University of Leicester

computerscience

Computer Science Seminars

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Semester 1, 2014/15

Seminar programme


Seminar details

Jorge Perez (Groningen, The Netherlands)
21st Nov, 14:00 in (Host: Paula Severi, Alexander Kurz)


Toward a Comprehensive Framework for Business Process Compliance

Amal Elgammal (Trinity College Dublin)
14th Nov, 14:00 in (Host: Artur Boronat)

Today's enterprises demand a high degree of compliance of business processes to meet ‎regulations, such as Sarbanes-Oxley and Basel III. To ensure continuous guaranteed ‎compliance, it should be enforced during all phases of the business process lifecycle, from the ‎phases of analysis and design to deployment, monitoring and adaptation. This course of ‎research primarily concentrates on design-time aspects of compliance management and ‎secondarily on business process runtime monitoring; hence, providing preventive lifetime ‎compliance support. While current manual or ad-hoc solutions provide limited assurances ‎that business processes adhere to relevant constraints, there is still a lack of an established ‎generic framework for managing these constraints; integrating their relationships and ‎maintaining their traceability to sources and processes; and automatically verifying their ‎satisfiability. In this talk, I will present my research results that address the problems of automating the compliance verification activities during design-time and the reasoning and analysis of detected compliance violations.


Andrzej Murawski (University of Warwick)
31st Oct, 14:00 in ATT LT1 (Host: Thomas Erlebach)


Measuring User Experience in Human-Computer Interaction: Current status, caveats and alternative methods

Anders Bruun (Aalborg University)
24th Oct, 14:00 in ATT LT1 (Host: Effie Law)

The concept of User Experience (UX) in Human-Computer Interaction has evolved over the past 10 years. UX is typically considered to cover a broad range of dimension going beyond usability of interactive products. This talk will firstly provide a brief overview of state-of-the art in UX research. Secondly, the talk will present results from a recent experiment questioning the reliability of current practices for assessing UX.

UX is typically measured retrospectively through subjective questionnaire ratings, yet we know little of how well these retrospective ratings reflect concurrent experiences of an entire interaction sequence. The talk will present findings from an empirical study of the gap between concurrent and retrospective ratings of UX. Alternative methods of assessing UX will be discussed, which have considerable implications for practice.


Jun Zhao (Lancaster University)
17th Oct, 14:00 in ATT LT1 (Host: Ruzanna Chitchyan)


Cecilia Mascolo (University of Cambridge)
10th Oct, 14:00 in ATT LT1 (Host: Thomas Erlebach)


Fast Statistical Assessment for Combinatorial Hypotheses Based on Frequent Itemset Enumeration

Shin-Ichi Minato (Hokkaido University, Japan)
Fri, 19th Sept, 13:00 in Bennett LT5 (Host: Rajeev Raman)

In many scientific communities using experiment databases, one of the crucial problems is how to assess the statistical significance (p-value) of a discovered hypothesis. Especially, combinatorial hypothesis assessment is a hard problem because it requires a multiple-testing procedure with a very large factor of the p-value correction. Recently, Terada et al. proposed a novel method of the p-value correction, called "Limitless Arity Multiple-testing Procedure" (LAMP), which is based on frequent itemset enumeration to exclude meaninglessly infrequent itemsets which will never be significant. The LAMP makes much more accurate p-value correction than previous method, and it empowers the scientific discovery. However, the original LAMP implementation is sometimes too time-consuming for practical databases. We propose a new LAMP algorithm that essentially executes itemset mining algorithm once, while the previous one executes many times. Our experimental results show that the proposed method is much (10 to 100 times) faster than the original LAMP. This algorithm enables us to discover significant p-value patterns in quite short time even for very large-scale databases.


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Author: Alexander Kurz (kurz mcs le ac uk), T: 0116 252 5356.
University of Leicester . Last modified: 19th September 2014, 09:54:17.
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