CIIP-RAM - a step-wise security risk analysis methodology for critical information infrastructure protection

Wilh the protection of critical information infrastructure becoming a priority for all levels of management. there is a need for a new security methodology to deal with the new and unique attack threats and vulnerabilities associated with the new information technology security paradigm. The fourth generation security risk analysis melhod which copes wilh the shift from computer/information security to critical information iinfrastructure protectionl is lhe next step toward handling security risk at all levels. The paper will present the methodology of
fourth generation models and their application to critical information infrastructure protection and the associated advantagess of this methodology.

Unifying manufacturing simulation models using HLA

The increasing use of simulation in manufacturing has seen an increase in simulation models created using many simulation package. This use of different simulators can create simulation islands in a manufacturing factory, making it difficult to get a true simulated overview of the factory. At present, there are only a few cases where manufacturing simulations have been linked to enable multiple simulation models to run as one. This research expands upon these cases. For this paper the topic of discussion is the research in connecting different 'Commercial Off The Shelf' simulators together to allow flow of all information through the connected models using high level architecture.

Contemporaneous intra-day volume, option, and futures volatility transmissions across parallel markets

The primary objective of this article is to investigate volatility transmission across three parallel markets operating on the Sydney Futures Exchange (SFE), both within and out of sample. Half-hourly observations are sampled from transaction data for the share price index (SPI) futures, SPI futures options, and 90-day bank accepted bill (BAB) futures markets, and the analysis is carried out using the simultaneous volatility (SVL) system of equations as well as competing volatility models. The results confirm the poor ability of GARCH models to fit intraday data. This study also applies an artificial nesting procedure to evaluate the out-of-sample volatility forecasts. Implied volatility has very limited (if any) predictive power when evaluated in isolation, whereas the SVL model with implied volatility embedded provides incremental information relative to competing model forecasts.

Conceptualising best practices in cooperative industry-based learning in information systems and information technology: an inter-institutional investigation in Australian higher education

The professional fields of information systems and information technology are drivers and enablers of the global economy. Moreover, their theoretical scope and practices are global in focus. University graduates need to develop a range of leadership, conceptual and technical capacities to work effectively in, and contribute to, the shaping of companies, business models and systems which operate in globalised settings. This paper reports a study of the operation of industry-based learning (IBL) at three Australian universities, which employ different models and approaches, as part of a series of investigations of the needs, circumstances and perspectives of various stakeholders (program coordinator, faculty teaching staff, the students, industry mentors, and the professional body which has supported the most recent stage of this study). The focus of this paper is a discussion of salient pragmatic considerations as we attempt to conceptualise what constitutes best practice in offering industry-based learning for higher education students in the disciplines of information systems and information technology.

Roles, responsibilities and futures of chief information officers (CIOs) in the public sector

This paper discusses in its entirety, the CIO (Chief Information Officer) position in the public sector. Governments are increasingly adopting Information Technology for internal processes and for the delivery of service to their citizens. It includes literature on CIO roles and responsibilities which is heavily based on the private sector, due to the recognition of the role in this sector over twenty years. The position is just evolving in the public sector, and due to the context in which the public sector operates there are some similarities as well as vast differences. An evaluation of existing CIO models and theories form the basis for research on CIO roles, responsibilities and future within the public sector.

Evaluation of the robustness of predictive yield models based on catchment characteristics using GIS for reservoir fisheries in Sri Lanka

Land-use patterns in the catchment areas of Sri Lankan reservoirs, which were quantified using Geographical Information Systems (GIS), were used to develop quantitative models for yield prediction. The validity of these models was evaluated through the application to five reservoirs that were not used in the development of the models, and by comparing with the actual fish yield data of these reservoirs collected by an independent body. The robustness of the predictive models developed was tested by principal component analysis (PCA) on limnological characteristics, land-use patterns of the catchments and fish yields. The predicted fish yields in five Sri Lankan reservoirs, using the empirical models based on the ratios of forest cover and/or shrub cover to reservoir capacity or reservoir area were in close agreement with the observed fish yields. The scores of PCA ordination of productivity-related limnological parameters and those of land-use patterns were linearly related to fish yields. The relationship between the PCA scores of limnological characteristics and land-use types had the appropriate algebraic form, which substantiates the influence of the limnological factors and land-use types on reservoir fish yields. It is suggested that the relatively high predictive power of the models developed on the basis of GIS methodologies can be used for more accurate assessment of reservoir fisheries. The study supports the importance and the need for an integrated management strategy for the whole watershed to enhance fish yields.

