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102 results for “GUIs”
Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring (Open Pit Extraction, Valea Sesei and Roșia Poieni (Romania)).
<p>Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring in the Open Pit Extraction (mine located at Valea Sesei and Roșia Poieni (Romania)) (3D view mode).</p> <p>Accessing the GOLDENAI GUI, please refer to the following link (<strong>login required</strong>): <a href="https://next-gui.goldenai.opt-net.eu/ ">https://next-gui.goldenai.opt-net.eu/ </a></p>
Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring (Underground Extraction, Pyhäsalmi (Finland)).
<p>Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring in the Underground Extraction (mine located at Pyhäsalmi (Finland)) (2D view mode).</p> <p>Accessing the GOLDENAI GUI, please refer to the following link (<strong>login required</strong>): <a href="https://next-gui.goldenai.opt-net.eu/ ">https://next-gui.goldenai.opt-net.eu/ </a></p>
Figs 25–29. Artema atlanta Walckenaer, 1837, ZFMK Gui 82 and ZFMK Gui 111. 25–26, 28 in Daddy-long-leg giants: revision of the spider genus Artema Walckenaer, 1837 (Araneae, Pholcidae)
Figs 25–29. Artema atlanta Walckenaer, 1837, ZFMK Gui 82 and ZFMK Gui 111. 25–26, 28. Male ALS and PMS (arrows: one of the six small cylindrically-shaped spigots wider than others). 27. Male left bulb, prolateral-distal view. 29. Small teeth prolaterally on round end of process c. Scale lines: 25 = 0.06 mm; 26, 29 = 0.03 mm; 27 = 0.2 mm; 28 = 0.04 mm.
Replication Package of "Exploiting Vision-Language Models in GUI Reuse"
<p>This replication package is for the paper entitled "Exploiting Vision-Language Models in GUI Reuse". The authors remain anonymous for double-blind review purposes. The package contains six files. If the paper is accepted, then the authors will move the replication package to a public repository hosted by an institution.</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>
Dataset for Code Review Guidelines for GUI-based Testing Artifacts
<p>The Excel file contains meta-data about collected white and gray literature, applied inclusion/exclusion criteria, the code system, and a list of identified guidelines.</p>
The ReDraw Dataset: A Set of Android Screenshots, GUI Metadata, and Labeled Images of GUI Components
<p>This is the dataset used to train and evaluate the CNN and KNN machine learning techniques for the ReDraw paper, published in IEEE Transactions on Software Engineering in 2018.</p> <p>Link to ReDraw Paper: https://arxiv.org/abs/1802.02312 </p>
A simplified palaeoceanography archiving system (PARIS) and GUI for storage and visualisation of marine sediment core proxy data vs age and depth.
<p>Scientific discovery can be aided when data is shared following the principles of findability, accessibility, interoperability, reusability (FAIR) data (Wilkinson et al., 2016). Recent discussions in the palaeoclimate literature have focussed on defining the ideal database format for storing data and associated metadata. Here, we highlight an often overlooked primary process in widespread adoption of FAIR data, namely the systematic creation of machine readable data at source (i.e. at the field and laboratory level). We detail a file naming and structuring method that was used at LSCE to store data in text file format in a way that is machine-readable, and also human-friendly to persons of all levels of computer proficiency, thus encouraging the adoption of a machine-readable ethos at the very start of a project. Thanks to the relative simplicity of downcore palaeoclimate data, we demonstrate the power of this simple but powerful file format to function as a basic database in itself: we provide a Matlab-based GUI tool that allows users to search and visualise data by sediment core location, proxy type and species type. The adoption of similarily accessible, machine-readable file formats at other laboratories will promote data sharing within projects, while also allowing for the automation of submission of data to online database repositories with particular formatting and/or metadata requirements, thus reducing post-hoc workload.</p>
Figure 15. Haldanodon exspectatus, Gui Mam 132 in Postcranial anatomy of Haldanodon exspectatus (Mammalia, Docodonta) from the Late Jurassic (Kimmeridgian) of Portugal and its bearing for mammalian evolution
Figure 15. Haldanodon exspectatus, Gui Mam 132/74, phalanges. (A–F) and metapodials (G–H) in (1) dorsal and (2) side view (stereo-pairs). Distal articulation facet is facing to the top of page. A, Gui Mam 132/74–1, phalanx II. B, Gui Mam 132/ 74–2, phalanx II. C, Gui Mam 132/74–3, phalanx II. D, Gui Mam 132/74–4, phalanx II. E, Gui Mam 132/74–5, phalanx II. F, Gui Mam 132/74–6, phalanx I. G, Gui Mam 132/74–7, metapodial, probably from central position. H, Gui Mam 132/74– 8, metapodial, from medial or lateral position.
