Clustering composite SaaS components in cloud computing using a grouping genetic algorithm

Recently, Software as a Service (SaaS) in Cloud computing, has become more and more significant among software users and providers. To offer a SaaS with flexible functions at a low cost, SaaS providers have focused on the decomposition of the SaaS functionalities, or known as composite SaaS. This approach has introduced new challenges in SaaS resource management in data centres. One of the challenges is managing the resources allocated to the composite SaaS. Due to the dynamic environment of a Cloud data centre, resources that have been initially allocated to SaaS components may be overloaded or wasted. As such, reconfiguration for the components’ placement is triggered to maintain the performance of the composite SaaS. However, existing approaches often ignore the communication or dependencies between SaaS components in their implementation. In a composite SaaS, it is important to include these elements, as they will directly affect the performance of the SaaS. This paper will propose a Grouping Genetic Algorithm (GGA) for multiple composite SaaS application component clustering in Cloud computing that will address this gap. To the best of our knowledge, this is the first attempt to handle multiple composite SaaS reconfiguration placement in a dynamic Cloud environment. The experimental results demonstrate the feasibility and the scalability of the GGA.

Analytical solutions for the multi-term time-fractional diffusion-wave/diffusion equations in a finite domain

Multi-term time-fractional differential equations have been used for describing important physical phenomena. However, studies of the multi-term time-fractional partial differential equations with three kinds of nonhomogeneous boundary conditions are still limited. In this paper, a method of separating variables is used to solve the multi-term time-fractional diffusion-wave equation and the multi-term time-fractional diffusion equation in a finite domain. In the two equations, the time-fractional derivative is defined in the Caputo sense. We discuss and derive the analytical solutions of the two equations with three kinds of nonhomogeneous boundary conditions, namely, Dirichlet, Neumann and Robin conditions, respectively.

In the pursuit of effective affective computing : the relationship between features and registration

For facial expression recognition systems to be applicable in the real world, they need to be able to detect and track a previously unseen person's face and its facial movements accurately in realistic environments. A highly plausible solution involves performing a "dense" form of alignment, where 60-70 fiducial facial points are tracked with high accuracy. The problem is that, in practice, this type of dense alignment had so far been impossible to achieve in a generic sense, mainly due to poor reliability and robustness. Instead, many expression detection methods have opted for a "coarse" form of face alignment, followed by an application of a biologically inspired appearance descriptor such as the histogram of oriented gradients or Gabor magnitudes. Encouragingly, recent advances to a number of dense alignment algorithms have demonstrated both high reliability and accuracy for unseen subjects [e.g., constrained local models (CLMs)]. This begs the question: Aside from countering against illumination variation, what do these appearance descriptors do that standard pixel representations do not? In this paper, we show that, when close to perfect alignment is obtained, there is no real benefit in employing these different appearance-based representations (under consistent illumination conditions). In fact, when misalignment does occur, we show that these appearance descriptors do work well by encoding robustness to alignment error. For this work, we compared two popular methods for dense alignment-subject-dependent active appearance models versus subject-independent CLMs-on the task of action-unit detection. These comparisons were conducted through a battery of experiments across various publicly available data sets (i.e., CK+, Pain, M3, and GEMEP-FERA). We also report our performance in the recent 2011 Facial Expression Recognition and Analysis Challenge for the subject-independent task.

Sensitivity of the NMR density matrix to pulse sequence parameters : a simplified analytic approach

We present a formalism for the analysis of sensitivity of nuclear magnetic resonance pulse sequences to variations of pulse sequence parameters, such as radiofrequency pulses, gradient pulses or evolution delays. The formalism enables the calculation of compact, analytic expressions for the derivatives of the density matrix and the observed signal with respect to the parameters varied. The analysis is based on two constructs computed in the course of modified density-matrix simulations: the error interrogation operators and error commutators. The approach presented is consequently named the Error Commutator Formalism (ECF). It is used to evaluate the sensitivity of the density matrix to parameter variation based on the simulations carried out for the ideal parameters, obviating the need for finite-difference calculations of signal errors. The ECF analysis therefore carries a computational cost comparable to a single density-matrix or product-operator simulation. Its application is illustrated using a number of examples from basic NMR spectroscopy. We show that the strength of the ECF is its ability to provide analytic insights into the propagation of errors through pulse sequences and the behaviour of signal errors under phase cycling. Furthermore, the approach is algorithmic and easily amenable to implementation in the form of a programming code. It is envisaged that it could be incorporated into standard NMR product-operator simulation packages.

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.

