scholarly journals A new model driven architecture for deep learning-based multimodal lifelog retrieval

2018 ◽  
Author(s):  
Fatma Ben Abdallah ◽  
Ghada Feki ◽  
Anis Ben Ammar ◽  
Chokri Ben Amar
Keyword(s):  
Deep Learning ◽  
Model Driven Architecture ◽  
New Model ◽  
Model Driven

Use of Business Models within Model Driven Architecture

2009 ◽  
Vol 38 (38) ◽  
pp. 119-130
Author(s):  
Erika Asnina
Keyword(s):  
Business Models ◽  
Business Processes ◽  
Use Cases ◽  
Separation Of Concerns ◽  
Model Driven Architecture ◽  
Model Driven ◽  
Development Processes ◽  
Business Requirements ◽  
Complex Computer ◽  
Independent Model

Use of Business Models within Model Driven Architecture Model Driven Architecture is a framework dedicated for development of large and complex computer systems. It states and implements the principle of architectural separation of concerns. This means that a system can be modeled from three different but related to each other viewpoints. The viewpoint discussed in this paper is a Computation Independent one. MDA specification states that a model that shows a system from this viewpoint is a business model. Taking into account transformations foreseen by MDA, it should be useful for automation of software development processes. This paper discusses an essence of the Computation Independent Model (CIM) and the place of business models in the computation independent modeling. This paper considers four types of business models, namely, SBVR, BPMN, use cases and Topological Functioning Model (TFM). Business persons use SBVR to define business vocabularies and business rules of the existing and planned domains, BPMN to define business processes of both existing and planned domains, and use cases to define business requirements to the planned domain. The TFM is used to define functionality of both existing and planned domains. This paper discusses their capabilities to be used as complete CIMs with formally defined conformity between planned and existing domains.

scholarly journals Model‐driven architecture based security analysis

2021 ◽  
Author(s):  
Saoussen Mili ◽  
Nga Nguyen ◽  
Rachid Chelouah
Keyword(s):  
Security Analysis ◽  
Model Driven Architecture ◽  
Model Driven

scholarly journals Fast inverse lithography based on dual-channel model-driven deep learning

2020 ◽  
Vol 28 (14) ◽  
pp. 20404 ◽  
Author(s):  
Xu Ma ◽  
Xianqiang Zheng ◽  
Gonzalo R. Arce
Keyword(s):  
Deep Learning ◽  
Channel Model ◽  
Model Driven ◽  
Dual Channel

scholarly journals Learning Feature Interactions with Lorentzian Factorization Machine

2020 ◽  
Vol 34 (04) ◽  
pp. 6470-6477
Author(s):  
Canran Xu ◽  
Ming Wu
Keyword(s):  
Deep Learning ◽  
Hyperbolic Space ◽  
Recommendation System ◽  
Triangle Inequality ◽  
State Of The Art ◽  
Learning Methods ◽  
New Model ◽  
User Behaviors ◽  
Feature Interactions ◽  
Factorization Machine

Learning representations for feature interactions to model user behaviors is critical for recommendation system and click-trough rate (CTR) predictions. Recent advances in this area are empowered by deep learning methods which could learn sophisticated feature interactions and achieve the state-of-the-art result in an end-to-end manner. These approaches require large number of training parameters integrated with the low-level representations, and thus are memory and computational inefficient. In this paper, we propose a new model named “LorentzFM” that can learn feature interactions embedded in a hyperbolic space in which the violation of triangle inequality for Lorentz distances is available. To this end, the learned representation is benefited by the peculiar geometric properties of hyperbolic triangles, and result in a significant reduction in the number of parameters (20% to 80%) because all the top deep learning layers are not required. With such a lightweight architecture, LorentzFM achieves comparable and even materially better results than the deep learning methods such as DeepFM, xDeepFM and Deep & Cross in both recommendation and CTR prediction tasks.

scholarly journals Formalism Challenges of the Cougaar Model Driven Architecture

2004 ◽  
pp. 57-71 ◽  
Author(s):  
Shawn A. Bohner ◽  
Boby George ◽  
Denis Gračanin ◽  
Michael G. Hinchey
Keyword(s):  
Model Driven Architecture ◽  
Model Driven

scholarly journals Productivity Evaluation of Self-Adaptive Software Model Driven Architecture

2014 ◽  
pp. 191-210
Author(s):  
Basel Magableh ◽  
Stephen Barrett
Keyword(s):  
Dynamic Decision Making ◽  
Development Effort ◽  
Model Driven Architecture ◽  
Model Driven ◽  
Software Model ◽  
Source Line ◽  
Formal Procedure ◽  
Context Dependent ◽  
Application Requirements ◽  
Self Adaptive

Anticipating context changes using a model-based approach requires a formal procedure for analysing and modelling context-dependent functionality and stable description of the architecture which supports dynamic decision-making and architecture evolution. This article demonstrates the capabilities of the context-oriented component-based application model-driven architecture (COCA-MDA) to support the development of self-adaptive applications; the authors describe a state-of-the-art case study and evaluate the development effort involved in adopting the COCA-MDA in constructing the application. An intensive analysis of the application requirements simplified the process of modelling the application’s behavioural model; therefore, instead of modelling several variation models, the developers modelled an extra-functionality model. COCA-MDA reduces the development effort because it maintains a clear separation of concerns and employs a decomposition mechanism to produce a context-oriented component model which decouples the applications’ core functionality from the context-dependent functionality. Estimating the MDA approach’s productivity can help the software developers select the best MDA-based methodology from the available solutions. Thus, counting the source line of code is not adequate for evaluating the development effort of the MDA-based methodology. Quantifying the maintenance adjustment factor of the new, adapted, and reused code is a better estimate of the development effort of the MDA approaches.

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