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The data in this paper are related to the research article entitled “Automated characterization of anthropomorphicity of prosthetic feet fitted to bone-anchored transtibial prosthesis” (Frossard et al., 2019: DOI: 10.1109/TBME.2019.2904713). This article contains the individual angles of dorsiflexion and bending moments generated while walking with transtibial bone-anchored prostheses including prosthetic feet with different index of anthropomorphicity. Inter-participant variability were presented for the (A) position of the load cell measuring directly to the bending moments, (B) patterns of angles of dorsiflexion and bending moment as well as moment-angle curves and (C) variations of magnitude of angles of dorsiflexion as well as the raw and bodyweight-normalized bending moments between toe contact and heel off. These initial inter-participant variability benchmark datasets are critical to design future automated algorithms and clinical trials. Online repository contains the files: https://eprints.qut.edu.au/127745/1/127745.pdf. Keywords: Amputation, Artificial limb, Bone-anchored prosthesis (BAP), Direct skeletal attachment, Osseointegrated implants, Osseointegration, Prosthesis, Loading, Kinetics, Feet, Stiffness

World over, power consumption by “server farms” or data centers is a growing concern. With time dependent job arrival rates at the servers, the power dissipation, and concomitantly the cooling requirements are dynamic. However, due to the lack of more efficient control algorithms, cooling systems are set to conservatively pre-determined set points, which result in sub-optimal energy consumption. An important step in the development of smarter control algorithms is detailed knowledge about the dynamic behavior of the data center system, which is missing in the open literature. In this paper, we have conducted transient experiments and CFD modeling to understand data center behavior for a broad range of time-varying input parameters such as CPU utilization, cooling air temperature and flow rates. Experiments at the server, rack, and facility level of raised floor plenum data centers were designed to record system responses to these input variable perturbations. The output responses were rack inlet-outlet air temperatures & CPU temperatures. A transient CFD model was validated with experiments. We foresee that these experimental results and the validated CFD model can be used in designing smarter control algorithms as a next step., by Yogesh Fulpagare, Atul Bhargav and Yogendra Joshi

The paper at hand motivates, proposes, demonstrates, and evaluates a novel systematic approach to discovering causal dependencies between events encoded in large arrays of data, called causality mining. The approach has emerged in the discussions with our project partner, an Australian public energy company. It was successfully evaluated in a case study with the project partner to extract valuable, and otherwise unknown, information on the causal dependencies between observations reported by the company’s employees as part of the organizational health and safety management practices and incidents that had occurred at the organization’s sites. The dependencies were derived based on the notion of proximity of the observations and incidents. The setup and results of the evaluation are reported in this paper. The new approach and the delivered insights aim at improving the overall health and safety culture of the project partner practices, as they can be applied to caution and, thus, prevent future incidents.

This paper proposes the use of Energy Storage (ES) to support frequency fluctuations in weak grids powered by low-power synchronous generators (SGs). Traditionally, large synchronous generators rely on high inertia and governor control to guarantee the frequency stability of the network. The scenario changes in networks with a large penetration of small distributed generators (DGs) with smaller inertia, where load variations can lead to large frequency deviations, violating the over/under frequency protection limits. The proposed energy storage control injects active power during load transients so that the frequency is constrained within predefined thresholds. The system frequency is detected by a Double Second-Order Generalised Integrator Frequency Locked Loop (DSOGI-FLL), eliminating the need for communication between energy storage and synchronous generator and providing “plug-and-play” capability. The proposed method combined with the continuous speed governing supports the frequency well within the primary frequency transient requirements. The solution has been validated using an 8kW laboratory system.

Metaheuristics provide high-level instructions for designing heuristic optimisation algorithms and have been successfully applied to a range of computationally hard real-world problems. Local search metaheuristics operate under a single-point based search framework with the goal of iteratively improving a solution in hand over time with respect to a single objective using certain solution perturbation strategies, known as move operators, and move acceptance methods starting from an initially generated solution. Performance of a local search method varies from one domain to another, even from one instance to another in the same domain. There is a growing number of studies on `more general' search methods referred to as cross-domain search methods, or hyperheuristics, that operate at a high-level solving characteristically different problems, preferably without expert intervention. This paper provides a taxonomy and overview of existing local search metaheuristics along with an empirical study into the effects that move acceptance methods, as components of singlepoint based local search metaheuristics, have on the cross-domain performance of such algorithms for solving multiple combinatorial optimisation problems. The experimental results across a benchmark of nine different computationally hard problems highlight the shortcomings of existing and well-known methods for use as components of cross-domain search methods, despite being re-tuned for solving each domain.

A problem faced by some Logistic Support Organisations (LSOs) is that of forecasting the demand for spare parts, corresponding to equipment failures within the system. Here we are particularly concerned with a final phase of operations and the opportunity to place only a single order to cover demand during this phase. The problem is further complicated when the service logistics context can change during this final phase, e.g. as the number of systems supported or the LSO's resources change. Such a problem is typical of the final phase of many military operations. The LSO operates the recovery and repair loop for the equipment in question. By developing a simulation of the LSO, we can generate synthetic operational data regarding equipment breakdowns, etc. We then split that data into a training set and a test set in order to compare several approaches to forecasting demand in the final operational phase. We are particularly interested in the application of Bayesian network models for this type of forecasting since these offer a way of combining hard observational data with subjective expert opinion. Different LSO configurations were simulated to create a test dataset and the simulation results were compared with the various forecasts. The BN that learned from training data performed best, followed by a hybrid BN design combining expert elicitation and machine learning, and then a logistic regression model. An expert-adjusted exponential smoothing model was the poorest performer and these differences were statistically significant. The paper concludes with a discussion of the results, some implications for practice and suggestions for future work.

Mining operations record a large amount of data from multiple sources (such as block model and online processing data) which is neither effectively nor systematically used to understand and improve operational performance. This paper proposes a generic semi-automatable data analytics method, the Integrated Analysis Method (IAM), that addresses the disconnection between disparate datasets. IAM enables evidence-based understanding of rock and machine parameters, laying the foundation for a potentially more sophisticated way to model and predict mining processes to deliver financial value. IAM systematically combines and analyses both rock characteristics and operational data to isolate the impact of the variability in rock characteristics and operational settings on key performances. Insights extracted from IAM allow one to narrow down key operating conditions, specific to a particular plant, that are correlated to, for example, significant differences in daily throughput while processing batches of ore with similar metallurgical characteristics. Such insights can be used for multiple purposes, for instance, to learn optimal processing recipes for a given set of rock properties. We applied IAM to a combined data set recorded at a Chilean ore deposit and evaluated our findings with domain experts.

