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12,393 results for “materiality”
Supplementary Material for Documentation artifacts for conversation-related requirements specification in chatbots: a systematic review and a meta-model proposal
<p>This is a supplementary data of the tertiary systematic literature review conducted in the paper "Conversation-related requirements specification in chatbots: a systematic review and a meta-model proposal".</p> <p>Context: Chatbots are complex applications due to their capacity to engage and maintain a conversation with humans. However, the conversational-related requirements of chatbots are hard to elicit, document, and test. Another challenge is the documentation since there are not so many directions on how to register and test subjective requirements.</p> <p>Methods: We followed systematic literature review (SLR) guidelines and identified 42 relevant papers that address the artifacts used by practitioners to document conversational-related requirements in literature. We also investigated what conversational requirements are addressed in requirements documentation.</p> <p>Results: The main results indicate that UML diagrams, prototypes, tables of requirements, conversational flows, and scenarios are present in most chatbot documentation. Except for UML diagrams, those artifacts are used to document standard requirements or conversational requirements. In those artifacts, context-dependent behavior, assertivity, error handling, and human-like attitude are the most approached conversational requirements in the studies. In sequence, based on our findings, we propose the conversational integrated map, a meta-model solution as documentation of conversational requirements.</p>
Supporting material for: MoonIndex, an Open-Source Tool to Generate Spectral Indexes for the Moon from M3 Data
<p>Supplementary material for the paper called: MoonIndex, an Open-Source Tool to Generate Spectral Indexes for the Moon from M3 Data. The data without "indexes" in the name are map-projected M3 cubes, they can be used in the python library <i><strong>MoonIndex </strong></i>to obtain the spectral indexes stored in the files with "indexes" in the name.</p><p>This research was done on the framework of the EXPLORE project, that has received funding from the European Union's 2020 research and innovation program under grant agreement No 101004214. </p>
Supplemental Material: Research Artifacts for Human-Oriented Experiments in Software Engineering: An ACM Badges–driven Structure Proposal
<p>This Research Artifact contains supplemental material from the study: "Supplemental Material: Research Artifacts for Human-Oriented Experiments in Software Engineering: An ACM Badges–driven Structure Proposal". The supplementary material contains:</p> <ol> <li>The list of the 106 primary studies classified by journals.</li> <li>The list of the 12 research artifacts classified by conferences.</li> <li>The .xlsx file of the dataset used to analyze the RQs.</li> <li>The .xlsx file of the dataset used to analyze the research artifacts problems (Table 3). </li> <li>The list of the figures published in the scientific article.</li> </ol>
Supplementary Materials for Learning Manufacturing Computer Vision Systems Using Tiny YOLOv4
<h1>About This Dataset</h1> <p>This repository contains the supplementary materials presented in the publication “Learning Manufacturing Computer Vision Systems Using Tiny YOLO v4” by Medina, A., Bradley, R., Xu, W., Ponce, P., Anthony, B., and Molina, A. that can be found with the following DOI <a href="https://www.frontiersin.org/articles/10.3389/frobt.2024.1331249/">10.3389/frobt.2024.1331249</a></p> <p>There are three files in this repository:</p> <ol> <li>dataset.zip</li> <li>YOLOv4_object_detection.ipynb</li> <li>deploy.py</li> </ol> <h1>dataset.zip</h1> <p>This Dataset is for an example used for education purposes. It is a small dataset that is adapted from the following Kaggle repository, authored by Ruthger Righart <a href="https://www.kaggle.com/datasets/rrighart/jarlids/data">https://www.kaggle.com/datasets/rrighart/jarlids/data</a>. One of the activities proposed is to teach students how to find, download and review a free dataset, so this is the example given.