On-line tool condition monitoring and control system in forging processes

Cold bulk metal forming has made large-scale production of small complex solid parts economically feasible. Tooling used in metal forming poses many uncertainties in the preliminary cost estimation and production process and continual tool replacement and maintenance dramatically reduces productivity and raises manufacturing cost. In order to tackle this, an on-line tool condition monitoring system using artificial neural network (ANN) to integrate information from multiple sensors for forging process has been developed. Together with the force, acoustic emission signals and process conditions, information developed from theoretical models is integrated into the ANN tool monitoring system to predict tool life and provide the maintenance schedule.

Decision-making models used by `graduate nurses` managing patients` medications

Background. Nurses in a graduate programme in Australia are those who are in the first year of clinical practice following completion of a 3-year undergraduate nursing degree. When working in an acute care setting, they need to make complex and ever-changing decisions about patients' medications in a clinical environment affected by multifaceted, contextual issues. It is important that comprehensive information about graduate nurses' decision-making processes and the contextual influences affecting these processes are obtained in order to prepare them to meet patients' needs.
Aim. The purpose of this paper is to report a study that sought to answer the following questions: What are the barriers that impede graduate nurses' clinical judgement in their medication management activities? How do contextual issues impact on graduate nurses' medication management activities? The decision-making models considered were: hypothetico-deductive reasoning, pattern recognition and intuition.
Methods. Twelve graduate nurses who were involved in direct patient care in medical and surgical wards of a metropolitan teaching hospital located in Melbourne, Australia participated in the study. Participant observations were conducted with the graduate nurses during a 2-hour period during the times when medications were being administered to patients. Graduate nurses were also interviewed to elicit further information about how they made decisions about patients' medications.
Results. The most common model used was hypothetico-deductive reasoning, followed by pattern recognition and then intuition. The study showed that graduate nurses had a good understanding of how physical assessment affected whether medications should be administered or not. When negotiating treatment options, graduate nurses readily consulted with more experienced nursing colleagues and doctors.
Study limitations. It is possible that graduate nurses demonstrated a raised awareness of managing patients' medications as a consequence of being observed.
Conclusions. The complexity of the clinical practice setting means that graduate nurses need to adapt rapidly to make sound and appropriate decisions about patient care.

Constructing artificial images of facial expressions

This paper presents a method for construction of artificial images of facial expressions. The proposed fractal-based synthesis procedure called pixel-based correspondence works on 2D images and does not require any depth information. This method can generate artificial images of an object when only a single image is given. Using the proposed method, effective example-based facial analysis systems can be trained and utilised in various applications.

Predictive distribution models and their application in wildlife conservation

Wildlife managers are often faced with the difficult task of determining the distribution of species, and their preferred habitats, at large spatial scales. This task is even more challenging when the species of concern is in low abundance and/or the terrain is largely inaccessible. Spatially explicit distribution models, derived from multivariate statistical analyses and implemented in a geographic information system (GIS), can be used to predict the distributions of species and their habitats, thus making them a useful conservation tool. We present two such models: one for a dasyurid, the Swamp Antechinus (Antechinus minimus), and the other for a ground-dwelling bird, the Rufous Bristlebird (Dasyornis broadbenti), both of which are rare species occurring in the coastal heathlands of south-western Victoria. Models were generated using generalized linear modelling (GLM) techniques with species presence or absence as the independent variable and a series of landscape variables derived from GIS layers and high-resolution imagery as the predictors. The most parsimonious model, based on the Akaike Information Criterion, for each species then was extrapolated spatially in a GIS. Probability of species presence was used as an index of habitat suitability. Because habitat fragmentation is thought to be one of the major threats to these species, an assessment of the spatial distribution of suitable habitat across the landscape is vital in prescribing management actions to prevent further habitat fragmentation.

