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9,302 results for “DETECT”
Data and code for "A Signal-Detection Approach to Modeling Forgiveness Decisions"
<p>Contains the data and code that supplements the journal article, "A Signal-Detection Approach to Modeling Forgiveness Decisions".</p> <p>Article doi: 10.1016/j.evolhumbehav.2016.06.004</p>
Chromosome images used for "Centromere detection of human metaphase chromosome images using a candidate based method"
<p>Chromosome image data used in the paper "Centromere detection of human metaphase chromosome images using a candidate based method". Images are in tiff format</p>
Chromosome images used for "Centromere detection of human metaphase chromosome images using a candidate based method"
<p>Chromosome image data used in the paper "Centromere detection of human metaphase chromosome images using a candidate based method". Images are in tiff format.</p>
Dataset for paper: J. A. Cerón-Guzmán and E. León (2015), Detecting Social Spammers in Colombia 2014 Presidential Election. MICAI 2015. DOI: 10.1007/978-3-319-27101-9_9
<p>Dataset for paper: J. A. Cerón-Guzmán and E. León (2015), Detecting Social Spammers in Colombia 2014 Presidential Election. The 14th Mexican International Conference on Artificial Intelligence. DOI: 10.1007/978-3-319-27101-9_9</p>
Evaluation of Android Malware Detection Based on System Calls - Dataset
<p>This is a data set accompanying a paper "Evaluation of Android Malware Detection Based on System Calls" published in the proceedings of the International Workshop on Security and Privacy Analytics, New Orleans, USA, 2016.</p> <p>See README.md for details of the data set.</p>
Concept detection scores for the IACC.3 dataset (TRECVID AVS Task)
<p>We provide concept detection scores for the IACC.3 dataset (600 hr internet archive videos), which is used in the TRECVID Ad-hoc Video Search (AVS) task [1]. Concept detection scores for 1345 concepts (1000 ImageNet concepts provided for the ILSVRC challenge [2] and 345 TRECVID SIN concepts [3]) have been generated as follows:<br> 1) To generate scores for the ImageNet concepts, 5 pre-trained ImageNet networks were applied on the IACC.3 dataset and their output was fused in terms of arithmetic mean.<br> 2) To generate scores for the TRECVID SIN concepts, two pre-trained ImageNet networks were fine-tuned on these concepts using a combination of our methods presented in the following papers: [4], [5]. We provide two different sets of concept scores for the TRECVID SIN concepts: a) The output of the two fine-tuned networks was fused in terms of arithmetic mean in order to return a single score for each concept. b) The last fully-connected layer was used as feature to train SVM classifiers separately for each fine-tuned network and each concept. Then, the SVM classifiers were applied on the IACC.3 dataset and the prediction scores of the SVMs for the same concept were fused in terms of arithmetic mean in order to return a single score for each concept. We evaluated the two different sets of concepts in terms of MXInfAP on a subset of 38 TRECVID SIN concepts for which ground-truth annotation exists, and the MXInfAP of each set of concept scores is: a) 30.04% for the networks' direct output, b) 35.81% for the SVM classifiers.</p> <p>Three different files of concept detection scores can be downloaded (after unpacking the compressed file):<br> 1) scores_ImageNet.txt<br> 2a) scores_SIN_direct.txt<br> 2b) scores_SIN_svm.txt<br> In total there are 335944 rows in each file; 1002 columns in the first file and 347 columns in each of the other two. Each row in any of these files corresponds to a different video shot; the video shot IDs appear in the first two columns. (Note: the shot IDs are the ones from the mp7 files in the TRECVID AVS master shot reference, with the format shotFILENUMBER_SHOTNUMBER). Then, each column (except for the fist two) corresponds to a different concept, with all concept scores being in [0,1] range. The higher the score the more likely that the corresponding concept appears in the video shot. Files “concept_names_ImageNet.txt” and “concept_names_SIN.txt” indicate the order of the concepts that is used in the concept score files. </p> <p>[1] G. Awad, J. Fiscus, M. Michel et al. 2016. TRECVID 2016: Evaluating Video Search, Video Event Detection, Localization, and Hyperlinking. In TRECVID 2016 Workshop. NIST, USA.<br> [2] O. Russakovsky, J. Deng, H. Su et al. 2015. ImageNet Large Scale Visual Recognition Challenge. Int. Journal of Computer Vision (IJCV) 115, 211–252.<br> [3] G. Awad, C. Snoek, A. Smeaton, and G. Quénot. 2016. TRECVid semantic indexing of video: a 6-year retrospective. ITE Transactions on Media Technology and Applications, 4 (3). pp. 187-208.<br> [4] N. Pittaras, F. Markatopoulou, V. Mezaris, I. Patras. 2017. Comparison of Fine-tuning and Extension Strategies for Deep Convolutional Neural Networks, Proc. 23rd Int. Conf. on MultiMedia Modeling (MMM'17), Reykjavik, Iceland, Springer LNCS vol. 10132, pp. 102-114, Jan. 2017.<br> [5] F. Markatopoulou, V. Mezaris, and I. Patras. 2016. Deep Multi-task Learning with Label Correlation Constraint for Video Concept Detection, Proc. ACM Multimedia 2016, Amsterdam, Oct. 2016.</p>
Dataset supporting 'What Does "Without Loss of Generality" Mean (And How Do We Detect It)'
<p>This is the dataset (Maple files and PDF equivalents) supporting the paper 'What Does "Without Loss of Generality" Mean (And How Do We Detect It)', presented at the Symbolic Computation and Satisfiability Checking workshop at SYNASC 2016 ( 24-27September 2016, Timisoara) and published by CEUR-WS.</p>
Figure 6. from Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda) - ZooKeys 156: 49-66 (20 December 2011) https://doi.org/10.3897/zookeys.156.1997
Figure 6. - Strobe models of five positions along the canonical variates indicated in Fig. 5. CV-1, CV-2, and CV-3 axes account for 79.5% of the observed between-species shape variation. Landmarks and semi-landmarks are superimposed in the figure to the right of each sequence to express the magnitudes and directions (arrows) of shape trends. In all models, the mesial margin of the coxa is depicted to the left, the lateral margin to the right.