</p> <p>Another activity is to teach how to label images to create a custom dataset. The images (with extension .JPG) from the original repository are used. The labels (with extension .txt) were created by the authors of the Learning Manufacturing Computer Vision Systems Using Tiny YOLOv4 paper. The authors used the free tool labelImg, from GitHub repository (<a href="https://github.com/HumanSignal/labelImg">https://github.com/HumanSignal/labelImg</a>), to label the images with object bounding boxes and corresponding labels in the YOLO format.</p> <p>The dataset contains 238 images and corresponding labels, with files named “p<num>.JPG” and “p<num>.txt”. The text labels are formatted in the YOLO format with each row in the .txt file corresponding to one object in the image. Each row contains 5 elements: The object identifier, top left corner x coordinate, top left corner y coordinate, height, and width, separated by a whitespace. The object identifier represents good cans as 0 and defective cans as 1.</p> <h1>YOLOv4_object_detection.ipynb</h1> <p>This notebook was created to give the user a step-by-step tutorial on how to train a YOLOv4 algorithm with a custom dataset using a free GPU on Google Collab, the prerequisite to use it are:</p> <ul> <li>To have ready the dataset.</li> <li>Have the training txt file with the path to all images used for training.</li> <li>Have the test txt file with the path to all images used for testing.</li> </ul> <p>There are other requirements like cloning a GitHub repository and altering certain files on that repository; however, those steps are discussed within the notebook.</p> <p>At the end of the notebook an example on how to test the trained model with images and/or videos is shown, however since Google Collab doesn’t have access to the physical computer of the user live stream video is not part of the example.</p> <h1>deploy.py</h1> <p><em>Disclaimer: This code is not optimized, and its intended purpose is to teach students how to run YOLO on a raspberry pi using the OpenCV library.</em></p> <p>To use this code with different files or datasets, be sure to change the two parameters inside the net3 variable which are the cfg file used while training the algorithm and the weights file. You should also change the class list to include your classes, keeping in mind that the classes order must correspond to the order of the labeling process and class 0 is the first one on the list.</p> <p>Also to change the Title of the created image prompt you shout go to the line calling the imshow method and change the ‘Tiny YOLOv4’ string.</p> <p>This algorithm uses the first camera it finds and opens up a display image with the detected objects surrounded by a bounding box, on top of that box the top predicted class is going to show, to change color of bounding boxes or text change the rectangle method where it says GREEN as well as in the next code line ant change the number to change the thickness of the line.</p> <p>This code has a hardcoded confidence threshold for both the YOLO objectevness score and the class score, this can be found in the NMSBoxes method and the if confidence line accordingly. The main value to change first is the if confidence value.</p> <p>To close the image, you need to press the key ‘q’ as closing the display window is not going to work as it will reopen again.</p> <p>Note: This code allows the pop-up window, which displays the detections, to be closed only when the "q" key is pressed. Simply closing the window will not work.</p>
