Reviewerly/exHarmony huggingface dataset:
Robust and Low-Computational Cost Eye Tracking System Using a Generic Webcam

Eye-tracking is a process for measuring eye movement and gaze location. It's considered a standard approach for inspecting the visual attention allocation. In this paper, a new eye-tracking approach was tested and evaluated principally for the first time. The detected gaze location values were used to control the mouse cursor in real time on a computer device. A generic web camera was placed on the eyeglasses frame and aimed at the eye; no Infrared light source was used to illuminate the eye. Every captured video frame of the eye is transformed to a grey level, then the contrast is enhanced using histogram equalization. Finally, the image of the eye is identically divided into four groups. The eye gaze position in the horizontal direction is detected by calculating the difference between the left and right groups of pixels. Similarly, the eye gaze in the vertical direction is detected by calculating the difference between lower and upper groups of pixels. The results show high accuracy, low computational cost, and confirmed perfect linearity. Moreover, it is a non-invasive method, and easy to calibrate.
Sentiment Analysis of COV19 Impact on Education and Economy Sectors using Arabic Tweets

The coronavirus (COVID-19) infected millions of people around the world. Due to the disease's rapid spread, all governments have decided to implement lockdowns. The two key pillars of any nation, education and economy were negatively impacted. This study investigates public opinion on COVID-19 effects on these two key sector by collecting Arabic tweets from Twitter from the MENA region. Sentiment analysis was applied using the collected dataset for each sector separately. We created five cutting-edge deep learning models to apply them in the prediction process. Such models named Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), LSTM plus CNN, BERT, and AraBERT. The outcomes showed that in two datasets, the LSTM with CNN and LSTM provided the greatest performance results in the education data set, while BERT and AraBERT has the best performance in the economy dataset. The results presented that these algorithms created information that helped several government decision-makers make important and wise decisions on the standard of living in the Arab World Country.
Cybersecurity Empirics: Evaluating Machine Learning Techniques for Phishing Detection

TIn an age dominated by internet usage, the threat of phishing attacks continues to plague users and organizations alike. Traditional cybersecurity mechanisms often fail to cope with the evolving tactics of cybercriminals, leading to a growing interest in machine learning-based solutions. This research evaluates the performance of four different machine learning classifiers-K-Nearest Neighbors (KNN), Naïve Bayes (NB), Decision Tree, and Artificial Neural Network (ANN)-in identifying phishing activities. A Python code framework is utilized for this evaluation, employing stratified k-fold cross-validation for reliable results. Multiple performance metrics indicate that machine learning provides an adaptable and effective tool against phishing. This research serves as a stepping stone for future development in this crucial area, urging continuous advancements to stay ahead of ever-evolving cybersecurity threats.
Ovarian Cancer Detection in CT Scan Images Using Transfer Learning

This research paper presents a system for classifying and detecting ovarian CT scan images using transfer learning and examines the effects of different tools on the solution quality. The methodology involves preprocessing, data augmentation, training, transformation, and filtering. The implementation experiments evaluate the system's performance with regard to using different combinations of color modes, filters, and training methods with and without augmentation of the training dataset in addition to the effect of using the discrete wavelet transform (DWT). Analysis of the implementation results indicated that augmentation and the DWT generally improved the results, while the choice of filters and color mode had a limited impact. Furthermore, using the stochastic gradient descent with momentum (SGDM) training method yielded better results compared to the Adam method. Overall, the recommended approach for the best outcomes consists of using the RGB color mode, SGDM training, augmentation, and the DWT without additional filters.
HyPA: Hybrid Horizontal Pod Autoscaling with Automated Model Updates

Due to changing demand patterns driven by technological advancements and the rise of new applications and services, the provisioning of heterogeneous workloads is a crucial component of the resource allocation problem. Traditional resource allocation strategies such as reactive autoscaling or prediction-based proactive solutions, fail to meet the desired performance goals when the underlying demand arrival pattern changes. In this paper, we present Hypa, which combines reactive and proactive components to autoscale pods in a Kubernetes environment. In contrast to previous approaches of hybrid autoscaling, HYPA automatically reacts to drifts in the request arrival pattern. Specifically, it updates the model of its proactive component when the prediction performance decreases. The evaluation in a simulation on a variety of real-world traces, spanning multiple days, demonstrates that HYPA improves upon existing purely reactive and purely proactive horizontal pod autoscalers.
Towards Accessible and Embodied Control of Telematic Sonic Space Through Browser-Based Facial Tracking

