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45 results for “detection dog”
Today's cat is tomorrow's dog: accounting for time-based changes in the labels of ML vulnerability detection approaches (Replication Package Part 3: OpenSSL dataset)
<h1><strong>The Replication Package of</strong></h1> <h1><strong>"Today's cat is tomorrow's dog: accounting for time-based changes in the labels of ML vulnerability detection approaches"</strong></h1> <h3><strong>Part 3 (OPENSSL Dataset)</strong></h3> <div> <div>This repository includes:</div> <ol> <li><em><strong>Code.zip</strong></em> that contains the codes to replicate some parts of this study:<br>a. <em>1_generate_datasets</em> implements our methodology to generate the datasets.<br>b. <em>2_run_models</em> runs the ML models during the evaluation.<br>c. <em>3_result_replication </em>generates charts presented in the paper from the ML evaluation results.</li> <li><em><strong>Datasets.zip</strong></em> that contain 2 folders:<br>a. <em>original</em> datasets: 1 from <a href="https://github.com/CGCL-codes/VulDeePecker" target="_blank" rel="noopener">NVD Vuldeepecker</a> and 3 extracted from <a href="https://github.com/ZeoVan/MSR_20_Code_vulnerability_CSV_Dataset" target="_blank" rel="noopener">BigVul</a>.<br> <div> <div>b. <em>OPENSSL</em> datasets: train, validation, test sets for each time of observation extracted using our methodology from <a href="https://github.com/ZeoVan/MSR_20_Code_vulnerability_CSV_Dataset" target="_blank" rel="noopener">BigVul</a> dataset for project <em>openssl</em>.</div> </div> </li> <li><em><strong>Pretrained-models.zip</strong></em> that we generated during our evaluation (3 test results for each time point in the timeline [2013-2019]).</li> <li><em><strong>Results.zip</strong></em> of our evaluation, the folder <em>ALL</em> contains the overall results and other folders are results by model.</li> </ol> <p><strong>UPDATED version 5<br></strong>- added a GLOBAL_README.md which contains the 3 stages and how they are connected to each other<br>- updated LineVul.ipynb: import AdamW from torch.optim instead of transformers<br>- updated README.md in Code2Vec with the prerequisites of Java to run gradlew for astmine</p> <p><strong>UPDATED version 6<br></strong>- updated CodeBert.ipynb: import AdamW from torch.optim instead of transformers</p> <p>Documentations</p> <ol> <li><em><strong>INSTALL.pdf </strong></em>: how to install the codes</li> <li><em><strong>README.pdf</strong></em>: readme file</li> <li><em><strong>REQUIREMENTS.pdf</strong></em>: hardware and software requirements</li> <li><em><strong>STATUS.pdf</strong></em> : status for artifact submission</li> <li><em><strong>LICENSE.pdf</strong></em>: the license of this artifact</li> <li><em><strong>PAPER.pdf</strong></em>: the camera-ready version of the paper</li> </ol> </div> <div> <div>Please refer to the following repositories for the other datasets and pre-trained models:</div> <div>- Part 1 NVD Vuldeeepecker : <a href="https://doi.org/10.5281/zenodo.8207883" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.8207883</a></div> - Part 2 LINUX : <a href="https://doi.org/10.5281/zenodo.10960662" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10960662</a><br> <div>- Part 4 POPPLER : <a href="https://doi.org/10.5281/zenodo.14713143">https://doi.org/10.5281/zenodo.14713143</a></div> <div> </div> <div>This work was partly funded by the EU under the H2020 Program AssureMOSS (Grant n. 952647) and the Horizon Europe Program Sec4AI4Sec (Grant n. 101120393), by the Italian Ministry of University and Research (MUR) under the P.N.R.R. – NextGenerationEU grant n.\ PE00000014 (SERICS subproject COVERT), and by the Dutch Research Council (NWO) under the grant NWA.1215.18.006 (Theseus) and grant KIC1.VE01.20.004 (HEWSTI). </div> </div>
