Search results for "computer.software_genre"
showing 10 items of 3858 documents
Natural Language Inference in Ordinary and Support Verb Constructions
2020
The family of clause types known as 'support (or 'light') verb construction' (SVC) manifests a peculiar syntax-semantics interface if compared with ordinary verb constructions (OVC). If, in e.g. She laughed, the verb licenses an argument and assigns it a semantic role, syntacticians of every stripe nowadays agree that it is the noun laugh, in She gave a laugh, which fulfils the same function. The differences between the two types have been extensively discussed in the linguistics literature (systematic research started in the 1970s), less so in Computational Linguistics. This paper has two objectives. First, it will propose an innovative type of semantic role, which is termed Cognate Semant…
Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG Signals
2019
Existing correlations between features extracted from Electroencephalography (EEG) signals and emotional aspects have motivated the development of a diversity of EEG-based affect detection methods. Both intra-subject and inter-subject approaches have been used in this context. Intra-subject approaches generally suffer from the small sample problem, and require the collection of exhaustive data for each new user before the detection system is usable. On the contrary, inter-subject models do not account for the personality and physiological influence of how the individual is feeling and expressing emotions. In this paper, we analyze both modeling approaches, using three public repositories. T…
A Comparative Analysis of Residual Block Alternatives for End-to-End Audio Classification
2020
Residual learning is known for being a learning framework that facilitates the training of very deep neural networks. Residual blocks or units are made up of a set of stacked layers, where the inputs are added back to their outputs with the aim of creating identity mappings. In practice, such identity mappings are accomplished by means of the so-called skip or shortcut connections. However, multiple implementation alternatives arise with respect to where such skip connections are applied within the set of stacked layers making up a residual block. While residual networks for image classification using convolutional neural networks (CNNs) have been widely discussed in the literature, their a…
In-depth evaluation of software tools for data-independent acquisition based label-free quantification.
2015
Label-free quantification (LFQ) based on data-independent acquisition workflows currently experiences increasing popularity. Several software tools have been recently published or are commercially available. The present study focuses on the evaluation of three different software packages (Progenesis, synapter, and ISOQuant) supporting ion mobility enhanced data-independent acquisition data. In order to benchmark the LFQ performance of the different tools, we generated two hybrid proteome samples of defined quantitative composition containing tryptically digested proteomes of three different species (mouse, yeast, Escherichia coli). This model dataset simulates complex biological samples con…
BANΔIT: B’‐factor Analysis for Drug Design and Structural Biology
2020
The analysis of B‐factor profiles from X‐ray protein structures can be utilized for structure‐based drug design since protein mobility changes have been associated with the quality of protein‐ligand interactions. With the BANΔIT (B’‐factor analysis and ΔB’ interpretation toolkit), we have developed a JavaScript‐based browser application that provides a graphical user interface for the normalization and analysis of B’‐factor profiles. To emphasize the usability for rational drug design applications, we have analyzed a selection of crystallographic protein‐ligand complexes and have given exemplary conclusions for further drug optimization including the development of a B’‐factor‐supported pha…
On a new robust workflow for the statistical and spatial analysis of fracture data collected with scanlines (or the importance of stationarity)
2020
Abstract. We present an innovative workflow for the statistical analysis of fracture data collected along scanlines, composed of two major stages, each one with alternative options. A prerequisite in our analysis is the assessment of stationarity of the dataset, which is motivated by statistical and geological considerations. Calculating statistics on non-stationary data can be statistically meaningless, and moreover the normalization and/or sub-setting approach that we discuss here can greatly improve our understanding of geological deformation processes. Our methodology is based on performing non-parametric statistical tests, which allow detecting important features of the spatial distrib…
A quantitative analysis of Educational Data through the Comparison between Hierarchical and Not-Hierarchical Clustering
2017
Many research papers have studied the problem of taking a set of data and separating it into subgroups through the methods of Cluster Analysis. However, the variables and parameters involved in Cluster Analysis have not always been outlined and criticized, especially in the field of Science Education. Moreover, in the field of Science Education, a comparison between two different Clustering methods is not discussed in the literature. Conceptions of students about modeling in physic are investigated by using an open-ended questionnaire. The questionnaire is analyzed through Clustering methods. The clustering results obtained by using the two methods are compared and show a good coherence bet…
Notice of Violation of IEEE Publication Principles<BR>An adaptive routing mechanism for P2P resource discovery
2005
The key to the usability of large-scale decentralize peer-to-peer (P2P) systems, and one of the most challenge design aspects, is efficient mechanism for distributed resource discovery. Unstructured P2P networks are very attractive because they do not suffer the limitations of centralized systems an the drawbacks of highly structured approaches. However the search algorithms are usually based on simple flooding scheme generating large loads on the network participants. In this paper to address this major limitation, we present the design an evaluation of an innovative searching protocol in unstructured P2P networks. The approach aims at dynamically adapting the network topology to peers' in…
A Novel Technique for Fingerprint Classification based on Fuzzy C-Means and Naive Bayes Classifier
2014
Fingerprint classification is a key issue in automatic fingerprint identification systems. One of the main goals is to reduce the item search time within the fingerprint database without affecting the accuracy rate. In this paper, a novel technique, based on topological information, for efficient fingerprint classification is described. The proposed system is composed of two independent modules: the former module, based on Fuzzy C-Means, extracts the best set of training images, the latter module, based on Fuzzy C-Means and Naive Bayes classifier, assigns a class to each processed fingerprint using only directional image information. The proposed approach does not require any image enhancem…
Privacy preserving via tree augmented naïve Bayesian classifier in multimedia database
2011
International audience; In this paper, we propose a novel technique for privacy preserving in multimedia databases. Our technique is based on a multimedia co-occurrence matrix and a tree augmented naive Bayesian classifier (TAN) to detect possible data associations making confidential multimedia objects at risk.