When measuring revolving objects, it is often desired to obtain not only the geometrical form of the workpiece, but also the topography of the surface, as they both affect the performance of the part. However, holistic measurement of the entire three-dimensional surface of a revolving part is challenging since most surface measurement instruments only have limited measurement ability, where the bottom and the side surfaces cannot be measured. One solution to obtain geometrical form and surface topography information simultaneously is to add a precision axis to rotate the object while performing surface topography measurement. However, this solution requires a high-cost precision rotation stage and adjustable mounting and alignment aids. Moreover, errors in the rotation will be added to the measurement result, which can be difficult to compensate. Stitching is a method often used for measuring revolving surfaces without the need for precision motion axes, as the method is applied at the software level, and errors in the rotation can be compensated by the stitching algorithm. Nevertheless, the overall accuracy of stitching is limited when the number of sub-surfaces is large, since the measurement and stitching error accumulate along the stitching chain. In this paper, a self-calibration rotational stitching method is presented which can compensate for the accumulated error. The self-calibration method utilises the inherent nature of a revolving surface and compensates for the registration error by aligning the last dataset with the first dataset. The proposed method is demonstrated by measuring grinding wheels with a coherence scanning interferometer and simultaneously rotating the grinding wheels with a low-cost stepper-motor. It is demonstrated that the proposed stitching measurement method is effective in compensating for accumulated registration error. The proposed self-calibration rotational stitching method can be easily extended to a wide range of applications for measuring revolving surfaces using various measuring instruments.

© 2018 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license: http://creativecommons.org/licenses/by-nc-nd/4.0/ This author accepted manuscript is made available following 24 month embargo from date of publication (April 2018) in accordance with the publisher’s archiving policy, This paper presents the design and development of a smartphone-based urinalysis device that has the ability for chronic kidney disease (CKD) patients themselves to conduct rapid and reliable quantitative urinalysis of human serum albumin (HSA) using an aggregation-induced emission (AIE) nanomaterial bioprobe with their own smartphones. The focus of this paper is a novel solution to the device agnosticism issue as a wide diversity of smartphones co-exist in the market. The solution comprises: a) custom-design and fabrication of an imaging housing that provides a consistent imaging condition regardless of the physical dimensions and the camera position of the smartphone used, b) orchestration of an image processing and analysis process that produces consistent image colour intensity values regardless of the camera sensor and imaging software used by the smartphone, and c) special design and development of an intuitive cross-platform mobile application that is scalable to growth, adaptable to changes, resilient to loss of data, and has an extremely low requirement for smartphone hardware. Preliminary evaluation of the device has confirmed the effectiveness of the proposed solution and the viability of such a smartphone-based device for people who have already developed or are prone to CKD to regularly perform point-of-care (POC) urine testing in order to self monitor their own health conditions without the burden of frequent visits to their doctors.

Published by Elsevier. This is the Author Accepted Manuscript issued with: Creative Commons Attribution Non-Commercial Licence (CC:BY:NC 4.0). The final published version (version of record) is available online at DOI: 10.1016/j.jnca.2018.03.022 . Please refer to any applicable publisher terms of use., Channel availability probability (CAP) and channel quality (CQ) are two key metrics that can be used to efficiently design a channel selection strategy in cognitive radio networks. For static scenarios, i.e., where all the users are immobile, the CAP metric depends only on the primary users' activity whereas the CQ metric remains relatively constant. In contrast, for mobile scenarios, the values of both metrics fluctuate not only with time (time-variant) but also over different links between users (link-variant) due to the dynamic variation of primary- and secondary-users' relative positions. As an attempt to address this dynamic fluctuation, this paper proposes L-CAQ: a link-oriented channel-availability and channel-quality based channel selection strategy that aims to maximize the link throughput. The L-CAQ scheme considers accurate estimation of the aforementioned two channel selection metrics, which are governed by the mobility-induced non-stationary network topology, and endeavors to select a channel that jointly maximizes the CAP and CQ. The benefits of the proposed scheme are demonstrated through numerical simulation for mobile cognitive radio networks.

The inclusion of Renewable Energy Resources (RER) and Electric Energy Storage (EES) can significantly improve the reliability of rural feeder customers with no cross connect switches to alternative supply. In such setups, there can be a financial incentive for aggregators to facilitate bulk storage to deal electricity with energy supplier and customers by using optimal scheduling strategy. Within this context, this paper proposes a framework for network reliability assessment to include bulk storage scheduling strategy in the evaluation. In this technique, seasonal effects on load demand and RER output, electricity market price, islanding provisions and EES state of charge (SOC) are taken into consideration. Finally, a case study is presented to illustrate the application of this approach and to evaluate the results.

Novel lighting strategies have the potential to create luminous environments that are more satisfactory to building occupants and reduce occupants' interventions in lighting conditions. Testing innovative design systems in the immersive virtual reality (IVR) environment could be a useful approach to investigate these systems quickly and easily. This paper explores how increasing the luminance of areas surrounding the window using an electric wall-washing system could improve subjective rated contrast (RC) scores on the windowed wall, as well as reducing negative lighting interventions in an IVR office room with different window-to-exterior-wall ratios. The results indicate that participants report greater lighting contrast between the window and its surroundings in the room with a 15% window-to-exterior-wall ratio (WWR) compared with other lighting conditions. The findings of this research also show that the proposed electric wall-washing system with a low power level could significantly reduce the likelihood of users' propensity to intervene in lighting conditions in rooms with different window sizes. Refereed/Peer-reviewed

As a main driving force of electric vehicles (EVs), the losses of in-wheel permanent-magnet synchronous motor (PMSM) direct drive system can seriously affect the energy consumption of EVs. This paper proposes a loss optimization control strategy for in-wheel PMSM direct drive system of EVs which optimizes the losses of both the PMSM and the inverter. The proposed method adjusts the copper losses and iron losses by identifying the optimal flux-weakening current, which results in the PMSM achieving the lower losses in the whole operational range. Moreover there are strongly nonlinear characteristics for the power devices, this paper creates a nonlinear loss model for three-phase half-bridge inverters to obtain accurate inverter losses under space vector pulse width modulation (SVPWM). Based on the inverter loss model and double Fourier integral analysis theory, the PWM frequency is optimized by the control strategy in order to maximize the inverter efficiency without affecting the operational stability of the drive. The proposed loss optimization control strategy can quickly find the optimum flux-weakening current and PWM frequency, and as a result, significantly broaden the high efficiency area of the PMSM direct drive system. The effects of the aforementioned strategy are verified by both theoretical analysis and experimental results.

