Reviewerly/exHarmony huggingface dataset:
Providing Recommendations to New ILMCI Edu Voucher Customers Using the Market Basket Analyst Algorithm

Every company involved in the education business has sales data. One thing that really needs to be considered is the process of providing the best recommendations to customers. PT ILMCI INDONESIA as one of the companies involved in the education sector has not implemented the best recommendations regarding the education products offered, in running its business, of course, it is necessary to do good planning to increase the number of new customers. The purpose of this research is to provide the best recommendations to new customers in order to increase product sales by using sales data attributes from the sales process that has been done before. This recommendation analysis is carried out using market basket analysis algorithm, FP-Growth algorithm, W-Apriori algorithm, and W-Tertius. The highest confidence value from the market basket analysis algorithm is 67.6%. The highest support value generated from the FP-Growth algorithm is 70.2%. The highest confidence value from the W-Apriori algorithm is 63%. The highest confidence of the W-Tertius algorithm is 96.2%.
Learning Quantum System Disturbance Models with Probabilistic Bayesian Neural Networks

This paper presents a probabilistic machine learning approach that is well suited to learning unknown disturbance dynamics (which may be caused by errors, noise, and parameter variations) in quantum processes or systems from operational data using a Bayesian neural network. This approach considers the dependence of the quantum circuit fidelity (or quantum state output deviation) on the variations of parameters (or factors) such as relaxation time TI, coherence time T2, crosstalk intensity, extra decay, etc. These disturbance factors are used as input variables in the disturbance model, and the quantum circuit fidelity is the output. The respective probability distribution of the weights capturing the disturbance-to-output relationships are learnable parameters of the disturbance model and can be learned from operational data. The learning process may apply to quantify the uncertainty of disturbance in the partial system dynamics, while portions of the nominal system dynamics may be given (or learned from a separate dynamic model). The learned stochastic quantum disturbance models together with the known nominal physical models can be used in the design of quantum system optimal control. As an example, two cases about the applications of probabilistic modeling are studied to learn the noise dynamics in a particular quantum circuit based on simulated datasets. The results are analyzed in different scenarios, and a plan for future work is identified.
Do Not Feed the Phish: Phishing Website Detection Using URL-based Features

Due to the onset of the pandemic in previous years, industries have shifted to the use of digitalized services. Since most transactions were done online, people have become more susceptible to phishing attacks. With this, a steady rise in phishing attacks has been observed. Though cybersecurity awareness can help counter phishing attacks, improvement of existing phishing detection approaches remains desirable. Evolution of phishing websites is inevitable as phishers persist in bypassing current phishing detection approaches. This study then suggests a phishing detection model that makes use of URL-based features to detect phishing websites. Models were developed using two well-known classifiers, XGBoost and Random Forest. XGBoost classifier demonstrated the most promising performance with an accuracy of 96.51% and a kappa of 0.930.
Forensic Sciences and Ethics in the Era of Application of Artificial Intelligence

Forensic sciences are an indispensable segment of criminal investigations. Forensics, as a dynamic science that is constantly developing, follows the development of modern scientific trends. The application of artificial intelligence has not bypassed forensic science, which by definition discovers modern scientific methods, adapts them and applies them with the aim of discovering and interpreting (expert) material traces from the scene of a crime. With the initial optimism of the application of artificial intelligence, especially in the development and application of information technologies in forensic databases, comes (un)justified caution. If artificial intelligence were to take over the simulation of shell, cognitive thinking and decision-making more and more over time, the question of ethical responsibility arises. This raises a number of questions, one of the most important of which is who is responsible in the event of an error in the analysis. Furthermore, if artificial intelligence also takes over the interpretations of forensic analyses, who in that case bears the responsibility for a possible complaint about the end result - the opinion.&#x0D; Our paper will deal with the mentioned problems, emphasizing that the European Union, through the ENFSI network, was the first to react in the direction of studying the application of artificial intelligence in forensic sciences, with the strategic document ENFSI - Vision of the European Forensic Science Area 2030 „Improving the reliability and validity of forensic science and encouraging implementation of new technologies“, the most important parts of which will be presented in the paper.
Uniformity of Batch Job Recommendations and Real-time Recommendation

Collaborative filtering recommends items based on similarity measures between users and/or items. The basic assumption behind the algorithm is that users with similar interests have common preferences. There are a lot of applications where websites collect data from their users and use that data to predict the likes and dislikes of their users. This allows them to recommend the content that they like. Recommender systems are a way of suggesting similar items and ideas to a user’s specific way of thinking
Clinical intelligent diagnosis and treatment analysis of hemorrhagic stroke

