Accessing the mobile web: myth or reality?

Emerging technologies for learning report - Article exploring open web standard

Privacy Implications of Health Information Seeking on the Web

This article investigates privacy risks to those visiting health- related web pages. The population of pages analyzed is derived from the 50 top search results for 1,986 common diseases. This yielded a total population of 80,124 unique pages which were analyzed for the presence of third-party HTTP requests. 91% of pages were found to make requests to third parties. Investigation of URIs revealed that 70% of HTTP Referer strings contained information exposing specific conditions, treatments, and diseases. This presents a risk to users in the form of personal identification and blind discrimination. An examination of extant government and corporate policies reveals that users are insufficiently protected from such risks

Interactive learning aided by JavaScript

This paper presents a case study in which some of the features of JavaScript have been employed to support the learning environment of students. Students have access to notes, self‐assessment tests, and revision crossword puzzles. JavaScript is sufficiently advanced to permit the writing of a simple nutritional analysis program. However, there are some problems caused by slight incompatibilities between browsers, but this complication is of no importance when students have access only to one browser on the network

Ariadne: Analysis for Machine Learning Program

Machine learning has transformed domains like vision and translation, and is now increasingly used in science, where the correctness of such code is vital. Python is popular for machine learning, in part because of its wealth of machine learning libraries, and is felt to make development faster; however, this dynamic language has less support for error detection at code creation time than tools like Eclipse. This is especially problematic for machine learning: given its statistical nature, code with subtle errors may run and produce results that look plausible but are meaningless. This can vitiate scientific results. We report on Ariadne: applying a static framework, WALA, to machine learning code that uses TensorFlow. We have created static analysis for Python, a type system for tracking tensors---Tensorflow's core data structures---and a data flow analysis to track their usage. We report on how it was built and present some early results