In this report, we present a new version of the SOFA component model – SOFA 2.0. SOFA component model now seamlessly integrates a component-based technology with service-oriented technology. Such a technology merge takes advantages of both approaches and allows for better management of features like dynamic reconfiguration, supporting multiple communication styles, heterogeneous applications, etc.

In this paper, we study the use of heterogeneous data for training of acoustic models. In initial experiments, a significant drop of accuracy has been observed on in-domain test set if the data was added without any regularization. A solution is proposed by getting control over the training data by optimization of the weights of different data-sets. The final models shows good performance on al...

We explore the idea of heterogeneous NAND flash which possesses pages/blocks of multiple sizes. This heterogeneity can then be exploited to accommodate the diversity in data access patterns found in most storage workloads. We identify various tradeoffs offered by such pages/blocks. By characterizing seven real-world I/O traces, we identify metrics that have a bearing on the efficacy as well as ...

A persistent problem in software engineering is the problem of software composition. The emergence of software architectures and architectural styles has focused attention on a new set of abstractions with which we can create and compose software systems. We examine the problem of providing a model for the composition of different architectural styles within software systems, i.e. the problem o...

This working notes describe the experiments we conducted in the Microblog Cultural Contextualization Lab [2] of CLEF 2017 [3]. The microblog data is composed of very short texts, with very heterogeneous styles. Some of them are written in more than one language. We decided to takle the entity recognition problem by using a non-statistical, dictionary-based, multiword term extractor. On the othe...

Every driver behaves differently in traffic. However, when it comes to micro-simulation of drivers with a high level of detail no framework manages to model the complexities of various driving styles as well as scale up to larger simulations. We propose a framework of micro-simulation combined with cognitive agents to facilitate such simulation tasks. Our goal is to (i) model individual drivers...

We address the problem of analysing how users actually interact with software. Users are heterogeneous: they adopt different usage styles and each individual user may move between different styles, from one interaction session to another, or even during an interaction session. For analysis, we require new techniques to model and analyse temporal data sets of logged interactions with the purpose...

In this study, an intelligent learning style aware diagnosis agent for computer-supported cooperative learning is proposed. Learners are first assigned to heterogeneous groups based on their learning styles questionnaire given right before the beginning of learning activities on the e-learning platform. The proposed diagnosis agent then scrutinizes each learner’s learning portfolio on e-learnin...

Group formation is a key issue in e-learning environments that make use of collaborative work to enhance student performance. While there are many ways to arrange students to work in cooperative groups, recent works have shown that learning styles offer good opportunities to organize students. Particularly, it seems the case that regarding learning styles, heterogeneous groups tend to perform b...