Reviewerly/exHarmony huggingface dataset:
Metasomatized mantle sources for orogenic gold deposits hosted in high-grade metamorphic rocks: Evidence from Hg isotopes

Investigation of Hg isotope ratios of gold-related sulfides and penecontemporaneous mafic dikes from four orogenic gold provinces on the margins of the North China Craton and Yangtze Craton identifies three orogenic gold deposit (OGD) groups from different tectonic regimes. Ore-related sulfides of group 1 OGDs and mafic dikes from the craton margin reworked via oceanic subduction mostly have positive Δ199Hg values. The group 2 OGDs and mafic dikes from the margin that witnessed complex oceanic and continental subductions have mixed positive to negative Δ199Hg values. The group 3 OGDs on the margin that experienced continental subduction have dominantly negative Δ199Hg values. These isotopic differences indicate subduction histories have a first-order control on the distinct sources for the OGDs. It indicates that OGDs were derived from fluids from the mantle lithosphere metasomatized by contrasting subduction components, not from metamorphic fluids as is widely accepted. Group 1 OGDs and dikes were sourced from metasomatized mantle, which inherited the positive Δ199Hg of both recycled marine sediments and seawater during oceanic subduction, whereas group 3 with negative Δ199Hg was derived from mantle lithosphere metasomatized by subduction of mainly continental components. This genetic model identifies regions with high-grade metamorphic rocks above metasomatized mantle lithosphere as promising new OGD exploration targets.
Research on Chinese machine reading comprehension method based on deep learning

Current machine reading comprehension methods focus on modeling local or global interactions between texts. This method, which does not pay attention to both local and global structures, easily leads to problems such as insufficient machine understanding of semantics and inaccurate answering of questions. Aiming at the problem that when the traditional LSTM is used as the encoder at the encoding layer, there is a problem that the current input and the hidden state are independent of each other, resulting in the loss of context information. This paper introduces a variant of the LSTM network to enhance the information perception and interaction between text contexts and obtain richer information. Semantic representation. At the same time, considering that the attention mechanism cannot effectively extract the local structure of the text, which hinders the model from understanding the deep semantics of the text, a combination of dynamic convolutional attention and multiple attention mechanisms is used in the attention layer to capture text from different scales. Structure. Experimental verification is carried out on the DuReader2.0 data set, and the results are analyzed, which proves that the proposed model can effectively improve the ability of the machine to answer questions.
Study on Seismic Evaluation for Anchor Bolts in Circular Arrangement Using Elastic-Plastic Analysis

Abstract In a seismic evaluation of process equipment, anchor bolts for a component are required to resist against seismic conditions to support the component on the basement. Some components are directly fixed to the basement by anchor bolts, and anchor bolts of many cylindrical tanks or vessels are arranged on the circumference. Anchor bolts are basically designed in elastic base, but actual materials of them are confirmed to show plastic behavior by tensile testing. For rational evaluation for increasing seismic condition, an elastic-plastic analysis method has been studied to take account the condition that a few of the anchor bolts in circular arrangement become beyond elastic limit by seismic load. In this condition, even if yielding of a few bolts is occurred, it is expected that the other bolts within elastic limit bear against the seismic loads and keep equipment support function of the anchor bolts. To evaluate this condition, static loading tests using a reduced scale model of a cylindrical storage tank and static pushover analyses taking into account the elastic-plastic characteristics of the anchor bolt material were performed.
Construction and Practice of Online Open Course Group of Applied College English and Assessment Data Analysis Based on Online Platform

In recent years, various new types of online open courses and platforms have risen rapidly, and the effective integration of College English teaching and online open courses has become necessary means of teaching reform.This paper describes the construction and practice of the online open course group for applied College English from the perspectives of teaching content, teaching resources, teaching models, assessment and teachers' development and assessment data analysis based on these online platform.
Developing Chinese College English Learners’ Vocabulary Proficiency with a Mobile-assisted Language Learning Application

By introducing the language learning mobile application BaiCiZhan, this paper conducts an experimental study on mobile technology-assisted vocabulary teaching for college non-English majors in order to explore the effectiveness of BaiCiZhan in English vocabulary learning and teaching. The results sh
Gate-Attention and Dual-End Enhancement Mechanism for Multi-Label Text Classification

