2023
Autores
Marchamalo-Sacristán, M; Ruiz-Armenteros, AM; Lamas-Fernández, F; González-Rodrigo, B; Martínez-Marín, R; Delgado-Blasco, JM; Bakon, M; Lazecky, M; Perissin, D; Papco, J; Sousa, JJ;
Publicação
Remote Sensing
Abstract
2023
Autores
Dunne, S; Ferreira, JF; Mendes, A; Ritchie, C; Stoddart, B; Zeyda, F;
Publicação
JOURNAL OF LOGICAL AND ALGEBRAIC METHODS IN PROGRAMMING
Abstract
We present an imperative refinement language for the development of backtracking programs and discuss its semantic foundations. For expressivity, our language includes prospective values and preference - the latter being a variant of Nelson's biased choice that backtracks from infeasibility of a continuation. Our key contribution is to examine feasibility-preserving refinement as a basis for developing backtracking programs, and several key refinement laws that enable compositional refinement in the presence of non -monotonic program combinators.
2023
Autores
Portela, B; Pacheco, H; Jorge, P; Pontes, R;
Publicação
2023 IEEE 36TH COMPUTER SECURITY FOUNDATIONS SYMPOSIUM, CSF
Abstract
Conflict-free Replicated Data Types (CRDTs) are a very popular class of distributed data structures that strike a compromise between strong and eventual consistency. Ensuring the protection of data stored within a CRDT, however, cannot be done trivially using standard encryption techniques, as secure CRDT protocols would require replica-side computation. This paper proposes an approach to lift general-purpose implementations of CRDTs to secure variants using secure multiparty computation (MPC). Each replica within the system is realized by a group of MPC parties that compute its functionality. Our results include: i) an extension of current formal models used for reasoning over the security of CRDT solutions to the MPC setting; ii) a MPC language and type system to enable the construction of secure versions of CRDTs and; iii) a proof of security that relates the security of CRDT constructions designed under said semantics to the underlying MPC library. We provide an open-source system implementation with an extensive evaluation, which compares different designs with their baseline throughput and latency.
2023
Autores
da Conceição, EL; Alonso, AN; Oliveira, RC; Pereira, JO;
Publicação
Distributed Applications and Interoperable Systems - 23rd IFIP WG 6.1 International Conference, DAIS 2023, Held as Part of the 18th International Federated Conference on Distributed Computing Techniques, DisCoTec 2023, Lisbon, Portugal, June 19-23, 2023, Proceedings
Abstract
2023
Autores
Cunha, J; Madeira, A; Barbosa, LS;
Publicação
Theoretical Aspects of Software Engineering - 17th International Symposium, TASE 2023, Bristol, UK, July 4-6, 2023, Proceedings
Abstract
2023
Autores
da Costa, TS; Andrade, MT; Viana, P; Silva, NC;
Publicação
PROCEEDINGS OF THE 2023 PROCEEDINGS OF THE 14TH ACM MULTIMEDIA SYSTEMS CONFERENCE, MMSYS 2023
Abstract
Immersive video applications impose unpractical bandwidth requirements for best-effort networks. With Multi-View(MV) streaming, these can be minimized by resorting to view prediction techniques. SmoothMV is a multi-view system that uses a non-intrusive head tracking mechanism to detect the viewer's interest and select appropriate views. By coupling Neural Networks (NNs) to anticipate the viewer's interest, a reduction of view-switching latency is likely to be obtained. The objective of this paper is twofold: 1) Present a solution for acquisition of gaze data from users when viewing MV content; 2) Describe a dataset, collected with a large-scale testbed, capable of being used to train NNs to predict the user's viewing interest. Tracking data from head movements was obtained from 45 participants using an Intel Realsense F200 camera, with 7 video playlists, each being viewed a minimum of 17 times. This dataset is publicly available to the research community and constitutes an important contribution to reducing the current scarcity of such data. Tools to obtain saliency/heat maps and generate complementary plots are also provided as an open-source software package.
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