Reviewerly/exHarmony huggingface dataset:
Exploring English Teachers’ Views on the Compulsory English Subject for Non-English Major Students: A Case Study at an Indonesian University

This research investigates the perspectives of English teachers regarding the factors influencing the teaching and learning of English as a compulsory subject, specifically the English conversation course (Matakuliah Institusi), for non-English major students at Dayanu Ikhsanuddin University in Baubau. The study employed semi-structured interviews with six English teachers affiliated with the English study program at the university. By examining both positive and negative aspects, the research aimed to identify the factors that impact the quality of instruction and learning in the English conversation course. The findings highlight several factors, including the syllabus, availability of resources and equipment, students' attitudes and motivation towards learning English, participation in extracurricular activities, class size, institutional support, and teachers' enthusiasm. These factors were identified by the teachers as having both positive and negative influences on the course. Furthermore, the English teachers were given the opportunity to provide suggestions for program improvement, such as emphasizing the importance of support and commitment from the heads of each study program.
Product design innovation based on the functional characteristics of the eight-masted windmill

Abstract To catch and research the functional characteristics of the eight-masted vertical windmill and its automatic readjustment of sail angle in the Lixia River area of northern Jiangsu Province, to permit this intangible cultural heritage a new practical meaning in modern tourism through the innovative design of the sail structure of the eight-masted windmill, and to come up with a portable folding windmill product suitable for outdoor sports enthusiasts. Firstly, we spell out the history of the eight-masted windmill and its “trotting horse lamp” structure through literature research and draw the working principle of the eight-masted windmill in operation, and combine the eight-masted windmill with power generation device by converting wind energy into electricity. The design direction was to design a lightweight, breeze start, and stable power output windmill generator; secondly, the sketch of the windmill main shaft structure and scheme were selected, and the integrated form of the fan blade and the mast was selected as the sketch scheme; finally, the model experiment of the prototype of the folding scheme was conducted, and the absolute pressure difference between blades was analyzed by hydrodynamics to determine that the windmill can be used to generate electricity. After verifying its feasibility, the portable folding generator was modeled in 3D by MCAD, and make a physical model of the generator was made in a reduced scale of 1:2.5. Through the innovative design of the eight-masted windmill, it is made to serve the production life of modern society.
28. Security and smart cities

This chapter explains the profound implications of digital society on the questions of crime, security, surveillance, and policing. In recent decades, digital technologies brought profound changes to human societies and the way they are governed. The chapter explains that digital processes do more than accelerate the production and availability of information, which highlights the implications of digital society from the abundance of data. The digital revolution has rendered criminological preoccupations more urgent and pressing than ever before. The chapter also provides an overview of the concept of a smart city by considering its criminogenic consequences and its potential for crime prevention and surveillance.
Incoherent nonlinear deconvolution using an iterative algorithm for recovering limited-support images from blurred digital photographs

Recovering original images from blurred images is a challenging task. We propose a new deconvolution method termed incoherent nonlinear deconvolution using an iterative algorithm (INDIA). Two inputs are introduced into the algorithm: one is a random or engineered point spread function of the scattering system, and the other is a blurred or distorted image of some object outputted from this system. The two functions are Fourier transformed, and their phase distributions are processed independently of their magnitude. The algorithm yields the image of the original object with reduced blurring effects. The results of the new method are compared to two linear and two nonlinear algorithms under various types of blurs. The root mean square error and structural similarity between the original and recovered images are chosen as the comparison criteria between the five different algorithms. The simulation and experimental results confirm the superior performance of INDIA compared to the other tested deblurring methods.
Multi-Task Unsupervised Question Retrieval Method with Enhanced Keyword Features

Abstract Currently, a large number of question retrieval solutions rely on supervised methods, which require expensive manual annotations. However, existing unsupervised question retrieval methods lack the reinforcement of keyword features in question representations. To address this issue, this paper proposes an unsupervised question retrieval method called QRTM (Question Retrieval based on Topic Filtering and Multi-Task Learning). Firstly, a topic filtering algorithm called GF is designed, which calculates the distribution of topics and keywords using topic models. It sequentially extracts the topic keywords from the questions to construct unsupervised learning corpus data. Secondly, a multi-task learning approach is adopted to build the question retrieval model. Three tasks are designed in this paper: a short question contrastive learning task; a question generation task with corresponding sequential topic keywords; and the SDN (Similarity Distribution Network) task, which combines autoencoders and attention mechanisms to measure the similarity between questions and sequential topic keywords. These three tasks are trained in parallel to obtain enhanced embeddings of topic keywords in question representations, capturing accurate semantic information of the questions. Comparative and ablation experiments on publicly available question datasets validate that the proposed QRTM method outperforms baseline methods. When using the BERT pre-trained model, the P@1, MAP, and MRR metrics are respectively improved by 8.5%, 5.4%, and 5.0% compared to the baseline model. When using the Roberta model, the corresponding metrics are improved by 4.8%, 4.3%, and 4.4%. QRTM can be applied to transfer learning with pre-trained models such as BERT and RoBERTa, effectively enhancing the accuracy of unsupervised similar question retrieval, with the retrieved similar questions appearing at a higher rank.
Financial Shared Service, Digital Transformation and Corporate Value Creation

