Reviewerly/exHarmony huggingface dataset:
Development of a Navigator with Artificial Intelligence Applied to Smoothing and Execution of Paths in a Dynamic Two-Dimensional Environment

This paper shows the design and development of a navigation system that allows the execution of smoothed paths in a dynamic two-dimensional environment, which was applied to a quadruped robot. In the proposal of this work, it is worth mentioning the use of the Robot Operating System (ROS) for the development and implementation of the navigator in a Raspberry Pi A+, Bezier Curve, used to smoothing the robot path, Video Tracking technique, that allows the identification of the movement of dynamic objects in the environment, and also the use of the Multilayer Perceptrons Neural Network to make the robot intelligent enough to get away from objects without leaving its route. The proposed system allows a robot to move from a starting point to an end of a 2D environment without colliding with a moving object also inserted into the environment. This was designed, developed and tested resulting in a functional prototype. Although this prototype is running on a quadruped robot, it can be easily imported into other types of robots.
COMPETENCE ORIENTED TASKS IN FORMATION OF FUTURE SOFTWARE ENGINEERS PROFESSIONAL COMPETENCES

The actual direction of research in the field of higher professional education is the improvement of the quality of future software engineers’ professional training in a reduced period of study at universities. This is due to contradictions between the list of competencies defined in curricula, fewer numbers of disciplines in the curriculum and the competencies already available to students. We believe that competence oriented tasks are means to gain this point. Such tasks involve the organization of students self-work to solve professional problems. This orientation motivates undergraduates to cognitive activity and contributes to the formation of professional competencies. The purpose of this article is to analyze the use of competence oriented tasks in the process of future software engineers’ short-term training at universities. Scientific publications devoted to various aspects of the use of competence oriented tasks in the training process are analyzed. Their definition, classification, structure, and recommendations for design are given. The concept of professional competence by definition of the TUNING project and professional competence of the software engineer in particular are considered. The characteristics that allow to consider competence oriented tasks as means for formation and evaluation of future software engineers professional competencies are determined. Examples of such tasks aimed at the formation and evaluation of the level of professional competencies in «Programming» course are given. Further research is aimed at evaluation the effectiveness of the implementation of educational and criterion functions of these ones in the training of future software engineers.
Privacy and Security Aware Cryptographic Algorithm for Secure Cloud Data Storage

Cloud computing is an effective framework to enable on demand network access for sharing configurable computing resources such as applications, network, servers, services, and storages. It depends on two different technologies like distributed computing and virtualization. Cloud technology offers the users to store and retrieve their data easily in a remote manner. However, privacy and security are considered as the most challenging concerns in cloud data storage. Users are not ready to believe to store their data on any third party or companies without guaranteeing that will not access their data whenever they need. In this paper, a novel Privacy and Security aware Cryptographic Algorithm (PSCA) for data storage is proposed which is based on Invertible Non-linear Function (INF). In proposed technique, the encrypted data is only stored at the cloud storage without key and the encryption is done by the data authorities who also secure the decryption key. The end user has to send request to the data authority and after all verification process the encrypted data is given and with the obtained key and the knowledge of inverse INF the decryption is performed. Thus, both the internal and external attacks are avoided. The observed results are plotted and the proposed technique is compared with the existing AES and RSA techniques. From the results, it is observed that the proposed PSCA has better performance in terms of reduced time in all four parameters.
Effects of Equivalent Sources Arrangement on Spatial Interpolation of Impulse Responses

In this paper, we focus on spatial interpolation of impulse responses. In conventional studies, the amplitude value of the interpolated impulse responses at the initial part is small. To solve this problem, we propose a method of changing the arrangement of equivalent sources used to reconstruct the impulse responses. From the experiment result, we confirmed that the error of the impulse responses at the initial part decreased.
Method Research and Mechanism Design of Automatic Weaving for Beaded Pads

This paper aims to seek a weaving method and mechanical structure for automatic beading in view of the current increasing cost of hand-knitting and the expanding market demand. Based on the difficulty of using the existing hand-weaving method to achieve automatic weaving on a mechanism device, three automatic weaving methods, including the warp and weft weaving, the lock stitch sewing weaving and the single-line straight-through weaving, are proposed and discussed in this paper. Then the specific weaving process of these three methods are analyzed respectively, and compared with the hand weaving method. After analyzing the three methods, the single-line straight-through weaving method is determined for the further development on the basis of better realizing the automatic weaving compared with the other two methods. On account of the single-line straight weaving method, a kind of beaded pad weaving device was designed, and the working principle of each functional device is explained.
Sublinear Algorithms for Local Graph-Centrality Estimation

