Reviewerly/exHarmony huggingface dataset:
Distinguishing Characteristics of Digital Format for Students of New Generation

This article actualizes the set of existing methodological problems in pedagogics of high school. The optimal combination of digital technologies and traditional teaching methods are considered to be the main focus. The choice of research direction was validated, the objective was cleared – study of satisfaction among present students with methods of training and control with the usage of elements of digital technology. Generation of young people, who were born in the beginning of 21 century, make up the main contingent of higher educational establishments. Their psychological and communicative peculiarities in the context of digital format of studies were outlined. Methods and results of author-developed research, applied on selection of students from younger courses of engineering training program were presented. Deficiency of communication with professors and troubles in self-dependent learning of training material form in students the requirement for expansion of interactive and active means and tools combining traditional and digital formats of education. It was established that the students experience technical and personal complications while working with digital analogues of training and controlling resources. The variety of advantages of digital format for the new generation of students was defined. The received results on the questions of satisfaction, interdependence of trainees self-organization and their motivation for acquiring high educational qualification and objective assessment of educational activity don’t contradict results received by Russian and overseas researchers. #CSOC1120.
Internet Olympiad in Computer Science in the Context of Assessing the Scientific Potential of the Student

Currently, the state of higher education requires solving many tasks, one of which is to work with young people to identify their scientific potential, which is emphasized by the provisions of the Strategy of digital transformation of science and education in the direction of personalized approach. The implementation of this task can be carried out within the framework of the Olympiad movement and can act as a tool for assessing the scientific potential of the student. In this context, digital technologies are also an important factor. Conducting the Olympiad in a digital space provides conditions for the participation of a wide mass of creative young people from different countries, allows to assess the level of development of scientific potential, increases the information and research competence of young scientists, which determines the relevance of this study. The paper states and solves the problem of identifying the capabilities of the Internet Olympiad in Computer Science as a tool for assessing the scientific potential of students. The aim of the study is to identify the level of scientific potential of its participants according to the results of the Internet Olympiad in Computer Science. The objectives of the study determine the necessity of compiling the Olympiad tasks in the framework of the competence approach and the methodology of their assessment as the level of scientific potential; representation of the contingent of participants according to the profile of knowledge; the analysis of the results. The following methods were used in the study: analysis, comparison, comparison, design, testing, systematization of the results of scientific research. Diagnostic materials of assessment of the level of scientific potential were applied. In the present study we proposed the implementation of the process of assessing the level of development of scientific potential of students on the criterion of scientific and creative activity of personality by the tools of Olympiad movement, testing of which showed its viability. The implementation of the process of assessing the level of development of scientific potential of student youth on the criterion of scientific and creative activity of personality with the tools of Olympiad movement allows to accumulate the base of diagnostics in pedagogical science. #CSOC1120.
Special Controller Design for Implementation the Desired Motion of the Moving Object

Nowadays the problem of automatic motion control systems design for different moving objects, particularly for marine vessels, that perform a wide spectrum of missions, has received significant attention in various scientific investigations. In regard to the continuous expansion of the range of requirements to such systems, and the growing capabilities of the devices that implement the control laws, there is a need to use special control laws, allowing to take into account the complex of conditions, requirements and restrictions that must definitely be performed in all modes of operation of the rolling object. This paper provides a general idea of ​​the design and transformation of the stabilizing controller for its use for the implementation the desired motion of the object, as well as to achieve astaticism in the presence of step disturbances. Particular attention is paid to the problem of transfer the control object from an arbitrary starting point to a given endpoint using a nonlinear control law, as well as ensuring the asymptotic stability of motion in the absence of disturbances. At the same time the proposed controller ensures the providing of the astatism in relation to permanent external influences. #CSOC1120.
An Overview: Image Processing Techniques and Its Applications

The use of digital image processing techniques has been widely flourished and they are now used for all kinds of tasks in various areas. Image processing helps in the identification of objects that are invisible, and different techniques makes it faster and cost effective. This paper gives you an overview on various digital image processing techniques and its applications like remote sensing, medical imaging, forensic studies etc.
Data Processing by Genetic and Pattern Search Algorithms

