2,258 research outputs found
Strategic Directions in Object-Oriented Programming
This paper has provided an overview of the field of object-oriented programming. After presenting a historical perspective and some major achievements in the field, four research directions were introduced: technologies integration, software components, distributed programming, and new paradigms. In general there is a need to continue research in traditional areas:\ud
(1) as computer systems become more and more complex, there is a need to further develop the work on architecture and design; \ud
(2) to support the development of complex systems, there is a need for better languages, environments, and tools; \ud
(3) foundations in the form of the conceptual framework and other theories must be extended to enhance the means for modeling and formal analysis, as well as for understanding future computer systems
Prototyping the recursive internet architecture: the IRATI project approach
In recent years, many new Internet architectures are being proposed to solve shortcomings in the current Internet. A lot of these new architectures merely extend the current TCP/IP architecture and hence do not solve the fundamental cause of these problems. The Recursive Internet Architecture (RINA) is a true new network architecture, developed from scratch, building on lessons learned in the past. RINA prototyping efforts have been ongoing since 2010, but a prototype on which a commercial RINA implementation can be built has not been developed yet. The goal of the IRATI research project is to develop and evaluate such a prototype in Linux/OS. This article focuses on the software design required to implement a network stack in Linux/OS. We motivate the placement of, and communication between, the different software components in either the kernel or user space. The first open source prototype of the IRATI implementation of RINA will be available in June 2014 for researchers, developers, and early adopters
A short curriculum of the robotics and technology of computer lab
Our research Lab is directed by Prof. Anton Civit. It is an interdisciplinary group of 23
researchers that carry out their teaching and researching labor at the Escuela
Politécnica Superior (Higher Polytechnic School) and the Escuela de Ingeniería
Informática (Computer Engineering School). The main research fields are: a)
Industrial and mobile Robotics, b) Neuro-inspired processing using electronic spikes,
c) Embedded and real-time systems, d) Parallel and massive processing computer
architecture, d) Information Technologies for rehabilitation, handicapped and elder
people, e) Web accessibility and usability
In this paper, the Lab history is presented and its main publications and research
projects over the last few years are summarized.Nuestro grupo de investigación está liderado por el profesor Civit. Somos un grupo
multidisciplinar de 23 investigadores que realizan su labor docente e investigadora
en la Escuela Politécnica Superior y en Escuela de Ingeniería Informática. Las
principales líneas de investigaciones son: a) Robótica industrial y móvil. b)
Procesamiento neuro-inspirado basado en pulsos electrónicos. c) Sistemas
empotrados y de tiempo real. d) Arquitecturas paralelas y de procesamiento masivo.
e) Tecnología de la información aplicada a la discapacidad, rehabilitación y a las
personas mayores. f) Usabilidad y accesibilidad Web.
En este artículo se reseña la historia del grupo y se resumen las principales
publicaciones y proyectos que ha conseguido en los últimos años
An ontology framework for developing platform-independent knowledge-based engineering systems in the aerospace industry
This paper presents the development of a novel knowledge-based engineering (KBE) framework for implementing platform-independent knowledge-enabled product design systems within the aerospace industry. The aim of the KBE framework is to strengthen the structure, reuse and portability of knowledge consumed within KBE systems in view of supporting the cost-effective and long-term preservation of knowledge within such systems. The proposed KBE framework uses an ontology-based approach for semantic knowledge management and adopts a model-driven architecture style from the software engineering discipline. Its phases are mainly (1) Capture knowledge required for KBE system; (2) Ontology model construct of KBE system; (3) Platform-independent model (PIM) technology selection and implementation and (4) Integration of PIM KBE knowledge with computer-aided design system. A rigorous methodology is employed which is comprised of five qualitative phases namely, requirement analysis for the KBE framework, identifying software and ontological engineering elements, integration of both elements, proof of concept prototype demonstrator and finally experts validation. A case study investigating four primitive three-dimensional geometry shapes is used to quantify the applicability of the KBE framework in the aerospace industry. Additionally, experts within the aerospace and software engineering sector validated the strengths/benefits and limitations of the KBE framework. The major benefits of the developed approach are in the reduction of man-hours required for developing KBE systems within the aerospace industry and the maintainability and abstraction of the knowledge required for developing KBE systems. This approach strengthens knowledge reuse and eliminates platform-specific approaches to developing KBE systems ensuring the preservation of KBE knowledge for the long term
An Introduction to Programming for Bioscientists: A Python-based Primer
Computing has revolutionized the biological sciences over the past several
decades, such that virtually all contemporary research in the biosciences
utilizes computer programs. The computational advances have come on many
fronts, spurred by fundamental developments in hardware, software, and
algorithms. These advances have influenced, and even engendered, a phenomenal
array of bioscience fields, including molecular evolution and bioinformatics;
genome-, proteome-, transcriptome- and metabolome-wide experimental studies;
structural genomics; and atomistic simulations of cellular-scale molecular
assemblies as large as ribosomes and intact viruses. In short, much of
post-genomic biology is increasingly becoming a form of computational biology.
