Reviewerly/exHarmony huggingface dataset:
LA–ICP–MS analysis of sulfides from the Jianzhupo deposit, Guangxi Province, China: Insights into element incorporation mechanisms and ore genesis

The Jianzhupo deposit, the largest Zn-Sb deposit in the Wuxu orefield in China, contains abundant sphalerite and jamesonite, contrasting with typical Sb deposits elsewhere. Whether the Sb and Zn in the deposit derived from similar parental sources is not clearly resolved. Here, trace-element compositions of sulfides from the deposit were determined by laser ablation–inductively coupled plasma–mass spectrometry (LA-ICP-MS). Results indicate that jamesonite is an important Cd-bearing sulfide with Cd contents of 523–764 ppm and minor or no significant Cd variations were observed within jamesonite crystals. Trace-element compositions of jamesonite indicates that Cd enters the lattice by coupled substitution as 2Fe2+ ↔ Zn2+ + Cd2+. Due to similar geochemical behaviors between Zn and Cd in hydrothermal systems, Zn/Cd ratio of sphalerite was suggested as a robust geochemical tracer to distinguish metal sources in such systems. Here, we summarized the Zn/Cd ratios of sphalerite from various hydrothermal systems and the results show that values between 169 and 233, and lower than 44 are from high- and low-temperature temperature fluid conditions, respectively. Due to different Zn/Cd ratios between sediments and igneous rocks, we conclude that metals in sphalerite from the Jianzhupo deposit were likely derived from granite; in contrast, metals in jamesonite were related to sedimentary rocks. These conclusions are consistent with previous studies on the deposit. Overall, our results indicate that ore-forming fluids of the Jianzhupo deposit were derived from magmatic–hydrothermal and basin water. This study extends the application of trace-element compositions in resolving the sources of sulfides in hydrothermal systems.
Early detection of new web tracking methods across 1.5 million sites

Current web tracking practices pose a constant threat to the privacy of Internet users. As a result, the research community has recently proposed different tools to combat well-known tracking methods. However, the early detection of new, previously unseen tracking systems is still an open research problem. In this paper, we present TrackSign+, a novel approach to discovering new web tracking methods. The main idea behind TrackSign+ is the use of code fingerprinting to identify common pieces of code shared across multiple domains. To detect tracking fingerprints, TrackSign+ builds a novel 4-mode network graph that captures the relationship between domains, URLs, online resources, and code fingerprints. We evaluated TrackSign+ with the 1.5M most popular Internet domains, including more than 45M web resources from almost 77M HTTP requests. Our results show that our method can detect new web tracking resources with high precision (over 92%). TrackSign+ was able to detect more than 300k new trackers, 800k new tracking resources, and 4.5M new tracking URLs, not yet detected by most popular pattern lists at the time. Finally, we also validated the effectiveness of TrackSign+ with more than 20 years of historical data from the Internet Archive.
Deep estimation for Q⁎ with minimax Bellman error minimization

In this paper we consider the estimation of optimal state-action value function Q⁎ with ReLU ResNet based on minimax Bellman error minimization. We construct the non-asymptotic error bounds for the minimax estimator and the estimated Q function induced by the estimated greedy policy. To bound the Bellman residual error, we guarantee the approximation errors based on deep approximation theory and the statistical ones by utilizing empirical processes taking into account the Markov decision process dependency. We provide a novel generalization bound with dependent data and an approximation bound in the Hölder class which are of independent interest. This bound depends on the sample size, the ambient dimension, the width and depth of the neural network, which can bring prior insights into tuning these hyper-parameters to achieve a desired convergence rate in practice. Furthermore, the bound circumvents the curse of dimensionality if the distribution of state-action pairs is assumed to be supported on a set of low intrinsic dimension.
The Power Generation Prediction of Chungju Floating Photovoltaic Utilizing RNN

Wind and the sun are major sources of renewable energy; however, operating a power grid is difficult, as renewable energy can infiltrate the grid rapidly because of the volatility and intermittent nature of renewable energy. To reduce the volatility of renewable energy, predicting solar power generation is important. The solar radiation amount is an important factor in solar power prediction. Based on the data recorded by the Chungju floating photovoltaic system, a prediction model was created in this study, and the power generation amount was predicted hourly. The prediction method used a recurrent neural network LSTM model, and the prediction model for predicting solar power generation was presented as well as the hyperparameter selection process. In addition, the accuracy of the prediction models was presented based on the different parameters. The prediction error decreased, and the optimal model was selected based on the optimal setting of the different hyperparameters. Results with few errors were obtained through 24-hour bundled calculation, the Sigmoid activation function, the Nadam optimum, and a batch size of 30.
THE CORRELATION OF STUDENTS’ MOTIVATION AND ENGLISH LEARNING ACHIEVEMENT

