Dynamic cloud service selection using an adaptive learning mechanism in multi-cloud computing

Cloud service selection in a multi-cloud computing environment is receiving more and more attentions. There is an abundance of emerging cloud service resources that makes it hard for users to select the better services for their applications in a changing multi-cloud environment, especially for online real time applications. To assist users to efficiently select their preferred cloud services, a cloud service selection model adopting the cloud service brokers is given, and based on this model, a dynamic cloud service selection strategy named DCS is put forward. In the process of selecting services, each cloud service broker manages some clustered cloud services, and performs the DCS strategy whose core is an adaptive learning mechanism that comprises the incentive, forgetting and degenerate functions. The mechanism is devised to dynamically optimize the cloud service selection and to return the best service result to the user. Correspondingly, a set of dynamic cloud service selection algorithms are presented in this paper to implement our mechanism. The results of the simulation experiments show that our strategy has better overall performance and efficiency in acquiring high quality service solutions at a lower computing cost than existing relevant approaches.

Pedagogy of the rural: teaching and learning for rural futures?

Pedagogy of the Rural illustrates the complexities of rural space and considers some of the underlying assumptions, ‘truths’ and ‘realities’ about rural education and teaching in a complicated and dynamic policy context. Pedagogy of the Rural offers an alternative to current teacher education practice – it is responsive to policy demands as well as local conditions and traditions, and has a futures orientation, in that it provides a way forward for valuing rural contexts for what they bring to teacher identities beyond traditional deficit positionings dominant in current discourses on rural. The authors examine notions of size and how this impacts on the ways in which beginning teachers in rural locations are positioned in terms of identity at a macro, meso and micro level. They also examine what it means to ‘be rural’ and use Pedagogy of the Rural to conceptualise rural understandings as a pedagogy that is not a pedagogy ‘for’ or ‘about’ but rather ‘of’ the rural. Complexities of the Pedagogy of the Rural are understood through Harré’s (2004) positioning theory, Baudrillard’s (1983) notion of simulation and simulacra and Lefebvre’s (2009) arguments around space and economic geographies. The interrelationship of place, space and identity unify teachers’ understandings of who they (or we) are, and are becoming, in a specific time and geographical location, raising questions about: subjectivity - who we are; power - what we can do; and desire- who we might become (Harré, Moghaddam, Cairnie, Rothbart, & Sabat, 2009), and the influence of personal and professional histories and what rural brings to our pedagogy within this.

‘I’m not like that, why treat me the same way?’ The impact of stereotyping international students on their learning, employability and connectedness with the workplace

A significant body of literature on international education examines the experiences of international students in the host country. There is however a critical lack of empirical work that investigates the dynamic and complex positioning of international students within the current education-migration nexus that prevails international education in countries such as Australia, Canada and the UK. This paper addresses an important but under-researched area of the education-migration landscape by examining how the stereotyping of students as mere ‘migration hunters’ may impact their study and work experiences. It draws on a four-year research project funded by the Australian Research Council that includes more than 150 interviews and fieldwork in the Australian vocational education context. Positioning theory is used as a conceptual framework to analyse how generalising international students as ‘mere migration hunters’ has led to the disconnectedness, vulnerability and marginalization of the group of international students participating in this research.

Professionalism in workplace learning: understanding interprofessional dilemmas through healthcare student narratives

Drawing on theory, research, and practice, the contributors to this interdisciplinary volume systematically analyze these ethical dilemmas and offer practical suggestions that are sure to interest students, academics, and professionals.

Collaborating differently on different topics: a multi-relational approach to multi-task learning

Multi-task learning offers a way to benefit from synergy of multiple related prediction tasks via their joint modeling. Current multi-task techniques model related tasks jointly, assuming that the tasks share the same relationship across features uniformly. This assumption is seldom true as tasks may be related across some features but not others. Addressing this problem, we propose a new multi-task learning model that learns separate task relationships along different features. This added flexibility allows our model to have a finer and differential level of control in joint modeling of tasks along different features. We formulate the model as an optimization problem and provide an efficient, iterative solution. We illustrate the behavior of the proposed model using a synthetic dataset where we induce varied feature-dependent task relationships: positive relationship, negative relationship, no relationship. Using four real datasets, we evaluate the effectiveness of the proposed model for many multi-task regression and classification problems, and demonstrate its superiority over other state-of-the-art multi-task learning models

