Outcomes of the Chemistry Discipline Network Mapping Exercises: Are the Threshold Learning Outcomes met?

The Chemistry Discipline Network has recently completed two distinct mapping exercises. The first is a snapshot of chemistry taught at 12 institutions around Australia in 2011. There were many similarities but also important differences in the content taught and assessed at different institutions. There were also significant differences in delivery, particularly laboratory contact hours, as well as forms and weightings of assessment. The second mapping exercise mapped the chemistry degrees at three institutions to the Threshold Learning Outcomes for chemistry. Importantly, some of the TLOs were addressed by multiple units at all institutions, while others were not met, or were met at an introductory level only. The exercise also exposed some challenges in using the TLOs as currently written.

Component modeling for SCADA network mapping

Supervisory Control and Data Acquisition systems (SCADA) are widely used to control critical infrastructure automatically. Capturing and analyzing packet-level traffic flowing through such a network is an essential requirement for problems such as legacy network mapping and fault detection. Within the framework of captured network traffic, we present a simple modeling technique, which supports the mapping of the SCADA network topology via traffic monitoring. By characterizing atomic network components in terms of their input-output topology and the relationship between their data traffic logs, we show that these modeling primitives have good compositional behaviour, which allows complex networks to be modeled. Finally, the predictions generated by our model are found to be in good agreement with experimentally obtained traffic.

NETEMBED: A Network Resource Mapping Service for Distributed Applications

Emerging configurable infrastructures such as large-scale overlays and grids, distributed testbeds, and sensor networks comprise diverse sets of available computing resources (e.g., CPU and OS capabilities and memory constraints) and network conditions (e.g., link delay, bandwidth, loss rate, and jitter) whose characteristics are both complex and time-varying. At the same time, distributed applications to be deployed on these infrastructures exhibit increasingly complex constraints and requirements on resources they wish to utilize. Examples include selecting nodes and links to schedule an overlay multicast file transfer across the Grid, or embedding a network experiment with specific resource constraints in a distributed testbed such as PlanetLab. Thus, a common problem facing the efficient deployment of distributed applications on these infrastructures is that of "mapping" application-level requirements onto the network in such a manner that the requirements of the application are realized, assuming that the underlying characteristics of the network are known. We refer to this problem as the network embedding problem. In this paper, we propose a new approach to tackle this combinatorially-hard problem. Thanks to a number of heuristics, our approach greatly improves performance and scalability over previously existing techniques. It does so by pruning large portions of the search space without overlooking any valid embedding. We present a construction that allows a compact representation of candidate embeddings, which is maintained by carefully controlling the order via which candidate mappings are inserted and invalid mappings are removed. We present an implementation of our proposed technique, which we call NETEMBED – a service that identify feasible mappings of a virtual network configuration (the query network) to an existing real infrastructure or testbed (the hosting network). We present results of extensive performance evaluation experiments of NETEMBED using several combinations of real and synthetic network topologies. Our results show that our NETEMBED service is quite effective in identifying one (or all) possible embeddings for quite sizable queries and hosting networks – much larger than what any of the existing techniques or services are able to handle.

Mapping the Australian political blogosphere

Network crawling and visualisation tools and other datamining systems are now advanced enough to provide significant new impulses to the study of cultural activity on the Web. A growing range of studies focus on communicative processes in the blogosphere – including for example Adamic & Glance’s 2005 map of political allegiances during the 2004 U.S. presidential election and Kelly & Etling’s 2008 study of blogging practices in Iran. There remain a number of significant shortcomings in the application of such tools and methodologies to the study of blogging; these relate both to how the content of blogs is analysed, and to how the network maps resulting from such studies are understood. Our project highlights and addresses such shortcomings.

Mapping the Australian networked public sphere

This article reports on a research program that has developed new methodologies for mapping the Australian blogosphere and tracking how information is disseminated across it. The authors improve on conventional web crawling methodologies in a number of significant ways: First, the authors track blogging activity as it occurs, by scraping new blog posts when such posts are announced through Really Simple Syndication (RSS) feeds. Second, the authors use custom-made tools that distinguish between the different types of content and thus allow us to analyze only the salient discursive content provided by bloggers. Finally, the authors are able to examine these better quality data using both link network mapping and textual analysis tools, to produce both cumulative longer term maps of interlinkages and themes, and specific shorter term snapshots of current activity that indicate current clusters of heavy interlinkage and highlight their key themes. In this article, the authors discuss findings from a yearlong observation of the Australian political blogosphere, suggesting that Australian political bloggers consistently address current affairs, but interpret them differently from mainstream news outlets. The article also discusses the next stage of the project, which extends this approach to an examination of other social networks used by Australians, including Twitter, YouTube, and Flickr. This adaptation of our methodology moves away from narrow models of political communication, and toward an investigation of everyday and popular communication, providing a more inclusive and detailed picture of the Australian networked public sphere.

