Reviewerly/exHarmony huggingface dataset:
Using Contextual Graphs as a Decision-making Tool in the Process of Hiring Candidates

Poor selection of employees can be a first step towards a lack of motivation, poor performance, and high turnover, to name a few. It's no wonder that organizations are trying to find the best ways to avoid these slippages by finding the best possible person for the job. Therefore, it is very important to understand the context of hiring process to help to understand which recruiting mistakes are most damaging to the organization in order to reduce the recruiting challenges faced by Human resource managers by building their capacity to ensure optimal HR performance. This paper initiates a research about how Contextual Graphs Formalism can be used for improving the decision making in the process of hiring potential candidates. An example of a typical procedure for visualization of recruiting phases is presented to show how to add contextual elements and practices in order to communicate the recruitment policy in a concrete and memorable way to both hiring teams and candidates.
Study on Using Scenarios of Linear and Nonlinear Classifiers

Classifier plays a crucial role in data mining by establishing a function or model based on existing data, which can then map all the data in the database to one of a given category to help with prediction. However, as the known data have various problems, such as errors in samples and how to define categories accurately, which will have a huge impact on the accuracy and temporal cost of its results, it is very important to select the optimal classifier based on the known samples. Before selecting a classifier, a detailed understanding of the advantages and disadvantages of each classifier can produce twice the result with half the effort. In this paper, classifiers are divided into linear classifiers and non-linear classifiers, and some typical linear classifiers and nonlinear classifiers are listed, comparing their advantages and disadvantages via their classification principles. This helps to select the most appropriate classifier and improved classifier.
Learning rule with fractional-order average momentum based on Tustin generating function for convolution neural networks

In this paper, we propose a fractional-order average momentum (FOAM) method based on Tustin generating function to train parameters in convolution neural networks. Taking the classical data set MNIST as the training and testing data, the effectiveness of the FOAM for CNNs is verified. The experimental results show that the stochastic gradient descent method based on FOAM can improve the recognition accuracy and learning convergence speed of convolution neural networks.
Heterogeneous Multi-Robot Path Planning Based on Probabilistic Motion Model

An important problem in multi-robot system is how to coordinate robot's motion such that each robot can complete its task without collision. Previous approaches assume that robots' motion is deterministic once their paths have been planned, which is however not realistic because random interferences such as noise, friction and inaccurate control input in real-life system could disturb the robot motion, leading to a stochastic behavior. In this paper, we take this stochastic behavior into account when planning the path for a heterogeneous multi-robot system. We assume that the motion time of a robot from a location to another can be modeled as a probability distribution. Every robot has its own probability distribution of motion time between any two neighbor locations. We develop a conflict-detection scheme for this model and propose using the conflict-based search algorithm via probability calculation to find the optimal path that minimizes the entire motion time. We also simplify this conflict detection such that our proposed approach is applicable online for a large-scale system. Experimental results demonstrate the high effectiveness of our proposed approaches.
Automation of Precise Positioning of a Vehicle Trailer when Driving Backwards

We consider the problem of creating an automatic system for assisting a driver of a vehicle with a trailer when driving backwards with precise target positioning of the trailer (as illustrated by a vehicle entering a gate). We propose the use of proximity sensors for detecting the distance from the center of the back edge of the trailer to the edge of the gate. Relative angular velocity of the trailer, i.e. the angular velocity of the trailer relative to the vehicle speed is used as the controlled variable. An algorithm for the generation of the set value of this variable depending on the readings of a distance sensor is provided. The algorithm is based on setting the reference behavior of the control error. The law of deviation of the steered wheels of the vehicle, aimed at achieving the set value of the relative angular velocity of the trailer, is designed on an adaptive level based on the assumption of an initial rough estimate of the length of the trailer and the action of uncontrolled external disturbances. An algorithm of current parametric identification of the controlled object and an implicit reference model of the controlled variable behavior are used. The functioning of the closed-loop control system is based on “simplified” conditions of adaptability. A model example is provided.
The Challenges and Methods of Natural Scene Text Detection

The detection and recognition of text in natural scenes are basis to computer vision application field. Text detection is designed to localize text areas in natural scenes for further usages. This article first elaborates on the challenges of scene text detection from the perspective of the characteristics of natural scene text. Secondly, the current common methods of scene text detection are introduced. Furthermore, the application of scene text detection is discussed. Finally, its research and development trend are prospected.
Implementing Inclusive Design in Architectural Education and Design Practice

