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102 results for “GUIs”
Gui Food Vessel, 11th Century BCE
Gui Food Vessel, 11th Century BCE, now in the collection of the Minneapolis Institue of Art. Description from artsmia.org: *'This gui demonstrates the inventiveness with which Western Zhou artisans adapted Shang forms. The main body is a standard gui with a deep bowl, while the looped handles on either side of the body are innovatively rendered in the form of an elephant's head and trunk. The bow displays the wings of a bird in relief, and the projection at the bottom contains the bird's curled tail and its feet descending almost to rest on the tail. The elephant head has large C-shaped ears, raised trunk, and protruding tusks.'* For more information, visit https://collections.artsmia.org/art/1141/gui-food-vessel-china Source: Objaverse 1.0 / Sketchfab
POPC/Cholesterol (50:50) lipid membrane, 303K, Charmm36 force field from charmm-gui, simulation files and 200 ns trajectory for Gromacs MD simulation engine v5.1.2
<p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with Gromacs 5.1.2 software package and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Conditions: T=303, 80 POPC and 80 Cholesterol molecules, 7200 tip3p waters, 200ns trajectory (preceded with equilibration)</p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</p>
POPC/Cholesterol (50:50) lipid membrane, 303K, Charmm36 force field from charmm-gui, simulation files and 100 ns trajectory for openMM simulation engine v7
<p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with openMM simulation engine v7 and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Conditions: T=303, 80 POPC and 80 Cholesterol molecules, 7200 tip3p waters, 100ns trajectory (preceded with equilibration)</p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</p>
GUI evaluation data for an IDE command recommender system
<p>This dataset contains results of the study conducted among the participants of the XP 2016 (a scientific conference with a strong participation of practitioners from the industry). The objective of the study was to evaluate the acceptance and usability of the proposed Graphical User Interface (GUI) for an Integrated Development Environment (IDE) command recommender system (RS). The data was collected by the questionnaire and the interviews. The data is anonymized.</p> <p>Content:</p> <ul> <li>README.txt</li> <li>./Survey answers.csv - the questionnaire answers</li> <li>./Interviews <ul> <li>./interviewXXX.txt - a file with a transcribed interview</li> <li>./mapping-codes-to-primary-documents.csv - a binary table summarizing interviews</li> </ul> </li> </ul>
Gui (Chinese food vessel)
12th-11th Century BCE Gui (food vessel) in the collection of the Minneapolis Institute of Art. From the object's description on artsmia.org: "Stylized animal motifs predominate Shang bronze decor. The dragon, snake, cicada, ram, owl, tiger and even elephant can all be found. These creatures were almost always subordinate to the taotie mask which typically dominates the largest or most important register of nearly all Shang bronzes. This exquisite food bowl has three such masks clearly cast in high relief encircling its cauldron. Below each mask, in the foot band, are two confronting dragons shown in profile, while four similar dragons appear above each mask in the top decorative band..." More information: http://collections.artsmia.org/art/833/food-vessel-yu-china Made with a few hundred 20MP photos, built in PhotoScan. Contact permissions@artsmia.org for a higher-res file. Here's the STL of the object on Thingiverse: http://www.thingiverse.com/thing:1561311 Source: Objaverse 1.0 / Sketchfab
Gui Food Vessel, 10th Century BCE
Bronze Gui Food Vessel, 10th Century BCE, now in Mia's collection. From the gui's description on artsmia.org: *Toward the mid–Western Zhou, two of the most noticeable changes that occurred in bronze casting involved the abandonment of the taotie (composite animal) mask motif and the adoption of long inscriptions. This gui displays elaborate birds, a popular motif, on its main decorative register. The birds' flamboyant design, with their crests and peacock-like plumage, is unique. The lengthy inscription, neatly cast into the cauldron's bottom, reads: 'Zhou King went out to attack Laiyu, then Naohei. Upon his return after victory, he held a liao sacrifice at the capital Zongzhou . He presented to me, X, Duke of Yong, ten strings of cowries. In response, to extol the king's grace, I have made this precious gui, dedicating it to ancestors. May sons and grandsons forever treasure and use it.'* More information on Mia's website: https://collections.artsmia.org/art/830/gui-food-vessel-china Source: Objaverse 1.0 / Sketchfab
The codes and datasets for the paper titled "Don't Confuse! Redrawing GUI Navigation Flow in Mobile Apps for Visually Impaired Users"
