We present a model for freight train time prediction based on station network analysis and specific feature engineering. We discuss the first pipeline to improve the freight flight duration prediction in Russia. While every freight company use only reference book made by RZD (Russian Railways) based on railroad distances with accuracy measured in days, we argue that one could predict the flight duration with error less than twenty hours while decreasing error to twelve hours for certain type of freight trains.
The purpose of the paper is to determine the perspectives of diversification of educational services in the conditions of industry 4.0 on the basis of artificial intelligence (AI) training, determine the consequences of this process for academic and teaching staff and to develop recommendations for its practical implementation.
Logical frameworks allow the specification of deductive systems using the same logical machinery. Linear logical frameworks have been successfully used for the specification of a number of computational, logics and proof systems. Its success relies on the fact that formulas can be distinguished as linear, which behave intuitively as resources, and unbounded, which behave intuitionistically. Commutative subexponentials enhance the expressiveness of linear logic frameworks by allowing the distinction of multiple contexts. These contexts may behave as multisets of formulas or sets of formulas. Motivated by applications in distributed systems and in type-logical grammar, we propose a linear logical framework containing both commutative and non-commutative subexponentials. Non-commutative subexponentials can be used to specify contexts which behave as lists, not multisets, of formulas. In addition, motivated by our applications in type-logical grammar, where the weakenening rule is disallowed, we investigate the proof theory of formulas that can only contract, but not weaken. In fact, our contraction is non-local. We demonstrate that under some conditions such formulas may be treated as unbounded formulas, which behave intuitionistically.
In November 2014, Team DESCARTES led by Newton Lee and sponsored by the Institute for Education, Research, and Scholarships (IFERS) was among one of the 104 teams registered with the Defense Advanced Research Projects Agency (DARPA) for the first-ever Cyber Grand Challenge (CGC). Only 28 teams, including Team DESCARTES, made it through two DARPA-sponsored dry runs and into the CGC Qualifying Event in June 2015. We proposed a system—Distributed Expert Systems for Cyber Analysis, Reasoning, Testing, Evaluation, and Security (DESCARTES)—that would be a fully autonomous cyber defense system that is capable of autonomous analysis, autonomous patching, autonomous vulnerability scanning, autonomous service resiliency, and autonomous network defense.
Proceedings of Machine Learning Research: Volume 97: International Conference on Machine Learning, 9-15 June 2019, Long Beach, California, USA
The paper analyses some legal issues of artificial intelligence. In the first part of the paper authors provide classification and overview of the interdisciplinary research in this field. The next part of the paper illustrates artificial intelligence legal issues and provides approaches to mitigate these challenges. In particular, authors examine artificial intelligence influence on the protection of personal data, intellectual property rights and civil liability. The authors conclude that the development of artificial intelligence requires a change in the legal framework.
We compared the ability of various empirical methods to reproduce public credit ratings (PCRs) of industrial companies (ICs) from BRICS countries using publicly available information. This task is important for researchers and practitioners because many of BRICS’ ICs lack PCRs from reputable rating agencies such as Moody’s, Standard and Poor’s, and Fitch. This paper aimed at filling the gap in the existing research as insufficient efforts were focused on prediction of PCRs of ICs from the entire BRICS IC community. The modeled variables are credit ratings (CRs) of 208 BRICS’ ICs assigned by Moody’s at the year-end from 2006 to 2016. The sample included 1217 observations. Financial explanatory variables included companies’ revenue, operating profitability, interest coverage ratio, debt/book capitalization, and cash flow debt coverage. Non-financial explanatory variables included dummies for home region, industry, affiliation with the state, and a set of macroeconomic data of IC’s home countries. The set of statistical methods included linear discriminant analysis (LDA), ordered logit regression (OLR), support vector machine (SVM), artificial neural network (ANN), and random forest (RF). The resulting models were checked for in-sample and out-of-sample predictive fit. Our findings revealed that among considered methods of artificial intelligence models (AI), SVM, ANN, and RF outperformed LDA and OLR by predictive power. On testing sample, AI gave on average 55% of precise results and up to 99% with an error within one rating grade; RF demonstrated the best outcome (58% and 100%). Conversely, LDA and OLR on average gave only 37% of precise results and up to 70% with an error within one grade. LDA and OLR also gave higher share of Type I errors (overestimation of ratings) than that of AI. Therefore, AI should have higher practical application than DA and OLR for predicting the ratings of BRICS ICs
Let G be a semisimple algebraic group whose decomposition into the product of simple components does not contain simple groups of type A, and P⊆G be a parabolic subgroup. Extending the results of Popov , we enumerate all triples (G, P, n) such that (a) there exists an open G-orbit on the multiple flag variety G/P × G/P × . . . × G/P (n factors), (b) the number of G-orbits on the multiple flag variety is finite.
I give the explicit formula for the (set-theoretical) system of Resultants of m+1 homogeneous polynomials in n+1 variables