Tracking and surveillance in wide-area spatial environments using the abstract hidden markov model.

In this paper, we consider the problem of tracking an object and predicting the object's future trajectory in a wide-area environment, with complex spatial layout and the use of multiple sensors/cameras. To solve this problem, there is a need for representing the dynamic and noisy data in the tracking tasks, and dealing with them at different levels of detail. We employ the Abstract Hidden Markov Models (AHMM), an extension of the well-known Hidden Markov Model (HMM) and a special type of Dynamic Probabilistic Network (DPN), as our underlying representation framework. The AHMM allows us to explicitly encode the hierarchy of connected spatial locations, making it scalable to the size of the environment being modeled. We describe an application for tracking human movement in an office-like spatial layout where the AHMM is used to track and predict the evolution of object trajectories at different levels of detail.

A probabilistic framework for tracking in wide-area environments

Surveillance in wide-area spatial environments is characterised by complex spatial layouts, large state space, and the use of multiple cameras/sensors. To solve this problem, there is a need for representing the dynamic and noisy data in the tracking tasks, and dealing with them at different levels of detail. This requirement is particularly suited to the Layered Dynamic Probabilistic Network (LDPN), a special type of Dynamic Probabilistic Network (DPN). In this paper, we propose the use of LDPN as the integrated framework for tracking in wide-area environments. We illustrate, with the help of a synthetic tracking scenario, how the parameters of the LDPN can be estimated from training data, and then used to draw predictions and answer queries about unseen tracks at various levels of detail.

Social connections among parents of pre-school-age children in an inner and outer area of Melbourne, Australia

Abstract
Parents’ social connectedness is an important factor in child health and development outcomes and has been strongly linked to place. This study aimed to compare social connectedness amongst parents in inner versus outer-suburbs of Melbourne using a mixed methods approach. Parents were recruited via playgroups, mother’s groups and preschools and interviewed face- to-face regarding their social networks, with a second open-ended interview focusing on parents’ ideals and experiences of raising children in their current location. Parents in the two areas identified a similar number of contacts, but had differently structured networks. Outer-suburban parents were more likely than inner-suburban parents to have very few contacts, and to name their general practitioner as among their significant contacts. They were less likely to have more extended networks or to include neighbours among their contacts. Parents in both areas had met at least some of their network members through local organisations or services with outer-suburban parents having met a greater proportion of their contacts in this way. Qualitative interview data supported the network analysis revealing the different priorities parents placed on neighbours, barriers experienced in connecting with neighbours in the outer- suburbs and the consequent heavy reliance on organised activities to form social connections. The different types of social connections parents in inner and outer Melbourne made in relation to raising their preschool-aged children revealed in this study have implications for both service delivery and social planning of new developments.

A general collaborative framework for modeling and perceiving distributed network behavior

Collaborative Anomaly Detection (CAD) is an emerging field of network security in both academia and industry. It has attracted a lot of attention, due to the limitations of traditional fortress-style defense modes. Even though a number of pioneer studies have been conducted in this area, few of them concern about the universality issue. This work focuses on two aspects of it. First, a unified collaborative detection framework is developed based on network virtualization technology. Its purpose is to provide a generic approach that can be applied to designing specific schemes for various application scenarios and objectives. Second, a general behavior perception model is proposed for the unified framework based on hidden Markov random field. Spatial Markovianity is introduced to model the spatial context of distributed network behavior and stochastic interaction among interconnected nodes. Algorithms are derived for parameter estimation, forward prediction, backward smooth, and the normality evaluation of both global network situation and local behavior. Numerical experiments using extensive simulations and several real datasets are presented to validate the proposed solution. Performance-related issues and comparison with related works are discussed.

Controller area network (CAN) modelling for automotive body control applications

This research details methods to improve upon current worst-case message response time analysis of CAN networks. Also, through the development of a CAN network model, and using modern simulation software, methods were shown to provide more realistic analyses of both sporadic and periodic messages on CAN networks prior to implementation.

Role of catecholaminergic inputs to the medial prefrontal cortex in local and subcortical expression of Fos after psychological stress

A wide variety of stressors elicit Fos expression in the medial prefrontal cortex (mPFC). No direct attempts, however, have been made to determine the role of the inputs that drive this response. We examined the effects of lesions of mPFC catecholamine terminals on local expression of Fos after exposure to air puff, a stimulus that in the rat acts as an acute psychological stressor. We also examined the effects of these lesions on Fos expression in a variety of subcortical neuronal populations implicated in the control of adrenocortical activation, one classic hallmark of the stress response. Lesions of the mPFC that were restricted to dopaminergic terminals significantly reduced numbers of Fos-immunoreactive (Fos-IR) cells seen in the mPFC after air puff, but had no significant effect on stress-induced Fos expression in the subcortical structures examined. Lesions of the mPFC that affected both dopaminergic and noradrenergic terminals also reduced numbers of Fos-IR cells observed in the mPFC after air puff. Additionally, these lesions resulted in a significant reduction in stress-induced Fos-IR in the ventral bed nucleus of the stria terminalis. These results demonstrate a role for catecholaminergic inputs to the mPFC, in the generation of both local and subcortical responses to psychological stress.

Dynamic threshold determination by local and global edge evaluation

In this paper we consider two methods for automatically determining values for thresholding edge maps. Rather than use statistical methods they are based on the figural properties of the edges. Two approaches are taken. We investigate applying an edge evaluation measure based on edge continuity and edge thinness to determine the threshold on edge strength. However, the technique is not valid when applied to edge detector outputs that are one-pixel wide. In this case, we use a measure based on work by Lowe for assessing edges. This measure is based on length and average strength of complete linked edge lists.

The relation between local and global influence of individuals in scale-free networks

Large-degree nodes in scale-free networks are normally responsible for large cascades of epidemics. However, recent research shows small-degree nodes can also produce large-scale epidemics in the real world. In this letter, we investigate the relation between local and global influence of individuals in scale-free network in order to theoretically explain this real-world phenomenon. The local influence of an individual corresponds to the node degree, and the global influence of an individual reflects the expected number of individuals directly or indirectly influenced by this individual in epidemics. We formalize the later as the novel epidemic betweenness concept, to mathematically estimate the global influence of individuals. Our analysis shows that the global influence follows power-law distributions in scale-free networks. We also observe that the average global influence of individuals is power-law to the degree of nodes, which well explains the reason why large-degree nodes are more likely to produce large cascades of epidemics. In addition, we discover that some smalldegree nodes also possess large global influence in terms of epidemics betweenness. This well explains the counter-intuitive phenomenon in recent research.

Developing and asynchronous learning network

Deakin University, Australia, has committed resources over a number of years to developing the use of information and communication technologies in all aspects of teaching and learning. This paper focuses on the development over a four year period of an Asynchronous Learning Network (ALN) for distance education students studying undergraduate introductory macroeconomics. The research is based on quantitative and qualitative data gained from student evaluations, academic staff interviews, participation levels and an analysis of the online communication. Key findings from the research relate to the quality of the learning environment, the level of communication, and the role of academic staff in the learning experience. Strategies discussed for the successful use of an ALN include the nurturing of a collaborative learning environment, the adaptation of curriculum and pedagogy, the role of assessment, and the role of academic staff training and development.

Defining diversity and its relation to the local and global: a question of methodology

This paper emerges from research to define the dimensions of diversity and difference within a local Melbourne, Australian school and the requirement to understand these changes in times of increasing globalisation.