Reviewerly/exHarmony huggingface dataset:
Pengaruh Model Problem Based Learning (PBL) terhadap Kemampuan Membangun Sikap Mandiri dan Hasil Belajar Siswa Pada Mata Pelajaran PKn Kelas IV SD Negeri 75 Kota Bengkulu

// This study aims to determine the effect of the Problem Based Learning model on the ability to build independent attitudes and student learning outcomes in the fourth grade Pkn Subject SD Negeri 75 Kota Bengkulu. This type of research is the Mix Method. The research samples obtained were students of grade IV A and IV D SD Negeri 75 Bengkulu City, amounting to 38 and 37 students. The research instruments used were questionnaire sheets, interview sheets and test sheets. The research data on the independent attitude showed that the result of t count was 1.746 with a significant level of 5%, namely 0.020 &lt;0.05, meaning that there was an influence of the PBL model on the independent attitude of students in class IV SD Negeri 75 Bengkulu City. In the results of the pretest value in the experimental class and control class, namely t count of 1.528 with a significant level of 5%, namely 0.058&gt; 0.05, it means that there is no influence of the PBL model on the independent attitude of students in class IV SD Negeri 75 Bengkulu City. Whereas the results of the posstest score in the experimental class and control class obtained t count of 3.345 with a significant level of 5%, namely 0.001 &lt;0.05, meaning that there is an effect of the PBL model on the independent attitude of students in class IV SD Negeri 75 Bengkulu City so it can be concluded that PBL model on the ability of independent attitudes and learning outcomes of fourth grade students of SD Negeri 75 Kota Bengkulu.
Modeling Barriers to the Adoption of Blockchain Technology in the Indian Healthcare Sector: An Integrated Fuzzy-Dematel Approach

Blockchain is an Internet-level revolution. The Internet solves information and communication problems but cannot cater to trust and intermediaries’ issues. Blockchain is the solution to these. The implementation of blockchain technology in India’s healthcare industry is still in its infancy, with a few pilot projects and initiatives being undertaken by start-ups and government agencies. These projects aim to leverage the secure and transparent nature of blockchain to address challenges such as data privacy, interoperability, and fraud prevention. However, there are also concerns about the high costs of implementation and the need for regulatory frameworks to govern the use of blockchain technology in the healthcare sector. Overall, while the potential of blockchain in healthcare sector is promising, its widespread adoption in India’s healthcare system is likely to be a gradual and ongoing process. Using the Fuzzy-DEMATEL methodology, this research paper seeks to identify and model the barriers to blockchain technology adoption in India’s healthcare industry. Originality/value: Blockchain technology in healthcare is in a nascent stage. This study outspreads the knowledge base by recognizing and further prioritizing the significant barriers that need to be overcome to adopt blockchain in India’s healthcare sector effectively.
Neural Style Preserving Visual Dubbing

Videos are becoming one of the most consumed sources of information. When a person obtains information with audio as well as visual stimulus, that information is retained more prominently in his/her mind. But most video content is made in a single language. Dubbing is used for translating videos from one language to another. But while consuming this dubbed media, there is a clear difference between dubbed audio and facial expressions of the actor. Lack of synchronization between dubbed audio and original video impacts the immersion of the content. The proposed solution aims to solve this problem by modifying the facial expressions of the target actor with reference to the dubbing artist. Regional languages with demanding facial articulation, such as mouth and lip movements and various facial expressions, can make use of this technique for better preservation of the original style of the language. Our method is based on image processing techniques with neural network enhancement. Use of deep learning techniques will give us better results than other techniques like superimposing two faces of different images.
Classification of Breast Cancer Using Machine Learning: An In-Depth Analysis

This article provided a comprehensive overview of the current state of breast imaging machine learning methods. Breast cancer is the second leading cause of cancer deaths among women, behind lung cancer. To make an accurate diagnosis of breast cancer, pathologists use a systematic and objective process that begins with the categorization of discovered tumors. The application of machine learning techniques has dramatically improved breast cancer staging and diagnosis. One million women are diagnosed with breast cancer each year. An effective detection system should produce few false positives. In the past, we would get where we needed to go by reviewing the most current research that attempted to classify these cancers. Machine learning algorithms like support vector machine (SVM), K-NN, and random forest are used to determine if a medical image is cancerous or benign. Preliminary preprocessing and feature selection in traditional machine learning take a considerable amount of time and computer resources, hence recent studies have included deep learning. There has been an increase in the usage of CNNs for the classification of breast cancers. This investigation provides a comprehensive literature review of machine learning-based methods for breast cancer detection, which may be useful to both researchers and clinicians.
PENINGKATAN KETERAMPILAN MENULIS TEKS PERSUASI DENGAN STRATEGI THINK TALK WRITE

