Reviewerly/exHarmony huggingface dataset:
Distributed photovoltaic index insurance premium prediction method based on ARIMA model

Photovoltaic power generation is an important field of the current new energy development, and distributed photovoltaic mode has gradually become an important trend in the development of the photovoltaic industry, for the distributed photovoltaic industry in China's domestic development status, there is an urgent need for a new kind of insurance products, to support photovoltaic enterprises to promote the development of the industry. ARIMA distributed PV and index insurance are reviewed. After researching and synthesizing the mature power generation index insurance in foreign countries, this paper collects 57 distributed PV projects and takes the GHI data of distributed PV projects in Shaoxing City, Zhejiang Province, as a representative, obtains the GHI data from NASA, and explores the process and significance of constructing the power generation index insurance model based on ARIMA model by using the method of machine science.
Calibration Error Estimation Using Fuzzy Binning

Neural network-based decisions tend to be overconfident, where their raw outcome probabilities do not align with the true decision probabilities. Calibration of neural networks is an essential step towards more reliable deep learning frameworks. Prior metrics of calibration error primarily utilize crisp bin membership-based measures. This exacerbates skew in model probabilities and portrays an incomplete picture of calibration error. In this work, we propose a Fuzzy Calibration Error metric (FCE) that utilizes a fuzzy binning approach to calculate calibration error. This approach alleviates the impact of probability skew and provides a tighter estimate while measuring calibration error. We compare our metric with ECE across different data populations and class memberships. Our results show that FCE offers better calibration error estimation, especially in multi-class settings, alleviating the effects of skew in model confidence scores on calibration error estimation. We make our code and supplementary materials available at: https://github.com/bihani-g/fce .
A Mixed Reality Setup for Prototyping Holographic Cockpit Instruments

This paper discusses a possible solution to allow rapid prototyping of augmented reality content for evaluating user assistance systems. Building upon current approaches from the automotive context, it presents options to transfer these to the aerospace context, focusing on upcoming electric Vertical Take-Off and Landing aircraft. By showing issues of using optical-see-through glasses inside a moving simulator, a solution for using video-see-through head-mounted displays instead is presented. As a solution, a modular system setup that can handle different rendering sources to create contact-analog augmentations on top of a virtual environment while using video-see-through capabilities to interact with the physical cockpit environment is presented. The main contribution is a process to avoid manual calibration between the different coordinate systems across the involved applications. To further discuss holographic content for vehicle-based augmentations, a first taxonomy is discussed on locating augmentations to prototype such concepts with the presented Mixed-Reality-Simulator.
The WalkingSeat: A Leaning Interface for Locomotion in Virtual Environments

When users experience immersive Virtual Reality (VR), the limited physical space available can become a significant problem when they use their real walk to navigate the virtual environment (VE). To address this limitation, various interfaces and metaphors have been proposed that combine different user inputs ranging from controllers, gestures, and body tracking. However, not all of these solutions are natural and intuitive, reducing the level of immersion and presence in VR. In this paper, we present Walking Seat (WS), a novel leaning interface (LI) for locomotion in VEs that the user can operate while seated. The main objective of this work is to design an interface that provides an intuitive and immersive locomotion experience. The WS interface uses pressure mapping and a gyroscope to track the user’s upper body tilt and waist orientation. The WS interface has been thoroughly evaluated, using an articulated locomotion testbed and compared its performance with three non-leaning interfaces previously tested with the same reference protocol. The experimental results suggest that the WS interface could be a promising solution for navigation in immersive VR applications and open new directions for further research to refine and improve the WS interface for more complex tasks and scenarios.
Based on Socket IO + Unity Building the Metaverse of &amp;lt;i&amp;gt;Dream of the Red Chamber&amp;lt;/i&amp;gt;

The Metaverse of Dream of the Red Chamber has a natural fit with Unity + Socket.IO, both in virtual space and functional design. Using Unity, you can generate and build the foundation of the Metaverse of Dream of the Red Chamber: virtual reality, including real-time dynamic internal generation and external loading of virtual characters, scenes, and various other elements, and can achieve various controls over these elements without delay. The Metaverse space built by Unity has full immersion and diversity; Socket.IO realizes Internet connection, Unity C# script creates Socket.IO client, Unity + Socket.IO connection realizes interpersonal communication, thus jointly realizing digital identity management in the Metaverse of Dream of the Red Chamber. Including virtual identity registration, Unity to achieve login authentication, digital avatar unique distribution; jointly create a network social space including games, business transactions, friends and entertainment, and provide corresponding safety information management for it, which is enough to reflect the special code of conduct and civilization ecology in the Metaverse. In short, Unity + Socket.IO can build a complete system of the Metaverse of Dream of the Red Chamber from the digital technology level.
Image Stitching Method of Non-Ferrous Smelting Scene Based on SIFT Algorithm and Color Constancy