This paper presents in-progress research on a browser-based solution for unique sonic perspectives in telematic musical performance via facial tracking. Angular rotation of the face is calculated for the direction of focus in relation to a webcam, changing the virtual orientation of the user within a shared virtual acoustics environment. This sonic perspective is relayed back to the player, allowing for personal embodied control of spatial audio. Current outcomes, limitations, and future research goals of the system are addressed.
Evaluation of Very-high Resolution Satellite Image Compression to Support Agriculture Monitoring

Recently, the need for detailed images such as very high-resolution satellite imagery has been increasing, but there is an issue of heavy processing for the image as the size of the file is very big. To address this issue, this study aims to evaluate the image compression method in terms of decreasing the size of the imagery. To assess the reliability of the method in preserving the quality of the compressed image, Structural Similarity Index, Peak Signal-To-Noise Ratio, and Universal Quality Index were applied to the compressed image. The results show that the compressed image successfully decreased up to 95% in the quality value of 5 and the method can preserve the quality of the compressed images very well.
Just a Sounding Object Notation: Sharing Objects for Sonic Interaction Design with JSON and OSC

We address the challenges encountered in sharing and communicating complex sounding objects in the field of Sonic Interaction Design. To overcome these challenges, we propose the adoption of JSON as a representation syntax and exchange format, accompanied by the integration of an Open Sound Control (OSC) server for enhanced interoperability and centralized control. By leveraging JSON, we provide a standardized method for capturing and sharing comprehensive information about sounding objects, streamlining the transition from prototyping to production software, and enabling efficient communication between analysis and synthesis tools. The integration of an OSC server further enhances real-time interoperability with other software platforms. To demonstrate the efficacy and viability of JSON and OSC as powerful tools for sharing objects in Sonic Interaction Design, this paper presents a practical use case that combines the SAMPLE software for modal analysis with the Sound Design Toolkit for real-time sound synthesis. Notably, SAMPLE generates JSON files that align with our proposed solution, highlighting the seamless compatibility and applicability of these technologies.
Method of Mobile Object Navigation Based on Recursive Galois Coding

Automated control of mobile vehicles involves the precise positioning of a moving object in space as a function of time along a given trajectory in a network or coordinate system. Binary code systems such as Rademacher, Gray, and Haar bases are employed to establish coordinate bindings in parallel codes. However, as the accuracy of positioning increases, this approach necessitates: a significant expansion of the number of digits used to represent data; an increase in the computational power of tracking hardware; an enhancement in the bandwidth of communication channels; a rise in energy consumption by equipment; ensuring the security of information exchange; an escalation in the cost of moving vehicle motion control systems. This means that achieving high-accuracy positioning necessitates the use of more powerful and expensive hardware, as well as ensuring the security of information exchange. Consequently, improving positioning accuracy can result in an increase in the cost of control systems for mobile vehicles. The automated significance of this research lies in reducing the requirements for the aforementioned parameters by implementing more efficient data encoding methods in the mobile control system. Methods: The use of recursive cyclic Galois code systems for the first time made it possible to implement vertical information technology with sequentially (bit)-oriented data streams with Hartley density. The objective of this work is to develop methods, software, and hardware for mobile object control systems based on the recursive encoding of motion routes and their positioning in space. The novelty of the proposed methods lies in the transition from parallel to vertical information technology for encoding and exchanging information. The practical aspect involves the development and implementation of mathematical, software, and technical support for mobile object control systems. Conclusion: The relative efficiency of recursive encoding, as compared to the method of parallel binary encoding, has been analyzed and graphically represented. The possibility of three-dimensional positioning of mobile objects in space is presented.
Perfect bitrades in Johnson graphs<sup>*</sup>

We study perfect and spherical bitrades in Johnson graphs. These combinatorial objects could be used in a switching construction given a 1-perfect code containing it. Despite the fact that the well-known Delsarte hypothesis on nonexistence of perfect codes in Johnson graphs J(n,w) is proven for all Johnson graphs for very wide spectra of n, we show the existence of two nontrivial examples of bitrades arising from the 3 − (12,6,2) Hadamard design and 3-design of the Nordstrom-Robinson code. Using weight distribution approach, we show the nonexistence of perfect bitrades in an infinite series of Johnson graphs.
Vulnerability Analysis of Azure Blockchain Workbench Key Management System