The goal of paper is to reveal the probable structure of the blurred distributions and estimation of their parameters. Genetic and pattern search algorithms together with maximum entropy and least square methods were applied to decomposition of complex distribution on the components. Algorithms were tested on the models and were used for determination the structures of complex IR spectra of minerals and X-ray diagrams of amorphous materials and also for selection of geophysics anomalies. #CSOC1120.
A Web Application for Moving from One Spot to Another by Using Different Transport—A Scenario of the Research

This document describes the scenario of the research of a Web application for moving from one spot to another by using different means of transport (trains or buses transport). The idea for creating this application appears at a moment, when moving from one spot to another seems almost impossible, because it either takes too much time if you use only one means of transport (train transport) or a possibility for transport using a road system is missing (buses road system Dijkstra’s algorithm for finding the shortest path in a weighted graph with weights mapped to travel time and was used to create this application.
Spider Colony Optimization Algorithm: Bio-Heuristic for Global Optimization Problem

Bioheuristics play a crucial role in solving optimization problems. The article describes an algorithm that models the behavior patterns of a colony of spiders for solving optimization problems of multidimensional multiextremal functions. The authors model various types of agent behavior and computational mechanisms to maintain a balance between the rate of convergence of the algorithm and the diversification of the solution search space. The study analyzes the performance of the algorithm using experiments for the problem of finding a global minimum on a set of test functions. The accuracy of the algorithm is compared with competing bioheuristics, and non-parametric proof of the statistical significance of the results obtained using the Wilcoxon signed-rank test is provided.
Linear Complexity of New r-Ary Sequences of Length pnqm

In this paper, we construct the family of non-binary sequences and show that these sequences have high linear complexity over the finite field of odd order. These sequences are constructed with using new generalized cyclotomic classes modulo pnqm. We generalized the results about the linear complexity of new generalized cyclotomic binary and non-binary sequences obtained earlier. #CSOC1120.
User Interface Generated Distraction Study Based on Accented Visualization Approach

There are considered the peculiarities of users’ perception of distracting user interface elements when performing visual search tasks. Research is based on the formal model of accented visualization. The study includes analysis of the user focus changes recorded by an external eye-tracker. Based on the results of the experiment, conclusions were drawn about the most distracting elements. The research results will be useful in studying the peculiarities of perception by users of interactive interfaces of immersive reality. #CSOC1120.
Detection of Cancer Using Deep Learning Techniques

Various kinds of Cancers have been the most dreaded diseases, which are responsible for a substantial portion of deaths year after year. Early detection of Cancer is crucial in saving the lives of patients. Although manual detection methods are available, the accuracy of detection and hence their reliability is low. In order to assist the physicians in this process, several machine learning techniques have been proposed by different researchers. Machine learning algorithms have made tremendous strides in handling challenging problems. Consequently, combining picture tools with a CAD technique that employs a machine learning approach has created a successful outcome. Most of these algorithms deal with image processing in general and classification of images in particular. Benefits of the medical imaging technologies can vary depending on the expertise of the doctors or the experience of the experts interpreting the images. Advent of deep learning (DL) techniques during the past few years is instrumental in image classification efficiently and is useful in identification of cancer. DL algorithms have excelled in identification, segmentation, extraction of features, and classifying cancer images. Identification of cancer using various DL algorithms is the main topic of the analysis of cancer detection. Convolutional Neural networks (CNNs) are very adept at separating the foreground from the background in images using image segmentation. To be precise CNNs evaluate the images using the kernel window sliding technique to identify the region of interest (ROI), which is the area of aberrant malignant cell development. Segmentation and analysis images can lead to early detection of cancer to a high degree with certainty. DL algorithms are used for detecting cancer from the relevant images for a variety of cancers, like breast cancer, skin cancer, gastrointestinal cancer etc. However, the biggest issues in using DL are lack of suitable digitized environment inside medical facilities; a dearth of adequate readily available and comprehensive databases on which the algorithms can be evaluated; and a lack of the resilience of frequently used training techniques to certain pervasive pathological aspects of medical images and their collections. Despite the challenges posed by visual inspection and traditional machine learning methods, research using DL approaches on cancer molecular pictures are growing rapidly. In this chapter an attempt is made to discuss fruitful application of various DL models and techniques in successful identification of malignant cell proliferation in general and especially spotting breast and skin cancer problems.
Statistical Inference