Supplementary material 1: Appendix from Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda) - ZooKeys 156: 49-66 (20 December 2011) https://doi.org/10.3897/zookeys.156.1997
Voucher data for specimens use din morphometric analyses and supplementary figures of Canonical Variates scatterplots
Figure 5. from Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda) - ZooKeys 156: 49-66 (20 December 2011) https://doi.org/10.3897/zookeys.156.1997
Figure 5. - Results of the CVA of coxal shape data for all eight species, showing the subspaces formed by the first three discriminant axes, which together account for more than 79% of observed between-group shape variation. Within each subspace plot the black circles represent the coordinate locations for each of the five along-axis shape models depicted in Fig. 6.
Figure 4. from Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda) - ZooKeys 156: 49-66 (20 December 2011) https://doi.org/10.3897/zookeys.156.1997
Figure 4. - Scatterplots of Procrustes PCA scores for coxal shape data. The first two shape variation axes (top) together account for 62.63% of the observed shape variation; PC-2 and PC-3 axes (bottom) together account for 27.58% of the observed shape variation.
Figure 3. from Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda) - ZooKeys 156: 49-66 (20 December 2011) https://doi.org/10.3897/zookeys.156.1997
Figure 3. - Thereuopoda longicornis scatterplot of coxal shape data along the discriminant subspace formed by the first two CV axes, which together account for 74.17% of observed between-group shape variation.
Figure 2. from Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda) - ZooKeys 156: 49-66 (20 December 2011) https://doi.org/10.3897/zookeys.156.1997
Figure 2. - Landmarks (L1-L10) used in morphometric analysis. Diagonal line to L1 is the longest line from anterolateral to posteromedial corners of the coxa. Spine-bristles numbered 1-4 (blue) from interior to exterior. Throughout text, left and right coxae refer to dorsal orientation (inverted 180° relative to this ventral view).
Figure 1. from Detecting taxonomic signal in an under-utilised character system: geometric morphometrics of the forcipular coxae of Scutigeromorpha (Chilopoda) - ZooKeys 156: 49-66 (20 December 2011) https://doi.org/10.3897/zookeys.156.1997
Figure 1. - Ventral view of head and forcipules of Thereuopoda longicornis placed in a standard horizontal position. BM 1952.9.8.574-575, Kuching, Sarawak, Malaysia.
