In search of best practices in responsible gambling ( RG ) : a report on a comparative study of RG among Macau, Las Vegas, and Melbourne, presented to the MSAR Bureau of Gaming Inspection and Coordination (DICJ).

This is a project sponsored by the Asia Pacific Association for Gambling Studies (APAGS) and supported by funds from the MSAR’s Bureau of Gambling Inspection and Coordination (DICJ). The research team comprises as Chief Investigators: Prof. Zhidong Hao of the University of Macau; Prof. Linda Hancock of Deakin University, Australia, and Prof. William Thompson, University of Las Vegas (UNLV). The project research was conducted between the end of December 2012 and July 2013.
The starting point for the research was to select four out of the six casino companies licensed to operate in Macau that also operate transnationally, that is, either in Las Vegas or Melbourne. Hence, the Venetian, Wynn, MGM, and the Melco-Crown Entertainment are the focus of research. The main objectives of the project are to explore how responsible gambling is framed in each of the three jurisdictions (Macau, Las Vegas and Melbourne); how it is approached cross-jurisdictionally by each of the companies; and to assess current approaches within a broader comparative context against international best practice. The research explores Responsible Gambling measures taken by a range of stakeholders including the government/regulators in each of the three jurisdictions, casino managements, problem gambling counselling services, unions and community organizations. The research emphasizes what problems prevail, and the implications of this research for enhancing Responsible Gambling in Macau.

A modified Intelligent Water Drops algorithm and its application to optimization problems

The Intelligent Water Drop (IWD) algorithm is a recent stochastic swarm-based method that is useful for solving combinatorial and function optimization problems. In this paper, we investigate the effectiveness of the selection method in the solution construction phase of the IWD algorithm. Instead of the fitness proportionate selection method in the original IWD algorithm, two ranking-based selection methods, namely linear ranking and exponential ranking, are proposed. Both ranking-based selection methods aim to solve the identified limitations of the fitness proportionate selection method as well as to enable the IWD algorithm to escape from local optima and ensure its search diversity. To evaluate the usefulness of the proposed ranking-based selection methods, a series of experiments pertaining to three combinatorial optimization problems, i.e., rough set feature subset selection, multiple knapsack and travelling salesman problems, is conducted. The results demonstrate that the exponential ranking selection method is able to preserve the search diversity, therefore improving the performance of the IWD algorithm. © 2014 Elsevier Ltd. All rights reserved.

Tree-based iterated local search for Markov random fields with applications in image analysis

The maximum a posteriori assignment for general structure Markov random fields is computationally intractable. In this paper, we exploit tree-based methods to efficiently address this problem. Our novel method, named Tree-based Iterated Local Search (T-ILS), takes advantage of the tractability of tree-structures embedded within MRFs to derive strong local search in an ILS framework. The method efficiently explores exponentially large neighborhoods using a limited memory without any requirement on the cost functions. We evaluate the T-ILS on a simulated Ising model and two real-world vision problems: stereo matching and image denoising. Experimental results demonstrate that our methods are competitive against state-of-the-art rivals with significant computational gain.

Development of guidelines for adults on how to communicate with adolescents about mental health problems and other sensitive topics: a Delphi study

There is little evidence-based information available to guide adults in the general community on communicating effectively with adolescents about mental health problems or other sensitive topics. The Delphi methodology was used to develop guidelines to fill this evidence gap. An online questionnaire containing potential guideline statements was developed following a literature search and input from two focus groups. Two expert panels (Youth Mental Health First Aid instructors and young consumer advocates) rated the questionnaire over three rounds, according to whether or not they believed that the statements should be included in the guidelines. Results were analyzed by comparing endorsement rates between the panels. Of the 175 statements presented, 80 were rated as essential or important by ≥90% of both panels and were included in the guidelines. The Delphi process has offered an effective way to achieve consensus between expert panels on useful tips to help adults communicate with adolescents.

Parent and family factors associated with service use by young people with mental health problems: a systematic review

AIM: To conduct a systematic review of parent and family factors associated with service use for young people with mental health problems, to inform early intervention efforts aimed at increasing service use by young people. METHODS: A systematic search of academic databases was performed. Articles were included in the review if they had: a sample of young people aged between 5 and 18 years; service use as the outcome measure; one or more parental or family variables as a predictor; and a comparison group of non-service using young people with mental health problems. In order to focus on factors additional to need, the mental health symptoms of the young person also had to be controlled for. Stouffer's method of combining P-values was used to draw conclusions as to whether or not associations between variables were reliable. RESULTS: Twenty-eight articles were identified investigating 15 parental or family factors, 7 of which were found to be associated with service use for a young person with mental health needs: parental burden, parent problem perception, parent perception of need, parent psychopathology, single-parent household, change in family structure and being from the dominant ethnic group for the United States specifically. Factors not found to be related to service use were: family history of service use, parent-child relationship quality, family functioning, number of children, parent education level, parent employment status, household income and non-urban location of residence. CONCLUSIONS: A number of family-related factors were identified that can inform effective interventions aimed at early intervention for mental health problems. Areas requiring further research were also identified.