With the rise of blockchain popularity, more and more people seek to implement blockchain technology into their projects. The most common way is to use an existing blockchain stack, such as the Azure Blockchain Workbench or Oracle Blockchain Platform. While blockchain technology is well protected by its algorithms, it is still vulnerable because its privacy relies on regular cryptography. And mistakes or vulnerabilities in key management protocols can affect even the most secure blockchain projects. This article considers the question of vulnerabilities within the Azure Blockchain Workbench key management system. We describe potential threats for each stage of the key management lifecycle based on public reports and then assess how likely those threats are to be realized within the Azure Blockchain Workbench environment based on the technical documentation for the Azure Blockchain Workbench and Azure Key Vault. Finally, we compile the results of our assessment into the key management threat table with three distinct degrees of protection: fully protected, partially protected, and not protected.
Machine Learning Method for Optimal Route Calculation

The industrialization of society and the transition to Industry 4.0 requires the use of advanced data processing and decision-making technologies. In connection with the utilization of perfect measuring systems and monitoring of a large number of parameters, the volume of statistical data increases when it is necessary to process them in real time. On the other hand, the computing possibilities of information systems is also increasing, which contributes to expanding the opportunity of using artificial intelligence technologies based on machine learning and deep learning methods. This paper is devoted to the development and analysis of a new machine learning method for determining the optimal route of an unmanned system or a guided vehicle. The initial information for building a route is a map with the designation of permitted and prohibited zones of movement from a given point to the final destination. The proposed method is an improvement of DBSCAN nonparametric clustering technologies and Dijkstra's algorithm.
An Image Watermarking Algorithm in DCT Domain Based on Optimal Patterns

Sharing images via social media and specialized sites creates a copyright issue. Image watermarking methods provide copyright protection for authors and owners of digital content. The security level of a watermarking algorithm depends on a watermark’s resistance to various distorting effects, such as brightness changing, contrast changing, applying a Gaussian filter, and others. At the same time, a watermark needs to be invisible in most cases, so as not to disturb a perception of an image by a viewer. In this paper, we propose a new image watermarking algorithm in a domain of discrete cosine transform, which provides a balance between embedding imperceptibility and robustness. This algorithm uses redundant embedding based on patterns and synchronization blocks to provide embedding robustness to image processing operations. A high level of imperceptibility is provided by optimizing patterns using a genetic algorithm. Experimental results demonstrate high values of key quality metrics such as PSNR, SSIM, and BER.
Robust Stabilization of an Autonomous Marine Vehicle on Programmed Trajectories

To solve the problems of effective manoeuvring of an autonomous marine vehicle, the principles of formation of programmed trajectories of polynomial forms, algorithm for determining the necessary switching moments and the types of optimal control functions in feedback circuits, considering the limitations on the physical control actions of the propulsions of an autonomous marine vehicle, are proposed. The use of a robust corrective control loop providing compensation of deviation of the current trajectory of the physical object from the optimal trajectory arising from the mismatch between the parameters of the model and the physical object and the influence of uncontrollable disturbances is proposed. Thus, invariance of the control system to incomplete information of models and minimum values of control errors are achieved. The given results of simulation modeling of the process of stabilization of an autonomous marine vehicle on a programmed trajectory confirm the correctness and efficiency of the proposed approach.
A Reaction Attack against Cryptosystems Based on Quasi–Group MDPC Codes

Cryptosystems based on quasi–cyclic moderate density parity–check (QC–MDPC) codes are considered among the most perspective post–quantum public–key encryption schemes due to small public–key sizes and excellent performance. However, due to probabilistic decoding of MDPC codes, there is non–zero decryption failure rate. In 2016, Q. Guo, T. Johansson, P. Stankovski showed that decryption failures can be used to construct a key–recovery attack against QC–MDPC cryptosystems. Recently, in order to mitigate GJS attack, P. Santini, E. Persichetti, and M. Baldi proposed generalization of quasi–cyclic codes called quasi–reproducible (QR) codes and QR–MDPC cryptosystems. In this paper, we consider cryptosystems based on binary and non–binary quasi–group (QG) MDPC codes and propose a generalization of GJS reaction attack against these cryptosystems. We show that many efficient QR–MDPC cryptosystems are in fact equivalent to QG–MDPC cryptosystems, making the proposed attack applicable to them as well.
Using Recurrent and Convulation Neural Networks to Indentify the Fake Audio Messages

With the improvement of deep fake technology, the problem of detecting fabricated audio has gained significant proportions. And if in peacetime, people, in the majority, can detect a fake by themselves, due to the imperfection of the generation algorithms, during crisis events, it becomes much more difficult to do this. The article is devoted to determining the effectiveness of using recurrent and convolutional neural networks to classify the content of audio information to detect fakes. The results of the research conducted on a self-created data set dedicated to Russia's invasion of Ukraine allow us to assert the high efficiency of using the selected models.
On the classification of completely regular codes with covering radius two and antipodal dual*

