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.

Intelligent fusion of structural and citation-based evidence for text classification

Abstract: This paper shows how different measures of similarity derived from the citation information and the structural content (e.g., title, abstract) of the collection can be fused to improve classification effectiveness. To discover the best fusion framework, we apply Genetic Programming (GP) techniques. Our experiments with the ACM Computing Classification Scheme, using documents from the ACM Digital Library, indicate that GP can discover similarity functions superior to those based solely on a single type of evidence. Effectiveness of the similarity functions discovered through simple majority voting is better than that of content-based as well as combination-based Support Vector Machine classifiers. Experiments also were conducted to compare the performance between GP techniques and other fusion techniques such as Genetic Algorithms (GA) and linear fusion. Empirical results show that GP was able to discover better similarity functions than other fusion techniques.

Multi-level parallelization of advanced video coding on hybrid CPU+GPU platforms

Abstract: A dynamic model for parallel H.264/AVC video encoding on hybrid GPU+CPU systems is proposed. The entire inter-prediction loop of the encoder is parallelized on both the CPU and the GPU, and a computationally efficient model is proposed to dynamically distribute the computational load among these processing devices on hybrid platforms. The presented model includes both dependency aware task scheduling and load balancing algorithms. According to the obtained experimental results, the proposed dynamic load balancing model is able to push forward the computational capabilities of these hybrid parallel platforms, achieving a speedup of up to 2 when compared with other equivalent state-of-the-art solutions. With the presented implementation, it was possible to encode 25 frames per second for HD 1920×1080 resolution, even when exhaustive motion estimation is considered.

Machiavellian Characters and the Edutainment Paradox

Abstract: Purely script-based approaches to building interactive narratives have often limited interaction capabilities where variability demand exponential work. This is why Intelligent Virtual Agents (IVAs) are a transparent technique to handle user interaction in interactive narrative systems. However, it is hard to predict a sense of educational purpose in the global behavior of a group of IVAs, if no script or control is given. Efforts have been channelled to achieve such control, but are yet to achieve truly satisfactory results. These efforts are usually based on a direct connection between the control and the IVA architecture, which is a source of exponential complication. We propose a system that, based on a common ontology, can flexibly support the human authoring of educational goals independently of any specific IVA architecture. This is done by having a stage manager that follows an episodic based narrative where each episode is only specified through a set of properties and conditions that set the context for the characters. Although acting as they please, this contextualization will limit their range of action thereby facilitating the achievement of dramatic and educational goals.

SEMilarity: Towards a Model-Driven Approach to Similarity.

Abstract: Enabling the Semantic Web requires solving the semantic heterogeneity problem, for which ontology matching methods have been proposed. These methods rely on similarity measures that are mainly focused on terminological, structural and extensional properties of the ontologies. Semantics rarely play a direct role on the ontology matching process, albeit some algorithms have been proposed. On the other hand, many ontology engineers choose representation languages that have an underlying formal logic, providing well-defined model-theoretic semantics. Since semantics are a key advantage of ontologies, we believe that semantics-based similarity measures are crucial. In this paper, we present a novel approach to semantic similarity.