Reviewerly/exHarmony huggingface dataset:
A cross-cultural study of giving and responding to some compliments in English and in Vietnamese

Complimenting and responding to compliments reflect the&#x0D; norms and values of a nation and a country and are also expressed&#x0D; differently across cultures. In this article, the author conducts a&#x0D; cross-cultural study on giving and responding to compliments in&#x0D; English and in Vietnamese. The purpose of this study was to&#x0D; investigate how people give and respond to compliments in English&#x0D; and in Vietnamese and analyze and compare them to find similarities&#x0D; and differences. This analysis and comparison can help learners&#x0D; understand and use the target language appropriately so that cultural&#x0D; bottlenecks and communication breakdown can be avoided. This&#x0D; study used a quantitative technique method and was conducted using&#x0D; a questionnaire, focusing on how to give compliments and feedback&#x0D; about their appearance, assets, or work in English and Vietnamese.&#x0D; The findings of this study on compliments are useful in teaching&#x0D; English to Vietnamese as well as teaching Vietnamese to foreigners.&#x0D; The act of complimenting and responding to compliments - an aspect&#x0D; of communicative competence-needs to be carefully observed in&#x0D; order to maintain good relations among people in society.
Research on Encryption and Key Aggregate Searchable Decryption Methods for Data Storage in Cloud Securely

In today’s global, several corporations use data structures for their management of strategic and enterprise-important information. This kind of crucial problem wishes to be covered. no precedence can be given to the company. cloud computing ensures to extensively alternate the method we generally tend to use computers, access and preserve our private and industrial employer info. With this new computing and communications, paradigms arise brought statistics protection challenges. present statistics protection mechanisms have barriers in stopping records felony assaults, particularly those perpetrated with the aid of the usage of partner diploma agency executives to the cloud supplier to watch facts get right of entry to inside the cloud and find out peculiar facts get proper of entry to styles. Those kinds of challenges need to be addressed by the manner of growing a gadget with robust safety and privateness for cloud facts storage and facts sharing thru an encryption process. The variation to cloud-sharing files for the person for an encryption key technique to be used for lots of documents. Now, not entirely the encryption keys but additionally, the search keys need for use, and customers ought to maintain their keys impervious and disbursed. The present-day gadget the owner of the fact can solely percentage one key with the consumer, whether or no longer it's miles any kind of file, massive or small variety of documents, and the characteristic of the person is to cross a trap inside the cloud of ordinary overall performance finding out and safety evaluation of shared files, that's an impenetrable and great proposed gives schemes. And clients are additionally very concerned about information sharing garages, unexplained information leaks inside the cloud, and malicious attackers. In this paper, a trial has been made to layout an encryption mechanism for impenetrable records sharing.
Implementasi Data Mining Untuk Menentukan Minat Siswa Dalam Menentukan Jurusan Pada Perguruan Tinggi

Class XII high school students are students who occupy a period of formal education before entering lectures, students who are in the teenage age range where at this age they have to make decisions for their future. In making these decisions, adolescents are often accompanied by confusion, uncertainty, and even stress so that making decisions that result in regrets in the future. Every year there are many vocational high school students who want to go to a college but do not know what major they want and apply in the world of work according to their talents and interests, so there are still many students who make decisions that are not in accordance with their interests and talents. make decisions based on the opinions of parents, friends or others. For this reason, a model is needed to classify these problems. In this study, three classification algorithms were used: decision tree, nave Bayes, and k-nearest neighbor with data mining techniques to find patterns from the model used, the results of this study are expected to help students determine the majors to be taken in lectures. From the results of this research test, the factor that most influences errors in majoring in college is the variable of majoring based on (self/friends/parents), and of the three algorithms used, the decision tree algorithm is the best algorithm with a high level of accuracy 75.38%.
Human Activity Recognition Algorithm Based on ACNN-GRU

In the field of human activity recognition using wearable sensors, the accurate extraction of features from raw data and the development of an appropriate classification model are crucial for improving recognition accuracy. To respond to this and other challenges, we are put forward an ACNN-GRU deep learning model that amalgamate a convolutional neural network, a gated recursive unit, and an attention mechanism. The Convolutional Neural Network (CNN) virtually catch the spatial information in the sensor data, while the Gated Recursive Unit (GRU) models the temporal correspondence in the data sequence. By incorporating the attention mechanism, our model automatically learns and focuses on the salient features essential to activity recognition, resulting in more efficient extraction of fine-grained features from sensor data. Our method well identified six everyday activities: Walking, Standing, Sitting, Laying, Upstairs and Downstairs. Experimental results on the UCI open-source dataset demonstrate an impressive recognition rate of 97.65%, surpassing the performance of traditional classification models.
Digital technology in university mathematics education

