Volunteers 'piggy-backing' on their private insurance : an uncertain protection against liability

Volunteering is a very important part of life in Australia with an estimated 36% of the adult population volunteering in 2010. Voluntary work generates economic benefits, addresses community needs and develops the social networks that form the backbone of civil society. Without volunteers, many essential services would either cease to exist or become too expensive for many people to afford. These volunteers, who by definition are not in receipt of any remuneration for their work and services, are exposed to personal injury and to legal liability in the discharge of their functions. It is therefore appropriate that statutory protection is extended to volunteers and that volunteer organisations procure public liability and personal accident cover where possible. However, given the patchwork quilt of circumstances where statutory or institutional cover is available to volunteers and the existence of many and diverse exclusions, it is important to have regard also to what scope a volunteer may have to avail themselves of protection against liability for volunteering activity by relying upon their own personal insurance cover. This article considers the extent of private insurance cover and its availability to volunteers under home and contents insurance and under comprehensive motor vehicle insurance. The most common policies in the Australian market are examined and the uncertain nature of protection against liability afforded by these policies is discussed. This uncertainty could be reduced should the Federal Government through amendments to the Insurance Contracts Regulations standardise the circumstances and extent to which liability protection was afforded to an insured holding home and contents insurance and comprehensive motor vehicle insurance cover.

The stitchery collective

The stitchery collective is fashion-based design collective. Founded in February 2010 the collective brings together creative practitioners from across an expanded field of fashion design to develop innovative new models for both the design, production and consumption of fashion in the 21st century. Under the broad question "can fashion be more than pretty clothes for pretty people?" the collective has developed a range of workshops, exhibitions and creative projects that both engage the wider public and targeted community groups. The projects include "consciousness raising: up cycling workshops, zero-waste pattern cutting workshops, and sewing workshops with members of Brisbane's Karen, Sudansese, and Iraqi communities. Through these projects we test how innovative fashion design practice can engage with questions of environmental sustainability, ethical practices, and social inclusion. Established around a set of people -centred values, the stitchery collective therefore seeks to re-cast fashion as a 'less bad' field of creative endeavor and, one that sustains, inspires and connects individuals and communities. In seeking to develop new models of fashion practice that are socially oriented and environmentally responsible the stitchery outcomes align with the broader field of Design for Sustainability.

Iris based identity verification robust to sample presentation security attacks

Iris based identity verification is highly reliable but it can also be subject to attacks. Pupil dilation or constriction stimulated by the application of drugs are examples of sample presentation security attacks which can lead to higher false rejection rates. Suspects on a watch list can potentially circumvent the iris based system using such methods. This paper investigates a new approach using multiple parts of the iris (instances) and multiple iris samples in a sequential decision fusion framework that can yield robust performance. Results are presented and compared with the standard full iris based approach for a number of iris degradations. An advantage of the proposed fusion scheme is that the trade-off between detection errors can be controlled by setting parameters such as the number of instances and the number of samples used in the system. The system can then be operated to match security threat levels. It is shown that for optimal values of these parameters, the fused system also has a lower total error rate.

