Training in supervised machine learning is based on the availability of datasets; however, medical datasets must comply with stringent privacy regulations. Generative Adversarial Networks (GANs) are a relevant alternative to solve the limitation of small medical datasets due to their ability to generate additional data with desired features. A significant drawback of these models is that they may produce unrealistic, blurred, or insufficiently diverse images. This paper proposes a data augmentation technique using GANs to create synthetic Magnetic Resonance Imaging (MRI) of four stages of Alzheimer's Disease (AD): non-demented, very mild demented, mild demented, and moderate demented. We designed a GAN based on the Pix2Pix model, which learns the features of each AD stage. Generated images are evaluated by multistage Convolutional Neural Network (CNN) models, greyscale histograms of the distribution of pixel intensities, and brain mass measurements on binarized images. The results indicate that AD synthetic MRI effectively captures disease patterns, demonstrating the potential of GANs to improve training and diagnosis of neurodegenerative diseases.

Construed as a shift in the distribution or structure of data over time, data drift can adversely affect the performance of machine learning models and data-driven decisions. This study examines two data drift metrics, denoted as dE,PCAand dE,AE, that are derived from unsupervised ML models: the reconstruction error-based metrics of Principal Component Analysis (PCA) and Autoencoders (AE). To investigate the robustness of these metrics, we have systematically accessed time-series datasets from the European Data Portal. Our experiments have examined data versioning through three basic events: creation, update, and deletion. The results are summarised and aggregated for all datasets, and unsupervised analysis based on Robust PCA and AE has been performed to examine patterns within the impact of dataset characteristics on data drift detection and computational efficiency. Our results indicate that both metrics aligned closely in performance with new records, suggesting consistent drift detection under normal conditions with FAIR compliance. However, high-dimensional datasets posed challenges for both PCA and AE models. Update events revealed discrepancies between the two metrics, suggesting that non-linear shifts affected AE-based metrics more than PCA-based ones. Deletion events demonstrated the resilience of these metrics against data loss, but also revealed variability in the reliability of the PCA model; i.e., data drift metrics derived from PCA and AE can be effective but sensitive to certain dataset characteristics.

Over the past five years, different organisations have increasingly called for science to become more open and reproducible. They have endorsed a set of data-management principles known as the FAIR (Findable, Accessible, Interoperable, Reusable) principles. As such, there is a growing trend towards the open availability of research data, as researchers continue to enhance reproducibility by enabling sharing and opening of their findings and datasets. However, there is not yet a standardised way to openly enable access to datasets while keeping control of their final use, potentially obtaining benefits from their utilisation. This paper introduces Smardy, an EU-funded project which is deploying a traceable FAIR-compliant open innovation marketplace for data. Its innovative method for data exchange consists of the use of blockchain for controlling access rights to data, with data models able to grant access according to policies completely kept under the control of the data owner/producer. We also describe how Smardy employs dimensionality reduction techniques to automatically generate FAIR–compliant metadata, statistical fingerprinting to identify derivated datasets, and watermarking to help data owners trace the distribution of multiple copies of a dataset.

Open Science is widely regarded as a culture that is characterised by the transparency and broad accessibility of scholarly work, where researchers share openly artefacts almost immediately and with a very wide audience. The overarching aim of this paper is to document the systematic development of a European postgraduate programme on Open Science and Research Data Management developed by the TRAINRDM project. TRAINRDM is a 30-month European Union funded project, which aims to develop a training network around Open Science and Research Data Management. We have applied a comprehensive survey collecting 239 responses from researchers across Europe, representative of 2.58 million individuals i.e. the total number of researchers employed in the EU-27 region. We then mapped out existing skills and offerings at different TRAINRDM partner institutions to produce a fully-online postgraduate programme with micro-credentials, fully distributed delivery, and compliance to FAIR principles to address academic and industrial research needs. The main outputs of the project are a training programme for Early Career Researchers delivered in Summer 2022, and a the postgraduate programme (Master degree) to be fully validated under the European Qualifications Framework at Level 7 and delivered in 2023. The TRAINRDM curricula, teaching materials, data, and software are openly released under CC BY 4.0 and GPL licenses.

