Reviewerly/exHarmony huggingface dataset:
The Impact of the Digital Economy on the Development of Higher Education

Long time ago, education belonged to a conservative industry, changes in it took place slowly, without sharp jumps and bursts. But the development of science, engineering and technology in the 20th century, and especially at the beginning of the 21st century, has led to the fact that over the past decades, higher education in the service market has repeatedly undergone dramatic changes in generally accepted development trends. The competition in the education market is now very high, and with the expansion of international integration, it has become an obvious global competition. The structure of the service market is changing as a result of the introduction of digital technologies, and accordingly, the structure of the higher education market is also changing dramatically. The number of participants in the global higher education market, according to the results of the analysis of Transparency market Research [1] is growing steadily. At the moment, the composition of the participants consists of private institutions, government organizations, ministries of education, government bodies, educational organizations, consulting firms, rating agencies, etc. The integration of education into the international community has led to the fact that the coverage of countries, people and information becomes colossal. To simplify the work with such a database and the number of people, digital technologies came to the rescue. This, in turn, has changed the traditional model of higher education. As the era of digitalization begins, when there is no need for a real presence.
Application of Fisher information to CMOS noise estimation

&lt;abstract&gt;&lt;p&gt;Analysis of the accuracy of estimated parameters is an important research direction. In the article, the maximum likelihood estimation is used to estimate CMOS image noise parameters and Fisher information is used to analyse their accuracy. The accuracies of the two parameters are different in different situations. Two applications of it are proposed in this paper. The first one is a guide to image representation. The standard pixel image has higher accuracy for signal-dependent noise and higher error for additive noise, in contrast to the normalised pixel image. Therefore, the corresponding image representation is chosen to estimate the noise parameters according to the dominant noise. The second application of the conclusions is a guide to algorithm design. For standard pixel images, the error of additive noise estimation will largely affect the final denoising result if two kinds of noise are removed simultaneously. Therefore, a divide-and-conquer hybrid total least squares algorithm is proposed for CMOS image restoration. After estimating the parameters, the total least square algorithm is first used to remove the signal-dependent noise of the image. Then, the additive noise parameters of the processed image are updated by using the principal component analysis algorithm, and the additive noise in the image is removed by BM3D. Experiments show that this hybrid method can effectively avoid the problems caused by the inconsistent precision of the two kinds of noise parameters. Compared with the state-of-art methods, the new method shows certain advantages in subjective visual quality and objective image restoration indicators.&lt;/p&gt;&lt;/abstract&gt;
Analisis Sentimen Wisatawan Melalui Data Ulasan Candi Borobudur di Tripadvisor Menggunakan Algoritma Naïve Bayes Classifier

Sentiment analysis of visitors to the tourist destinations of Borobudur Temple in Indonesia needs to be done to determine the expected product and service preferences. In addition, sentiment analysis is also helpful for managers to adjust the needs of tourists to the infrastructure provided in the tourist destination area. The classification method used in the sentiment analysis is the Naïve Bayes Classifier (NBC) against 3850 visitor reviews at Borobudur Temple. Review data is pulled from Tripadvisor web pages filtered by language, review time, and travel characteristics to analyze foreign traveler preferences comprehensively. This research stage is divided into three parts: data preparation, data processing, sentiment analysis, and algorithm performance evaluation. In addition, SMOTE Upsampling is used to balance data. The results of implementing the Naïve Bayes Classifier (NBC) classification method obtained an accuracy value of 96.36%, a precision value of 93.23%, and a recall value of 100% with an Area Under Curve (AUC) value of 0.714. In addition, the results of ranking five famous words from the review data show that there are highlights of the physical condition of the temple, scenery, and tourist visit activities at Borobudur Temple, where the four most famous words in visitor reviews are the “temple,” “visit,” “Borobudur,” “sunrise” and “place.”
Hate Speech Detection on Twitter through Natural Language Processing using LSTM Model

Currently, social media is a place to express opinions. This opinion can be positive or negative. However, lately, the opinion that often appears is a negative opinion, such as hate speech. Hate speech is often found on social media, such as malicious comments intended to insult individuals or groups. Based on WeAreSocial data in 2021, one of the most used social media platforms in Indonesia is Twitter, with 63.6% of users. According to the Indonesia National Police, hate speech cases were more dominant during the period from April 2020 to July 2021. Therefore, efforts are needed to identify hate speech on the Twitter platform. One way to detect hate speech is by using deep learning. In this research, we use a deep learning model of Long Short-Term Memory (LSTM) with word embedding. FastText and Global Vector (GloVe) is the word embeddings that we use as input for word representation and classification. FastText embeddings make use of subword information to create word embeddings and GloVe embeddings using an unsupervised learning method trained on a corpus to generate distributional feature vectors. From the evaluation results on the experimental model, LSTM-FastText using random oversampling has an advantage with an F1-score of 89.91% compared to LSTM-GloVe to obtain an F1-score of 82.14%.
IoT-Assisted Blockchain-Based Car Rental System Supporting Traceability

