Reviewerly/exHarmony huggingface dataset:
Methods and means of production generation of tests on computer disciplines

The article describes the problem of automatic test generation, which is important in the modern education system, especially in distance learning. Various developments in this area have been considered. The development of new methods and means of generating precisely test tasks has been submitted. In order to reduce the complexity and time in the course of developing tests on computer disciplines, a new type of the test task template has been produces using the production rules specified by the teacher. They make it possible to generate tests from a group of such templates (test template) suitable to be imported into the Moodle distance learning system. The development of a module for the Moodle system provides generating tests from templates directly at the time of testing and allows to create a test template instead of writing all the options for this test in hand, which should greatly facilitate the work and save the teacher’s time. The algorithm for generating a test task and examples of ready-made templates on the topics “Operating Systems” and &#x0D; “Python” have been presented. The proposed approach can be used in the teacher’ work as a tool for test training depending on its purpose and the nature of the discipline studied.
Determining input variable ranges given a trained regression model and an output range

Industrial process control systems try to keep an output variable within a given tolerance around a target value. PID control systems have been widely used in industry to control input variables in order to reach this goal. However, this kind of Transfer Function based approach cannot be extended to complex processes where input data might be non-numeric, high dimensional, sparse, etc. In such cases, there is still a need for determining the subspace of input data that produces an output within a given range. This paper presents a non-stochastic approximation to determine input values for a mathematical function or trained regression model given an output range. The proposed method creates a synthetic training data set of input combinations with a class label that indicates whether the output is within the given target range or not. Then, a decision tree classifier is used to determine the subspace of input data of interest. This method is more general than a traditional controller as the target range for the output does not have to be centered around a reference value and it can be applied given a regression model of the output variable, which may have categorical variables as inputs and may be high dimensional, sparse... The proposed method is validated with a proof of concept on a real use case where the quality of a lamination factory is established to identify the suitable subspace of production variable values.
User interface model-based for basic-skills training in medical applications

Creating scenarios for training motor skills such as hand-eye coordination using augmented reality (AR) technology has several challenges. One of them, the application of task variability, a principle to foster flexibility and to unanticipated tasks adaptation. Due to its benefits, a model-based approach was applied to implement it in the training scenario development. In this paper, concretization rules were proposed and evaluated, from a task model that describes the practical exercise. The results show the concretization effectiveness. A case study is made for the design of hand-eye coordination exercises, a skill required for surgical procedures such as the transpedicular screws placement in spine surgery.
Numerical Simulation of Spear Motion as Game Items

Abstract Game development in 3D is mostly done by characters, items and the environment. Game items such as character weapons modeled in 3D will be the attraction of a game. In this paper, spear motion as a game items is modeled in 3D. Nonlinear Equations Six Degrees of Freedom (6 DOF) are used for mathematical models of spear motion. The parameters studied in the motion model are: geometry, mass and aerodynamics. Spear aerodynamic parameters were analyzed using the Datcom method. Numerical simulation of mathematical models of spear motion with variations in the initial velocity of the throw and the direction of the throw. From the results of numerical simulation, the maximum range R = 131.7 m at the initial velocity V 0 = 40 m/s, the direction of throw (angle θ 0 = 35 deg, angle φ 0 = 10 deg, ψ 0 = 0 deg). And the maximum height H max = 12.18 m is achieved at the initial velocity V 0 = 20 m/s, direction of throw (angle θ 0 = 35 deg, angle φ 0 = 40 deg, angle ψ 0 = 0 deg).
Fingertip Detection through Atrous Convolution and Grad-CAM

With the development of deep learning technology, research is being actively carried out on user-friendly interfaces that are suitable for use in virtual reality or augmented reality applications.To support the interface using the user's hands, this paper proposes a deep learning-based fingertip detection method to enable the tracking of fingertip coordinates to select virtual objects, or to write or draw in the air.After cutting the approximate part of the corresponding fingertip object from the input image with the Grad-CAM, and perform the convolution neural network with Atrous Convolution for the cut image to detect fingertip location.This method is simpler and easier to implement than existing object detection algorithms without requiring a pre-processing for annotating objects.To verify this method we implemented an air writing application and showed that the recognition rate of 81% and the speed of 76 ms were able to write smoothly without delay in the air, making it possible to utilize the application in real time.
Collaboration network analysis of scientific production at UB-SEE