As one of the fundamental concepts underpinning the FAIR (Findability, Accessibility, Interoperability, and Reusability) guiding principles, data provenance entails keeping track of each version for a given dataset from its original to its latest version. However, standard terms to determine and include versioning information in the metadata of a given dataset are still ambiguous and do not explicitly define how to assess the overlap of information between items along a versioning stream. In this work, we propose a novel approach for automatic versioning of time series datasets, based on the use of parameters from two dimensionality reduction approaches, namely Principal Component Analysis and Autoencoders. That is to say, we systematically detect and measure similarities (information distances) in datasets via dimensionality reduction, encode them as different versions, and then automatically generate provenance metadata via a FAIR versioning service using the W3C DCAT 3.0 nomenclature. We illustrate this approach with two time series datasets and demonstrate how the proposed parameters effectively assess the similarity between different data versions. Our results have shown that the proposed version similarity metrics are robust$(s^{(0,1)}=1)$to the alteration of up to 60% of cells, the removal of up to 60% of rows, and the log-scale transformation of variables. In contrast, row-wise transformations (e.g. converting absolute values to a percentage of a second variable) yield minimal similarity values$(s^{(0,1)} < 0.75)$. Our code and datasets are openly available to enable reproducibility.

As a disruptive paradigm in the cloud landscape, Serverless Computing is attracting attention because of its unique value propositions to reduce operating costs and outsource infrastructure management. Nevertheless, enterprise Functionas-a-Service (FaaS) platforms may pose significant risks such as vendor lock-in, lack of security control due to multi-tenancy, complicated pricing models, and legal and regulatory compliance- particularly in mobile computing scenarios. This work proposes a production-grade fault-tolerant serverless architecture based on a highly-available Kubernetes topology using an open-source framework, deployed on OpenStack instances, and benchmarked with a realistic scaled-down Azure workload traces dataset. By measuring success rate, throughput, latency, and auto scalability, we have managed to assess not only resilience but also sustained performance under a logistic model for three distinct representative workloads. Our test executions show, with 95%-confidence, that between 70 and 90 concurrent users can access the system while experiencing acceptable performance. Beyond the breaking point identified (i.e. 91 transactions per second), the Kubernetes cluster has to be scaled-up or scaled out to meet the QoS and availability requirements.

Multi-service networks aim to efficiently supply distinct goods within the same infrastructure by relying on a (typically centralised) authority to manage and coordinate their differential delivery at specific prices. In turn, final customers constantly seek to lower costs whilst maximising quality and reliability. This paper proposes a decentralised business model for multi-service networks using Ethereum blockchain features – gas, transactions, and smart contracts – to execute multiple services at different prices. By employing the Ethereum cryptocurrency token, Ether, to quantify the quality of service and reliability of distinct private Ethereum networks, our model concurrently processes streams of services at different gas prices while differentially delivering reliability and service quality. This multi-service business model has been extensively tested on five concurrent Ethereum networks with various combinations of gas prices, miners, and regular nodes using a Proof of Authority consensus algorithm and throughput as the evaluation metric. It has exhibited linear scalability, providing increased throughput in high-quality Ethereum networks, i.e., composed of more validator nodes. The results also indicate that different mining prices do not impact the network performance, but networks with more miners had limited scalability and an increased level of trustworthiness and reliability.

This paper aims to address the issue of CPU-memory intercommunication latency with the help of 3D stacked memory. We propose a 3D-stacked memory configuration, where a DRAM module is mounted on top of the CPU to reduce latency. We have used a comprehensive simulation environment to assure both fabrication feasibility and energy efficiency of the proposed 3D stacked memory modules. We have evaluated our proposed architecture by running PARSEC 2.1, a benchmark suite for shared-memory multithreaded workloads. The results demonstrate an average of 40% improvement over conventional DDR3/4 memory architectures.

