Search

The Star-Planet Activity Research CubeSat (SPARCS) is positioned to revolutionize our understanding of M-dwarf star evolution, activity, variability, and the habitability of surrounding exoplanets. SPARCS will be the first mission to observe M stars for long periods of time simultaneously using a dual channel FUV (153 – 171 nm) and NUV (260 - 300 nm) imaging system. Anticipated to launch in 2023, SPARCS will provide key UV context to future observations by TESS and JWST, and the spaceflight application of advanced new detector technologies will pave the way for their implementation into future missions like LUVOIR and HabEx. To realize the scientific potential of SPARCS against the challenges of the ultraviolet spectrum, we are developing the specialized facilities, procedures, and tests necessary to assemble, integrate, and test the SPARCS science payload and spacecraft. A thorough testing campaign will verify the performance of individual payload components and obtain calibration baselines from the fully assembled science instrument that are vital to the data reduction process and in-flight contamination monitoring. SPARCS requires extensive contamination control to maintain its sensitivity in the FUV and NUV, which means all of AIT must occur in controlled and precisely monitored environments. This work will result in: (1) The delivery of the assembled and tested SPARCS spacecraft for launch in 2023. (2) A comprehensive performance validation and calibration baseline for SPARCS including a measurement of system throughput to for every wavelength across the SPARCS bandpasses, maps of NUV and FUV sensitivity across the payload field of view, and a full set of calibration products like flatfield images and dark current measurements for data reduction and comparison with calibration products acquired in orbit to monitor spacecraft conditions. (3) The establishment of a fully operational CubeSat AIT laboratory at ASU equipped to handle CubeSats up to 6U in size requiring meticulous contamination control up to the levels required for working in the FUV. This paper presents the work completed so far on the development and early operation of assembly, integration, and testing facilities for SPARCS. A custom thermal vacuum (TVAC) chamber facility was created and one of Arizona State University’s cleanroom environments was retrofitted to accommodate a 6U ultraviolet CubeSat requiring strict contamination control. We will describe the TVAC facility design and early testing, the cleanroom operation and contamination monitoring, and the development of an optical system and procedures to characterize the optical performance.

A 65 nm CMOS integrated circuit implementation of a bio-physiological signal compression device is presented, reporting exceptionally low power, and extremely low silicon area cost, relative to state-of-the-art. A novel ‘xor-log2-sub-band’ data compression scheme is evaluated, achieving modest compression, but with very low resource cost. With the intent to design the 'simplest useful compression algorithm’, the outcome is demonstrated to be very favourable where power must be saved by trading off compression effort against data storage capacity, or data transmission power, even where more complex algorithms can deliver higher compression ratios. A VLSI design and fabricated Integrated Circuit implementation are presented, and estimated performance gains and efficiency measures for various bio-medical use-cases are given. Power costs as low as 1.2 pJ per sample-bit are suggested for a 10 kSa/s data-rate, whilst utilizing a power-gating scenario, and dropping to 250 fJ/bit at continuous conversion data-rates of 5 MSa/sec. This is achieved with a diminutive circuit area of 155 um2. Both power and area appear to be state-of-the-art in terms of compression versus resource cost, and this yields benefit for system optimization.

Rodent electroencephalography (EEG) in preclinical research is frequently conducted in behaving animals. However, the difficulty inherent in identifying EEG epochs associated with a particular behavior or cue is a significant obstacle to more efficient analysis. In this paper we highlight a new solution, using infrared event stamping to accurately synchronize EEG, recorded from superficial sites above the hippocampus and prefrontal cortex, with video motion tracking data in a transgenic Alzheimer’s disease (AD) mouse model. Epochs capturing specific behaviors were automatically identified and extracted prior to further analysis. This was achieved by the novel design of a ultra- miniature wearable EEG recorder, the NAT-1 device, and its in- situ IR recording module. The device is described in detail, and its contribution to enabling new neuroscience is demonstrated.

Further space exploration in the far-infrared (FIR) requires larger apertures in order to improve the spatial resolution of captured images. To this purpose, the Thinned Aperture Light Collector (TALC) concept of a deployable annular telescope has been recently developed at CEA, which offers novel perspectives for FIR space missions. The consortium ELICSIR consortium of European institutes and companies has been created to improve the technological readiness level (TRL) of its key systems and components.

