Safeguarding rural Australia: addressing masculinity and violence in rural settings : unintentional violent harm including injuries in an Australian rural context : analysis of secondary data analysis

This report is an update of an earlier one produced in January 2010 (see Carrington et al. 2010) which remains as an ePrint through the project’s home page. This report focuses on our examination of extant data which have been sourced with respect to unintentional serious and violent harm, including injuries, to males living in regional and remote Australia . and which were available in public data bases at production. Such harm typically might be caused by, for example, transport accidents, occupational exposures and hazards, burns and so on. Thus unintentional violent harm can cause physical trauma the consequences of which can lead to chronic conditions including psychological harm or substance abuse.

Safeguarding rural Australia : addressing masculinity and violence in rural settings : risky behaviour in a rural Australian context : analysis of secondary data analysis

This report is an update of an earlier one produced in January 2010 (see Carrington et al. 2010) which remains as an ePrint through the project’s home page. The report focus on our examination of extant data which have been sourced with respect to personally and socially risky behaviour associated with males living in regional and remote Australia and which were available in public data bases at production.

Safeguarding rural Australia : addressing masculinity and violence in rural settings : consequences of violent harm in a rural Australian context : analysis of secondary data analysis

This report is an update of an earlier one produced in January 2010 (see Carrington et al. 2010) which remains as an ePrint through the project’s home page. The report considers extant data which have been sourced with respect to some of the consequences of violent acts, incidents, harms and risky behaviour involving males living in regional and remote Australia and which were available in public data bases at production.

Proximity-to-goal as a constraint on patterns of behaviour in attacker–defender dyads in team games

The aim of this study was to determine whether spatiotemporal interactions between footballers and the ball in 1 vs. 1 sub-phases are influenced by their proximity to the goal area. Twelve participants (age 15.3 ± 0.5 years) performed as attackers and defenders in 1 vs. 1 dyads across three field positions: (a) attacking the goal, (b) in midfield, and (c) advancing away from the goal area. In each position, the dribbler was required to move beyond an immediate defender with the ball towards the opposition goal. Interactions of attacker-defender dyads were filmed with player and ball displacement trajectories digitized using manual tracking software. One-way repeated measures analysis of variance was used to examine differences in mean defender-to-ball distance after this value had stabilized. Maximum attacker-to-ball distance was also compared as a function of proximity-to-goal. Significant differences were observed for defender-to-ball distance between locations (a) and (c) at the moment when the defender-to-ball distance had stabilized (a: 1.69 ± 0.64 m; c: 1.15 ± 0.59 m; P < 0.05). Findings indicate that proximity-to-goal influenced the performance of players, particularly when attacking or advancing away from goal areas, providing implications for training design in football. In this study, the task constraints of football revealed subtly different player interactions than observed in previous studies of dyadic systems in basketball and rugby union.

A Context Space Model for Detecting Anomalous Behaviour in Video Surveillance

Having a good automatic anomalous human behaviour detection is one of the goals of smart surveillance systems’ domain of research. The automatic detection addresses several human factor issues underlying the existing surveillance systems. To create such a detection system, contextual information needs to be considered. This is because context is required in order to correctly understand human behaviour. Unfortunately, the use of contextual information is still limited in the automatic anomalous human behaviour detection approaches. This paper proposes a context space model which has two benefits: (a) It provides guidelines for the system designers to select information which can be used to describe context; (b)It enables a system to distinguish between different contexts. A comparative analysis is conducted between a context-based system which employs the proposed context space model and a system which is implemented based on one of the existing approaches. The comparison is applied on a scenario constructed using video clips from CAVIAR dataset. The results show that the context-based system outperforms the other system. This is because the context space model allows the system to considering knowledge learned from the relevant context only.

