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30 results for “demo videos”
Design of an Ontology-Driven Constraint Tester (ODCT) and Application to SAREF & Smart Energy Appliances: Datasets, SHACL Shapes, Demo Video of Web Application, and Detailed Performance Reports
<h2>Description</h2> <p>This repository presents the resources used for validating the compliance of <strong>smart energy appliances</strong> against the <strong>Smart Appliances REFerence (SAREF)</strong> ontology and its extension <strong>SAREF4ENER</strong>, as part of the <strong>Ontology-Driven Constraint Tester (ODCT)</strong> project. The ODCT tool is specifically designed to ensure <strong>semantic interoperability</strong> and adherence to standardized ontological frameworks, which are crucial for integrating smart devices into modern energy management systems.</p> <h2>ODCT Overview</h2> <p>The <strong>Ontology-Driven Constraint Tester (ODCT)</strong> is a robust framework created to validate datasets against ontologies defined by <strong>SAREF</strong> and <strong>SAREF4ENER</strong>, both established under ETSI SmartM2M. This tool has been applied to the <strong>Flexible Start use case</strong> from the <strong>Joint Research Centre’s (JRC) Code of Conduct for Energy Smart Appliances</strong>. The ODCT tool ensures that smart devices like energy-efficient washing machines, thermostats, and connected lighting operate in compliance with established ontologies, thereby enhancing their <strong>interoperability</strong> within energy management systems and smart grids.</p> <h2>Repository Contents</h2> <p>This repository contains essential resources used in the ODCT compliance testing process:</p> <ul> <li> <p><strong>Compliant Dataset</strong>: This dataset represents a fully compliant scenario where no errors are present in the smart energy appliances’ profiles, demonstrating the ODCT’s accuracy under ideal conditions.</p> </li> <li> <p><strong>Modified Datasets</strong>: These datasets introduce various types of errors to showcase ODCT’s ability to handle diverse compliance scenarios:</p> <ol> <li><strong>Modified Dataset 1</strong>: Introduces type mismatches and spelling errors in key attributes.</li> <li><strong>Modified Dataset 2</strong>: Contains extraneous properties and missing required properties, including details about energy consumption and efficiency class.</li> <li><strong>Modified Dataset 3</strong>: Includes both extraneous and missing properties, and additional priority levels for energy profiles.</li> </ol> </li> <li> <p><strong>SHACL Shapes</strong>: The SHACL shapes used in the compliance testing for both SAREF and SAREF4ENER ontologies are included in this repository to allow reproducibility of the validation process.</p> </li> </ul> <ul> <li> <p><strong>Error Detection Results and Performance Reports</strong>: After conducting compliance tests using ODCT we got the Results and Performance Reports, the repository includes comprehensive reports detailing the results. These reports highlight the types of errors detected and provide a performance analysis of the tool under various scenarios.</p> </li> <li> <p><strong>Demonstration Video</strong>: A video is provided to guide users through the <strong>ODCT web application</strong>, showcasing how the tool detects errors and generates detailed compliance reports based on smart energy appliance datasets.</p> </li> </ul> <h2>Background</h2> <p>The integration of smart energy appliances into modern power grids is key to improving <strong>energy management</strong> and supporting <strong>sustainability goals</strong> like the <strong>European Green Deal</strong>. However, ensuring that these devices communicate effectively and conform to <strong>standardized protocols</strong> is a challenge. The <strong>ODCT</strong> tool addresses this challenge by providing a rigorous, ontology-based validation framework that is both <strong>protocol-agnostic</strong> and <strong>technology-flexible</strong>.</p> <p>This work is grounded in the broader context of <strong>global warming</strong> and the need for <strong>energy efficiency</strong> and <strong>demand-side flexibility</strong> in energy systems. By ensuring compliance with <strong>SAREF</strong> and <strong>SAREF4ENER</strong>, ODCT supports the EU’s ambitions for <strong>carbon neutrality</strong> by 2050, contributing to a connected, efficient, and sustainable energy ecosystem.