Despite their relative simplicity, correlation matrix memories (CMMs) are an active area of research, as they are able to be integrated into more complex architectures such as the Associative Rule Chaining Architecture (ARCA) "Austin et al. (International conference on artificial neural networks, pp 49---56, 2012)". In this architecture, CMMs are used effectively in order to reduce the time complexity of a tree search from $$O(b^d)$$ O ( b d ) to $$O(d)$$ O ( d ) --where $$b$$ b is the branching factor and $$d$$ d is the depth of the tree. This paper introduces the Extended Neural Associative Memory Language (ENAMeL)--a domain specific language developed to ease development of applications using CMMs. We discuss various considerations required while developing the language, and techniques used to reduce the memory requirements of CMM-based applications. Finally we show that the memory requirements of ARCA when using the ENAMeL interpreter compare favourably to our original results "Austin et al. (International conference on artificial neural networks, pp 49---56, 2012)" run in MATLAB.

We address the problem of automatically detecting a sparse set of 3D mesh vertices, likely to be good candidates for determining correspondences, even on soft organic objects. We focus on 3D face scans, on which single local shape descriptor responses are known to be weak, sparse or noisy. Our machine-learning approach consists of computing feature vectors containing $$D$$ different local surface descriptors. These vectors are normalized with respect to the learned distribution of those descriptors for some given target shape (landmark) of interest. Then, an optimal function of this vector is extracted that best separates this particular target shape from its surrounding region within the set of training data. We investigate two alternatives for this optimal function: a linear method, namely Linear Discriminant Analysis, and a non-linear method, namely AdaBoost. We evaluate our approach by landmarking 3D face scans in the FRGC v2 and Bosphorus 3D face datasets. Our system achieves state-of-the-art performance while being highly generic.

This paper proposes Logical Matching Strategy for sequential pattern matching. We show the two real-world applications of the method: (1) locate repeating sequential pattern and (2) alignment-free comparison of sequential pattern of finite length using fuzzy membership values that generate automatically from the number of matches and mismatches. The results show the utility of the method by analyzing DNA sequences taken from the National Center for Biotechnology Information (NCBI) databank. The Logical Matching Strategy can possibly be applied to develop a method of research in sequental pattern matching.

In the study of information flow in the nervous system, component processes can be investigated using a range of electrophysiological and imaging techniques. Although data is difficult and expensive to produce, it is rarely shared and collaboratively exploited. The Code Analysis, Repository and Modelling for e-Neuroscience (CARMEN) project addresses this challenge through the provision of a virtual neuroscience laboratory: an infrastructure for sharing data, tools and services. Central to the CARMEN concept are federated CARMEN nodes, which provide: data and metadata storage, new, thirdparty and legacy services, and tools. In this paper, we describe the CARMEN project as well as the node infrastructure and an associated thick client tool for pattern visualisation and searching, the Signal Data Explorer (SDE). We also discuss new spike detection methods, which are central to the services provided by CARMEN. The SDE is a client application which can be used to explore data in the CARMEN repository, providing data visualization, signal processing and a pattern matching capability. It performs extremely fast pattern matching and can be used to search for complex conditions composed of many different patterns across the large datasets that are typical in neuroinformatics. Searches can also be constrained by specifying text based metadata filters. Spike detection services which use wavelet and morphology techniques are discussed, and have been shown to outperform traditional thresholding and template based systems. A number of different spike detection and sorting techniques will be deployed as services within the CARMEN infrastructure, to allow users to benchmark their performance against a wide range of reference datasets.

The CARMEN Virtual Laboratory (VL) is a cloud-based platform which allows neuroscientists to store, share, develop, execute, reproduce and publicise their work. This paper describes new functionality in the CARMEN VL: an interactive publications repository. This new facility allows users to link data and software to publications. This enables other users to examine data and software associated with the publication and execute the associated software within the VL using the same data as the authors used in the publication. The cloud-based architecture and SaaS (Software as a Service) framework allows vast data sets to be uploaded and analysed using software services. Thus, this new interactive publications facility allows others to build on research results through reuse. This aligns with recent developments by funding agencies, institutions, and publishers with a move to open access research. Open access provides reproducibility and verification of research resources and results. Publications and their associated data and software will be assured of long-term preservation and curation in the repository. Further, analysing research data and the evaluations described in publications frequently requires a number of execution stages many of which are iterative. The VL provides a scientific workflow environment to combine software services into a processing tree. These workflows can also be associated with publications and executed by users. The VL also provides a secure environment where users can decide the access rights for each resource to ensure copyright and privacy restrictions are met.