Abstract This chapter covers the fundamentals of statistical inference. Topics include discrete and continuous probability distributions, conditional probability, Central Limit Theorem (CLT), confidence intervals, hypothesis testing, multiple testing, and statistical power.
Bayesian Inference for Multivariate Categorical Data

In this chapter we describe approaches for Bayesian estimation and model determination for multivariate categorical data, typically summarised in the form of a multiway contingency table. The focus is on log-linear models, particularly log-linear interaction models which describe associations and (conditional) independence of the variables. We view modelling both as a mechanism for obtaining smooth estimated of cell probabilities, and for drawing inferences about associations between the categorical variables being studied. We discuss possible families of prior distributions, with a focus on conjugate prior distributions. For some models these lead to convenient, analytically tractable, posterior summaries, but generally further computation is required. Computational methodology is introduced and discussed.
Graphical Models for Categorical Data

This chapter describes the use of graphical models to analyse categorical data. After defining the (conditional) independence graph, the core ingredients of graphical modelling, conditional independence and graphs, are reviewed. The Markov properties for undirected independence graphs are then presented. These properties facilitate the interpretation of the association structure displayed in the independence graph. Subsequently, three types of graphical models for categorical data are discussed: undirected graphical log-linear models, where all the variables are treated on an equal footing; directed graphical models, where the variables are assumed to be completely ordered; and graphical chain models, where the variables are partitioned into ordered blocks. A simple example is used to illustrate the ideas, and the chapter concludes with some suggestions for further reading.
Requirements extraction from engineering standards – systematic evaluation of extraction techniques

Working with information and knowledge is highly important in engineering design. Especially due to the continuous growth of information embedded in documents, the extraction process needs to be automated and accelerated. Hereby, engineering standards are an important source of knowledge in product development. However, the current way of working with these documents is inefficient and requires a lot of manual steps. This research paper focuses on the automatic extraction of requirements from standards documents and aims to systematically evaluate different extraction techniques in order to identify the best suitable technique for the given problem. For this, a dataset with approximately 10,000 entries is generated from existing standards. It is used to evaluate different extraction techniques whereas rule-based as well as supervised and unsupervised machine-learning techniques are implemented. It is shown that requirements are extracted efficiently, i.e. the F1-score is mostly greater than 80%. This proves the overall suitability for automatic requirements extraction from standards documents. The best results are achieved by Support Vector Machine (F1-scoreSVM=89.6%), followed by the fine-tuned BERT model (F1-scoreBERT=87.9%). Additionally, the rule-based extraction shows promising results and should be considered in future due to its high transparency. Future work will focus on the optimization and combination of different techniques as well as the automatic integration into requirements engineering tools.
A Visual Reasoning-based AR-HUD Service Design Approach for Better Driving Experience

Smart traffic or transportation, as a critical part of realizing the smart city, utilizes various smart product-service systems to support it. Among them, the augmented reality head-up display (AR-HUD) system is a typical smart driving solution, which projects vital information about driving situations with pre-defined AR graphics onto the windshield of vehicles. The AR-HUD service design attracts increasing concerns owing to its enhancement of drivers’ situational awareness without glancing down at the instrument cluster. Nevertheless, current in-car AR-HUDs only stack some simple driving information from sensors, such as the driving speed. It's difficult to meet the demand of drivers since they ignore a higher level of drivers’ situation awareness, i.e., driving situation prediction and recommendation of driving advice. To overcome the challenge, this paper proposes a visual reasoning-based service design approach for advancing AR-HUDs’ digital servitization and better user experience. Additionally, drivers are involved in the service design development in a value co-creation manner through human-computer interaction design. With the combination of experiential driving knowledge and cognitive intelligence computing on the driving scene, the proposed novel service design approach enables AR-HUD to percept driving scenarios and infer good strategies accordingly. Finally, an illustrative example is carried out to validate the feasibility of the proposed AR-HUD service design approach.
A first step towards automatic identification and provision of user-specific knowledge: A verification of the feasibility of automatic text classification using the example of standards