The problem that arises in this classroom action research is the low learning outcomes of Indonesian in the remaining class VIII of SMPN One Roof 3 clan Jaya. The purpose of this study was to determine the increase in Indonesian learning outcomes for students through the Improvement of Persuasion Text Writing Skills with the Think Talk Write Strategy for Class VIII Students of SMPN One Roof 3 Marga Jaya in the Academic Year 2020/2021. This classroom action research was conducted in two cycles, each cycle consisting of 4 stages, namely: planning, action, observation, and reflection. The method of collecting research data is through observation and tests. While the research indicator is KKM 72%. From the research results indicate an increase in student achievement. Based on the results of data analysis and discussion, it can be concluded that pre-cycle learning completeness by 72% increases to cycle I which reaches 82% means an increase of 9%. From cycle I to cycle II there was also an increase in learning completeness, namely from 82% in cycle I to 86% in cycle II. Thus, from the initial condition to the final condition, learning completeness has increased by 14%.
Interactive active learning for fairness with partial group label

The rapid development of AI technologies has found numerous applications across various domains in human society. Ensuring fairness and preventing discrimination are critical considerations in the development of AI models. However, incomplete information often hinders the complete collection of sensitive attributes in real-world applications, primarily due to the high cost and potential privacy violations associated with such data collection. Label reconstruction through building another learner on sensitive attributes is a common approach to address this issue. However, existing methods focus solely on improving the prediction accuracy of the sensitive learner as a separate model, while ignoring the disparity between its accuracy and the fairness of the base model. To bridge this gap, this paper proposes an interactive learning framework that aims to optimize the sensitive learner while considering the fairness of the base learner. Furthermore, a new active sampling strategy is developed to select the most valuable data for the sensitive learner regarding the fairness of the base model. The effectiveness of our proposed method in improving model fairness is demonstrated through comprehensive evaluations conducted on various datasets and fairness criteria.
Be careful what you explain: Benefits and costs of explainable AI in a simulated medical task

We investigated the impact of explainability instructions with respect to system limitations on trust behavior and trust attitude when using an artificial intelligence (AI) support agent to perform a simulated medical task. In an online experiment (N = 128), participants performed a visual estimation task in a simulated medical setting (i.e., estimate the percentage of bacteria in a visual stimulus). All participants were supported by an AI that gave perfect recommendations for all but one color of bacteria (i.e., error-prone color with 50% reliability). We manipulated between-subjects whether participants knew about the error-prone color (XAI condition) or not (nonXAI condition). The analyses revealed that participants showed higher trust behavior (i.e., lower deviation from the AI recommendation) for the non-error-prone trials in the XAI condition. Moreover, participants showed lower trust behavior for the error-prone color in the XAI condition than in the nonXAI condition. However, this behavioral adaptation only applied to the subset of error-prone trials in which the AI gave correct recommendations, and not to the actual erroneous trials. Thus, designing explainable AI systems can also come with inadequate behavioral adaptations, as explainability was associated with benefits (i.e., more adequate behavior in non-error-prone trials), but also costs (stronger changes to the AI recommendations in correct error-prone trials).
Blockchain for public safety: A survey of techniques and applications

As an emerging technology, blockchain provides a range of advantages, such as decentralized and transparent data storage, secure access control, and enhanced data traceability. However, it is rarely applied in the field of public safety. This paper presents an in-depth survey of blockchain technology, focusing on its potential applications and implications within the field of public safety research. We explore the practical needs of multi-party data collaboration in emergency management and discusses the applicability and value of blockchain technology in this context. Additionally, this paper introduces and compares several popular blockchain platforms. By providing a comprehensive examination of blockchain technology and its potential benefits for public safety, this paper seeks to enhance understanding of the technology's capabilities, encourage further research, and inspire innovation in this domain.
THE EFFECTIVENESS OF WEB-BASED LEARNING ON THE STUDENTS’ MOTIVATION AND ABILITY IN WRITING NARRATIVE TEXT

In English, there are four skills which must be mastered, they are listening, speaking, reading, and writing. One of the language skills is writing that important for learners to practice their capability and their understanding, how to send ideas, and how to arrange words well. The objectives of this study were students’ motivation and ability to write narrative text who are taught through web-based learning, the writing motivation and ability of students who are not taught through web-based learning and to find out whether or not there is a significant effect of web-based learning to the students' motivation and ability to write narrative text for tenth-grade students of senior high school. The research was conducted at Muhammadiyah Boarding School in Prambanan Yogyakarta. This study used experimental research with nonrandomized-groups pre-test post-test control group design as the research design. The data were collected by using tests and questionnaires in the form of pre-test and post-test. The result of inferential analysis, showed by the result of the t-test, was found that the value of the ttable (2.001) was lower than the tobtained (3.532). Thus, H0 is rejected and H1 is accepted. It means that web-based learning had a significant effect on improving students’ writing motivation and ability.
sRIFD: A Shift Rotation Invariant Feature Descriptor for multi-sensor image matching

