1,721,083 research outputs found
Special Issue: Selected papers from the AIxIA 2023 Workshops
No full textThe 2023 edition of the AIxIA Conference, held in Rome, brought together a large number of researchers and practitioners to discuss the most recent and important advancements in Artificial Intelligence (AI). The conference featured 19 workshops, organized by 77 experts, attracting 248 submissions and resulting in 16 proceedings. This special issue presents extended versions of selected papers initially showcased at these workshops. Each paper underwent rigorous review and represents a diverse array of topics, reflecting the multifaceted nature of the Italian AI community. The topics covered include ethical foundations to symbiotic AI, symbolic knowledge extraction from black-box models, creative influence prediction using graph theory, AI approaches to multidimensional poverty prediction, an assessment of AI-based supports for informal caregivers, deep learning-based EEG denoising, AI-assisted board-game-based learning, large language models for assessment and feedback in higher education, geometric reasoning in the Traveling Salesperson Problem, defeasible reasoning in weighted knowledge bases, and conditional computation in neural networks. These contributions demonstrate the innovative and interdisciplinary research within the AI community, offering valuable insights and advancing the field
Worsening of osteonecrosis of the jaw during treatment with sunitinib in a patient with metastatic renal cell carcinoma.
No full textWe report on the potential association of suspected bisphosphonate-associated osteonecrosis of the jaw (BRONJ) recurrence with the use of the novel antiangiogenic drug sunitinib. A 59 year-old patient affected by metastatic renal cell carcinoma (RCC) and established BRONJ experienced consecutive episodes of painful jaw infection with cutaneous fistula and bone sequestration which occurred during active treatment with sunitinib, improved after discontinuation and antibiotic therapy, then rapidly worsened with resumption of sunitinib. We hypothesize that the potent antiangiogenic activity of sunitinib may amplify the inhibition of bone remodeling exerted by aminobisphosphonates entrapped within the osteonecrotic mineral matrix, antagonize mucosal healing and expose to infections during treatment. This supports the emerging role of soft-tissue damage in the pathogenesis of osteonecrosis of the jaw
Cinetica cellulare, contenuto in DNA e Grading istologico nella valutazione iniziale dei carcinomi del distretto cervico-facciale
No full textPairing monitoring with machine learning for smart system verification and predictive maintenance
No full textOver the last decades, the advancements in microelectronic technologies allowed for the embedding of complex digital sensors in several systems, ranging from home appliances to health tracking devices and industrial plant machinery. The resulting systems are, in general, quite complex, given the possible heterogeneity of their components and the non-trivial ways in which sensors may interact. In critical domains, formal methods have been employed to ensure the correct behaviour of a system. However, a complete specification of all the properties that have to be guaranteed turns out to be often out of reach, due to the inherent complexity of the system and of its interactions with the environment in which it operates. To overcome these limitations, some approaches that complement formal verification with model-based testing and monitoring have been recently proposed. In this paper, we argue for the opportunity of pairing monitoring with machine learning techniques in order to improve its ability of detecting critical system behaviours
A Framework for Indoor Positioning Including Building Topology
Full text linkIn many application domains, position information is of fundamental importance. However, unlike the case of outdoor positioning, producing an accurate position estimation in the indoor setting turns out to be quite difficult. One of the most common localisation strategies makes use of fingerprinting. Research in this area has been faced with a number of challenges, leading to the proposal of a number of localisation algorithms, sampling strategies, benchmark datasets, and representations of building information. This proliferation made the modeling of the indoor positioning domain quite hard from both a theoretical and a practical point of view. In this paper, we propose a general and extensible framework, based on a relational database, that pairs fingerprints with building information. We show how the proposed system successfully deals with a number of problems that affect indoor positioning, supporting a large set of relevant tasks. The source code of the framework is available online, as well as an implementation of it, that provides an interactive open repository of indoor positioning data
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Learning how to monitor: Pairing monitoring and learning for online system verification
No full textIn several domains, the execution of a system is associated with the generation of continuous streams of data. Such streams may contain important telemetry information, which can be used to perform tasks like predictive maintenance and preemptive failure detection, in order to issue early warnings. In critical contexts, formal methods have been recognized as an effective approach to ensure the correct behaviour of a system. However, they have at least two significant weaknesses: (i) a complete, hand-made specification of all the properties that have to be guaranteed during the execution of the system turns out to be often out of reach when complex systems have to be handled and, for the same complexity reasons, (ii) it may be difficult to derive a complete model of the system against which to check the properties of interest. In this paper, to overcome these limitations, we extend a recently presented framework that pairs monitoring with machine learning, in order to allow for the preemptive detection of critical system behaviours in an on-line setting. The framework is tested on a practical use-case based on the public NASA C-MAPSS dataset, and is shown to obtain promising performance in terms of its ability to forecast the approach of failures, and to provide interpretable results
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