Reviewerly/exHarmony huggingface dataset:
Evaluation of Financial Stability of Azerbaijan Commercial Banks Using the Fuzzy Logic Methods

The paper considers two fuzzy methods of multifactor assessment of alternatives and groups of financial indicators, which are used to assess the financial stability of the four leading commercial banks of Azerbaijan. At the same time, the choice of financial ratios that have the greatest impact on the financial stability of a bank is dictated by the desire to establish a strict correlation between the financial condition of banks and these factors. Therefore, not trying to invent new ratios to assess liquidity, profitability, capital adequacy, quality of assets and liabilities, the paper employs a set of ratios that are most commonly used in various methods for selected financial indicators of bank stability.
Promoting Autonomous Learning Using Self-Discovery Technique with Self-Prepared Worksheets in Extensive Reading Class

This research is an action research that investigates the implementation of self-discovery autonomous learning using self-discovery technique with self-prepared worksheet in extensive reading class. The study was conducted in the contexts of a classroom teaching and learning situation. Self-discovery learning is believed to be effective in helping the students to improve their reading skills because this technique forces student to be autonomous learner. The study used action research model by Kemmis and Taggart. However, the researcher does not use cyclic treatment in this study, instead its implemented in one big cycle in one semester. As it is needed to be implemented in the whole semester to get the real result. The autonomous learning using self-discovery technique with self-prepared worksheet in extensive reading class was able to improve the studentsâ€™ achievement in extensive reading, it is indicated in the post test scores compared to the pre-test score. Nevertheless, there are positives and negative effects found during the acting and observing phase. The positive effects are the students become more active in class, the students can explore the text freely as they like, thus, lead to the studentsâ€™ better communication. Whereas, the class becomes noisy since the students have to discuss in their group and the lecturer had more burdens in finding, selecting, choosing and providing the text for the class activities. Keywords: autonomous learning, self-prepared worksheet, extensive reading.
THE FEATURES OF THE INFLUENCE OF OCCUPATIONAL ACTIVITY OF ACCOUNTANT UPON ITS PSYCHOLOGICAL STATUS

Introduction. In this paper, the authors presented findings of comparative psychodiagnostics of certain indices of personality sphere of accountants before and after implementation of the author’s training program “11 steps of perfection”. material and methods. Two preferable behavior strategies (“Collaboration and “Compromise”) of accountants in conflict situation were revealed. Pronounced levels of indices of reticence, emotional instability, anxiety and lower levels of indices of functional status were found in elder accountants. Results of approbation of the training program aimed to form a positive dynamics of personality sphere indices in this occupational group provided evidence about the effectiveness of this program. conclusion. The decrease of levels of indices of reticence, emotional instability, anxiety; elevated levels of indices of functional status; the broadening of behavior strategy spectrum in a conflict situation was observed.
Object Classification from Shape Detection

We evaluate the problem of object detection and classification based on a single model for five diverse classes. The class detection problem is implemented by enabling a method which detects the presence or absence of every shape-based model in every instance of a class. Low-level feature extraction is also performed to facilitate object categorization on one-dimensional information of the dataset. We compute the categorization performance for both the modalities in separate as well as combined representations to produce improved experimental results. The combined obtained solutions provide a classification of the object into the five classes. We have evaluated our approach on the ETHZ dataset and found that it performs with an accuracy of 88.2% in classification based on object detection.
Identification of Articulated Components in 3D Digital Objects Using Curve Skeleton

A novel combinatorial algorithm for the segmentation of the articulated components of a 3D digital object is presented in the paper. As a preprocessing step, the single voxel thick 3D curve skeleton of the object is extracted by a topological technique. The curve skeleton is segmented by exploiting the concept of antipodal points in 3D orthogonal domain and calculating the isothetic distance of each voxel to a leaf voxel. The isothetic distance of the voxels on the object surface to the voxels on the segmented curve skeleton is used to separate the articulated components of the object into segments. The segmentation of the curve skeleton is based on the object geometry rather than the topology of the skeleton. The effectiveness of the algorithm has been demonstrated by the experimental results on a variety of articulated objects.
Meta-cluster Based Consensus Clustering with Local Weighting and Random Walking