FREE-ROAMING DOGS DETECTED USING GOOGLE STREET VIEW
<p>Datasets to count free-roaming dogs using Google Street View, and compare with population of free-roaming dog from surveys in Arequipa, Peru.</p>
Animal Recognition Using Methods Of Fine-Grained Visual Analysis - YOLOv5 Object Detection Dataset (Tsinghua Dogs)
<p>Preprocessed dataset for Tsinghua Dogs in YOLOv5 format.. Ground truth labels for head bounding boxes, body bounding boxes</p>
Air temperature and humidity effects on the performance of conservation detection dogs
<p>This is the dataset which underlies the submitted manuscript entitled " <strong>Air temperature and humidity effects on the performance of conservation detection dogs</strong>".</p> <p>The uploaded data contain an xlsx file,which includes the data, as well as a txt readme file, which explains the header information in the data file.</p>
Fig. 1 in Molecular detection and genotypes of Enterocytozoon bieneusi in farmed mink (Neovison vison), blue foxes (Alopex lagopus), and raccoon dogs (Nyctereutes procyonoides) in Xinjiang, China
Fig. 1. Phylogenetic relationships of the E. bieneusi genotypes. The relationships were inferred using NJ analysis of the ITS rRNA gene and the values generated greater than 50% are shown beside the nodes. Genotypes with hollow circles and filled circles are known and novel genotypes identified in this study, respectively.
Fig. 2 in First detection and molecular identification of Babesia gibsoni and Hepatozoon canis in an Asiatic wild dog (Cuon alpinus) from Thailand
Fig. 2. Neighbor-joining (NJ) tree of the Hepatozoon partial 18S ribosomal RNA (18S rRNA) gene sequence. Hepatozoon canis (MK144332) was amplified from an Asiatic wild dog in Thailand and analyzed for comparison with other Hepatozoon spp. from the GenBank database. The numbers on branches indicate percent bootstrap support based on 1000 bootstrap replications and only bootstrap values ≥ 50% are shown.
Fig. 1 in First detection and molecular identification of Babesia gibsoni and Hepatozoon canis in an Asiatic wild dog (Cuon alpinus) from Thailand
Fig. 1. Neighbor-joining (NJ) tree of the Babesia partial 18S ribosomal RNA (18S rRNA) gene sequence. Babesia gibsoni (MK144331) was amplified from an Asiatic wild dog in Thailand and analyzed for comparison with other Babesia spp. from the GenBank database. The numbers on branches indicate percent bootstrap support based on 1000 bootstrap replications and only bootstrap values ≥ 50% are shown.
Fig. 1 in Estimating parasite infrapopulation size given imperfect detection: Proof-of-concept with ectoparasitic fleas on prairie dogs
Fig. 1. Left: Frequency histogram of raw (field) flea count indices from prairie dogs. Middle and right: Huggins closed captures model estimates for fleas combed from prairie dogs, including a histogram of estimated flea counts (infrapopulation size = ̂N) and a positive correlation between Julian date and individual flea detection probability (here, p from the first combing occasion within primary trapping occasions). In the histograms, counts of 0 fleas (gray bars) are presented for illustration; those data were not analyzed herein, because the Huggins closed captures models 'condition' on primary occasions with at least 1 flea being detected (black bars). Model output is from the top model in Table 1. On the right, dotted lines are 95% confidence intervals.
Does the tail show when the nose knows? AI-enhanced analysis of tail kinematics outperforms human experts at predicting when detection dogs find their target odor
Open the record for dataset details and reuse information.
