Efficient querying of large process model repositories

Recent years have seen an increased uptake of business process management technology in industries. This has resulted in organizations trying to manage large collections of business process models. One of the challenges facing these organizations concerns the retrieval of models from large business process model repositories. For example, in some cases new process models may be derived from existing models, thus finding these models and adapting them may be more effective and less error-prone than developing them from scratch. Since process model repositories may be large, query evaluation may be time consuming. Hence, we investigate the use of indexes to speed up this evaluation process. To make our approach more applicable, we consider the semantic similarity between labels. Experiments are conducted to demonstrate that our approach is efficient.

Speaker diarization : "who spoke when"

Speaker diarization is the process of annotating an input audio with information that attributes temporal regions of the audio signal to their respective sources, which may include both speech and non-speech events. For speech regions, the diarization system also specifies the locations of speaker boundaries and assign relative speaker labels to each homogeneous segment of speech. In short, speaker diarization systems effectively answer the question of ‘who spoke when’. There are several important applications for speaker diarization technology, such as facilitating speaker indexing systems to allow users to directly access the relevant segments of interest within a given audio, and assisting with other downstream processes such as summarizing and parsing. When combined with automatic speech recognition (ASR) systems, the metadata extracted from a speaker diarization system can provide complementary information for ASR transcripts including the location of speaker turns and relative speaker segment labels, making the transcripts more readable. Speaker diarization output can also be used to localize the instances of specific speakers to pool data for model adaptation, which in turn boosts transcription accuracies. Speaker diarization therefore plays an important role as a preliminary step in automatic transcription of audio data. The aim of this work is to improve the usefulness and practicality of speaker diarization technology, through the reduction of diarization error rates. In particular, this research is focused on the segmentation and clustering stages within a diarization system. Although particular emphasis is placed on the broadcast news audio domain and systems developed throughout this work are also trained and tested on broadcast news data, the techniques proposed in this dissertation are also applicable to other domains including telephone conversations and meetings audio. Three main research themes were pursued: heuristic rules for speaker segmentation, modelling uncertainty in speaker model estimates, and modelling uncertainty in eigenvoice speaker modelling. The use of heuristic approaches for the speaker segmentation task was first investigated, with emphasis placed on minimizing missed boundary detections. A set of heuristic rules was proposed, to govern the detection and heuristic selection of candidate speaker segment boundaries. A second pass, using the same heuristic algorithm with a smaller window, was also proposed with the aim of improving detection of boundaries around short speaker segments. Compared to single threshold based methods, the proposed heuristic approach was shown to provide improved segmentation performance, leading to a reduction in the overall diarization error rate. Methods to model the uncertainty in speaker model estimates were developed, to address the difficulties associated with making segmentation and clustering decisions with limited data in the speaker segments. The Bayes factor, derived specifically for multivariate Gaussian speaker modelling, was introduced to account for the uncertainty of the speaker model estimates. The use of the Bayes factor also enabled the incorporation of prior information regarding the audio to aid segmentation and clustering decisions. The idea of modelling uncertainty in speaker model estimates was also extended to the eigenvoice speaker modelling framework for the speaker clustering task. Building on the application of Bayesian approaches to the speaker diarization problem, the proposed approach takes into account the uncertainty associated with the explicit estimation of the speaker factors. The proposed decision criteria, based on Bayesian theory, was shown to generally outperform their non- Bayesian counterparts.

Re-framing fashion : from original and copy to adaptation

Partington notes that clothing produced by individual consumers through adaptation of patterns is contextualised as a watered down version of original couture. In its most reductive form, this notion characterises fashion as commercial and exploitative. Descriptors such as appropriation, imitation, copy and so forth have restricted the opportunity to understand fashion as a major global cultural form and institution. Therefore exploring and understanding the concept of adaptation will shift the attention from a superficial assessment of original versus imitation or copy to adaptation as a practice that provides a better framework for the understanding of designers’ and couturiers’ innovative practices and creativity, describing also the active engagement of consumers with fashion at the micro level. Adaptation can also provide a way to understand different historical shifts in the fashion system, from individual creative agency with home dressmaking and re-making to the explosion of the mass market and the consequent abandonment of such practices. Home dressmaking has been replaced by fashion remix of mass produced garments, a practice that thrives in our environment of globalised fast fashion. Thus this chapter suggests the need for a contextual requalification of concepts such as original, copy, imitation and copyright, and argues that these categories have been played against each other, but they are in fact interdependent. Today, big labels and conglomerates try to control knowledge and innovation through copyright, but, fashion escapes copyright because, in fashion, creativity is contextual. The institutionalisation of couture from 1868 served as a way to control knowledge about production processes in fashion; on the other hand, adaptation practices, often subversive, have been fundamental to the democratisation of fashion.

