Reviewerly/exHarmony huggingface dataset:
Emotional Expression and Information Communication in English Texts Based on Artificial Intelligence Technology

Abstract This paper firstly researches English text emotion expression and information communication, classifies English text emotion expression and information communication according to the human emotion-value relationship, and summarizes the characteristics of English emotion expression and information communication. Secondly, using artificial intelligence technology, it is proposed to construct an analysis model for English text emotion and information communication using the BiLSTM neural network. To deal with the characteristics of English text quickly and efficiently, it is necessary to encode the emotional information of English text, and based on encoding, the BiLSTM neural network is applied to extract the emotional features of English text and solve the problem of the loss of emotional features through the loss function. Then, the crawler tool is used to obtain the dataset from the Chinese English module under the MOOC of Chinese universities, and the evaluation indexes are set according to the model’s performance, followed by the experimental analysis of the English text emotion expression and information conveyance. The results show that compared with the original CNN, LSTM, and T-LSTM, the BiLSTM-based neural network performs better in the task of text emotion expression and information conveyance, with the accuracy rate staying above 0.925, and the effect on the English dataset is a bit better than that on the Chinese dataset. This study aims to enhance English teaching and communication between Chinese and foreign cultures.
Diversified Curriculum Innovation in College Vocal Music Education under Deep Learning Modeling

Abstract This paper takes the problems and ways of diversified curriculum construction of vocal music education in colleges and universities as the entry point and constructs a deep blended learning model of vocal music diversified curriculum based on the deep learning model. The deep evolutionary knowledge tracking model is constructed by combining the self-attention mechanism embedding model with vocal music knowledge evolution based on Transformer. To verify the effectiveness of the deep blended learning model constructed in this paper, model comparison experiments and empirical analysis were conducted. The results show that compared with the SAKT model, the DBL model in this paper has a 5.19% improvement in the average ROC-AUC value. The vocal posttest scores of the students in the experimental class were 5.09 points higher than those of the control class, with a two-tailed significance value of 0.027, which is less than 0.05 and a significant difference. This indicates that the deep learning model can effectively promote the innovative design of diversified curricula for vocal music education in colleges and universities and enhance students’ vocal music learning ability.
Pengaruh Penerapan Model Pembelajaran Berbasis Proyek Terhadap Kreativitas Belajar PAK Siswa

Abstrak:Tujuan penelitian ini adalah untuk mengetahui Pengaruh Penerapan Model Pembelajaran Berbasis Proyek Terhadap Kreativitas Belajar Pendidikan Agama Kristen Siswa Kelas VII SMP Negeri 2 Girsang Sipangan Bolon Kabupaten Simalungun Tahun Pembelajaran 2022/2023. Metode yang digunakan dalam penelitian ini adalah metode penelitian kuantitatif. Populasi adalah seluruh siswa kelas VII SMP Negeri 2 Girsang Sipangan Bolon Kabupaten Simalungun Tahun Pembelajaran 2022/2023 yang berjumlah 122 orang dan ditetapkan sampel sebanyak 30 orang dengan teknik purposive sampling. Data dikumpulkan dengan 16 item tes untuk variabel X dan 20 item angket tertutup positif untuk variabel Y. Hasil analisis data menunjukkan bahwa terdapat Pengaruh Penerapan Model Pembelajaran Berbasis Proyek Terhadap Kreativitas Belajar Pendidikan Agama Kristen Siswa Kelas VII SMP Negeri 2 Girsang Sipangan Bolon Kabupaten Simalungun Tahun Pembelajaran 2022/2023, melalui Uji pengaruh: a) Uji persamaan regresi, diperoleh persamaan regresi . b) Uji pengaruh yang signifikan diperoleh nilai thitung= 2,248 ttabel(α=0,05,dk=n-2=28)=2,048 dengan demikian terdapat pengaruh yang signifikan antara variabel X dengan variabel Y. Dengan demikian Ha diterima dan H0 ditolak.Kata Kunci: Model Pembelajaran Berbasis Proyek, Kreativitas Belajar Siswa Abstract:This research is to know the effect of application of project-based learning model to the creativity of learning Christianity education of students Class VII SMP Negeri 2 Girsang Sipangan Bolon Regency Simalungun Besuan Palenten 2022/2023. The method used in this research is quantitative research method. The population is all students of class VII SMP Negeri 2 Girsang Sipangan Bolon Simalungun Regency Year of Learning 2022/2023 amounting to 122 people and set by samples as many as 30 people with purposive sampling technique. Data were collected with 16 test items for Variable X and 20 positive enclosed shortened items for the variable y. Data analysis results showed that there is an effect on the application of project-based learning model to the creativity of learning of Christian education of students Christian class VII SMP Negeri 2 Girsang Sipangan Bolon Regency Simalungun Besuan Pulangan 2022/2023, through the test influence: a) Test Regression Equation, obtained regression equation Y = 51,797 + 0.973x. b) Testing effects significantly obtained value Thitung = 2,248 ttable (a = 0.05, dk = n-2 = 28) = 2,048 thus there was a significant relationship between variable X with variable y. thus ha accepted and H0 was rejected.Keywords: The project-based learning model, Creativity learning
Objective knowledge and “open society” of K. Popper in the modern information field

