Reviewerly/exHarmony huggingface dataset:
A Study on the Direction of Future Art Education: An Aesthetic Experience in Art Curriculum

The purpose of this paper is to pursue art educational meaning and direction of future by understanding aesthetic experience in the aesthetic practice of human beings who encounter art. I regard art as a prac-tice or act rather than a specific object, incident or thing. I hope to further clarify its aesthetic significance and its perspective on the educational aspect. Today, art is a culture that has unique characteristics that distinguish it from other academic disciplines such as society, politics, and economics, and it serves to broaden the horizon of understanding of the nature of the human spirit. Examine human aesthetic practice based on experience with art, and examine the meaning and character of 'aesthetic' as an educational ac-tivity of human beings surrounding art, treating art as a kind of human practical form as visual art. Aesthetic experience is the practice in which the object and the subject communicate with each other vigorously, and the practice is not a passive process of accepting the aesthetic attributes of the object, but a concrete practice. Aesthetic experience is differentiated from everyday human practice and has the power to re-flectively reflect on history and society, the world and myself. Through such aesthetic experiences, practical forms of aesthetic situations can be discovered and educational outcomes that are meaningful to human beings can be derived.
Implementation of Elementary and MI Students' Basic Literacy Strengthening Program in East Lombok Regency

This study aims to identify the level of students' reading ability; described the implementation of the basic literacy strengthening program in schools; and described the reflection on basic literacy strengthening programs. This study used descriptive qualitative research, with a target school of 30 SD and MI in East Lombok, and involves supervisors, principals, teachers, and students. Data collection techniques used reading competency assessment, interviews, observation, FGD, and documentation. Data analysis used an interactive model, including data reduction, data presentation, and concluding. The results of the study found that: 1) student literacy at the proficient level was 338, 200 proficient abilities, 217 basic abilities, and 2.067 special interventions; 2) program implementation is divided into the stages of preparation and capacity building, collaboration, and active participation; 3) the results of program reflection show that there are 21 schools with an optimal score, 7 schools with a targeted score, and 2 schools with a score just starting. The reflection process is very important to review the achievements made by the school and the things that need to be improved from various aspects.
Architecture Domains

The present chapter discusses and explains the existence of and difference between architecture layers and domains. The importance of information concepts is discussed as the main factor for the introduction of an additional architecture domain: Information Architecture.
Design Considerations for Virtual Reality (VR) Vaping Applications: Co-Designing Persuasive Computing Scenarios

Tobacco smoking and vaping remain a global public health concern, causing millions of premature deaths and imposing substantial economic losses. In spite of persistent efforts to combat the health risks, smoking continues to persist, necessitating the exploration of innovative interventions or technology solutions such as mobile applications, wearables, virtual reality, and more. Despite being included in public health programs worldwide, vaping or smoking awareness and/or cessation campaigns face barriers as they pertain to scalability and accessibility. Virtual reality (VR) experiences could potentially contribute towards remedial programs, but virtual reality experiences, that cater for both pre-vaping awareness, cessation and post cessation awareness, are scarce. Co-design, involving public health policy makers and subject matter experts is crucial for creating meaningful technology solutions. However, few researchers have explored this approach in VR contexts, despite its potential for authentic public health campaign support. Recognizing the need for a holistic approach, this paper used a co-design approach, collaborating with public health policy makers and subject matter experts to design and evaluate a VR experience for the contexts of pre-vaping, cessation and post cessation awareness. In this paper, we present necessary design guidelines and an evaluation framework for the creation of virtual reality experiences in persuasive computing contexts for public health.
Challenges in AutoML and Declarative Studies Using Systematic Literature Review

Machine Learning (ML) technologies have become essential tools, transforming industries and unlocking incredible potential in various fields. ML is now widely used for data-driven decision-making and predictive analytics across fields like healthcare, finance, transportation, and more. However, building and implementing ML models can be complex and time-consuming, often requiring programming proficiency and data science skills. Despite significant progress in ML, non-experts often struggle with selecting algorithms, optimizing models, and deploying ML solutions. This paper conducts a systematic literature review to explore challenges in the area of machine learning based on multiple categories involving features engineering and data extraction, learning model structure and activities, learning-based analysis and visualization, analysis algorithms in data-based systems, machine learning algorithms and systems development, and declarative ML-based prediction. Addressing these challenges underlines the importance of following AutoML and Declarative ML strategies in simplifying the ML process.
Measuring User Engagement in Virtual, Augmented, and Mixed Reality Interventions for Stress Reduction

