Search results for "Intelligence"

Semantic models of musical mood: Comparison between crowd-sourced and curated editorial tags

2013

Social media services such as Last.fm provide crowd-sourced mood tags which are a rich but often noisy source of information. In contrast, editorial annotations from production music libraries are meant to be incisive in nature. We compare the efficiency of these two data sources in capturing semantic information on mood expressed by music. First, a semantic computing technique devised for mood-related tags in large datasets is applied to Last.fm and I Like Music (ILM) corpora separately (250,000 tracks each). The resulting semantic estimates are then correlated with listener ratings of arousal, valence and tension. High correlations (Spearman's rho) are found between the track positions in…

Change-driven Image Architecture on FPGA with adaptive threshold for Optical-Flow Computation

2006

Optical flow computation has been extensively used for object motion estimation in image sequences. However, the results obtained by most optical flow techniques are as accurate as computationally intensive due to the large amount of data involved. A new strategy for image sequence processing has been developed; pixels of the image sequence that significantly change fire the execution of the operations related to the image processing algorithm. The data reduction achieved with this strategy allows a significant optical flow computation speed-up. Furthermore, FPGAs allow the implementation of a custom data-flow architecture specially suited for this strategy. The foundations of the change-dr…

Integrating miniMin-HSP agents in a dynamic simulation framework

2004

In this paper, we describe the framework created for implementing AI-based animations for artificial actors in the context of IVE (Intelligent Virtual Environments). The minMin-HSP (Heuristic Search Planner) planner presented in [12] has been updated to deal with 3D dynamic simulation environments, using the sensory/actuator system fully implemented in UnrealTM and presented in [10]. Here, we show how we have integrated these systems to handle the necessary balance between the reactive and deliberative skills for 3D Intelligent Virtual Agents (3DIVAs). We have carried out experiments in a multi-agent 3D blocks world, where 3DIVAs will have to interleave sensing, planning and execution to be…

New systems for extracting 3-D shape information from images

1993

Neural architectures may offer an adequate way to deal with early vision since they are able to learn shape features or classify unknown shapes, generalising the features of a few meaningful examples, with a low computational cost after the training phase. Two different neural approaches are proposed by the authors: the first one consists of a cascaded architecture made up by a first stage named BWE (Boundary Webs Extractor) which is aimed to extract a brightness gradient map from the image, followed by a backpropagation network that estimates the geometric parameters of the object parts present in the perceived scene. The second approach is based on the extraction of the boundary webs map …

Word sense disamibiguation combining conceptual distance, frequency and gloss

2004

Word sense disambiguation (WSD) is the process of assigning a meaning to a word based on the context in which it occurs. The absence of sense tagged training data is a real problem for the word sense disambiguation task. We present a method for the resolution of lexical ambiguity which relies on the use of the wide-coverage noun taxonomy of WordNet and the notion of conceptual distance among concepts, captured by a conceptual density formula developed for this purpose. The formula we propose, is a generalised form of the Agirre-Rigau conceptual density measure in which many (parameterised) refinements were introduced and an exhaustive evaluation of all meaningful combinations was performed.…

Interpretable machine learning models for single-cell ChIP-seq imputation

2019

AbstractMotivationSingle-cell ChIP-seq (scChIP-seq) analysis is challenging due to data sparsity. High degree of data sparsity in biological high-throughput single-cell data is generally handled with imputation methods that complete the data, but specific methods for scChIP-seq are lacking. We present SIMPA, a scChIP-seq data imputation method leveraging predictive information within bulk data from ENCODE to impute missing protein-DNA interacting regions of target histone marks or transcription factors.ResultsImputations using machine learning models trained for each single cell, each target, and each genomic region accurately preserve cell type clustering and improve pathway-related gene i…

The CogALex-IV Shared Task on the Lexical Access Problem

2014

The shared task of the 4th Workshop on Cognitive Aspects of the Lexicon (CogALexIV) was devoted to a subtask of the lexical access problem, namely multi-stimulus association. In this task, participants were supposed to determine automatically an expected response based on a number of received stimulus words. We describe here the task definition, the theoretical background, the training and test data sets, and the evaluation procedure used for ranking the participating systems. We also summarize the approaches used and present the results of the evaluation. In conclusion, the outcome of the competition are a number of systems which provide very good solutions to the problem.

An evolutionary restricted neighborhood search clustering approach for PPI networks

2014

Protein-protein interaction networks have been broadly studied in the last few years, in order to understand the behavior of proteins inside the cell. Proteins interacting with each other often share common biological functions or they participate in the same biological process. Thus, discovering protein complexes made of a group of proteins strictly related can be useful to predict protein functions. Clustering techniques have been widely employed to detect significant biological complexes. In this paper, we integrate one of the most popular network clustering techniques, namely the Restricted Neighborhood Search Clustering (RNSC), with evolutionary computation. The two cost functions intr…

Corrigendum to “Intelligent agents for feature modelling in computer aided design” [J. Comput. Des. Eng. (2018) 19–40]

2018

A Structural $\mathcal{ SHOIN(D)}$ Ontology Model for Change Modelling

2013

This paper presents a complete structural ontology model suited for change modelling on \(\mathcal{ SHOIN(D)}\) ontologies. The application of this model is illustrated along the paper through the description of an ontology example inspired by the UOBM ontology benchmark and its evolution.