Reviewerly/exHarmony huggingface dataset:
Forensic Search for Traces of Unauthorized Access Using the Kerberos Authentication Protocol

Organizations use Windows Active Directory service to authenticate users on the network with Kerberos extended Authentication Protocol. According to IT Governance for 2022, there were revealed 1,063 incidents involving approximately 480,014,323 compromised network user accounts. Therefore, based on statistical data, it is necessary to analyze the most popular type of attack using Kerberos authentication protocol, in particular, what traces remain in logs and, based on this forensic data, determine a more secure usage environment offering mitigation and response precautions. As a conclusion to this study it is necessary to highlight the fact that it is difficult to implement and detect them, that’s why it is necessary to make a thorough log analysis and analysis of other environments where it’s possible to detect them.
Segmenting Prostate Cancer on TRUS Images with a Small Dataset: A Comprehensive Methodology

The use of mathematical algorithms for disease identification has gained traction in recent years and has paved the way for the creation of novel tools that can swiftly and accurately detect pathologies. In particular, modern machine learning techniques have garnered significant attention in this domain and are currently among the most widely used algorithms. Despite their popularity, the implementation and training of these neural networks can be daunting, owing to the intricate nature of the data and the complexity of the training process. To address these challenges, this paper suggests an efficient neural network training algorithm that employs iterative analysis and gradient computation for each data packet, thus ensuring the attainment of optimal quality metrics.
Deep Learning in Automation of Checking Homework Assignments

The article deals with the current issues of knowledge management automation. The authors describe the possibilities of deep learning methods for checking homework and control tasks in such school subjects as Russian language, literature, social studies, history. As a rule, the main form of presenting answers to tasks is text. In this regard, it is proposed to automate the process of recognizing handwritten students texts and checking for compliance of the student’s response with the template document proposed by the teacher for this task. The main process of the service is handwritten text recognition technology, which is implemented on the basis of convolutional and recurrent neural network architecture using a decoding algorithm based on connective time classification. The article also provides a description of the online service, which implements the ability to download the answer to the task by students, recognition of the answer, determination and output of the answer similarity percentage with the attached answer from the teacher. According to the authors, this service will automate the teachers routine operations to check homework. It will be especially useful during the implementation of distance learning during pandemics, quarantines, etc.
Building Deep Neural Networks for solving Machine Learning Problems in Agricultural Production

In the tasks of agricultural production, it is necessary to identify unfavorable situations of agricultural farming that arise in the process of cultivating agricultural crops. These include soil erosion or salinization, damage from crop diseases, pests, and others. Timely and prompt identification of such situations is possible with the use of technical vision and methods of intellectual analysis and image processing. The most effective means of machine learning (ML) for such tasks are deep neural networks (DNN), primarily based on a parallel architecture containing convolutional layers of neurons. The purpose of the study was to build and study the effectiveness of DNN, which are used in intellectual land use tasks. The Python-based Google Collaboration cloud service, including ML libraries, was used as the DNN development environment.. When designing DNN, the features of the functioning of the CPU and GPU were taken into account. The results obtained make it possible to optimize the architecture and hyperparameters of DNN, as well as their training time. This approach increases the efficiency of the information and analytical complexes being developed to support the solution of various land use problems.
Nature-Like Structural Node Components for Software and Hardware Complexes on the Basis of Single-Board Computers

The paper presents the results of research on the possibility of using single-board computers in instrumentation and control systems. It is shown on the example of the OPERATOR software platform it is possible to create nature-like structural nodal components for software and hardware complexes on the base of sinlge-board computers. The authors called these node elements as "veurons". The advantages (the reduction of manufacturing costs, complete hardware unification, easy repair and replacement of units) and limitations (need of parallel computational algorithms, moderate network and data flows) of veurons are given. The examples of veuron- based configurations of the top-level of instrumentation and control systems are presented.
Optimal Parameters Determination for Extreme Learning Machine in the Human Activity Recognition

Extreme Learning Machine is a single-hidden-layer feed-forward neural network that has the advantage of high learning speed and ease of implementation. The efficiency of an extreme learning machine in human activity recognition largely depends on three parameters, such as the weight matrix, the hidden layer neurons number, and the activation functions. This study is aimed at building an artificial neural network to solve the problem of human activity recognition to analyze the influence on the accuracy of parameters neural networks such as input weights, activation functions, and the neurons number in the hidden layer. For the experiment, an open dataset was used, which includes information about seven physical activity types. We compared the accuracy when training a neural network with an extreme learning machine and an extreme learning machine with the weight coefficient values calculated using the particle swarm optimization method. Also, the influence on the accuracy of such activation functions as a hyperbolic tangent, rectified linear unit, sigmoid, sinusoidal, and binary step function for different hidden layer neurons numbers was evaluated.
Formation of student’s self-education and competence in the process of developing mental skills