There are many performance based techniques that aim to improve the safety of neural networks for safety critical applications. However, many of these approaches provide inadequate forms of safety assurance required for certification. As a result, neural networks are typically restricted to advisory roles in safety-related applications. Neural networks have the ability to operate in unpredictable and changing environments. It is therefore desirable to certify them for highly-dependable roles in safety critical systems. This paper outlines the safety criteria which are safety requirements for the behaviour of neural networks. If enforced, the criteria can contribute to justifying the safety of ANN functional properties. Characteristics of potential neural network models are also outlined and are based upon representing knowledge in interpretable and understandable forms. The paper also presents a safety lifecycle for artificial neural networks. This lifecycle focuses on managing behaviour represented by neural networks and contributes to providing acceptable forms of safety assurance.

The use of search engines within the Internet is now ubiquitous. This work examines how Grid technology may affect the implementation of search engines by focusing on the Signal Data Explorer application developed within the Distributed Aircraft Maintenance Environment (DAME) project. This application utilizes advanced neural-network-based methods (Advanced Uncertain Reasoning Architecture (AURA) technology) to search for matching patterns in time-series vibration data originating from Rolls-Royce aeroengines (jet engines). The large volume of data associated with the problem required the development of a distributed search engine, where data is held at a number of geographically disparate locations. This work gives a brief overview of the DAME project, the pattern marching problem, and the architecture. It also describes the Signal Data Explorer application and provides an overview of the underlying search engine technology and its use in the aeroengine health-monitoring domain.

Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review.

The availability of high frequency data sets in finance has allowed the use of very data intensive techniques using large data sets in forecasting. An algorithm requiring fast k-NN type search has been implemented using AURA, a binary neural network based upon Correlation Matrix Memories. This work has also constructed probability distribution forecasts, the volume of data allowing this to be done in a nonparametric manner. In assistance to standard statistical error measures the implementation of simulations has allowed actual measures of profit to be calculated.

In this paper, we propose a simple, flexible, and efficient hybrid spell checking methodology based upon phonetic matching, supervised learning, and associative matching in the AURA neural system. We integrate Hamming Distance and n-gram algorithms that have high recall for typing errors and a phonetic spell-checking algorithm in a single novel architecture. Our approach is suitable for any spell checking application though aimed toward isolated word error correction, particularly spell checking user queries in a search engine. We use a novel scoring scheme to integrate the retrieved words from each spelling approach and calculate an overall score for each matched word. From the overall scores, we can rank the possible matches. We evaluate our approach against several benchmark spellchecking algorithms for recall accuracy. Our proposed hybrid methodology has the highest recall rate of the techniques evaluated. The method has a high recall rate and low-computational cost.

The automatic identification of sleep stages in electroencephalography (EEG) time-series is a long desired goal for researchers concerned with the study of sleep disorders. This paper presents advances towards achieving this goal, with particular application to EEG time-series recorded from mice. Approaches in the literature apply supervised learning classifiers, however, these do not reach the performance levels required for use within a laboratory. In this paper, detection reliability is increased, most notably in the case of REM stage identification, by naturally decomposing the problem and applying a support vector machine (SVM) based classifier to each of the EEG channels. Their outputs are integrated within a multiple classifier system. Furthermore, there exists no general consensus on the ideal choice of parameter values in such systems. Therefore, an investigation into the effects upon the classification performance is presented by varying parameters such as the epoch length; features size; number of training samples; and the method for calculating the power spectral density estimate. Finally, the results of these investigations are brought together to demonstrate the performance of the proposed classification algorithm in two cases: intra-animal classification and inter-animal classification. It is shown that, within a dataset of 10 EEG recordings, and using less than 1% of an EEG as training data, a mean classification errors of Awake 6.45%, NREM 5.82%, and REM 6.65% (with standard deviations less than 0.6%) are achieved in intra-animal analysis and, when using the equivalent of 7% of one EEG as training data, Awake 10.19%, NREM 7.75%, and REM 17.43% are achieved in inter-animal analysis (with mean standard deviations of 6.42%, 2.89%, and 9.69% respectively). A software package implementing the proposed approach will be made available through Cybula Ltd.

In this paper, we propose a simple and flexible spell checker using efficient associative matching in the AURA modular neural system. Our approach aims to provide a pre-processor for an information retrieval (IR) system allowing the user's query to be checked against a lexicon and any spelling errors corrected, to prevent wasted searching. IR searching is computationally intensive so much so that if we can prevent futile searches we can minimise computational cost. We evaluate our approach against several commonly used spell checking techniques for memory-use, retrieval speed and recall accuracy. The proposed methodology has low memory use, high speed for word presence checking, reasonable speed for spell checking and a high recall rate.

