Reviewerly/exHarmony huggingface dataset:
Tourism Destination Recommendation System Using Collaborative Filtering and Modified Neural Network

Tourism is one of the driving sectors of the national economy.Nowadays, the normal opening of tourist destinations after COVID-19 pandemic, tourist visits are currently increasing rapidly.Indonesia has a unique culture, nature, language, and cuisine.This is certainly a potential that can attract tourists to visit this archipelago country.To increase the attractiveness of tourism, one of the things that can be done is to create a recommendation system.This system needs to be built to provide users with personalized recommendations based on the input that the user has given.This research uses primary data.The data is a tourist place in the island of Madura.The amount of data consists of 160 tourist places.While the rating is done by 120 users.The system built consists of steps: preprocessing, creating, and training the model based on the data split, calculating the error, and showing destination recommendations to a user.Preprocessing converts "user" and "place" into integer indexes.Creating the model is done by embedding between "user" and "place".The rating is normalized to a number between zero and one using a sigmoid.Furthermore, training data is carried out using Modified Neural Network.The test results show validation_RMSE for each regency (Bangkalan, Sampang, Pamekasan, and Sumenep) is 0.3663, 0.3523, 0.3581, 0.3905.The recommendation system produces seven destinations as recommendations for places that have not been visited by the user.
From deadpan machine to relating socially: Middle school students’ experiences speaking English with embodied conversational agents

Speaking with an artificial counterpart in simulated environments has been shown beneficial for foreign language learning. Still, few studies have explored how it is experienced by younger students. We report on a study of Swedish students (N = 22) speaking English with embodied conversational agents (ECAs) in everyday-life scenarios. Data were collected on students’ rankings, choices, and open-response items in logbooks and questionnaires. Self-reported data on experiences were analyzed through three dimensions: cognitive, emotional, and social. Findings show that students were generally satisfied with the activity and emotionally engaged with large individual differences within the social dimension. We unpack aspects regarding social relating to the ECA, analyzing the space between experiencing the ECA as a socially distant “deadpan machine” to humanizing and relating socially.
Tracking Brand-Associated Polarity-Bearing Topics in User Reviews

Abstract Monitoring online customer reviews is important for business organizations to measure customer satisfaction and better manage their reputations. In this paper, we propose a novel dynamic Brand-Topic Model (dBTM) which is able to automatically detect and track brand-associated sentiment scores and polarity-bearing topics from product reviews organized in temporally ordered time intervals. dBTM models the evolution of the latent brand polarity scores and the topic-word distributions over time by Gaussian state space models. It also incorporates a meta learning strategy to control the update of the topic-word distribution in each time interval in order to ensure smooth topic transitions and better brand score predictions. It has been evaluated on a dataset constructed from MakeupAlley reviews and a hotel review dataset. Experimental results show that dBTM outperforms a number of competitive baselines in brand ranking, achieving a good balance of topic coherence and uniqueness, and extracting well-separated polarity-bearing topics across time intervals.1
PROBLEMS OF TEACHINGMATHEMATICAL DISCIPLINES DEPENDING ON THE DIRECTION OF TRAINING

The article discusses the problems arising in the implementation of mathematical training of bachelors and masters in technical areas that meet the requirements of the time in terms of digitalization of education, including the use of computer technology
Study on UV optical field distribution of partial discharge in GIS disconnector based on ray tracing method

Abstract In order to clarify the distribution law of the radiation optical field of the gas insulated switchgear disconnector defect and determine the detection range of partial discharge optical sensor, the optical model of the gas insulated switchgear disconnector based on the uniform geometrical theory of diffraction theory is established based on ray tracing method, and the distribution of the radiation light field is calculated when partial discharge occurs at the key positions such as the basin insulator, shield, and support insulator barrel. Based on the maximum irradiance, average irradiance and attenuation characteristics on the shell interface, the distribution law is further analysed, and based on this, the optimal location of the ultraviolet detector is proposed.
Greene and Petty's Criteria: The Quality of Grade II Students' Theme 4 Books