We classify all linear completely regular codes which have covering radius 2 and whose dual are antipodal. For this, we firstly show several properties of such dual codes, which are two-weight codes.
McEliece-type cryptosystem based on correction of errors and erasures

Recently, a modification of the classical McEliece cryptosystem has been proposed by introducing an auxiliary matrix, by which an artificial error vector is multiplied. The system was broken. In this work, we propose a simpler attack on the system, and at the same time, we propose a generalization of the system, free from the identified shortcomings. We assume that the new scheme is at least as secure as McEliece’s cryptosystem.
Completely Regular Codes as Optimal Structures

We survey several results showing that optimal structures from different classes attaining a specific bound are described as completely regular codes with certain parameters. Examples of such structures are error-correcting codes, orthogonal arrays, edge cuts. We prove two new results of such kind.At first, we prove that in an arbitrary finite regular graph, an algebraic T-design attaining the generalized Bierbrauer– Friedman–Potapov lower bound on its size is a completely regular code. (An algebraic T -design is a set of vertices whose characteristic function is orthogonal to all eigenfunctions corresponding to the T largest non-main eigenvalues of the graph.)At second, we show that every diameter-perfect code is completely regular in a specially constructed graph. (A diameter-perfect code of is a minimum-distance-d code C attaining the code-anticode bound |C| • |A| ≤ |S|, where S is the finite ambient metric space with transitive group of isometries and A, an anticode, is a set of diameter less than d).
Super-Resolution of Images Received from UAV

This work concerns the problem of real-time image super-resolution (RTISR) for drone navigational video feed. A dual-step convolutional neural network approach based on residual learning and sub-pixel convolutions is proposed. The feature maps are extracted in low-dimensional space via residual feature extraction head and then passed through subpixel deconvolutional layers. The results indicate that the efficiency of the proposed approach is comparable to the state-of-the-art approach, but further improvements can be made to increase the throughput of the proposed architecture.
Methods of Satellite Images Segmentation Analysis

This paper delves into the realm of satellite image segmentation, a critical process for extracting valuable insights from complex satellite imagery. Focusing on the Otsu, watershed and k-means methods, this study provides a comprehensive overview of these foundational techniques. They can be applied to various types of images, including satellite imagery, for different purposes such as object detection, feature extraction and more. The experimental evaluation of segmentation methods using the MATLAB environment is demonstrated and discussed. The paper not only demonstrates the implementation of methods but also engages in an insightful discourse on their respective performance, strengths and limitations.
Existence of balanced functions that are not derivative of bent functions

It is disproved the conjecture (see [10], [12]) that any balanced boolean function of the appropriate algebraic degree is a derivative of some bent function. This result is based on new upper bounds for the numbers of bent and plateaued functions.
Permissioned Blockchain-Based Techniques for Refining the Data Security in Commercial Aviation

A major challenge for data security in the aviation industry is security and privacy because of the requirement of immense scale of data transfer. Blockchain and its related approaches provide decentralized security and privacy, however, they involve significant energy, delay, and computational overhead. The proposed security models of this paper acknowledged these drawbacks by circumventing the computational resource-intensive Proof of Work (PoW) concepts. The models consist of 4 main components: Channel CA and Identity manager, Smart contract (validator), Non-CA Channels, and Distributed Ledger. A set of new methods are proposed to outline various phases of Aviation data security. The introduced methods are the addition and deletion of actors, permission and access control for actors using smart contracts, storing airline data on blockchain storage networks, creating blockchain using consensus by trusted blockchain admin, and diverse types of blockchain storage networks in channels. This paper explores the data security realm and outlines cryptography and blockchain-based approaches. Extensive empirical work is done to demonstrate that the proposed models are viable concerning confidentiality, integrity, and availability.
Recommendations of Security Controls Using Threat Modeling in Information Systems in Higher Education Institutions

In Higher Education Institutions (HEIs) more and more information systems are used to automate their educational, administrative, or strategic processes. The information systems process, transmit and store a large amount of information on the processes and personal data of workers and students, which is why the information systems in HEIs have become the target of malicious people to obtain information easily and cause damage. Identifying threats to which information systems in HEIs are exposed will allow them to identify some security controls that must be implemented to reduce level of risk. This document presents recommendation of security controls determined from the use of threat modeling.