Massive open online courses (MOOCs) have commanded considerable public attention due to their sudden rise and disruptive potential. But there are no robust, published data that describe who is taking these courses and why they are doing so. As such, we do not yet know how transformative the MOOC phenomenon can or will be. We conducted an online survey of students enrolled in at least one of the University of Pennsylvania’s 32 MOOCs offed on the Coursera platform. The student population tends to be young, well educated, and employed, with a majority from developed countries. There are significantly more males than females taking MOOCs, especially in BRIC and other developing countries. Students’ main reasons for taking a MOOC are advancing in their current job and satisfying curiosity. The individuals the MOOC revolution is supposed to help the most — those without access to higher education in developing countries — are underrepresented among the early adopters.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2350964_code2143609.pdf?abstractid=2350964.&mirid=5

The MOOC Phenomenon: Who Takes Massive Open Online Courses and Why?

Cloud computing for teaching and learning: strategies for design and implementation / Lee Chao, editor. p. cm. Includes bibliographical references and index. Summary: " This book provides the latest information about cloud development and cloud applications in teaching and learning, including empirical research findings in these areas for professionals and researchers working in the field of e-learning who want to implement teaching and learning with cloud computing "-All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the authors, but not necessarily of the publisher. Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark.

Cloud Computing for Teaching and Learning: Strategies for Design and Implementation

Search-Based Software Testing is a well-established research area, whose goal is to apply meta-heuristic approaches, like Genetic Algorithms, to address optimization problems in the testing domain. Even if many interesting results have been achieved in this field, the heavy computational resources required by these approaches are limiting their practical application in the industrial domain. In this chapter, the authors propose the migration of Search-Based Software Testing techniques to the Cloud aiming to improve their performance and scalability. Moreover, they show how the use of the MapReduce paradigm can support the parallelization of Genetic Algorithms for test data generation and their migration in the Cloud, thus relieving software company from the management and maintenance of the overall IT infrastructure and developers from handling the communication and synchronization of parallel tasks. Some preliminary results are reported, gathered by a proof-of-concept developed on the Google’s Cloud Infrastructure.

http://www0.cs.ucl.ac.uk/staff/fsarro/resource/papers/B1_EditorProof.pdf

Towards Migrating Genetic Algorithms for Test Data Generation to the Cloud

Image segmentation is an important process that facilitates image analysis such as in object detection. Because of its importance, many different algorithms were proposed in the last decade to enhance image segmentation techniques. Clustering algorithms are among the most popular in image segmentation. The proposed algorithms differ in their accuracy and computational efficiency. This paper studies the most famous and new clustering algorithms and provides an analysis on their feasibility for parallel implementation. We have studied four algorithms which are: fuzzy C-mean, type-2 fuzzy C-mean, interval type-2 fuzzy C-mean, and modified interval type-2 fuzzy C-mean. We have implemented them in a sequential (CPU only) and a parallel hybrid CPU---GPU version. Speedup gains of 6$$\times $$× to 20$$\times $$× were achieved in the parallel implementation over the sequential implementation. We detail in this paper our discoveries on the portions of the algorithms that are highly parallel so as to help the image processing community, especially if these algorithms are to be used in real-time processing where efficient computation is critical.

Accelerating compute-intensive image segmentation algorithms using GPUs

Cloud service providers offer a huge variety of schema-less NoSQL data storage solutions. The flexibility of these data stores offer greater freedom in structuring the data than relational databases. However, it would be desirable to make use of the strong mathematical background of relational data structures. In this paper, we introduce an automatic NoSQL schema optimization that uses a normalized data schema as starting point. We analyze the predefined set of queries, and compile the schema that can serve the queries with minimal cost at a certain query load. The introduced process is performed on a conceptual model of the database, and the queries are defined in Object Constraint Language to simplify the analysis. The optimization algorithm is introduced through a case study.

https://www.actapress.com/PaperInfo.aspx?paperId=455440

Automatic NoSQL Schema Development: A Case Study

With this book, you can build exciting, scalable web applications quickly and confidently, using Google App Engine -- even if you have little or no experience in programming or web development. App Engine is one of the most exciting web technologies to appear in the last year, providing a simple, easy-to-use application framework with basic web tools. While Google's own tutorial assumes significant development experience, Using Google App Engine is for anyone who wants to get started with this platform. By the end of the book, you'll know how to build complete, interactive applications and deploy them to "the cloud," using the same servers that power Google applications. You will: Get an overview of all the technologies necessary to use Google App Engine Learn how to use Python, HTML, Cascading Style Sheets (CSS), HTTP, and DataStore, App Engine's database Grasp the technical aspects necessary to create sophisticated, dynamic web applications Understand what's required to deploy your applications to Google servers In addition to being a foundational resource for beginning programmers, Using Google App Engine is also an excellent book for experienced programmers who want to acquire working knowledge of web technologies. Building web applications used to be for experts only, but with Google App Engine -- and this book -- anyone can create a dynamic web presence.

Using Google App Engine

The impact of technology and networks on our lives, culture, and society continues to increase. The very fact that you can take this course from anywhere in the world requires a technological infrastructure that was designed, engineered, and built over the past sixty years. To function in an information-centric world, we need to understand the workings of network technology. This course will open up the Internet and show you how it was created, who created it and how it works. Along the way we will meet many of the innovators who developed the Internet and Web technologies that we use today. ¶ Course Audience ¶ This course has no prerequisites and there will be no programming. Literally anyone can and everyone should take this course. ¶ What You Will Learn ¶ After this course you will not take the Internet and Web for granted. You will be better informed about important technological issues currently facing society. You will realize that the Internet and Web are spaces for innovation and you will get a better understanding of how you might fit into that innovation. If you get excited about the material in this course, it is a great lead-in to taking a course in Web design, Web development, programming, or even network administration. At a minimum, you will be a much wiser network citizen.

SI/Coursera - Internet History, Technology, and Security

The purpose of this book is to teach new programmers and scientists about the basics of High Performance Computing. Too many parallel and high performance computing books focus on the architecture, theory and computer science surrounding HPC. This book speaks to the practicing chemistry student, physicist, or biologist who need to write and run their programs as part of their research. ¶ High Performance Computing, originally published by O’Reilly–but out of print since 2003, has been republished on Connexions. Book author Severance, Charles;, with his editor Mike Loukides, worked with O’Reilly to release the book under a CC-BY license, then coordinated with the Connexions staff to republish it.

High Performance Computing, an open textbook

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SI/Coursera - Internet History, Technology, and Security

High Performance Computing, an open textbook