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.

Identification of pre-spike network in patients with mesial temporal lobe epilepsy

Background: Seizures and interictal spikes in mesial temporal lobe epilepsy (MTLE) affect a network of brain regions rather than a single epileptic focus. Simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) studies have demonstrated a functional network in which hemodynamic changes are time-locked to spikes. However, whether this reflects the propagation of neuronal activity from a focus, or conversely the activation of a network linked to spike generation remains unknown. The functional connectivity (FC) changes prior to spikes may provide information about the connectivity changes that lead to the generation of spikes. We used EEG-fMRI to investigate FC changes immediately prior to the appearance of interictal spikes on EEG in patients with MTLE. Methods/principal findings: Fifteen patients with MTLE underwent continuous EEG-fMRI during rest. Spikes were identified on EEG and three 10 s epochs were defined relative to spike onset: spike (0–10 s), pre-spike (−10 to 0 s), and rest (−20 to −10 s, with no previous spikes in the preceding 45s). Significant spike-related activation in the hippocampus ipsilateral to the seizure focus was found compared to the pre-spike and rest epochs. The peak voxel within the hippocampus ipsilateral to the seizure focus was used as a seed region for FC analysis in the three conditions. A significant change in FC patterns was observed before the appearance of electrographic spikes. Specifically, there was significant loss of coherence between both hippocampi during the pre-spike period compared to spike and rest states. Conclusion/significance: In keeping with previous findings of abnormal inter-hemispheric hippocampal connectivity in MTLE, our findings specifically link reduced connectivity to the period immediately before spikes. This brief decoupling is consistent with a deficit in mutual (inter-hemispheric) hippocampal inhibition that may predispose to spike generation.

A Memetic Algorithm for the location-based continuously operating reference stations placement problem in Network Real-Time Kinematic

Network Real-Time Kinematic (NRTK) is a technology that can provide centimeter-level accuracy positioning services in real time, and it is enabled by a network of Continuously Operating Reference Stations (CORS). The location-oriented CORS placement problem is an important problem in the design of a NRTK as it will directly affect not only the installation and operational cost of the NRTK, but also the quality of positioning services provided by the NRTK. This paper presents a Memetic Algorithm (MA) for the location-oriented CORS placement problem, which hybridizes the powerful explorative search capacity of a genetic algorithm and the efficient and effective exploitative search capacity of a local optimization. Experimental results have shown that the MA has better performance than existing approaches. In this paper we also conduct an empirical study about the scalability of the MA, effectiveness of the hybridization technique and selection of crossover operator in the MA.

How innovation and R&D happen in the upstream oil & gas industry : insights from a global survey

Few would disagree that the upstream oil & gas industry has become more technology-intensive over the years. But how does innovation happen in the industry? Specifically, what ideas and inputs flow from which parts of the sector׳s value network, and where do these inputs go? And how do firms and organizations from different countries contribute differently to this process? This paper puts forward the results of a survey designed to shed light on these questions. Carried out in collaboration with the Society of Petroleum Engineers (SPE), the survey was sent to 469 executives and senior managers who played a significant role with regard to R&D and/or technology deployment in their respective business units. A total of 199 responses were received from a broad range of organizations and countries around the world. Several interesting themes and trends emerge from the results, including: (1) service companies tend to file considerably more patents per innovation than other types of organization; (2) over 63% of the deployed innovations reported in the survey originated in service companies; (3) neither universities nor government-led research organizations were considered to be valuable sources of new information and knowledge in the industry׳s R&D initiatives, and; (4) despite the increasing degree of globalization in the marketplace, the USA still plays an extremely dominant role in the industry׳s overall R&D and technology deployment activities. By providing a detailed and objective snapshot of how innovation happens in the upstream oil & gas sector, this paper provides a valuable foundation for future investigations and discussions aimed at improving how R&D and technology deployment are managed within the industry. The methodology did result in a coverage bias within the survey, however, and the limitations arising from this are explored.

