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31 results for “user model”
AI-TAM: a model to investigate user acceptance and collaborative intention in human-in-the-loop AI applications
<p>More and more frequently, digital applications make use of Artificial Intelligence (AI) capabilities<br> to provide advanced features; on the other hand, human-in-the-loop approaches are on the<br> rise to involve people in AI-powered pipelines for data collection, results validation and decision making.<br> Does the introduction of AI features affect user acceptance? Does the AI result quality<br> affect people’s willingness to use such applications? Does the additional user effort required in<br> human-in-the-loop mechanisms change the application adoption and use?<br> This study aims to provide a reference approach to answer those questions. We propose a model<br> that extends the Technology Acceptance Model (TAM) with further constructs explicitly related to<br> AI – user trust in AI and perceived quality of AI output, from explainable AI (XAI) literature – and<br> collaborative intention – willingness to contribute to AI pipelines.<br> We tested the proposed model with an application for car damage claim reporting with AI-powered<br> damage estimation for insurance customers. The results showed that the XAI related factors have<br> a strong and positive effect on behavioral intention, perceived usefulness, and ease of use of the<br> application. Moreover, there is a strong link between behavioral intention and collaborative intention,<br> indicating that indeed human-in-the-loop approaches can be successfully adopted in final user<br> applications.</p> <p>Users were invited to test the interactive prototype of the BumpOut application and to report the given car accident from start to finish. These are the two interactive prototypes experienced by users:</p> <ul> <li> <p><a href="https://bit.ly/bo-prototype-flawlessAI">FlawlessAI-Group prototype</a></p> </li> <li> <p><a href="https://bit.ly/bo-prototype-failingAI">FailingAI-Group prototype</a></p> </li> </ul> <p> </p> <p>This study is shared as a research object adopting the <a href="https://www.researchobject.org/ro-crate/1.0/">RO-Crate</a> specification.</p>
Distinguishing GUI Component States for Blind Users using Large Language Models
<p><strong># Data Code Repository</strong></p><p> </p><p>This repository contains open-source data code that provides utilities for the paper named "Here comes trouble! Distinguishing GUI Component States for Blind Users using Large Language Models". The code is designed to facilitate data-related tasks and promote reproducibility in research and data analysis projects.</p><p> </p><p><strong>## Features</strong></p><p> </p><p>- Attribute identification and extraction: Including real-time recognition and extraction of GUI components in the view type, resource-id, color, action of four attributes</p><p>- Components State Distinction: Provides the prompt needed for large language models, covering their specific design schemes and chain of thought reasoning processes as well as contextual learning content.</p><p>- Implementation: Offers specific methods to realize the process, including the setting of relevant parameters and the use of functions.</p><p> </p><p><strong>## Installation</strong></p><p> </p><p>To use the data code, you can down or clone the required code.</p><p>Notably, before using the code, make sure the necessary environment configuration is done.</p><p> </p><p><strong>## Dependencies</strong></p><p>The data code has the following dependencies:</p><p> </p><p>Python (version 3.6 or higher)</p><p>NumPy</p><p>Pandas</p><p>Seaborn</p><p>Scikit-learn</p><p>Openai</p><p>Android Studio (version 4.0)</p><p> </p><p>Install the required dependencies using pip:</p><p>pip install numpy..</p><p> </p><p><strong>##License</strong></p><p>This data code is distributed under the MIT License. See LICENSE for more information.</p><p> </p><p><strong>##Copyright</strong></p><p>All copyright of the tool is owned by the author of the paper.</p>
Code and Data for the Study "A User-Centric Model of Connectivity in Street Networks"
<p>This resource contains the code and results used in the paper:</p> <p>Corcoran, P. and R. Lewis (Pending) “A User-Centric Model of Connectivity in Street Networks”</p> <p>Please consult <strong>UserGuide.pdf</strong> for further information. </p>
Enhancing Smartphone Battery Life: A Deep Learning Model Based on User-Specific Application and Network Behaviour