Imagining neighbourhood-based community : an autoethnography

This research is an autoethnographic investigation of consumption experiences, public and quasi-public spaces, and their relationship to community within an inner city neighbourhood. The research specifically focuses on the gentrifying inner city, where class-based processes of change can have implications for people’s abilities to remain within, or feel connected to place. However, the thesis draws on broader theories of the throwntogetherness of the contemporary city (e.g., Amin and Thrift, 2002; Massey 2005) to argue that the city is a space where place-based meanings cannot be seen to be fixed, and are instead better understood as events of place – based on ever shifting interrelations between the trajectories of people and things. This perspective argues the experience of belonging to community is not just born of a social encounter, but also draws on the physical and symbolic elements of the context in which it is situated. The thesis particularly explores the ways people construct identifications within this shifting urban environment. As such, consumption practices and spaces offer one important lens through which to explore the interplay of the physical, social and symbolic. Consumer research tells us that consumption practices can facilitate experiences in which identity-defining meaning can be generated and shared. Consumption spaces can also support different kinds of collective identification – as anchoring realms for specific cultural groups or exposure realms that enable individuals to share in the identification practices of others with limited risk (Aubert-Gamet & Cova, 1999). Furthermore, the consumption-based lifestyles that gentrifying inner city neighbourhoods both support and encourage can also mean that consumption practices may be a key reason that people are moving through public space. That is, consumption practices and spaces may provide a purpose for which – and spatial frame against which – our everyday interactions and connections with people and objects are undertaken within such neighbourhoods. The purpose of this investigation then was to delve into the subjectivities at the heart of identifying with places, using the lens of our consumption-based experiences within them. The enquiry describes individual and collective identifications and emotional connections, and explores how these arise within and through our experiences within public and quasi-public spaces. It then theorises these ‘imaginings’ as representative of an experience of community. To do so, it draws on theories of imagination and its relation to community. Theories of imagined community remind us that both the values and identities of community are held together by projections that create relational links out of objects and shared practices (e.g., Benedict Anderson, 2006; Urry, 2000). Drawing on broader theories of the processes of the imagination, this thesis suggests that an interplay between reflexivity and fantasy – which are products of the critical and the fascinated consciousness – plays a role in this imagining of community (e.g., Brann, 1991; Ricoeur, 1994). This thesis therefore seeks to explore how these processes of imagining are implicated within the construction of an experience of belonging to neighbourhood-based community through consumption practices and the public and quasi-public spaces that frame them. The key question of this thesis is how do an individual’s consumption practices work to construct an imagined presence of neighbourhood-based community? Given the focus on public and quasi-public spaces and our experiences within them, the research also asked how do experiences in the public and quasi-public spaces that frame these practices contribute to the construction of this imagined presence? This investigation of imagining community through consumption practices is based on my own experiences of moving to, and attempting to construct community connections within, an inner city neighbourhood in Melbourne, Australia. To do so, I adopted autoethnographic methodology. This is because autoethnography provides the methodological tools through which one can explore and make visible the subjectivities inherent within the lived experiences of interest to the thesis (Ellis, 2004). I describe imagining community through consumption as an extension of a placebased self. This self is manifest through personal identification in consumption spaces that operate as anchoring realms for specific cultural groups, as well as through a broader imagining of spaces, people, and practices as connected through experiences within realms of exposure. However, this is a process that oscillates through cycles of identification; these anchor one within place personally, but also disrupt those attachments. This instability can force one to question the orientation and motives of these imaginings, and reframe them according to different spaces and reference groups in ways that can also work to construct a more anonymous and, conversely, more achievable collective identification. All the while, the ‘I’ at the heart of this identification is in an ongoing process of negotiation, and similarly, the imagined community is never complete. That is, imagining community is a negotiation, with people and spaces – but mostly with the different identifications of the self. This thesis has been undertaken by publication, and thus the process of imagining community is explored and described through four papers. Of these, the first two focus on specific types of consumption spaces – a bar and a shopping centre – and consider the ways that anchoring and exposure within these spaces support the process of imagining community. The third paper examines the ways that the public and quasi-public spaces that make up the broader neighbourhood context are themselves throwntogether as a realm of exposure, and considers the ways this shapes my imaginings of this neighbourhood as community. The final paper develops a theory of imagined community, as a process of comparison and contrast with imagined others, to provide a summative conceptualisation of the first three papers. The first paper, chapter five, explores this process of comparison and contrast in relation to authenticity, which in itself is a subjective assessment of identity. This chapter was written as a direct response to the recent work of Zukin (2010), and draws on theories of authenticity as applied to personal and collective identification practices by consumer researchers Arnould and Price (2000). In this chapter, I describe how my assessments of the authenticity of my anchoring experiences within one specific consumption space, a neighbourhood bar, are evaluated in comparison to my observations of and affective reactions to the social practices of another group of residents in a different consumption space, the local shopping centre. Chapter five also provides an overview of the key sites and experiences that are considered in more detail in the following two chapters. In chapter six, I again draw on my experiences within the bar introduced in chapter five, this time to explore the process of developing a regular identity within a specific consumption space. Addressing the popular theory of the cafe or bar as third place (Oldenburg, 1999), this paper considers the purpose of developing anchored relationships with people within specific consumption spaces, and explores the different ways this may be achieved in an urban context where the mobilities and lifestyle practices of residents complicate the idea of a consumption space as an anchoring or third place. In doing so, this chapter also considers the manner in which this type of regular identification may be seen to be the beginning of the process of imagining community. In chapter seven, I consider the ways the broader public spaces of the neighbourhood work cumulatively to expose different aspects of its identity by following my everyday movements through the neighbourhood’s shopping centre and main street. Drawing on the theories of Urry (2000), Massey (2005), and Amin (2007, 2008), this chapter describes how these spaces operate as exposure realms, enabling the expression of different senses of the neighbourhood’s spaces, times, cultures, and identities through their physical, social, and symbolic elements. Yet they also enable them to be united: through habitual pathways, group practices of appropriation of space, and memory traces that construct connections between objects and experiences. This chapter describes this as a process of exposure to these different elements. Our imagination begins to expand the scope of the frames onto which it projects an imagined presence; it searches for patterns within the physical, social, and symbolic environment and draws connections between people and practices across spaces. As the final paper, chapter eight, deduces, it is in making these connections that one constructs the objects and shared practices of imagined community. This chapter describes this as an imagining of neighbourhood as a place-based extension of the self, and then explores the ways in which I drew on physical, social, and symbolic elements in an attempt to construct a fit between the neighbourhood’s offerings and my desires for place-based identity definition. This was as a cumulative but fragmented process, in which positive and negative experiences of interaction and identification with people and things were searched for their potential to operate as the objects and shared practices of imagined community. This chapter describes these connections as constructed through interplay between reflexivity and fantasy, as the imagination seeks balance between desires for experiences of belonging, and the complexities of constructing them within the throwntogether context of the contemporary city. The conclusion of the thesis describes the process of imagining community as a reflexive fantasy, that is, as a product of both the critical and fascinated consciousness (Ricoeur, 1994). It suggests that the fascinated consciousness imbues experiences with hope and desire, which the reflexive imagining can turn to disappointment and shame as it critically reflects on the reality of those fascinated projections. At the same time, the reflexive imagination also searches the practices of others for affirmation of those projections, effectively seeking to prove the reality of the fantasy of the imagined community.

