Reviewerly/exHarmony huggingface dataset:
Multidisciplinary Expert System for Etymologization of Oghuz Ethnonyms

There is an urgent need for expert systems (ESs) that will solve tasks that are very complex to solve by human experts alone. The challenge for etymologization of 22 Oghuz ethnonyms (names of tribes) of M. Kashgari’s list exists, the solution of which could be used to argue the relationship between early Nine Oghuz people of Mongolia and late Oghuz people of the Syr Darya basin. The authors of existing etymologies did not take into account a multidisciplinary approach to the etymological study of ethnonyms. In this paper, we present the development of Oghuz Ethnonyms ES - multidisciplinary ES as the best choice to solve this problem. The ES collects and processes expert knowledge not only from Linguistics but also from interdisciplinary sciences, uses advantages of AI technique, and consists of two linked parts: The Rule-based Sub-ES (to produce etymons-sources for 22 target ethnonyms) and the Fuzzy Sub-ES (to estimate etymons). This ES was developed using Java, SWI-Prolog, and Fuzzy Control programming languages. The study presents some hypothetical etymons suggested, some of which have confirming outside the linguistics facts and consequently are close to true etymons. Most of etymons-ethnonyms presented are Nine Oghuz's ethnonyms which may be a good argument in favour of the opinion that the late Syr Darya Oghuz likely could be the descendants of the early Nine Oghuz.
Multi-scale-based Network for Image Dehazing

Image and video dehazing is a difficult subject that has received a lot of attention in the field of computer vision. The presence of air haze in photos and movies can reduce visual quality dramatically, resulting in a loss of contrast, color accuracy, and sharpness. To address this issue, in this paper, we propose a deep-learning-based method for image dehazing. The proposed network consists of using multi-scale representation at every VGG-16 block to conserve the high quality of the image during the learning process. The collaboration of convolutional layers and the multi-scale block make the network learn from different scales combined with the outputs of the previous layers of the networks. This can conserve the high quality as well as remove the haze. The proposed method is trained and tested on four datasets including BESIDE, DENSE, O-HAze, and I-HAZE, and hives promising results compared to some of the state-of-the-art methods.
Intelligent Inductive Modelling Technology Based on Recurrent-and-Parallel Computations

This paper presents a new intelligent information technology that can build inductive models of complex processes from experimental data. The technology uses a novel concept of combining recurrent and parallel computations to achieve high productivity. By integrating this technology into modern intelligent information-and-analytical systems, the efficiency and validity of managerial decisions for complex process management can be significantly enhanced. The paper demonstrates an application of the technology for evaluating and forecasting the investment activity in Ukraine.
A Demonstrable Break of PCBC Mode

Modes have long been used to help ciphers remain secure under cryptanalysis. The goal of modes is to randomize the cipher so that for any two identical input blocks the cipher text output is likely to be different. This apparent morphing of the mapping between input and output is generally thought to make decryption more difficult. However, this may not be true. Side channel attacks against some modes have proven that they carry sufficient information to be able to return the original message without having to also break the cipher. Of the identified and approved modes, one quarter have been broken (return the key for the cipher) and this paper presents a verifiable break for another mode. Further, a concept for breaking a fourth mode is also presented. If more modes are broken, then the concept of modes may be proved ineffective, and abandoning modes may be indicated.
Explainability of Artificial Intelligence Systems: A Survey

Complex machine learning (ML) algorithms consistently outperform traditional ML models, yielding significantly improved results. Furthermore, these complex system uses large amounts of data with many features to train, increasing their predictive power but reducing our ability to explain them. Thus, complex ML models primarily operate like black boxes. Explaining their inference processes and predictions poses a challenge due to the necessity to account for millions of interacting weights in a complicated way. Still, we need to clarify their inner workings and procedures, as understanding a model can improve transparency and promote trustworthiness. Therefore, ‘Explainability’ assumes pivotal importance, as it enhances accuracy and broadens the scope of applicability for complex ML methods within critical domains. Thus, in this review paper, we have investigated the complete ML system development lifecycle, compiled recommendations to cover explainability gaps from diverse literature sources, listed different methods of interpretability and explainability, and improved our explainability-augmented ML system development life cycle for all stakeholders to enhance trustworthiness.
Customer Satisfaction Toward Commercial E-Services in Saudi Arabia: A Sentiment Analysis