In this paper, we propose a hierarchical, lexical clustering neural network algorithm that automatically generates a thesaurus (synonym abstraction) using purely stochastic information derived from unstructured text corpora and requiring no prior word classifications. The lexical hierarchy overcomes the Vocabulary Problem by accommodating paraphrasing through using synonym clusters and overcomes Information Overload by focusing search within cohesive clusters. We describe existing word categorisation methodologies, identifying their respective strengths and weaknesses and evaluate our proposed approach against an existing neural approach using a benchmark statistical approach and a human generated thesaurus for comparison. We also evaluate our word context vector generation methodology against two similar approaches to investigate the effect of word vector dimensionality and the effect of the number of words in the context window on the quality of word clusters produced. We demonstrate the effectiveness of our approach and its superiority to existing techniques.

This paper introduces a binary neural network-based prediction algorithm incorporating both spatial and temporal characteristics into the prediction process. The algorithm is used to predict short-term traffic flow by combining information from multiple traffic sensors (spatial lag) and time series prediction (temporal lag). It extends previously developed Advanced Uncertain Reasoning Architecture (AURA) k-nearest neighbour (k-NN) techniques. Our task was to produce a fast and accurate traffic flow predictor. The AURA k-NN predictor is comparable to other machine learning techniques with respect to recall accuracy but is able to train and predict rapidly. We incorporated consistency evaluations to determine whether the AURA k-NN has an ideal algorithmic configuration or an ideal data configuration or whether the settings needed to be varied for each data set. The results agree with previous research in that settings must be bespoke for each data set. This configuration process requires rapid and scalable learning to allow the predictor to be set-up for new data. The fast processing abilities of the AURA k-NN ensure this combinatorial optimisation will be computationally feasible for real-world applications. We intend to use the predictor to proactively manage traffic by predicting traffic volumes to anticipate traffic network problems.

We propose a hierarchical clustering algorithm (TreeGCS) based upon the Growing Cell Structure (GCS) neural network of B. Fritzke (1993). Our algorithm refines and builds upon the GCS base, overcoming an inconsistency in the original GCS algorithm, where the network topology is susceptible to the ordering of the input vectors. Our algorithm is unsupervised, flexible, and dynamic and we have imposed no additional parameters on the underlying GCS algorithm. Our ultimate aim is a hierarchical clustering neural network that is both consistent and stable and identifies the innate hierarchical structure present in vector-based data. We demonstrate improved stability of the GCS foundation and evaluate our algorithm against the hierarchy generated by an ascendant hierarchical clustering dendogram. Our approach emulates the hierarchical clustering of the dendogram. It demonstrates the importance of the parameter settings for GCS and how they affect the stability of the clustering.

This paper describes a novel search and pattern matching technology, AURA, based on neural networks. The technology exploits the simple and fast training of correlation matrix memories and their ability to match noisy and incomplete data. Unlike other neural network approaches, the method scales well to accommodate very large datasets, and has a simple and effective hardware implementation. It achieves this by storing basic feature data and relying on the fast matching ability of correlation matrix memory methods, as well as a two-stage processing approach. The paper demonstrates that the technology is applicable to a wide range of problems and shows how it has been implemented in dedicated hardware for high-performance applications.

This paper presents a study of two learning criteria and two approaches to using them for training neural network classifiers, specifically a Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) networks. The first approach, which is a traditional one, relies on the use of two popular learning criteria, i.e. learning via minimising a Mean Squared Error (MSE) function or a Cross Entropy (CE) function. It is shown that the two criteria have different charcteristics in learning speed and outlier effects, and that this approach does not necessarily result in a minimal classification error. To be suitable for classification tasks, in our second approach an empirical classification criterion is introduced for the testing process while using the MSE or CE function for the training. Experimental results on several benchmarks indicate that the second approach, compared with the first, leads to an improved generalisation performance, and that the use of the CE function, compared with the MSE function, gives a faster training speed and improved or equal generalisation performance.

This paper describes an improvement to the Cellular Associative Neural Network, an architecture based on the distributed model of a cellular automaton, allowing it to perform scale invariant pattern matching. The use of tensor products and superposition of patterns allows the system to recall patterns at multiple resolutions simultaneously. Our experimental results show that the architecture is capable of scale invariant pattern matching, but that further investigation is needed to reduce the distortion introduced by image scaling.

