Reviewerly/exHarmony huggingface dataset:
Penggunaan Media Powerpoint Pada Pembelajaran Tematik di SD Muhammadiyah 1 Krian

Education is the basis for advancing a Country. In order to achieve a successful education system, therefore educators are expected to be able to improve the quality of educators who are able to educate by using various methods, and strategies and also present innovations prioritizing the use of technology. A problem found in the research is the thematic learning process, students seem to lack enthusiasm and do not pay attention to the teacher in following the learning process. In an era that is increasingly advanced in civilization, it also has an effect on the development of a field, including in the field of education. The existence of learning support media by utilizing technological advances will facilitate students in the learning process. One of the learning media used is power point learning media. The selection of the right learning media will have a good effect on student learning results, including thematic learning. The purpose of the research was to witness the use of digital learning media with the help of power points in thematic learning and to find out the student's response to the use of thematic learning media in grade 4 of SD Muhammadiyah 1 Krian. The research method used is qualitative, which is an elaboration method that describes a phenomenon that occurs in the field in a real way and as it is. Data collection instruments were carried out, including observations, interviews, and documentation at SD Muhammadiyah 1 Krian. The results of the research analysis that has been carried out refer to 4 aspects. As for the use of PowerPoint media in thematic subjects at SD Muhammadiyah Krian, it can be concluded in its implementation, the use of PowerPoint media in thematic learning at SD Muhammadiyah 1 Krian can attract and increase student motivation n in the learning delivered. With the use of PowerPoint learning media, the results of the assessment of student thematic learning above KKM
A SAT Solver + Computer Algebra Attack on the Minimum Kochen-Specker Problem

Abstract One of the foundational results in quantum mechanics is the Kochen--Specker (KS) theorem, which states that any theory whose predictions agree with quantum mechanics must be contextual, i.e., a quantum observation cannot be understood as revealing a pre-existing value. The theorem hinges on the existence of a mathematical object called a KS vector system. While many KS vector systems are known to exist, the problem of finding the minimum KS vector system has remained stubbornly open for over 55 years, despite significant attempts by leading scientists and mathematicians. In this paper, we present a new method based on a combination of a SAT solver and a computer algebra system (CAS) to address this problem. Our approach improves the lower bound on the minimum number of vectors in a KS system from 22 to 24, and is about 35,000 times more efficient compared to the previous best computational methods. The increase in efficiency derives from the fact we are able to exploit the powerful combinatorial search-with-learning capabilities of a SAT solver together with the isomorph-free exhaustive generation methods of a CAS. The quest for the minimum KS vector system is motivated by myriad applications such as simplifying experimental tests of contextuality, zero-error classical communication, dimension witnessing, and the security of certain quantum cryptographic protocols. To the best of our knowledge, this is the first application of a novel SAT+CAS system to a problem in the realm of quantum foundations.
An image patch selection algorithm for the detection of Oral Squamous Cell Carcinoma using textural and morphological features

Abstract A significant proportion of cancer-related deaths are caused by oral cancer, and oral squamous cell carcinoma is prevalent. Systems for computer-aided diagnosis can lower subjective errors and assist the pathologist in making a more accurate diagnosis. Feature extraction from the whole histopathology images is difficult due to the structural variation of the oral tissue images. A new patch selection algorithm can be used to create and select image patches containing nuclei-specific information and extract their textural and morphological characteristics, improving cancer diagnosis. We extract the morphological characteristics of the nucleus from the selected patches using five pretrained networks. The texture from the regions of the selected patches is also extracted from the Haar wavelet decomposed components using the gray-level co-occurrence matrix. Then, we combine the textural and morphological features to create the final feature vector, followed by feature selection using an extra trees classifier. In order to detect oral squamous cell carcinoma, we examined the feasibility of using six classifiers, including voting classifier, logistic regression, random forest, Naive Bayes, K nearest neighbor, and support vector machine. The performance of the algorithm is evaluated using accuracy, precision, sensitivity, confusion matrix, ROC curves, and AUC values of characteristic curves. Together, ResNet 50 and the voting classifier produce results with a high accuracy of 97.66 % and a precision of 98.00 %. The suggested patch-based method outperforms the image based method and is accurate and efficient for identifying oral squamous cell carcinoma and will be a reliable and precise support tool for oral pathologists.
Effects of Applying Drama-Based Activities in Speaking Classes on EFL Students’ Speaking Performance

