Kernel performance in meeting hard real-time system characteristics (the ARTOS real-time kernel)

Describes the design and implementation of an operating system kernel specifically designed to support real-time applications. It emphasises portability and aims to support state-of-the-art concepts in real-time programming. Discusses architectural aspects of the ARTOS kernel, and introduces new concepts on the areas of interrupt processing, scheduling, mutual exclusion and inter-task communication. Also explains the programming environment of ARTOS kernal and its task model, defines the real-time task states and system data structures and discusses exception handling mechanisms which are used to detect missed deadlines and take corrective action.

Exemplifying a model-eliciting task for primary school pupils

Mathematical modelling is a field that is gaining prominence recently in mathematics educaiton research and has generated interests in schools as well.  In Singapore, modelling and applications are included as process componens in revised 2007 curriculum document (MOE, 2007) as keeping to reform efforst. In Indonesia, efforts to place stronger emphasis on connecting school mathematics with real-world contexts and applications have started in Indonesian primary schools with the Pendidikan Matematika Realistik Indonesia (PMRI) movement a decade ago (Sembiring, Hoogland, Dolk, 2010). Amidst others, modeling activities are gradually introduced in Singapore and Indonesian schools to demonstrte the relevance of school mathematics with real-world problems. However, on order for it to find a place in the mathematics classroom, ther eis a need for teacher-practitioners to know what mathematical modelling and what a modelling task is. This paper sets out to exemplify a model-eliciting task that has been designed and used in both a Singapore and Indonesian mathematics classroom. Mathematical modelling, the features of a model-eliciting task, and its potential and advice on implementation are discussed. 

A shadow-like task migration model based on context semantics for mobile and pervasive environments

Pervasive computing is a user-centric mobile computing paradigm, in which tasks should be migrated over different platforms in a shadow-like way when users move around. In this paper, we propose a context-sensitive task migration model that recovers program states and rebinds resources for task migrations based on context semantics through inserting resource description and state description sections in source programs. Based on our model, we design and develop a task migration framework xMozart which extends the Mozart platform in terms of context awareness. Our approach can recover task states and rebind resources in the context-aware way, as well as support multi- modality I/O interactions. The extensive experiments demonstrate that our approach can migrate tasks by resuming them from the last broken points like shadows moving along with the users.

A new transfer learning framework with application to model-agnostic multi-task learning

Learning from small number of examples is a challenging problem in machine learning. An effective way to improve the performance is through exploiting knowledge from other related tasks. Multi-task learning (MTL) is one such useful paradigm that aims to improve the performance through jointly modeling multiple related tasks. Although there exist numerous classification or regression models in machine learning literature, most of the MTL models are built around ridge or logistic regression. There exist some limited works, which propose multi-task extension of techniques such as support vector machine, Gaussian processes. However, all these MTL models are tied to specific classification or regression algorithms and there is no single MTL algorithm that can be used at a meta level for any given learning algorithm. Addressing this problem, we propose a generic, model-agnostic joint modeling framework that can take any classification or regression algorithm of a practitioner’s choice (standard or custom-built) and build its MTL variant. The key observation that drives our framework is that due to small number of examples, the estimates of task parameters are usually poor, and we show that this leads to an under-estimation of task relatedness between any two tasks with high probability. We derive an algorithm that brings the tasks closer to their true relatedness by improving the estimates of task parameters. This is achieved by appropriate sharing of data across tasks. We provide the detail theoretical underpinning of the algorithm. Through our experiments with both synthetic and real datasets, we demonstrate that the multi-task variants of several classifiers/regressors (logistic regression, support vector machine, K-nearest neighbor, Random Forest, ridge regression, support vector regression) convincingly outperform their single-task counterparts. We also show that the proposed model performs comparable or better than many state-of-the-art MTL and transfer learning baselines.

Naturalistic decision making: a model to overcome methodological challenges in the study of critical care nurses` decision making about patients` hemodynamic status

The quality of critical care nurses' decision making about patients' hemodynamic status in the immediate period after cardiac surgery is important for the patients' well-being and, at times, survival. The way nurses respond to hemodynamic cues varies according to the nurses' skills, experiences, and knowledge. Variability in decisions is also associated with the inherent complexity of hemodynamic monitoring. Previous methodological approaches to the study of hemodynamic assessment and treatment decisions have ignored the important interplay between nurses, the task, and the environment in which these decisions are made. The advantages of naturalistic decision making as a framework for studying the manner in which nurses make decisions are presented.

