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1,855 results for “Framework”
Prediction of Personality Traits using the Big 5 Framework
<p>The methodology is the core component of any research-related work. The methods used to gain the results are shown in the methodology. Here, the whole research implementation is done using python. There are different steps involved to get the entire research work done which is as follows:</p> <p><strong>1. Acquire Personality Dataset</strong></p> <p>The kaggle machine learning dataset is a collection of datasets, data generators which are used by machine learning community for analysis purpose. The personality prediction dataset is acquired from the kaggle website. This dataset was collected (2016-2018) through an interactive on-line personality test. The personality test was constructed from the IPIP. The personality prediction dataset can be downloaded in zip file format just by clicking on the link available. The personality prediction file consists of two subject CSV files (test.csv & train.csv). The test.csv file has 0 missing values, 7 attributes, and final label output. Also, the dataset has multivariate characteristics. Here, data-preprocessing is done for checking inconsistent behaviors or trends.</p> <p> </p> <p><strong>2. Data preprocessing</strong></p> <p>After, Data acquisition the next step is to clean and preprocess the data. The Dataset available has numerical type features. The target value is a five-level personality consisting of serious,lively,responsible,dependable & extraverted. The preprocessed dataset is further split into training and testing datasets. This is achieved by passing feature value, target value, test size to the train-test split method of the scikit-learn package. After splitting of data, the training data is sent to the following Logistic regression & SVM design is used for training the artificial neural networks then test data is used to predict the accuracy of the trained network model.</p> <p> </p> <p><strong>3. Feature Extraction</strong></p> <p>The following items were presented on one page and each was rated on a five point scale using radio buttons. The order on page was EXT1, AGR1, CSN1, EST1, OPN1, EXT2, etc. The scale was labeled 1=Disagree, 3=Neutral, 5=Agree</p> <p> </p> <pre><code> EXT1 I am the life of the party. EXT2 I don't talk a lot. EXT3 I feel comfortable around people. EXT4 I am quiet around strangers. EST1 I get stressed out easily. EST2 I get irritated easily. EST3 I worry about things. EST4 I change my mood a lot. AGR1 I have a soft heart. AGR2 I am interested in people. AGR3 I insult people. AGR4 I am not really interested in others. CSN1 I am always prepared. CSN2 I leave my belongings around. CSN3 I follow a schedule. CSN4 I make a mess of things. OPN1 I have a rich vocabulary. OPN2 I have difficulty understanding abstract ideas. OPN3 I do not have a good imagination. OPN4 I use difficult words.</code></pre> <p> </p> <p><strong>4. Training the Model</strong></p> <p>Train/Test is a method to measure the accuracy of your model. It is called Train/Test because you split the the data set into two sets: a training set and a testing set. 80% for training, and 20% for testing. You train the model using the training set.In this model we trained our dataset using linear_model.LogisticRegression() & svm.SVC() from sklearn Package</p> <p> </p> <p><strong>5. Personality Prediction Output</strong></p> <p>After the training of the designed neural network, the testing of Logistic Regression & SVM is performed using Cohen_kappa_score & Accuracy Score.</p>
Gamification of Joint Rehabilitation Utilizing Non-Invasive Sensors and Vision-Based Software: Framework Development and Validation
<p>This file contains the video supplementary materials for an MDPI Sensor paper publication.</p>
Open Research Data for "A New Framework for Evaluating Model Simulated Inland Tropical Cyclone Wind Fields"
<p>The (1) NOAA GFDL T-SHiELD outputs, (2) processed ASOS data, and (3) observation-based, theory-driven wind profiles data used in the manuscript "A New Framework for Evaluating Model Simulated Inland Tropical Cyclone Wind Fields". </p>
Qualitative Interview Study to Assess the Usefulness of the IaC-based Application Compliance Management Framework (IACMF)
<p>This dataset contains anonymized answers from nine industry experts interviewed to assess the usefulness of the IaC-based Application Compliance Management Framework (IACMF). The interviews were held online after the participants <a href="https://clipchamp.com/watch/5tUiwnTqyXW">watched a video tutorial of the framework</a>. The dataset also contains the questions that were asked during the interviews, a fundamentals document sent to the participants before the interviews, and the results of structuring and summarizing the answers.</p>
A federated learning framework based on transfer learning and knowledge distillation for targeted advertising-Ad Display/Click Data on Taobao.com dataset
<p>https://tianchi.aliyun.com/dataset/56</p>
Emotion-Aware Control Framework for HRC
<p>Video of submitted paper entitled "Emotion-Aware Control Framework for HRC"</p>
Nonresonant scattering of energetic electrons by electromagnetic ion cyclotron waves: spacecraft observations and theoretical framework
<p>This repository contains the simulation dataset, plotting scripts, and optimization package for the manuscript ``Nonresonant scattering of energetic electrons by electromagnetic ion cyclotron waves: spacecraft observations and theoretical framework''.</p> <p>*.h5 are snapshots of EMIC wave field (By and Bz) from deltaf-PIC simulations.</p> <p>test_particle.zip contains the test particle data in the wave field of deltaf-PIC simulations.</p> <p>*.py are plotting scripts to visualize the wave field, test particle simulation results, and theoretical predictions.</p> <p>optimization.zip contains the main subroutines for optimization of wave power spectrum to produce the measured electron precipitation.</p> <p>plt_style.txt is an auxillary style file for matplotlib plot.</p> <p> </p>
HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing
<p>Data set provided with the paper "HotProtein: A Novel Framework for Protein Thermostability Prediction and Editing". Please solely cite the <a href="https://openreview.net/forum?id=YDJRFWBMNby&noteId=vl18g7_lVT">paper</a> if you use the data.</p> <p>Data was downloaded from the provided <a href="https://github.com/VITA-Group/HotProtein">GitHub</a> links 2023/07/11:</p> <p>Datasets</p> <ul> <li>HP-S2C2</li> <li>HP-S2C5</li> <li>HP-S:</li> </ul>
Fig. 9 in Structurally diverse alkaloids with nine frameworks from Zephyranthes candida and their acetylcholinesterase inhibitory and anti-inflammatory activities
Fig. 9. ORTEP drawing of the X-ray structures of haemanthamine (18) and (+)-tazettine (24).
