Organizational change in the public sector: augmenting the demand control model to predict employee outcomes under New Public Management

This study identifies the environmental and personal characteristics that predict employee outcomes within an Australian public sector organization that had, under New Public Management (NPM), implemented a variety of practices traditionally found in the private sector. These are more results-oriented, and their adoption can be accompanied by increased strain for employees. The current investigation was guided by two complementary theories, the Demand Control Support (DCS) model and Conservation of Resources (COR) theory, and sought to examine the benefits of building on the DCS to include both situation-specific stressors and internal coping resources. Survey responses from 1,155 employees were analysed. The hierarchical regression analyses indicated that both external and employee-centred variables made significant contributions to variations in psychological health, job satisfaction, and organizational commitment. The external resources, work based support and, to a lesser extent, job control, predicted relatively large proportions of the variance in the target variables. The situation-specific stressors, particularly those involving harmful management practices (e.g., insufficient time to do job as well as you would like, lack of recognition for good work), made significant contributions to the outcome measures and generally supported the process of augmenting the generic components of the DCS with more situation-specific variables. In terms of internal resources, problem and emotion-based coping improved the capacity of the model to predict psychological health. The results suggest that the impact of NPM can be ameliorated by incorporating the dimensions of the augmented DCS and coping resources into the change programme.

Selective hydrogen bonding and hierarchical nanostructures in poly(hydroxyether of bisphenol A)/poly(ε-caprolactone)-block-poly(2-vinyl pyridine) blends

The phase behavior, hydrogen bonding interactions and morphology of poly(hydroxyether of bisphenol A) (phenoxy) and poly(var epsilon-caprolactone)-block-poly(2-vinyl pyridine) (PCL-b-P2VP) were investigated using differential scanning calorimetry (DSC), Fourier transform infrared (FTIR) spectroscopy, optical microscopy and atomic force microscopy (AFM). In this A-b-B/C type block copolymer/homopolymer system, both P2VP and PCL blocks have favorable intermolecular interaction towards phenoxy via hydrogen bonding. However, the hydrogen bonding between P2VP and phenoxy is significantly stronger than that between PCL and phenoxy. Selective hydrogen bonding between phenoxy/P2VP pair at lower phenoxy contents and co-existence of two competitive hydrogen bonding interactions between phenoxy/P2VP and phenoxy/PCL pairs at higher phenoxy contents were observed in the blends. This leads to the formation of a variety of composition dependent nanostructures including wormlike, hierarchical and core–shell morphologies. The blends became homogeneous at 95 wt% phenoxy where both blocks of the PCL-b-P2VP were miscible with phenoxy due to hydrogen bonding. In the end, a model was proposed to explain the microphase morphology of blends based on the experimental results obtained. The swelling of the PCL-b-P2VP block copolymer by phenoxy due to selective hydrogen bonding causes formation of different microphases

Instructional strategies integrating cognitive style construct : a meta-knowledge processing model

