Network modelling for Townsville distribution system

Drinking water quality guidelines are becoming increasingly stringent. To comply with these guidelines and to manage water quality in a distribution system, improved understanding of the movement and fate of drinking water constituents within the system is required. This study illustrates the construction and calibration of an electronic model of the Townsville drinking water distribution system. Being in the tropics, the temperature of the water in the distribution system changes little throughout the year (usually between 20 and 25°C); also, water is supplied to the system from two sources, the location of the blending of these waters is varies with demand.

Spatial modeling of electrical conductivity with neural network

Many policy decisions for agricultural management in the coastal region closely depend on the extent of intrusion of sea water. In this study, Artificial Neural Network (ANN) is used to model the spatial variation of Electrical Conductivity to determine the extent of sea water intrusion in the coastal area of Brisbane, Australia. Quarterly EC data obtained from the observation (monitoring) wells located along the coast is used for training ANN architecture. The study demonstrates that ANN is able to model the spatial variation of EC with very good accuracy (even with very less training records) when some spatial information is used as one of the inputs in the network training. The results considerable improvement when compared with the network trained without the distance information.

The microcosmic model of worm propagation

Each year, large amounts of money and labor are spent on patching the vulnerabilities in operating systems and various popular software to prevent exploitation by worms. Modeling the propagation process can help us to devise effective strategies against those worms' spreading. This paper presents a microcosmic analysis of worm propagation procedures. Our proposed model is different from traditional methods and examines deep inside the propagation procedure among nodes in the network by concentrating on the propagation probability and time delay described by a complex matrix. Moreover, since the analysis gives a microcosmic insight into a worm's propagation, the proposed model can avoid errors that are usually concealed in the traditional macroscopic analytical models. The objectives of this paper are to address three practical aspects of preventing worm propagation: (i) where do we patch? (ii) how many nodes do we need to patch? (iii) when do we patch? We implement a series of experiments to evaluate the effects of each major component in our microcosmic model. Based on the results drawn from the experiments, for high-risk vulnerabilities, it is critical that networks reduce the number of vulnerable nodes to below 80%. We believe our microcosmic model can benefit the security industry by allowing them to save significant money in the deployment of their security patching schemes.

Implementation of the nurse practitioner role within a Victorian healthcare network : an organisational perspective

Objective
This paper presents a discussion of the development of a framework to implement and sustain the nurse practitioner (NP) role within one health service designed to strengthen the capacity of the health system and which could be readily transferable to other health services.
Setting
Eastern Health (EH) is a multi‑campus tertiary health care organisation servicing a population of approximately 800,000 people in the east and outer eastern suburbs of Melbourne, Australia. EH is committed to advancing the nursing profession and exploring innovative, research based models of practice that are responsive to the needs of the community it serves.
Primary argument
The Framework documents the processes of providing a new career pathway for advanced practice nurses that incorporates education and training, and utilises current evidenced‑based practice guidelines to define and promote the scope of practice.
Conclusion
Strong organisational support to facilitate interdisciplinary and multidisciplinary learning opportunities assists integration of the NP role into the healthcare team. Role clarity will assist interprofessional teams to understand and value the role NPs provide.

Hybrid neural-evolutionary model for electricity price forecasting

Evolving artificial neural networks has attracted much attention among researchers recently, especially in the fields where plenty of data exist but explanatory theories and models are lacking or based upon too many simplifying assumptions. Financial time series forecasting is one of them. A hybrid model is used to forecast the hourly electricity price from the California Power Exchange. A collaborative approach is adopted to combine ANN and evolutionary algorithm. The main contributions of this thesis include: Investigated the effect of changing values of several important parameters on the performance of the model, and selected the best combination of these parameters; good forecasting results have been obtained with the implemented hybrid model when the best combination of parameters is used. The lowest MAPE through a single run is 5. 28134%. And the lowest averaged MAPE over 10 runs is 6.088%, over 30 runs is 6.786%; through the investigation of the parameter period, it is found that by including future values of the homogenous moments of the instant being forecasted into the input vector, forecasting accuracy is greatly enhanced. A comparison of results with other works reported in the literature shows that the proposed model gives superior performance on the same data set.

