Reviewerly/exHarmony huggingface dataset:
An Interaction between Teacher and Student Digital Involvement in a Pandemic-Building Resilient Classrooms

The adoption of the new Hybrid Education model during the pandemic altered the teaching-learning techniques that posed instructors and students with a slew of obstacles as they attempted to adapt to the digital modes of communication.The objective of this research is to discover new challenges faced by teachers and students which stayed as roadblocks in the Technology adoption process.The research provides a new framework for depicting several stages of student-teacher digital involvement.A qualitative approach is employed in order to arrive at a grounded Theory.Purposive and snowball sampling techniques are used to contact teachers and students.The findings indicate that teachers and students are now confronted with a variety of new obstacles like Unstable internet connectivity, work-life imbalance, unethical methods, and response verification dilemma.The Exploration resulted in the development of a new framework that identified four quadrants depicting phases of student-teacher digital interaction.The outcomes of this study assist institutional management authorities in identifying the numerous problems that they would face when implementing online teaching-learning methods.The research is the first attempt to unite student-teacher conflicts by proposing a four-quadrant framework, thereby giving a road map for educational institutions seeking to increase students' self-reliance. .
A Unique Assessment Framework for Employee Engagement in Software Development Projects

An employee engagement assessment is implemented in many software development projects to access cognitive engagement and overall employee satisfaction.This paper develops the assessment framework for employee engagement levels in Software Development Projects (SDP) using the multi-grade fuzzy approach.The case study has been conducted and the results found that the employee engagement index of case SDP organization is 7.11, which indicates that the 'moderately engaged'.An Importance Performance Analysis is used to identify the weaker attributes.This framework will help SDP organization's managers to measure the employee engagement and give more importance to the weaker attributes to boost their engagement level.
Assessing the Usability of User Interfaces Game Tapel Saga Through Heuristics Evaluation

Tapel Saga is a game that was developed by processing the Malangan mask visual, which is a work of Indonesian culture in the Malang area, East Java.This game gives a message about heroism, solidarity, community involvement and friendship so that it is hoped that through the Tapel Saga game, teenagers experience a real projection and are helped in responding to mental problems in their daily lives.This study aims to maximize the quality of the game by evaluating the usability of the game's user interface using the heuristics method.The research method was carried out using Neuroresearch which focused on the exploratory stage.In the exploratory research stage, the heuristic evaluation of the Tapel Saga game was carried out using a focus group discussion system so that there was an in-depth discussion between resource persons and game developers to find input and evaluate the ten criteria of the heuristic evaluation.The results showed that out of the ten criteria, 1 had been completed, while the others were minor fixes and still needed development.The results of the heuristic evaluation help evaluate the usability of the game interface so that it is of higher quality.Heuristic evaluation helps highlight potential usability issues early in game development.
TAM Applied for Serious Video Games: A Systematic Literature Review

Since their inception, video games have greatly interested different types of users and organizations, and the academy is not an exception.The principal purpose of serious video games in the academy is to discover new ways to interact with students and strengthen learning and teaching.Video games in education are constantly used in a greater scope to improve the educational process.On the other hand, it is essential to consider the characteristics that must be considered in these types of tools so that they fulfil their purpose without neglecting the interest and motivation of the player.The objective of this work was to perform a systematic review of the topic "technological acceptance models (TAM) applied to serious video games".The investigation was done by performing searches in 6 scientific libraries to obtain a corpus of papers published between 2015 and 2021.Consequently, a lexical analysis was performed, and 4 clusters were identified about this topic: i) Emotions, social influence, and gratifications; ii) Structural equations; iii) Educational challenges with technology, and iv) The technology acceptance model.
Sciencetometric Forensic Linguistics Investigation

This research used a qualitative approach in literature review, using inclusion criteria and flowcharts to compile data into a synthesis based on scientific sources related to linguistic forensics that were obtained from various sources both journals and books using inclusion criteria. The researchers found 2436 journals that fit these keywords. A total of 439 journals found according to the search keywords were then screened, 163 journals were excluded because full text articles were not available. Eligibility assessment of 279 full text journals was carried out, journals that were duplicated and did not meet the inclusion criteria were excluded as many as 273, so that 6 full text journals were reviewed. Based on the results of the analysis and discussion above from this study, it can be concluded that with the development of forensic linguistics as a scientific discipline, linguistic studies are certainly needed both for oral variety data and written variety data related to language in legal processes, language in legal products and language in legal evidence.
PREDIKSI HARGA PANGAN DI PASAR TRADISIONAL KOTA SURABAYA DENGAN METODE LSTM

