Use Of Random Numbers Research Articles

One time pad for enhanced steganographic security using least significant bit with spiral pattern

Data is an important commodity in today’s digital era. Therefore, data needs to get adequate security to prevent misuse. A common data security practice in the transmission of information is cryptography. Another approach is steganography, which hides secret messages in other media that are not confidential and can be accessed by the public. In this study, the spiral pattern is used for data placement using the least significant bit (LSB) method. Modifications were made to the 2-bits LSB to increase the data capacity that can be hidden. In order to increase security, the data is first converted into a datastream using random numbers as one time pad (OTP). exclusive-OR (XOR) operation is performed on datastream and OTP to get encrypted data to be hidden. The results showed that the image quality of the steganography results at a capacity close to 100% was still fairly good, as indicated by a peak signal-to-noise ratio (PSNR) value greater than 46 dB. Visually, the steganographic image does not look different from the original one. Likewise, the use of random numbers as OTP succeeded in changing the hidden data significantly, as indicated by the avalanche effect value above 50%.

Improved Masking Multiplication with PRGs and Its Application to Arithmetic Addition

At Eurocrypt 2020, Coron et al. proposed a masking technique allowing the use of random numbers from pseudo-random generators (PRGs) to largely reduce the use of expansive true-random generators (TRNGs). For security against d probes, they describe a construction using 2d PRGs, each of which is fed with at most 2d random variables in a finite field, resulting in a randomness requirement of O∼d2. In this paper, we improve the technique on multiple frontiers. On the theoretical level, we push the limits of the randomness requirement by providing an improved masking multiplication using only d PRGs, each of which is fed with d random variables, saving more than half random bits. On the practical level, considering that the masking of arithmetic addition usually requires more randomness (than multiplication), we apply the technique to the algorithm proposed at FSE 2015 that is a very efficient scheme performing arithmetic addition modulo 2w. It significantly reduces the randomness cost of masked arithmetic addition, and further advocates the advantage of masking with PRGs. Furthermore, we apply our masking scheme to the Speck, XTEA, and Sparkle, and provide the first (to the best of our knowledge) higher order masked implementations for the ciphers using ARX structure.

Blockchain-based fairness-enhanced federated learning scheme against label flipping attack

The federated learning technology provides a new method for data integration, which realize the sharing of a global model and prevent the leakage of the user’s original data. In order to resist label flipping attack, to ensure the reliability and accuracy of the global model, and to guarantee the fairness of federated learning, we propose a blockchain-based fairness enhanced federated learning scheme. The accuracy of global model and the fairness of the aggregation process are guaranteed by an adaptive aggregation algorithm. The reliability of federated learning process is ensured by recording the entire process of the model training on the blockchain and by using digital signature. The privacy of each participant of federated learning is protected by public key encryption combined with the use of random numbers. Theoretical analysis and experiments show that the scheme can protect the privacy of each participant, mitigate label flipping attack and ensure the reliability and fairness of the entire federated learning process.

Reproduction management of soil fertility for innovative approach in agromelioration of Ukraine

The object of research is managing reproduction of soil fertility using innovations in the field of agromelioration. One of the most problematic places is actualization the role and impact of innovations in agromelioration to ensure their effective management for the reproduction of soil fertility, increase land productivity and the sustainability of their use. Issues that raise in research are essential for the agricultural economy, but not always achieve goals for this reason, including natural and climatic factors. The set of such causes is not always possible to predict and to consider it especially at macro level. In the course of the study, the modeling approaches of the economic processes in land agromelioration on the basis of widespread use of random numbers (Monte Carlo), according to the received series production studies of changes in crop yields by crop rotation. Separately were used methods the Economic and Statistical – with the determination of the processes of validization and evaluation of the obtained results, Balance – when calculating the indicators of aggregated states, Planned and Calculated according to the development of targets and measurement of economic and ecological effects, etc. The results of the studies allow general condition of land resources in which there is a need for agromelioration measures due to disturbance of the soil environment and deviation of the pH value from the permissible level. It’s noted that in Ukraine due to the existing unbalanced land use there is a further acidification of soils, at the same time there are significant areas of land that need desalination and/or desalinization. We carried out an in-depth analysis and substantiation of the transition to innovations in land agromelioration technologies, which are not inferior to the best world standards and are modern resource-saving due to the use of land agromelioration. The ecological and economic efficiency of the use of calcium-iron sludge (CIS) in relation to the desalination action in comparison with other agromelioration measures is revealed. We proposed to improve scientific and methodological approaches to estimation the effectiveness of management the reproduction of soil fertility by randomness (uncertainty) conditions the based on production approbation, using a variety of agromeliorative techniques (variances).

