Using viewer’s facial expression and heart rate for sports video highlights detection

Viewer interests, evoked by video content, can potentially identify the highlights of the video. This paper explores the use of facial expressions (FE) and heart rate (HR) of viewers captured using camera and non-strapped sensor for identifying interesting video segments. The data from ten subjects with three videos showed that these signals are viewer dependent and not synchronized with the video contents. To address this issue, new algorithms are proposed to effectively combine FE and HR signals for identifying the time when viewer interest is potentially high. The results show that, compared with subjective annotation and match report highlights, ‘non-neutral’ FE and ‘relatively higher and faster’ HR is able to capture 60%-80% of goal, foul, and shot-on-goal soccer video events. FE is found to be more indicative than HR of viewer’s interests, but the fusion of these two modalities outperforms each of them.

Experimental and numerical investigation of strain-rate dependent mechanical properties of single living cells

The objective of this project is to investigate the strain-rate dependent mechanical behaviour of single living cells using both experimental and numerical techniques. The results revealed that living cells behave as porohyperlastic materials and that both solid and fluid phases within the cells play important roles in their mechanical responses. The research reported in this thesis provides a better understanding of the mechanisms underlying the cellular responses to external mechanical loadings and of the process of mechanical signal transduction in living cells. It would help us to enhance knowledge of and insight into the role of mechanical forces in supporting tissue regeneration or degeneration.

Parallel attentive visual tracking

The research reported here addresses the problem of detecting and tracking independently moving objects from a moving observer in real-time, using corners as object tokens. Corners are detected using the Harris corner detector, and local image-plane constraints are employed to solve the correspondence problem. The approach relaxes the restrictive static-world assumption conventionally made, and is therefore capable of tracking independently moving and deformable objects. Tracking is performed without the use of any 3-dimensional motion model. The technique is novel in that, unlike traditional feature-tracking algorithms where feature detection and tracking is carried out over the entire image-plane, here it is restricted to those areas most likely to contain-meaningful image structure. Two distinct types of instantiation regions are identified, these being the “focus-of-expansion” region and “border” regions of the image-plane. The size and location of these regions are defined from a combination of odometry information and a limited knowledge of the operating scenario. The algorithms developed have been tested on real image sequences taken from typical driving scenarios. Implementation of the algorithm using T800 Transputers has shown that near-linear speedups are achievable, and that real-time operation is possible (half-video rate has been achieved using 30 processing elements).

Terra Preta (Agriculture+Art+Algorithm)

Terra Preta is a site-specific bio-energy project which aims to create a synergy between the public and the pre-existing engineered landscape of Freshkills Park on Staten Island, New York. The project challenges traditional paradigms of public space by proposing a dynamic and ever-changing landscape. The initiative allows the publuc to self-organise the landscape and to engage in 'algorithmic processes' of growth, harvest and space creation.

Melanoma cell colony expansion parameters revealed by approximate Bayesian computation

In vitro studies and mathematical models are now being widely used to study the underlying mechanisms driving the expansion of cell colonies. This can improve our understanding of cancer formation and progression. Although much progress has been made in terms of developing and analysing mathematical models, far less progress has been made in terms of understanding how to estimate model parameters using experimental in vitro image-based data. To address this issue, a new approximate Bayesian computation (ABC) algorithm is proposed to estimate key parameters governing the expansion of melanoma cell (MM127) colonies, including cell diffusivity, D, cell proliferation rate, λ, and cell-to-cell adhesion, q, in two experimental scenarios, namely with and without a chemical treatment to suppress cell proliferation. Even when little prior biological knowledge about the parameters is assumed, all parameters are precisely inferred with a small posterior coefficient of variation, approximately 2–12%. The ABC analyses reveal that the posterior distributions of D and q depend on the experimental elapsed time, whereas the posterior distribution of λ does not. The posterior mean values of D and q are in the ranges 226–268 µm2h−1, 311–351 µm2h−1 and 0.23–0.39, 0.32–0.61 for the experimental periods of 0–24 h and 24–48 h, respectively. Furthermore, we found that the posterior distribution of q also depends on the initial cell density, whereas the posterior distributions of D and λ do not. The ABC approach also enables information from the two experiments to be combined, resulting in greater precision for all estimates of D and λ.

Reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm

In this paper, we propose a highly reliable fault diagnosis scheme for incipient low-speed rolling element bearing failures. The scheme consists of fault feature calculation, discriminative fault feature analysis, and fault classification. The proposed approach first computes wavelet-based fault features, including the respective relative wavelet packet node energy and entropy, by applying a wavelet packet transform to an incoming acoustic emission signal. The most discriminative fault features are then filtered from the originally produced feature vector by using discriminative fault feature analysis based on a binary bat algorithm (BBA). Finally, the proposed approach employs one-against-all multiclass support vector machines to identify multiple low-speed rolling element bearing defects. This study compares the proposed BBA-based dimensionality reduction scheme with four other dimensionality reduction methodologies in terms of classification performance. Experimental results show that the proposed methodology is superior to other dimensionality reduction approaches, yielding an average classification accuracy of 94.9%, 95.8%, and 98.4% under bearing rotational speeds at 20 revolutions-per-minute (RPM), 80 RPM, and 140 RPM, respectively.

How does Australian diabetic foot screening rate on an international Stage?

Background From the conservative estimates of registrants with the National Diabetes Supply Scheme, we will be soon passing 1.1 Million Australians affected by all types of diabetes. The diabetes complications of foot ulceration and amputation are costly to all. These costs can be reduced with appropriate prevention strategies, starting with identifying people at risk through primary care diabetic foot screening. However, levels of diabetic foot screening in Australia are difficult to quantify. Methods This presentation reports on foot screening rates as recorded in the academic literature, national health surveys and national database reports. The focus is on type 1 and type 2 diabetes in adults, and not gestational diabetes or children. Literature searches included diabetic foot screening that occurred in the primary care setting for populations over 2000 people from 2002 to 2014. Searches were performed using Medline and CINAHL as well as internet searches of OECD health databases. The primary outcome measure was foot -screening rates as a percentage of adult diabetic population. Results The lack of a national diabetes database and register hampers efforts to analyse diabetic foot screening levels. The most recent and accurate level for Australian population review was in the AUSDIAB (Australian Diabetes and lifestyle survey) from 2004. This survey reported screening in primary care to be as low as 50%. Countries such as the United Kingdom and United States of America report much higher rates of foot screening (67-88%) using national databases and web based initiatives that involve patients and clinicians. Conclusions Australian rates of diabetic foot screening in primary care centres is ambiguous. Uptake of national registers, incentives and web based systems improve levels of diabetic foot assessment which are the first steps to a healthier diabetic population.

Parallel streaming signature EM-tree: A clustering algorithm for web scale applications

The proliferation of the web presents an unsolved problem of automatically analyzing billions of pages of natural language. We introduce a scalable algorithm that clusters hundreds of millions of web pages into hundreds of thousands of clusters. It does this on a single mid-range machine using efficient algorithms and compressed document representations. It is applied to two web-scale crawls covering tens of terabytes. ClueWeb09 and ClueWeb12 contain 500 and 733 million web pages and were clustered into 500,000 to 700,000 clusters. To the best of our knowledge, such fine grained clustering has not been previously demonstrated. Previous approaches clustered a sample that limits the maximum number of discoverable clusters. The proposed EM-tree algorithm uses the entire collection in clustering and produces several orders of magnitude more clusters than the existing algorithms. Fine grained clustering is necessary for meaningful clustering in massive collections where the number of distinct topics grows linearly with collection size. These fine-grained clusters show an improved cluster quality when assessed with two novel evaluations using ad hoc search relevance judgments and spam classifications for external validation. These evaluations solve the problem of assessing the quality of clusters where categorical labeling is unavailable and unfeasible.

