AN APPLICATION OF CYBERNETIC ALGORITHM DESIGN

Abstract

Principles for cybernetic generation of algorithms are applied to a specific problem class in order to gain insight needed for the development of a general design methodology. The principles are formalized and implemented in a working program. Specifically, the family of problems considered consists of those where instances consist of finite sequences of distinct elements chosen from some linearly ordered set and the operation among elements is the comparison. The results obtained with the program indicate that the program can provide many insights into problem solution. Problems inherent in the cybernetic algorithm design process are discussed and applications to other types of problems are suggested.     

Adaptive nonlinear composite filters for pattern recognition

Adaptive composite nonlinear filters for reliable illumination-invariant pattern recognition are proposed. The information about objects to be recognized, false objects, and a background to be rejected is utilized in an iterative training procedure to design a nonlinear adaptive correlation filter with a given value of discrimination capability. The designed filter during recognition process adapts its parameters to local statistics of the input image. Computer simulation results obtained with the proposed filters in test nonuniform illuminated scenes are discussed and compared with those of linear composite correlation filters in terms of recognition performance. The text was submitted by the authors in English. Saul Martínez Diaz. Received his MSc degree in Computer Science from Instituto Tecnologico de La Paz, Mexico in 2005. He is currently a PhD student at Department of Computer Science, CICESE, Mexico. His research interests include nonlinear image processing and pattern recognition. Vitaly Kober. Obtained his MS degree in Applied Mathematics from the Air-Space University of Samara (Russia) in 1984, his PhD degree in 1992, and Doctor of Sciences degree in 2004 in Image Processing from the Institute of Information Transmission Problems, Russian Academy of Sciences. Now he is a titular researcher at CICESE, Mexico. His research interests include signal and image processing, pattern recognition. Iosif A. Ovseyevich. Graduated from the Moscow Electrotechnical Institute of Telecommunications. Received Candidate’s degree in 1953 and Doctor’s degree in information theory in 1972. At present he is Emeritus Professor at the Institute of Information Transmission Problems of the Russian Academy of Sciences. His research interests include information theory, signal processing, and expert systems. He is a Member of IEEE, Popov Radio Society.     

Adaptive correlation filters for pattern recognition

Adaptive correlation filters based on synthetic discriminant functions (SDFs) for reliable pattern recognition are proposed. A given value of discrimination capability can be achieved by adapting a SDF filter to the input scene. This can be done by iterative training. Computer simulation results obtained with the proposed filters are compared with those of various correlation filters in terms of recognition performance. The text was submitted by the authors in English. Vitaly Kober obtained his MS degree in Applied Mathematics from the Air-Space University of Samara (Russia) in 1984 and his PhD degree in 1992 and Doctor of Sciences degree in 2004 in Image Processing from the Institute of Information Transmission Problems, Russian Academy of Sciences. He is now a titular researcher at the Centro de Investigatión Cientifica y de Educatión Superior de Ensenada (Cicese), Mexico. His research interests include signal and image processing and pattern recognition. Mikhail Mozerov received his MS degree in Physics from Moscow State University in 1982 and his PhD degree in Image Processing from the Institute of Information Transmission Problems, Russian Academy of Sciences, in 1995. He is with the Laboratory of Digital Optics of the Institute of Information Transmission Problems, Russian Academy of Sciences. His research interests include signal and image processing, pattern recognition, and digital holography. Iosif A. Ovseyevich graduated from the Moscow Electrotechnical Institute of Telecommunications. He received his candidate’s degree in 1953 and doctoral degree in Information Theory in 1972. At present he is Emeritus Professor at the Institute of Information Transmission Problems, Russian Academy of Sciences. His research interests include information theory, signal processing, and expert systems. He is a Member of the IEEE and Popov Radio Society.     