METHOD OF STATISTICAL ANALYSIS OF RANDOM SEQUENCES GENERATED BY GENERATORS OF RANDOM AND PSEUDO-RANDOM NUMBER

The problems of the Internet of Things, in particular the need to protect confidential data, lead to the use of encryption and decryption methods, which, in turn, rely on the use of random numbers. The use of HVDC can help ensure the security of this process, but it is important to use high-quality HVDC and thoroughly test them to avoid security vulnerabilities. Statistical analysis is used to assess the randomness of the generated sequences by testing them using statistical tests of randomness. These tests evaluate various statistical properties, such as the frequency distribution of the generated numbers, the distribution of consecutive numbers, and the presence of patterns. By performing a statistical analysis, you can identify any flaws or biases in the operation of the random number generator and improve its quality. The article checks the sequence for randomness using two- and three-dimensional statistics. A mathematical model is built, a theorem is given. A scheme of statistical analysis of sequences was built, based on which software engineering methods were used. Examples of using binary statistics for bit sequence analysis are given. The use of statistical analysis of the randomness of sequences generated by random (or pseudo-random) number generators is a popular trend, as it is critical to the reliability and security of various computing systems and applications, including the Internet of Things (IoT), cryptography, simulations, and scientific calculation. With the growing importance of cybersecurity and data privacy, the demand for reliable and secure random number generators is increasing, and the use of statistical analysis to assess the randomness of sequences generated by random (or pseudo-random) number generators is becoming increasingly critical.

Design of pseudo-random number generator from turbulence padded chaotic map

Transmission of the information in any form requires security. Security protocols used for communication rely on the use of random numbers. Pseudo-random numbers are required with good statistical properties and efficiency. The use of a single chaotic map may not produce enough randomness. The turbulence is padded into the existing map to improve its chaotic behaviour and increase the periodicity. A Pseudo-random number generator (PRNG) with this architecture is devised to generate random bit sequences from secret keys. The statistical properties of newly constructed PRNG are tested with NIST SP 800–22 statistical test suite and were shown to have good randomness. To ensure its usability in cryptographic applications, we analysed the size of its key space, key sensitivity, and performance speed. The test results show that the newly designed PRNG has a 3.6% increase in key space and a 5% increase in its performance speed compared to existing chaotic PRNGs. The novel PRNG with faster performance is found suitable for lightweight cryptographic applications.

Difusi Bebas 1D dan 2D dengan Monte Carlo: Perbandingan Distribusi Bilangan Random Normal dan Seragam dengan Box-Müller

Many biological processes in the human body are based on the diffusion system. Diffusion is defined as a process of random movement of the particle whose the direction is from high concentrations to low concentrations. Many of various study of diffusion have been done both experimentally and computationally. Because the particle interaction is stochastic, the Monte Carlo (MC) method is used in performing particle simulations. The main of MC method is the use of random numbers. Many software have provided uniform random number generators. But based on the analytic results, the solution is normal distribution. Therefore, Box-Müller can be used as a transformation of particle distribution. The software used, MATLAB, has a normal random generator. Therefore, the aims of this study is comparing particle distribution of these two different random number generator with MATLAB and showing the impact of timestep parameter to these random number generator. This result can be used as based for the modelling of more complex biological systems.

Level of Education, Leadership Style and Employee Engagement

This study sought to establish the relationship between the level of education, leadership style and teacher engagement in public secondary schools of Murang’a County, Kenya. A survey research design was used. A sample of 368 respondents was selected from a target population of 3,860 teachers in 306 public secondary schools using systematic random sampling followed by use of random numbers. Data was collected using a questionnaire. Descriptive statistical methods like mean and percentage, and inferential statistical methods like F-test and t-tests (at 0.05 significance level) were used for data analysis. The research findings indicated that more teachers (75.7%) were Bachelors Degree holders in relation to the principals (51.4%) while more principals (41.2%) had attained their Masters Degrees compared to teachers (12.8%). The results also revealed that there was a significant difference on the perception about employee engagement among respondents due to their level of education. The ANOVA-test results indicated that there was no statistically significant difference between the compared levels of education except between Diploma and Masters. The study recommended that the Teachers’ Service Commission should come up with a policy that guides on brilliant ways of engaging the knowledge, skills and abilities of the teachers who had earned postgraduate education while teaching in secondary schools and also compensate them appropriately. This study contributes to the existing body of knowledge in drivers of employee engagement domain that are significant to school leaders. Keywords: Teachers, Principals, Education, Leadership Style, Employee Engagement DOI: 10.7176/JEP/12-2-08 Publication date: January 31 st 2021

Comparison of Queueing Data-Structures for Kinetic Monte Carlo Simulations of Heterogeneous Catalysts.

