Reviewerly/exHarmony huggingface dataset:
Playing Card Games on Adjustable Shuffling with Reinforcement Learning Agents

This paper shows an example application system that supports the users to find a suitable customized shuffling method for playing card games. To enable the users to utilize the characteristics of each shuffling method in each game playing, we develop a prototype gaming platform with a shuffling adjustment support mechanism that assists players in selecting the preferred shuffling method and the custom number of shuffling times to fit to their playing styles. We also develop a prototype player agent that behaves as the good counterpart in the exciting gameplay defined by Isabella et al. in 2004, applying QS-learning technique to allow the agent to change its behavior according to the characteristics of the shuffling.
Prediction performance on each interpolation and extrapolation for corrosion rates with laws using machine learning

Corrosion problems affects widely such as strength reduction, collapse of objects due to damage, damage to neighborhoods due to falling damaged objects. In order to prevent accidents before they are happen, it is necessary to be able to predict limitation of durability loss due to corrosion and to accurately calculate maintenance cycles. So the correct prediction for corrosion change is important significance. We are attempting to predict corrosion by deep learning, machine learning and statistical regression methods. This research aims to reveal prediction performance for corrosion change using their methods, specially by comparing with approximation functions based on supervised learning and approximation functions following physical law’s change based on statistical method. In addition to that we reveal the difference of the predict score between interpolation and extrapolation. Because the prediction on interpolation and extrapolation shows completely difference. The result showed that the best prediction method on extrapolation was logarithmic approximation (Feature: Day, MSE: 4.8, $R^{2}: 0.7$), Neural Network(Feature: A-D, MSE: 35.1, $R^{2}: 1.19)$. The best method on interpolation was exponential approximation (Feature: Day, MSE: $19.7, R^{2}: 0.98$), Neural Network(Feature: AD, MSE: $.23.9, R^{2}: 0.97)$.
A Game-based Augmented Reality Navigation System to Support Makerspace User Education in a University Library

In recent years, a makerspace has been regarded as an important learning field for cultivating students’ creative and thinking abilities. However, the diversified operations and applications of makerspace equipment may cause the librarians who are responsible for makerspace user education cannot burden their working load due to the limitation of manpower. Therefore, this study develops a game-based augmented reality navigation system (GARNS) to support makerspace user education by combining a game-based learning method and augmented reality (AR) tools, hoping to promote learners’ learning motivation and immersive experience, and enhances the learning performance of makerspace user education. Furthermore, this study also gathered learners’ learning behaviors for behavior analysis based on lag sequential analysis to understand the effective learning mode of learners using the GARNS. With a true experimental research method, a total of 24 Grade 11 students from a high school in Keelung City, Taiwan were recruited to participate in the makerspace user education. Among them, a total of 10 students were randomly assigned to the experimental group using the GARNS, the other 7 students were randomly assigned to control group 1 using the web navigation system, and the remaining 7 students were assigned to the control group 2 using the narrative guided tour with librarian to conduct makerspace user education. Analytical results show that learners can achieve significant learning effectiveness no matter using the GARNS, web navigation system, or traditional narrative guided tour with librarian for makerspace user education. There was no significant difference between the GARNS group and the narrative guided tour with librarian group, and between the web navigation system group and the narrative guided tour with librarian group. However, there was a significant difference between the GARNS group and the web navigation system group, and the GARNS group was significantly better than the web navigation system group.
Exploring the Impact of Syntactic Structure Information on Unknown Entity Recognition in Transformer-based Natural Language Understanding

Natural language understanding (NLU) of user utterances is an essential task in dialogue systems. However, existing machine learning models suffer from low accuracy in recognizing unknown entities that are not present in the training data. This paper investigates the importance of syntactic structure information in recognizing unknown entities for NLU. The study introduces a transformer-based language understanding method that incorporates syntactic structure information into a neural network using a Graph Neural Network (GNN) and evaluates its performance in recognizing unknown entities. The proposed neural network model combines features obtained from the pre-trained Transformer model ELECTRA and the optional Bi-LSTM layer and feeds them to the GNN layer. The findings of evaluating the model’s performance in unknown entity recognition and its impact on a transformer-based NLU model suggest that incorporating a GNN layer, in some cases, enhances the baseline models’ performance. However, the degree of enhancement depends on the dataset, which implies that the addition of syntactic structure information is not always helpful for entity recognition tasks.
Sufficient Algorithm for Inclusion of Pattern Caterpillar in Text Tree

