Reviewerly/exHarmony huggingface dataset:
Cross-view Topology Based Consistent and Complementary Information for Deep Multi-view Clustering

Multi-view clustering aims to extract valuable information from different sources or perspectives. Over the years, the deep neural network has demonstrated its superior representation learning capability in multi-view clustering and achieved impressive performance. However, most existing deep clustering approaches are dedicated to merging and exploring the consistent latent representation across multiple views while overlooking the abundant complementary information in each view. Furthermore, finding correlations between multiple views in an unsupervised setting is a significant challenge. To tackle these issues, we present a novel Cross-view Topology based Consistent and Complementary information extraction framework, termed CTCC. In detail, deep embedding can be obtained from the bipartite graph learning module for each view individually. CTCC then constructs the cross-view topological graph based on the OT distance between the bipartite graph of each view. Utilizing the above graph, we maximize the mutual information across views to learn consistent information and enhance the complementarity of each view by selectively isolating distributions from each other. Extensive experiments on five challenging datasets verify that CTCC outperforms existing methods significantly.
Iterative Denoiser and Noise Estimator for Self-Supervised Image Denoising

With the emergence of powerful deep learning tools, more and more effective deep denoisers have advanced the field of image denoising. However, the huge progress made by these learning-based methods severely relies on large-scale and high-quality noisy/clean training pairs, which limits the practicality in real-world scenarios. To overcome this, researchers have been exploring self-supervised approaches that can denoise without paired data. However, the unavailable noise prior and inefficient feature extraction take these methods away from high practicality and precision. In this paper, we propose a Denoise-Corrupt-Denoise pipeline (DCD-Net) for self-supervised image denoising. Specifically, we design an iterative training strategy, which iteratively optimizes the denoiser and noise estimator, and gradually approaches high denoising performances using only single noisy images without any noise prior. The proposed self-supervised image denoising framework provides very competitive results compared with state-of-the-art methods on widely used synthetic and real-world image denoising benchmarks.
Discrepant and Multi-instance Proxies for Unsupervised Person Re-identification

Most recent unsupervised person re-identification methods maintain a cluster uni-proxy for contrastive learning. However, due to the intra-class variance and inter-class similarity, the cluster uni-proxy is prone to be biased and confused with similar classes, resulting in the learned features lacking intra-class compactness and inter-class separation in the embedding space. To completely and accurately represent the information contained in a cluster and learn discriminative features, we propose to maintain discrepant cluster proxies and multi-instance proxies for a cluster. Each cluster proxy focuses on representing a part of the information, and several discrepant proxies collaborate to represent the entire cluster completely. As a complement to the overall representation, multi-instance proxies are used to accurately represent the fine-grained information contained in the instances of the cluster. Based on the proposed discrepant cluster proxies, we construct cluster contrastive loss to use the proxies as hard positive samples to pull instances of a cluster closer and reduce intraclass variance. Meanwhile, instance contrastive loss is constructed by global hard negative sample mining in multiinstance proxies to push away the truly indistinguishable classes and decrease inter-class similarity. Extensive experiments on Market-1501 and MSMT17 demonstrate that the proposed method outperforms state-of-the-art approaches.
IntentQA: Context-aware Video Intent Reasoning

In this paper, we propose a novel task IntentQA, a special VideoQA task focusing on video intent reasoning, which has become increasingly important for AI with its advantages in equipping AI agents with the capability of reasoning beyond mere recognition in daily tasks. We also contribute a large-scale VideoQA dataset for this task. We propose a Context-aware Video Intent Reasoning model (CaVIR) consisting of i) Video Query Language (VQL) for better cross-modal representation of the situational context, ii) Contrastive Learning module for utilizing the contrastive context, and iii) Commonsense Reasoning module for incorporating the commonsense context. Comprehensive experiments on this challenging task demonstrate the effectiveness of each model component, the superiority of our full model over other baselines, and the generalizability of our model to a new VideoQA task. The dataset and codes are open-sourced at: https://github.com/JoseponLee/IntentQA.git.
IMPLEMENTATION OF RANDOM FOREST AND SMOTE METHODS FOR ECONOMIC STATUS CLASSIFICATION IN CIREBON CITY