The development of the technology in the past decade, called Industry 4.0 has an effect on every segment of everyday life, as well as on education. However the use of digital technology in education was launched earlier and it has become an increasingly more important part of education in the last decades, the digital education appearing due to the COVID-19 pandemic has stimulated this paradigm shift. Merely integrating digital technologies does not ensure effectiveness. There are solutions required that seamlessly blend with the methodological practices of traditional education to form an organic and cohesive whole. In this paper, we share the insights acquired from the period of distance education and explore additional potential applications of digital technology. Our goal is to show what opportunities these platforms offer.
Sistem Pembatasan Jaringan Internet Wi-Fi berbasis Voucher menggunakan Perangkat MikroTik

The high mobility in this information technology era has led many people to use wireless (Wi-Fi) internet access media instead of wired media. Devices such as laptops, tablets, PCs, and gadgets are inseparable from wireless technology. This study aims to limit Wi-Fi users and improve the efficiency and performance of the hotspot network. This study implements a voucher system using MikroTik devices and AAA (Authentication, Authorization, and Accounting) functions. The research development method uses a waterfall model consisting of 5 stages: requirements analysis, topology design, system implementation, performance testing, and maintenance. Data collection techniques involve direct observation of hotspot users who use vouchers, recording data on voucher duration, the number of users, and usage time. The collected data is analyzed quantitatively to evaluate the efficiency and performance of the voucher system and to gain insights into user experiences. The research results show successful implementation with 1, 3, 5, 8, 15, and 20 users using vouchers for 30-, 60-, and 120 minutes during morning, afternoon, and evening testing periods. Based on the testing and data analysis, this voucher-based Wi-Fi user restriction system successfully improves the efficiency of the Wi-Fi hotspot network by reducing the risk of illegal access and data theft.
Content is Fire and Virtual Reality is Gasoline: Understanding Users Attention, Comprehension, and Attitude in Destination Promotion

Virtual Reality (VR) has become an emerging technology that benefits the tourism industry by promoting destinations through content stimulation and interactive feature. Extant research overlooks understanding the effect of content stimulation and modality interactivity on persuasive outcomes: attention, comprehension, and visit intention. Hereby, this research tries to uncover this effect by conducting a randomized lab experiment with 14 undergraduate students from the information system department. Observing recall and recognition memory, we extend this study by understanding how repetition might influence individual cognition. This study found that respondents can recognize and recall more information in interactive VR, and repetition does not influence this outcome, as indicated by an inverted U-shaped graph. It also shows that users have more attention to the museum collections through interactive features with less intention to visit the destination. This study provides novel insights for practitioners and service scholars in utilizing VR features to promote tourism services.
Aplikasi Pengamanan Data Karyawan menggunakan Algoritma Advanced Encryption Standard dan Cloud Computing berbasis Mobile

Documents are valuable assets that often contain important information, including employee personal data, and are vulnerable to security threats such as theft and alteration by unauthorized parties. This research aims to develop an Android-based document security application that combines the Advanced Encryption Standard (AES) algorithm and cloud computing technology. This type of research is developed using the System Development Life Cycle (SDLC) model, which involves requirements analysis, design, coding, and testing. At the needs analysis stage, focus is given to employee data that needs to be secured. System design includes data structure, system structure, and user interface (UI). System design includes use case elements, class diagrams, and application architecture. Coding was done using Flutter as the user interface, Laravel as the backend, and MySQL as the database. System testing uses black box testing. Our test results show that the data security application is successful in carrying out the data encryption and decryption processes. Compared to previous web and desktop-based developments, this application provides a more adaptive solution to high mobility in the work environment. Cloud computing integration provides flexibility in data access, and the use of the Android platform significantly increases user mobility.
Predicting Experience Satisfaction and Use Intention in the Virtual Reality (VR) Fashion Store: Comparison between Immersive vs. Non-Immersive Types