Рис. 5. Морские двустворчатые моллюски иЗ раскопов памЯтника Константиновка-1: A–D, G–K – Anadara talmiensis Kalishevich, 1976 (A, B – раскоп 3, постройка № 40, пласт 6, квадрат Ж.3-10, длина раковины 49.3 мм; C, D – раскоп 3, постройка № 40, квадрат Ж.3-10, длина фрагмента 40.8 мм; G, H – раскоп 3, постройка № 40, квадрат Ж.3-10, длина фрагмента 41.6 мм; I–K – раскоп 1, пласт 1, квадрат Б2, длина фрагмента 35.8 мм; E, F – Crenomytilus grayanus (Dunker, 1853), подъемный материал, длина фрагмента 100.7 мм. Fig. 5. Marine bivalves from the Konstantinovka-1 site excavations: A–D, G–K – Anadara talmiensis Kalishevich, 1976 (A, B – excavation 3, construction N 40, layer 6, square Ж.3-10, shell length 49.3 mm; C, D – excavation 3, construction N 40, square Ж.3-10, fragment length 40.8 mm; G, H – excavation 3, construction N 40, square Ж.3-10, fragment length 41.6 mm; I–K – excavation 1, formation 1, square B2, fragment length 35.8 mm); E, F – Crenomytilus grayanus (Dunker, 1853), lifting material, fragment length 100.7 mm. in Mollusks from the archaeological site Konstantinovka-1 in Primorye (Russian Far East)
Рис. 5. Морские двустворчатые моллюски иЗ раскопов памЯтника Константиновка-1: A–D, G–K – Anadara talmiensis Kalishevich, 1976 (A, B – раскоп 3, постройка № 40, пласт 6, квадрат Ж.3-10, длина раковины 49.3 мм; C, D – раскоп 3, постройка № 40, квадрат Ж.3-10, длина фрагмента 40.8 мм; G, H – раскоп 3, постройка № 40, квадрат Ж.3-10, длина фрагмента 41.6 мм; I–K – раскоп 1, пласт 1, квадрат Б2, длина фрагмента 35.8 мм; E, F – Crenomytilus grayanus (Dunker, 1853), подъемный материал, длина фрагмента 100.7 мм. Fig. 5. Marine bivalves from the Konstantinovka-1 site excavations: A–D, G–K – Anadara talmiensis Kalishevich, 1976 (A, B – excavation 3, construction N 40, layer 6, square Ж.3-10, shell length 49.3 mm; C, D – excavation 3, construction N 40, square Ж.3-10, fragment length 40.8 mm; G, H – excavation 3, construction N 40, square Ж.3-10, fragment length 41.6 mm; I–K – excavation 1, formation 1, square B2, fragment length 35.8 mm); E, F – Crenomytilus grayanus (Dunker, 1853), lifting material, fragment length 100.7 mm.
Supplementary material for the paper: "One-shot procedures for efficient minimum compliance topology optimization"
<p>MATLAB codes and results used in the article: "One-shot procedures for efficient minimum compliance topology optimization", published in Structural and Multidisciplinary Optimization, 2024.</p>
Рис. 1. ВоЗмоЖный синтип Mactra sulcataria Deshayes in Reeve, 1854 иЗ коллекции МуЗеЯ естественной истории, Лондон (коллекциЯ Х. Каминга), регистрационный номер № NHMUK 20130005. Рисунок Этого ЭкЗемплЯра опубликован Л. Ривом [Reeve, 1854, pl. 2, fig. 5]; воспроиЗведен в настоЯЩей статье (рис. 2А). in Notes on type material of Mactra sulcataria Deshayes in Reeve, 1854 (Bivalvia: Mactridae) and taxonomic history of the species
Рис. 1. ВоЗмоЖный синтип Mactra sulcataria Deshayes in Reeve, 1854 иЗ коллекции МуЗеЯ естественной истории, Лондон (коллекциЯ Х. Каминга), регистрационный номер № NHMUK 20130005. Рисунок Этого ЭкЗемплЯра опубликован Л. Ривом [Reeve, 1854, pl. 2, fig. 5]; воспроиЗведен в настоЯЩей статье (рис. 2А).
Fig. 2 in Notes on type material of Mactra sulcataria Deshayes in Reeve, 1854 (Bivalvia: Mactridae) and taxonomic history of the species
Fig. 2. Illustrations of Mactra sulcataria Deshayes in Reeve, 1854 in monographs of the 19th century: A – Reeve [1854, pl. 2, fig. 5]; B – Weinkauff [1881, Taf. 18, Fig. 3]; in references as: Küster, Weinkauff [1841–1884].
Рис. 3. ОписаниЯ Mactra sulcataria Deshayes in Reeve, 1854 в статье Ж.П. ДехЭ [Deshayes, 1854, p. 15] (вверху) и монографии Л. Рива [Reeve, 1854, species 5] (вниЗу). in Notes on type material of Mactra sulcataria Deshayes in Reeve, 1854 (Bivalvia: Mactridae) and taxonomic history of the species
Рис. 3. ОписаниЯ Mactra sulcataria Deshayes in Reeve, 1854 в статье Ж.П. ДехЭ [Deshayes, 1854, p. 15] (вверху) и монографии Л. Рива [Reeve, 1854, species 5] (вниЗу).