Recently, many meta-heuristic algorithms have been proposed to serve as the basis of a t-way test generation strategy (where t indicates the interaction strength) including Genetic Algorithms (GA), Ant Colony Optimization (ACO), Simulated Annealing (SA), Cuckoo Search (CS), Particle Swarm Optimization (PSO), and Harmony Search (HS). Although useful, meta-heuristic algorithms that make up these strategies often require specific domain knowledge in order to allow effective tuning before good quality solutions can be obtained. Hyper-heuristics provide an alternative methodology to meta-heuristics which permit adaptive selection and/or generation of meta-heuristics automatically during the search process. This paper describes our experience with four hyper-heuristic selection and acceptance mechanisms namely Exponential Monte Carlo with counter (EMCQ), Choice Function (CF), Improvement Selection Rules (ISR), and newly developed Fuzzy Inference Selection (FIS), using the t-way test generation problem as a case study. Based on the experimental results, we offer insights on why each strategy differs in terms of its performance.

This work was supported by the research projects TIN2014-57251-P, TIN2013-47210-P and P12-TIC-2958. D. Peralta holds an FPU scholarship from the Spanish Ministry of Education and Science (FPU12/04902). I. Triguero holds a BOF postdoctoral fellowship from the Ghent University., Nowadays, many companies and institutions need fast and reliable identification systems that are able to deal with very large databases. Fingerprints are among the most used biometric traits for identification. In the current literature there are fingerprint matching algorithms that are focused on efficiency, whilst others are based on accuracy. In this paper we propose a flexible dual phase identification method, called DPD-DFF, that combines two fingers and two matchers within a hybrid fusion scheme to obtain both fast and accurate results. Different alternatives are designed to find a trade-off between runtime and accuracy that can be further tuned with a single parameter. The experiments show that DPD-DFF obtains very competitive results in comparison with the state-of-the-art score fusion techniques, especially when dealing with large databases or impostor fingerprints., Spanish Government FPU12/04902, Ghent University, TIN2014-57251-P, TIN2013-47210-P, P12-TIC-2958

The purpose of this research to explore the use of modelling in the field of Purchasing and Supply Management (P/SM). We are particularly interested in identifying the specific areas of P/SM where there are opportunities for the use of modelling based methods. The paper starts with an overview of main types of modelling and also provides a categorisation of the main P/SM research themes. Our research shows that there are many opportunities for using descriptive, predictive and prescriptive modelling approaches in all areas of P/SM research from the ones with a focus on the actual function from a purely operational and execution perspective (e.g. purchasing processes and behaviour) to the ones with a focus on the organisational level from a more strategic perspective (e.g. strategy and policy). We conclude that future P/SM research needs to explore the value of modelling not just at the functional or operational level, but also at the organisation and strategic level respectively. We also acknowledge that while using empirical results to inform and improve models has advantages, there are also drawbacks, which relate to the value, the practical relevance and the generalisability of the modelling based approaches.

Organisations use Enterprise Architecture (EA) to reduce organisational complexity, improve communication, align business and information technology (IT), and drive organisational change. Due to the dynamic nature of environmental and organisational factors, EA descriptions need to change over time to keep providing value for its stakeholders. Emerging business and IT trends, such as Service-Oriented Architecture (SOA), may impact EA frameworks, methodologies, governance and tools. However, the phenomenon of EA evolution is still poorly understood. Using Archer's morphogenetic theory as a foundation, this research conceptualises three analytical phases of EA evolution in organisations, namely conditioning, interaction and elaboration. Based on a case study with a government agency, this paper provides new empirically and theoretically grounded insights into EA evolution, in particular in relation to the introduction of SOA, and describes relevant generative mechanisms affecting EA evolution. By doing so, it builds a foundation to further examine the impact of other IT trends such as mobile or cloud-based solutions on EA evolution. At a practical level, the research delivers a model that can be used to guide professionals to manage EA and continually evolve it.

Provision of daylighting and external view in hospitals is crucial as they create a positive healing environment. They could help in reducing patient stress, fatigue and length of stay, while increasing patient and staff safety and satisfaction. However, in desert locations that are typically characterized by year-long clear skies, control of solar penetration is essential. Window blinds were used to decrease patients’ visual discomfort and improve illuminance levels. The shape of blind slats influences daylighting performance and exposure to external view. The objective of this paper was to identify the shapes of horizontal blind slats that best suit a common hospital patient room layout design under the predominantly clear-sky of Cairo, Egypt. The paper utilized parametric tools and simulation software to systematically test possible slat shapes. A total of 77 slat shapes were examined. The objective was to fulfill year-round daylighting adequacy on the bed surface and the room area, and to maximize patient’s access to external view. The shading system examined in this paper was an internal horizontal blind that looks similar to Venetian blinds, but fixed with no movement. The shape of all slats of the tested blinds was simultaneously adjusted for examination. The effect of changing slat shapes was examined by evaluating daylighting performance in terms of Spatial Daylight Autonomy (sDA) and Annual Sunlight Exposure (ASE) on both the bed surface and the room surface planes. Exposure to external view, expressed as External View Factor (EVF) was also addressed. The two tips of each slat were kept fixed in position. The position of an intermediate point on the slat surface was systematically adjusted in the horizontal and vertical directions creating curved blind slat shapes. Results revealed that the sDA on the room surface plane was the limiting factor in determining acceptable slat shapes. The range of acceptable slat shapes according to the sDA on the bed surface criterion was larger than that of the whole room area. In addition, all tested slat shapes provided acceptable ASE performance. Blinds with flat or gently curved slat shapes scored better results in both daylighting and external view exposure. Position of the intermediate point of the slat – in most of the accepted cases – fell in a narrow vertical range around the horizontal base line. Changing the position of the intermediate point horizontally in either direction resulted in a marginal effect on the performance of the accepted cases. It was observed that the closer the intermediate point to the center of the slat, the better was the performance.

