Reviewerly/exHarmony huggingface dataset:
Help to Autocar Insurance Companies: Forecast of Polices Sales on Dynamics of Search Queries

There are two traditional ways of quantitative forecast for sales of autocar insurance polices: autoregression on dynamics of sales and regression on dynamics of investments to various channels of advertisement. In both cases the linear models are used. To improve such forecasts, we propose to use: 1) search queries to Internet as a source of independent data reflecting interests of drivers; 2) algorithms of GMDH as a tool, which builds noise-resistant lineal and non-lineal models under limited experimental data. We compare our results with several baselines: Holt-Winters method and ARIMA model in case of autoregression, GMDH models with investments in case of regression. Unlike the typical week and month horizons of forecast we consider month and quarter horizons. In addition, we study possibilities of joint model, where regression includes both investments and queries. Our results show: 1) GMDH-based algorithms without application of investments and queries dynamics decrease errors of forecast (MAPE) approximately in 2.5 times in comparison with Holt-Winters and ARIMA methods; 2) GMDH-based algorithms with application of search queries dynamics decrease errors of forecast (MAPE) in 20% in comparison with application of investments dynamics; 3) joint model with investments and queries doesn't improve forecasts. The proposed technologies may be useful both for autocar insurance companies and for other companies related to sales and advertisements.
Investigation of hybrid deep learning networks and LSTM in the short term forecasting in financial sphere

In this paper the problems of short term forecasting at the financial sphere are considered. For this problem intelligent forecasting methods: LSTM and hybrid deep learning networks based on GMDH are suggested. The optimal parameters of LSTM network and hybrid networks were found. Optimal structures of hybrid networks were constructed for short term forecasting. The experimental investigations of LSTM and hybrid DL networks were carried out and their accuracy was compared. The fields of preferable application of LSTM and hybrid DL networks in forecasting problems in finance were determined.
Video Surveillance Shoplifting Recognition Based on a Hybrid Neural Network

Understanding dynamic human behavior based on online video has many applications in security control, crime surveillance, sports, and industrial IoT systems. This paper solves the problem of classifying video data recorded on surveillance cameras in order to identify fragments with instances of shoplifting. It is proposed to use a classifier that is a symbiosis of two neural networks: convolutional and recurrent. The convolutional neural network is used for extraction of features from each frame of the video fragment, and the recurrent network for processing the temporal sequence of processed frames and subsequent classification.
Offline evaluation of the serendipity in recommendation systems

Offline optimization of recommender systems is a difficult task. Popular optimization criteria such as RMSE, Recall, and NDCG do not correlate much with online performance, especially when the recommendation algorithm is largely different from the one used to generate the offline data. An exciting direction of research to mitigate this problem is to use more robust optimization criteria. Serendipity is reported to be a promising proxy. However, more variants exist, and it is unclear whether they can be used as a single criterion to optimize. This paper analyzes how serendipity relates to other optimization criteria for three different recommendation algorithms. Based on our findings, we propose to modify the way serendipity is computed. We conduct experiments using three collaborative filtering algorithms: K-Nearest Neighbors, Matrix Factorization, and Embarrassingly Shallow Autoencoder $(EASE^{R})$. We also employ and evaluate the ensemble learning approach and analyze the importance of the individual components of serendipity.
Improving the Machine Translation Model in Specific Domains for the Ukrainian Language

One of the main tasks of natural language generation is to improve the quality of translation. For a morphologically rich language like Ukrainian, there are few ordered datasets that can be the basis for further training of a machine learning model.
Investigated Insider and Outsider Attacks on the Federated Learning Systems

With the vast usage of smart system devices, such as those used in 6G applications, that rely heavily on distributed machine learning technologies such as Federated Learning. Thus, there is an urgent need to provide a secure federated learning environment. The main challenge that faces federated Learning is thepoising attack. Therefore, this paper proposes a new framework for federated Learning that makes it more secure against GANs (Generative Adversarial Networks) attacks and Sybil attacks. The proposed framework is based on Federated Learning with Microsoft Confidential Consortium Framework (FED_CCF) to create a secure and reliable environment that deceives attackers of the federated learning environment. The performance of the proposed FED_CCF is evaluated using the MNIST dataset in terms of accuracy, where 30% of the devices were malicious devices represented by GAN or Sybil attacks. The experiment results of the proposed FED_CCF system show 96% better accuracy, with no effect of Sybil poisoning attacks, and only 0.18% of GAN poisoning attacks could affect it.
Bayesian Convolutional Neural Networks for Image Classification with Uncertainty Estimation