<p>This work presents an analysis based on training AI models directly on devices to make personalized predictions tailored to individual usage patterns, ensuring that each user benefits from a personalized approach to battery management. By integrating these AI-based insights, mobile devices can proactively manage power consumption, improving battery performance and user satisfaction. This personalized, intelligent approach to battery management represents a significant advance in optimizing device efficiency and addresses the growing demand for longer-lasting mobile technology.</p>
Survey questionnaire and results on user needs for energy models for the European energy transition, related to Süsser et al. (2021)
<p>The online survey was designed and conducted in the framework of the EU H2020 project SENTINEL in collaboration with the project openENTRANCE. The aim of the survey was to identify needs by modellers and model result users across Europe for energy modelling. We developed it as an explorative, semi-quantitative, self-completion online questionnaire, using the online tool “LimeSurvey”. We performed the online survey among different stakeholders from academia, policy, NGO’s and energy industry. </p> <p>The study by Süsser <em>et al.</em> (2021) investigates the differences between energy model improvements and adjustments as perceived by modellers, and the actual needs of users of model results. If you use this questionnaire in an academic publication, please cite the corresponding article:</p> <p><em>Süsser, D., Gaschnig, H., Ceglarz, A., Stavrakas, V., Flamos, A. & Lilliestam, J. (under review). Better suited or just more complex? </em><em>On the fit between user needs and modeller-driven improvements of energy system models. Energy.</em></p>
US4USec: A User Story Model for Usable Security
<p>Excel sheet containing information used for the construction of the US4USec: A User Story Model for Usable Security </p>
Study Data: Obtaining Semi-Formal Models from Qualitative Data: From Interviews into BPMN Models in User-Centered Design Processes
<p>This dataset (Data.zip) contains the raw data of a user study on the investigation of transforming think aloud interviews into BPMN models. All information on how to use the data are provide in the SPSS files and as a readme file. This transformation is executed following a manual additionally provided in Documents.zip. For the training phase, a website was used provided in Website.zip including Screenshots for simpler re-use. Further information are also included as readme file in the zip container.</p> <p>Main research question answered is in how far the manual reduces interpretation and variance in the created models. </p>
Dataset: A Study on the Mental Models of Users Concerning Existing Software
<p>In 2022, we conducted a study on the mental models of users concerning existing software.</p> <p>Information on the execution of the study are presented in the paper linked below:</p> <p>https://doi.org/10.1007/978-3-030-98464-9_18</p>
SSP2017 - Experiment Data - Towards Extracting Realistic User Behavior Models
<p>This package contains the monitoring data, the ideal behavior models, computed clustering results for behavior models based on the monitoring data, and the interpretation of the analysis results. We processed these data with the following two tooling sources:</p> <p>Experiment setup: https://doi.org/10.5281/zenodo.883069</p> <p>Analysis software: https://doi.org/10.5281/zenodo.883061</p>
Replication Data for: CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model
<p>This dataset includes three files necessary for understanding CHEEREIO model output in the demo section of my initial submission to GMD for the paper: <em>CHEEREIO 1.0: a versatile and user-friendly ensemble-based chemical data assimilation and emissions inversion platform for the GEOS-Chem chemical transport model.</em> Detailed guides for how to handle these datasets are provided in the <a href="https://cheereio.readthedocs.io/en/latest/Postprocess-workflow.html">CHEEREIO documentation postprocessing page</a>.</p> <ul> <li>control_hemco_diagnostics.nc contains the source-separated prior methane emissions.</li> <li>combined_hemco_diagnostics.nc contains the source-separated and ensemble member separated posterior methane emissions.</li> <li>bigY.pkl contains a Python dictionary which aligns TROPOMI XCH4 with simulated prior and posterior GEOS-Chem XCH4.</li> </ul>
AccessFixer: Enhancing GUI Accessibility for Low Vision Users with R-GCN model