Classifier selection using sequential error ratio criterion for multi-instance and multi-sample fusion

Classifier selection is a problem encountered by multi-biometric systems that aim to improve performance through fusion of decisions. A particular decision fusion architecture that combines multiple instances (n classifiers) and multiple samples (m attempts at each classifier) has been proposed in previous work to achieve controlled trade-off between false alarms and false rejects. Although analysis on text-dependent speaker verification has demonstrated better performance for fusion of decisions with favourable dependence compared to statistically independent decisions, the performance is not always optimal. Given a pool of instances, best performance with this architecture is obtained for certain combination of instances. Heuristic rules and diversity measures have been commonly used for classifier selection but it is shown that optimal performance is achieved for the `best combination performance' rule. As the search complexity for this rule increases exponentially with the addition of classifiers, a measure - the sequential error ratio (SER) - is proposed in this work that is specifically adapted to the characteristics of sequential fusion architecture. The proposed measure can be used to select a classifier that is most likely to produce a correct decision at each stage. Error rates for fusion of text-dependent HMM based speaker models using SER are compared with other classifier selection methodologies. SER is shown to achieve near optimal performance for sequential fusion of multiple instances with or without the use of multiple samples. The methodology applies to multiple speech utterances for telephone or internet based access control and to other systems such as multiple finger print and multiple handwriting sample based identity verification systems.

A pattern based two-stage text classifier

In a classification problem typically we face two challenging issues, the diverse characteristic of negative documents and sometimes a lot of negative documents that are closed to positive documents. Therefore, it is hard for a single classifier to clearly classify incoming documents into classes. This paper proposes a novel gradual problem solving to create a two-stage classifier. The first stage identifies reliable negatives (negative documents with weak positive characteristics). It concentrates on minimizing the number of false negative documents (recall-oriented). We use Rocchio, an existing recall based classifier, for this stage. The second stage is a precision-oriented “fine tuning”, concentrates on minimizing the number of false positive documents by applying pattern (a statistical phrase) mining techniques. In this stage a pattern-based scoring is followed by threshold setting (thresholding). Experiment shows that our statistical phrase based two-stage classifier is promising.

Quantum one-time programs

A one-time program is a hypothetical device by which a user may evaluate a circuit on exactly one input of his choice, before the device self-destructs. One-time programs cannot be achieved by software alone, as any software can be copied and re-run. However, it is known that every circuit can be compiled into a one-time program using a very basic hypothetical hardware device called a one-time memory. At first glance it may seem that quantum information, which cannot be copied, might also allow for one-time programs. But it is not hard to see that this intuition is false: one-time programs for classical or quantum circuits based solely on quantum information do not exist, even with computational assumptions. This observation raises the question, "what assumptions are required to achieve one-time programs for quantum circuits?" Our main result is that any quantum circuit can be compiled into a one-time program assuming only the same basic one-time memory devices used for classical circuits. Moreover, these quantum one-time programs achieve statistical universal composability (UC-security) against any malicious user. Our construction employs methods for computation on authenticated quantum data, and we present a new quantum authentication scheme called the trap scheme for this purpose. As a corollary, we establish UC-security of a recent protocol for delegated quantum computation.