This paper proposes an algorithm for alignment-free sequence comparison using Logical Match. Here, we compute the score using fuzzy membership values which generate automatically from the number of matches and mismatches. We demonstrate the method with both the artificial and real datum. The results show the uniqueness of the proposed method by analyzing DNA sequences taken from NCBI databank with a novel computational time., Comment: Computer and Automation Engineering (ICCAE), 2010 The 2nd International Conference on

This paper proposes a method for sequential data mining using correlation matrix memory. Here, we use the concept of the Logical Match to mine the indices of the sequential pattern. We demonstrate the uniqueness of the method with both the artificial and the real datum taken from NCBI databank., Comment: Networking and Information Technology (ICNIT), 2010 International Conference on

We introduce a theoretical framework for estimating the matching performance of binary correlation matrices acting as hetero-associative memories. The framework is applicable to non-recursive, fully connected systems with binary (0, 1) Hebbian weights and hard-limited threshold. It can handle both full and partial matching of single or multiple data items in non-square memories. Theoretical development takes place under a probability theory framework. Inherent uncertainties in the matching process are accommodated through the use of probability distributions to describe the numbers of correct and incorrect neuron responses during retrieval. Theoretical predictions are verified experimentally for medium-sized memories and are used to aid in the design of larger systems. The results highlight the fact that correlation-based models can act as highly efficient memories provided a small probability of retrieval error is accepted.

We describe a new approach to sharing software simulations that is of great potential benefit to Artificial Life researchers. youShare is an online collaborative facility that allows users to upload data, and software in the form of services. An attached execution environment allows services to be run over a heterogeneous cluster of compute nodes, where the service infrastructure guarantees that the service will be executed in the correct environment, and provide consistent results. It allows software to be made available as a service regardless of the operating system they run upon. This allows software to be maintained more easily, and to be available to all researchers with internet access. We demonstrate this by making three Artificial Life simulations available over the web: Tierra, Avida and Stringmol. These services form the foundation of an ALife “Zoo”, in which visitors can interact with ALife simulations for research and education. In addition, youShare offers a workflow facility whereby multiple services can be connected to create more complex tasks. We demonstrate the utility of this system in Artificial Life research via a workflow which calculates evolutionary activity for runs of Tierra and Stringmol.

To date, in computing technology, wireless connections have not been good choices for connecting processors in a massively parallel system. This is mainly because wireless devices are considered to have both more energy demands and have lower data communication rates compared to their wireline counterparts. In light of developments in wireless technologies in the last decade, this paper investigates if (and, to what extent) that assumption is still valid. Capacitive coupling, inductive coupling and radio waves are reviewed. These technologies are compared with each other as well as with wireline technologies based on factors including data rate, energy consumption and occupied area. We survey the current trends and emerging technology and consider how they can be exploited. For an evaluation case we simulate a 3D wireless parallel platform. This model is the basis for our research to test the viability of a concept 3D wireless interconnect network for a massively parallel computer.

Service-orientation is effective at managing complexity and dynamicity at a programmatic level, but there is still much work to be done in understanding and improving the trust that users place in a system's outputs, and the extent to which they understand the associated risks of decisions recommended by a system. This is crucial if we are to improve the uptake and real-world effectiveness of service-based decision-support systems whilst also reducing the risks (both perceived and actual) of using such systems. This paper presents the current progress of the STRAPP project, which is designing and engineering novel trust and risk assessment mechanisms for services computing and applying these to a number of real-world service-based decision-support systems. A new layered architecture model for trust and risk is introduced and described in detail, and we present our state-of-the-art work in risk-assessment, demonstrating the relationship between provenance data and risk via a mathematical model. We then give a detailed description of our latest software demonstrator, integrated into the Rolls-Royce Equipment Health Management system, and comprehensively discuss the lessons we have learnt from developing such a complex, holistic, and applied real-world system. Finally, we describe the future work we plan to complete.

This paper describes improvements to the rule chaining architecture presented in [1]. The architecture uses distributed associative memories to allow the system to utilise memory efficiently, and superimposed distributed representations in order to reduce the time complexity of a tree search to O(d), where d is the depth of the tree. This new work reduces the memory required by the architecture, and can also further reduce the time complexity.