The works of Karl Popper, exploring new approaches to the consideration of theories of knowledge, the formation of objective knowledge, the creation of an open society and the justification of its advantages, not only remain relevant today, but, along with his critical works, are of particular importance in the modern world of a globalized information space. His ideas can and should find practical application in the areas of acquisition, analysis and further formation of knowledge in the field of science and at the level of ordinary network users and consumers of media content. The purpose of this work is to review and study the approaches identified by K. Popper, which can have a positive impact on the formation of public and private opinions in the current huge flow of information and its availability. Today, every user of social networks and other communication channels is a potential distributor of information and messages based on their own opinion. The significance of this work may lie in finding and identifying ways to apply the theories of the philosopher and scientist K. Popper to form more qualified opinions, avoid errors and inaccuracies in public statements, and more fruitfully consume content. The exegesis of K. Popper's methods in the area of modern communication environment can be considered as a new side of studying the problem, and can be applied in the scientific community, among professional journalists, content makers and the general public.
Semi-Automatic Category Estimation and Data Augmentation for Opinion Extraction of Product Components

When customers purchase a product online, they use reviews to gather information about that product to help them make a purchase decision. Aspect-based Sentiment Analysis is a task that analyzes the review content from various perspectives, including the product itself, its components, and its retail outlets. We focus on comparing the characteristics of each component in a product with those of other products at the time of purchase. We define a task called component-based sentiment analysis (CBSA), which analyzes the review content from the perspective of only each component in the product. The CBSA task consists of opinion target extraction and polarity analysis. We approach that task with a classifier. We describe a semi-automatic category determination method for creating classification labels for CBSA and a data augmentation method to improve its classification performance. In experiments, we show that our category determination method can generate categories that cover 95% of the existing categories on e-commerce sites and that our data augmentation method improves the macro-F1-measure for uncommon opinions by 10%.
Different-resolution solar radiation prediction with accompanying microclimate data and long-term monitoring experiment by LSTM, RF, and SVR: a case of campus area

Solar radiation plays a pivotal role in urban microclimate, significantly, impacting building energy consumption and renewable energy utilization. Utilizing a comprehensive three- and-a- half-year dataset of high-precision urban microclimate, this study proposes a three-step approach to solar radiation prediction: 1) Data Preprocessing and Characterization: The microclimate data are meticulously preprocessed and categorized, with solar radiation values categorized into three resolutions: total daily solar radiation, hourly solar radiation, and 30-min solar radiation. 2) Feature Selection: Various climatic parameters are carefully selected for solar radiation prediction at different resolutions. 3) Machine Learning Models: Three machine learning models, namely Long Short-Term Memory (LSTM), Random Forest (RF) and Support Vector Regression (SVR), are employed for solar radiation prediction. The results show that all three models exhibit prediction accuracy with closely matched errors. Particularly noteworthy findings include SVR’s superior performance in predicting total daily solar radiation with a minimal error rate of about 6.0% and an impressive coefficient of determination of 0.9789. LSTM exhibits adaptability, achieving R2 values of 0.9906 and 0.9956 for hourly and 30-minute radiation predictions, respectively. This study offers valuable insights into solar radiation and its implications for building energy research. It underscores that large datasets enable accurate solar radiation predictions, even when excluding certain parameters like daily maximum temperature and relative humidity.
STUDY OF SOLAR POWER PLANT (SPP) WITH 1.51 MWP CAPACITY AT TANJUNGPURA UNIVERSITY

