70,447 research outputs found
A formal support to business and architectural design for service-oriented systems
Architectural Design Rewriting (ADR) is an approach for the design of software architectures developed within Sensoria by reconciling graph transformation and process calculi techniques. The key feature that makes ADR a suitable and expressive framework is the algebraic handling of structured graphs, which improves the support for specification, analysis and verification of service-oriented architectures and applications. We show how ADR is used as a formal ground for high-level modelling languages and approaches developed within Sensoria
A Graph-based Framework for Transmission of Correlated Sources over Broadcast Channels
In this paper we consider the communication problem that involves
transmission of correlated sources over broadcast channels. We consider a
graph-based framework for this information transmission problem. The system
involves a source coding module and a channel coding module. In the source
coding module, the sources are efficiently mapped into a nearly semi-regular
bipartite graph, and in the channel coding module, the edges of this graph are
reliably transmitted over a broadcast channel. We consider nearly semi-regular
bipartite graphs as discrete interface between source coding and channel coding
in this multiterminal setting. We provide an information-theoretic
characterization of (1) the rate of exponential growth (as a function of the
number of channel uses) of the size of the bipartite graphs whose edges can be
reliably transmitted over a broadcast channel and (2) the rate of exponential
growth (as a function of the number of source samples) of the size of the
bipartite graphs which can reliably represent a pair of correlated sources to
be transmitted over a broadcast channel.Comment: 36 pages, 9 figure
Software process modelling as relationships between tasks
Systematic formulation of software process models is currently a challenging problem in software engineering. We present an approach to define models covering the phases of specification, design, implementation and testing of software systems in the component programming framework, taking into account non-functional aspects of software (efficiency, etc.), automatic reusability of implementations in systems and also prototyping techniques involving both specifications and implementations. Our proposal relies on the identification of a catalogue of tasks that appear during these phases which satisfy some relationships concerning their order of execution. A software process model can be defined as the addition of more relationships over these tasks using a simple, modular process language. We have developed also a formal definition of correctness of a software development with respect to a software process model, based on the formulation of models as graphs.Peer ReviewedPostprint (published version
Visibly Pushdown Modular Games
Games on recursive game graphs can be used to reason about the control flow
of sequential programs with recursion. In games over recursive game graphs, the
most natural notion of strategy is the modular strategy, i.e., a strategy that
is local to a module and is oblivious to previous module invocations, and thus
does not depend on the context of invocation. In this work, we study for the
first time modular strategies with respect to winning conditions that can be
expressed by a pushdown automaton.
We show that such games are undecidable in general, and become decidable for
visibly pushdown automata specifications.
Our solution relies on a reduction to modular games with finite-state
automata winning conditions, which are known in the literature.
We carefully characterize the computational complexity of the considered
decision problem. In particular, we show that modular games with a universal
Buchi or co Buchi visibly pushdown winning condition are EXPTIME-complete, and
when the winning condition is given by a CARET or NWTL temporal logic formula
the problem is 2EXPTIME-complete, and it remains 2EXPTIME-hard even for simple
fragments of these logics.
As a further contribution, we present a different solution for modular games
with finite-state automata winning condition that runs faster than known
solutions for large specifications and many exits.Comment: In Proceedings GandALF 2014, arXiv:1408.556
Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension
In this work, we introduce a novel algorithm for solving the textbook
question answering (TQA) task which describes more realistic QA problems
compared to other recent tasks. We mainly focus on two related issues with
analysis of the TQA dataset. First, solving the TQA problems requires to
comprehend multi-modal contexts in complicated input data. To tackle this issue
of extracting knowledge features from long text lessons and merging them with
visual features, we establish a context graph from texts and images, and
propose a new module f-GCN based on graph convolutional networks (GCN). Second,
scientific terms are not spread over the chapters and subjects are split in the
TQA dataset. To overcome this so called "out-of-domain" issue, before learning
QA problems, we introduce a novel self-supervised open-set learning process
without any annotations. The experimental results show that our model
significantly outperforms prior state-of-the-art methods. Moreover, ablation
studies validate that both methods of incorporating f-GCN for extracting
knowledge from multi-modal contexts and our newly proposed self-supervised
learning process are effective for TQA problems.Comment: ACL2019 Camera-read
Dynamic reconfiguration of human brain networks during learning
Human learning is a complex phenomenon requiring flexibility to adapt
existing brain function and precision in selecting new neurophysiological
activities to drive desired behavior. These two attributes -- flexibility and
selection -- must operate over multiple temporal scales as performance of a
skill changes from being slow and challenging to being fast and automatic. Such
selective adaptability is naturally provided by modular structure, which plays
a critical role in evolution, development, and optimal network function. Using
functional connectivity measurements of brain activity acquired from initial
training through mastery of a simple motor skill, we explore the role of
modularity in human learning by identifying dynamic changes of modular
organization spanning multiple temporal scales. Our results indicate that
flexibility, which we measure by the allegiance of nodes to modules, in one
experimental session predicts the relative amount of learning in a future
session. We also develop a general statistical framework for the identification
of modular architectures in evolving systems, which is broadly applicable to
disciplines where network adaptability is crucial to the understanding of
system performance.Comment: Main Text: 19 pages, 4 figures Supplementary Materials: 34 pages, 4
figures, 3 table
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