The purpose of this study is to investigate the effects of applying drama-based activities in speaking classes on high school English as a foreign language (EFL) students' speaking performance in terms of grammar, vocabulary, pronunciation, fluency and discourse management; and their perceptions of implementing dramabased activities in speaking lessons.This quasi-experimental study which used the mixed method approach was conducted with the participation of sixty eleventhgrade students at a high school in Can Tho city.The quantitative data from the results of the pre-test and post-test were done to find out the effects of applying drama-based activities on high school EFL students' speaking performance.The quantitative and qualitative data from the questionnaire and interviews focused on students' perceptions of implementing drama-based activities in speaking lessons.The findings of the study reveal that drama-based activities had positive effects on students' speaking performance, especially in term of discourse management and students had positive perceptions of implementing drama-based activities in speaking lessons.
An image copyright authentication model based on blockchain and digital watermarking

Abstract This paper proposes an image copyright authentication model that combines blockchain technology, digital watermarking, IPFS(InterPlanetary File System) and perceptual hash function , aiming to solve the secure storage problem of watermark images in digital image copyright authentication. This paper designs a spatial domain watermarking algorithm to implement watermark embedding and detection, and verifies its imperceptibility and correctness through experiments. This paper also uses the content addressing feature of IPFS and perceptual hash function to obtain a unique identifier, and records it on the blockchain to achieve secure storage and distribution of watermark images, and adopts image encryp-tion technology to make them visible but unreadable to unauthorized visitors. The innovation of this paper lies in proposing a digital image copyright authen-tication model that combines multiple technologies, which can effectively protect watermark images from tampering, leakage and infringement.
Heuristics and Usability Testing of a Project-Based Learning Online Course: A Case Study with Structural Mathematical Concepts

Usability refers to the system's functionality by the user, allowing the system to be used with ease of learning, efficiency in carrying out tasks and satisfaction.The article presents the design and development of an online course about structural mathematics concepts with a project-based learning approach.With this study, we intend, through the development of an online course, to explore the contribution of project-based learning to approach mathematical structural concepts.The methodological option was development research, fulfilling the following phases of the protocol: (1) preliminary investigation, (2) theoretical embedding, (3) empirical testing and (4) documentation, analysis and reflection on process and outcomes.We started with an exploratory analysis aimed at identifying a group of teachers' knowledge about structural mathematical concepts and the potential of a project-based learning to support students in apprehending these concepts.Based on the data obtained, the design and development of the online course were carried out.Then, heuristic evaluation tests were conducted with experts and usability tests with the target audience to assess the adoption of the online course.The online course was designed to involve teachers in learning mathematics concepts through a project methodology.In the preliminary investigation, all teachers showed interest in knowing the project learning methodology and identified concepts that, from their practice, are usually difficult for their students.In the heuristic evaluation test with the experts, linguistic, structural and course content corrections were made.The results allow us to say that the online course with the projectbased learning approach responded to the target audience's needs, arousing their interest in using project learning in teaching mathematics structural concepts.
Differences in Self-Regulated Learning Between Gifted Students, Students with Special Needs and Other Students in Slovenian Schools

Self-regulated learning strategies play a crucial role in learning progress and academic achievement of different groups of students.The purpose of the present study is to investigate differences in the use of self-regulatory strategies among a sample of 1,495 students, aged 12 to 15 years, representing three groups: gifted students, students with special needs, and other students.The theoretical framework for the study is Pintrich's (1991) model of self-regulated learning.Data were collected using the Motivated Strategies for Learning Questionnaire (MSLQ).Results indicated that gifted students scored significantly higher on the MSLQ subscales of motivation and learning strategies than students with special needs and other peers.Special needs students reported lower intrinsic and extrinsic goal orientation and weaker self-efficacy in learning and achievement than other students.There were no significant differences between these two groups on the MSLQ learning strategies subscales.Positive and statistically significant associations between the MSLQ subscales and final grades in three school subjects (Slovenian, mathematics, and foreign language) were also confirmed.We discuss the implications of our findings for future research and the educational context that contributes most to the development of self-regulated learning in all groups of students.
qLEET: visualizing loss landscapes, expressibility, entangling power and training trajectories for parameterized quantum circuits

We present qLEET, an open-source Python package for studying parameterized quantum circuits (PQCs), which are widely used in various variational quantum algorithms (VQAs) and quantum machine learning (QML) algorithms. qLEET enables the computation of properties such as expressibility and entangling power of a PQC by studying its entanglement spectrum and the distribution of parameterized states produced by it. Furthermore, it allows users to visualize the training trajectories of PQCs along with high-dimensional loss landscapes generated by them for different objective functions. It supports quantum circuits and noise models built using popular quantum computing libraries such as Qiskit, Cirq, and Pyquil. In our work, we demonstrate how qLEET provides opportunities to design and improve hybrid quantum-classical algorithms by utilizing intuitive insights from the ansatz capability and structure of the loss landscape.
Evaluation of the School Literacy Movement in Primary Schools