The implications of cross-regional differences for the design of In-vehicle Information Systems : a comparison of Australian and Chinese drivers

The increasing global distribution of automobiles necessitates that the design of In-vehicle Information Systems (IVIS) is appropriate for the regions to which they are being exported. Differences between regions such as culture, environment and traffic context can influence the needs, usability and acceptance of IVIS. This paper describes two studies aimed at identifying regional differences in IVIS design needs and preferences across drivers from Australia and China to determine the impact of any differences on IVIS design. Using a questionnaire and interaction clinics, the influence of cultural values and driving patterns on drivers' preferences for, and comprehension of, surface- and interaction-level aspects of IVIS interfaces was explored. Similarities and differences were found between the two regional groups in terms of preferences for IVIS input control types and labels and in the comprehension of IVIS functions. Specifically, Chinese drivers preferred symbols and Chinese characters over English words and were less successful (compared to Australians) at comprehending English abbreviations, particularly for complex IVIS functions. Implications in terms of the current trend to introduce Western-styled interfaces into other regions with little or no adaptation are discussed.

The knock-on from the knock-off : recent shifts within Australian mass market fashion design practice

Australia's mass market fashion labels have traditionally benefitted from their peripheral location to the world's fashion centres. Operating a season behind, Australian mass market designers and buyers were well-placed to watch trends play out overseas before testing them in the Australian marketplace. For this reason, often a designer's role was to source and oversee the manufacture of 'knock-offs', or close copies of northern hemisphere mass market garments. Both Weller and Walsh have commented on this practice.12 The knock-on effect from this continues to be a cautious, derivative fashion sensibility within Australian mass market fashion design, where any new trend or product is first tested and proved overseas months earlier. However, there is evidence that this is changing. The rapid online dissemination of global fashion trends, coupled with the Australian consumer’s willingness to shop online, has meant that the ‘knock-off’ is less viable. For this reason, a number of mass market companies are moving away from the practice of direct sourcing and are developing product in-house under a northern hemisphere model. This shift is also witnessed in the trend for mass market companies to develop collections in partnership with independent Australian designers. This paper explores the current and potential effects of these shifts within Australian mass market design practice, and discusses how they may impact on both consumers and on the wider culture of Australian fashion.

The contribution of psychosocial factors to socioeconomic differences in food purchasing

