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997 results for “awareness”
Data for "Improving semantic video retrieval models by training with a relevance-aware online mining strategy"
<p>This repository contains all the data available for the publication:</p> <p><a href="https://doi.org/10.1016/j.cviu.2024.104035">Alex Falcon, Giuseppe Serra, and Oswald Lanz. <em>Improving semantic video retrieval models by training with a relevance-aware online mining strategy</em>. <strong>Computer Vision and Image Understanding</strong>. 2024.</a></p> <p>Code is available at: <a href="https://github.com/aranciokov/ranp/">https://github.com/aranciokov/ranp/</a></p> <p>The data includes:</p> <ul> <li>pre-extracted features (ordered_feature_*.zip files)</li> <li>annotations, such as pre-extracted semantic graphs, glove checkpoints, class annotations, etc (annotations_*.zip files)</li> <li>train/val/test, when available, split information (public_split_*.zip) files</li> <li>pretrained models for HGR and EAO (details in the github repo)</li> </ul>
Technical Debt Awareness: Measuring the Success of Technical Debt Management Approaches
<p>The additional material for the paper "Technical Debt Awareness: Measuring the Success of Technical Debt Management Approaches" comprises the following documents:<br>(Sec_XX is refering to the paper's sections)<br>* Sec_II_Semi_SLR_papers.xlsx<br> All excluded and included papers of our smei-SLR whit resons for exclusion and extracted data for included papers.<br>* Sec_III_Literature review in Psychology.pdf<br> Types of Awareness found with their respective definitions<br>* Sec_III_Guideline_to_Use_TD-SAGAT.pdf<br> A short guideline on how the use the TD-SAGAT method.* Participant_and_Meetings.xslx<br> overview of all participants with role and years of experience <br> overview of all meetings, we used for analysis<br>* Sec_III_SAGAT_for_TD_from_Goals_to_Queries.pdf<br> Determination of survey questions by following the SAGAT approach of following the hierarchy of goals, subgoals, decisions, requirements, queries <br>* Sec_IV_Delphi_Surveys.pdf<br> List of all questions asked and explanations given during the Delphi panel study<br>* Sec_IV_Delphi_Ranking_of_metrics.xslx<br> resulting ranking from third Delphi survey <br>* Sec_V_Survey_and_Observation_Template.xlsx<br> Templates for: questions for surveys and TD-SAGAT survey plus empty observation protocol</p>
Knowledge Graph Neural Network with Spatial-Aware Capsule for Drug-Drug Interaction Prediction
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ARC³N: A Collaborative Uncertainty Catalog to Address the Awareness Problem of Model-Based Confidentiality Analysis - Data Set
<p>Data set of the Paper "ARC³N: A Collaborative Uncertainty Catalog to Address the Awareness Problem of Model-Based Confidentiality Analysis". For more information, please see the README.md. For even more information please visit https://abunai.dev</p>
ICSME 2024 Research Track: "What Happened to my Models?" History-Aware Co-Existence and Co-Evolution of Metamodels and Models
<p> </p> <h1>ICSME 2024 Research Track: “What Happened to my Models?” History-Aware Co-Existence and Co-Evolution of Metamodels and Models</h1> <p> </p> <p>This repository provides the dataset and results for the evaluation of the paper “What Happened to my Models?” of the ICSME 2024 Research track.<br>The dataset consists of the following files:</p> <ul> <li><strong>RQ1-Type-Refactors.zip </strong>contains the operations and refactoring performed on each of the given metamodels used by our approach in RQ1.</li> <li><strong>RQ1-Type-Results.zip</strong>: contains the group results of RQ1 as shown in our paper with additional metrics of other operations not highlighted in our paper due to space limitations.</li> <li><strong>RQ2-3-PlantUML-Models.zip</strong>: contains the 250 PlantUML models and the metamodel. This folder contains the co-evolved PlantUML models, the operations performed during the co-evolution, their metrics and the state of the models bore and after the co-evolution. </li> <li><strong>RQ2-3-PlantUML-Results.zip:</strong> contains the group results of RQ2 and RQ3 as shown in our paper with additional metrics of other operations not highlighted in our paper due to space limitations</li> <li><strong>RQ2-3-FHIR-Models.zip</strong>: contains the 1180 FHIR models and the metamodel. This folder contains the co-evolved FHIR models, the operations performed during the co-evolution, their metrics and the state of the models bore and after the co-evolution.