In the realm of Multi-Label Text Classification (MLTC), the dual challenges of extracting rich semantic features from text and discerning inter-label relationships have spurred innovative approaches. Many studies in semantic feature extraction have turned to external knowledge to augment the model’s grasp of textual content, often overlooking intrinsic textual cues such as label statistical features. In contrast, these endogenous insights naturally align with the classification task. In our paper, to complement this focus on intrinsic knowledge, we introduce a novel Gate-Attention mechanism. This mechanism adeptly integrates statistical features from the text itself into the semantic fabric, enhancing the model’s capacity to understand and represent the data. Additionally, to address the intricate task of mining label correlations, we propose a Dual-end enhancement mechanism. This mechanism effectively mitigates the challenges of information loss and erroneous transmission inherent in traditional long short term memory propagation. We conducted an extensive battery of experiments on the AAPD and RCV1-2 datasets. These experiments serve the dual purpose of confirming the efficacy of both the Gate-Attention mechanism and the Dual-end enhancement mechanism. Our final model unequivocally outperforms the baseline model, attesting to its robustness. These findings emphatically underscore the imperativeness of taking into account not just external knowledge but also the inherent intricacies of textual data when crafting potent MLTC models.
Comparative Analysis of PID and Fuzzy Logic Controllers for Position Control in Double-Link Robotic Manipulators

This study presents a comprehensive evaluation of linear and non-linear control systems, specifically Proportion Integration Differentiation (PID) and fuzzy logic controllers, in the context of position control within double-link robotic manipulators. The effectiveness of these controllers was rigorously assessed in a simulated environment, utilizing MATLAB Simulink for the simulation and SOLIDWORKS for the model design. The PID controller, characterized by its Kp, Ki, and Kd components, was implemented both in the simulation and on the hardware. However, due to the constraints of the microcontroller's RAM and processor, which facilitate the hardware's connection with MATLAB, the application of the Fuzzy Logic concept to hardware was not feasible. In the simulated environment, the fuzzy logic controller demonstrated superior stability in comparison to the PID controller, evidenced by a lower settling time (1.0 seconds) and overshoot (2%). In contrast, the PID controller exhibited a settling time of 0.2 seconds and an overshoot of 32%. Additionally, the fuzzy logic controller showcased a 44% reduction in steady-state error relative to the PID controller. When applied to hardware, the PID controller maintained stable results, achieving a settling time of 0.6 seconds and an overshoot of 2%. The steady-state errors for Link 1 and Link 2 were recorded as 3.6° and 1.4°, respectively. The findings highlight the fuzzy logic controller's enhanced stability, rendering it more suitable for ensuring the accuracy and protection of the manipulator system. As a non-linear controller, the fuzzy logic controller efficiently addresses various potential errors through its intelligent control mechanism, which is embedded in its fuzzy rules. Conversely, the PID controller, a linear controller, responds rapidly but may lack flexibility in complex scenarios due to its inherent linearity. This study underscores the importance of selecting an appropriate controller based on the specific requirements of robotic manipulator systems, with a focus on achieving optimal performance and stability.
Nutrition Control and Moisture Control in Hydroponic Plants Based on IoT (Internet of Things)

Hydroponics is a method of growing plants using nutrient solutions. The advantage of hydroponics is that soil can be replaced with a nutrient solution because it can produce its advantages, such as saving land, planting in any area without the need for soil, and producing optimal quality compared to using soil. Technology development is growing rapidly and has created a lot of IoT (Internet of Things) technology. IoT technology can be implemented in hydroponic methods to produce more accurate and well-managed hydroponic plants so that plants are not treated manually. This method comprises six stages: analysis, planning, implementation, testing, and conclusion. Nutrient control in IoT-based hydroponic plants has been widely researched. Many researchers use different work systems and add new features to improve the quality and quantity of hydroponic plant production. Data control is monitored using an application called HyGrow. In obtaining data and information, the author uses observation and qualitative methods by observing and digging up information directly on the hydroponic system in the RH Farm environment. From the test results obtained, this system has successfully controlled and monitored the acidity, temperature conditions, and humidity in hydroponic plants. The percent accuracy of Nutrition and humidity sensors is 98%.
Peningkatan Kemampuan Baca Tulis Aksara Jawa Melalui Media Pembelajaran Berbasis Game Kwartet Aksara Jawa

The Javanese language subject is a subject at the Elementary School (SD) level which includes learning related to Javanese literature, language, and culture. One indicator of the success of learning Javanese is that students have the ability to read and write Javanese script. Students' skills in reading and writing Javanese script are influenced by the condition of students in knowing Javanese script letters. So that students can be skilled in reading and writing Javanese script. We conducted this research to improve the ability of fifth grade students at Wirogunan 2 Public Elementary School in writing and reading Javanese script using the Javanese Script Quarted Game. This research involved 22 students of class V at SD Negeri Wirogunan 2 as research subjects. In this study, the authors used a qualitative descriptive approach. Data collection techniques used are observation, interviews, documentation, and tests. Data validity uses source triangulation, namely comparing information obtained from different sources to re-check the degree of trustworthiness of information. The results of the research explain that the learning media innovation uses this quartet game, namely the students are very enthusiastic about learning to memorize Javanese script and it is hoped that through this game over time students can memorize all forms of Javanese script. In addition, the students will be challenged and enthusiastic in memorizing the Javanese script letters, because if they have memorized all the Javanese script letters, it will be easy to win this game.
Automated Academic Advisory System Based on Students’ Emotional Intelligence: A Study of University of Nizwa, Sultanate of Oman

