Reviewerly/exHarmony huggingface dataset:
Research of Computational Complexity of Cost Functions in S-boxes Generation Problems

The generation of non-linear substitutions (S-boxes) is an important task in the design of cryptographic algorithms with a secret key. The properties of S-boxes determine the cryptographic strength of symmetric ciphers against various attacks, for example, linear and differential cryptanalysis. In addition, substitutions must be random in order to be resistant to algebraic cryptanalysis methods. Many authors explore the problem of generating random S-boxes. The most effective technique is heuristic search, which is based on the use of various cost functions (special heuristics). Heuristic search consists of iteratively modifying a randomly generated substitution. At each iteration, the value of the cost function is calculated, the search continues until a substitution is found that minimizes the cost function. In this article we explore several options for cost functions and evaluate the complexity of their calculation. We estimate the number of iterations required by the heuristic search to generate S-boxes with given cryptographic indicators as well as the computational complexity of generation taking into account the complexity of calculating the cost function.
Some Questions of Cybersecurity in Ukrainian Modern Conditions

Since the beginning of the war, Ukraine has been the target of numerous cyberattacks that have affected state institutions, private organizations, and citizens. Those businesses that are part of critical infrastructure, including energy, telecommunications, media and financial companies, should also be on high alert, as these industries are often considered priority targets in times of war. Business must be prepared to counter these challenges – companies must assess their preparedness for cyber incidents and their ability to resume operations. We have prepared recommendations that can help assess the company's level of cyber security readiness.
Research of Image Preprocessing Methods for Enhancement of Information Technology for Person Identification

Research presented in this paper describes the experiments conducted on image preprocessing methods, such as anisotropic diffusion and image histogram equalization, with an aim to enhance the performance of the information technology for person identification based on Gabor wavelet transform, local binary patterns in 1-dimensional space and histogram of oriented gradients for image processing, feature vector extraction, and classification. The experimental research of the algorithm was performed on three face image databases with the use of program software created in Python. Experiments were divided into three sets: the original set of the methods without preprocessing, set with the method of anisotropic diffusion, set with the method of image histogram equalization, and set with both these methods. Analysis of obtained results of the experimental research has shown that chosen methods haven’t provided the improvement of the algorithm, therefore other image preprocessing methods must be considered for further exploring.
Optimization of the PCF Cost Function for the Generation of Highly Nonlinear S-boxes

Creation of reliable telecommunication communication channels providing confidential data, it is currently impossible to have reliable encryption algorithms. The most popular services that provide encryption algorithms include confidentiality, integrity, and authenticity services. Most modern symmetric encryption algorithms use nonlinear substitutions or abbreviated S-boxes. Their properties have a direct impact on the cryptographic strength of symmetric encryption. So, generating S-boxes with the right properties is certainly an important task for creating S-boxes. In this paper, we consider heuristic methods for generating nonlinear substitutions that use special cost functions (heuristics). During generation, the search algorithm changes the randomly generated substitution at each iteration and tries to reduce the value of the cost function. Search attempts are stopped either after finding the S-box with the desired properties, or after performing a certain number of iterations. Doubtless, the search efficiency depends on the chosen heuristic, namely, on the parameters of the cost function. In this paper, we consider the PCF cost function and optimize its parameters. We managed to optimize the parameters of this heuristic and achieve the least number of search iterations.
Strong Encryption Based on the Small Ree Groups

An improved design of cryptosystem based on small Ree groups is proposed. We have changed the encryption algorithm and propose to use a logarithmic signature for the entire Ree group. This approach improves security against sequential key recovery attacks. Hence, the complexity of the key recovery attack will be defined by a brute-force attack over the entire group. In this paper, we have proved that to construct secure cryptosystem with group computations over a small finite field its needed to use 3-parametric small Ree group.
Agent Modeling of Educational Motivation in Online Learning Conditions