Investigating ways of seeing IT research : a tool for facilitating effective research partnerships?

Effective research partnerships, both intra- and interdisciplinary, as well as academy-industry partnerships, rely on shared understandings of particular aspects of the research endeavour. Research partnerships are essentially learning partnerships, if we accept the argument that research may be seen as learning at the collective level. This paper establishes the need to investigate information technology (IT) researchers' varying ways of seeing certain aspects of their research world, in order to assist the process of forging effective research partnerships. We analyse the importance of facilitating effective partnerships in IT research, discuss our plan for investigating the collective consciousness of IT researchers, and explain some of the strategies to be used in the investigation.

Sampling environmental acoustic recordings to determine bird species richness

Acoustic sensors can be used to estimate species richness for vocal species such as birds. They can continuously and passively record large volumes of data over extended periods. These data must subsequently be analyzed to detect the presence of vocal species. Automated analysis of acoustic data for large numbers of species is complex and can be subject to high levels of false positive and false negative results. Manual analysis by experienced surveyors can produce accurate results; however the time and effort required to process even small volumes of data can make manual analysis prohibitive. This study examined the use of sampling methods to reduce the cost of analyzing large volumes of acoustic sensor data, while retaining high levels of species detection accuracy. Utilizing five days of manually analyzed acoustic sensor data from four sites, we examined a range of sampling frequencies and methods including random, stratified, and biologically informed. We found that randomly selecting 120 one-minute samples from the three hours immediately following dawn over five days of recordings, detected the highest number of species. On average, this method detected 62% of total species from 120 one-minute samples, compared to 34% of total species detected from traditional area search methods. Our results demonstrate that targeted sampling methods can provide an effective means for analyzing large volumes of acoustic sensor data efficiently and accurately. Development of automated and semi-automated techniques is required to assist in analyzing large volumes of acoustic sensor data. Read More: http://www.esajournals.org/doi/abs/10.1890/12-2088.1

Pre-crash traffic flow trend analysis on motorways

Crashes on motorway contribute to a significant proportion (40-50%) of non-recurrent motorway congestions. Hence reduce crashes will help address congestion issues (Meyer, 2008). Crash likelihood estimation studies commonly focus on traffic conditions in a Short time window around the time of crash while longer-term pre-crash traffic flow trends are neglected. In this paper we will show, through data mining techniques, that a relationship between pre-crash traffic flow patterns and crash occurrence on motorways exists, and that this knowledge has the potential to improve the accuracy of existing models and opens the path for new development approaches. The data for the analysis was extracted from records collected between 2007 and 2009 on the Shibuya and Shinjuku lines of the Tokyo Metropolitan Expressway in Japan. The dataset includes a total of 824 rear-end and sideswipe crashes that have been matched with traffic flow data of one hour prior to the crash using an incident detection algorithm. Traffic flow trends (traffic speed/occupancy time series) revealed that crashes could be clustered with regards of the dominant traffic flow pattern prior to the crash. Using the k-means clustering method allowed the crashes to be clustered based on their flow trends rather than their distance. Four major trends have been found in the clustering results. Based on these findings, crash likelihood estimation algorithms can be fine-tuned based on the monitored traffic flow conditions with a sliding window of 60 minutes to increase accuracy of the results and minimize false alarms.

Pre-crash and non-crash traffic flow trends analysis on motorways

Crashes that occur on motorways contribute to a significant proportion (40-50%) of non-recurrent motorway congestions. Hence, reducing the frequency of crashes assists in addressing congestion issues (Meyer, 2008). Crash likelihood estimation studies commonly focus on traffic conditions in a short time window around the time of a crash while longer-term pre-crash traffic flow trends are neglected. In this paper we will show, through data mining techniques that a relationship between pre-crash traffic flow patterns and crash occurrence on motorways exists. We will compare them with normal traffic trends and show this knowledge has the potential to improve the accuracy of existing models and opens the path for new development approaches. The data for the analysis was extracted from records collected between 2007 and 2009 on the Shibuya and Shinjuku lines of the Tokyo Metropolitan Expressway in Japan. The dataset includes a total of 824 rear-end and sideswipe crashes that have been matched with crashes corresponding to traffic flow data using an incident detection algorithm. Traffic trends (traffic speed time series) revealed that crashes can be clustered with regards to the dominant traffic patterns prior to the crash. Using the K-Means clustering method with Euclidean distance function allowed the crashes to be clustered. Then, normal situation data was extracted based on the time distribution of crashes and were clustered to compare with the “high risk” clusters. Five major trends have been found in the clustering results for both high risk and normal conditions. The study discovered traffic regimes had differences in the speed trends. Based on these findings, crash likelihood estimation models can be fine-tuned based on the monitored traffic conditions with a sliding window of 30 minutes to increase accuracy of the results and minimize false alarms.