<p>Here is the relevant dataset and open-source code for the article titled "AccessFixer: Enhancing GUI Accessibility for Low Vision Users with RGCN-based Method"</p> <p><strong>Introduction</strong></p> <p>In this work, we designed and implemented a tool, named <strong>AccessFixer</strong>, capable of fixing GUI accessibility issues in terms of small size, narrow interval, and low color contrast. It can ensure the consistency of GUI visual effects in the repair process, and will not produce inconsistent components.</p> <p><strong>Functions</strong></p> <ol> <li><strong>Convert the GUIs screenshots to GUI-graphs. </strong>AccessFixer converts the GUIs into their corresponding GUI-graphs by parsing the XML file (in Graph_Presentation()), and meanwhile, our tool could remove all edges that connected the problematic nodes detected by Google Accessibility Scanner.</li> <li><strong>Pre-trained model based on Relational-Graph Convolutional Neural Network (R-GCN). </strong>Along with Capture_Signal(model), users can obtain the spectral signals of predicted links. Encoder and Decoder are responsible for generating new edges and scoring edges.</li> <li><strong>AccessFixer</strong> builds a mapping relationship between signal values and attribute values of GUI components in Attribute_Mapp(). Based on this mapping, it is possible to input a GUI to be repaired and generate repair strategies for its various components.</li> </ol> <p><strong>Environment</strong></p> <p>This tool can be run on an Android emulator based on Android 11.0 on a typical development machine, using Windows 11 with 2.4GHz core i7 CPU and 16 GB memory. The pre-trained RGCN model is run in PyCharm 4.5.4 with the packages of tensorflow, GraphConvolution, pandas, time, numpy, argparse, optimization, Parameter, and Module.</p> <p><strong>Installation</strong></p> <ol> <li>Using pip install package-name to install the required packages.</li> <li>Configure GCN model training in the same directory.</li> <li>Click the run button in PyCharm or use the command run-train.sh [configuration]</li> </ol> <p><strong>Frequently Asked Questions</strong></p> <ol> <li>Unable to install packages using pip</li> </ol> <p>Answer: It could be the version of the pip install command. Detailed solution could be found in https://stackoverflow.com/questions/17869101/unable-to-install-pygame-using-pip/74229901#74229901</p> <ol> <li>Some GUIs fail to build GUI-graphs</li> </ol> <p>Answer: To create GII-graphs, the user is required to provide the GUI screenshot and the layout file parsed by UIAutomator.</p> <p><strong>Contact Information</strong></p> <p>If you have any questions about this tool, you can contact the author of this work at <a href="mailto:zmxalakay@126.com">zmxalakay@126.com</a></p> <p><strong>Copyright</strong></p> <p>All copyright of the tool is owned by the author of the paper.</p> <p> </p>
Manually Annotated Event Log of Users Prompts in LLMs for Conceptual Modeling
<p>This repository contains the supplementary material for our paper at ER 2024 Conference. </p> <p>The data contains the results of an empirical study with 76 undergraduate information systems students. The students submitted the course assignments in 39 groups (of one or two students). The assignment used for the study required use case modeling with UML use case diagrams and domain modeling with UML class diagrams. The groups were first expected to interact with an LLM and then, if needed, to manually improve their models. Groups were randomly assigned to interact with either GPT 4.0 or Code Llama 34B Instruct in one of three application domains. </p> <p>The participants were instructed to engage with the LLM until they were satisfied with the results or opted to skip further refinement. The interaction log contains the following fields: User ID, Input (the user prompt), Response (the modeling artifacts), and the Prompt Number (within user ID).</p>
Intensive Models of HCV Care for Injection Drug Users
ClinicalTrials.gov study NCT01857245. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Comparing power-system- and user-oriented battery electric vehicle charging representation and its implications on energy system modeling
<p>This supplementary material includes data and code for the research described in the paper "Comparing power-system- and user-oriented battery electric vehicle charging representation and its implications on energy system modeling". The code containts an interface between the output files of the agent-based simulation model CURRENT and the energy system optimization model REMix as well as some scripts for analyzing REMix results. The data folder contains input data for REMix, the complete list of all model runs analyzed in the paper in the GAMS format .gdx as well as Excel files containing annual results of the sensitivity runs and respective pivot tables and figures for respective analysis.</p>