Over the past decade, deep learning has led to a cutting-edge performance in a variety of fields. However, it faces a fundamental constraint which is the treatment of uncertainty. The representation of the model's uncertainty is of significant importance in areas subject to strict safety or reliability re-quirements. Bayesian deep learning offers a new approach that showcases the degree of reliability of predictions made by neural networks. The present work tests deep learning with Bayesian thinking through a case study of image classification. It puts into practice Bayesian inference to tackle the problem of uncertainty in deep learning and shows its correlation with data quality and model accuracy. To reach this goal, we have implemented a Bayesian convolutional neural network using the variational inference algorithm, Bayes by Backprop. The proposed model was evaluated on an image classification task, with two benchmark datasets. The results' review allowed for validation of the Bayesian approach and showed that it obtains comparable results to those of a non-Bayesian convolutional neural network. Furthermore, the uncertainty of the model was estimated in terms of aleatory and epistemic uncertainty.
Determining the Priority of Factors Influencing the Selection of Information Technology for Distance Education

This article considers the task of determining the priority of factors influencing the selection of information technology for distance education. Since this process is one of those ones that are difficult to describe in the form of mathematical formulas, it is suggested to use the graph method and the method of analysis of hierarchies for calculations. These methods have shown themselves to be good in solving this kind of problems.In the course of the conducted survey among the participants of the educational process, eight factors are singled out, which, according to the correspondents, have a certain weight when selecting the information technology for distance education. Thus, such factors are singled out: communication tools; educational facilities; user data management; usability; adaptation; technical aspects; administration; course management. As the conducted studies show, the factor of usability acquires the highest priority level. The factor of course management, which is on the fourth step of the hierarchy, is at a lower level in terms of importance. According to calculations, the factors of the first hierarchy level turn out to be the least important: communication tools; educational facilities. These research findings are important for those educational institutions that are at a crossroads in their choice and they will help to decide on such information technologies.
Entity Resolution in graph databases: comparison study

Entity Resolution is the process of identifying whether or not various entities from different sources are referring to the same real-world entity. Entity Resolution hasn't been extensively researched in graph databases, whereas it has been for relational databases. This paper focuses on providing comparisons of experiments on various datasets to determine the most appropriate method used in the Entity Resolution process from among literature's similarity algorithms, graph embedding techniques, and graph embedding algorithms combined to link prediction. Moreover, if the embedding algorithm employed has an impact on the given results. The results show that the Entity Resolution process performed better when graph embedding techniques were paired with link prediction, and the chosen graph embedding algorithm also has an impact on the results.
Systematic Literature Review: Comparison on Collaborative Filtering Algorithms for Recommendation Systems

The recommendation system is divided into collaborative filtering (CF), content-based (CB), and hybrid approaches. This paper focuses on the CF approach which has many algorithms such as K-Nearest Neighbor (KNN), K-Means, Singular Value Decomposition (SVD), etc. We used the systematic literature review approach to gather papers related to CF and 28 research papers were eventually considered for analysis in KNN, deep learning, and SVD. From the review results, most of the datasets used in CF were movie datasets to test the recommendation model, and most of the models produced a good result in recommending items. To achieve good results, the majority of existing works combine more than one method to overcome or reduce the impact of CF problems (cold-start, sparsity, shilling attacks, etc.) which can affect the recommendation performance.
Basic Mobile Robot Prototyping Using RFID

Robotic platforms use RFID (Radio Frequency IDentification) technology in various fields of application (industrial, service, etc.). RFID tags can stores many information about the environment such as type of obstacles and their dimensions, humans, robots types, etc. Robot motion guided with RFID, has, thus, a tags stored data based behavior This article presents the design and the implementation of hardware and software architectures of a basic mobile robot prototype moving with RFID. The actions (walk straight, turn left/right etc.) executed by the mobile robot depend on data stored on the RFID tags. The proposed hardware architecture is based on a Raspberry Pi (single-board nano computer) for sending commands to the actuators and receiving sensorial data, and the Arduino controller-based development board for the low level control. The software architecture is based on Robot Operating System (ROS) nodes for the implementation and approach execution. The proposed architecture provides a fully functional and scalable system with integrating RFID technology.
Parameter Estimation of LFM-BPSK Composite Modulated Radar Signal Based on Multi-Output Regression Network

