Strategy management through quantitative modelling of performance measurement systems

This paper is based on previous works on performance measurement and on quantification of relationships between factors which affect performance. It demonstrates how tools and techniques developed can be used to evaluate the performance of alternative strategic choices through a quantitative approach to modelling of performance measurement systems. The paper provides a brief background to the research problem and preceding works. The tools and techniques used are briefly introduced. Use of these tools and techniques to evaluate the performance of alternative manufacturing strategies is demonstrated. Finally, the capability of the approach to deal with dynamic environments is demonstrated using sensitivity analysis

Query Modification in Object-oriented Database Federation

We discuss the modification of queries against an integrated view in a federation of object-oriented databases. We present a generalisation of existing algorithms for simple global query processing that works for arbitrarily defined integration classes. We then extend this algorithm to deal with object-oriented features such as queries involving path expressions and nesting. We show how properties of the OO-style of modelling relationships through object references can be exploited to reduce the number of subqueries necessary to evaluate such querie

Enhancing Fault / Intrusion Tolerance through Design and Configuration Diversity

Fault/intrusion tolerance is usually the only viable way of improving the system dependability and security in the presence of continuously evolving threats. Many of the solutions in the literature concern a specific snapshot in the production or deployment of a fault-tolerant system and no immediate considerations are made about how the system should evolve to deal with novel threats. In this paper we outline and evaluate a set of operating systems’ and applications’ reconfiguration rules which can be used to modify the state of a system replica prior to deployment or in between recoveries, and hence increase the replicas chance of a longer intrusion-free operation

After the negotiations: assessing the impact of free trade agreements in Southern Africa

After protracted and difficult negotiations, agreement was recently reached on the dimensions of a South African-EU free trade deal. Because of South Africa's prominence in the sub-region, implementation of this agreement will have an impact not only on South Africa, but on all the SADC economies. This paper traces how this impact may be felt over time, using a multi-region model constructed to focus on the determination of sectoral and geographic trade patterns. By separatelymodeling South Africa and the rest of southern Africa, the model can be used to evaluate how alternative SADC regional trade strategies can influence how the EU deal affects the region's economies; by distinguishing among major trading partners (EU, North America, East Asia), the simulations can help illuminate how the trade deal will likely affect current trade patterns The empirical results lead to a number of conclusions: (1) trade creation dominates trade diversion for the region under all FTA arrangements; (2) the rest of southern Africa benefits from an FTA between the EU and South Africa — the recently signed bilateral agreement is not a “beggar thy neighbor” policy; (3) the rest of southern Africa gains more from zero-tariff access to EU markets than from a partial (50 percent) reduction in global tariffs; and (4) the South African economy is not large enough to serve as a growth pole for the region. Access to EU markets provides substantially bigger gains for the rest of southern Africa than does access to South Africa.Trade policy Africa., Free trade., South Africa.,

Evaluation - the educational context

Evaluation comes in many shapes and sizes. It can be as simple and as grounded in day to day work as a clinical teacher refl ecting on a lost teaching opportunity and wondering how to do it better next time or as complex, top down and politically charged as a major government led evaluation of use of teaching funds with the subtext of re-allocating them. Despite these multiple spectra of scale, perceived ownership, fi nancial and political implications, the underlying principles of evaluation are remarkably consistent. To evaluate well, it needs to be clear who is evaluating what and why. From this will come notions of how it needs to be done to ensure the evaluation is meaningful and useful. This paper seeks to illustrate what evaluation is, why it matters, where to start if you want to do it and how to deal with evaluation that is external and imposed

Using a Bayesian averaging model for estimating the reliability of decisions in multimodal biometrics

The issue of reliable authentication is of increasing importance in modern society. Corporations, businesses and individuals often wish to restrict access to logical or physical resources to those with relevant privileges. A popular method for authentication is the use of biometric data, but the uncertainty that arises due to the lack of uniqueness in biometrics has lead there to be a great deal of effort invested into multimodal biometrics. These multimodal biometric systems can give rise to large, distributed data sets that are used to decide the authenticity of a user. Bayesian model averaging (BMA) methodology has been used to allow experts to evaluate the reliability of decisions made in data mining applications. The use of decision tree (DT) models within the BMA methodology gives experts additional information on how decisions are made. In this paper we discuss how DT models within the BMA methodology can be used for authentication in multimodal biometric systems

Combining Static and Dynamic Features for Multivariate Sequence Classification

Model precision in a classification task is highly dependent on the feature space that is used to train the model. Moreover, whether the features are sequential or static will dictate which classification method can be applied as most of the machine learning algorithms are designed to deal with either one or another type of data. In real-life scenarios, however, it is often the case that both static and dynamic features are present, or can be extracted from the data. In this work, we demonstrate how generative models such as Hidden Markov Models (HMM) and Long Short-Term Memory (LSTM) artificial neural networks can be used to extract temporal information from the dynamic data. We explore how the extracted information can be combined with the static features in order to improve the classification performance. We evaluate the existing techniques and suggest a hybrid approach, which outperforms other methods on several public datasets.Comment: Presented at IEEE DSAA 201