Reviewerly/exHarmony huggingface dataset:
SOFTWARE QUALITY PREDICTION USING MACHINE LEARNING TECHNIQUES AND SOURCE CODE METRICS: A REVIEW

Software quality prediction is the Machine Learning (ML) based technique in which ML models are trained using historical data.Output from these quality models can be used by software experts in the early phase of software development for improving the quality of software by controlling the various quality attributes like maintainability, reliability, security issues of software etc.In this study a systematic review of studies from 2005 to 2021 is performed.Studies that use ML techniques and source code metrics for Software Quality Prediction (SQP) are included for review.Study assesses the commonly used machine learning techniques and source code metric for SQP.Commonly used datasets, feature selection techniques and commonly used performance measures in software quality prediction are also as sessed.In this paper 53 primary studies are selected for systematic review.Results of this study prove that Bayesian Learning (BL), Regression, Ensemble Learning (EL), Decision Tree (DT) and Support Vector Machine (SVM) are most commonly ML techniques used for quality prediction which comprises 58%, 52%, 41%, 32%, and 32% of the overall studies respectively.It is also assessed that NASA, PROMISE, Apache, Mozilla Firefox and Eclipse are the most commonly used datasets for training and testing the SQP models.LOC, CC, CBO, RFC, WMC, LCOM, DIT and NOC are among the most commonly used source code metrics in SQP.Based on the results from the selected studies it is concluded that ML techniques and source code metrics have the ability to improve the overall quality of the software.
Ensuring the Innovativeness of Vocational Education: Neuropedagogical Aspect

The significance of the outlined direction research is determined by the fact that in modern conditions of postmodern society development, the issues of improving higher education and ensuring the quality of the educational process become relevant. The need for innovative renewal of the educational process of the higher school is determined by the emergence of a number of psychological and pedagogical problems, taking into account the conditions of postmodernity(mismatch between the needs of employers, the existing state of future professionals training and scientific and pedagogical staff’s qualifications, lack of mechanisms for selective processing of information, insufficient provision of information and psychological security of the individual, the need to develop an inclusive educational environment in higher education, etc.). The main conceptual principles of the innovative development of the educational environment in postmodern conditions are highlighted (humanization of the educational process, providing developmental learning and personality-oriented approach). The article analyzes the foreign and domestic experience of using innovative educational technologies (individualized, personality-oriented learning, as well as gamification of the future training professionals’ process), identifies their advantages and disadvantages. Peculiarities of their introduction into the practice of higher education in postmodern conditions are outlined. Prospects for further research are the development of practical recommendations for the introduction of innovative educational technologies in the future professionals training in higher education.
Evaluation of High School Students' Perceptions and Views on Distance Education During COVID-19 Pandemic

The COVID-19 disease, which spread all over the world and caused a pandemic, disrupted education the most. Countries had to quickly switch to the distance education model in schools and universities in order to prevent the spread of the epidemic. In our study, in which we evaluated the perceptions and views of high school students about distance education, almost two-thirds of the students stated that distance education is not an adequate and effective learning model. Despite the fact that today is the age of technology, students' reporting that a more effective learning will be achieved with face-to-face education shows that the distance education model should be developed and improved.
A Study To Determine The eTwinning-Related Views Of The Teachers In The eTwinning Network Countries, And Their Digital Literacy Levels

The main aim of this study is to compare the digital literacy levels of teachers in Turkey and of those in other member countries within the European Union eTwinning network. The present study has been conducted with a mixed methods research design, in which quantitative and qualitative methods are used together. The quantitative data of the research have been collected online from a total of 181 teachers, 106 from Turkey and 75 from the member countries of the eTwinning network by using the Digital Literacy Scale, while the qualitative data have been obtained from a total of 111 teachers. The quantitative data have been analyzed by using descriptive statistics and independent sample t test, whereas the qualitative data have been analyzed in line with the descriptive analysis approach by using the MaxQDA 2020 program. As a result of the research, the digital literacy levels of the teachers working in Turkey and those in the member countries of the European Union eTwinning network have been found to be at high levels, with no statistically significant difference between the gender variable and digital literacy.
THE CONSTRUCTION OF THE MATHEMATICS TEACHING PROCESS IN AN AFFECTIVE APPROACH