The objective of this study was to figure out whether there is any correlation between students’ motivation and English learning achievement at Grade XI Madrasah Aliyah Negeri 1 Kota Bengkulu. In this research, the researcher used Correlational research in a quantitative design. In correlational method, the researcher used the correlation statistical test to describe and measure between two variables. The population of this study was the whole students of XI grade MAN 1 Bengkulu City with total students 355. The researcher took all of population as the sample of the research. Therefore, the sample of this research was about 350 students at eleventh grade MAN Model Bengkulu. To obtain the data, the researcher used questionnaire and documentation as the instruments of this research. The questionnaire of students’ motivation was adopted from The Attitude / Motivation Test Battery by R. C Gardner cited in (Rafasah, 2019). Moreover, the students’ learning achievement was identified by collecting the students’ English score in UTS or UAS or daily test. After collecting and analyzing the data, the researcher found that the sign. (2-tailed) Pearson correlation = 0.00 &gt; 0.05 which means that there was a correlation between students’ motivation and English learning achievement at XI A students of SMAN 1 Kota Bengkulu. The researcher suggested for English teachers in developing some strategies to facilitate the students to have a good motivation. The researcher also suggested for the students to be more active in practicing English even though this research findings showed that the students’ motivation was good.&#x0D; Keywords: The Correlation, Students’ Motivation, English Learning Achievement
Field Experiment of the Memory Retention of Programmers Regarding Source Code

"Program comprehension is a continuously important topic in computer science since the spread of personal computers, and several program comprehension models have been identified as possible directions of active code comprehension. There has been little research on how much programmers remember the code they have once written. We conducted two experiments with a group of Computer Science MSc students. In the first experiment, we examined the code comprehension strategies of the participants. The students were given a task to implement a minor feature in a relatively small C++ project. In the second experiment, we asked the students 2 months later to complete the same task again. Before starting the clock, we asked the students to fill a questionnaire which aimed to measure program code-related memory retention: we inquired about how much the students remembered the code, down to the smallest relevant details, e.g. the name of functions and variables they had to find to complete the task. After the second experiment, we could compare the solution times of those students who participated in both parts. As one result, we could see that these students could solve the task in shorter time than they did in the first experiment. We also looked at the results of the questionnaire: the vast majority of students could not precisely remember more than two or three identifiers from the original code. In this paper, we will show how this result compares to the forgetting curve. 2010 Mathematics Subject Classification. 68U99. 1998 CR Categories and Descriptors. I.m [Computing Methodologies]: Miscellaneous; J.m [Computer Applications]: Miscellaneous. Key words and phrases. code comprehension, memory retention, experiment."
Design of the Integrated Cognitive Perception Model for Developing Situation-Awareness of an Autonomous Smart Agent

This study explores the potential for autonomous agents to develop environmental awareness through perceptual attention. The main objective is to design a perception system architecture that mimics human-like perception, enabling smart agents to establish effective communication with humans and their surroundings. Overcoming the challenges of modeling the agent's environment and addressing the coordination issues of multi-modal perceptual stimuli is crucial for achieving this goal. Existing research falls short in meeting these requirements, prompting the introduction of a novel solution: a cognitive multi-modal integrated perception system. This computational framework incorporates fundamental feature extraction, recognition tasks, and spatial-temporal inference while facilitating the modeling of perceptual attention and awareness. To evaluate its performance, experimental tests and verification are conducted using a software framework integrated into a sandbox game platform. The model's effectiveness is assessed through a simple interaction scenario. The study's results demonstrate the successful validation of the proposed research questions.
Efficiency of estimators for locally asymptotically normal quantum statistical models

We herein establish an asymptotic representation theorem for locally asymptotically normal quantum statistical models. This theorem enables us to study the asymptotic efficiency of quantum estimators, such as quantum regular estimators and quantum minimax estimators, leading to a universal tight lower bound beyond the i.i.d. assumption. This formulation complements the theory of quantum contiguity developed in the previous paper [Fujiwara and Yamagata, Bernoulli 26 (2020) 2105–2141], providing a solid foundation of the theory of weak quantum local asymptotic normality.
Exploring Online Technology Resources for English Speaking Skills: A Case Study of Students’ Experiences