Large-aperture Experiment to Detect the Dark Ages -- February 2016
<p>Initial data release of LEDA characterization data from February 2016 testing campaign. Please see manuscript, "Design and Characterization of a Radiometric Receiver for the Large-Aperture Experiment to Detect the Dark Ages (LEDA)", D. C. Price et. al.</p>
Concept detection scores for the MED16train dataset (TRECVID MED task)
<p>We provide concept detection scores for the MED16train dataset which is used at the TRECVID Multimedia Event Detection (MED) task [1]. First, each video is decoded into a set of keyframes at fixed temporal intervals (2 keyframes per second). Then, we calculated concept detection scores for the two following concept sets: i) 487 sport-related concepts from YouTube Sports-1M Dataset[1] and ii) 345 TRECVID SIN concepts [3]. The scores have been generated as follows:<br> 1) For the 487 concepts for the Sports-1M Dataset, a Googlenet network [4] originally trained on 5055 ImageNet concepts was fine-tuned, following the extension strategy of [2] with one extension layer of dimension 128.<br> 2) For the 345 TRECVID SIN concepts, a pre-trained Googlenet network [4] on 5055 ImageNet concepts was fine-tuned on these concepts, again following the extension strategy of [2] with one extension layer of dimension 1024. </p> <p>After unpacking the compressed file two different folders can be found, namely "Prob_sports_MED16train" and "Prob_SIN_MED16train", one for each concept set. We provide one file for every video of the MED16train dataset for each concept set. Each file consists of N columns (where N = 345 for TRECVID SIN and N = 487 for Sports-1M Dataset) and M rows (where M is the number of extracted keyframes for the corresponding video). Each column corresponds to a different concept, with all concept scores being in the range [0,1]. The higher the score the more likely that the corresponding concept appears in the keyframe. Two additional files are provided; files "sports_487_Classes.txt" and "SIN_345_Classes.txt" indicate the order of the concepts that is used in the concept score files.</p> <p>[1] A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar and L. Fei-Fei, "Large-scale video classification with convolutional neural networks", In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 1725-1732, 2014.<br> [2] N. Pittaras, F. Markatopoulou, V. Mezaris and I. Patras, "Comparison of Fine-tuning and Extension Strategies for Deep Convolutional Neural Networks", Proc. 23rd Int. Conf. on MultiMedia Modeling (MMM'17), Reykjavik, Iceland, Springer LNCS vol. 10132, pp. 102-114, Jan. 2017.<br> [3] G. Awad, C. Snoek, A. Smeaton, and G. Quénot, "TRECVid semantic indexing of video: a 6-year retrospective", ITE Transactions on Media Technology and Applications, 4 (3). pp. 187-208, 2016.<br> [4] C. Szegedy, Wei Liu, Yangqing Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke and A. Rabinovich, "Going deeper with convolutions", In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9, 2015.</p>
Generic, Scalable and Decentralized Fault Detection for Robot Swarms
<p>This raw data archive includes the data on fault detection in a simulated swarm of 20 e-puck robots. The data was used in the paper Generic, Scalable and Decentralized Fault Detection for Robot Swarms by D. Tarapore et al. (2017).</p> <p>See readme.txt for more details.</p>
FIRESENSE database of videos for flame and smoke detection
<p>The FIRESENSE database, developed within the FP7-ENV-244088<br> ''FIRESENSE - Fire Detection and Management through a Multi-Sensor Network for<br> the Protection of Cultural Heritage Areas from the Risk of Fire and Extreme<br> Weather" project contains videos for testing flame and smoke detection algorithms.</p> <p>Specifically:</p> <p>a) for flame detection 11 positive and 16 negative videos are provided, while</p> <p>b) for smoke detection, 13 positive and 9 negative videos are provided.</p> <p>Results using this database are presented in many papers, including:</p> <ol> <li><strong>K. Dimitropoulos, P. Barmpoutis, N. Grammalidis, </strong>"<em>Spatio-Temporal Flame Modeling and Dynamic Texture Analysis for Automatic Video-Based Fire Detection</em>", IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), Vol. 25, No. 2, February 2015, pp. 339-351.</li> </ol> <p> </p> <p> </p>
Evaluation of two 4th generation point-of-care assays for the detection of Human Immunodeficiency Virus infection.
<p>Background. Fourth generation assays detect simultaneously antibodies for HIV and the p24 antigen, identifying HIV infection earlier than previous generation tests. Previous studies have shown that the Alere Determine HIV-1/2 Combo has lower than anticipated performance in detecting antibodies for HIV and the p24 antigen. Furthermore, there are currently very few studies evaluating the performance of Standard Diagnostics BIOLINE HIV Ag/Ab Combo.</p> <p>Objective: To evaluate the performance of the Alere Determine HIV-1/2 Combo and the Standard Diagnostics BIOLINE HIV Ag/Ab Combo in a panel of frozen serum samples.</p> <p>Study Design: The testing panel included 133 previously frozen serum specimens from the UCLA Clinical Microbiology & Immunoserology laboratory. Reference testing included testing for HIV antibodies by a 3<sup>rd</sup> generation enzyme immunoassay followed by HIV RNA detection. Antibody negative and RNA positive sera were also tested by a laboratory 4<sup>th</sup> generation HIV Ab/Ag enzyme immunoassay.</p> <p>Results: Reference testing yielded 97 positives for HIV infection and 36 negative samples. Sensitivity of the Alere test was 95% (88-98%), while the SD Bioline sensitivity was 91% (83-96%). Both assays showed 100% (90-100%) specificity. No indeterminate or invalid results were recorded. Among 13 samples with acute infection (HIV RNA positive, HIV antibody negative), 12 were found positive by the first assay and 8 by the second. The antigen component of the Alere assay detected 10 acute samples, while the SD Bioline assay detected only one.</p> <p>Conclusions: Both rapid assays showed very good overall performance in detecting HIV infection in frozen serum samples, but further improvements are required to improve the performance in acute infection.</p>
Global dry and hot extreme events detection
<p>Workflow for the global detection of dry and hot extreme weather events. ERA5 is the ECMWF Reanalysis of the climate. PET is potential reference evapotranspiration. PEI is the daily difference between precipitation and evapotranspiration averaged over the preceding days (here 30, 90 and 180). Data cubes are stored in zarr format. Statistics are saved in a csv table.</p>
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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.