Search results for "Data mining"

Stereotype threat and lift effects on perceived ability and motor task performance of high school physical education students: the moderating role of…

2016

This study investigated the effects of stereotype threat and lift on perceived ability and motor task performance, and tested the moderating effects of stereotype endorsement and domain identification. One hundred and twenty French high school students were randomly assigned to control, stereotype threat, or stereotype lift conditions, in a 3 (condition) × 2 (sex) study design. The results revealed a stereotype lift effect on boys’ performance moderated by domain identification and a stereotype threat effect on girls’ perceived ability moderated by domain identification and stereotype endorsement. Perceived ability did not mediate the effects of stereotype threat and lift on performance. Th…

Five Ways in Which Computational Modeling Can Help Advance Cognitive Science

2019

Abstract There is a rich tradition of building computational models in cognitive science, but modeling, theoretical, and experimental research are not as tightly integrated as they could be. In this paper, we show that computational techniques—even simple ones that are straightforward to use—can greatly facilitate designing, implementing, and analyzing experiments, and generally help lift research to a new level. We focus on the domain of artificial grammar learning, and we give five concrete examples in this domain for (a) formalizing and clarifying theories, (b) generating stimuli, (c) visualization, (d) model selection, and (e) exploring the hypothesis space.

Deterministic Linkage as a Preceding Filter for Other Record Linkage Methods

2015

Deterministic record linkage (RL) is frequently regarded as a rival to more sophisticated strategies like probabilistic RL. We investigate the effect of combining deterministic linkage with other linkage techniques. For this task, we use a simple deterministic linkage strategy as a preceding filter: a data pair is classified as ‘match' if all values of attributes considered agree exactly, otherwise as ‘nonmatch'. This strategy is separately combined with two probabilistic RL methods based on the Fellegi–Sunter model and with two classification tree methods (CART and Bagging). An empirical comparison was conducted on two real data sets. We used four different partitions into training data a…

A Novel Approach for Faulty Sensor Detection and Data Correction in Wireless Sensor Network

2013

he main Wireless Sensor Networks purpose is represented by areas of interest monitoring. Even if the Wireless sensor network is properly initialized, errors can occur during its monitoring tasks. The present work describes an approach for detecting faulty sensors in Wireless Sensor Network and for correcting their corrupted data. The approach is based on the assumption that exist a spatio-temporal cross- correlations among sensors. Two sequential mathematical tools are used. The first stage is a probabilistic tools, namely Markov Random Field, for a two-fold sensor classification (working or damaged). The last stage is represented by the Locally Weighted Regression model, a learning techniq…

Statistical Modeling of Huffman Tables Coding

2005

An innovative algorithm for automatic generation of Huffman coding tables for semantic classes of digital images is presented. Collecting statistics over a large dataset of corresponding images, we generated Huffman tables for three images classes: landscape, portrait and document. Comparisons between the new tables and the JPEG standard coding tables, using also different quality settings, have shown the effectiveness of the proposed strategy in terms of final bit size (e.g. compression ratio).

Performance evaluation of different techniques to estimate subjective quality in live video streaming applications over LTE-Advance mobile networks

2018

Abstract Current mobile service providers are offering Gigabit Internet access over LTE-Advanced networks. Traditional services, such as live video streaming, over wired networks are feasible on these networks. However different aspects should be taken into account due to the fast changing network conditions as well as the constrained resources of the mobile phones, in order to provide a good subjective video quality in terms of Mean Opinion Score (MOS). Our goal is to estimate and predict this subjective metric without information or reference from the original video, known as Non Reference approach. This approach is important for the Service Provider from a practical point of view, becaus…

Gender analysis and attention to gender: An experimental framework

Gender aspects are gaining more and more attention for policy makers, practitioners and faculties. They also have a great importance for funding purposes, since many calls for proposals by national and international agencies require a gender plan and/or an analysis of the gender aspect, especially referring to the extent to which a candidate research project affects differently men and women. In this context, we want to understand whether there exists a relationship between the gender diversity of corporate boards of directors and the way a business articulates gender aspects on their corporate communications and activities on the Internet. To achieve this goal, we created a set of meaningf…

Source-Target Mapping Model of Streaming Data Flow for Machine Translation

2017

Streaming information flow allows identification of linguistic similarities between language pairs in real time as it relies on pattern recognition of grammar rules, semantics and pronunciation especially when analyzing so called international terms, syntax of the language family as well as tenses transitivity between the languages. Overall, it provides a backbone translation knowledge for building automatic translation system that facilitates processing any of various abstract entities which combine to specify underlying phonological, morphological, semantic and syntactic properties of linguistic forms and that act as the targets of linguistic rules and operations in a source language foll…

2014

Large data sets classification is widely used in many industrial applications. It is a challenging task to classify large data sets efficiently, accurately, and robustly, as large data sets always contain numerous instances with high dimensional feature space. In order to deal with this problem, in this paper we present an online Logdet divergence based metric learning (LDML) model by making use of the powerfulness of metric learning. We firstly generate a Mahalanobis matrix via learning the training data with LDML model. Meanwhile, we propose a compressed representation for high dimensional Mahalanobis matrix to reduce the computation complexity in each iteration. The final Mahalanobis mat…

Ranking Series of Cancer-Related Gene Expression Data by Means of the Superposing Significant Interaction Rules Method

2020

The Superposing Significant Interaction Rules (SSIR) method is a combinatorial procedure that deals with symbolic descriptors of samples. It is able to rank the series of samples when those items are classified into two classes. The method selects preferential descriptors and, with them, generates rules that make up the rank by means of a simple voting procedure. Here, two application examples are provided. In both cases, binary or multilevel strings encoding gene expressions are considered as descriptors. It is shown how the SSIR procedure is useful for ranking the series of patient transcription data to diagnose two types of cancer (leukemia and prostate cancer) obtaining Area Under Recei…