Reviewerly/exHarmony huggingface dataset:
THE STRATEGY PECULIARITIES IN THE PROTEST FOR SAVING URBAN FABRIC IN TBILISI

After gaining its independence in 1991, Georgia faced significant challenges on all levels like all other post-communist countries. Therefore, against this background, care for urban historical and cultural heritage, and ecological health was less visible on the agenda. As a result of the Rose Revolution of 2003, various reforms were carried out. After strengthening state institutions, the re-urbanization of cities was gradually included on the agenda. At the same time, growing urbanization resulted in an acute shortage of green space and an uprising of the urban grassroots movements in Georgia. We have selected three cases of urban movement developed in Tbilisi, the capital of Georgia. “Save Gudiashvili Square,” “Defend Vake Park,”and “No to Panorama Tbilisi!”. Even though these cases occurred almost simultaneously, and the activists involved were quite the same, their achievements are different. The presented research attempts to determine the challenges that arose during the formation of the urban movement agenda and study the activism strategy and how it influenced the results of the movements. By triangulation of multiple methods – analyzing primary and secondary sources and interviews of involved actors –, we argue that the strategy of the movements played an important role, while not crucial, with regard to the studied civic activism.
Research on Human-computer Interaction Design System based on Computer Artificial Intelligence technology

This paper discusses the usability of electric vehicle control interface from the perspective of differentiated human-computer interaction design. Virtual reality technology is used to evaluate the human-computer interaction design scheme of vehicle to improve the iterative efficiency and reduce the investment of capital in the process of interaction design. This paper uses the interactive design scheme of real person and virtual vehicle to interact. At the same time, the test equipment was used to collect video, audio, eye movement track, skin electroencephalography, infrared brain and other data and information, and to conduct data analysis and design evaluation. Experiments show that the software system has good effectiveness and stability.
Bivariate Correlative Modest Adaptive Boost Resource Aware Task Scheduling in Cloud Environment

Cloud computing is a rapidly evolving paradigm that provides accessible and virtualized resources through Internet technology. In this model, Cloud Service Providers (CSPs) offer online access to computing resources for users to execute their application tasks. Task scheduling in cloud computing involves the allocation of computational tasks to available resources within the cloud environment. The primary objectives of task scheduling are to optimize resource utilization, minimize task completion time, and enhance overall system performance. Task scheduling plays a vital role in cloud resource management as it directly impacts the efficiency of cloud data centers. With the increasing number of cloud computing users, task scheduling has become more challenging, requiring the use of suitable scheduling algorithms. To improve task scheduling efficiency, a novel method called Bivariate Correlative Modest Adaptive Boost Resource Optimized Task Scheduling (BCMABROTS) is developed for efficient service provisioning in the cloud environment. The BCMABROTS method begins by collecting the number of incoming user-requested tasks. After that, the resources of virtual machines, such as energy, bandwidth, memory, and CPU, are measured. The ensemble classifier constructs the set of weak learners as the nearest prototype centroid classifier. The weak classifier uses Bivariate Correlation to classify the resource-optimal virtual machine based on the available resources. The results of the weak learners are combined to provide strong classification results. Once the optimal virtual machine is categorized, the task is scheduled for that particular virtual machine by the task assigner. This approach ensures efficient cloud service provisioning with minimal time consumption. Experimental evaluation is carried out to assess factors such as task scheduling efficiency, makespan, throughput and average response time in relation to the number of cloud-requested tasks. The observed performance results confirm that the BCMABROTS method improves the task scheduling efficiency, throughput and minimizes the makespan as well as the average response time than the conventional machine learning methods.
STUDY OVER EL NIÑO AND LA NIÑA INFLUENCE ON MEAN TEMPERATURE TRENDS AND PRECIPITATION IN BRAZILIAN REGIONS

The objective of this work is to describe and analyze historical series of climatic parameters of mean air temperature and rainfall, in June and December, in cities in Brazil. The data related to the daily historical series were obtained from 243 meteorological stations of the National Institute of Meteorology (INMET) and analyzes were performed by applying statistical tests for detecting possible trends in the referred time series. It was observed that the non-parametric tests of Mann-Kendall and Pettitt are the most suitable for the analysis of climatic variables, allowing to detect when a significant trend in a certain region of the country occurs. The correlation of these climatic magnitudes with the occurrence dates of the El Niño and La Niña phenomena allowed us to observe a strong association of the average temperature increase in years of strong El Niño events, especially in the month of December.
Enhancing Transaction Speed of Blockchain System via Parallel Proof of Work

