Reviewerly/exHarmony huggingface dataset:
MARKETPLACE SELECTION STRATEGY WITH INTEGRATION OF FUZZY AHP AND FUZZY MOORA METHODS

Currently, many business actors have used marketplaces in online product marketing. Choosing the right marketplace is one of the most important parts, because it affects business profitability. This study will explain the strategy in marketplace selection with the integration of the fuzzy AHP and fuzzy MOORA method. Fuzzy AHP method is used in weighting the criteria, while the fuzzy MOORA method is used in the alternative assessment. Sources of data obtained from questionnaires distributed to marketplace users. The weighting of criteria using fuzzy AHP method obtained service quality weight of 32.76%, system quality weight of 24.35%, information quality weight of 27.42% and vendor quality weight of 15.47. In the alternative assessment using fuzzy MOORA, the Shopee performance score is 0.0285, Tokopedia's performance score is 0.0266 and Bukalapak's performance score is 0.0256. SH has the highest performance value, so SH can be used as the best alternative in the marketplace selection strategy with the integration of fuzzy AHP MOORA methods. The results of this study can be used as input for business actors in their marketplace selection strategy, so that they are expected to increase business profitability. Keywords: Fuzzy , AHP, MOORA, Marketplace
Discussions of Memorization in Education

The general purpose of education is to raise individuals within advanced language, mental, social and emotional skills. New approaches and models are used to achieve this goal. Ineffective, useless practices, one of which is memorization are avoided in this process. Memorization has been used in the history of education for a long time. In the past, memorization was considered as an important method in schools and information was memorized with constant repetition within this concept. In today's educational approaches, memorization is regarded as useless and argued because it is not learning, yet it is the lowest level of the learning process. Memorization includes repeating information and keeping it in mind for a certain period of time. Later, this information is forgotten and all efforts are wasted in vain. There is no mental change in individuals through memorization. Conversely, it causes students to have mental and physical laziness, whereas learning includes understanding, thinking, integrating with prior knowledge, producing new information and applying it. With learning, the individual constantly improves and renews himself. Therefore, memorization is avoided. Although this issue is constantly debated among educators, it is known that students in the world and in our country still tend to memorize. Educators are expected to focus on qualified education and train individuals who will shape the future of our country.
Geological conditions of karst development in the Nizhneirensko-Orda area (Perm Krai, Russia)

The article presents the results of research on the use of remote sensing methods in combination with general geological methods to study the patterns of karst development. Practical aspects of expanding the possibilities of using remote sensing methods for engineering and geological surveys in territories of karst development are considered. Analysis and interpretation of the research materials were carried out using spatial analysis and mathematical methods. The priority tasks of the research were to evaluate and expand the possibilities of using remote methods as part of a complex of engineering and geological surveys, to combine remote research methods with general geological methods for solving specific practical problems, to establish general laws of the influence of various natural factors on the intensity and parameters of surface karst forms, their analysis and mathematical justification.
Machine learning in blockchain

The promise and potential of blockchain to drive social impact is enormous. Blockchain will touch every significant industry which people interact with in dayto-day life. Blockchain will enable solutions that are not previously possible. Health sector recently attracted more initiatives than any other industry. Applications for blockchain in health include digital health records exchange and pharmaceutical supply chain management. In many of these areas, blockchain offers a more secure, decentralized and efficient solution than would otherwise be possible. Blockchain and machine learning (ML) technologies are gaining strong momentum and thrust around the world. Blockchain, a disruptive technology, made its big splash with crypto currencies invention and trading. On the other hand, with predictive and descriptive algorithms, ML is making considerable waves in harnessing existing data to identify patterns and gain insights. Congregating the two technologies can only make them super disruptive! Both have the potential to hasten data exploration and analysis as well as intensify transactions security. Additionally, distributed blockchains can be a significant and proven input for ML, which requires big datasets to make quality predictions. It goes without saying that each technology has its degree of complexity, but both artificial intelligence (AI) and blockchain are in situations where they can benefit from each other and help one another. Both these technologies are able to effect and enact upon data in different ways as their combination makes sense, which can take the exploitation of data to new levels. At the same time, the integration of ML and AI into the blockchain, and vice versa, can enhance blockchain&apos;s underlying architecture and boost AI&apos;s potential. Additionally, blockchain can also make AI more coherent and understandable for tracing and decision-making using ML techniques. Blockchain and its ledger can record all data and variables that go through a decision made under ML. The present chapter focuses on a few standard ML algorithms that are useful in supporting blockchain technology.
HANDWRITING YONDER RELIGION: A STUDY OF HANDWRITING ANALYSIS AMONG HINDUS AND MUSLIMS OF LUCKNOW, UTTAR PRADESH