Feature descriptors such as SIFT (Scale Invariant Feature Transform) have been widely used in various computer vision applications, and many methods have been proposed to adapt the descriptors to multi-sensor images. Recently, RIFT (Radiation variation Insensitive Feature Transform) utilized the phase congruency that is invariant to the illumination and radiometric variation to construct descriptors for multi-sensor images. However, RIFT does not perform well on images of certain rotation degrees even with accurate main orientation of keypoints. To address this issue, this work designs a SoMIM (second-order maximum index map) by using the orientational response of Log-Gabor filters, and then constructs a novel descriptor, sRIFD (shift Rotation Invariant Featured Descriptor), without the computing main orientations. sRIFD performs evenly well on images of almost all rotation angles. To evaluate the performance of sRIFD, we use three image datasets, Infrared-Optical, Thermal-Optical, and Optical-Optical datasets. The quantitative results demonstrated that sRIFD performed better on multi-sensor image datasets under varying rotation angles.
ANALYSIS OF THE EFFECT OF INTEGRATING DIGITAL GAME-BASED DECISION-MAKING INTO THE APPROPRIATE TEACHING OF BUSINESS ETHICS

As the increasing emphasis of game-based learning on teaching strategies, many researchers in dictated that implanting game-based learning strategies, which include the enjoyment and tasks for student to pass, in the digital learning system can guide students to enhance their learning motive to win game. Many teaching strategies were developed to improve programming skills instead of traditional business ethical class lecture. Among them, game-based learning are applied to enhance students learning. Nonetheless, they fail to engage students in deep thinking and reflections. To cope with this issue, a two-tier test-based learning system is proposed to enhance students’ learning performances and the flow experience in business ethical courses in this study.
Cognitive Load Theory for ESL Students: Mixed Method to Employ Difficulty in Using Tenses While Writing

The present study aimed to determine learners’ problems in using English tenses. By utilizing the pre-test to have students compose their own paragraphs in 12 different tenses and through LearningApps.org, the researcher distributed the post-test self-created questions. The research employed a mixed research method, and the findings were presented. This study focused on 96 Bachelor of Technology students also learning English as a second language. The results show that past perfect tense, future perfect tense and simple past tenses are the most commonly problematic writing skills by the learners. These issues are linked to the difficulties of employing the appropriate aspect and tense in the appropriate context, interference from second languages, a lack of repetition in practice, and student’s level of language proficiency. In addition, it is recommended that teachers employ various methods that are appropriate to teaching tenses to help students develop their writing abilities.
Simultaneous interferometric profilometry of surface and thickness of transparent plate using wavelength-modulation

Wavelength-modulation interferometers have been widely used to measure the geometric characteristics of transparent plates. However, several errors can be generated by multiple reflections of the laser beams and environmental uncertainties when the wavelength is modulated. In this research, two novel phase-calculation formulas with the same number of interference fringes (15) are proposed to measure the geometric characteristics of a transparent plate. First, a 15s-sample phase-calculation formula for measuring the surface shape of a transparent wedge plate was developed using a system of linear equations. Second, a 15t-sample phase-calculation formula for measuring the optical thickness irregularity of transparent parallel plate was developed by using the characteristic-polynomial theory. The two 15-sample formulas were visualized in the frequency domain and complex plane to confirm the properties of their error-compensation ability. Furthermore, the two formulas were compared with conventional phase-calculation formulas through the numerical analysis. Finally, the surface profile of the wedge plate and optical thickness irregularity of the parallel plate were measured via the two proposed formulas using wavelength-modulation Fizeau interferometer. The standard deviations of 15s-sample formula and the 15t-sample formula were confirmed as 4.3616 nm and 1.2875 nm, respectively. Using these formulas to measure the geometric properties of transparent plate on nanoscale can contribute to the production of high-performance semiconductor devices.
The Interplay of Human Resource Practices and Employee Innovative Behavior in Employee Performance

The COVID-19 pandemic greatly affected the global economy with lockdown measures and reduced economic activity. This study investigates the relationship between employee innovative behavior, HR practices implementation, and performance at one of the biggest car distributors in Indonesia. The study determines the impact of employee's innovative ideas in daily work activities and changes in HR practices on the company's recent performance improvements. The research uses a quantitative approach by using a questionnaire of 120 employee respondents and applying the PLS-SEM (Partial Least Square – Structural Equation Modeling) to analyze the data. The result shows that employee's innovative behavior and HR practices significantly influence employee performance.
Practicality And Effectiveness Game Snake Chemistry Ladder as a Learning Media on Material Reaction Reduction and Oxidation Class X SMA/MA

