140,995 research outputs found
Collaborative Verification-Driven Engineering of Hybrid Systems
Hybrid systems with both discrete and continuous dynamics are an important
model for real-world cyber-physical systems. The key challenge is to ensure
their correct functioning w.r.t. safety requirements. Promising techniques to
ensure safety seem to be model-driven engineering to develop hybrid systems in
a well-defined and traceable manner, and formal verification to prove their
correctness. Their combination forms the vision of verification-driven
engineering. Often, hybrid systems are rather complex in that they require
expertise from many domains (e.g., robotics, control systems, computer science,
software engineering, and mechanical engineering). Moreover, despite the
remarkable progress in automating formal verification of hybrid systems, the
construction of proofs of complex systems often requires nontrivial human
guidance, since hybrid systems verification tools solve undecidable problems.
It is, thus, not uncommon for development and verification teams to consist of
many players with diverse expertise. This paper introduces a
verification-driven engineering toolset that extends our previous work on
hybrid and arithmetic verification with tools for (i) graphical (UML) and
textual modeling of hybrid systems, (ii) exchanging and comparing models and
proofs, and (iii) managing verification tasks. This toolset makes it easier to
tackle large-scale verification tasks
Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media
Sentiment analysis has been emerging recently as one of the major natural
language processing (NLP) tasks in many applications. Especially, as social
media channels (e.g. social networks or forums) have become significant sources
for brands to observe user opinions about their products, this task is thus
increasingly crucial. However, when applied with real data obtained from social
media, we notice that there is a high volume of short and informal messages
posted by users on those channels. This kind of data makes the existing works
suffer from many difficulties to handle, especially ones using deep learning
approaches. In this paper, we propose an approach to handle this problem. This
work is extended from our previous work, in which we proposed to combine the
typical deep learning technique of Convolutional Neural Networks with domain
knowledge. The combination is used for acquiring additional training data
augmentation and a more reasonable loss function. In this work, we further
improve our architecture by various substantial enhancements, including
negation-based data augmentation, transfer learning for word embeddings, the
combination of word-level embeddings and character-level embeddings, and using
multitask learning technique for attaching domain knowledge rules in the
learning process. Those enhancements, specifically aiming to handle short and
informal messages, help us to enjoy significant improvement in performance once
experimenting on real datasets.Comment: A Preprint of an article accepted for publication by Inderscience in
IJCVR on September 201
A model-based approach to hypermedia design.
This paper introduces the MESH approach to hypermedia design, which combines established entity-relationship and object-oriented abstractions with proprietary concepts into a formal hypermedia data model. Uniform layout and link typing specifications can be attributed and inherited in a static node typing hierarchy, whereas both nodes and links can be submitted dynamically to multiple complementary classifications. In addition, the data model's support for a context-based navigation paradigm, as well as a platform-independent implementation framework, are briefly discussed.Data; Model; Specifications; Classification;
Object-Oriented Paradigms for Modelling Vascular\ud Tumour Growth: a Case Study
Motivated by a family of related hybrid multiscale models, we have built an object-oriented framework for developing and implementing multiscale models of vascular tumour growth. The models are implemented in our framework as a case study to highlight how object-oriented programming techniques and good object-oriented design may be used effectively to develop hybrid multiscale models of vascular tumour growth. The intention is that this paper will serve as a useful reference for researchers modelling complex biological systems and that these researchers will employ some of the techniques presented herein in their own projects
Information hiding through variance of the parametric orientation underlying a B-rep face
Watermarking technologies have been proposed for many different,types of digital media. However, to this date, no viable watermarking techniques have yet emerged for the high value B-rep (i.e. Boundary Representation) models used in 3D mechanical CAD systems. In this paper, the authors propose a new approach (PO-Watermarking) that subtly changes a model's geometric representation to incorporate a 'transparent' signature. This scheme enables software applications to create fragile, or robust watermarks without changing the size of the file, or shape of the CAD model. Also discussed is the amount of information the proposed method could transparently embed into a B-rep model. The results presented demonstrate the embedding and retrieval of text strings and investigate the robustness of the approach after a variety of transformation and modifications have been carried out on the data
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