An exhaustive review on the use of structured stochastic search approaches towards system identification and digital filter design is presented in this paper. In particular, the paper focuses on the identification of various systems using infinite impulse response adaptive filters and Hammerstein models as well as on the estimation of chaotic systems. In addition to presenting a comprehensive review on the various swarm and evolutionary computing schemes employed for system identification as well as digital filter design, the paper is also envisioned to act as a quick reference for a few popular evolutionary computing algorithms., by Akhilesh Gotmare, Sankha Subhra Bhattacharjee, Rohan Patidar and Nithin V. George

This paper presents a two-output indirect matrix converter feeding an open-ended winding induction machine. The modulation strategy for the converter input stage, which provides the DC voltage for the output stages, exploits the capability of the input rectifier to produce different DC voltage levels. Moreover, this paper includes a space vector modulation strategy for the converter output stages intended to eliminate the zero sequence load voltage. Furthermore, in order to decrease commutation losses, output stage commutation will take place at reduced voltage when load voltage requirements are low. Modulation strategies and overall system operation are verified via simulation in a PSim/Matlab platform with the machine operating under an open loop V/f control strategy. Experimental results are also presented to validate the control strategies. A topology for an open-end winding induction motor drive is presented.The topology is based on an Indirect Matrix Converter.Space Vector Modulation (SVM) strategies are presented for the converter.Occurrence of machine zero sequence voltage is eliminated by the SVM proposed.The system is modeled and simulated in Matlab/Psim platform.

As part of the cosmos of digital fabrication technology, Additive Manufacturing (AM) systems are able to manufacture three-dimensional components and products directly from raw material and 3D design data. The layer-by-layer operating process of these systems does not require the use of tools, moulds or dies. Technology observers speculate that AM will have a profound economic impact on the manufacturing sector and indeed on wider society. By constructing a model of production cost for two different AM systems used commercially for the manufacture of end-use metal parts, Electron Beam Melting (EBM) and Direct Metal Laser Sintering (DMLS), this paper performs an inter-process comparison of cost performance. High specific costs, measured at £2.39 and £6.18 per cm3 of material deposited respectively, are identified as a central impediment to more widespread technology adoption of such additive systems. The research demonstrates differing levels of system productivity, suggesting that the observed deposition rates are not sufficient for the adoption of EBM and DMLS in high volume manufacturing applications. Despite the absence of amortisable tooling costs, the analysis also reveals that economies of scale are achievable in AM. The results reached are further discussed in the light of the varying strategic requirements posed by the market-pull and technology-push modes of innovation which are both found in the AM industry.

Display Omitted Bradford Hill's causality considerations were used as a basis to engineer features.ADR reference sets were used to create labels by identifying known ADR/non-ADRs.The supervised ADR detection approach resulted in a low false positive rate.The supervised ADR detection approach outperformed existing unsupervised methods. Big longitudinal observational medical data potentially hold a wealth of information and have been recognised as potential sources for gaining new drug safety knowledge. Unfortunately there are many complexities and underlying issues when analysing longitudinal observational data. Due to these complexities, existing methods for large-scale detection of negative side effects using observational data all tend to have issues distinguishing between association and causality. New methods that can better discriminate causal and non-causal relationships need to be developed to fully utilise the data.In this paper we propose using a set of causality considerations developed by the epidemiologist Bradford Hill as a basis for engineering features that enable the application of supervised learning for the problem of detecting negative side effects. The Bradford Hill considerations look at various perspectives of a drug and outcome relationship to determine whether it shows causal traits. We taught a classifier to find patterns within these perspectives and it learned to discriminate between association and causality. The novelty of this research is the combination of supervised learning and Bradford Hill's causality considerations to automate the Bradford Hill's causality assessment.We evaluated the framework on a drug safety gold standard known as the observational medical outcomes partnership's non-specified association reference set. The methodology obtained excellent discrimination ability with area under the curves ranging between 0.792 and 0.940 (existing method optimal: 0.73) and a mean average precision of 0.640 (existing method optimal: 0.141). The proposed features can be calculated efficiently and be readily updated, making the framework suitable for big observational data.

Proposed a novel extension to Histogram Equalization method for noise compensation.We perform a sub-band specific equalization on the noisy cepstral features.Histogram analysis and recognition results show usefulness of the proposed approach.Favorable in real-time systems due to superior performance and computational benifits. This paper describes a novel framework to sub-band based Histogram Equalization (HEQ) applied to robust speech recognition. We propose a frequency band specific equalization to compensate the noise distortion on the individual frequency bands. The proposed equalization framework is a two step process. In the first step, conventional histogram equalization is done. By analyzing the histograms of equalized cepstra, we show that the first stage of conventional HEQ approach does not compensate the sub-band specific noise distortion, even though the overall histogram is normalized. Hence, in the second stage, sub-band specific histogram equalization is done. Every frame of cepstral coefficients is decomposed into low-frequency (LF) cepstra and high-frequency (HF) cepstra. Separate equalization is done on LF and HF cepstra to compensate LF and HF specific noise distortion. The cepstra corresponding to the LF and HF bands are obtained by using simple averaging and differencing filters on the cepstral components within a particular frame. The proposed approach is referred to as Sub-band Histogram Equalization (S-HEQ). Using histogram analysis, we show that the S-HEQ approach is able to compensate for the sub-band specific noise distortion. S-HEQ approach shows a consistent improvement over the conventional HEQ approach with a relative improvement of 12 % and 22.10 % over conventional HEQ in WER on Aurora-2 and Aurora-4 databases respectively. Proposed equalization approach can also be used with the deep neural network based systems and has shown a consistent improvement in the recognition accuracies over conventional HEQ. Finally, the efficacy of the proposed S-HEQ approach for embedded real-time speech applications is shown by comparing the performance and computational complexity trade-off with other state-of-the-art noise compensation methods.

