Repositório Colecção:https://hdl.handle.net/1822/213142024-03-29T12:52:31Z2024-03-29T12:52:31ZPreface [International Conference on Rigorous State-Based Methods (ABZ 2023)]Campos, José C.Glässer, UweMéry, DominiquePalanque, Philippehttps://hdl.handle.net/1822/902712024-03-28T20:43:27Z2024-03-28T13:28:15ZTítulo: Preface [International Conference on Rigorous State-Based Methods (ABZ 2023)]
Autor: Campos, José C.; Glässer, Uwe; Méry, Dominique; Palanque, Philippe
Resumo: [Excerpt] The International Conference on Rigorous State-Based Methods (ABZ 2023) was an
international forum for the cross-fertilization of related state-based and machine-based
formal methods, mainly Abstract StateMachines (ASM), Alloy, B, TLA +, VDM and
Z. Rigorous state-based methods share common conceptual foundations and are widely
used in both academia and industry for the design and analysis of hardware and software
systems. The acronym ABZ was invented at the first conference, held in London in
2008, where the ASM, B and Z conference series merged into a single event. The
second ABZ 2010 conference was held in Orford (Canada), where the Alloy community
joined the event; ABZ 2012 was held in Pisa (Italy), which saw the inclusion of the
VDM community (but not in the title); ABZ 2014 was held in Toulouse (France), which
brought the inclusion of the TLA + community into the ABZ conference series. Lastly,
the ABZ 2016 conference was held in Linz, Austria and ABZ 2018 in Southampton, UK.
In 2018 the steering committee decided to retain the (well-known) acronym ABZ and
add the subtitle ‘International Conference on Rigorous State-Based Methods’ to make
more explicit the intention to include all state-based formal methods. Two successive
ABZ events have been organized in Ulm (Germany) and these were the two first virtual
ABZ events. [...]
<b>Tipo</b>: bookEditorial2024-03-28T13:28:15ZBeyond Average Performance -- exploring regions of deviating performance for black box classification modelsTorgo, LuisAzevedo, Paulo J.Areosa, Inêshttps://hdl.handle.net/1822/902652024-03-28T12:51:11Z2024-03-28T12:50:59ZTítulo: Beyond Average Performance -- exploring regions of deviating performance for black box classification models
Autor: Torgo, Luis; Azevedo, Paulo J.; Areosa, Inês
Resumo: Machine learning models are becoming increasingly popular in different types
of settings. This is mainly caused by their ability to achieve a level of
predictive performance that is hard to match by human experts in this new era
of big data. With this usage growth comes an increase of the requirements for
accountability and understanding of the models' predictions. However, the
degree of sophistication of the most successful models (e.g. ensembles, deep
learning) is becoming a large obstacle to this endeavour as these models are
essentially black boxes. In this paper we describe two general approaches that
can be used to provide interpretable descriptions of the expected performance
of any black box classification model. These approaches are of high practical
relevance as they provide means to uncover and describe in an interpretable way
situations where the models are expected to have a performance that deviates
significantly from their average behaviour. This may be of critical relevance
for applications where costly decisions are driven by the predictions of the
models, as it can be used to warn end users against the usage of the models in
some specific cases.
<b>Tipo</b>: researchReport2024-03-28T12:50:59ZTwo-level adaptive sampling for illumination integrals using Bayesian Monte CarloMarques, R.Bouville, C.Santos, Luís PauloBouatouch, K.https://hdl.handle.net/1822/901872024-03-27T20:59:02Z2024-03-27T16:33:56ZTítulo: Two-level adaptive sampling for illumination integrals using Bayesian Monte Carlo
Autor: Marques, R.; Bouville, C.; Santos, Luís Paulo; Bouatouch, K.
Resumo: Bayesian Monte Carlo (BMC) is a promising integration technique which considerably broadens the theoretical tools that can be used to maximize and exploit the information produced by sampling, while keeping the fundamental property of data dimension independence of classical Monte Carlo (CMC). Moreover, BMC uses information that is ignored in the CMC method, such as the position of the samples and prior stochastic information about the integrand, which often leads to better integral estimates. Nevertheless, the use of BMC in computer graphics is still in an incipient phase and its application to more evolved and widely used rendering algorithms remains cumbersome. In this article we propose to apply BMC to a two-level adaptive sampling scheme for illumination integrals. We propose an efficient solution for the second level quadrature computation and show that the proposed method outperforms adaptive quasi-Monte Carlo in terms of image error and high frequency noise.
<b>Tipo</b>: conferencePaper2024-03-27T16:33:56ZHeterogeneous models and modelling approaches for engineering of interactive systemsAït-Ameur, YamineBowen, JudyCampos, José C.Palanque, PhilippeWeyers, Benjaminhttps://hdl.handle.net/1822/901452024-03-27T20:57:18Z2024-03-27T12:34:26ZTítulo: Heterogeneous models and modelling approaches for engineering of interactive systems
Autor: Aït-Ameur, Yamine; Bowen, Judy; Campos, José C.; Palanque, Philippe; Weyers, Benjamin
Resumo: [Excerpt] The selection of articles that follow were solicited from attendees of several workshops that are related in their focus on engineering methods for interactive systems design and development. The first of these was the Workshop on Heterogeneous Models and Modelling Approaches for Engineering of Interactive Systems, which took place in Paris in June 2018, colocated with the 10th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS).[...]
<b>Tipo</b>: journalEditorial2024-03-27T12:34:26ZProceedings of the 16th ACM SIGPLAN International conference on software language engineeringSaraiva, JoãoDegueule, ThomasScott, Elizabethhttps://hdl.handle.net/1822/900002024-03-25T19:09:52Z2024-03-25T17:46:14ZTítulo: Proceedings of the 16th ACM SIGPLAN International conference on software language engineering
Autor: Saraiva, João; Degueule, Thomas; Scott, Elizabeth
<b>Tipo</b>: conferenceProceedings2024-03-25T17:46:14Z