Prediciton of emergency events: a multi-task multi-label learning approach

Prediction of patient outcomes is critical to plan resources in an hospital emergency department. We present a method to exploit longitudinal data from Electronic Medical Records (EMR), whilst exploiting multiple patient outcomes. We divide the EMR data into segments where each segment is a task, and all tasks are associated with multiple patient outcomes over a 3, 6 and 12 month period. We propose a model that learns a prediction function for each task-label pair, interacting through two subspaces: the first subspace is used to impose sharing across all tasks for a given label. The second subspace captures the task-specific variations and is shared across all the labels for a given task. The proposed model is formulated as an iterative optimization problems and solved using a scalable and efficient Block co-ordinate descent (BCD) method. We apply the proposed model on two hospital cohorts - Cancer and Acute Myocardial Infarction (AMI) patients collected over a two year period from a large hospital emergency department. We show that the predictive performance of our proposed models is significantly better than those of several state-of-the-art multi-task and multi-label learning methods.

Distributed Q-learning controller for a multi-intersection traffic network

This paper proposes a Q-learning based controller for a network of multi intersections. According to the increasing amount of traffic congestion in modern cities, using an efficient control system is demanding. The proposed controller designed to adjust the green time for traffic signals by the aim of reducing the vehicles’ travel delay time in a multi-intersection network. The designed system is a distributed traffic timing control model, applies individual controller for each intersection. Each controller adjusts its own intersection’s congestion while attempt to reduce the travel delay time in whole traffic network. The results of experiments indicate the satisfied efficiency of the developed distributed Q-learning controller.

A systematic design of interval type-2 fuzzy logic system using extreme learning machine for electricity load demand forecasting

This paper presents a novel design of interval type-2 fuzzy logic systems (IT2FLS) by utilizing the theory of extreme learning machine (ELM) for electricity load demand forecasting. ELM has become a popular learning algorithm for single hidden layer feed-forward neural networks (SLFN). From the functional equivalence between the SLFN and fuzzy inference system, a hybrid of fuzzy-ELM has gained attention of the researchers. This paper extends the concept of fuzzy-ELM to an IT2FLS based on ELM (IT2FELM). In the proposed design the antecedent membership function parameters of the IT2FLS are generated randomly, whereas the consequent part parameters are determined analytically by the Moore-Penrose pseudo inverse. The ELM strategy ensures fast learning of the IT2FLS as well as optimality of the parameters. Effectiveness of the proposed design of IT2FLS is demonstrated with the application of forecasting nonlinear and chaotic data sets. Nonlinear data of electricity load from the Australian National Electricity Market for the Victoria region and from the Ontario Electricity Market are considered here. The proposed model is also applied to forecast Mackey-glass chaotic time series data. Comparative analysis of the proposed model is conducted with some traditional models such as neural networks (NN) and adaptive neuro fuzzy inference system (ANFIS). In order to verify the structure of the proposed design of IT2FLS an alternate design of IT2FLS based on Kalman filter (KF) is also utilized for the comparison purposes.

A case study approach to classroom experiential learning of problem structuring methods

The proposed research aims at consolidating two years of practical experience in developing a classroom experiential learning pedagogic approach for the problem structuring methods (PSMs) of operational research. The results will be prepared as papers to be submitted, respectively, to the Brazilian ISSS-sponsored system theory conference in São Paulo, and to JORS. These two papers follow the submission (in 2004) of one related paper to JORS which is about to be resubmitted following certain revisions. This first paper draws from the PSM and experiential learning literatures in order to introduce a basic foundation upon which a pedagogic framework for experiential learning of PSMs may be built. It forms, in other words, an integral part of my research in this area. By September, the area of pedagogic approaches to PSM learning will have received its first official attention - at the UK OR Society conference. My research and paper production during July-December, therefore, coincide with an important time in this area, enabling me to form part of the small cohort of published researchers creating the foundations upon which future pedagogic research will build. On the institutional level, such pioneering work also raises the national and international profile of FGVEAESP, making it a reference for future researchers in this area.