The product development process faces major challenges. One major challenge is the time-efficient retrieval of information from engineering documents. In times of machine learning - as an enabler as well as a driver of digitization - new possibilities are opening up to meet the need for intelligent provision of knowledge and to make the flood of information in the product development process more manageable. To achieve the superordinate – future – goal of automatic and user-specific identification and provision of knowledge, a step-by-step approach is required. Thus, existing approaches dealing with the solution of classification problems by ontology-based methods are evaluated, transferred to the domain of standards, and extended. As an initial step, the technical feasibility of automatic text classification for the assignment of business departments to corresponding contents of documents will be examined within the scope of this research work, so that users are provided with the possibility of support when searching for information. Based on this, future research activities will primarily deal with the development of a role model so that, with the help of defined criteria, an unambiguous assignment can be made between content from different engineering documents and a "person of interest".
A Method for Reinforcement Learning-based Development of a System's Information Processing in the Product Development

In current product development methods, the development of a system's information processing control unit is done by defining the exact algorithm of "how" to solve a problem. This is a major restriction because it relies on human creativity and problem-solving skills. Additionally and particularly in complex technical systems, problems exist that are intuitive for a human to solve, but the definition of a problem-solving algorithm is not trivial. In this paper, a method is presented and described that addresses the current challenges in the product development process by leveraging the advantages and properties of reinforcement learning. The presented method aims to avoid the complex programming process for the information processing of a system by not providing a specific definition of "how" a problem should be solved, but rather a specific definition of "what" should be solved. Through reinforcement learning-based development, the solution for a defined problem can be generated. In addition, this method is intended to respond to changing conditions, and the ability to make rapid adjustments when already deployed. This reduces the overall complexity during the product development process. The presented method is successfully applied to an exemplary use-case.
A Methodology for the Systematic Selection of Human-Machine Interface Device Types in Production Machinery Development

Progressive digitization accelerates the evolution of human-machine interface (HMI) technologies. In mechanical engineering, HMIs are decisive for accomplishing user-friendly, efficient, and effective machine control. Although various technologies, such as AR-glasses, have already been applied in industrial practice, mechanical engineering companies still face tremendous challenges in selecting and implementing suitable HMI devices. Therefore, this paper presents a novel methodology for systematically evaluating and selecting a suitable HMI device type for production machinery in mechanical engineering product development. The methodology provides a use case-specific process considering the company's resources, innovation strategy, and other relevant criteria enabling the systematic selection of a suitable HMI device type out of a predefined set of existing options. A case study demonstrates the applicability and practicability of the methodology.
Towards the Development of a Standard Elements Toolbox for the Digitalization of Work Instructions in Engineering Apprenticeships

With increasing digitalization and a shift towards Industry 4.0, new technologies are also finding their way into production. Especially in the training of technical professions, many processes are becoming more digital in order to also create a familiarization for the workforce of the future in manufacturing. To support the training of aircraft mechanics, a concept to transfer work instructions into a digital format was developed. For this purpose, a digitized system based on paper instructions was designed, in which manufacturing procedures are stored as templates in a standard elements toolbox (SET), and instructors can assemble these into work instructions in a workflow editor (WFE). Furthermore, an augmented reality (AR) display supports trainees in implementing projects using these instructions, as well as a learning platform to overview the project progress. The concept was implemented in interactive prototypes and evaluated in a training centre for aircraft mechanics. The results show a good usability of the system and give a positive impression with special requirements for apprenticeships for further development of the prototypes into a usable product and an integration into the general workflow of an aircraft mechanic.
Analyzing current Challenges on Scaled Agile Development of Physical Products

Agile development has successfully established itself for coping with VUCA conditions on a team level. Furthermore, in developing cyber-physical systems or mechatronic products, the collaboration of multiple teams is often necessary. This necessity to scale contrasts with the paradigm of agile development from which additional challenges arise. The study examines the current challenges in scaling agile product development, characterized by scale-related challenges and physical constraints. This paper provides an empirical investigation to address these challenges. This publication shows that product development's top challenges are synchronization, coordination, resource allocation, and dependencies between different organizational units. It is determined that the challenges in the non-scaled environment also apply to scaling; however, substantial differences in their dependencies exist. Furthermore, an attempt to explain differences in scaled agile software development using comparative analysis is given. It becomes apparent that challenges such as production setup or physical limitations are non-existent.