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46 results for “Detection Strategies”
Result dataset for our experimental analysis on multi-cepstral projection representation strategies for dysphonia detection
<p>Database containing the results of the analyzed versions of the framework proposed in our paper submitted to the journal Sensors (Basel) under the title "An experimental analysis on multi-cepstral projection representation strategies for dysphonia detection".</p> <p>In this database, we have the following information:</p> <p>"gender": Gender of individuals referring to the selected voice database. For this field, we have the following possible values: “male” for a selection of male individuals, “female” for a selection of female individuals, and “both” for a selection considering both genders.</p> <p>“Techniques”: Concerns about the techniques for extracting cepstral coefficients that we are analyzing. The identifier “nonceps” refers to the use of non-cepstral features.</p> <p>“vowel”: Vowel considered in the database selection. The following values are possible: “a”, “i” and “u”.</p> <p>“intonation”: Tone used by individuals when pronouncing the analyzed vowel. Possible values are: “h” for high; “l” for low; “n” is normal; and “lhl” for low-high-low.</p> <p>“coordinates”: Number of coordinates that make up the feature vector that represents the voice signal after the dimensionality reduction routines.</p> <p>“scale”: Normalization function used on the feature vector. The possible values of this field are the following: “MinMax” for the min-max scale; “Robust” for the robust scale; “Standard” for the standard scale; and “Unscaled” for the unscaled vector.</p> <p>“ACC”: Accuracy obtained by the analyzed version on the considered voice database clipping.</p> <p>“AUC”: Area under the ROC curve obtained by the analyzed version on the considered voice database clipping.</p> <p>“EER”: Equal Error Rate obtained by the analyzed version on the considered voice database clipping.</p> <p>“F1”: F1-score obtained by the analyzed version on the considered voice database clipping.</p> <p>“EH”: Rate of healthy voice signals classified as pathological on the considered voice database clipping.</p> <p>“EP”: Rate of pathological voice signals classified as healthy on the considered voice database clipping.</p> <p>“KFCV”: Average accuracy score of a 5-fold Cross Validation over the training dataset on the considered voice database clipping.</p> <p>“Balancing”: Indication of the use of balancing technique (SMOTE) by the considered framework version.</p> <p>“Classifier”: Classifier used, being possible the use of Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM).</p> <p>“Multi-Projection”: Multi-projection strategies employed by the evaluated technique.</p> <p>“Features”: Type of feature that defines the feature vector. In this case, the following values are possible in this field: “NonCeps” for non-cepstral features; “Ceps” for cepstral features only; and “Ceps and NonCeps” for features of cepstral and non-cepstral types.<br> .</p> <p>It is worth noting that the symbol “-”, present in some fields, represents the “non-use” of any technique of the type indicated by the field. For example, in the case of the “Balancing” field, the value “-” means that no data balancing technique was used in the evaluated version of the framework.</p>
Microservice Security Detectors & Metrics & Detection Strategies: Dataset
<p>This is the dataset for replicability for the article "Detection Strategies for Microservice Security Tactics." It provides the code needed to replicate the study in the article and the model data set of 10 system models and 20 variants of those models.</p> <p> </p> <p>The abstract of the article is:</p> <p> </p> <p>Microservice architectures are widely used today to implement distributed systems. Securing microservice architectures is challenging because of their polyglot nature, continuous evolution, and various security concerns relevant to such architectures. This article proposes a novel, model-based approach providing detection strategies to address the automated detection of security tactics (or patterns and best practices) in a given microservice architecture decomposition model. Our novel detection strategies are metrics-based rules that decide conformance to a security recommendation based on a statistical predictor. The proposed approach models this recommendation using Architectural Design Decisions (ADDs). We apply our approach for four different security-related ADDs on access management, traffic control, and avoiding plaintext sensitive data in the context of microservice systems. We then apply our approach to a model data set of 10 open-source microservice systems and 20 variants of those systems. Our results are detection strategies showing a very low bias, a very high correlation, and a low prediction error in our model data set.<br> <br> The dataset is based on a dataset from a previous article: https://zenodo.org/record/6424722<br> </p>