Long Short-Term Memory is the development of an artificial neural network that has the ability to overcome the vanishing gradient problem, and makes it possible to remember long-term information, and understand temporal patterns in time series data, so that LSTM has good performance in predicting food prices [1]. In Indonesia, especially in Surabaya, food prices are often unstable. Fluctuations in food prices can be caused by many factors such as weather, growing season and production. Under these conditions, this research was conducted to predict future food prices. The purpose of this study is to apply the LSTM method in predicting food prices so that it can provide maximum results and can be used by the community in making good decisions. In this study the dataset used included 5 types of food, namely rice, beef, chicken eggs, granulated sugar, and cooking oil. The dataset was obtained from the website of the National Strategic Food Price Information Center (PIHPS Nasional, https://www.bi.go.id/hargapangan). Predictive results are evaluated with RMSE and MAE. RMSE and MAE values of 5 types of food, namely rice 32 and 27, beef 229 and 125, chicken eggs 319 and 213, cooking oil 424 and 215, granulated sugar 30 and 18.
PENERAPAN FUZZY TAHANI PADA REKOMENDASI OBJEK WISATA DI JAKARTA

Tourism is an important sector in building the Indonesian economy. Jakarta as the nation's capital has a variety of tourist destinations that can be an attraction for tourists to visit the city. Due to diverse destinations, tourists find it difficult to choose destinations to visit according their preferences. Therefore, a decision support system is needed in recommending tourist destinations in terms of the average 3 stars hotel price, the number of nearby restaurants, the distance from airport to tourism and destinations rating. This study aims to develop a decision support system based on Fuzzy Tahani in recommending Jakarta tourist destinations. The decision support system development method uses the stages of Rapid Application Development,, which consist of requirement analysis, design, implementation and testing. The results of this study are designing a decision support system that applies the Fuzzy Tahani algorithm method with an interface in the form of a web page that helps tourists get recommendations based on the criteria entered. For the testing there are two tests, fuzzy algorithm testing by comparing fuzzy algorithm calculations manually with calculations from the system and Black Box testing to test the suitability of the functionality of the decision support system.
EDUCATIONAL TECHNOLOGY IN PROBLEM-BASED LEARNING

The problem-based approach to teaching aims to enable students to understand and evaluate the content taught.Organizing activities that involve problem solving includes: Creating a problem situation, defining the problem, selecting a problem-solving method, analyzing the results, drawing conclusions, and applying the acquired knowledge to new problem situations.Active learning with the help of technology enables faster implementation of planned activities.The main task of the teacher in problem-based teaching is to ensure conditions for creative and research activity of students in all the mentioned phases.The Faculty of Teacher Education in Rijeka, Croatia participated as a partner in several European projects aimed at implementing problem-based methods using educational technology in the learning and teaching processes of students, preschool educators and primary school teachers.The goal of these projects was to contribute to the development of creativity and logical thinking in children so that they can apply what they have learned to new problem situations.The most important activities of the project included the teaching of students as well as the professional development of educators and teachers in various innovative teaching methods using information and communication technology.This paper presents the educational activities for solving problems in practice with the aim of achieving the planned learning outcomes.
STANDARD OF FINAL YEAR STUDENT TEACHERS’ MAIN SUMMATIVE ASSESSMENT PAPERS AT A UNIVERSITY OF TECHNOLOGY

There are specific standards that are recommended for setting main summative assessment papers in accordance with Bloom's taxonomy of the cognitive domain.For final year student teachers, the recommendation is that eighty percent of the question paper must be pitched at Bloom taxonomy's upper cognitive levels, which are analyzing, evaluating, and creating.Only twenty percent of the question paper must be pitched at Bloom's lower cognitive levels, namely, remembering, understanding, and applying.This distribution is designed to assess higher order thinking and thus instill, promote, and reinforce independent and critical thinking, as well as problem-solving skills in final year students from the faculty of humanities as the final measure to prepare them for the envisaged world of work.To determine whether examiners comply with this recommendation, I analyzed ten question papers from the faculty of humanities through document analysis.The study found that some examiners pitch their question papers at Bloom's lower cognitive levels.Some spread the questions almost evenly throughout the paper, while only a few distribute the questions close to the required recommendations.Of concern was that some examiners inappropriately used action verbs belonging to Bloom's higher levels.This was evident through the posed questions and what the memorandum or marking guide revealed.It is imperative that questions are pitched at the recommended level, most significantly for final year student teachers.It is recommended that examiners be re-trained in setting question papers in line with the revised Bloom's taxonomy protocol.It is recommended also, that experts in assessment and Bloom's taxonomy be brought in to conduct workshops on the appropriate use of appropriate action verbs.
GENERATIONAL INTELLIGENCE AS AN INTEGRAL ASPECT OF PRE-SERVICE TEACHERS’ INTERPERSONAL COMMUNICATION COMPETENCE