The Indonesian government has tried various methods to eradicate poverty throughout the country, one of which is the fair distribution of social assistance to households based on their economic status classification. The determination of social assistance recipients can be influenced by political factors or personal relationships and can be misused, leading to assistance being given to individuals with specific political connections or support. This research aims to develop a household economic status classification system in Cirebon City using the Random Forest algorithm to address these issues. The data used in this research experienced an imbalance in the number of class instances, where the high-class instances were much fewer than the low and medium classes. To address this, the Synthetic Minority Oversampling Technique (SMOTE) was employed. In this study, various testing scenarios were conducted to obtain the best model for accurately predicting household economic status. Based on the research results, the best testing was achieved using Random Forest and SMOTE with a random state of 0, obtaining an accuracy of 93% and excellent performance in classifying each class. When testing unlabeled data, Random Forest successfully predicted 24 out of 30 actual data, resulting in an accuracy rate of 80%. Although this accuracy is lower than what was achieved by Random Forest and SMOTE with a random state of 0, it can be said that the application successfully classifies household economic status in Cirebon City effectively.
TECHNOLOGY OF SOCIO-CULTURAL PROJECTING: INTEGRATION OF SCIENCE AND PRACTICE

The article considers the connection of science and practice in the tech-nology of socio-cultural projecting, emphasizes its effectiveness in solving the society problems, multitasks of its development and well as the necessity of studying the discipline «Socio-cultural projecting» (as unity of science-practice) in the process of training personnel for the leisure and educational institutions. Some results of the empirical study, the examples from the au-thor’s pedagogical experience, are given.
Analysis of LQ45 Index Stock Movements using the ARIMA Method during Uncertainty in Global Economic Conditions in 2023

This research aims to analyze the movement of the LQ45 stock index using the ARIMA method during the uncertainty of the global economic conditions in 2023. The study used a quantitative approach with secondary data sourced from yahoo.finanace website from April 2021 to April 2022. The data were analyzed using the ARIMA method to identify the patterns and trends of the LQ45 stock index movement during the uncertain economic conditions in 2023. The results show that the LQ45 stock index is affected by the uncertain global economic conditions in 2023. The ARIMA method was able to capture the patterns and trends of the stock index movement and predict its future values. The study found that the LQ45 stock index is expected to experience fluctuations in the short term but will likely trend upward in the long run. The findings of this research can be used as a reference for investors and policymakers in making investment decisions and policies related to the stock market during uncertain economic conditions. The study also contributes to the literature on the application of the ARIMA method in analyzing stock market movements during periods of economic uncertainty.
Enhancing Trustworthiness and Interoperability of Electronic Voting Systems through Blockchain Bridges

Decentralized applications leveraging blockchain technology are gaining widespread adoption within the decentralized applications ecosystem. Interoperability, a fundamental concept facilitating seamless data and processing power exchange across diverse blockchain networks, is paramount in this context. The primary objective of this paper is to explore the transformative potential of "blockchain bridges" in facilitating secure and transparent electronic voting processes across multiple blockchain networks. The study employs a comprehensive analysis of various approaches, including atomic exchanges, sidechains, cross-chain bridges, token wrappers, and interledger protocols. The selection of a specific method is guided by the unique requirements and privacy considerations of the electronic voting use case. The application of two distinct blockchains serves as a practical demonstration, illustrating the principles of blockchain bridges in real-world scenarios. The research reveals that blockchain bridges not only streamline the exchange of data between diverse blockchain networks but also establish a dual decentralization paradigm. This paradigm enables the creation of openly maintained, purpose-specific, decentralized ledgers for electronic voting. The integration of blockchain bridges significantly reduces the risk of fraud, instilling greater confidence in the accuracy of election results. Thus, by presenting a comprehensive array of approaches and emphasizing their practical application, this research contributes to advancing the understanding and implementation of blockchain technology in the critical domain of electronic voting. Doi: 10.28991/HIJ-2023-04-04-04 Full Text: PDF
Exploring User Interactions in AR/VR Interfaces: A Simulation-Based Study

Virtual Reality (VR) and Augmented Reality (AR) technologies have gained significant attention in various domains. User interactions and error rates in VR/AR environments are crucial for improving system performance and user experience. In this study, a simulated dataset of 20,000 data points representing interactions of 1,000 users with four actions: “Select,” “Drag,” “Resize,” and “Rotate” is presented. Likelihood analysis is employed to explore error rates and their correlations with user-related factors such as action duration, fatigue, task success, preference, and learning rate. The findings deepen the understanding of error rates in VR/AR environments and provide insights into optimizing user experiences. The methodology of using simulated data and likelihood analysis can serve as a foundation for future studies investigating error modeling and prediction in real-world VR/AR applications.
The effectiveness of Konjo traditional game “Ak Bombo-Bombo” in English vocabulary mastery