Abstract The trend of Chinese enterprises has defied the hard-hit international investment and trade activities of recent years. The scale and globalization of local enterprises continue to develop, and the enterprise management efficiency has become increasingly prominent. This paper delves into the strategic importance of FSSC in augmenting corporate value, and examines the link between digital transformation and the formation of FSSC. The introduction of FSSC and its accompanying digital technology marks a new juncture for corporate reform. Based on RBV and DCV, this paper studies the strategic significance of FSSC to enhance corporate value, and deconstructs the relationship between digital transformation and the development process of financial shared service centers. Based on the time-varying DID, 335 listed companies were selected for a quasi-natural experiment. The results showed that: (1) financial shared service centers can significantly promote the improvement of corporate value. (2) Digital transformation can promote the establishment and development of financial sharing service centers, thus promoting the improvement of corporate value. (3) The effects of FSSC and digital transformation are characterized by heterogeneity. The worth of financial shared service centers for non-state-owned and manufacturing companies is substantial. Digital transformation has a noteworthy positive moderating effect on them but has no considerable moderating effect on state-owned and non-manufacturing companies. The government ought to bolster policy direction, keep up the digital transformation of businesses, and aid them in achieving rapid financial transformation; senior executives should remain cognizant of their strategic position and strive to execute a satisfactory job of both internal and external coordination, as the research findings suggest.
Application of Training Skills in Nepali Classrooms

This study aims to explore the application of training skills in Nepali classrooms. The study is conducted through secondary resources, such as websites, document analysis, and evaluating interrelated publications, articles, and journals, to examine one's own insight and sensory experience. Many problems and issues arise when transferring teacher training into classroom practice, including the lack of teachers, inefficient strategies and approaches to training, obsolete curricula, poor supervision services, insufficient empirical research, profit-driven education, etc. Applying teacher training skills in the classroom in Nepal appears to be especially unsuccessful, according to studies with a fifty percent success rate. However, a survey of the literature on teacher training in Nepal that is currently available reveals that not much has been accomplished in this area during the past few decades. This study focuses on theoretical applications, the current situation of teacher preparation, and integrating that knowledge, skill, and attitude into classroom activities.
Token-Based Smart Power Contract for Interoperable Blockchains of Networked Microgrid System

Designing the secure and privacy-protected smart power contract between electricity suppliers and consumers, considered agents, of different microgrids, is a challenging task in the networked- microgrid system. A framework is suggested in which each microgrid implements a heterogeneous or isomorphic blockchain based platform. The blockchain interoperability, inherently, is present in different blockchains implemented by various microgrids. This paper reviews the interoperability issues and smart contract designs in blockchain based systems. The paper proposes new mechanisms to cater blockchain interoperability challenges to facilitate the design of secure and seamless smart contracts among different blockchains of microgrids. A network hub of heterogeneous or isomorphic blockchains of network microgrids has been created. A methodology has been developed to transfer tokens between interoperable blockchains. Distributed identity-based microgrid (DIBM) scheme is incorporated to make the networked microgrid system secure and trustworthy. This paper suggests an effective consensus protocol for cross-chain architecture that improves the tokenization system and smart power contract designs. For simulation purposes, MATLAB and python programming have been used with real-time data of microgrids.
LeNet Convolutional Neural Network for Face Mask Usage Classification Using a Low-Cost Device