Improved biodiversity detection using a large-volume environmental DNA sampler with in situ filtration and implications for marine eDNA sampling strategies
<p>Metabarcoding analysis of environmental DNA samples is a promising new tool for marine biodiversity and conservation. Typically, seawater samples are obtained using Niskin bottles and filtered to collect eDNA. However, standard sample volumes are small relative to the scale of the environment, conventional collection strategies are limited, and the filtration process is time consuming. To overcome these limitations, we developed a new large – volume eDNA sampler with in situ filtration, capable of taking up to 12 samples per deployment. We conducted three deployments of our sampler on the robotic vehicle <em>Mesobot</em> in the Flower Garden Banks National Marine Sanctuary in the northwestern Gulf of Mexico and collected samples from 20 to 400 m depth. We compared the large volume (~40 – 60 liters) samples collected by <em>Mesobot</em> with small volume (~2 liters) samples collected using the conventional CTD rosette – mounted Niskin bottle approach. We sequenced the V9 region of 18S rRNA, which detects a broad range of invertebrate taxa, and found that while both methods detected biodiversity changes associated with depth, our large volume samples detected approximately 66% more taxa than the CTD small volume samples. We found that the fraction of the eDNA signal originating from metazoans relative to the total eDNA signal decreased with sampling depth, indicating that larger volume samples may be especially important for detecting metazoans in mesopelagic and deep ocean environments. We also noted substantial variability in biological replicates from both the large volume <em>Mesobot</em> and small volume CTD sample sets. Both of the sample sets also identified taxa that the other did not – although the number of unique taxa associated with the <em>Mesobot</em> samples was almost four times larger than those from the CTD samples. Large volume eDNA sampling with in situ filtration, particularly when coupled with robotic platforms, has great potential for marine biodiversity surveys, and we discuss practical methodological and sampling considerations for future applications.</p>
Apple orchard production estimation using deep learning strategies: a comparison of tracking-by-detection algorithms - CaseStudy
<p>The dataset "Case Study" consists of image sequences (videos) for apple detection and tracking and its corresponding ground truth. The ground truth is presented in MOT format. This dataset is part of the paper:</p> <p>Villacrés, J., Viscaino, M., Delpiano, J., Vougioukas, S. & Cheein, F. A. (2022). Apple orchard production estimation using deep learning strategies: a comparison of tracking-by-detection algorithms. <em>Computers and Electronics in Agriculture</em>.</p> <p>The article is currently accepted. For a better reference format, please refer to the journal's official website.</p> <p>If you have used the material presented in this data set, please cite the previous article.</p> <p>For more information regarding the dataset, please refer to the paper mentioned below.</p>
Improved biodiversity detection using a large-volume environmental DNA sampler with in situ filtration and implications for marine eDNA sampling strategies
Open the record for dataset details and reuse information.
Imperfect detection alters the outcome of management strategies for protected areas
<p>Designing protected areas configurations to maximize biodiversity is a critical conservation goal. The configuration of protected areas can significantly impact the richness and identity of the species found there; one large patch supports larger populations but can facilitate competitive exclusion. Conversely, many small habitats spreads risk but may exclude predators that typically require large home ranges. Identifying how best to design protected areas is further complicated by monitoring programs failing to detect species. Here we test the consequences of different protected area configurations using multi-trophic level experimental microcosms. We demonstrate that for a given total size, many small patches generate higher species richness, are more likely to contain predators, and have fewer extinctions compared to single large patches. However, the relationship between the size and number of patches and species richness was greatly affected by insufficient monitoring, and could lead to incorrect conservation decisions, especially for higher trophic levels.</p>
Data availability: Clustered and rotating designs as a strategy to obtain precise detection rates in camera trapping studies
<p>Manuscript data "Clustered and rotating designs as a strategy to obtain precise detection rates in camera trapping studies" published in Journal of Applied Ecology. R code to replicate the simulations can be found in the supplementary materials of the manuscript.</p>