In non-ferrous smelting scenes, complex environments, uneven lighting conditions, and inaccurate automatic exposure parameters result in the panoramic image having obvious seams and significant brightness differences in different regions. To address these issues, a novel image stitching method based on Scale-Invariant Feature Transform (SIFT) and color constancy theory was proposed. Initially, the input image was preprocessed followed by feature extraction and matching. Subsequently, color constancy processing based on matching points was performed to acquire an image with consistent brightness to be spliced. This process was complemented by the integration of multi-band fusion to enhance the original fusion procedure. Eventually, a spliced image with seamless blending and even luminosity was generated. The method proposed in this paper can obtain panoramic images that were more suitable for human eyes to observe, and greatly improve the subjective and objective performance of panoramic images. In the non-ferrous smelting scene, the PSNR and SSIM scores were improved by 3.555 dB and 0.16 respectively.
Digital Wealth Management

As an emerging form of wealth management, digital wealth management is gradually gaining attention.
Emotional Linkage among Teacher-Student in English Multimedia Smart Classroom Teaching in the Internet of Things Big Data Era

At this stage, multimedia has been increasingly widely used in college English education with the advantages of rich content, convenient updating and various forms, providing rich and novel resources for college English teaching. However, compared with the traditional classroom, the multimedia smart classroom relatively reduces the linkage between teachers and students, making it difficult to form emotional linkage between teachers and students in the classroom teaching situation, which seriously affects the teaching effect. In view of this situation, this paper studied the English multimedia smart classroom in the era of Internet of Things big data and the emotional linkage between teachers and students in classroom teaching. This paper conducted experiments and analysed on the emotional linkage between teachers and students from five aspects: students' learning enthusiasm, learning efficiency, academic level, teachers and students' extra-classroom linkage, and teachers' satisfaction. The experimental results showed that under the condition of active emotional linkage between teachers and students, students' English learning enthusiasm has increased by 6.76%, students' English academic level has increased by 4.08%, and students' satisfaction with teachers has increased by 4.92%. Positive emotional linkage between teachers and students can effectively improve students' English learning enthusiasm and English learning performance, and can increase students' recognition of teachers.
Construction of the Evaluation System of Classroom Practice Teaching in Colleges and Universities Driven by 5G Network Communication Technology

As higher education reform continues to progress, university classrooms are making continuous efforts to cultivate well-rounded and high-quality talents. In order to follow the path of socialist modernization with Chinese characteristics and implement the national quality education policy, improving practical teaching and its teaching evaluation is a major breakthrough to realize the construction of the road of higher education reform. The importance of practical teaching can be imagined as an irreplaceable teaching link in the teaching process. In particular, under the current situation of education reform, classroom teaching practice has gradually been emphasized. In the past, more attention was paid to students' mastery of theoretical knowledge, while the requirements of modern teaching quality for college classroom no longer stay at the level of theoretical teaching. However, the modern college classroom does not really combine teaching with happiness and classroom with action. For the evaluation of practical teaching, there are also a series of problems. In order to optimize the teaching evaluation system and truly integrate the classroom into practice, this paper introduced 5G network communication technology and conducted a comparative experiment. The teachers and students of the experimental class jointly scored the classroom practice teaching evaluation system before and after the experiment. The experimental data showed that the teachers and students' rating of the evaluation subject has risen from 4.3 to 8.3; the score of the evaluation index rose from 5.6 to 9.1; the change of scoring results can show the satisfaction of teachers and students with 5G network communication technology. On this basis, an interview survey was conducted. The survey results showed that after the introduction of 5G network communication technology, the evaluation indicators were more optimized and the evaluation process became more scientific. The proposed research provided a value of reference for the construction of classroom practice teaching evaluation system in universities driven by 5G network communication technology and provided a direction for the future development of practice teaching evaluation.
Model Geographically Weighted Weibull Regression Pada Indikator Pencemaran Air COD di Daerah Aliran Sungai Mahakam Kalimantan Timur

The Geographically Weighted Weibull Regression (GWWR) model is a Weibull regression model applied to spatial data. Parameter estimation is carried out at each observation location using spatial weighting. This study aimed to determine the GWWR model on the Chemical Oxygen Demand (COD) water pollution indicator data and to obtain the factors that influence COD in the Mahakam watershed. The parameter estimation method was Maximum Likelihood Estimation (MLE). Spatial weighting in parameter estimation has been determined using the adaptive tricube weighting function and the criteria for determining the optimum bandwidth was Generalized Cross-Validation (GCV). The research sample was 20 location points of the Mahakam river determined by the Environmental Department of East Kalimantan Province. The results showed that the factors that influence COD locally was temperature, while the factors that influence globally were temperature, Total Suspended Solids (TSS), and Fecal Coli.
Exploring Bias and Accountability in Military Artificial Intelligence