SARS-CoV-2 detection dogs - a pilot study
<p>The outstanding olfactory acuity of canines led us to consider whether dogs are able to reliably detect the odour of respiratory diseases associated with a SARS-CoV-2 infection in saliva or tracheobronchial secretion of hospitalized COVID-19 patients. Furthermore, we examined if SARS-CoV-2 detection dogs could provide an appropriate screening method for the human virus.The aim of this data publication is to provide the data acquired in the controlled, randomized and double-blinded pilot study `Scent dog identification of SARS-CoV-2 infection’ (submitted to BMC Infectious Diseases).</p>
Fecal standing crop with real time correction using scat detection dogs to estimate population density
<p>Population density is fundamental information for assessing the conservation status of species and support management and conservation actions for in situ populations, but is unknown for many forest species due to their difficulty in detection. The Fecal Standing Crop (FSC) method using detection dogs is an alternative for cryptic or elusive species. An intrinsic difficulty of FSC is the ability to find fecal samples in the field and to estimate the probability of which feces detection is influenced by degradation due to climatic conditions. Our goal was to propose a concurrent FSC parameter estimation using a scat detection dog under different climatic conditions and apply those parameters in a wild deer population. Ten fecal samples of gray brocket deer (Subulo gouazoubira) were placed weekly in a transect (24 x 1200 m) in both dry and wet seasons (12 weeks each). A scat detection dog was then employed to find experimental fecal samples to determine the FSC parameters that were subsequently used with naturally occurring fecal samples (also dog-detected) to estimate population density. The oldest dog found samples were 21 (Dry) and seven (Wet) days after placement, resulting in dog efficiency of 23% (Dry) and 30% (Wet). Adjusting the model to account for efficiency and scat durability, we estimated similar, seasonal, densities of 4.54 individuals km-2 (SD = 2.21, Dry) and 5.52 indiv. km-2 (SD = 3.71, Wet).</p> <p><em>Synthesis and applications:</em> Our results demonstrate that our concurrent methodology corrected the effects of weather and habitat on FSC parameters thereby allowing for accurate population density estimation. Additionally, this method can provide reasonably precise density estimates with a logistically feasible sample size, as demonstrated by simulation. Following our recommendations, this method allows a reliable estimate of population density because it incorporates any influence of study area, dog ability, and climate in fecal sample detection, providing fundamental information for the conservation of many cryptic and elusive species.</p>
Data for: Speech naturalness detection and language representation in the dog brain
<p>Abstract<br> Family dogs are exposed to a continuous flow of human speech throughout their lives. However, the extent of their abilities in speech perception is unknown. Here, we used functional magnetic resonance imaging (fMRI) to test speech detection and language representation in the dog brain. Dogs (n = 18) listened to natural speech and scrambled speech in a familiar and an unfamiliar language. Speech scrambling distorts auditory regularities specific to speech and to a given language, but keeps spectral voice cues intact. We hypothesized that if dogs can extract auditory regularities of speech, and of a familiar language, then there will be distinct patterns of brain activity for natural speech vs. scrambled speech, and also for familiar vs. unfamiliar language. Using multivoxel pattern analysis (MVPA) we found that bilateral auditory cortical regions represented natural speech and scrambled speech differently; with a better classifier performance in longer-headed dogs in a right auditory region. This neural capacity for speech detection was not based on preferential processing for speech but rather on sensitivity to sound naturalness.<br> Furthermore, in case of natural speech, distinct activity patterns were found for the two languages in the secondary auditory cortex and in the precruciate gyrus; with a greater difference in responses to the familiar and unfamiliar languages in older dogs, indicating a role for the amount of language exposure. No regions represented differently the scrambled versions of the two languages, suggesting that the activity difference between languages in natural speech reflected sensitivity to language-specific regularities rather than to spectral voice cues. These findings suggest that separate cortical regions support speech naturalness detection and language representation in the dog brain.<br> <br> This dataset contains</p> <ul> <li>Raw data (four functional runs and Matlab logs n = 18)</li> <li>Dog brain template</li> <li>Stimuli (natural and scrambled speech in Hungarian and Spanish) </li> <li>MVPA main results maps (Speech detection and Language discrimination, n = 18)</li> <li>GLM All sounds > Silence contrast (n = 18) </li> </ul>
Fecal standing crop with real time correction using scat detection dogs to estimate population density
Open the record for dataset details and reuse information.
Data from: Wildlife detection dogs effectively survey a terrestrial amphibian, but differ among individuals, weather and habitat
Open the record for dataset details and reuse information.