A good knowledge book is a novel that can accommodate students in solving environmental cases, does not bring wrong thoughts, and can be presented to scientific laws. This book is very useful for teachers as a learning resource to improve student learning outcomes. Through the evaluation of Greene and Petty theory textbooks, this study intends to evaluate the caliber of grade II students' books on subject 4, Clean and Healthy Living, in primary schools. Researchers employed qualitative research methodologies and qualitative research analysis approaches in this study. According to Greene and Petty, the study used ten criteria for high-quality books. The data source used is the book of grade II student themes 4 of the 2013 curriculum. Data were collected during this study using documentation and observation techniques. The strategies used in this work for interactive model data analysis include data reduction, data storage, and data validation. The method used to collect data is sheet analysis, consisting of a set of data collection criteria and tools. Based on the study's results, grade II student books theme 4 of the 2013 curriculum scored 93,5% with high-quality categories.
Persuasive presuppositions

A recurrent claim, coming from different approaches to pragmatics, argumentation theory and related disciplines, is that informative presuppositions have a special persuasive force. My aim in this paper is threefold: first, it is to achieve a better understanding of this claim. I argue that we should carefully distinguish between persuasion, which involves forming a belief, and accommodation, which requires acceptance of a proposition for the purpose of the conversation. Consequently, we should distinguish between the persuasive force of presuppositions and a different claim concerning our tendency to accommodate presuppositions. Second, I briefly discuss the evidence available for these claims, from the perspective of the distinction introduced. Finally, I argue that the Stalnakerian approach to presuppositions has the resources needed to offer an account of their persuasive force.
Problems and prospects of the federal project “Professionalitet” implementation as a tool for the secondary vocational education development

The paper considers the problems of implementing the federal project “Professionalitet”, which began in 2022 and is aimed at integrating technical schools (colleges) with enterprises of the real sector of the economy, with the participation of state support for the development of educational infrastructure at the expense of budget allocations of the federal budget. Based on the analysis of the performance of the indicators of the federal project “Professionalitet”, a generalization and systematization of the effectiveness of participation in it of the Rostov State Transport University was carried out. A systematic analysis of regulatory legal acts on the implementation of the federal project “Professionalitet” has been carried out, shortcomings of legal regulation have been identified and proposals for its improvement have been formulated. Taking into account the historically strong interaction of industrial technical schools (colleges) with railway transport enterprises, the old approaches in improving the activities of educational organizations for personnel training have been rethought and new approaches have been identified. The proposed approaches to the construction of educational and production centers (clusters) and the model of the infrastructure sheet can be useful for educational organizations when forming an application for participation in a competition for the creation and development of educational and production centers (clusters). The author suggests possible directions for the development of the federal project “Professionalitet”, first of all, meeting new trends in legislation in the field of education on the creation of educational and industrial complexes as part of educational organizations of secondary vocational education.
Transformer-Based Feature Fusion Approach for Multimodal Visual Sentiment Recognition Using Tweets in the Wild

We present an image-based real-time sentiment analysis system that can be used to recognize in-the-wild sentiment expressions on online social networks. The system deploys the newly proposed transformer architecture on online social networks (OSN) big data to extract emotion and sentiment features using three types of images: images containing faces, images containing text, and images containing no faces/text. We build three separate models, one for each type of image, and then fuse all the models to learn the online sentiment behavior. Our proposed methodology combines a supervised two-stage training approach and threshold-moving method, which is crucial for the data imbalance found in OSN data. The training is carried out on existing popular datasets (i.e., for the three models) and our newly proposed dataset, the Domain Free Multimedia Sentiment Dataset (DFMSD). Our results show that inducing the threshold-moving method during the training has enhanced the sentiment learning performance by 5-8% more points compared to when the training was conducted without the threshold-moving approach. Combining the two-stage strategy with the threshold-moving method during the training process, has been proven effective to further improve the learning performance (i.e. by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx ~12$ </tex-math></inline-formula> % more enhanced accuracy compared to the threshold-moving strategy alone). Furthermore, the proposed approach has shown a positive learning impact on the fusion of the three models in terms of the accuracy and F-score.
Barabási-Albert Çizgesinde K-Derece Anonimleştirmenin Performans Analizi

Anonymity is one the most important problems that emerged with the increasing number of graph-based social networks. It is not straightforward to ensure anonymity by adding or removing some nodes from the graph. Therefore, a more sophisticated approach is required. The consideration of the degree of the nodes in a graph may facilitate having knowledge about specific nodes. To handle this problem, one of the prominent solutions is k-degree anonymization where some nodes involving particular degree values are anonymized by masking its information from the attackers. Our objective is to evaluate the achievement of k-degree anonymization with a well-known graph structure, namely, Barabási-Albert graph, which is similar to the graphs on social networks. Hence, we generate multiple synthetic Barabási-Albert graphs and evaluate the k-degree anonymization performance on these graphs. According to experimental results, the success of k-degree anonymity approximately proportional to the number of edges or nodes.
Mobile robot vision navigation strategy based on Pixy2 camera

