Standards Research Data
This is an example of clustering to replicate OpenNym experiments
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An ontology for the multidisciplinary phenomenon of creating web application ove encrypted data
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Nodes represent personality facets (a description of each facet is provided in Table 3), green lines represent positive connections and red lines represent negative connections. Thicker lines represent stronger connections and thinner lines represent weaker connections. The node placement of all graphs is based on the adaptive LASSO network to facilitate comparison. The width and color are scaled to the strongest edge and are not comparable between graphs; edge strengths in the correlation network are generally stronger than edge strengths in the partial correlation network.
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We develop a general group-based continuous-time Markov epidemic model (GgroupEM) framework for any compartmental epidemic model (e.g., susceptible-infected-susceptible, susceptible-infected-recovered, susceptible-exposed-infected-recovered). Here, a group consists of a collection of individual nodes of a network. This model can be used to understand the critical dynamic characteristics of a stochastic epidemic spreading over large complex networks while being informative about the state of groups.
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This is a file containing the codes for IEEE paper
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Evidence-Based Medicine (EBM) aims to apply the best available evidence gained from scientific methods to clinical decision making. A generally accepted criterion to formulate evidence is to use the PICO framework, where PICO stands for Problem/Population, Intervention, Comparison, and Outcome. Automatic extraction of PICO-related sentences from medical literature is crucial to the success of many EBM applications. In this work, we present our Aceso system, which automatically generates PICO-based evidence summaries from medical literature.
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The age of Artificial Intelligence (AI) is coming. Since Natural Language Processing (NLP) is a core AI technology for communication between humans and devices, it is vital to understand technological trends. Early research on NLP focused on syntactic processing such as information extraction and subject modeling but later developed into the semantic-oriented analysis. To analyze technological trends concerning NLP, especially semantic analysis, patent data that contains objective and extensive information is analyzed.
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The dataset is system activities captured by Procmon on Windows, including running malware WannaPeace and Infostealer.Dexter.
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Considering the ongoing works in Natural Language Processing (NLP) with the Nepali language, it is evident that the use of Artificial Intelligence and NLP on this Devanagari script has still a long way to go. The Nepali language is complex in itself and requires multi-dimensional approaches for pre-processing the unstructured text and training the machines to comprehend the language competently. There seemed a need for a comprehensive Nepali language text corpus containing texts from domains such as News, Finance, Sports, Entertainment, Health, Literature, Technology.
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