The sideways hourglass : establishing the lemniscate as a narrative structure for writing and reading non-linear stories

This thesis is a study in narratology that examines the pre-theoretical ideas that underlie the study of narrative and time. The thesis explores how the lemniscate can be transported from geometry to narrative in order to structure a non-linear story that breaks the rules of causality and chronology by coupling physical movement through space with the backward pull of memory. The findings offer new possibilities for understanding the nexus between shape and story and for recording non-linear narratives that are marked by simultaneity, counterpoint, and reversal.

Building resilient communities : creating effective multi-channel communication during disaster response and recovery

Aim: To examine evidence-based strategies that motivate appropriate action and increase informed decision-making during the response and recovery phases of disasters.

Enhancing sense of recovery and self-reflectivity in people with schizophrenia : a pilot study of Metacognitive Narrative Psychotherapy

Objective This study investigated the effectiveness of an innovative, manualized psychotherapy aimed at enhancing recovery and self-experience in people with schizophrenia, Metacognitive Narrative Psychotherapy. Design Treatment effects were assessed using a mixed methodology. Data were quantitatively assessed using a single sample, pre- and post-therapy design and qualitatively assessed using a case-study methodology. Methods Eleven patients diagnosed with schizophrenia received Metacognitive Narrative Psychotherapy over the course of 11 to 26 months. Therapists were seven supervised postgraduate psychology students. On average patients attended 49 sessions over the course of therapy. Patients completed interview-based and self-report measures for general and treatment-specific outcomes at pre-, mid-, and post-treatment. Results Quantitative analyses showed that patients significantly improved on the general outcome of subjective recovery, as well as the treatment-specific outcome of self-reflectivity, with medium to large effect sizes. Case-study evidence also showed improvements for some patients in symptom severity, and narrative coherence and complexity. Conclusions These results are consistent with previous case-study evidence and suggest that this manualized version of Metacognitive Narrative Psychotherapy produces general and approach-specific improvements for people with schizophrenia. Replication is needed to ascertain its effectiveness with a larger sample size and within a controlled design.

Brief interventions to reduce ecstasy use : a multi-site randomized controlled trial

Studies examining the ability of motivational enhancement therapy (MET) to augment education provision among ecstasy users have produced mixed results and none have examined whether treatment fidelity was related to ecstasy use outcomes. The primary objectives of this multi-site, parallel, two-group randomized controlled trial were to determine if a single-session of MET could instill greater commitment to change and reduce ecstasy use and related problems more so than an education-only intervention and whether MET sessions delivered with higher treatment fidelity are associated with better outcomes. The secondary objective was to assess participants’ satisfaction with their assigned interventions. Participants (N = 174; Mage = 23.62) at two Australian universities were allocated randomly to receive a 15-minute educational session on ecstasy use (n = 85) or a 50-minute session of MET that included an educational component (n = 89). Primary outcomes were assessed at baseline, and then at 4-, 16-, and 24-weeks post-baseline, while the secondary outcome measure was assessed 4-weeks post-baseline by researchers blind to treatment allocation. Overall, the treatment fidelity was acceptable to good in the MET condition. There were no statistical differences at follow-up between the groups on the primary outcomes of ecstasy use, ecstasy-related problems, and commitment to change. Both interventions groups reported a 50% reduction in their ecstasy use and a 20% reduction in the severity of their ecstasy-related problems at the 24-week follow up. Commitment to change slightly improved for both groups (9% - 17%). Despite the lack of between-group statistical differences on primary outcomes, participants who received a single session of MET were slightly more satisfied with their intervention than those who received education only. MI fidelity was not associated with ecstasy use outcomes. Given these findings, future research should focus on examining mechanisms of change. Such work may suggest new methods for enhancing outcomes.

Triumphs and tribulations in the psychotherapy of schizophrenia : reflections on a pilot study of metacognitive narrative psychotherapy

Schizophrenia results in a profound disruption of one’s capacity to make sense of mental states, coherently narrate self-experiences, and meaningfully relate to others. While current treatment options for people with schizophrenia tend to be symptom-focused, experience in designing and implementing a study focusing on enhancing sense of self demonstrates the feasibility of developing and implementing models of treatment that prioritize the subjective distress and self-experience of people with schizophrenia. There is emerging research evidence, based upon dialogical theory of self, that posits the potential of people with deficits of self to engage in meaningful therapeutic relationships and work toward greater integrity of self and degrees of recovery. The challenge is to translate these ideas into a research methodology that can be successfully applied within therapeutic contexts with people who meet the diagnostic criteria for schizophrenia. Based upon dialogical theory, we developed a principle-based manual for metacognitive narrative psychotherapy: a psychological approach to the treatment of people with schizophrenia, which aims to enhance metacognitive capacity and ability to narrate self-experiences. Five phases of treatment were identified: (1) developing a therapeutic relationship, (2) eliciting narratives, (3) enhancing metacognitive capacity, (4) enriching narratives, and (5) living enriched stories. Proscribed practices were also identified. We then implemented the manual within a university clinic context. Six therapists were trained to implement the model and, in turn, provided therapy to 11 patients who completed 12 to 24 months of treatment. Participants were assessed on metacognitive capacity, narrative coherence, narrative richness, self-reported recovery, and symptomatology at three points in time over the course of therapy. Contrary to expectations, participants were highly engaged in the therapeutic process, with minimal dropout. Overall, over 75% of participants evidenced improvement in their level of recovery over the course of therapy. The manualization and outcome findings demonstrate the feasibility of applying such interventions to a broader clinical population.