Optimal lag-length choice in stable and unstable VAR models under situations of homoscedasticity and ARCH

The performance of different information criteria - namely Akaike, corrected Akaike (AICC), Schwarz-Bayesian (SBC), and Hannan-Quinn - is investigated so as to choose the optimal lag length in stable and unstable vector autoregressive (VAR) models both when autoregressive conditional heteroscedasticity (ARCH) is present and when it is not. The investigation covers both large and small sample sizes. The Monte Carlo simulation results show that SBC has relatively better performance in lag-choice accuracy in many situations. It is also generally the least sensitive to ARCH regardless of stability or instability of the VAR model, especially in large sample sizes. These appealing properties of SBC make it the optimal criterion for choosing lag length in many situations, especially in the case of financial data, which are usually characterized by occasional periods of high volatility. SBC also has the best forecasting abilities in the majority of situations in which we vary sample size, stability, variance structure (ARCH or not), and forecast horizon (one period or five). frequently, AICC also has good lag-choosing and forecasting properties. However, when ARCH is present, the five-period forecast performance of all criteria in all situations worsens.

Information leakage and informed trading around unscheduled earnings announcements

While there has been much judicial discussion regarding the competency of Australia's continuous disclosure regime with reference to contemporaneous international standards, there has to date been limited empirical analysis of the Australian system's effectiveness in preventing selective disclosure and information leakage. This paper presents an empirical study of information content and trading behaviour around unscheduled earnings announcements - comprising of profit upgrades, profit warnings and neutral trading statements - made by ASX-listed companies during 2004. The contention is that informed trading impacts on the stock returns and trading volumes of listed entities, and hence abnormal returns or trading volumes observed prior to an announcement provide evidence of information leakage. The paper models a range of factors that potentially influence firm disclosure practices and contribute to the level information asymmetry in the market during the pre- announcement period. Previous research has investigated the influence of firm size and information content in contributing to information leakage. This study further considers the variables of firm growth, capital structure and industry group.

Expanding the realm of best practices in cooperative industry-based learning in information systems and information technology : an inter-institutional investigation in Australian higher education

The professional fields of information systems and information technology are drivers and enablers of the global economy. Moreover, their theoretical scope and practices are global in focus. University graduates need to develop a range of leadership, conceptual and technical capacities to work effectively in, and contribute to, the shaping of companies, business models and systems which operate in globalised settings. This paper reports a study of the operation of industry‐based learning (IBL) at three Australian universities, which employ different models and approaches, as part of a series of investigations of the needs, circumstances and perspectives of various stakeholders (program coordinator, faculty teaching staff, the students, industry mentors, and the professional body). The focus of this paper is a discussion of salient pragmatic considerations in an attempt to conceptualize what constitutes best practice in offering industry‐based learning for higher education students in the disciplines of information systems and information technology (Asia‐Pacific Journal of Cooperative Education, 9(2), 73‐80).

Dispersion of information or market behaviour : general public trading in S & P 500 index futures

This paper considers 15 minute records of trading volume and traded prices coinciding with the reporting intervals required by the Commodity Futures Trading Commission. Records are extracted from trade records for two way trade between market makers (CTI1) and the general public (CTI4) from January 1994 to June 2004. Futures price records are matched with S&P500 cash index price records. Simultaneous volatility models are specified and estimated to test trading volume to futures volatility lead/lag effects and also futures volatility to cash index volatility lead/lag effects. There is evidence that existing theoretical models of the general public trading behaviour do not explain such behaviour in these very actively traded markets. These effects can depend more on market conditions than what is suggested in theoretical models.

An artificial neural network-based hairiness prediction model for worsted wool yarns

This study evaluated the performance of multilayer perceptron (MLP) and multivariate linear regression (MLR) models for predicting the hairiness of worsted-spun wool yarns from various top, yarn and processing parameters. The results indicated that the MLP model predicted yarn hairiness more accurately than the MLR model, and should have wide mill specific applications. On the basis of sensitivity analysis, the factors that affected yarn hairiness significantly included yarn twist, ring size, average fiber length (hauteur), fiber diameter and yarn count, with twist having the greatest impact on yarn hairiness.