Interpersonal communication is widely recognized as an important skill for success in personal and professional settings and is listed as a core competency in many educational frameworks around the world.Interpersonal communication is also a critical skill for teachers to establish and maintain cooperative relationships.As several East Asian countries have made the transition to an aging society, intergenerational communication has become a pressing need for teachers, as many students' primary caregivers are their grandparents, which is especially the case in rural areas.Teacher education usually emphasizes communication with students, their parents, and colleagues.However, interaction with older people is neglected.Clearly, teachers of the younger generation should have intergenerational knowledge and skills.This article reviews publications on intergenerational communication and presents an example of a communication accommodation theory to cultivate college teachers' perspectives when interacting with a heterogeneous group of people, the elderly.The results and findings of the activities shed light on the design of interpersonal communication activities in teacher education.
Meta Analysis of Marketing Innovation on Firm’s Performance of Small &amp; Medium Enterprises with the Moderating effect of Government Support Program: In case of Selected Sub-Cities of Addis Ababa, Ethiopia

This study looked at how market innovation affected SMEs' performance in Addis Abeba, Ethiopia, with the use of government support programmes as a moderator. The study's meta-analysis will be guided by the Schumpeter innovation theory, which also incorporates the diffusion of innovation theory, expectancy theory, institutional theory, stakeholder theory, absorptive capacity theory, resource-based view/theory, dynamic capability theory, R-A theory, and unified theory of acceptance and use of technology. The researcher used an effect size approach based on a forest and funnel plot to scan, exclude, and include relevant material. Researchers discovered a connection between the Dimension of Innovation and company performance on the basis of an extensive literature review. Also Based on the studied literature, the researcher discovered a link between the moderation of the government support programme and the performance of SMEs' firms in the area of innovation. To carry out this study, an exploratory sequential mixed research design will be used. Based on the current literature, the study attempts to create a conceptual framework and testable hypotheses. It was discovered that the impact of marketing innovation on a firm's performance was moderated by government support programmes. Businesses must be encouraged to adopt government support programmes with a moderating influence as a result.
Source-free unsupervised domain adaptation via bi-classifier confidence score weighting

Source-free unsupervised domain adaptation (SFUDA) seeks to adapt a pre-trained model from the source domain to the unlabeled target domain without using any source domain data. Some existing methods focus on learning transferable representations with only one classifier and cannot exploit full knowledge of the target domain. Therefore, they are unable to obtain a clear classification decision boundary between the source domain and the target domain. Therefore, we propose a Bi-classifier Confidence Score Weighting (BCSW) approach for addressing these issues. We first introduce a novel confidence score for sample weighting adaptation to boost the performance of the target model. Additionally, we exploit the sample mixup strategy to adequately utilize target domain knowledge to form better clusters. We also further design bi-classifier and adopt classifier disparity distance(CDD) to train the target model to effectively push cross-domain distributions close. Experiments show that our method achieves results equal to or better than the most advanced methods on standard benchmarks.
The Effect of Line Webtoon in Improving Vocabulary Mastery

This study aims to find out whether the students at SMAN 16 Banda Aceh who are taught English vocabulary by using line Webtoon get higher score compared to those who are not taught by using line Webtoon. This research used a quantitative approach with pre-experimental design, one group pre-test and post-test. The population in this study were 20 of class XI (IPA), and the total sample was 20 students consisting of one class, consisting of 11 males and 9 females. The data was collected through tests, namely a multiple choice question test. The results of data analysis showed that the average pretest score of students was 38.00. While the average score of the students' post-test was 77.25. The increase obtained from the average score of the pre-test and post-test was 39.25. In addition, the SPSS results show that a significant value (sig-2 tailed) of 0.00 is less than 0.05 (0.000.05), which means that Ho is rejected and Hₐ is accepted. Thus, it can be concluded that there is a significant difference between students who are taught vocabulary using the line Webtoon get a higher score than students who are not taught using the line Webtoon. It is concluded that line Webtoon has an effect on improving students’ vocabulary mastery.
Students’ Difficulties in Answering the TOEFL Listening Test