With the evolution of machine learning, predictive risk assessment will establish itself as the standard rather than the exception. However, the excessive reliance on computer software can be detrimental to a state’s international human rights framework, threatening the fundamentality of customary international law. This letter highlights the concerns with algorithmic detention during armed conflict, a procedure where the detainee is attributed a recidivist score by an algorithm based on the degree of threat they pose to national security. Accordingly, the algorithm determines whom to detain and the duration of their detention. The primary concern with predictive detention is the instability of data collection during hostility, making the entire procedure manifestly arbitrary. Furthermore, algorithms are pre-set with data inputted by humans. As a result, there will always be room for human error or discriminatory biases that affect its decision-making. These concerns require immediate redressal before military-owned algorithms for the purpose of security detention enter the mainstream.
Perbandingan Metode C-Means dan Fuzzy C-Means Pada Pengelompokan Kabupaten/Kota Di Kalimantan Berdasarkan Indikator IPM Tahun 2019

The Human Development Index is an indicator used to measure one important aspect related to the quality of the results of economic development, namely the degree of human development. Data Mining is a technique or process for obtained information from large database warehouses. Based on its function, one of the data mining tasks was to group data, where the method used in this study was the C-Means and Fuzzy C-Means grouping methods. The two classification methods were applied to the human development index indicator data. The purpose of this study was to determined the best method based on the ratio of the standard deviation in clusters to the standard deviation between clusters. Based on the results of the analysis, it was concluded that the best method is the C-Means method with the value of the standard deviation value in the cluster against the standard deviation between clusters of 0.434 which results in 5 clusters, namely cluster 1 consisting of 9 districts / cities, cluster 2 consisting of 7 districts / cities, cluster 3 consists of 10 districts / cities, cluster 4 consists of 15 districts / cities and cluster 5 consists of 15 districts / cities.
Penerapan Metode Klasifikasi Multinomial Naive Bayes

Life insurance is a risk management service provide payment to policyholders in the event of a disaster that has been stipulated in the agreement. A classification system needs to be done to facilitate the company in making decisions to provide policies to customers. One system that can be used is multinomial Naive Bayes. Multinomial Naive Bayes is a simple probabilistic classification that has more than two groups or categories. An algorithm using Bayes theorem assumes all independent variables. The aim of this study is to obtain an accuracy level of 5 different proportions with the Naive Bayes multinomial method used in insurance customer payment status data. The data used is the customer data of PT. Prudential Life Samarinda in 2019 with the status of current premium payment, substandard and non-current and using 5 independent variables, namely income, age, amount of premium payment, sex and employment. The results of the measurement of classification accuracy using APER status premium payment on insurance customer data of PT. Prudential Life 2019 Naive Bayes multinomial method showed 22,96% misclassification at 50:50 proportion, at the proportion of 60:40 there were 21,43% misclassification, at the proportion of 70:30 there were 19,05% misclassified, at proportions 80:20 had a misclassification of 14,29%, and a proportion of 90:10 has a misclassification of 7,14%.
Optimalisasi K-Means Cluster dengan Principal Component Analysis pada Pengelompokan Kabupaten/Kota di Pulau Kalimantan Berdasarkan Indikator Tingkat Pengangguran Terbuka

Data mining or often also called knowledge discovery in databases is an activity that includes collecting, using historical data to find regularity, patterns, or relationships in large data sets resulting in useful new information. Cluster analysis is an analysis that aims to group data based on its likeness. This research uses the K-Means method combined with PCA. The K-Means method groups data in the form of one or more clusters that share the same characteristics. While the PCA method was used to reduce research variables. This grouping method was applied to the data indicator of the unemployment rate of districts/cities in Kalimantan Island in 2018. The cluster validation used in this study was the Davies-Bouldin Index (DBI). Based on the results of the analysis, it was concluded that the number of principal components formed was as many as 2 principal components. The most optimal grouping of districts/cities in Kalimantan island in 2018 was to use 2 clusters with a DBI value of 0,507. The grouping of districts/cities in Kalimantan Island in 2018 produced 2 clusters, cluster 1 consisting of 51 districts/cities and clusters of 2 consisting of 5 districts/cities. Cluster 1 was a cluster that has the highest percentage of the poor population and the highest labor force participation rate when compared to cluster 2. While cluster 2 was a cluster that has an index value of human development, population, number of the labor force, number of unemployed, population density, and the minimum wage of district/city was high compared to cluster 1.
Perbandingan Metode Klasifikasi Naive Bayes dan K-Nearest Neighbor pada Data Status Pembayaran Pajak Pertambahan Nilai di Kantor Pelayanan Pajak Pratama Samarinda Ulu