The overarching goal of this dissertation was to evaluate the contextual components of instructional strategies for the acquisition of complex programming concepts. A meta-knowledge processing model is proposed, on the basis of the research findings, thereby facilitating the selection of media treatment for electronic courseware. When implemented, this model extends the work of Smith (1998), as a front-end methodology, for his glass-box interpreter called Bradman, for teaching novice programmers. Technology now provides the means to produce individualized instructional packages with relative ease. Multimedia and Web courseware development accentuate a highly graphical (or visual) approach to instructional formats. Typically, little consideration is given to the effectiveness of screen-based visual stimuli, and curiously, students are expected to be visually literate, despite the complexity of human-computer interaction. Visual literacy is much harder for some people to acquire than for others! (see Chapter Four: Conditions-of-the-Learner) An innovative research programme was devised to investigate the interactive effect of instructional strategies, enhanced with text-plus-textual metaphors or text-plus-graphical metaphors, and cognitive style, on the acquisition of a special category of abstract (process) programming concept. This type of concept was chosen to focus on the role of analogic knowledge involved in computer programming. The results are discussed within the context of the internal/external exchange process, drawing on Ritchey's (1980) concepts of within-item and between-item encoding elaborations. The methodology developed for the doctoral project integrates earlier research knowledge in a novel, interdisciplinary, conceptual framework, including: from instructional science in the USA, for the concept learning models; British cognitive psychology and human memory research, for defining the cognitive style construct; and Australian educational research, to provide the measurement tools for instructional outcomes. The experimental design consisted of a screening test to determine cognitive style, a pretest to determine prior domain knowledge in abstract programming knowledge elements, the instruction period, and a post-test to measure improved performance. This research design provides a three-level discovery process to articulate: 1) the fusion of strategic knowledge required by the novice learner for dealing with contexts within instructional strategies 2) acquisition of knowledge using measurable instructional outcome and learner characteristics 3) knowledge of the innate environmental factors which influence the instructional outcomes This research has successfully identified the interactive effect of instructional strategy, within an individual's cognitive style construct, in their acquisition of complex programming concepts. However, the significance of the three-level discovery process lies in the scope of the methodology to inform the design of a meta-knowledge processing model for instructional science. Firstly, the British cognitive style testing procedure, is a low cost, user friendly, computer application that effectively measures an individual's position on the two cognitive style continua (Riding & Cheema,1991). Secondly, the QUEST Interactive Test Analysis System (Izard,1995), allows for a probabilistic determination of an individual's knowledge level, relative to other participants, and relative to test-item difficulties. Test-items can be related to skill levels, and consequently, can be used by instructional scientists to measure knowledge acquisition. Finally, an Effect Size Analysis (Cohen,1977) allows for a direct comparison between treatment groups, giving a statistical measurement of how large an effect the independent variables have on the dependent outcomes. Combined with QUEST's hierarchical positioning of participants, this tool can assist in identifying preferred learning conditions for the evaluation of treatment groups. By combining these three assessment analysis tools into instructional research, a computerized learning shell, customised for individuals' cognitive constructs can be created (McKay & Garner,1999). While this approach has widespread application, individual researchers/trainers would nonetheless, need to validate with an extensive pilot study programme (McKay,1999a; McKay,1999b), the interactive effects within their specific learning domain. Furthermore, the instructional material does not need to be limited to a textual/graphical comparison, but could be applied to any two or more instructional treatments of any kind. For instance: a structured versus exploratory strategy. The possibilities and combinations are believed to be endless, provided the focus is maintained on linking of the front-end identification of cognitive style with an improved performance outcome. My in-depth analysis provides a better understanding of the interactive effects of the cognitive style construct and instructional format on the acquisition of abstract concepts, involving spatial relations and logical reasoning. In providing the basis for a meta-knowledge processing model, this research is expected to be of interest to educators, cognitive psychologists, communications engineers and computer scientists specialising in computer-human interactions.

Mining multi-modal crime patterns at different levels of granularity using hierarchical clustering

The appearance of patterns could be found in different modalities of a domain, where the different modalities refer to the data sources that constitute different aspects of a domain. Particularly, the domain of our discussion refers to crime and the different modalities refer to the different data sources such as offender data, weapon data, etc. in crime domain. In addition, patterns also exist in different levels of granularity for each modality. In order to have a thorough understanding a domain, it is important to reveal the hidden patterns through the data explorations at different levels of granularity and for each modality. Therefore, this paper presents a new model for identifying patterns that exist in different levels of granularity for different modes of crime data. A hierarchical clustering approach - growing self organising maps (GSOM) has been deployed. Furthermore, the model is enhanced with experiments that exhibit the significance of exploring data at different granularities.

Secure connectivity model in wireless sensor networks (WSN) using first order Reed-Muller codes

In this paper, we suggest the idea of separately treating the connectivity and communication model of a Wireless Sensor Network (WSN). We then propose a novel connectivity model for a WSN using first order Reed-Muller Codes. While the model has a hierarchical structure, we have shown that it works equally well for a Distributed WSN. Though one can use any communication model, we prefer to use the communication model suggested by Ruj and Roy [1] for all computations and results in our work. Two suitable secure (symmetric) cryptosystems can then be applied for the two different models, connectivity and communication respectively. By doing so we have shown how resiliency and scalability are appreciably improved as compared to Ruj and Roy [1].