This article is devoted to agent modeling the features of student learning motivation in online learning conditions. Maintaining motivation plays a special role during the introduction of distance learning in connection with the coronavirus epidemic. To analyze the features of motivation in different learning conditions agent model of the student motivation has been used. The effectiveness of the model was tested in both offline and online learning. The results of modeling showed that the influence of the main motivational components in the conditions of offline learning varies from primary school to higher education. The youngest students are best motivated to learn in a situation where the inner desire to learn something new is constantly supported by external stimulation. In primary school, the motivating influence of teachers and parents gradually decreases, but both the negative and positive influence of an important environment (friends, reference adults) increases. Adolescents have clearly defined learning goals. The impact of online learning on this category of students is quite controversial. The study has found mixed trends - both an increase in motivation for learning and a sharp decrease. The first group is quietly moving to online learning, making extensive use of the Internet. The second either completely lose motivation to learn or shows unstable motivation, which either sharply decreases, then just as sharply increases. Agent modeling technologies allows tracking the conditions under which stimulation will be change student learning motivation.
The effectiveness of the auditing of the Financial and Administrative Audit Bureau on government agencies in Jordan

In line with the Bureau's strategic vision of sustainable professional oversight excellence to enhance public accountability, consolidate concepts of disclosure and transparency, and assist the public sector to work efficiently and effectively, based on the provisions of Article (119) of the Jordanian Constitution, and Article (22) of the Audit Bureau Law No. (28) of 1952 and its amendments, which The President of the Audit Bureau shall submit an annual report to the National Assembly, both the Senate and the Representatives, that includes his opinions and observations, and a statement of the most important violations committed and the responsibility resulting therefrom, with the start of each regular session of the National Assembly, or whenever the House of Representatives requests him to do so.
Active diversification of head-class features in bilateral-expert models for enhanced tail-class optimization in long-tailed classification

Training deep learning models on long-tailed datasets is a challenging task since the classification performance of tail classes with fewer samples is always unsatisfactory. Currently, many long-tailed methods have achieved success. However, some methods always improve tail-class performance at the expense of head-class performance due to limited model capability. To address this issue, we propose a novel algorithm-level method inspired by information theory to balance the information space of each class and boost tail-class performance while minimizing head-class sacrifice. Our method involves actively eliminating the redundant feature information of head classes to save space for tail classes during training. Specifically, we use a bilateral-expert model and design a duplicate information disentanglement (DID) module that can extract duplicate and redundant information from bilateral-expert features. This allows us to develop a head diversity loss to decrease the extracted duplicate and redundant information of head classes and a tail distillation loss to increase the label information of tail classes. The joint result of these two losses allows our model to fully leverage the information space for improved tail-class performance without compromising head-class performance. The effectiveness and practicability of our method are verified by five datasets with long-tailed distributions for visual recognition or fault diagnosis tasks. Experimental results demonstrate that our method outperforms currently available mainstream methods, which we attribute to the effectiveness of our proposed DID module and the incorporation of two long-tailed losses.
Assessment of the global applicability of three leading indices that characterize the spectral distribution of solar irradiance impacting various photovoltaic technologies

At each instant, the actual solar irradiance has a specific spectral distribution that most generally differs substantially from the standardized reference conditions under which photovoltaic (PV) cells are rated. To help reduce the dimensionality of the calculations needed to quantify the spectral effects on PV cells, various spectral indices have been introduced in the literature to simply characterize the incident spectral distribution with a single number. In previous studies, diverging conclusions have been reached about whether or not a single number can effectively represent a specific spectral distribution. This disagreement needs to be resolved before the usage of spectral indices can be considered best practice. To that effect, this study proposes a novel modeling approach. Here, three spectral indices (Average Photon Energy, APE; Blue Fraction, BF; and Useful Fraction, UF) are critically evaluated and compared using the SMARTS radiative transfer model and a large database of atmospheric inputs that are representative of the vast diversity of possible conditions worldwide, thus avoiding the rarity and limitations of costly experimental measurements. A total of 28,260 pairs of direct normal and global horizontal spectra are obtained, from which the three indices are calculated over six different wavebands that characterize different PV technologies. The key variables that have a significant impact on the indices are found to be: air mass, aerosol optical depth, Ångström exponent, and precipitable water. Using a variety of tests to evaluate the behavior of the three indices relatively to the reference direct and global spectra, results show that UF is prone to anomalous variations under a variety of circumstances. APE and BF behave similarly and reliably, but APE is found more responsive to even small changes in atmospheric conditions, and is thus preferable. A critical finding is that none of the indices can be considered bijective because compensations between red-shifting and blue-shifting atmospheric processes often result in different spectral distributions having the same index value.
Development and testing of a ground recognition system for tractor field operations