Figure 13. Haldanodon exspectatus, Gui Mam 30 in Postcranial anatomy of Haldanodon exspectatus (Mammalia, Docodonta) from the Late Jurassic (Kimmeridgian) of Portugal and its bearing for mammalian evolution
Figure 13. Haldanodon exspectatus, Gui Mam 30/79. Right tibia in: A, anterior; B, lateral; and C, posterior view. bord., border; cran., cranial; proxlat., proximolateral; tub., tuberosity.
Figure 12. Haldanodon exspectatus, Gui Mam 47 in Postcranial anatomy of Haldanodon exspectatus (Mammalia, Docodonta) from the Late Jurassic (Kimmeridgian) of Portugal and its bearing for mammalian evolution
Figure 12. Haldanodon exspectatus, Gui Mam 47/75. Right femur in: A, anterior (= dorsal); B, medial; C, posterior (= ventral); D, lateral; E, distal and F, proximal view. cond., condyle; lat., lateral; med., medial; troch., trochanter.
Figure 10. Haldanodon exspectatus, Gui Mam 3011 in Postcranial anatomy of Haldanodon exspectatus (Mammalia, Docodonta) from the Late Jurassic (Kimmeridgian) of Portugal and its bearing for mammalian evolution
Figure 10. Haldanodon exspectatus, Gui Mam 3011. Left ilium in: A, lateral and B, medial aspects (stereo-pairs).
Figure 7. Haldanodon exspectatus, Gui Mam 30 in Postcranial anatomy of Haldanodon exspectatus (Mammalia, Docodonta) from the Late Jurassic (Kimmeridgian) of Portugal and its bearing for mammalian evolution
Figure 7. Haldanodon exspectatus, Gui Mam 30/79. Right forelimb as originally preserved, with (from right to left) humerus in anterior view (distal end pointing upwards), radius in an anterior view, and ulna in a lateral aspect. entepic., entepicondyle; fac., facet.
Figure 5. Haldanodon exspectatus, Gui Mam 3008 in Postcranial anatomy of Haldanodon exspectatus (Mammalia, Docodonta) from the Late Jurassic (Kimmeridgian) of Portugal and its bearing for mammalian evolution
Figure 5. Haldanodon exspectatus, Gui Mam 3008. Right scapulocoracoid in: A, lateral; B, anterior and C, medial views (stereo-pairs). infrasp., infraspinous.
Figure 4. Haldanodon exspectatus, Gui Mam 3000 in Postcranial anatomy of Haldanodon exspectatus (Mammalia, Docodonta) from the Late Jurassic (Kimmeridgian) of Portugal and its bearing for mammalian evolution
Figure 4. Haldanodon exspectatus, Gui Mam 3000. Left scapulocoracoid in: A, lateral; B, caudal and C, medial views. ant. mar., anterior margin; 'infrasp. fo.', 'infraspinous fossa'.
Figure 2. Haldanodon exspectatus, Gui Mam 30 in Postcranial anatomy of Haldanodon exspectatus (Mammalia, Docodonta) from the Late Jurassic (Kimmeridgian) of Portugal and its bearing for mammalian evolution
Figure 2. Haldanodon exspectatus, Gui Mam 30/79. Left thoracal rib in a caudal aspect (posterior view).
Figure 3. Haldanodon exspectatus, Gui Mam 30 in Postcranial anatomy of Haldanodon exspectatus (Mammalia, Docodonta) from the Late Jurassic (Kimmeridgian) of Portugal and its bearing for mammalian evolution
Figure 3. Haldanodon exspectatus, Gui Mam 30/79. A and B, right scapulocoracoid in: A, lateral and B, medial views. C–E, left scapulocoracoid in: C, lateral; D, caudal and E, medial views.