The world is moving towards digitalisation and technology is advancing, but handwriting remains persistent and is still maintaining its place when it comes to recording information, noting down points, mode of communication in day to day life. Writing is a complex act and is a highly developed skill. Variation in handwriting is foremost principle of handwriting. This paper investigated English handwriting characteristics of Hindus and Muslims in Lucknow, Uttar Pradesh, India. Since English is not a primary language of the nation all of them have learned English as a second language. 174 exemplars were collected from the students of different colleges and Universities across Lucknow city of age group 18 to 24 years. It is an approach to identify the writer and assists in Forensic document examination.
A Standard 8-Fold Symmetry Model of Elementary Particles Compared to the Present 6-Fold Symmetry Standard Model

The Up neutron quark is in a 2-quark format to allow its birth in the 2nd cyclic universe in 2-digit form. No Wigner, Feynman, heavy neutrino, dark neutrino, cosmophoton, archaic electron, graviton, or 7th quark exists with 6- fold symmetry.
Strategy and tactics of teaching mathematics at a technical university

Abstract The role of mathematics in the formation of innovative thinking of students is determined. The strategy and tactics of teaching mathematics in solving the problem of integration of fundamental and professional education are proposed. The role of integrative disciplines as a means of unifying the links between mathematics and special disciplines was revealed. It is shown that the course of mathematics in a technical university should reflect continuously developing trends in applied mathematics. The need to include general mathematical courses in the curricula at all stages of training including in the magistracy is justified. Examples illustrating the proposed concept are given.
Risk-Sensitive Deep RL: Variance-Constrained Actor-Critic Provably Finds Globally Optimal Policy

While deep reinforcement learning has achieved tremendous successes in various applications, most existing works only focus on maximizing the expected value of total return and thus ignore its inherent stochasticity. Such stochasticity is also known as the aleatoric uncertainty and is closely related to the notion of risk. In this work, we make the first attempt to study risk-sensitive deep reinforcement learning under the average reward setting with the variance risk criteria. In particular, we focus on a variance-constrained policy optimization problem where the goal is to find a policy that maximizes the expected value of the long-run average reward, subject to a constraint that the long-run variance of the average reward is upper bounded by a threshold. Utilizing Lagrangian and Fenchel dualities, we transform the original problem into an unconstrained saddle-point policy optimization problem, and propose an actor-critic algorithm that iteratively and efficiently updates the policy, the Lagrange multiplier, and the Fenchel dual variable. When both the value and policy functions are represented by multi-layer overparameterized neural networks, we prove that our actor-critic algorithm generates a sequence of policies that finds a globally optimal policy at a sublinear rate. Further, We provide numerical studies of the proposed method using two real datasets to back up the theoretical results.
Non-native speakers’ attitudes towards the use of short Arabic stories in language classes

This study aims to investigate the attitudes of non-Arabic students towards reading short stories in Arabic at universities in Malaysia, with a focus on three variables: the students' university, gender, and mother tongue.A mixed-methods approach was employed, using a questionnaire and semi-structured focus group discussion with 58 participants.The study found that the students had positive attitudes toward reading short stories.There were no statistically significant differences between the participants' attitudes based on type of university, gender, and mother tongue.With respect to the types of short stories students prefer, comic stories were read most frequently by students.Finally, students also found it very encouraging and supporting to use short stories to enhance their Arabic language skills.The study concludes with some recommendations for further research.
Data augmentation and image understanding.

Interdisciplinary research is often at the core of scientific progress. This dissertation explores some advantageous synergies between machine learning, cognitive science and neuroscience. In particular, this thesis focuses on vision and images. The human visual system has been widely studied from both behavioural and neuroscientific points of view, as vision is the dominant sense of most people. In turn, machine vision has also been an active area of research, currently dominated by the use of artificial neural networks. This work focuses on learning representations that are more aligned with visual perception and the biological vision. For that purpose, I have studied tools and aspects from cognitive science and computational neuroscience, and attempted to incorporate them into machine learning models of vision. A central subject of this dissertation is data augmentation, a commonly used technique for training artificial neural networks to augment the size of data sets through transformations of the images. Although often overlooked, data augmentation implements transformations that are perceptually plausible, since they correspond to the transformations we see in our visual world -- changes in viewpoint or illumination, for instance. Furthermore, neuroscientists have found that the brain invariantly represents objects under these transformations. Throughout this dissertation, I use these insights to analyse data augmentation as a particularly useful inductive bias, a more effective regularisation method for artificial neural networks, and as the framework to analyse and improve the invariance of vision models to perceptually plausible transformations. Overall, this work aims to shed more light on the properties of data augmentation and demonstrate the potential of interdisciplinary research.
The Teaching Design and Application of Vocal Music Education in Normal University

