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1,036 results for “Modernization”
Fig. 2 in Frederick II of Hohenstaufen and modern ecology
Fig. 2 - Frederick received hunting falcons and hawks from the different parts of Europe, which gave him an opportunity to compare plumage and morphology among individuals and species from different areas. / Federico ricevette falconi e falchi da caccia dalle diverse parti d'Europa, il che gli diede l'opportunità di confrontare il piumaggio e la morfologia tra individui e specie di diverse aree. © Biblioteca Apostolica Vaticana, Pal.lat.1071: f.55v.
Fig. 1 in Frederick II of Hohenstaufen and modern ecology
Fig. 1 - Falconers training and taking care of their falcons. / Falconieri che addestrano e si prendono cura dei loro falchi. © Biblioteca Apostolica Vaticana, Pal.lat.1071: f.79r.
Figure 3. Logoped 1.0 program-Modern Tools in Patient-Centred Speech Therapy for Romanian Language
<p>The program was designed for the therapy of logoneurosis, while being equally useful for the therapeutic activities used in the treatment of dyslexic-dysgraphic disorders. Logoped 1.0 provides a vast lexical material, which is organised into several sections: exercises involving reading the syllables and the words, sentences reading, followed by phrase and text reading. The colourful design of the words and sentences, the attractive way in which they are displayed on the monitor, and the fact that it allows choosing the exercises level of difficulty render the reading activity much more attractive for the pupil (Tobolcea, 2001). Its functional schema is presented in figure 3.</p>
Figure 9. A model that helps the diagnosis prediction-Modern Tools in Patient-Centred Speech Therapy for Romanian Language
<p>We have already implemented many modules of Logo-DM, such as: data cleaning module, data transformation module, feature extraction module, data clustering module and a classification module for diagnosis prediction. Figure 9 shows the model achieved using a decision tree built on complex examination data that aims to predict the patient’s diagnosis. Currently, we are testing the built models on new cases in order to estimate their quality.</p>
Figure 8. The end-to-end operations in Logo-DM-Modern Tools in Patient-Centred Speech Therapy for Romanian Language
<p>The useful data mining tasks for speech therapy fall into three categories: classification, clustering, and association rules. Classification places children with different speech impairments in predefined classes, and makes possible to track the characteristics of various groups. To model different classes we use many predictor variables (e.g. personal or familial anamnesis data or related to lifestyle). By clustering we group people with speech disorders on the basis of similarity of different features. This helps therapists to understand their patients. Clustering aims to find subsets of a predetermined segment, with homogeneous behavior towards various methods of therapy that can be effectively targeted by a specific therapy, but it is not based on the previous definition of groups (Danubianu, Tobolcea, & Pentiuc, 2009). Association rules aim to find out relationships between different data which seem to have no semantic dependence. The built patterns might be very useful to determine why a specific therapy program has been successful on a segment of patients with speech disorders, and on the other was ineffective. The Logo-DM system was designed to help the speech therapists to optimize the personalized therapy of dyslalia. To understand what kind of knowledge we could discover in TERAPERS’ dataset to improve speech therapy, we have to describe the collected data.</p>
Figure .5 Architecture of the Terapers system-Modern Tools in Patient-Centred Speech Therapy for Romanian Language
<p>Shown in Figure 5, the architecture of the Terapers system implies the existence of two main connected components: on the one hand, an intelligent system which is installed on the office computer of each speech therapist and, on the other, a mobile system which is used as a virtual friend in the therapy applied to the child (Danubianu et al., 2008). The intelligent system – which represents the fixed component of the system – is installed on each computer from the office of the speech therapist; it is made up of the following parts: • an information management module for children; • an expert system, able to produce inferences based on the data given by the assessment module; • a mouth virtual module which allows the display of all hidden movements that are likely to occur during speech; • a management module of the exercises uses, which allows creating or modifying the exercises, depending on the various therapy stages, as well as their organization into complex issues.</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>
Figure 6. Activities dealing with microlearning-Micro Learning: A Modernized Education System
<p>There are many unique activities involved in microlearning, which provides a high rate of successful knowledge transformation. To the inquiry made in research on the activities dealing with microlearning that support knowledge transformation, figure 6 shows that mind mapping has the highest view of respondents, up to 72%, the nearest to it is story telling with 50%. The variation displayed in the figure is significant. 44% of the respondents opt for tagging followed by text with 38%.</p>
Figure 5. Microlearning dimensions-Micro Learning: A Modernized Education System
