1,170,276 research outputs found
Analysis of Blended Learning Development in Distance Learning in Variation of Borg & Gall and Addie Models
With the success of blended learning and the use of online media on learning outcomes and from article search results, it shows that there have been many articles that contain blended learning and various media uses, and reviews are needed about it by reviewing existing articles or commonly called literature reviews. Borg & Gall and ADDIE models. The Borg & Gall model and ADDIE are two teaching models used in colleges and universities. ADDIE stands for Analyze, Design, Development, Implementation, and Evaluation. In the Borg & Gall model, the steps taken are research and information. Research and information is used to collect information about the need for learning evaluation instruments for learning media development courses for students. In the ADDIE model the steps taken are the same as the original which includes aspects of Analyze, Design, Development, Implementation, and Evaluation. Thus, what is needed in this development is a reference about the product procedure to be developed. The description of the development model of Borg and Gall, described as follows; Educational research and development (R&D) is the process used to develop and validate educational products. The validity of interactive blended learning is: (1) according to expert reviews the content of metrics shows a good category (92%), (2) according to expert reviews learning design is in the good category (88%), (3) according to expert reviews learning media shows a good category (86%), Thus, this interactive blended eLearning does not need to be revised and can be used for further research
Predicting Network Attacks Using Ontology-Driven Inference
Graph knowledge models and ontologies are very powerful modeling and re
asoning tools. We propose an effective approach to model network attacks and
attack prediction which plays important roles in security management. The goals
of this study are: First we model network attacks, their prerequisites and
consequences using knowledge representation methods in order to provide
description logic reasoning and inference over attack domain concepts. And
secondly, we propose an ontology-based system which predicts potential attacks
using inference and observing information which provided by sensory inputs. We
generate our ontology and evaluate corresponding methods using CAPEC, CWE, and
CVE hierarchical datasets. Results from experiments show significant capability
improvements comparing to traditional hierarchical and relational models.
Proposed method also reduces false alarms and improves intrusion detection
effectiveness.Comment: 9 page
Reasoning About Pragmatics with Neural Listeners and Speakers
We present a model for pragmatically describing scenes, in which contrastive
behavior results from a combination of inference-driven pragmatics and learned
semantics. Like previous learned approaches to language generation, our model
uses a simple feature-driven architecture (here a pair of neural "listener" and
"speaker" models) to ground language in the world. Like inference-driven
approaches to pragmatics, our model actively reasons about listener behavior
when selecting utterances. For training, our approach requires only ordinary
captions, annotated _without_ demonstration of the pragmatic behavior the model
ultimately exhibits. In human evaluations on a referring expression game, our
approach succeeds 81% of the time, compared to a 69% success rate using
existing techniques
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