This research aims to optimize solar power generation at Tanjungpura University's Bralian Merdeka Yanuar Building by harnessing solar energy as a renewable resource. Indonesia, being near the equator with abundant solar radiation, holds significant potential for solar energy utilization. The main goal is to design and implement a 1.51 MWp capacity Solar Power Plant at the building to reduce reliance on fossil fuels and minimize carbon emissions on campus. The study utilizes a descriptive-analytical method, conducting literature reviews, site surveys, and electricity generation calculations using PVSyst software. Solar radiation data, electricity consumption, and building energy requirements guide the analysis. The SPP, comprising 2800 solar panels and 12 inverters, can effectively meet the Bralian Merdeka Yanuar Building's electricity needs. This research highlights solar energy's potential as a renewable resource to reduce carbon emissions and enhance energy independence. Successful SPP implementation serves as an alternative to fulfill the building's electricity demands, The azimuth and tilt angles significantly influence the electricity production of the Photovoltaic Solar Power Plant built to meet the electricity needs of the building. The study deepens understanding of solar energy potential and its benefits in electricity generation for Indonesia.
Unlocking Sustainable Success in Saudi Arabia: The Interplay of Knowledge Management and Green Innovation

As organizations around the world address sustainability issues, increasing attention is being devoted to the role that knowledge management can play in encouraging ecologically desirable behavior. The primary goal of the study was to evaluate how knowledge management affects sustainable performance, with an emphasis on the mediating function of green innovation. Data were acquired from 275 managers and senior employees working in the oil, gas, electricity, water desalination, and minerals industries in Saudi Arabia. The primary method of data collection was a self-administered and online survey. Using the Advanced Structural Equation Modeling (AMOS) tool, the data were assessed, and a thorough examination of the connections between knowledge management, green innovation, and sustainable performance was created. According to the results, there is a direct link between knowledge management and sustained performance. Furthermore, it was discovered that green innovation acted as a mediating component in this relationship, shedding light on the advantages that knowledge management brought to green innovation and how those advantages benefitted sustainable performance. This study adds to the body of current literature by empirically proving the linkages between knowledge management, green innovation, and sustainable performance. It emphasizes how important knowledge management is in promoting environmentally friendly behaviors and provides practical advice for companies trying to match their strategy with sustainability goals.
An End-Process Blockchain-Based Secure Aggregation Mechanism Using Federated Machine Learning

Federated Learning (FL) is a distributed Deep Learning (DL) technique that creates a global model through the local training of multiple edge devices. It uses a central server for model communication and the aggregation of post-trained models. The central server orchestrates the training process by sending each participating device an initial or pre-trained model for training. To achieve the learning objective, focused updates from edge devices are sent back to the central server for aggregation. While such an architecture and information flows can support the preservation of the privacy of participating device data, the strong dependence on the central server is a significant drawback of this framework. Having a central server could potentially lead to a single point of failure. Further, a malicious server may be able to successfully reconstruct the original data, which could impact on trust, transparency, fairness, privacy, and security. Decentralizing the FL process can successfully address these issues. Integrating a decentralized protocol such as Blockchain technology into Federated Learning techniques will help to address these issues and ensure secure aggregation. This paper proposes a Blockchain-based secure aggregation strategy for FL. Blockchain is implemented as a channel of communication between the central server and edge devices. It provides a mechanism of masking device local data for secure aggregation to prevent compromise and reconstruction of the training data by a malicious server. It enhances the scalability of the system, eliminates the threat of a single point of failure of the central server, reduces vulnerability in the system, ensures security, and transparent communication. Furthermore, our framework utilizes a fault-tolerant server to assist in handling dropouts and stragglers which can occur in federated environments. To reduce the training time, we synchronously implemented a callback or end-process mechanism once sufficient post-trained models have been returned for aggregation (threshold accuracy achieved). This mechanism resynchronizes clients with a stale and outdated model, minimizes the wastage of resources, and increases the rate of convergence of the global model.
A Unified Efficient Deep Learning Architecture for Rapid Safety Objects Classification Using Normalized Quantization-Aware Learning

The efficient recognition and classification of personal protective equipment are essential for ensuring the safety of personnel in complex industrial settings. Using the existing methods, manually performing macro-level classification and identification of personnel in intricate spheres is tedious, time-consuming, and inefficient. The availability of several artificial intelligence models in recent times presents a new paradigm shift in object classification and tracking in complex settings. In this study, several compact and efficient deep learning model architectures are explored, and a new efficient model is constructed by fusing the learning capabilities of the individual, efficient models for better object feature learning and optimal inferencing. The proposed model ensures rapid identification of personnel in complex working environments for appropriate safety measures. The new model construct follows the contributory learning theory whereby each fussed model brings its learned features that are then combined to obtain a more accurate and rapid model using normalized quantization-aware learning. The major contribution of the work is the introduction of a normalized quantization-aware learning strategy to fuse the features learned by each of the contributing models. During the investigation, a separable convolutional driven model was constructed as a base model, and then the various efficient architectures were combined for the rapid identification and classification of the various hardhat classes used in complex industrial settings. A remarkable rapid classification and accuracy were recorded with the new resultant model.
Spatial Analysis of Vibration Frequency Distribution of Jatibarang Dam Semarang Using Raspberryshake 4D