Linear causal model discovery using the MML criterion

Determining the causal structure of a domain is a key task in the area of Data Mining and Knowledge Discovery.The algorithm proposed by Wallace et al. [15] has demonstrated its strong ability in discovering Linear Causal Models from given data sets. However, some experiments showed that this algorithm experienced difficulty in discovering linear relations with small deviation, and it occasionally gives a negative message length, which should not be allowed. In this paper, a more efficient and precise MML encoding scheme is proposed to describe the model structure and the nodes in a Linear Causal Model. The estimation of different parameters is also derived. Empirical results show that the new algorithm outperformed the previous MML-based algorithm in terms of both speed and precision.

Improving an inverse model of sheet metal forming by neural network based regression

An inverse model for a sheet meta l forming process aims to determine the initial parameter levels required to form the final formed shape. This is a difficult problem that is usually approached by traditional methods such as finite element analysis. Formulating the problem as a classification problem makes it possible to use well established classification algorithms, such as decision trees. Classification is, however, generally based on a winner-takes-all approach when associating the output value with the corresponding class. On the other hand, when formulating the problem as a regression task, all the output values are combined to produce the corresponding class value. For a multi-class problem, this may result in very different associations compared with classification between the output of the model and the corresponding class. Such formulation makes it possible to use well known regression algorithms, such as neural networks. In this paper, we develop a neural network based inverse model of a sheet forming process, and compare its performance with that of a linear model. Both models are used in two modes, classification mode and a function estimation mode, to investigate the advantage of re-formulating the problem as a function estimation. This results in large improvements in the recognition rate of set-up parameters of a sheet metal forming process for both models, with a neural network model achieving much more accurate parameter recognition than a linear model.

Redefining teacher education through a `multiple space` model: examining the forgotten spaces that teachers work in

This paper maps the current debates surrounding school-based and university-based teacher education models, and presents a ‘multiple-space’ model of teacher education that both explores and values the many ‘forgotten’ spaces that teachers work in. It draws from a variety of research studies, including my own doctoral work, to argue for a new approach to teacher education programs. I suggest that in order for teacher education to move beyond separatist, binary models, we need to adopt a ‘multiple-space’ view of learning to be a teacher that embraces the notion that teachers do not learn about theory in a university space, nor do they simply work in a classroom space.

Developing the task and ego orientation at work questionnaire

Although widely researched in education and sport, little research examines employee achievement goal orientations in a work context. This article provides validity and reliability evidence for the Task and Ego Orientation at Work Questionnaire (TEOWQ) from a study of378 employees representing from eight different occupational categories. Confirmatory factor analyses indicate that are-specified model comprising two ego ("Being the best" and "being better than others") and two task sub-factors ("Learning" and "Effort") fit the data better than the original two-factor model. Temporal stationarity and stability of the constructs over time receive support. As hypothesized, task and task-effort orientations relate positively with persistence while ego orientation does not. The TEOWQ appears to be a valid and reliable instrument of achievement orientation in a work setting.

Influence of task properties and subjectivity on consistency of triage: a simulation study

Aim. This paper reports a study to determine nurses' levels of agreement using a standard 5-point triage scale and to explore the influence of task properties and subjectivity on decision-making consistency.

Background. Triage scales are used to define time-to-treatment in hospital emergency departments. Studies of the inter-rater reliability of these scales using paper-based simulation methods report varying levels of consistency. Understanding how various components of the decision task and individual perceptions of the case influence agreement is critical to the development of strategies to improve consistency of triage.

Method. Simulations were constructed from naturalistic observation, cue types and frequencies were classified. Data collection was conducted in 2002, and the final response rate was 41·3%. Participants were asked to allocate an urgency code for 12 scenarios using the Australasian Triage Scale, and provide estimates of case complexity, levels of certainty and available information. Data were analysed descriptively, agreement between raters was calculated using kappa. The influence of task properties and participants' subjective estimates of case complexity, levels of certainty and available information on agreement were explored using a general linear model.

Findings. Agreement among raters varied from moderate to poor (κ = 0·18–0·64). Participants' subjective estimates of levels of available information were found to influence consistency of triage by statistically significant amounts (F 5·68; ≤0·01).

