Cross database training of audio-visual hidden Markov models for phone recognition

Speech recognition can be improved by using visual information in the form of lip movements of the speaker in addition to audio information. To date, state-of-the-art techniques for audio-visual speech recognition continue to use audio and visual data of the same database for training their models. In this paper, we present a new approach to make use of one modality of an external dataset in addition to a given audio-visual dataset. By so doing, it is possible to create more powerful models from other extensive audio-only databases and adapt them on our comparatively smaller multi-stream databases. Results show that the presented approach outperforms the widely adopted synchronous hidden Markov models (HMM) trained jointly on audio and visual data of a given audio-visual database for phone recognition by 29% relative. It also outperforms the external audio models trained on extensive external audio datasets and also internal audio models by 5.5% and 46% relative respectively. We also show that the proposed approach is beneficial in noisy environments where the audio source is affected by the environmental noise.

The link between marketing instruments and customer perceptions

Measuring marketing effectiveness is a major challenge for marketing academics and practitioners. An investigation that uses dependence analysis to examine the link between objective marketing instruments and subjective customer perceptions of them is missing from existing approaches. In examining this link, the present article redresses this deficit. A conceptual model is developed in order to structure and explain the effects of instruments on perceptions for 3 marketing instrument categories (value, relationship, and brand). The links are analyzed in an empirical study with bank customers. Instruments are measured by using objective internal data. Perceptions are measured based on a customer survey. The relationships between instruments and perceptions are examined using regression analysis. Most of the effects of instruments on perceptions are significant, signaling that the activities conducted are perceived as intended. In particular, some interesting results become evident, such as the negative effect on relationship perceptions of follow-up calls subsequent to direct mail services.

Estimating link-dependent origin-destination matrices from sample trajectories and traffic counts

Origin-Destination matrices (ODM) estimation can benefits of the availability of sample trajectories which can be measured thanks to recent technologies. This paper focus on the case of transport networks where traffic counts are measured by magnetic loops and sample trajectories available. An example of such network is the city of Brisbane, where Bluetooth detectors are now operating. This additional data source is used to extend the classical ODM estimation to a link-specific ODM (LODM) one using a convex optimisation resolution that incorporates networks constraints as well. The proposed algorithm is assessed on a simulated network.

Is there a link between poverty and food security?

This paper addresses the gap in economic theory underlying the multidimensional concept of food security and observed data by deriving a composite food security index using the latent class model. The link between poverty and food security is then examined using the new food security index and the robustness of the link is compared with two unidimensional measures often used in the literature. Using Vietnam as a case study, it was found that a weak link exists for the rural but not for the urban composite food security index. The unidimensional measures on the other hand show a strong link in both the rural and urban regions. The results on the link are also different and mixed when two poverty types given by persistent and transient poverty are considered. These findings have important policy implications for a targeted approach to addressing food security.

Online hidden Markov model parameter estimation and minimax robust quickest change detection in uncertain stochastic processes

Stochastic (or random) processes are inherent to numerous fields of human endeavour including engineering, science, and business and finance. This thesis presents multiple novel methods for quickly detecting and estimating uncertainties in several important classes of stochastic processes. The significance of these novel methods is demonstrated by employing them to detect aircraft manoeuvres in video signals in the important application of autonomous mid-air collision avoidance.

Linear programming for large-scale Markov decision problems

We consider the problem of controlling a Markov decision process (MDP) with a large state space, so as to minimize average cost. Since it is intractable to compete with the optimal policy for large scale problems, we pursue the more modest goal of competing with a low-dimensional family of policies. We use the dual linear programming formulation of the MDP average cost problem, in which the variable is a stationary distribution over state-action pairs, and we consider a neighborhood of a low-dimensional subset of the set of stationary distributions (defined in terms of state-action features) as the comparison class. We propose a technique based on stochastic convex optimization and give bounds that show that the performance of our algorithm approaches the best achievable by any policy in the comparison class. Most importantly, this result depends on the size of the comparison class, but not on the size of the state space. Preliminary experiments show the effectiveness of the proposed algorithm in a queuing application.

Design and flight testing of an integrated solar powered UAV and WSN for greenhouse gas monitoring emissions in agricultural farms

There is an increased interest in measuring the amount of greenhouse gases produced by farming practices . This paper describes an integrated solar powered Unmanned Air Vehicles (UAV) and Wireless Sensor Network (WSN) gas sensing system for greenhouse gas emissions in agricultural lands. The system uses a generic gas sensing system for CH4 and CO2 concentrations using metal oxide (MoX) and non-dispersive infrared sensors, and a new solar cell encapsulation method to power the unmanned aerial system (UAS)as well as a data management platform to store, analyze and share the information with operators and external users. The system was successfully field tested at ground and low altitudes, collecting, storing and transmitting data in real time to a central node for analysis and 3D mapping. The system can be used in a wide range of outdoor applications at a relatively low operational cost. In particular, agricultural environments are increasingly subject to emissions mitigation policies. Accurate measurements of CH4 and CO2 with its temporal and spatial variability can provide farm managers key information to plan agricultural practices. A video of the bench and flight test performed can be seen in the following link: https://www.youtube.com/watch?v=Bwas7stYIxQ