Consensus clustering has in recent years become one of the most popular topics in the clustering research, due to its promising ability in combining multiple weak base clusterings into a strong consensus result. In this paper, we aim to deal with three challenging issues in consensus clustering, i.e., the high-order integration issue, the local reliability issue, and the efficiency issue. Specifically, we present a new consensus clustering approach termed meta-cluster based consensus clustering with local weighting and random walking (MC$$^3$$LR). To ensure the computational efficiency, we use the base clusters as the graph nodes to construct a cluster-wise similarity graph. Then, we perform random walks on the cluster-wise similarity graph to explore its high-order structural information, based on which a new cluster-wise similarity measure is derived. To tackle the local reliability issue, all of the base clusters are assessed and weighted according to the ensemble-driven cluster index (ECI). Finally, a locally weighted meta-clustering process is performed on the newly obtained cluster-wise similarity measure to build the consensus clustering result. Experimental results on multiple datasets have shown the effectiveness and efficiency of the proposed approach.
Research on Academic Analysis Based on Hadoop Platform Collaborative Filtering Algorithm

With the rapid development of Internet+, it is still necessary for users to extract valuable data from massive data using classical algorithms. Based on the Hadoop platform, this paper uses collaborative filtering algorithm to study the overall employment situation of IT industry across the country. It has important reference value for college students working in the IT industry, such as working place, salary, skill points, etc. The results of study analysis have certain theoretical research significance in talent cultivation in colleges and universities.
Relationship between External Information and Structures of Cultural Property Braids

Reproduction of cultural property braids has been largely depending on the experience of only a limited number of braid experts, while extraction of their knowledge at high availability are needed for the consistent reproduction to be practiced over the subsequent generations.In this paper, interview and eye tracking analysis, using the photographs of two cultural property scrolls, were performed during the reproduction work of the expert, Makiko Tada, and important braiding parameters such as the interactions of ribs with the neighbors and the number of transits were derived.It was found that patterns consisting of multiple color yarns play a core role in the parameter analysis.
Digital Transformation Legacy Social Service Information System

Abstract The article considers the methodological aspects of the digital transformation of legacy social service information systems. The result of this transformation is the social service digital platform that ensure the use of new innovative technologies for processing and analysing big data, machine learning and artificial intelligence. The base of this transformation is cloud computing; big data technology; microservice architecture with container-based software development technology. We propose a person digital profile as a basis of data management in the social service digital platform. The proposed one is based on a new abstraction level of software stack – dataware. The implementation uses a microservice architecture and a container-based software development technology in according to the Data-as-a-Service concept.
Can You See It? Perceived Variance in Scatterplot Visualization

Understanding uncertainty in spatial domains (such as hurricane forecasting) is both important and challenging, often leading to overconfidence and underestimation of variability. The underlying source of the difficulty is not well understood, as this complex domain requires perceptual, attentional, and memory related cognitive abilities. The present study explores the impact of perception on understanding of variability in an abstract spatial task. Individuals were shown a scatter chart type display of possible endpoints of an uncertain trajectory and asked to make two separate judgements of variability: adjusting a circle to encompass 75% of endpoints and estimating the likelihood the trajectory would fall within an experimenter defined probe circle. Calibration to changes in variability were calculated for each individual on both measures and compared against each other. Results indicate a lack of sensitivity in interpreting visualized spatial uncertainty among many individuals, with performance differing depending on the probe method employed. Implications are discussed.
A Cross-Situational Learning Based Framework for Grounding of Synonyms in Human-Robot Interactions

Natural human-robot interaction requires robots to link words to objects and actions through grounding. Although grounding has been investigated in previous studies, not many considered grounding of synonyms and the majority of employed models only worked offline. In this paper, we try to fill this gap by introducing an online learning framework for grounding synonymous object and action names using cross-situational learning. Words are grounded through geometric characteristics of objects and kinematic features of the robot joints during action execution. An interaction experiment between a human tutor and HSR robot is used to evaluate the proposed framework. The results show that the employed framework is able to successfully ground all used words.
Examples of problem statements and functionals

In this chapter, we begin a detailed presentation of our approach to the construction of neural network models on heterogeneous information, including differential equations, boundary, initial and other conditions, measurement data, etc. The first stage of our approach consists in the transition from the given information to the functional, which numerically characterizes the correspondence of the model to the available information about the simulated object. We described this stage in the first chapter, both in general form and by many specific examples. We have shown a large number of examples so that the reader can find an example that is most suitable for his task and make the necessary modifications to the functional we have considered. As examples, we considered both problems for ordinary differential equations and problems for partial differential equations, both simple problems and problems having complicating features. The problems with parameters, stiff, differential-algebraic, nonlinear, are considered as such features. The problems for composite domains with fixed and variable boundaries (both free and determined in the process of solving the problem based on the given criterion of optimality) are also studied. Also, we do not ignore ill-posed (in particular, inverse) problems in which measurement data replace all or part of the initial or boundary conditions. We describe the concrete results of computational experiments on the listed problems in Chapter 4.
The math modeling analysis based on Simplex method