Detection dogs in nature conservation: a database on their worldwide deployment with a review on breeds used and their performance compared to other methods
<p>Over the last century, dogs have been increasingly used to detect rare and elusive species or traces of them. The use of wildlife detection dogs (WDD) is particularly well established in North America, Europe and Oceania, and projects deploying them have increased worldwide. However, if they are to make a significant contribution to conservation and management, their strengths, abilities, and limitations should be fully identified. We reviewed the use of WDD with particular focus on the breeds used in different countries and for various targets, as well as their overall performance compared to other methods, by developing and analysing a database of 1220 publications, including 916 scientific ones, covering 2464 individual cases - most of them (1840) scientific. With the worldwide increase in the use of WDD, associated tasks have changed and become much more diverse. Since 1930, reports exist for 62 countries and 407 animal, 42 plant, 26 fungi and 6 bacteria species. Altogether, 108 FCI-classified and 20 non-FCI-classified breeds have worked as WDD. While certain breeds have been preferred on different continents and for specific tasks and targets, they were not generally better suited for detection tasks than others. Overall, WDD usually worked more effectively than other monitoring methods. For each species group, regardless of breed, detection dogs were better than other methods in 88.71% of all cases and only worse in 0.98%. It was only for arthropods that Pinshers and Schnauzers performed worse than other breeds. For mono- and dicotyledons, detection dogs did less often outperform other methods. Although every breed can be trained as a WDD, choosing the most suitable dog for the task and target may speed up training and increase the chance of success. Albeit selection of the most appropriate WDD is important, excellent training, knowledge about the target density and suitability, and a proper study design all appeared to have the highest impact on performance. Moreover, an appropriate area, habitat and weather are crucial for detection dog work. When these factors are taken into consideration, WDD can be an outstanding monitoring method.</p>
Data from: Evaluating the performance of selection scans to detect selective sweeps in domestic dogs
Selective breeding of dogs has resulted in repeated artificial selection on breed-specific morphological phenotypes. A number of quantitative trait loci associated with these phenotypes have been identified in genetic mapping studies. We analyzed the population genomic signatures observed around the causal mutations for 12 of these loci in 25 dog breeds, for which we genotyped 25 individuals in each breed. By measuring the population frequencies of the causal mutations in each breed, we identified those breeds in which specific mutations most likely experienced positive selection. These instances were then used as positive controls for assessing the performance of popular statistics to detect selection from population genomic data. We found that artificial selection during dog domestication has left characteristic signatures in the haplotype and nucleotide polymorphism patterns around selected loci that can be detected in the genotype data from a single population sample. However, the sensitivity and accuracy at which such signatures were detected varied widely between loci, the particular statistic used, and the choice of analysis parameters. We observed examples of both hard and soft selective sweeps and detected strong selective events that removed genetic diversity almost entirely over regions >10 Mbp. Our study demonstrates the power and limitations of selection scans in populations with high levels of linkage disequilibrium due to severe founder effects and recent population bottlenecks.
Canine 230K Consortium chip data originated from the drug detection dogs
<p>Drug detection dogs play integral roles in society. However, the interplay between their behaviors and genetic characteristics underlying their performance remains uninvestigated. Herein, more than 120,000 genetic variants were evaluated in 326 German Shepherd or Labrador Retriever dogs to profile the genetic traits associated with various behavioral traits related to the successful training of drug detection dogs.</p>
Determining the efficacy of camera traps, live capture traps, and detection dogs for locating cryptic small mammal species
