Semantics-supported cooperative learning for enhanced awareness

Awareness is required for supporting all forms of cooperation. In Computer Supported Collaborative Learning (CSCL), awareness can be used for enhancing collaborative opportunities across physical distances and in computer-mediated environments. Shared Knowledge Awareness (SKA) intends to increase the perception about the shared knowledge, students have in a collaborative learning scenario and also concerns the understanding that this group has about it. However, it is very difficult to produce accurate awareness indicators based on informal message exchange among the participants. Therefore, we propose a semantic system for cooperation that makes use of formal methods for knowledge representation based on semantic web technologies. From these semantics-enhanced repository and messages, it could be easier to compute more accurate awareness

Context modeling and constraints binding in web service business processes

Context awareness is a principle used in pervasive services applications to enhance their exibility and adaptability to changing conditions and dynamic environments. Ontologies provide a suitable framework for context modeling and reasoning. We develop a context model for executable business processes { captured as an ontology for the web services domain. A web service description is attached to a service context profile, which is bound to the context ontology. Context instances can be generated dynamically at services runtime and are bound to context constraint services. Constraint services facilitate both setting up constraint properties and constraint checkers, which determine the dynamic validity of context instances. Data collectors focus on capturing context instances. Runtime integration of both constraint services and data collectors permit the business process to achieve dynamic business goals

Collaboration in the Semantic Grid: a Basis for e-Learning

The CoAKTinG project aims to advance the state of the art in collaborative mediated spaces for the Semantic Grid. This paper presents an overview of the hypertext and knowledge based tools which have been deployed to augment existing collaborative environments, and the ontology which is used to exchange structure, promote enhanced process tracking, and aid navigation of resources before, after, and while a collaboration occurs. While the primary focus of the project has been supporting e-Science, this paper also explores the similarities and application of CoAKTinG technologies as part of a human-centred design approach to e-Learning

Knowledge web: realising the semantic web... all the way to knowledge-enhanced multimedia documents

The semantic web and semantic web services are major efforts in order to spread and to integrate knowledge technology to the whole web. The Knowledge Web network of excellence aims at supporting their developments at the best and largest European level and supporting industry in adopting them. It especially investigates the solution of scalability, heterogeneity and dynamics obstacles to the full development of the semantic web. We explain how Knowledge Web results should benefit knowledge-enhanced multimedia applications

Enhancing knowledge management in online collaborative learning

This study aims to explore two crucial aspects of collaborative work and learning: on the one hand, the importance of enabling collaborative learning applications to capture and structure the information generated by group activity and, on the other hand, to extract the relevant knowledge in order to provide learners and tutors with efficient awareness, feedback and support as regards group performance and collaboration. To this end, in this paper we first propose a conceptual model for data analysis and management that identifies and classifies the many kinds of indicators that describe collaboration and learning into high-level aspects of collaboration. Then, we provide a computational platform that, at a first step, collects and classifies both the event information generated asynchronously from the users' actions and the labeled dialogues from the synchronous collaboration according to these indicators. This information is then analyzed in next steps to eventually extract and present to participants the relevant knowledge about the collaboration. The ultimate aim of this platform is to efficiently embed information and knowledge into collaborative learning applications. We eventually suggest a generalization of our approach to be used in diverse collaborative learning situations and domains

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Semantic Wide and Deep Learning for Detecting Crisis-Information Categories on Social Media

When crises hit, many flog to social media to share or consume information related to the event. Social media posts during crises tend to provide valuable reports on affected people, donation offers, help requests, advice provision, etc. Automatically identifying the category of information (e.g., reports on affected individuals, donations and volunteers) contained in these posts is vital for their efficient handling and consumption by effected communities and concerned organisations. In this paper, we introduce Sem-CNN; a wide and deep Convolutional Neural Network (CNN) model designed for identifying the category of information contained in crisis-related social media content. Unlike previous models, which mainly rely on the lexical representations of words in the text, the proposed model integrates an additional layer of semantics that represents the named entities in the text, into a wide and deep CNN network. Results show that the Sem-CNN model consistently outperforms the baselines which consist of statistical and non-semantic deep learning models

Design and semantics of form and movement (DeSForM 2006)

Design and Semantics of Form and Movement (DeSForM) grew from applied research exploring emerging design methods and practices to support new generation product and interface design. The products and interfaces are concerned with: the context of ubiquitous computing and ambient technologies and the need for greater empathy in the pre-programmed behaviour of the ‘machines’ that populate our lives. Such explorative research in the CfDR has been led by Young, supported by Kyffin, Visiting Professor from Philips Design and sponsored by Philips Design over a period of four years (research funding £87k). DeSForM1 was the first of a series of three conferences that enable the presentation and debate of international work within this field: • 1st European conference on Design and Semantics of Form and Movement (DeSForM1), Baltic, Gateshead, 2005, Feijs L., Kyffin S. & Young R.A. eds. • 2nd European conference on Design and Semantics of Form and Movement (DeSForM2), Evoluon, Eindhoven, 2006, Feijs L., Kyffin S. & Young R.A. eds. • 3rd European conference on Design and Semantics of Form and Movement (DeSForM3), New Design School Building, Newcastle, 2007, Feijs L., Kyffin S. & Young R.A. eds. Philips sponsorship of practice-based enquiry led to research by three teams of research students over three years and on-going sponsorship of research through the Northumbria University Design and Innovation Laboratory (nuDIL). Young has been invited on the steering panel of the UK Thinking Digital Conference concerning the latest developments in digital and media technologies. Informed by this research is the work of PhD student Yukie Nakano who examines new technologies in relation to eco-design textiles

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page