Mapping intermedia news flows : topical discussions in the Australian and French political blogospheres

The growth of technologies and tools branded as =new media‘ or =Web 2.0‘ has sparked much discussion about the internet and its place in all facets of social life. Such debate includes the potential for blogs and citizen journalism projects to replace or alter journalism and mainstream media practices. However, while the journalism-blog dynamic has attracted the most attention, the actual work of political bloggers, the roles they play in the mediasphere and the resources they use, has been comparatively ignored. This project will look at political blogging in Australia and France - sites commenting on or promoting political events and ideas, and run by citizens, politicians, and journalists alike. In doing so, the structure of networks formed by bloggers and the nature of communication within political blogospheres will be examined. Previous studies of political blogging around the world have focussed on individual nations, finding that in some cases the networks are divided between different political ideologies. By comparing two countries with different political representation (two-party dominated system vs. a wider political spectrum), this study will determine the structure of these political blogospheres, and correlate these structures with the political environment in which they are situated. The thesis adapts concepts from communication and media theories, including framing, agenda setting, and opinion leaders, to examine the work of political bloggers and their place within the mediasphere. As well as developing a hybrid theoretical base for research into blogs and other online communication, the project outlines new methodologies for carrying out studies of online activity through the analysis of several topical networks within the wider activity collected for this project. The project draws on hyperlink and textual data collected from a sample of Australian and French blogs between January and August 2009. From this data, the thesis provides an overview of =everyday‘ political blogging, showing posting patterns over several months of activity, away from national elections and their associated campaigns. However, while other work in this field has looked solely at cumulative networks, treating collected data as a static network, this project will also look at specific cases to see how the blogospheres change with time and topics of discussion. Three case studies are used within the thesis to examine how blogs cover politics, featuring an international political event (the Obama inauguration), and local political topics (the opposition to the =Création et Internet‘, or HADOPI, law in France, the =Utegate‘ scandal in Australia). By using a mixture of qualitative and quantitative methods, the study analyses data collected from a population of sites from both countries, looking at their linking patterns, relationship with mainstream media, and topics of interest. This project will subsequently help to further develop methodologies in this field and provide new and detailed information on both online networks and internet-based political communication in Australia and France.

A ‘Big Data’ Approach to Mapping the Australian Twittersphere

This chapter discusses the methodological aspects and empirical findings of a large-scale, funded project investigating public communication through social media in Australia. The project concentrates on Twitter, but we approach it as representative of broader current trends toward the integration of large datasets and computational methods into media and communication studies in general, and social media scholarship in particular. The research discussed in this chapter aims to empirically describe networks of affiliation and interest in the Australian Twittersphere, while reflecting on the methodological implications and imperatives of ‘big data’ in the humanities. Using custom network crawling technology, we have conducted a snowball crawl of Twitter accounts operated by Australian users to identify more than one million users and their follower/followee relationships, and have mapped their interconnections. In itself, the map provides an overview of the major clusters of densely interlinked users, largely centred on shared topics of interest (from politics through arts to sport) and/or sociodemographic factors (geographic origins, age groups). Our map of the Twittersphere is the first of its kind for the Australian part of the global Twitter network, and also provides a first independent and scholarly estimation of the size of the total Australian Twitter population. In combination with our investigation of participation patterns in specific thematic hashtags, the map also enables us to examine which areas of the underlying follower/followee network are activated in the discussion of specific current topics – allowing new insights into the extent to which particular topics and issues are of interest to specialised niches or to the Australian public more broadly. Specifically, we examine the Twittersphere footprint of dedicated political discussion, under the #auspol hashtag, and compare it with the heightened, broader interest in Australian politics during election campaigns, using #ausvotes; we explore the different patterns of Twitter activity across the map for major television events (the popular competitive cooking show #masterchef, the British #royalwedding, and the annual #stateoforigin Rugby League sporting contest); and we investigate the circulation of links to the articles published by a number of major Australian news organisations across the network. Such analysis, which combines the ‘big data’-informed map and a close reading of individual communicative phenomena, makes it possible to trace the dynamic formation and dissolution of issue publics against the backdrop of longer-term network connections, and the circulation of information across these follower/followee links. Such research sheds light on the communicative dynamics of Twitter as a space for mediated social interaction. Our work demonstrates the possibilities inherent in the current ‘computational turn’ (Berry, 2010) in the digital humanities, as well as adding to the development and critical examination of methodologies for dealing with ‘big data’ (boyd and Crawford, 2011). Out tools and methods for doing Twitter research, released under Creative Commons licences through our project Website, provide the basis for replicable and verifiable digital humanities research on the processes of public communication which take place through this important new social network.

