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This study focuses on the detection of seeded porosity during metal additive manufacturing by employing convolutional neural networks (CNN). The aim of the study is to demonstrate the application of Machine Learning (ML) in in-process monitoring. Laser Powder Bed Fusion (LPBF) is a selective laser melting technique used to build complex 3D parts. The current monitoring system in LPBF is inadequate to produce safety-critical parts due to the lack of automated processing of collected data. To assess the efficacy of applying ML to defect detection in LPBF by in-process images, a range of synthetic defects have been designed into cylindrical artefacts to mimic porosity occurring in different locations, shapes, and sizes. Empirical analysis has revealed insights into the importance of accurate labelling strategies required for data-driven solutions. Two labelling strategies based on the computer aided design (CAD) file and X-ray computed tomography (XCT) scan data was formulated. A novel CNN was trained from scratch and optimised by selecting the best values of an extensive range of hyper-parameters by employing Hyperband tuner. The accuracy of the model was 90% when trained using a CAD-assisted labelling, and 97% when using XCT-assisted labelling. The model successfully spotted pores as small as 0.2mm. Experiments revealed that balancing the data set improved the model's precision from 89% to 97% and recall from 85% to 97% when compared to training on an imbalanced data set. We strongly believed that the proposed model would significantly reduce post-processing cost and provide a better base model network for transfer learning of future ML models aimed at LPBF micro-defects detection.

Behavioural biometrics have the potential to provide an additional or alternative authentication mechanism to those involving a shared secret (i.e. a password). Keystroke timings are the focus of this study, where key press and release timings are acquired whilst monitoring a user typing a known phrase. Many studies exist in keystroke biometrics, but there is an absence of literature aiming to understand the relationship between characteristics of password policies and the potential of keystroke biometrics. Furthermore, benchmark datasets used in keystroke biometric research do not enable useful insights into the relationship between their capability and password policy. Herein, substitutions of uppercase, numeric, special characters, and their combination of passwords derived from English words are considered. Timings for 42 participants for the same 40 passwords are acquired. A matching system using the Manhattan distance measure with seven different feature sets is implemented, culminating in an Equal Error Rate of between 6% and 11% and accuracy values between 89% and 94%, demonstrating comparable accuracy to other threshold‐based systems. Further analysis suggests that the best feature sets are those containing all timings and trigraph press to press. Evidence also suggests that phrases containing fewer characters have greater accuracy, except for those with special character substitutions.

This work focuses on the problem of constructing accurate prediction models using an adaptive neuro-fuzzy inference system (ANFIS) from data that are scarce and poorly scaled. Many real-world problems have limited data to work with and yet require models with high prediction accuracy. Due to the fuzzy nature of many data sets, it is appropriate to construct prediction models using fuzzy inference systems (FIS), rather than traditional artificial neural network (ANN) models. However, the scarcity of the data raises serious concerns when attempting to construct models of this type. In this paper, we address the problem of constructing accurate prediction models using ANFIS in circumstances where capturing large amounts of data is difficult. We propose a new approach, using robust data expansion methods to enhance the data carefully. This approach provides the learning algorithm with enough representative data to allow all of the parameters (premise and consequent) to be optimised. We compare and contrast the effectiveness in reducing prediction error for three specific expansion models using radial basis function (RBF) kernels. When expanding data, for which the underlying smoothness properties are not known, there is often a danger of over-smoothing. This is usually noticeable at turning points and can occur when fitting the data using polynomials or cubic splines for example. To avoid over-smoothing, we introduce a modified multiquadric approach that allows smooth, near-interpolation while maintaining the desirable shape-preserving properties of the data. The proposed method is explained through two case studies — the first to predict gasoline consumption for the oil/fuel industry in Iraq and the second for predicting natural gas consumption in Iran. This is the first study that uses adjustable, shape-preserving re-sampling methods for optimising ANFIS models in low data environments.

