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.

Efficient Extraction of Structured Motifs Using Box-Links

Abstract: In this paper we propose a new data structure for the efficient extraction of structured motifs from DNA sequences. A structured motif is defined as a collection of highly conserved motifs with pre-specified sizes and spacings between them. The new data structure, called box-link, stores the information on how to jump over the spacings which separate each motif in a structured motif. A factor tree, a variation of a suffix tree, endowed with box-links provide the means for the efficient extraction of structured motifs.

Runtime Object Lifetime Profiler for Latency Sensitive Big Data Applications

Abstract: Latency sensitive services such as credit-card fraud detection and website targeted advertisement rely on Big Data platforms which run on top of memory managed runtimes, such as the Java Virtual Machine (JVM). These platforms, however, suffer from unpredictable and unacceptably high pause times due to inadequate memory management decisions (e.g., allocating objects with very different lifetimes next to each other, resulting in severe memory fragmentation). This leads to frequent and long application pause times, breaking Service Level Agreements (SLAs). This problem has been previously identified, and results show that current memory management techniques are ill-suited for applications that hold in memory massive amounts of long-lived objects (which is the case for a wide spectrum of Big Data applications). Previous works reduce such application pauses by allocating objects in off-heap, in special allocation regions/generations, or by using ultra-low latency Garbage Collectors (GC). However, all these solutions either require a combination of programmer effort and knowledge, source code access, offline profiling (with clear negative impacts on programmer's productivity), or impose a significant impact on application throughput and/or memory to reduce application pauses. We propose ROLP, a Runtime Object Lifetime Profiler that profiles application code at runtime and helps pretenuring GC algorithms allocating objects with similar lifetimes close to each other so that the overall fragmentation, GC effort, and application pauses are reduced. ROLP is implemented for the OpenJDK 8 and was evaluated with a recently proposed open-source pretenuring collector (NG2C). Results show long tail latencies reductions of up to 51% for Lucene, 85% for GraphChi, and 69% for Cassandra. This is achieved with negligible throughput (

Age and gender detection in the I-DASH project

Abstract: This article presents a description of the INESC-ID Age and Gender classification systems which were developed for aiding the detection of child abuse material within the scope of the European project I-DASH. The Age and Gender classification systems are composed respectively by the fusion of four and six individual subsystems trained with short- and long-term acoustic and prosodic features, different classification strategies, Gaussian Mixture Models-Universal Background Model (GMM-UBM), Multi-Layer Perceptrons (MLP) and Support Vector Machines (SVM), trained over five different speech corpus. The best results obtained by the calibration and linear logistic regression fusion back-end show an absolute improvement of 2p on the unweighted accuracy value for the Age and 1p for the Gender when compared to the best individual frontend systems in the development set. The final age/gender detection system evaluated using a six-hour child abuse (CA) test set achieved promising results given the extremely difficult conditions of this type of video material. In order to further improve the performance in the CA domain, the classification modules were adapted using unsupervised selection of training data. An automatic data selection algorithm using frame-level posterior probabilities was developed. Performance improvement after adapting the classification modules was around 10p relative when compared with the baseline classifiers.

Detection of stator winding fault in induction motors using a motor square current signature analysis (MSCSA)

Abstract: In this paper the short-circuit fault inter-turn on the stator of an induction motor is analysed by an online diagnostic method. For the diagnostic method it is proposed the motor square current signature analysis (MSCSA). This method uses the results of spectral analysis of the instantaneous square stator current. The effects of stator square current spectrum are described and the related frequencies determined. This method is similar to the instantaneous power signature analysis, however has the advantage of just require one current sensor. Several simulation and experimental results are presented in order to illustrate the characteristics of the proposed method.