An efficient and robust system for multi-person event detection in real world indoor surveillance scenes

Due to the popularity of security cameras in public places, it is of interest to design an intelligent system that can efficiently detect events automatically. This paper proposes a novel algorithm for multi-person event detection. To ensure greater than real-time performance, features are extracted directly from compressed MPEG video. A novel histogram-based feature descriptor that captures the angles between extracted particle trajectories is proposed, which allows us to capture motion patterns of multi-person events in the video. To alleviate the need for fine-grained annotation, we propose the use of Labelled Latent Dirichlet Allocation, a “weakly supervised” method that allows the use of coarse temporal annotations which are much simpler to obtain. This novel system is able to run at approximately ten times real-time, while preserving state-of-theart detection performance for multi-person events on a 100-hour real-world surveillance dataset (TRECVid SED).

Supervisor conflict management, justice and strain : multi-level relationships

Purpose The purpose of this paper is to test a multilevel model of the main and mediating effects of supervisor conflict management style (SCMS) climate and procedural justice (PJ) climate on employee strain. It is hypothesized that workgroup-level climate induced by SCMS can fall into four types: collaborative climate, yielding climate, forcing climate, or avoiding climate; that these group-level perceptions will have differential effects on employee strain, and will be mediated by PJ climate. Design/methodology/approach Multilevel SEM was used to analyze data from 420 employees nested in 61 workgroups. Findings Workgroups that perceived high supervisor collaborating climate reported lower sleep disturbance, job dissatisfaction, and action-taking cognitions. Workgroups that perceived high supervisor yielding climate and high supervisor forcing climate reported higher anxiety/depression, sleep disturbance, job dissatisfaction, and action-taking cognitions. Results supported a PJ climate mediation model when supervisors’ behavior was reported to be collaborative and yielding. Research limitations/implications The cross-sectional research design places limitations on conclusions about causality; thus, longitudinal studies are recommended. Practical implications Supervisor behavior in response to conflict may have far-reaching effects beyond those who are a party to the conflict. The more visible use of supervisor collaborative CMS may be beneficial. Social implications The economic costs associated with workplace conflict may be reduced through the application of these findings. Originality/value By applying multilevel theory and analysis, we extend workplace conflict theory.

Audio signalling as a backup communication channel for multi-robot systems

This paper presents a low-bandwidth multi-robot communication system designed to serve as a backup communication channel in the event a robot suffers a network device fault. While much research has been performed in the area of distributing network communication across multiple robots within a system, individual robots are still susceptible to hardware failure. In the past, such robots would simply be removed from service, and their tasks re-allocated to other members. However, there are times when a faulty robot might be crucial to a mission, or be able to contribute in a less communication intensive area. By allowing robots to encode and decode messages into unique sequences of DTMF symbols, called words, our system is able to facilitate continued low-bandwidth communication between robots without access to network communication. Our results have shown that the system is capable of permitting robots to negotiate task initiation and termination, and is flexible enough to permit a pair of robots to perform a simple turn taking task.

Multi-ciphersuite security of the Secure Shell (SSH) protocol

The Secure Shell (SSH) protocol is widely used to provide secure remote access to servers, making it among the most important security protocols on the Internet. We show that the signed-Diffie--Hellman SSH ciphersuites of the SSH protocol are secure: each is a secure authenticated and confidential channel establishment (ACCE) protocol, the same security definition now used to describe the security of Transport Layer Security (TLS) ciphersuites. While the ACCE definition suffices to describe the security of individual ciphersuites, it does not cover the case where parties use the same long-term key with many different ciphersuites: it is common in practice for the server to use the same signing key with both finite field and elliptic curve Diffie--Hellman, for example. While TLS is vulnerable to attack in this case, we show that SSH is secure even when the same signing key is used across multiple ciphersuites. We introduce a new generic multi-ciphersuite composition framework to achieve this result in a black-box way.

