Evaluating Bad Query Abandonment in an Iterative SMS-Based FAQ Retrieval System

In this paper, we investigate how many iterations users are willing to tolerate in an iterative Frequently Asked Ques- tion (FAQ) system that provides information on HIV/AIDS. This is part of work in progress that aims to develop an automated Frequently Asked Question system that can be used to provide answers on HIV/AIDS related queries to users in Botswana. Our system engages the user in the question answering process by following an iterative interaction approach in order to avoid giving inappropriate answers to the user. Our findings provide us with an indication of how long users are willing to engage with the system. We sub- sequently use this to develop a novel evaluation metric to use in future developments of the system. As an additional finding, we show that the previous search experience of the users has a significant effect on their future behaviour

Evaluating the effectiveness of the Disability Discrimination (NI) Order (2006) duties

This report evaluates the effectiveness of the Disability Discrimination (NI) Order (2006) duties. These duties require public authorities in Northern Ireland to promote positive attitudes towards disabled people and to encourage the participation of disabled people in public life. The duties also require public authorities to produce disability action plans and to report annually on progress towards disability equality. The report provides a framework for evaluation of progress and applies this to provide an assessment of the implementation of these duties up to 2009 and makes recommendations to improve the effectiveness and implementation of the duties

Detecting missing content queries in an SMS-Based HIV/AIDS FAQ retrieval system

Automated Frequently Asked Question (FAQ) answering systems use pre-stored sets of question-answer pairs as an information source to answer natural language questions posed by the users. The main problem with this kind of information source is that there is no guarantee that there will be a relevant question-answer pair for all user queries. In this paper, we propose to deploy a binary classifier in an existing SMS-Based HIV/AIDS FAQ retrieval system to detect user queries that do not have the relevant question-answer pair in the FAQ document collection. Before deploying such a classifier, we first evaluate different feature sets for training in order to determine the sets of features that can build a model that yields the best classification accuracy. We carry out our evaluation using seven different feature sets generated from a query log before and after retrieval by the FAQ retrieval system. Our results suggest that, combining different feature sets markedly improves the classification accuracy

Anticommuting Variables, Fermionic Path Integrals and Supersymmetry

(Replacement because mailer changed `hat' for supercript into something weird. The macro `\sp' has been used in place of the `hat' character in this revised version.) Fermionic Brownian paths are defined as paths in a space para\-metr\-ised by anticommuting variables. Stochastic calculus for these paths, in conjunction with classical Brownian paths, is described; Brownian paths on supermanifolds are developed and applied to establish a Feynman-Kac formula for the twisted Laplace-Beltrami operator on differential forms taking values in a vector bundle. This formula is used to give a proof of the Atiyah-Singer index theorem which is rigorous while being closely modelled on the supersymmetric proofs in the physics literature.Comment: 18 pages, KCL-TH-92-

Incorporating peak grouping information for alignment of multiple liquid chromatography-mass spectrometry datasets

Motivation: The combination of liquid chromatography and mass spectrometry (LC/MS) has been widely used for large-scale comparative studies in systems biology, including proteomics, glycomics and metabolomics. In almost all experimental design, it is necessary to compare chromatograms across biological or technical replicates and across sample groups. Central to this is the peak alignment step, which is one of the most important but challenging preprocessing steps. Existing alignment tools do not take into account the structural dependencies between related peaks that co-elute and are derived from the same metabolite or peptide. We propose a direct matching peak alignment method for LC/MS data that incorporates related peaks information (within each LC/MS run) and investigate its effect on alignment performance (across runs). The groupings of related peaks necessary for our method can be obtained from any peak clustering method and are built into a pairwise peak similarity score function. The similarity score matrix produced is used by an approximation algorithm for the weighted matching problem to produce the actual alignment result.<p></p> Results: We demonstrate that related peak information can improve alignment performance. The performance is evaluated on a set of benchmark datasets, where our method performs competitively compared to other popular alignment tools.<p></p> Availability: The proposed alignment method has been implemented as a stand-alone application in Python, available for download at http://github.com/joewandy/peak-grouping-alignment.<p></p&gt

Fast inference in nonlinear dynamical systems using gradient matching

Parameter inference in mechanistic models of coupled differential equations is a topical problem. We propose a new method based on kernel ridge regression and gradient matching, and an objective function that simultaneously encourages goodness of fit and penalises inconsistencies with the differential equations. Fast minimisation is achieved by exploiting partial convexity inherent in this function, and setting up an iterative algorithm in the vein of the EM algorithm. An evaluation of the proposed method on various benchmark data suggests that it compares favourably with state-of-the-art alternatives

Pre-Interaction Identification by Dynamic Grip Classification

We present a novel authentication method to identify users as they pick up a mobile device. We use a combination of back-of-device capacitive sensing and accelerometer measurements to perform classification, and obtain increased performance compared to previous accelerometer-only approaches. Our initial results suggest that users can be reliably identified during the pick-up movement before interaction commences

Towards Better Understanding Researcher Strategies in Cross-Lingual Event Analytics

With an increasing amount of information on globally important events, there is a growing demand for efficient analytics of multilingual event-centric information. Such analytics is particularly challenging due to the large amount of content, the event dynamics and the language barrier. Although memory institutions increasingly collect event-centric Web content in different languages, very little is known about the strategies of researchers who conduct analytics of such content. In this paper we present researchers' strategies for the content, method and feature selection in the context of cross-lingual event-centric analytics observed in two case studies on multilingual Wikipedia. We discuss the influence factors for these strategies, the findings enabled by the adopted methods along with the current limitations and provide recommendations for services supporting researchers in cross-lingual event-centric analytics.Comment: In Proceedings of the International Conference on Theory and Practice of Digital Libraries 201

How head and neck consultants manage patients’ emotional distress during cancer follow-up consultations : a multilevel study

Head and neck cancer (HNC) patients suffer substantial emotional problems. This study aimed to explore how utterance-level variables (source, type and timing of emotional cues) and patient-level variables (e.g. age, gender and emotional well-being) relate to consultants’ responses (i.e. reducing or providing space) to patient expressions of emotional distress. Forty-three HNC outpatient follow-up consultations were audio recorded and coded, for patients’ expressions of emotional distress and consultants’ responses, using the Verona Coding Definitions of Emotional Sequence. Multilevel logistic regression modelled the probability of the occurrence of consultant reduce space response as a function of patient distress cue expression, controlling for consultation and patient-related variables. An average of 3.5 cues/concerns (range 1–20) was identified per consultation where 84 out of 152 total cues/concerns were responded by reducing space. Cue type did not impact on response; likewise for the quality of patient emotional well-being. However, consultants were more likely to reduce space to cues elicited by patients, as opposed to those initiated by themselves. This reduced space response was more pronounced as the consultation continued. However, about 6 min into the consultation, this effect (i.e. tendency to block patients) started to weaken. Head and neck consultants’ responses to negative emotions depended on source and timing of patient emotional expressions. The findings are useful for training programme development to encourage consultants to be more flexible and open in the early stages of the consultation.PostprintPeer reviewe

Inference in Nonlinear Differential Equations

Parameter inference in mechanistic models of coupled differential equations is a challenging problem. We propose a new method using kernel ridge regression in Reproducing Kernel Hilbert Spaces (RKHS). A three-step gradient matching algorithm is developed and applied to a realistic biochemical model