Training data for 'Long non-coding RNAs (lncRNAs) annotation with FEELnc' tutorial (Galaxy Training Material)
<p>Data needed for the 'Long non-coding RNAs (lncRNAs) annotation with FEELnc' tutorial (Galaxy Training Material).<br>The assembly was generated following the 'Genome assembly using PacBio data' tutorial.<br>The annotation was generated following the 'Genome annotation with Funannotate ' tutorial.</p> <p>The bam file is RNASeq SRR8534859_1.fastq.gz and SRR8534859_2.fastq.gz mapping on the genome assembly.</p>
Dataset for an article "Numerical and Experimental Evaluation of Structured Material for Use in Multi-scale Topology Optimization"
<p><span>The dataset contains data from compression mechanical testing of 6 basic truss-based lattice cells with relative density between 0.3 and 0.7 in two directions (parallel and perpendicular to build direction). Additionally, the dataset contains simulation of the experiments by finite element method. Due to a big difference between results from experiment and simulation with nominal material model, parametric material model with Young's modulus set as parameter was used. Resulting Young's moduli that correspond with the experiments are also included. Details can be found in the article: “Numerical and Experimental Evaluation of Structured Material for Use in Multi-scale Topology Optimization”.</span></p>
"Monthly velocity and seasonal variations of the Mont Blanc glaciers derived from Sentinel-2 between 2016-2024" - supplementary materials
<p>The repository contains the supplementary materials to be downloaded relative to the research article:</p> <p>“Monthly velocity and seasonal variations of the Mont Blanc glaciers derived from Sentinel-2 between 2016-2024” </p> <p>https://doi.org/10.5194/egusphere-2023-2771</p> <p>The available files are:</p> <p>-92 raster maps of monthly velocity of the study area.</p> <p>-Shapefiles whith the glacier outlines of the 30 studied glaciers.</p> <p>-Shapefiles of the velocity time series extraction areas.</p> <p>-Velocity time series 2016-2024 of the 30 glaciers from the study. </p>
Supplementary material for the paper: "Mesh-based topology, shape and sizing optimization of ribbed plates"
<p>Source code and results, accompanying the paper "Mesh-based topology, shape and sizing optimization of ribbed plates", published in Structural and Multidisciplinary Optimization, 2024. </p>
Predicting particle quality attributes of organic crystalline materials using Particle Informatics
<p>Dataset related to the publication: "Predicting particle quality attributes of organic crystalline materials using Particle Informatics" published in Powder Technology (<a title="Go to table of contents for this volume/issue" href="https://www.sciencedirect.com/journal/powder-technology/vol/443/suppl/C"><span>Volume 443</span></a>, 1 July 2024, 119927Volume 443, 1 July 2024, 119927). In this work, a novel quercetin solvate of dimethylformamide (QDMF) was studied. The crystal structure was solved using single crystal X-ray diffraction and analysed using synthon analysis and other particle informatics tools (<em>e.g.</em>, solvate analyser). The thermal behaviour and thermodynamic stability of QDMF were studied experimentally using Raman spectroscopy, ATR-FTIR spectroscopy, differential scanning calorimetry, and thermogravimetric analysis. A clear relationship between the two-step desolvation behaviour of QDMF and the type, strength, and directionality of the main bulk synthons characterizing the QDMF structure was observed. Additionally, the attachment energy model was used to predict the QDMF morphology, together with facet-specific topology and chemical nature of each of the dominant {001}, {110}, and {200} facets. The {200} facet was found to be significantly rougher than the other two; whereas, the {110} was characterized by a higher percentage of exposed DMF molecules compared to the other two facets. Specific scanning electron microscopy and contact angle measurements were used to experimentally detect differences among the three facets and validate the modelling results.</p>
FIG. 7 in Crocodylomorph and dinosaur tracks from the lowermost Jurassic of Le Veillon (western France): ichnotaxonomic revision of the type material (Lapparent collection)
FIG. 7. — Batrachopus deweyi (Hitchcock, 1843) Hitchcock, 1845: A-C, trackway ULB-04C10_E, photograph (A), DEM and false-colour depth map (B) and interpretative sketch (C); D-F, pes/manus set 04C10_A_1 (holotype of "B. gilberti" that is here invalidated), photograph (D), DEM and false-colour depth map (E) and interpretative sketch (F); G-I, pes/manus set 04C14_C, photograph (G), DEM and false-colour depth map (H) and interpretative sketch (I); J-L, pes/manus set 04C14_C, photograph (J), DEM and false-colour depth map (K) and interpretative sketch (L). Scale bars represent: A-C, 10 cm; D-L, 1 cm.
