1,720,964 research outputs found
Improving the Performance of Already Trained Classifiers Through an Automatic Explanation-Based Learning Approach
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Enhancement of SSVEPs Classification in BCI-based Wearable Instrumentation Through Machine Learning Techniques
No full textThis work addresses the adoption of Machine Learning classifiers and Convolutional Neural Networks to improve the performance of highly wearable, single-channel instrumentation for Brain-Computer Interfaces. The proposed measurement system is based on the classification of Steady-State Visually Evoked Potentials (SSVEPs). In particular, Head-Mounted Displays for Augmented Reality are used to generate and display the flickering stimuli for the SSVEPs elicitation. Four experiments were conducted by employing, in turn, a different Head-Mounted Display. For each experiment, two different algorithms were applied and compared with the state-of-the-art-techniques. Furthermore, the impact of different Augmented Reality technologies in the elicitation and classification of SSVEPs was also explored. The experimental metrological characterization demonstrates (i) that the proposed Machine Learning-based processing strategies provide a significant enhancement of the SSVEP classification accuracy with respect to the state of the art, and (ii) that choosing an adequate Head-Mounted Display is crucial to obtain acceptable performance. Finally, it is also shown that the adoption of inter-subjective validation strategies such as the Leave-One-Subject-Out Cross Validation successfully leads to an increase in the inter-individual 1-σ reproducibility: this, in turn, anticipates an easier development of ready-to-use systems
Exploring the Latent Space of Person-Specific Convolutional Autoencoders for Eye-Blink Artefact Mitigation in EEG Signals
No full textElectroencephalography (EEG) is a non-invasive and
cost-effective technique that allows the investigation of brain
activity. However, EEG recordings often suffer from artefacts
that complicate signal analysis. Eye-blink artefacts pose a sig
nificant challenge among these artefacts due to their frequency
overlap with neural signals. Machine Learning, notably semi
supervised Autoencoders (AEs), appears promising in removing
EEGartefacts. This research investigates the use of Convolutional
Autoencoders (CAEs) for mitigating eye blinks in EEG signals,
deviating from a previous use of Convolutional Variational AEs.
This shift can offer a simpler approach with reduced computa
tional complexity. Specifically, the latent space of CAEs, trained
on spatially preserving EEG topographic maps, was explored
to identify latent components selective for eye blinks. Receiver
Operating Characteristic (ROC) curves and Area Under the
Curve (AUC) were employed to evaluate each latent component’s
discriminative performance. The most discriminative component,
determined by the highest AUC, is subsequently modified to
mitigate eye blinks. Specifically, the median is chosen to mask
this discriminative latent component for blink artefact removal.
Visual inspections and Pearson correlation indices between the
original EEG signal and the reconstructed clean version were
used to evaluate the effectiveness of artefact removal. This study
contributes to the knowledge for introducing an offline pipeline
able to detect and remove eye blinks from EEG recordings
without human intervention
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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