This paper presents an analysis of the coauthors collaboration network based on research papers published in journals, where at least one author is affiliated to the University of Belgrade, School of Electrical Engineering (UB-SEE). We have extracted data from the institutional database and processed 2000 papers published in journals during the period from 2000 to 2017. After the initial refinement of the dataset we created and visualized the network of coauthors and carried out further analysis based on metrics specifically used for social network analysis.
A Method of Fault Identification Considering High Fix Priority in Open Source Project

Open source software is adopted as embedded systems, server usage and so on because of quick delivery, cost reduction and standardization of systems. Many open source software are developed under the peculiar development style known as bazaar method, in which faults are found and fixed by developers around the world, and the result will be reflected in the next release. However, several massive open source projects have a problem that faults fixing takes a lot of time because faults corrector cannot handle many faults reports briefly. In this paper, we make an index to detect faults that require high fix priority and long fault fixing time when faults are reported in specific version of open source project. In addition, we try to improve the detection accuracy of the proposed index by learning not only the specific version but also the fault report data of the past version by using random forest considering the characteristic similarities of faults fix among different versions. As a result, the detection accuracy has highly improved compared with using only specific version data.
Homomorphic Image Processing Over Geometric Product Spaces and Finite P-Adic Arithmetic

The ability of securely perform computations over encrypted data creates the opportunity of outsourcing data processing without compromising its privacy. With the always increasing data availability, cloud-based applications are becoming the new norm, which makes securely storing homomorphically encrypted data on the cloud a much current need since it allows one to conciliate availability with confidentiality. We introduce a homomorphic image encryption framework with which images can be processed while encrypted. We demonstrate the practicability of our construction by implementing a set of pixel transformations that are only performed over encrypted images. We present the results of our experiments in terms of image transformations, security and efficiency.
A 3D Model Authentication Scheme Based on Secret Sharing in Cloud Environment

Aiming at the problem that the data of the 3D model is lost or unavailable in the cloud storage of multiple nodes, the node data is easily destroyed and the data integrity cannot be guaranteed, a 3D model authentication scheme based on secret sharing in the cloud environment is proposed. Firstly, the original STL secret file of 3D model was selected from the standard image library, and Lagrange interpolation polynomial was used to construct the secret polynomial to generate secret fragments of 3D model files, which were distributed and stored in multiple secret participants. Secondly, the source file and secret sharing sub-fragment are used as the original structural carrier, and the SM3 hash algorithm of Chinese commercial password is used to generate the digital digest of the source file and each secret fragment, and the digital digest generated by each secret fragment is distributed upload to the cloud. Finally, Shamir's (k, n) threshold secret sharing algorithm is used to detect the change of 3D model information and to protect the integrity of the encrypted 3d model data, the consistency of the digital digests of the encrypted 3D model before and after encryption is verified. Experimental analysis shows that the invisibility, robustness and analyzability of the dense 3D model have been improved, effectively preventing fraud in the secret sharing process, and providing a reliable technical means for protecting 3d model in cloud environment.
Development of One Hand Drive Wheelchair System : 2<sup>nd</sup> report : Evaluation of the designed hand rim

The purpose of this research is to develop the wheelchair system for hemiplegic users. The proposed system is configured by the front wheel with active steering and hand rim for detecting user's driving force. The user's operational intention is distinguished by the sign and amplitude of force along the rotational axis of the hand rim. The steering angle of the front wheel is controlled by an actuator based on extracted intention from the hand rim. By proposed system, user can operate the wheelchair by using only single side hand rim. In particular, in this paper, we evaluate the designed hand rim by conducting some experiments.
Design and Implementation of Bitrate Adaptation Schemes for Power Capping in Wi-Fi Video Streaming

In dynamically adaptive streaming over HTTP (DASH), which is the de facto standard for streaming, each video is divided into segments, and each segment is further transcoded into multiple bitrate versions.This allows a client device to select the most appropriate bitrate version that matches the network bandwidth to avoid jitters or stalls.However, Wi-Fi download of a high-bitrate version may consume significant energy, especially when network conditions are good.To address this, we propose a new streaming method that limits the energy consumed by mobile devices but maintains an acceptable video quality.First, we derive a power model to analyze how bitrate selection affects power consumption in smartphones.Based on this, we propose two algorithms that determine the bitrate of each segment with the aim of maximizing overall video quality while limiting energy consumption.We use dynamic programming and heuristics to address the tradeoff between algorithm complexity and video quality.The proposed scheme was implemented on an Android-based DASH streaming platform, and various issues were resolved to cope with varying network conditions.Experimental results demonstrated that our scheme effectively optimized the video quality while limiting the energy consumption.For example: 1) our scheme uses 4% and 10% less power than DASH while maintaining an excellent video quality, and 2) the average difference between estimated and actual power consumption is 0.8%, thus keeping a precise energy bound.
Polynomial-Basis Arithmetic

