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625 results for “anomaly”
Interviewguide: Classification for System Anomalies
<p>Interviewguide to evaluate the quality in use of a classifaction for anomlies for IoT Systems.</p> <p> </p>
RARE: A Labeled Dataset for Cloud-Native Memory Anomalies
<p>The dataset is linked to the paper RARE: A Labeled Dataset for Cloud-Native Memory Anomalies (<a href="https://doi.org/10.1145/3416505.3423560">https://doi.org/10.1145/3416505.3423560</a>)</p> <p>This dataset has been generated using a microservice for injecting artificial byte stream in order to overload the nodes, provoking memory anomalies,</p> <p>It includes 2 files:</p> <p>- List_of_anomalies.csv includes the details on the anomalies injected</p> <p>- RARE.csv is the actual dataset.</p> <p>There are a total of 30 anomalies injected in the dataset. The full dataset comprise 10K labelled time-series, each with 7062 metrics.</p>
Machine Learning for Anomaly Detection in Cyanobacterial Fluorescence Signals
<p>Excel files containing chlorophyll a and phycocyanin fluorescence data imported from <a href="https://www.glerl.noaa.gov/res/HABs_and_Hypoxia/habTracker.html">https://www.glerl.noaa.gov/res/HABs_and_Hypoxia/habTracker.html</a> for buoys WE2, WE4, WE8, and WE13. The Python code used to manipulate the data is also included.</p>
Climate anomalies and neighbourhood crowding interact in shaping tree growth in old-growth and selectively-logged tropical forests
<p>Species mean information for the six leaf water-related traits used in the paper titled: Climate anomalies and neighbourhood crowding interact in shaping tree growth in old-growth and selectively-logged tropical forests.</p>
CADeSH Dataset: Collaborative Anomaly Detection for Smart Homes
<p>Dataset used for quantitative evaluation in the paper:</p> <p>Y. Meidan, D. Avraham, H. Libhaber and A. Shabtai, "CADeSH: Collaborative Anomaly Detection for Smart Homes," in IEEE Internet of Things Journal, 2022, doi: 10.1109/JIOT.2022.3194813.</p> <p> </p> <p>This is a table of flow-level traffic data which was continuously captured during a period of 21 days from five real home networks which were subscribed to a smart home security service, and from our lab at Ben-Gurion University of The Negev. This security service provider shared with us these network traffic flows, plus the related DNS requests and responses, and reputation intelligence of the destination IP addresses. Each instance in this dataset represents an outbound network traffic flow (in the form of an IPFIX) which emanated from an instance of the IoT model streamer.Amazon.Fire_TV_Gen_3.</p> <p>In our lab, we infected our streamer.Amazon.Fire_TV_Gen_3 with a cryptominer and executed cryptomining from this device. To imitate a scanning activity typically performed by some botnets, we also scanned the network using Nmap. In accordance, we labeled these malicious activities as (1) `is executing cryptomining,' or (2) `being scanned by Nmap.' All of the remaining IPFIXs captured in our lab or on the home networks were labeled as `assumed benign'.</p> <p>The multitude of real home networks, and the multitude of identical source devices, enable using this dataset for quantitative evaluation of (collaborative) anomaly/attack detection methods, especially for the IoT.</p>
Figure 3 from: Yi D-A, Kuprin AV, Bae YJ (2017) Morphological anomalies of endangered Korean relict long-horned beetle Callipogon relictus during ontogenesis and possible causes of their occurrence (Cerambycidae, Coleoptera). ZooKeys 714: 53-60. https://doi.org/10.3897/zookeys.714.21112
Figure 3 - Adults of C. relictus with various developmental deformations of the body.
Figure 2 from: Yi D-A, Kuprin AV, Bae YJ (2017) Morphological anomalies of endangered Korean relict long-horned beetle Callipogon relictus during ontogenesis and possible causes of their occurrence (Cerambycidae, Coleoptera). ZooKeys 714: 53-60. https://doi.org/10.3897/zookeys.714.21112
Figure 2 - Pupae C. relictus with various deformations of teguments and limbs.
Figure 1 from: Yi D-A, Kuprin AV, Bae YJ (2017) Morphological anomalies of endangered Korean relict long-horned beetle Callipogon relictus during ontogenesis and possible causes of their occurrence (Cerambycidae, Coleoptera). ZooKeys 714: 53-60. https://doi.org/10.3897/zookeys.714.21112
Figure 1 - Larvae of Callipogon relictus with various deformations of the teguments.