<p>The inquiry on the different aspects of the various dimension of microlearning is reflected in figure 5. The result is obtained based on 82 respondents who were learners from different groups. 74% of the respondents consider that process to be a micro learning dimension which strengthens the knowledge input followed by time, up to 60%. The curriculum and mediality had the same 46% of the respondents. 32% and 30% of the respondents believe that the learning type and the content strengthen the knowledge input through microlearning. The lowest percentage is of 20% and 14%.</p>
Figure 2. Current status and necessity of micro learning-Micro Learning: A Modernized Education System
<p>The authors made a percentage analysis based on the basic data collected from the respondents. Out of 100 questionnaires distributed for data collection for the research study, only 91 were received. After reviewing the questionnaires, 7 were incomplete; therefore they were removed from the analysis. The following analysis is based on 84 full and complete questionnaires.</p> <p>To the inquiry on the current status of the learning methodology, 90% of the respondents welcomed a new and smart version of software which in turn leads to micro learning system. 80% of the respondents are currently relying on the work infrastructure to support their learning, whereas 75% of the respondents use more often PLE. 80% of the respondents are looking towards a simple and smart learning system in regular basics and microcontent to transform knowledge.</p>
Figure 4.The purpose for using Smartphones-Micro Learning: A Modernized Education System
<p>Figure 4 shows the purpose of using smartphones. The current status implies that a maximum number of 91% of the respondents use it for social networking. 77% of the respondents use it for communication (WhatsApp, Viber, etc.) followed by e-mails at 55%. Only 43% of the respondents use it for learning and updating (translation, dictionary, etc). Comparing figure 3 andfigure 4 the research identified the gap between electronic device which they prefer for learning and the usage of that electronic devices for learning and updating.</p>
Figure 7. Comparison for identifying the success factor key versus the learning approach-Micro Learning: A Modernized Education System
<p>There are many unique activities involved in microlearning, which provides a high rate of successful knowledge transformation. To the inquiry made in research on the activities dealing with microlearning that support knowledge transformation, figure 6 shows that mind mapping has the highest view of respondents, up to 72%, the nearest to it is story telling with 50%. The variation displayed in the figure is significant. 44% of the respondents opt for tagging followed by text with 38%.</p>
Figure 1. Mind mapping of Concepts and Versions of Micro learning (Hug, 2005)-Micro Learning: A Modernized Education System
<p>The methods of micro learning are in line with the way that the learner’s brain naturally takes in information, so that the body does not get stressed-out. One of the salient features of micro learning is that it allows the user to find exactly what he or she is looking for. When the mind focuses on a particular question, it is the most open to receiving that answer (www.digitalpromise.org/microcredentials dated on 10/10/2015). It allows the learner’s brain to explore its own curiosity and its own patterns.</p>
Figure 3. Impact of the Boom in digital data in learning-Micro Learning: A Modernized Education System-
<p>Figure 3 portrays the research guiding the impact of the boom in digital data in knowledge codification. The current status shows that 80% of the respondents are interested in learning via electronic devices, followed by e-mails at 75%. 72% and 70% of respondents opt for video clips and sound and voice recording. 65% of the respondents selected images followed by graphical display at 61%. 50% of the respondents selected Journals. Further observation from the figure reveals that books and reference volumes had a very insignificant impact, as expected by merely 45% and 30% of the respondents. This directed us towards the necessity for micro learning, and encourages the increase of the usage of electronic devices.</p>
On the Popularity of Modern Open Source Software
<p><strong>This dataset contains the data analyzed on the paper:</strong></p> <p>Hudson Borges and Marco Tulio Valente. <em>On the Popularity of Modern Open Source Software</em>. Submitted to Journal of Systems and Software (JSS), 2018.</p> <p><strong>Files:</strong></p> <ul> <li><em>cdf.csv</em>: Cumulative distribution function data.</li> <li><em>contributos.csv</em>: List of contributors of the repositories.</li> <li><em>developers_perceptions.csv</em>: Survey of Developers' Perceptions on Growth Patterns.</li> <li><em>factors.[activity,owner,repository].csv</em>: Additional information of the analyzed repositories and their owners.</li> <li><em>growth_patterns.zip</em>: A compressed file containing the output of the KSC algorithm (time series clusters).</li> <li><em>motivations_for_starring.csv</em>: Developers' motivations for starring projects.</li> <li><em>owners.csv</em>: Information of the repositories' owners.</li> <li><em>releases.csv</em>: Releases considered in the study (i.e., major and minor releases only).</li> <li><em>repositories.csv</em>: Information of the analyzed repositories (e.g., stars, forks, owner, creation date, etc.).</li> <li><em>timeseries.json</em>: File containing the number of stars gained by week for each repository since their creation.</li> </ul>
A Corpus of Modern Burmese
<p>This is a corpus of modern Burmese compiled by John Okell in the 1990s and converted into Unicode more recently.</p>
R replication code and data for: Do modern hunter-gatherers live in marginal habitats?