An interpretative structural modeling based network reconfiguration strategy for power systems

The network reconfiguration is an important stage of restoring a power system after a complete blackout or a local outage. Reasonable planning of the network reconfiguration procedure is essential for rapidly restoring the power system concerned. An approach for evaluating the importance of a line is first proposed based on the line contraction concept. Then, the interpretative structural modeling (ISM) is employed to analyze the relationship among the factors having impacts on the network reconfiguration. The security and speediness of restoring generating units are considered with priority, and a method is next proposed to select the generating unit to be restored by maximizing the restoration benefit with both the generation capacity of the restored generating unit and the importance of the line in the restoration path considered. Both the start-up sequence of generating units and the related restoration paths are optimized together in the proposed method, and in this way the shortcomings of separately solving these two issues in the existing methods are avoided. Finally, the New England 10-unit 39-bus power system and the Guangdong power system in South China are employed to demonstrate the basic features of the proposed method.

Wrecking ball: could big tech firms ruin the Spotify party?

YouTube is contemplating the launch of a new music service. But how would such a service fare against established music services like Spotify, Rdio, and Pandora? All these services are referred to as “access-based music services”. They offer music listeners access to millions of songs they can listen to as much as they want for free (with advertising and only basic functionality) or for a monthly subscription fee (without advertising)...

Association hierarchy mining and its application for network traffic characterisation

This thesis presents an association rule mining approach, association hierarchy mining (AHM). Different to the traditional two-step bottom-up rule mining, AHM adopts one-step top-down rule mining strategy to improve the efficiency and effectiveness of mining association rules from datasets. The thesis also presents a novel approach to evaluate the quality of knowledge discovered by AHM, which focuses on evaluating information difference between the discovered knowledge and the original datasets. Experiments performed on the real application, characterizing network traffic behaviour, have shown that AHM achieves encouraging performance.

Co-movements in commodity prices : a note based on network analysis

This article analyses co-movements in a wide group of commodity prices during the time period 1992–2010. Our methodological approach is based on the correlation matrix and the networks inside. Through this approach we are able to summarize global interaction and interdependence, capturing the existing heterogeneity in the degrees of synchronization between commodity prices. Our results produce two main findings: (a) we do not observe a persistent increase in the degree of co-movement of the commodity prices in our time sample, however from mid-2008 to the end of 2009 co-movements almost doubled when compared with the average correlation; (b) we observe three groups of commodities which have exhibited similar price dynamics (metals, oil and grains, and oilseeds) and which have increased their degree of co-movement during the sampled period.

Identifying influential users of micro-blogging services : a dynamic action-based network approach

In this paper, we present a dynamic model to identify influential users of micro-blogging services. Micro-blogging services, such as Twitter, allow their users (twitterers) to publish tweets and choose to follow other users to receive tweets. Previous work on user influence on Twitter, concerns more on following link structure and the contents user published, seldom emphasizes the importance of interactions among users. We argue that, by emphasizing on user actions in micro-blogging platform, user influence could be measured more accurately. Since micro-blogging is a powerful social media and communication platform, identifying influential users according to user interactions has more practical meanings, e.g., advertisers may concern how many actions – buying, in this scenario – the influential users could initiate rather than how many advertisements they spread. By introducing the idea of PageRank algorithm, innovatively, we propose our model using action-based network which could capture the ability of influential users when they interacting with micro-blogging platform. Taking the evolving prosperity of micro-blogging into consideration, we extend our actionbaseduser influence model into a dynamic one, which could distinguish influential users in different time periods. Simulation results demonstrate that our models could support and give reasonable explanations for the scenarios that we considered.