The COVID-19 pandemic has brought outward unparalleled difficulties in the field of education, emphasizing the need for unique solutions. This study aimed to evaluate the determinants that affect the effective implementation of the automated student academic advisory system at the University of Nizwa of the Sultanate of Oman, with a particular emphasis on its influence on students' emotional intelligence during the pandemic The research utilized the Unified Theory of Acceptance and Use of Technology (UTAUT) model to investigate how factors such as performance expectancy, effort expectancy, social influence, and facilitating conditions influence students' behavioral intentions. In addition, the study examined the behavioral intention effects on the use of the automated system, emotional intelligence effects and the experience of COVID-19 specifically influences the actual use of the system. The study employed a case study methodology in combination with a quantitative survey method to gather data from 272 students and advisors at the University of Nizwa. The collected data was analyzed using SmartPLS, a technique known as structural equation modeling. The research provides useful information on the adoption of technology in an educational environment and its impact on student well-being and emotional intelligence. The study found that only performance expectancy and facilitating condition factors had a substantial impact on behavioral intention, whereas effort expectancy and social influence did not. Behavioral intention showed a positive correlation with actual usage, but performance expectancy, effort expectancy, and social influence did not directly influence the actual utilization. Moreover, behavioral intention served as a mediator for the indirect impact of performance expectancy and facilitating conditions on actual usage. However, it did not mediate the indirect impact of social influence and effort expectancy on actual utilization. Furthermore, the impact of COVID-19 and emotional intelligence did not influence the relationship between facilitating conditions and actual use. These findings offer valuable information into understanding educational technology, particularly in situations of global crises, and offer practical recommendations for educators, legislators, and academic institutions seeking to enhance student participation, support, and general well-being.&#x0D;
IMPLEMENTASI SISTEM INFORMASI PENERIMAAN PESERTA DIDIK BARU BERBASIS WEB

Education is very important for everyone. Education is generally defined as a life process in developing each individual to develop and be able to live. The first time we get education in the family, school and community environment, in schools without students, an educational institution cannot carry out its function to provide educational services to the community. Every educational institution seeks to acquire students every new academic year through the acceptance of new students, especially SMK Indra Adnan Indragiri COLLEGE, based on this, a web-based information system for new student admissions is needed. In the pandemic, it is very necessary to accept online-based new students in its development, the Waterfall method was chosen as a flow of thought and for analysis using PIECES, in system design using UML (Unified Modeling Language). With the implementation of this web-based new student admissions information system, it aims to provide convenience in accepting new students so that they can be widely reached and follow the current era.
Prediction of Material Surface Area using Multiple Linear Regression Algorithm with Independent Variables Time, T emperature and Quantity

Objective: In this paper, the surface area of MnO2 was predicted by multiple linear regression algorithm (MLRA) in python Jupyter notebook with accuracy and best correlation between parameters. Methods: A data set was collected from different recent research papers. The dataset underwent data processing and factor analysis, involving the removal of unnecessary data. The surface area was predicted by the different experimentally synthesis parameters (Temperature, Material quantity, reaction time and experimental evaluated surface area). For prediction of surface area the total data set of 11 different research pater data was divided in two part as 72% for training dataset and 28% kept for test dataset. Findings: The surface area of the material can be enhanced from 92 to 196.67 m2/g by changing the synthesis parameters during hydrothermal process. For prediction, the best relationship was found between temperature and surface area in heatmap. The predictions yielded accuracy levels was 94%. Novelty: The surface area of MnO2 was first time predicted by MLRA with high accuracy by tuning the hydrothermally synthesis parameters. Keywords: Multiple Linear Regression Algorithm (MLRA), Machine Learning, Python, Heatmap, Prediction Of Surface Area
A Study on Concentric Feature Embeddings in Variational Multiclass Classification for Small-Scale Quantum Computers

This study proposes a concentric feature embedding method to effectively implement quantum machine learning on small-scale real quantum computers. This approach enables the embedding of more data points in fewer qubits, reducing quantum circuit cost and improving the performance of multi-class classification tasks. We apply the proposed concentric feature embeddings with variational quantum circuits on the real-world datasets, and verify the performance on the QASM simulator and a real quantum computer. The results show that the accuracy of the multi-class dataset in a two qubits system achieves more than 80% without error correction in both QASM simulation and real quantum computer.
Application-Oriented Benchmarking of Quantum Generative Learning Using QUARK