The majority of existing car rental systems rely on a centralized database to manage the data. Although these centralized architectures could make the system design and operation costs are reduced, there are many security problems in real world applications. If the information of the car obtained by the tenant is not completely credible, it will lead to security risks in the rental process. This paper proposes a blockchain-based car rental system with the Internet of Things (IoT), where the blockchain is engaged to store the use history of the car, so that the car condition can be traced to the source and the risk of using the car for the tenant can be reduced. The IoT technology is employed to enable renters to flexibly rent and unlock platform cars. Our system also provides visual operation interface. The evaluation results demonstrate the practicality of our system in the online car rental market with higher security and more flexible rental methods.
Analisis Sentimen Konsumen terhadap Food, Services, and Value di Restoran dan Rumah Makan Populer Kota Makassar Berdasarkan Rekomendasi Tripadvisor Menggunakan Metode CRISP-DM dan SERVQUAL

Culinary is one of the economic activities that support national economic growth and represents the gastronomy of the archipelago in Indonesia. Culinary tourism activities have become famous for domestic and foreign tourists to experience the taste of a food based on the culture of each region. Makassar is one of the regions with diverse types of food and beverages and has a relationship with local socio-cultural values. Considering this, this study aims to analyze consumer sentiment toward food and services in ten restaurants in Makassar based on the recommendations of the Tripadvisor website using the Cross-Industry Standard Process for Data Mining (CRISPD-DM) and Service Quality (SERVQUAL) methods. The stages of CRISP-DM are as follows: the stage of understanding business processes; the stage of understanding data; the stage of preparing data; the modeling stage; the evaluation stage; and the deployment stage. The algorithms used as models are k-Nearest Neighbor (kNN), Naïve Bayes Classifier (NBC), Decision Tree (DT), and Support Vector Machine (SVM). The results of this study show that the DT algorithm when using the SMOTE operator where the resulting accuracy value is 93.25%, precision is 88.74%, recall is 99.10%, and f-measure is 93.62%. In addition, the k-NN algorithm without using the SMOTE operator showed an accuracy value of 98.72%, a precision of 98.72%, a recall of 100%, and an f-measure of 99.36%. However, the resulting AUC value is 0.905 (90.5%). Meanwhile, when using the SMOTE operator, the SVM algorithm produces an accuracy value of 99.42%, a precision of 100%, a recall of 98.84%, and an f-measure of 99.42%. Meanwhile, the resulting AUC value is 1,000 (100%). Based on the ROC value, three algorithms can be used as models in the CRISPP-DM and SERVQUAL frameworks: the k-NN algorithm without SMOTE and the DT and SVM algorithms using the SMOTE operator
Impact of Ethical Leadership and Employee Engagement on Customers’ Perceived Service Quality: A Study of Sri Lankan Public Service

This paper investigates how ethical leader influences employee engagement for enhancing the quality of public service in Sri Lanka while examines their customers’ perception of service quality, employees’ perception of the style of ethical leadership, and the level of employee engagement. For that, two questionnaires were distributed. The first questionnaire was distributed through 500 employees in 83 Divisional Secretaries to identify the level of ethical leadership and employee engagement. The second questionnaire was distributed to 2500 customers to identify the level of service quality. To measure these constructs customized scales were used. The results found that, the customers were generally satisfied with the service provided by the public service. Employees in public service were satisfied with the ethical leadership of their administrators and they engage well. The results indicate that ethical leadership and employee engagement significantly affect customers’ perceived service quality and employee engagement is partially mediates the association between ethical leadership and service quality. This study helps to identify the extent of ethical leadership exhibited by public officers, and how it influences service quality through employee engagement. The findings will help the public service to identify its strengths and weaknesses, and will serve as an important source of feedback to the public service that can be used in decision-making processes to better serve customers. In addition, this paper filled the gap in the literature by addressing the relationship between ethical leadership, employee engagement and customers’ perceived service quality. The empirical findings of this will help to enhance the body of knowledge in the area of service quality. The findings will help to uncover the nature of ethical leadership, and employee engagement in the existing public service in Sri Lanka
Double Inverse-Gaussian Distributions and Associated Inference