</p> <h2>Methodology</h2> <p>ODCT uses a structured methodology that involves:</p> <ol> <li><strong>Generating relevant datasets</strong> for validation.</li> <li><strong>Defining SHACL shape constraints</strong> based on ontologies.</li> <li><strong>Developing a user-friendly web application</strong> to facilitate compliance testing.</li> <li><strong>Performing compliance tests</strong> that validate datasets against SHACL shapes, ensuring interoperability and adherence to energy management standards.</li> </ol> <h2>Why It Matters</h2> <p>Researchers and developers working on smart energy appliances will benefit from ODCT by:</p> <ul> <li>Ensuring their devices meet standardized ontological requirements for <strong>interoperability</strong>.</li> <li>Reducing <strong>compliance issues</strong> in the development phase, leading to smoother integration into energy management systems.</li> <li>Supporting the <strong>sustainability efforts</strong> by enhancing device communication in <strong>smart grids</strong>.</li> </ul> <p>This repository showcases the potential of ODCT in fostering <strong>data accuracy</strong>, <strong>semantic interoperability</strong>, and <strong>compliance</strong> with essential energy standards. It offers comprehensive resources for furthering research and development in the field of smart energy appliances and energy management.</p>
Unitymol demo on generating 360 degree videos
<p>This video provides more detailed supportive information about using Unitymol.</p> <p>It requires to run UnityMol from the Unity editor. It will not work with the UnityMol executable.</p> <p>1) from within the Unity editor with an opened UnityMol project, open the Recorder window<br> 2) add a new Movie recorder, setting parameters as shown in the tutorial video<br> 3) launch the UnityMol project (play mode) and start recording<br> 4) do what you want to be captured within UnityMol (can be live action, can be executing a pre-recorded script ..)<br> 5) stop recording when finished</p>
CS3MESH4EOSC ScienceMesh Demo Videos
<p>The Science Mesh serves all kinds of research and also non-research disciplines, where users can be either in well-organised user communities within existing Research infrastructures (e.g. European Research Infrastructure Consortia (ERICs), National Research and Education Network (NRENs) and other official clusters), but also individual researchers and smaller research groups that are not part of any internationally organised community, let alone, have their own e-infrastructure. These videos demonstrate the value and how Science Mesh is helping different communities in their data sharing and synchronisation activities.</p> <p>They also show how how the Science Mesh federated interoperability and file-sharing features are possible thanks to the usage of OpenCloudMesh (OCM) and application programming interfaces (API) from the well-known EFSS OwnCloud and NextCloud.</p>
Demo video of ImAc player
<p>This is a demo video of the open-source accessibility-enabled VR360 player developed within the framework of the EU H2020 ImAc project.</p> <p>Link: <a href="https://www.google.com/url?q=http://imac.i2cat.net/player/&sa=D&source=hangouts&ust=1587208804555000&usg=AFQjCNE2SkQgDVXom19pepQ6LlIgkEZL1Q">http://imac.i2cat.net/player/</a> </p> <p>Github (opne source repository): <a href="https://www.google.com/url?q=https://github.com/ua-i2cat/ImAc&sa=D&source=hangouts&ust=1587208804555000&usg=AFQjCNEfoKiBuJA0c0WhRVS64Nxrsn6P9Q">https://github.com/ua-i2cat/ImAc</a> </p>
Video demo of the ADOLIMIS experiment
<p>The video demo shows the ADOLIMIS experiment performed by adolescents with borderine personality disorder and healthy controls. All participants performed a socially evaluated mental arithmetic test. The task was composed of six steps of increasing difficulty, with a break period of 5 seconds between two steps (total duration 12 minutes). During the task, each participant was video recorded using both a Kinect® device and a HD video. Each participant was also wearing a portable device (Nexus10, Mind Media lab) to record physiological data. Several features were extracted online through specific sensors and algorithms. From the Kinect recording, we extracted 15 features and metrics (means) associated with body movements of the participants. From the HD video recording of the face, we extracted 12 facial landmarks/action units (AU) and 24 metrics (means, SD) associated with facial movements. From Nexus10 recording, we extracted 13 features and 52 metrics (mean, SD, minimum, maximum) associated with physiological parameters (Blood Pressure, Heart Rate, Respiratory Frequency, Skin Conductance, Body Temperature).</p>
