Next-Generation text entry

Users are reluctant to learn new text entry methods, as they demand a substantial training investment. Allowing users to flexibly combine multiple existing probabilistic text entry modalities is a way to still provide performance benefits to users.This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/MC.2015.18

Security and confidentiality approach for the Clinical E-Science Framework (CLEF)

CLEF is an MRC sponsored project in the E-Science programme that aims to establish policies and infrastructure for the next generation of integrated clinical and bioscience research. One of the major goals of the project is to provide a pseudonymised repository of histories of cancer patients that can be accessed by researchers. Robust mechanisms and policies are needed to ensure that patient privacy and confidentiality are preserved while delivering a repository of such medically rich information for the purposes of scientific research. This paper summarises the overall approach adopted by CLEF to meet data protection requirements, including the data flows and pseudonymisation mechanisms that are currently being developed. Intended constraints and monitoring policies that will apply to research interrogation of the repository are also outlined. Once evaluated, it is hoped that the CLEF approach can serve as a model for other distributed electronic health record repositories to be accessed for research

Automatic generation of hardware Tree Classifiers

Machine Learning is growing in popularity and spreading across different fields for various applications. Due to this trend, machine learning algorithms use different hardware platforms and are being experimented to obtain high test accuracy and throughput. FPGAs are well-suited hardware platform for machine learning because of its re-programmability and lower power consumption. Programming using FPGAs for machine learning algorithms requires substantial engineering time and effort compared to software implementation. We propose a software assisted design flow to program FPGA for machine learning algorithms using our hardware library. The hardware library is highly parameterized and it accommodates Tree Classifiers. As of now, our library consists of the components required to implement decision trees and random forests. The whole automation is wrapped around using a python script which takes you from the first step of having a dataset and design choices to the last step of having a hardware descriptive code for the trained machine learning model

SkillSum: basic skills screening with personalised, computer-generated feedback

We report on our experiences in developing and evaluating a system that provided formative assessment of basic skills and automatically generated personalised feedback reports for 16-19 year-old users. Development of the system was informed by literacy and numeracy experts and it was trialled 'in the field' with users and basicskills tutors. We experimented with two types of assessment and with feedback that evolved from long, detailed reports with graphics to more readable, shorter ones with no graphics. We discuss the evaluation of our final solution and compare it with related systems

BIF : a behavioral intermediate format for high level synthesis

This report describes a new intermediate format for behavioral synthesis systems, based on annotated state tables. It supports user control of the synthesis process by allowing specification of partial design structures, user-bindings and user modification of compiled designs. It is a simple and uniform representation that can be used as an intermediate exchange format for various behavioral synthesis tools. The format captures synchronous and asynchronous behavior, and serves as a good interface to the user by linking behavior and structure at each level of abstraction in the behavioral synthesis process

Organizing information on the next generation web - Design and implementation of a new bookmark structure

The next-generation Web will increase the need for a highly organized and ever evolving method to store references to Web objects. These requirements could be realized by the development of a new bookmark structure. This paper endeavors to identify the key requirements of such a bookmark, specifically in relation to Web documents, and sets out a suggested design through which these needs may be accomplished. A prototype developed offers such features as the sharing of bookmarks between users and groups of users. Bookmarks for Web documents in this prototype allow more specific information to be stored such as: URL, the document type, the document title, keywords, a summary, user annotations, date added, date last visited and date last modified. Individuals may access the service from anywhere on the Internet, as long as they have a Java-enabled Web browser

Automatically Discovering, Reporting and Reproducing Android Application Crashes

Mobile developers face unique challenges when detecting and reporting crashes in apps due to their prevailing GUI event-driven nature and additional sources of inputs (e.g., sensor readings). To support developers in these tasks, we introduce a novel, automated approach called CRASHSCOPE. This tool explores a given Android app using systematic input generation, according to several strategies informed by static and dynamic analyses, with the intrinsic goal of triggering crashes. When a crash is detected, CRASHSCOPE generates an augmented crash report containing screenshots, detailed crash reproduction steps, the captured exception stack trace, and a fully replayable script that automatically reproduces the crash on a target device(s). We evaluated CRASHSCOPE's effectiveness in discovering crashes as compared to five state-of-the-art Android input generation tools on 61 applications. The results demonstrate that CRASHSCOPE performs about as well as current tools for detecting crashes and provides more detailed fault information. Additionally, in a study analyzing eight real-world Android app crashes, we found that CRASHSCOPE's reports are easily readable and allow for reliable reproduction of crashes by presenting more explicit information than human written reports.Comment: 12 pages, in Proceedings of 9th IEEE International Conference on Software Testing, Verification and Validation (ICST'16), Chicago, IL, April 10-15, 2016, pp. 33-4