Aiming at the problem of high relative error in parameter estimation of LFM-BPSK radar signal by traditional decomposition algorithm, this paper proposes a parameter estimation algorithm for LFM-BPSK based on Multi-Output Regression Network (MORN). The algorithm first innovatively designs the ID-CNN-BiGRU network pre-training for feature extraction, then weights migration to the MORN network. The simulation results show that the relative errors of symbol rate, modulation frequency slope and carrier frequency are as low as 9.73 %, 10.80 % and 6.65 % at −6dB SNR respectively. The algorithm has good engineering value and can be extended to more composite modulated radar signals.
Investigating Methods of Input Data Preparation for Person Identification Information Technology

This paper describes the investigation of image preprocessing methods that can be used for preparation of input image data for the algorithm underlying in person identification information technology. Experiments were performed on Python software with developed methods of anisotropic diffusion and image histogram equalization, separately and simultaneously applied, on three sets of images from different databases. As a result, method of image histogram equalization in most experiment cases did not improved the identification accuracy rate of the algorithm. For image from SCface database the identification accuracy rate is stably equal to 92.5%. However, on the images from the Database of Faces the promising result was obtained, that improved algorithm performance on 5%.
Comparative Study on Correlation Filtering Algorithms for Sea Surface Target Tracking

Aiming at the target tracking problem of optoelectronic tracking system for border and coastal defense facing various challenges on the sea surface, by comprehensively evaluating the tracking effect of several typical correlation filtering algorithms, this paper provides the basis for improving the target tracking algorithm. Firstly, the research status and basic theory of target tracking algorithm based on correlation filtering are introduced. Then, four representative correlation filtering tracking algorithms are described in detail, including KCF algorithm, SAMF algorithm, SRDCF algorithm and ECO algorithm. Finally, multiple video sequences of the ship moving at sea level are used as the test set of the tracking algorithm, the accuracy and real-time performance of the algorithms are analyzed qualitatively and quantitatively, which lays the foundation for further improving the target tracking performance.
Conceptual Model of a System for Controlling the Process of Egg-Hatching in Incubators

This paper presents a conceptual model of a system for controlling the process of egg-hatching in incubators, which is based on the Sufficiently general control theory and its concept for sustainability in terms of predictability. The key factors influencing the predictability of the egg-hatching processes are identified, and using the subjective ones are selected the main functionalities that an incubator control system should provide. Next, is presented the conceptual model of an incubator control system with all its subsystems and regimes of operation. The study also presents the algorithm of the system divided into two parallel processes. The developed model offers a new conceptual approach for increasing the sustainability of the process, which increases the chances for efficient control of the process and reduces the chances for complete loss of control. Furthermore, the study gives footprints about other important processes that should be monitored, even though they are controlled by other subjects.
Path Planning of Load Transportation by Multi-Quadrotor Formation Based on RRT-APF Method

In this work, we propose an integration algorithm of rapidly-exploring random trees method and artificial potential field method in path planning for a multi-quadrotor unmanned aerial vehicles formation with a load. Specifically, we establish potential field to guide the stretch direction of branches and extract the key points of the final planned path for optimization. For the lack of intelligence of the artificial potential field method, the sampling method gets improvements both on that and rapidity. In the same way, the artificial potential field method also makes up for the lack of planned path's smoothness with the sampling method. Aiming at the complicated structure of the transporting system, we set strict safety constraints to ensure the success of obstacle avoidance. In addition, an algorithm of searching along the wall is proposed to fit in the scene with unknown information obstacles. The methods' feasibility and effectiveness are verified by simulation results and relevant comparative analyses.
High Accuracy Discretization-based Integer Programming for the Dubins Multiple Traveling Salesman Problem with Min-max Objective

Many robotic exploration and surveillance applications can be modeled as a single-robot Euclidean traveling salesman problem (TSP). However, the robot in the real-world is usually limited by turning radius or curvature; Moreover, multiple robots can accelerate the completion of the applications. This paper thus studies the Dubins multiple traveling salesman problem (DMTSP) with a min-max objective where the robots are with limited turning radius. Compared to the single robot, multiple robots also need a high corporation to min-max the completion time of the application, and the cooperation makes the problem more difficult. As no mathematical programming work for DMTSP yet has been found in the literature, we propose two approximate (discretization-based) mixed integer linear program-ming (MILP) formulations for the studied problem in this paper. These formulations were compared to the existing Euclidean multiple TSP (MTSP) method and the genetic algorithms for Dubins TSP (DTSP). The results show the effectiveness of our methods on mild-large instances and high accuracy results towards genetic algorithms. The results also are with a fairly small 5% gap to the Euclidean MTSP results which ignores the curvature limits.
Research on key technology of infrared detection of power equipment