Community wide interventions for increasing physical activity

Not having enough physical activity leads to poorer health. Regular physical activity can reduce the risk of chronic disease and improve one's health and well being. The lack of physical activity is a common and growing health problem. To address this, 25 studies have used improvement activities directed at communities using more than one approach in a single program. When we looked at the available research, we observed that there was a lack of good studies which could show whether this approach was or wasn't beneficial. For example, some research studies claimed that community wide programs improved physical activities and other studies did not. It was not possible to determine what might work. Future research is needed with improved designs, measures of outcomes and larger samples of participants.

Geoproof : proofs of geographic location for cloud computing environment

Cloud computing has emerged as a major ICT trend and has been acknowledged as a key theme of industry by prominent ICT organisations. However, one of the major challenges that face the cloud computing concept and its global acceptance is how to secure and protect the data that is the property of the user. The geographic location of cloud data storage centres is an important issue for many organisations and individuals due to the regulations and laws that require data and operations to reside in specific geographic locations. Thus, data owners may need to ensure that their cloud providers do not compromise the SLA contract and move their data into another geographic location. This paper introduces an architecture for a new approach for geographic location assurance, which combines the proof of storage protocol (POS) and the distance-bounding protocol. This allows the client to check where their stored data is located, without relying on the word of the cloud provider. This architecture aims to achieve better security and more flexible geographic assurance within the environment of cloud computing.

The social car : new interactive vehicular applications derived from social media and urban informatics

Digital information that is place- and time-specific, is increasingly becoming available on all aspects of the urban landscape. People (cf. the Social Web), places (cf. the Geo Web), and physical objects (cf. ubiquitous computing, the Internet of Things) are increasingly infused with sensors, actuators, and tagged with a wealth of digital information. Urban informatics research explores these emerging digital layers of the city at the intersection of people, place and technology. However, little is known about the challenges and new opportunities that these digital layers may offer to road users driving through today’s mega cities. We argue that this aspect is worth exploring in particular with regards to Auto-UI’s overarching goal of making cars both safer and more enjoyable. This paper presents the findings of a pilot study, which included 14 urban informatics research experts participating in a guided ideation (idea creation) workshop within a simulated environment. They were immersed into different driving scenarios to imagine novel urban informatics type of applications specific to the driving context.

Testing a model of physical activity among mothers and fathers of young children: integrating self-determined motivation, planning, and the theory of planned behavior

Parents are at risk for inactivity; however, research into understanding parental physical activity (PA) is scarce. We integrated self-determined motivation, planning, and the theory of planned behavior (TPB) to better understand parental PA. Parents (252 mothers, 206 fathers) completed a main questionnaire assessing measures underpinning these constructs and a 1-week follow-up of PA behavior to examine whether self-determined motivation indirectly influenced intention via the TPB variables (i.e., attitude, subjective norm, and perceived behavioral control) and intention indirectly influenced behavior via planning. We found self-determined motivation on intention was fully mediated by the TPB variables and intention on behavior was partially mediated by the planning variables. In addition, slight differences in the model’s paths between the sexes were revealed. The results illustrate the range of important determinants of parental PA and provide support for the integrated model in explaining PA decision making as well as the importance of examining sex differences.

An evolving perspective on physical activity counselling by medical professionals

Background Physical inactivity is a modifiable risk factor for many chronic conditions and a leading cause of premature mortality. An increasing proportion of adults worldwide are not engaging in a level of physical activity sufficient to prevent or alleviate these adverse effects. Medical professionals have been identified as potentially powerful sources of influence for those who do not meet minimum physical activity guidelines. Health professionals are respected and expected sources of advice and they reach a large and relevant proportion of the population. Despite this potential, health professionals are not routinely practicing physical activity promotion. Discussion Medical professionals experience several known barriers to physical activity promotion including lack of time and lack of perceived efficacy in changing physical activity behaviour in patients. Furthermore, evidence for effective physical activity promotion by medical professionals is inconclusive. To address these problems, new approaches to physical activity promotion are being proposed. These include collaborating with community based physical activity behaviour change interventions, preparing patients for effective brief counselling during a consultation with the medical professional, and use of interactive behaviour change technology. Summary It is important that we recognise the latent risk of physical inactivity among patients presenting in clinical settings. Preparation for improving patient physical activity behaviours should commence before the consultation and may include physical activity screening. Medical professionals should also identify suitable community interventions to which they can refer physically inactive patients. Outsourcing the majority of a comprehensive physical activity intervention to community based interventions will reduce the required clinical consultation time for addressing the issue with each patient. Priorities for future research include investigating ways to promote successful referrals and subsequent engagement in comprehensive community support programs to increase physical activity levels of inactive patients. Additionally, future clinical trials of physical activity interventions should be evaluated in the context of a broader framework of outcomes to inform a systematic consideration of broad strengths and weaknesses regarding not only efficacy but cost-effectiveness and likelihood of successful translation of interventions to clinical contexts.