In this article, based on the objective trend of the mathematical modeling technology of the Simplex method, this is been analyzed and studied by using the large number of reference analysis and contrast method. The four instances about the math which aims to the objective development trend of Simplex method math modeling which begins from the concept of Simplex method. Firstly, the initial adoption based on Simplex method had been realized by the first case. Secondly, through the second case, the in-depth application of mathematical modeling based on Simplex method is realized. We hope that it can provide the effective reference to student for high-efficiency learning of math modeling.
An examination of the effort hypothesis for the perceived strength of passwords

Previous work has examined whether keyboards that have multiple levels of entry (e.g., soft keyboards, found on mobile devices) increase users’ perceptions of password security. That work found that passwords that required greater numbers of keyboard transitions had higher perceived strength. Unfortunately, it was impossible to determine whether this increase in perceived strength was due solely to the extra keyboard transition steps or whether the inclusion of special characters in the passwords (which is required in order to increase the number of keyboard transitions) was driving that perception. The research described in this paper aims to disambiguate these two possibilities. In these experiments, rather than typing the passwords on keyboards, the users simply spoke the password. In this case, there is no additional effort or keypress required in order to enter the special character(s). Results showed that there was a positive correlation between the perceived password strength and the number of keyboard transition for spoken passwords, in a fashion nearly identical to the typed password study. This suggests that the increase in perceived password strength cannot be explained simply as a function of the additional expended effort required to enter additional keyboard transition steps, although it may play a role.
Cleaning Robot for Free Stall Dairy Barns: Sequential Control for Cleaning and Littering of Cubicles

Rising cost pressure and growing farm sizes in dairy farming with constant or even declining working capacity require new robotic solutions in this field. A logical approach is to automate the cleaning due to the high manual workload at this task. Therefore, a cleaning robot is being developed which removes liquid manure from the running surfaces and maintains the cubicles. The cubicles are cleaned of liquid manure with a brush and maintained by the help of a conveyor belt with bedding. The navigation of the robot is presented which is based on a 2.5-D SLAM and localization with two 2-D lidars. The focus is however on the sequential control of this cleaning task which is solved by using semantic maps, work routes, an active collision avoidance strategy and a brush force control. This functioning procedure is shown with experiments in a artificial stall and in a real stall barn.
Universal mathematical model of SAR signals for natural surfaces

The implementation of the basic version of the universal mathematical model of the formation of reflected signals for various airborne radar systems, including the synthetic antenna aperture radar, when working on natural surfaces are discussed in the article. A number of parameters are required to study, which must be taken into account for the correct modeling of echo signals, which must then be processed to calculate radar images. A method for constructing such a mathematical model in MATLAB is presented with some modeling results for a homogeneous rough surface and two typical pulse and continuous modulation signals. But the facet parameter structure of the simulated surface can take into account the possibilities of representing various types of terrain and objects created by man.
Per una semiotica della rima

Abstract The article, following a suggestion of Barthes, attempts to outline a semiotics of the technique of rhyming considered as a sub-code of poetic language, in order to better understand the important contribution provided by rhyme, on many levels, to the strategies of signification of the literary text. This semiotic approach is inscribed in a general theory of rhyme under development by the author, which aims at explaining the raison d’être and fortune of this poetic device in relation to its decisive contribution to the rhetorical and figurative elaboration of the poetic text. In a semiotic perspective, three fundamental levels of structuring rhyme as a sign can be distinguished: its signifier (the partial phonic identity which, in the current view, is considered to be rhyme’s exhaustive definition), its meaning (the semantic interference between rhyming words only) and its sense (the relation between rhyme’s meaning and context). A short reference is also made to further aspects of the the sub-code of rhyme, such as its syntax (strophic patterns) and the role of pools of rhymes. This leads to a reappraisal of some significant questions raised by the technique of rhyme, e. g. whether or not it always makes a semantic contribution, or what the interplay between its limitations to freedom of expression and its contribution to the expressiveness and poetic value of the text is. The theoretical tools are finally put to the test in a short analysis of the rhyme-words in the form of the sestine.
Semantic Approach to Visualization of Evolution Dynamics of Topic Trends in Space of Scientific Publications Using t-SNE and Web-based 3D Graphics

