INESC-ID

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.

ARMS - automatic residue-minimization based sampling for multi-point modeling techniques

Abstract: This paper describes an automatic methodology for optimizing sample point selection for using in the framework of model order reduction (MOR). The procedure, based on the maximization of the dimension of the subspace spanned by the samples, iteratively selects new samples in an efficient and automatic fashion, without computing the new vectors and with no prior assumptions on the system behavior. The scheme is general, and valid for single and multiple dimensions, with applicability on rational nominal MOR approaches, and on multi-dimensional sampling based parametric MOR methodologies. The paper also presents an integrated algorithm for multi-point MOR, with automatic sample and order selection based on the transfer function error estimation. Results on a variety of industrial examples demonstrate the accuracy and robustness of the technique.

Lite-CNN: A High-Performance Architecture to Execute CNNs in Low Density FPGAs

Abstract: Due to the computational complexity of Convolutional Neural Networks (CNNs), high performance platforms are generally considered for their execution. However, CNNs are very useful in embedded systems and its execution right next to the source of data has many advantages, like avoiding the need for data communication. In this paper, we propose an architecture for CNN inference (Lite-CNN) that can achieve high performance in low density FPGAs. Lite-CNN adopts a fixed-point representation for both neurons and weights, which was already shown to be sufficient for most CNNs. Also, with a simple and known dot product reorganization, the number of multiplications is reduced to half. We show implementation results for 8 bit fixed-point in a ZYNQ7020 and extrapolate for other larger FPGAs. Lite-CNN achieves 410 GOPs in a ZYNQ7020.

Classifying documents with link-based bibliometric measures

Abstract: Automatic document classification can be used to organize documents in a digital library, construct on-line directories, improve the precision of web searching, or help the interactions between user and search engines. In this paper we explore how linkage information inherent to different document collections can be used to enhance the effectiveness of classification algorithms. We have experimented with three link-based bibliometric measures, co-citation, bibliographic coupling and Amsler, on three different document collections: a digital library of computer science papers, a web directory and an on-line encyclopedia. Results show that both hyperlink and citation information can be used to learn reliable and effective classifiers based on a kNN classifier. In one of the test collections used, we obtained improvements of up to 69.8% of macro-averaged F 1 over the traditional text-based kNN classifier, considered as the baseline measure in our experiments. We also present alternative ways of combining bibliometric based classifiers with text based classifiers. Finally, we conducted studies to analyze the situation in which the bibliometric-based classifiers failed and show that in such cases it is hard to reach consensus regarding the correct classes, even for human judges.

Multi-room speech activity detection using a distributed microphone network in domestic environments

Abstract: Domestic environments are particularly challenging for distant speech recognition: reverberation, background noise and interfering sources, as well as the propagation of acoustic events across adjacent rooms, critically degrade the performance of standard speech processing algorithms. In this application scenario, a crucial task is the detection and localization of speech events generated by users within the various rooms. A specific challenge of multi-room environments is the inter-room interference that negatively affects speech activity detectors. In this paper, we present and compare different solutions for the multi-room speech activity detection task. The combination of a model-based room-independent speech activity detection module with a room-dependent inside/outside classification stage, based on specific features, provides satisfactory performance. The proposed methods are evaluated on a multi-room, multi-channel corpus, where spoken commands and other typical acoustic events occur in different rooms.

Virtual labs in electrical engineering education - The VEMA environment

Abstract: 3D worlds provide excellent platforms to achieve performance results when designed for the needs of students, helping them to improve acquisition and allowing greater interaction with contents in a unique way