Stress affects individuals across the world, posing significant challenges on the mental and physical states and overall quality of life. These detrimental consequences may include increased risk of mental disorders, such as anxiety, depression, and higher risk to physical ailments. Therefore, there is an urgent need for effective interventions which can reduce stress. Cognitive Behavioral Therapy (CBT) videos and Biofeedback techniques offer supportive approaches in alleviating stress. CBT provides cognitive restructuring and behavioral strategies to modify negative patterns, while Biofeedback utilizes physiological measurements to provide real-time feedback for stress symptom regulation. Integrating CBT, Biofeedback, and immersive technology, such as immersive technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), enhances the effectiveness of stress reduction interventions through immersive and engaging experiences. This paper proposes a solution that combines the principles of CBT videos and Biofeedback techniques within immersive environments. CBT videos within immersive environments provide engaging and immersive therapeutic content, while Biofeedback interventions enable users to regulate their physiological responses in an immersive environment but in real-time, empowering the users to regulate stress levels effectively. Building upon this premise, the study aims to comprehensively assess user engagement and evaluate the effectiveness of immersive interventions in stress reduction across VR, AR, and MR platforms. In addition to measuring user engagement, the study also aims to investigate factors such as user presence, immersion, emotion, and perceived usability to identify the most conducive immersive environment for stress reduction interventions. The research seeks to contribute to the development of personalized and effective stress reduction interventions, providing valuable insights into the potential of immersive technologies for stress minimization. The expected outcome of this study is to demonstrate the effectiveness of immersive interventions in minimizing stress and promoting wellness.
The Strength of Predict, Organize, Rehearse, Practice, Evaluate With Kahoot in The Teaching Reading for Junior High School

The purpose of this study was to determine how well junior high school reading instruction using Kahoot and PORPE (Predict, Organize, Rehearse, Practice, Evaluate) works. This study employed a quasi-experimental method. The research sample consisted of forty-one students, with eighteen students serving as the experimental class and twenty-three students serving as the control group. The subjects of in this study were students VIII A and VIII B SMP Alam Al-Ghifari. The researcher applied ANCOVA to calculate the data. The teaching reading using the Predict, Organize, Rehearse, Practice, Evaluate With Kahoot on students' reading comprehension revealed that the value of F= 29.591. and 0.000 was displayed by the probability (P). Using the PORPE method With Kahoot, the class's Kahoot score was 77.529. The class using the conventional method yielded 58.117. This indicates that the PORPE method using Kahoot produced superior results with a 95% confidence level. It is possible to get the conclusion that the PORPE method combined with Kahoot worked well for teaching reading.
A Deep Learning Framework for Classification of Immature White Blood Cells in Acute Myeloid Leukemia Using Convolutional Autoencoder and Transfer-Learning Ensemble

Acute myeloid leukemia (AML) is a type of acute leukemia that affects myeloid cells. It is characterized by uncontrolled proliferation of immature WBCs causing an accumulation of immature WBCs in the bone marrow and peripheral circulation. AML is an aggressive cancer that can be life-threatening if not treated promptly, therefore, early detection is crucial. Identification of immature WBCs and their subtypes is the first step towards AML diagnosis. Microscopic blood smear image analysis is commonly used to diagnose AML as it is less expensive non-invasive diagnostic tool compared to bone marrow biopsy and immunophenotype. Nonetheless, classifying immature WBCs presents challenges due to their high similarity and minimal interclass variation, especially for intermediate stages of myelopoiesis which make is susceptible to misclassification. The current diagnostic methods of AML are based on manual identification of immature WBCs using peripheral blood smear. These methos are time-consuming, prone to errors, and subject to inter-observer variation. Therefore, this study aims to develop a computer-aided diagnostic framework to accurately identify immature WBCs and classify them into various subtypes. The proposed framework consists of two main components: a binary classification system to classify cells into mature versus immature cells and a multiclassification system to further classify immature cells into four subtypes including myeloblast, monoblast, and promyelocytes. The proposed framework was designed to undergo four distinct phases: the first phase involves preprocessing, which includes data augmentation techniques aimed at addressing the imbalance distribution of WBCs. Augmentation encompasses geometric transformation and generation of synthetic images using convolutional Autoencoder (CAE). Feature extraction phase involves using image embedding and transfer-learning. Image embedding representation was obtained using CAE and then utilized by two pre-trained models: VGG19 and Resnet50. Classification phase was carried out using weighted ensemble of the VGG19 and Resnet50 with the optimal weights determined through Grid search. The validation phase evaluated the model performance using overall accuracy, precision, and sensitivity. The area under the ROC curve (AUC) was used to assess model discrimination ability. The proposed framework demonstrated excellent results, achieving an average accuracy of 99.5%, a sensitivity of 97.97%, and a precision of 94.12%. The model exhibited an excellent discriminatory capability, with an AUC of 99.9%. Furthermore, the proposed framework outperformed previous methods, showcasing improved performance. Key word: Acute myeloid leukemia, peripheral blood smear, immature white blood cells, deep learning, feature extraction, computer-aided diagnosis.
ANALISIS STRATEGI PELAYANAN PRA DAN PASCA BENCANA KEBAKARAN PADA DINAS PEMADAM KEBAKARAN DAN PENYELAMATAN DI KABUPATEN SUMBAWA BARAT