In developed countries the relationship between socioeconomic position (SEP) and health is unequivocal. Those who are socioeconomically disadvantaged are known to experience higher morbidity and mortality from a range of chronic diet-related conditions compared to those of higher SEP. Socioeconomic inequalities in diet are well established. Compared to their more advantaged counterparts, those of low SEP are consistently found to consume diets less consistent with dietary guidelines (i.e. higher in fat, salt and sugar and lower in fibre, fruit and vegetables). Although the reasons for dietary inequalities remain unclear, understanding how such differences arise is important for the development of strategies to reduce health inequalities. Both environmental (e.g. proximity of supermarkets, price, and availability of foods) and psychosocial (e.g. taste preference, nutrition knowledge) influences are proposed to account for inequalities in food choices. Although in the United States (US), United Kingdom (UK), and parts of Australia, environmental factors are associated with socioeconomic differences in food choices, these factors do not completely account for the observed inequalities. Internationally, this context has prompted calls for further exploration of the role of psychological and social factors in relation to inequalities in food choices. It is this task that forms the primary goal of this PhD research. In the small body of research examining the contribution of psychosocial factors to inequalities in food choices, studies have focussed on food cost concerns, nutrition knowledge or health concerns. These factors are generally found to be influential. However, since a range of psychosocial factors are known determinants of food choices in the general population, it is likely that a range of factors also contribute to inequalities in food choices. Identification of additional psychosocial factors of relevance to inequalities in food choices would provide new opportunities for health promotion, including the adaption of existing strategies. The methodological features of previous research have also hindered the advancement of knowledge in this area and a lack of qualitative studies has resulted in a dearth of descriptive information on this topic. This PhD investigation extends previous research by assessing a range of psychosocial factors in relation to inequalities in food choices using both quantitative and qualitative techniques. Secondary data analyses were undertaken using data obtained from two Brisbane-based studies, the Brisbane Food Study (N=1003, conducted in 2000), and the Sixty Families Study (N=60, conducted in 1998). Both studies involved main household food purchasers completing an interviewer-administered survey within their own home. Data pertaining to food-purchasing, and psychosocial, socioeconomic and demographic characteristics were collected in each study. The mutual goals of both the qualitative and quantitative phases of this investigation were to assess socioeconomic differences in food purchasing and to identify psychosocial factors relevant to any observed differences. The quantitative methods then additionally considered whether the associations examined differed according to the socioeconomic indicator used (i.e. income or education). The qualitative analyses made a unique contribution to this project by generating detailed descriptions of socioeconomic differences in psychosocial factors. Those with lower levels of income and education were found to make food purchasing choices less consistent with dietary guidelines compared to those of high SEP. The psychosocial factors identified as relevant to food-purchasing inequalities were: taste preferences, health concerns, health beliefs, nutrition knowledge, nutrition concerns, weight concerns, nutrition label use, and several other values and beliefs unique to particular socioeconomic groups. Factors more tenuously or inconsistently related to socioeconomic differences in food purchasing were cost concerns, and perceived adequacy of the family diet. Evidence was displayed in both the quantitative and qualitative analyses to suggest that psychosocial factors contribute to inequalities in food purchasing in a collective manner. The quantitative analyses revealed that considerable overlap in the socioeconomic variation in food purchasing was accounted for by key psychosocial factors of importance, including taste preference, nutrition concerns, nutrition knowledge, and health concerns. Consistent with these findings, the qualitative transcripts demonstrated the interplay between such influential psychosocial factors in determining food-purchasing choices. The qualitative analyses found socioeconomic differences in the prioritisation of psychosocial factors in relation to food choices. This is suggestive of complex cultural factors that distinguish advantaged and disadvantaged groups and result in socioeconomically distinct schemas related to health and food choices. Compared to those of high SEP, those of lower SEP were less likely to indicate that health concerns, nutrition concerns, or food labels influenced food choices, and exhibited lower levels of nutrition knowledge. In the absence of health or nutrition-related concerns, taste preferences tended to dominate the food purchasing choices of those of low SEP. Overall, while cost concerns did not appear to be a main determinant of socioeconomic differences in food purchasing, this factor had a dominant influence on the food choices of some of the most disadvantaged respondents included in this research. The findings of this study have several implications for health promotion. The integrated operation of psychosocial factors on food purchasing inequalities indicates that multiple psychosocial factors may be appropriate to target in health promotion. It also seems possible that the inter-relatedness of psychosocial factors would allow health promotion targeting a single psychosocial factor to have a flow-on affect in terms of altering other influential psychosocial factors. This research also suggests that current mass marketing approaches to health promotion may not be effective across all socioeconomic groups due to differences in the priorities and main factors of influence in food purchasing decisions across groups. In addition to the practical recommendations for health promotion, this investigation, through the critique of previous research, and through the substantive study findings, has highlighted important methodological considerations for future research. Of particular note are the recommendations pertaining to the selection of socioeconomic indicators, measurement of relevant constructs, consideration of confounders, and development of an analytical approach. Addressing inequalities in health has been noted as a main objective by many health authorities and governments internationally. It is envisaged that the substantive and methodological findings of this thesis will make a useful contribution towards this important goal.