To overcome the problems of poor recognition accuracy, slow response speed, and unsuitable for farmland ground environment based on the traditional ground recognition method of vehicle mathematical model, this paper proposes a ground recognition system for tractor field operation based on wheel acceleration. A wheel acceleration sensor and data acquisition device are designed, and a ground recognition algorithm based on Principal Component Analysis - Genetic Algorithm optimization of an Extreme Learning Machine (PCA-GA-ELM) is investigated. The system can collect acceleration samples and extract statistical features in different frequency bands in real time according to the cycle of wheel rotation and then use the trained recognition model to recognize the ground type. In the actual operation process, this system only needs to collect the acceleration test data of one rotation of the tractor wheels to achieve the recognition of the ground. The test set data show that this system achieves 96 % discrimination accuracy, higher than the 87 % of the ELM model without genetic algorithm optimization and 92 % of the GA-BP neural network model. In addition, the real-time ground recognition test shows that the increase in operation speed will slightly reduce the accuracy of the recognition model, but the overall can still maintain 94–98 % accuracy, indicating that the system has good stability. This system can provide a reference for real-time speed adjustment, implement position, and other control parameters during tractor operation.
Bidirectional piecewise linear representation of time series with application to collective anomaly detection

Directly mining high-dimensional time series presents several challenges, such as time and space costs. This study proposes a new approach for representing time series data and evaluates its effectiveness in detecting collective anomalies. The proposed method, called bidirectional piecewise linear representation (BPLR), represents the original time series using a set of linear fitting functions, which allows for dimensionality reduction while maintaining its dynamic characteristics. Similarity measurement is then performed using the piecewise integration (PI) approach, which achieves good detection performance with low computational overhead. Experimental results on synthetic and real-world data sets confirm the effectiveness and advantages of the proposed approach. The ability of the proposed method to capture more dynamic details of time series leads to consistently superior performance compared to other existing methods.
MeshCLIP: Efficient cross-modal information processing for 3D mesh data in zero/few-shot learning

Text, 2D, and 3D information are crucial information representations in modern science and management disciplines. However, complex and irregular 3D data produce data scarcity and expensive generation that limit their processing and application. In this paper, we present MeshCLIP, a new cross-modal information learning paradigm to directly process 3D mesh data end-to-end in a zero/few-shot manner. Specifically, we design a novel pipeline based on visual factors and graphics principles, bridging the gap between 3D mesh data and other modal data, thereby joining 2/3D visual and textual information for zero/few-shot learning. Then, we construct a self-attention adapter for 3D mesh key information learning when training only a few priors, significantly improving the model’s discriminative ability. Extensive experiments demonstrate that the proposed MeshCLIP can achieve state-of-the-art results on multiple challenging 3D mesh datasets. In the whole 3D domain, the proposed zero-shot approach significantly outperforms the existing other 3D representation methods with an accuracy 3 × better ( increased by 41.5%) on the ModelNet40 dataset. Furthermore, in few-shot learning, the proposed MeshCLIP uses only a few supervised priors (only less than 10% of the sample size) to achieve results close to those of methods trained on a full dataset.
Secured Two-Layer Encryption and Pseudorandom-Based Video Steganography into Cipher Domain Using Machine Learning Technique

The recent development of the multimedia industry gives more preference to secure data on communication and leads researches to get into some ideas. The proposed method of data hiding scheme uses a hiding technique in the ciphered domain, which combines multi-dimensional chaotic maps and support vector machine (SVM) image classifier. The video frames encrypted by modifying the pixels with a random matrix generated using the 2-D logistic map. The ciphered frame divided into non-overlapping blocks, and each block XORed with blocks of the public key table generated by the 3D Lorenz map. The public key blocks are selected based on the K binary information bits stream. Data extraction is carried out by performing XORing with non-overlapping blocks and the public key table. The SVM classifier extracts the information data based on the decoded block. Both cryptographic and steganography analyses performed on the ciphered frames for security analysis. The experiment results show that the proposed method is secure, and it extracts the reversibly using machine learning technique.
Influences of Requirement Change on Software Failure