For over a decade now, Amazon Web Services (AWS) has offered its spare capacity at a discounted price in the form of EC2 spot instances. This discount comes at the price of variable pricing and sudden instance termination. In this paper, we present a machine-learning solution to one of the challenges when using AWS Spot Instances, namely the termination of the instance on short notice. Our system, Spot Instance Management System (SimS), can effectively manage spot instances and keep up the availability at the desired level using 100-tree Random Forest Regression model. By using a risk assessment mechanism and proactive actions, SimS assures a three-nines SLA using AWS spot instances with lower running costs on workloads for a major European financial institution.

Contemporary software deployments rely on cloud-based package managers for installation, where existing packages are installed on demand from remote code repositories. Usually frameworks or common utilities, packages increase the code reusability within the ecosystem, whilst keeping the code base small. However, disruptions in the package management services can potentially affect development and deployment workflows. Furthermore, cloud package managers have arguably an ambiguous ownership model and offer limited visibility of packages to the users. This work describes the development of a blockchain-based package control system which is decentralised, reliable, and transparent. Blockchain nodes are installed within the distributed infrastructure to provide immutability, and then a dependency graph is constructed with the help of smart contracts to trace the software provenance. Our system has been successfully tested with 4338 packages from NPM, 950 out of which are the top depended-upon packages.

Metal oxide (MOX) gas detectors based on SnO_2 provide low-cost solutions for real-time sensing of complex gas mixtures for indoor ambient monitoring. With high sensitivity under ideal conditions, MOX detectors may have poor long-term response accuracy due to environmental factors (humidity and temperature) along with sensor aging, leading to calibration drifts. Finding a simple and efficient solution to correct such calibration drifts has been the subject of numerous studies but remains an open problem. In this work, we present an efficient approach to MOX calibration using active and transfer sampling techniques coupled with non-linear machine learning algorithms, namely neural networks, extreme gradient boosting (XGBoost) and radial kernel support vector machines (SVM). Applied on the UCI's HT detectors dataset, the study evaluates methods for active sampling, makes an assessment of suitable neural networks architectures and compares the performance of neural networks, XGBoost and radial kernel SVM to classify gas mixtures (banana and wine odours, clean air) in the presence of humidity and temperature changes. The results show high classification accuracy levels (above 90%) and confirm that active sampling can provide a suitable solution.

Today, auto-scaling solutions are still largely reactive and are based on the load measured on existing nodes nearing a threshold or traffic forecast information provided in advance of a scheduled event. Despite these advancements, events which cause a flash flood of web traffic do not always benefit from this approach to auto-scaling because the latency of provisioning new nodes is not sufficient to prevent resource saturation. This paper proposes a novel approach to preemptive compute scaling based on the buzz of specific social network hashtags. We argue that improved scaling latency and service availability can be achieved for web services that receive intermittent as well as unexpected traffic load patterns. By combining social network monitoring, with auto-scaling frameworks, this approach can reduce the operational expense impact of over- and under-provisioning as well as the business costs of the latter. We demonstrate our approach using the AWS application suite, and demonstrate how our approach expedites the auto-scaling strategy for our use case: public transportation web sites.

Based on historical user information, collaborative filters predict for a given user the classification of unknown items, typically using a single criterion. However, a crowd typically rates tourism resources using multi-criteria, i.e., each user provides multiple ratings per item. In order to apply standard collaborative filtering, it is necessary to have a unique classification per user and item. This unique classification can be based on a single rating – single criterion (SC) profiling – or on the multiple ratings available – multicriteria (MC) profiling. Exploring both SC and MC profiling, this work proposes: (ı) the selection of the most representative crowd-sourced rating; and (ıı) the combination of the different user ratings per item, using the average of the non-null ratings or the personalised weighted average based on the user rating profile. Having employed matrix factorisation to predict unknown ratings, we argue that the personalised combination of multi-criteria item ratings improves the tourist profile and, consequently, the quality of the collaborative predictions. Thus, this paper contributes to a novel approach for guest profiling based on multi-criteria hotel ratings and to the prediction of hotel guest ratings based on the Alternating Least Squares algorithm. Our experiments with crowd-sourced Expedia and TripAdvisor data show that the proposed method improves the accuracy of the hotel rating predictions.