We study the complexity of local graph centrality estimation, with the goal of approximating the centrality score of a given target node while exploring only a sublinear number of nodes/arcs of the graph and performing a sublinear number of elementary operations.We develop a technique, that we apply to the PageRank and Heat Kernel centralities, for building a low-variance score estimator through a local exploration of the graph.We obtain an algorithm that, given any node in any graph of m arcs, with probability (1δ) computes a multiplicative (1±ǫ)-approximation of its score by examining only Õ min m 2/3 ∆ 1/3 d -2/3 , m 4/5 d -3/5 nodes/arcs, where ∆ and d are respectively the maximum and average outdegree of the graph (omitting for readability poly(ǫ -1 ) and polylog(δ -1 ) factors).A similar bound holds for computational complexity.We also prove a lower bound of Ω min m 1/2 ∆ 1/2 d -1/2 , m 2/3 d -1/3 for both query complexity and computational complexity.Moreover, our technique yields a Õ(n 2/3 ) query complexity algorithm for the graph access model of Brautbar et al. [14], widely used in social network mining; we show this algorithm is optimal up to a sublogarithmic factor.These are the first algorithms yielding worst-case sublinear bounds for general directed graphs and any choice of the target node.
Aspect-Oriented Modeling in Concurrent System

An approach on modeling traversing features in concurrent software system was presented, which is based on aspect-oriented techniques and statecharts of unified modeling language (UML). Modeled with UML statecharts diagrams, the primary system functions and corresponding traversal features are enveloped into various orthogonal regions of a composite state respectively. The mutual relationships between orthogonal regions are implied by the orders of broadcast events. With modular transition system, a basic computational model, the formalization description of aspect-oriented statechart models was proposed. The precise semantics of model elements and modeling procedures was also given. The case study indicates that the strategy of separation of traversing features is implemented in design phrase of concurrent software system with this method. And, the advantages of this software modeling approach are loose couple, adaptability and traceability.
Sensor less Speed Control for Permanent Magnet Motors with Non- Sinusoidal Back EMF

Brushless DC motor has a rotor with permanent magnet and has no mechanical commutator which leads to many advantages like less maintenance, long life, high reliability, low inertia, low friction and low noise. Since the brush system/commutator assembly is replaced by an electronic controller which can be easily integrated into other required electronics, thereby improving the effective power to weight and power to volume ratio. In this paper the direct torque control method is employed to improve its torque ripple content which is been compared with conventional method by using MATLAB. In this project the three phase brushless DC motor model is designed with proportional integral controller and tested in MATLAB software. The PI controller is used to control the speed of the brushless DC motor. On the other hand parameters like Back EMF, current; speed and torque are evaluated for the designed models of BLDC motor. Due to the easy implementation and simple control structure the convectional PI controller are used in industries.
Multi-attribute task builder

The multi-attribute task is a framework used by social scientists to study decision-making processes.In a typical experimental setup, a participant must select one of the options in each trial.The options differ in their attribute values, which are presented in a table format (see Figure 1).This paradigm allows researchers to explore what decision strategies people use, how they search and aggregate available information.In addition to the actual choices, a researcher can analyze process parameters such as response times and information search trajectories.
Thermal Print Scanning Attacks in Theretail Environments

The residual heat left by fingers on a PIN pad may breach the confidentiality of the card access codes. With over five billion chip-enabled cards across the globe, thermal imaging attack may create new crime avenue. This paper studies various vectors of thermal image attacks on PIN pad terminals with the main goal to outline potential controls to prevent such attacks. Previous research work confirms that the success of attack depends upon various factors like camera angle, camera-to-PIN pad distance, time between key punching and image taken, and the room temperature. These factors have been revisited as per the potential attack scenarios in a typical retail setup to find adoptable countermeasures. The research suggested deterring and preventive controls against thermal image attack on PIN terminals with emphasis on the applicability of these controls. The control measures such as the use of on-demand virtual keyboard and redesigned curved PIN pad terminal have been studied in details as an extra layer in physical security.
Linear filtering method for bone structures in computerized ultrasonic tomography images

Computerized ultrasonic tomography is an important radiological technique because of its non-ionizing properties. However, it has been the subject of several studies due to the complexity of its visualization. In this work, a Ram-Lak filter is implemented in the Radon domain to reduce the noise levels in the images and facilitate their observation. Results obtained were superior to those reported in the state of the art with an average improvement of 8.1512 dB.
Bio-inspired Expert System for Identifying Questioned Documents' Printer Source Suitable for Digital Forensics