Theorizing Police Response to Domestic Violence in Singapore: Police Subculture Revisited

This article focuses on the well documented, yet potentially contested concept of rank-and-file policesubculture to conceptualize policeresponse to situations of domesticviolence in Singapore. It argues that the utility of the concept to explaining police behavior is often undermined by an all-powerful, homogenous, and deterministic conception of it that fails to take into account the value of agency in police decision-making and the range of differentiated policeresponse in situations of domesticviolence. Through reviewing the literature on policeresponse to domesticviolence, this study called for the need to rework the concept of policesubculture by treating it as having a relationship with, and response to, the structural conditions of policing, while retaining a conception of the active role played by street-level officers in instituting a situational practice. Using Pierre Bourdieu's relational concepts of ‘habitus’ and ‘field,’ designating the cultural dispositions of policesubculture and structural conditions of policing respectively, the study attempted to reconceptualize the problem of policing domesticviolence with reference to the Singaporean context.

The Operational Policing of Domestic Violence in Singapore: An Exploratory Study

This article examines the formal processing of domestic violence as accomplished by institutionalized policing in Singapore. The description of the process through which domestic calls for assistance were shaped and translated into relevant categories for appropriating a particular police response was facilitated through the use of the participant observation method. The ethnographic fieldwork reported here, including observations of call screening in action, is an attempt to explicate the phenomenological grounds employed by organizational members to constitute calls as instances of categories for practical policing purposes. Theoretically, the data point to the need for a reconceptualization of the problem of policing domestic violence by emphasizing the point that the eventual institutional response be understood as a product of the relationship that exists between police subculture and structural conditions of policing unique to contemporary Singapore society.

Between the Devil and the Deep-blue Sea: Conceptualizing Victims Experiences of Policing in Domestic Violence in the Singaporean Context

In contextualising victims' experiences of policing in domestic violence situations in Singapore, two extreme but interrelated sets of responses have been observed. At one end of the continuum, criminal justice sanctions are strictly contingent upon victim willingness to initiate criminal proceedings against the perpetrator, and at the other, victims' rights, needs and preferences seem to be usurped by the justice system regardless of victims' choice. Neither of these positions takes victims' interests into account. Nor do they stem from an understanding of the sociocultural, economic and structural circumstances in which victims experienced violence, and continued to experience it, long after a police intervention. Data from the research revealed that criminalisation as an ideological and legally practical tool was not only rendered ineffective but irrelevant to the experiences of women in the Singaporean context.Two factors account for this phenomenon. First, the absence of support structures to achieve criminalisation and address victims' needs in the aftermath of criminalisation; second, the authoritative, paternalistic and patriarchal state impedes processes aimed at the empowerment of women victims.

Can you describe him for me? A technique for semantic person search in video

From a law enforcement standpoint, the ability to search for a person matching a semantic description (i.e. 1.8m tall, red shirt, jeans) is highly desirable. While a significant research effort has focused on person re-detection (the task of identifying a previously observed individual in surveillance video), these techniques require descriptors to be built from existing image or video observations. As such, person re-detection techniques are not suited to situations where footage of the person of interest is not readily available, such as a witness reporting a recent crime. In this paper, we present a novel framework that is able to search for a person based on a semantic description. The proposed approach uses size and colour cues, and does not require a person detection routine to locate people in the scene, improving utility in crowded conditions. The proposed approach is demonstrated with a new database that will be made available to the research community, and we show that the proposed technique is able to correctly localise a person in a video based on a simple semantic description.

Heteroscedastic probabilistic linear discriminant analysis for manifold learning in video-based face recognition

To recognize faces in video, face appearances have been widely modeled as piece-wise local linear models which linearly approximate the smooth yet non-linear low dimensional face appearance manifolds. The choice of representations of the local models is crucial. Most of the existing methods learn each local model individually meaning that they only anticipate variations within each class. In this work, we propose to represent local models as Gaussian distributions which are learned simultaneously using the heteroscedastic probabilistic linear discriminant analysis (PLDA). Each gallery video is therefore represented as a collection of such distributions. With the PLDA, not only the within-class variations are estimated during the training, the separability between classes is also maximized leading to an improved discrimination. The heteroscedastic PLDA itself is adapted from the standard PLDA to approximate face appearance manifolds more accurately. Instead of assuming a single global within-class covariance, the heteroscedastic PLDA learns different within-class covariances specific to each local model. In the recognition phase, a probe video is matched against gallery samples through the fusion of point-to-model distances. Experiments on the Honda and MoBo datasets have shown the merit of the proposed method which achieves better performance than the state-of-the-art technique.