This paper describes a semantic approach to visualization of 3D cyberspace of Artificial Intelligence (AI) publications and their topic trends evolution using web-based 3D graphics. The purpose of research is to group AI publications with same subject into clusters for further visualization of topic trends dynamics. An unsupervised method and algorithm for visualizing the dynamics of topic trends by generating a time series of 2D and 3D semantic visual maps with predictive information is described. The method includes semantic similarity measure and citation prediction for documents, topic modeling and clustering, dimensionality reduction, virtual reality technology, representation of dynamics using time filters. As an example of particular implementation, the method is demonstrated on AI collection data using technologies of neural network prediction, LDA clustering, t-SNE dimensionality reduction, WebVR visualization. Cluster dynamics associated with scientific trends is analyzed. The growth in number of clusters and their consolidation during the period from 1954 to 1993 is demonstrated. It is shown that 3D visual map better preserves articles similarity and highdimensional clusters structure than 2D visual map. The proposed cyberspace implemented by WebVR and interactive 3D graphics can be considered as a dynamic learning environment that is convenient for discovering new significant articles, ideas and trends.
DEPENDENCE OF THE STRUCTURAL PROPERTIES OF THE DUAL BASIS ON THE TYPE OF THE CHARACTERISTIC POLYNOMIAL

The relevance of the topic is connected with the solution of problems of processing of recurrent sequences. The method used to determine the initial phase of a pseudorandom sequence constructed by the characteristic polynomial allows its processing directly in the receiving process. This approach does not require mandatory accumulation of the entire sequence, which significantly increases the efficiency of the system, which uses a dual basis as a method of decoding recurrent sequences. The method of decoding using the dual basis of the Galois field is an effective means of protection against errors in a digital data transmission system, where the codes of BCH, Reed-Solomon and other varieties of recurrent sequences can be used as noise resistant codes. At the same time, the calculation of the elements of the basis dual to the left power basis generates a number of problems related to mathematical calculations over the elements of a finite field. In this paper, the methods of finding the elements of the dual basis are considered, the variants of simplified procedures for their calculation are proposed, and the task is to study the structural properties of the dual basis, which depend on the type of the characteristic polynomial. Research subject. The work is devoted to the study of binary Galois fields, in particular, the properties of the left power and dual basis are affected. Method. On the example of M-sequences, the procedure for finding the elements of the dual basis through the trace function is considered in detail using the mathematical apparatus developed by O. S. Korovitsky. Core results. The analysis of the given properties of the dual basis allowed to reveal new structural dependences between its elements. Practical relevance. The revealed structural dependences between the elements of the dual basis of the Galois field make it possible to significantly simplify the implementation of the procedure for the formation of its elements.
Planar Pose Estimation Using Object Detection and Reinforcement Learning

Pose estimation concerns systems or models dealing with the determination of a static object’s pose using, in this case, vision. This paper approaching the problem with an active vision-based solution, that integrates both perception and action in the same model. The problem is solved using a combination of neural networks for object detection and a reinforcement learning architecture for moving a camera and estimating the pose. A robotic implementation of the proposed active vision system is used for testing with promising results. Experiments show that our approach does not only solve the simple task of planar visual pose estimation, but also exhibits robustness to changes in the environment.
Development of a Pointing Device that Directly Measures the Tilt Angles of a Head

In situations such as endoscopic surgeries, a leader cannot use their hands to instruct the other surgeons to perform a task on the monitor because he/she must use both hands to control the surgical equipment. Although there are several pointing methods such as eye gaze tracking, these methods are not suitable for endoscopic surgeries, where optical sensors (e.g. infrared sensors) cannot be used due to the existence of physical obstacles in the operating theater. Therefore, an assisting pointing device apparatus that directly measures the behavior of users and is not based on hands is specifically needed. To tackle this problem, we built a pointing device that measures the tilt angle of a user's head by gyro and acceleration sensors. Furthermore, we evaluated the performance of our device compared to that of a mouse using statistical analysis based on Fitts' law and other commonly used parameters. We considered a mouse as a benchmark to clarify the limitations and possible usage of our device. We found that, although it does not perform as good as a mouse, our device can still suitably perform as an assisting device for limited usage such as for tracking pre-defined lines.