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43 results for “knowledge systems”
C-RIDGE: Indoor CO2 Data Collection System for Large Venues Based on Prior Knowledge
<p>This CO2 of C-RIDGE system dataset contains the high spatial and temporal resolution of the CO2<br> measures with the corresponding timestamp of static wireless sensors. <br> 45 sensors are densely deployed on the stand in the venue. The id and relative positions of sensors <br> are shown in device message. The sampling rate is adaptively adjusted according to the competition schedule. <br> The sampling interval is 5 minutes during the competition and 15 minutes otherwise. Please refer to <br> the description of the experimental setup in the data descriptor paper.</p> <p>In the process of data collection, the data cleaning process is performed to remove <br> and calibrate outliers and abnormal trend data. The script of data cleaning algorithm is <br> provided in this repository. For details about the data cleaning process, please refer to the script in <br> this repository and data descriptor paper.</p> <p>The dataset in this repository is processed version. The raw dataset is not included in this repository.</p> <p>Data is stored as CSV file. Each device is numbered in order of placement. There are 45 sensors in total,<br> 1 to 45 in the csv file are sensor numbers, timestamp as the China Standard Time (GMT+8), Each timestamp <br> corresponds to 45 CO2 concentration data from different sensors.</p> <p>To access the dataset, any programming language that can access the CSV file is appropriate. Users can <br> also directly open the CSV file. To successfully execute script files, Pycharm with Python 3.0 is required.</p>
Figure 4. CBR Implementation-Intelligent Flowcharting Developmental Approach to Legal Knowledge Based System
<p>The development of the case based reasoning module is in done in java net-beans. Proper<br> verification and validation of this module was done by the legal experts. The cases related to<br> Transfer of property act were collected from different legal databases and compiled. The necessary<br> keywords were framed, which were used in searching for the related cases. The following Fig 2.0<br> gives the screen shot of the CBR module develoed in Java Net beans.</p>
Fig 3 : VisiRule Implementation Module-1-Intelligent Flowcharting Developmental Approach to Legal Knowledge Based System
<p>The code of this flowchart is developed by the VisiRule in FLEX/ Prolog. As the source<br> code very huge it has not be incorporated in the paper.</p>
Figure 1 - VisiRule architecture-Intelligent Flowcharting Developmental Approach to Legal Knowledge Based System
<p>In the development of RBR we used the intelligent flowcharting approach. VisiRule is a tool<br> for creating decision support software purely by drawing flowcharts. The end result is Flex or<br> Prolog code which is automatically generated, compiled and ready to run, but which can also be<br> copied and used in a separate program. Not only can VisiRule be used by people with minimal<br> programming skills. VisiRule also enhances productivity by considerably reducing the time it takes<br> to produce a decision support system. VisiRule is an intelligent flowcharting tool in two senses.<br> Firstly, it is used to create knowledge-based systems and, secondly, it intelligently guides the<br> construction process by constraining what you can and can't do on the basis of the semantic content<br> of the emerging program. VisiRule provides the automatic construction of menu dialogues from<br> questions. These are populated by items inferred from expression boxes throughout the flowchart<br> tree which have a path to the question.</p>
Figure 5. Multi-agent system information and knowledge scheme.-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System
<p>The negotiations between agents are subject to optimization based on “knowledge” that is<br> derived from complete production models, yield models or even sparse models as expressed in<br> fuzzy expert rules or practical rules of thumb. In addition, pest control and plant disease models<br> provide additional information useful to the design of a successful strategy for optimal management<br> [15] (illustrated in Figure 5).</p>
Figure 3. Traditional Approach and Process Management System Approach – Effort Percentage Comparison-Business Process Management – A Traditional Approach versus a Knowledge Based Approach
<p>Comparing the results obtained in using the two approaches (Figure 3), it is possible to note<br> a significant reduction in terms of both effort and working hours in correspondence of design and<br> development phases.</p>
Figure 8. Knowledge input dimension of microlearning-Micro Learning: A Modernized Education System
<p>Figure 8 portrays the research directing the studies on the knowledge input dimension of microlearning. Almost 81% of the respondents believed that microlearning is the best learning system for integrated mashup PLE, followed by 66% of the respondents whose opinion was that dynamic applications of microlearning enhances knowledge. Almost 72% of the respondents believed that microlearning is suitable for diverse subjects, whereas 67% of the respondents believed that the use of digital artifacts maximizes the process of aggregation.</p>
BRAIN Journal-Right-Linear Languages Generated in Systems of Knowledge Representation based on LSG-Right-Figure 3. Representation of the grammar G1 in the labelled graph G0 1
