78,576 research outputs found
Uniform: The Form Validation Language
Digital forms are becoming increasingly more prevalent but the ease of creation is not. Web Forms are difficult to produce and validate. This design project seeks to simplify this process. This project is comprised of two parts: a logical programming language (Uniform) and a web application.
Uniform is a language that allows its users to define logical relationships between web elements and apply simple rules to individual inputs to both validate the form and manipulate its components depending on user input. Uniform provides an extra layer of abstraction to complex coding.
The web app implements Uniform to provide business-level programmers with an interface to build and manage forms. Users will create form templates, manage form instances, and cooperatively complete forms through the web app.
Uniformâs development is ongoing, it will receive continued support and is available as open-source. The web application is software owned and maintained by HP Inc. which will be developed further before going to market
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Use of Questions to Facilitate Social Learning in a Web 2.0 Environment
Online social learning involves distributed learners interacting through the use of Web 2.0. In many cases, Web 2.0 interactions are limited to information exchange and do not provoke knowledge construction. Studies of concept mapping suggest that engaging with social learning via a question could encourage meaningful interaction, although this would be likely to depend upon affective conditions and the effort involved in asking and responding to these questions. In order to investigate this, the interactions of 1,229 participants on a social learning site were studied over an 11-week period. Data were also collected from a questionnaire distributed to all participants, and from feedback contributed during the project. These were analysed thematically to investigate the ways in which questions can be used to facilitate learning in a Web 2.0 environment. Analysis showed that participants were interested in broad topic areas, themes and issues rather than specific questions about these areas. They did not treat questions related to learning about the community and the website in the same way as questions related to learning about subject areas and content. The social use of questions online was identified as supporting meaningful learning interaction in nine ways
Twelve Theses on Reactive Rules for the Web
Reactivity, the ability to detect and react to events, is an
essential functionality in many information systems. In particular, Web
systems such as online marketplaces, adaptive (e.g., recommender) systems,
and Web services, react to events such as Web page updates or
data posted to a server.
This article investigates issues of relevance in designing high-level programming
languages dedicated to reactivity on the Web. It presents
twelve theses on features desirable for a language of reactive rules tuned
to programming Web and Semantic Web applications
Login Authentication with Facial Gesture Recognition
Facial recognition has proven to be very useful and versatile, from Facebook photo tagging and Snapchat filters to modeling fluid dynamics and designing for augmented reality. However, facial recognition has only been used for user login services in conjunction with expensive and restrictive hardware technologies, such as in smart phone devices like the iPhone x. This project aims to apply machine learning techniques to reliably distinguish user accounts with only common cameras to make facial recognition logins more accessible to website and software developers. To show the feasibility of this idea, we created a web API that recognizes a users face to log them in to their account, and we will create a simple website to test the reliability of our system. In this paper, we discuss our database-centric architecture model, use cases and activity diagrams, technologies we used for the website, API, and machine learning algorithms. We also provide the screenshots of our system, the user manual, and our future plan
Generating dynamic higher-order Markov models in web usage mining
Markov models have been widely used for modelling usersâ web navigation behaviour. In previous work we have presented a dynamic clustering-based Markov model that accurately represents second-order transition probabilities given by a collection of navigation sessions. Herein, we propose a generalisation of the method that takes into account higher-order conditional probabilities. The method makes use of the state cloning concept together with a clustering technique to separate the navigation paths that reveal differences in the conditional probabilities. We report on experiments conducted with three real world data sets. The results show that some pages require a long history to understand the users choice of link, while others require only a short history. We also show that the number of additional states induced by the method can be controlled through a probability threshold parameter
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