On-lattice kinetic Monte Carlo (KMC) is a computational method used to simulate (among others) physicochemical processes on catalytic surfaces. The KMC algorithm propagates the system through discrete configurations by selecting (with the use of random numbers) the next elementary process to be simulated, e.g., adsorption, desorption, diffusion, or reaction. An implementation of such a selection procedure is the first-reaction method in which all realizable elementary processes are identified and assigned a random occurrence time based on their rate constant. The next event to be executed will then be the one with the minimum interarrival time. Thus, a fast and efficient algorithm for selecting the most imminent process and performing all of the necessary updates on the list of realizable processes post execution is of great importance. In the current work, we implement five data-structures to handle the elementary process queue during a KMC run: an unsorted list, a binary heap, a pairing heap, a one-way skip list, and finally, a novel two-way skip list with a mapping array specialized for KMC simulations. We also investigate the effect of compiler optimizations on the performance of these data-structures on three benchmark models, capturing CO oxidation, a simplified water gas shift mechanism, and a temperature-programmed desorption run. Excluding the least efficient and impractical for large-problems unsorted list, we observe a 3× speedup of the binary or pairing heaps (most efficient) compared to the one-way skip list (least efficient). Compiler optimizations deliver a speedup of up to 1.8×. These benchmarks provide valuable insight into the importance of, often-overlooked, implementation-related aspects of KMC simulations, such as the queueing data-structures. Our results could be particularly useful in guiding the choice of data-structures and algorithms that would minimize the computational cost of large-scale simulations.

A Lightweight Authentication and Authorization Framework for Blockchain-Enabled IoT Network in Health-Informatics

Blockchain and IoT are being deployed at a large scale in various fields including healthcare for applications such as secure storage, transactions, and process automation. IoT devices are resource-constrained, have no capability of security and self-protection, and can easily be hacked or compromised. Furthermore, Blockchain is an emerging technology with immutability features which provide secure management, authentication, and guaranteed access control to IoT devices. IoT is a cloud-based internet service in which processing and collection of user’s data are accomplished remotely. Smart healthcare also requires the facility to provide the diagnosis of patients located remotely. The smart health framework faces critical issues such as data security, costs, memory, scalability, trust, and transparency between different platforms. Therefore, it is important to handle data integrity and privacy as the user’s authenticity is in question due to an open internet environment. Several techniques are available that primarily focus on resolving security issues i.e., forgery, timing, denial of service and stolen smartcard attacks, etc. Blockchain technology follows the rules of absolute privacy to identify the users associated with transactions. The motivation behind the use of Blockchain in health informatics is the removal of the centralized third party, immutability, improved data sharing, enhanced security, and reduced overhead costs in distributed applications. Healthcare informatics has some specific requirements associated with the security and privacy along with the additional legal requirements. This paper presents a novel authentication and authorization framework for Blockchain-enabled IoT networks using a probabilistic model. The proposed framework makes use of random numbers in the authentication process which is further connected through joint conditional probability. Hence, it establishes a secure connection among IoT devices for further data acquisition. The proposed model is validated and evaluated through extensive simulations using the AVISPA tool and the Cooja simulator, respectively. Experimental results analyses show that the proposed framework provides robust mutual authenticity, enhanced access control, and lowers both the communication and computational overhead cost as compared to others.

APPLICATION OF THE COMPLEX APPROACH FOR USAGE OF MUSIC COMPOSING SOFTWARE

A complex approach of using the electronic composer has been proposed. Today, the scientific community has shown great interest in the problems of studying the interrelationships of music, mathematics and psychology. Therefore, creation and giving recommendations on the practical use of musical biotechnical automated control system, which allows to compose monophonic and polyphonic sound series using a stochastic basis or a regular random signal with a uniform distribution, with an objective to further application of this sound series for musical or musicaltherapeutic effects on the object of influence — cognitive functions of the human brain (aviation dispatcher, pilot, cosmonaut, etc.), has been considered. The modernized concept of automated music synthesis is highlighted, taking into account the requirements of both the quality of composed music and a significant reduction of the composer’s routine work. Such a strategy seems to be optimal today, or at least very promising. Automated music creation is one of the most difficult tasks in both applied mathematics and the development of targeted software. Within the framework of the proposed integrated approach, an exposition of a musical piece together with a cadenza is composed by the author of a musical work manually, while its polyphonic development is carried out with the help of the music composing software. The use of random numbers or a stochastic basis in a musical composition makes it possible to get a musical signal that is original in structure but somewhat monotonous in sound. To make it more sophisticated and eliminate the triviality of the musical texture, which is a consequence of the automation of the music composition process, a digital filter based on solution of a linear differential equation in fractional derivatives (according to Caputo) is used as the activation function of the neural scheme. The result of semi-automated modeling of the system is estimated as more natural, correct and strategically perspective.