With the continuous progress and development of artificial intelligence, it has been widely applied in many fields. In the current stage of rapid development of medical technology, many medical technologies need to analyze complex basic data based on artificial intelligence to provide further treatment plans or predict and analyze patient conditions. This article focuses on the risk factors of hematoma after hemorrhagic stroke in patients, and combines the patient's personal information, treatment plans, and other data provided. Based on factor analysis, a quantitative data dimensionality reduction model is established using BP neural network, GABP neural network, and other related models to achieve accurate and personalized prognosis prediction, treatment evaluation, and opinions. To predict the probability of hematoma expansion in patients, complex patient information is first dimensionally reduced based on factor analysis. Then, the personal history and disease history of the top 100 patients, as well as relevant treatment plans, are used as input features, and the prediction probability of hematoma expansion is used as output. BP neural network is used to predict the probability of hematoma expansion in patients.
Breaking the Bias: Gender Fairness in LLMs Using Prompt Engineering and In-Context Learning

Large Language Models (LLMs) have been identified as carriers of societal biases, particularly in gender representation. This study introduces an innovative approach employing prompt engineering and in-context learning to rectify these biases in LLMs. Through our methodology, we effectively guide LLMs to generate more equitable content, emphasizing nuanced prompts and in-context feedback. Experimental results on openly available LLMs such as BARD, ChatGPT, and LLAMA2-Chat indicate a significant reduction in gender bias, particularly in traditionally problematic areas such as ‘Literature’. Our findings underscore the potential of prompt engineering and in-context learning as powerful tools in the quest for unbiased AI language models.
On the Issue of Professional Self-Determination of Students (In the Case of Research at KNRTU)

Professional self-determination is accompanied by such concepts as formation, development, identification, self-realization in the profession. The article presents a brief review of the theoretical and methodological basis of the conceptual apparatus of professional self-determination from the point of view of sociology and peda-gogy. The results of the study of first-year students of the Institute of Innovation Management of Kazan National Research University and Kazan Technological College are described. The subject of the study is the profes-sional self-determination of students of institutions of higher and secondary vocational education. According to the results of the author’s research, the student's path, the needs and expectations of students, channels and sources of information, groups of reference persons who can influence the student’s professional self-determination are determined. Conditions for professional self-realization of students in the chosen profession are formed as a result of the study, the correlation of self-determination factors among university and college students is carried out.
Terraform and Import Support on Other Platforms

Our main focus up until this point has been to understand Terraform, reverse engineer it, and effectively import an existing VMware virtual machine into Terraform. Because Terraform is a single platform that can assist in defining our infrastructure as code (IaC) and manage numerous platforms, it is becoming more and more popular.
Comparative Analysis of Encapsulation in Java And Python: Syntax And Implementation Differences

Encapsulation is a fundamental principle of object-oriented programming, which allows for the hiding of implementation details and the protection of data from unauthorized access. This paper evaluates the concept of encapsulation in the programming languages Java and Python. The study examines the ways in which encapsulation is implemented in each language, including access modifiers and methods of data hiding. This research paper’s findings demonstrate that while both languages support encapsulation, the syntax and specific implementation vary in each of them. In particular, Java's use of access modifiers such as private, protected and public allows for a stricter level of encapsulation compared to Python's use of the '_' and '__' prefix to denote private variables and methods.
Forming graduate students’ vision regarding pedagogical activity of a classical university professor

In the introduction it is stated that forming graduate students’ readiness for pedagogical activity is based on the methodology of the post-non-classical science, which allows to use qualitative research methods aimed at interpreting opinions, standpoints, values of the actors as legitimate “data” and to define understanding of pedagogical activity’s specifics and the key trends in the development of higher education. Theoretical analysis is dedicated to understanding the purpose and meaning of pedagogical activity of a university professor from the standpoint of holistic approach. The article analyses valuable, theoretical and technological components of activity. It is pointed out that pedagogical activity can be logically analysed as a type of activity aimed at certain pedagogical problems. Empirical analysis is built around describing research results within the framework of solving academic pedagogical tasks by graduate students of Saratov State National Research University within the framework of the course named “University-level Pedagogics”. The article presents the results of academic tasks performed by graduate students. Conclusion Graduate students working as teaching assistants and instructors showed greater psychological readiness to carry out teaching activities at university. On the one hand, a significant fraction of graduate students has virtually no experience in teaching and, due to this, has certain expectations of teaching practice; on the other hand, the empirical study results indicate that it is this category that is interested in working under conditions of changes in the digital environment and mastering new educational technologies.
Code-Switching Analysis in EFL Classroom Interaction: A Sociolinguistic Study