Blockchain is an online distributed and decentralised ledger system that records transactions and maintains an eternal, verifiable record set of data. While blockchain is employed in many domains, its application in the financial sector is still in its infancy. In this sector, consensus algorithms are used in numerous forms for transaction verification, such as in Bitcoin, Ethereum, Litecoin, etc. Many resources and time are needed for this process. Using concurrent mining instead of solo mining is one way to solve this issue. The main goal of parallel miming is to ensure that the effort required to solve and verify a given block is not shared by more than two processes. In this book, we present several consensus algorithms used to various cryptocurrencies with various blockchain types, explain the bitcoin mining process, and attempt to employ parallel proof of work to accelerate the transaction verification process.
THE INFLUENCE OF PERSONALITY &amp; TRAINING ON EMPLOYEE PERFORMANCE WITH JOB SATISFACTION AS INTERVENING VARIABLES

This study is the result of research that describes and analyzes how much factors affect personality, training, and performance with job satisfaction as an intervening variable in the Agriculture and Food Security Service of the Special Region of Yogyakarta. This type of research uses quantitative methods with data collection techniques using questionnaires distributed manually to 110 employees at the Yogyakarta Special Region Agriculture and Food Office. The sampling technique uses simple random sampling, which is a technique of taking samples from members of the population that is carried out randomly without regard to the strata in the population(Sugiyono, 2015). Based on the results of the study after previously conducting validity and reliability tests, it shows evidence that the first direct effect, namely; personality has a direct effect on performance; second, training has no direct effect on performance; third, personality has a positive effect on job satisfaction; fourth, training has a positive effect on job; and fifth, job satisfaction affects performance; while the sixth, the results of indirect effects show that personality affects performance through job satisfaction, and seventh; training affects performance through job satisfaction.
AI-Driven Emotional Recognition in Digital Ads: A Novel Approach to Consumer Engagement

This This research aims to comprehensively review the current state of artificial intelligence techniques for emotional recognition and their potential applications in optimizing digital advertising strategies. A systematic literature review was conducted involving searches of academic databases and screening of papers on topics relating to emotion recognition using facial analysis, sentiment analysis, computational advertising, and measuring digital engagement.
Hybrid Swarm Algorithm for Mobile Robot Path Planning

The adoption of lightweight and effective swarm algorithms is required for low resource usage algorithms for mobile robot path planning crises. We present a hybrid swarm approach in this study that combines the best features of particle swarm optimization and river formation dynamics. This method looks for the shortest route while keeping the path as smooth as feasible. The best qualities of both approaches are combined and leveraged by the hybrid RFD-PSO methodology. While the RFD algorithm is well known for its smooth path discovery, it needs a lot of drops for good convergence and suffers from sinuosity problems. The generated hybrid RFD-PSO algorithm synergistically balances PSO's fast convergence with the river method's adaptive exploration and exploitation. Comparing the simulation results of the proposed method versus the Ant Colony Optimization (ACO), modified Ant Colony Optimization ACO*, PSO, RFD, A*, and Dijkstra’s, Hybrid RFD-PSO have better results in creating optimal path.
Festive or Failure? Differences in Sentiments of Consumer Tweets on Amazon Festive Flash Sale before and after COVID-19

Amazon’s Great Indian Sale is an annual mega shopping festival and provides an opportunity for retailers selling on e-commerce platforms to increase their sales, traffic as well as visibility. This study aims to understand consumer behaviour through the analysis of user-generated content (UGC) on social media concerning Amazon’s Great Indian Sale from 2015 to 2020. This time period was especially significant since it allowed for the analysis of differences caused by the pandemic. It was further divided into pre-COVID-19, during COVID-19 and post-COVID-19 period. The study also aims to establish a causal relationship between the feelings of engagement of the UGC on Twitter, with the strategies of marketing and promotion of the companies. Twitter’s API was used to extract the UGC. A two-step methodology was used to extract meaningful insights. First, Latent Dirichlet Allocation (LDA) was used to identify major topics related to the Flash sales organized during the festive season. Next, Sentiment Analysis was used to classify each tweet into Positive, Negative and Neutral Sentiments in order to understand user opinions and feelings towards the marketing campaign. It was found that both during the COVID period as well as the post COVID period, topics like Deals &amp; Offers and Exclusive Promotions were positively perceived by consumers. Topics like Pre-event Excitement, Diwali Shopping Experience, Customer Support, and Money-saving Opportunities generated positive feelings during the pre-COVID times. During COVID times, positive sentiments were generated by topics like Delivery Workers and Attractive Deals for Gifting. On the other hand, topics like Insults and Noise generated negative sentiments in both the COVID and post COVID periods. The findings of this study are significant not only to improve and devise marketing strategies and social media content for E-commerce companies, but also to provide an understanding of the effectiveness of festive sales.
Water Demand Modeling using Machine Learning Method in Bandung City, Indonesia

