Tensorizing neural networks
Deep neural networks currently demonstrate state-of-the-art performance in several domains.At the same time, models of this class are very demanding in terms of computational resources. In particular, a large amount of memory is required by commonly used fully-connected layers, making it hard to use the models on low-end devices and stopping the further increase of the model size. In this paper we convert the dense weight matrices of the fully-connected layers to the Tensor Train format such that the number of parameters is reduced by a huge factor and at the same time the expressive power of the layer is preserved.In particular, for the Very Deep VGG networks we report the compression factor of the dense weight matrix of a fully-connected layer up to 200000 times leading to the compression factor of the whole network up to 7 times.
The issues of information support based on the use of artifical neural networks for the rapid recognition of odors using devices such as "elecronic nose" are considered. The variants the reducing the test sampl for an artifical neural network are proposed with the aim of increasing the stabilutyof computatijns and the speed of calculations. A method for the rapid recognition of odors in the presence of background odors is proposed.
The secular outcome of our investigation is development of new monitoring service for glucose control related to diabetes. It is based on the main results of research: 1) New innovative wearable sensor that carry non-invasive measurement of glucose level. Sensor uses several independent technologies, simultaneously: radio-frequency with different levels of signal, ultrasonic, electromagnetic and thermal; 2) Special mobile application as the principal interface monitor for personal usage; 3) The unique proprietary algorithm, based on neural net, which calculates the weighted average and returns the user's glucose level. The algorithm précises the results of measurements, based on genetic neural net ideas; 4) Special designed Data Base Storage in our cloud software specialized for gathering information and giving the predictions for patient. All results together makes the essence of the research.
The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018. The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.
This book constitutes the proceedings of the 7th International Conference on Analysis of Images, Social Networks and Texts, AIST 2018, held in Moscow, Russia, in July 2018.
The 29 full papers were carefully reviewed and selected from 107 submissions (of which 26 papers were rejected without being reviewed). The papers are organized in topical sections on natural language processing; analysis of images and video; general topics of data analysis; analysis of dynamic behavior through event data; optimization problems on graphs and network structures; and innovative systems.
One of the main objectives of strategic management is the development and selection of strategies to achieve the desired results. The main goal of this paper is the analysis of the main domains or areas of machine learning application to support the process of strategic planning and decision making. The scientific methodology of the research studies is methods and procedures of modeling and intelligent analysis. This is theoretical and empirical paper in equal measure. This paper deals with the issues of machine learning implementation and how intellectual models and systems can be used to support the process of strategic planning in the context of theory of economic growth and development. At the preprocessing stage on the basis of a modeled base of examples of strategy options, the use of clustering methods for forming groups of similar parameters that influence the choice of strategies and groups of similar enterprise objects, each of which has a certain type of strategy, are demonstrated. On the next step the selection of ranked characteristics that affect the choice of strategy is made. At the stage of solving the problem of choosing strategies, neural network and neuro-fuzzy approaches are used. The advantage of this hybrid method is based on the fact that the hybrid technology can combine the advantages of neural networks as well as the advantages of fuzzy logic.
The relevance of scientific research in the field of quantum informatics is grounded. Highlighted promising areas of research. For foreign and Russian publications and materials, an overview of the main scientific results characterizing the current state of quantum informatics has been made. It is concluded that knowledge and resources are most intensively invested in the development of a quantum computer, its architecture and elements, quantum algorithms in the field of cryptography and artificial quantum intelligence. Developments are also actively underway in the field of modeling complex natural and artificial phenomena and processes, the application of quantum computing in the cognitive and social sciences
In Tomsk University of Control Systems and Radioelectronics (TUSUR) one of the main areas of research is information security. The work is carried out by a scientific group under the guidance of Professor Shelupanov. One of the directions is the development of a comprehensive approach to assessing the security of the information systems. This direction includes the construction of an information security threats model and a protection system model, which allow to compile a complete list of threats and methods of protection against them. The main directions of information security tools development are dynamic methods of biometrics, methods for generating prime numbers for data encryption, steganography, methods and means of data protection in Internet of Things (IoT) systems. The article presents the main results of research in the listed areas of information security. The resultant properties in symmetric cryptography are based on the properties of the power of the generating functions. The authors have obtained symmetric principles for the development of primality testing algorithms, as discussed in the Appendix.
The task of determining information security state of objects using the information of signals of electromagnetic emissions of individual elements of devices of cyber-physical systems was investigated. We consider the main side channels of information with which it is possible to monitor the state of the system and analyze the software and hardware environment. Such «independent» methods of monitoring allow analyzing the state of the system based on external behavioral characteristics within the framework of conceptual models of autonomous agents. The statistical characteristics of signals allowing to identify changes in the state of local devices of systems are considered. Was described an experiment aimed at obtaining statistical information on the operation of individual elements of cyber-physical systems. The efficiency of the neural networks approach for solving the described classification problem, in particular, two-layer feed-forward neural networks with sigmoid hidden neurons was investigated. The results of the experiments showed that the proposed approach is superior to the quality of detection of anomalous states by classification based on internal indicators of the functioning of the system. With minimal time of accumulation of statistical information using the proposed approach based on neural networks, it becomes possible to identify the required state of the system with a probability close to 0.85. The proposed approach of the analysis of the statistical data based on neural networks can be used for definition of states of information safety of independent devices of cyber-physical systems.