Fire disasters occur due to excessive chemical reaction processes with oxygen and other substances such as carbon. In dealing with fire disasters, a fire prevention policy is needed to reduce the impact and occurrence of fire disasters. The purpose of this research is to determine the pre- and post-fire disaster service strategies carried out by the Fire and Rescue Service. The method used in this research is qualitative method. The data collection process uses observation, interviews and documentation. The results of this research are, 1) The West Sumbawa Regency fire and rescue service regarding the Pre and Post Fire Disaster Service Strategy has a work program in implementing its work strategy, 2) Factors that influence the Pre and Post Fire Disaster Service Strategy are infrastructure readiness and technological preparation , 3) Pre and Post Fire Disaster Service Strategy, namely the West Sumbawa Regency Fire and Rescue Service has an important role in efforts to prevent and prepare for fires
Research and Application of Anomaly Detection of Bridge Data Based on Improved Transformer

In the past, learning point-wise representations through recursive networks has been effective for addressing anomaly detection problems, but it has been difficult to extend to more complex temporal patterns. Transformer’s self-attention weights, on the other hand, can capture rich associations between each time point and the entire sequence. Because of the rarity of anomalies, it is difficult for anomalous points to establish associations with the entire sequence, but they tend to have close associations with neighboring regions.This difference can be leveraged to differentiate anomalies.By improving the self-attention module of the original Transformer model, we construct a Transformer model with dual-path modeling capability. Using the idea of anomaly detection based on association modeling, we perform anomaly detection on bridge data. The experimental results show that the precision reaches 91.5%. This detection method is effective in distinguishing anomalies.This paper takes the bridge of urban rail transportation in Wuhan as the background, with a large amount of data and a high demand for real-time support for the detection of numerous sensor data. After adopting the sparse attention mechanism, the time cost decreased by 10.7% while the precision did not decrease by about 1%. By combining deep learning with the time series database IoTDB, the ability to detect anomalies in time series data is improved, providing data protection for bridge safety assessment.
Comparison of CNN and CNN-LSTM Algorithms based on Earthquake Magnitude Estimation

After the earthquake, the rapid determination and estimation of earthquake magnitude is one of the most important tasks of the earthquake early warning system. In the model construction of magnitude determination, it is necessary to comprehensively consider the seismograph response time, the error of arrival time selection, and the inaccurate velocity model, which will lead to the incompleteness of the current earthquake catalog and the insufficient accuracy of magnitude determination. In this paper, based on the STanford EArthquake Dataset (STEAD), convolution neural network (CNN) and long short-term memory (LSTM) are mainly used for fast estimation. This paper compares the timeliness and accuracy of the two algorithms for magnitude assessment. By comparing the characteristics of absolute error standard deviation and mean value, it provides a new idea for the improvement of subsequent magnitude determination methods to better enhance the disaster reduction effect of earthquake early warning.
The Effect of Using Wattpad Application to Improving Student’s Reading Comprehension of Recount Text at Tenth Grade Students of Senior High School Number 11 Jambi

Many students are more captivated by reading through the internet and application media than reading a book, one example of reading application the Wattpad application, this application is an electronic book in the form of an application, on Wattpad there are many things that can be read in the Wattpad application. This study employed a true experiment design, using quantitatively approved pre-test and post-test. Genuine experiment with pre- and post-tests This study's type of research design is group design. Or in Indonesian known as true experiment, is the most rigorous and powerful experimental design because in true experiment research the research subject is randomly selected. Before providing treatment, the exam should determine the kids'' reading comprehension. In the pre-test experimental group, the researcher obtained 36 sample scores with means of 55, 35, 63, and 40. In the post-test score following treatment, the researcher obtained 36 sample scores with means of 75, 35, 90, and 35. Based on the theory of reading using the Wattpad application it can help students to improve their reading skill and students can read understand a content of the story text.
Reading Comprehension for the Tenth Grade Students of SMAN 11 Jambi