A Theory of voter experimentation in repeated elections

Nesse artigo, eu desenvolvo e analiso um modelo de dois perí odos em que dois polí ticos competem pela preferência de um eleitor representativo, que sabe quão benevolente é um dos polí ticos mas é imperfeitamente informado sobre quão benevolente é o segundo polí tico. O polí tico conhecido é interpretado como um incumbente de longo prazo, ao passo que o polí tico desconhecido é interpretado como um desa fiante menos conhecido. É estabelecido que o mecanismo de provisão de incentivos inerente às elei cões - que surge através da possibilidade de não reeleger um incumbente - e considerações acerca de aquisi cão de informa cão por parte do eleitor se combinam de modo a determinar que em qualquer equilí brio desse jogo o eleitor escolhe o polí tico desconhecido no per íodo inicial do modelo - uma a cão à qual me refi ro como experimenta cão -, fornecendo assim uma racionaliza cão para a não reelei cão de incumbentes longevos. Especifi camente, eu mostro que a decisão do eleitor quanto a quem eleger no per odo inicial se reduz à compara cão entre os benefí cios informacionais de escolher o polí tico desconhecido e as perdas econômicas de fazê-lo. Os primeiros, que capturam as considera cões relacionadas à aquisi cão de informa cão, são mostrados serem sempre positivos, ao passo que as últimas, que capturam o incentivo à boa performance, são sempre não-negativas, implicando que é sempre ótimo para o eleitor escolher o polí tico desconhecido no per íodo inicial.

A satisfação e a intenção de continuidade de uso em servços de e-learning:validação empírica de um modelo aplicado no serviço público

The present study aims to investigate the constructs of Technological Readiness Index (TRI) and the Expectancy Disconfirmation Theory (EDT) as determinants of satisfaction and continuance intention use in e-learning services. Is proposed a theoretical model that seeks to measure the phenomenon suited to the needs of public organizations that offer distance learning course with the use of virtual platforms for employees. The research was conducted from a quantitative analytical approach, via online survey in a sample of 343 employees of 2 public organizations in RN who have had e-learning experience. The strategy of data analysis used multivariate analysis techniques, including structural equation modeling (SEM), operationalized by AMOS© software. The results showed that quality, quality disconfirmation, value and value disconfirmation positively impact on satisfaction, as well as disconfirmation usability, innovativeness and optimism. Likewise, satisfaction proved to be decisive for the purpose of continuance intention use. In addition, technological readiness and performance are strongly related. Based on the structural model found by the study, public organizations can implement e-learning services for employees focusing on improving learning and improving skills practiced in the organizational environment

On-line collaborative learning for in-service teacher education

This article brings some of the results of a study that analyzes a hybrid course for in-service teachers in the Project Teletandem Brazil: foreign languages for all. In this project, Brazilian teachers of Spanish as a foreign language took part in a blended tandem learning course, communicating via videoconferencing with Uruguayan teachers of Portuguese as a foreign language. The aim of the study was to verify Brazilian teachers' concepts and beliefs concerning language and culture and how the teletandem interactions affected them. After the interactions, teachers' views of culture seemed to also incorporate aspects of culture as an interpersonal process, instead of the factual and static view which was previously predominant. Therefore teacher education programs must consider the possibility of conjugating theory and reflective practice through the use of videoconference tools in order to allow teachers to experience culture rather learn facts about it. © 2011 ACADEMY PUBLISHER.

Power Electronics course: Analysis and evaluation of the educational software and the environment learning

This paper presents the analysis and evaluation of the Power Electronics course at So Paulo State University-UNESP-Campus of Ilha Solteira(SP)-Brazil, which includes the usage of interactive Java simulations tools and an educational software to aid the teaching of power electronic converters. This platform serves as an oriented course for the lectures and supplementary support for laboratory experiments in the power electronics courses. The simulation tools provide an interactive and dynamic way to visualize the power electronics converters behavior together with the educational software, which contemplates the theory and a list of subjects for circuit simulations. In order to verify the performance and the effectiveness of the proposed interactive educational platform, it is presented a statistical analysis considering the last three years. © 2011 IEEE.

A multiple intelligences theory-based 3D virtual lab environment for digital systems teaching.

This paper describes a 3D virtual lab environment that was developed using OpenSim software integrated into Moodle. Virtuald software tool was used to provide pedagogical support to the lab by enabling to create online texts and delivering them to the students. The courses taught in this virtual lab are methodologically in conformity to theory of multiple intelligences. Some results are presented.