Spectral analysis and data classification in magnetoencephalography

A method for classifying types of brain activity in magnetoencephalographic (MEG) signals is proposed. Sources of abnormal cortical activity are localized by performing a generalized spectral analysis in the space of Fourier coefficients of the expansions of recorded signals in spherical harmonics. The basic principles of the method are discussed, and the results of its application to actual MEG records are presented in the case of Parkinson’s disease. Arkadii V. Derguzov. Born 1974. Graduated from Bauman Moscow State Technical University in 2000. Researcher at the Institute of Mathematical Problems of Biology, Russian Academy of Sciences. Scientific interests: data processing, pattern recognition. Six published paper. Sergey A. Makhortykh. Born 1963. Graduated from Moscow Institute of Physics and Technology in 1986. Defended dissertation (Cand. Sci.) in 1990. Academic secretary of the Institute of Mathematical Problems of Biology, Russian Academy of Sciences, Head of the Laboratory of Data Processing. Scientific interests: data processing, pattern recognition, and physical acoustics. Over 60 publications.     

The Fifth International Students’ Olympiad in cryptography—NSUCRYPTO: Problems and their solutions

Abstract

Problems and their solutions of the Fifth International Students’ Olympiad in cryptography NSUCRYPTO’2018 are presented. We consider problems related to attacks on ciphers and hash functions, Boolean functions, quantum circuits, Enigma, etc. We discuss several open problems on orthogonal arrays, Sylvester matrices, and disjunct matrices. The problem of existing an invertible Sylvester matrix whose inverse is again a Sylvester matrix was completely solved during the Olympiad.     

Fuzzy Sequential Functions

Abstrat

In this paper, the sequential functions or input-output relations of fuzzy systems which are realizable by maximin machines are studied. Various problems related to these functions are examined, which include characterization and decision Problems, completion problems, extension problems, closure properties and their relations to maximin regular events. Some basic properties of maximin regular events are also presented including the extension of Kleene's theorem to the maximin case.     

Changing local health and safety practices at work within the explosives industry

Abstract

An action research project was carried out over a period of 18 months at three companies engaged in the manufacture of explosives. The aim of the research was to evaluate a scheme for health and safety practices which provided for worker participation and a structured decisionmaking process. Problems encountered when attempting to improve provisions for health and safety at work are discussed. At all the workplaces involved, however, the experiment resulted in active efforts to reduce health and safety risks particularly with reference to risks of explosions, acceptance of remedial action among workers who participated in the decisionmaking process and increased knowledge and a broader understanding of problems related to health and safety at work among those directly participating in the experiment.     

Increasing the efficiency of combinatorial logical data analysis in recognition and classification problems

Problems of increasing the efficiency of combinatorial logical data analysis in recognition problems are examined. A technique for correct conversion of initial information for reduction of its dimensionality is proposed. Results of testing this technique for problems of real medical prognoses are given. Djukova Elena V. Born 1945. Graduated from Moscow State University in 1967. Candidate’s degree in Physics and Mathematics in 1979. Doctoral degree in Physics and Mathematics in 1997. Dorodnicyn Computing Center, Russian Academy of Sciences, leading researcher. Moscow State University, lecturer. Moscow Pedagogical University, lecturer. Scientific interests: discrete mathematics and mathematical method of pattern recognition. Author of 70 papers. Peskov Nikolai V. Born 1978. Graduated from Moscow State University in 2000. Candidate’s degree in 2004. Dorodnicyn Computing Center, Russian Academy of Sciences, junior researcher. Scientific interests: discrete mathematics and mathematical methods of pattern recognition. Author of ten papers. Inyakin Andrey S. Born 1978. Graduated from Moscow State University in 2000. Dorodnicyn Computing Center, Russian Academy of Sciences, junior researcher. Scientific interests: discrete mathematics and mathematical methods of pattern recognition. Author of ten papers. Sakharov Aleksei A. Born 1980. Graduated from Moscow State University in 2003. Moscow Pedagogical University, graduate student. Scientific interests: discrete mathematics and mathematical method of pattern recognition. Author of three papers.     

The impact of inadequate and dysfunctional training on Agile transformation process: A Grounded Theory study

ContextTraining is an essential facilitator in moving from traditional to Agile software development.ObjectiveThis paper addresses the importance of adequate and functional training in Agile transformation process, the causes of inadequate and dysfunctional training, and the heuristic strategies that can be used in software companies for dealing with this phenomenon.MethodA Grounded Theory study was conducted with participation of 35 Agile experts from 13 different countries.ResultsThis research discovered that inadequate and dysfunctional training was one of the critical issues that affected Agile transformation process. This study shows that comprehensive and functional training is not often provided to support Agile transformation. This paper shows the primary causes of inadequate and dysfunctional training, its adverse consequences on the transformation process, and the heuristic and ad-hoc treatments as the strategies used by Agile teams to cope with this challenge.ConclusionComprehensive training is important in Agile transformation process. Inadequate and dysfunctional training causes several challenges and problems for software companies and development teams when moving to Agile. Several ad-hoc strategies identified by this study can be employed to help software teams and companies facing similar problems.     