The e-government in Saudi Arabia has evolved significantly over the last two decades. Therefore, this study investigated the commercial e-services provided by the Saudi Ministry of Commerce (MOC). Sentiment analysis was conducted to assess customer satisfaction using these MOC's e-services. To this end, data were collected from Twitter and App reviews, after which two approaches were applied to the analysis: manual annotation- and lexicon-based approaches. Then, each experiment was evaluated using three machine learning classifiers, namely, support vector machine (SVM), Na誰ve Bayes (NB), and logistic regression (LR), based on several metrics such as accuracy, precision, recall, and the F1-score. The results showed that more than 90% of the data was neutral, and that more users were negatively satisfied with the MOC's e-services than were satisfied with them. Furthermore, based on the accuracy of all three classifiers, the results indicated that while the manual annotation approach outperformed the lexicon-based approach, SVM showed better performance than NB and LR in the two experiments.
No Filters: A Deep Dive into Photo Sharing Apps on Android and iOS

Photo and video-sharing applications have gained popularity among all age groups. These applications allow users to create, share, and interact with multimedia content. Unfortunately, some of these applications have been used to harass, exploit, cyberbully, solicit/groom minors, or track any users' physical location. Consequently, they have become a significant source of evidence in digital investigations. In this paper, we conduct a comprehensive forensic investigation of two relatively new and highly popular photo-sharing applications on both Android and iOS devices. Our goals are to (1) provide a forensic roadmap for forensic investigators who may encounter these or similar applications during a case, (2) highlight the privacy concerns that these applications' users should be made aware of, and (3) discuss the implications of not using multiple tools for artifact validation. Our findings illustrate the lack of privacy and security measures in place for these applications' users, which are location-specific artifacts that can be easily recovered along with plaintext chat messages. Additionally, our findings highlight the inability of commercial tools to uncover pertinent evidence and thus reinforce the need for investigators to use multiple tools during a forensic investigation.
A High Speed Convergent Formula for Time-Variant Generalized Sylvester Equation Solving

The construction of novel and faster categories of recurrent neural networks (RNN) is an important and interesting field of study in domains, which are related with engineering and applied mathematics problems. Different nonlinear odd and increasing functions called activation functions (AFs) have been used for the acceleration of the convergence speed of each RNN formula. In the context of this research a faster model based on a novel AF will be constructed and applied for time-varying generalized Sylvester equation (TVGSE) solving, while theoretical convergence analysis and simulation experiment in MATLAB’s environment, will show the effectiveness of the proposed formula.
Autoencoder-based Feature Extraction for the Classification Tasks with Insufficient Dataset Size

The presented work proposes an effective approach for extracting abstract characteristics from image data using the autoencoder-based models. Since simple autoencoders do not deliver the desired result in building a feature map between the data samples, variations and domain-specific adjustments might improve the performance. To assist a model with more informative and representative samples, augmentation technique on small subset with the position and size invariance have been applied. To evaluate the efficiency, we employ simple autoencoder and U-Net models that take both data features and their relationships into consideration. The suggested autoencoder models are assessed on a collection of benchmark datasets, and the experimental findings demonstrate that, in comparison to other autoencoder variants, taking data relationships into account can lead to more robust features that accomplish reduce construction loss and then reduced rate of errors in subsequent classification.
Modern approaches to interact Smart-Contracts in React.js development with ThirdWeb framework

Blockchain technology has gained significant popularity in recent years, with its ability to create secure, transparent and decentralized systems. React.js is a popular frontend development library, known for its performance and reusability. There are several common ways to create front-end dApps on blockchain networks, including using libraries such as Web3.js, web3-react, and ThirdWeb framework (later ThirdWeb). There are also several ways to create a complex dApp like NFT Marketplace, and the methods of architecture creating and implementing further projects have their own nuances and limitations that have to be discussed in this article.
Shooting Method for Finding Cost Optimal Trajectory

We consider the problem of finding a trajectory of the road which is optimal in the sense of construction cost. Variational ideas lead us to the integral cost functional which should be minimized. The necessary optimality condition for the functional has a form of an integro-differential equation. The main goal of the paper is the numerical solution of this equation. We use linearization and shooting method to reduce the boundary value problem to an initial value one. The initial value problem is solved via FDM. Numerical examples are presented.
Validation of Multipliers for Elements of Extended Galois Fields GF(p<sup>n</sup>) and Multipliers VHDL-descriptions Generator

This article discusses a VHDL-description (IPcore generator, IP – intellectual property) generator developed by the authors for multiplication of elements in extended Galois fields $\mathrm{GF(p^{n}})$. Such multipliers are used in information security systems based on elliptic curves. This generator simplifies the process of generating VHDL-descriptions for multipliers utilizing modified Guild cells that are interconnected. The count of modified Guild cells can reach $2^{999}$. The evaluation of the generated multipliers’ performance is presented for Galois fields: $GF(3^{30}),GF(7^{17}),GF(23^{10})$, and $GF(53^{8})$, which exhibit similar orders. The testing procedure involved simulations within the Active HDL environment, with the simulation outcomes being compared to the multiplication results obtained through the GaloisCPP C++ library.
Predicting fraudulent card transactions using machine learning methods

