Unveiling hidden unstructured regions in process models

Process models define allowed process execution scenarios. The models are usually depicted as directed graphs, with gateway nodes regulating the control flow routing logic and with edges specifying the execution order constraints between tasks. While arbitrarily structured control flow patterns in process models complicate model analysis, they also permit creativity and full expressiveness when capturing non-trivial process scenarios. This paper gives a classification of arbitrarily structured process models based on the hierarchical process model decomposition technique. We identify a structural class of models consisting of block structured patterns which, when combined, define complex execution scenarios spanning across the individual patterns. We show that complex behavior can be localized by examining structural relations of loops in hidden unstructured regions of control flow. The correctness of the behavior of process models within these regions can be validated in linear time. These observations allow us to suggest techniques for transforming hidden unstructured regions into block-structured ones.

Educational paradox : the hidden obstacles to the integration of mobile phones in the language classroom

This paper reports on the findings of an international telecollaboration study using Facebook, in which teachers studying in M. Ed programs in Australia and Greece, discussed the use of mobile phones in language classrooms. Results suggest that invisible barriers exist in the use of mobile phones in the classroom, including bans on use in schools, lack of familiarity with educational uses for mobile phones, and negative perceptions about mobile phones specifically in terms of classroom management.

Hidden innovation : Policy, industry and the creative sector

The term ‘two cultures’ was coined more than 50 years ago by scientist and novelist C.P. Snow to describe the divergence in the world views and methods of scientists and the creative sector. This divergence has meant that innovation systems and policies have focused for decades on science, engineering, technology and medicine and the industries that depend on them. The humanities, arts and social sciences have been bit players at best; their contributions hidden from research agendas, policy and program initiatives, and the public mind. But structural changes to advanced economies and societies have brought services industries and the creative sector to greater prominence as key contributors to innovation. Hidden Innovation peels back the veil, tracing the way innovation occurs through new forms of screen production enabled by social media platforms as well as in public broadcasting. It shows that creative workers are contributing fresh ideas across the economy and how creative cities debates need reframing. It traces how policies globally are beginning to catch up with the changing social and economic realities. In his new book, Cunningham argues that the innovation framework offers the best opportunity in decades to reassess and refresh the case for the public role of the humanities, particularly the media, cultural and communication studies disciplines.

Compressed sensing using hidden Markov models with application to vision based aircraft tracking

This paper investigates compressed sensing using hidden Markov models (HMMs) and hence provides an extension of recent single frame, bounded error sparse decoding problems into a class of sparse estimation problems containing both temporal evolution and stochastic aspects. This paper presents two optimal estimators for compressed HMMs. The impact of measurement compression on HMM filtering performance is experimentally examined in the context of an important image based aircraft target tracking application. Surprisingly, tracking of dim small-sized targets (as small as 5-10 pixels, with local detectability/SNR as low as − 1.05 dB) was only mildly impacted by compressed sensing down to 15% of original image size.

Degradation modeling and monitoring of machines using operation-specific hidden Markov models

In this paper, a novel data-driven approach to monitoring of systems operating under variable operating conditions is described. The method is based on characterizing the degradation process via a set of operation-specific hidden Markov models (HMMs), whose hidden states represent the unobservable degradation states of the monitored system while its observable symbols represent the sensor readings. Using the HMM framework, modeling, identification and monitoring methods are detailed that allow one to identify a HMM of degradation for each operation from mixed-operation data and perform operation-specific monitoring of the system. Using a large data set provided by a major manufacturer, the new methods are applied to a semiconductor manufacturing process running multiple operations in a production environment.

Guardians of hidden universes

“Guardians of Hidden Universes” is a playful story for experiencing on your phone in the street. You’re a Guardian of Hidden Universes, and when you check your Communication Terminal you find the Guild has made a terrible mistake they need you to repair. The experience lasts about 15-25 minutes, and takes you around whatever city you happen to be in. That’s right – you can play it in any city in the world! The app was launched at Popup Playground‘s 2013 Fresh Air Festival – a new pervasive gaming festival in Melbourne, Australia. This playful story is inspired by the idea of creating a locative-experience that is not site-specific. How can we have an experience than engages with the environment that also can work worldwide? The story is also playing with the idea of engaging with a character and a space and intuition within a short period of time. The short and playful experience is a Universe Creation 101 production. It was conceived, written, and designed by Christy Dena; with artwork, coding, and interface design by Elroy.

Adaptive estimation of hidden semi-Markov chains with parameterised transition probabilities and exponential decaying states

This paper develops maximum likelihood (ML) estimation schemes for finite-state semi-Markov chains in white Gaussian noise. We assume that the semi-Markov chain is characterised by transition probabilities of known parametric from with unknown parameters. We reformulate this hidden semi-Markov model (HSM) problem in the scalar case as a two-vector homogeneous hidden Markov model (HMM) problem in which the state consist of the signal augmented by the time to last transition. With this reformulation we apply the expectation Maximumisation (EM ) algorithm to obtain ML estimates of the transition probabilities parameters, Markov state levels and noise variance. To demonstrate our proposed schemes, motivated by neuro-biological applications, we use a damped sinusoidal parameterised function for the transition probabilities.

Reduced complexity on-line estimation of hidden Markov model parameters

In this paper we propose and study low complexity algorithms for on-line estimation of hidden Markov model (HMM) parameters. The estimates approach the true model parameters as the measurement noise approaches zero, but otherwise give improved estimates, albeit with bias. On a nite data set in the high noise case, the bias may not be signi cantly more severe than for a higher complexity asymptotically optimal scheme. Our algorithms require O(N3) calculations per time instant, where N is the number of states. Previous algorithms based on earlier hidden Markov model signal processing methods, including the expectation-maximumisation (EM) algorithm require O(N4) calculations per time instant.

An external field prior for the hidden Potts model with application to cone-beam computed tomography

In images with low contrast-to-noise ratio (CNR), the information gain from the observed pixel values can be insufficient to distinguish foreground objects. A Bayesian approach to this problem is to incorporate prior information about the objects into a statistical model. A method for representing spatial prior information as an external field in a hidden Potts model is introduced. This prior distribution over the latent pixel labels is a mixture of Gaussian fields, centred on the positions of the objects at a previous point in time. It is particularly applicable in longitudinal imaging studies, where the manual segmentation of one image can be used as a prior for automatic segmentation of subsequent images. The method is demonstrated by application to cone-beam computed tomography (CT), an imaging modality that exhibits distortions in pixel values due to X-ray scatter. The external field prior results in a substantial improvement in segmentation accuracy, reducing the mean pixel misclassification rate for an electron density phantom from 87% to 6%. The method is also applied to radiotherapy patient data, demonstrating how to derive the external field prior in a clinical context.

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 hidden injuries of mining: Frontier cultural conflict

This chapter addresses a topic of growing significance to green criminology - the harmful effects of mining on local communities and the environment (Ruggiero and South 2013; White 2013a). While mining has long been recognised as an agent of environmental harm (White 2013a), less recognised is that its global expansion also has harmful effects on localised patterns of violence, work and community life in mining towns. Australia provides an excellent case study for exploring some of these mining impacts.

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.

Male nursing students in training: A comparative analysis of hidden advantage and discrimination

Aim The aim of this study was to examine the lived experience of men training to be registered nurses within a regional New Zealand context. Design This study draws upon the key principles of descriptive phenomenology. Sample Five male students enrolled from the 1st and 3rd year of the BN programme. Findings - A Career with Prospects - Gender inequality by superiors; - Developing professional boundaries with female colleagues; - Being unique has its advantages.