The CARMEN platform allows neuroscientists to share data, metadata, services and workflows, and to execute these services and workflows remotely via a Web portal. This paper describes how we implemented a service-based infrastructure into the CARMEN Virtual Laboratory. A Software as a Service framework was developed to allow generic new and legacy code to be deployed as services on a heterogeneous execution framework. Users can submit analysis code typically written in Matlab, Python, C/C++ and R as non-interactive standalone command-line applications and wrap them as services in a form suitable for deployment on the platform. The CARMEN Service Builder tool enables neuroscientists to quickly wrap their analysis software for deployment to the CARMEN platform, as a service without knowledge of the service framework or the CARMEN system. A metadata schema describes each service in terms of both system and user requirements. The search functionality allows services to be quickly discovered from the many services available. Within the platform, services may be combined into more complicated analyses using the workflow tool. CARMEN and the service infrastructure are targeted towards the neuroscience community; however, it is a generic platform, and can be targeted towards any discipline.

We present a machine learning framework that automatically generates a model set of landmarks for some class of registered 3D objects: here we use human faces. The aim is to replace heuristically-designed landmark models by something that is learned from training data. The value of this automatically generated model is an expected improvement in robustness and precision of learning-based 3D landmarking systems. Simultaneously, our framework outputs optimal detectors, derived from a prescribed pool of surface descriptors, for each landmark in the model. The model and detectors can then be used as key components of a landmark-localization system for the set of meshes belonging to that object class. Automatic models have some intrinsic advantages; for example, the fact that repetitive shapes are automatically detected and that local surface shapes are ordered by their degree of saliency in a quantitative way. We compare our automatically generated face landmark model with a manually designed model, employed in existing literature.

Large-scale data processing systems frequently require users to make timely and high-value business decisions based upon information that is received from a variety of heterogeneous sources. Such heterogeneity is especially true of service-oriented systems, which are often dynamic in nature and composed of multiple interacting services. However, in order to establish user trust in such systems, there is a need to determine the validity and reliability of all the data sources that go into the making of a decision. This paper analyses the concept of provenance and discusses how the establishment of personalized provenance recording and retrieval systems can be used to increase the utility of data and engender user trust in complex service-based systems. An overview of current provenance research is presented, and a real-world project to address the abstract concepts of trust and data quality in industrial and clinical settings is presented. From this, we conclude that the addition of provenance into data processing and decision making systems can have a tangible benefit to improving the trust of system users.

This paper describes an architecture based on superimposed distributed representations and distributed associative memories which is capable of performing rule chaining. The use of a distributed representation allows the system to utilise memory efficiently, and the use of superposition reduces the time complexity of a tree search to O(d), where d is the depth of the tree. Our experimental results show that the architecture is capable of rule chaining effectively, but that further investigation is needed to address capacity considerations.

Modern organizations increasingly depend heavily on information stored and processed in distributed, heterogeneous data sources and services to make critical, high-value decisions. Service-oriented systems are dynamic in nature and are becoming ever more complex systems of systems. In such systems, knowing how data was derived is of significant importance in determining its validity and reliability. To address this, a number of advocates and theorists postulate that provenance is critical to building trust in data and the services that generated it as it provides evidence for data consumers to judge the integrity of the results. This paper provides an overview of provenance research with an emphasis on its application in the domain of service-oriented computing. The goal of this paper is not to provide an exhaustive survey of the provenance literature but rather to highlight key work, themes, challenges and issues as well as emerging areas related to the use of provenance as a mechanism for improving trust in data utilized in distributed computing environments.

Keypoints on 3D surfaces are points that can be extracted repeatably over a wide range of 3D imaging conditions. They are used in many 3D shape processing applications, for example, to establish a set of initial correspondences across a pair of surfaces to be matched. Typically, keypoints are extracted using extremal values of a function over the 3D surface, such as the descriptor map for Gaussian curvature. That approach works well for salient points, such as the nose-tip, but can not be used with other less pronounced local shapes. In this paper, we present an automatic method to detect keypoints on 3D faces, where these keypoints are locally similar to a set of previously learnt shapes, constituting a 'local shape dictionary'. The local shapes are learnt at a set of 14 manually-placed landmark positions on the human face. Local shapes are characterised by a set of 10 shape descriptors computed over a range of scales. For each landmark, the proportion of face meshes that have an associated keypoint detection is used as a performance indicator. Repeatability of the extracted keypoints is measured across the FRGC v2 database.