Preoperative planning of pelvic and lower limbs surgery by CT image processing

This paper outlines main functions of a system for preoperative planning of pelvic and lower limbs surgery. A generic scheme of operation planning using the system as well as the main system features and methods applied at the time of development are discussed. Eight steps of planning procedure using the presented system are described. The article is published in the original. Vasil Hancharenka. Born in 1979, graduated from Minsk State Higher Radioengineering College in 2002. 2002–2005: PhD student of the United Institute of Informatics Problems of the National Academy of Sciences of Belarus. At present: junior researcher at the United Institute of Informatics Problems of the National Academy of Sciences of Belarus. Professional interest: CT image processing, image segmentation, watershed transformation, development of systems for computer support in radiology and surgery. Alexander Tuzikov. Graduated from the Belarus State University (Minsk, 1980), received the candidate of physics-mathematical sciences degree of Institute of Mathematics (1985) and doctor of physics-mathematical sciences degree of the Institute of Engineering Cybernetics of the National Academy of Sciences of Belarus (2000). He is a Deputy General Director of the United Institute of Informatics Problems of the National Academy of Sciences of Belarus. His research subjects include image processing and analysis, medical imaging, stereo image reconstraction, mathematical morphology, discrete applied mathematics. Viachaslau Arkhipau. Born in 1982, graduated from Belarusian State University in 2006. At present he is a PhD student and junior researcher (part of time) in the United Institute of Informatics Problems of the National Academy of Sciences of Belarus. His interest is; medical image processing, image registration, tomography image visualization. In 1994 Aleh Kryvanos receved a dyploma at the Belarusian State University, Faculty of Mathematics and Informatics. In 2002 he defended a PhD thesis at the University of Mannheim, Germany. His field of specialization is medical Image Processing, Image Analysis, Operation Planning, Surgical Navigation, Image Restoration. His research activities are currently directed to medical equipment for diagnosing, surgery supporting, and archiving.     

Tabu search for a class of single-machine scheduling problems

In this paper we develop a tabu search-based solution procedure designed specifically for a certain class of single-machine scheduling problems with a non-regular performance measure. The performance of the developed algorithm is tested for solving the variance minimization problem. Problems from the literature are used to test the performance of the algorithm. This algorithm can be used for solving other problems such as minimizing completion time deviation from a common due date.Scope and purposeScheduling problems with non-regular performance measures has gained a great importance in modern manufacturing systems. These problems are found to be hard to solve and analyze. The purpose of this paper is to present a tabu search approach for solving a certain class of single-machine scheduling problems with non-regular performance measure. Minimizing the variance of completion times and the total deviation from a common due date are two examples of such problems. The proposed approach is found to perform better than the simulated annealing approach for the variance minimization problem.     

An automated scheme for the removal of cloud contamination from AVHRR radiances over western Europe

Abstract

Various cloud-detection schemes are applied to 1.1 km Advanced Very High Resolution Radiometer (AVHRR) day- and night-time data to determine an optimum automated scheme for deriving cloud-free radiances over both land and sea. A combination of the spatial coherence method at infrared wavelengths (11 μm) and dynamic visible threshold methods proved to be the most effective scheme for day-time use. Uniform thin cirrus (i.e. reflectance less than 15 per cent) was difficult to detect with all methods. Problems were also encountered over regions with a changing underlying surface type (e.g. coastal areas) where the automated scheme was not as effective as over uniform surfaces. At night a combination of the spatial coherence method and a scheme based on the differences in brightness temperatures between the three infrared channels at 37, II and 12 μm wavelength was successfully used. Results obtained by applying these algorithms to AVHRR data are presented and the different problems encountered with each algorithm are discussed.     