Dams are building constructions that have a high risk of construction vulnerability, one of the dams located in Semarang City is Jatibarang Dam. Spatial analysis is applied to group the analysis of dam monitoring points so that the deformation behavior of the dam can be analyzed. RaspberryShake 4D can be used in Structural Health Monitoring (SHM) which uses vibration to determine changes in the dynamic characteristics of the dam structure. Therefore, research is needed that is able to analyze the vibrations obtained at the Dam. The research data is primary data conducted at 10 research points with 2 measurements. This research aims to determine the value of the dominant frequency (f0) and amplification, determine the spatial analysis of the response spectrum, and determine the direction of particle motion. The HVSR method used obtained the value of the dominant frequency (f0) and amplification at the research point with 2 measurements which are in the range of 0.570897 Hz - 0.708169 Hz for measurement 1 and 0.593621 Hz - 0.911603 Hz for measurement 2 and the amplification value is 0.98424 - 2.22027 for measurement 1 and 0.984486 - 1.48635 for measurement 2. The results of the response spectrum of the dam have a peak value of 0.51 mm/s for measurement 1 and 0.43 mm/s for measurement 2 and have mechanical vibrations with a range of 0 - 194 mm/s in measurements 1 and 2 and the results of particle motion in the horizontal and vertical directions for measurements 1 and 2 have an angle and direction at each research point.
Virtual Pedagogy: Scenarios for Future Learning with VR and AR Technologies

Modern education and science are at the stage of active practical implementation of digital technologies aimed at improving the efficiency of the educational process. The fourth wave of industrial development offers digital and technological solutions that enhance the practical learning experience through the use of immersive technologies such as augmented and virtual reality. The purpose of this study is to assess the possibilities of using currently available Virtual Reality / Augmented Reality (VR/AR) technologies and to develop specific recommendations for improving the quality of the educational process, as well as to explore promising areas for the development of immersive education. The study used methods of criterion-based selection of the optimal tool for creating educational virtual content. This selection was made among the research nomenclature of currently available relevant digital platforms based on the calculation of specialised metrics for assessing the focal technical and technological capabilities of the studied cloud resources. Based on the results of this study, the optimal and currently available means of creating educational content has been identified, which provides users with the opportunity to use VR/AR technologies without acquiring special skills and abilities. Pedagogical scenarios that have already been immersed and can be adapted to different sectors of education are proposed. Probable vectors for the further development of educational VR/AR technologies are considered, and a hypothesis is formed about the need to use real experience in implementing immersive and virtualised educational processes to adjust and moderate the future development of global education in the context of the and Education 4.0 paradigms. The results of this study indicate the dynamic development of the field of immersive and virtualised educational space, formulate practical recommendations for using currently available tools for creating virtual educational content, and also point to the problem of low scientific support for the development of the studied field, which requires a systematic approach and a generally accepted strategy in the transformational processes of civilisational evolution. According to the results of the study, it is recommended to actively introduce available cloud-based platforms for creating virtual educational content into educational processes, as well as to use a nomenclature of pedagogical scenarios that have already been adapted and transformed to the use of VR/AR technologies in educational processes. It is necessary to constantly monitor and moderate the processes of involving immersive and virtualisation tools in the educational space.
Adapting Bitcoin between jurisprudential differences and legitimate purposes

This research aims to illuminate jurisprudential differences in the adaptation of Bitcoin. It first addresses the resolution of disputes surrounding Bitcoin and examines the reasons for variations in its adaptation. The study presents the cryptocurrency’s characteristics, issuance method, and trading mechanisms, all in the context of legal considerations. The research adopts an inductive and descriptive approach suitable for its subject matter. It comprises an introduction and two main sections. In the introduction, the study defines Bitcoin, outlines its characteristics, and discusses the current practices associated with it. The first section explores the diverse jurisprudential adaptations of Bitcoin after resolving the disputes and identifying the factors contributing to the differing viewpoints. The second section examines how legal objectives influence the adaptation of Bitcoin. The research concludes with several key findings and recommendations. These include the necessity to legalize trading in Bitcoin and other cryptocurrencies, as well as the importance of finding alternatives that align with Islamic economic principles, meeting both efficiency and legal criticism requirements.