The probability model of peer-to-peer botnet propagation

Active Peer-to-Peer worms are great threat to the network security since they can propagate in automated ways and flood the Internet within a very short duration. Modeling a propagation process can help us to devise effective strategies against a worm's spread. This paper presents a study on modeling a worm's propagation probability in a P2P overlay network and proposes an optimized patch strategy for defenders. Firstly, we present a probability matrix model to construct the propagation of P2P worms. Our model involves three indispensible aspects for propagation: infected state, vulnerability distribution and patch strategy. Based on a fully connected graph, our comprehensive model is highly suited for real world cases like Code Red II. Finally, by inspecting the propagation procedure, we propose four basic tactics for defense of P2P botnets. The rationale is exposed by our simulated experiments and the results show these tactics are of effective and have considerable worth in being applied in real-world networks.

Policy recognition in the Abstract Hidden Markov Model

In this paper, we present a method for recognising an agent's behaviour in dynamic, noisy, uncertain domains, and across multiple levels of abstraction. We term this problem on-line plan recognition under uncertainty and view it generally as probabilistic inference on the stochastic process representing the execution of the agent's plan. Our contributions in this paper are twofold. In terms of probabilistic inference, we introduce the Abstract Hidden Markov Model (AHMM), a novel type of stochastic processes, provide its dynamic Bayesian network (DBN) structure and analyse the properties of this network. We then describe an application of the Rao-Blackwellised Particle Filter to the AHMM which allows us to construct an efficient, hybrid inference method for this model. In terms of plan recognition, we propose a novel plan recognition framework based on the AHMM as the plan execution model. The Rao-Blackwellised hybrid inference for AHMM can take advantage of the independence properties inherent in a model of plan execution, leading to an algorithm for online probabilistic plan recognition that scales well with the number of levels in the plan hierarchy. This illustrates that while stochastic models for plan execution can be complex, they exhibit special structures which, if exploited, can lead to efficient plan recognition algorithms. We demonstrate the usefulness of the AHMM framework via a behaviour recognition system in a complex spatial environment using distributed video surveillance data.

Tracking and surveillance in wide-area spatial environments using the abstract hidden markov model.

In this paper, we consider the problem of tracking an object and predicting the object's future trajectory in a wide-area environment, with complex spatial layout and the use of multiple sensors/cameras. To solve this problem, there is a need for representing the dynamic and noisy data in the tracking tasks, and dealing with them at different levels of detail. We employ the Abstract Hidden Markov Models (AHMM), an extension of the well-known Hidden Markov Model (HMM) and a special type of Dynamic Probabilistic Network (DPN), as our underlying representation framework. The AHMM allows us to explicitly encode the hierarchy of connected spatial locations, making it scalable to the size of the environment being modeled. We describe an application for tracking human movement in an office-like spatial layout where the AHMM is used to track and predict the evolution of object trajectories at different levels of detail.

Distributed audio network for speech enhancement in challenging noise backgrounds

This paper presents a new approach to enhance speech based on a distributed microphone network. Each microphone is used to simultaneously classify the input into either one of the noise types or as speech. For enhancing the speech signal a modified spectral subtraction approach is used that utilise the sound information of the entire network to update the noise model even during speech. This improves the reduction of the ambient noise, especially for non-stationary noise types such as street or beach noise. Experiments demonstrate the effectiveness of the proposed system.

Human behavior recognition with generic exponential family duration modeling in the hidden semi-Markov model

The ability to learn and recognize human activities of daily living (ADLs) is important in building pervasive and smart environments. In this paper, we tackle this problem using the hidden semi-Markov model. We discuss the state-of-the-art duration modeling choices and then address a large class of exponential family distributions to model state durations. Inference and learning are efficiently addressed by providing a graphical representation for the model in terms of a dynamic Bayesian network (DBN). We investigate both discrete and continuous distributions from the exponential family (Poisson and Inverse Gaussian respectively) for the problem of learning and recognizing ADLs. A full comparison between the exponential family duration models and other existing models including the traditional multinomial and the new Coxian are also presented. Our work thus completes a thorough investigation into the aspect of duration modeling and its application to human activities recognition in a real-world smart home surveillance scenario.