Genome-wide mapping of cystitis due to Streptococcus agalactiae and Escherichia coli in mice identifies a unique bladder transcriptome that signifies pathogen-specific antimicrobial defense against urinary tract infection

The most common causes of urinary tract infections (UTIs) are Gram-negative pathogens such as Escherichia coli; however, Gram-positive organisms including Streptococcus agalactiae, or group B streptococcus (GBS), also cause UTI. In GBS infection, UTI progresses to cystitis once the bacteria colonize bladder, but the host responses triggered in the bladder immediately following infection are largely unknown. Here, we used genome-wide expression profiling to map the bladder transcriptome of GBS UTI in mice infected transurethrally with uropathogenic GBS that was cultured from a 35 year-old women with cystitis. RNA from bladders was applied to Affymetrix Gene-1.0ST microarrays; qRT-PCR was used to analyze selected gene responses identified in array datasets. A surprisingly small significant gene list of 172 genes was identified at 24h; this compared to 2507 genes identified in a side-by-side comparison with uropathogenic E. coli (UPEC). No genes exhibited significantly altered expression at 2h in GBS-infected mice according to arrays despite high bladder bacterial loads at this early time point. The absence of a marked early host response to GBS juxtaposed with broad-based bladder responses activated by UPEC at 2h. Bioinformatics analyses including integrative systems-level network mapping revealed multiple activated biological pathways in the GBS cystitis transcriptome that regulate leukocyte activation, inflammation, apoptosis, and cytokine-chemokine biosynthesis. These findings define a novel, minimalistic type of bladder host response triggered by GBS UTI, which comprises collective antimicrobial pathways that differ dramatically from those activated by UPEC. Overall, this study emphasizes the unique nature of bladder immune activation mechanisms triggered by distinct uropathogens.

Network virtualisation from an operator perspective

Network virtualisation is seen as a promising approach to overcome the so-called “Internet impasse” and bring innovation back into the Internet, by allowing easier migration towards novel networking approaches as well as the coexistence of complementary network architectures on a shared infrastructure in a commercial context. Recently, the interest from the operators and mainstream industry in network virtualisation has grown quite significantly, as the potential benefits of virtualisation became clearer, both from an economical and an operational point of view. In the beginning, the concept has been mainly a research topic and has been materialized in small-scale testbeds and research network environments. This PhD Thesis aims to provide the network operator with a set of mechanisms and algorithms capable of managing and controlling virtual networks. To this end, we propose a framework that aims to allocate, monitor and control virtual resources in a centralized and efficient manner. In order to analyse the performance of the framework, we performed the implementation and evaluation on a small-scale testbed. To enable the operator to make an efficient allocation, in real-time, and on-demand, of virtual networks onto the substrate network, it is proposed a heuristic algorithm to perform the virtual network mapping. For the network operator to obtain the highest profit of the physical network, it is also proposed a mathematical formulation that aims to maximize the number of allocated virtual networks onto the physical network. Since the power consumption of the physical network is very significant in the operating costs, it is important to make the allocation of virtual networks in fewer physical resources and onto physical resources already active. To address this challenge, we propose a mathematical formulation that aims to minimize the energy consumption of the physical network without affecting the efficiency of the allocation of virtual networks. To minimize fragmentation of the physical network while increasing the revenue of the operator, it is extended the initial formulation to contemplate the re-optimization of previously mapped virtual networks, so that the operator has a better use of its physical infrastructure. It is also necessary to address the migration of virtual networks, either for reasons of load balancing or for reasons of imminent failure of physical resources, without affecting the proper functioning of the virtual network. To this end, we propose a method based on cloning techniques to perform the migration of virtual networks across the physical infrastructure, transparently, and without affecting the virtual network. In order to assess the resilience of virtual networks to physical network failures, while obtaining the optimal solution for the migration of virtual networks in case of imminent failure of physical resources, the mathematical formulation is extended to minimize the number of nodes migrated and the relocation of virtual links. In comparison with our optimization proposals, we found out that existing heuristics for mapping virtual networks have a poor performance. We also found that it is possible to minimize the energy consumption without penalizing the efficient allocation. By applying the re-optimization on the virtual networks, it has been shown that it is possible to obtain more free resources as well as having the physical resources better balanced. Finally, it was shown that virtual networks are quite resilient to failures on the physical network.