This chapter is on arithmetic and related operations over the field GF(2m) with polynomial-basis representations. The first section is on addition, subtraction, multiplication, and squaring; although subtraction is just an instance of addition, optimal squaring is not just multiplication with the same operand. The second section is on reduction. And the third section is on exponentiation, inversion, and division.
Ensemble of Receptive Fields for Training Central-Focused Convolutional Neural Networks

Translation is a data augmentation method widely used in the image classification tasks. We analyze the mechanism of translation and discover that the central area of the images is more likely to be selected as convolutional neural network's input. Inspired by the structure of human retina, we propose the hypothesis that the central area of the image contains more significant information than the marginal one. Comprehensive experiments are presented to prove that hypothesis and reach the conclusion that the receptive field is nonuniform. The central part of the image that is always selected by translation is called the focused area. Motivated by the demand to take use of different focused area and thus take use of different receptive fields, we propose a novel training mechanism that integrate different focused areas in one training process. Our method consists of several stages, each has its own focused area and learning rate, and achieves considerable result in the experiments. We call the integration of focused areas the ensemble of receptive fields.
Design Process for Accessible Virtual Reality Learning Environments

Virtual reality has captured the attention of people. Since virtual reality has become more realistic, with the evolution of the technology like the innovation of smartphones, it has been more accessible for society, and many industries have begun research on the application of VR for training and for learning about certain specific topics as it allows reducing accidents and maximizes safety. Their use in the education industry has been best seen as a tool to complement certain issues that may be difficult to understand since it can allow one to virtually move to certain areas from safe areas. However, the development virtual environments is not fully specialized to implicitly include and promote learning, much less the consideration of people with disabilities. The main objective of this chapter is the presentation of a design process for the development of virtual learning environments that allows accessibility.
A Secure Voice Signature Based Lightweight Authentication Approach for Remote Access Control

Abstract Crypto-authentication schemes become unreliable whenever the private key is compromised, making them unfit for any system or network that requires high level of confidentiality. Key compromise is inevitable due to the wider space of operability of key in most of the cryptography based authentication schemes. To improve the performance of any authentication system, there is need to narrow its key operability to the key owner, that is eliminating the influence of other parties in the key generation and operability. However, this is quite difficult especially for any network that is characterised with low computation and energy, and relied on the third party for key management. In this paper, we propose the adoption of Mel-Frequency Cepstral Coefficients (MFCC) based voice signature based authentication scheme with a taint of cryptography operation. The scheme extracts the user’s voice signature as the hash of the MFCC parameters of the voice and unique passcode which is used for the authentication. We used exclusive or to filter off remnant of noise from MFCC values without incurring extra hardware cost. The performance evaluation results show authentication accuracy of 87.8% at low computation cost and communication overhead.
A Graph based Keyword Extraction from Twitter using Node and Edge Weight

With the advent of mobile and internet technology, social media has experienced a tremendous growth over the past few years. Social networking site such as Twitter provides the platform for content generation, information dissemination and communications. Summarizing the textual contents or extracting the influential words is a challenging task. Therefore an unsupervised graph based approach is proposed for automatic Keyword Extraction from the collection of Tweets using Node and Edge Weight (KETNEW). The node weight and edge weight depends on various parameters such as tweet frequency, position of node, clustering coefficient, co-occurrence frequency and shared neighbors. The important aspect of our approach is that it is a hybrid approach i.e. a graph based approach (structural approach) which depends upon statistical as well as linguistic features. Apart from this, it also depends on the position of the word in the text. These numerous features made it perform better than existing techniques.
Knowledge discovery model based on the effective control theory for decision support systems

This article proposes a knowledge discovery model based on effective control theory intended for validating a fuzzy cognitive map (FCM). The model considers the system from the point of view of the spectral properties of the adjacency matrix of the directed weighted signed graph representing FCM. The effective controls algorithm is used, which determines the direction of the eigenvector of the system adjacency matrix. This property defines the validation criteria for the created FCM.
Multilevel resource allocation for performance-aware energy-efficient cloud data centers