The aim of this study was to know the difficulties faced by students in answering the TOEFL listening test at the English Department of the University of Serambi Mekkah, Banda Aceh The research design used in this study was a descriptive quantitative study. The participants of this study were 25 students who have taken the TOEFL preparation course. In collecting the data, the author distributed a questionnaire, and all the obtained data were analyzed using the percentage formula. The result of the study showed that the student’s difficulties in answering the TOEFL, especially in the listening section are mainly caused by four factors as follows: (1) conversational implicature factor, (2) idiomatic expression factor, (3) lack of vocabulary factor, and (4) lack of practice factor. The most important factor was the first factor or conversational implicatures, which is 76%. Based on the conclusions above, the writer offers some recommendations with regard to the results of the study. Since the implicature factors seem one of the difficult factors for the students in answering the listening section of TOEFL thus the lecturers should encourage students to pay more attention to implicature factors.
The Effect of Crypto Currency Return and Volume on The Indonesian, Philipphine and Thailand Stock Exchange period of 2020-2022

Currently the crypto currency market continues to surge, and is experiencing explosive growth. These fast-rising crypto currencies are attracting the attention of investors and traders, as they offer the potential for huge profits quickly. This study aims to analyze the effect of Crypto currency Return and Volume on Stock Exchanges in 3 Countries, namely Indonesia, Thailand and the Philippines. This analysis uses the time series data regression method. The research period uses a monthly scale from 1 January 2020 to 31 December 2022, with a total of 108 data. The test results show data that crypto currency returns and crypto currency volume have an effect on the movement of the Stock Price Index in Indonesia, Thailand and the Philippines
Text and Instruction Necessary for the Facilitation of EFL Reader’s Predictive Inference Generation

Inference generation is necessary for reading to understand stories. However, it may be difficult for English as a Foreign Language (EFL) readers to generate inferences owing to their limited cognitive resources that are mostly used in lower-level processing such as lexical and syntactical analysis. Predictive inference, which facilitates readers’ context processing and construction of a situational model, can be challenging for EFL readers to produce during reading. This study focuses on textual conditions and teacher’s task instructions to suggest the appropriate text choices and task instructions to help EFL readers produce predictive inferences. Two story versions (High-predictability Target vs. Control) and two different instructions (Predict the end of the story vs. Memorize the names of characters) were compared using recall rates. The findings indicated that the combination of a high-predictability target story with instructions to predict the end of the story would be the most effective pair for predictive inference.
Downstream Task-agnostic Transferable Attacks on Language-Image Pre-training Models

Vision-language pre-trained models (e.g., CLIP) trained on large-scale datasets via self-supervised learning, are drawing increasing research attention since they can achieve superior performances on multi-modal downstream tasks. Nevertheless, we find that the adversarial perturbations crafted on vision-language pre-trained models can be used to attack different corresponding downstream task models. Specifically, to investigate such adversarial transferability, we introduce a task-agnostic method named Global and Local Augmentation (GLA) attack to generate highly transferable adversarial examples on CLIP, to attack black-box downstream task models. GLA adopts random crop and resize at both global and local patch levels, to create more diversity and make adversarial noises robust. Then GLA generates the adversarial perturbations by minimizing the cosine similarity between intermediate features from augmented adversarial and benign examples. Extensive experiments on three CLIP image encoders with different backbones and three different downstream tasks demonstrate the superiority of our method compared with other strong baselines. The code is available at https://github.com/yqlvcoding/GLAattack.
Minimum Max-Rank Method for High-Sigma Yield Analysis

In the field of high-sigma yield analysis, it is common to utilize machine learning-based surrogate models. However, current methodologies struggle with balancing the accuracy of the models with the computational cost. Our findings indicate that the conventional definition of accuracy is inadequate for high-sigma yield analysis, with the order or ranking of predictions being more crucial than relative error. To address this, we introduce the concept of Max-Rank as a Figure of Merit(FOM) for evaluating different models, and present the Minimum Max-Rank method as a means of producing models that result in faster, more accurate yield analysis. Finally, we compared our algorithm with commercial tools such as Cadence, WICkeD, varman, and others, and obtained satisfactory accuracy.
MRCap: Multi-modal and Multi-level Relationship-based Dense Video Captioning

Dense video captioning, with the objective of describing a sequence of events in a video, has received much attention recently. As events in a video are highly correlated, leveraging relationships among events helps generate coherent captions. To utilize relationships among events, existing methods mainly enrich event representations with their context, either in the form of vision (i.e., video segments) or combining vision and language (i.e., captions). However, these methods do not explicitly exploit the correspondence between these two modalities. Moreover, the video-level context spanning multiple events is not fully exploited. In this paper, we propose MRCap, a novel relationship-based model for dense video captioning. The key of MRCap is a multi-modal and multi-level event relationship module (MMERM). MMERM exploits the correspondence between vision and language at both the event level and the video level via contrastive learning. Experiments on ActivityNet Captions and YouCook2 datasets demonstrate that MRCap achieves state-of-the-art performance.