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While aerial shark spotting has been a standard practice for beach safety for decades, new technologies offer enhanced opportunities, ranging from drones/unmanned aerial vehicles (UAVs) that provide new viewing capabilities, to new apps that provide beachgoers with up-to-date risk analysis before entering the water. This report describes the Sharkeye platform, a first-of-its-kind project to demonstrate personal shark alerting for beachgoers in the water and on land, leveraging innovative UAV image collection, cloud-hosted machine learning detection algorithms, and reporting via smart wearables. To execute, our team developed a novel detection algorithm trained via machine learning based on aerial footage of real sharks and rays collected at local beaches, hosted and deployed the algorithm in the cloud, and integrated push alerts to beachgoers in the water via a shark app to run on smartwatches. The project was successfully trialed in the field in Kiama, Australia, with over 350 detection events recorded, followed by the alerting of multiple smartwatches simultaneously both on land and in the water, and with analysis capable of detecting shark analogues, rays, and surfers in average beach conditions, and all based on ~1 h of training data in total. Additional demonstrations showed potential of the system to enable lifeguard-swimmer communication, and the ability to create a network on demand to enable the platform. Our system was developed to provide swimmers and surfers with immediate information via smart apps, empowering lifeguards/lifesavers and beachgoers to prevent unwanted encounters with wildlife before it happens.

Agent based modelling has been widely accepted as a promising tool for urban planning purposes thanks to its capability to provide sophisticated insights into the social behaviours and the interdependencies that characterise urban systems. In this paper, we report on an agent based model, called TransMob, which explicitly simulates the mutual dynamics between demographic evolution, transport demands, housing needs and the eventual change in the average satisfaction of the residents of an urban area. The ability to reproduce such dynamics is a unique feature that has not been found in many of the like agent based models in the literature. TransMob, is constituted by six major modules: synthetic population, perceived liveability, travel diary assignment, traffic micro-simulator, residential location choice, and travel mode choice. TransMob is used to simulate the dynamics of a metropolitan area in South East of Sydney, Australia, in 2006 and 2011, with demographic evolution. The results are favourably compared against survey data for the area in 2011, therefore validating the capability of TransMob to reproduce the observed complexity of an urban area. We also report on the application of TransMob to simulate various hypothetical scenarios of urban planning policies. We conclude with discussions on current limitations of TransMob, which serve as suggestions for future developments.

In many developed countries including Australia, infrastructure services at local and state levels are being provided by an increasing number of disjointed public and private agencies. There is an urgent need for an integrated view on the provision and use of these services for better governance and productivity. Developing an integrated view is challenging due to the dispersion of relevant data sets and the underlying complexity of increasingly interconnected infrastructure networks. Using a case study in New South Wales (Australia), we demonstrate how tools and processes in Geospatial Business Intelligence (Geo-BI) can be harnessed using a collective design approach to develop an integrated solution; the SMART Infrastructure Dashboard (SID). While providing a much needed planning and policy support tool for the local governance of infrastructure services, SID pushes the boundary of Geo-BI beyond its traditional use.

We present a method for characterizing text based on a statistical analysis of word recurrence interval. This method can be used for extracting keywords from text, and also for comparing texts by an unknown author against a set of known authors. We also use these methods to comment on the controversial question of who wrote the letter to the Hebrews in the New Testament.

Over the past several decades, neural networks have evolved into powerful computation systems, which are able to learn complex nonlinear input-output relationship from data. However, the structure optimization problem of neural network is a big challenge for processing huge-volumed, diversified and uncertain data. This paper focuses on this problem and introduces a network pruning algorithm based on sparse representation, termed SRP. The proposed approach starts with a large network, then selects important hidden neurons from the original structure using a forward selection criterion that minimizes the residual output error. Furthermore, the presented algorithm has no constraints on the network type. The efficiency of the proposed approach is evaluated based on several benchmark data sets. We also evaluate the performance of the proposed algorithm on a real-world application of individual travel mode choice. The experimental results have shown that SRP performs favorably compared to alternative approaches.