Different approaches of applying single-objective binary genetic algorithm on the wind farm design

This paper discusses three different ways of applying the single-objective binary genetic algorithm into designing the wind farm. The introduction of different applications is through altering the binary encoding methods in GA codes. The first encoding method is the traditional one with fixed wind turbine positions. The second involves varying the initial positions from results of the first method, and it is achieved by using binary digits to represent the coordination of wind turbine on X or Y axis. The third is the mixing of the first encoding method with another one, which is by adding four more binary digits to represent one of the unavailable plots. The goal of this paper is to demonstrate how the single-objective binary algorithm can be applied and how the wind turbines are distributed under various conditions with best fitness. The main emphasis of discussion is focused on the scenario of wind direction varying from 0° to 45°. Results show that choosing the appropriate position of wind turbines is more significant than choosing the wind turbine numbers, considering that the former has a bigger influence on the whole farm fitness than the latter. And the farm has best performance of fitness values, farm efficiency, and total power with the direction between 20°to 30°.

Complete-linkage clustering for voice activity detection in audio and visual speech

We propose a novel technique for conducting robust voice activity detection (VAD) in high-noise recordings. We use Gaussian mixture modeling (GMM) to train two generic models; speech and non-speech. We then score smaller segments of a given (unseen) recording against each of these GMMs to obtain two respective likelihood scores for each segment. These scores are used to compute a dissimilarity measure between pairs of segments and to carry out complete-linkage clustering of the segments into speech and non-speech clusters. We compare the accuracy of our method against state-of-the-art and standardised VAD techniques to demonstrate an absolute improvement of 15% in half-total error rate (HTER) over the best performing baseline system and across the QUT-NOISE-TIMIT database. We then apply our approach to the Audio-Visual Database of American English (AVDBAE) to demonstrate the performance of our algorithm in using visual, audio-visual or a proposed fusion of these features.

Public use and perceptions of emergency departments: A population survey

Objectives To inform demand management strategies aimed at reducing congestion in EDs by: (i) identifying public use of EDs, decision-making and reasons; and (ii) measuring acceptance of alternative care models. Methods A cross-sectional telephone survey of a random sample of Queensland population aged 18 years or older residing in a dwelling unit in Queensland that could be contacted on a land-based telephone service was conducted. One person per household was selected according to a predetermined algorithm to ensure sex and regional balance were interviewed. The main outcome measures were: ED use, attitudes towards ED staff and services, and alternative models of care. Results The final sample included a total of 1256 respondents (response rate = 40.3%). Twenty-one per cent attended EDs in the preceding 12 months. The decision to attend was made by patients (51%), health and medical professionals (31%), and others (18%). The main reasons included perceived severity of the illness (47%), unavailability of alternative services (26%) and better care (11%). Most respondents agreed with more flexible care models of service delivery including incentives for general practitioners (90%), private health insurance coverage for ED use (89%), and enhanced roles for paramedics and nurses. Conclusions Main reason for attending ED is perceived severity of illness, followed by lack of alternative care. The majority of both consumers and the public are in favour of more flexible care models. However, further research is necessary to detail those alternatives and to test and validate their effectiveness.

Structural identification of a laboratory-based steel through truss cantilevered bridge with suspended span using a layered genetic algorithm with patternsearch step (GAPS)

Structural identification (St-Id) can be considered as the process of updating a finite element (FE) model of a structural system to match the measured response of the structure. This paper presents the St-Id of a laboratory-based steel through-truss cantilevered bridge with suspended span. There are a total of 600 degrees of freedom (DOFs) in the superstructure plus additional DOFs in the substructure. The St-Id of the bridge model used the modal parameters from a preliminary modal test in the objective function of a global optimisation technique using a layered genetic algorithm with patternsearch step (GAPS). Each layer of the St-Id process involved grouping of the structural parameters into a number of updating parameters and running parallel optimisations. The number of updating parameters was increased at each layer of the process. In order to accelerate the optimisation and ensure improved diversity within the population, a patternsearch step was applied to the fittest individuals at the end of each generation of the GA. The GAPS process was able to replicate the mode shapes for the first two lateral sway modes and the first vertical bending mode to a high degree of accuracy and, to a lesser degree, the mode shape of the first lateral bending mode. The mode shape and frequency of the torsional mode did not match very well. The frequencies of the first lateral bending mode, the first longitudinal mode and the first vertical mode matched very well. The frequency of the first sway mode was lower and that of the second sway mode was higher than the true values, indicating a possible problem with the FE model. Improvements to the model and the St-Id process will be presented at the upcoming conference and compared to the results presented in this paper. These improvements will include the use of multiple FE models in a multi-layered, multi-solution, GAPS St-Id approach.