Procedural modeling facilities for hierarchical object generation

We modify a selection of interactive modeling tools for use in a procedural modeling environment. These tools are selection, extrusion, subdivision and curve shaping. We create human models to demonstrate that these tools are appropriate for use on hierarchical objects. Our tools support the main benefits of procedural modeling, which are: the use of parameterisation to control and very a model, varying levels of detail, increased model complexity, base shape independence and database amplification. We demonstrate scripts which provide each of these benefits.

Clusters driven implementation of a brain inspired model for multi-view pattern identifications

The human brain processes information in both unimodal and multimodal fashion where information is progressively captured, accumulated, abstracted and seamlessly fused. Subsequently, the fusion of multimodal inputs allows a holistic understanding of a problem. The proliferation of technology has produced various sources of electronic data and continues to do so exponentially. Finding patterns from such multi-source and multimodal data could be compared to the multimodal and multidimensional information processing in the human brain. Therefore, such brain functionality could be taken as an inspiration to develop a methodology for exploring multimodal and multi-source electronic data and further identifying multi-view patterns. In this paper, we first propose a brain inspired conceptual model that allows exploration and identification of patterns at different levels of granularity, different types of hierarchies and different types of modalities. Secondly, we present a cluster driven approach for the implementation of the proposed brain inspired model. Particularly, the Growing Self Organising Maps (GSOM) based cross-clustering approach is discussed. Furthermore, the acquisition of multi-view patterns with clusters driven implementation is demonstrated with experimental results.

Identifying multi-view patterns with hierarchy and granularity based multimodal (HGM) cognitive model

Humans perceive entities such as objects, patterns, events, etc. as concepts, which are the basic units in human intelligence and communications. In addition, perceptions of these entities could be abstracted and generalised at multiple levels of granularity. In particular, such granulation allows the formation and usage of concepts in human intelligence. Such natural granularity in human intelligence could inspire and motivate the design and development of pattern identification approach in Data Mining. In our opinion, a pattern could be perceived at multiple levels of granularity and thus we advocate for the co-existence of hierarchy and granularity. In addition, granular patterns exist across different sources of data (multimodality). In this paper, we present a cognitive model that incorporates the characteristics of Hierarchy, Granularity and Multimodality for multi-view patterns identification in crime domain. Such framework is implemented with Growing Self Organising Maps (GSOM) and some experimental results are presented and discussed.

Activity recognition and abnormality detection with the switching hidden semi-Markov model

This paper addresses the problem of learning and recognizing human activities of daily living (ADL), which is an important research issue in building a pervasive and smart environment. In dealing with ADL, we argue that it is beneficial to exploit both the inherent hierarchical organization of the activities and their typical duration. To this end, we introduce the Switching Hidden Semi-Markov Model (S-HSMM), a two-layered extension of the hidden semi-Markov model (HSMM) for the modeling task. Activities are modeled in the S-HSMM in two ways: the bottom layer represents atomic activities and their duration using HSMMs; the top layer represents a sequence of high-level activities where each high-level activity is made of a sequence of atomic activities. We consider two methods for modeling duration: the classic explicit duration model using multinomial distribution, and the novel use of the discrete Coxian distribution. In addition, we propose an effective scheme to detect abnormality without the need for training on abnormal data. Experimental results show that the S-HSMM performs better than existing models including the flat HSMM and the hierarchical hidden Markov model in both classification and abnormality detection tasks, alleviating the need for presegmented training data. Furthermore, our discrete Coxian duration model yields better computation time and generalization error than the classic explicit duration model.