In this paper, as the restriction of a rooted labeled unordered tree (tree, for short), we introduce a rooted labeled k-caterpillar(k-caterpillar, for short) that is a tree T whose number of the leaves in the tree obtained by deleting all of the leaves in T is at most k. Then, we design the sufficient algorithm to return “yes” implying that a pattern caterpillar P is included in a text tree T as a $k -$caterpillar. It runs in $O(k(h+H)\sigma)$ time, where h is the height of $P, H$ is the height of T, and $\sigma$ is the number of labels occurring in P and T. Finally, we give experimental results for the algorithm by using real data for caterpillars and trees as k - caterpillars.
Disfluency detection with context information from real utterances and generative utterances

Disfluency is one of the linguistic phenomenons that often occurs in speech. Recently, automatic speech recognition has improved the accuracy of its transcript. The perfect transcript contains filler, self-repairs, distluency, and so on, because they often occurs. We focus on distluency in speech recognition outputs. The distluency in the transcript can be noise for the downstream tasks that use the transcript. In this paper, we propose a BERT-based distluency detection model for dialogue utterance. BERT handles contextual information of a target utterance to detect distluency. However, dialogue utterances can be interrupted by other speakers in conversation. Hence, utterances in dialogue tend to be short and have less contextual information. Therefore, the context of the target utterances should be complemented. In this paper, we propose two types of complementing approaches. The complement is preprocessing for the input of BERT. The first complement approach utilizes real utterances around the target utterance. In this approach, we need to select sentences from real utterances, based on some conditions: e.g., the number of utterances and the same speaker or not. The second complement approach utilizes utterances generated by a language model. The language model is trained to generate continuous utterances from the input. The generated utterance is utilized for expanding the context of the input data. In experiments, utterances from the speaker of target utterances improve the distluency detection performance. However, generated context could not outperform the real context.
Application of 5-gram to Obtain Factor Scores of Japanese Q&amp;A Statements

In order to clarify the issue of mismatches between the questioners and respondents of Question and Answer (Q&A) sites, nine factors of impressions for Q&A statements were obtained through impression evaluation experiments. Then through multiple regression analysis, factor scores were estimated with the usage of feature values of statements. The factor scores estimated and obtained were subsequently utilized for detecting respondents who would be expected to appropriately answer a posted question. Nevertheless, this method has considerably been dependent on the syntactic information extracted through morphological analysis. Moreover, this method has a vital downside of requiring numerous explanatory variables and complicated multiple regression equation to estimate factor scores. Therefore, the principle has been shifted to applying N-gram over morphological analysis. In this paper, 5-gram is applied to the feature values, in a similar way as the previous analysis using shorter N-grams. Further analysis has shown that 5-gram would be also applicable to the method. In terms of estimation accuracy, N-grams also outscore morphological analysis; above all 2-gram and 3-gram show the best accuracy. Hence, it could be suggested that N-gram should play more important role in estimating factor scores than mere morphological analysis.
Hybrid Crowd-AI Learning for Human-Interpretable Symbolic Rules in Image Classification

Explainable AI is an indispensable goal for an AI-based society with trust, and deriving human-interpretable symbolic rules is one of the promising ways to verify whether the decision is appropriate. This paper explores a hybrid crowd-AI approach to develop white-box ML models associated with human-interpretable symbolic rules. The key idea of the proposed method is to discover human-interpretable latent features from trained neural networks by leveraging human abductive reasoning. The proposed method automatically generates crowdsourcing tasks that display subsets of images corresponding to each latent feature and ask crowd workers to provide the semantics of the features in natural language. The obtained semantics allow us to use the latent features as human-interpretable predicates that form symbolic rules to define target classes. We provide experimental results showing that the proposed approach can obtain interpretable symbolic rules and explanations.
CT Reconstruction from X-ray Videos with Conditional GAN Image Translation

Advancements in medical imaging technology have led to the emergence of rotatable X-ray scanning devices, which can capture multiple X-ray images from various angles (X-ray videos) with reduced radiation exposure and subsequent burden compared to computed tomography (CT) scans. However, the potential utility of X-ray videos remain underexplored. This study introduces a novel CT reconstruction technique that combines the Filtered Back Projection method (FBP) with image-to-image translation using a conditional Generative Adversarial Network (conditional GAN). While conventional FBP from X-ray videos produces CT images marred by artifacts and noise due to perspective distortion, our results demonstrate that the Pix2Pix image translation network can effectively mitigate these artifacts, yielding CT scans of superior qualitative and quantitative value.
Penerapan Model Make A Match Berbantuan Media ICT Pada Pembelajaran Tematik Untuk Meningkatkan Prestasi Belajar Siswa SD