THE EFFECTS OF PHYSICAL TRAINING IN A TEMPERATE AND HOT CLIMATE ON THE PHYSIOLOGICAL RESPONSES TO HEAT STRESS

Abstract

The effects of vigorous physical training in a hot climate were assessed and compared with those produced by identical physical training under cooler ambient conditions. Both types of training resulted in a lowering of physiological strain during a standardized heat exposure. The effect was greater for those trained in the hot climate. The results are discussed in relation to other artificial acclimatization techniques.     

Computer program in the treatment for major depression and cognitive impairment in university students

Introduction

A depressed patient presents cognitive impairment that remains in spite of depression’s remission. This study intends to evaluate the impact of cognitive training in the treatment of depression, and also of the impairment that depression causes.

Method

A program for cognitive training (Alcor) was designed for and applied to a group of patients (n = 10) with non-medicated MDD; a group (N = 10) with MDD that was treated with the program and with anti-depressants, and to another group (n = 11) that was given anti-depressors only. The impact of this intervention was assessed by applying the following instruments: Beck Depression Inventory, WAIS, Spielberger State-Trait Anxiety Inventory, Externalized Problems Assessment Scale for Adolescents and Young Adults, and Attention Problems Assessment Scale. The program was applied to University students with MDD twice a week, until they had reached adequate levels of execution.

Results

The patients of all three groups showed MDD event remission. Those who received cognitive training showed a substantial increase of intellectual performance. The cognitive treatment group increased IQ in 12.9 units and the combined group increase in 13.3 units. There was a slight decrease of 1.9 units within the anti-depressant treatment group. The changes in attention and in externalized problems showed the same trends.     

主动学习的多标签图像在线分类

目的在多标签有监督学习框架中,构建具有较强泛化性能的分类器需要大量已标注训练样本,而实际应用中已标注样本少且获取代价十分昂贵。针对多标签图像分类中已标注样本数量不足和分类器再学习效率低的问题,提出一种结合主动学习的多标签图像在线分类算法。方法基于min-max理论,采用查询最具代表性和最具信息量的样本挑选策略主动地选择待标注样本,且基于KKT(Karush-Kuhn-Tucker)条件在线地更新多标签图像分类器。结果在4个公开的数据集上,采用4种多标签分类评价指标对本文算法进行评估。实验结果表明,本文采用的样本挑选方法比随机挑选样本方法和基于间隔的采样方法均占据明显优势;当分类器达到相同或相近的分类准确度时,利用本文的样本挑选策略选择的待标注样本数目要明显少于采用随机挑选样本方法和基于间隔的采样方法所需查询的样本数。结论本文算法一方面可以减少获取已标注样本所需的人工标注代价;另一方面也避免了传统的分类器重新训练时利用所有数据所产生的学习效率低下的问题,达到了当新数据到来时可实时更新分类器的目的。     

Construction of three-dimensional models using stereo images and taking into consideration its multisequencing

Methods for the automatic construction of a three-dimensional model based on stereoimages are described in the report. Algorithms of the construction of disparity maps are described. Approaches to their parallel implementation are discussed. Additionally, results from computing experiments on the comparison of the efficiency of the execution of sequential and parallel implementations of algorithm are presented. The text was submitted by the author in English. Alexander Nikolaevich Volkovich. Born in 1982 in Grodno. Received the high distinction diploma of High Education in 2005 in Yanka Kupala State University of Grodno. Now he is a PhD student at the United Institute of Informatics Problems of the National Academy of Sciences of Belarus. His research interests include stereovision and 3D-reconstructing.     

生成式对抗网络及其计算机视觉应用研究综述

目的 生成式对抗网络（GAN）的出现为计算机视觉应用提供了新的技术和手段,它以独特零和博弈与对抗训练的思想生成高质量的样本,具有比传统机器学习算法更强大的特征学习和特征表达能力。目前在机器视觉领域尤其是样本生成领域取得了显著的成功,是当前研究的热点方向之一。方法 以生成式对抗网络的不同模型及其在计算机视觉领域的应用为研究对象,在广泛调研文献特别是GAN的最新发展成果基础上,结合不同模型的对比试验,对每种方法的基本思想、方法特点及使用场景进行分析,并对GAN的优势与劣势进行总结,阐述了GAN研究的现状、在计算机视觉上的应用范围,归纳生成式对抗网络在高质量图像生成、风格迁移与图像翻译、文本与图像的相互生成和图像的还原与修复等多个计算机视觉领域的研究现状和发展趋势,并对每种应用的理论改进之处、优点、局限性及使用场景进行了总结,对未来可能的发展方向进行展望。结果 GAN的不同模型在生成样本质量与性能上各有优劣。当前的GAN模型在图像的处理上取得较大的成就,能生成以假乱真的样本,但是也存在网络不收敛、模型易崩溃、过于自由不可控的问题。结论 GAN作为一种新的生成模型具有很高的研究价值与应用价值,但目前存在一些理论上的桎梏亟待突破,在应用方面生成高质量的样本、逼真的场景是值得研究的方向。     