SCADA (Supervisory Control and Data Acquisition) systems are a critical part of modern national critical infrastructure (CI) systems. Due to the rapid increase of sophisticated cyber threats with exponentially destructive effects, intrusion detection systems (IDS) must systematically evolve. Specific intrusion detection systems that reassure both high accuracy, low rate of false alarms and decreased overhead on the network traffic must be designed for SCADA systems. In this book chapter we present a novel IDS, namely K-OCSVM, that combines both the capability of detecting novel attacks with high accuracy, due to its core One-Class Support Vector Machine (OCSVM) classification mechanism and the ability to effectively distinguish real alarms from possible attacks under different circumstances, due to its internal recursive k-means clustering algorithm. The effectiveness of the proposed method is evaluated through extensive simulations that are conducted using realistic datasets extracted from small and medium sized HTB SCADA testbeds.

In this chapter, we explore the meaning of the term “global graduate” and discuss the multifaceted approach taken by the School of Computing and Engineering at the University of Huddersfield, UK, to expose students to a range of experiences and opportunities in order to facilitate their development. Specifically, we focus on both the opportunities available and the challenges faced by examining the different ways that the School works with its students. We highlight some of the key areas in which students are exposed to globalisation, through its award-winning placements unit, and cross-curriculum team challenges led by global organisations. Our discussion draws on current literature and the experience and expertise of academic and professional services staff working with the students. In order to capture the students’ voice, we have undertaken a focus group of both national and international students. The approach taken in gathering student opinion on how well the University encourages and supports students to be global graduates is presented together with a summary of its findings.

With organisations and governments significantly investing in cyber de-fenses, there is a significant need to develop tools and technologies to help analystsunderstand cyber security within their domain of application. A critical aspect ofthis is to develop and maintain Situation Awareness of security aspects within cyberinfrastructures. Visual analytics provides much support to analysts to help under-stand evolving situations and overall status of systems and situations, particularlywhen dealing with the challenge of dealing with massive volumes of data. Thischapter explores situation awareness in cyber security in more details, aligning de-sign recommendations for visual analytics to help analysts with progressive levelsof situation awareness

Cloud computing has many commercial advantages; however, companies tend to avoid trusting cloud service providers (CSPs) with their sensitive data because of the associated risks. Cloud infrastructures can be vulnerable due to a single flaw in any node with networking, computing, storage and authentication services capabilities, which can result in the exposure of vulnerabilities with severe consequences. This paper reviews various recent articles and determines the most prominent security challenges. The paper also includes the critical analysis of existing solutions to determine their limitations. Using these identified limitations, a multi-layered security framework is proposed that can be integrated within cloud components to resolve data, virtualization, access control, insiders and service unavailability challenges. A theoretical evaluation of the framework is also conducted to explore its benefits.

The evolution in precision manufacturing has resulted in the requirement to produce and maintain more accurate machine tools. This new requirement coupled with desire to reduce machine tool downtime places emphasis on the calibration procedure during which the machine’s capabilities are assessed. Machine tool downtime is significant for manufacturers because the machine will be unavailable for manufacturing use, therefore wasting the manufacturer’s time and potentially increasing lead-times for clients. In addition to machine tool downtime, the uncertainty of measurement, due to the schedule of the calibration plan, has significant implications on tolerance conformance, resulting in an increased possibility of false acceptance and rejection of machined parts. The work presented in this paper is focussed on expanding a developed temporal optimisation model to reduce the uncertainty of measurement. Encoding the knowledge in regular PDDL requires the discretization of non-linear, continuous temperature change and implementing the square root function. The implementation shows that not only can domainindependent automated planning reduce machine downtime by 10.6% and the uncertainty of measurement by 59%, it is also possible to optimise both metrics reaching a compromise that is on average 9% worse that the best-known solution for each individual metric.

Machine tool owners require knowledge of their machine’s capabilities, and the emphasis increases with\ud areas of high accuracy manufacturing. An aspect of a machine’s capability is its geometric accuracy. International\ud Standards and best-practice guides are available to aid understanding of the required measurements\ud and to advise on how to perform them. However, there is an absence of any intelligent method\ud capable of optimising the duration of a calibration plan, minimising machine down-time. In this work,\ud artificial intelligence in the form of automated planning is applied to the problem of machine tool\ud pseudo-static geometric error calibration. No prior knowledge of Artificial Intelligence (AI) planning is\ud required throughout this paper. The authors have written this paper for calibration engineers to see\ud the benefits that automated planning can provide. Two models are proposed; the first produces a sequential\ud calibration plan capable of finding the optimal calibration plan. The second model has the additional\ud possibility of planning for concurrent measurements, adding the possibility of further reducing machine\ud down-time. Both models take input regarding a machine’s configuration and available instrumentation.\ud The efficacy of both models is evaluated by performing a case study of a five-axis gantry machine,\ud whereby calibration plans are produced and compared against both an academic and industrial expert.\ud From this, the effectiveness of this novel method for producing optimal calibration plan is evaluated,\ud stimulating potential for future work.