<p>Data and R replication code for testing the Marginal Habitat Hypothesis. The R code files contain all models and are organized by the figures they generate for the associated paper.</p> <p>The data are sourced from:</p> <p>1) the Standard Cross Cultural Sample (SCCS).</p> <p>2) NASA Moderate Resolution Imaging Spectroradiometer (MODIS) NPP data (MOD17A3 algorithm) from Numerical Terra Dynamic Simulation Group at the University of Montana.</p> <p>3) Marine Ecoregions Of the World (MEOW): <a href="http://maps.tnc.org/files/metadata/MEOW.xml">http://maps.tnc.org/files/metadata/MEOW.xml</a></p> <p>4) Terrestrial Ecoregions Of the World (TEOW): <a href="http://maps.tnc.org/files/metadata/TerrEcos.xml">http://maps.tnc.org/files/metadata/TerrEcos.xml</a></p>
GT4HistOCR: Ground Truth for training OCR engines on historical documents in German Fraktur and Early Modern Latin
<p><strong>GT4HistOCR</strong> contains ground truth for research in Optical Character Recognition (OCR) technology applied to historical printings in German Fraktur and Early Modern Latin.</p> <p>The ground truth comes in pairs of images of single printed lines as they appear in book pages (*.png) and their corresponding diplomatic transcriptions (*.gt.txt), which are UTF-8 strings preserving the character forms (glyphs) as much as possible within the UNICODE standard. These pairs of line images and their transcriptions can be directly used to train recognition models with, e.g., the open source OCR engines <em>OCRopy</em> or <em>Tesseract</em>. A total of 313,173 ground truth lines are provided.</p> <p><strong>Please note that the subcorpora making up this collection used different transcription guidelines, so it is a bad idea to train a recognition model on the total collection! Rather train individual models for each subcorpus.</strong> Fur further information about the subcorpora, please see the README file and the accompanying publication.</p> <p>If these data are useful for you, please cite the accompanying publication:</p> <pre>@article{<a href="http://springmann.net/publications.html#springmann2018gt4hist">springmann2018gt4hist</a>, author = {Uwe Springmann and Christian Reul and Stefanie Dipper and Johannes Baiter}, title = {{Ground Truth for training {OCR} engines on historical documents in German Fraktur and Early Modern Latin}}, journal = {J. Lang. Technol. Comput. Linguistics}, volume = {33}, number = {1}, pages = {97--114}, year = {2018}, url = {https://jlcl.org/content/2-allissues/1-heft1-2018/jlcl_2018-1_5.pdf} }</pre>
Shared unique tetragrams between early modern plays
<p>This spreadsheet lists plays in Martin Mueller’s corpus Shakespeare His Contemporaries according to dislegomena consisting of at least four words i.e. tetragrams that occur in only two plays of the period. </p>
Phrasal repetitions between Arden of Faversham and other early modern plays
<p>This spreadsheet ranks Arden of Faversham against all other plays in Martin Mueller’s corpus Shakespeare His Contemporaries according to the number and type of shared phrases</p>
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.