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.

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.

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.

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.

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.