Segmentation of cone-beam CT using a hidden Markov random field with informative priors

Purpose: Flat-detector, cone-beam computed tomography (CBCT) has enormous potential to improve the accuracy of treatment delivery in image-guided radiotherapy (IGRT). To assist radiotherapists in interpreting these images, we use a Bayesian statistical model to label each voxel according to its tissue type. Methods: The rich sources of prior information in IGRT are incorporated into a hidden Markov random field (MRF) model of the 3D image lattice. Tissue densities in the reference CT scan are estimated using inverse regression and then rescaled to approximate the corresponding CBCT intensity values. The treatment planning contours are combined with published studies of physiological variability to produce a spatial prior distribution for changes in the size, shape and position of the tumour volume and organs at risk (OAR). The voxel labels are estimated using the iterated conditional modes (ICM) algorithm. Results: The accuracy of the method has been evaluated using 27 CBCT scans of an electron density phantom (CIRS, Inc. model 062). The mean voxel-wise misclassification rate was 6.2%, with Dice similarity coefficient of 0.73 for liver, muscle, breast and adipose tissue. Conclusions: By incorporating prior information, we are able to successfully segment CBCT images. This could be a viable approach for automated, online image analysis in radiotherapy.

Segmentation of cone-beam CT using a hidden Markov random field with informative priors

Cone-beam computed tomography (CBCT) has enormous potential to improve the accuracy of treatment delivery in image-guided radiotherapy (IGRT). To assist radiotherapists in interpreting these images, we use a Bayesian statistical model to label each voxel according to its tissue type. The rich sources of prior information in IGRT are incorporated into a hidden Markov random field model of the 3D image lattice. Tissue densities in the reference CT scan are estimated using inverse regression and then rescaled to approximate the corresponding CBCT intensity values. The treatment planning contours are combined with published studies of physiological variability to produce a spatial prior distribution for changes in the size, shape and position of the tumour volume and organs at risk. The voxel labels are estimated using iterated conditional modes. The accuracy of the method has been evaluated using 27 CBCT scans of an electron density phantom. The mean voxel-wise misclassification rate was 6.2\%, with Dice similarity coefficient of 0.73 for liver, muscle, breast and adipose tissue. By incorporating prior information, we are able to successfully segment CBCT images. This could be a viable approach for automated, online image analysis in radiotherapy.

Intrusion detection in distributed systems, an approach based on taint marking

This paper presents a new framework for distributed intrusion detection based on taint marking. Our system tracks information flows between applications of multiple hosts gathered in groups (i.e., sets of hosts sharing the same distributed information flow policy) by attaching taint labels to system objects such as files, sockets, Inter Process Communication (IPC) abstractions, and memory mappings. Labels are carried over the network by tainting network packets. A distributed information flow policy is defined for each group at the host level by labeling information and defining how users and applications can legally access, alter or transfer information towards other trusted or untrusted hosts. As opposed to existing approaches, where information is most often represented by two security levels (low/high, public/private, etc.), our model identifies each piece of information within a distributed system, and defines their legal interaction in a fine-grained manner. Hosts store and exchange security labels in a peer to peer fashion, and there is no central monitor. Our IDS is implemented in the Linux kernel as a Linux Security Module (LSM) and runs standard software on commodity hardware with no required modification. The only trusted code is our modified operating system kernel. We finally present a scenario of intrusion in a web service running on multiple hosts, and show how our distributed IDS is able to report security violations at each host level.

Real-time video event detection in crowded scenes using MPEG derived features : a multiple instance learning approach

This paper presents an investigation into event detection in crowded scenes, where the event of interest co-occurs with other activities and only binary labels at the clip level are available. The proposed approach incorporates a fast feature descriptor from the MPEG domain, and a novel multiple instance learning (MIL) algorithm using sparse approximation and random sensing. MPEG motion vectors are used to build particle trajectories that represent the motion of objects in uniform video clips, and the MPEG DCT coefficients are used to compute a foreground map to remove background particles. Trajectories are transformed into the Fourier domain, and the Fourier representations are quantized into visual words using the K-Means algorithm. The proposed MIL algorithm models the scene as a linear combination of independent events, where each event is a distribution of visual words. Experimental results show that the proposed approaches achieve promising results for event detection compared to the state-of-the-art.