<h3>Project Title:</h3> <p>Redrawing GUI Navigation Flow in Mobile Apps for Visually Impaired Users</p> <h3>Description:</h3> <p>This project enhances GUI navigation accessibility for visually impaired users by analyzing GUI structures, identifying issues, and optimizing navigation flow.</p> <h3>Contents:</h3> <ol> <li><strong>Risk Warnings</strong></li> <li><strong>Variable Explanations</strong></li> <li><strong>Function Descriptions</strong></li> <li><strong>Usage Instructions</strong></li> <li><strong>Contact Information</strong></li> </ol> <h3>1. Risk Warnings:</h3> <ul> <li>Navigation analysis focuses on visible nodes only.</li> <li>Code maintenance issue in loop C.</li> <li>Prior reading of the "Info" button warning is essential.</li> <li>Potential information loss in the reordering algorithm.</li> </ul> <h3>2. Variable Explanations:</h3> <ul> <li>Constants: parameter1, parameter2, outputSign.</li> <li>Global Variables: nodeString, nodeList, intToRect, intToDir, intToInfo, infoToInt, intToSubRect, intToSubKind.</li> <li>Local Variables: sortedSon, sign, acceptable.</li> </ul> <h3>3. Function Descriptions:</h3> <ul> <li><strong>isNodeVisibleOnScreen</strong>: Checks if a node is visible on the screen.</li> <li><strong>Gestalt</strong>: Conducts a depth-first search traversal of all nodes and records the Gestalt_inspired order.</li> <li><strong>checkNodeNecessity</strong>: Further checks if a node is necessary for navigation.</li> <li><strong>DFS</strong>: Used for the reordering algorithm.</li> </ul> <h3>4. Usage Instructions:</h3> <ul> <li>Ensure thorough understanding of risk warnings.</li> <li>Modify and maintain the code as necessary.</li> <li>Read the warning prompt before using the "Info" button.</li> <li>Exercise caution with potential information loss in reordering.</li> </ul> <h3>5. Contact Information:</h3> <ul> <li>Developer: Mengxi Zhang</li> <li>Email: <a target="_new">zmxalakay@126.com</a></li> </ul> <p><strong>Note</strong>: This README provides a brief overview. Refer to the User_Guidelines documentation for detailed information.</p>
Bronze gui food vessel, 11th-10th C BCE
Bronze gui food vessel, 11th-10th C BCE, now in the collection of the Minneapolis Institute of Art. From the object's description on artsmia.org: *"...The most interesting feature of the vessel is the decor on the bottom surface. It is a coiled dragon, in threadlike relief, with a rolled-up nose and a body adorned with a row of big scales."* More information about the object here: https://collections.artsmia.org/art/1125/gui-food-vessel-china Source: Objaverse 1.0 / Sketchfab
The code and data for the paper entitled 'Facilitating Efficient Discovery: A GUI-Oriented Approach for Exploring Functionality using Machine Learning Model'
<h2><strong>Code</strong></h2> <p><strong><span>dataCollection.py</span></strong></p> <p><span>The collection of app data is primarily accomplished by processing and storing XML files and screenshots of the app. Firstly, the uiautomator is utilized to connect to the smart device and obtain screenshots and XML files. Subsequently, the XML files are analyzed to identify clickable functions within the GUI, and the textual information contained within the functions, along with their specific locations, is extracted. Finally, the Python file also includes handling of interface elements, such as determining if elements are obscured and verifying the legitimacy of the text.</span></p> <p><strong><span>tagData.py</span></strong></p> <p><span>The main implementation involves volunteers annotating app functionalities, including HTML generation, user data analysis, and retrieval. Flask framework is employed, presenting one GUI to the user each time while randomly prompting them to click on three functionalities. Ultimately, the time taken by users to locate these three functionalities is collected.</span></p> <p><strong><span>userPersonalization.py</span></strong></p> <p><span>Separating out the data annotated by each user facilitates personalized analysis. This process involves extraction, storage, and loading of individual user annotations.</span></p> <p><strong><span>dataPreprocessing.py</span></strong></p> <p><span>For a user-annotated functionality, completing the conversion from user time to either "hard-to-find" or "easy-to-find" involves several steps. First, the functionalities are vectorized, extracting relevant parameters from the XML files and computing their correlation with the time users spent searching for the functionalities. These parameters are then normalized to obtain feature vectors for the functionalities. Additionally, an initial determination is made regarding whether the annotated functionalities are "hard-to-find" or "easy-to-find" for each user. Subsequently, clustering is performed on all annotated data from users, and based on the clustering results, the outcomes are filtered and adjusted.