This research aims to evaluate the implementation of a literacy program based on predetermined indicators, with the expectation that the findings will benefit future policy formulation. The research used a qualitative approach, specifically the Context, Input, Process, and Product (CIPP) evaluation. Data collection involved interviews and observations. The School Literacy Movement is a government initiative aimed at improving literacy levels in Indonesia. In the Muaro Jambi District, a primary school serving as an implementer of the School Literacy Movement experienced a decline in literacy education based on educational report card scores. After conducting the research, it was found that the school already had clear goals in the context, but it required additional inputs such as integrating the vision and mission to motivate teachers and students, establishing a School Literacy Team (TLS), implementing Standard Operating Procedures (SOP), and optimizing the use of facilities and resources. In the process, academic supervision and creativity in finding appropriate learning media were still needed, along with personalized guidance for students to identify their learning styles to ensure that the outcomes aligned with the intended goals. The research highlights the importance of various factors in implementing the literacy program, and the findings can contribute to improving future literacy policies in schools.
BLOCK CIPHER PERFORMANCE AND RISK ANALYSIS

Block cipher algorithms are a very important issue in the field of information security. Their simple structure and software-based encryption allow users to implement them in several applications such as: data security and cloud computing. In this paper, we reviewed 8 block ciphers that had been presented in the recently five years and were used in different applications. The 8 block ciphers are: DES, 3DES, Blowfish, Twofish, PRISENT, KLEIN, IDEA and AES. All of them are symmetric block ciphers with different designs. The comparative results showed that the block ciphers can still be used in different applications and fields. They showed that many modifications had been presented in which, chaotic maps had been implemented in key generation to enhance the robustness of the block ciphers and its randomness. The comparative results also showed that the AES is one of the block ciphers that is still unbroken algorithm and still modified to suit other new applications. Since less robust ciphers have continuous modification and enhancement in order to be more robust against some of probable attacks, the length of the key and its complexity and the structure of the ciphers are essential directions for improving block ciphers.
The Effectiveness of the Think Talk Write (TTW) Learning Technique Aided by Word Cards Media in Improving Sentence Writing Ability

This research aims to examine the effectiveness of the Think Talk Write (TTW) technique supported by word cards in enhancing sentence writing skills. The research method is quasi-experimental, utilizing a nonequivalent group's pretest-posttest design. Data collection techniques encompassed sentence writing tests and angle distribution. The research sample comprised Class IX C as the experimental group and Class IX A as the control group. Data processing techniques included the Shapiro-Wilk test to assess data normality, paired sample T-tests, and the NGain-Score test. The paired sample T-tests for the experimental group yielded a Sig. (2-tailed) value of 0.00 &lt; 0.005, indicating a significant distinction between the pretest and posttest scores. Similarly, the paired sample T-test outcomes for the control group demonstrated a Sig. (2-tailed) value of 0.00 &lt; 0.005. Moreover, based on the NGain-Score calculation, the average gain score for the experimental group was 71.25. In contrast, for the control group, it stood at 50.28, implying that the Think Talk Write (TTW) model, supplemented by word cards, effectively improves sentence writing skills.
Unsupervised and Semi-Supervised Speech Recognition System: A Review

Voice is a behavioral biometric that may reveal a person's age, gender, ethnicity, and emotional state. Speaker recognition is the method of identifying individuals through their sounds. Despite the fact that over the last eight decades, academics have already been focusing on speaker identification, technological advancements like the Internet of Things (IoT), smart homes, voice assistants, smart gadgets and humanoids have made their use popular in modern society. This study offers a thorough analysis of the speaker identification literature. It looks at recent developments as well as problems in this area of study. This study looks into feature extraction, classifiers, and the structure of the speaker recognition system. Also covered is how speaker recognition is used in apps. The objective is to increase researchers' understanding of speaker identification by machine learning since recent research has shown that it is easy to deceive machine learning into producing an incorrect prediction.
Comparative Analysis of Machine Learning and Deep Learning Techniques for Intrusion Detection