In a globalized world, where society overvalues cognitive performance, there has been a trend that consolidates theories that understand human beings as affective subjects.Thus, it is observed that there is an intellect/ affection axis, that the ability to feel emotions is indispensable for the establishment of rational behaviors.It is noticed that the difficultiesin learning seem to occur as a result of an imbalance, between cognition or affectivity, or in the relationship between both.For example, with regard to cognition, from the pedagogical, psychological and psychoanalytical point of view, an individual may have learning difficulties in several areas of education, especially in mathematics, as it requires a great cognitive effort.DSeveral psychological and sociological currents demonstrate that action and reason correspond to cognitive functions driven by mental structures and affective aspects.From this perspective, when the cognitive capacity of the human being or intelligence is mentioned, questions are raised about the individual's learning capacity in relation to the object of knowledge.This article, which aims to investigate the importance of affectivity in the construction of the teaching-learning process of mathematics, will be based on the learning theories of Piaget, Vygotsky and Wallon, who conceive the relationship between cognitive and affective processes in psychic functioning as intrinsic human.The methodology undertaken was of a qualitative nature.The instruments used were the Affect-Performance Diagram (DAP) of adapted analysis, developed by Silva and Teixeira (2009).The results point to the affective aspect, as well as the relationship between teacher and student, as fundamental variables to determine the teaching-learning process.
Towards The Ultimate Brain: Exploring Scientific Discovery with ChatGPT AI 

We introduce a powerful general probabilistic theory, GPT$^4$, that extends classical and quantum theories to include higher-dimensional probabilistic models. GPT$^4$ results from the four-fold integration of GPT in physics (General Probabilistic Theory) and GPT in AI (Generative Pretrained Transformer). In this paper, which is entirely generated by GPT$^4$, we demonstrate the capabilities of GPT$^4$ by using it to construct a new model of the universe that includes all known physical laws and phenomena. We show that GPT$^4$ can use its built-in mathematical and statistical capabilities to simulate and analyze the behavior of the universe, making predictions and testing the validity of different physical theories. As a demonstration of its language capabilities, GPT$^4$ also generates a limerick about itself. GPT$^4$ estimates that AI will have a probability of over 95% to surpass humanity within approximately 5 years. Our results show that GPT$^4$ provides a self-aware description of the physical world, making it a useful tool for understanding and analyzing the universe.
Artistic and aesthetic development of the personality of a younger student in the lessons of fine arts

One of the aspects in the teaching of fine arts in primary school is considered – the education of a literate viewer, which is related to the content and pedagogical characteristics of the subject, reflected in regulatory documents. A diagnostic study is presented, the content of which is aimed at identifying how much a child manifests himself as a competent viewer.
Research and Evaluation of Early Warning Models for Crowd Density Monitoring Based on Drones

High-density crowd areas are receiving more and more research attention as a key area of concern for law enforcement agencies. This project focuses on combining emerging drones with crowd monitoring algorithms to build early warning prediction models that can be embedded into the backend systems of relevant departments to provide references for the holding of large events and the control of popular scenic spots.
Unsupervised Face Image Quality Assessment Based on Face Recognition

Face quality evaluation can filter out low quality face image to save computational resources and improve the system performance, labeling the face image quality score by manual consume too much manpower. To solve this problem, an unsupervised face image evaluation based on face recognition is proposed. We use the face recognition model to calculate the features of faces and label the images quality score. The face recognition model is compressed by knowledge distillation method to obtain efficient quality assessment model. Experimental results show that this method can effectively evaluate the quality of face image and improve the performance of face recognition.
Research on the Construction and Evolution Model of Cloud Ecosystem

Clarifying the positioning of participants in the cloud ecosystem and analyzing the competition and cooperation among subjects are important for the sustainable and healthy development of cloud ecosystem. This paper analyzed the structure and the evolution law of the participants in the cloud ecosystem. The results show that, in a certain degree of competitive environment, companies with different positions and competitive relationships have different competitiveness. Cooperation is conducive to the coexistence of individuals. The stronger the cooperative relationship, the greater the enterprise’s profit. These findings give a beneficial motivation for the coordinated relationship and development strategy for cloud providers.
Algorithm Based on Deep Learning to Improve the Logistics Management of a Company That Distributes Reading Material