<p>Metal box (e.g., Elliott, Sherman) traps and remote cameras are two of the most commonly employed methods presently used to survey terrestrial mammals. However, their relative efficacy at accurately detecting cryptic small mammals has not been adequately assessed. The present study therefore compared the effectiveness of metal box (Elliott) traps and vertically oriented, close range, white flash camera traps in detecting small mammals occurring in the Scenic Rim of eastern Australia. We also conducted a preliminary survey to determine effectiveness of a conservation detection dog (CDD) for identifying presence of a threatened carnivorous marsupial, <i>Antechinus arktos,</i> in present-day and historical locations, using camera traps to corroborate detections. 200 Elliott traps and 20 white flash camera traps were set for four deployments per method, across a site where the target small mammals, including <i>A. arktos</i>, are known to occur. Camera traps produced higher detection probabilities than Elliott traps for all four species. Thus, vertically mounted white flash cameras were preferable for detecting the presence of cryptic small mammals in our survey. The CDD, which had been trained to detect <i>A. arktos</i> scat, indicated in total 31 times when deployed in the field survey area, with subsequent camera trap deployments specifically corroborating <i>A. arktos</i> presence at 100% (3) indication locations. Importantly, the dog indicated twice within Border Ranges National Park, where historical (1980s-1990s) specimen-based records indicate the species was present, but extensive Elliott and camera trapping over the last 5-10 years have resulted in zero <i>A. arktos</i> captures. Camera traps subsequently corroborated <i>A. arktos</i> presence at these sites. This demonstrates that detection dogs can be a highly effective means of locating threatened, cryptic species, especially when traditional methods are unable to detect low-density mammal populations.</p>
Calculations for: Detector dog work assessing probability of detection for Yellow crazy ant
<p class="MsoNormal">The use of detector dogs within environmental programs has increased greatly over the past few decades, yet their<span> </span><span>search methods are not standardised, and variation in dog performance remains not well quantified or understood. There is much science to be done to improve the general utility of detector dogs, especially for invertebrate surveys.</span></p> <p class="MsoNormal">We report research for detector dog work conducted as part of yellow crazy ant eradication. One dog was first used to quantify probability of detection (POD) within a strictly controlled trial. We then investigated the search patterns of two dogs when worked through sites using different transect spacings. Specifically we quantified their presence within set distances of all locations in each assessment area, as well as the time they took to assess each area. In a GIS we then calculated the relative percentage of the entire search area within six distance categories, and combined this information with the POD values to obtain a site-level POD.</p> <p class="MsoNormal">The calculated relationship between distance and POD was extremely strong (R<sup>2</sup> = 0.998), with POD being 86% at 2 m and 28% at 25 m. For site-level assessments conducted by the two dogs, both dogs achieved highest site-level POD when operated on the lowest transect spacing (15 m), with POD decreasing significantly as transect spacing increased. Both dogs had strong linear relationships between area assessed and time, with the area assessed being greater when the transects had greater spacing. The working style of the two dogs also resulted in significantly different assessment outcomes. In one hour one dog could assess approximately 9.2 ha with transects spaced 20m apart, and 6.8ha with transects spaced 15 m apart, whereas the second dog could only assess approximately 6.9 ha with transects spaced 20 m apart, and 4.9 ha with transects spaced 15 m apart.</p> <p class="MsoNormal">Our study provides insight into the ability of dogs to detect yellow crazy ants, and sets the basis for further science and protocol development for ant detection. With the lessons learnt from this work we then detail protocols for using detector dogs for ant eradication assessments.</p>
Data for: An experimental assessment of detection dog ability to locate great crested newts (Triturus cristatus) at a channeled distance and through soil
<p>Detection dogs are increasingly used to locate cryptic wildlife species, but their use for amphibians is still rather underexplored. In the present paper we focus on the great crested newts (<em>Triturus cristatus</em>), a European species which is experiencing high conservation concerns across its range, and assess the ability of a trained detection dog to locate individuals during their terrestrial phase. More specifically, we used a series of randomised, double-blinded experiments to document whether a range of distances between target newts and the detection dog affects the ability of localisation, and to assess the ability and efficiency of target newt detection in simulated subterranean refugia through 20 cm of two common soil types (clay and sandy soil, both with and without air vents to mimic mammal burrows, a common refuge used by <em>T. cristatus</em>). The detection dog accurately located all individual <em>T. cristatus</em> across the entire range of tested distances (25 cm - 2 m). The substrate trials revealed that the detection dog could locate individuals also through soil. As expected, the detection time during the soil discrimination trials was significantly reduced for treatments with vents. Contrary to existing studies with detection dogs in human forensic contexts, however, detection was generally faster for <em>T. cristatus</em> under clay soil compared to sandy soil. Our study provides a general baseline for the use of detection dogs in locating <em>T. cristatus</em> and similar amphibian species during their terrestrial phase. </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.