Investigation of a prefetch model for low bandwidth networks

We investigate speculative prefetching under a model in which prefetching is neither aborted nor preempted by demand fetch but instead gets equal priority in network bandwidth utilisation. We argue that the non-abortive assumption is appropriate for wireless networks where bandwidth is low and latency is high, and the non-preemptive assumption is appropriate for Internet where prioritization is not always possible. This paper assumes the existence of an access model to provide some knowledge about future accesses and investigates analytically the performance of a prefetcher that utilises this knowledge. In mobile computing, because resources are severely constrained, performance prediction is as important as access prediction. For uniform retrieval time, we derive a theoretical limit of improvement in access time due to prefetching. This leads to the formulation of an optimal algorithrn for prefetching one access ahead. For non-uniform retrieval time, two different types of prefetching of multiple documents, namely mainline and branch prefetch, are evaluated against prefetch of single document. In mainline prefetch, the most probable sequence of future accesses is prefetched. In branch prefetch, a set of different alternatives for future accesses is prefetched. Under some conditions, mainline prefetch may give slight improvement in user-perceived access time over single prefetch with nominal extra retrieval cost, where retrieval cost is defined as the expected network time wasted in non-useful prefetch. Branch prefetch performs better than mainline prefetch but incurs more retrieval cost.

Polymorphic malware detection using Hierarchical Hidden Markov Model

Binary signatures have been widely used to detect malicious software on the current Internet. However, this approach is unable to achieve the accurate identification of polymorphic malware variants, which can be easily generated by the malware authors using code generation engines. Code generation engines randomly produce varying code sequences but perform the same desired malicious functions. Previous research used flow graph and signature tree to identify polymorphic malware families. The key difficulty of previous research is the generation of precisely defined state machine models from polymorphic variants. This paper proposes a novel approach, using Hierarchical Hidden Markov Model (HHMM), to provide accurate inductive inference of the malware family. This model can capture the features of self-similar and hierarchical structure of polymorphic malware family signature sequences. To demonstrate the effectiveness and efficiency of this approach, we evaluate it with real malware samples. Using more than 15,000 real malware, we find our approach can achieve high true positives, low false positives, and low computational cost.

Application of a brain inspired model for profiling multi-view crime patterns

With the massive amount of crime data generated daily, this has put law enforcement under intensive stress. This means that law enforcement has to compete against the time to solve crime. In addition, the focus of crime investigation has been expanded from the ability to catch the criminals towards the ability to act before a crime happens (i.e pre-crime). Given such situation, creation of crime profiles is very important to law enforcement, especially in understanding the behaviours of criminals and identifying the characteristics of similar crimes. In fact, crime profiles could be used to solve similar crimes and thus pre-crime action could be conducted. In this paper, a brain inspired conceptual model is proposed and a structurally adaptive neural network is deployed for its implementation. Subsequently, the proposed model is applied for the identification and presentation of multi-view crime patterns. Such multi-view crime patterns could be useful for the construction of crime profiles. Moreover, the suitability of the proposed model in crime profiling is discussed and demonstrated through some experimental results.

A neural network-based multi-agent classifier system with a Bayesian formalism for trust measurement

In this paper, a neural network (NN)-based multi-agent classifier system (MACS) utilising the trust-negotiation-communication (TNC) reasoning model is proposed. A novel trust measurement method, based on the combination of Bayesian belief functions, is incorporated into the TNC model. The Fuzzy Min-Max (FMM) NN is used as learning agents in the MACS, and useful modifications of FMM are proposed so that it can be adopted for trust measurement. Besides, an auctioning procedure, based on the sealed bid method, is applied for the negotiation phase of the TNC model. Two benchmark data sets are used to evaluate the effectiveness of the proposed MACS. The results obtained compare favourably with those from a number of machine learning methods. The applicability of the proposed MACS to two industrial sensor data fusion and classification tasks is also demonstrated, with the implications analysed and discussed.