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Institution

INESC-ID

NonprofitLisbon, Portugal
About: INESC-ID is a nonprofit organization based out in Lisbon, Portugal. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 932 authors who have published 2618 publications receiving 37658 citations.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors exploit the transport characteristic of a solar plant where the transport velocity (a flow) is the manipulated variable, i.e. the control input, and use a state-space description of the plant in conjunction with the non-uniform sampling that allows to introduce the feedback mechanism through the state observer.

25 citations

Proceedings ArticleDOI
31 Oct 2005
TL;DR: A novel framework using Genetic Programming to combine image database descriptors for content-based image retrieval (CBIR) is proposed and validated through several experiments involving two image databases and specific domains.
Abstract: In this paper, we propose a novel framework using Genetic Programming to combine image database descriptors for content-based image retrieval (CBIR). Our framework is validated through several experiments involving two image databases and specific domains, where the images are retrieved based on the shape of their objects.

25 citations

Proceedings ArticleDOI
14 Sep 2014
TL;DR: An experimental recognition framework comprising a multi-room, multi-channel corpus and the accompanying evaluation tools is made publicly available to represent a common platform for comparing state-of-the-art algorithms and integrate several components in a realistic distant-talking recognition chain.
Abstract: Distant speech recognition in real-world environments is still a challenging problem and a particularly interesting topic is the investigation of multi-channel processing in case of distributed microphones in home environments. This paper presents an initiative oriented to address the challenges of such a scenario; an experimental recognition framework comprising a multi-room, multi-channel corpus and the accompanying evaluation tools is made publicly available. The overall goal is to represent a common platform for comparing state-of-the-art algorithms, share ideas of different research communities and integrate several components in a realistic distant-talking recognition chain, e.g., voice activity detection, speech/feature enhancement, channel selection and fusion, model compensation. The recordings include spoken commands (derived from the well-known GRID corpus) mixed with other acoustic events occurring in different rooms of a real apartment. The work provides a detailed description of data, tasks and baseline results, discussing the potential and limits of the approach and highlighting the impact of single modules on recognition performance.

25 citations

Journal ArticleDOI
01 Nov 2014-Energy
TL;DR: In this article, the authors proposed a conjectural variations model to study the competitive behavior of generating firms acting in liberalized electricity markets, which computes a parameter that represents the degree of competition of each generating firm in each trading period.

25 citations

Journal ArticleDOI
TL;DR: It is shown that the best performing algorithms are LSA, for news articles and documentaries, and LexRank and Support Sets, for films, despite the different nature of films and documentaries.

25 citations


Authors

Showing all 967 results

NameH-indexPapersCitations
João Carvalho126127877017
Jaime G. Carbonell7249631267
Chris Dyer7124032739
Joao P. S. Catalao68103919348
Muhammad Bilal6372014720
Alan W. Black6141319215
João Paulo Teixeira6063619663
Bhiksha Raj5135913064
Joao Marques-Silva482899374
Paulo Flores483217617
Ana Paiva474729626
Miadreza Shafie-khah474508086
Susana Cardoso444007068
Mark J. Bentum422268347
Joaquim Jorge412906366
Network Information
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202311
202252
202196
2020131
2019133
2018126