Model-By-Voice New Interaction Model - Video Demo
<p>Demo of the main features added to the new interaction model of the platform Model-By-Voice, including:<br> <br> - Modeling through non-vocal sounds</p> <p>- Modeling through drawn gestures</p> <p>- Modeling through a table navigation paradigm</p> <p>- Addition of shortcuts such as "copy"</p> <p>- Customization of the voice synthesizer</p> <p>- Notion of "User" and "profile" where you can save your preferences, gestures, non-vocal-sounds and patterns</p> <p>Features not covered in the demo:<br> <br> - Navigating on the table through keyboard arrows</p> <p>- Designing the interaction protocol that adjust the platform to the level of knowledge in modeling engineering and visual difficulties.</p> <p>- Other shortcuts such as "use pattern" and "cut"</p> <p> </p> <p>The demo represents the usage of the platform by a first time user.</p>
AIR demo videos
<p>Aerial drone video material for testing <a href="https://github.com/Accenture/AIR">the AIR detector</a>.</p>
BINCI 360º demo video with binaural audio experimental
<p>BINCI - Binaural Tools for the Creative Industries</p> <p>There are multiple use cases as well as interpretations about immersive audio. As an aid for understanding BINCI approach of immersive audio, we created this first 360 demo video with Matroska Spatial Workstation 8 Channel. You can download this video and watch and hear with Google Cardboard or even better, with Samsung Gear. </p>
BINCI 360º demo video with 2nd Ambisonics audio
<p>BINCI - Binaural Tools for the Creative Industries</p> <p>There are multiple use cases as well as interpretations about immersive audio. As an aid for understanding BINCI approach of immersive audio we created this first 360 demo video with a basi introduction. You can download this video and watch and hear with Google Cardboard or even better, with Samsung Gear. </p>
Virtual reality bimodal neurofeedback paradigm for fMRI/EEG/MEG/fNIRS (video demo)
<p>To watch the video demo of the software, please download this file: <a href="https://zenodo.org/api/files/28e810fb-4f02-4301-ad77-a8323a81a313/VR_NF_OHBM_demo.mp4?versionId=1aaea91b-984d-4b66-9a61-78eedfba7112">VR_NF_OHBM_demo.mp4</a></p> <p> </p>
CuratorBot Visboeck Demo Video, July 2023
<p>A short demo video of the Visboeck Curatorbot, a collaboration between the National Library of the Netherlands and TU Delft. It is a generative machine-learning chatbot that engages visitors in a conversation about the 'Visboeck' of the Scheveningen butcher Adriaen Coenen (https://www.kb.nl/ontdekken-bewonderen/topstukken/visboeck). This manuscript from the late 16th century is particularly popular with visitors. It contains numerous illustrations of creatures of the sea, from fish to sea monsters. To an untrained eye, however, the text is not readable, and many illustrations raise questions. What is this? What does this have to do with the sea? Why did Adriaen Coenen add this to his book? </p> <p>The CuratorBot has been “fed” multiple sources of information sources about the Visboeck, from the web to publications to knowledge from our own collection specialists. Therefore, the CuratorBot can tell you all about unicorns, manatees and tree geese.</p> <p>The CuratorBot gives us insight into the usability of AI chatbots for heritage institutions. What questions does the CuratorBot struggle with? What do visitors to the KB like to know? The curatorbot was available for live testing in the KB's reception area until June 2023.</p>
Listening to Listening: Sonic Sculpture Video Demo
<p>A demo of the sonic sculpture system and "listening to listening" sound artwork developed to explore the use of AR to engage with physical sculptures through sound art.</p>
Elastic Data Analytics for the Cloud-to-Things Continuum - Demo Video
<p>The massive deployment of Internet-connected devices has led to an increase in the collection of data that are then used by companies to improve their decision-making processes. This growing trend demands more and more cloud and communications infrastructure. The limited resources, the need of sharing them, and the fact that many consumers are interested in the same data call for an efficient management of the available resources. The cloud-to-thing continuum can be used to execute different analytics closer to the data source so infrastructure consumption and data circulation can be optimized. In this paper, different dimensions for achieving elastic analytics and a framework for dynamically modifying their behavior, is proposed.</p> <p>This artifact corresponds with a descriptive video of the framework presented in the paper. </p>