Neural network enhanced generalised minimum variance self-tuning controller for nonlinear discrete-time systems

A neural network enhanced self-tuning controller is presented, which combines the attributes of neural network mapping with a generalised minimum variance self-tuning control (STC) strategy. In this way the controller can deal with nonlinear plants, which exhibit features such as uncertainties, nonminimum phase behaviour, coupling effects and may have unmodelled dynamics, and whose nonlinearities are assumed to be globally bounded. The unknown nonlinear plants to be controlled are approximated by an equivalent model composed of a simple linear submodel plus a nonlinear submodel. A generalised recursive least squares algorithm is used to identify the linear submodel and a layered neural network is used to detect the unknown nonlinear submodel in which the weights are updated based on the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model therefore the nonlinear submodel is naturally accommodated within the control law. Two simulation studies are provided to demonstrate the effectiveness of the control algorithm.

Mapping virtual networks onto substrate networks

Network virtualization is a promising technique for building the Internet of the future since it enables the low cost introduction of new features into network elements. An open issue in such virtualization is how to effect an efficient mapping of virtual network elements onto those of the existing physical network, also called the substrate network. Mapping is an NP-hard problem and existing solutions ignore various real network characteristics in order to solve the problem in a reasonable time frame. This paper introduces new algorithms to solve this problem based on 0–1 integer linear programming, algorithms based on a whole new set of network parameters not taken into account by previous proposals. Approximative algorithms proposed here allow the mapping of virtual networks on large network substrates. Simulation experiments give evidence of the efficiency of the proposed algorithms.

Neural network enhanced self tuning adaptive control application for non-linear control of dynamic systems

The main theme of research of this project concerns the study of neutral networks to control uncertain and non-linear control systems. This involves the control of continuous time, discrete time, hybrid and stochastic systems with input, state or output constraints by ensuring good performances. A great part of this project is devoted to the opening of frontiers between several mathematical and engineering approaches in order to tackle complex but very common non-linear control problems. The objectives are: 1. Design and develop procedures for neutral network enhanced self-tuning adaptive non-linear control systems; 2. To design, as a general procedure, neural network generalised minimum variance self-tuning controller for non-linear dynamic plants (Integration of neural network mapping with generalised minimum variance self-tuning controller strategies); 3. To develop a software package to evaluate control system performances using Matlab, Simulink and Neural Network toolbox. An adaptive control algorithm utilising a recurrent network as a model of a partial unknown non-linear plant with unmeasurable state is proposed. Appropriately, it appears that structured recurrent neural networks can provide conveniently parameterised dynamic models for many non-linear systems for use in adaptive control. Properties of static neural networks, which enabled successful design of stable adaptive control in the state feedback case, are also identified. A survey of the existing results is presented which puts them in a systematic framework showing their relation to classical self-tuning adaptive control application of neural control to a SISO/MIMO control. Simulation results demonstrate that the self-tuning design methods may be practically applicable to a reasonably large class of unknown linear and non-linear dynamic control systems.

Monitoring the Australian Blogosphere through the 2007 Australian Federal Election

This paper examines the observable patterns of content creation by Australian political bloggers dur‐ing the 2007 election and its aftermath, thereby providing insight into the level and nature of activity in the Australian political blogosphere during that time. The performance indicators which are identi‐fied through this process enable us to target for further in‐depth research, to be reported in subse‐quent papers, those individual blogs and blog clusters showing especially high or unusual activity as compared to the overall baseline. This research forms the first stage in a larger project to investigate the shape and internal dynamics of the Australian political blogosphere. In this first stage, we tracked the activities of some 230 political blogs and related Websites in Australia from 2 November 2007 (the final month of the federal election campaign, with the election itself taking place on 24 Novem‐ber) to 24 January 2008. We harvested more than 65,000 articles for this study.

Challenges of tracking topical discussion networks online

Attempts to map online networks, representing relationships between people and sites, have covered sites including Facebook, Twitter, and blogs. However, the predominant approach of static network visualization, treating months of data as a single case rather than depicting changes over time or between topics, remains a flawed process. As different events and themes provoke varying interactions and conversations, it is proposed that case-by-case analysis would aid studies of online social networks by further examining the dynamics of links and information flows. This study uses hyperlink analysis of a population of French political blogs to compare connections between sites from January to August 2009. Themes discussed in this period were identified for subsequent analysis of topic-oriented networks. By comparing static blogrolls with topical citations within posts, this research addresses challenges and methods in mapping online networks, providing new information on temporal aspects of linking behaviors and information flows within these systems.