This study aims to improve the learning achievement of fourth grade students at SDN 133 Seluma in thematic learning by applying the Make a Match type cooperative model assisted by ICT media. This type of research was Classroom Action Research (CAR). The subjects in this study were teachers and fourth grade students (17 boys and 16 girls) at SDN 133 Seluma. Student achievement data in this study used the average formula and the percentage of learning completeness. The results of student achievement in thematic learning cycle I for SBdP subjects the average grade of 69.24 with classical learning completeness 60.60% experienced a very good increase in cycle II with an average value of 82.12 and classical learning completeness 87 ,87%. Meanwhile, in the first cycle of Indonesian language subjects with an average score of 63.63 and classical learning completeness 57.57%, there was an increase in the second cycle with an average value of 80.39 and 81.81% learning completeness. Thus, it can be concluded that the application of the Make a Match cooperative learning model assisted by ICT media can improve student achievement in class IV SDN 133 Seluma in thematic learning.
Analyzing CLS Vector Variation in CodeBERT with One-Token Source Code Modification

This study aims to investigate the properties of CodeBERT, a Large-scale Programming Language Model, when editing source code from its original purpose to another. In programming languages, even small changes in the code can have a significant impact on the functionality and behavior of the program. One-Token changes, which refer to modifying a single character or word within the code, can have a particularly important role in this regard. In this study, we examine the vectors representing the programs that cannot be compiled or do not serve a clear purpose, which often occur during the process of adding features or modifying existing programs, with respect to the semantic distance from which they were edited.
Perbandingan Hasil Belajar Menggunakan Model Picture and Picture dengan Examples Non Examples Pada Pembelajaran PKn

This research aims to analyzed comparison the outcomes cognitif learning use Picture And Picture learning model with Examples Non Examples learning model. This type of research is a quantitative research. The research design used was Quasi Experimental Design with design type The Matching Pretest-Posttest Comparison Group Design. The test is done twice before and after giving treatment (treatment) in the class. The subjeck in this study is divided into two groups: students of class IV A in SDN 76 Kota Bengkulu as a experimental group 1 and students of class IV B at SDN 76 Kota Bengkulu as experimental group 2. The research instrument used is a learning result test with multiple choice questions given through pretest before the learning takes place and posttest after the learning takes place. Data of this research are analyzed by using descriptive analysis and inferential analysis that is t-test (independent sample t-test). Data of research result which is processed by using t test obtained t hitung equal to 2,34 and ttabel value equal to 1,667. Systematically seen thitung 2,34 &gt; ttabel 1.667 at 5% significant level, This shows that there are differences in learning outcomes between students taught by using pictures and pictures learning model with examples non examples learning model. then the average of the result study of Picture and Picture learning model was (68,22) and Example Non Example was (58,03).The concluded that the Picture and Picture learning model more effective to highest students result study.
A Review of the Use of Virtual Reality Studies in Social Psychology

Experience in virtual environments is becoming widespread in both daily life and scientific studies. With the advancement of virtual reality technology, new research questions can be posed. Thus, different experiences of the body, relationships with avatars, and the use of mobile and interactive media are increasingly preferred as research topics and research methods. In addition, the relationships that people establish in virtual environments have become a new type of interaction in social psychology that needs to be investigated. The aim of this review is to examine how virtual reality is handled both as a study tool and as a field of study in social psychology. First, technical terms and the necessary software and hardware are introduced, and the concepts of presence and immersion, which are important in the virtual reality literature, are explained. Subsequently, examples of studies conducted with virtual reality in areas such as the self, morality, identity, terror management, and social impact in the field of social psychology are presented. Technical limitations, such as the uncanny valley, virtual reality sickness, ethical issues, and experimental design issues that may arise in virtual reality studies, are discussed. Finally, the contributions of virtual reality to the field and what it can do are emphasized.
Investigating the Performance Impact of Dimensionality Reduction on Word Vectors

Word embedding has been essential in advancing state-of-the-art benchmarks in many natural language processing tasks. In training such word embeddings, the dimension of the vectors is an important hyperparameter that determines the cost in terms of training time and storage. Because it can be costly to retrain and find a good balance between storage volume and performance, practitioners can be interested in using dimensionality reduction algorithm such as PCA to obtain smaller-sized vectors for smaller computational and storage costs. This experimental study explores how far PCA can be used in reducing the dimension of word vectors without sacrificing too much performance. Our findings suggest that PCA can be used to reduce word vectors by up to half their size, within a range of frequently used dimensions for word vectors, while keeping reasonable performances on downstream tasks, but at the cost of possibly missing optimal performance from retraining.
TEACHING QUALITY IMPROVEMENT THROUGH “SIULIN”