This research aimed to find out the effectiveness of using Listen – Read – Discuss (LRD) strategy towards students’ reading comprehension for tenth grade students at SMAN 11 Jambi. This method of research is quantitative and the design of this research is quasi-experimental design. This research used two classes, experimental class and control class, X-E7 as experimental class were taught by using Listen-Read-Discuss (LRD) Strategy, and X-E3 as control class were taught without Listen-Read-Discuss (LRD) strategy. Both classes consist of 36 students. This research used a test instrument with multiple choice questions with total number questions is 16 items. The test consisted of Narrative Text. The technique of data analysis is calculation the students’ score and analyzing using SPSS. The data was analyzed by T-test using SPSS for windows version 21.0. The result of this research is found that the mean score of post-test in experiment class with using LRD strategy was 87,86 and the mean of post-test in control class without using LRD Strategy was 77.67. Meanwhile, from T-test analysis found that the value of sig (2-tailed) was 0.000. As the result, it can be seen that 0.00 &lt; 0.05 which mean that null hypothesis (Ho) was rejected ad the alternative hypothesis (Ha) was accepted. It means that there is significant effect of using Listen-Read-Discuss (LRD) strategy toward students reading comprehension at tenth grade students of SMAN 11 Jambi.
Analyzing The Ability of Twelfth Grade Students in Writing Application Letter at Senior High School Number 11 jambi

The Purpose of the research is to analyzed the ability of twelfth grade students to write application letter at senior high school number 11 Jambi. In conducting the research, the researcher used descriptive quantitative. The researcher used quantitative method to analyze the data. It can be seen there are 18 students got excellent score in 80-100, 12 students got good score 66-79, 1 student got fear score in 56-65, 2 students got weak score in 46-55 and no students got poor score in 0-45. This condition showed that some of the twelfth grade of Senior High School number 11 Jambi were able to write the application letter. Because they know how to make a good application letter such as the salutation, the content, and closing.
The Effectiveness of Using Shadowing Technique Towards Students’ Pronunciation Skill at Twelfth Grade Students in Senior Highschool Number 02 Muaro Jambi

This research was made to figure out if there is an improvement after using Shadowing Technique that applied to twelfth grade students in senior high school number 02 Muaro Jambi. This research used Quantitative method with Quasi-Experimental design and using SPSS as data analysis technique. The total population in this school was 239 students. In this research, researcher use one experimental group and one control group. The experimental group that chosen in this research was XII MIPA 2 and XII IPS 2 as the control group with the total of 61 samples (30 students for experimental group and 31 students for control group). The technique that used in this research to collect the data was students must immediately follow the words that speaker said. The results that were found in this research are: (1) Students pronunciation skill taught by using Shadowing Technique now can pronounce the words in English pronunciation and less using Indonesian pronunciation (2) Some students’ who categorized in poor pronunciation skill now become average criteria after the treatment of Shadowing Technique. Therefore, at the end of this research, there is an improvement of students’ pronunciation skill after using Shadowing Technique.
Enablers or Inhibitors? Unpacking the Emotional Power Behind In-Vehicle AI Anthropomorphic Interaction: A Dual-Factor Approach by Text Mining

The intelligent strategy of the new energy vehicle (NEV) industry has triggered the rapid prevalence of in-vehicle anthropomorphic artificial intelligence (AI) assistants. There is still a lack of clarity regarding NEV users' attitudes toward this cutting-edge technology and whether they receive a satisfactory intelligent service experience. To circumvent potential emerging technology resistance, in this article, we utilize text analysis techniques for the identification of AI interaction emotions, love and disgust (enablers and inhibitors) with significant influence on user satisfaction, and validates the improving role of multimodality on the effectiveness of anthropomorphic interaction. In addition, this study innovatively constructs a multidimensional corpus of modality × emotion, using structural topic modeling to uncover the constituent elements and real-time changes of love and disgust emotions in different modalities, from which development opportunities and improvement directions for AI anthropomorphic interaction technologies are identified. The findings provide new insights into the application of emotion analysis methods to improve users' intelligent service experience and provide a realistic reference for mitigating emerging technology resistance in the NEV industry.
Ensuring Trustworthy Automated Road Vehicles: A Software Integrity Validation Approach

This paper presents a cascading scheme designed to measure and validate the software integrity of interconnected and automated road vehicles. With modern vehicular architectures decoupling hardware from specific functions and supporting frequent updates, we require proactive means to detect software manipulations, especially as compromised control units pose a severe risk to passenger safety. Therefore, we must ensure the benignity of critical software components that impact the vehicle's dynamics while allowing for rapid adaptions and user customizations. Our scheme requires vehicles to obtain proof of trustworthiness before performing specific actions, such as driving autonomously or joining a vehicle fleet. To achieve this, we employ remote attestation techniques to compute an integrity identifier verified and validated by a trusted third party. We specifically design the process of integrity measurement for hierarchically organized networks of control units, as is the case in domain-based and zonal architectures. For evaluation, we utilize a recently presented zonal architecture deployed in four fully automated road vehicles and demonstrate that our scheme incurs an acceptable overhead.