Query: Model driven

title: Grand challenges in model driven engineering: an analysis of the state of the research
abstract: In 2017 and 2018, two events were held in Marburg, Germany, and San Vigilio di Marebbe, Italy, respectively focusing on an analysis of the state of research, state of practice, and state of the art in model driven engineering (MDE) The events brought together experts from industry, academia, and the open source community to assess what has changed in research in MDE over the last 10 years, what challenges remain, and what new challenges have arisen. This article reports on the results of those meetings, and presents a set of grand challenges that emerged from discussions and synthesis. These challenges could lead to research initiatives for the community going forward.

title: A Model Driven Deep Neural Network for Single Image Rain Removal
abstract: Deep learning (DL) methods have achieved state of the art performance in the task of single image rain removal. Most of current DL architectures, however, are still lack of sufficient interpretability and not fully integrated with physical structures inside general rain streaks. To this issue, in this paper, we propose a model driven deep neural network for the task, with fully interpretable network structures. Specifically, based on the convolutional dictionary learning mechanism for representing rain, we propose a novel single image deraining model and utilize the proximal gradient descent technique to design an iterative algorithm only containing simple operators for solving the model. Such a simple implementation scheme facilitates us to unfold it into a new deep network architecture, called rain convolutional dictionary network (RCDNet) with almost every network module one to one corresponding to each operation involved in the algorithm. By end to end training the proposed RCDNet, all the rain kernels and proximal operators can be automatically extracted, faithfully characterizing the features of both rain and clean background layers, and thus naturally lead to its better deraining performance, especially in real scenarios. Comprehensive experiments substantiate the superiority of the proposed network, especially its well generality to diverse testing scenarios and good interpretability for all its modules, as compared with state of the arts both visually and quantitatively.

title: Optimal strong convergence rate of a backward Euler type scheme for the Cox Ingersoll Ross model driven by fractional Brownian motion
abstract: In this paper, we investigate the optimal strong convergence rate of numerical approximations for the Cox Ingersoll Ross model driven by fractional Brownian motion with Hurst parameter H 1 2 1 To deal with the difficulties caused by the unbounded diffusion coefficient, we study an auxiliary equation based on Lamperti transformation. By means of Malliavin calculus, we prove that the backward Euler scheme applied to this auxiliary equation ensures the positivity of the numerical solution, and is of strong order one. Furthermore, a numerical approximation for the original model is obtained and converges with the same order.

title: "Why is 'Chicago' deceptive?" Towards Building Model Driven Tutorials for Humans
abstract: To support human decision making with machine learning models, we often need to elucidate patterns embedded in the models that are unsalient, unknown, or counterintuitive to humans. While existing approaches focus on explaining machine predictions with real time assistance, we explore model driven tutorials to help humans understand these patterns in a training phase. We consider both tutorials with guidelines from scientific papers, analogous to current practices of science communication, and automatically selected examples from training data with explanations. We use deceptive review detection as a testbed and conduct large scale, randomized human subject experiments to examine the effectiveness of such tutorials. We find that tutorials indeed improve human performance, with and without real time assistance. In particular, although deep learning provides superior predictive performance than simple models, tutorials and explanations from simple models are more useful to humans. Our work suggests future directions for human centered tutorials and explanations towards a synergy between humans and AI.

title: A model driven approach to automate data visualization in big data analytics
abstract: In big data analytics, advanced analytic techniques operate on big datasets aimed at complementing the role of traditional OLAP for decision making. To enable companies to take benefit of these techniques despite the lack of in house technical skills, the H2020 TOREADOR Project adopts a model driven architecture for streamlining analysis processes, from data preparation to their visualization. In this article, we propose a new approach named SkyViz focused on the visualization area, in particular on (1) how to specify the user's objectives and describe the dataset to be visualized, (2) how to translate this specification into a platform independent visualization type, and (3) how to concretely implement this visualization type on the target execution platform. To support step (1) we define a visualization context based on seven prioritizable coordinates for assessing the user's objectives and conceptually describing the data to be visualized. To automate step (2) we propose a skyline based technique that translates a visualization context into a set of most suitable visualization types. Finally, to automate step (3) we propose a skyline based technique that, with reference to a specific platform, finds the best bindings between the columns of the dataset and the graphical coordinates used by the visualization type chosen by the user. SkyViz can be transparently extended to include more visualization types on one hand, more visualization coordinates on the other. The article is completed by an evaluation of SkyViz based on a case study excerpted from the pilot applications of the TOREADOR Project.