This paper describes a novel system for automatic classification of images obtained from Anti-Nuclear Antibody (ANA) pathology tests on Human Epithelial type 2 (HEp-2) cells using the Indirect Immunofluorescence (IIF) protocol. The IIF protocol on HEp-2 cells has been the hallmark method to identify the presence of ANAs, due to its high sensitivity and the large range of antigens that can be detected. However, it suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg. speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. We propose a novel automatic cell image classification method termed Cell Pyramid Matching (CPM), which is comprised of regional histograms of visual words coupled with the Multiple Kernel Learning framework. We present a study of several variations of generating histograms and show the efficacy of the system on two publicly available datasets: the ICPR HEp-2 cell classification contest dataset and the SNPHEp-2 dataset., arXiv admin note: substantial text overlap with arXiv:1304.1262

Automated process discovery techniques aim at extracting process models from information system logs. Existing techniques in this space are effective when applied to relatively small or regular logs, but generate spaghetti-like and sometimes inaccurate models when confronted to logs with high variability. In previous work, trace clustering has been applied in an attempt to reduce the size and complexity of automatically discovered process models. The idea is to split the log into clusters and to discover one model per cluster. This leads to a collection of process models - each one representing a variant of the business process - as opposed to an all-encompassing model. Still, models produced in this way may exhibit unacceptably high complexity and low fitness. In this setting, this paper presents a two-way divide-and-conquer process discovery technique, wherein the discovered process models are split on the one hand by variants and on the other hand hierarchically using subprocess extraction. Splitting is performed in a controlled manner in order to achieve user-defined complexity or fitness thresholds. Experiments on real-life logs show that the technique produces collections of models substantially smaller than those extracted by applying existing trace clustering techniques, while allowing the user to control the fitness of the resulting models.

This paper presents a segment-based probabilistic approach to robustly recognize continuous sign language sentences. The recognition strategy is based on a two-layer conditional random field (CRF) model, where the lower layer processes the component channels and provides outputs to the upper layer for sign recognition. The continuously signed sentences are first segmented, and the sub-segments are labeled SIGN or ME (movement epenthesis) by a Bayesian network (BN) which fuses the outputs of independent CRF and support vector machine (SVM) classifiers. The sub-segments labeled as ME are discarded and the remaining SIGN sub-segments are merged and recognized by the two-layer CRF classifier; for this we have proposed a new algorithm based on the semi-Markov CRF decoding scheme. With eight signers, we obtained a recall rate of 95.7% and a precision of 96.6% for unseen samples from seen signers, and a recall rate of 86.6% and a precision of 89.9% for unseen signers., by W.W. Konga and Surendra Ranganath

Stigmergy is a biological term used when discussing a sub-set of insect swarm-behaviour describing the apparent organisation seen during their activities. Stigmergy describes a communication mechanism based on environment-mediated signals which trigger responses among the insects. This phenomenon is demonstrated in the behavior of ants and their food gathering process when following pheromone trails, where the pheromones are a form of environment-mediated communication. What is interesting with this phenomenon is that highly organized societies are achieved without an apparent management structure. Stigmergy is also observed in human environments, both natural and engineered. It is implicit in the Web where sites provide a virtual environment supporting coordinative contributions. Researchers in varying disciplines appreciate the power of this phenomenon and have studied how to exploit it. As stigmergy becomes more widely researched we see its definition mutate as papers citing original work become referenced themselves. Each paper interprets these works in ways very specific to the research being conducted. Our own research aims to better understand what improves the collaborative function of a Web site when exploiting the phenomenon. However when researching stigmergy to develop our understanding we discover a lack of a standardized and abstract model for the phenomenon. Papers frequently cited the same generic descriptions before becoming intimately focused on formal specifications of an algorithm, or esoteric discussions regarding sub-facets of the topic. None provide a holistic and macro-level view to model and standardize the nomenclature. This paper provides a content analysis of influential literature documenting the numerous theoretical and experimental papers that have focused on stigmergy. We establish that stigmergy is a phenomenon that transcends the insect world and is more than just a metaphor when applied to the human world. We present from our own research our general theory and abstract model of semantics of stigma in stigmergy. We hope our model will clarify the nuances of the phenomenon into a useful road-map, and standardise vocabulary that we witness becoming confused and divergent. Furthermore, this paper documents the analysis on which we base our next paper: Special Theory of Stigmergy: A Design Pattern for Web 2.0 Collaboration.

3GPP has developed the Discontinuous Reception (DRX) power saving mechanism, which periodically turns off the radio interface to reduce the power consumption. However, packets cannot be received during the turn-off duration, as a result that the Quality of Services (QoS) may be violated. Therefore, optimal DRX parameters should be configured to satisfy QoS and minimize power consumption. In addition, channel condition may be unstable during the transmission, so the DRX parameters should also be dynamically adjusted. In this paper, we propose the Dynamic Scheduling with Extensible Allocation and Dispersed Offsets (DXD) scheme, which is composed of two algorithms, DRX parameters decision, to determine DRX period by considering channel condition and QoS constraints, and DRX-aware scheduling, to determine whether to extend the on duration so that QoS would not be affected by DRX. Simulation results demonstrate that the proposed DXD approach can reduce power consumption up to 96.7% compared to No-DRX scheme and guarantee QoS as good as No-DRX scheme. In addition, the power consumption can be reduced by 70.1% and 45.7% compared to the Fixed-DRX (100 ms), start-offset scheme and TS scheme in good channel condition. Our approach effectively reduces power consumption in varied channel conditions and high loading system and still achieves better delay satisfaction ratio than others. � 2014 Elsevier B.V. All rights reserved.

The development of user expertise is a strategic imperative for organizations in hyper-competitive markets. This paper conceptualizes opreationalises and validates user expertise in contemporary Information Systems (IS) as a formative, multidimensional index. Such a validated and widely accepted index would facilitate progression of past research on user competence and efficacy of IS to complex contemporary IS, while at the same time providing a benchmark for organizations to track their user expertise. The validation involved three separate studies, including exploratory and confirmatory phases, using data from 244 respondents.

