The ParaPhrase Project:Parallel patterns for adaptive heterogeneous multicore systems

This paper describes the ParaPhrase project, a new 3-year targeted research project funded under EU Framework 7 Objective 3.4 (Computer Systems), starting in October 2011. ParaPhrase aims to follow a new approach to introducing parallelism using advanced refactoring techniques coupled with high-level parallel design patterns. The refactoring approach will use these design patterns to restructure programs defined as networks of software components into other forms that are more suited to parallel execution. The programmer will be aided by high-level cost information that will be integrated into the refactoring tools. The implementation of these patterns will then use a well-understood algorithmic skeleton approach to achieve good parallelism. A key ParaPhrase design goal is that parallel components are intended to match heterogeneous architectures, defined in terms of CPU/GPU combinations, for example. In order to achieve this, the ParaPhrase approach will map components at link time to the available hardware, and will then re-map them during program execution, taking account of multiple applications, changes in hardware resource availability, the desire to reduce communication costs etc. In this way, we aim to develop a new approach to programming that will be able to produce software that can adapt to dynamic changes in the system environment. Moreover, by using a strong component basis for parallelism, we can achieve potentially significant gains in terms of reducing sharing at a high level of abstraction, and so in reducing or even eliminating the costs that are usually associated with cache management, locking, and synchronisation. © 2013 Springer-Verlag Berlin Heidelberg.

Multiple Biomarkers for the prediction of ischemic stroke: the PRIME study

Objective: To simultaneously evaluate 14 biomarkers from distinct biological pathways for risk prediction of ischemic stroke, including biomarkers of hemostasis, inflammation, and endothelial activation as well as chemokines and adipocytokines.
Methods and Results: The Prospective Epidemiological Study on Myocardial Infarction (PRIME) is a cohort of 9771 healthy men 50 to 59 years of age who were followed up over 10 years. In a nested case–control study, 95 ischemic stroke cases were matched with 190 controls. After multivariable adjustment for traditional risk factors, fibrinogen (odds ratio [OR], 1.53; 95% confidence interval [CI], 1.03–2.28), E-selectin (OR, 1.76; 95% CI, 1.06–2.93), interferon-γ-inducible-protein-10 (OR, 1.72; 95% CI, 1.06–2.78), resistin (OR, 2.86; 95% CI, 1.30–6.27), and total adiponectin (OR, 1.82; 95% CI, 1.04–3.19) were significantly associated with ischemic stroke. Adding E-selectin and resistin to a traditional risk factor model significantly increased the area under the receiver-operating characteristic curve from 0.679 (95% CI, 0.612–0.745) to 0.785 and 0.788, respectively, and yielded a categorical net reclassification improvement of 29.9% (P=0.001) and 28.4% (P=0.002), respectively. Their simultaneous inclusion in the traditional risk factor model increased the area under the receiver-operating characteristic curve to 0.824 (95% CI, 0.770–0.877) and resulted in an net reclassification improvement of 41.4% (P<0.001). Results were confirmed when using continuous net reclassification improvement.
Conclusion: Among multiple biomarkers from distinct biological pathways, E-selectin and resistin provided incremental and additive value to traditional risk factors in predicting ischemic stroke.

Strange, But True? Object-oriented Programming Is Best Taught, And Learnt, While Sitting On The Floor.

There is a perception amongst some of those learning computer programming that the principles of object-oriented programming (where behaviour is often encapsulated across multiple class files) can be difficult to grasp, especially when taught through a traditional, didactic ‘talk-and-chalk’ method or in a lecture-based environment.
We propose a non-traditional teaching method, developed for a government funded teaching training project delivered by Queen’s University, we call it bigCode. In this scenario, learners are provided with many printed, poster-sized fragments of code (in this case either Java or C#). The learners sit on the floor in groups and assemble these fragments into the many classes which make-up an object-oriented program.
Early trials indicate that bigCode is an effective method for teaching object-orientation. The requirement to physically organise the code fragments imitates closely the thought processes of a good software developer when developing object-oriented code.
Furthermore, in addition to teaching the principles involved in object-orientation, bigCode is also an extremely useful technique for teaching learners the organisation and structure of individual classes in Java or C# (as well as the organisation of procedural code). The mechanics of organising fragments of code into complete, correct computer programs give the users first-hand practice of this important skill, and as a result they subsequently find it much easier to develop well-structured code on a computer.
Yet, open questions remain. Is bigCode successful only because we have unknowingly predominantly targeted kinesthetic learners? Is bigCode also an effective teaching approach for other forms of learners, such as visual learners? How scalable is bigCode: in its current form can it be used with large class sizes, or outside the classroom?