Background: One of the efforts to prevent the spread of the COVID-19 virus is to wear a face mask in public places.However, there are still many people who use masks in the wrong way, and some do not even wear masks when in public places. From these problems, we need an image-based classification system that can be employed to identify the use of face masks. The system built must use a low-cost device to be purchased by sundry groups. In previous studies, some classification systems for face mask use were designed using various methods, but there were limitations. The Convolutional Neural Network (CNN) method provides high accuracy. However, it has a heavy computational level and cannot be used in real-time on low-cost devices. In contrast, the haar-cascade method provides a fast processing time but is less accurate than the CNN method.Objective: In this article, research was conducted on the development of image processing algorithms for the classification process of face mask use using low-cost devices.Methods: The method used was CNN with LeNet architecture which has a light computational level. In the machine learning process, a dataset of 400 images was used, which was split into 240 images for training needs and 160 images for validation needs.Result: This study produced a classification with an accuracy rate of 98.75%. The prediction process that is carried out using a low-cost device requires an average time of 0.235 seconds.Conclusion: This research showed that the system can be run in real time
The Application of Recurrent Neural Network Method with the Long Short Term Memory (LSTM) Approach to Forecast Hybe Corporation's Stock Price

Background: Indonesia has a large land and sea area that has the potential for salt production. Efforts to increase salt production are still not optimal because there are still several areas that produce salt products in small quantities even though they have a large land area and a large number of farmers, so for areas like this, it is necessary to carry out guidance so that they can increase the amount of salt production. So far, no system has been used to group salt-producing regions, so it is impossible to know which areas still have the potential to increase their production.Objective: This research aims to create an application system to classify salt-producing regions.Methods: The clustering method used is K-Means, where this method can group datasets into several predetermined clusters.Result: The results of application testing carried out using 10 salt-producing region datasets obtained 4 regions with small production volume clusters, 3 regions with sufficient production volume clusters, 3 regions with large production volume clusters.Conclusion: The application of grouping salt-producing areas using the K-Means method can help the process of grouping salt-producing regions easier, faster, and more objectively.
ASMAM: An Answer Summarization Mechanism Based on Multi-layer Attention Model

At present, deep learning technologies have been widely used in the field of natural language process, such as text summarization. In Community Question Answering (CQA), the answer summary could help users get a complete answer quickly. There are still some problems with the current answer summary mechanism, such as semantic inconsistency, repetition of words, etc. In order to solve this, we propose a novel mechanism called ASMAM, which stands for Answer Summarization based on Multi-layer Attention Mechanism. Based on the traditional sequence to sequence (seq2seq), we introduce self-attention and multi-head attention mechanism respectively during sentence and text encoding, which could improve text representation ability of the model. In order to solve “long distance dependence” of Recurrent Neural Network (RNN) and too many parameters of Long Short-Term Memory (LSTM), we all use gated recurrent unit (GRU) as the neuron at the encoder and decoder sides. Experiments over the Yahoo! Answers dataset demonstrate that the coherence and fluency of the generated summary are all superior to the benchmark model in ROUGE evaluation system.
Research on Low Illumination Image Enhancement Algorithm Based on Convolutional Neural Network

Low illumination images have insufficient local and global light exposure, loss of structural and detail information, and are prone to generating a large amount of noise. The overall image is grayish or even completely dark, and people often cannot recognize the content of the image with the naked eye. Image enhancement technology aims to enhance image brightness, adjust image contrast, restore hidden details in the Fark, and enhance the utilization value of images through corresponding technical means.The traditional low illumination image enhancement methods mainly focus on Histogram equalization and Retinex methods. Based on the drawbacks of the traditional methods, this paper studies the low illumination image enhancement algorithm based on Convolutional neural network, builds a mathematical model, and lays the foundation for subsequent experimental research and application.
The Virtual Machine Migration Strategy Based on Dynamic Threshold

With the rapid development of cloud computing, virtualization is widely used in data centers for resource management. However, traditional static threshold-based virtual machine migration strategies struggle to adapt to changing workloads. To address this, we propose a dynamic threshold-based strategy that monitors resource utilization to optimize migration timing and reduce costs. Simulation experiments on a real data center confirm the superior performance of our approach compared to static methods. This intelligent and efficient strategy enhances resource management, and energy efficiency in data centers.
Large Deformation Features Guided Network for Medical Image Registration

Aiming at the problem of the loss of detail information in medical image registration, which leads to poor registration results in large deformation regions, a large deformation features guided network(LDGNet) for medical image registration is proposed. LDGNet mainly includes two contributions: first, a large deformation feature enhancement module is designed at the encoding and decoding connection to enable the network to enhance the extraction of large deformation features. Secondly, a large deformation feature guidance module is designed at the skip connection, which can help fully fuse the large deformation features from the encoded feature map, and effectively improve the registration accuracy of the network in large deformation regions. Registration experiments on the brain dataset IXI show that LDGNet achieves higher registration accuracy compared with current popular medical image registration methods.
Algorithmic Implementation for Insurance Fraud Detection