A neuro-fuzzy hybrid scheme for design and simulation of human machine systems

In this article, a new neuro-fuzzy hybrid approach to human workplace design and simulation is proposed. Problems related to human workplace design such as human-machine modeling, measurement and analysis, workplace layout design and planning, workplace evaluation and simulation are discussed in detail. The complex human-machine interactions in workplace design are described with human and workstation parameters within a comprehensive human-machine system model. Based on this model, procedures and algorithms for workplace design, ergonomic evaluation, and optimization are presented in an integrated framework. With a combination of individual neural and fuzzy techniques, the neuro-fuzzy hybrid scheme implements fuzzy if-then rules block for workplace design and evaluation by trainable neural network architectures. For training and test purposes, simulated assembly tasks are carried out on a self-built multiadjustable laboratory workstation with a flexible PEAK Motus motion measurement and analysis system. The trained fuzzy neural networks are capable of predicting the operator's posture and joint angles of motion associated with a range of workstation configurations. They can also be used for design/layout and adjustment of manual assembly workstations. The developed system provides a unified, intelligent computational framework for human-machine system design and simulation. In the end, case studies for workplace design and simulation are presented to validate and illustrate the developed neuro-fuzzy design scheme and system.     

Preprocessing of alphanumeric images to be recognized by autonomous computing systems

Methods and algorithms for digital image processing that increase the probability of correct recognition of alphanumeric data are considered. The computational costs of the control block of an autonomous image recognition system based on TMS320C5416 signal processor are analyzed. Natalia S. Novozhilova. Born 1955. Graduated from Moscow Institute of Electronic Engineering in 1978. Received her candidate’s degree (Mathematics and Physics) in 1984. At present, she is a senior researcher at Lukin Scientific Research Institute of Physical Problems. Scientific interests: pattern recognition and mathematical methods for processing the results of a physical experiment. Author of 22 publications. Aleksandr G. Safonov. Born 1952. Graduated from Moscow Power Engineering Institute in 1975. Received his candidate’s degree (Technical Sciences) in 1982. At present, he is a head of department at Lukin Scientific Research Institute of Physical Problems. Scientific interests: automated data processing complexes, pattern recognition, and microelectronics. Author of more than 50 publications.     

An algorithm for median filtering on the basis of merging of ordered columns

This paper presents algorithms for optimization of the median filtering of images. A median filtering algorithm on the basis of merging ordered columns of an image in the 3 × 3 window of a filter is developed, which allows us to perform the filtering on a personal computer in real time without use of additional hardware. This algorithm outperforms other median filtering algorithms in execution speed on a personal computer. Zalesky Boris Andreevich. Born 1953. Graduated from the Lomonosov Moscow University in 1979. Received the candidate’s degree in 1982 and doctoral degree in Physics and Mathematics in 1990. Leading researcher at the United Institute of Informatics Problems of the National Academy of Sciences of Belarus. Scientific interests: image processing, pattern recognition. Author of 81 papers. In 1987 awarded Leninskii Komsomol prize in the area of mathematics. Kravchonok Aleksandr Ivanovich. Born 1982. Graduated from the Belarussian State University in 2004. Junior researcher at the United Institute of Informatics Problems of the National Academy of Sciences of Belarus. Scientific interests: image processing, pattern recognition. Author of two papers. Lukashevich Pavel Vladimirovich. Born 1982. Graduated from the Moscow Institute of Physics and Technology in 2005. Junior researcher at the United Institute of Informatics Problems of the National Academy of Sciences of Belarus. Scientific interests: image processing, pattern recognition. Author of one paper.