Injury narrative text classification : a preliminary study

Description of a patient's injuries is recorded in narrative text form by hospital emergency departments. For statistical reporting, this text data needs to be mapped to pre-defined codes. Existing research in this field uses the Naïve Bayes probabilistic method to build classifiers for mapping. In this paper, we focus on providing guidance on the selection of a classification method. We build a number of classifiers belonging to different classification families such as decision tree, probabilistic, neural networks, and instance-based, ensemble-based and kernel-based linear classifiers. An extensive pre-processing is carried out to ensure the quality of data and, in hence, the quality classification outcome. The records with a null entry in injury description are removed. The misspelling correction process is carried out by finding and replacing the misspelt word with a soundlike word. Meaningful phrases have been identified and kept, instead of removing the part of phrase as a stop word. The abbreviations appearing in many forms of entry are manually identified and only one form of abbreviations is used. Clustering is utilised to discriminate between non-frequent and frequent terms. This process reduced the number of text features dramatically from about 28,000 to 5000. The medical narrative text injury dataset, under consideration, is composed of many short documents. The data can be characterized as high-dimensional and sparse, i.e., few features are irrelevant but features are correlated with one another. Therefore, Matrix factorization techniques such as Singular Value Decomposition (SVD) and Non Negative Matrix Factorization (NNMF) have been used to map the processed feature space to a lower-dimensional feature space. Classifiers with these reduced feature space have been built. In experiments, a set of tests are conducted to reflect which classification method is best for the medical text classification. The Non Negative Matrix Factorization with Support Vector Machine method can achieve 93% precision which is higher than all the tested traditional classifiers. We also found that TF/IDF weighting which works well for long text classification is inferior to binary weighting in short document classification. Another finding is that the Top-n terms should be removed in consultation with medical experts, as it affects the classification performance.

Narrative and multimodality in English language arts curricula : a tale of two nations

In this policy column within this special edition on "The Arts in Language Arts", we critique the current place of multimodality and narratives in research and curriculum policy. This is a vital issue of significance for literacy educators, researchers, and policy makers because the narrative texts that circulate in our everyday lives are multimodal, tied to the ever-broadening range of narratives forms in digital sites of display. Here, we critically evaluate the place of multimodality and narratives in the language arts or English curriculum policies of two nations, the USA and Australia. In particular, we highlight the silence on multimodality within the Common Core State Standards, USA, and the contrasting centrality of multimodality in the National Curriculum: English, Australia.

Multi-agent systems for modeling high penetration photovoltaic system impacts in distribution networks

This paper reviews the use of multi-agent systems to model the impacts of high levels of photovoltaic (PV) system penetration in distribution networks and presents some preliminary data obtained from the Perth Solar City high penetration PV trial. The Perth Solar City trial consists of a low voltage distribution feeder supplying 75 customers where 29 consumers have roof top photovoltaic systems. Data is collected from smart meters at each consumer premises, from data loggers at the transformer low voltage (LV) side and from a nearby distribution network SCADA measurement point on the high voltage side (HV) side of the transformer. The data will be used to progressively develop MAS models.

Robust clustering of multi-type relational data via a heterogeneous manifold ensemble

High-Order Co-Clustering (HOCC) methods have attracted high attention in recent years because of their ability to cluster multiple types of objects simultaneously using all available information. During the clustering process, HOCC methods exploit object co-occurrence information, i.e., inter-type relationships amongst different types of objects as well as object affinity information, i.e., intra-type relationships amongst the same types of objects. However, it is difficult to learn accurate intra-type relationships in the presence of noise and outliers. Existing HOCC methods consider the p nearest neighbours based on Euclidean distance for the intra-type relationships, which leads to incomplete and inaccurate intra-type relationships. In this paper, we propose a novel HOCC method that incorporates multiple subspace learning with a heterogeneous manifold ensemble to learn complete and accurate intra-type relationships. Multiple subspace learning reconstructs the similarity between any pair of objects that belong to the same subspace. The heterogeneous manifold ensemble is created based on two-types of intra-type relationships learnt using p-nearest-neighbour graph and multiple subspaces learning. Moreover, in order to make sure the robustness of clustering process, we introduce a sparse error matrix into matrix decomposition and develop a novel iterative algorithm. Empirical experiments show that the proposed method achieves improved results over the state-of-art HOCC methods for FScore and NMI.

Non-monetised multi-objective decision making system for road management

This paper presents a performance-based optimisation approach for conducting trade-off analysis between safety (roads) and condition (bridges and roads). Safety was based on potential for improvement (PFI). Road condition was based on surface distresses and bridge condition was based on apparent age per subcomponent. The analysis uses a non-monetised optimisation that expanded upon classical Pareto optimality by observing performance across time. It was found that achievement of good results was conditioned by the availability of early age treatments and impacted by a frontier effect preventing the optimisation algorithm from realising of the long-term benefits of deploying actions when approaching the end of the analysis period. A disaggregated bridge condition index proved capable of improving levels of service in bridge subcomponents.