</li> <li><strong>RQ2-3-Results.zip:</strong> contains the grouped results of RQ2 and RQ3 as presented in our paper.</li> <li><strong>Additionalnformation.pdf:</strong> contains additional information on how to read the files extracted by our approach, i.e., how to read the models and operations and how our executable refactoring catlaog works since we only focused on Property Refactorings in the paper.</li> <li><strong>Results.pdf</strong>: Contains an overview of the results (the results from our paper + additional results)</li> <li><strong>Tools.zip: </strong>Contains the tools used for the evalution. For an explaination how to use it read the <strong>Additionalnformation.pdf, </strong>see below<strong> </strong>or contact the authors</li> </ul> <p><strong>Running the tools:</strong></p> <p><em>Windows 10/11<br></em><em>JDK 20 or above</em></p> <p>The tools consist of two programs: </p> <ul> <li><strong>importer.jar<br></strong>This file is used to import a FHIR or PlantUML file into the server and co-evolve it.</li> <li><strong>server_FHIR_.jar<br></strong>The server stores the models and co-evolves them. The files provided already have the metamodels preloaded, that are used in RQ2 and RQ3, i.e., FHIR_STU3 contains the FHIR metamodel version DSTU2 and STU3 and our hybrid PlantUML, while FHIR_ synthetic contains the FHIR metamodel DSTU2 and our synthetically created one.</li> </ul> <p><strong>How to use the Tools</strong></p> <p>First, start the server by starting the jar. The server also has an experimental GUI mode that allows engineers to check the types and instances that were created. <em><strong>Note: </strong>This mode is currently under development and is still unstable. The mode <em>is accessible</em> by adding -gui as a parameter.</em></p> <p><em>java -jar server_FHIR_STU3.jar -gui</em></p> <p>Otherwise, just run the server normally:</p> <p><em>java -jar server_FHIR_STU3.jar</em></p> <p>After the server has booted up, it exports InstanceTypes and operations created for the FHIR and PlantUML metamodels. Next, you start the importer. The importer has two modes: the PlantUML mode, where it imports a PlantUML state machine and co-evolves it and the FHIR mode, where it imports an FHIR file and co-evolves it into either STU3 or our synthetic version (depending on which of the preloaded servers is running). Just run the tool by providing either FHIR or PlantUML, the imported file and the path where the output should be stored.</p> <p><strong><em>FHIR-Mode:</em></strong></p> <p><em>java -jar importer.jar FHIR C:\Users\Admin\Desktop\Aaron697_Brekke496_2fa15bc7-8866-461a-9000-f739e425860a.json C:\Users\Admin\Desktop\results</em></p> <p><strong>PlantUML Mode:</strong></p> <p><em>java -jar importer.jar PlantUML C:\Users\Admin\Desktop\branch.puml C:\Users\Admin\Desktop\results</em></p> <p><em><strong>Note: </strong></em><em>Please run both the tool and the server in a command line to receive additional information about the importing and co-evolution since the tool is otherwise without a user interface.</em></p> <p> </p>
The replication page for paper: Revisiting Supervised and Unsupervised Models for Effort-Aware Just-in-Time Defect Prediction
<p>The replication page for the paper submitted to EMSE: Revisiting Supervised and Unsupervised Models for Effort-Aware Just-in-Time Defect Prediction.</p> <p>There are four java files in the package named "model". Each file corresponds to one specific model (CBS and CBS+ are in the same file). Each file has a main function and you will get the experiment results when running the main function.</p> <p> </p>
Wikipedia Dataset for "Structure Tree-LSTM: Structure-aware Attentional Document Encoders"
<p>The Wikipedia dataset collected for the paper "Structure Tree-LSTM: Structure-aware Attentional Document Encoders".</p> <p>Paper link: <a href="https://arxiv.org/abs/1902.09713">https://arxiv.org/abs/1902.09713</a></p>
InCLosure Code for Enclosures of Real Functions: Supplementary Material for Chapter "Universal Intervals: Towards a Dependency-Aware Interval Algebra"
<p>InCLosure Code for Enclosures of Real Functions: Supplementary Material for Chapter "Universal Intervals: Towards a Dependency-Aware Interval Algebra", In S. Chakraverty, editor, Mathematical Methods in Interdisciplinary Sciences. John Wiley & Sons, Hoboken, New Jersey, March, 2020. ISBN: 9781119585503.</p>
Artifact of An LLM-based Readability Measurement for Unit Tests' Context-aware Inputs