It is part and parcel to carry out scientific positioning on the education of vocal music in normal university, putting forward the teaching design and application of the vocal music education as well as effectively guiding the reform and development of it in normal university. Based on the basic characteristics of the vocal music education, this paper expounds the design of the teaching objectives, content, object, methodology, media, assessment and so on in order to offer theoretical guidance on the education of vocal music in normal university.
THE STUDENTS' PERCEPTION ON LEARNING USING ONLINE METHOD IN PANDEMIC ERA

The study is investigating the perceptions of students about learning using onlinemethod, the problems they encountered during e-learning process and theirstrategy to face e-learning method. The subject of the research was coming from 4students who were divided into third and fifth semester from 2 universities, suchas Bina Sarana Informatika and Muhammadiyah Tangerang University. Thedescriptive qualitative is used to gather the required data. Five questions areasked to get the data through online interview. Data shows that most of thestudents’ perceptions on online learning method is quite similar. Most of thestudents feel the same problems which relate to internet connection problems. Theproblem distracted their understanding of the materials given by the lecturers.Then, the strategy to encounter the problems are asking and discussing withfriends. Besides, they also find their own way through internet. Despite of beingeasy to find many ways to solve the problems, the offline method becomes theirmost favourable learning method.
Pengembangan Media Pembelajaran Ekonomi Berbasis Komputer Model Drills untuk Siswa Kelas X SMA Islam Bina Insan

Media is anything that can be used to transmit messages from sender to receiver so that it can stimulate thoughts, feelings, attention, and student interests in such a way that the learning process occurs, media in education is used to improve the quality of learning, one of which is based learning media. computer. It is the duty and responsibility of the teacher in utilizing learning media that is in accordance with the needs and interests of student learning, and one of the media that is in accordance with student learning interests, namely computer-based learning media drills model. The objectives of this research and development are: 1) to produce a Drills model of computer-based economic learning media which will be used for the following years in class X students of Bina Insan Islamic Senior High School 2) to determine the feasibility of computer-based economic learning media developed according to the assessment of media experts and experts material, 3) knowing the responses of expert validators and students to the Drills model of computer-based economics teaching materials that have been developed, and 4) knowing the effectiveness of the developed drills model of computer-based economic learning media. This type of research is development research with the Borg and Gall model which has been modified into eight development steps. Development steps include: 1) potentials and problems, 2) data collection, 3) product design, 4) design validation, 5) design revision, 6) product testing, 7) product revision, and 8) final product. Data collection was carried out by means of questionnaires and tests. The target output of this research is publication in accredited national journals, another output can be a computer-based learning media product model of drills. The results showed that, 1). There is a product in the form of a computer-based economic learning media with the Drills model for Bina Insan Islamic Senior High School. 2). This Drills model of computer-based economic learning media is feasible to use, it can be seen from the validation results of economic material experts of 89.09% (feasible), for media experts it is 92.30% (feasible). 3). The teacher and student responses showed a positive response seen from the results of field trials, namely the results of the teacher's questionnaire response of 100% and student responses of 89.6%. 4). For the results of student learning activities of 3.7, thus the Driils Model of computer-based economic learning media is effectively used
Using mobile learning applications for the development of students’ universal compe-tencies: Evaluation of the effectiveness