As a recommendation technique based on historical user information, collaborative filtering typically predicts the classification of items using a single criterion for a given user. However, many application domains can benefit from the analysis of multiple criteria, e.g. tourists usually rate attractions (hotels, attractions, restaurants, etc.) using multiple criteria. In this paper, we argue that the personalised combination of multi-criteria data together with the creation and application of trust models should not only refine the tourist profile, but also improve the quality of the collaborative recommendations. The main contributions of this work are: (1) a novel profiling approach which takes advantage of the multi-criteria crowdsourced data and builds pairwise trust models and (2) the k-NN prediction of user ratings using trust-based neighbour selection. Significant experimental work has been performed using crowdsourced datasets from the Expedia and TripAdvisor platforms.

Task scheduling in large-scale distributed High Performance Computing (HPC) systems environments remains challenging research and engineering problem. There is a need of development of novel advanced scheduling techniques in order to optimise the resource utilisation. In this work, we develop the Agent Supported Non-Deterministic Meta Scheduler for cloud environments. This scheduling model is a simple combination of intelligent agent-based monitoring model for cloud system and security-aware cloud scheduler. In our model, scheduling, monitoring and reporting are provided in nondeterministic time intervals. An empirical case study using a FastFlow task farm was presented. It has demonstrates the effectiveness of the proposed solution.

Cloud computing has enabled the Database-as-a-Service (DBaaS) model to manage large volumes of user-generated data using NoSQL data repositories. There are several NoSQL implementations such as document, columnar, and key-value which ensure high availability, fault tolerance and scalability to serve distinct client requirements. Nonetheless, different NoSQL data models may also introduce unnecessary heterogeneity in DBaaS, which further restricts the user to migrate the application services according to business or technology changes. In this paper, we propose a NoSQL data migration framework to foster data portability across cloud-based heterogeneous NoSQL data repositories. The proposed approach involves data standardisation and classification stages to render an efficient mapping, and translation between cloud-based different NoSQL data stores. The current implementation of the framework supports three different data models: document, columnar and graph. Moreover, the framework is meta-model driven, and therefore allows developers to extend the support for new database models. Our approach includes an online compression algorithm for data migration (document to graph) whereby a graph database requires up to 46% less space. There is also a significant reduction (37% to 55%) in the number of nodes in the compressed graph database.

The Hadoop framework has been developed to effectively process data-intensive MapReduce applications. Hadoop users specify the application computation logic in terms of a map and a reduce function, which are often termed MapReduce applications. The Hadoop distributed file system is used to store the MapReduce application data on the Hadoop cluster nodes called Data nodes, whereas Name node is a control point for all Data nodes. While its resilience is increased, its current data-distribution methodologies are not necessarily efficient for heterogeneous distributed environments such as public clouds. This work contends that existing data distribution techniques are not necessarily suitable, since the performance of Hadoop typically degrades in heterogeneous environments whenever data-distribution is not determined as per the computing capability of the nodes. The concept of data-locality and its impact on the performance of Hadoop are key factors, since they affect the performance in the Map phase when scheduling tasks. The task scheduling techniques in Hadoop should arguably consider data locality to enhance performance. Various task scheduling techniques have been analysed to understand their data-locality awareness while scheduling applications. Other system factors also play a major role while achieving high performance in Hadoop data processing. The main contribution of this work is a novel methodology for data placement for Hadoop Data nodes based on their computing ratio. Two standard MapReduce applications, Word Count and Grep, have been executed and a significant performance improvement has been observed based on our proposed data distribution technique.