In order to have more flexibility, several double distributions have been studied in the literature. In this paper, we propose a double inverse Gaussian distribution using random sign mixture transform and study its associated inferences. The maximum likelihood estimation is performed to estimate the parameters. Extensive simulation studies are carried out to examine the performance of the estimators and the corresponding confidence intervals of the parameters. A real data set example is presented to illustrate the procedure.
ADDITION: Detecting Adversarial Examples With Image-Dependent Noise Reduction

Notwithstanding the tremendous success of deep neural networks in a range of realms, previous studies have shown that these learning models are exposed to an inherent hazard called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adversarial example</i> — images to which an elaborate perturbation is maliciously added could deceive a network, which entails the study of countermeasures urgently. However, existing solutions suffer from some weaknesses, e.g. parameters are usually determined empirically in some processing-based detection methods might result in a sub-optimal effect, and the directly performed processing on images might affect the classification of benign samples, leading to increment of false positive. In this paper, we propose a novel imAge-DepenDent noIse reducTION (ADDITION) model based on deep learning for adversarial detection. The ADDITION model can adaptively convert the adversarial perturbation in each image to approximate Gaussian noise by injecting image-dependent additional noise, then perform noise reduction to eliminate the adversarial perturbation, and finally detect adversarial examples by examining the classification inconsistency between the input image and its denoised version. The ADDITION model is trained end-to-end on benign samples without any prior knowledge of adversarial attacks, and thus avoid time-consuming task of generating adversarial examples in practical use. We generate more than 220,000 adversarial examples based on six attack algorithms for evaluation and present state-of-the-art comparisons on three real-word datasets. Extensive experiments demonstrate that our proposed method achieves improved performance in both detection accuracy rate and false positive rate.
Locking Down Secure Open Source Software

Can even secure open source software ever be considered truly safe?
Interdisciplinary communicative competence: From conceptualising to operationalising

Introduction . Communication complexities which often occur in interdisciplinary work gave rise to the studies on teaching interdisciplinary communication. A growing need to provide pedagogical solutions to facilitate teaching interdisciplinary communication stimulated the research into language as a social practice to better understand communication process for interdisciplinary purposes. Aim . This exploratory study investigates the concept of interdisciplinary communicative competence and proposes a framework of interdisciplinary communicative competence with the focus on three underlying components: knowledge, skills, and personal attributes of interdisciplinary team members. Methodology and research methods . Qualitative and quantitative methods were used. The data obtained from 24 in-depth semi-structured interviews with five groups of higher education stakeholders (employers, academic directors of the programmes, professors, students, and alumni) revealed the existing interdisciplinary practices in the university and cross-functional practices in the companies. The proposed framework was empirically tested using an online survey with 139 responses from professors, students, and employers. The data processing techniques included the use of Kendall’s concordance coefficient, Cronbach’s alpha, and the principal component analysis. Results . The study presents the authors’ conceptualisation of interdisciplinary communicative competence and its framework as the result of the literature analysis and the empirical research. The findings provided evidence on the importance of language skills for effective interdisciplinary communication as perceived by 5 groups of respondents. The choice of language skills as a basic component of interdisciplinary communicative competence is justified. Scientific novelty . The study contributes to the conceptualisation of a framework of interdisciplinary communicative competence. The elements of the framework are identified and their relevance is empirically tested. Practical significance . The results of the empirical part of the study can be applied in the design of interdisciplinary learning process in higher education, for example, in the design of interdisciplinary courses, and teaching materials.
Artificial intelligence-based assessment in education

Artificial Intelligence- (AI-) based assessments are commonly used in a variety of settings including business, healthcare, policing, manufacturing, and education. In education, AI-based assessments undergird intelligent tutoring systems as well as many tools used to evaluate students and, in turn, guide learning and instruction. This chapter provides a framework for AI-based Assessment (AIBA) in Education that describes systems and tools. The framework comprises five interrelated dimensions that broadly address the purposes and procedures of AIBA for educational technologies: purposes (i.e., summative feedback, formative feedback, training, and instruction), constructs (e.g., knowledge, skills, strategies, and emotion), data sources (e.g., task performance, behaviors, language, and multimodal data), computational methods (e.g., Bayesian statistics, machine learning, and natural language processing), and visibility (e.g., task-related vs. stealth). This chapter describes systems and tools used to provide AIBA and instantiate these tools within these dimensions. Framing systems within these five dimensions of AIBA enables understanding the landscape of tools and systems, and, in turn, pointing to gaps in the literature and future directions.
Power Grid Fault Diagnosis Method Based on LightGBM