Through the rise of cloud computing, on-demand applications, and business networks, services are increasingly being exposed and delivered on the Internet and through mobile communications. So far, services have mainly been described through technical interface descriptions. The description of business details, such as pricing, service-level, or licensing, has been neglected and is therefore hard to automatically process by service consumers. Also, third-party intermediaries, such as brokers, cloud providers, or channel partners, are interested in the business details in order to extend services and their delivery and, thus, further monetize services. In this paper, the constructivist design of the Unified Service Description Language (USDL), aimed at describing services across the human-to-automation continuum, is presented. The proposal of USDL follows well-defined requirements which are expressed against a common service discourse and synthesized from currently available service description efforts. USDL's concepts and modules are evaluated for their support of the different requirements and use cases.

The suitability of Role Based Access Control (RBAC) is being challenged in dynamic environments like healthcare. In an RBAC system, a user's legitimate access may be denied if their need has not been anticipated by the security administrator at the time of policy specification. Alternatively, even when the policy is correctly specified an authorised user may accidentally or intentionally misuse the granted permission. The heart of the challenge is the intrinsic unpredictability of users' operational needs as well as their incentives to misuse permissions. In this paper we propose a novel Budget-aware Role Based Access Control (B-RBAC) model that extends RBAC with the explicit notion of budget and cost, where users are assigned a limited budget through which they pay for the cost of permissions they need. We propose a model where the value of resources are explicitly defined and an RBAC policy is used as a reference point to discriminate the price of access permissions, as opposed to representing hard and fast rules for making access decisions. This approach has several desirable properties. It enables users to acquire unassigned permissions if they deem them necessary. However, users misuse capability is always bounded by their allocated budget and is further adjustable through the discrimination of permission prices. Finally, it provides a uniform mechanism for the detection and prevention of misuses.

Network coding is a new communication technique that generalizes routing, where, instead of simply forwarding the packets they receive, intermediate nodes are allowed to recombine (code) together some of the data packets received from different incoming links if necessary. By doing so, the maximum information flow in a network can always be achieved. However, performing coding operations (i.e. recombining data packets) incur computational overhead and delay of data processing at the corresponding nodes. In this paper, we investigate the optimization of the network coding based multicast routing problem with respect to two widely considered objectives, i.e. the cost and the delay. In general, reducing cost can result into a cheaper multicast solution for network service providers, while decreasing delay improves the service quality for users. Hence we model the problem as a bi-objective optimization problem to minimize the total cost and the maximum transmission delay of a multicast. This bi-objective optimization problem has not been considered in the literature. We adapt the Elitist Nondominated Sorting Genetic Algorithm (NSGA-II) for the new problem by introducing two adjustments. As there are many infeasible solutions in the search space, the first adjustment is an initialization scheme to generate a population of feasible and diversified solutions. These initial solutions help to guide the search towards the Pareto-optimal front. In addition, the original NSGA-II is very likely to produce a number of solutions with identical objective values at each generation, which may seriously deteriorate the level of diversity and the optimization performance. The second adjustment is an individual delegate scheme where, among those solutions with identical objective values, only one of them is retained in the population while the others are deleted. Experimental results reveal that each adopted adjustment contributes to the adaptation of NSGA-II for the problem concerned. Moreover, the adjusted NSGA-II outperforms a number of state-of-the-art multiobjective evolutionary algorithms with respect to the quality of the obtained nondominated solutions in the conducted experiments.

This paper proposes the use of Bayesian approaches with the cross likelihood ratio (CLR) as a criterion for speaker clustering within a speaker diarization system, using eigenvoice modelling techniques. The CLR has previously been shown to be an effective decision criterion for speaker clustering using Gaussian mixture models. Recently, eigenvoice modelling has become an increasingly popular technique, due to its ability to adequately represent a speaker based on sparse training data, as well as to provide an improved capture of differences in speaker characteristics. The integration of eigenvoice modelling into the CLR framework to capitalize on the advantage of both techniques has also been shown to be beneficial for the speaker clustering task. Building on that success, this paper proposes the use of Bayesian methods to compute the conditional probabilities in computing the CLR, thus effectively combining the eigenvoice-CLR framework with the advantages of a Bayesian approach to the diarization problem. Results obtained on the 2002 Rich Transcription (RT-02) Evaluation dataset show an improved clustering performance, resulting in a 33.5% relative improvement in the overall diarization error rate (DER) compared to the baseline system.

Process modeling is an important design practice in organizational improvement projects. In this paper, we examine the design of business process diagrams in contexts where novice analysts only have basic design tools such as paper and pencils available, and little to no understanding of formalized modeling approaches. Based on a quasi-experimental study with 89 BPM students, we identify five distinct process design archetypes ranging from textual to hybrid and graphical representation forms. We examine the quality of the designs and identify which representation formats enable an analyst to articulate business rules, states, events, activities, temporal and geospatial information in a process model. We found that the quality of the process designs decreases with the increased use of graphics and that hybrid designs featuring appropriate text labels and abstract graphical forms appear well-suited to describe business processes. We further examine how process design preferences predict formalized process modeling ability. Our research has implications for practical process design work in industry as well as for academic curricula on process design.

Process-aware information systems, ranging from generic workflow systems to dedicated enterprise information systems, use work-lists to offer so-called work items to users. In real scenarios, users can be confronted with a very large number of work items that stem from multiple cases of different processes. In this jungle of work items, users may find it hard to choose the right item to work on next. The system cannot autonomously decide which is the right work item, since the decision is also dependent on conditions that are somehow outside the system. For instance, what is ''best'' for an organisation should be mediated with what is ''best'' for its employees. Current work-list handlers show work items as a simple sorted list and therefore do not provide much decision support for choosing the right work item. Since the work-list handler is the dominant interface between the system and its users, it is worthwhile to provide an intuitive graphical interface that uses contextual information about work items and users to provide suggestions about prioritisation of work items. This paper uses the so-called map metaphor to visualise work items and resources (e.g., users) in a sophisticated manner. Moreover, based on distance notions, the work-list handler can suggest the next work item by considering different perspectives. For example, urgent work items of a type that suits the user may be highlighted. The underlying map and distance notions may be of a geographical nature (e.g., a map of a city or office building), but may also be based on process designs, organisational structures, social networks, due dates, calendars, etc. The framework proposed in this paper is generic and can be applied to any process-aware information system. Moreover, in order to show its practical feasibility, the paper discusses a full-fledged implementation developed in the context of the open-source workflow environment YAWL, together with two real examples stemming from two very different scenarios. The results of an initial usability evaluation of the implementation are also presented, which provide a first indication of the validity of the approach.