Various learning resources are designed and used to enhance students’ English speaking skills because the speaking skill plays a significant role in communication and in language acquisition. For the past few decades, the rapid development of information and communication technologies have made a significant contribution to English language teaching and learning. In other words, the development of technology has been the driving force that leads to a transformation in English language learning. Particularly, the mushrooming online learning resources have been providing valuable support for students when it comes to learning English speaking skills. Nonetheless, not many studies have looked into how low-proficiency English learners utilized the online technology resources for English speaking skills. Thus, this study aims to study the online technology resources used by low-proficiency English learners to improve their communication and speaking skills. This study adopted a quantitative approach. The instrument used to elicit the data for the study was questionnaire. A total of 62 undergraduate students were recruited as the respondents of this study. The results of the study showed that YouTube (58%) was one of the most popular online technology resources used by students to improve their communication and speaking skills. Most of them perceived that online technology resources helped to build their confidence and reduce their speaking anxiety (32%). However, internet/Wi-Fi (26%) problem was the biggest challenge encountered by them. It is believed that this paper could make a contribution to the body of knowledge as it explored students’ experiences in using online technology resources. Lastly, the recommendations for future studies and limitations were also discussed in this paper.
Educational Robot AI-Assisted Reading Promotes Foreign Children's Chinese Acquisition: An Experimental Study

Three foreign children from Grade 1 of an international primary school in Hangzhou, China, were selected for a six-week after-school autonomous AI-assisted reading experiment using educational robots. The reading materials used were the electronic audio picture books Fun Chinese 1000 Characters, Illustrated Tang and Song Poems and Illustrated Three-Character Classics. At the end of the experiment, it was found that their recognized Chinese characters exceeded 2,000 characters, with a reading comprehension accuracy rate of 80%, which was about 10 times higher than that of the control group. Furthermore, the students who participated in the AI reading experiment demonstrated better accuracy and standard Mandarin pronunciation in their Chinese spoken expression than their non-participating classmates. They also developed a love for Chinese and ancient Chinese poetry and established good reading habits. This experiment effectively and economically promoted Chinese language acquisition among foreign children using educational robots with AI-assisted reading. Both children and parents highly appreciated this approach, and it provides valuable experience and reference for second language acquisition in the digital age.
Intelligent Sanskrit translator using NLP

The rapid evolution of Human beings as a species can be credited to their ability to commune with one another and efficiently drive ideas, messages and intent past each other. One of the antediluvian and well-structured languages, Sanskrit, is being relegated only to use in scriptures during modern times. Our intent is to build a virtual assistant (voice/chat) which communicates through Sanskrit ensuring this language becomes the linchpin of understanding machines and relaying information and knowledge not only for an extensive heterogeneity of vernacular population but for the world. Studying various Machine Learning and Neural Network models, understanding their scope, underlying principles and application hence facilitating deep understanding of the scope of AI Assistants and aid in building a Sanskrit Voice Bot. Various algorithm explore include linear regression and logistic regression, whose reach is limited to linearly related/ separable data, which was test by deploying gradient descent algorithm. Support Vector Machine kernels resolve this problem by providing linear as well as polynomial decision boundary. Principal Component Analysis finds its major application in dimensionality reduction and Anomaly Detection would be used to detect any out of the bound data input. Furthermore, Sequence Models would play a major role in all the required Natural Language Processing
WeConnect: Class Attendance Monitoring System with the use of Mobile Tethering

This study, entitled WeConnect: Class Attendance Monitoring System with the Use of Mobile Tethering, focuses on the development of an attendance monitoring system for National University Laguna (NU Laguna). The objective of this study is to automate attendance monitoring in NU Laguna. The attendance monitoring system was developed using Android Studio SDK, Java SDK, and an integrated development kit dedicated to Android mobile app development. In addition to tethering technology, the system also incorporates the utilization of QR codes. This project demonstrates the significance of promoting NU Laguna as a premier educational institution and highlights the benefits of an automated attendance recording and monitoring system. The study involved 15 testers, including 3 full-time faculty members from the Bachelor of Science in Information Technology program and 12 randomly selected students from the College of Computer Studies (CCS), majoring in Computer Science, Information Technology, and Information Systems. The comprehensive testing and evaluation aimed to assess the functionality, reliability, usability, and portability of the system.
A Higher-Accuracy Color- and Gray-Image Training Method for Road-Object Detection with YOLO