Classification is a systematic grouping of objects into certain groups based on the same characteristics. The classification method used in this research are naive Bayes and K-Nearest Neighbor which has a relatively high degree of accuracy. This research aims to compare the level of classification accuracy on the status data of value-added tax (VAT) payment. The data used is data on corporate taxpayers at Samarinda Ulu Tax Office in 2018 with the status of VAT payment being compliant or non-compliant and used 3 independent variables are income, type of business entity and tax reported status. Measurement of accuracy using APER in the Naive Bayes method is 17.07% and in K-Nearest Neighbor method is 19,51%. The comparison results of accuracy measurements between the two methods show that the naive Bayes method has a higher level of accuracy than the K-Nearest Neighbor method
An extensive review on block chain

The blockchain technology is the most recent technology in the current monetary system. Blockchain technology arranges blocks separately identified linked transaction history into a chain. As it is a chain it includes the order of blocks. It would be beneficial as it is reliable. Also, it can be making use like assured money transactions, tracing items and make cocksure of data veracity. Blockchain makes it almost impossible to hack transactions. A huge number of industries can benefit from this growing technology as it has the ability to make a cheap cost by gathering the processes and breaking form or paperwork.
Types And Levels Of Education In The National Education System In Indonesia

Education is a very important thing in human life so that they can compete in the future. Education also varies over time, so in education that has the development of cognitive and intellectual goals for students and also education that always encourages moral and ethical education in students. This research is aimed at describing and analyzing a national education system which is mentioned in law number 20 of 2003, which consists of types, pathways and levels of education. The national education system, which consists of types, pathways and levels of education, must be supported by quality human resources and supporting facilities. Law No. 20 of 2003 in article 15 explains that the types of education are general, vocational, academic, professional, vocational, religious and special education. There are 8 types of education consisting of: general education, (2) vocational education, (3) academic education, (4) professional education, (5) vocational education, (6) religious education, (7) special education, (8) official education.
Urgensi Perpustakaan IAIN Pare - Pare Dalam Meningkatkan Literasi Mahasiswa Fakultas Tarbiyah IAIN Pare-Pare

The urgency of the IAIN Parepare library for tarbiyah faculty students is one of the places to study and add insight. With the existence of a library, students can use the container to be used as a recreation area and look for reading references to support their literacy. This research is a type of qualitative research. The targets in this study were tarbiyah faculty students and library managers at IAIN Parepare. Data collection techniques were carried out through observation and interviews, equipped with data analysis techniques using data reduction and data presentation. The results of this study indicate that: 1) The use of libraries to improve student literacy at the Tarbiyah Faculty of IAIN Parepare, namely library development is a student need. 2) Increasing the literacy of Tarbiyah students of IAIN Parepare through the use of libraries, namely literacy as a supplement to improve students' mindsets and reasoning
Can Artificial Intelligence (AI) like QuillBot AI Assist Students’ Writing Skills? Assisting Learning to Write Texts using AI

At present, it is undeniable that the influence of Artificial Intelligence (AI) in learning English is increasing. AI helps students develop English sentences. This study aims to assist eleventh-grade students’ skills in creating hortatory exposition texts using QuillBot AI. It was Classroom Action Research (CAR) using the Kemmis, McTaggart, and Nixon paradigm conducted in two cycles, each consisting of three meetings. Each cycle included planning, acting, observing, and reflecting. The participants of this study were students from XI IPA 2A, consisting of 20 students. The data collection technique employed was a test. Before giving treatment, the students did a pre-test in the form of a writing test. Then, the researcher taught the students using Quillbot AI in several meetings. A paired sample t-test was administered using the SPSS version 25 to analyze the obtained data. The findings revealed that using QuillBot AI in the writing class considerably improved students’ writing skills. It was demonstrated by the results of the students’ writing test scores. In the pre-test, the students’ mean score was 53.55. Then, after utilizing QuillBot AI, the students’ mean post-test score was 78.90. In addition, the significance of the difference could also be seen in the acquisition of a sig. (2-tailed) value of 0.000, which meant it was smaller than 0.05. Based on the statistics presented, Ho was refused, and Ha was approved, indicating that QuillBot AI’s usage impacted the students’ ability to create hortatory exposition texts.