Printer identification models are provided for the goal of distinguishing the printer that produced a suspicious imprinted document. Source identification of a published document can easily be a significant procedure intended for the forensic science. The arising problem is that the extraction of many features of the printed document for printer identification sometimes increases time and reduces the classification accuracy since a lot of the document features may come to be repetitive and non-beneficial. Distinct combinatorial collection of features will need to be acquired in order to preserve the most effective fusion to accomplish the maximum accuracy. This paper presents an intelligent machine learning algorithm for printer identification that adopts both of texture features formulated from gray level co-occurrence matrix of the printed letter "WOO" and genetic heuristic search to select the optimal reduced feature set. This integration aims to achieve high classification accuracy based on small group of discriminative features. For classification, the system utilizes k-nearest neighbors (KNN) to recognize the source model of the printer for its simplicity. Experimental results validate that the suggested system has high taxonomy accuracy and requires less computation time.
Dynamic Rules Extraction in Big Data Context for Knowledge Capitalization Systems

Big data is particularly challenging, when focusing on pattern mining to find rules that describe the hidden behavior over time. Traditional formalism for rules extraction paradigms, has been extended to a high abstraction level, and improved with the automatic choice of the feature space dimension. This paper presents a novel comprehensive theory of large-scale learning with $$\beta $$ random walk, and variational autoencoder. The new theory has the following components: 1. Rethinking learning theory; it validates the two bounds context, the local and the global by which the knowledge behavior is caught. 2. Hidden features extraction; large scale variational autoencoder provides a complete decentralization of the latent distribution resided in the latent space. Thus, as a result a new representation of the high dimensionality is replaced by a more relevant low dimensionality distribution. 3. Rules construction, the optimal bound of pattern recognition is achieved by a high abstraction level. In that sense, the proposed theory provides a new understating of the benefit of the hidden features, and gives concrete response to the diversity of rules in the big data context. The results show that the extracted rules are solid by achieving high accuracy, as well as, a high precision.
Sampling Rank Correlated Subgroups

Data mining, a key technique in knowledge discovery, is the process of identifying useful patterns from a collection of data. This process is made difficult for complex data combining, for example, numeric and symbolic attributes, or also when the number of observations is large. In this paper, we present a pattern mining approach to identify local correlations in the data, that is to say, sets of numerical attributes that strongly co-vary together in a subset of the data. The sets of numerical attributes and the subset of data are automatically (inductively) identified by the method. Whereas the space of patterns to be potentially explored is exponential, the complexity of the problem can be overcome by using sampling techniques that have several advantages: (1) reducing the computation cost, (2) identifying most important patterns, and (3) making possible to process large databases by distributed computing on multiple machines.
Firefly Algorithm and Grey Wolf Optimizer for Constrained Real-Parameter Optimization

The main goal of this paper is to present the performance of two popular algorithms, the first is the Firefly Algorithm (FA) and the second one is the Grey Wolf Optimizer (GWO) algorithm for complex problems. In this case the problems that we are presenting are of the CEC 2017 Competition on Constrained Real-Parameter Optimization in order to realize a brief analysis, study and comparison between the FA and GWO algorithms respectively.
A demodulation method with high stability for interferometric type vector fiber hydrophone

Signal detection stability is very important for vector fiber-optic hydrophone and hydrophone array, because the instability of the demodulated signal directly leads to the target azimuth estimation error and the degradation of system performance. In this paper, a method to achieve high-stability signal demodulation for interferometric vector fiber-optic hydrophone is studied. A parameter estimation and demodulation parameter compensation method for phase generated carrier demodulation system is proposed based on elliptic curve parameter fitting algorithm. An elliptic curve is constructed using the second and third frequency of the reference interference signal. The ellipse curve fitting algorithm is introduced to estimate the distortion parameter of the modulation and demodulation system by fitting the value of each elliptic curve parameter. By compensating the PGC demodulation for the tested signal with the estimated parameters, the instability of demodulation system caused by PGC modulation depth variation and additional modulation intensity of the light source can be effectively reduced. The feasibility of the method is verified by simulation experiments and actual system experiments. High stability signal detection is realized using the proposed method, which can effectively improves the detection effect of the vector fiber-optic hydrophone array.
Application and comparative analysis on three dimensional camera measurement system in full size inspection of batch production parts