An ensemble of intelligent water drop algorithms and its application to optimization problems

Crown Copyright © 2015 Published by Elsevier Inc. All rights reserved. The Intelligent Water Drop (IWD) algorithm is a recent stochastic swarm-based method that is useful for solving combinatorial and function optimization problems. In this paper, we propose an IWD ensemble known as the Master-River, Multiple-Creek IWD (MRMC-IWD) model, which serves as an extension of the modified IWD algorithm. The MRMC-IWD model aims to improve the exploration capability of the modified IWD algorithm. It comprises a master river which cooperates with multiple independent creeks to undertake optimization problems based on the divide-and-conquer strategy. A technique to decompose the original problem into a number of sub-problems is first devised. Each sub-problem is then assigned to a creek, while the overall solution is handled by the master river. To empower the exploitation capability, a hybrid MRMC-IWD model is introduced. It integrates the iterative improvement local search method with the MRMC-IWD model to allow a local search to be conducted, therefore enhancing the quality of solutions provided by the master river. To evaluate the effectiveness of the proposed models, a series of experiments pertaining to two combinatorial problems, i.e., the travelling salesman problem (TSP) and rough set feature subset selection (RSFS), are conducted. The results indicate that the MRMC-IWD model can satisfactorily solve optimization problems using the divide-and-conquer strategy. By incorporating a local search method, the resulting hybrid MRMC-IWD model not only is able to balance exploration and exploitation, but also to enable convergence towards the optimal solutions, by employing a local search method. In all seven selected TSPLIB problems, the hybrid MRMC-IWD model achieves good results, with an average deviation of 0.021% from the best known optimal tour lengths. Compared with other state-of-the-art methods, the hybrid MRMC-IWD model produces the best results (i.e. the shortest and uniform reducts of 20 runs) for all13 selected RSFS problems.

The mythical properties of police body-worn cameras: A solution in the search of a problem

Over the past few years, Australian police agencies have begun to enthusiastically introduce body-worn cameras on police personnel. These devices are now either implemented or under trial across the country. There is also an emergent ‘surveillance consensus’ (Hempel and Töpfer 2009) concerning their use amongst Australian police. While more detailed empirical examination of information flows that shape this surveillance consensus is warranted, this contribution to the debate seeks to draw from policing scholarship to critically explore the intersections between the rationalizations for body-worn cameras and the broader policing scholarship. More directly, body-worn cameras cannot be understood in narrow instrumental terms, but must be located within the broader literature on governing police and the law and order politics that surrounds many contemporary police and criminal justice reforms (Cox 2015; Gregg and Wilson 2015). I begin with a summary of the introduction of body-worn cameras in Australia. The article then identifies five problems body-worn cameras purportedly address and provides a brief case summary indicating how current ‘privacy protections’ fail to establish real limits to the collection, use, and dissemination of images from body-worn cameras.

Heterogeneous cooperative co-evolution memetic differential evolution algorithms for big data optimisation problems

Evolutionary algorithms (EAs) have recently been suggested as candidate for solving big data optimisation problems that involve very large number of variables and need to be analysed in a short period of time. However, EAs face scalability issue when dealing with big data problems. Moreover, the performance of EAs critically hinges on the utilised parameter values and operator types, thus it is impossible to design a single EA that can outperform all other on every problem instances. To address these challenges, we propose a heterogeneous framework that integrates a cooperative co-evolution method with various types of memetic algorithms. We use the cooperative co-evolution method to split the big problem into sub-problems in order to increase the efficiency of the solving process. The subproblems are then solved using various heterogeneous memetic algorithms. The proposed heterogeneous framework adaptively assigns, for each solution, different operators, parameter values and local search algorithm to efficiently explore and exploit the search space of the given problem instance. The performance of the proposed algorithm is assessed using the Big Data 2015 competition benchmark problems that contain data with and without noise. Experimental results demonstrate that the proposed algorithm, with the cooperative co-evolution method, performs better than without cooperative co-evolution method. Furthermore, it obtained very competitive results for all tested instances, if not better, when compared to other algorithms using a lower computational times.