This paper studies a power equipment identification method based on infrared image technology. The methods are mainly divided into image sharpening, edge extraction, feature extraction, and electrical equipment detection. First, the infrared signal is processed explicitly to obtain a higher quality infrared signal, then the boundary analysis is used to obtain the signal boundary, the characteristics are extracted from the boundary graph, and the data of each positive and negative sample is trained by SVM to obtain the exercise mode, and finally use the training mode to realize the power equipment identification on the infrared signal.
Convolution Neural Network on BeagleBone Black Wireless for Machine Learning Applications

This study explores building and characterizing a Convolutional Neural Network (CNN) onto a Beaglebone Black Wireless. Two case studies are tested, hand drawn digits and facial recognition. The Beaglebone represents an edge node on a cloud network and by moving the machine learning (ML) computations to the edge node, response times and bandwidth usage can be reduced. We characterize execution times and power consumption on standard BeagleBone Black and on the AI version.
Research on building smart monitoring system through data acquisition and calculation

This paper studies a civil engineering building monitoring system, which involves the technical field of engineering building monitoring, including the data acquisition system used to obtain the damage information and damage coordinate information of the building structure; the data modeling system used to generate the health index report of the building structure and Build a building structure evaluation model; the damage identification system is used to divide the building structure and carry out damage identification and damage location; the audit evaluation system is used to evaluate the damage degree of the building structure, the future change trend of the damage degree and the future use period, and for correction Evaluate models and recommend fixes. The invention can monitor the health state information of the building structure in real time, classify the damage degree of the building structure and trace the damage location in time, infer the future development trend of the damage and the future service life of the building structure, and simulate and repair the repairable damaged parts., and recommend repair suggestions to the monitoring personnel, so that the monitoring personnel can take timely measures to deal with the damage.
Analysis of the Optimal Adjustment Frequency for a Dual-Axis Solar Tracker

With the rising need for clean energy around the world, sources of clean energy must be improved upon to increase their efficiency and meet global demands. This project aims to investigate an improvement to solar panel efficiency through the construction of a dual-axis solar tracker equipped with light sensors and an accompanying robotic light source that can simulate the motion of the Sun. The performance of the solar tracker was compared to a fixed solar panel, revealing a significant improvement in power generation. The frequency at which the solar tracker automatically adjusts throughout the day was varied and analyzed to determine the rate of adjustment that causes the greatest panel output. The dual-axis solar tracker and robotic light source system created also enable future research in the tracker’s performance in additional lighting scenarios and the comparison of tracking algorithms.
An On-device Federated Learning System for SMS Spam Classification

Federated Learning (FL) is a privacy-preserving machine learning technique that allows training of models on edge devices which are then aggregated centrally, ensuring private data never leaves edge devices. In this paper, we aim to bridge the research gap in on-device FL, by designing and testing an on-device FL system for classifying SMS spam on Android devices. We study the feasibility of training deep learning models on phones: optimizing models to fit within memory/network constraints, analyzing effects of different word embeddings, IID/non-IID data and finally optimizing FL performance to be on par with centralized training while preserving privacy.
Determining the most ideal practical quantum decoherence-reduction method by simulating superconducting qubit coherence rates in a Qiskit Pulse program and IBM’s Dyanmic Decoupling Program

Quantum decoherence is the loss of quantum information preserved in quantum states. This phenomenon occurs because the wavefunctions of the qubits have interference, which allows them to change their frequencies and wavelengths when other waves are applied to them. While the physics behind using gates to qubits is the application of waves to qubits in order to change their properties and thus its state, these properties can also be altered by environmentally occurring waves, such as heat and radio waves. However, many decoherence-reduction methods have been discovered and can be viably implemented into quantum systems to mitigate or counter quantum decoherence to a degree. We perform an assessment to determine the best method to reduce quantum decoherence by assessing the qubit coherence of qubits under each reduction scheme by plugging in a percentage value stating how much larger the qubit coherence time is into a proposed Qiskit Pulse program that utilizes dynamic decoupling much more accurately and quickly than the standard dynamic decoupling program available in the Qiskit Pulse documentation, “Calibrating Qubits with Qiskit Pulse.”
Substation Power Equipment Monitoring System Based on Infrared Detection

At present, the temperature measurement pictures of infrared thermometers commonly used at home and abroad all use manual methods for infrared temperature measurement and atlas analysis. However, a large number of input work in the traditional sense seriously occupies the working time of operation and maintenance personnel, and the work efficiency is low., time-consuming and labor-intensive. The purpose of this paper is to provide a substation infrared detection method and system to solve the problems of low work efficiency, time-consuming and labor-intensive, and randomness of the existing substation infrared detection technology, which is not conducive to later analysis and judgment,