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8 results for “Definition generation”
Figure 3. Autogenerative Learning Object Model Definition (2)Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>Formally, the theory section (see line 11) contains only static data. Here the educator places<br> theoretical information about the learned concept in HTML format.<br> The question, answers and feedbacks sections (see lines 12-13, 14-17, 18-21) contain a mix<br> of static data and dynamic values referring the previously computed symbol values. The semantic of<br> these sections is to create a dynamic content to be presented to the learner. Formalisms for specific<br> details are also present, like:<br> i) correctness - because we need to know which is the correct answer if they are multiple;<br> ii) feedbacks answer identifier list - because feedbacks may be related to certain answers and<br> showed to the learner only in certain conditions.</p>
Figure 2. Autogenerative Learning Object Model Definition (1)-Generative Learning Objects Instantiated with Random Numbers Based Expressions
<p>The proposed model is defined using the EBNF meta-language resulting in the concise<br> grammar from figures 2 and 3. We will not present the full XML Schema because of clarity and<br> space reasons.<br> Structurally, the AGLO model has six sections: i) name; ii) scenario; iii) theory; iv) question;<br> v) answers; vi) feedbacks. Next, we will explain each section in details.</p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 3. Symbols definition for a graph-based test
<p>In this scenario, we intend to generate a random graph and compute a deep first-search node list. The first defined random symbol is n, namely the number of nodes in the graph as an integer from 5 to 9. The next symbol is named g and denotes the graph object created randomly using 3 parameters: the number of nodes, the minimum, and the maximum value for the weight. For the number of nodes, we used the previously computed value of n, whereas for the weights, we used two constants 0 and 1 since the graph is not weighted</p>
BRAIN Journal-Auto-generative Learning Objects in Online Assessment of Data Structures Disciplines-Figure 2. Auto-generative Learning Object Model Definition
<p>In this section, we will present the structure of AGLOs in the context of our approach. The AGLO meta-model is structured in XML as in Figure 2,a refinement from Chirila, Ciocarlie, and Stoicu (2015). The AGLO definition contains several sections like name, scenario, theory, question, answers, and feedback (line 01). The name element contains the name of the AGLO, possibly a small description in the human language (line 02). The section of the scenario (line 03) contains a comment (line 04) followed by a set of symbol definitions. The comment should describe the imagined scenario in details and it has the same role as code comments. The symbol is the central element of the AGLO model. The symbol has a name and is very similar to programming language variables. Symbols may be called also parameters since they control the content of the AGLO content in the process of instantiation. </p>
Graphine: A Dataset for Graph-aware Terminology Definition Generation
<p>This is the dataset of our EMNLP 2021 paper:</p> <p>Graphine: A Dataset for Graph-aware Terminology Definition Generation. </p> <p>Please read the "readme.md" in it for the format of the dataset.</p>
Artifacts for the paper "Automated and Complete Generation of Traffic Scenarios at Road Junctions Using a Multi-level Danger Definition"
<div> <div> <div>This deposit contains measurement data and additional artifacts pertaining to the "<em>Automated and Complete Generation of Traffic Scenarios at Road Junctions Using a Multi-level Danger Definition</em>" paper. The deposit is structured as follows:</div> <br> <div>Data pertaining to <strong>RQ1</strong> is found in the <em>baseline-comparison/</em> directory, which contains (1) statistics measured during scenario generation and (2) data analysis results for both our proposed approach (which we name <em>complete</em>) and the baseline <em>Scenic</em> approach, including statistical significance data.</div> <br> <div>Data pertaining to <strong>RQ2-4</strong> and to the following <strong>Discussion</strong> is found in the following three directories.</div> </div> </div> <div> </div> <ul> <li><em>0-generated-scenarios/ </em>contains (1) the generated abstract scenario specifications (i.e. maneuver instance and path region assignments) and (2) concrete scenarios represented in an `xml` format that are executable through the <a href="https://github.com/carla-simulator/scenario_runner">CARLA Scenario Runner</a> framework.</li> <li><em>1-simulation-results/ </em>contains the simulation results for our measurement runs. We include simulation traces, which show the exact position of each actor at each frame, in a human-readable, textual format. We also include result analysis (i.e. pertaining to simulation runtime, outcome, preventive maneuvers, etc.) within `measurements.json` files.</li> <li><em>2-generated-figures/ </em>contains figures derived from the contents of <em>1-simulation-results/</em> (including additional figures not included in the publication).</li> </ul> <div>All the code of the proposed scenario generation approach is implemented as extensions to the <a href="https://github.com/ArenBabikian/concretize">Concretize</a> framework (for scenario generation and analysis), and to the <a href="https://github.com/ArenBabikian/transfuser/tree/complete-gen">Transfuser</a> repository (for simulation). <a href="https://github.com/ArenBabikian/concretize">Concretize</a> is available under the <a href="https://www.eclipse.org/legal/epl-2.0/">Eclipse Public License - v 2.0</a>, while <a href="https://github.com/autonomousvision/transfuser">Transfuser</a> is available under the <a href="https://opensource.org/license/mit">MIT License</a>.</div>
Characterization and generation of human definitive multipotent hematopoietic stem/progenitor cells
GEO Series GSE148215. Homo sapiens. 2 samples. Type: Expression profiling by high throughput sequencing.
Generation of pancreatic β cells from CD177+ anterior definitive endoderm
GEO Series GSE113791. Homo sapiens. 9 samples. Type: Expression profiling by array.
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
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DANDI Archive for NWB datasets
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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.