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.

An MRF based abnormal event detection approach using motion and appearance features

Abnormal event detection has attracted a lot of attention in the computer vision research community during recent years due to the increased focus on automated surveillance systems to improve security in public places. Due to the scarcity of training data and the definition of an abnormality being dependent on context, abnormal event detection is generally formulated as a data-driven approach where activities are modeled in an unsupervised fashion during the training phase. In this work, we use a Gaussian mixture model (GMM) to cluster the activities during the training phase, and propose a Gaussian mixture model based Markov random field (GMM-MRF) to estimate the likelihood scores of new videos in the testing phase. Further-more, we propose two new features: optical acceleration, and the histogram of optical flow gradients; to detect the presence of any abnormal objects and speed violations in the scene. We show that our proposed method outperforms other state of the art abnormal event detection algorithms on publicly available UCSD dataset.

Mainstreaming biophilic urbanism in Australian cities : a response to climate change, resource shortages and population pressures

This thesis explored how biophilic urbanism, or the integration of natural features into increasingly dense urban environments, has become mainstream in cities around the world. Fourteen factors uncovered through a case study investigation provide insight for decision makers and change agents in Australia to use biophilic urbanism to address impacts of population growth, climate change and resource shortages. The thesis uses an inductive research approach to explore how barriers to the integration of multi-functional vegetated and water design elements into the built environment, such that these become and standard inclusions in urban design and development processes.

Anomaly detection in online social networks : using data-mining techniques and fuzzy logic

This research is a step forward in improving the accuracy of detecting anomaly in a data graph representing connectivity between people in an online social network. The proposed hybrid methods are based on fuzzy machine learning techniques utilising different types of structural input features. The methods are presented within a multi-layered framework which provides the full requirements needed for finding anomalies in data graphs generated from online social networks, including data modelling and analysis, labelling, and evaluation.

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.

Evaluation of features for leaf classification in challenging conditions

Fine-grained leaf classification has concentrated on the use of traditional shape and statistical features to classify ideal images. In this paper we evaluate the effectiveness of traditional hand-crafted features and propose the use of deep convolutional neural network (ConvNet) features. We introduce a range of condition variations to explore the robustness of these features, including: translation, scaling, rotation, shading and occlusion. Evaluations on the Flavia dataset demonstrate that in ideal imaging conditions, combining traditional and ConvNet features yields state-of-theart performance with an average accuracy of 97:3%�0:6% compared to traditional features which obtain an average accuracy of 91:2%�1:6%. Further experiments show that this combined classification approach consistently outperforms the best set of traditional features by an average of 5:7% for all of the evaluated condition variations.

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.

Achieving competence: The interactional features of children's storytelling

Early years researchers interested in storytelling have largely focused on the development of children’s language and social skills within constructed story sessions. Less focus has been given to the interactional aspects of storytelling in children’s everyday conversation and how the members themselves, the storytellers and story recipients, manage storytelling. An interactional view, using ethnomethodological and conversation analytic approaches, offers the opportunity to study children’s narratives in terms of ‘members work’. Detailed examination of a video-recorded interaction among a group of children in a preparatory year playground shows how the children managed interactions within conversational storytelling. Analyses highlight the ways in which children worked at gaining a turn and made a story tellable within a round of second stories. Investigating children’s competence-in-action ‘from within’, the findings from this research show how children invoke and accomplish competence through their interactions.

Identifying weak linear features with the "coalescing shortest path image transform"

The detection of line-like features in images finds many applications in microanalysis. Actin fibers, microtubules, neurites, pilis, DNA, and other biological structures all come up as tenuous curved lines in microscopy images. A reliable tracing method that preserves the integrity and details of these structures is particularly important for quantitative analyses. We have developed a new image transform called the "Coalescing Shortest Path Image Transform" with very encouraging properties. Our scheme efficiently combines information from an extensive collection of shortest paths in the image to delineate even very weak linear features. © Copyright Microscopy Society of America 2011.

Using upper bounds on attainable discrimination to select discrete valued features

Selection of features that will permit accurate pattern classification is a difficult task. However, if a particular data set is represented by discrete valued features, it becomes possible to determine empirically the contribution that each feature makes to the discrimination between classes. This paper extends the discrimination bound method so that both the maximum and average discrimination expected on unseen test data can be estimated. These estimation techniques are the basis of a backwards elimination algorithm that can be use to rank features in order of their discriminative power. Two problems are used to demonstrate this feature selection process: classification of the Mushroom Database, and a real-world, pregnancy related medical risk prediction task - assessment of risk of perinatal death.

Multi-modal deformable medical image registration

Non-rigid image registration is an essential tool required for overcoming the inherent local anatomical variations that exist between images acquired from different individuals or atlases. Furthermore, certain applications require this type of registration to operate across images acquired from different imaging modalities. One popular local approach for estimating this registration is a block matching procedure utilising the mutual information criterion. However, previous block matching procedures generate a sparse deformation field containing displacement estimates at uniformly spaced locations. This neglects to make use of the evidence that block matching results are dependent on the amount of local information content. This paper presents a solution to this drawback by proposing the use of a Reversible Jump Markov Chain Monte Carlo statistical procedure to optimally select grid points of interest. Three different methods are then compared to propagate the estimated sparse deformation field to the entire image including a thin-plate spline warp, Gaussian convolution, and a hybrid fluid technique. Results show that non-rigid registration can be improved by using the proposed algorithm to optimally select grid points of interest.

A conceptual approach to track design changes within a multi-disciplinary BIM environment

Successful management of design changes is critical for the efficient delivery of construction projects. Building Information Modeling (BIM) is envisioned to play an important role in integrating design, construction and facility management processes through coordinated changes throughout the project life-cycle. BIM currently provides significant benefits in coordinating changes across different views in a single model, and identifying conflicts between different discipline-specific models. However, current BIM tools provide limited support in managing changes across several discipline-specific models. This paper describes an approach to represent, coordinate, and track changes within a collaborative multi-disciplinary BIM environment. This approach was informed by a detailed case study of a large, complex, fast-tracked BIM project where we investigated numerous design changes, analyzed change management processes, and evaluated existing BIM tools. Our approach characterises design changes in an ontology to represent changed component attributes, dependencies between components, and change impacts. It explores different types of dependencies amongst different design changes and describes how a graph based approach and dependency matrix could assist with automating the propagation and impact of changes in a BIM-based project delivery process.