The reliance on expert knowledge—required for analysing security logs and performing security audits—has created an unhealthy balance, where many computer users are not able to correctly audit their security configurations and react to potential security threats. The decreasing cost of IT and the increasing use of technology in domestic life are exacerbating this problem, where small companies and home IT users are not able to afford the price of experts for auditing their system configuration. In this paper, we present GraphBAD, a graph-based analysis tool that is able to analyse security configurations in order to identify anomalies that could lead to potential security risks. GraphBAD, which does not require any prior domain knowledge, generates graph-based models from security configuration data and, by analysing such models, is able to propose mitigation plans that can help computer users in increasing the security of their systems. A large experimental analysis, conducted on both publicly available (the well-known KDD dataset) and synthetically generated testing sets (file system permissions), demonstrates the ability of GraphBAD in correctly identifying security configuration anomalies and suggesting appropriate mitigation plans.

Intelligent agents solving problems in the real world require domain models containing widespread knowledge of the world.

Pseudo spectral methods offer an attractive alternative to finite element procedures for the solution of problems in elasticity. Especially for simple domains, questions involving both two and three dimensional elasticity (Navier's Equations or their non-linear generalisations) would seem to be reasonable candidates for a pseudo spectral approach. This paper examines some simple vibrational eigenvalue type problems, demonstrating how Navier's equations can be recast into pseudo- spectral format, including first derivative boundary conditions representing zero traction. Fourier---Chebyshev methods are shown to give solutions with typical spectral accuracy, with the addition of pole conditions being necessary for the case of a two dimensional disc. There is also consideration given to time-stepping solutions of elastodynamic problems, especially those involving non-linear friction effects, the authors particular interest being the study of disc brake noise. It is shown that, at least for relatively simple cases, it is possible to model systems in such a way that animated graphical output can be provided as the system of partial differential equations is numerically integrated. This provides a useful tool for engineers to rapidly examine the effect of parameter changes on a system model.

As sequels go, Gulf War II: Unfinished Business was a popular and omnipotent movie spectacle. The very process of naming the conflict implied a notion of watching a second episode and encountering the war from afar, whilst numerous media commentators likened their experience of the war to that of watching a movie, or even a movie of a movie. In turn political leaders from the ‘coalition of the willing’ were regularly able to call upon a stock of filmic cultural reference points in their representations of unfolding events in the Gulf. With Hollywood in a long-established role as geopolitician and film as a key geopolitical site, the latest conditions of conflict in the theatre of international politics could be (re)staged for the viewing American public. These events raise some important issues about the production of subject positions through cinema and highlight some of the ways in which film is cast in the role of director on the world geopolitical stage, writing the scripts of global politics as theat...

Discretely defined surfaces that exhibit vertical displacements across unknown fault lines can be difficult to approximate accurately unless a representation of the faults is known. Accurate representations of these faults enable the construction of constrained approximation models that can successfully overcome common problems such as over-smoothing. In this paper we review an existing method for detecting fault lines and present a new detection approach based on data triangulations and discrete Gaussian curvature (DGC). Furthermore, we show that if the fault line can be described non-parametrically, then accurate support vector machine (SVM) models can be constructed that are independent of the type of triangulation used in the detection algorithms. We shall also see that SVM models are particularly effective when the data produced by the detection algorithms are noisy. We compare the performances of the various new and established models.

