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.

TUGAS: Exploiting unlabelled data for Twitter sentiment analysis

Abstract: This paper describes our participation in the message polarity classification task of SemEval 2014. We focused on exploiting unlabeled data to improve accuracy, combining features leveraging word representations with other, more common features, based on word tokens or lexicons. We analyse the contribution of the different features, concluding that unlabeled data yields significant improvements.

Real-Time Integration of Building Energy Data

Abstract: An Energy Management System (EMS) is a monitoring tool that tracks buildings energy consumption with the purpose of enhancing energy efficiency, by identifying savings opportunities and misuse situations. To achieve this, EMSs collect data flows-data streams-from a network of energy meters and sensors, which are then combined into useful information. Data must be processed in real-time, to support a timely decision making process. Traditionally EMSs use Database Management Systems (DBMSs) to process data, introducing a persistence step that leads to an unacceptable latency on data evaluation and do not properly support many types of time-series queries. This work explores the feasibility of Data Stream Management Systems (DSMSs) to support Energy Management applications, pointing out how to implement an EMS capable of real-time data processing.

Depression Detection Using Automatic Transcriptions of De-Identified Speech.

Abstract: Depression is a mood disorder that is usually addressed by outpatient treatments in order to favour patient’s inclusion in society. This leads to a need for novel automatic tools exploiting speech processing approaches that can help to monitor the emotional state of patients via telephone or the Internet. However, the transmission, processing and subsequent storage of such sensitive data raises several privacy concerns. Speech deidentification can be used to protect the patients’ identity. Nevertheless, these techniques modify the speech signal, eventually affecting the performance of depression detection approaches based on either speech characteristics or automatic transcriptions. This paper presents a study on the influence of speech de-identification when using transcription-based approaches for depression detection. To this effect, a system based on the global vectors method for natural language processing is proposed. In contrast to previous works, two main sources of nuisance have been considered: the de-identification process itself and the transcription errors introduced by the automatic recognition of the patients’ speech. Experimental validation on the DAIC-WOZ corpus reveals very promising results, obtaining only a slight performance degradation with respect to the use of manual transcriptions.

Fault Simulation Using Partially Reconfigurable Hardware

Abstract: This paper presents a fault simulation algorithm and that uses efficient partial reconfiguration of FPGAs. The methodology is particularly useful for evaluation of BIST effectiveness, and for applications in which multiple fault injection is mandatory, such as safety-critical applications. A novel fault collapsing methodology is proposed, which efficiently leads to the minimal stuck-at fault list at the look-up-tables’ terminals. Fault injection is performed using local partial reconfiguration with small binary files. Our results on the ISCAS’89 sequential circuit benchmarks show that our methodology can be orders of magnitude faster than software or fully reconfigurable hardware fault simulation..

Computing distribution of scale independent motifs in biological sequences.

Abstract: The use of Chaos Game Representation (CGR) or its generalization, Universal Sequence Maps (USM), to describe the distribution of biological sequences has been found objectionable because of the fractal structure of that coordinate system. Consequently, the investigation of distribution of symbolic motifs at multiple scales is hampered by an inexact association between distance and sequence dissimilarity. A solution to this problem could unleash the use of iterative maps as phase-state representation of sequences where its statistical properties can be conveniently investigated. In this study a family of kernel density functions is described that accommodates the fractal nature of iterative function representations of symbolic sequences and, consequently, enables the exact investigation of sequence motifs of arbitrary lengths in that scale-independent representation. Furthermore, the proposed kernel density includes both Markovian succession and currently used alignment-free sequence dissimilarity metrics as special solutions. Therefore, the fractal kernel described is in fact a generalization that provides a common framework for a diverse suite of sequence analysis techniques.