OpenVINO Deep Learning Workbench: Towards Analytical Platform for Neural Networks Inference Optimization
Modern neural network methods make it possible to obtain qualitative results that open the colossal potential for integrating them into industrial applications. Therefore, there is a need for systems like the OpenVINO Deep Learning Workbench to analyze and optimize neural networks' performance on target devices. As the primary tool for inference, the OpenVINO open environment is used, which provides a wide variety of options to speed up network execution on the target hardware.
In article features of non-uniform knowledge are considered, and the hypothesis of location on their structure is offered. This hypothesis allows to express work of the corresponding mechanism of a logic conclusion in the verbal form.
A new computer architecture named object-attribute is offered in the article. Computer of the architecture have all necessary properties for Artificial Intelligence: abstraction of data and program, height concurrency, isomorphism of data and program (i.e. possibility of painless changing of program and data structures), training and self-training of computer system, dataflow, integration of data and program, generation of object description from simple description to complex description, implementation of distribute computer system.
The article is devoted to the history and problems of creating interfaces. Shows the complexity and importance of effective interfaces, noted that this problem is a system of multilevel interdisciplinary. The new systems should be given serious attention to issues of human efficiency level. Man is still the leading element in determining the efficiency of any ergatic system. The main means of control in ergatic systems including computers, is the graphic manipulator (GM), with which to control the on-screen controls. Are the main styles of user interface. The most popular are GUI-interface (GUI - GraphicalUserInterface) and based on them WUI-interface (WUI-WebUserInterface). The development of equipment and technology of computer modeling led to the active introduction of virtual reality technology to ensure the inclusion of people in artificial worlds. Their main feature - full control of all the parameters of the development and the emergence of a sense of presence in people who live in these environments, which are called immersive. Technology induced environments allow a number of new, not generally applicable to the present, of interfaces using specially engineered virtual environments. Much attention is paid to creating the most advanced systems - systems contact management, which are the camera and sophisticated software. The drawbacks of modern non-contact control. Is being developed to create a contactless intelligent interface, which will allow: to control with data from a video camera, which is installed on your computer have a high noise immunity, clearly identify the user to recognize the situational environment, have an acceptable cost.
Most of today’s machine learning techniques requires large manually labeled data. This problem can be solved by using synthetic images. Our main contribution is to evaluate methods of traffic sign recognition trained on synthetically generated data and show that results are comparable with results of classifiers trained on real dataset. To get a representative synthetic dataset we model different sign image variations such as intra-class variability, imprecise localization, blur, lighting, and viewpoint changes. We also present a new method for traffic sign segmentation, based on a nearest neighbor search in the large set of synthetically generated samples, which improves current traffic sign recognition algorithms.
The volume contains the abstracts of the 12th International Conference "Intelligent Data Processing: Theory and Applications". The conference is organized by the Russian Academy of Sciences, the Federal Research Center "Informatics and Control" of the Russian Academy of Sciences and the Scientific and Coordination Center "Digital Methods of Data Mining". The conference has being held biennially since 1989. It is one of the most recognizable scientific forums on data mining, machine learning, pattern recognition, image analysis, signal processing, and discrete analysis. The Organizing Committee of IDP-2018 is grateful to Forecsys Co. and CFRS Co. for providing assistance in the conference preparation and execution. The conference is funded by RFBR, grant 18-07-20075. The conference website http://mmro.ru/en/.
This book constitutes the refereed proceedings of the 12th Industrial Conference on Data Mining, ICDM 2012, held in Berlin, Germany in July 2012. The 22 revised full papers presented were carefully reviewed and selected from 97 submissions. The papers are organized in topical sections on data mining in medicine and biology; data mining for energy industry; data mining in traffic and logistic; data mining in telecommunication; data mining in engineering; theory in data mining; theory in data mining: clustering; theory in data mining: association rule mining and decision rule mining.