<p>If we take the labeled graph G0 1 given in Figure 3 and construct the stratified graph structure over (99) such that (100) we obtain (101), (102). </p> <p>In this paper, we proposed a new system for formal language generation by means of stratified graphs structures. This mechanism can generate languages of the first type and of the second type. More precisely, we propose a new system for formal language generation by means of a system of knowledge based on stratified graphs. We exemplified that, using an interpretation system specially defined for stratified graphs representations, a particular formal language can be obtained by means of the resulted accepted structured paths.</p>
BRAIN Journal-Right-Linear Languages Generated in Systems of Knowledge Representation based on LSG-Right-Figure 2. The representation of the rule
<p>In order to model these derivations in the stratified graph G, each production of the grammar will be represented in the labeled graph G0 by a direct arc of the form given in Figure 2.</p> <p> </p>
BRAIN Journal-Right-Linear Languages Generated in Systems of Knowledge Representation based on LSG-Right-Figure 1. The graphical representation of the morphism
<p>A morphism of partial algebras such that (30) and if (31), then (32) (see Figure 1). We obtain f(L) = T which means that “for every element of L the associated element of T is computed by the morphism f” (Ţăndăreanu, 2000).</p>
SPARC Connectivity Knowledge base of the Autonomic Nervous System
<p>The SPARC Knowledge base of the Autonomic Nervous System (SCKAN) is an integrated graph database composed of three parts: the SPARC dataset metadata graph, ApiNATOMY and NPO models of connectivity, and the larger ontology used by SPARC which is a combination of the NIF-Ontology and community ontologies.</p> <p>The fastest way to get querying is to follow the instructions in the <a href="https://github.com/SciCrunch/sparc-curation/blob/master/docs/sckan/README.org#getting-started">SCKAN readme file</a>.</p> <p>For background information please see <a href="https://scicrunch.org/sawg/about/SCKAN">https://scicrunch.org/sawg/about/SCKAN</a> and <a href="https://sparc.science/resources/6eg3VpJbwQR4B84CjrvmyD">the SPARC portal resource page about SCKAN.</a></p> <p>This release contains the raw and compiled data for SCKAN. The release-*.zip contains raw data inputs along with the Blazegraph journal file, the sparc-sckan-graph-*.zip contains the SciGraph database, and sckan-data-*.tar.gz is a Docker image that contains the Blazegraph journal file and the SciGraph database along with the configuration files for running each of the servers. The image is intended to be used as a data volume with another Docker container that runs the SciGraph and Blazegraph server software.</p> <p>The Docker image containing this data is available live and is likely easier to use than the archived image included in this release. See the <a href="https://github.com/SciCrunch/sparc-curation/blob/master/docs/sckan/README.org#getting-started">SCKAN readme file</a> for the most up-to-date instructions.</p> <p>We would like to thank the members of the SAWG (SPARC Anatomy Working Group, RRID:SCR_018709) for their work on the various connectivity models included in this release.</p> <p>This work was funded by the NIH Common Fund under 3OT2OD030541-01S1.</p>
Data from: Building on 150 years of knowledge: the freshwater isopod Asellus aquaticus as an integrative eco-evolutionary model system
<p><strong>Introduction</strong></p> <p>This is a literature database with reference information of all papers that use the freshwater isopod <em>Asellus aquaticus</em>; published between the years 1867 and 2020. This database is intended as a starting point for scientists interested in conducting research on and with this organism. The database is currently only available as a single CSV file; future versions may be made available through a more frequently updated SQL database. The database includes specific information about the subject area and content of each paper, as well as bibliographic information. This repository is associated with the paper "Building on 150 years of knowledge: the freshwater isopod<em> Asellus aquaticus</em> as an integrative eco-evolutionary model system", published in Frontiers in Ecology and Evolution.</p> <p><strong>Details on Methods from the electronic supplement:</strong></p> <p>We used the we online search tools of Web of Science (WOS; Clarivate analytics) by searching for the term "asellus aquaticus" in six relevant databases (BIOSIS, CABI, FSTA, Medline, WOS Core Collection and Zoological Records). The database was accessed with a University License (Lund University). We manually downloaded the results and combined them to a single CSV file in Excel (Microsoft). All further processing was done in the statistical programming language R, version 4.0.2 (R Core Team 2020).</p> <p>From the 1238 obtained records we discarded three papers that were published after the year 2020 to work with completed years only. We used the subject areas assigned by WOS to provide an overview of the fields of science in which A. aquaticus has been most studied. Each paper had between one and ten subject areas assigned by WOS (2845 assignments to 1235 papers, meaning 2.3 assignments per paper, on average). To represent these multiple assignments in relation to the actual number of papers per year, we calculated "fractional assignments" by adding up all assignments to a field per year, divided by the total number of assignments in that year, and then multiplied by the number of papers. For example, if there were 12 assignments to "toxicology" in 1993, and 133 assignments in 1993, but only 21 papers published, "toxicology" would get a score of 1.9 papers in 1993 (as calculated by = (12/133)*21). In Figure 1, we represent these "fractional assignments" in the top panel, and the total number of assignments in the lower panel.