Having become the leading trend in IT infrastructure, service delivering, and multi-layered resource sharing, cloud services typically include SaaS (Software as a service), PaaS (Platform as a service) and Iaas (Infrastructure as a service). With the increasing popularity of cloud computing, users store large amounts of data as documents, text files, databases, and more relevant to this work, system logs. Current cloud services are getting more decoupled with each layer in the cloud stack generating different logs for network, applications, database, and programming interfaces on different machines. At any point in time, cloud providers, users, or application developers arguably require to understand the status of different components, monitor business processes, and analyse machine logs in real time. However, there are no specialised search engines for the systematic analysis of logs by different cloud providers. Hence, this paper presents Simha, an agent-based document search service for cloud platforms. It implements a proof of concept system to analyse user documents, logs, and folders in real time from different virtual machines. Based on an Elastic search server, our overall search process has been extended to distributively search data stored into cloud. So, we propose an application which looks for data in private cloud and public clouds. In this paper, we describe its design and implementation. We have obtained initial encouraging results, and we further discuss how to extend our scheme in several ways.

Parallel scientific workloads typically entail highly-customised software environments, involving complex data structures, specialised systems software, and even distinct hardware, where virtualisation is not necessarily supported by third-party providers. Considering the expansion of cloud computing in different domains and the development of different proprietary (e.g. Amazon Web Services, Azure) and open source cloud platforms (Eucalyptus, OpenStack, OpenNebula), users should arguably be able to automatically and seamlessly migrate their parallel workloads across cloud platforms using standardised virtual machines. However, even if it is easier to migrate the workload between nodes when the nodes have a similar configuration on the same platform, the transition between different platforms typically raises different issues such as vendor lock-in, portability, and interoperability. In this paper, we describe our work to automatically deploy a complex parallel software stack on heterogeneous hybrid cloud platforms. We have elastically deployed FastFlow - a C/C++ pattern-based programming framework for multi-/many-core and distributed platforms -- using virtual machines on both CPU and GPU-based architectures between heterogeneous virtualised platforms. Our approach relies on the standard Open Virtualization Format (OVF) in order to achieve a universal description of virtual appliances. Such a description is not only useful for elastically migrating and deploying, but also to determine the hardware/system software configuration needed switching to any new (cloud) image format. We have successfully evaluated our work using virtual machines based on VirtualBox and Amazon Web Services on local cluster and public cloud providers.

SUMMARY With the emergence of general‐purpose computation on graphics processing units, high‐level approaches that hide the conceptual complexity of the low‐level Compute Unified Device Architecture and Open Computing Language platforms are the subject of active research. However, these approaches may require a trade‐off in terms of achieved performance and utilisation on graphics processing units hardware and may impose algorithmic limitations. In this paper, we present and systematically evaluate the parallel performance of three implementations of the brute force, all‐pairs N‐body algorithm with skeletal deployments based on the FastFlow, SkePU and Thrust frameworks. Our results indicate that the skeletal framework implementation achieves up to two orders of magnitude speed‐up over serial version with a Tesla M2050 with lower implementation complexity than low‐level Compute Unified Device Architecture programming. Copyright © 2013 John Wiley & Sons, Ltd.

In this paper we investigate the deployment of FastFlow applications on multi-core virtual platforms. The overhead introduced by the virtual environment has been measured using a well-known application benchmark both in the sequential and in the FastFlow parallel setting. The overhead introduced for both the sequential and the parallel executions of CPU and memory-intensive applications is in the range of 2-30%, while the execution speedup is almost preserved. Additionally, we have ported the FastFlow benchmark to a cloud-based distributed environment in which a task-intensive application has been tested and the performance compared with the corresponding run on a smaller cluster of multi-core machines without virtualisation. From a parallel programming perspective, we have demonstrated how a unique programming framework based on the structured parallel programming paradigm can cope with very different kind of target architectures without any (or minimal) code intervention.