Introduction. Nowadays, the widespread use of mobile learning applications has brought significant changes in education. This paper addresses the problem of using mobile learning applications for developing universal competencies among students. The purpose of the research is to assess the effectiveness of using mobile applications for the development of universal competencies in students. Materials and Methods. The data were collected through reviewing and critical analysis of research literature on the development of competencies among students of higher educational institutions and the use of information, computer and mobile technologies for educational purposes. The empirical study was conducted using methods of educational research from 2017 to 2019. The sample consisted of students in years 3-5 doing an undergraduate degree in Education. Methods of mathematical statistics were employed for processing empirical data (correlation analysis). Results. The paper presents the results of evaluating the effectiveness of using mobile applications for developing students’ universal competencies. The authors identified the indicators for assessing the development of the selected universal competencies. The authors examined the following stages of developing universal competences by means of using mobile learning applications: (1) knowledge and skills alignment in the field of using mobile learning applications; (2) demonstrating knowledge and skills of using mobile learning applications; (3) inclusion of mobile learning applications in studying various disciplines (modules); (4) using mobile learning applications as a part of a larger activity (group projects); (5) using mobile learning applications for academic assessment (self-evaluation). It is emphasized that it is necessary to purposefully include students' work with mobile applications in the educational process of the university, when a student, being an active participant in the educational process, uses mobile applications to master the content of the discipline, enhance competencies and evaluate them. Conclusions. The article concludes that characteristics of using mobile learning applications identified and described in this research facilitate the development of students’ universal competencies. The findings can be used to improve the efficiency of the educational process at the university. Keywords Educating students; Universal competencies; Indicators of the formation of universal competencies; Mobile learning applications; E-portfolio; Self-assessment; Demonstration of competence; Group work
Assessing Diagnostic Accuracy of Quantitative Data in Biomedical Studies Using Descriptive Statistics and Standardized Mean Difference

ROC analysis is the most used method for analyzing the diagnostic accuracy of quantitative data in biomedical research. ROC analysis generates a curve describing the frequencies of true positive and false positive results for different degrees of the analyzed variable. However, in many publications devoted to the application of quantitative diagnostic methods, this analysis is not carried out: researchers report only analysis of statistical significance for the groups difference. In meta-analyses, the estimated parameter is the effect size expressed through standardized mean difference. The article describes the approach, which allows performing ROC analysis using cumulative normal distribution functions for studied and controlling groups. The proposed approach can be used to evaluate the diagnostic accuracy of quantitative variables on the base of one of the sets of descriptive statistics (mean and standard deviation, or median and quartiles) or the value of standardized mean difference. Examples of application of the proposed approach on model data, on data from literature sources, as well as on the authors' own observations are given as an example of assessment of diagnostic accuracy of quantitative variables analyzed in the microcirculation studies in various diseases. The results presented in the article can be used by medical and biological specialists to assess the diagnostic accuracy of various quantitative variables without access to primary data.
Python 3 Lambda

Python coders are traditionally imperative. They may program in the paradigms of structured or object-oriented programming, but seldom in functional program. The resurgence of functional programming in the past decade or so has sent Python coders into tailspin. Lambda functions appear in countless of code, not as pure functional but as hybrid to the traditional imperative. Various resources about lambda function add even more confusion. This article address lambda in the context of Python, with minimum jargon but with a number of illustrative examples.
Revisiting the Challenges of Input Parsing for Robust and Secure Software

A wide variety of programs exchange data in our internet-connected world and one of the perpetual security challenges is the ingestion of data produced by communication partners. The insufficient robustness in parsing and processing of input data by programs still leads to security incidents. This paper revisits and explores the problem of parsing input data, with a consideration of the programming language Rust. By its novel combination of features, Rust discourages certain programming errors which are the cause of common security issues. Consequently, this motivates its adoption in cases where a secure and robust implementation is required. Our exploration in this work suggests that Rust is a worthwhile programming language choice for implementation of input data parsing and processing.
BCTH: A Novel Text Hashing Approach via Bayesian Clustering

Similarity search is to find the most similar items for a certain target item. The ability of similarity search at large scale plays a significant role in many information retrieval applications, and thus has received much attention. Text hashing is a promising strategy, which utilizes binary encoding to represent documents, obtaining attractive performance. This paper makes the first attempt to utilize Bayesian Clustering for Text Hashing, dubbed as BCTH. Specifically, BCTH is able to map documents to binary codes by utilizing multiple Bayesian Clusterings in parallel, where each Bayesian Clustering is responsible for one bit. Our approach employs the bit-balanced constraint to maximize the amount of information in each bit. Meanwhile, the bit-uncorrected constraint is adopted to keep the independence among all bits. The time complexity of BCTH is linear, where the hash codes and hash function are jointly learned. The experimental results, based on four widely-used datasets, demonstrate that BCTH is competitive, compared with currently competitive baselines in the perspective of both precision and training speed.
Imperialist competitive algorithm for determining the parameters of a Sugeno fuzzy controller

Abstract We used an imperialist competitive algorithm to determine the parameters of a fuzzy controller of type Sugeno that would ensure a good unit step response of a second-order single-input and single-output automatic system.