Using games in an English class can enhance the student’s learning process. It offers an environment where the students can practice using new words and express themselves freely. This study aims to measure the effect of the traditional game the Konjo community “Äk’ Bombo-Bombo” in improving the student’s vocabulary mastery. The game is one of the popular traditional games in rural areas of the Kajang sub-district. Therefore, it is proper to be applied for students of elementary 115 in Tana Toa Village, Kajang. The study conducted a pre-experimental method with one group pre-test and post-test design. Independent and dependent variables were determined as the traditional game of Ak Bombo Bombo and the student’s English vocabulary development scores, respectively. There were 20 students in sixth grade in elementary school as the samples, which were chosen by using the total sampling technique. The results of this study showed that the student's mean post-test score (3.10) was higher than the pre-test score (3.05). Statistical tests also show that the value of the t-test, pre-test, and post-test is less than the alpha value (0.000 &lt; 0.05). It can be concluded that the traditional game Ak Bombo-Bombo increased the student’s English vocabulary significantly in a positive way.
Analisis Pengukuran Tingkat Kepuasan Pengguna Aplikasi Helpdesk Menggunakan Metode End User Computing Satisfaction (EUCS) pada UIN Raden Fatah Palembang

Purpose: This research aims to assess the satisfaction level of students with the online helpdesk application at UIN Raden Fatah Palembang, employing the End User Computing Satisfaction (EUCS) approach. The study involves the collection of data through questionnaires distributed to 100 users of the Helpdesk UIN Raden Fatah Palembang website. Research Methodology: Data analysis is conducted using the IBM software SPSS Statistica 26, involving initial stages of validity testing, reliability testing, and multiple linear regression. Five variables are explored in the research: content, accuracy, format, ease of use, and timeliness. A total of five hypotheses are tested, each associated with a specific variable. Results: The analysis reveals that out of the five hypotheses tested, two are considered insignificant as the t-values are smaller than the critical t-values. The t-values for content (X1) and timeliness (X5) are 1.105 and 1.888, respectively, leading to the conclusion that these two variables are not significant. From the multiple linear regression results, the independent variables (content, accuracy, format, ease of use, and timeliness) collectively influence the dependent variable, attachment, by 91.7%, while the remaining 8.3% is influenced by other unexplored factors in this study. Limitation: It is important to acknowledge the limitations of this study. The research is confined to the specific context of UIN Raden Fatah Palembang and may not be universally applicable. Additionally, the study does not delve into certain factors that could contribute to user satisfaction. Contribution: The findings contribute insights into factors influencing user satisfaction with the helpdesk application. In conclusion, the research employs rigorous testing procedures, including validity and reliability tests, and utilizes multiple linear regression for hypothesis testing.
DEVELOPING ENGLISH INTERACTIVE LEARNING MEDIA BASED ON ANDROID BY USING ARTICULATE STORYLINE 3 APPS

This study was conducted in response to the challenges encountered by students in learning English within an academic context. The identified issues included a lack of interactive learning resources, difficulties in comprehending English materials, limited opportunities for practice, and time constraints within the classroom. Consequently, the aim of this research was to develop an Android-based English interactive learning app using the Articulate Storyline 3 application, specifically tailored to support students in an academic setting.The research employed the Research and Development (R&amp;D) methodology, utilizing the 4-D (four-D) version and following the Luther-Sutopo model for system development. The target population consisted of seventh-grade students at SMPIT Al-Fityah Pekanbaru, with a sample size of thirty students. Data were collected through questionnaires and a pretest-posttest procedure. Statistical analysis involved the utilization of Aiken's V formula, the Moment Kappa formula, and the t-test formula. The study yielded three key results. Firstly, the validity test demonstrated a high validity score of 0.88, indicating the reliability and appropriateness of the developed learning app for its intended academic purposes. Secondly, the practicality test produced a score of 0.98, indicating that the learning app was practical and feasible to be used within the academic teaching and learning process. Lastly, the effectiveness test revealed a significant improvement, with a two-tailed significance score of 0.00, which was smaller than the significance level of 0.005. This indicates that the learning app was highly effective in facilitating the academic teaching and learning process. Based on the results of the three tests, it can be concluded that the developed app provides a valuable tool for both teachers and students in the academic English learning process. &#x0D;
An Experiment on Leveraging ChatGPT for Online Teaching and Assessment of Database Students