Laurent Pade-Chebyshev rational approximants,A m (z,z −1)/B n (z, z −1), whose Laurent series expansions match that of a given functionf(z,z −1) up to as high a degree inz, z −1 as possible, were introduced for first kind Chebyshev polynomials by Clenshaw and Lord [2] and, using Laurent series, by Gragg and Johnson [4]. Further real and complex extensions, based mainly on trigonometric expansions, were discussed by Chisholm and Common [1]. All of these methods require knowledge of Chebyshev coefficients off up to degreem+n. Earlier, Maehly [5] introduced Pade approximants of the same form, which matched expansions betweenf(z,z −1)B n (z, z −1)). The derivation was relatively simple but required knowledge of Chebyshev coefficients off up to degreem+2n. In the present paper, Pade-Chebyshev approximants are developed not only to first, but also to second, third and fourth kind Chebyshev polynomial series, based throughout on Laurent series representations of the Maehly type. The procedures for developing the Pade-Chebyshev coefficients are similar to that for a traditional Pade approximant based on power series [8] but with essential modifications. By equating series coefficients and combining equations appropriately, a linear system of equations is successfully developed into two sub-systems, one for determining the denominator coefficients only and one for explicitly defining the numerator coefficients in terms of the denominator coefficients. In all cases, a type (m, n) Pade-Chebyshev approximant, of degreem in the numerator andn in the denominator, is matched to the Chebyshev series up to terms of degreem+n, based on knowledge of the Chebyshev coefficients up to degreem+2n. Numerical tests are carried out on all four Pade-Chebyshev approximants, and results are outstanding, with some formidable improvements being achieved over partial sums of Laurent-Chebyshev series on a variety of functions. In part II of this paper [7] Pade-Chebyshev approximants of Clenshaw-Lord type will be developed for the four kinds of Chebyshev series and compared with those of the Maehly type.

In this paper we are interested in the approximation of continuous surface texture profiles defined only at discretely obtained points. A continuous representation is required in order to be able to apply filtration methods that assume uniform data spacing to practical data that in general is not uniformly spaced. By reconstructing surface profiles as natural cubic spline interpolants accurately using numerically stable fitting algorithms, we can provide a mathematically sound basis on which to compute surface texture profile parameters. As an important direct benefit, parameters involving the integrals of surface profiles can be calculated directly from the spline interpolant. Examples are given to illustrate the advantages of using this reconstruction method.

A set of discrete data (xk, f (xk)) (k = 1, 2, , m) may be fitted in any lp norm by a nonlinear form derived from a function g (L) of a linear form L = L(x). Such a nonlinear approximation problem may under appropriate conditions be (asymptotically) replaced by the fitting of g-1 (f) by L in any lp norm with respect to a weight function w = g (g-1 (f)). In practice this direct method can yield very good results, sometimes coming close to a best approximation. However, to ensure a near-best approximation, by using an iterative procedure based on fitting L, two algorithms are proposed in the l2 norm - one already established by Mason and Upton (1989) and one completely new, based on minimising the two algorithms and multiplicative combinations of errors, respectively. For a general g we prove they converge locally and linearly with small constants. Moreover it is established that they converge to different (nonlinear) Galerkin type approximations, the first based on making the explicit error f - g (L) orthogonal to a set of functions forming a basis for L, and the second based on making the implicit error * w(g-1 (f) - L) orthogonal to such a basis. Finally, and mainly for comparison purposes, the well known Gauss-Newton algorithm is adopted for the determination of a best (nonlinear) approximation. Illustrative problems are tackled and numerical results show how effective all of the algorithms can be. To add a further novel feature, L is here chosen throughout to be a radial basis function (RBF), and, as far as we are aware, this is one of the first successful uses of a (nonlinear) function of an RBF as an approximation form in data fitting. (© 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

This paper examines the relationship between museums and representations of national identity in South Africa. From their early development in Western Europe in the nineteenth century there has been a close relationship between museums and other exhibitionary spaces and the production of national identities. In South Africa, museum displays have historically supported colonial and apartheid ideologies, but with the transition to a post-apartheid society museums have reassessed their divisive roles and repositioned themselves within South Africa’s contemporary nation-building project, organized around building unity from diversity. The development of this new relationship between museums and democratic nation-building is examined here through discussing the attempts of South Africa’s museums to become more inclusive in their exhibitions, and analysing debates in the museological community concerning the challenges facing museums in a post apartheid society. These issues then inform an analysis of an exhibition at the South African National Gallery titled Contemporary South African art 1985-1995. The exhibition attempted to use art to document the political transition and contribute to building national unity. However, by discussing the political operation of classification in museums I argue that the exhibition ultimately fails to represent this period inclusively, and I question, more generally, the possibility of art museums engaging in a properly inclusive nation-building project.

