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INDUSTRY PROGRAM. October 14-19, 2018

Industry Day @ MODELS
Modeling in practice
  • Tailor-made by industry for industry
  • Talks by internationally renowned speakers (e.g., B. Selic, L. Briand, M. Völter, F. Bordeleau), and experience reports from various application domains and companies (e.g., Thales, Ericsson, CEA, Toyota)
  • Showcase latest advancements of modelling technologies and tools
  • Unique opportunity to interact with the world's leading practitioners, architects, researchers, and students of advanced SE
Model-Based Systems Engineering Meeting
Adoptions and remaining challenges
  • Keynote: Ed Seidewitz (Model Driven Solutions)
    SysML v2 and MBSE: The next ten years
  • Morning focusing on automotive challenges (talks by HCL, Continental, AVL,...)
  • Afternoon focusing on challenges towards Adoption of MBSE (talks by Bernafon, Airbus, Thales, Bombardier Transport, PRFC)
  • Panels and discussions on automotive challenges and MBSE adoption in industry
Silvja Seres
Independent Advisor and Investor

Incremental and disruptive models for business development

This talk will focus on addressing the current lack of strategies for future business models in face of rapid technological change. The narrative of finding and keeping a position sustainable competitive advantage breaks when dominant strategic positions shift in a matter of months, driven by new business models and new critical resources, such as good clean data and sharp analytic tools. Most board members, just as most owners and leaders, still think in terms of the previous industrial revolution, and attempt to regain competitive positions by incremental improvements to their production or marketing processes, while the new players uninhibitedly accumulate the real competitive advantages. Most incumbents are in a paradoxical position where their proprietary data and their unique understanding of critical processes present a strategis liability, while it should and could be used as a clear advantage. Old models of good leadership forces them to think, in Henry Ford's terms, of ever faster horses rather than new vehicles and infrastructure. We'll outline a simple model for constructive strategies for the future, built on three levels: new efficiencies, new business drivers, and new externalities. This is not a research-based talk, but rather a set of observations from an ex-academic techie's wanderings in the world of business, board rooms and investment partnerships.
Martijn Wisse
Directory of the Robotics Institute at TU Delft

Models for motion prediction; robotic brains versus biological brains

This talk will focus on robot and animal motions. For any system in the real world (robots, animals, and humans alike), for any task that they need to execute, the world needs to be predictable. Accurate predictions facilitate anticipation, planning, and optimization. And predictions are made with a model, i.e. a description of (relevant parts of) the world written in a well-defined language. So the key question for this talk is which models and which modelling language are used by robots and by the biological brain, and is there any similarity between the two? We will first review which models and which language are used for the prediction of dynamic motions for robots. We will survey the basics of (systems of) differential equations illustrated with our work on walking robots, self-driving cars, robotic arms, hands, and integrated systems for logistic applications (the "Amazon Picking Challenge"). Next, in this presentation we survey the reigning neuroscientific theories for how the brain does modelling and prediction. Prediction will appear to play a deep fundamental role in all of our brain functions, and we will analyse how this is related to the robot approach; the brain has no processing unit for matrix multiplications or differential equations, so how does the brain do dynamic prediction? Finally, we will share a preliminary breakthrough result termed "Dualistic Prediction Error Minimization", a key insight from the brain's ways of prediction error minimization, which will allow for a tremendous improvement in modelling accuracy for our future complex robot systems that require predictability within a complex environment.
Jim Cordy
School of Computing, Queen's University

Genetics of Computer Programs

Language use vectors (LUVs) encode the vocabulary and frequency of programming language feature use by computer programs and applications. In some sense, they encode the potential and expressed vocabulary of a program in a way analogous to the way that the genome (DNA) and phenome (RNA) describe the potential and expressed properties of a living thing. In this talk I will explore this analogy and its potential for predicting hidden properties of programs, models and applications such as changes, faults and security flaws.
Practice & Innovation Track
The main conference track where science meets practice
  • Lessons learned from applying advanced model technology
  • Papers co-authored by industry and academia on CPS, safety, automotive, space, smart mobility, low code, ...
5th xtUML Days 2018
The highest density gathering of
Shlaer-Mellor and xtUML modelers
  • Mapping out the future of the method and the tooling
  • Research and work on end-to-end modeling, execution, translation and deployment of models-as-code in mission critical settings
  • Every model and application is deployed and running in the real world
  • Interactive breakout session on Tuesday
JetBrains MPS Day
Create your own domain-specific language
  • Be inspired by real-life examples of model-driven software development and domain-specific languages' capability to increase users' productivity
  • Join JetBrains' Vaclav Pech, as he provides insight into why modern languages workbenches matter
  • See industry expert Markus Völter live coding with KernelF, an extensible and embeddable functional language