Thermoelastic anomaly of iron carbonitride across the spin transition and implications for planetary cores
<p>This is the dataset for the manuscript entitled "Thermoelastic anomaly of iron carbonitride across the spin transition and implications for planetary cores".</p>
AnoShift: A distribution shift benchmark for unsupervised anomaly detection
<p>Analyzing the distribution shift of data is a growing research direction in nowadays Machine Learning (ML), leading to emerging new benchmarks that focus on providing a suitable scenario for studying the generalization properties of ML models. The existing benchmarks are focused on supervised learning, and to the best of our knowledge, there is none for unsupervised learning. Therefore, we introduce an unsupervised anomaly detection benchmark with data that shifts over time, built over Kyoto-2006+, a traffic dataset for network intrusion detection. This type of data meets the premise of shifting the input distribution: it covers a large time span (10 years), with naturally occurring changes over time (eg users modifying their behavior patterns, and software updates). We first highlight the non-stationary nature of the data, using a basic per-feature analysis, t-SNE, and an Optimal Transport approach for measuring the overall distribution distances between years. Next, we propose AnoShift, a protocol splitting the data in IID, NEAR, and FAR testing splits. We validate the performance degradation over time with diverse models, ranging from classical approaches to deep learning. Finally, we show that by acknowledging the distribution shift problem and properly addressing it, the performance can be improved compared to the classical training which assumes independent and identically distributed data (on average, by up to 3% for our approach).</p>
Traces, Metrics, and Logs for Anomaly Detection and Root Cause Localization in Microservices
<p>Here are the data used in our paper published at ICSE 2023: </p><p>"Eadro: An End-to-End Troubleshooting Framework for Microservices on Multi-source Data".<br><br>Please make sure to cite our paper whenever you use the data in your research:<br><br>@inproceedings{DBLP:conf/icse/LeeYCSL23, author = {Cheryl Lee and Tianyi Yang and Zhuangbin Chen and Yuxin Su and Michael R. Lyu}, title = {Eadro: An End-to-End Troubleshooting Framework for Microservices on Multi-source Data}, booktitle = {45th {IEEE/ACM} International Conference on Software Engineering, {ICSE} 2023, Melbourne, Australia, May 14-20, 2023}, pages = {1750--1762}, publisher = {{IEEE}}, year = {2023}, url = {https://doi.org/10.1109/ICSE48619.2023.00150}, doi = {10.1109/ICSE48619.2023.00150}, timestamp = {Wed, 19 Jul 2023 10:09:12 +0200}, biburl = {https://dblp.org/rec/conf/icse/LeeYCSL23.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} }</p>
Anomalies Detection in Student Activity in Solving Unique Programming Exercises: Motivated Students against Suspicious Ones
<p>The proposed dataset contains messages from the Digital Teaching Assistant (DTA) system, which captures the results of automatic verification of students’ solutions of unique programming exercises for 11 tasks of various types, which are automatically generated by the same system that automates a massive Python programming course at MIREA - Russian Technological University (RTU MIREA). The dataset contains anonymous information about students, their groups, variants, the time of successful and unsuccessful attempts to submit solutions to exercises for 11 tasks to the DTA system, as well as the ways in which a particular student performed a particular unique exercise. The dataset can be the subject of exploratory analysis in terms of detecting various anomalies and outbursts both in the structure of a multidimensional time series formed from messages sent to the DTA system as a whole, and in the behavior models of student groups and individual students when they interact with the DTA system.</p>
Role of Cardiac AngioMR in Diagnosis of Cardiac and Vascular Anomalies in Adult Patients with Turner Syndrome
ClinicalTrials.gov study NCT06178887. IPD Sharing: NO. Countries: 1. Publications: 0.
Ultrasound Screening of Congenital Anomalies of Kidney and Urinary Tract in Neonates
ClinicalTrials.gov study NCT06534749. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Comparison of Karyotyping, CMA and NIPT for Prenatal Diagnosing Chromosomal Anomalies
ClinicalTrials.gov study NCT03201666. IPD Sharing: NO. Countries: 1. Publications: 0.
AVAST Anomalies Vasculaires Associées au Syndrome de Turner (Vascular Abnormalities Associated With Turner Syndrome)
ClinicalTrials.gov study NCT02250456. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Diagnostic, Therapeutic and Reproductive Aspects of Patients With Mullerian Anomalies (MULAN)
ClinicalTrials.gov study NCT04732546. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Percutaneous Administration of Sirolimus in the Treatment of Superficial Complicated Vascular Anomalies
ClinicalTrials.gov study NCT04921722. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Value of Prenatal Ultrasound in Complicated Twin Pregnancy and Its Correlation With Chromosomal Anomalies
ClinicalTrials.gov study NCT02732717. IPD Sharing: NO. Countries: 1. Publications: 0.
Congenital Uterine Anomalies: Identifying Cancer Associations and Genetic and Environmental Factors to Improve Clinical Care
ClinicalTrials.gov study NCT04661072. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.