The role of monuments in producing contested national histories and identities is increasingly receiving critical attention in geography. This paper contributes to the existing literature on monuments and nationalism by examining the Voortrekker Monument in Pretoria and illustrating how it became a key site at which Afrikaners produced a distinct political identity in the late 1940s. Through examining the discourse surrounding the monument's inauguration in December 1949 it looks at five key discursive themes that attached different, but often complimentary, meanings to the monument. Through analysing these themes, I demonstrate how a contemporary Afrikaner identity was constructed through a particularistic interpretation of an heroic Voortrekker past and argue that the construction of this history and identity was governed by political debates in the 1930s and 1940s rather than any historical authenticity to Afrikaner nationalist claims. Moreover, the monument's inauguration just months after the election of the first apartheid government provides a useful window into the specificity of apartheid political identity. I argue that the representation of Afrikaner identity at the monument in distinctive ethnicist terms both legitimised, and was fundamental to the possibility of, apartheid. Finally, building on this analysis of apartheid I conclude by suggesting how this illuminates debates concerning South Africa's contemporary nation building project and the content of post apartheid identities.

Robert Strausz-Hupe is a somewhat forgotten and unexamined figure in the history of ‘American geopolitics’. In the early 1940s Strausz-Hupe, an Austrian emigre to the United States, was one of a number of intellectuals who introduced ‘geopolitics’ to the American public and championed a ‘geopolitical approach’ to international politics in his work for the US government during the Second World War. With the help of funding from American conservatives in the 1950s, Strausz-Hupe established the Foreign Policy Research Institute at the University of Pennsylvania and its influential journal Orbis. Together with a number of others, Strausz-Hupe produced a remarkable number of books and articles on the ‘Soviet threat’ to the USA and the western world. The influence of Strausz-Hupe and the FPRI extended into the US military, where its form of anti-communist indoctrination was challenged by Senator William Fulbright in the 1960s. In the 1970s, Strausz-Hupe served as US ambassador for the Nixon and Ford administrations and as a member of the revitalized Committee on the Present Danger. With Ronald Reagan's ascent to power, he once again became a US ambassador, this time to Turkey. From the early 1940s to the mid-1980s, Strausz-Hupe made a career in the analysis but also production of geopolitics as propaganda. The circumstances of his biography are a fascinating window into the intellectuals, institutions and ideology of a dominant strain of ‘American geopolitics’.

—In this paper, we present a Monte-Carlo policy rollout technique (called MOCART-CGA) for path planning in dynamic and partially observable real-time environments such as Real-time Strategy games. The emphasis is put on fast action selection motivating the use of Monte-Carlo techniques in MOCART-CGA. Exploration of the space is guided by using corridors which direct simulations in the neighbourhood of the best found moves.\ud \ud MOCART-CGA limits how many times a particular stateaction pair is explored to balance exploration of the neighbourhood of the state and exploitation of promising actions. MOCART-CGA is evaluated using four standard pathfinding benchmark maps, and over 1000 instances. The empirical results show that MOCART-CGA outperforms existing techniques, in terms of search time, in dynamic and partially observable environments. Experiments have also been performed in static (and partially observable) environments where MOCARTCGA still requires less time to search than its competitors, but typically finds lower quality plans.

This paper introduces a novel path planning technique called MCRT which is aimed at non-deterministic, partially known, real-time domains populated with dynamically moving obstacles, such as might be found in a real-time strategy (RTS) game. The technique combines an efficient form of Monte-Carlo tree search with the randomized exploration capabilities of rapidly exploring random tree (RRT) planning. The main innovation of MCRT is in incrementally building an RRT structure with a collision-sensitive reward function, and then re-using it to efficiently solve multiple, sequential goals. We have implemented the technique in MCRT-planner, a program which solves non-deterministic path planning problems in imperfect information RTS games, and evaluated it in comparison to four other state of the art techniques. Planners embedding each technique were applied to a typical RTS game and evaluated using the game score and the planning cost. The empirical evidence demonstrates the success of MCRT-planner.