Dataset for the workshop paper titled "Automating GUI-based Software Testing with GPT-3" published at AIST 2023
<p>The training dataset for the research paper "Automating GUI-based Software Testing with GPT-3" presented at the 3rd International Workshop on Artificial Intelligence in Software Testing (AIST 2023), which was a part of the 16th IEEE International Conference on Software Testing, Verification and Validation (ICST 2023). The dataset contains prompt completion pairs acquired through user interaction with the software and was used to fine-tune the GPT-3 model. The dataset is in the .jsonl format specified by OpenAI.</p>
Data and Code for the paper "GUI Testing of Android Applications: Investigating the Impact of the Number of Testers on Different Exploratory Testing Strategies"
<p>This package contains data and code to replicate the findings presented in our paper titled "<em>GUI Testing of Android Applications: Investigating the Impact of the Number of Testers on Different Exploratory Testing Strategies</em>".</p> <p><strong>Abstract</strong></p> <p>Graphical User Interface (GUI) testing plays a pivotal role in ensuring the quality and functionality of mobile apps. In this context, Exploratory Testing (ET), a distinctive methodology in which individual testers pursue a creative, and experience-based approach to test design, is often used as an alternative or in addition to traditional scripted testing. Managing the exploratory testing process is a challenging task, that can easily result either in wasteful spending or in inadequate software quality, due to the relative unpredictability of exploratory testing activities, which depend on the skills and abilities of individual testers. A number of works have investigated the<br> diversity of testers’ performance when using ET strategies, often in a crowdtesting setting. These works, however, investigated ET effectiveness in detecting bugs, and not in scenarios in which the goal is to generate a re-executable test suite, as well. Moreover, less work has been conducted on evaluating the impact of adopting different exploratory testing strategies. As a first step towards filling this gap in the literature, in this work we conduct an empirical evaluation involving four open-source Android apps and twenty masters students, that we believe can be representative of practitioners partaking in exploratory testing activities. The students were asked to generate test suites for the apps using a Capture and Replay tool and different exploratory testing strategies. We then compare the effectiveness, in terms of aggregate code coverage, that different-sized groups of students using different exploratory testing strategies may achieve. Results provide deeper insights into code coverage dynamics to project managers interested in using exploratory approaches to test simple Android apps, on which they can make more informed decisions.</p> <p> </p> <p><strong>Contents and Instructions</strong></p> <p>This package contains:</p> <ul> <li><strong>apps-under-test.zip</strong> A zip archive containing the source code of the four Android applications we considered in our study, namely MunchLife, TippyTipper, Trolly, and SimplyDo.</li> <li><strong>apps-under-test-instrumented.zip</strong> A zip archive containing the instrumented source code of the four Android applications we used to compute branch coverage.</li> <li><strong>students-test-suites.zip</strong> A zip archive containing the test suites developed by the students using Uninformed Exploratory Testing (referred to as "Black Box" in the subdirectories) and Informed Exploratory Testing (referred to as "White Box" in the subdirectories). This also includes coverage reports.</li> <li><strong>compute-coverage-unions.zip </strong>A zip archive containing Python scripts we developed to compute the aggregate LOC coverage of all possible subsets of students. The scripts have been tested on MS Windows. To compute the LOC coverage achieved by any possible subsets of testers using IET and UET strategies, run the <em>analysisAndReport.py</em> script. To compute the LOC coverage achieved by mixed crowds in which some testers use a U+IET approach and others use a UET approach, run the <em>analysisAndReport_UET_IET_combinations_emma.py</em> script.</li> <li><strong>branch-coverage-computation.zip </strong>A zip archive containing Python scripts we developed to compute the aggregate branch coverage of all considered subsets of students. The scripts have been tested on MS Windows. To compute the branch coverage achieved by any possible subsets of testers using UET and I+UET strategies, run the <em>branch_coverage_analysis.py</em> script. To compute the code coverage achieved by mixed crowds in which some testers use a U+IET approach and others use a UET approach, run the <em>mixed_branch_coverage_analysis.py</em> script.</li> <li><strong>data-analysis-scripts.zip</strong> A zip archive containing R scripts to merge and manipulate coverage data, to carry out statistical analysis and draw plots. All data concerning RQ1 and RQ2 is available as a ready-to-use R data frame in the <em>./data/all_coverage_data.rds</em> file. All data concerning RQ3 is available in the <em>./data/all_mixed_coverage_data.rds </em>file.</li> </ul>
Gui food vessel, 10th century BCE
Gui food vessel, 10th century BCE, now in the collection of the Minneapolis Institute of Art. From the description of the vessel on artsmia.org: "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." More information: https://collections.artsmia.org/art/830/gui-food-vessel-china Source: Objaverse 1.0 / Sketchfab
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.