Benchmarking of quantum machine learning (QML) algorithms is challenging due to the complexity and variability of QML systems, e. g., regarding model ansatzes, data sets, training techniques, and hyper-parameter selection. The QUantum computing Application benchmaRK (QUARK) framework simplifies and standardizes benchmarking studies for quantum computing applications. Here, we propose several extensions of QUARK to include the ability to evaluate the training and deployment of quantum generative models. We describe the updated software architecture and illustrate its flexibility through several example applications: (1) We trained different quantum generative models using several circuit ansatzes, data sets, and data transformations. (2) We evaluated our models on GPU and real quantum hardware. (3) We assessed the generalization capabilities of our generative models using a broad set of metrics that capture, e. g., the novelty and validity of the generated data.
THE-V: Verifiable Privacy-Preserving Neural Network via Trusted Homomorphic Execution

Privacy-preserving machine learning (PPML) schemes aim at protecting client-side data privacy in two-party secure computing tasks such as private deep neural network (DNN) inference. While fully homomorphic encryption (FHE) can provide provable security for client data privacy, efficiently verifying that such homomorphic DNN inference protocol is honestly executed on the server presents to be challenging. In this work, we propose THE-V, a novel DNN inference framework that combines FHE and Trusted Execution Environment (TEE) to achieve data privacy, verifiable execution and efficient computation all at once. We first point out that, while the trivial solution of executing FHE entirely within TEE can ensure both private and verifiable computing, the limited resource within TEE becomes a severe computational bottleneck. To solve such dilemma, we devise a new strategy of securely outsourcing computation-heavy tasks in TEE to untrusted environments. By rigorous experiments, we show that we can achieve verifiable and private DNN inference with up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$15\times$</tex> speedup compared with the state-of-the-art solution.
On the Usage of Continual Learning for Out-of-Distribution Generalization in Pre-trained Language Models of Code

Pre-trained language models (PLMs) have become a prevalent technique in deep learning for code, utilizing a two-stage pre-training and fine-tuning procedure to acquire general knowledge about code and specialize in a variety of downstream tasks. However, the dynamic nature of software codebases poses a challenge to the effectiveness and robustness of PLMs. In particular, world-realistic scenarios potentially lead to significant differences between the distribution of the pre-training and test data, i.e., distribution shift, resulting in a degradation of the PLM's performance on downstream tasks. In this paper, we stress the need for adapting PLMs of code to software data whose distribution changes over time, a crucial problem that has been overlooked in previous works. The motivation of this work is to consider the PLM in a non-stationary environment, where fine-tuning data evolves over time according to a software evolution scenario. Specifically, we design a scenario where the model needs to learn from a stream of programs containing new, unseen APIs over time. We study two widely used PLM architectures, i.e., a GPT2 decoder and a RoBERTa encoder, on two downstream tasks, API call and API usage prediction. We demonstrate that the most commonly used fine-tuning technique from prior work is not robust enough to handle the dynamic nature of APIs, leading to the loss of previously acquired knowledge i.e., catastrophic forgetting. To address these issues, we implement five continual learning approaches, including replay-based and regularization-based methods. Our findings demonstrate that utilizing these straightforward methods effectively mitigates catastrophic forgetting in PLMs across both downstream tasks while achieving comparable or superior performance.
Automated Testing and Improvement of Named Entity Recognition Systems

Named entity recognition (NER) systems have seen rapid progress in recent years due to the development of deep neural networks. These systems are widely used in various natural language processing applications, such as information extraction, question answering, and sentiment analysis. However, the complexity and intractability of deep neural networks can make NER systems unreliable in certain circumstances, resulting in incorrect predictions. For example, NER systems may misidentify female names as chemicals or fail to recognize the names of minority groups, leading to user dissatisfaction. To tackle this problem, we introduce TIN, a novel, widely applicable approach for automatically testing and repairing various NER systems. The key idea for automated testing is that the NER predictions of the same named entities under similar contexts should be identical. The core idea for automated repairing is that similar named entities should have the same NER prediction under the same context. We use TIN to test two SOTA NER models and two commercial NER APIs, i.e., Azure NER and AWS NER. We manually verify 784 of the suspicious issues reported by TIN and find that 702 are erroneous issues, leading to high precision (85.0%-93.4%) across four categories of NER errors: omission, over-labeling, incorrect category, and range error. For automated repairing, TIN achieves a high error reduction rate (26.8%-50.6%) over the four systems under test, which successfully repairs 1,056 out of the 1,877 reported NER errors.