The ability to design and write computer programs is among the most
indispensable skills that a modern researcher can cultivate. Python has become
a popular programming language in the biosciences, largely because (i) its
straightforward semantics and clean syntax make it a readily accessible first
language; (ii) it is expressive and well-suited to object-oriented programming,
as well as other modern paradigms; and (iii) the many available libraries and
third-party toolkits extend the functionality of the core language into
virtually every biological domain (sequence and structure analyses,
phylogenomics, workflow management systems, etc.). This primer offers a basic
introduction to coding, via Python, and it includes concrete examples and
exercises to illustrate the language's usage and capabilities; the main text
culminates with a final project in structural bioinformatics. A suite of
Supplemental Chapters is also provided. Starting with basic concepts, such as
that of a 'variable', the Chapters methodically advance the reader to the point
of writing a graphical user interface to compute the Hamming distance between
two DNA sequences.Comment: 65 pages total, including 45 pages text, 3 figures, 4 tables,
numerous exercises, and 19 pages of Supporting Information; currently in
press at PLOS Computational Biolog
Can geocomputation save urban simulation? Throw some agents into the mixture, simmer and wait ...
There are indications that the current generation of simulation models in practical,
operational uses has reached the limits of its usefulness under existing specifications.
The relative stasis in operational urban modeling contrasts with simulation efforts in
other disciplines, where techniques, theories, and ideas drawn from computation and
complexity studies are revitalizing the ways in which we conceptualize, understand,
and model real-world phenomena. Many of these concepts and methodologies are
applicable to operational urban systems simulation. Indeed, in many cases, ideas from
computation and complexity studies—often clustered under the collective term of
geocomputation, as they apply to geography—are ideally suited to the simulation of
urban dynamics. However, there exist several obstructions to their successful use in
operational urban geographic simulation, particularly as regards the capacity of these
methodologies to handle top-down dynamics in urban systems.
This paper presents a framework for developing a hybrid model for urban geographic
simulation and discusses some of the imposing barriers against innovation in this
field. The framework infuses approaches derived from geocomputation and
complexity with standard techniques that have been tried and tested in operational
land-use and transport simulation. Macro-scale dynamics that operate from the topdown
are handled by traditional land-use and transport models, while micro-scale
dynamics that work from the bottom-up are delegated to agent-based models and
cellular automata. The two methodologies are fused in a modular fashion using a
system of feedback mechanisms. As a proof-of-concept exercise, a micro-model of
residential location has been developed with a view to hybridization. The model
mixes cellular automata and multi-agent approaches and is formulated so as to
interface with meso-models at a higher scale
The Effectiveness of Aural Instructions with Visualisations in E-Learning Environments
Based on Mayer’s (2001) model for more effective learning by exploiting the brain’s dual sensory channels for information processing, this research investigates the effectiveness of using aural instructions together with visualisation in teaching the difficult concepts of data structures to novice computer science students. A small number of previous studies have examined the use of audio and visualisation in teaching and learning environments but none has explored the integration of both technologies in teaching data structures programming to reduce the cognitive load on learners’ working memory.
A prototype learning tool, known as the Data Structure Learning (DSL) tool, was developed and used first in a short mini study that showed that, used together with visualisations of algorithms, aural instructions produced faster student response times than did textual instructions. This result suggested that the additional use of the auditory sensory channel did indeed reduce the cognitive load.
The tool was then used in a second, longitudinal, study over two academic terms in which students studying the Data Structures module were offered the opportunity to use the DSL approach with either aural or textual instructions. Their use of the approach was recorded by the DSL system and feedback was invited at the end of every visualisation task.
The collected data showed that the tool was used extensively by the students. A comparison of the students’ DSL use with their end-of-year assessment marks revealed that academically weaker students had tended to use the tool most. This suggests that less able students are keen to use any useful and available instrument to aid their understanding, especially of difficult concepts.
Both the quantitative data provided by the automatic recording of DSL use and an end-of-study questionnaire showed appreciation by students of the help the tool had provided and enthusiasm for its future use and development. These findings were supported by qualitative data provided by student written feedback at the end of each task, by interviews at the end of the experiment and by interest from the lecturer in integrating use of the tool with the teaching of the module. A variety of suggestions are made for further work and development of the DSL tool. Further research using a control group and/or pre and post tests would be particularly useful
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