Abstract The objective of the article is to present working methods in didactics and in design practice in terms of the implementation of the inclusive design concept. It obliges the students and participants to be involved in the design process in order to recognize the needs and preferences of different user groups. Additionally, since the development of ICT technology allows us to increase the availability of space, the expansion of the range of tools in the design process requires the expansion of the knowledge of future designers. Therefore, the authors pose questions: how to educate future architects in this context? what tools and technologies to offer to local communities so that they can actively participate in such design processes? The subject of the analyses involves the implemented didactic projects regarding the solutions for the elderly and the disabled, both in facilities having the caring function and in public spaces. The effects of the completed didactic projects and project activities had a diverse character: from conceptual designs to the prototypes of small architecture elements. The basic assumption in the implemented projects is to adopt the formula of education through experience and the use of pre-project research. According to the state of the knowledge in this respect, the effectiveness of a design process for people with disabilities requires direct contact with users, research on the functioning of space and how it is experienced. The article presents the tools and techniques of work applied in the method ‘education trough experience’: focus meetings, individual interviews, observational studies, role playing, experimenting, testing the solutions.
Design of Western Yugur Language Speech Database

The Yugur nationality is one of the ethnic groups with a small population in China, and the Yugur nationality has its own language. In the course of development, the Yugur language is divided into two types: Eastern Yugur and Western Yugur. The native language has been lost. Yugur language currently has a very small population and is an endangered language, and the special attributes displayed in the evolution of the language make Yugur language research of great value. This article will start from the design of the voice database, with the purpose of protecting and preserving the existing Yugur language, taking the western Yugur language voice database design as an example, introduce the design of the western Yugur language voice database, and propose the western Yugur language signals and parameters Set standards.
It is double pleasure to deceive the deceiver: disturbing classifiers against adversarial attacks

Convolutional neural networks (CNNs) for image classification can be fragile to small perturbations in the images they ought to classify. This fragility exposes CNNs to malicious attacks, resulting in safety concerns in many application domains. In this paper, we propose a simple yet efficient strategy for decreasing the effectiveness of black-box attacks that need to sequentially query the classifier network in order to build an attack. The general idea consists of applying controlled random disturbances (noise) at the softmax output layer of neural network classifiers, changing the confidence scores according to a set of design requirements. To evaluate this defense strategy, we employ a CNN, trained on the MNIST data set, and attack it with a black-box attack method from the literature called ZOO. The results show that our defense strategy: a) decreases the attack success rate of the adversarial examples; and b) forces the attack algorithm to insert larger perturbations in the input images.
A conceptual framework for cloud-based advanced planning systems

In the last decade, Cloud Computing (CC) and Advanced Planning Systems (APS) are two emerging topics. However, no comprehensive model that considers both topics at a conceptual level exists. Purpose of this article is to provide a conceptual framework for cloud-based APS. Within the framework, we propose a generic structure of fundamental services and components to fulfill different consumer requirements from the area of supply chain planning. In addition, we elaborate appropriate service, deployment, and pricing models and introduce the ‘Result-as-a-Service’ model. The research is generally based on the design science research approach by extending CC to the problem domain of supply chain planning. Based on the framework, several findings concerning the potential of cloud-based APS but also caveats like insufficient planning capabilities are elaborated. Furthermore, several most important research topics (e.g. process and object reference models) are formulated and form an initial agenda for research on cloud-based APS.
Video inpainting using object motion rate and color variance in spatiotemporal domain

Inpainting is a technique to enhance digital videos. Based on the spatiotemporal domain, we herein propose a video inpainting method to repair the removal objects in the videos. The method consists of an adaptive foreground model, the motion rate estimation of objects, and a repairing scheme. Initially, the adaptive foreground model based on the background subtraction method is developed. The model is used to estimate the motion rate for each moving object in the frame. According to the estimated motion rate, the model specifies an adaptive interval between the forwarding reference frame and backward reference frame to obtain the useful information and to repair the removal objects. The remaining un-repaired areas are filled using an exemplar-based inpainting technique with color variance. The results show that the proposed method can produce visually pleasing results. Additionally, it reduces the inpainting time and provides efficient computing.
México, the Americas, and Spain Perspectives and IB Education Innovations: ¿Hacia Dónde Vamos?