title: A novel tDCS sham approach based on model driven controlled shunting
abstract: Transcranial direct current stimulation (tDCS) a non invasive brain stimulation technique able to transiently modulate brain activity, is surging as one of the most promising therapeutic solutions in many neurological and psychiatric disorders. However, profound limitations exist in current placebo (sham) protocols that limit single and double blinding, especially in non naive subjects.  To ensure better blinding and strengthen reliability of tDCS studies and trials, we tested a new optimization algorithm aimed at creating an "active" sham tDCS condition (ActiSham hereafter) capable of inducing the same scalp sensations perceived during real stimulation while preventing currents from reaching the cortex and cause changes in brain excitability.  A novel model based multielectrode technique optimizing the location and currents of a set of small electrodes placed on the scalp was used to control the relative amount of current delivered transcranially in real and placebo multichannel tDCS conditions. The presence, intensity and localization of scalp sensations during tDCS was evaluated by means of a specifically designed questionnaire administered to the participants. We compared blinding ratings by directly addressing subjects' ability to discriminate across conditions for both traditional (Bifocal tDCS and Sham, using sponge electrodes) and our novel multifocal approach (both real Multifocal tDCS and ActiSham) Changes in corticospinal excitability were monitored based on Motor Evoked Potentials (MEPs) recorded via concurrent Transcranial Magnetic Stimulation (TMS) and electromyography (EMG) RESULTS Participants perceived Multifocal tDCS and ActiSham similarly in terms of both localization and intensity of scalp sensations, whereas traditional Bifocal stimulation was rated as more painful and annoying compared to its Sham counterpart. Additionally, differences in scalp localization were reported for active/sham Bifocal tDCS, with Sham tDCS inducing more widespread itching and burning sensations. As for MEPs amplitude, a main effect of stimulation was found when comparing Bifocal Sham and ActiSham (F(1,13) 6.67, p .023) with higher MEPs amplitudes after the application of Bifocal Sham.  Compared to traditional Bifocal tDCS, ActiSham offers better participants' blinding by inducing very similar scalp sensations to those of real Multifocal tDCS both in terms of intensity and localization, while not affecting corticospinal excitability.

title: Integrated model driven development of self adaptive user interfaces
abstract: Modern user interfaces (UIs) are increasingly expected to be plastic in the sense that they retain a constant level of usability, even when subjected to context changes at runtime. Self adaptive user interfaces (SAUIs) have been promoted as a solution for context variability due to their ability to automatically adapt to the context of use at runtime. The development of SAUIs is a challenging and complex task as additional aspects like context management and UI adaptation have to be covered. In classical model driven UI development approaches, these aspects are not fully integrated and hence introduce additional complexity as they represent crosscutting concerns. In this paper, we present an integrated model driven development approach where a classical model driven development of UIs is coupled with a model driven development of context of use and UI adaptation rules. We base our approach on the core UI modeling language IFML and introduce new modeling languages for context of use ContextML and UI adaptation rules AdaptML The generated UI code, based on the IFML model, is coupled with the context and adaptation services, generated from the ContextML and AdaptML model, respectively. The integration of the generated artifacts, namely UI code, context, and adaptation services in an overall rule based execution environment, enables runtime UI adaptation. The benefit of our approach is demonstrated by two case studies, showing the development of SAUIs for different application scenarios and a usability study which has been conducted to analyze end user satisfaction of SAUIs.

title: Model Driven Development of a Digital Twin for Injection Molding
abstract: Digital Twins (DTs) of Cyber Physical Production Systems (CPPSs) enable the smart automation of production processes, collection of data, and can thus reduce manual efforts for supervising and controlling CPPSs. Realizing DTs is challenging and requires significant efforts for their conception and integration with the represented CPPS. To mitigate this, we present an approach to systematically engineering DTs for injection molding that supports domain specific customizations and automation of essential development activities based on a model driven reference architecture. In this approach, reactive CPPS behavior is defined in terms of a Domain Specific Language (DSL) for specifying events that occur in the physical system. The reference architecture connects to the CPPS through a novel DSL for representing OPC UA bindings. We have evaluated this approach with a DT of an injection molding machine that controls the machine to optimize the Design of Experiment (DoE) parameters between experiment cycles before the products are molded. Through this, our reference implementation of the DT facilitates the time consuming setup of a DT and the subsequent injection molding activities. Overall, this facilitates to systematically engineer digital twins with reactive behavior that help to optimize machine use.

title: Estimation for the Discretely Observed Cox Ingersoll Ross Model Driven by Small Symmetrical Stable Noises
abstract: This paper is concerned with the least squares estimation of drift parameters for the Cox Ingersoll Ross (CIR) model driven by small symmetrical a stable noises from discrete observations. The contrast function is introduced to obtain the explicit formula of the estimators and the error of estimation is given. The consistency and the rate of convergence of the estimators are proved. The asymptotic distribution of the estimators is studied as well. Finally, some numerical calculus examples and simulations are given.

title: Environment interaction model driven smart products through life design framework
abstract: With the advent of Industry 4.0, design of a smart product to work in a smart factory, or at home or city over its lifecycle has to consider its intelligent interaction with its external environment (physical, human and cyberspace environments) Thus, environment interaction driven design of smart products becomes an important design research field, facing a huge challenge of integrating a smart product and its environment interaction design crossing all its life phases. In this paper, a high profile design framework is proposed for guiding environment interaction model driven smart products through life design. It has three core elements: (1) a generic environment interaction model of a smart product through life phases, which can help map out the interaction requirements between the smart product and other interaction elements through life phases, (2) a smart product design process model for guiding product and its interaction design at each phase and (3) a design strategy of smart products through life design. Its implementation feasibility is demonstrated with an industrial case study, showing that it is helpful to implement design solutions to a smart product for satisfying intelligent interactions with its external environments through life phases.