Design Space Exploration in Cyber-Physical Systems

Cyber physical systems (CPS) integrate a variety of engineering areas such as control, mechanical and computer engineering in a holistic design effort. While interdependencies between the different disciplines are key attributes of CPS design science, little is known about the impact of design decisions of the cyber part on the overall system qualities. To investigate these interdependencies, this paper proposes a simulation-based Design Space Exploration (DSE) framework that considers detailed cyber system parameters such as cache size, bus width, and voltage levels in addition to physical and control parameters of the CPS. We propose an exploration algorithm that surfs the parameter configurations in the cyber physical sub-systems, in order to approximate the Pareto-optimal design points with regards to the trade-os among the design objectives, such as energy consumption and control stability. We apply the proposed framework to a network control system for an inverted-pendulum application. The presented holistic evaluation of the identified Pareto-points reveals the presence of non-trivial trade-os, which are imposed by the control, physical, and detailed cyber parameters. For instance the identified energy and control optimal design points comprise configurations with a wide range of CPU speeds, sample times and cache configuration following non-trivial zig-zag patterns. The proposed framework could identify and manage those trade-os and, as a result, is an imperative rst step to automate the search for superior CSP configurations

Improved Cyberbullying Detection Through Personal Profiles

Online social networks brought a new definition to relationships and communications. One may have hundreds of friends in cyberspace without even having seen their real faces. Along with this transition there is increasing evidence that bullying has transformed as well, from school yards to internet precincts – cyberbullying. Although bullying draws a lot of attention, due to its technical aspects, cyberbullying is not fully understood yet. State-of-the-art studies in cyberbullying detection have mainly focused on the sentiment of terms and the content of conversations, while largely ignoring the involved actors and their interactions. A funny chat between teenage friends, just because of having foul words, can be flagged as bullying while a tenacious intruder with subtle but hurtful comments sneaks out. We hypothesize that incorporation of the potential victim’s profile and their characteristics, into cyberbullying detection improves the discrimination capacity of the procedure. This study outlines a framework for this faceted approach. Our study demonstrated that deploying gender-specific and age-specific features improve the cyberbullying detection accuracy for the MySpace dataset, compared to the conventional approaches. Analysis showed that authors’ information can be leveraged to discriminate between harassing posts and the bullying ones. The main limitation of our experiment was the limited size of the dataset. A larger and more diverse dataset should be developed for future work in cyberbullying detection. Other features which may differentiate writing styles, such as profession, and educational level can also be investigated in this matter. In future stages this approach will be extended by considering the behaviour of actors across social networks, and how they react to a potentially cyberbullying incident. A second line of future research will be to address the various use scenarios for the detection of bullying as well as the corresponding detection approaches that may be required in each of the different types of cyber contexts

Estimating New Keynesian Phillips Curves Using Exact Methods

The authors use simple new finite-sample methods to test the empirical relevance of the New Keynesian Phillips curve (NKPC) equation. Unlike tests based on the generalized method of moments, the generalized Anderson-Rubin (1949) tests are immune to the presence of weak instruments and allow, by construction, the identification status of a model to be assessed. The authors illustrate their results using Gali and Gertler's (1999) NKPC specifications and data, as well as a survey-based inflation-expectation series from the Federal Reserve Bank of Philadelphia. The test the authors use rejects Gali and Gertler's estimates (conditional on the latters' choice of instruments). Nevertheless, and in contrast with results obtained by Ma (2002), the authors do obtain relatively informative confidence sets. This provides support for NKPC equations and illustrates the usefulness of using exact procedures in estimations based on instrumental variables. The authors' results also reveal that the least well-identified parameter is w; namely, the proportion of firms that do not adjust their prices in period t.Econometric and statistical methods; Inflation and prices

A Forecasting Model for Inventory Investments in Canada

The authors present an empirical model to forecast short-run inventory investment behaviour for Canada. As with other recent studies that examine this series, they adopt an error-correction framework. Estimations using non-linear least squares and quarterly data yield both a good model fit and good out-of-sample forecasts. Given the debate in the United States on whether the adoption by firms of new information-technology-based methods of inventory management led to a decline in the volatility of U.S. output growth, the authors examine this issue for Canada. Results of the heteroscedasticity-robust Quandt likelihood ratio test advocated by Stock and Watson (2002) reveal very different dates for structural breaks in the volatilities of the growth contribution of inventory investment and of Canadian output growth: 1984Q1 and 1991Q2, respectively. Thus, the authors conclude that the "inventory hypothesis" is likely not an important explanation for the decline in the volatility of Canadian GDP growth.Domestic demand and components; Econometric and statistical methods

TESTING THE PRICING-TO-MARKET HYPOTHESIS CASE OF THE TRANSPORTATION EQUIPMENT INDUSTRY

Most of the evidence in favor of pricing-to-market (PTM) was obtained by estimating partial equilibrium models using OLS, instrumental variable (IV) and single-equation error-correction methods. However, we know from the recent econometric literature that Wald tests applied to some of these estimates may give erroneous results in the presence of endogeneity and weak instruments. In this paper we examine the reliability of the evidence supporting the hypothesis of pricing-to-market using LIML-based LR Monte Carlo tests. These tests, developed by Dufour and Khalaf (1998), have good power and, unlike the Wald test, also have the correct test size.We first estimate a typical PTM model by OLS and subject certain regressors to a test for exogeneity which does not depend on the "quality" of instruments used. Since the null is rejected, we then re-estimate the model by both IV and limited information maximum likelihood methods. Subsequently, we apply Wald and LR-based tests to the parameters of interest to examine the hypothesis of PTM. We find that the size-correct Monte Carlo LR-based test reverses half of the results obtained from the popular Wald test indicating that PTM may not be as widespread as previously believed. In addition, our results support the viewpoint suggesting that PTM behavior is likely to be present in the same industry across different countries and that pass-through is possibly higher with a larger market share of exports.The above findings are illustrated using the model developed by Marston (1990) and our analysis is conducted for export pricing firms in the transportation equipment industry for three country pairs: Canada exporting to the United States, the United States exporting to Canada, and Japan exporting to (mainly) the United States.

Testing the Stability of the Canadian Phillips Curve Using Exact Methods

Postulating two different specifications for the Canadian Phillips curve (a purely backwardlooking model, and a partly backward-, partly forward-looking model), the authors test for structural breaks in the parameters of the equation. In each case, they account for the possibilities that: (i) breaks can be discrete, or continuous, and (ii) available data samples may be too small to justify using asymptotically valid structural-change tests. Thus, the authors use recent testing procedures that are valid in finite samples, applying the Dufour-Kiviet (1996) methodology for discrete-type breaks, and the Dufour (2002) Maximized Monte Carlo test method for continuous-type shifts. The second test accounts for nuisance parameters that appear only under the alternative. The proposed alternative is a Kalman-filter-based time-varying-parameter specification, with coefficients that follow random walks. The authors find evidence for linear and non-linear breaks, the latter being characterized by continuous and unpredictable-type shifts in the inflation-dynamics coefficients.Econometric and statistical methods

Structural Change in Covariance and Exchange Rate Pass-Through: The Case of Canada

The authors address empirically the implications of structural breaks in the variance-covariance matrix of inflation and import prices for changes in pass-through. They define pass-through within a correlated vector autoregression (VAR) framework as the response of domestic inflation to an impulse in import price inflation. This approach allows them to examine changes in both the amount and the duration of pass-through.Econometric and statistical methods