KEYWORDS: Mexico. the Americas, and Spain (MAS)IB ducation perspectives and innovationsinternational experiential learning
La construcción de textos escritos desde diferentes asignaturas del 1er año de la Educación Técnica y Profesional

In this article offers a brief theoretical analysis of the most current criteria for the construction of written texts and their application to the teaching of this functional component of the Integral Practice of the Spanish Language, in the first year in the major degree of Labor Education-Computing. In turn, methodological suggestions for working with this component are presented from the perspective of different subjects within the curriculum. For the preparation of the article, methods such as the historical-logical, the analytical-synthetic, the inductive-deductive and the system approach, the document review, the survey, the interview and pedagogical tests were applied.
On Black-Box Extension of a Non-Interactive Zero-Knowledge Proof System for Secret Equality

A non-interactive zero-knowledge proof system (NIZK) for a composite language (i.e., a language that combines distinct statements by AND or OR) is widely used in cryptographic applications. The (im)possibility of black-box constructions of such NIZKs has been studied so far. We extend it to the case of proving equality of a witness behind distinct statements that often plays a central role in cryptographic constructions as illustrated by the seminal work by Naor and Yung (STOC, 1990). In this paper, we investigate the impossibility of a black-box construction of NIZKs for equality of witnesses from those proving the validity of the witnesses. Concretely, we prove that there exists no black-box construction of NIZKs for equality of plaintexts in two distinct ciphertexts of public-key encryption schemes based on NIZKs for proving validity of ciphertexts. It implies that one has to exploit specific properties of the underlying encryption schemes or NIZKs to construct NIZKs for proving equality, and hence justifies existing constructions.
Prediction Intervals for Mechanical Property Forecasting with Improved ANFIS

Application of Adaptive-Network-based Fuzzy Inference System (ANFIS) for point forecast has burgeoned in recent years. While the performance of ANFIS drops when the uncertainty level increases in dataset, and point forecasts also suffer from unreliable and uninformative problems. Prediction interval (PI) has been proposed as a powerful tool to address these drawbacks. In spite of plentiful application of ANFIS for constructing PIs, the use in mechanical property forecasting is limited. Compared with traditional computational expensive algorithms, in this paper, we combine Particle Swarm optimization (PSO) and Genetic Algorithm (GA) to minimize the cost function and optimize the parameters of ANFIS. The proposed novel approach is applied to a real mechanical property forecasting case study. The outcomes show modified PSO-based ANFIS not only computational less expensive, but also with good quality of constructing better PIs.
A systematic approach to schoolchildren’s environmental thinking development and prospective teachers’ training for this work organization

The following paper discusses one of the aspects of environmental education. It is related to the necessity to use a systematic approach for schoolchildrens environmental thinking development. The process of environmental thinking development is linked with environmental education implementation. For a long time, environmental education was considered as an integral part of biological education, but now an interdisciplinary approach to this work is considered. Different models of cognition are also considered, the recognition or denial of which influenced the nature of science development and the content of the educational process. Cognitive models in varying degrees, depending on specific conditions, determined the level and nature of ecological knowledge system development. A systematic approach for schoolchildrens environmental thinking development should be based on the main positions, which are the basis of cognitive models that act as paradigmatic methodological guidelines. The implementation of a systematic approach to environmental thinking development proceeds more successfully when using the practice-oriented approach, which lies at the basis of the lesson and students extracurricular activities organization. The above mentioned approach can be now implemented in the form of project development. While working with students - prospective teachers, ecologization of disciplines is widely implemented.
기초 어휘 선정을 위한 형태소 분석기의 오류 유형에 대하여

This paper aims at investigating the error types of morpheme analyser ‘Utagger,’ which can differentiate homonyms for the decision of basic vocabulary. Samples for analysing are composed of a corpus of 10,000 words of written Korean and 6,000 words of spoken Korean. An analysis showed that the error rate of spoken Korean is higher than that of written Korean. Also the error types of spoken Korean was shown to be much wider than that of written Korean. These error types deserve special attention in that they may have a significant impact on the classification of basic vocabulary as well as on the decision of basic vocabulary.
Graph Theoretical Analysis in Particle Swarm Optimization Based on Random Topologies

Particle Swarm Optimization (PSO) is a swarm intelligence method which is employed frequently for solving real-world problems. After its inception, many variants of PSO devote to improving its performance by modifying the behavior of each particle, in which the population topologies of the particle swarm may alter. This paper investigates how population topology influences the performance of PSO. A random topology generation algorithm that adopts both the greedy strategy and randomized algorithm is proposed in the paper. The randomly generated topologies are applied in PSO-w, which introduces no modification to the population topology of the original PSO. Experimental results demonstrate that algorithms using topologies with more edges tend to converge faster and generally obtain a more accurate solution. Another major result in this paper is that how clustering coefficient affects PSO largely depends on the sparsity of the topology. A lower clustering coefficient in sparse topology conduces to faster convergence and a more precise result, but a higher clustering coefficient is preferred when the topology is dense.
Kernel Pattern Recognition Algorithm in the Task of Testing the Hypothesis of Distribution of Multidimensional Random Quantities