</span></p> <p><strong><span>difficultFindClassifier.py</span></strong></p> <p><span>Train the classifier and use it to predict "hard-to-find" functionalities, then display the results.</span></p> <h2><span>Data</span></h2> <p><span>The data is located in the "static" folder:</span></p> <p><span>- The "persistentData" folder contains the trained classifier.</span></p> <p><span>- The "picture" folder contains screenshots of the app.</span></p> <p><span>- The "requestTime" folder stores data for when volunteers annotate only one function in a GUI.</span></p> <p><span>- The "threeResponseTime" folder saves data for when volunteers annotate three functions in a GUI.</span></p> <p><span>- The "userData" folder stores personalized user data.</span></p> <p><span><span>- The "xml_information" folder stores XML files of the app.</span></span></p>
Delta GUI change detection using inferred models replication package
<p><strong>TESTAR </strong>is a scriptless automated open-source tool developed by the Universitat Politècnica de València and the Open University of the Netherlands.</p> <p><strong>TESTAR Change detection .NET</strong> is an open-source tool that simultaneously transits and compares state models inferred by a scriptless testing tool, enabling the detection and highlighting of GUI changes to detect the widgets or functionalities that have been added, removed, or modified. This tool is also developed by the Universitat Politècnica de València and the Open University of the Netherlands.</p> <p>This replication package contains:</p> <ul> <li>The Excel file that was used to perform and store the systematic mapping of the literature.</li> <li>The TESTAR Change detection .NET version that was used to compare the state models inferred from the OBS, Calibre, and MyExpenses applications. </li> <li>The state models that were inferred from the OBS, Calibre, and MyExpenses applications. These are stored in the OrientDB graph database. </li> <li>Three documents (OBS, Calibre, MyExpenses) that detail with images the GUI changes results detected using the TESTAR Change detection .NET tool.</li> <li>A video demo that shows how to use the TESTAR Change detection .NET tool with the inferred models from the Calibre web system.</li> </ul>
PopSweeper: Automatically Detecting and Resolving App-Blocking Pop-Ups to Assist Automated Mobile GUI Testing
<p>Collected data for paper: PopSweeper: Automatically Detecting and Resolving App-Blocking Pop-Ups to Assist Automated Mobile GUI Testing</p>
Um estudo sobre as práticas de testes funcionais de GUI na indústria brasileira de software
<p>Teste de software é uma importante atividade no ciclo de desenvolvimento de software, a qual busca garantir a qualidade dos software criados. Quando um software não supre as necessidades implícitas e explicitas de seus usuários, pode-se dizer que ele carece de qualidade. A falta de qualidade de um software pode gerar vários problemas para o projeto de desenvolvimento de software, como atritos entre fornecedor e clientes/usuários, perdas financeiras, problemas legais por descumprimento de contratos e até risco à vida dos seus usuários em contextos críticos.</p> <p>Nesse sentido, distintos níveis de testes foram desenvolvidos para garantir a qualidade em diferentes aspectos do software. Uma abordagem popular é o teste funcional de interface, que avalia o software através da execução de eventos na tela do software, como o preenchimento de um campo de texto ou o clique de um botão. Os testes funcionais de interface ou GUI (\textit{Graphical User Interface}), podem ser executados tanto de forma manual, com a interação direta do testador no software, quanto automatizada, com a execução do software através de um \textit{script} programado.</p> <p>Nos últimos anos, é possível encontrar na literatura diversas soluções e técnicas propostas para lidar com os testes de GUI. No entanto, as pesquisas não se aprofundam em analisar como os profissionais da indústria vem executando esse tipo de teste, deixando assim uma lacuna na área. Por isso, este estudo propõe investigar como os testadores estão realizando testes de interface em seus projetos. Assim, primeiramente foi realizado um questionário online com 222 profissionais de teste com distintas experiências, cargos e funções. O resultado indicou que ainda há muitos profissionais que realizam testes exclusivamente de forma manual. Além disso, a maioria dos profissionais concordam que as ferramentas utilizadas na automação de testes atendem as necessidades de negócio, embora tenham limitações técnicas. </p> <p>Em seguida, realizamos uma entrevista semiestruturada com 20 profissionais de testes para complementar os resultados obtidos com o questionário. Os resultados indicaram que testadores que realizam apenas testes de GUI manuais não criam testes de GUI automatizados devido ao contexto do projeto em que trabalham, suas preferências profissionais e também por questões educacionais, como dificuldade com programação. Além disso, foram observadas distintas limitações para testes de GUI manuais e automatizados que impactam negativamente as atividades desempenhadas pelos profissionais, como dificuldades relacionadas à cultura da empresa e problemas técnicos relacionados ao software sob teste. Os resultados combinados fornecem uma visão geral dos testes de GUI na indústria de software brasileira.</p>