Algorithmic skeletons (`skeletons') abstract commonly-used patterns of parallel computation, communication, and interaction. They provide top-down design composition and control inheritance throughout the whole structure. The efficient execution of skeletal applications on a heterogeneous environment has long been of interest to the research community. Arguably, executing a coarse-grained resource-intensive skeletal workloads ought to achieve higher resource utilisation and, ultimately, better job makespan on heterogeneous systems due to the structured parallelism model. This paper presents a heterogeneous OpenCL-based GPU back-end for FastFlow, a widely-used skeletal framework. Our back-end allows the user to easily write any arbitrary OpenCL code inside an heterogeneous algorithmic skeleton and seamlessly control the allocation of OpenCL kernel over the hybrid (CPU/GPU) architecture. Our performance evaluation indicate that a skeletal program which employs our back-end is around one order of magnitude faster than a skeletal parallel program using the traditional homogeneous FastFlow skeletons with the serial version of OpenCL code.

This paper explores the early implementation of high-performance routines for the solution of multiple large Hermitian eigenvector and eigenvalue systems on a Graphics Processing Unit (GPU). We report a perfor-mance increase of up to two orders of magnitude over the original EISPACK routines with a NVIDIA Tesla C2050 GPU, potentially allowing an order of magnitude in-crease in the complexity or resolution of a neutron scat-tering modeling application.

This work analyses the performance of Hadoop, an implementation of the MapReduce programming model for distributed parallel computing, executing on a virtualisation environment comprised of 1+16 nodes running the VMWare workstation software. A set of experiments using the standard Hadoop benchmarks has been designed in order to determine whether or not significant reductions in the execution time of computations are experienced using Hadoop on this virtualisation platform on a local area network. Our findings indicate that a significant decrease in computing times is observed under these conditions. They also highlight how overheads and virtualisation in a distributed environment hinder the possibility of achieving the maximum (peak) performance.

This paper examines the initial parallel implementation of SCATTER, a computationally intensive inelastic neutron scattering routine with polycrystalline averaging capability, for the General Utility Lattice Program (GULP). Of particular importance to structural investigation on the atomic scale, this work identifies the computational features of SCATTER relevant to a parallel implementation and presents initial results from performance tests on multi-core and multi-node environments. Our initial approach exhibits near-linear scalability up to 256 MPI processes for a significant model.

Abstract Algorithmic skeletons abstract commonly used patterns of parallel computation, communication, and interaction. Based on the algorithmic skeleton concept, structured parallelism provides a high‐level parallel programming technique that allows the conceptual description of parallel programs while fostering platform independence and algorithm abstraction. This work presents a methodology to improve skeletal parallel programming in heterogeneous distributed systems by introducing adaptivity through resource awareness. As we hypothesise that a skeletal program should be able to adapt to the dynamic resource conditions over time using its structural forecasting information, we have developed adaptive structured parallelism (ASPARA). ASPARA is a generic methodology to incorporate structural information at compilation into a parallel program, which will help it to adapt at execution. ASPARA comprises four phases: programming, compilation, calibration, and execution. We illustrate the feasibility of this approach and its associated performance improvements using independent case studies based on two algorithmic skeletons—the task farm and the pipeline—evaluated in a non‐dedicated heterogeneous multi‐cluster system. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract Structured parallel programs ought to be conceived as two separate and complementary entities: computation, which expresses the calculations in a procedural manner, and coordination, which abstracts the interaction and communication. By abstracting commonly used patterns of parallel computation, communication, and interaction, algorithmic skeletons enable programmers to code algorithms without specifying platform‐dependent primitives. This article presents a literature review on algorithmic skeleton frameworks (ASKF), parallel software development environments furnishing a collection of parameterizable algorithmic skeletons, where the control flow, nesting, resource monitoring, and portability of the resulting parallel program is dictated by the ASKF as opposed to the programmer. Consequently, the ASKF can be positioned as high‐level structured parallel programming enablers, as their systematic utilization permits the abstract description of programs and fosters portability by focusing on the description of the algorithmic structure rather than on its detailed implementation. Copyright © 2010 John Wiley & Sons, Ltd.