Self-education is aimed at heights and perfection, self-education puts simpler issues. An impulsive person with an explosive character gets used to not yelling, self-control helps – this is self-education. A calm and adequate person teaches himself/herself wisdom – this is self-education. The child teaches himself/herself not to lie and not to fight – this is self-education. A young graduate of the conservatory willingly performs transitions masterfully day after day – this is self-improvement. Self-education is the first step towards self-development. Self-education is one of the very necessary links of a single complex of intellectual labour. This type of activity, which includes the elements of independent activity, has been the centre of attention in all structures and stages of social life as a means of improving a person's socio-political and professional-specialization training and acquiring knowledge, it has always engaged people with progressive views and has been highly valued based on its usefulness “coefficient”. It is necessary to perform the opposite operation in the mental work process. In addition to giving these operations a great place in training, the teacher should also take care of their formation in students. Insufficient development of students’ analysis-composition activities negatively affects not only the depth of their knowledge, but also the level of their skills. The purpose of the article is to examine the high demands placed on school preparation in the era of scientific and technical progress, in a situation where information is growing rapidly. In addition to arming the young generation with scientific knowledge, the school should teach them to acquire independent knowledge, free work habits, intellectual work culture, independence of thought, self-education habits, and better prepare for independent work activities. This is one of the important directions of educational reforms.
Multi-Class Classification Using Quantum Kernel Methods

Quantum machine learning has recently attracted attention in various research fields. One of the most promising areas are kernel methods in quantum computers as they leverage the quantum computers advantage over classical kernels. The embedding of data in Hilbert space of quantum computers is called Quantum Embedding Kernels (QEKs). Many previous researches have explored the idea of using quantum embedding kernels for binary classification problems, demonstrating the advantage of quantum computing. This research is concerned with using these methods for multi-class classification problem and benchmark the results against well-known datasets such as IRIS and MNIST.
Network Stegoinsider Detection

The number of crimes committed by insiders has increased dramatically over the past 10 years. Insiders use various technologies, including steganography, to bypass security measures and steal confidential information. In this paper, we analyse the methods that can be used to identify the steganographic channel and steganographic insider, such as Shannon information entropy method, Kolmogorov-Smirnov method, probability distribution variance comparison method, machine learning method and neural network method. Based on these methods, some programs were written to identify steganographic channels in the network. Each of the methods was tested on real enterprise traffic. As a result it is possible to say whether these methods are suitable for use in the enterprise network or nor.
Digital Twin as an Effective and Versatile Tool for Modeling and Optimizing Hybrid Energy Complexes at All Stages of the Life Cycle

The aim of the work is to create a universal mechanism that will quickly and accurately determine the optimal composition, parameters and modes of operation of hybrid energy complexes with power plants based on the use of renewable energy sources and energy storage systems from the point of view of safe operation and guaranteed electricity and heat supply to consumers of isolated and hard-to-reach territories. The paper shows the effectiveness of research and development of recommendations on the choice of parameters and modes of operation of hybrid power complexes having a diverse composition of generating plants and high dependence on natural uncertain factors at all stages of the life cycle using the developed universal digital twin, which was verified by the example of real power complexes with different equipment composition.
Development of an Automated Method for Compiling a List of Protected Objects Based on a Special Classification of Information Assets in the Field of Information Security

This article reviewed the sources on the research topic, as a result of which it was determined that the research topic is discussed, relevant in the Russian and international agenda. A comparison of asset classifications in legal acts was carried out, which determines the recommended basic list of assets. A special classification of information assets has been developed, which systematizes the classes of financial assets according to the criterion of the possibility of information security risks affecting an information asset. An algorithm for the rational construction of a list of financial assets in the field of information security has been developed. An automated tool has been developed to determine the totality of assets based on special allocations.
Classification of Incoming Messages of the University Admission Campaign