Formalisation of probabilistic concealment

In probabilistic concealment, cryptographic protocols are used to ensure concealment through the use of random numbers. This paper proposes a formalisation of probabilistic concealment and a formal logical system under which probabilistic concealment is proven. Using a secret sharing scheme as an example, we demonstrate probabilistic concealment. As a result, we point out that evenness and independence are the essential notions in the proof of probabilistic concealment.

Fragmentation in Distributed Database Design Based on Ant Colony Optimization Technique

Distributed database design solutions depend heavily on the exploitation of input data sources by using clustering techniques in data mining. A new approach of biomimetic computation systems such as ant colony optimization (ACO) for this solution is of interest to informatics experts. Using ACO techniques for this solution has the advantages such as faster algorithms thanks to the randomness of ant colony behavior. The use of random numbers based on heuristic information to pickup (drop) points will facilitate the flexible search on a large data space, so that it provides us with a better answer. In this article, the authors present ACO algorithms application solutions to clustering techniques for the problem of vertical fragmentation of distributed data.

EFFECT OF TRANSFORMATIONAL LEADERSHIP STYLE ON EMPLOYEE ENGAGEMENT IN PUBLIC SECONDARY SCHOOLS, MURANG’A COUNTY, KENYA

ABSTRACT This study sought to examine the effect of employee transformational leadership style on employee engagement in public secondary schools of Murang’a County in Kenya. A survey research design was employed. The target population was 3,860 teachers. Systematic random sampling followed by use of random numbers were applied to sample 368 respondents in 306 Public Secondary Schools. Data analysis was carried out using descriptive statistical methods that provide measures of central tendency like the mean, standard deviation and percentages to describe the characteristics of the variables of interest in the study. The inferential statistical tools applied in this research were correlation analysis and linear regression. Statistical Package for the Social Sciences software (SPSS) version 23 was used to assist in data analysis. The result showed that transformational leadership had a positive significant effect on employee engagement and its dimensions. Based on the findings of this study, it was established that using transformational leadership style could enhance employee engagement. Key Words: Transformational Leadership, Employee Engagement, Teachers, Principals, Secondary Schools

EFFECT OF AUTHENTIC LEADERSHIP STYLE ON EMPLOYEE ENGAGEMENT IN PUBLIC SECONDARY SCHOOLS, MURANG’A COUNTY, KENYA

ABSTRACT This study sought to examine the effect of employee authentic leadership style on employee engagement in public secondary schools of Murang’a County in Kenya. A survey research design was employed. The target population was 3,860 teachers. Systematic random sampling followed by use of random numbers were applied to sample 368 respondents in 306 Public Secondary Schools. Data analysis was carried out using descriptive statistical methods that provide measures of central tendency like the mean, standard deviation and percentages to describe the characteristics of the variables of interest in the study. The inferential statistical tools applied in this research were correlation analysis and linear regression. Statistical Package for the Social Sciences software (SPSS) version 23 was used to assist in data analysis. The result showed that authentic leadership had a positive significant effect on employee engagement and its dimensions. Based on the findings of this study, it was established that using authentic leadership style could enhance employee engagement. This study contributes to the general understanding of leadership behaviors that are significant in encouraging employee engagement to both practicing and aspiring school leaders and teachers, and professional educator preparation programs. Key Words: Authentic Leadership, Employee Engagement, Teachers, Principals, Secondary Schools

Global maximum power point tracking of PV arrays under partial shading conditions using a modified particle velocity‐based PSO technique

Modern PV arrays are generally designed with bypass diodes to avoid damage. However, such arrays exhibit multiple peaks in their P - V characteristics under partial shading conditions. Owing to the limitation in the abilities of conventional maximum power point tracking algorithms in such cases, the application of other optimisation algorithms has been explored. This study proposes a modified particle velocity-based particle swarm optimisation (MPV-PSO) algorithm for tracking the global power peak of the multiple peak P - V characteristics. The MPV-PSO algorithm is both adaptive and deterministic in nature. It eliminates the inherent randomness in the conventional PSO algorithm by excluding the use of random numbers in the velocity equation. The proposed algorithm also eliminates the need for tuning the weight factor, the cognitive and social acceleration coefficients by introducing adaptive values for them which adjust themselves based on the particle position. These adaptive values also solve problems like oscillations about the global best position during steady-state operation and particles getting trapped in local minima. The effectiveness of the proposed MPV-PSO algorithm is validated through MATLAB/Simulink simulations and hardware experiments.