The main purpose of this paper is to develop a conceptual framework for understanding the literature on the consequences of contemporary performance measurement (CPM) systems and the theories that explain these consequences. The framework is based on an in-depth review of 76 empirical studies published in high-quality academic journals in the areas of accounting, operations, and strategy. The framework classifies the consequences of CPM into three categories: people's behaviour, organizational capabilities, and performance consequences. This paper discusses our current knowledge on the impact of CPM, highlighting inconsistencies and gaps as well as providing direction for future research.

Increasingly, studies are reported that examine how conceptual modeling is conducted in practice. Yet, typically the studies to date have examined in isolation how modeling grammars can be, or are, used to develop models of information systems or organizational processes, without considering that such modeling is typically done by means of a modeling tool that extends the modeling functionality offered by a grammar through complementary features. This paper extends the literature by examining how the use of seven different features of modeling tools affects usage beliefs users develop when using modeling grammars for process modeling. We show that five distinct tool features positively affect usefulness, ease of use and satisfaction beliefs of users. We offer a number of interpretations about the findings. We also describe how the results inform decisions of relevance to developers of modeling tools as well as managers in charge for making modeling-related investment decisions.

Many modern business environments employ software to automate the delivery of workflows; whereas, workflow design and generation remains a laborious technical task for domain specialists. Several differ- ent approaches have been proposed for deriving workflow models. Some approaches rely on process data mining approaches, whereas others have proposed derivations of workflow models from operational struc- tures, domain specific knowledge or workflow model compositions from knowledge-bases. Many approaches draw on principles from automatic planning, but conceptual in context and lack mathematical justification. In this paper we present a mathematical framework for deducing tasks in workflow models from plans in mechanistic or strongly controlled work environments, with a focus around automatic plan generations. In addition, we prove an associative composition operator that permits crisp hierarchical task compositions for workflow models through a set of mathematical deduction rules. The result is a logical framework that can be used to prove tasks in workflow hierarchies from operational information about work processes and machine configurations in controlled or mechanistic work environments.

This paper presents work towards recognizing facial expressions that are used in sign language communication. Facial features are tracked to effectively capture temporal visual cues on the signers' face during signing. Face shape constraints are used for robust tracking within a Bayesian framework. The constraints are specified through a set of face shape subspaces learned by Probabilistic Principal Component Analysis (PPCA). An update scheme is also used to adapt to persons with different face shapes. Two tracking algorithms are presented, which differ in the way the face shape constraints are enforced. The results show that the proposed trackers can track facial features with large head motions, substantial facial deformations, and temporary facial occlusions by hand. The tracked results are input to a recognition system comprising Hidden Markov Models (HMM) and a support vector machine (SVM) to recognize six isolated facial expressions representing grammatical markers in American sign language (ASL). Tracking error of less than four pixels (on 640×480 videos) was obtained with probability greater than 90%; in comparison the KLT tracker yielded this accuracy with 76% probability. Recognition accuracy obtained for ASL facial expressions was 91.76% in person dependent tests and 87.71% in person independent tests., by Tan Dat Nguyena and Surendra Ranganath

Audio-visual speech recognition, or the combination of visual lip-reading with traditional acoustic speech recognition, has been previously shown to provide a considerable improvement over acoustic-only approaches in noisy environments, such as that present in an automotive cabin. The research presented in this paper will extend upon the established audio-visual speech recognition literature to show that further improvements in speech recognition accuracy can be obtained when multiple frontal or near-frontal views of a speaker's face are available. A series of visual speech recognition experiments using a four-stream visual synchronous hidden Markov model (SHMM) are conducted on the four-camera AVICAR automotive audio-visual speech database. We study the relative contribution between the side and central orientated cameras in improving visual speech recognition accuracy. Finally combination of the four visual streams with a single audio stream in a five-stream SHMM demonstrates a relative improvement of over 56% in word recognition accuracy when compared to the acoustic-only approach in the noisiest conditions of the AVICAR database.

This paper presents a practical scheme to control heave motion for hover and automatic landing of a Rotary-wing Unmanned Aerial Vehicle (RUAV) in the presence of strong horizontal gusts. A heave motion model is constructed for the purpose of capturing dynamic variations of thrust due to horizontal gusts. Through the construction of an effective gust estimator, a feedback-feedforward controller is developed which uses available measurements from onboard sensors. The proposed controller dynamically and synchronously compensates for aerodynamic variations of heave motion, enhancing disturbance-attenuation capability of the RUAV. Simulation results justify the reliability and efficiency of the suggested gust estimator. Moreover, flight tests conducted on our Eagle helicopter verify suitability of the proposed control strategy for small RUAVs operating in a gusty environment.

Process compliance measurement is getting increasing attention in companies due to stricter legal requirements and market pressure for operational excellence. In order to judge on compliance of the business processing, the degree of behavioural deviation of a case, i.e., an observed execution sequence, is quantified with respect to a process model (referred to as fitness, or recall). Recently, different compliance measures have been proposed. Still, nearly all of them are grounded on state-based techniques and the trace equivalence criterion, in particular. As a consequence, these approaches have to deal with the state explosion problem. In this paper, we argue that a behavioural abstraction may be leveraged to measure the compliance of a process log – a collection of cases. To this end, we utilise causal behavioural profiles that capture the behavioural characteristics of process models and cases, and can be computed efficiently. We propose different compliance measures based on these profiles, discuss the impact of noise in process logs on our measures, and show how diagnostic information on non-compliance is derived. As a validation, we report on findings of applying our approach in a case study with an international service provider.