Supporting Data Set for Paper "Using GUI Test Videos to Obtain Stakeholders' Feedback"
<p>This data set is a supporting material for an accepted paper "Using GUI Test Videos to Obtain Stakeholders’ Feedback" on <a href="https://conf.researchr.org/track/icssp-2023/">ICSSP 2023</a>.</p> <p>This dataset consists of</p> <ul> <li>Questionnaires of control and experimental groups <ul> <li> <p>Questionnaire-Control Group (German).pdf -> Original quetionnaire for control group (in German)</p> </li> <li> <p>Questionnaire-Control Group (translated)-v03.pdf -> Translated quetionnaire for control group (in English)</p> </li> <li> <p>Questionnaire-Experimental Group (German).pdf -> Original quetionnaire for experimental group (in German)</p> </li> <li> <p>Questionnaire-Experimental Group (translated)-v03.pdf -> Translated quetionnaire for experimental group (in English)</p> </li> </ul> </li> <li>Survey-Data-v25.ods -> Collected data through questionnaires</li> <li>Calculate-Mann-Whitney-U-Test-v07.ods -> Detailed calculation of Mann Whitney U Test</li> <li>The videos of the ten scenarios in this study are also available on OneDrive <a href="https://1drv.ms/f/s!AtqkJ5cB802BoABI7w_Bft9H8Psi?e=upToep">https://1drv.ms/f/s!AtqkJ5cB802BoABI7w_Bft9H8Psi?e=upToep</a> , where you can play them directly in browsers.</li> </ul>
FLUTE: a Python GUI for interactive phasor analysis of FLIM data
<p>This repository contains the Fluorescence lifetime imaging microscopy (FLIM) data relative to the following publication <em>"FLUTE: a Python GUI for interactive phasor analysis of FLIM data" </em>https://www.biorxiv.org/content/10.1101/2023.03.31.534529v1</p> <p><em><strong>Fluorescein.tif</strong> </em>stack contains the fluorescence intensity decay of fluorescein solution with a known lifetime of 4ns, used as calibration.</p> <p><strong><em>Embryo.tif</em></strong> file contains the fluorescence intensity decay of a zebrafish embryo at 3 days post fertilisation.</p> <p>Both files have been acquired with the following parameters:</p> <ul> <li>temporal bin number = 56</li> <li>laser repetition rates = 80 MHz</li> <li>bin width = 0.223ns</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>
A Comprehensive Evaluation of Q-Learning Based Automatic Web GUI Testing
<p>This repository holds experimental data and figures of our paper "A Comprehensive Evaluation of Q-Learning Based Automatic Web GUI Testing" in 10th International Conference on Dependable Systems and Their Applications (DSA) 2023.</p>
Supporting Data Set for Paper "Can Videos as a By-Product of GUI Testing Help Developers Understand GUI Tests?"
<p>This data set is a supporting material for an accepted paper "Can Videos as a By-Product of GUI Testing Help Developers Understand GUI Tests?" on 2023 IEEE 31st International Requirements Engineering Conference Workshops (REW 2023).</p> <p>This dataset consists of</p> <ul> <li>a consent form of the study in English;</li> <li>a tutorial video for TakeNote App (see <em>TakeNote Tutorial-v02</em>);</li> <li>a questionnaire in HTML format;</li> <li>used videos (in <em>HTML Video Player with Videos and VTT files</em>) and screenshots;</li> <li>the source code of the HTML Video Player (in <em>HTML Video Player with Videos and VTT files</em>);</li> <li>obtained and coded results from the questionnaire (see <em>Study-Data-4EmpiRE-v22</em>);</li> <li>calculation steps of the Mann-Whitney U Test;</li> <li>the source code of the TakeNote App.</li> </ul>
Bronze gui food vessel, 11th C BCE
Chinese bronze gui food vessel, 11th C BCE, now in the collection of the Minneapolis Institute of Art. From artsmia.org: *This vessel is among the earliest gui with looped handles, which emerged in the latest period of the Shang dynasty. The surfaces of the vessel are divided into four vertical panels by four flanges on the foot and two flanges and two handles on the body.* More information about the object: https://collections.artsmia.org/art/1068/gui-food-vessel-china Source: Objaverse 1.0 / Sketchfab
A network pharmacology approach to predict potential targets and mechanisms of Gui Zhi-Shao Yao herb pair in treating chronic pain with comorbid anxiety and depression
Open the record for dataset details and reuse information.
"An Empirical Study of i18n Collateral Changes and Bugs in GUIs of Android apps" Complementary Material
<p>Study results for the paper: "An Empirical Study of i18n Collateral Changes and Bugs in GUIs of Android apps"</p>
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OpenNeuro
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