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1,041 results for “Internet”
Internet Of Things,K.J.Somaiya Institute of Engineering & Information Technology, Mumbai,
<p>Internet Of Things,K.J.Somaiya Institute of Engineering & Information Technology, Mumbai,</p>
Index of supplementary files from "Perils of Zero-Interaction Security in the Internet of Things"
<p>This record serves an an index to the other dataset releases that are part of the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1.</p> <p>We have chosen to split the dataset into several parts to meet Zenodo size requirements and make it easier to find specific pieces of data. In total, the following datasets exist:</p> <ol> <li><strong>Raw data</strong><br> These datasets contain raw data, as collected directly from the devices doing the recording. It includes readings from several different sensors, as well as observed WiFi and BLE signals with their signal strength, and in one case, audio recordings. This raw data can be used to repeat our own experiments, or to apply different schemes to it to have a baseline for comparisons. Four datasets exist, mapped to the three scenarios discussed in the paper: <ol> <li><a href="http://dx.doi.org/10.5281/zenodo.2537699">Car Scenario</a></li> <li><a href="http://dx.doi.org/10.5281/zenodo.2537701">Office Scenario</a></li> <li><a href="http://dx.doi.org/10.5281/zenodo.2537703">Mobile Scenario</a> + <a href="http://dx.doi.org/10.5281/zenodo.2537984">audio data in separate deposit</a> (with access control)</li> </ol> </li> <li><strong>Processed Data</strong><br> The processed data is generated from the raw data using the processing code (which can be found in <a href="https://dx.doi.org/10.5281/zenodo.2543721">the code repository</a>). The resulting data contains computed features from the five papers under investigation plus derived machine learning datasets, and can be used to see in detail how the schemes behave in specific situations. These datasets tend to be fairly large. Three datasets exist: <ol> <li><a href="http://dx.doi.org/10.5281/zenodo.2537705">Car Scenario</a></li> <li><a href="http://dx.doi.org/10.5281/zenodo.2537707">Office Scenario</a></li> <li><a href="http://dx.doi.org/10.5281/zenodo.2537709">Mobile Scenario</a></li> </ol> </li> <li><strong>Result Data</strong><br> Finally, the result datasets contain the results of the evaluation (i.e., the computed error rates and generated plots, plus associated caches). The code used to derive these results can once again be found in the <a href="http://dx.doi.org/10.5281/zenodo.2543721">source code repository</a>. Here, five datasets exist, one for each investigated paper: <ol> <li><a href="http://dx.doi.org/10.5281/zenodo.2537711">Karapanos et al.</a></li> <li><a href="http://dx.doi.org/10.5281/zenodo.2537713">Schürmann and Sigg</a></li> <li><a href="http://dx.doi.org/10.5281/zenodo.2537715">Miettinen et al.</a></li> <li><a href="http://dx.doi.org/10.5281/zenodo.2537717">Truong et al.</a></li> <li><a href="http://dx.doi.org/10.5281/zenodo.2537719">Shrestha et al.</a></li> </ol> </li> </ol>
Raw data from Car scenario from "Perils of Zero-Interaction Security in the Internet of Things"
<p>This deposit contains the raw data from the Car scenario in the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1. See the <a href="https://dx.doi.org/10.5281/zenodo.2537721">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p>
Raw data from Office scenario from "Perils of Zero-Interaction Security in the Internet of Things"
<p>This deposit contains the raw data from the Office scenario in the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1. See the <a href="https://dx.doi.org/10.5281/zenodo.2537721">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p>
Processed data from Car scenario from "Perils of Zero-Interaction Security in the Internet of Things"
<p>This deposit contains the processed data from the Car scenario in the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1. See the <a href="https://dx.doi.org/10.5281/zenodo.2537721">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p>
Results for scheme by Schürmann and Sigg from "Perils of Zero-Interaction Security in the Internet of Things"
<p>This deposit contains the results from the evaluation of the paper by Schürmann and Sigg in the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1. See the <a href="https://dx.doi.org/10.5281/zenodo.2537721">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p>
Processed data from Mobile scenario from "Perils of Zero-Interaction Security in the Internet of Things"
<p>This deposit contains the processed data from the Mobile scenario in the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1. See the <a href="https://dx.doi.org/10.5281/zenodo.2537721">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p>
Results for scheme by Truong et al. from "Perils of Zero-Interaction Security in the Internet of Things"
