane con le forme effettive della guerra marittima praticata dalle potenze islamiche. Cronache, testi agiografici e documentazione ecclesiastica tendono spesso a ricondurre incursioni, razzie, spedizioni militari e iniziative corsare a un’unica immagine stereotipata, dominata dalla violenza, dall’empietà e dall’alterità religiosa. Un’analisi più ravvicina...

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