Fundamentos de simulación de materiales por medio del método de Monte Carlo

El método Monte Carlo es una herramienta que ha sido ampliamente utilizada desde sus inicios en la década de los años 1940 principalmente en la ciencia e ingeniería de materiales aunque es aplicable a temáticas tan diversas como la economía, la sociedad y su comportamiento, la biología y hasta la medicina. El funcionamiento del método Monte Carlo se basa en el uso de números aleatorios y en poder llegar a describir el comportamiento de un sistema o explicar un fenómeno difícil de comprender y de presentar analíticamente. Se pretende dar una introducción a los fundamentos básicos del método Monte Carlo aplicado a la ciencia de materiales como también mostrar algunos ejemplos basados en el algoritmo propuesto por N. Metropolis y que ha permitido abordar problemas interesantes en el tema. Al mismo tiempo se incluyen ejemplos básicos con sus propios algoritmos desarrollados en el lenguaje de programación FORTRAN 95 y que ilustran bastante bien las bases del método de Monte Carlo.

Assessing the effects of audio-visual stimulation on the prefrontal EEG of good & poor sleepers

Purpose – The purpose of this paper was to investigate the response of good and poor sleepers toward audio-visual stimulation via prefrontal theta EEG measurement. Design/methodology/approach – The experiment included ten healthy subjects that were chosen after going through the Pittsburgh Sleep Quality Index (PSQI). They were divided into two groups that include five good and five poor sleepers. Next, in order to clarify the effects of audio-visual biofeedback during daytime, each subject was asked to go through six two-minute tasks that include: pre-baseline, eyes open at rest, eyes closed at rest, audio biofeedback with eyes open, video biofeedback also with eyes open, and post-baseline. Findings – In Task 4, the audio stimulation task, both types of sleepers elicited higher theta waves due to demand in mental activity and also a meditation state. It was significantly higher in poor sleeper that demonstrated a peak difference of 25 percent compared to its good sleeper counterpart. In Task 5, the visual stimulation task, through the use of random numbers having blue and red color background, the theta amplitudes of good and poor sleepers drop together, due to beta waves becoming dominant, as the task required attention and focussed accounting for reduced theta amplitudes. The study was able to prove the use of prefrontal EEG in measuring and evaluating sleep quality by examining theta variation. Originality/value – This paper proposed a novel and convenient method for evaluating sleep quality by utilizing only a single channel prefrontal EEG measurement.

�������� ������� ������������� � ������ ���������� ����������������� ������� � ������������� ���������

The project duration reduction problem in the conditions of limited renewable
 resources is one of the most popular themes within researches in area
 of project management�s mathematical models during more than 50 years.
 Nowadays applying of exact optimization methods in real practice is impossible,
 so heuristic methods are used in this problem resolution. Among a set
 of heuristic methods there is a considerable share of so-called multipass
 scheduling methods based on consecutive application of different heuristic
 rules of resource conflicts resolution to the same project. If at this each new
 schedule turns out with use of random numbers, they speak about sampling.
 In this paper a review of existing sampling methods will be made, and
 their efficiency research on the basis of PSPLIB projects will be conducted.
 Besides, has been received a confirmation that at a large number of iterations
 in a sampling method the parallel schemes surpass consecutive ones,
 and a new method of sampling combining various schemes and rules of
 priority has been constructed.

Application of random search method for maximum power point tracking in partially shaded photovoltaic systems

The power–voltage (P–V) curve of a photovoltaic (PV) power generation system under partially shaded conditions (PSCs) is largely non-linear and multimodal, and hence, global optimisation techniques are required for maximum power point tracking. A traditional optimisation algorithm is proposed here, namely random search method (RSM) for tracking the global maximum power point in a solar power system under PSC. The RSM is based on the use of random numbers in finding the global optima and is a gradient independent method. The major advantage of RSM is its very simple computational steps, which requires very less memory. The performance of RSM in tracking the peak power is studied for a variety of shading patterns and the tracking performance is compared with two-stage perturb and observe (P&O) and population-based particle swarm optimisation (PSO) methods. The simulation results strongly suggest that the RSM is far superior to two-stage P&O method and better than PSO method. Experimental results obtained from a 120-watt prototype PV system validate the effectiveness of the proposed scheme.