1,721,000 research outputs found
Improving Auditory CAPTCHA Security
Full text linkCAPTCHAs are tests used by resource-rich websites to ensure that humans, but not malicious automated programs, have access to their resources. Most CAPTCHAs are visual tests (e.g. identifying distorted text), but auditory versions are necessary to provide access to the visually impaired, and are currently deployed at commonly used websites such as Google and Facebook. To be effective at deterring automated programs, they must be at least as secure as their visual counterparts. Assuming that the attacks against auditory CAPTCHAs will depend on automatic speech recognition systems (ASRs), we undertook the project of designing auditory CAPTCHAs that would take advantage of the weaknesses in ASRs as compared to the human auditory system. Examples of such weaknesses of ASRs, relative to humans, include impeded recognition in the presence of broadband and time-varying noise such as multiple simultaneous speakers. Results show that a combination of such disruptive noise types can outperform currently employed techniques while still maintaining human intelligibility.NSF #064732
Data from: Neural speech restoration at the cocktail party
No full textThis dataset is incremental to http://hdl.handle.net/1903/21109 and contains independent component analysis (ICA) used for additional preprocessing, gammatone stimulus representations, and STRFs that were used to generate the figures. The ICA is stored in MNE *-ica.fif files which can be opened with MNE-Python (http://mne.tools). Gammatone spectrograms and STRFs are stored in Python pickle files which require the Eelbrain library to be opened (http://eelbrain.readthedocs.io).Data from the article titled: Neural speech restoration at the cocktail party: Auditory cortex recovers masked speech of both attended and ignored speakers.This work was supported by a National Institutes of Health grant R01-DC-014085 (to JZS) and by a University of Maryland Seed Grant (to LEH and JZS).https://doi.org/10.1371/journal.pbio.300088
Data from: Rapid Transformation from Auditory to Linguistic Representations of Continuous Speech
No full textMagnetoencephalography (MEG) research study datasetMagnetoencephalography (MEG) data and predictor variables from the article titled: Transformation from auditory to linguistic representations across auditory cortex is rapid and attention dependent for continuous speechThis work was supported by a National Institutes of Health grant R01-DC-014085 (to JZS) and by a University of Maryland Seed Grant (to LEH and JZS).https://doi.org/10.1016/j.cub.2018.10.04
Low-power EEG monitor based on compressed sensing with compressed domain noise rejection
Full text linkWireless sensor nodes capable of acquiring and transmitting biosignals are increasingly important to address future needs in healthcare monitoring. One of the main issues in designing these systems is the unavoidable energy constraint due to the limited battery lifetime, which strictly limits the amount of data that may be transmitted. Compressed Sensing (CS) is an emerging technique for introducing low-power, real-time compression of the acquired signals before transmission. The recently developed rakeness approach is capable of further increasing CS performance. In this paper we apply the rakeness-CS technique to enhance compression capabilities for electroencephalographic (EEG) signals, and particularly for Evoked Potentials (EP), which are recordings of the neural activity evoked by the presentation of a stimulus. Simulation results demonstrate that EPs are correctly reconstructed using rakeness-CS with a compression factor of 16. Additionally, some interesting denoising capabilities are identified: the high-frequency noise components are rejected and the 60 Hz power line noise is decreased by more than 20 dB with respect to the state-of-the-art filtering when rakeness-CS techniques are applied to the EEG data stream
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
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
Cortical Processing of Arithmetic and Simple Sentences in an Auditory Attention Task - Dataset
No full textMEG dataset collected for a study on arithmetic and language processing. Full details of experiment design, stimuli and data preprocessing can be found at https://doi.org/10.1101/2021.01.31.429030. Additional information: Joshua P. Kulasingham - [email protected] processing of arithmetic and of language rely on both shared and task-specific neural mechanisms, which should also be dissociable from the particular sensory modality used to probe them. Here, spoken arithmetical and non-mathematical statements were employed to investigate neural processing of arithmetic, compared to general language processing, in an attention-modulated cocktail party paradigm. Magnetoencephalography (MEG) data were recorded from 22 human subjects listening to audio mixtures of spoken sentences and arithmetic equations while selectively attending to one of the two speech streams. Short sentences and simple equations were presented diotically at fixed and distinct word/symbol and sentence/equation rates. Critically, this allowed neural responses to acoustics, words, and symbols to be dissociated from responses to sentences and equations. Indeed, the simultaneous neural processing of the acoustics of words and symbols were observed in auditory cortex for both streams. Neural responses to sentences and equations, however, were predominantly to the attended stream, originating primarily from left temporal, and parietal areas, respectively. Additionally, these neural responses were correlated with behavioral performance in a deviant detection task. Source-localized Temporal Response Functions revealed distinct cortical dynamics of responses to sentences in left temporal areas and equations in bilateral temporal, parietal, and motor areas. Finally, the target of attention could be decoded from MEG responses, especially in left superior parietal areas. In short, the neural responses to arithmetic and language are especially well segregated during the cocktail party paradigm, and the correlation with behavior suggests that they may be linked to successful comprehension or calculation.This work was supported by DARPA (N660011824024), the National Science Foundation (SMA-1734892 and DGE-1449815), and the National Institutes of Health (R01-DC014085). The views, opinions and/or findings expressed are those of the authors and
should not be interpreted as representing the official views or policies of the Department of Defense or the U.S. Government.https://doi.org/10.1523/JNEUROSCI.0269-21.202
Data for: Eelbrain: A Python toolkit for time-continuous analysis with temporal response functions
No full textThis dataset accompanies “Eelbrain: A Python toolkit for time-continuous analysis with temporal response functions” (Brodbeck et al., 2021) and is a derivative of the Alice EEG datasets collected at the University of Michigan Computational Neurolinguistics Lab (Bhattasali et al., 2020), licensed under CC BY (https://creativecommons.org/licenses/by/4.0/) and the original work can be found at DOI: 10.7302/Z29C6VNH.
The files were converted from the original matlab format to fif format in order to be compatible with Eelbrain. This dataset includes the EEG data for 33 participants, which were used in the example analyses for the paper. The original Alice dataset included data from all 49 participants and participants were excluded due to artifacts and incorrect behavioral responses (for more details see Bhattasali et al., 2020).
You can use the Python script data_grab.py at https://github.com/christianbrodbeck/Alice-Eelbrain to download and unzip these files into a specified destination folder.https://doi.org/10.1101/2021.08.01.45468
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