FIG. 8 in Crocodylomorph and dinosaur tracks from the lowermost Jurassic of Le Veillon (western France): ichnotaxonomic revision of the type material (Lapparent collection)
FIG. 8. — Tracks ascribed to Dahutherium sp. by Lapparent & Montenat (1967) and that are here reinterpreted: A-C, two superimposed tridactyl grallatorid footprints, ULB-04C18_B, photograph (A), interpretative sketch of Lapparent & Montenat (1967) (B), and our interpretation (C), showing a small footprint (dark grey) and larger footprint (light grey); D-F, Batrachopus isp., ULB-04C15_A, photograph (D), interpretative sketch of Lapparent & Montenat (1967) (E), and our interpretation (F). Scale bars: A-C, 5 cm; D-F, 2 cm.
FIG. 11 in Crocodylomorph and dinosaur tracks from the lowermost Jurassic of Le Veillon (western France): ichnotaxonomic revision of the type material (Lapparent collection)
FIG. 11. — Plaster cast of one footprint from the type trackway of "Saltopoides igalensis", that is here invalidated: A-C, track ULB-04C01_B, photograph (A), DEM and false-colour depth map (B) and interpretative sketch (C). Scale bars: 5 cm.
FIG. 3 in Crocodylomorph and dinosaur tracks from the lowermost Jurassic of Le Veillon (western France): ichnotaxonomic revision of the type material (Lapparent collection)
FIG. 3. — Grallator olonensis Lapparent & Montenat, 1967: A, B, slab ULB-04D19 bearing the trackway T1-Go, as well as T2-Go; photograph (A) and interpretative sketch (B); C-E, track 04D19_A_4 (syntype) of the trackway T1-Go, photograph (C), DEM and false-colour depth map (D) and interpretative sketch (E); F-H, ULB- 04C11_A_6, photograph (F), DEM and false-colour depth map (G) and interpretative sketch (H). Scale bars: A, B, 20 cm; C-H, 2 cm.
FIG. 6 in Crocodylomorph and dinosaur tracks from the lowermost Jurassic of Le Veillon (western France): ichnotaxonomic revision of the type material (Lapparent collection)
FIG. 6. — "Anatopus palmatus" Lapparent & Montenat, 1967, that is here invalidated: A-C, ULB-04C15_B (holotype of "A. palmatus"), photograph (A), interpretative sketch of Lapparent & Montenat (1967: fig. 16A and pl.XII.3) (B), and our interpretation (C); D-F, ULB-04C15_C, photograph (D), interpretative sketch of Lapparent & Montenat (1967: fig. 16B1) (E), and our interpretation (F). Scale bars: 5 cm.
FIG. 13 in Crocodylomorph and dinosaur tracks from the lowermost Jurassic of Le Veillon (western France): ichnotaxonomic revision of the type material (Lapparent collection)
FIG. 13. — Other problematic tracks in Lapparent & Montenat 1967: A-C, Batrachopus isp., ULB-04C14_I, photograph (A), interpretative sketch of Lapparent & Montenat (1967) (B) ("Unnamed track n°1" in Lapparent & Montenat 1967: fig. 18), and our interpretation (C); D-F, Grallator cf. variabilis, ULB-04D22_A, photograph (D), interpretative sketch of Lapparent & Montenat (1967) (E) ("Unnamed track n°2" in Lapparent & Montenat 1967: fig. 18 and pl. XIII.5), and our interpretation (F). Scale bars: A-C, 1 cm; D-F, 5 cm.
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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.