TravelBot: Journey Disruption Alerts Utilising Social Media and Linked Data

Postprint

Comprehensive security in data processing systems : the state of the art /

"UIUCDCS-R-75-725"

The precompiler component of a data base dictionary system /

Thesis (M.S.)--University of Illinois at Urbana-Champaign.

A synchronization system for a television bandwidth compression scheme /

COO 1469-0194.

Data dependence in ordinary programs /

Thesis (M. S.)--University of Illinois at Urbana-Champaign.

A study of data manipulation algorithms in magnetic bubble memories /

Thesis--Illinois.

Electronic data processing for business and industry,

Bibliography: p. 325-327.

Social media data in research : provenance challenges?

Postprint

Modelling Provenance of Sensor Data for Food Safety Compliance Checking

Postprint

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

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).

A methodology for spreadsheet development based on data structure

Abstract not available

An assessment of data leakage in Firefox under different conditions.

Data leakage is a serious issue and can result in the loss of sensitive data, compromising user accounts and details, potentially affecting millions of internet users. This paper contributes to research in online security and reducing personal footprint by evaluating the levels of privacy provided by the Firefox browser. The aim of identifying conditions that would minimize data leakage and maximize data privacy is addressed by assessing and comparing data leakage in the four possible browsing modes: normal and private modes using a browser installed on the host PC or using a portable browser from a connected USB device respectively. To provide a firm foundation for analysis, a series of carefully designed, pre-planned browsing sessions were repeated in each of the various modes of Firefox. This included low RAM environments to determine any effects low RAM may have on browser data leakage. The results show that considerable data leakage may occur within Firefox. In normal mode, all of the browsing information is stored within the Mozilla profile folder in Firefox-specific SQLite databases and sessionstore.js. While passwords were not stored as plain text, other confidential information such as credit card numbers could be recovered from the Form history under certain conditions. There is no difference when using a portable browser in normal mode, except that the Mozilla profile folder is located on the USB device rather than the host's hard disk. By comparison, private browsing reduces data leakage. Our findings confirm that no information is written to the Firefox-related locations on the hard disk or USB device during private browsing, implying that no deletion would be necessary and no remnants of data would be forensically recoverable from unallocated space. However, two aspects of data leakage occurred equally in all four browsing modes. Firstly, all of the browsing history was stored in the live RAM and was therefore accessible while the browser remained open. Secondly, in low RAM situations, the operating system caches out RAM to pagefile.sys on the host's hard disk. Irrespective of the browsing mode used, this may include Firefox history elements which can then remain forensically recoverable for considerable time.

Hierarchical clustering with multiple-height branch-cut applied to short time-series gene expression data.

Rigid adherence to pre-specified thresholds and static graphical representations can lead to incorrect decisions on merging of clusters. As an alternative to existing automated or semi-automated methods, we developed a visual analytics approach for performing hierarchical clustering analysis of short time-series gene expression data. Dynamic sliders control parameters such as the similarity threshold at which clusters are merged and the level of relative intra-cluster distinctiveness, which can be used to identify "weak-edges" within clusters. An expert user can drill down to further explore the dendrogram and detect nested clusters and outliers. This is done by using the sliders and by pointing and clicking on the representation to cut the branches of the tree in multiple-heights. A prototype of this tool has been developed in collaboration with a small group of biologists for analysing their own datasets. Initial feedback on the tool has been positive.