We present a technique for initial location of scattered chromosomal objects within multi-resolution images of human blood cells. Kohonen Self Organising Maps learn to extract salient image features in the vicinity of located objects. Feature extraction is to form the first stage in a neural network system applied to the problem of recognizing structural aberrations in chromosomes.

The Code Analysis Repository & Modelling for E-Neuroscience (CARMEN) project aims to enable broad sharing of resources, through the provision of a secure, online environment for storage and curation of data, analysis code and experimental protocols, together with the ability to execute data analysis. While the CARMEN system is initially focused on electrophysiology data, it is equally applicable to many domains outside neuroscience. Metadata are essential for a system such as CARMEN that has the potential to store thousands of data collections and analysis codes; without metadata, resource discovery, interpretation, evaluation and re-use would be severely impeded. Therefore, when any resource (data, service or workflow) is added to the system, users must provide adequate descriptions. These descriptions form a metadata repository that is searchable to allow users to find any kind of resource held in the system, assuming that the user has appropriate access rights. This paper discusses and explores the project’s approach to implementing such a metadata repository that meets both system requirements and user expectations. Initial approaches were refined after user evaluations, and a more practical approach was followed that better aligned with the aims of the users and the project as a whole.

Using probability theory, it is shown that tuple preprocessing in ADAM fails to store patterns in a uniform manner in the first stage memory matrix. Instead, patterns tend to be clustered at the edges of tuple unit addressed matrix regions. A technique for overcoming this is given. Simulations show that this new technique is a great advantage for fault tolerance.

The Support Vector Machine (SVM) provides a robust, accurate and effective technique for pattern recognition and classification. Although the SVM is essentially a binary classifier, it can be adopted to handle multi-class classification tasks. The conventional way to extent the SVM to multi-class scenarios is to decompose an m-class problem into a series of two-class problems, for which either the one-vs-one (OVO) or one-vs-all (OVA) approaches are used. In this paper, a practical and systematic approach using a kernelised SVM is proposed and developed such that it can be implemented in embedded hardware within a road-side camera. The foreground segmentation of the vehicle is obtained using a Gaussian mixture model background subtraction algorithm. The feature vector describing the foreground (vehicle) silhouette encodes size, aspect ratio, width, solidity in order to classify vehicle type (car, van, HGV), In addition 3D colour histograms are used to generate a feature vector encoding vehicle color. The good recognition rates achieved in the our experiments indicate that our approach is well suited for pragmatic embedded vehicle classification applications.

Studies of neural networks and the processes they controlfrequently employ recording techniques to determinetemporal patterns of activity within individual neuronsand their interactions. Neuroinformatics is the rapidlygrowing science that addresses the manipulation andanalysis of the vast volumes of data generated from suchtechniques. However, although these data are often diffi-cult and expensive to produce, they are rarely shared andcollaboratively exploited, and dissemination of new anal-ysis methods may be restricted by issues of software andfile compatibility. CARMEN (Code Analysis, Repositoryand Modeling for e-Neuroscience) aims to address theseissues by creating an environment for handling time seriesdata and for deploying analysis algorithms using distrib-uted computing technology.The CARMEN infrastructure builds heavily on softwaredeveloped in previous e-science projects. The "Cloud"architecture allows the co-location of data and computa-tion (avoiding the need to repeatedly transfer large quan-tities of data) and enabling users to conduct their sciencethrough a web browser. The data handling capabilities ofthe CARMEN portal have recently been deployed, ena-bling registration of users and upload of data files. Theprimary data consists mainly of files of electrophysiologi-cal data, for which we use Storage Resource Broker tomanage the distributed store. To provide a description ofexperimental protocols, an extensible metadata schemahas been developed [1] and implemented using templatesto avoid the necessity of re-entering values for commonprotocols. A security layer enables the contributor to con-trol access rights to both the data and metadata, so thatthe originator and collaborators can share and analyze thedata in a private environment until publication when thedata may be made public. This repository satisfies therequirements of funding agencies to make research outputpublicly available and provides a resource for computa-tional neuroscientists.The project consortium is developing new analysis meth-ods including spike detection services that use waveletand morphology techniques [2], a spike sorting method-ology that extends WaveClus [3], information theoreticanalysis and Bayesian network analysis to determinecausal relations, and algorithms for resolving spike syn-chrony. An associated thick client tool, Signal DataExplorer, provides data visualization, signal processingand pattern matching capabilities. Because analysis appli-cations need to be executed on a wide range of data for-mats, we have specified a uniform file and formatstructure for data sharing and communication betweenapplications [4]. We are implementing an enactmentengine to enable linking of applications into more com-plex and user-defined workflows.