Study this is study advanced with a development model Plomp done by Adelina and Iswendi (2021) with title development game snake ladder chemistry as a learning medium on material reduction and oxidation as well as nomenclature compound class X SMA/MA which is already valid, however Not yet tested level practicality and level its effectiveness. Objective study for disclose level practicality and level effectiveness game snake ladder chemistry as a learning medium on material Reaction Reduction and Oxidation class X MAN 1 Padang Panjang.Design study is One-Group Pretest-Posttest Design. Subject study is class X MIPA 3. Instruments study on test practicality form questionnaire practicality to teachers and participant educate and test effectiveness form question pre-test and completed post - test tested try it. Practicality data analyzed with percentage and effectiveness data with N-Gain. Based on results analysis of the data obtained mark teacher practicality is 91% (very practical) and participant educate on test small group by 86% (very practical), and test field-test obtained by 87% (very practical). Results analysis effectiveness obtained mark N-Gain by 76% with category effective. Based on results study can concluded that game snake ladder chemistry as a learning medium on material reaction reduction and oxidation class X MAN 1 Padang Panjang very practical and effective.
Application of The Discovery Learning Model with a Thinking Approach to Improving Students' Activity on The Content Indonesian Language in Class III

This research was motivated by the low activity of class III students at SDN 83/1 Bajubang. This research is a Classroom Action Research which aims to describe the increase in student activity through the application of the Discovery Learning model with the MIKiR approach to Indonesian content in class III. This research was conducted at SDN 83/I Bajubang. Research data obtained by means of observation, interviews and also documentation. The data in this study were analyzed descriptively qualitatively. The results of the study show that applying the discovery learning model with the MIKiR approach to the learning process can increase student activity. This increase can be seen in the data analysis of each meeting cycle. Cycle I meetings I and II were 42.85%, cycle II meeting I was 66.66%, cycle II meeting II was 80% which increased by 13.34%. In this case it is concluded that the use of the discovery learning model with the MIKiR approach can increase the activity of students in Indonesian language content in class III SDN 83/1 Bajubang. Proven by the success of this research. The activeness of students can be increased by the discovery learning model with the MIKiR approach.
The Metaverse Hype: Identifying Bubbles and Comovements of Metaverse Tokens

This study provides novel insights into the financial dynamics of the most capitalized metaverse tokens. It examines the relationship between metaverse tokens, public sentiment and global stock indices, examining their correlation with related market attention (“NFT” and “Metaverse” Google Trends Indices) and selected stock markets (NASDAQ 100 and Shanghai Composite Index). In this paper SADF and GSADF tests for bubble detection as well as Wavelet Coherency analyses for frequency dependent comovement detection get applied. By analyzing the period from January 1st, 2021, to December 31st, 2022 we cover the first significant rise of the so-called ‘metaverse’ including the big announcement made by Mark Zuckerberg on October 28th, 2021. Our results show that metaverse tokens display periods of bubble- behavior, coinciding with spikes in the NFT and Metaverse Google Trends indices. Furthermore, the analysis unveils medium-frequency connections of the metaverse tokens with the overall technology sector in the post-hype episode in 2022. These observations underscore the need for individualized analysis in this digital space, impacting investment strategies and risk management. While policymakers are also urged to consider that the Metaverse and the still quite unknown dynamics and potential connections that require further research.
A Study on the Use of Attention for Explaining Video Summarization

In this paper we present our study on the use of attention for explaining video summarization. We build on a recent work that formulates the task, called XAI-SUM, and we extend it by: a) taking into account two additional network architectures and b) introducing two novel explanation signals that relate to the entropy and diversity of attention weights. In total, we examine the effectiveness of seven types of explanation, using three state-of-the-art attention-based network architectures (CA-SUM, VASNet, SUM-GDA) and two datasets (SumMe, TVSum) for video summarization. The conducted evaluations show that the inherent attention weights are more suitable for explaining network architectures which integrate mechanisms for estimating attentive diversity (SUM-GDA) and uniqueness (CA-SUM). The explanation of simpler architectures (VASNet) can benefit from taking into account estimates about the strength of the input vectors, while another option is to consider the entropy of attention weights.
Real-Time Conversational Gaze Synthesis for Avatars

Eye movement plays an important role in face-to-face communication. In this work, we present a deep learning approach for synthesizing the eye movements of avatars for two-party conversations and evaluate viewer perception of different types of eye motions. We aim to synthesize believable gaze behavior based on head motions and audio features as they would typically be available in virtual reality applications. To this end, we captured the head motion, eye motion, and audio of several two-party conversations and trained an RNN-based model to predict where an avatar looks in a two-person conversational scenario. We evaluated our approach with a user study on the perceived quality of the eye animation and compared our method with other eye animation methods. While our model was not rated highest, our model and our user study lead to a series of insights on model features, viewer perception, and study design that we present.