Many simulations of networks in computational neuroscience assume completely homogenous random networks of the Erdös–Rényi type, or regular networks, despite it being recognized for some time that anatomical brain networks are more complex in their connectivity and can, for example, exhibit the “scale-free” and “small-world” properties. We review the most well known algorithms for constructing networks with given non-homogeneous statistical properties and provide simple pseudo-code for reproducing such networks in software simulations. We also review some useful mathematical results and approximations associated with the statistics that describe these network models, including degree distribution, average path length, and clustering coefficient. We demonstrate how such results can be used as partial verification and validation of implementations. Finally, we discuss a sometimes overlooked modeling choice that can be crucially important for the properties of simulated networks: that of network directedness. The most well known network algorithms produce undirected networks, and we emphasize this point by highlighting how simple adaptations can instead produce directed networks. Refereed/Peer-reviewed

Complex adaptive systems typically contain multiple, heterogeneous agents, with non-trivial interactions. They tend to produce emergent (larger-scale) phenomena. Agent-based modelling allows one to readily capture the behaviour of a group of heterogeneous agents (such as people, animals, et cetera), with diverse behaviour and important interactions, so it is a natural t to modelling complex systems. Many complex systems (and agent-based models thereof) can be thought of as containing networks, either explicitly or implicitly. Therefore for complex systems research it is important to have a good understanding of network analysis techniques. This chapter is aimed at beginners to complex systems modelling and network analysis, using NetLogo (Section 1.1) and Pajek (Section 1.2) respectively. It is also aimed at more advanced complex systems modellers who want an introduction to these platforms.

The trade-off between pleiotropy and redundancy in telecommunications networks is analyzed in this paper. They are optimized to reduce installation costs and propagation delays. Pleiotropy of a server in a telecommunications network is defined as the number of clients and servers that it can service whilst redundancy is described as the number of servers servicing a client. Telecommunications networks containing many servers with large pleiotropy are cost-effective but vulnerable to network failures and attacks. Conversely, those networks containing many servers with high redundancy are reliable but costly. Several key issues regarding the choice of cost functions and techniques in evolutionary computation (such as the modeling of Darwinian evolution, and mutualism and commensalism) will be discussed, and a future research agenda is outlined. Experimental results indicate that the pleiotropy of servers in the optimum network does improve, whilst the redundancy of clients do not vary significantly, as expected, with evolving networks. This is due to the controlled evolution of networks that is modeled by the steady-state genetic algorithm; changes in telecommunications networks that occur drastically over a very short period of time are rare.

Authorship attribution has a range of applications in a growing number of fields such as forensic evidence, plagiarism detection, email filtering, and web information management. In this study, three attribution techniques are extended, tested on a corpus of English texts, and applied to a book in the New Testament of disputed authorship. The word recurrence interval based method compares standard deviations of the number of words between successive occurrences of a keyword both graphically and with chi-squared tests. The trigram Markov method compares the probabilities of the occurrence of words conditional on the preceding two words to determine the similarity between texts. The third method extracts stylometric measures such as the frequency of occurrence of function words and from these constructs text classification models using multiple discriminant analysis. The effectiveness of these techniques is compared. The accuracy of the results obtained by some of these extended methods is higher than many of the current state of the art approaches. Statistical evidence is presented about the authorship of the selected book from the New Testament.

We explore a variety of network models describing transmission across a network. In particular we focus on transmission across composite networks, or “networks of networks”, in which a finite number of networked objects are then themselves connected together into a network. In a disease context we introduce two interrelated viruses to hosts on a network, to model the infection of hosts in a classroom situation, with high rates of infection within a classroom, and lower rates of infection between classrooms. The hosts can be either susceptible to infection, infected, or recovering from each virus. During the infection stage and recovery stage there is some level of cross-immunity to related viruses. We explore the effects of immunizing sections of the community on transmission through social networks. In a stock market context we introduce memes, or virus-like ideas into a virtual agent-based model of a stock exchange. By varying the parameters of the individual traders and the way in which they are connected we are able to show emergent behaviour, including boom and bust cycles.