Abstract: This comparative analysis examines the application of both machine learning and deep learning methods in network traffic classification. Network traffic classification holds significant importance in network security, traffic management, and Quality of Service provisioning. The analysis covers a range of popular machine learning techniques, such as Decision Tree, KNearest Neighbours, Naive Bayes, Logistic Regression, Multi-Layer Perceptron, and Feed Forward Neural Network with a sigmoid activation function. Each technique's strengths and weaknesses are discussed, along with the factors that influence the selection of a particular technique. Ultimately, the choice of machine learning approach depends on data characteristics, performance requirements, and available resources. The demand for prompt and accurate classification of Internet traffic has been steadily increasing, driven by the emergence of new applications in the field. Traditional approaches based on port numbers and packet payloads have become insufficient, prompting the adoption of pattern recognition techniques that leverage statistical flow-based features in training samples to classify unknown flows. To ensure real-time identification of traffic types, the chosen method must be capable of swift classification before the entire flow is completed. In this study, a supervised machine learning approach and deep learning techniques are proposed for the identification of various Internet applications. The proposed system exhibits the ability to detect application types based on just a few initial packets within each flow, enabling realtime operation. Promising results were achieved, with the Logistic Regression algorithm attaining the highest accuracy of 80.7%.
Detect Multi Spoken Languages Using Bidirectional Long Short-Term Memory

Many speaker language detection systems depend on deep learning (DL) approaches, and utilize long recorded audio periods to achieve satisfactory accuracy. This study aims to extract features from short recording audio files that are convenient in order to detect the spoken languages under test successfully. This detection process is based on audio files of (1 or 2) seconds whereas most of the previous languages Classification systems were based on much longer time frames (from 3 to 10 seconds). This research defined and implemented many low-level features using Mel Frequency Cepstral Coefficients (MFCCs), the dataset compiled by the researcher containing speech files in three languages (Arabic, English. Kurdish), which is called M2L_dataset is the source of data used in this paper.A Bidirectional Long Short-Term Memory (BiLSTM) algorithm applied in this paper for detection speaker language and the result was perfect, binary language detection had a test accuracy of 100%, and three languages detection had a test accuracy of 99.19%.
Non-Markovianity in the time evolution of open quantum systems assessed by means of quantum state distance

Abstract We provide a quantitative evaluation of non-Markovianity (NM) for an XX chain of interacting qubits with one end coupled to a reservoir. The NM of several non-Markovian spectral densities is assessed in terms of various quantum state distance (QSD) measures. Our approach is based on the construction of the density matrix of the open chain, without the necessity of a master equation. For the quantification of NM we calculate the dynamics of the QSD measures between the Markovian-damped and various types of non-Markovian-damped cases. Since in the literature several QSD measures, appear in forms that imply trace preserving density matrices, we introduced appropriate modifications so as to render them applicable to the case of decaying traces. The results produce remarkable consistency between the various QSD measures. They also reveal a subtle and potentially useful interplay between qubit–qubit interaction and non-Markovian damping. Our calculations have also uncovered a surprisingly dramatic slowing-down of dissipation by the squared Lorentzian reservoir.
Tourism Information QA Datasets for Smart Tourism Chatbot

Smart tourism uses artificial intelligence (AI) technology to provide easy and convenient travel services to tourists. The task-oriented chatbot system is a way to provide tourists more efficiently with travel services that were previously provided on the web or apps. In this paper, we develop the question answering (QA) dataset for an AI-based tourism information chatbot system. The tourism information QA dataset is developed in JSON format of KLUE MRC based on the tourism information database and tourism knowledge base built for smart tourism apps and rule-based chatbot services, respectively. To apply the QA model along with the DST and NER models to the smart tourism chatbot system, we develop the QA dataset by considering the previously developed the tourism information NER dataset and the smart tourism DST dataset. We evaluate the tourism information QA datasets with the koBigBird model, which can handle sequences of 4,096 tokens, and the EM (Exact Match) and F1 score are 96.85 and 98.84, respectively.
The Development of a Web Application for Tracking Medical Cannabis Products

This research aims to develop a web application for tracking medical cannabis product information, designed and developed according to the System Development Life Cycle (SDLC) process. The study was conducted in the province of Buriram, Thailand, and the sample group was selected through purposive sampling, consisting of 30 individuals from Play La Ploen Community Enterprise, representing cannabis cultivators and medical cannabis product processors. Data was collected through in-depth interviews to gather database design and system development requirements. The tools used for system development were PHP, HTML, CSS, JavaScript, Bootstrap, Visual Studio Code, and XAMPP, with MySQL used as the database management system. The effectiveness of the information system was evaluated using a 5-point Likert scale questionnaire developed based on the PIECES framework. The research findings indicate that the medical cannabis product tracking system is functional, as users are able to scan the QR code on the packaging to obtain comprehensive and complete information about the product.