A parallel cooperative co-evolutionary genetic algorithm for the composite SaaS placement problem in Cloud computing

A composite SaaS (Software as a Service) is a software that is comprised of several software components and data components. The composite SaaS placement problem is to determine where each of the components should be deployed in a cloud computing environment such that the performance of the composite SaaS is optimal. From the computational point of view, the composite SaaS placement problem is a large-scale combinatorial optimization problem. Thus, an Iterative Cooperative Co-evolutionary Genetic Algorithm (ICCGA) was proposed. The ICCGA can find reasonable quality of solutions. However, its computation time is noticeably slow. Aiming at improving the computation time, we propose an unsynchronized Parallel Cooperative Co-evolutionary Genetic Algorithm (PCCGA) in this paper. Experimental results have shown that the PCCGA not only has quicker computation time, but also generates better quality of solutions than the ICCGA.

The Attack on Political Correctness: Islamophobia and the Erosion of Multiculturalism In Australia Under the Howard Regime

This article reports on civil society in Australia between 1996 and 2007 related to former Prime Minister John Howard. The article discusses Howard's neo-conservative ideology and Liberal-National coalition, noting his views on political correctness. Howard's administration is also discussed in terms of immigration, multiculturalism, indigenous land rights, othering, and Islamaphobia. Information on the effect of Islamaphobia on Australian perceptions and the treatment of Muslims is also provided

Tampere region under the microscope : a critical analysis from the knowledge perspective

Purpose – In the context of global knowledge economy, knowledge-based urban development (KBUD) is seen as an effective development strategy for city-regions to survive, flourish and become highly competitive urban agglomerations – i.e., a knowledge city-region. This paper aims to evaluate the KBUD dynamics, capacity and potentials of a rapidly emerging knowledge city-region of Finland – Tampere region. Design/methodology/approach – The paper undertakes a review of the literature on regional development in the knowledge economy era. It adopts a qualitative analysis technique to scrutinize the dynamics, capacity and potentials of Tampere region. The semi-structured interview process starts with the pre-determined key actors of the city-region with an aim of determining the other key players. Next, with the participation of all key players to the interviews, the research reveals the principal issues, assets and mechanisms that relate to KBUD, and portrays the strengths, weaknesses, opportunities and threats of the city-region. A critical analysis of the findings along with the previous studies is undertaken to provide a clear picture of the dynamics, capacity and potentials of the emerging knowledge city-region. Originality/value – This paper reports the findings of a pioneering study focusing on the investigation of the KBUD dynamics, capacity and potentials of Tampere region. The paper critically evaluates the city-region from the knowledge perspective with the lens of KBUD, and the lessons learned and the methodological approach of the paper shed light to other city-regions seeking such development. Practical implications – The paper discusses the findings of a study from Tampere region that critically scrutinizes the KBUD experience of the city-region. The research provides an invaluable opportunity to inform the regional decision-, policy- and plan-making mechanisms by determining key issues, actors, assets, processes and potential development directions for the KBUD of Tampere region.

A comparison of methods for classifying clinical samples based on proteomics data : a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.

Acceptability of the distress thermometer and problem list to community-based telephone cancer helpline operators, and to cancer patients and carers

Background Cancer can be a distressing experience for cancer patients and carers, impacting on psychological, social, physical and spiritual functioning. However, health professionals often fail to detect distress in their patients due to time constraints and a lack of experience. Also, with the focus on the patient, carer needs are often overlooked. This study investigated the acceptability of brief distress screening with the Distress Thermometer (DT) and Problem List (PL) to operators of a community-based telephone helpline, as well as to cancer patients and carers calling the service. Methods Operators (n = 18) monitored usage of the DT and PL with callers (cancer patients/carers, >18 years, and English-speaking) from September-December 2006 (n = 666). The DT is a single item, 11-point scale to rate level of distress. The associated PL identifies the cause of distress. Results The DT and PL were used on 90% of eligible callers, most providing valid responses. Benefits included having an objective, structured and consistent means for distress screening and triage to supportive care services. Reported challenges included apparent inappropriateness of the tools due to the nature of the call or level of caller distress, the DT numeric scale, and the level of operator training. Conclusions We observed positive outcomes to using the DT and PL, although operators reported some challenges. Overcoming these challenges may improve distress screening particularly by less experienced clinicians, and further development of the PL items and DT scale may assist with administration. The DT and PL allow clinicians to direct/prioritise interventions or referrals, although ongoing training and support is critical in distress screening.