This paper explores the authorship of the Letter to the Hebrews using a number of different measures of relationship between different texts of the New Testament. The methods used in the study include file zipping and compression techniques, prediction by the partial matching technique and the word recurrence interval technique. The long term motivation is that the techniques employed in this study may find applicability in future generation web search engines, email authorship identification, detection of plagiarism and terrorist email traffic filtration.

This paper reviews applications of signal processing techniques to a number of areas in the field of genetics. We focus on techniques for analyzing DNA sequences, and briefly discuss applications of signal processing to DNA sequencing, and other related areas in genetics that can provide biologically significant information to assist with sequence analysis.

This paper explores fluctuations and noise in various facets of cancer development. The three areas of particular focus are the stochastic progression of cells to cancer, fluctuations of the tumor size during treatment, and noise in cancer cell signalling. We explore the stochastic dynamics of tumor growth and response to treatment using a Markov model, and fluctutions in tumor size in response to treatment using partial differential equations. We also explore noise within gene networks in cancer cells, and noise in inter-cell signalling., 11 pages, 6 figures

This paper explores the use of genetic algorithms for the design of networks, where the demands on the network fluctuate in time. For varying network constraints, we find the best network using the standard genetic algorithm operators such as inversion, mutation and crossover. We also examine how the choice of genetic algorithm operators affects the quality of the best network found. Such networks typically contain redundancy in servers, where several servers perform the same task and pleiotropy, where servers perform multiple tasks. We explore this trade-off between pleiotropy versus redundancy on the cost versus reliability as a measure of the quality of the network., 9 pages, 5 figures

Evolutionary computation algorithms are increasingly being used to solve optimization problems as they have many advantages over traditional optimization algorithms. In this paper we use evolutionary computation to study the trade-off between pleiotropy and redundancy in a client-server based network. Pleiotropy is a term used to describe components that perform multiple tasks, while redundancy refers to multiple components performing one same task. Pleiotropy reduces cost but lacks robustness, while redundancy increases network reliability but is more costly, as together, pleiotropy and redundancy build flexibility and robustness into systems. Therefore it is desirable to have a network that contains a balance between pleiotropy and redundancy. We explore how factors such as link failure probability, repair rates, and the size of the network influence the design choices that we explore using genetic algorithms., Comment: 10 pages, 6 figures

Noise is present in the wide variety of signals obtained from sleep patients. This noise comes from a number of sources, from presence of extraneous signals to adjustments in signal amplification and shot noise in the circuits used for data collection. The noise needs to be removed in order to maximize the information gained about the patient using both manual and automatic analysis of the signals. Here we evaluate a number of new techniques for removal of that noise, and the associated problem of separating the original signal sources., Comment: 9 pages, 3 figures

This paper examines two methods for finding whether long-range correlations exist in DNA: a fractal measure and a mutual information technique. We evaluate the performance and implications of these methods in detail. In particular we explore their use comparing DNA sequences from a variety of sources. Using software for performing in silico mutations, we also consider evolutionary events leading to long range correlations and analyse these correlations using the techniques presented. Comparisons are made between these virtual sequences, randomly generated sequences, and real sequences. We also explore correlations in chromosomes from different species., Comment: 8 pages, 3 figures

A number of signal processing and statistical methods can be used in analyzing either pieces of text or DNA sequences. These techniques can be used in a number of ways, such as determining authorship of documents, finding genes in DNA, and determining phylogenetic and linguistic trees. Signal processing methods such as spectrograms provide useful new tools in the area of genomic information science. In particular, fractal analysis of DNA ``signals'' has provided a new way of classifying organisms., 10 pages, 5 figures