Recognising team activities from noisy data

Recently, vision-based systems have been deployed in professional sports to track the ball and players to enhance analysis of matches. Due to their unobtrusive nature, vision-based approaches are preferred to wearable sensors (e.g. GPS or RFID sensors) as it does not require players or balls to be instrumented prior to matches. Unfortunately, in continuous team sports where players need to be tracked continuously over long-periods of time (e.g. 35 minutes in field-hockey or 45 minutes in soccer), current vision-based tracking approaches are not reliable enough to provide fully automatic solutions. As such, human intervention is required to fix-up missed or false detections. However, in instances where a human can not intervene due to the sheer amount of data being generated - this data can not be used due to the missing/noisy data. In this paper, we investigate two representations based on raw player detections (and not tracking) which are immune to missed and false detections. Specifically, we show that both team occupancy maps and centroids can be used to detect team activities, while the occupancy maps can be used to retrieve specific team activities. An evaluation on over 8 hours of field hockey data captured at a recent international tournament demonstrates the validity of the proposed approach.

Representing and discovering adversarial team behaviors using player roles

In this paper, we describe a method to represent and discover adversarial group behavior in a continuous domain. In comparison to other types of behavior, adversarial behavior is heavily structured as the location of a player (or agent) is dependent both on their teammates and adversaries, in addition to the tactics or strategies of the team. We present a method which can exploit this relationship through the use of a spatiotemporal basis model. As players constantly change roles during a match, we show that employing a "role-based" representation instead of one based on player "identity" can best exploit the playing structure. As vision-based systems currently do not provide perfect detection/tracking (e.g. missed or false detections), we show that our compact representation can effectively "denoise" erroneous detections as well as enabe temporal analysis, which was previously prohibitive due to the dimensionality of the signal. To evaluate our approach, we used a fully instrumented field-hockey pitch with 8 fixed high-definition (HD) cameras and evaluated our approach on approximately 200,000 frames of data from a state-of-the-art real-time player detector and compare it to manually labelled data.

Sequential decision fusion of multibiometrics applied to text-dependent speaker verification for controlled errors

Reliability of the performance of biometric identity verification systems remains a significant challenge. Individual biometric samples of the same person (identity class) are not identical at each presentation and performance degradation arises from intra-class variability and inter-class similarity. These limitations lead to false accepts and false rejects that are dependent. It is therefore difficult to reduce the rate of one type of error without increasing the other. The focus of this dissertation is to investigate a method based on classifier fusion techniques to better control the trade-off between the verification errors using text-dependent speaker verification as the test platform. A sequential classifier fusion architecture that integrates multi-instance and multisample fusion schemes is proposed. This fusion method enables a controlled trade-off between false alarms and false rejects. For statistically independent classifier decisions, analytical expressions for each type of verification error are derived using base classifier performances. As this assumption may not be always valid, these expressions are modified to incorporate the correlation between statistically dependent decisions from clients and impostors. The architecture is empirically evaluated by applying the proposed architecture for text dependent speaker verification using the Hidden Markov Model based digit dependent speaker models in each stage with multiple attempts for each digit utterance. The trade-off between the verification errors is controlled using the parameters, number of decision stages (instances) and the number of attempts at each decision stage (samples), fine-tuned on evaluation/tune set. The statistical validation of the derived expressions for error estimates is evaluated on test data. The performance of the sequential method is further demonstrated to depend on the order of the combination of digits (instances) and the nature of repetitive attempts (samples). The false rejection and false acceptance rates for proposed fusion are estimated using the base classifier performances, the variance in correlation between classifier decisions and the sequence of classifiers with favourable dependence selected using the 'Sequential Error Ratio' criteria. The error rates are better estimated by incorporating user-dependent (such as speaker-dependent thresholds and speaker-specific digit combinations) and class-dependent (such as clientimpostor dependent favourable combinations and class-error based threshold estimation) information. The proposed architecture is desirable in most of the speaker verification applications such as remote authentication, telephone and internet shopping applications. The tuning of parameters - the number of instances and samples - serve both the security and user convenience requirements of speaker-specific verification. The architecture investigated here is applicable to verification using other biometric modalities such as handwriting, fingerprints and key strokes.