Feature-Oriented Modelling Using Event-B

Event-B is a formal method for specification and verification of reactive systems. Its Rodin toolkit provides comprehensive support for modelling, refinement and analysis using theorem proving, animation and model checking. There has always been a need to reuse existing models and their associated proofs when modelling related systems to save time and effort. Software product lines (SPLs) focus on the problem of reuse by providing ways to build software products having commonalities and managing variations within products of the same family. Feature modelling is a well know technique to manage variability and configure products within the SPLs. We have combined the two approaches to formally specify SPLs using Event-B. This will contribute the concept of formalism to SPLs and re-usability to Event-B. Existing feature modelling notations were adapted and extended to include refinement mechanism of Event-B. An Eclipse-based graphical feature modelling tool has been developed as a plug-in to the Rodin platform. We have modelled the "production cell" case-study in Event-B, an industrial metal processing plant, which has previously been specified in a number of formalisms. We have also highlighted future directions based on our experience with this framework so far

Attention Mechanism for Adaptive Feature Modelling

This thesis presents groundbreaking contributions in machine learning by exploring and advancing attention mechanisms within deep learning frameworks. We introduce innovative models and techniques that significantly enhance feature recognition and analysis in two key application areas: computer vision recognition and time series modeling. Our primary contributions include the development of a dual attention mechanism for crowd counting and the integration of supervised and unsupervised learning techniques for semi-supervised learning. Furthermore, we propose a novel Dynamic Unary Convolution in Transformer (DUCT) model for generalized visual recognition tasks, and investigate the efficacy of attention mechanisms in human activity recognition using time series data from wearable sensors based on the semi-supervised setting. The capacity of humans to selectively focus on specific elements within complex scenes has long inspired machine learning research. Attention mechanisms, which dynamically modify weights to emphasize different input elements, are central to replicating this human perceptual ability in deep learning. These mechanisms have proven crucial in achieving significant advancements across various tasks. In this thesis, we first provide a comprehensive review of the existing literature on attention mechanisms. We then introduce a dual attention mechanism for crowd counting, which employs both second-order and first-order attention to enhance spatial information processing and feature distinction. Additionally, we explore the convergence of supervised and unsupervised learning, focusing on a novel semi-supervised method that synergizes labeled and unlabeled data through an attention-driven recurrent unit and dual loss functions. This method aims to refine crowd counting in practical transportation scenarios. Moreover, our research extends to a hybrid attention model for broader visual recognition challenges. By merging convolutional and transformer layers, this model adeptly handles multi-level features, where the DUCT modules play a pivotal role. We rigorously evaluate DUCT's performance across critical computer vision tasks. Finally, recognizing the significance of time series data in domains like health surveillance, we apply our proposed attention mechanism to human activity recognition, analyzing correlations between various daily activities to enhance the adaptability of deep learning frameworks to temporal dynamics

FEATURE MODELLING

U radu su predstavljeni osnovni načini razvoja modela značajke proizvoda. Istraživanja i unapređenja modeliranja pomoću značajke, kao modeliranja proizvoda koji omogućava pohranu geometrijskih i funkcijskih informacija u jednom modelu, nisu u potpunosti ostvarena. Uz pregled većih nedostataka koji prate sadašnje sustave za modeliranje pomoću značajke, a to su održanje značenja značajke, postojanje samo jednog modela proizvoda, ograničene mogućnosti kolaborativnog modeliranja i ograničena domena oblika, prikazana su i istraživanja usmjerena rješavanju tih nedostataka.The paper presents basic ways for the development of product feature model. The investigation and improvement of feature modelling, as product modelling that enables geometrical and functional information to be stored in a single model, has not been accomplished completely. Along with survey of major shortcomings that accompany current feature modelling systems, consisting of maintenance of feature semantics, the existence of only one product feature model, the limited facilities of collaborative modelling, and the limited shape domain the investigations aimed at solving these shortcomings are also presented

Evaluation of a feature modelling validation method

Geometric modelling techniques for computer-aided design are provided with formal validation methods to ensure that a valid model is made available to applications such as interference checking. A natural and popular extension to geometric modelling is to group geometric entities into features that provide some extra meaning for one or more aspects of design or manufacture. These extra meanings are typically loosely formulated, in which case it is not possible to validate the feature-based model to ensure that it provides a correct representation for a downstream activity such as process planning. Earlier research established that validation methods can be based on the capture of designers' intents related to functional, relational and volumetric aspects of component geometry. This paper describes how this feature-based validation method has itself been validated through it's application to a series of test parts which have been either drawn from the literature or created to demonstrate particular aspects. It is shown that the prototype system that has been developed is indeed capable of meaningful featurebased model validation and additionally provides extensive information that is potentially useful to a range of engineering analysis activities

Feature modelling: a validation methodology and its evaluation

Geometric modelling techniques for computer-aided design are provided with formal validation methods to ensure that a valid model is made available to applications such as interference checking. A natural and popular extension to geometric modelling is to group geometric entities into features that provide some extra meaning for one or more aspects of design or manufacture. These extra meanings are typically loosely formulated, in which case it is not possible to validate the feature-based model to ensure that it provides a correct representation for a downstream activity such as process planning. This paper presents a methodology used to validate the feature-based representation which is based on the capture of designer’s intents related to functional, relational and volumetric aspects of the component geometry. The feature-based validation method has itself been validated through its application to a series of test parts which have been either drawn from the literature or created to demonstrate particular aspects. It is shown that the prototype system that has been developed is indeed capable of meaningful feature-based model validation and additionally provides extensive information that is potentially useful to a range of engineering and manufacturing analysis activities

Automated Analysis in Feature Modelling and Product Configuration

The automated analysis of feature models is one of the thriving topics of research in the software product line and variability management communities that has attracted more attention in the last years. A recent literature review reported that more than 30 analysis operations have been identi ed and di erent analysis mechanisms have been proposed. Product con guration is a well established research eld with more than 30 years of successful applications in di erent industrial domains. Our hypothesis, that is not really new, is that these two independent areas of research have interesting synergies that have not been fully explored. To try to explore the potential synergies systematically, in this paper we provide a rapid review to bring together these previously disparate streams of work. We de ne a set of research questions and give a preliminary answer to some of them. We conclude that there are many research opportunities in the synergy of these independent areas.Ministerio de Ciencia e Innovación TIN2009- 07366Junta de Andalucía TIC-590

Using Feature Modelling and Automations to Select among Cloud Solutions

International audienceCloud computing is a major trend in distributed computing environments. Resources are accessed on demand by customers and are delivered as services by cloud providers in a pay-per-use model. Companies provide their applications as services and rely on cloud providers to provision, host and manage such applications on top of their infrastructure. However, the wide range of cloud solutions and the lack of knowledge in this domain is a real problem for companies when facing the cloud solution choice. In this paper, we propose to use Software Product Line Engineering (SPLE) and Feature Model (FM) configuration to develop a decision-supporting tool. Using such modelling techniques and automations, this tool takes into consideration the application technical requirements as well as the user quality requirements to provide an accurate result among cloud solutions that best fits both requirements