Abstract With access to distributed power generation and interconnection between regions, China’s power system structure is becoming increasingly complex, and the requirements for power grid dispatching capacity are getting higher and higher. When the power grid fails, a large amount of alarm information appears in the monitoring window, which needs to be processed and classified first to speed up the fault diagnosis and processing. Therefore, this paper proposes a power grid fault diagnosis method based on LightGBM. First, after pre-processing the power grid alarm information, Word2vec is used to vectorize the text, and then the LightGBM model is used to process the text, extract the information features and continue to train. Finally, realizing the three classifications of such simple faults as the transformer fault, bus fault, and line fault. Finally, through the test and verification of the actual power grid case data, it is verified that the method can effectively classify simple faults and meet the needs of the actual power grid online diagnosis.
Evaluation of Learning Effect of Reverse Teaching Design Based on BP Neural Network

A BP neural network model is built for the comprehensive evaluation of students' learning effects of market research courses using Python. Combined with the reverse instructional design in market research courses, a learning effect evaluation index system with 13 indicators under the three dimensions of knowledge, ability, and quality goal achievement is constructed. By using 263 sets of sample data of simulation experiment learning training, students' learning effect in the market research course is evaluated based on BP neural network. The BP neural network-based evaluation model of the learning effect of the course avoids the subjectivity of evaluation index weight evaluation and improves the speed of evaluation. Compared with traditional weight evaluation, the proposed method has better efficiency and effect. The test result shows that the comprehensive evaluation model has strong applicability and provides new methods and ideas for the evaluation of the learning effect.
Emerging Role of Blockchain in Banking Operations: An Overview

Introduction: Blockchain technology is the method of storing the data systematically, such that it is impossible to change, defraud or hack the data. Distributed Ledger Technology is another name for this technology. It is like a digital ledger of numerous transactions stored or distributed throughout the Blockchain extensive network by a computer system. Banks offer critical services like payments, clearance and settlement systems, trade finance, securities, etc. Hence, there are fraud and mistakes in these key services due to many manual procedures and human mediators.Purpose: The main purpose of this chapter is to study the emerging role of Blockchain in banking services. This chapter will attempt to examine the significance and applications of Blockchain in banking operations. This chapter will also investigate the challenges encountered by banks in adopting Blockchain technology.Research Methodology: In this research chapter, secondary data are collected by studying the various journal papers and scholarly articles, with exact keywords like: blockchain, banking sector, applications and blockchain role. Data are collected from the Emerald, Springer Open, Google Scholar, and Science Direct databases.Findings: This technology will enhance transparency in banking transactions in the coming future. The adoption of Blockchain will transform the banking system in many ways, such as faster payment and settlement systems, security management and fundraising.
Design a wing flange lever mechanism using Giza pyramid algorithm

In this work, a well-known mechanism that is referred to as piston lever mechanism is design to control the wing’s flange. The design targets the mechanism components and their location while maintaining the minimum oil level required to lift the flanges from 0 to 45 degrees. The design is considered a multi-objective optimization problem; in which we propose the Giza Pyramid Algorithm (GPA) to optimize. GPA is a newly developed metaheuristic technique of type ancientinspired that was developed in 2021. The GPA shall obtain the best design while satisfying four constrains. The algorithm will be tested in Monte Carlo simulation to show its effectiveness in terms of stability and robustness. The performance is then compared to the particle swarm optimization. The proposed method shows a superior result compared to the PSO. Keywords: Giza pyramid, metaheuristic, optimization, design.
A bilateral context and filtering strategy-based approach to Chinese entity synonym set expansion

Abstract Entity synonyms play a significant role in entity-based tasks. Previous approaches use linguistic syntax, distributional, and semantic features to expand entity synonym sets from text corpora. Due to the flexibility and complexity of the Chinese language expression, the aforementioned approaches are still difficult to expand entity synonym sets robustly from Chinese text, because these approaches fail to track holistic semantics among entities and suffer from error propagation. This paper introduces an approach for expanding Chinese entity synonym sets based on bilateral context and filtering strategy. Specifically, the approach consists of two novel components. First, a bilateral-context-based Siamese network classifier is proposed to determine whether a new entity should be inserted into the existing entity synonym set. The classifier tracks the holistic semantics of bilateral contexts and is capable of imposing soft holistic semantic constraints to improve synonym prediction. Second, a filtering-strategy-based set expansion algorithm is presented to generate Chinese entity synonym sets. The filtering strategy enhances semantic and domain consistencies to filter out wrong synonym entities, thereby mitigating error propagation. Experimental results on two Chinese real-world datasets demonstrate that the proposed approach is effective and outperforms the selected existing state-of-the-art approaches to the Chinese entity synonym set expansion task.