Background/Objectives: This study was to predict experience satisfaction and use intention in the context of shopping at the virtual reality (VR) fashion store. Specific objectives were to compare regression models on the predictors of experience satisfaction and use intention between different types of VR devices, such as Head Mounted Display (HMD) vs. Personal Computer (PC).&#x0D; Methods: The VR store of fashion brand X was selected as a stimulus for experimental design with immers ive (HMD) and non-immersive (PC) types. A self-administered questionnaire was developed to measure technological attributes, mental imagery, choice heuristics, experience satisfaction, and use intentions. Two groups of participants were separately experienced the VR store with HMD and PC. A total of 262 usable data were obtained from consumers who were aged from 20s. For data analysis, exploratory factor analysis, t-test, and regression analysis were used via SPSS program.&#x0D; Results: The control and vividness were significant predictors of experience satisfaction in the immer sive type, while technological attributes and mental imagery were significant predictors in the non- immersive type. Also, there were differences on the predictors of use intentions between two types. The non-immersive type had a higher prediction power than did the immersive type.&#x0D; Conclusion/Implications: This study discussed managerial implications for the strategic VR store management in the fashion retail context.
Utilizing iPad in TEYL: Some Benefits and Barriers

At present, tablet devices are used in teaching in the schools across the nation, the most popular device is iPad and the role of&#x0D; teachers in using technology especially iPad is very crucial to serve the learners in order to make them learn better in a fun&#x0D; way. The objective of this study was to investigate the use of the iPad for teaching and learning in terms of benefits and the&#x0D; barriers through the experiences of classroom teachers and students in one of Primary School in Palembang, Indonesia.&#x0D; Qualitative methods, including teacher and student-participant interviews, classroom observations, and document review&#x0D; were utilized in this study. The purposefully selected sample consisted of three primary English teachers and two students&#x0D; grade 6 who were used iPad for teaching and learning. The research revealed that the use of iPad for teaching English for&#x0D; young learners was good. It had some advantages that support student learning and teacher teaching. However, despite the&#x0D; positive sides of using iPad, teachers and students faced numerous barriers, such as technical problem, lack of proper skill,&#x0D; and others.
Comparison of Support Vector Machine (SVM) and Linear Regression (LR) for Stock Price Prediction

Stocks are a type of investment with high risk and high returns because the data is dynamic. One of the capital market participating companies is PT. Vale Indonesia or commonly known by its issuer code INCO. Production PT. Vale Indonesia supplies 4% of the world’s nickel needs. In the future, the need for nickel will continue to increase because it is used for electric vehicle batteries. As demand increases, selling prices will increase and share price movements will fluctuate. Therefore, we need a prediction that is able to describe stock prices based on stock price movements in the previous period. This research compares the Support Vector Machine (SVM) and Linear Regression (LR) algorithms in predicting stock prices. This research is expected to obtain an algorithm with the best level of accuracy as a consideration for investors in choosing an algorithm to make predictions. The results this research state that Linear Regression (LR) algorithm is better than the Support Vector Machine (SVM) in predicting stock prices at PT. Vale Indonesia. The best Linear Regression Model divides the data into 90% training data and 10% test data to obtain an RMSE of 42.82, which means it is quite good at making predictions because it is quite close to zero. Meanwhile, the best Support Vector Machine model divides the data into 90% training data and 10% testing data to obtain an RMSE of 64.32, which means the model is not good at making predictions because the accuracy results are not close to zero.
COMPARISON OF HUMAN AND MODERN LARGE LANGUAGE MODELS THINKING

Thinking is a complex process, which humans try to understand since the very beginning of our existence. It includes generation of thoughts, making sense of the existing experience, analysis, drawing conclusions and making conclusions. Modern artificial intelligence systems are trying to replicate this process for different domains starting with texts and now even audio or video. However, generation of new knowledge, correct answers from existing knowledge or solving complex tasks requires step-by-step approach, analysis, sources comparison and even full-on planning or ability to reassess previous decisions. We try to describe the thinking process in this article, derive main properties and compare it to the way modern artificial intelligence models learn information and make decisions. Nowadays the necessity to understand this process more clearly is as relevant as ever, because it will give us an opportunity to classify current or future models as “artificial general intelligence” [1].
DC - DRC Optimization using Load Balancer (Case Study: Karawang Regional Hospital)

The development of modern applications to support company business processes requires management with high service availability. Most application development still implements traditional client server architecture, without considering the need for high availability. Karawang Regional Hospital currently still uses this traditional architecture, so data availability has the potential to experience problems. Data Center – Disaster Recovery Center (DC-DRC) is an opportunity to meet high availability needs, so the right method is needed to build a DC-DRC that has optimal performance. This research aims to increase data availability at Karawang Regional Hospital through the implementation of DC-DRC which has high availability capabilities. The load balancer method was chosen to divide the distribution of server performance loads. This research method adopts a network development process model, namely Network Development Life Cycle (NDLC) with stages including analysis, design, simulation, and implementation. Based on the test results, the applied load balancer can share the load on DC-DRC and support high availability at Karawang Regional Hospital. The results of this research can be applied to the development of DC-DRC which can be used to increase the availability of service quality.
Deepfake Forensics: Identifying Real Regions in Altered Videos with Digital Watermarking