A distributed decision support system involving multiple clinical centres is crucial to the diagnosis of rare diseases. Although sharing of valid diagnosed cases can facilitate later decision making, possibly from geographically different centres, the released information could reveal patient privacy if it is not properly protected. Clinical centres may have to impose their distinct regulations and rules that govern the use of their data externally. The collaboration of centres, therefore, must respect the collective policies and ideally, serve users the most appropriate and useful resources possible in the system according to the past experience. In this way, the system’s value is entrusted and even elevated through continuous collaboration. We present in this paper a link-anonymised data scheme and in addition to that, a security model that together enforce privacy data security and secure resource access for distributed clinical centres. Our illustration of the approach involves a prototype medical decision support system, HealthAgents, for brain tumour diagnosis.

This paper introduces an adaptive parallel pipeline pattern which follows the GRASP (grid-adaptive structured parallelism) methodology. GRASP is a generic methodology to incorporate structural information at compile time into a parallel program that enables it to adapt automatically to dynamic variations in resource performance. GRASP instruments the pipeline with a series of pragmatic rules, which depend on particular performance thresholds based on the computation/communication patterns of the program and the availability of resources in the grid. Our parallel pipeline pattern is implemented as a parameterisable C/MPI API using a variable-size input data vector and a stage function array. We have evaluated its efficiency using a numerical benchmark stage function in a non-dedicated computational grid environment.

We present in this paper an adaptive security model for Multi-agent systems. A security meta-model has been developed in which the traditional role concept has been extended. The new concept incorporates the need of both security management as used by role-based access control (RBAC) and agent functional behaviour in agent-oriented Software Engineering (AOSE). Our approach avoids weaknesses of traditional RBAC approaches and provides a practically usable security model for multi-agent systems (MAS). A unified role interaction model framework has been put forward that incorporates not only functional requirements but also security constraints in MAS. A security policy rule scheme has been used to express security requirements in relation to affective roles. The major contribution of the work is that little redevelopment effort will be required when security is to be engineered into the overall MAS architecture, hence minimising the impact of the security requirements changes to the MAS architecture. We illustrate the approach through its potential application in a clinical trial setting involving a prototype medical decision support system, HealthAgents.

No abstract available.

We present a stochastic simulation of L-type Ca/sup 2+/ current assuming thousands of calcium channels on the membrane of a spherical cell. We propose a three-state Markov model to simulate the individual contribution of each channel. Rather than using a statistical approximation, we actually consider each individual channel transitions between states and evaluate the unitary channel current. We compare this aggregated unitary contributions with simulated whole cell currents, both in response to a depolarising voltage pulse. On the computational side, we have employed a parameter-sweep, component based approach. Being embarrassingly parallel by design, we have parallelised it in a naive manner. We argue its possible extension using algorithmic skeletons. The results presented account for hours of processing time on a dedicated grid.

The authors describe the implementation of SISMAPEO, a database system designed to manage brain electrical activity mapping (BEAM) information which enables practitioners to carry out statistical analysis on groups and individuals based on topography maps. In order to evaluate their system they followed a methodology in which each patient is exposed to eyes-closed (OC) and eyes-open (OA) activation methods. Twenty 4-second epochs with a subset of 18 electrodes selected from the 10-20 International System referential montage are recorded. The register is transformed to the frequency domain via a FFT algorithm and then it is analyzed using statistical parameters for each typical band of the EEG spectrum. They present comparisons based on the t-score test, between two infant groups: a group with learning disabilities and a control. An analysis of the resulting coloured brain-mapping studies is presented. These patients show no evidence of abnormality in any imaging study such as CT or NMR. That is why they are using alternative ways of evaluating disorders such as neurophysiologic ones. At this point, they concentrate only on brain mapping records but the SISMAPEO system is planned to manage other relevant records, e.g., EMG or evoked potentials. They are carrying out clinical validations on infants with language and learning disorders.