<div> <p><strong>Artifact of An LLM-based Readability Measurement for Unit Tests' Context-aware Inputs</strong></p> <p> </p> <p>A README.md file can be found in the base folder after unzipping the artifact.</p> <p> </p> </div>
Benchmarking datasets used in the manuscript "HyLight: Strain aware assembly of low coverage metagenomes"
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Datasets and models for constrained retrosynthesis with multi-objective search and disconnection-aware Chemformer
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Improved cross-scale snow cover simulations by developing a scale-aware parameterization in the Noah-MP land surface model
<p>Noah-MP data used to support the analyses conducted by Abolafia-Rosenzweig et al.: <strong>Improved </strong><strong>cross-scale </strong><strong>snow cover simulations by developing a scale-aware parameterization in the Noah-MP land surface model</strong></p>
Dataset Questionnaire Green Marketing Mix, Green Awarness, and Green Purchase
<p>The following dataset is a dataset from a study that investigated green marketing mix, green awareness, and green purchase intention in the context of electric vehicle in Indonesia.</p>
Survey Appendix - User awareness about Meta's Data for Good
<p>This survey aims to explore user awareness regarding Meta's 'Data for Good' program in the context of the upcoming EU Data Act. The study seeks to understand how many users are informed about the sharing of their data for public purposes. </p>
Dataset The Importance of Interface Design and Security Awareness in Improving Service Quality and Its Impact on Digital Customer Loyalty of Smart Mobile Banking
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The relation between deviation from the balanced time perspective and quality of life among older people. The mediating role of death anxiety and the moderating role of awareness of aging
<p><em><span>Objectives</span></em><span>. In the current study, one of the objectives was to check if the deviation from a balanced time perspective (DBTP) is a predictor of the quality of life of the elderly. Another objective investigated whether awareness of aging is a moderator and death anxiety a mediator of the relationship between PTSD and quality of life. <em>Method.</em> 436 elderly people participated in this research (M = 68.27; SD= 6.56; 66.7% female, 33.3% male). They completed 4 instruments to evaluate the variables included in the study: World Health Organization Quality of Life, Zimbardo Time Perspective Inventory - Short version, Death Anxiety Scale and Awareness of Age-Related Change 10-Item Short Form (awareness of aging). <em>Results</em>. Statistical analyzes highlighted the fact that DBTP is not a predictor of quality of life, but it can positively predict death anxiety. Also, awareness of aging does not moderate the relationship between DBTP and quality of life. Finally, death anxiety negatively predicts quality of life, but also mediates the relationship between DBTP and quality of life. <em>Discussion</em>. This study brought to the fore some concepts less addressed in the case of the elderly population in Romania, which could lead to the identification of some benefits for improving the quality of life of the elderly. Starting from the conclusions obtained, promotion and intervention plans can be developed to improve mental and physical health services. The inclusion process of the elderly in as many social activities as possible can also be considered.</span></p> <p><strong><span> </span></strong></p>
Artifacts for paper "CITYWALK: Enhancing LLM-Based C++ Unit Test Generation via Project-Dependency Awareness and Language-Specific Knowledge" submitted to TOSEM
<p>The project includes the data and code used in the submitted TOSEM paper titled "CITYWALK: Enhancing LLM-Based C++ Unit Test Generation via Project-Dependency Awareness and Language-Specific Knowledge"</p>
Identification of local thresholds of TWL for triggering the European coastal flood awareness system, Deliverable 4.3 – Report on the identification of local thresholds of TWL for triggering coastal flooding - ECFAS project (GA 101004211). www.ecfas.eu