This paper deals with the task of text messages classification. The authors analyzed and reviewed the results of other researchers in this task and provided a brief overview of the machine learning and deep learning methods used in the study. The dataset of 1200 incoming messages of university admission campaign was used in the study. The authors pre-processed message texts, classified messages in three ways and applied three types of text vectorization. Based on machine learning and deep learning methods, the authors developed and applied multiclass and binary message classifiers. The paper presents classification metrics and confusion matrices for tasks of multiclass and multilabel classification. The models that provide the highest f1 score were selected as the best models.
The Automated Efficiency Enhancement of Photovoltaic cells using Artificial Intelligence and Internet of Things

Many solar plants adopt fixed solar panels in their plants for power production. Due to these fixed panels, maximum utilization of power output from the solar panels is not obtained during morning and evening time. This limitation can be overcome by using dual-axis solar trackers. With dual-axis solar radiation trackers, we could obtain more power output from solar panels even in morning and evening time compared to fixed solar panels. Compared to the average power output produced from the fixed panels, the average power output from the dual-axis solar trackers is 9.916% more than the fixed panels.
Arduino Based Voice Controlled Wheelchair For Physically Challenged Persons

As the time being, Technology is growing at a faster rate. Improvement in Technology makes the lives simple, everyone wants to live individually using the technology. However, this is deceptive for physically challenged persons. Majority of people with disabilities having trouble moving various body parts. The only solution is to depend on their family and friends to complete their day to day works. Even for moving from one place to other place the persons must use a wheelchair. For moving the wheelchair, the person should have enough strength in hands or requires other person to assist the wheelchair. The persons having disability are less communicating with the outside world which make them week and reluctant. The proposed machine is a wheelchair-manipulated gadget to help disabled human beings to turn out to be unbiased. The device uses a voice recognition unit which will take human voice as command and control the wheelchair movement based on the command. The device integrates Arduino, HC-05 Bluetooth module, speech-to-text voice recognition android utility, and motor driver through which the customers can perform the wheelchair with the aid of absolutely speaking to the wheelchair microphone. The wheelchair is designed at low cost in such a way that needy users will benefit from the machine.
A dynamic few-shot learning framework for medical image stream mining based on self-training

Abstract Few-shot semantic segmentation (FSS) has been widely used in the field of information medicine and intelligent diagnosis. Due to the high cost of medical data collection and the privacy protection of patients, labeled medical images are difficult to obtain. Compared with other semantic segmentation dataset which can be automatically generated in a large scale, the medical image data tend to be continually generated. Most of the existing FSS techniques require abundant annotated semantic classes for pre-training and cannot deal with its dynamic nature of medical data stream. To deal with this issue, we propose a dynamic few-shot learning framework for medical semantic segmentation, which can fully utilize the features of newly-collected/generated data stream. We introduce a new pseudo-label generation strategy for continuously generating pseudo-labels and avoiding model collapse during self-training. Furthermore, an efficient consistency regularization strategy is proposed to fully utilize the limited data. The proposed framework is iteratively trained on three tasks: abdominal organ segmentation for CT and MRI, and cardiac segmentation for MRI. Experiments results demonstrate significant performance gain on medical data stream mining compared with the baseline method.
Automatic Speech Recognition trained with Convolutional Neural Network and predicted with Recurrent Neural Network

An important method for interacting between humans and computers is Automatic-Speech-Recognition (ASR). This technique enables a computer to recognize the word (acoustic) spoken into a microphone and convert it into text or other suitable formats for further processing. The w i d e applications using Convolutional Neural Networks (CNNs) towards Voice Recognizing systems becomes widespread. This study investigates the use of deep CNNs to detect potentially mispronounced English phonemes. Machine learning models built on deep learning have produced noteworthy outcomes in a variety of vision-related applications, including Speech Recognition. In this work, Recurrent Neural Network (RNN) is used to process feature maps from CNN architecture trained on Speech Command dataset. The simulation results reveal that the proposed CNN architecture with RNN performs significantly, with an accuracy rate of 91.81%and a trained Word Error Rate (WER) of 12.4% on comparing with existing works.
Form and form creation in the art of painting

The article draws attention to the need to study and master the concepts "form ‒ form creation" in the art of painting, relying on their unique importance in the process of creating an artistic image. Analysing the current state of the problem, and understanding its role in the future professional activity of artist-pedagogues, we are forced to state that this important section of art pedagogy still remains outside the boundaries of professional attention, despite the active request of student youth. However, we admit that the interest in studying the "form ‒ form creation" expressed by the students of the Faculty of Arts and Graphics as well as the impossibility of its implementation in the educational process depend not only on the professional wishes of teachers and students, but also on the lack of software, the shortage of special literature and the laboriousness of extracting necessary data from its volume, and, most importantly, ‒ on the difficulty of assimilating material during independent study. Similar factors forced the authors of the article to compile and offer a shortened version of the textual information from theoretical provisions and examples of form creation in the world art of painting, giving further analysis of the quality of its perception by the student audience. For the practical implementation of the event, a number of milestone topics were developed, which made it possible to create a certain methodical course for solving strategically selected issues.
Detection of Real Time Face Mask using Convolutional Neural Network