Box-type parts are the basic parts of general machines, they assemble the shaft, sleeve, gear and other related parts of the machine into a whole, so as to maintain the correct mutual position accuracy between the parts. Their complex forming structure, large matching dimensions and high precision requirements directly affect the accuracy, performance and life of the machine. Aiming at the problems of large size and low efficiency of single piece inspection for box parts, this paper takes the spindle box of horizontal lathe as the research object, simulates through virtual testing platform, puts several parts of the same box parts in a certain order, determines the appropriate angle and shoots batch parts placement model in many directions, and adopts three-dimensional method. The camera measurement system detects the full size of the same batch of products and obtains the test results. On this basis, the traditional laser tracking measurement method is used to detect and compare the randomly selected single spindle box. The comparison results show that the three-dimensional camera measurement method can detect the forming accuracy of batch box parts well under the appropriate position and photographic angle, and the full-scale measurement error can be effectively controlled within the technical allowable range. This study provides a preliminary basis for the three-dimensional camera measurement technology in improving the efficiency and accuracy of full-scale measurement of batch box parts.
Object Detection and Tracking Based on Deep Learning

Object detection and tracking is an exciting and interesting research area in the field of computer vision, and its technologies have been widely used in various applications such as surveillance, military and augmented reality. This paper suggests and implements a robust object detection and tracking scheme to localize and to track multiple objects from input images, which estimates target state using the likelihoods obtained from convolutional neural networks. As the experimental results, the proposed system is effective to handle multiple target appearances and other exceptions, and it is able to detect the interesting object accurately in various environments, compared to the traditional method.
Controlled Quantum Dialogue Based on Logical Qubits

Alice prepares logical qubits, each of the two adjacent logical qubits is in the same quantum state, it is sent to Charlie, the controller, and after the unitary transformation, it is sent to Bob. Bob encodes the encoding sequence and then sends it back to Alice. Alice then encodes, published the measurement results. With the help of Charlie, Alice and Bob can obtain secret messages. The security analysis of the protocol proves the validity of the protocol. The biggest advantage of the protocol proposed by the protocol is that the third party can be controlled, the efficiency is higher, and there is no information leakage.
A Novel Traffic Flow prediction model based on the Improved Extreme Learning Machine

The changes of urban traffic flow is affected by many factors, and during predicting the traffic flow, it should consider each factor. The paper considers the characteristic between time and traffic flow, and introduces the time factor as a separated input parameter. Besides, some unexpected situations have large impacts on the predicting results, so, the unexpected factor is introduced as an improved factor to the input samples. Based on these, a novel prediction model is proposed, called at-ELM, which determines the hidden node number by using PCA, and reduces the impacts of the hidden node numbers on the predicting results. After performance tests and analysis, the novel model is a little improved in prediction accuracy and completely acceptable.
Determination of paraxial focal length of lens using Strehl definition measurement

This work describes a method for an experimental determination of a paraxial back focus position and a paraxial focal length of optical systems. It is analyzed an influence of spherical aberration on the value of the measured effective focal length of an optical system and the method is proposed for an elimination of this influence and the determination of the paraxial back focus position and the paraxial focal length of a lens from its effective focal length and the Strehl definition measurements.
Orbit-based conditional tests. A link between permutations and Markov bases

Algebraic sampling methods are a powerful tool to perform hypothesis testing for non-negative discrete exponential families, when the exact computation of the test statistic null distribution is computationally infeasible. We propose an improvement of the accelerated sampling described by Diaconis and Sturmfels (1998) based on permutations. We thus establish a link between standard permutation and algebraic-statistics-based sampling. We prove that the permutations-based sampling gives the lowest approximation errors and we validate our algorithm through a simulation study on three applications (data fitting, two sample tests and linear regression).
THE NATIONAL SEGMENT IN THE INFORMATION SUPPORT FOR THE EDUCATIONAL PROCESS: CONTEMPORARY BELARUSIAN REALITIES

National information resources are an important component of the resource base of the education space. Features of their development in Belarus pose a number of pressing challenges for the library community of this country. The article covers the main types of information resources that were created in Belarus in the last decade and have become an integral part of resourcing for the educational process. It shows the specifics of how the aggregated collection of national printed publications, collections of fulltext electronic information are formed. Possibilities for the development of the national segment in the information support for various entities in the education space involve creating National Electronic Library and implementing a number of other national projects that aim to cumulate separate information units and design information and analytical tools based on them.