The rapid increase in online financial transactions has led to a growing demand for effective and efficient fraud detection systems. The purpose of this work is to assess the performance of machine learning algorithms on the task of fraud detection using relevant evaluation metrics and to compare their effectiveness. Using transaction data from various publicly available datasets, the algorithms are trained and tested under different configurations to determine their optimal parameters and suitability for the task. The evaluation of these models is performed based on their precision, recall, and F1-score metrics. The object of this work is to provide insights into the best practices and potential challenges associated with implementing these machine learning methods for detecting fraudulent transactions The findings of this investigation will contribute to the understanding of the capabilities and limitations of these algorithms for fraud detection, while also shedding light on potential improvements in their performance.
Project for the development of an online betting game “Live Roulette”

The research examines the main approaches to visualization and modeling of the main processes of IT project that develops the online betting game Live Roulette. Live Roulette is a Windows-based game that includes a cashier side, a player screen, and an admin panel. The game is designed for gaming terminals. The goal of the development of this game is to improve the system of percentage payments in order to transfer the profit to the reconstruction of cities. During the project implementation a comparison of the Live Roulette game with analogues was made, and mathematical models for predicting the result of the roulette game were also considered. Various methods were used to design the project, namely: building a design plan with the help of Microsoft Project, implementing various types of UML diagrams, and creating a User Case. The testing process is described in detail.
Comparison of Image Deraining Methods Using Deep Learning

this paper examines the most popular methods of decolorizing single images. An explanation of the models and their pros and cons is provided. The methods of decolorization of single images are analyzed and their mathematical basis is presented.
Intelligent Analysis of Ukrainian-Language Tweets of Regional Public Opinion Study and Management

The paper considers the problems of public opinion research in Ukraine (cost, vulnerability, short-term) and develops a system that allows bypassing these problems by automating and digitalizing research based on the analysis of Ukrainian-language tweets. The functional capabilities of the developed system are presented, which include the preparation and display of graphs of public opinion statistics such as trends, and positive and negative topics of Ukrainians. These statistics are a relevant source of information that can be used for the benefit of Ukraine.
The Indoor People Tracking and Counting System

COVID-19 outbreaks and becomes serious from 2019 winter. Taking body temperature, wearing masks, recording footprint and avoiding crowds become important tasks and require a lot of manpower support for the prevention of the epidemic in every. In this study, we have integrated the infrared thermometer devices to measure people temperature and the people identities through RGB images at the entrance of buildings. Using the computer vision and deep learning methods, we would like to build a 24/7 and region-wide coverage assistance system to automatically record the footprint of a specific people in the building. We also detect if people taking off their masks in the yard at any time. The processing videos are grabbed from the existing CCTV systems. The developed system also locates the crowd and calculate the number of people in a specified area, and the manager can know the number of people and control the access in real time. It will reduce the burden of manpower. In addition, we have optimized the system performance to handle multiple camera videos and to reduce the hardware cost at the same time.
Artificial Intelligence for Anomaly Detection in IoMTs

The exponential development and widespread emergence of the Internet of Medical Things (IoMT) have led to a growing need for effective anomaly detection techniques to ensure the reliability and security of healthcare systems. This article provides a review of existing machine learning and deep learning algorithms for anomaly detection in IoMT, followed by the presentation of a novel approach combining ARIMA for predicting health parameter values and a decision tree for anomaly detection. This hybrid approach aims to improve the accuracy and efficiency of anomaly detection in IoMT by leveraging both time series models and the discriminative features of decision trees. The preliminary results of this approach are presented and discussed, highlighting its potential to enhance early detection of anomalies in IoMT and contribute to safer and more reliable healthcare.
Application of Hybrid Neural Networks based on bagging and Group Method of Data Handling for forecasting

In this paper hybrid Deep learning network based on bagging and Group Method of Data Handling is considered. This network differs from earlier developed hybrid DL networks by application of bagging method. The system consists of a sequence of stacks, wherein odd stacks are essentially ensembles formed by parallel connected different models that solve the same problem, while even ones are metamodels that implement the bagging procedure and calculate the levels of fuzzy membership of each member of the ensemble to the optimal result. The process of increasing the number of stacks is based on GMDH principles until the desired accuracy of the final results is achieved. The proposed system does not require large volumes of training samples, provides online work and automatically determines the number of its layers - stacks in the learning process. The algorithm of training hybrid DL network based on bagging in on-line mode is developed. The experimental investigations of suggested Hybrid DL network based on bagging were carried out at the problem of forecasting Dow Jones Ind. Aver and comparison by accuracy with models ARIMA, GMDH, GMDH-neo-fuzzy network was performed.
Still Computers Networking is Less Secure Than It should be, Causes and Solution

