Data Mining of Changing Rules in Time Series
Morrill and Valent´ın in the paper “Computational coverage of TLG: Nonlinearity” considered an extension of the Lambek calculus enriched by a so-called “exponential” modality. This modality behaves in the “relevant” style, that is, it allows contraction and permutation, but not weakening. Morrill and Valent´ın stated an open problem whether this system is decidable. Here we show its undecidability. Our result remains valid if we consider the fragment where all division operations have one direction. We also show that the derivability problem in a restricted case, where the modality can be applied only to variables (primitive types), is decidable and belongs to the NP class.
This volume contains the papers presented at the session "Data Science" within the V International Conference on Information Technology and Nanotechnology (ITNT-2019). The conference was held in Samara, Russia, during May 21-24, 2019 (itnt-conf.org). The conference is a forum for leading researchers from all over the world aimed to discuss the latest advances in the basic and applied research in the field of Information Technology and Nanotechnology. It is also aimed to attract young people to advanced scientific research and share the latest trends in training and research programs for future ITNT specialists . In addition to the session "Data Science", ITNT-2019 also included three other sessions: "Computer Optics and Nanophotonics", "Image Processing and Earth Remote Sensing" and "Mathematical Modeling of Physico-Technical Processes and Systems". The whole forum brought together more than 450 scientists from United Kindom, Japan, Switzerland, Iran, Poland, Bulgaria, Finland, China, Kazakhstan and Russia, as well as representatives of global high-tech corporations, developers of modern electronics – Huawei, Nvidia, Intel, and Azimuth Photonics, and more than 60 cities in the world. 436 talks enabled discussion on a wide range of topics. The topics of the session "Data Science" were grouped into the following key directions: Data Mining (Big data, Systems and platforms, Methods); Machine Learning (Neural networks, Statistical methods, Feature-based classification, Applications); Security, Cryptography (Cryptosystems design and analysis, Mathematical and algorithmic aspects, Efficient implementations of algorithms, Network security); High Performance Computing (Parallel programming models and languages, Highperformance implementations, Complex systems simulation).
An approach to discovering rules in nonstationary k-valued Multidimensional time series is proposed. It allows one to discover rules that are subject to “smooth” structural changes with the course of time. A measure of rule similarity is proposed to describe such changes, and its application in the form of weight in the graph of rules is discussed. The discovered rules can be used to predict the next elements in the multidimensional time series, to analyze the phenomenon described by this multidimensional time series, and to model it. This allows one to use the proposed algorithm for predicting time series and for examining and describing the processes that can be represented by a multidimensional time series. Means for the direct practical application of the proposed methods of the analysis and prediction of time series are described, and the use of those methods for the short-range prediction of a real-life multidimensional time series consisting of the stock prices of companies operating in similar fields is discussed.
A scalable method for mining graph patterns stable under subsampling is proposed. The existing subsample stability and robustness measures are not antimonotonic according to definitions known so far. We study a broader notion of antimonotonicity for graph patterns, so that measures of subsample stability become antimonotonic. Then we propose gSOFIA for mining the most subsample-stable graph patterns. The experiments on numerous graph datasets show that gSOFIA is very efficient for discovering subsample-stable graph patterns.
The Fifth HCT Information Technology Trends (ITT 2018) is a major international research conference for the presentation of innovative ideas, approaches, technologies, research findings and outcomes, best practices and case studies, national and international projects, institutional standards and policies on Emerging Technologies for Artificial Intelligence. ITT 2018 will provide an outstanding forum for researchers, practitioners, students, policy makers, and users to exchange ideas, techniques and tools, raise awareness and share experiences related to all practical and theoretical aspects of Emerging Technologies for Artificial Intelligence, so as to develop solutions related to communications, computer science and engineering, control systems as well as interdisciplinary research and applications.