In recent years, the global pandemic like COVID - 19 has changed the lifestyle of people. Wearing face mask is must in order to stay safe and healthy. This paper presents a real-time face mask detector which identifies whether a human is wearing a mask or not. Moreover, this system can also recognize the person wearing a face mask inappropriately or wear other things except a face mask. The proposed algorithm for face mask detection in this system utilizes Haar cascade classifier to detect the face and Convolutional Neural Networks to detect the mask. The whole system has been demonstrated in a practical application for checking people wearing face mask.
Semi-Supervised Medical Image Classification Combined with Unsupervised Deep Clustering

An effective way to improve the performance of deep neural networks in most computer vision tasks is to improve the quantity of labeled data and the quality of labels. However, in the analysis and processing of medical images, high-quality annotation depends on the experience and professional knowledge of experts, which makes it very difficult to obtain a large number of high-quality annotations. Therefore, we propose a new semi-supervised framework for medical image classification. It combines semi-supervised classification with unsupervised deep clustering. Spreading label information to unlabeled data by alternately running two tasks helps the model to extract semantic information from unlabeled data, and prevents the model from overfitting to a small amount of labeled data. Compared with current methods, our framework enhances the robustness of the model and reduces the influence of outliers. We conducted a comparative experiment on the public benchmark medical image dataset to verify our method. On the ISIC 2018 Dataset, our method surpasses other methods by more than 0.85% on AUC and 1.08% on Sensitivity. On the ICIAR BACH 2018 dataset, our method achieved 94.12% AUC, 77.92% F1-score, 77.69% Recall, and 78.16% Precision. The error rate is at least 1.76% lower than that of other methods. The result shows the effectiveness of our method in medical image classification.
Comprehensive approach of real time web-based face recognition system using Haar Cascade and LBPH algorithm

The goal of our research is to propose a system that automatically marks the attendance among the students in class using a webcam with Face Recognition. Since traditional ways of taking attendance are not that useful as it is highly time-consuming to mark the attendance of every student by calling their names as well as the use of papers to mark the attendance also affects our environment, therefore our system helps to save time as well as its eco-friendly as no paper is needed, helps to solve problems of fake attendance and proxies. The Haar Cascade & LBPH algorithms are used in this work to develop a Student’s Attendance System with facial recognition technology. Face detection and recognition are two steps in realtime human face recognition. As of Haar Cascades algorithm’s excellent precision and high real-time permit rate, which is implemented in OpenCV via the Python language, we use it for face detection. The training phase and evaluation phase are two categories taken into consideration in face recognition. The algorithm is trained during the training phase using samples of the image to be learned and the test image is compared across all trained samples in the dataset during the estimate step. By using Local Binary Patterns Histogram(LBPH), the live stream’s faces are recognized & the face characteristics are taken out. Face recognition is completed using the Euclidean distance classifier using the LBPH. The control of the system in computer-based communication is significantly impacted by authentication. The system will automatically keep track of students' attendance in a classroom and will give faculty members the ability to conveniently access student data by keeping a log of their clock-in times.
Integrating Global and Local Features for Efficient Face Identification Using Deep CNN Classifier

This work analyses the effectiveness of integrating global (complete face) and local (eyes, nose, mouth part) features for face identification using deep CNN classifier for different percentage of training dataset and different number of users. In this work, we have used HOG for face detection followed by DHE technique for image enhancement as low-illumination image contain important distinct facial features. The role of global and local features in perceiving a face is distinct and substantial. Thus, to examine the efficacy we have extracted and integrated the global feature (DCT) with local feature (ULBP) as the proposed facial feature. For dimensionality reduction of DCT-ULBP feature, 2DPCA is used. The reduced feature vectors are fed to deep CNN classifier for face identification task. The experimental result on ORL face database, shows that the proposed features are discriminative and complement with each other. The proposed DCT-ULBP feature with deep CNN helps us to achieve an accuracy of 94.58% and outperforms the contemporary DCT and ULBP feature extraction techniques.