Automatic Classification of Non-functional Requirements in App User Reviews Based on System Model and Artificial Intelligence
<p>This is the replication package for the paper: "Automatic Classification of Non-functional Requirements in App User Reviews Based on System Model and Artificial Intelligence". It contains the dataset of our experiment for the replication by other researchers. In the meanwhile, we provide brief description of the files in the replication package in the following.</p> <p><strong>1. dataset folder</strong></p> <ul> <li>dataset_user_reviews.xlsx contains 1278 labelled non-requirement user reviews.</li> <li>readme.txt describes the meaning of the data in dataset_user_reviews.xlsx in detail.</li> </ul>
User Modeling in MDE - Data Sheet and Scripts
<p>This replication package contains both the filled out excel template and the used scripts to filter the results at an abstract/title level for ScienceDirect and SpringerLink. <br><br>For ScienceDirect: </p> <ol> <li>Perform the search on the ScienceDirect page and download the results.</li> <li>ScienceDirect proposes a .bib file download. Transform this .bib file into a .ris file using any available online tools for such a conversion.</li> <li>Execute the script 'sciencedirect_filter_script.py' that will ingest a ScienceDirect.ris file and produce a file called 'output_filtered_included.ris' file.</li> </ol> <p>For SpringerLink:</p> <ol> <li>Perform the search on SpringerLink and download the resulting .csv file. and name it SearchResults.csv.</li> <li>The downloaded file does not contain the abstracts, that is why the 'springerlink_abstract_scrape.py' script that will perform a scraping to fetch the abstracts. Execute 'python springerlink_abstract_scrape.py SearchResults.py output.csv'. This will start the scraping and save the results in output.csv. Note that you will need to manually change the name of the columns ['Item Title', 'Authors', 'Publication Year', 'Publication Title', 'URL'] to ['title', 'author', 'year', 'publication', 'link']</li> <li>Perform a manual check if some abstracts are missing. Simply text search the .csv file for 'ABSTRACT NOT FOUND ERROR' and manually enter the missing results.</li> <li>Now use the second script by executing 'python springerlink_filter_script.py' and it will generate a file called 'outputfinalkeyword.ris'</li> </ol> <p> </p> <p>The data extraction excel file contains all the relevant information concerning the data extraction. This includes the reviewed papers, a summary of the numbers, the RQs and metadata. </p>
Material of the paper "Deriving Domain Models from User Stories: Human vs. Machines"
<p>This folder represents the online appendix for the paper "Deriving Domain Models from User Stories: Human vs. Machines" It contains all relevant code, results, and data sets.</p>
Deep Neural Models for Medical Concept Normalization in User-Generated Texts
<p>PsyTar folds used for experiments in the paper "Deep Neural Models for Medical Concept Normalization in User-Generated Texts" to be published at ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Student Research Workshop. </p> <p>All other datasets used in the paper can be found in the following places:</p> <p>Cadec random: https://zenodo.org/record/55013#.XPE1MC1eN24<br> Cadec custom: https://yadi.sk/d/GZoWm1wBxzyW_w</p> <p>SMM4H dataset: in the paper "Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H) - 2017 shared task"<br> <br> Bibtex:</p> <p>@inproceedings{miftahutdinov2019,<br> title = "Deep Neural Models for Medical Concept Normalization in User-Generated Texts",<br> author = "Miftahutdinov, Zulfat and Tutubalina, Elena",<br> booktitle = "Proceedings of {ACL} 2019, Student Research Workshop",<br> month = jul,<br> year = "2019",<br> address = "Florence, Italy",<br> publisher = "Association for Computational Linguistics",<br> }</p>
D3.10 - Users document for methods and models of the long-term coastline evolution.
<p>The D3.10, related to Task 3.5 and entitled "User Document for Methods and Models of the Long-term Coastline Evolution," is a WP3 deliverable, specifically a report describing the methods and models used to study long-term morpho-dynamic processes in a climate-change scenario. </p>
A User Model to Reach Intelligent User Interfaces: Experimental material
<p>Material of the paper: A User Model to Reach Intelligent User Interfaces</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)
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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.