Use of aggregation functions in decision making

A key component of many decision making processes is the aggregation step, whereby a set of numbers is summarised with a single representative value. This research showed that aggregation functions can provide a mathematical formalism to deal with issues like vagueness and uncertainty, which arise naturally in various decision contexts.

Maximising clinical trial participation - how important is the social context?

Individuals participate in clinical research for a variety of reasons, dependant not only on the trial phase and their own clinical status but also their sense of optimism, altruism, clinician influence, or financial gain. Practical factors influencing participation may include geographical access, motivation, availability, and language spoken. Widely-used health psychology models (e.g the Theory of Reasoned Action, the Health Belief Model) demonstrate that, in addition to these factors, the primary reason for non-participation in clinical research is social perception of risk. These models detail how beliefs and attitudes toward clinical research develop within, and are influenced by, an individual's social context. Perceived social benefits and barriers toward participation are considered alongside perceived susceptibility and severity of side-effects from participation, or symptoms of disease. A major factor in such models is the subjective norm i.e. individuals' beliefs that important others expect or wish him/her to perform this behaviour, and the motivation to act in accordance with their expectations or wishes. This includes, but is not limited to, the role of the media, peers and family members, clarification of risk associated with trial participation and discussion of comparative risk, and risk equivalence between trials and same treatment outside trials.
The workshop will involve a 30 minute presentation from the discussion leaders and a 30 minute group-work session to explore how an understanding of the social context of participation in clinical research can maximise appropriate clinical trial participation.

Attractive forces between surfaces : what can and cannot be learned from a jump-in study with the surface forces apparatus?

We study theoretically the dynamics of film thinning under the action of an attractive surface force near the point of a jump instability. Our approach is illustrated by modeling van der Waals and hydrophobic attractive forces. The main result is that with the hydrophobic force law reported previously it is often impossible to establish the jump separation with any certainty. The surfaces instead approach slowly from a distance which is much larger than the point where an actual jump is expected. We conclude that an attractive force measured by the static jump technique is overestimated, and we formulate principles of a new dynamic jump method. The use of this new technique would permit direct measurements of attractive forces at separations below the static jump distance down to contact of the surfaces.

Interface slip control for CFRP-concrete composite beams

Carbon fibre reinforced polymer (CFRP) has been used frequently to retrofit concrete structures. Strengthening efficiency is related to the CFRP application process and the characteristics of the bonding agent. In this paper the mechanism of interface shear behaviour in CFRP to concrete beams is discussed considering previous test observations and mathematical models. This paper then discusses the consequences of introducing interface slip which reduces the integrity of the composite section, however improve ductility and delay debonding failure. The paper suggests that using softer bonding agent as well as setting limits on the interface slip could ensure acceptable serviceability and ductile behaviour.

Edge evaluation using necessary components

We classify all the different kinds of errors that can occur in edge detection and then develop measures for quantifying these errors. It is shown that these sets of measures are complete and independent and form necessary components of an edge-evaluation scheme. The principle that an edge-evaluation measure should have certain qualitative properties is used to develop a method for combining these error components into a single combined measure.

Policy recognition in the Abstract Hidden Markov Model

In this paper, we present a method for recognising an agent's behaviour in dynamic, noisy, uncertain domains, and across multiple levels of abstraction. We term this problem on-line plan recognition under uncertainty and view it generally as probabilistic inference on the stochastic process representing the execution of the agent's plan. Our contributions in this paper are twofold. In terms of probabilistic inference, we introduce the Abstract Hidden Markov Model (AHMM), a novel type of stochastic processes, provide its dynamic Bayesian network (DBN) structure and analyse the properties of this network. We then describe an application of the Rao-Blackwellised Particle Filter to the AHMM which allows us to construct an efficient, hybrid inference method for this model. In terms of plan recognition, we propose a novel plan recognition framework based on the AHMM as the plan execution model. The Rao-Blackwellised hybrid inference for AHMM can take advantage of the independence properties inherent in a model of plan execution, leading to an algorithm for online probabilistic plan recognition that scales well with the number of levels in the plan hierarchy. This illustrates that while stochastic models for plan execution can be complex, they exhibit special structures which, if exploited, can lead to efficient plan recognition algorithms. We demonstrate the usefulness of the AHMM framework via a behaviour recognition system in a complex spatial environment using distributed video surveillance data.

Spatial reasoning via active observation

This paper presents a model for space in which an autonomous agent acquires information about its environment. The agent uses a predefined exploration strategy to build a map allowing it to navigate and deduce relationships between points in space. The shapes of objects in the environment are represented qualitatively. This shape information is deduced from the agent's motion. Normally, in a qualitative model, directional information degrades under transitive deduction. By reasoning about the shape of the environment, the agent can match visual events to points on the objects. This strengthens the model by allowing further relationships to be deduced. In particular, points that are separated by long distances, or complex surfaces, can be related by line-of-sight. These relationships are deduced without incorporating any metric information into the model. Examples are given to demonstrate the use of the model.

Learning other agents' preferences in multiagent negotiation

In multiagent systems, an agent does not usually have complete information about the preferences and decision making processes of other agents. This might prevent the agents from making coordinated choices, purely due to their ignorance of what others want. This paper describes the integration of a learning module into a communication-intensive negotiating agent architecture. The learning module gives the agents the ability to learn about other agents' preferences via past interactions. Over time, the agents can incrementally update their models of other agents' preferences and use them to make better coordinated decisions. Combining both communication and learning, as two complement knowledge acquisition methods, helps to reduce the amount of communication needed on average, and is justified in situations where communication is computationally costly or simply not desirable (e.g. to preserve the individual privacy).