This research aims to analyze the types and the functions of code-switchingin EFL Classroom Interaction by teacher and students. The type of this research is descriptive using a qualitative approach. The instruments which are used are observation, interview, and documentation. The observation is conducted 3 times while interview was done to English teacher and some students to know the use of code switching in EFL classroom. The result shows that code switching is mostly used especially in explaining material to students based on Poplack theory. Three types of code switching which was used by the teacher: tag switching occurred 5 times in 3 meetings, intrasentential code-switching occored 33 times in 3 meetings, and interrsentential code-switching occurred 22 times in 3 meetings. Meanwhile, Based on Sert, there are the three functions of teachers’ code-switching accomplished. They were topic switch occurred 37 times in 3 meetings, affective function occurred 16 times in 3 meeting), and repetitive function occurred 16 times in 3 meetings. Besides, there are two functions of students’ code-switching accomplished. These functions are equivalence appeared 3 times in 3 meetings and reiteration appeared 1 time in three meetings. In conclusion, the type of code switching that is most often done by teachers is intersential switching, which occurs in one sentence, when the speaker says a complete sentence in one language into another language. Meanwhile, the code switching function that is most often used by teachers is topic switch, namely the teacher does code switching when explaining material to students, while the code switching function that is most often used by students is equivalence, namely repeating to avoid mistakes.
Advancing Healthcare Security: A Cutting-Edge Zero-Trust Blockchain Solution for Protecting Electronic Health Records

The effective management of electronic health records (EHRs) is vital in healthcare. However, traditional systems often need help handling data inconsistently, providing limited access, and coordinating poorly across facilities. This study aims to tackle these issues using blockchain technology to improve EHR systems' data security, privacy, and interoperability. By thoroughly analyzing blockchain's applications in healthcare, we propose an innovative solution that leverages blockchain's decentralized and immutable nature, combined with advanced encryption techniques such as the Advanced Encryption Standard and Zero Knowledge Proof Protocol, to fortify EHR systems. Our research demonstrates that blockchain can effectively overcome significant EHR challenges, including fragmented data and interoperability problems, by facilitating secure and transparent data exchange, leading to enhanced coordination, care quality, and cost-efficiency across healthcare facilities. This study offers practical guidelines for implementing blockchain technology in healthcare, emphasizing a balanced approach to interoperability, privacy, and security. It represents a significant advancement over traditional EHR systems, boosting security and affording patients greater control over their health records. Doi: 10.28991/HIJ-2023-04-03-012 Full Text: PDF
The Digital Democracy of Blockchain: Beyond Libertarianism to Commonism

Blockchain, the latest form of open-source, peer-to-peer networking, has garnered much attention as a groundbreaking technology that may expand free and equal social relations. The progressive forecasts about blockchain’s possible contribution to digital democracy, however, tend to deal short shrift with the libertarian implications of blockchain-based projects. Critiques of the libertarian basis of blockchain, on the other hand, also tend to throw the baby with the bathwater by disparaging the technology’s commonist potential. The question at the heart of this chapter is how the combination of the commons and blockchain is distinct from the libertarian approach, on the one hand, and from most cryptocurrencies, including Bitcoin, on the other. By analyzing the case of FairCoin, the cryptocurrency of FairCoop, this chapter examines the implications of blockchain-based cryptocurrencies in organizing the common’s independent, autonomous, and global production and distribution of value.
Factors influencing students’ academic performance in universities: Mediated by knowledge sharing behavior

Recently, using Social Network Sites has been rising noticeably, especially among young people (18 years-34 years) who compose more than 50% of Facebook users. Accordingly, university students may find Facebook familiar and easier to share knowledge. This study attempts to investigate the impact of knowledge sharing using Facebook on student performance. This study examines the effect of Technology Self-Efficacy (TS), Information Self-efficacy (IS), Social Expectations (SE), Enjoyment (EN), Size of Social Network (SN), Personal Trust (PT), and Reciprocity (REC) as main factors on students’ academic performance as mediated by knowledge sharing behavior on Facebook in Jordanian University. Accordingly, a Covariance-based structural equation modeling (CB-SEM) using AMOS 22 was applied to test the model and posited hypotheses. A questionnaire-based survey was designed to collect the data based on a dataset of 450 university students from various five Jordanian universities. The results indicated that TS, IS, SE, EN, SN, PT, and REC positively and significantly affect students’ academic performance. Additionally, knowledge sharing on Facebook significantly as well as positively mediated the relationship between the proposed factors and students’ academic performance. This is one of the few studies which investigate the interrelationships among the examined factors, and the first to test the model on university students in Jordan. Practically, policymakers can depend on the results to create or modify the current learning environment by introducing the usage of social networks to help in increasing academic performance. Lastly, future research could study some other social network platforms looking at gender, educational level, or region.
Engineering of Information Monitoring System Sensor Reading Data Based on Smart Wireless using NVDIA Jetson Nano and Arduino Mega on Agricultural Spraying Machines