This paper aims to present an efficient navigation method for collecting tennis balls using a visual sensor, specifically the Pixy2 camera, which, in addition to the sensor, also has a high-quality processor built in. In order to achieve this goal, a navigation algorithm based on three situations encountered in practice when detecting a tennis ball was developed and the information received from the Pixy2 camera, that is width, height and two coordinates on the x-axis and y-axis was processed. Basically, two hypotheses were considered, namely: area decides the order and object coordination defines the choice of navigation strategy. Using a square consisting of 100x100 [mm] gray squares, the navigation method was validated based on a visual sensor, by studying five individual and representative cases of tennis ball location. For each case, the four data provided by the sensor were collected and then processed in order to highlight the two hypotheses initially established.
Decision Support System Design and Development for Determine Graduate Phase of College Students with Naïve Bayes Algorithm Web-Based in Indonesia Institute of Business and Technology

In the course of the lecture period, each student is different, various factors can affect the mental and academic achievement of students which have an impact on their graduation period. This things also impact on the campus of the Indonesian Institute of Business and Technology, until now there is no system that can determine the graduation period of students at the campus, therefore it is necessary to build a decision support system to determine the graduation period of students, especially students in the Informatics Engineering Study Program and Program Study of Computer Systems at the Indonesian Institute of Business and Technology (INSTIKI) using the Naïve Bayes algorithm that utilizes parameters, namely student academic data consisting of work status, Semester Achievement Index (IPS) scores semesters one untill four, and Grade Point Average (IPK). The system designed by using the GaussianNB library in python and website based. From the results of this study, the accuracy of the model made on the system produces an accuracy value of 87.5% with a dataset rule of 800 data and the data is divided and used as test data by 20%, while the accuracy value of classifying 250 test data on the system is 80.8%.
THE SIGNIFICANCE OF ANTI-CORRUPTION EDUCATION IN UNIVERSITIES

The importance of anti-corruption education in universities, formal and informal methods of promoting the fight against corruption are considered. The need for regular anti-corruption activities among students of all forms of education is emphasized
Hybrid multi-strategy chaos somersault foraging chimp optimization algorithm research

To address the problems of slow convergence speed and low accuracy of the chimp optimization algorithm (ChOA), and to prevent falling into the local optimum, a chaos somersault foraging ChOA (CSFChOA) is proposed. First, the cat chaotic sequence is introduced to generate the initial solutions, and then opposition-based learning is used to select better solutions to form the initial population, which can ensure the diversity of the algorithm at the beginning and improve the convergence speed and optimum searching accuracy. Considering that the algorithm is likely to fall into local optimum in the final stage, by taking the optimal solution as the pivot, chimps with better adaptation at the mirror image position replace chimps from the original population using the somersault foraging strategy, which can increase the population diversity and expand the search scope. The optimization search tests were performed on 23 standard test functions and CEC2019 test functions, and the Wilcoxon rank sum test was used for statistical analysis. The CSFChOA was compared with the ChOA and other improved intelligent optimization algorithms. The experimental results show that the CSFChOA outperforms most of the other algorithms in terms of mean and standard deviation, which indicates that the CSFChOA performs well in terms of the convergence accuracy, convergence speed and robustness of global optimization in both low-dimensional and high-dimensional experiments. Finally, through the test and analysis comparison of two complex engineering design problems, the CSFChOA was shown to outperform other algorithms in terms of optimal cost. For the design of the speed reducer, the performance of the CSFChOA is 100% better than other algorithms in terms of optimal cost; and, for the design of a three-bar truss, the performance of the CSFChOA is 6.77% better than other algorithms in terms of optimal cost, which verifies the feasibility, applicability and superiority of the CSFChOA in practical engineering problems.
Determinants of Cyberattack Prevention in UAE Financial Organizations: Assessing the Mediating Role of Cybersecurity Leadership