Companies in the field of sales and distribution of reading material, which have the problem of great variability in the demand for their products, which causes higher costs and customer dissatisfaction. Deep Learning, which is a branch of Machine Learning which is responsible for training a computer to learn on its own, works by recognizing patterns using many processing layers and learns through repetition in its training. Using the KDD methodology, we have implemented a deep learning algorithm to improve the logistics management of a reading material distribution company through demand forecasting and other aspects of logistics. It was shown that with the use of certain functions in the neural network of the algorithm it can be predicted with a low deviation and a minimum level of error, giving us results for 85% of improvement in demand prediction, also an 80% of improvement in material devolution, increasing the precision of the company on buying material aiming to get the exact amount of material to distribute. However, we can improve the results for a more accurate prediction by adapting the algorithm to the situation. Also, we share our challenges, and other opportunities for future research. This paper can help other investigators being a guide for beginners in deep learning application in logistics management.
Research on Human-Computer Interaction on the Field of Intelligent Vehicles

With the development of information technology and Internet technology, the functions inside the car are constantly integrated, and the operation mode is increasingly complex. The traditional functions of the car cannot meet the growing needs of human beings. The new era of intelligent cars requires the participation of human-computer interaction, so that the car is more intelligent and more modern. Human-computer interaction refers to the process of information exchange between human and computer by using a certain dialogue language and in a certain interaction way to complete certain tasks. An important problem of human-computer interaction is that different computer users have different usage styles – they have different educational backgrounds, different ways of understanding, different learning methods and different skills, as well as cultural and ethnic factors. Secondly, the research and design of human-computer interaction needs to consider that the user interface technology changes rapidly, and the new interaction technology provided may not be applicable to the previous research. This paper discusses the research of human-computer interaction in the field of intelligent vehicles from the perspectives of voice interaction, vehicle voice image personality, visual gesture recognition and on-board music control.
Continuous-Speech Spoken Dialogue System Based on Joint Decision-Making of Audio and Semantics

To avoid the frequent wakeups of general spoken dialogue systems, we studied continuous-speech spoken dialogue systems. The experiment results show that continuous-speech systems can efficiently enhance the success rate of interactions by 8%, in contrast with discontinuous ones. The full-duplex continuous-speech dialogue systems, which are continuously listening surrounding voices, are often interfered with by invalid sounds, such as the voice played on TV. Therefore, we have studied the audio recognition algorithm to reject those noises, and the experiments show that the recognition accuracy of this algorithm can reach about 94%. In this paper, we propose joint decision-making of audio and semantics, which can effectively reject invalid speech and further improve the success rate of interaction in continuous-speech spoken dialogue systems.
“Password Authentication Key Exchange Protocol using Fish Tank Image based True Random Number Generator”

Abstract: We employ a straightforward configuration of a fish tank as the changeable environment, recording its photos over time, in order to limit the computational complexity. To create the initial seed, the image data is then submitted to a reduction method and hash function. In order to get true random numbers, we suggest a method for economically removing the true seed from the image data and applying it to a pseudo-random generator, in this case, a Linear Congruential Generator (LCG). The need for information security is increasing across a range of industries, and security concerns are receiving more and more attention as a result of the Internet's explosive growth in recent years. Any future value cannot be accurately anticipated based on the current set of values and random numbers are those that are a part of a sequence in which values are uniformly distributed over a defined set. A safe session key can be provided to a pair of users interacting over an unstable channel using password-based encrypted key exchange protocols, even if the top secret key or password they share is chosen at random from a tiny set of values. We offer two straightforward Bellovin and Merritt-based encrypted key exchange systems..
MULTITASKING BEHAVIOR IN THE WORKPLACE: A SYSTEMATIC REVIEW

The multitasking behavior is burgeoning in today’s work environment which reflects capabilities of individual to manage multiple things simultaneously to attain the efficient work performance. Study examines effect of multitasking on human behavior in workplace and its impact upon work performance. A search strategy was formulated to conduct this literature review-based study, and different databases containing scholarly research material were explored to find related literature. Inclusion-exclusion criteria were defined &amp; included studies fulfilled selection criteria. PRISMA-P (2015) guidelines were followed to complete study. After reviewing literature pertaining to subject, finding established the fact, internal and external interruption triggers the individuals’ working behavior towards multitasking at the workplace. Multitask working behavior exerts an impact on overall work performance particularly, on work quality, productivity, and working memory. In this linking, no prior literature-based evidence is available in this domain in particular context. The scope of study is confined to published literature on human multitasking and human performance, literature on system multitasking and media multitasking is not included.
EXPLORING THE ANTECEDENTS OF EMPLOYEE TURNOVER INTENTIONS: THE SCALE VALIDATION THROUGH PILOT TEST