The focus of the research is monitoring data from sensors on the agricultural sprayer. The monitoring system support by some sensors in camera, tank capacity, boom sprayer balance and battery capacity. The research method was carried out using the waterfall model, because according to the needs that require a sequential flow in the process. This model is divided into four parts, namely analysis (to identify problems and needs), design (plans to solve problems to be solved), implementation (implementation of plans that have been made), and testing. Engineering of Information Monitoring System Sensor Reading Data Based on Smart Wireless using NVDIA Jetson Nano and Arduino Uno on Agricultural Spraying Machines. The test results for the CNN model for the detection of the “Jajar Legowo” object were carried out to obtain 90% accuracy, 82.35% precision and 100% recall. Tests an accuracy value cappacity tank of 100%. Testing the balance sensor, if rotates clockwise on the Y axis the output voltage decreases, and vice versa. However, if the sensor at rest, the output voltage will same as the offset value. Besides that, testing the optimum PWM value fuzzy approach is carried out with aim that the droplets hit the target zone when sprayer is working. The result are Arduino IDE and Matlab produce same value, which is 42 for the optimum PWM value. Testing the battery capacity sensor get accuracy value of 100% by difference in the voltage increase of 0.5 volts is equivalent to increase of 10%. All information read by the sensors is displayed on the LCD using WMS-2000 (smart wireless). Keyword: Fuzzy, Microcontroller, Monitoring, Sensor data, Smart wireless
Unveiling the Influencing Factors of Cryptocurrency Return Volatility

The blossoming of cryptocurrencies during the last decade has largely influenced both the financial and the technological world. Bitcoin emerged on the edge of the financial crisis in 2008, signaling the very beginning of a financial and technological innovation, which in continuance would eventually create a lot of questions and debate previously unforeseeable. This paper aims to explore the impact of factors such as trading volume, information demand, stock returns, and exchange rates on the volatility of returns for decentralized and unbacked cryptocurrencies from 2016 to 2022 by employing the GARCH model. Based on each coin’s innate functional characteristics and market performance quantified by their respective market capitalization, the selection included Bitcoin, Ether, and XRP as representative crypto coins for the category of decentralized and unbacked cryptocurrencies. The implementation of correlation analysis and the use of the GARCH model on influencing factors for each coin revealed that decentralized and unbacked cryptocurrencies are positively related to trading volume, information demand, and exchange rates while being indifferent to a certain extent to the stock market returns of the world stock index MSCI ACWI. The results of this study provide further insight into the behavior of cryptocurrency return volatility in the new, ever-changing, and highly unpredictable crypto market as well as aid investors in their decision-making process concerning portfolio optimization.
Teacher Creativity in Implementing Indonesian Language Learning Using Cursive Letter Writing

This qualitative research aims to explore the teacher's creativity in implementing Indonesian language learning through cursive letters at MI Nahdlatul Ulama 01 Purwosari Kudus and its impact. The study involved the school principal, the Class III Indonesian language teacher, and 28 Class III students. Data were collected through interviews, observations, and documentation. The findings revealed that the teacher's creativity in teaching Indonesian language through cursive letters involved the use of a scientific approach and the alternating habituation method accompanied by ice-breaking activities. Media such as posters, whiteboards, and fine notepads were also employed. The positive impacts of this implementation included increased student enthusiasm, improved and neater student writing, and gradual enhancements in learning outcomes for cursive letter writing. However, negative impacts were observed in the prolonged time needed for the process and the perceived difficulty in writing cursive letters, posing a distinct obstacle for students.
Development of Android-Based Indonesian Cultural Exploration Educational Game for Madrasah Ibtidaiyah Students

This research aims to develop an Android-based educational game to introduce Indonesian culture to Madrasah Ibtidaiyah students. Following the Research and Development methodology with ADDIE model, the study comprises three key stages: 1) analysis of students' and teachers' needs, 2) development of the educational game, and 3) testing the product's effectiveness. The study involved test subjects from twenty schools in Lamongan Regency. Data were collected using questionnaires and interviews, and data analysis encompassed both qualitative and quantitative methods. The material expert validation, media validation, and validation by learning experts collectively resulted in a 'very good' categorization. The educational game's effectiveness was substantiated by a t-test (tvalue &gt; ttable) and practicality tests, revealing a high interest level of 94.86%. In conclusion, this research significantly contributes to the development of an Android-based educational game for exploring Indonesian culture.