Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Federico Cerutti"'
Publikováno v:
MLSP
When the historical data are limited, the conditional probabilities associated with the nodes of Bayesian networks are uncertain and can be empirically estimated. Second order estimation methods provide a framework for both estimating the probabiliti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a7ef598badc138b593dc793ef98d8b70
Publikováno v:
IoTDI
The ubiquity of mobile, wearable, and IoT devices enhances humans with a network of environmental sensors. These devices capture raw, time-series measurements of scalar physical phenomena. To transform the data into human-digestible representations,
Autor:
Liam Hiley, Dave Braines, Lance M. Kaplan, Mani Srivastava, Mark Law, Marc Roig Vilamala, Alessandra Russo, Federico Cerutti, Tianwei Xing, Jack Furby, Alun Preece
Publikováno v:
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III.
Recent years have seen significant advances in artificial intelligence (AI) and machine learning (ML) technologies applicable to coalition situational understanding (CSU). However, state-of-the-art ML techniques based on deep neural networks require
Publikováno v:
AIxIA 2020 – Advances in Artificial Intelligence ISBN: 9783030770907
AI*IA
AI*IA
In this paper we introduce \(\textsf {AASExts}\), an algorithm for computing semi–stable extensions. We improve techniques developed for other semantics, notably preferred semantics, as well as leverage recent advances in All-SAT community. We prov
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a415a31e7ce54bcd725161ac6132d64c
https://doi.org/10.1007/978-3-030-77091-4_8
https://doi.org/10.1007/978-3-030-77091-4_8
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030893903
CLAR
CLAR
The paper introduces a general model for the study of decomposability in abstract argumentation, i.e. the possibility of determining the semantics outcome based on local evaluations in subframeworks. As such, the paper extends a previous work by gene
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ec53501e5e806427e27b7c35a42c2f36
http://hdl.handle.net/11379/551275
http://hdl.handle.net/11379/551275
Autor:
Pietro Baroni, Francesco Gringoli, Massimiliano Giacomin, Federico Cerutti, Paul Sullivan, Daniela Fogli, Giovanni Guida
Publikováno v:
CyCon
Artificial intelligence (AI) techniques can significantly improve cyber security operations if tasks and responsibilities are effectively shared between human and machine. AI techniques excel in some situational understanding tasks; for instance, cla
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b4fe2cf94f47e144fb22045a67acc81
http://hdl.handle.net/11379/551393
http://hdl.handle.net/11379/551393
Current solutions to tackle phishing employ blocklists that are built from user reports or automatic approaches. They, however, fall short in detecting zero-day phishing attacks. We propose the use of Generative Adversarial Networks (GANs) to automat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c939f813c60b91d040f0884e8cad7986
https://hdl.handle.net/11379/565241
https://hdl.handle.net/11379/565241
Publikováno v:
FUSION
Efficient second-order probabilistic inference in uncertain Bayesian networks was recently introduced. However, such second -order inference methods presume training over complete training data. While the expectation-maximization framework is well-es
Publikováno v:
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II.
Coalition situational understanding (CSU) is fundamental to support decision-making and autonomy in multi-domain operations involving multiple allied partners. Our work aims to advance the algorithms and techniques to develop CSU, addressing key scie
Autor:
Luis Garcia, Lance M. Kaplan, Mani Srivastava, Alun Preece, Tianwei Xing, Federico Cerutti, Marc Roig Vilamala
Publikováno v:
SenSys
Despite the remarkable success in a broad set of sensing applications, state-of-the-art deep learning techniques struggle with complex reasoning tasks across a distributed set of sensors. Unlike recognizing transient complex activities (e.g., human a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5319131f5361250eaa96695c583bbdc3
http://hdl.handle.net/11379/538197
http://hdl.handle.net/11379/538197