Design-driven digital futures: The role of design-led innovation in the creation of online connected communities for new mobility solutions

Online communities have fundamentally changed how humans connected and are now so common they are fundamental to the human experience. As the Internet developed for Web 1.0 to Web 2.0, the functionality of these communities has far exceeded initial expectations. These communities have shifted from simply places to share information to ways to access products and services that bridge the online and offline worlds. This shift has led to the disruption of many industries with the transportation industry being one such sector. Both private transport providers and public transport systems face competition from online communities who are able to link services providers and customers more effectively and innovatively. These types of communities fall under what has been popularised as collaborative consumption or the sharing economy. The aim of this study is to explore the role of Design-led Innovation in the creation of digital futures, specifically online connected communities for successful new mobility solutions. To explore this proposition multiple data collection methods are proposed;Content Analysis, ii) A Comparative Qualitative Study consisting of Qualitative Interviews and Focus Groups / Design Workshops and iii) An Action Research Cycle of Embedded Practice. The multidisciplinary nature of this study grounds this research in a novel position contributing to new knowledge in both the field of design, and also a deeper understanding of the larger fast-growing online community phenomena.

The missing link of crime analysis: A systematic approach to testing competing hypotheses

Crime analysts have traditionally received little guidance from academic researchers in key tasks in the analysis process, specifically the testing of multiple hypotheses and evaluating evidence in a scientific fashion. This article attempts to fill this gap by outlining a method (the Analysis of Competing Hypotheses) of systematically analysing multiple explanations for crime problems. The method is systematic, avoids many cognitive errors common in analysis, and is explicit. It is argued that the implementation of this approach makes analytic products audit-able, the reasoning underpinning them transparent, and provides intelligence managers a rational professional development tool for individual analysts.

The cost-effectiveness of the MobileMums intervention to increase physical activity among mothers with young children: A Markov model informed by a randomised controlled trial

Objectives: To determine the cost-effectiveness of the MobileMums intervention. MobileMums is a 12-week programme which assists mothers with young children to be more physically active, primarily through the use of personalised SMS text-messages. Design: A cost-effectiveness analysis using a Markov model to estimate and compare the costs and consequences of MobileMums and usual care. Setting: This study considers the cost-effectiveness of MobileMums in Queensland, Australia. Participants: A hypothetical cohort of over 36 000 women with a child under 1 year old is considered. These women are expected to be eligible and willing to participate in the intervention in Queensland, Australia. Data sources: The model was informed by the effectiveness results from a 9-month two-arm community-based randomised controlled trial undertaken in 2011 and registered retrospectively with the Australian Clinical Trials Registry (ACTRN12611000481976). Baseline characteristics for the model cohort, treatment effects and resource utilisation were all informed by this trial. Main outcome measures: The incremental cost per quality-adjusted life year (QALY) of MobileMums compared with usual care. Results: The intervention is estimated to lead to an increase of 131 QALYs for an additional cost to the health system of 1.1 million Australian dollars (AUD). The expected incremental cost-effectiveness ratio for MobileMums is 8608 AUD per QALY gained. MobileMums has a 98% probability of being cost-effective at a cost-effectiveness threshold of 64 000 AUD. Varying modelling assumptions has little effect on this result. Conclusions: At a cost-effectiveness threshold of 64 000 AUD, MobileMums would likely be a cost-effective use of healthcare resources in Queensland, Australia. Trial registration number: Australian Clinical Trials Registry; ACTRN12611000481976.

Promoting and providing expert guidance in work-intensive clinical settings

This paper discusses how expert guidance can be best provided in work intensive clinical settings. The adequacy for supporting learning in the clinical practicum for health care disciplines is often complicated by the intensive work practices in healthcare settings. Often, clinicians' work is so intense that the scope for providing close guidance for students is quite restricted. The case advanced here draws on a range of empirical work to propose how clinician-student interactions might be optimized through the provision of a clinical ccn guided learning such as demonstrating and role-modeling. These roles can contribute in essential ways to the development of learning environments where clinicians have the opportunity to facilitate the learning of others as part of their workload, and without being burdened by the requirements of teaching and assessment processes. It differs from other approaches because although clinicians partner students and provide feedback to them, clinicians are not expected to formally assess or award a grade for student performance. Assessment and remedial action, when required, is undertaken by the role of a designated clinical supervisor qualified to perform such activities. © 2010 Springer Science+Business Media B.V.