Modelling Provenance of Sensor Data for Food Safety Compliance Checking

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Assessing the Quality of Semantic Sensor Data

Acknowledgements The research described here is supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub; award reference: EP/G066051/1

FdDCA : A Novel Fuzzy Deterministic Dendritic Cell Algorithm

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A framework for modelling mobile radio access networks for intelligent fault management

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Summarising News Stories for Children

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Semantic Stream Processing for IoT Devices in the Food Safety Domain

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Models and empirical data for the production of referring expressions

Article Accepted Date: 29 May 2014 Acknowledgements The authors gratefully acknowledge the support of the Cognitive Science Society for the organisation of the Workshop on Production of Referring Expressions: Bridging the Gap between Cognitive and Computational Approaches to Reference, from which this special issue originated. Funding Emiel Krahmer and Albert Gatt thank The Netherlands Organisation for Scientific Research (NWO) for VICI grant Bridging the Gap between Computational Linguistics and Psycholinguistics: The Case of Referring Expressions (grant number 277-70-007).

Production of Referring Expressions for an Unknown Audience : a Computational Model of Communal Common Ground

The research reported in this article is based on the Ph.D. project of Dr. RK, which was funded by the Scottish Informatics and Computer Science Alliance (SICSA). KvD acknowledges support from the EPSRC under the RefNet grant (EP/J019615/1).

Sentiment-Topic Modelling in Text Mining

Peer reviewed

Spacetime foam induced collective bundling of intense fields

This research is supported by the Carnegie Trust for the Universities of Scotland (TO) and by the EPSRC GG-Top Project and the Cruickshank Trust (CW).

Parallel and Streaming Truth Discovery in Large-Scale Quantitative Crowdsourcing

ACKNOWLEDGEMENTS This research is based upon work supported in part by the U.S. ARL and U.K. Ministry of Defense under Agreement Number W911NF-06-3-0001, and by the NSF under award CNS-1213140. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views or represent the official policies of the NSF, the U.S. ARL, the U.S. Government, the U.K. Ministry of Defense or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.

Software Components for Serious Game Development

The large upfront investments required for game development pose a severe barrier for the wider uptake of serious games in education and training. Also, there is a lack of well-established methods and tools that support game developers at preserving and enhancing the games’ pedagogical effectiveness. The RAGE project, which is a Horizon 2020 funded research project on serious games, addresses these issues by making available reusable software components that aim to support the pedagogical qualities of serious games. In order to easily deploy and integrate these game components in a multitude of game engines, platforms and programming languages, RAGE has developed and validated a hybrid component-based software architecture that preserves component portability and interoperability. While a first set of software components is being developed, this paper presents selected examples to explain the overall system’s concept and its practical benefits. First, the Emotion Detection component uses the learners’ webcams for capturing their emotional states from facial expressions. Second, the Performance Statistics component is an add-on for learning analytics data processing, which allows instructors to track and inspect learners’ progress without bothering about the required statistics computations. Third, a set of language processing components accommodate the analysis of textual inputs of learners, facilitating comprehension assessment and prediction. Fourth, the Shared Data Storage component provides a technical solution for data storage - e.g. for player data or game world data - across multiple software components. The presented components are exemplary for the anticipated RAGE library, which will include up to forty reusable software components for serious gaming, addressing diverse pedagogical dimensions.

Quantitative Comparison of SensibleThings and Dweet.io

The objective of this paper is to perform a quantitative comparison of Dweet.io and SensibleThings from different aspects. With the fast development of internet of things, the platforms for internet-of-things face bigger challenges. This paper will evaluate both systems in four parts. The first part shows the general comparison of input ways and output functions provided by the platforms. The second part shows the security comparison, which focuses on the protocol types of the packets and the stability during the communication. The third part shows the scalability comparison when the value becomes bigger. The fourth part shows the scalability comparison when speeding up the processes. After the comparisons, I concluded that Dweet.io is more easy to use on devices and supports more programming languages. Dweet.io realizes visualization and it can be shared. Dweet.io is safer and more stable than SensibleThings. SensibleThings provides more openness. SensibleThings has better scalability in handling big values and quick speed.

Numerical simulation of the mass loss process in pyrolizing char materials

Abstract not available

Statistical software usage by student and consultant statisticians in the UK - A comparative study

Abstract not available