Defining adaptation in a generic multi layer model : CAM: the GRAPPLE conceptual adaptation model

Authoring of Adaptive Hypermedia is a difficult and time consuming task. Reference models like LAOS and AHAM separate adaptation and content in different layers. Systems like AHA! offer graphical tools based on these models to allow authors to define adaptation without knowing any adaptation language. The adaptation that can be defined using such tools is still limited. Authoring systems like MOT are more flexible, but usability of adaptation specification is low. This paper proposes a more generic model which allows the adaptation to be defined in an arbitrary number of layers, where adaptation is expressed in terms of relationships between concepts. This model allows the creation of more powerful yet easier to use graphical authoring tools. This paper presents the structure of the Conceptual Adaptation Models used in adaptive applications created within the GRAPPLE adaptive learning environment, and their representation in a graphical authoring tool

Building ArtBots to attract students into STEM learning

There is an increasing worldwide demand for people educated into science and technology. Unfortunately, girls and underprivileged students are often underrepresented in Science, Technology, Engineering and Mathematics (STEM) education programs. We believe that by inclusion of art in these programs, educational activities might become more attractive to a broader audience. In this work we present an example of such an educational activity: an international robotics and art week for secondary school students. This educational activity builds up on the project-based and inquiry learning framework. This article is intended as a brief manual to help others organise such an activity. It also gives insights in how we led a highly heterogeneous group of students into learning STEM and becoming science and technology ambassadors for their peers

ONTOLOGY-BASED WEB TOOLS for RETRIEVING PHOTOGRAMMETRIC CULTURAL HERITAGE MODELS

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A NeISS collaboration to develop and use e-infrastructure for large-scale social simulation

The National e-Infrastructure for Social Simulation (NeISS) project is focused on developing e-Infrastructure to support social simulation research. Part of NeISS aims to provide an interface for running contemporary dynamic demographic social simulation models as developed in the GENESIS project. These GENESIS models operate at the individual person level and are stochastic. This paper focuses on support for a simplistic demographic change model that has a daily time steps, and is typically run for a number of years. A portal based Graphical User Interface (GUI) has been developed as a set of standard portlets. One portlet is for specifying model parameters and setting a simulation running. Another is for comparing the results of different simulation runs. Other portlets are for monitoring submitted jobs and for interfacing with an archive of results. A layer of programs enacted by the portlets stage data in and submit jobs to a Grid computer which then runs a specific GENESIS model program executable. Once a job is submitted, some details are communicated back to a job monitoring portlet. Once the job is completed, results are stored and made available for download and further processing. Collectively we call the system the Genesis Simulator. Progress in the development of the Genesis Simulator was presented at the UK e- Science All Hands Meeting in September 2011 by way of a video based demonstration of the GUI, and an oral presentation of a working paper. Since then, an automated framework has been developed to run simulations for a number of years in yearly time steps. The demographic models have also been improved in a number of ways. This paper summarises the work to date, presents some of the latest results and considers the next steps we are planning in this work

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502