The task of generating SQL queries from natural language questions has long been a challenge for researchers, particularly in the context of teaching and tutoring database students. Existing efforts have yielded limited success, especially when it comes to assessing student assignments automatically. The discrepancy arises from the fact that students’ query solutions may differ significantly from those of instructors, despite producing identical results. The conventional approach to determining query correctness relies on establishing query equivalence, yet a satisfactory algorithmic solution remains elusive due to the lack of a solid theoretical foundation.In this paper, we explore an alternative approach by harnessing the capabilities of ChatGPT, an advanced language model with extensive knowledge. We conduct experiments to investigate if this "all-knowing" system can provide valuable assistance in generating SQL queries. Remarkably, our findings demonstrate that ChatGPT offers significantly more encouraging and superior results compared to existing solutions found in the literature. This research presents a promising avenue for leveraging ChatGPT in the realm of teaching and assessing database students. By capitalizing on the model’s extensive knowledge and language processing abilities, we open up new possibilities for improving the accuracy and efficiency of SQL query generation from natural language. These results contribute to the advancement of educational methodologies and provide valuable insights for researchers and educators seeking innovative approaches to enhance database instruction and assessment.
ANALISIS KEMAMPUAN MENYIMAK SISWA PADA PEMBELAJARAN BAHASA INDONESIA DI SEKOLAH DASAR

Language skills are needed for all individuals. This is because language skills are a model for developing students’ intellectual, social, and character abilities. The first language skill possessed by humans is listening. The purpose of writing this article is to analyze student listening skills in learning Indonesian at MI/SD. This research uses the library research method which is a series of activities related to the method of collecting library data, reading and recording and processing the research material. Listening is one of the most important language skills that must be mastered by students, but in reality many students do not like learning to listen. Most think that learning to listen is a boring lesson.
The implementation of CEFR in ESL Learning: Why does it matter to the Malaysian Education System?

Implementing the Common European Framework or CEFR in English as a Second Language learning in the Malaysian education system is the focus of this paper. CEFR is a generally established international standard for depicting language proficiency. It is also extensively recognised in Europe and is becoming more widely accepted globally; it is now incorporated into the Malaysian education system. The CEFR provides thorough descriptions of what foreign or second language learners can perform in listening, speaking, reading, and writing at six proficiency levels. Thus the methodology used in this paper is qualitative through review documents specifically on past studies from 2015 to 2021 highlighting CEFR implementation. This study aims to answer two research questions; what are the importance of implementation of CEFR in the Malaysian ESL classroom, and how CEFR can be implemented in classroom assessment in the Malaysian ESL classroom. The findings related to the first aim indicate that the CEFR is important for the needs of the international standard framework, benchmarking, alignment, and calibration. The findings also show that CEFR assists teachers in monitoring students' development in ESL classrooms. This article will also examine the history of the CEFR's implementation in the Malaysian education system and its influence on the curriculum, teaching and learning, and assessment. Then, the descriptors of CEFR in all four primary language skills, learning, speaking, listening, reading, and writing, will be briefly discussed before moving on to implementing the CEFR in classroom assessment. By the end of this paper, the recommendations for future study in implementing CEFR in the Malaysian ESL classroom will be proposed.
Genesis of group interaction phenomenology in complex social situations

Complex social situations, reflected in the experiences of group members, trigger the processes of group dynamics. These processes arise specific group phenomena, which reflect such experiences. In group interaction, there are processes and states (tension, confrontation, and conflict, crisis, trauma), which affect their member to feel discomfort, stress, frustration, traumatization. As a result, the social space of interaction has a change in the dynamics of integration and differentiation processes. According to the resources available in the group (values, status in society, creative potential), it uses available at the certain moment ways of cognitive and emotional processing of a complex social situation and produces group phenomenology. Group scenarios and interaction strategies can be classified as constructive and non-constructive ones, but their evaluation depends on the out- or ingroup affiliation of the one who evaluates the interaction. According to the resources, group phenomena can perform the functions of adaptation (maladaptation) of the group to complex social situations, as well as their acting-creative overcoming. In such conditions, the group solves two tasks that, depending on the circumstances, can compete with each other: overcoming social difficulties and preserving the group as a whole. Thus, in complex situations, in accordance with the dynamics of their development in the process of group interaction unfolds a consistent emergence process of psychological phenomena chain – phenomenogenesis. The scientific novelty of the research is that in contrast to the existing models and concepts of group dynamics in psychology, the concept of phenomenogenesis takes into account the social context of group interaction, brings together the whole spectrum of group phenomena, distinguishes their functions, and defines the tasks of groups
CEFR for Languages and Its Effective Implementation in Secondary Schools in Malaysia

The implementation and development of the Common European Framework of References (CEFR) have received recognition and enthusiasm in language learning globally. The purpose of the present study, therefore, is to investigate the effectiveness of the implementation of CEFR reference of language in other countries generally as well as in the Malaysian secondary schools specifically. The evaluation of Malaysian teachers’ perceptions toward the implementation of CEFR is substantially important to carry out the significant problems of implementing the framework. A systematic literature review was done to several research studies in Asian and especially Malaysia to achieve the fundamental research objectives. Despite the several challenges faced by English Second Language (ESL) teachers in implementing the framework. Therefore, it was found that CEFR has potentially improved in the language learning context.