This paper proposes a higher-accuracy color- and gray-image training method for road-object detection with You Only Look Once (YOLO). Image pre-processing is performed before feeding images to a network for object detection, i.e., color-to-gray conversion and edge detection. Then, the original images, the gray version of the original images, and the edge images are fed to the main network. YOLO version 5 (YOLOv5) was used as a backbone network. The model was trained by using custom traffic dataset which consists of 738 training images and 185 validating images, and they are separated into 7 classes. The proposed method achieved a mAP of 44.97% when performing a validation by using both RGB and grayscale images. It is higher than that of the conventional training method using only original images. The results also confirm that higher accuracy can be achieved even for night vision images. The proposed method could serve for applications which be used in various factors and environments.
Display of Ancient Sports Games in Dunhuang Frescoes Based on Virtual Reality Animation Technology

This paper takes the "Boy's Batting" movement on the south wall of Cave 45 in Dunhuang Yulin Grottoes as an example to carry out VR restoration display and study the application of VR animation technology to reproduce the game movement in ancient frescoes. We use MAYA and ZBRUSH to restore the image of "Boy Batting" in the mural. Combined with VR animation technology, we popularize the movement of ancient people to the audience through digital technology, and get closer to the life of ancient people more intuitively. It provides new ideas and methods for spreading and popularizing traditional culture.
Research on the Personalized Teaching Model of College English Based on Big Data

In the context of big data, according to the current situation of college English teaching, the implementation plan of personalized teaching is proposed according to the theory of constructivism, learner needs theory and the theory of multiple intelligences. Both quantitative and qualitative methods are adopted to research on the application effect of personalized teaching in GDUFE. The purpose is to provide a paradigm for college English teaching, improve students' independent learning ability, promote the individual development of students, and deepen the reform of college English teaching.
Construction of Knowledge Graph of Discrete Mathematics and Data Science and Big Data Technology Course Groups∗

Discrete mathematics is a core discipline basic course in data science and big data technology. In the process of teaching discrete mathematics, teacher pay more attention to theoretical explanation, and neglect the correlation and connection with the professional course groups of data science and big data technology, so that students do not have enough understanding of the core basic position of discrete mathematics courses and insufficient learning motivation. Build the knowledge map of each related course in the discrete mathematics and data science and technology major course group to realize the cultivation of students' logical thinking, abstract thinking, computer thinking, modeling thinking, engineering thinking, divergent thinking and critical thinking.
rFITb: Random Forest in the Browser

This paper introduces rFITb, a distributed computing platform that enables the execution of computationally intensive random forest jobs on personal devices such as smartphones and personal computers. The platform leverages the increased computational capacity of personal devices to distribute and execute jobs globally, providing an efficient alternative to cloud-based services. The paper describes rFITb's architecture and design optimizations, along with a comparative evaluation of its performance against Python's sklearn ensemble random forest classifier on various datasets. The results show that rFITb outperforms the sklearn classifier in terms of model time, while also providing a mechanism for managing failure-prone volunteers.
Visualization Analysis on Intellectual Structures and Research Fronts of “Cinemetrics”: Quantitative Film Studies

The integration of quantitative methods in the study of film has gained prominence in the digital humanities field, which leverages advancements in technology and information sciences. The increasing adoption of these methods shows the evolution of the film field and the impact of digital humanities. However, only a small amount of study has attempted to comprehensively characterize the current research trends and hotspots in the context of quantitative cinema and database film research. This study utilizes the latest information visualization software, Citespace, to analyze research papers published from 2005 to 2022 retrieved from the Web of Science related to Cinemetrics, with a total of 308 papers being identified. The top 15 co-occurring keywords among these papers include “digital humanities”, “visual analytics”, “model”, “film”, “attention”, “classification”, “emotion”, “design”, “collection”, “topics”, “computational analysis”, “social network”, “distant reading” and “close reading”. These papers can be grouped into nine clusters: digital humanities, user group analytics survey, visual text analysis, quantitative analysis, visual narrative, digital visual effect, affective characterization, genealogy, and criteria.
Rearch on the Framework and Index System of Asset Digitization in Higher Education

Digitalization is strongly impacting the traditional social system and education system at an unprecedented speed and momentum. The process of educational digital transformation is not a simple linear combination, but a series of complex behaviors such as the bifurcation, the mutation and the self-organization. This is a complicated system evolution process. Combined with the current situation of university assets management and the responsibilities of universities, we propose a framework for the construction of digital campus and the AHP is used to construct the digital evaluation index framework of university assets, including the asset status, the cost-effectiveness, the teaching effect, the scientific research ability and the network security management, to determine the weight, and to solve the problem that qualitative analysis and quantitative evaluation are difficult to combine. The results of index weights proves that it provides a set of feasible models and methods for constantly improving the asset management decision-making and digital development and evaluation of assets in universities.