Fig. 1 in Structurally diverse alkaloids with nine frameworks from Zephyranthes candida and their acetylcholinesterase inhibitory and anti-inflammatory activities
Fig. 1. Chemical structures of the isolated alkaloids 1–26.
Fig. 5 in Structurally diverse alkaloids with nine frameworks from Zephyranthes candida and their acetylcholinesterase inhibitory and anti-inflammatory activities
Fig. 5. Proposed biosynthetic pathway of zephyranine A (1).
Fig. 2. 1H–1H in Structurally diverse alkaloids with nine frameworks from Zephyranthes candida and their acetylcholinesterase inhibitory and anti-inflammatory activities
Fig. 2. 1H–1H COSY, key HMBC, and key NOESY correlations of zephyranines A D (1–4).
Dataset for "Adding the SMT solver OpenSMT2 to the JavaSMT Framework and Evaluation using CPAchecker"
<p>Benchmark data and code examples for the thesis. See README.md files for the exact locations.</p>
Files for Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation
<p>This are the files needed for running the experiments of "Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation".</p> <ul> <li> <p>listening_events.tsv.bz2 : Dataset excerpt from <a href="http://www.cp.jku.at/datasets/LFM-2b/">LFM-2b</a>, before filtering (see submission for details)</p> </li> <li> <p>BPR_item_embeddings.tsv.bz2 : Item embeddings obtained from the pre-trained BPR instance</p> </li> <li> <p>user_split.tar.bz2 : csv file of the listening history of each user</p> </li> <li> <p>2023_recsys_actr_poster.pdf ; poster presented at RecSys 2023</p> </li> </ul> <p>The code for running the experiments is available on <a href="https://github.com/hcai-mms/actr">GitHub</a><br> <br> If you use these files, please cite<br> </p> <blockquote> <p>@inproceedings{10.1145/3604915.3608838,<br> author = {Moscati, Marta and Wallmann, Christian and Reiter-Haas, Markus and Kowald, Dominik and Lex, Elisabeth and Schedl, Markus},<br> title = {Integrating the ACT-R Framework with Collaborative Filtering for Explainable Sequential Music Recommendation},<br> year = {2023},<br> isbn = {9798400702419},<br> publisher = {Association for Computing Machinery},<br> address = {New York, NY, USA},<br> url = {https://doi.org/10.1145/3604915.3608838},<br> doi = {10.1145/3604915.3608838},<br> abstract = {Music listening sessions often consist of sequences including repeating tracks. Modeling such relistening behavior with models of human memory has been proven effective in predicting the next track of a session. However, these models intrinsically lack the capability of recommending novel tracks that the target user has not listened to in the past. Collaborative filtering strategies, on the contrary, provide novel recommendations by leveraging past collective behaviors but are often limited in their ability to provide explanations. To narrow this gap, we propose four hybrid algorithms that integrate collaborative filtering with the cognitive architecture ACT-R. We compare their performance in terms of accuracy, novelty, diversity, and popularity bias, to baselines of different types, including pure ACT-R, kNN-based, and neural-networks-based approaches. We show that the proposed algorithms are able to achieve the best performances in terms of novelty and diversity, and simultaneously achieve a higher accuracy of recommendation with respect to pure ACT-R models. Furthermore, we illustrate how the proposed models can provide explainable recommendations.},<br> booktitle = {Proceedings of the 17th ACM Conference on Recommender Systems},<br> pages = {840–847},<br> numpages = {8},<br> keywords = {Music Recommender Systems, Psychology-Informed Recommender Systems, Collaborative Filtering, Adaptive Control Thought-Rational (ACT-R), Sequential Recommendation, Explainability},<br> location = {Singapore, Singapore},<br> series = {RecSys '23}<br> }</p> </blockquote> <p>This research was funded in whole, or in part, by the Austrian Science Funds (FWF): P33526 and DFH-23, and by the State of Upper Austria and the Federal Ministry of Education, Science, and Research, through grant LIT-2020-9-SEE-113.</p>
GraffiTE: a Unified Framework to Analyze Transposable Element Insertion Polymorphisms using Genome-graphs
<p>This archive contains the larger datasets associated with the GraffiTE project. </p> <p>See: https://github.com/cgroza/GraffiTE</p>
An Imaging Framework for Clinically Testing New Treatments to Prevent Post-traumatic OA
ClinicalTrials.gov study NCT03809520. IPD Sharing: NO. Countries: 1. Publications: 0.
A Framework For Linking Sequential Pattern Rules in DLD: Perception in Toddlers
ClinicalTrials.gov study NCT04531514. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Integrating the Fundamentals of Care Framework Into Italian Nursing Education
ClinicalTrials.gov study NCT05177627. IPD Sharing: NO. Countries: 0. Publications: 9.
Comparison Between Injection Molded Polyetheretherketone (PEEK) Framework and Cobalt Chromium Framework Removable Partial Dentures Regarding Periodontal Health of Abutment Teeth
ClinicalTrials.gov study NCT03777345. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Unmute's CARE Framework: A Novel Approach to Strengthen the Therapeutic Alliance and Reduce Treatment Disparities
ClinicalTrials.gov study NCT06625502. IPD Sharing: YES. 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.