Audit pricing and product differentiation in small private firms : evidence from Thailand

Purpose – There is limited evidence on how differences in economic environments affect the demand for and supply of auditing. Research on audit pricing has mainly focused on large client markets in developed economies; in contrast, the purpose of this paper is to focus on the small client segment in the emerging economy of Thailand which offers a choice between auditors of two different qualities. Design/methodology/approach – This paper is based on a random stratified sample of small clients in Thailand qualifying for audit exemption. The final sample consists of 1,950 firm-year observations for 2002-2006. Findings – The authors find evidence of product differentiation in the small client market, suggesting that small firms view certified public accountants as superior and pay a premium for their services. The authors also find that audit fees have a positive significant association with leverage, metropolitan location and client size. Audit risk and audit opinion are not, however, significantly associated with audit fees. Furthermore, the authors find no evidence that clients whose financial year ends in the auditors’ busy period pay significantly higher audit fees, and auditors engage in low-balling on initial engagements to attract audit clients. Research limitations/implications – The research shows the importance of exploring actual decisions regarding audit practice and audit pricing in different institutional and organizational settings. Originality/value – The paper extends the literature from developed economies and large/listed market setting to the emerging economy and small client market setting. As far as the authors are aware, this is the first paper to examine audit pricing in the small client market in an emerging economy.

Correlation based method for phase identification in a three phase LV distribution network

Low voltage distribution networks feature a high degree of load unbalance and the addition of rooftop photovoltaic is driving further unbalances in the network. Single phase consumers are distributed across the phases but even if the consumer distribution was well balanced when the network was constructed changes will occur over time. Distribution transformer losses are increased by unbalanced loadings. The estimation of transformer losses is a necessary part of the routine upgrading and replacement of transformers and the identification of the phase connections of households allows a precise estimation of the phase loadings and total transformer loss. This paper presents a new technique and preliminary test results for a method of automatically identifying the phase of each customer by correlating voltage information from the utility's transformer system with voltage information from customer smart meters. The techniques are novel as they are purely based upon a time series of electrical voltage measurements taken at the household and at the distribution transformer. Experimental results using a combination of electrical power and current of the real smart meter datasets demonstrate the performance of our techniques.

Consumer phase identification in a three phase unbalanced LV distribution network

A new technique is presented for automatically identifying the phase connection of domestic customers. Voltage information from a reference three phase house is correlated with voltage information from other customer electricity meters on the same network to determine the highest probability phase connection. The techniques are purely based upon a time series of electrical voltage measurements taken by the household smart meters and no additional equipment is required. The method is demonstrated using real smart meter datasets to correctly identify the phase connections of 75 consumers on a low voltage distribution feeder.

Constraints and characteristics of flexible work arrangements in small not-for-profit firms

There is a great deal of research that examines flexible working arrangements, but this work tends to be concentrated in large organisations. This research examines the approach taken to flexible working arrangements in five small community based, not for profit organisations. We present three propositions that aim to understand the constraints and the characteristics of flexible work in this rarely studied sector.

The flip side of fly-in-fly-out : the use of 457 visas by smaller firms in the Western Australian resources sector

The focus in this article is how the extensive use of fly-in fly-out (FIFO) working arrangements in the Western Australian resources sector has an impact directly and indirectly on smaller firms and their ability to recruit workers in remote locations. We argue that the growth of FIFO working arrangements has disadvantaged smaller resource-sector firms by increasing their employment costs and decreasing their ability to attract skilled workers. As a result, smaller resource-sector firms are recruiting skilled workers on 457 visas to secure their business stability and growth, despite the complexity, costs, and risks involved.

Convolutional Neural Network-based place recognition

Recently Convolutional Neural Networks (CNNs) have been shown to achieve state-of-the-art performance on various classification tasks. In this paper, we present for the first time a place recognition technique based on CNN models, by combining the powerful features learnt by CNNs with a spatial and sequential filter. Applying the system to a 70 km benchmark place recognition dataset we achieve a 75% increase in recall at 100% precision, significantly outperforming all previous state of the art techniques. We also conduct a comprehensive performance comparison of the utility of features from all 21 layers for place recognition, both for the benchmark dataset and for a second dataset with more significant viewpoint changes.