58,552 research outputs found
Modeling adaptation with a tuple-based coordination language
In recent years, it has been argued that systems and applications, in order to deal with their increasing complexity, should be able to adapt their behavior according to new requirements or environment conditions. In this paper, we present a preliminary investigation aiming at studying how coordination languages and formal methods can contribute to a better understanding, implementation and usage of the mechanisms and techniques for adaptation currently proposed in the literature. Our study relies on the formal coordination language Klaim as a common framework for modeling some adaptation techniques, namely the MAPE-K loop, aspect- and context-oriented programming
Modeling Adaptation with Klaim
In recent years, it has been argued that systems and applications, in order to deal with their increasing complexity, should be able to adapt their behavior according to new requirements or environment conditions. In this paper, we present an investigation aiming at studying how coordination languages and formal methods can contribute to a better understanding, implementation and use of the mechanisms and techniques for adaptation currently proposed in the literature. Our study relies on the formal coordination language Klaim as a common framework for modeling some well-known adaptation techniques: the IBM MAPE-K loop, the Accord component-based framework for architectural adaptation, and the aspect- and context-oriented programming paradigms. We illustrate our approach through a simple example concerning a data repository equipped with an automated cache mechanism
Modelling Requirements for Content Recommendation Systems
This paper addresses the modelling of requirements for a content
Recommendation System (RS) for Online Social Networks (OSNs). On OSNs, a user
switches roles constantly between content generator and content receiver. The
goals and softgoals are different when the user is generating a post, as
opposed as replying to a post. In other words, the user is generating instances
of different entities, depending on the role she has: a generator generates
instances of a "post", while the receiver generates instances of a "reply".
Therefore, we believe that when addressing Requirements Engineering (RE) for
RS, it is necessary to distinguish these roles clearly.
We aim to model an essential dynamic on OSN, namely that when a user creates
(posts) content, other users can ignore that content, or themselves start
generating new content in reply, or react to the initial posting. This dynamic
is key to designing OSNs, because it influences how active users are, and how
attractive the OSN is for existing, and to new users. We apply a well-known
Goal Oriented RE (GORE) technique, namely i-star, and show that this language
fails to capture this dynamic, and thus cannot be used alone to model the
problem domain. Hence, in order to represent this dynamic, its relationships to
other OSNs' requirements, and to capture all relevant information, we suggest
using another modelling language, namely Petri Nets, on top of i-star for the
modelling of the problem domain. We use Petri Nets because it is a tool that is
used to simulate the dynamic and concurrent activities of a system and can be
used by both practitioners and theoreticians.Comment: 28 pages, 7 figure
Pattern Reification as the Basis for Description-Driven Systems
One of the main factors driving object-oriented software development for
information systems is the requirement for systems to be tolerant to change. To
address this issue in designing systems, this paper proposes a pattern-based,
object-oriented, description-driven system (DDS) architecture as an extension
to the standard UML four-layer meta-model. A DDS architecture is proposed in
which aspects of both static and dynamic systems behavior can be captured via
descriptive models and meta-models. The proposed architecture embodies four
main elements - firstly, the adoption of a multi-layered meta-modeling
architecture and reflective meta-level architecture, secondly the
identification of four data modeling relationships that can be made explicit
such that they can be modified dynamically, thirdly the identification of five
design patterns which have emerged from practice and have proved essential in
providing reusable building blocks for data management, and fourthly the
encoding of the structural properties of the five design patterns by means of
one fundamental pattern, the Graph pattern. A practical example of this
philosophy, the CRISTAL project, is used to demonstrate the use of
description-driven data objects to handle system evolution.Comment: 20 pages, 10 figure
Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT
Pretrained contextual representation models (Peters et al., 2018; Devlin et
al., 2018) have pushed forward the state-of-the-art on many NLP tasks. A new
release of BERT (Devlin, 2018) includes a model simultaneously pretrained on
104 languages with impressive performance for zero-shot cross-lingual transfer
on a natural language inference task. This paper explores the broader
cross-lingual potential of mBERT (multilingual) as a zero shot language
transfer model on 5 NLP tasks covering a total of 39 languages from various
language families: NLI, document classification, NER, POS tagging, and
dependency parsing. We compare mBERT with the best-published methods for
zero-shot cross-lingual transfer and find mBERT competitive on each task.
Additionally, we investigate the most effective strategy for utilizing mBERT in
this manner, determine to what extent mBERT generalizes away from language
specific features, and measure factors that influence cross-lingual transfer.Comment: EMNLP 2019 Camera Read
Multilingual Adaptation of RNN Based ASR Systems
In this work, we focus on multilingual systems based on recurrent neural
networks (RNNs), trained using the Connectionist Temporal Classification (CTC)
loss function. Using a multilingual set of acoustic units poses difficulties.
To address this issue, we proposed Language Feature Vectors (LFVs) to train
language adaptive multilingual systems. Language adaptation, in contrast to
speaker adaptation, needs to be applied not only on the feature level, but also
to deeper layers of the network. In this work, we therefore extended our
previous approach by introducing a novel technique which we call "modulation".
Based on this method, we modulated the hidden layers of RNNs using LFVs. We
evaluated this approach in both full and low resource conditions, as well as
for grapheme and phone based systems. Lower error rates throughout the
different conditions could be achieved by the use of the modulation.Comment: 5 pages, 1 figure, to appear in 2018 IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP 2018
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