This paper describes a method for detecting Deepfake videos using a lightweight yet secure video encryption algorithm. With the increasing use of digital media, transferring data via the Internet or other mediums requires protection. In the proposed method a digital signature is generated and encrypted using Asymmetric Encryption (RSA). This encrypted signature is then used as a blind watermark for the video. This technique aims to detect “face swap” type of Deepfake videos. It is an efficient algorithm and has minimal impact on the perceptibility of the video quality.
Optimasi Gaussian Naïve Bayes dengan Hyperparameter Tuning dan Univariate Feature Selection dalam Prediksi Cuaca

The importance of conducting weather prediction research is due to the significant influence of weather changes on daily life. The purpose of this study is to apply an optimal machine-learning classification method for weather prediction. The method used is the Gaussian Naïve Bayes model, which has been optimized using Univariate Feature Selection ANOVA-f test and Hyperparameter Tuning GridsearchCV techniques. The data used consists of 6454 daily weather data in Palembang City. There are 5 tests on the Gaussian Naïve Bayes model before and after optimization. The research results show that the optimization of the model successfully improves the performance in weather prediction. The highest accuracy result after optimization reaches 98.33% with 644 test data, an improvement from the pre-optimization accuracy of only 96.95%. Before optimization, the predictions for weather conditions such as sunny, cloudy/rainy, light rain, and heavy rain match the actual data. However, there were 20 prediction errors when dealing with data that should represent very heavy rain conditions. After optimization, the number of prediction errors for the very heavy rain data was reduced to seven. The optimization approach used in this research helps find the most suitable parameter combinations and eliminates irrelevant features, allowing the model to consider only significant features in weather p
Comparing Feature Matching Methods to Identify Persian Writers

A variety of frequently utilized feature matching techniques are employed and integrated in this study to tackle the task of writer identification using Persian scripts. The literature demonstrates promise for feature matching methods in the writer identification field. Multiple methods, such as SIFT, SURF, BRISK, BRISK-SURF, FREAK, SURF-FREAK, SURF-BRISK, Harris-SURF, Harris-FREAK, and Harris-BRISK, were employed and their performance was analyzed and compared to establish the optimal method(s). The identification process involves the comparison of the query image with all reference images, and as such, the reference image with the highest number of matched feature points corresponds to the query image. To the best of our knowledge, no extensive study has compared the effectiveness of these methods in the context of identifying Persian writers. Among the methods compared, SIFT and SURF algorithms have demonstrated the highest accuracy in recognizing the correct writer, while other algorithms have exhibited encouraging results.
An extension to the RISC-V instruction set architecture for quantum systems

As researchers race to realize novel applications for quantum technologies, they require an increasing level of flexibility and performance from their control and data acquisition hardware. In response, commercial and open-source control hardware solutions have increased support for features found in standard classical processors with varying levels of feature parity. This paper suggests that full processor feature parity for quantum control is possible and proposes an extension to the RISC-V instruction architecture to make that feasible.
On the Narrative and Architectonics Applied to Daniil Kharms’ Prose

The present article was written as a response to the formulation of the theme of the conference Musical Composition: Narrative and Architectonics (Gnesin’ Russian Academy of Music, 2022), where in the commonality between music, literature and architecture was stated – i.e., between music and those forms of creativity to whom narrative and architectonics initially pertain. Music possesses the equable capability of perception of compositional principles relevant to both the temporal and the spatial arts, and this circumstance places music in the center of a triple juxtaposition with narrative (and literature presented by it) and architectonics (and, correspondingly, architecture). The author of the article suggests turning the scheme of the triple juxtaposition in such a way, that not music, but architecture would appear at the center. And the focus would be not literature in general, but specifically on Daniil Kharms’ flash fiction — the primary sphere of observation of the peculiarities of the narrative in his works. In the organization of Kharms’ literary works both musical and architectural analogies play a discernible role, the substantiation of which we find in a number of his diary notes, drawings and theoretical texts. The “musical” traits of Kharms’ poetry and prose have been studied to a sufficient degree, which cannot be said about the inherent architectonic qualities intrinsic to them — let us compare them with “the law of symmetry, the law of equilibrium” mentioned in Kharms’ notes (Treatise about the Infinite, 1932). The instrument of the revealing of symmetry in a literary text is the metrotectonic method of Konyus. The article examines three examples of Kharms’ symmetrically aligned prose — the short stories The Optical Illusion, Symphony No. 2 and The Trunk Box.