Multiple Markers of Inflammation in Crevicular Fluid During Experimental Gingivitis

Objective: To evaluate temporal changes in GCF levels of substance P, cathepsin G, interleukin 1 beta (IL-1&beta), neutrophil elastase and alpha1-antitrypsin (&alpha1AT) during development of and recovery from experimental gingivitis. Methods: Healthy human volunteers participated in a split-mouth study: experimental gingivitis was induced using a soft vinyl splint to cover test teeth during brushing over 21 days, after which normal brushing was resumed. Modified gingival index (MGI), gingival bleeding index (BI) and modified Quigley and Hein plaque index (PI) were assessed and 30-second GCF samples taken from 4 paired test and contra-lateral control sites in each subject at days 0, 7, 14, 21, 28 and 42. GCF volume was measured and site-specific quantification of one analyte per GCF sample was performed using radioimmunoassay (substance P), enzyme assay (cathepsin G) or ELISA (IL-1&beta, elastase, &alpha1AT). Site-specific data were analysed using analysis of repeated measurements and paired sample tests. Results: 56 subjects completed the study. All measurements at baseline (day 0) and at control sites throughout the study were low. Clinical indices and GCF volumes at the test sites increased from day 0, peaking at day 21 (difference between test and control for PI, BI, MGI and GCF all p<0.0001) and decreased again to control levels by day 28. Levels of four inflammatory markers showed a similar pattern, with significant differences between test and control apparent at 7 days (substance P p=0.0015; cathepsin G p=0.029; IL-1&beta p=0.026; elastase p=0.0129) and peaking at day 21 (substance P p=0.0023; cathepsin G, IL-1&beta and elastase all p<0.0001). Levels of &alpha1AT showed no apparent pattern over the course of the study. Conclusion: GCF levels of substance P, cathepsin G, IL-1&beta and neutrophil elastase have the potential to act as early markers of experimentally-induced gingival inflammation.

Optimization models for a short sea fuel oil distribution problem

O transporte marítimo e o principal meio de transporte de mercadorias em todo o mundo. Combustíveis e produtos petrolíferos representam grande parte das mercadorias transportadas por via marítima. Sendo Cabo Verde um arquipelago o transporte por mar desempenha um papel de grande relevância na economia do país. Consideramos o problema da distribuicao de combustíveis em Cabo Verde, onde uma companhia e responsavel por coordenar a distribuicao de produtos petrolíferos com a gestão dos respetivos níveis armazenados em cada porto, de modo a satisfazer a procura dos varios produtos. O objetivo consiste em determinar políticas de distribuicão de combustíveis que minimizam o custo total de distribuiçao (transporte e operacões) enquanto os n íveis de armazenamento sao mantidos nos n íveis desejados. Por conveniencia, de acordo com o planeamento temporal, o prob¬lema e divido em dois sub-problemas interligados. Um de curto prazo e outro de medio prazo. Para o problema de curto prazo sao discutidos modelos matemáticos de programacao inteira mista, que consideram simultaneamente uma medicao temporal cont ínua e uma discreta de modo a modelar multiplas janelas temporais e taxas de consumo que variam diariamente. Os modelos sao fortalecidos com a inclusão de desigualdades validas. O problema e então resolvido usando um "software" comercial. Para o problema de medio prazo sao inicialmente discutidos e comparados varios modelos de programacao inteira mista para um horizonte temporal curto assumindo agora uma taxa de consumo constante, e sao introduzidas novas desigualdades validas. Com base no modelo escolhido sao compara¬das estrategias heurísticas que combinam três heur ísticas bem conhecidas: "Rolling Horizon", "Feasibility Pump" e "Local Branching", de modo a gerar boas soluçoes admissíveis para planeamentos com horizontes temporais de varios meses. Finalmente, de modo a lidar com situaçoes imprevistas, mas impor¬tantes no transporte marítimo, como as mas condicões meteorológicas e congestionamento dos portos, apresentamos um modelo estocastico para um problema de curto prazo, onde os tempos de viagens e os tempos de espera nos portos sao aleatórios. O problema e formulado como um modelo em duas etapas, onde na primeira etapa sao tomadas as decisões relativas as rotas do navio e quantidades a carregar e descarregar e na segunda etapa (designada por sub-problema) sao consideradas as decisoes (com recurso) relativas ao escalonamento das operacões. O problema e resolvido por um metodo de decomposto que usa um algoritmo eficiente para separar as desigualdades violadas no sub-problema.