In the insurance sector, spotting insurance fraud is crucial. Insurance is vital for finance and societal security. Frequent fraud causes losses to insurers and the financial system, impacting insurance companies' functioning and trust. Insurance fraud involves policyholders giving false information or creating incidents to claim compensation. This harms insurers and raises premiums for honest policyholders. To combat frauds, insurers must use methods to detect and prevent them. This study assesses popular ML algorithms like Gradient Boosting Decision Trees and XGBoost for fraud detection efficiency and verifiability. Metrics such as efficiency, recall rate, precision F1 score, and AUC score are calculated using these methods.
An Image Classification and Retrieval Hybrid Model for Larger Healthcare Datasets using Deep Learning

Objectives: The objective of this work is to obtain an efficient medical image retrieval and classification from a larger healthcare datasets using Novel approach. Methods: In this study five different classes of Medical images are taken for input, features are extracted using GLCM (Grey Level Co-occurrence Matrix) by image attributes such as dissimilarity, correlation, homogeneity, contrast, ASM, and energy. The photos are examined at several angles (0, 45, 90, and 135) to extract the characteristics using the layers. The received feature vectors are input into the most often used deep learning models Artificial Neural Networks (ANN) and Convolution Neural Networks (CNN) for image classification. Then CNN model is integrated with a deep learning model based on Long-Short Term Memory (LSTM), which incorporates additional layers into its structure and works on large datasets. Further the retrieval performance is improved by Euclidean Distance Technique. Findings: Performance evaluation is performed by comparing and analyzing the experimental findings of proposed methods, ANN, CNN and CNN-LSTM yields the retrieval accuracy of 97.79%, 98.78% and 99.4%. The Precision, Recall and F1-Score are also compared, and they are more accurate when picture classification is performed on larger healthcare datasets. Novelty: The additional feature extraction using GLCM and the proposed hybrid model can extract better medical image features, and achieve higher classification accuracy compared with earlier image classification models. Keywords: ContentBased Image Retrieval; Grey Level Cooccurrence Matrix; Artificial Neural Networks; Convolution Neural Networks; LongShort Term Memory; Cloud Computing
Research on Integrated Mapping Mystem of Mand and Mea Basic Spatial Element Data Based on ArcGIS

Adhering to land and sea overall planning is the basic principle and important content of China's construction of Marine power, the basic geographic information of land and sea integration can provide data support and service guarantee for "land and sea overall planning" work. Aiming at the current land and ocean surveying and mapping system in China, an integrated data system of land and sea basic spatial elements is designed and implemented. The system uses C/S architecture and vector data integration technology to integrate and manage the data of land and sea basic spatial elements effectively. Based on ArcGIS secondary development method, this paper completed the establishment of land and sea basic spatial element data, designed the technical process and function of the integrated mapping system, and implemented the system by using.NET and ArcGIS Engine technologies. It has realized the functions of multi-field retrieval of massive surveying and mapping data, integrated spatial display, security storage and so on. Practice has proved that the data integration and management system designed and developed in this paper is feasible and effective, which can realize the efficient reuse of surveying and mapping data, provide convenient and fast data services for surveying and mapping workers, greatly improve their work efficiency, and promote the development of land and sea integration.
Algorithms Feasibility Inquiry Based on Data Mining in Privacy

This paper firstly summarizes the current research status of privacy protection data mining algorithms and the significance of researching privacy protection data mining; and then according to the different distribution of data objects, this paper discusses the corresponding privacy protection mining methods of integrated data and distributed data respectively, and then it analyses and studies association rule mining algorithms and SVM classification mining algorithms; And focusing on distributed database system classification data mining which is horizontal distribution, privacy protection classification algorithm based on the SVM is proposed. The mathematical model has been established, and experimented with the method of computer simulation. The results show that the algorithm has certain stability under the circumstances of distributed node increases, and the algorithm is feasible and has a practical guiding significance.
Insurance Fraud Detection Based on XGBoost

This research conducted a comprehensive study on predicting customer car insurance claims using Gradient Boosting Decision Tree (GBDT) and XGBoost models. The process included data exploration, feature engineering, model evaluation, and parameter tuning. The dataset was explored based on variable types and missing values, and further processed through mean encoding and outlier removal. Date features were also manipulated to create more meaningful features. Two models, GBDT and XGBoost, were trained and evaluated based on their AUC (Area Under the Curve) values. Both models demonstrated good predictive power, with GBDT slightly outperforming XGBoost. The results of this study provide valuable insights for predicting insurance claims, offering significant implications for further research and practical applications.