Data for "Evaluating disease surveillance strategies for early outbreak detection in contact networks with varying community structure"
<p>New York City contact network data used in the publication “<a href="https://doi.org/10.1016/j.socnet.2024.06.003">Evaluating disease surveillance strategies for early outbreak detection in contact networks with varying community structure</a>” (LA-UR-23-26868). This contact network comes in the form of a weighted edge list. Each row describes an edge, with the first and second column containing the labels of the nodes connected by the edge, and the third column contains the corresponding weight of the edge. In this network, an edge encodes an interaction between two individuals and the weight describes the duration of the interaction in seconds. In total the edge list describes 6,376,729,847 interactions among 6,813,615 individuals; the first 10 interactions are listed below as an example.</p> <p>2, 1, 84121<br>4, 3, 83654.4<br>5, 3, 79591.4<br>5, 4, 87642<br>6, 3, 79853<br>6, 4, 81604<br>6, 5, 79146<br>8, 7, 80604<br>10, 9, 84259.6<br>12, 11, 68990.8</p> <p> </p> <p>This work is approved for public distribution under LA-UR-24-25046.</p>
Apple orchard production estimation using deep learning strategies: a comparison of tracking-by-detection algorithms - SensitivityAnalysis
<p>The dataset "Sensitivity Analysis" consists of image sequences (videos) for apple detection and tracking and its corresponding ground truth. The ground truth is presented in MOT format. This dataset is part of the paper:</p> <p>Villacrés, J., Viscaino, M., Delpiano, J., Vougioukas, S. & Cheein, F. A. (2022). Apple orchard production estimation using deep learning strategies: a comparison of tracking-by-detection algorithms. <em>Computers and Electronics in Agriculture</em>.</p> <p>The article is currently accepted. For a better reference format, please refer to the journal's official website.</p> <p>If you have used the material presented in this data set, please cite the previous article.</p> <p>For more information regarding the dataset, please refer to the paper mentioned below.</p>
Pupillary dynamics reflect the impact of temporal expectation on detection strategy - data
<p>This repository contains data from work that investigated how humans can extract hidden temporal cues from the occurrences of probabilistic targets and utilize them to inform target detection in a complex acoustic stream, using a combination of behavioral measures and pupillometry. It accompanies the article entitled: " <strong>Pupillary dynamics reflect the impact of temporal expectation on detection strategy</strong>" from the same authors. It contains:<br> - preprocessed pupil data for all subjects<br> - similarly matched behavior data for all subjects<br> - custom code for both behavioral and pupil analysis</p>
Recurrent Disease Detection After Resection of Pancreatic Adenocarcinoma Using a Standardized Surveillance Strategy
ClinicalTrials.gov study NCT04875325. IPD Sharing: YES. Countries: 2. Publications: 1.
E6/E7 mRNA Performance to Detect HSIL and Cost-effectiveness Analysis of This Screening Strategy in HIV + MSM
ClinicalTrials.gov study NCT03357991. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effect of Community Active Case Finding Strategies for Detection of Tuberculosis in Cambodia
ClinicalTrials.gov study NCT04094350. IPD Sharing: NO. Countries: 1. Publications: 13.
Drug Sensitivity Detection of Micro Tumor (PTC) to Guide Postoperative Adjuvant Treatment Strategy of Colorectal Cancer
ClinicalTrials.gov study NCT05424692. IPD Sharing: NO. Countries: 1. Publications: 7.
Self-detection and Professional Screening Strategies for Early Detection of Periodontal Disease
ClinicalTrials.gov study NCT05513599. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Cardiac Magnetic Resonance Imaging Strategy for the Management of Patients With Acute Chest Pain and Detectable to Elevated Troponin
ClinicalTrials.gov study NCT01931852. IPD Sharing: Not stated. Countries: 1. Publications: 61.
Study to Compare Strategies to Improve Detection of Nutritional Disorders in Hospitalized Adults (Compass Project)
ClinicalTrials.gov study NCT02845895. IPD Sharing: NO. Countries: 1. Publications: 13.
Retrospective Evaluation of 3 Colonic Adenoma Detection Strategies
ClinicalTrials.gov study NCT05080088. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Impact of a Strategy Based on Bacterial DNA Detection to Optimize Antibiotics in Patients With Hospital-acquired Pneumonia
ClinicalTrials.gov study NCT03711331. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Sequential Strategy vs Palpation vs Routine Ultrasound for Detection of Cricothyroid Membrane
ClinicalTrials.gov study NCT05535127. IPD Sharing: NO. Countries: 1. Publications: 4.
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