Statistically assisted fluid registration algorithm - SAFIRA

In this paper, we develop and validate a new Statistically Assisted Fluid Registration Algorithm (SAFIRA) for brain images. A non-statistical version of this algorithm was first implemented in [2] and re-formulated using Lagrangian mechanics in [3]. Here we extend this algorithm to 3D: given 3D brain images from a population, vector fields and their corresponding deformation matrices are computed in a first round of registrations using the non-statistical implementation. Covariance matrices for both the deformation matrices and the vector fields are then obtained and incorporated (separately or jointly) in the regularizing (i.e., the non-conservative Lagrangian) terms, creating four versions of the algorithm. We evaluated the accuracy of each algorithm variant using the manually labeled LPBA40 dataset, which provides us with ground truth anatomical segmentations. We also compared the power of the different algorithms using tensor-based morphometry -a technique to analyze local volumetric differences in brain structure- applied to 46 3D brain scans from healthy monozygotic twins.

A nonconservative Lagrangian framework for statistical fluid registration - SAFIRA

In this paper, we used a nonconservative Lagrangian mechanics approach to formulate a new statistical algorithm for fluid registration of 3-D brain images. This algorithm is named SAFIRA, acronym for statistically-assisted fluid image registration algorithm. A nonstatistical version of this algorithm was implemented, where the deformation was regularized by penalizing deviations from a zero rate of strain. In, the terms regularizing the deformation included the covariance of the deformation matrices Σ and the vector fields (q). Here, we used a Lagrangian framework to reformulate this algorithm, showing that the regularizing terms essentially allow nonconservative work to occur during the flow. Given 3-D brain images from a group of subjects, vector fields and their corresponding deformation matrices are computed in a first round of registrations using the nonstatistical implementation. Covariance matrices for both the deformation matrices and the vector fields are then obtained and incorporated (separately or jointly) in the nonconservative terms, creating four versions of SAFIRA. We evaluated and compared our algorithms' performance on 92 3-D brain scans from healthy monozygotic and dizygotic twins; 2-D validations are also shown for corpus callosum shapes delineated at midline in the same subjects. After preliminary tests to demonstrate each method, we compared their detection power using tensor-based morphometry (TBM), a technique to analyze local volumetric differences in brain structure. We compared the accuracy of each algorithm variant using various statistical metrics derived from the images and deformation fields. All these tests were also run with a traditional fluid method, which has been quite widely used in TBM studies. The versions incorporating vector-based empirical statistics on brain variation were consistently more accurate than their counterparts, when used for automated volumetric quantification in new brain images. This suggests the advantages of this approach for large-scale neuroimaging studies.

Identifying rate-limiting nodes in large-scale cortical networks for visuospatial processing: An illustration using fMRI

With the advent of functional neuroimaging techniques, in particular functional magnetic resonance imaging (fMRI), we have gained greater insight into the neural correlates of visuospatial function. However, it may not always be easy to identify the cerebral regions most specifically associated with performance on a given task. One approach is to examine the quantitative relationships between regional activation and behavioral performance measures. In the present study, we investigated the functional neuroanatomy of two different visuospatial processing tasks, judgement of line orientation and mental rotation. Twenty-four normal participants were scanned with fMRI using blocked periodic designs for experimental task presentation. Accuracy and reaction time (RT) to each trial of both activation and baseline conditions in each experiment was recorded. Both experiments activated dorsal and ventral visual cortical areas as well as dorsolateral prefrontal cortex. More regionally specific associations with task performance were identified by estimating the association between (sinusoidal) power of functional response and mean RT to the activation condition; a permutation test based on spatial statistics was used for inference. There was significant behavioral-physiological association in right ventral extrastriate cortex for the line orientation task and in bilateral (predominantly right) superior parietal lobule for the mental rotation task. Comparable associations were not found between power of response and RT to the baseline conditions of the tasks. These data suggest that one region in a neurocognitive network may be most strongly associated with behavioral performance and this may be regarded as the computationally least efficient or rate-limiting node of the network.