This research was conducted at Bandung City with the aim of building a model using machine learning methods so that it can estimated clean water demands in Bandung City, as well as knowing the external factors that are considered to affect the model. Machine learning is a part of Artificial Intelligence (AI) discipline. The modeling is carried out using independent variables in the form of climate parameters which are rainfall, rainy days, and humidity, as well as the dependent variable in the form of drinking water needs which are represented by raw water. Data collection is done through secondary data. The model was built by using the TPOT module, and produces the AdaBoost.R2 algorithm as the most optimal model, by using the model algorithm, the best sub-model is produced with the most influential external factors, namely rainy days and humidity which has an MAE of 326,077.70 and a MAPE of 4.75%. This model is compared with the ARIMA model which has an MAE of 330,672.088 and an MAPE of 5.07%.
The Construction of Complex Sentences with Predicative Phrases of Transitive Verbs in Japanese Language Learners Utterances: A Transformational Generative Grammar Study

This research examines how fukubun (complex sentence) with transitive verbs is constructed in the utterance of Japanese language learners in universities. The fukubun constructions are described and analyzed through transformational generative grammar. This research method is descriptive-qualitative, with data collection techniques in the form of semi-structured interviews. Data analysis uses an analytical approach of transformational generative grammar, namely analysis of phrase structure rules and analysis of the transformation process. The results showed that there were 4 (four) types of B-derived constructs, namely: BB type, BB (1) type, BB (2) type, and BB (3) type. Among the four types of derivatives, type BB construction is dominant in the utterance of learners. This is indicated by the tendency to use the BB-type construction when describing various situations, even though the utterance events use various conjunctions. The dominance of the BB-type shows that the utterances by learners are still relatively simple because it only requires one object in the subordination clause and main clause. Furthermore, B fukubun found eight varieties of conjunctive transformation used in this type. All varieties of conjunctive transformation appeared in the BB-type construction, with relatively frequent use of causality, serial, selection/alternative, and temporal conjunctions. The T-process analysis shows several tendencies in the elementary transformation process in the utterance, namely the omission of the subject in both the subordination clause or the main clause, the merger of the subject or the predicate of the verb, whereas in causality conjunctive transformation learners showed a more varied and complex T-process.
Advancing 21st-Century Creativity and Innovation into Japanese Educational System

The 21st century requires a new set of skills for success in a global economy. Creativity and innovation are essential components of skills, yet the current Japanese educational system does not place a strong emphasis on these skills. This paper stated the importance of project-based learning and technology integration approaches that can be used to advance creativity and innovation in the Japanese educational system. By promoting creativity and innovation in the Japanese educational system, we can help to develop critical thinking, problem-solving, and collaboration skills that are essential for success in the 21st century. The paper provides detailed strategies for overcoming challenges in advancing 21st-century creativity and innovation in the Japanese educational system. Finally, the paper emphasizes the importance of providing teachers with the necessary training and support to implement innovative approaches effectively.
Influence of Western Modernist Trends on Kyiv Music end Theoretical School In 1915–1925 Years

The purpose of the article is to reveal the scientific ideas of the first in Ukraine center of musicologists, formed in Kyiv in 1915–1925 years. The methodology was formed on the application of historical, gnoseological, formal-logical, systematic approaches using special-historical (comparatives and historical typology) methods of research. The scientific novelty of the article is that for the first time in Ukrainian musical historiography, an attempt was made to analyze the scientific, organizational and musicaltheoretical achievements of Ukrainian musicologists on the platform of a certain philosophical and cultural movement with its transformations in education and science on the example of the Kiev Scientific and Music School of the early twentieth century. The Conclusions. The group of the first professional musicologists in Ukraine should enter the history of Ukrainian music science as the Kyiv Music and Theoretical School. The unifying factor for them were common tasks (building the structure of national music education, searching for the essence of music science and the directions of its research research) and fundamental musical and theoretical principles. Filled with special knowledge and enriched with certain experience, leading figures of musical culture in Kiev in a short time (apprehension from 1916 to 1924) for the first time in Ukraine developed a three-stage structure of musical education of youth (B. Yavorsky), painted ways of development of musical science (B. Yavorsky, A. Butskoy). In its organizational activity, new post-revolutionary opportunities were used, and in scientific research they were based on the world's most common moderator philosophical theories. In the teachings about musical thinking and the structure of musical language B. Yavorsky and A. Butskoy consciously or intuitively embodied the ideas of many leading modernist philosophers in the West (especially E. Husserl, A. Bergson, V. Warringer). Despite the rejection of their innovative views at that time, some of them remain effective and relevant today.
Dynamic Capacity Planning in Data Centers for Latency Sensitive Workloads like Immersive Media &amp; AR/VR: A Decision-Engine Approach