Association rule mining has contributed to many advances in the area of knowledge discovery. However, the quality of the discovered association rules is a big concern and has drawn more and more attention recently. One problem with the quality of the discovered association rules is the huge size of the extracted rule set. Often for a dataset, a huge number of rules can be extracted, but many of them can be redundant to other rules and thus useless in practice. Mining non-redundant rules is a promising approach to solve this problem. In this paper, we first propose a definition for redundancy, then propose a concise representation, called a Reliable basis, for representing non-redundant association rules. The Reliable basis contains a set of non-redundant rules which are derived using frequent closed itemsets and their generators instead of using frequent itemsets that are usually used by traditional association rule mining approaches. An important contribution of this paper is that we propose to use the certainty factor as the criterion to measure the strength of the discovered association rules. Using this criterion, we can ensure the elimination of as many redundant rules as possible without reducing the inference capacity of the remaining extracted non-redundant rules. We prove that the redundancy elimination, based on the proposed Reliable basis, does not reduce the strength of belief in the extracted rules. We also prove that all association rules, their supports and confidences, can be retrieved from the Reliable basis without accessing the dataset. Therefore the Reliable basis is a lossless representation of association rules. Experimental results show that the proposed Reliable basis can significantly reduce the number of extracted rules. We also conduct experiments on the application of association rules to the area of product recommendation. The experimental results show that the non-redundant association rules extracted using the proposed method retain the same inference capacity as the entire rule set. This result indicates that using non-redundant rules only is sufficient to solve real problems needless using the entire rule set.

Over recent years, many scholars have studied the conceptual modeling of information systems based on a theory of ontological expressiveness. This theory offers four constructs that inform properties of modeling grammars in the form of ontological deficiencies, and their implications for development and use of conceptual modeling in IS practice. In this paper we report on the development of a valid and reliable instrument for measuring the perceptions that individuals have of the ontological deficiencies of conceptual modeling grammars. We describe a multi-stage approach for instrument development that incorporates feedback from expert and user panels. We also report on a field test of the instrument with 590 modeling practitioners. We further study how different levels of modeling experience influence user perceptions of ontological deficiencies of modeling grammars. We provide implications for practice and future research.

The cascading appearance-based (CAB) feature extraction technique has established itself as the state-of-the-art in extracting dynamic visual speech features for speech recognition. In this paper, we will focus on investigating the effectiveness of this technique for the related speaker verification application. By investigating the speaker verification ability of each stage of the cascade we will demonstrate that the same steps taken to reduce static speaker and environmental information for the visual speech recognition application also provide similar improvements for visual speaker recognition. A further study is conducted comparing synchronous HMM (SHMM) based fusion of CAB visual features and traditional perceptual linear predictive (PLP) acoustic features to show that higher complexity inherit in the SHMM approach does not appear to provide any improvement in the final audio-visual speaker verification system over simpler utterance level score fusion.

Single clustering methods have often been used to elucidate clusters in high dimensional medical data, even though reliance on a single algorithm is known to be problematic. In this paper, we present a methodology to determine a set of ‘core classes’ by using a range of techniques to reach consensus across several different clustering algorithms, and to ascertain the key characteristics of these classes. We apply the methodology to immunohistochemical data from breast cancer patients. In doing so, we identify six core classes, of which several may be novel sub-groups not previously emphasised in literature.

Emerging data streaming applications in Wireless Sensor Networks require reliable and energy-efficient Transport Protocols. Our recent Wireless Sensor Network deployment in the Burdekin delta, Australia, for water monitoring [T. Le Dinh, W. Hu, P. Sikka, P. Corke, L. Overs, S. Brosnan, Design and deployment of a remote robust sensor network: experiences from an outdoor water quality monitoring network, in: Second IEEE Workshop on Practical Issues in Building Sensor Network Applications (SenseApp 2007), Dublin, Ireland, 2007] is one such example. This application involves streaming sensed data such as pressure, water flow rate, and salinity periodically from many scattered sensors to the sink node which in turn relays them via an IP network to a remote site for archiving, processing, and presentation. While latency is not a primary concern in this class of application (the sampling rate is usually in terms of minutes or hours), energy-efficiency is. Continuous long-term operation and reliable delivery of the sensed data to the sink are also desirable. This paper proposes ERTP, an Energy-efficient and Reliable Transport Protocol for Wireless Sensor Networks. ERTP is designed for data streaming applications, in which sensor readings are transmitted from one or more sensor sources to a base station (or sink). ERTP uses a statistical reliability metric which ensures the number of data packets delivered to the sink exceeds the defined threshold. Our extensive discrete event simulations and experimental evaluations show that ERTP is significantly more energy-efficient than current approaches and can reduce energy consumption by more than 45% when compared to current approaches. Consequently, sensor nodes are more energy-efficient and the lifespan of the unattended WSN is increased.

The study described in this paper developed a model of animal movement, which explicitly recognised each individual as the central unit of measure. The model was developed by learning from a real dataset that measured and calculated, for individual cows in a herd, their linear and angular positions and directional and angular speeds. Two learning algorithms were implemented: a Hidden Markov model (HMM) and a long-term prediction algorithm. It is shown that a HMM can be used to describe the animal's movement and state transition behaviour within several “stay” areas where cows remained for long periods. Model parameters were estimated for hidden behaviour states such as relocating, foraging and bedding. For cows’ movement between the “stay” areas a long-term prediction algorithm was implemented. By combining these two algorithms it was possible to develop a successful model, which achieved similar results to the animal behaviour data collected. This modelling methodology could easily be applied to interactions of other animal species.

The dynamic interaction between building systems and external climate is extremely complex, involving a large number of difficult-to-predict variables. In order to study the impact of climate change on the built environment, the use of building simulation techniques together with forecast weather data are often necessary. Since most of building simulation programs require hourly meteorological input data for their thermal comfort and energy evaluation, the provision of suitable weather data becomes critical. In this paper, the methods used to prepare future weather data for the study of the impact of climate change are reviewed. The advantages and disadvantages of each method are discussed. The inherent relationship between these methods is also illustrated. Based on these discussions and the analysis of Australian historic climatic data, an effective framework and procedure to generate future hourly weather data is presented. It is shown that this method is not only able to deal with different levels of available information regarding the climate change, but also can retain the key characters of a “typical” year weather data for a desired period.

This paper concerns classification by Boolean functions. We investigate the classification accuracy obtained by standard classification techniques on unseen points (elements of the domain, {0,1}n, for some n) that are similar, in particular senses, to the points that have been observed as training observations. Explicitly, we use a new measure of how similar a point xset membership, variant{0,1}n is to a set of such points to restrict the domain of points on which we offer a classification. For points sufficiently dissimilar, no classification is given. We report on experimental results which indicate that the classification accuracies obtained on the resulting restricted domains are better than those obtained without restriction. These experiments involve a number of standard data-sets and classification techniques. We also compare the classification accuracies with those obtained by restricting the domain on which classification is given by using the Hamming distance.