scholarly journals Digital Dialogue Games and InterLoc: A Deep Learning Design for Collaborative Argumentation on the Web

2012 ◽  
pp. 277-315 ◽  
Keyword(s):  
Deep Learning ◽  
Learning Design ◽  
Dialogue Games ◽  
Collaborative Argumentation ◽  
The Web

The Problem of Fraudulent Content on the Web: Deep Learning Approaches

2020 ◽  
Author(s):  
Priyanka Meel ◽  
Farhin Bano ◽  
Dr. Dinesh K. Vishwakarma
Keyword(s):  
Deep Learning ◽  
Learning Approaches ◽  
The Web

A Real-time Deep Learning Design Tool for Far-Field Radiation Profile

2021 ◽  
Author(s):  
Jinran Qie ◽  
Erfan Khoram ◽  
Dianjing Liu ◽  
Ming Zhou ◽  
Li Gao
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Design Tool ◽  
Far Field ◽  
Learning Design ◽  
Field Radiation ◽  
Radiation Profile

Using Expert Reviews to Enhance Learning Designs

2011 ◽  
pp. 243-262 ◽  
Author(s):  
Carmel McNaught ◽  
Paul Lam ◽  
Kin-Fai Cheng
Keyword(s):  
Hong Kong ◽  
Learning Strategies ◽  
Web Sites ◽  
Learning Design ◽  
Expert Review ◽  
Institutional Planning ◽  
Chinese University ◽  
E Learning ◽  
Evaluation Matrix ◽  
The Web

The chapter will describe an expert review process used at The Chinese University of Hong Kong. The mechanism used involves a carefully developed evaluation matrix which is used with individual teachers. This matrix records: (1) the Web functions and their use as e-learning strategies in the course Web site; (2) how completely these functions are utilized; and (3) the learning design implied by the way the functions selected are used by the course documentation and gauged from conversations with the teacher. A study of 20 course Web sites in the academic years 2005–06 and 2006–07 shows that the mechanism is practical, beneficial to individual teachers, and provides data of relevance to institutional planning for e-learning.

Ambient Pedagogies, Meaningful Learning and Social Software

2010 ◽  
pp. 415-433 ◽  
Author(s):  
Andrew Ravenscroft ◽  
Musbah Sagar ◽  
Enzian Baur ◽  
Peter Oriogun
Keyword(s):  
Digital Age ◽  
Learning Processes ◽  
Social Software ◽  
Learning Design ◽  
Future Trends ◽  
Experience Design ◽  
New Approach ◽  
Dialogue Games ◽  
Ongoing Work ◽  
Stable Learning

This chapter will present a new approach to designing learning interactions and experiences that reconciles relatively stable learning processes with relatively new digital practices in the context of social software and Web 2.0. It will begin with a brief position on current educational articulations of social software before offering some theoretical pointers and methodological perspectives for research and development in this area. The authors will then explain how an ongoing initiative in advanced learning design has developed notions of “ambient learning design” and “experience design” to address these issues and describe a new methodology for developing digital tools that incorporate these concepts. This approach is exemplified through ongoing work within an initiative in Digital Dialogue Games and the InterLoc tool that realises them. Finally, the implications this work has for future trends in designing for inclusive, highly communicative and engaging learning interactions and practices for the digital age are discussed.

scholarly journals Comparative study of deep learning models for sentiment analysis

2018 ◽  
Vol 7 (2.14) ◽  
pp. 5726
Author(s):  
Oumaima Hourrane ◽  
El Habib Benlahmar ◽  
Ahmed Zellou
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Word Embeddings ◽  
Human Language ◽  
Learning Models ◽  
Automatic Learning ◽  
Learning Capability ◽  
The Web

Sentiment analysis is one of the new absorbing parts appeared in natural language processing with the emergence of community sites on the web. Taking advantage of the amount of information now available, research and industry have been seeking ways to automatically analyze the sentiments expressed in texts. The challenge for this task is the human language ambiguity, and also the lack of labeled data. In order to solve this issue, sentiment analysis and deep learning have been merged as deep learning models are effective due to their automatic learning capability. In this paper, we provide a comparative study on IMDB movie review dataset, we compare word embeddings and further deep learning models on sentiment analysis and give broad empirical outcomes for those keen on taking advantage of deep learning for sentiment analysis in real-world settings.

Brewery: deep learning and deeper profiles for the prediction of 1D protein structure annotations

2020 ◽  
Vol 36 (12) ◽  
pp. 3897-3898
Author(s):  
Mirko Torrisi ◽  
Gianluca Pollastri
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Solvent Accessibility ◽  
Protein Structures ◽  
Evolutionary Information ◽  
Relative Solvent Accessibility ◽  
Multiple Sources ◽  
Contact Density ◽  
And Training ◽  
The Web

Abstract Motivation Protein structural annotations (PSAs) are essential abstractions to deal with the prediction of protein structures. Many increasingly sophisticated PSAs have been devised in the last few decades. However, the need for annotations that are easy to compute, process and predict has not diminished. This is especially true for protein structures that are hardest to predict, such as novel folds. Results We propose Brewery, a suite of ab initio predictors of 1D PSAs. Brewery uses multiple sources of evolutionary information to achieve state-of-the-art predictions of secondary structure, structural motifs, relative solvent accessibility and contact density. Availability and implementation The web server, standalone program, Docker image and training sets of Brewery are available at http://distilldeep.ucd.ie/brewery/. Contact [email protected]

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