For the objective of securing communication channels among physical systems, various security approaches have been deployed that secure networking protocols. However, these approaches focused on securing protocols individually, and none have addressed the intra-domain communication issue in a holistic manner. This results in questioning the security of intra-domain environments, where all communication is exchanged within a single domain. These contentions rise from the fact that intra-domain communication had been burdened with a vexing absence of interest, by both academic and industry realms. To indicate the data transmission issue within organizations, this paper conducts a review of the applied security mechanisms that focus on securing individual networking protocols and their drawbacks, plus the implemented software-based and hardware-based solutions that secure communication within a single domain and their weaknesses, to point out the existing insecurities in data transmission among physical systems within organizations. Then recommending a novel solution to overcome the addressed issues, to give a boost in providing a secure environment for physical systems.
A Hybrid Approach for Food Name Recognition in Restaurant Reviews

Food Computing is an emerging research field that leverages Natural Language Processing (NLP) techniques to extract valuable insights from textual data. A key task within NLP is Named Entity Recognition (NER), which involves identifying and categorizing words or phrases into predefined categories. Current, NER methods are limited in their capacity to recognize novel entity types, such as food names. Enhancing their capabilities to encompass new entities necessitates supervised training, that needs substantial labeled dataset. Labeling such datasets is time-intensive and challenging, particularly for novel entities like foods, that lack standardized definitions across various applications. Furthermore, existing state-of-the-art transformer-based techniques are not suitable for lightweight applications due to their large size and computational complexity. In this study, we present a neuro-heuristic based approach for food name recognition, specifically targeting food names or recipe names. To mitigate the need for extensive labeling, we adopt a template-based approach to prepare a dataset with labeled food entities. Our system achieves an impressive F1 accuracy of 0.97, on the dataset prepared by using multiple publicly available resources, including the Branded Food Dataset and NPR Dataset.
Cyberbullying Detection Through Acoustic and Linguistic Analysis

In this article, we present a method for detecting cyberbullying that goes beyond understanding the text's content and also considers the underlying intent. Our approach involves analyzing both the acoustic and linguistic features of audio to gain insights into the emotional intent conveyed in the text. Using the librosa library, we extract acoustic features from audio files to identify whether the emotions expressed are aggressive or non-aggressive. We then combine this information with the results of linguistic analysis to develop a comprehensive understanding of the text's content. By incorporating the emotional aspect, our method enables more precise and nuanced identification of cyberbullying instances.
Interactive Communication Assistance System Based on In-Air Handwriting Recognition

The idea for this project was born out of the necessity to enhance communication for people dealing with either short-term challenges, such as those arising from surgery or illness, or long-term challenges, such as individuals with hearing or speech impairments. We've noticed that individuals with hearing impairments often resort to using sign language as a means of communication, but the responses they receive may involve gestures or partial interpretation of body language. Unfortunately, not everyone is skilled in sign language, which can lead to misunderstandings, misinterpretations, and difficulties in conveying precise information. This challenge becomes even more significant for individuals who temporarily cannot speak or hear due to medical conditions. Therefore, our goal is to resolve these concerns by creating a lightweight and user-friendly device. The proposed system utilizes Jetson Nano as the computational core, leveraging the Mediapipe toolkit to recognize the user's finger joints and gestures within the camera's field of view. It then employs OpenCV image processing and convolutional neural networks to recognize airborne handwriting. An OLED screen functions as the user interface for displaying restaurant information. The ultimate outcomes are showcased on this OLED screen and audibly conveyed through a speaker to facilitate communication with others.
Segmentation of Weather News Headlines: Sentiment and Tone Shifts in Parallel Bilingual Corpora

This paper explores whether emotional tone/sentiment of the text is completely/partially lost, shifted or preserved in parallel bilingual corpora of weather news headlines and to what extent the translation techniques chosen influence this process. To this aim we have carried out both sentiment and tone analysis of weather news headlines, compared and critically evaluated the obtained results. For investigating the influence of translation techniques used on the sentiment/tone of the expression two programs were applied: The IBM Watson™ Tone Analyzer and MonkeyLearn. The first program is used for defining the tone of the text (in our work ˗ the headline), and the second points out its sentiment.