<p>The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.</p><p>The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the <strong>European Commission H2020 Framework Programme</strong> within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services. </p><p><i><strong>Description of the product</strong></i></p><p>The ECFAS Deliverable 4.3 - Report on the identification of local thresholds of TWL for triggering coastal flooding aims to describe the methodology developed to identify local thresholds that will trigger the coastal flood mapping activity. To this end, it was necessary to identify both a total water level triggering threshold, used as a local reference to trigger the system in case of forecasted TWL exceedence, and a duration threshold, used to set the storm duration. In order to compute both thresholds, an Extreme Value Analysis (EVA) and a Duration Analysis (DA) were performed on the ECFAS combined hindcast. As the local TWL thresholds (triggering and duration) were identified using the ECFAS combined hindcast, and the system will instead be operative with the input of CMEMS forecast, a methodology was developed to establish a correction to be applied before integrating the thresholds into the warning system. The document also describes some limitations and possible future improvements of the employed methodology.</p><p>The Deliverable 4.3 - Report on the identification of local thresholds of TWL for triggering coastal flooding is accompanied by an accessory data file. This file, named "ThresholdsFile.csv", contains the values of the triggering and duration thresholds for all the ECFAS combined hindcast of TWL points and their coordinates.</p><p>This <strong>ECFAS Thresholds Dataset</strong> is made available under the <strong>Open Database License</strong>: <a href="http://opendatacommons.org/licenses/odbl/1.0/">http://opendatacommons.org/licenses/odbl/1.0/</a>. Any rights in individual contents of the ECFAS Thresholds Dataset are licensed under the Database Contents License: <a href="http://opendatacommons.org/licenses/dbcl/1.0/">http://opendatacommons.org/licenses/dbcl/1.0/</a>.</p><p>This <strong>Report</strong> on the identification of thresholds is made available under the <strong>Creative Commons Attribution 4.0 International License</strong>.</p><p><i><strong>Disclaimer:</strong></i></p><p>ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.</p><p>This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211</p><p> </p>
Quality-aware Analysis and Optimisation of Virtual Network Functions
<p><strong>To watch it in Youtube:</strong></p> <p><a href="https://youtu.be/RGwIVCgANcU">https://youtu.be/RGwIVCgANcU</a></p> <p><strong>This is the live presentation of a conference paper. Please, access and cite the published version:</strong></p> <p><a href="https://doi.org/10.1145/3546932.3547007">https://doi.org/10.1145/3546932.3547007</a></p> <p>The softwarisation and virtualisation of network functionality is the last milestone in the networking industry. <em>Software-Defined Networks</em> (SDN) and <em>Network Function Virtualization</em> (NFV) offer the possibility of using software to manage computer and mobile networks and build novel <em>Virtual Network Functions</em> (VNFs) deployed in heterogeneous devices. To reason about the variability of network functions and especially about the quality of a software product defined as a set of VNFs instantiated as part of a service (i.e., Service Function Chaining), a variability model along with a quality model is required.</p> <p>However, this domain imposes certain challenges to quality-aware reasoning of service function chains, such as numerical features or configuration-level <em>Quality Attributes</em> (QAs) (e.g., energy consumption). Incorporating numerical reasoning with quality data into SPL analyses is challenging and tool support is rare. In this work, we present 3 groups of operations: model report, aggregate functions to dynamically convert QAs at the feature-level into the configuration-level, and quality-aware optimisation. Our objective is to test the most complete reasoning tools to exploit the extended variability with quality attributes needed for VNFs.</p>
Species-aware DNA language modeling - data
<p>Data accompanying the publication Species-aware DNA language modeling.</p> <p>For code, see: https://github.com/DennisGankin/species-aware-DNA-LM (for the latest version) or the code.zip file.</p> <p>The data directory contains model checkpoints, baselines models, evaluation results and datasets used for training, testing and downstream tasks. It has the following structure:</p> <p>data/ Datasets and subdirectories</p> <p> - data/results/ Results from different test runs</p> <p> - data/models/ Model checkpoints</p> <p> - data/baselines/ Baseline models</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.