Genetic programming and bacterial algorithm for neural networks and fuzzy systems design

In the field of control systems it is common to use techniques based on model adaptation to carry out control for plants for which mathematical analysis may be intricate. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this line, this paper gives a perspective on the quality of results given by two different biologically connected learning algorithms for the design of B-spline neural networks (BNN) and fuzzy systems (FS). One approach used is the Genetic Programming (GP) for BNN design and the other is the Bacterial Evolutionary Algorithm (BEA) applied for fuzzy rule extraction. Also, the facility to incorporate a multi-objective approach to the GP algorithm is outlined, enabling the designer to obtain models more adequate for their intended use.

Early nutritional programming in fish: tailoring the metabolic use of dietary carbohydrates

Tese de doutoramento, Ciências da Vida, do Mar, da Terra e do Ambiente (Nutrição), Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015

Nano-CF: a Coordination Framework for macro-programming in wireless sensor networks

Wireless Sensor Networks (WSN) are being used for a number of applications involving infrastructure monitoring, building energy monitoring and industrial sensing. The difficulty of programming individual sensor nodes and the associated overhead have encouraged researchers to design macro-programming systems which can help program the network as a whole or as a combination of subnets. Most of the current macro-programming schemes do not support multiple users seamlessly deploying diverse applications on the same shared sensor network. As WSNs are becoming more common, it is important to provide such support, since it enables higher-level optimizations such as code reuse, energy savings, and traffic reduction. In this paper, we propose a macro-programming framework called Nano-CF, which, in addition to supporting in-network programming, allows multiple applications written by different programmers to be executed simultaneously on a sensor networking infrastructure. This framework enables the use of a common sensing infrastructure for a number of applications without the users having to worrying about the applications already deployed on the network. The framework also supports timing constraints and resource reservations using the Nano-RK operating system. Nano- CF is efficient at improving WSN performance by (a) combining multiple user programs, (b) aggregating packets for data delivery, and (c) satisfying timing and energy specifications using Rate- Harmonized Scheduling. Using representative applications, we demonstrate that Nano-CF achieves 90% reduction in Source Lines-of-Code (SLoC) and 50% energy savings from aggregated data delivery.

A parallel programming model for ada

Over the last three decades, computer architects have been able to achieve an increase in performance for single processors by, e.g., increasing clock speed, introducing cache memories and using instruction level parallelism. However, because of power consumption and heat dissipation constraints, this trend is going to cease. In recent times, hardware engineers have instead moved to new chip architectures with multiple processor cores on a single chip. With multi-core processors, applications can complete more total work than with one core alone. To take advantage of multi-core processors, parallel programming models are proposed as promising solutions for more effectively using multi-core processors. This paper discusses some of the existent models and frameworks for parallel programming, leading to outline a draft parallel programming model for Ada.

A multi-objective approach for the motion planning of redundant manipulators

Kinematic redundancy occurs when a manipulator possesses more degrees of freedom than those required to execute a given task. Several kinematic techniques for redundant manipulators control the gripper through the pseudo-inverse of the Jacobian, but lead to a kind of chaotic inner motion with unpredictable arm configurations. Such algorithms are not easy to adapt to optimization schemes and, moreover, often there are multiple optimization objectives that can conflict between them. Unlike single optimization, where one attempts to find the best solution, in multi-objective optimization there is no single solution that is optimum with respect to all indices. Therefore, trajectory planning of redundant robots remains an important area of research and more efficient optimization algorithms are needed. This paper presents a new technique to solve the inverse kinematics of redundant manipulators, using a multi-objective genetic algorithm. This scheme combines the closed-loop pseudo-inverse method with a multi-objective genetic algorithm to control the joint positions. Simulations for manipulators with three or four rotational joints, considering the optimization of two objectives in a workspace without and with obstacles are developed. The results reveal that it is possible to choose several solutions from the Pareto optimal front according to the importance of each individual objective.

A framework for programming sensor networks with scheduling and resource-sharing optimizations

Several projects in the recent past have aimed at promoting Wireless Sensor Networks as an infrastructure technology, where several independent users can submit applications that execute concurrently across the network. Concurrent multiple applications cause significant energy-usage overhead on sensor nodes, that cannot be eliminated by traditional schemes optimized for single-application scenarios. In this paper, we outline two main optimization techniques for reducing power consumption across applications. First, we describe a compiler based approach that identifies redundant sensing requests across applications and eliminates those. Second, we cluster the radio transmissions together by concatenating packets from independent applications based on Rate-Harmonized Scheduling.