Most of the software requirements are not unfurnished in the initial level of development procedure of software. Software requirements are usually unavoidable due to frequent changes in user needs, changes in market and business policy and operating environment. Basically, requirement change management is also an important and challenging part of the software development process. Changing requirements are one of the main reasons of project failure or cancelled. The success and failure of any software project mostly depend upon project manager that how he manages the requirement change.Several Requirement change management models and methods are introduced and performed different activities for mitigating the impacts of requirement changes. The paper explores research effort done in the field of requirement change and helps to identify the goal of the study.
Methodological features of substantial explanation in contemporary materialism

In the paper, the content of the concept of substance is defined; the relevance of substantial explanation in philosophical and general scientific methodology of modern materialism is considered; the methodological features of substantial explanation are analyzed. Among these features are the choice of dialectics as general methodology for deriving theoretical ideas about substance (as opposed to the inductive approach and ideas about pramatter), the relations between the universal substance and special substances, the consideration of substance as an absolute reality, the actual indicators of substance in the context of post-nonclassical scientific rationality, the development of substantial explanation from the concrete to the abstract and vice versa (including the problem of the ontological and epistemological «cell»), as well as the substratum approach and the definition of substantial attribute. The concept of substantial attribute is introduced, which is the procedural basis for the self-reproduction of a form of matter or of the matter as a whole. This concept expresses a process without which this or that substance could not be the cause of itself, i.e. the substance. For an infinite material substance, self-development is a substantial attribute, but in each particular material substance it has specific features corresponding to the level of complexity of the substance. In this regard, different types of substantial attributes are identified in the structure of the main forms of matter: in the physical world — a self-sustaining thermonuclear reaction, in the chemical world — autocatalysis, in the biological world — autopoiesis, in the social world — labor. Thus, with the help of the above aspects, the paper substantiates the thesis that modern materialism, coupled with dialectics, creates a number of important methodological prerequisites for substantial explanation to be used not only as a philosophical but also as a general scientific method.
Factors of Practical Stress Experienced by Pre-Nursing Teacher and Coping Methods

The purpose of this study is to determine the stress factors and coping strategies of pre-nursing teachers. For this study, a questionnaire survey was conducted on 23 2nd year student of G University and the results were analyzed. The results of this study are as follows. First, it was found that pre-nursing teachers had the highest stress at 1 week out of 6 weeks of training period. The biggest reason was adapting to the new environment. Second, among the stress factors, the pre-school teachers were found to be the highest. Specifically, the lack of confidence in class progress was found to act as the greatest stress. In the nursery practice environment, there is no rest space and no free rest time. There is also difficulty in coaching problem children in relationships with infants and toddlers. In the relationship with the facility manager, it was found that the pre-nursing teachers were under stress in regards to the child care practice as a person who came to volunteer. While maintaining the class atmosphere in class planning and implementation, in the practical evaluation, it was found that they were experiencing stress due to the children's immaturity in class and lack of confidence. Third, it was found that the pre-nursing teachers mostly used the problem-centered solution. It was also found that they wished to continue communication and encouragement in common in schools, institutions, and themselves regarding measures to overcome the stress of pre-service nursery teachers. Through these results, it is expected that by predicting the factors influencing the stress of pre-nursing teachers in advance and seeking effective coping strategies, they will be able to help the growth of nursery teachers as professionals through a successful child care experience.
Testing Lotka’s Law and Pattern of Author Productivity in the Maharashtra University of Health Services (MUHS) Consortium: A Bibliometric Approach

The study analyzes the productivity patterns of authors in Health Science using publications indexed in Maharashtra University of Health Services (MUHS) Consortium from 2001 to 2013 based on Lotka’s Law. The study considered 20724 articles for the analysis. The findings of the study reveal only co-authors and non-collaborative authors’ categories fit in the Lotka’s Law, whereas all-authors and first-author categories differ from it.