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184 results for “personalisation”
Gene-based Personalised Diet and Physical Activity Advice on Adiposity Indices Personalised Diet and Physical Activity Advice on Adiposity Indices in University Students
ClinicalTrials.gov study NCT04096404. IPD Sharing: NO. Countries: 0. Publications: 0.
Dataset for manuscript "The effect of personalised versus non-personalised study invitations on recruitment: An embedded randomised controlled recruitment trial"
<p>This dataset contains the original data to the manuscript <em>The effect of personalised versus non-personalised study invitations on recruitment: An embedded randomised controlled recruitment trial. </em>The study is registered in the ISRCTN registry (ISRCTN18404129) and the Northern Ireland Hub for Trials Methodology Research SWAT repository (SWAT 112). A full protocol for the study has been published: Woodford J, Norbäck K, Hagström J, Grönqvist H, Parker A, Arundel C, et al. Study within a trial (SWAT) protocol. Investigating the effect of personalised versus non-personalised study invitations on recruitment: an embedded randomised controlled recruitment trial. Contemp Clin Trials Commun. 2020; doi: 10.1016/j.conctc.2020.100572</p>
PHyMAtt (Personalised Hygienic Mask Attacks)
<p>This dataset was used to perform the experiments reported in the IJCB2023 paper "Can personalised hygienic masks be used to attack face recognition systems?".</p> <p>The dataset consists of face videos captured using the ‘selfie’ cameras of five different smartphones : Apple iPhone 12, Apple iPhone 6s, Xiaomi Redmi 6 Pro, Xiaomi Redmi 9A and Samsung Galaxy S9. The dataset contains :</p> <ul> <li><strong>B</strong><strong>ona-fide </strong><strong>face videos</strong><strong>:</strong> 1400 videos of bona-fide (real, non-attack) faces. In total, there are 70 identities (data subjects). Each video is 10 seconds long, where for the first 5 seconds the data subject was required to stay still and look at the camera, then for the last 5 seconds the subject was asked to turn their head from one side to the other (such that profile views could be captured). The videos were acquired indoors, under normal office lighting conditions. The data subjects were volunteers, who were required to be present during two recording sessions, which on average were separated by about three weeks. In each recording session, the volunteers were asked to record a video of their own face using the front (i.e., selfie) camera of each of the five smartphones mentioned earlier. The face data was additionally captured while the data subjects wore plain (not personalised) hygienic masks, to simulate the scenario where face recognition might need to be performed on a masked face (e.g., during a pandemic like COVID-19).</li> <li> <p><strong>A</strong><strong>ttacks:</strong></p> <ul> <li> <p><em>Personalised </em><em>hygienic mask attack</em><em>s</em><em>:</em> Video recordings of an impostor wearing personalised hygienic masks (one at a time), on which the bottom part of each data subject’s face is printed. Please note that the dataset contains 350 personalised hygienic mask attack videos, whereas the IJCB2023 paper mentioned 345 videos. This is because, for the experiments reported in the paper, we excluded the videos of the attacker wearing their own hygienic mask (since the "attacker" was one of the 70 data subjects).</p> </li> <li> <p><em>P</em><em>rint attacks:</em> 1400 video recordings of the data subjects’ face photos printed on A4 matte paper, which was held up to the smartphone’s camera.</p> </li> <li> <p><em>R</em><em>eplay attacks:</em> 2800 video recordings of bona-fide face videos that were replayed to the target smartphone’s camera. Different phones were paired, such that one of the pair was used to replay the bona-fide videos while the second (attacked) phone recorded the videos using its front camera.</p> </li> </ul> </li> </ul> <p> </p> <p><strong>Reference</strong></p> <p>If you use the data for your research or publication, please cite the following paper :</p> <p><a href="https://publications.idiap.ch/authors/show/1883">Komaty, Alain</a>, <a href="https://publications.idiap.ch/authors/show/1934">Krivokuca Hahn, Vedrana</a>, <a href="https://publications.idiap.ch/authors/show/3114">Ecabert, Christophe</a> and <a href="https://publications.idiap.ch/authors/show/65">Marcel, Sébastien</a>, <a href="https://publications.idiap.ch/publications/show/5093">Can personalised hygienic masks be used to attack face recognition systems?</a>, in: Proceedings of IEEE International Joint Conference on Biometrics (IJCB2023), 2023</p>
MuSe-Personalisation: Personalisation Sub-Challenge (MuSe 2023)
<p><strong>Description</strong>: Predicting valence and arousal of individuals in a stressed disposition, as induced by the Trier Social Stress Test (TSST) protocol. Available modalities: audio, video, text and physiological signals (respiratory rate, ECG, BPM).</p> <p><strong>Labels</strong>: The valence labels are obtained by fusing the ratings of three human annotators. For arousal, in contrast, two human annotations have been fused with the TSST subject's electrodermal activity (EDA) signal in order to obtain a more objective arousal gold standard. Hence, this sub-challenge is <strong>not</strong> identical to the 2021 MuSe-Stress sub-challenge.</p> <p><strong>Dataset</strong>: MuSe-Stress is based on the Ulm-TSST data set as introduced in the MuSe 2021 challenge. It contains 69 recordings of individuals during a Trier Social Stress Test (TSST). <strong>In order to facilitate personalisation, parts of the test data labels are provided</strong>. Overall, about 6 hours of recordings are provided. The data is split in a speaker-independent manner with the training data set comprising 41 individuals, development and test each comprising 14 individuals. This split is identical to the split used in the MuSe 2022 challenge.</p> <p><strong>General</strong>: The <a href="http://muse-challenge.org">4th Multimodal Sentiment Analysis Challenge and Workshop (MuSe) 2023</a> adresses research questions that are of interest to affective computing, machine learning and multimodal signal processing communities and encourages a fusion of their disciplines. The goal of the MuSe workshop and challenge is to gain new insights into the merits of each of the core modalities and to serve as a stimulating environment for the development and evaluation of multimodal affect recognition approaches.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.