This chapter presents the Relaxation by Elimination methods (RBE) for searching large collections of graph data that has been implemented on a distributed platform and is in daily use for searching a database of molecules. The core of the approach uses an ‘inverted’ relaxation labelling method that finds a good match of the input data with stored examples. The method is shown to scale linearly with the number of graphs, and to scale linearly under given circumstances to the number of nodes in the graph. Key to the idea is that the system cuts the search time by removing a set of sub-optimal matches leaving those that could match. The system uses arrays of biologically plausible neural networks, Correlation Matrix Memories (CMMs) to store the constraints between the nodes of the graphs being searched. This is coupled to a novel search method. The system is highly parallel. Recently we have developed a parallel Grid enabled computer system (Cortex II) which utilises Digital Signal Processors (DSPs) and Field Programmable Gate Arrays (FPGAs) and have implemented the method on this system. A service for matching small molecules to a molecule database, in which the molecules are represented as attributed graphs, is currently running online. The methods have also been applied to searching trademark databases allowing people to find trademarks that are geometrically similar. The chapter describes the method in detail and its implementation and application. It also brings together work that has appeared separately and presents a new mathematical formulation of the mapping of RBE onto correlation matrix methods.

A performance analysis model for regular multicomputer networks is developed which covers both single- or multiple-message accepting PE models and buffered or unbuffered communication switches. The performance of a four-neighbour mesh with ‘wrapped-around’ boundaries as found in most transputer nets is evaluated in terms of the overall bandwidth vs. the message generation rate and ‘span’ radii. We show that the network will not saturate if the message generation rate is less than unity and the message hopcount is less than six.

Twenty years ago a paper detailing a novel neural network, called ADAM and that could be directly implemented in hardware RAM, was published in the first conference of this series. Subsequent research based directly/indirectly on this type of RAM-based neural network founded a research group that has produced over 200 research documents. This paper overviews that research and goes on to mathematically define a CMML, a generalised version of a CMM (the component at the heart of ADAM). The CMML can be trained to replicate the exact computational properties of a CMM and so is a plug-and-play replacement to a CMM; whilst a different training algorithm gives it different properties when used in recall.

In this paper, we develop a system to classify the outputs of image segmentation algorithms as perceptually relevant or perceptually irrelevant with respect to human perception. The work is aimed at figurative images. We previously investigated human visual perception of trademark images and established a body of ground truth data in the form of trademark images and their respective human segmentations. The work indicated that there is a core set of segmentations for each image that people perceive. Here we use this core set of segmentations to train a classifier to classify closed shapes output from an image segmentation algorithm so that the method returns the image segments that match those produced by people. We demonstrate that a perceptual relevance classifier is attainable and identify a good methodology to achieve this. The paper compares MLP, SVM, Bayes and regression classifiers for classifying shapes. MLPs perform best with an overall accuracy of 96.4%.

Ensuring the uniqueness of trademark images and protecting their identities are the most important objectives for the trademark registration process. To prevent trademark infringement, each new trademark must be compared to a database of existing trademarks. Given a newly designed trademark image, trademark retrieval systems are not only concerned with finding images with similar shapes but also locating images with similar layouts. Performing a linear-search, i.e., computing the similarity between the query and each database entry and selecting the closest one, is inefficient for large database systems. An effective and efficient indexing mechanism is, therefore, essential to select a small collection of candidates. This paper proposes a framework in which a graph-based indexing schema will be applied to facilitate efficient trademark retrieval based on spatial relations between image components, regardless of mutual shape similarity.Our framework starts by segmenting trademark images into distinct shapes using a shape identification algorithm. Identified shapes are then encoded automatically into an attributed graph whose vertices represent shapes and whose edges show spatial relations (both directional and topological) between the shapes. Using a graph-based indexing schema, the topological structure of the graph as well as that of its subgraphs are represented as vectors in which the components correspond to the sorted Laplacian eigenvalues of the graph or subgraphs. Having established the signatures, the indexing amounts to a nearest neighbour search in a model database. For a query graph and a large graph data set, the indexing problem is reformulated as that of fast selection of candidate graphs whose signatures are close to the query signature in the vector space. An extensive set of recognition trials, including a comparison with manually constructed graphs, show the efficacy of both the automatic graph construction process and the indexing schema.