Efficient cross-validation of the complete two stages in KFD classifier formulation

This paper presents an efficient evaluation algorithm for cross-validating the two-stage approach of KFD classifiers. The proposed algorithm is of the same complexity level as the existing indirect efficient cross-validation methods but it is more reliable since it is direct and constitutes exact cross-validation for the KFD classifier formulation. Simulations demonstrate that the proposed algorithm is almost as fast as the existing fast indirect evaluation algorithm and the twostage cross-validation selects better models on most of the thirteen benchmark data sets.

Hierarchical recognition of intentional human gestures for sports video annotation

We present a novel technique for the recognition of complex human gestures for video annotation using accelerometers and the hidden Markov model. Our extension to the standard hidden Markov model allows us to consider gestures at different levels of abstraction through a hierarchy of hidden states. Accelerometers in the form of wrist bands are attached to humans performing intentional gestures, such as umpires in sports. Video annotation is then performed by populating the video with time stamps indicating significant events, where a particular gesture occurs. The novelty of the technique lies in the development of a probabilistic hierarchical framework for complex gesture recognition and the use of accelerometers to extract gestures and significant events for video annotation.

Narrative structure analysis with education and training videos for e-learning

This paper deals with the problem ofstructuralizing education and training videos for high-level semantics extraction and nonlinear media presentation in e-learning applications. Drawing guidance from production knowledge in instructional media, we propose six main narrative structures employed in education and training videos for both motivation and demonstration during learning and practical training. We devise a powerful audiovisual feature set, accompanied by a hierarchical decision tree-based classification system to determine and discriminate between these structures. Based on a two-liered hierarchical model, we demonstrate that we can achieve an accuracy of 84.7% on a comprehensive set of education and training video data.

Temporal PDMs for gait classification

Gait classification is a developing research area, particularly with regards to biometrics. It aims to use the distinctive spatial and temporal characteristics of human motion to classify differing activities. As a biometric, this extends to recognising different people by the heterogeneous aspects of their gait. This research aims to use a modified deformable model, the temporal PDM, to distinguish the movements of a walking and miming person. The movement of 2D points on the moving form is used to provide input into the model and classify the type of gait present.

Adaptive model for foreground extraction in adverse lighting conditions

Background elimination models are widely used in motion tracking systems. Our aim is to develop a system that performs reliably under adverse lighting conditions. In particular, this includes indoor scenes lit partly or entirely by diffuse natural light. We present a modified "median value" model in which the detection threshold adapts to global changes in illumination. The responses of several models are compared, demonstrating the effectiveness of the new model.

The influence of hip circumference on the relationship between abdominal obesity and mortality

Background Higher waist circumference and lower hip circumference are both associated with increased cardiovascular disease (CVD) risk, despite being directly correlated. The real effects of visceral obesity may therefore be underestimated when hip circumference is not fully taken into account. We hypothesized that adding waist and hip circumference to traditional risk factors would significantly improve CVD risk prediction.

Methods In a population-based survey among South Asian and African Mauritians (n = 7978), 1241 deaths occurred during 15 years of follow-up. In a model that included variables used in previous CVD risk calculations (a Framingham-type model), the association between waist circumference and mortality was examined before and after adjustment for hip circumference. The percentage with an increase in estimated 10-year cumulative mortality of >25% and a decrease of >20% after waist and hip circumference were added to the model was calculated.

Results Waist circumference was strongly related to mortality only after adjustment for hip circumference and vice versa. Adding waist and hip circumference to a Framingham-type model increased estimated 10-year cumulative CVD mortality by >25% for 23.7% of those who died and 15.7% of those censored. Cumulative mortality decreased by >20% for 4.5% of those who died and 14.8% of those censored.

Conclusions The effect of central obesity on mortality risk is seriously underestimated without adjustment for hip circumference. Adding waist and hip circumference to a Framingham-type model for CVD mortality substantially increased predictive power. Both may be important inclusions in CVD risk prediction models.

Identification of surface heterogeneity effects in cyclic voltammograms derived from analysis of an individually addressable gold array electrode

Voltammetric behavior at gold electrodes in aqueous media is known to be strongly dependent on electrode polishing and history. In this study, an electrode array consisting of 100 nominally identical and individually addressable gold disks electrodes, each with a radius of 127 µm, has been fabricated. The ability to analyze both individual electrode and total array performance enables microscopic aspects of the overall voltammetric response arising from variable levels of inhomogeneity in each electrode to be identified. The array configuration was initially employed with the reversible and hence relatively surface insensitive [Ru(NH₃)₆]^3+/2+ reaction and then with the more highly surface sensitive quasi-reversible [Fe(CN)₆]^3−/4− process. In both these cases, the reactants and products are solution soluble and, at a scan rate of 50 mV s⁻¹, each electrode in the array is assumed to behave independently, since no evidence of overlapping of the diffusion layers was detected. As would be expected, the variability of the individual electrodesʼ responses was significantly larger than found for the summed electrode behavior. In the case of cytochrome c voltammetry at a 4,4′-dipyridyl disulfide modified electrode, a far greater dependence on electrode history and electrode heterogeneity was detected. In this case, voltammograms derived from individual electrodes in the gold array electrode exhibit shape variations ranging from peak to sigmoidal. However, again the total response was always found to be well-defined. This voltammetry is consistent with a microscopic model of heterogeneity where some parts of each chemically modified electrode surface are electroactive while other parts are less active. The findings are consistent with the common existence of electrode heterogeneity in cyclic voltammetric responses at gold electrodes, that are normally difficult to detect, but fundamentally important, as electrode nonuniformity can give rise to subtle forms of kinetic and other forms of dispersion.