A Contextualized Web-Based Learning Environments for DEVS Models

With the advance in applying technology in education, the traditional lecture-driven teaching style is gradually replaced by a more active teaching style where the students play a more active rule in the learning process. In this paper we introduce a new initiative to provide a suite of online tools for learning DEVS model. The uniqueness of this tutorial project is the integration of information technology and multimedia into education through the development of an interactive tutorial and the characteristic of contextualized learning. The tutorial teaches students about the basic aspects of discrete event system and simulation. The interactive tutorial fully utilizes the power of the information and multimedia technology, web application and the programming language Java, to enhance students’ learning to achieve rich interactivity. The tutorial greatly supports human-computer collaboration to enhance learning and to satisfy user goals by effectively allowing the user to interact

Supporting Constructive Learning with a Feedback Planner

A promising approach to constructing more effective computer tutors is implementing tutorial strategies that extend over multiple turns. This means that computer tutors must deal with (1) failure, (2) interruptions, (3) the need to revise their tactics, and (4) basic dialogue phenomena such as acknowledgment. To deal with these issues, we need to combine ITS technology with advances from robotics and computational linguistics. We can use reactive planning techniques from robotics to allow us to modify tutorial plans, adapting them to student input. Computational linguistics will give us guidance in handling communication management as well as building a reusable architecture for tutorial dialogue systems. A modular and reusable architecture is critical given the difficulty in constructing tutorial dialogue systems and the many domains to which we would like to apply them. In this paper, we propose such an architecture and discuss how a reactive planner in the context of this architecture can implement multi-turn tutorial strategies

Getting Started with Particle Metropolis-Hastings for Inference in Nonlinear Dynamical Models

This tutorial provides a gentle introduction to the particle Metropolis-Hastings (PMH) algorithm for parameter inference in nonlinear state-space models together with a software implementation in the statistical programming language R. We employ a step-by-step approach to develop an implementation of the PMH algorithm (and the particle filter within) together with the reader. This final implementation is also available as the package pmhtutorial in the CRAN repository. Throughout the tutorial, we provide some intuition as to how the algorithm operates and discuss some solutions to problems that might occur in practice. To illustrate the use of PMH, we consider parameter inference in a linear Gaussian state-space model with synthetic data and a nonlinear stochastic volatility model with real-world data.Comment: 41 pages, 7 figures. In press for Journal of Statistical Software. Source code for R, Python and MATLAB available at: https://github.com/compops/pmh-tutoria

A Systematic Review of Strong Gravitational Lens Modeling Software

Despite expanding research activity in gravitational lens modeling, there is no particular software which is considered a standard. Much of the gravitational lens modeling software is written by individual investigators for their own use. Some gravitational lens modeling software is freely available for download but is widely variable with regard to ease of use and quality of documentation. This review of 13 software packages was undertaken to provide a single source of information. Gravitational lens models are classified as parametric models or non-parametric models, and can be further divided into research and educational software. Software used in research includes the GRAVLENS package (with both gravlens and lensmodel), Lenstool, LensPerfect, glafic, PixeLens, SimpLens, Lensview, and GRALE. In this review, GravLensHD, G-Lens, Gravitational Lensing, lens and MOWGLI are categorized as educational programs that are useful for demonstrating various aspects of lensing. Each of the 13 software packages is reviewed with regard to software features (installation, documentation, files provided, etc.) and lensing features (type of model, input data, output data, etc.) as well as a brief review of studies where they have been used. Recent studies have demonstrated the utility of strong gravitational lensing data for mass mapping, and suggest increased use of these techniques in the future. Coupled with the advent of greatly improved imaging, new approaches to modeling of strong gravitational lens systems are needed. This is the first systematic review of strong gravitational lens modeling software, providing investigators with a starting point for future software development to further advance gravitational lens modeling research

Open TURNS: An industrial software for uncertainty quantification in simulation

The needs to assess robust performances for complex systems and to answer tighter regulatory processes (security, safety, environmental control, and health impacts, etc.) have led to the emergence of a new industrial simulation challenge: to take uncertainties into account when dealing with complex numerical simulation frameworks. Therefore, a generic methodology has emerged from the joint effort of several industrial companies and academic institutions. EDF R&D, Airbus Group and Phimeca Engineering started a collaboration at the beginning of 2005, joined by IMACS in 2014, for the development of an Open Source software platform dedicated to uncertainty propagation by probabilistic methods, named OpenTURNS for Open source Treatment of Uncertainty, Risk 'N Statistics. OpenTURNS addresses the specific industrial challenges attached to uncertainties, which are transparency, genericity, modularity and multi-accessibility. This paper focuses on OpenTURNS and presents its main features: openTURNS is an open source software under the LGPL license, that presents itself as a C++ library and a Python TUI, and which works under Linux and Windows environment. All the methodological tools are described in the different sections of this paper: uncertainty quantification, uncertainty propagation, sensitivity analysis and metamodeling. A section also explains the generic wrappers way to link openTURNS to any external code. The paper illustrates as much as possible the methodological tools on an educational example that simulates the height of a river and compares it to the height of a dyke that protects industrial facilities. At last, it gives an overview of the main developments planned for the next few years

Simulation discounted cash flow valuation for internet companies

Discounted cash flow (DCF) is the most accepted approach for company valuation. It is well grounded in theory and practice. However, the DCF approach, which is commonly used for traditional companies valuation, presents a number of serious weaknesses within the Internet companies’ context. One of these weaknesses is tackling the uncertainty that characterize future cash flows of these companies. Specifically DCF assumes that future cash flow streams are highly predictable. The effects of uncertainty are therefore tackled implicitly by discounting the expected value of the cash flows at a risk-adjusted interest rate. However, under uncertainty, future cash flows of these companies can no longer be characterized by a single value but rather by a range of values of its possible consequences. This paper looks at the way in which uncertainty can be incorporated into the traditional DCF approach so that the latter, which is otherwise conceptually sound, becomes relevant. This is done by recognizing that the DCF input variables are uncertain and will have a probability distribution pertaining to each of them. Thus by utilizing a probability-based valuation model (using Monte Carlo Simulation) it is possible to incorporate uncertainty into the analysis and address the shortcomings of the current model. The MC simulation assigns a range of values in order to cope with uncertainty underlies each key cash flow variable. The process leads to a probability distribution of the valuation criterion used, giving investors a quantitative measure of risk involved