scholarly journals Enhancing Interactivity: How has design exploration of physically and intellectually interactive picturebooks enhanced shared reading?

2020 ◽  
Author(s):  
Nicholas Vanderschantz ◽  
◽  
Claire Timpany ◽  
Kristy Wright
Keyword(s):  
Shared Reading ◽  
Design Exploration

Preschoolers Who Are Nonresponsive to Shared-Reading Instruction: Moderators and Mediators

2007 ◽  
Author(s):  
Catherine Roux ◽  
Jorge E. Gonzalez ◽  
Deborah C. Simmons ◽  
Sharolyn Pollard-Durodola ◽  
Vivina Y. Rivera ◽  
...  
Keyword(s):  
Reading Instruction ◽  
Shared Reading

A Design Exploration Framework for Secure IoT-Systems

2020 ◽  
Author(s):  
Lukas Gressl ◽  
Alexander Rech ◽  
Christian Steger ◽  
Andreas Sinnhofer ◽  
Ralph Weissnegger
Keyword(s):  
Design Exploration

Comparing the Effectiveness of Two Oral Reading Practices: Round-Robin Reading and the Shared Book Experience

1996 ◽  
Vol 28 (2) ◽  
pp. 201-225 ◽  
Author(s):  
J. Lloyd Eldredge ◽  
D. Ray Reutzel ◽  
Paul M. Hollingsworth
Keyword(s):  
Reading Fluency ◽  
Oral Reading ◽  
Experimental Studies ◽  
Second Graders ◽  
Round Robin ◽  
Shared Reading ◽  
Book Reading ◽  
Reading Practices ◽  
Basal Readers ◽  
Reading Errors

This study compared the effectiveness of two oral reading practices on second graders' reading growth: shared book reading and round-robin reading. The results indicated that the Shared Book Experience was superior to round-robin reading in reducing young children's oral reading errors, improving their reading fluency, increasing their vocabulary acquisition, and improving their reading comprehension. An analysis of the primary-grade basal readers submitted for adoption in 1993 revealed that most had incorporated “shared reading” into their instructional designs. Before “shared reading,” the common practice was “individual reading,” and although the authors of basals did not recommend it, round-robin oral reading was widely used. Although the Shared Book Experience had been widely used in schools prior to its inclusion in basal designs, there were no experimental studies supporting it. The findings of this study are discussed and related to these classroom practices and trends.

scholarly journals OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4805
Author(s):  
Saad Abbasi ◽  
Mahmoud Famouri ◽  
Mohammad Javad Shafiee ◽  
Alexander Wong
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Network Architectures ◽  
Design Exploration ◽  
Convolutional Autoencoder ◽  
Acoustic Anomaly ◽  
Human Operators ◽  
Computational Resources

Human operators often diagnose industrial machinery via anomalous sounds. Given the new advances in the field of machine learning, automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning-driven anomaly detection methods often require an extensive amount of computational resources prohibiting their deployment in factories. Here we explore a machine-driven design exploration strategy to create OutlierNets, a family of highly compact deep convolutional autoencoder network architectures featuring as few as 686 parameters, model sizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection accuracy matching or exceeding published architectures with as many as 4 million parameters. The architectures are deployed on an Intel Core i5 as well as a ARM Cortex A72 to assess performance on hardware that is likely to be used in industry. Experimental results on the model’s latency show that the OutlierNet architectures can achieve as much as 30x lower latency than published networks.

scholarly journals Interactive Exploration of Large-Scale UI Datasets with Design Maps

2021 ◽  
Author(s):  
Luis A Leiva ◽  
Asutosh Hota ◽  
Antti Oulasvirta
Keyword(s):  
User Interface ◽  
Point Cloud ◽  
Large Scale ◽  
Mobile Apps ◽  
Online Resources ◽  
Design Tools ◽  
Support Design ◽  
Design Exploration ◽  
Clustering Techniques ◽  
Cloud Points

Abstract Designers are increasingly using online resources for inspiration. How to best support design exploration without compromising creativity? We introduce and study Design Maps, a class of point-cloud visualizations that makes large user interface datasets explorable. Design Maps are computed using dimensionality reduction and clustering techniques, which we analyze thoroughly in this paper. We present concepts for integrating Design Maps into design tools, including interactive visualization, local neighborhood exploration and functionality to integrate existing solutions to the design at hand. These concepts were implemented in a wireframing tool for mobile apps, which was evaluated with actual designers performing realistic tasks. Overall, designers find Design Maps supporting their creativity (avg. CSI score of 74/100) and indicate that the maps producing consistent whitespacing within cloud points are the most informative ones.

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