The massive power consumption of data centers has been a recurring concern in current research. In cloud environments, lots of methods are being adopted that aim for energy efficiency. However, although such methods enable the decrease in power consumption, they regularly affect application performance. In this paper, we present a multilevel resource allocation approach towards dynamic network bandwidth at the physical substrate, managing different power-saving states and workload allocation at the cloud infrastructure at the same time employ virtual machine allocation and selection policies at the cloud platform. In order to evaluate our approach, tests were carried out in a simulated environment using scale-out application on a dynamic cloud infrastructure. Results showed that our proposal presents a better balance regarding a more energy-efficient data center with a smaller impact on application performance when compared with other works discussed in the literature.
Lossless File Compression using Redundant Ngrams in English

There are millions of files published and transferred every second on the internet. So, file compression is compelling when we are storing or transmitting colossal data over a Brobdingnagian distance as every bit and byte stored or transferred takes time, energy and money. This paper presents a lossless text file compression technique. The compression technique is implemented based on the process of encoding the redundant and most repeating n-grams in English.
SD: A Divergence-Based Estimation Method for Service Demands in Cloud Systems

Estimating performance models parameters of cloud systems presents several challenges due to the distributed nature of the applications, the chains of interactions of requests with architectural nodes, and the parallelism and coordination mechanisms implemented within these systems. In this work, we present a new inference algorithm for model parameters, called state divergence (SD) algorithm, to accurately estimate resource demands in a complex cloud application. Differently from existing approaches, SD attempts to minimize the divergence between observed and modeled marginal state probabilities for individual nodes within an application, therefore requiring the availability of probabilistic measures from both the system and the underpinning model. Validation against a case study using the Apache Cassandra NoSQL database and random experiments show that SD can accurately predict demands and improve system behavior modeling and prediction.
Robotic Navigation using Entropy-Based Exploration

Robotic navigation concerns the task in which a robot should be able to find a safe and feasible path and traverse between two points in a complex environment. We approach the problem of robotic navigation using reinforcement learning and use deep Q- -networks to train agents to solve the task of robotic navigation. We compare the Entropy-Based Exploration (EBE) with the widely used ε -greedy exploration strategy by training agents using both of them in simulation. The trained agents are then tested on different versions of the environment to test the generalization ability of the learnt policies. We also implement the learned policies on a real robot in complex real environment without any fine tuning and compare the effectiveness of the above mentioned exploration strategies in the real world setting. Video showing experiments on TurtleBot3 platform is available at https://youtu.be/NHT-EiN_4n8.
STRESS EFFECT FACTORS AND SOCIO-PATHOLOGICAL PHENOMENA IN TEACHING AND LEARNING ENVIRONMENT

Stress represents an inner human state in a positive or negative sense that can be con­sidered a threat. From the researchers conducted in the Czech and Slovak Republic, it can be shown that adequate stress has a significant effect on brain activity. Stress in teaching and learning environment leads to the emergence and development of socio-pathological phenomena. Contrib­utors focus on social, emotional and behaviour stress factors in pupils/students with an impact on the occurrence of socio-pathological phenomena and risk behaviours in the educational process. The main methods of realization of the empirical research were the questionnaire survey, the own structure of the contribution authors, the experiment method, the statistical method of the F-test of equivalence and the t-test with uneven dispersion. The authors of the paper further analysed the statements of teachers who reported the most frequent preferences of stress factors in students.
Relationship Estimation Metrics for Binary SoC Data

System-on-Chip (SoC) designs are used in every aspect of computing and their optimization is a difficult but essential task in today’s competitive market. Data taken from SoCs to achieve this is often characterised by very long concurrent bit vectors which have unknown relationships to each other. This paper explains and empirically compares the accuracy of several methods used to detect the existence of these relationships in a wide range of systems. A probabilistic model is used to construct and test a large number of SoC-like systems with known relationships which are compared with the estimated relationships to give accuracy scores. The metrics $${{\,\mathrm{\dot{C}ov}\,}}$$ and $${{\,\mathrm{\dot{D}ep}\,}}$$ based on covariance and independence are demonstrated to be the most useful, whereas metrics based on the Hamming distance and geometric approaches are shown to be less useful for detecting the presence of relationships between SoC data.
Design, Implementation and Characterization of a Low-Cost Stair-Climbing and Obstacle Dodging Robot for Emergency Situations

Disaster response robots (DRRs) designed to explore dangerous areas in emergency situations (collapses, urban conflagration, earthquakes, floods) represent a design challenge. Therefore, there is the constant need of innovation in the field of navigation through harsh environment. In this paper, we present a design and implementation of a low cost, low power consumption and high terrain adaptability continuous track articulated robot. The proposed design is capable of climbing obstacles and dodging them by adjusting the inner angle of a middle joint. Also the current research, provides new ideas towards the innovation and ad-hoc use of low-cost robots for emergency scenarios.