New method of testing hypotheses on distribution of multi-dimensional statistical data of large volume is considered. The possibility of replacing the hypothesis test on the identity of two laws of multivariate random values distribution with the hypothesis test on the equality of pattern recognition error with value 0,5 is proved. To test this hypothesis, the technique of trustworthy assessment of pattern recognition error probability or Kolmogorov-Smirnov test is used. Training sample is generated by statistical data of the compared distribution laws. Under conditions of large volumes of statistical data synthesis of nonparametric algorithm of pattern recognition is carried out on the basis of regression estimates of probability densities of random values distribution in classes. Proposed algorithms of pattern recognition allow reducing the amount of training sample due to decomposition of the range of random quantities values. Method of selection of decomposition optimal parameters of independent random values range is considered. The closest analogue of the proposed approach is the Pearson's chi-squared test.
The Linux Load Balance: Wasted vCPUs in Clouds

With the rise of multicore machines, the Linux scheduler has introduced a sophisticated load balancing mechanism to spread the tasks over the cores. Study [1] has shown that the load balancer implemented in the default Linux scheduler, Complete Fair Scheduler (CFS), works well to evenly distribute the workload and succeeds in maintaining high CPU utilization.
Balanced Accuracy of Collaborative Recommender System

Recommender systems (RSs) have emerged as a vital tool to handle the overloaded and huge information very effectively, caused by exceptional growth of means on Web. Collaborative recommender system (CRS) is one of the types of RSs that are being used tremendously by majority of e-commerce industry in present scenario. CRS help the users to opt for an item of their own interest easily from largely available options on Internet. CRS use the ratings given by similar users to make predictions to the interested users. However, these ratings are not always true and authentic. These un-authentic ratings are the biased ratings that lead to various attacks, such as Push and Nuke Attacks (P-N Attacks). In this work, P-N Attacks are, first, injected on available data and then the effect of these P-N Attacks are explored on the performance of CRS by using balanced accuracy (BAC) and other state-of-the-art measure metrics.
Principles of Design of a Learning Management System for Development of Economic Skills for Nuclear Engineering Education

The dramatic changes in education that we face in 2020 and which are the result of the accelerating digital revolution indicate the need to develop new tools for education and training. Leading universities are developing their own Learning Management Systems (LMS) based on information and Internet technologies. However, in higher education there are certain educational tasks in the interdisciplinary field. One of them is the task of successfully developing atomic engineering education. In this article, we present the basic design principles and architecture features of a specific LMS for the development of economic skills for education in the field of nuclear technology.
Improving the Quality of Speech Synthesis Using Semi-Syllabic Synthesis

This paper describes how to improve the quality of synthesized speech using semi-syllables. The transition to speech synthesis using semi-syllable speech fragments is substantiated. For speech synthesis, the Yakut language from the turkic group of languages is considered. The full base of syllables and semi-syllables is determined with the assistance of a machine newspaper corpora. Narration text is created using the base of semi-syllables for all received Yakut words. With the assistance of the narrator's text, material is collected for the speech corpora of the Yakut language. The speech corpora is divided into words and semi-syllables, and is divided into semi-syllable parts of speech, taking into account word stress. With the assistance of concatenation, a sound file is created for any word in the Yakut language. Measures are given to further improve the quality of speech synthesis.
E-Brainstorming Engenders Synergy Based on Ontology

Abstract The E-brainstorming system involves piggy bagging of ideas which results in more idea generation by the agents.Triggering of an idea for one participants happens from another participants, which will help the participants with the new idea. But with ordinary search and retrieval methods only few ideas are generated when compared with E-brainstorming method. More agents’ participation in E-brainstorming results in more ideas generation. The effect of combined participation of two or more agents is more than the sum of the effect of individual agents. The main goal of the project is to build an E-Brainstorming system where the agents represent the session participants and to build a idea evaluation system which is not domain specific. The E-brainstorming system is built so that it can be used both by the facilitator and the ordinary user who don’t have knowledge about the domain. The input idea given to the system is a sentence and the system solves the problem of input idea being a simple keyword or term.