The Brief summary: The study aimed to explore the antecedents of employee turnover intentions through the scale validation based on pilot test. Data and collection procedure: Primary data collected by close-ended questionnaire. Sampling: Convenience and snow-ball sampling techniques used to acquire data. Nature: The study was quantitative and descriptive in nature. Population and sample size: Approximately, 125 questionnaires delivered to the target population which including managers and non-managers, working in private banks of Sindh, Pakistan and out of that 100 received back as completed and fit for data analysis and interpretation. Data analysis &amp; interpretation: Data analyzed and interpreted by SPSS and PLS-SEM. Findings: Cronbach’s alpha, item analysis and factor analysis measured individually for overall variables of study. All variables and items measured internally consistent and reliable except few items. Such items were measured insignificant through the factor loading considered to be revised or revisited. Conclusion: This study assisted researcher to find out errors of instrument and suggested solutions to revise such items which may cause problems in making analysis and interpretation for the main study.
Factors Affecting of Electronic Learning Platform Using as Perceived by Nursing Educators and Students and its relation to Nursing Students' Satisfaction

The use of e-learning platform in the field of education has become popular duringCOVID-19 outbreak. However, there are many factors and challenges faced by educators and studentswhich affect their intention to use and satisfaction.
Statistical approach to interpretation of geochemical data of stream sediment in Pleše mining area

The Ba, Pb and Zn ore deposit Pleše near Ljubljana is one of the formerly productive mines. The stream sediments were sampled and analysed by XRF to establish the effect of grain size, mineralization, and downstream location of sampling sites on geochemical composition based on various statistical analyses. Statistical analyses of the geochemical data confirm the impact of mineralization. The parametric t-test, non-parametric Mann-Whitney test and cluster analysis showed only minor differences in the geochemical composition of the samples with different grain sizes (&lt; 0.063 mm and 0.063-2 mm). The parametric and non-parametric correlation coefficients as well as cluster analysis indicate that the contents of Si, Al, K, Rb, and Fe are associated with weathered rock forming minerals such as micas, and clay minerals, whereas Nb and Zr are associated with minerals resistant to weathering. Ca and Mg are associated with carbonates. S, Ba, Sr, Pb, Zn, and Mn indicate local mineralization with sulphates and sulphides. The results of the t-test and analysis of variance, Mann-Whitney tests and Kruskal-Wallis ANOVA of the groups established by the cluster analysis confirm that the contents of Ba, Pb and Sr have a statistically significant influence on the classification of the cluster group - i.e., the influence of sediment mineralization. There are no differences in elemental contents in the sediment samples downstream. The statistical approach to evaluate the geochemical data has proven useful and provides a good basis for further interpretation.
Unsupervised and Computationally Lightweight Spectrum Sensing in IoT Devices

Principal component analysis (PCA) is a widespread technique in data analysis. Recently, the L1-norm has been proposed as an alternative criterion to classical L2-norm in PCA due to its greater robustness to outliers. The present work shows that, with a whitening step, L1-PCA can perform spectrum sensing and modulation recognition in IoT applications. Numerical experiments confirm this finding.
التحذير وأنواعه في القرآن الكريم (دراسة موضوعية)

This study aims to clarify the cautioning and its types in the Holy Qur’an. It is an objective study that attempts to clarify the verses in the Holy Qur’an which caution and have connotative variation such as intimidation, and suspense. It also highlights the words of warning in the Holy Qur’an such as caution, precaution, and warning. They are intense and repetitive in nature with the express purpose to edify mankind.
CHATBOT DEVELOPMENT USING PYTHON

A chatbot is an artificial intelligence computer program which performs communication using audio and video system.A person can ask any questions and chatbot will answer accordingly.Nowadays a chatbot is highly popular and takes speed as a computer communication application.Chatbot system is in trend, thus it is being used on many websites.With the chatbot, one doesn't have to wait to talk to the customer helpline, they don't even have to search for shopping through Websites.A chatbot is used in many areas like order food, product suggestions, customer support, weather, personal finance assistance, scheduled a meeting, search and track flights, send money, and many more.The main objective that we will discuss in this paper is about creating a web API, and also about sample web and text messaging interfaces that demonstrate the use of API.In this research paper we are trying to understand these Chatbots and understand their shortcomings.
Features of “digital education” of specialists in the field of economic security

The article actualizes the problem of digital education in the training of specialists in the field of economic security. The paper defines the features of digitalization of modern vocational education. The analysis of the use of digital technologies in the activities of specialized educational organizations of the Ministry of Internal Affairs of Russia is presented. The positive and negative sides of the educational process with the use of digital innovations, the possibility of mastering by students of special knowledge and skills necessary for the implementation of successful practical activities to protect the economic interests of our country are identified.