Cyberattack prevention factors have a significant impact on the perception of social and moral values in the business context. Despite leaders’ significant role in encouraging and enculturating cybersecurity practices in their organizations, there is a noticeable gap in the literature to highlight empirically how leaders and top management in organizations foster organizational cybersecurity. Therefore, this study aims to explore the role of cybersecurity leadership in financial organizations in preventing cyberattacks and investigate other human and non-technical factors related to the individual in financial organizations. Based on Protection Motivation Theory (PMT), the research framework was developed with the tallying of new variables focusing on the role of an organization’s cybersecurity leadership, training frequency, and the role of government frequent alerting. This research employed a quantitative research method. The data were collected through a questionnaire from 310 financial executive officers from selected banks in UAE that use digital technology to enhance their daily banking operations. Using Structural Equation Modelling (SEM), the results indicated (1) a significant association between all investigated independent variables and cybersecurity leadership through hypothesis (H8–H14); (2) cybersecurity leadership mediates the relationship between investigated independent variables and cyberattack prevention, from hypothesis (H15, and H16–H22); (3) no significant association between investigated independent variables and cyberattack prevention from hypothesis (H1–H6), except hypothesis (H4 and H7), which show a significant association. The coefficient of cybersecurity leadership in this study is viewed as a prevention element against cyberattacks based on the findings. With greater cybersecurity leadership success, the implementation of cyberattack prevention increases. This study emphasizes the importance of cybersecurity leadership in a cyberspace environment that protects against cyberattacks and promotes cybersecurity awareness within financial organizations and society in UAE.
Construction of a virtual reality platform for computer-aided navigation Lingnan bone setting technique

To establish a standard Traditional Chinese medicine (TCM) bone setting technique, standardize the operation and inherit the TCM bone setting technique. This project was based on the interactive tracking of bone setting techniques with a dedicated position tracker, the motion tracking of bone setting techniques based on RGBD (Red Green Blue Depth) cameras, the digital analysis of bone setting techniques, and the design of the virtual reality platform for bone setting techniques. These key technical researches were combined to construct an interactive bone setting technique. The virtual simulation system can reproduce the implementation process of the expert's bone setting technique. The user can observe the implementation of the manipulative technique from multiple angles; through human-computer interaction, the whole process of implementation of the bone setting technique can be simulated, and the movement and reduction of the affected bone can be observed at the same time. It can be used as a teaching and training system for assisting bone setting techniques. Students can use the system to carry out repeated self-training, and can instantly compare with the standard techniques of the expert database, breaking the traditional teaching mode of 'expected and unspeakable' and avoid directly using patients. Therefore, this research makes it possible to reduce teaching costs, reduce risks, improve teaching quality, and make up for the lack of teaching conditions. It is very positive for the inheritance of the traditional Chinese 'intangible culture' of bone setting techniques, and to promote the digitalization and standardization of bone setting techniques.Using computer technology to digitally record bone-setting manipulations.Construct a virtual simulation system for interactive bone-setting manipulation.Promote the digitization and standardization of bone-setting techniques.
Graph drawing using Jaya

Graph drawing, involving the automatic layout of graphs, is vital for clear data visualization and interpretation but poses challenges due to the optimization of a multi-metric objective function, an area where current search-based methods seek improvement. In this paper, we investigate the performance of Jaya algorithm for automatic graph layout with straight lines. Jaya algorithm has not been previously used in the field of graph drawing. Unlike most population-based methods, Jaya algorithm is a parameter-less algorithm in that it requires no algorithm-specific control parameters and only population size and number of iterations need to be specified, which makes it easy for researchers to apply in the field. To improve Jaya algorithm's performance, we applied Latin Hypercube Sampling to initialize the population of individuals so that they widely cover the search space. We developed a visualization tool that simplifies the integration of search methods, allowing for easy performance testing of algorithms on graphs with weighted aesthetic metrics. We benchmarked the Jaya algorithm and its enhanced version against Hill Climbing and Simulated Annealing, commonly used graph-drawing search algorithms which have a limited number of parameters, to demonstrate Jaya algorithm's effectiveness in the field. We conducted experiments on synthetic datasets with varying numbers of nodes and edges using the Erdős-Rényi model and real-world graph datasets and evaluated the quality of the generated layouts, and the performance of the methods based on number of function evaluations. We also conducted a scalability experiment on Jaya algorithm to evaluate its ability to handle large-scale graphs. Our results showed that Jaya algorithm significantly outperforms Hill Climbing and Simulated Annealing in terms of the quality of the generated graph layouts and the speed at which the layouts were produced. Using improved population sampling generated better layouts compared to the original Jaya algorithm using the same number of function evaluations. Moreover, Jaya algorithm was able to draw layouts for graphs with 500 nodes in a reasonable time.