Institution
INESC-ID
Nonprofit•Lisbon, 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.
Topics: Computer science, Context (language use), Field-programmable gate array, Control theory, Adaptive control
Papers published on a yearly basis
Papers
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15 Jul 2010TL;DR: AGGRO as discussed by the authors is an innovative Optimistic Atomic Broadcast-based (OAB) active replication protocol that aims at maximizing the overlap between communication and processing through a novel AGGRessively Optimistic concurrency control scheme.
Abstract: Software Transactional Memories (STMs) are emerging as a potentially disruptive programming model. In this paper we are address the issue of how to enhance dependability of STM systems via replication. In particular we present AGGRO, an innovative Optimistic Atomic Broadcast-based (OAB) active replication protocol that aims at maximizing the overlap between communication and processing through a novel AGGRessively Optimistic concurrency control scheme. The key idea underlying AGGRO is to propagate dependencies across uncommitted transactions in a controlled manner, namely according to a serialization order compliant with the optimistic message delivery order provided by the OAB service. Another relevant distinguishing feature of AGGRO is of not requiring a-priori knowledge about read/write sets of transactions, but rather to detect and handle conflicts dynamically, i.e. as soon (and only if) they materialize. Based on a detailed simulation study we show the striking performance gains achievable by AGGRO (up to 6x increase of the maximum sustainable throughput, and 75% response time reduction) compared to literature approaches for active replication of transactional systems.
36 citations
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TL;DR: Several successful extensions to the standard hidden-Markov-model/artificial neural network (HMM/ANN) hybrid are reviewed, which have recently made important contributions to the field of noise robust automatic speech recognition and provide generic models for multi-modal data fusion.
36 citations
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02 Sep 2013TL;DR: This paper proposes a new approach to WIP, called Speed-Amplitude-Supported Walking-in-Place (SAS-WIP), which allows people, when walking along linear paths, to control their virtual speed based on footstep amplitude and speed metrics.
Abstract: Walking in Place (WIP) is an important locomotion technique used in virtual environments. This paper proposes a new approach to WIP, called Speed-Amplitude-Supported Walking-in-Place (SAS-WIP), which allows people, when walking along linear paths, to control their virtual speed based on footstep amplitude and speed metrics. We argue that our approach allows users to better control the virtual distance covered by the footsteps, achieve higher average speeds and experience less fatigue than when using state-of-the-art methods based on footstep frequency, called GUD-WIP.
36 citations
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TL;DR: Results show that performance alone can be used to predict student type with 79 percent accuracy by midterm, however, its accuracy improves when paired with gaming data at earlier stages of the course.
Abstract: State of the art research shows that gamified learning can be used to engage students and help them perform better. However, most studies use a one-size-fits-all approach to gamification, where individual differences and needs are ignored. In a previous study, we identified four types of students attending a gamified college course, characterized by different levels of performance, engagement and behavior. In this paper, we present a new experiment where we study what data best characterizes each of our student types and explore if this data can be used to predict a student's type early in the course. To this end, we used machine-learning algorithms to classify student data from one term and predict the students’ type on another term. We identified two sets of relevant features that best describe our types, one containing only performance measurements and another also containing data regarding the students’ gaming preferences. Results show that performance alone can be used to predict student type with 79 percent accuracy by midterm. However, its accuracy improves when paired with gaming data at earlier stages of the course. In this paper, we clearly describe our findings and discuss the lessons learned from this experiment.
36 citations
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TL;DR: An efficient algorithm is proposed that identifies the most interesting region to cut circular genomes in order to improve phylogenetic analysis when using standard multiple sequence alignment algorithms, and leads to more realistic phylogenetic comparisons between species.
Abstract: The comparison of homologous sequences from different species is an essential approach to reconstruct the evolutionary history of species and of the genes they harbour in their genomes. Several complete mitochondrial and nuclear genomes are now available, increasing the importance of using multiple sequence alignment algorithms in comparative genomics. MtDNA has long been used in phylogenetic analysis and errors in the alignments can lead to errors in the interpretation of evolutionary information. Although a large number of multiple sequence alignment algorithms have been proposed to date, they all deal with linear DNA and cannot handle directly circular DNA. Researchers interested in aligning circular DNA sequences must first rotate them to the "right" place using an essentially manual process, before they can use multiple sequence alignment tools.
36 citations
Authors
Showing all 967 results
Name | H-index | Papers | Citations |
---|---|---|---|
João Carvalho | 126 | 1278 | 77017 |
Jaime G. Carbonell | 72 | 496 | 31267 |
Chris Dyer | 71 | 240 | 32739 |
Joao P. S. Catalao | 68 | 1039 | 19348 |
Muhammad Bilal | 63 | 720 | 14720 |
Alan W. Black | 61 | 413 | 19215 |
João Paulo Teixeira | 60 | 636 | 19663 |
Bhiksha Raj | 51 | 359 | 13064 |
Joao Marques-Silva | 48 | 289 | 9374 |
Paulo Flores | 48 | 321 | 7617 |
Ana Paiva | 47 | 472 | 9626 |
Miadreza Shafie-khah | 47 | 450 | 8086 |
Susana Cardoso | 44 | 400 | 7068 |
Mark J. Bentum | 42 | 226 | 8347 |
Joaquim Jorge | 41 | 290 | 6366 |