Large-scale analysis of formations in soccer

Due to the demand for better and deeper analysis in sports, organizations (both professional teams and broadcasters) are looking to use spatiotemporal data in the form of player tracking information to obtain an advantage over their competitors. However, due to the large volume of data, its unstructured nature, and lack of associated team activity labels (e.g. strategic/tactical), effective and efficient strategies to deal with such data have yet to be deployed. A bottleneck restricting such solutions is the lack of a suitable representation (i.e. ordering of players) which is immune to the potentially infinite number of possible permutations of player orderings, in addition to the high dimensionality of temporal signal (e.g. a game of soccer last for 90 mins). Leveraging a recent method which utilizes a "role-representation", as well as a feature reduction strategy that uses a spatiotemporal bilinear basis model to form a compact spatiotemporal representation. Using this representation, we find the most likely formation patterns of a team associated with match events across nearly 14 hours of continuous player and ball tracking data in soccer. Additionally, we show that we can accurately segment a match into distinct game phases and detect highlights. (i.e. shots, corners, free-kicks, etc) completely automatically using a decision-tree formulation.

Online self-supervised multi-instance segmentation of dynamic objects

This paper presents a method for the continuous segmentation of dynamic objects using only a vehicle mounted monocular camera without any prior knowledge of the object’s appearance. Prior work in online static/dynamic segmentation is extended to identify multiple instances of dynamic objects by introducing an unsupervised motion clustering step. These clusters are then used to update a multi-class classifier within a self-supervised framework. In contrast to many tracking-by-detection based methods, our system is able to detect dynamic objects without any prior knowledge of their visual appearance shape or location. Furthermore, the classifier is used to propagate labels of the same object in previous frames, which facilitates the continuous tracking of individual objects based on motion. The proposed system is evaluated using recall and false alarm metrics in addition to a new multi-instance labelled dataset to evaluate the performance of segmenting multiple instances of objects.

Insider comes out : an artist's inquiry and narrative about the relationship of art and mental health

Background Through an account of prevailing experiences of art and mental illness, this paper aims to raise awareness, open dialogue and create agency about art created by people with experience of mental illness. Methods This paper draws on personal narrative and inquiry by an artist with mental illness and data collected as part of a larger participatory action research project that investigated understandings of identity, art and mental illness. Result An inquiry through art raised awareness and attentiveness to the importance of choice in identity construction and exposed frequent dichotomies in art and mental illness that were negotiated to eschew prescribed social stratification. As an artist, the first author challenged values present in one idea and absent in the other, and the options and concessions available to authorise her own dialogue and agency of being an artist. Conclusion Constructing an identity is an important part of being human, the labels that we choose or are chosen for us attribute to our identity. Reflections and recommendations are offered to consider expanded ways of thinking about art and mental illness and the functions that art play in identity construction.

The past, present and future of carbon labelling for construction materials – a review

Global climate change is one of the most significant environmental issues that can harm human development. One central issue for the building and construction industry to address global climate change is the development of a credible and meaningful way to measure greenhouse gas (GHG) emissions. While Publicly Available Specification (PAS) 2050, the first international GHG standard, has been proven to be successful in standardizing the quantification process, its contribution to the management of carbon labels for construction materials is limited. With the recent publication of ISO 14067: Greenhouse gases – carbon footprint of products – requirements and guidelines for quantification and communication in May 2013, it is necessary for the building and construction industry to understand the past, present and future of the carbon labelling practices for construction materials. A systematic review shows that international GHG standards have been evolving in terms of providing additional guidance on communication and comparison, as well as less flexibility on the use of carbon labels. At the same time, carbon labelling schemes have been evolving on standardization and benchmarking. In addition, future actions are needed in the aspect of raising consumer awareness, providing benchmarking, ensuring standardization and developing simulation technologies in order for carbon labelling schemes for construction materials to provide credible, accurate and transparent information on GHG emissions.

Probability and story-telling : normative and descriptive models of juridical proof

The operation of the law rests on the selection of an account of the facts. Whether this involves prediction or postdiction, it is not possible to achieve certainty. Any attempt to model the operation of the law completely will therefore raise questions of how to model the process of proof. In the selection of a model a crucial question will be whether the model is to be used normatively or descriptively. Focussing on postdiction, this paper presents and contrasts the mathematical model with the story model. The former carries the normative stamp of scientific approval, whereas the latter has been developed by experimental psychologists to describe how humans reason. Neil Cohen's attempt to use a mathematical model descriptively provides an illustration of the dangers in not clearly setting this parameter of the modelling process. It should be kept in mind that the labels 'normative' and 'descriptive' are not eternal. The mathematical model has its normative limits, beyond which we may need to critically assess models with descriptive origins.