13,311 research outputs found
Educational Technology as Seen Through the Eyes of the Readers
In this paper, I present the evaluation of a novel knowledge domain
visualization of educational technology. The interactive visualization is based
on readership patterns in the online reference management system Mendeley. It
comprises of 13 topic areas, spanning psychological, pedagogical, and
methodological foundations, learning methods and technologies, and social and
technological developments. The visualization was evaluated with (1) a
qualitative comparison to knowledge domain visualizations based on citations,
and (2) expert interviews. The results show that the co-readership
visualization is a recent representation of pedagogical and psychological
research in educational technology. Furthermore, the co-readership analysis
covers more areas than comparable visualizations based on co-citation patterns.
Areas related to computer science, however, are missing from the co-readership
visualization and more research is needed to explore the interpretations of
size and placement of research areas on the map.Comment: Forthcoming article in the International Journal of Technology
Enhanced Learnin
A Hybrid Technological Innovation Text Mining, Ensemble Learning and Risk Scorecard Approach for Enterprise Credit Risk Assessment
Enterprise credit risk assessment models typically use financial-based information as a predictor variable, relying on backward-looking historical information rather than forward-looking information for risk assessment. We propose a novel hybrid assessment of credit risk that uses technological innovation information as a predictor variable. Text mining techniques are used to extract this information for each enterprise. A combination of random forest and extreme gradient boosting are used for indicator screening, and finally, risk scorecard based on logistic regression is used for credit risk scoring. Our results show that technological innovation indicators obtained through text mining provide valuable information for credit risk assessment, and that the combination of ensemble learning from random forest and extreme gradient boosting combinations with logistic regression models outperforms other traditional methods. The best results achieved 0.9129 area under receiver operating characteristic. In addition, our approach provides meaningful scoring rules for credit risk assessment of technology innovation enterprises
Identifying communities of practice: analysing ontologies as networks to support community recognition
Communities of practice are seen as increasingly important for creating, sharing and applying organisational knowledge. Yet their informal nature makes them difficult to identify and manage. In this paper we set out ONTOCOPI, a system that applies ontology-based network analysis techniques to target the problem of identifying such communities
EMPATH: A Neural Network that Categorizes Facial Expressions
There are two competing theories of facial expression recognition. Some researchers have suggested that it is an example of "categorical perception." In this view, expression categories are considered to be discrete entities with sharp boundaries, and discrimination of nearby pairs of expressive faces is enhanced near those boundaries. Other researchers, however, suggest that facial expression perception is more graded and that facial expressions are best thought of as points in a continuous, low-dimensional space, where, for instance, "surprise" expressions lie between "happiness" and "fear" expressions due to their perceptual similarity. In this article, we show that a simple yet biologically plausible neural network model, trained to classify facial expressions into six basic emotions, predicts data used to support both of these theories. Without any parameter tuning, the model matches a variety of psychological data on categorization, similarity, reaction times, discrimination, and recognition difficulty, both qualitatively and quantitatively. We thus explain many of the seemingly complex psychological phenomena related to facial expression perception as natural consequences of the tasks' implementations in the brain
Case-Based Decision Support for Disaster Management
Disasters are characterized by severe disruptions of the societyâs functionality and adverse impacts on humans, the environment, and economy that cannot be coped with by society using its own resources. This work presents a decision support method that identifies appropriate measures for protecting the public in the course of a nuclear accident. The method particularly considers the issue of uncertainty in decision-making as well as the structured integration of experience and expert knowledge
PLD-SLAM: A Real-Time Visual SLAM Using Points and Line Segments in Dynamic Scenes
In this paper, we consider the problems in the practical application of
visual simultaneous localization and mapping (SLAM). With the popularization
and application of the technology in wide scope, the practicability of SLAM
system has become a new hot topic after the accuracy and robustness, e.g., how
to keep the stability of the system and achieve accurate pose estimation in the
low-texture and dynamic environment, and how to improve the universality and
real-time performance of the system in the real scenes, etc. This paper
proposes a real-time stereo indirect visual SLAM system, PLD-SLAM, which
combines point and line features, and avoid the impact of dynamic objects in
highly dynamic environments. We also present a novel global gray similarity
(GGS) algorithm to achieve reasonable keyframe selection and efficient loop
closure detection (LCD). Benefiting from the GGS, PLD-SLAM can realize
real-time accurate pose estimation in most real scenes without pre-training and
loading a huge feature dictionary model. To verify the performance of the
proposed system, we compare it with existing state-of-the-art (SOTA) methods on
the public datasets KITTI, EuRoC MAV, and the indoor stereo datasets provided
by us, etc. The experiments show that the PLD-SLAM has better real-time
performance while ensuring stability and accuracy in most scenarios. In
addition, through the analysis of the experimental results of the GGS, we can
find it has excellent performance in the keyframe selection and LCD
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