The exam is one of the curriculum implementation activities, which is part of the evaluation of learning activities. A midterm and final semester exam learning system is implemented in one of the integrated Islamic elementary schools in East Java. In contrast, currently, the exam is carried out manually on paper. It could be more efficient, especially with the use of paper. Besides that, the teachers will add working hours to correct exam results. An online exam application design is needed that can help teachers in the online exam process and corrections. The fundamental objective of this research is to create a website-based online exam information system at an integrated Islamic elementary school in one of the cities in East Java. The research method is descriptive qualitative to find facts and original information. Data collection techniques for information system development are used using observations, interviews, and literature studies. While the development method used is the waterfall method. The results of this study are that it can make it easier for teachers to carry out the process of evaluating, correcting, saving teacher time in correcting exam answers, helping teachers to recapitulate grades, reducing the impact of cheating during exams, reducing paper use and making it easier for students to see exam scores
Innovation and Practice of English Language Teaching for Doctoral Candidates in Institutions of Higher Education

Nowadays, the internationalisation of higher education has become an important development direction, and the requirements for talent cultivation have been gradually improved. In this context, the construction and implementation of traditional English courses for doctoral students in universities can hardly meet the needs of doctoral training. In order to achieve scientific education and consolidate the development foundation of internationalisation of higher education, it is necessary to actively innovate doctoral English courses, scientifically make up for the shortcomings of traditional doctoral English teaching, and then ensure the quality teaching effect and improve the English teaching level of doctoral students. Based on the current situation, this paper will analyse the problems existing in the optimization of doctoral English courses in colleges and universities, and put forward specific measures in order to provide reasonable reference for the innovative teaching of doctoral English in the future.
A Study on Phishing Attacks and Its Legal Remedies

There are various Data-security threats in the contemporary world. One such threat is Phishing, wherein the act entails tricking a user into giving the attacker private and sensitive information. It targets bank account information, credit card information, usernames, and passwords among other things. Phishing attacks take the form of malicious emails, texts, and phone calls. Once the attacker has the information, they can steal identities commonly known as Identity theft and cause financial damages, among other crimes. This article provides a fair description of phishing assaults, including the many types of phishing attempts, how they are carried out, and how to identify, prevent them and the legal remedies that follow.
Russian education: deviant reforms and the loss of educational sovereignty (historical and legal review)

The article analyzes the content of state policy, tasks and ways of reforming Russian education at the turn of the XX - XXI centuries. It is proved that the implementation of the set goals was designed to audit and discredit the achievements of Russian pedagogy, ignoring research experience, deviation from legal and other norms (deviations) of the evolutionary development of the system in favor of revolutionary liberal transformations. Particular attention is paid to the analysis of the adopted normative legal acts that laid the deviant foundations in the state educational policy. It is noted that the non-critical borrowing of foreign technologies deprived the domestic system of independence, deformed the multi-level professional training of personnel, which led to a shortage of highly qualified personnel in all sectors of the national economy. The country lost the &amp;quot;knowledge race&amp;quot; and, under the influence of foreign advisers and consultants, doomed education to develop along a fading trajectory.
Enhancing Assamese Word Recognition for CBIR: A Comparative Study of Ensemble Methods and Feature Extraction Techniques

This study conducts a thorough assessment of ensemble machine learning methods, specifically focusing on the identification of Assamese words. This task is crucial for improving Content-Based Image Retrieval systems and safeguarding the digital heritage of Assamese culture. We analyze the efficacy of different algorithms, such as CatBoost, XGBoost, Gradient Boosting, Random Forest, Bagging, AdaBoost, Stacking, and Histogram-Based Gradient Boosting, by thoroughly examining their performance in terms of accuracy, precision, recall, Kappa, F1-score, Matthews Correlation Coefficient, and AUC. The Cat-Boost algorithm stands out as the top performer, achieving an accuracy rate of 97.7%, precision rate of 95%, and recall rate of 96%. XGBoost is also acknowledged for its substantial effectiveness. This comparative analysis emphasizes CatBoost’s superiority in terms of precision and recall. Additionally, it underscores the strong ability of ensemble classifiers to enhance assistive technologies, promote social inclusivity, and seamlessly integrate the Assamese language into technological applications.