Demo data and model weights for "Direct Observation of Atmospheric Turbulence with a Video-rate Wide-field Wavefront Sensor"
<p>Demo data and model weights for "Direct Observation of Atmospheric Turbulence with a Video-rate Wide-field Wavefront Sensor". For usage, please refer to https://github.com/freemercury/Widefield_wavefront_sensor.</p>
Therapeutic body wrap video demo
<p> This10-minute-video presents video clips of the same child during several TBW sessions both at session<br> beginnings and session endings. This video is related to Delion et al. manuscript published in PlosONE and entitled "Therapeutic body wraps (TBW) for treatment of severe injurious behaviour in children with autism spectrum disorder (ASD): a 3-month randomized controlled feasibility study".</p> <p>The detailed clinical history of this child has been reported in Cravero C, Guinchat V, Xavier J, Meunier C, Diaz L, Mignot C, Doummar D, Chantot-Bastaraud S, Consoli A, Cohen D. Management of severe developmental regression in an autistic child with a 1q21.3 microdeletion and self-injurious blindness. Case Rep Psychiatry 2017; 2017: e7582780. https://doi.org/10.1155/2017/7582780</p>
IMPS: Interactive Musical Prediction System: Demo Video
<p>IMPS is a system for predicting musical control data in live performance. It uses a mixture density recurrent neural network (MDRNN) to observe control inputs over multiple time steps, predicting the next value of each step, and the time that expects the next value to occur. It provides an input and output interface over OSC and can work with musical interfaces with any number of real-valued inputs (we’ve tried from 1-8). Several interactive paradigms are supported for call-response improvisation, as well as independent operation, and “filtering” of the performer’s input. Whenever you use IMPS, your input data is logged to build up a training corpus and a script is provided to train new versions of your model.</p> <p>This video shows some demo musical interface designs with the IMPS controller.</p>
ACB Master's thesis demo video
<p>Video demonstrating the steps of the demo for the Master's thesis titled: "Enabling cross-resource query via indexing datasets and query component matching". Narrated by Alberto Cámara Ballesteros.</p>
AK_FRAEX - Azure Kinect Frame Extractor demo videos
<p>Video samples recorded in the field using the Azure Kinect DK. These videos are part of the <a href="https://pypi.org/project/ak-frame-extractor">AK-FRAEX</a> software to demonstrate the use of frame extraction tasks. Visit the project site:</p> <ul> <li>https://pypi.org/project/ak-frame-extractor</li> <li>https://github.com/GRAP-UdL-AT/ak_frame_extractor</li> </ul> <p>Explanations about the recording can be found at "AKFruitData: A dual software application for Azure Kinect cameras to acquire and extract informative data in yield tests performed in fruit orchard environments". <a href="https://doi.org/10.1016/j.softx.2022.101231">https://doi.org/10.1016/j.softx.2022.101231</a></p> <p>This version is updated with videos in MJPG mode (20210927_114012_k_r2_e_000_150_138.mkv, 20210927_192424_k_r2_e_000_150_138.mkv) and in BGRA32 mode (1080_06072022182211.mkv, 1080_07072022180929.mkv)</p> <p>** Thanks to Iva Xhimitiku (https://orcid.org/0000-0002-6205-3445) for the videos in BGRA32 mode. **</p> <p> </p>
Drinking Reminder - prototype of a smart jar. (Demo video)
<p>This is a demo Video accompanying the paper :</p> <p>Katharina Groß-Vogt. 2020. The Drinking Reminder - Prototype of a smart jar. In Proceedings of the 15th International Audio Mostly Conference (AM’20), September 15–17, 2020, Graz, Austria. ACM, New York, NY, USA, 4 pages. <a href="https://doi.org/10.1145/3411109.34111">https://doi.org/10.1145/3411109.34111</a></p>
Expergefactor - Sonic Interaction Design for an alarm clock app (Demo Video)
<p>Demo video accompanying the publication with DOI 10.1145/3411109.3411149.</p>
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
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.