A Java expression evaluator for nonlinear programming

Finding the optimal value for a problem is usual in many areas of knowledge where in many cases it is needed to solve Nonlinear Optimization Problems. For some of those problems it is not possible to determine the expression for its objective function and/or its constraints, they are the result of experimental procedures, might be non-smooth, among other reasons. To solve such problems it was implemented an API contained methods to solve both constrained and unconstrained problems. This API was developed to be used either locally on the computer where the application is being executed or remotely on a server. To obtain the maximum flexibility both from the programmers’ and users’ points of view, problems can be defined as a Java class (because this API was developed in Java) or as a simple text input that is sent to the API. For this last one to be possible it was also implemented on the API an expression evaluator. One of the drawbacks of this expression evaluator is that it is slower than the Java native code. In this paper it is presented a solution that combines both options: the problem can be expressed at run-time as a string of chars that are converted to Java code, compiled and loaded dynamically. To wide the target audience of the API, this new expression evaluator is also compatible with the AMPL format.

Modelling an e-learning environment for learning programming languages

It is widely accepted that solving programming exercises is fundamental to learn how to program. Nevertheless, solving exercises is only effective if students receive an assessment on their work. An exercise solved wrong will consolidate a false belief, and without feedback many students will not be able to overcome their difficulties. However, creating, managing and accessing a large number of exercises, covering all the points in the curricula of a programming course, in classes with large number of students, can be a daunting task without the appropriated tools working in unison. This involves a diversity of tools, from the environments where programs are coded, to automatic program evaluators providing feedback on the attempts of students, passing through the authoring, management and sequencing of programming exercises as learning objects. We believe that the integration of these tools will have a great impact in acquiring programming skills. Our research objective is to manage and coordinate a network of eLearning systems where students can solve computer programming exercises. Networks of this kind include systems such as learning management systems (LMS), evaluation engines (EE), learning objects repositories (LOR) and exercise resolution environments (ERE). Our strategy to achieve the interoperability among these tools is based on a shared definition of programming exercise as a Learning Object (LO).

PETCHA: a Programming Exercises Teaching Assistant

This paper presents a tool called Petcha that acts as an automated Teaching Assistant in computer programming courses. The ultimate objective of Petcha is to increase the number of programming exercises effectively solved by students. Petcha meets this objective by helping both teachers to author programming exercises and students to solve them. It also coordinates a network of heterogeneous systems, integrating automatic program evaluators, learning management systems, learning object repositories and integrated programming environments. This paper presents the concept and the design of Petcha and sets this tool in a service oriented architecture for managing learning processes based on the automatic evaluation of programming exercises. The paper presents also a case study that validates the use of Petcha and of the proposed architecture.

Bridging between programming exercises formats with BabeLO

Assessment plays a vital role in learning. This is certainly the case with assessment of computer programs, both in curricular and competitive learning. The lack of a standard – or at least a widely used format – creates a modern Ba- bel tower made of Learning Objects, of assessment items that cannot be shared among automatic assessment systems. These systems whose interoperability is hindered by the lack of a common format include contest management systems, evaluation engines, repositories of learning objects and authoring tools. A prag- matical approach to remedy this problem is to create a service to convert among existing formats. A kind of translation service specialized in programming prob- lems formats. To convert programming exercises on-the-fly among the most used formats is the purpose of the BabeLO – a service to cope with the existing Babel of Learning Object formats for programming exercises. BabeLO was designed as a service to act as a middleware in a network of systems typically used in auto- matic assessment of programs. It provides support for multiple exercise formats and can be used by: evaluation engines to assess exercises regardless of its format; repositories to import exercises from various sources; authoring systems to create exercises in multiple formats or based on exercises from other sources. This paper analyses several of existing formats to highlight both their differ- ences and their similar features. Based on this analysis it presents an approach to extensible format conversion. It presents also the features of PExIL, the pivotal format in which the conversion is based; and the function definitions of the proposed service – BabeLO. Details on the design and implementation of BabeLO, including the service API and the interfaces required to extend the conversion to a new format, are also provided. To evaluate the effectiveness and efficiency of this approach this paper reports on two actual uses of BabeLO: to relocate exercises to a different repository; and to use an evaluation engine in a network of heterogeneous systems.