INESC-ID

About: INESC-ID is a nonprofit organization based out in Lisbon, Portugal. It is known for research contribution in the topics: Field-programmable gate array & Control theory. The organization has 932 authors who have published 2618 publications receiving 37658 citations.

SQUARES: a SQL synthesizer using query reverse engineering

Abstract: Nowadays, many data analysts are domain experts, but they lack programming skills. As a result, many of them can provide examples of data transformations but are unable to produce the desired query. Hence, there is an increasing need for systems capable of solving the problem of Query Reverse Engineering (QRE). Given a database and output table, these systems have to find the query that generated this table. We present SQUARES, a program synthesis tool based on input-output examples that can help data analysts to extract and transform data by synthesizing SQL queries, and table manipulation programs using the R language.

Agents that remember can tell stories: integrating autobiographic memory into emotional agents

Abstract: For the past few years many new applications are being developed featuring interactive environments populated with autonomous virtual agents capable of acting according to their goals, beliefs and even social relations. Such agents must be able to interact with each other, and more importantly with the user, thus involving the users in an engaging narrative experience. To achieve these, in this paper we describe the essential event structure in an autobiographic memory, event reconstructions in memory retrieval process and the influences of such past events in interpersonal relations.

Automatic Detection of Parkinson’s Disease: An Experimental Analysis of Common Speech Production Tasks Used for Diagnosis

Abstract: Parkinson’s disease (PD) is the second most common neurodegenerative disorder of mid-to-late life after Alzheimer’s disease. During the progression of the disease, most individuals with PD report impairments in speech due to deficits in phonation, articulation, prosody, and fluency. In the literature, several studies perform the automatic classification of speech of people with PD considering various types of acoustic information extracted from different speech tasks. Nevertheless, it is unclear which tasks are more important for an automatic classification of the disease. In this work, we compare the discriminant capabilities of eight verbal tasks designed to capture the major symptoms affecting speech. To this end, we introduce a new database of Portuguese speakers consisting of 65 healthy control and 75 PD subjects. For each task, an automatic classifier is built using feature sets and modeling approaches in compliance with the current state of the art. Experimental results permit to identify reading aloud prosodic sentences and story-telling tasks as the most useful for the automatic detection of PD.