This paper explores the intricacies of data center planning, specifically focusing on the management of internet traffic and the optimization of capacity for edge servers. In an era where the internet is integral to daily life, effective capacity planning is essential to meet the escalating demands on network infrastructure. The paper introduces factors influencing internet and traffic demands, highlights the concept of capacity planning, and outlines key elements such as traffic analysis, scalability, edge server deployment, and resource monitoring, with a special focus on the implications in futuristic applications like Augmented &amp; Virtual Reality Two primary methodologies for ensuring required capacity are discussed: the Built to Forecast Methodology and the Decision Engine Methodology. The Built to Forecast approach relies on historical data and usage patterns for proactive infrastructure building, while the Decision Engine approach utilizes intelligent algorithms for dynamic capacity adjustments based on real-time data specific to AR/VR workloads. The latter incorporates push-pull mechanisms and multi-echelon inventory management, addressing the unique challenges posed by the dynamic nature of AR/VR traffic. The paper delves into challenges and inefficiencies associated with the Built to Forecast Methodology, emphasizing scalability issues, the risk of technological obsolescence, inefficient energy consumption, and operational strain. In response, the Decision Engine Methodology is presented as a more adaptive alternative, incorporating push-pull mechanisms and multi-echelon inventory management for efficient supply chain optimization. The conclusion underscores the critical decision between a recommendation-based model and a built-to-forecast approach, emphasizing the trade-offs involved. The optimal choice depends on organizational requirements, risk tolerance, and industry characteristics, with the recommendation-based model excelling in scenarios of high demand variability. Ultimately, the paper advocates for a tailored and effective inventory management strategy aligned with the unique needs and circumstances of the organization.
Keefektifan Teknik Cinematherapy untuk Mengurangi Kebiasaan Mengonsumsi Minuman Keras Mahasiswa Maumere Universitas PGRI Kanjuruhan Malang

Consuming too much alcohol will affect one's attitudes and actions. The factors that can trigger this habit can be in the form of an environment that has a habit of consuming liquor, joining friends, experimenting, and so on. Apart from that, consuming liquor is also usually used by some people as an escape from the problems that are being experienced, where the intoxicating effect of alcohol in liquor can provide a momentary sense of calm for those who consume it. This study aims to reduce the level of drinking habits in Maumere students with cienematherapy techniques with pre-experiments with a one-group pretest and post-test design. The research subjects were eight students who had high criteria for the habit of consuming liquor. Giving treatment using group counseling using Cinematherapy techniques for eight meetings. The data collection instrument in this study used a scale of student drinking habits which was given at the beginning and end of the treatment and observation of each meeting. The results of the study were in the form of observation sheets and the Wilcoxon Sign Rank Test showing changes in scores before and after treatment. So it can be concluded that the Cinematherapy technique is effective in reducing the habit of consuming liquor among Maumere students at Universitas PGRI Kanjuruhan Malang.
EFEKTIVITAS MEDIA PEMBELAJARAN WAYANG JĀ̄TAKA TERHADAP MINAT BELAJAR SISWA SEKOLAH MINGGU BUDDHA DHAMMA METTĀ KALISAT

This study aims to determine the effectiveness of the jātaka puppet learning media on the learning interest of Buddhist Dhamma Metta Kalisat Sunday school students. The type of research used is a type of research that uses a classroom action research design. Data collection in this study using observation, documentation, and interviews, guided by observation sheets and interviews. The subjects of this classroom action research are Dhamma Metta Kalisat Buddhist Sunday School students at the Kindergarten, Elementary, and Junior High School levels. The results of this study indicate that students' interest in learning during the use of jātaka puppet learning media has increased. It can be proven by the results that were carried out before the use of the jātaka puppet, that the student's interest was 16 points or 40%. After using the media, students' interest in learning in the pre-cycle was 24 points with a percentage of 60%, in cycle 1 they got 26 or in percentage form it was 65%, and in the second cycle it reached 35 points and a total percentage of 87.5%. Therefore, it can be concluded that the use of wayang jātaka learning media is effective in increasing students' interest in learning at the Buddhist Dhamma Metta Kalisat Sunday School.
Construction and Standardization of Economics Achievement Test Using Classical Testing and Item Response Methods

The paper reports the Economics Achievement Test (EAT) construction and standardization.&nbsp; This study used a multi-stage sampling process involving simple random, stratified, and purposive sampling techniques. Five research questions guided the study. A self-constructed valid and reliable 120-item Draft Economics Achievement Test (DEAT) was administered to 1,200 students. Students’ sex, school location (urban or rural), and school type (publicly or privately owned) were reflected in the selection of participants to ensure representativeness. The instrument was trial tested and the Cronbach Alpha reliability coefficient was .714. The study made use of Iteman-4, range, and BILOG to analyze data. Results show that the number of items generated was higher in IRT than in CTT. It is recommended that in the construction, standardization, and selection process, both CTT and IRT approaches should be employed. The standardized Economic Achievement Test will be a good instrument for preparing students for either internal or external assessments and comparing their performance. &nbsp; KEYWORDS:&nbsp; Achievement, Economics, CTT, IRT, Standardized Testing