Conclusions. Strategies employed to optimize consistency of triage should focus on improving the quality of the simulations that are used. In particular, attention should be paid to the development of interactive simulations that will accommodate individual differences in information-seeking behaviour.

Evidence for crossvergence in the perception of task and contextual performance : a study of western expatriates working in Thailand

The applicability of the Western model of task and contextual performance to the context of Thai and Western managers, professionals and consultants working together in Thailand is addressed in this research. The results show a clear difference in the factor structure of how Western and Thai managers perceive the importance of performance factors. Moreover, the task and contextual factor structure found for Western managers working in a Western culture did not hold for Westerners working within the Thai cultural environment. These findings provide evidence of adaptation by the Westerner to the Thai cultural environment, supporting the notion of crossvergence.

Development of a collaborative model to improve school health promotion in the Netherlands

In recent decades, school health promotion programs have been developing into whole-school health approaches. This has been accompanied by a greater understanding among health promoters of the core-business of schools, namely education, and how health promotion objectives can be integrated into this task. Evidence of the positive impact of school health promotion on health risk behavior of students is increasing. This article focuses on the processes and initial results of developing a collaborative model tailored for whole-school health in the Netherlands, named schoolBeat. The Dutch situation is characterized by fragmentation, a variety of health and welfare groups supporting schools, and a lack of sound integrated youth policies. A literature review, observations, and stakeholder consultation provided a clear picture of the current situation in school health promotion, and factors limiting a comprehensive and needs-based approach to school health. This revealed that a health promotion team within a school is fundamental to an effective approach to tailored school health promotion. A respected member of school staff should chair this team. To strengthen the link with the school care team, the school care coordinator should be a member of both teams. To provide coordinated support to all schools in a region, participating organizations decided to share advisory tasks. These tasks are included in the regular health promotion work of their staff. This means working with one advisor representing all school-health organizations per school, and using a comprehensive overview of possible support and projects promoting health. Empowering schools in needs assessments and comprehensive school health promotion is an important element of the developed approach. This article concludes with an examination of emerging issues in evaluating collaborative school health support during the first 18 months of development, and implementation and future perspectives regarding sustainable collaboration and quality improvement.

A performance model for analysis of heterogeneous multi-cluster systems

This paper addresses the problem of performance modeling for large-scale heterogeneous distributed systems with emphases on multi-cluster computing systems. Since the overall performance of distributed systems is often depends on the effectiveness of its communication network, the study of the interconnection networks for these systems is very important. Performance modeling is required to avoid poorly chosen components and architectures as well as discovering a serious shortfall during system testing just prior to deployment time. However, the multiplicity of components and associated complexity make performance analysis of distributed computing systems a challenging task. To this end, we present an analytical performance model for the interconnection networks of heterogeneous multi-cluster systems. The analysis is based on a parametric family of fat-trees, the m-port n-tree, and a deterministic routing algorithm, which is proposed in this paper. The model is validated through comprehensive simulation, which demonstrated that the proposed model exhibits a good degree of accuracy for various system organizations and under different working conditions.

A strategic approach to target marketing-choosing the preferred generic customer : a model of shareholder value performance

In recent years the contribution of the marketing function has changed and interest now centres on its contribution to a firm’s financial performance. The Marketing Science Institute in the USA has stated that it is the number one marketing issue facing corporate America. The Australian Marketing Institute is promoting a set of marketing metrics that will help Australian firms measure the function’s contribution to shareholder value creation. Much of the literature relates notions such as customer satisfaction and other marketing activities with a firm’s profit. A missing link appears to be the choice firms make in terms of which customer groups to target and the resultant impact on shareholder value performance. The generic customer groups comprise: existing customers, former customers and prospects. A review of the literature reveals that marketing costs and benefits vary across these groups. The challenge for management is to determine which group represents the best target and to allocate scarce marketing resources accordingly. The task is made even more challenging because the economic value of members within each group also varies and some product lines may be unprofitable and therefore, may not be worth pursuing. To generate superior shareholder value it may not simply be the case of acquiring the maximum number of new customers from any source but to find the appropriate mix of the generic customer groups and manage the individual customer relationships accordingly. This paper seeks to firstly summarise and review the recent literature on marketing and its relationship to shareholder value and secondly to propose a model for allocating marketing resources across generic customer groups in order to generate improved shareholder value performance. Importantly, the model not only covers increasing business with customers but also shedding customers or shedding the extent of business conducted with customers as means of generating shareholder value.