<p>This deposit contains the results from the evaluation of the paper by Truong et al. in the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1. See the <a href="https://dx.doi.org/10.5281/zenodo.2537721">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p>
Results for scheme by Shrestha et al. from "Perils of Zero-Interaction Security in the Internet of Things"
<p>This deposit contains the results from the evaluation of the paper by Shrestha et al. in the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1. See the <a href="https://dx.doi.org/10.5281/zenodo.2537721">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p>
Results for scheme by Miettinen et al. from "Perils of Zero-Interaction Security in the Internet of Things"
<p>This deposit contains the results from the evaluation of the paper by Miettinen et al. in the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1. See the <a href="https://dx.doi.org/10.5281/zenodo.2537721">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p>
Results for scheme by Karapanos et al. from "Perils of Zero-Interaction Security in the Internet of Things"
<p>This deposit contains the results from the evaluation of the paper by Karapanos et al. in the paper "Perils of Zero Interaction Security in the Internet of Things" by Mikhail Fomichev, Max Maass, Lars Almon, Alejandro Molina, Matthias Hollick, in Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, Issue 1. See the <a href="https://dx.doi.org/10.5281/zenodo.2537721">index of all related datasets</a> for more details on the paper, and see the included README for details on this dataset.</p>
PEMANFAATAN BIG DATA ANALYTIC BERBASIS INTERNET OF THINGS SEBAGAI SUMBER DATA DALAM PENGENDALIAN COVID-19
<p>Tingginya angka kasus dan angka kematian pasien terkonfirmasi positif COVID-19 masih merupakan masalah besar di dunia dan di Indonesia. Banyaknya tenaga medis yang terpapar bahkan meninggal akibat COVID-19 juga belum dapat teratasi. Oleh sebab itu para peneliti mengembangkan berbagai usaha pencegahan dan penanggulangan, termasuk memanfaatkan analisis Big Data. Tujuan review ini adalah untuk menjelaskan bagaimana potensi analisis Big Data berbasis Internet of Things (IoT) dalam pencegahan dan penanggulangan COVID-19. Metode yang digunakan adalah mengkaji literatur yang bersumber dari database Pubmed, Sciencedirect, dan Proquest. Penelusuran literatur menggunakan beberapa kata kunci yang terkait topik. Dari literatur yang membahas macam-macam teknologi IoT, dapat disimpulkan bahwa teknologi IoT seperti perangkat wearable, drone, robot, dan aplikasi smartphone sangat direkomendasikan penggunaannya dalam penanggulangan COVID-19. Namun tentu saja sebelum digunakan di masyarakat luas, perlu dipertimbangkan beberapa hal seperti sumber daya manusia, finansial, dan keamanan serta privasi data.</p>
Internet searches for diet, exercise, diabetes, BMI and a GLP1 analog, in the 10 most populous Muslim countries
<p>We conducted an analysis of internet search trends from January 2022 to June 2023 in the ten most populous Muslim countries, focusing on keywords such as "Ozempic"TM (a popular GLP1 analog), "Diet," "Exercise," and "Diabetes." The results showed a significant increase in searches for the GLP1 analog in eight countries, while searches for "Diet" and "Exercise" were mostly declining in nine and four countries, respectively. Searches for "Diabetes" were positive in only two countries. These trends suggest a growing reliance on pharmaceutical interventions for diabetes and weight management at the expense of diet and exercise. Clinicians and healthcare workers in Muslim countries should emphasize the importance of diet and exercise for diabetic patients, even during Ramadan.</p>
Pain in Pediatric Patients - Internet Interventions for Disorders of Gut-brain Interaction
ClinicalTrials.gov study NCT07319078. IPD Sharing: NO. Countries: 1. Publications: 0.
A Transdiagnostic, Self-guided Internet Intervention ("Velibra") for Waitlist Patients With Anxiety Disorders
ClinicalTrials.gov study NCT04793828. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Internet-based Cognitive Behavioral Therapy for Adolescents With Sleep Problems- a Feasibility Trial
ClinicalTrials.gov study NCT04616157. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Internet-administered CBT for Young Adults With Low Self-esteem
ClinicalTrials.gov study NCT05180084. IPD Sharing: NO. Countries: 1. Publications: 0.
Treatment of Panic Disorder Via Internet With a Wireless Temperature Biofeedback Ring
ClinicalTrials.gov study NCT00825136. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Internet-based Group Contingency Management to Promote Smoking Abstinence
ClinicalTrials.gov study NCT01181661. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Internet-Based Cognitive Behavioral Therapy for Depressive Symptoms in Adolescents With Type 1 Diabetes Mellitus
ClinicalTrials.gov study NCT03655067. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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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.