</p> <p><strong>Caption for figure (1) in publication:</strong></p> <p>FIGURE 1 | Over 150 years of research on and with Asellus aquaticus. The figure summarizes published scientific literature on A. aquaticus. We conducted a quantitative literature survey with the search tools of Web of Science (WOS; Clarivate analytics) by searching for the term "asellus aquaticus" in six databases (i.e., BIOSIS, CABI, FSTA, Medline, WOS Core Collection, and Zoological Records). We found 1235 records, published between 1867 and 2020. (A) The graph shows the number of publications per year within a given subject area, as designated by WOS. (B) The graph shows the total number of publications assigned to a specific subject area. The top 10 fields account for 72.58% of all publications, and are indicated by color coding in A and B (multiple assignments are possible, summing up to 2845 assignments). The inset in B shows a wordcloud with the 100 most used keywords from all A. aquaticus’ publications. Furthermore, we compiled all records with relevant information (e.g., title, keywords, research areas, and abstract) to a single file which is available online. More details can be found in the Supplementary Material.</p>
Data for "Transferring Chemical and Energetic Knowledge Between Molecular Systems With Machine Learning"
<p>Data used in the paper "Transferring Chemical and Energetic Knowledge Between Molecular Systems With Machine Learning."</p> <p>The following is a description of each file:</p> <p>- ala_dipep_full.zip contains the JSON files for alanine dipeptide</p> <p>- ala_dipep_full.txt contains the free energy values for alanine dipeptide</p> <p>- trialanine.zip contains the JSON files for trialanine</p> <p>- trialanine.txt contains the free energy values for trialanine</p> <p>- decaalanine.zip contains the JSON files for decaalanine, broken into groups</p> <p> </p> <p>Each JSON file contains the following properties:</p> <p>- atom_types: describing the short strings used for various types of atoms via their mass and radius.</p> <p>- atoms: describing each individual atom, with their type via the short string in atom_types, their partial charge, and coordinates.</p> <p>- angles: describing the angles formed between three atoms, their atom indices, as well as their angular value.</p> <p>- dihedrals: describing the dihedrals formed between four atoms, their atom indices, as well as their dihedral value.</p> <p>- bonds: describing the existence of pairwise bonds between atoms via a binary number.</p> <p>- van_der_waals: describing the van der Waals forces between pairs of atoms.</p> <p>- coulomb: describing the Coulomb forces between pairs of atoms.</p>
Learning stochastic process-based models of dynamical systems from knowledge and data - Libraries, incomplete models and data
<p>The archive contains all libraries of domain knowledge, the incomplete models and the data used in the experiments described in the manuscript titled "Learning stochastic process-based models of dynamical systems from knowledge and data" pubilshed in BMC Systems Biology</p>
Resources for BMF CP72: The effectiveness of knowledge management systems in motivation and satisfaction in Vietnamese higher education institutions
<p>Code and data for reproducing the results in "BMF CP72: The effectiveness of knowledge management systems in motivation and satisfaction in Vietnamese higher education institutions" are available here.</p>
Resources for "BMF CP 92: Who had no wildlife smoke notifications and knowledge of the air quality rating system?"
<p>The current study is conducted to examine the following research questions:</p> <ul> <li>Who were the people who had no access to wildfire smoke notifications during the smoke event in the summer of 2018 in the Boise Metropolitan Area in Idaho?</li> <li>Who were the people who were not familiar with the air quality rating system?</li> </ul>
Dataset: Towards a Knowledge Management Framework for LLM-Generated Personas in Collaborative Systems
Open the record for dataset details and reuse information.
Official Code and Dataset of Table Tennis Coaching System Based on a Multimodal Large Language Model with Knowledge Base
<p>Official Code and Dataset of Table Tennis Coaching System Based on a Multimodal Large Language Model with Knowledge Base</p>
Perceptions and Knowledge regarding the application of Artificial Intelligence (AI) technologies in healthcare system; A cross-sectional Study involving Medical Students.
<p>This is the data set of a cross-sectional study that was carried out on medical students at Rawalpindi medical university, Pakistan involving 211 participants.</p>
Artifact: Incorporating Failure Knowledge into Design Decisions for IoT Systems: A Controlled Experiment on Novices
<p>Artifact for "Incorporating Failure Knowledge into Design Decisions for IoT Systems: A Controlled Experiment on Novices"</p>
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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.