Hierarchical hidden Markov models with general state hierarchy

The hierarchical hidden Markov model (HHMM) is an extension of the hidden Markov model to include a hierarchy of the hidden states. This form of hierarchical modeling has been found useful in applications such as handwritten character recognition, behavior recognition, video indexing, and text retrieval. Nevertheless, the state hierarchy in the original HHMM is restricted to a tree structure. This prohibits two different states from having the same child, and thus does not allow for sharing of common substructures in the model. In this paper, we present a general HHMM in which the state hierarchy can be a lattice allowing arbitrary sharing of substructures. Furthermore, we provide a method for numerical scaling to avoid underflow, an important issue in dealing with long observation sequences. We demonstrate the working of our method in a simulated environment where a hierarchical behavioral model is automatically learned and later used for recognition.

Hierarchical monitoring of people's behaviors in complex environments using multiple cameras

We present a distributed, surveillance system that works in large and complex indoor environments. To track and recognize behaviors of people, we propose the use of the Abstract Hidden Markov Model (AHMM), which can be considered as an extension of the Hidden Markov Model (HMM), where the single Markov chain in the HMM is replaced by a hierarchy of Markov policies. In this policy hierarchy, each behavior can be represented as a policy at the corresponding level of abstraction. The noisy observations are handled in the same way as an HMM and an efficient Rao-Blackwellised particle filter method is used to compute the probabilities of the current policy at different levels of the hierarchy The novelty of the paper lies in the implementation of a scalable framework in the context of both the scale of behaviors and the size of the environment, making it ideal for distributed surveillance. The results of the system demonstrate the ability to answer queries about people's behaviors at different levels of details using multiple cameras in a large and complex indoor environment.

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.

Content structure discovery in educational videos using shared structures in the hierarchical hidden Markov models

In this paper, we present an application of the hierarchical HMM for structure discovery in educational videos. The HHMM has recently been extended to accommodate the concept of shared structure, ie: a state might multiply inherit from more than one parents. Utilising the expressiveness of this model, we concentrate on a specific class of video -educational videos - in which the hierarchy of semantic units is simpler and clearly defined in terms of topics and its subunits. We model the hierarchy of topical structures by an HHMM and demonstrate the usefulness of the model in detecting topic transitions.

Hierarchical representation for spatial knowledge

One of the fundamental issues in building autonomous agents is to be able to sense, represent and react to the world. Some of the earlier work [Mor83, Elf90, AyF89] has aimed towards a reconstructionist approach, where a number of sensors are used to obtain input that is used to construct a model of the world that mirrors the real world. Sensing and sensor fusion was thus an important aspect of such work. Such approaches have had limited success, and some of the main problems were the issues of uncertainty arising from sensor error and errors that accumulated in metric, quantitative models. Recent research has therefore looked at different ways of examining the problems. Instead of attempting to get the most accurate and correct model of the world, these approaches look at qualitative models to represent the world, which maintain relative and significant aspects of the environment rather than all aspects of the world. The relevant aspects of the world that are retained are determined by the task at hand which in turn determines how to sense. That is, task directed or purposive sensing is used to build a qualitative model of the world, which though inaccurate and incomplete is sufficient to solve the problem at hand. This paper examines the issues of building up a hierarchical knowledge representation of the environment with limited sensor input that can be actively acquired by an agent capable of interacting with the environment. Different tasks require different aspects of the environment to be abstracted out. For example, low level tasks such as navigation require aspects of the environment that are related to layout and obstacle placement. For the agent to be able to reposition itself in an environment, significant features of spatial situations and their relative placement need to be kept. For the agent to reason about objects in space, for example to determine the position of one object relative to another, the representation needs to retain information on relative locations of start and finish of the objects, that is endpoints of objects on a grid. For the agent to be able to do high level planning, the agent may need only the relative position of the starting point and destination, and not the low level details of endpoints, visual clues and so on. This indicates that a hierarchical approach would be suitable, such that each level in the hierarchy is at a different level of abstraction, and thus suitable for a different task. At the lowest level, the representation contains low level details of agent's motion and visual clues to allow the agent to navigate and reposition itself. At the next level of abstraction the aspects of the representation allow the agent to perform spatial reasoning, and finally the highest level of abstraction in the representation can be used by the agent for high level planning.