Reviewerly/exHarmony huggingface dataset:
EVALUATING LEGAL ENGLISH MAJOR FRESHMEN’S PERSPECTIVES TOWARDS LISTENING STRATEGIES THROUGH FLIPPED LEARNING MODEL

Listening skill plays an important role in students’ learning process and in the communication process. With effective listening strategies, students would have better chance to improve their listening comprehension. Accordingly, this study aims to explore first year legal English-major students' tendency in using listening strategies, the problems that they encounter during the process, and students’ feelings regarding the implementation of flipped learning method. By applying the quantitative method, a questionnaire survey was executed during a seven-day period. Due to the outbreak of the Covid-19 pandemic, the survey was conducted online with the participation of 77 respondents. Findings show that listening strategies are employed with high frequency by first year legal English-major students at HLU. Moreover, results indicate that students still encounter difficulties upon implementing listening strategies and they are willing to study listening skill through flipped learning method. The study illustrates HLU English-major students’ tendency and perception of legal English listening learning strategies&#x0D; during Covid-19 pandemic at HLU.
A Study of Password Management Behaviors of Young People

Password-based authentication is still the most widely used authentication method today. Unfortunately, passwords are the main culprit leading to cyberattacks. This study examines the behaviors of young people towards password generation and usage. The young people will ultimately become the future for society. An online survey with a sample of 265 respondents aged 10–24 was conducted between April and August 2021. The research utilized descriptive statistical analyses and compared the responses from young people with older people. The results suggest that although the survey participants seemed to have basic knowledge of creating complex passwords, they still possessed some aspects, which could lead to being a cyberattack target. This preliminary study provides information and increases awareness for policymakers and educators in such a way that it could be used to create an educational program on the importance of managing passwords securely. In addition, the study provides insights into the password management of young people between the ages of 10 and 24.
Advanced Machine Learning Techniques in Spam Medical Events

The detection of adverse events (spam) within a health establishment, associated or not with care, represents one of the axes of a risk management approach. It does not in any way constitute a means of denunciation, control, or sanction; the purpose being that the most important medical spam events do not recur. In this chapter the authors discuss the different algorithms of machine learning such as the KNN decision tree, naive bayes, etc., applicable to the filtering of medical spam event. The objective of these techniques is to control medical data in order to make decisions and achieve strategic objectives.
Cancer Data Pre-Processing Techniques

Automatic detection of cancer using deep learning is a popular topic that is constantly evolving. Technological advancements in computer processors, digital imaging, and mass storage devices have enhanced the growth of cancer data processing. Cancer data processing is a technique for extracting useful information from the data. This procedure also addresses (i) quality enhancement of data, (ii) proper data representation, (iii) restoring the original cancer data from its corrupted form, and (iv) compressing the bulk amount of cancer data to improve image retrieval efficiency. Automation in cancer data processing can be divided into three categories. The first category includes algorithms that work directly with raw data. The second category includes the algorithm that uses the first category’s results for additional processing. The third and final category includes algorithms that attempt to extract semantic information from lower-level data. This chapter discusses various cancer data pre-processing techniques that are necessary for data enhancement by reducing reluctant falsifications or improving some data features that are important for additional cancer data processing and automation in retrieval, classification, detection, and diagnosis.
A Blockchain-Based RegTech System for Product Safety Enforcement

Food safety is highly regulated in China. Moving from food hygiene to food safety regulation, the central government in China has developed more stringent regulatory measures over the past two decades for both national food production and imported food products. The compulsory observance of this regulation presents a specific challenge for all overseas companies that wish to export food to China. Blockchain has proved to be a technology able to ease regulatory burdens, especially in the finance field. Besides, the Chinese government has a strategy to develop the use of blockchain as a technology for establishing e-government and providing more transparent and better-quality services to the public. This chapter studies the use of blockchain as a regulatory technology to facilitate food product safety enforcement. The research focuses on food import regulations in China and studies the case of the business-to-business (B2B) food platform Foodgates that markets French food products to Chinese buyers. Foodgates manages all aspects of a transaction including regulatory compliance and transportation from producer warehouse to the final destination in China. In the process, Foodgates collects all pieces of information required by the Chinese authorities and stores them on a private blockchain. Even though the current adoption of blockchain by Chinese Customs authorities is limited, Foodgates’s information system enables a direct access to certified information for all stakeholders and therefore smoothens the path of food import into China.
ASSESSING SOFT AND HARD SKILLS FOR THE WORKPLACES OF TOMORROW

Leader to LeaderVolume 2023, Issue 107 p. 64-65 FROM THE FRONT LINES ASSESSING SOFT AND HARD SKILLS FOR THE WORKPLACES OF TOMORROW First published: 03 January 2023 https://doi.org/10.1002/ltl.20694Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume2023, Issue107Winter 2023Pages 64-65 RelatedInformation
Systematic Literature Review of Search-Based Software Engineering Techniques for Code Modularization/Remodularization

Due to rigorous changes, the maintenance of software systems has become difficult and complex. As a result, these rigorous changes can degrade the quality of the software process. One of the extensively used techniques, software modularization can be used to address this issue that converts the software classes attained from software products. Although to enhance the software quality, this can be a challenging task that automatically remodu-larizes the process. There are many parameters involved in improving the quality of the software process by automatically remodularizing the software system. Some of them include semantic domain, upgrading the continuous changing uniformity, number of the software changes by using search-based methods. SBSE (Search-based software engineering) study aims to transfer software engineering issues from human-based search to machine-based search using a range of methods from metaheuristic search, evolutionary computing paradigms, and operations research. The 242concept is to exploit humans’ reliability and creativity, and machines instead of requiring people to conduct the more tedious, error-prone, and thus expensive elements of the engineering process. Researchers have suggested some methodologies to increase the accuracy of the machine learning classifier for the prediction of software defects by assembling some machine learning methods, using an algorithm to improve, select features, and optimize parameters in some classifiers.
A Statistical Experimentation Approach for Software Quality Management and Defect Evaluations

Software development and metrics areas metrics lack information concerning metrics on what is applicable and what is actually practiced. In this work, the goal is to research software metrics about the source code to direct software quality measurement. Assessment of formulations is prearranged from two eras: first, when focus was on metrics based on complexity of the code and, second, when the center was on metrics based on object-oriented systems. The proposed technique for code analytics based on re-enactment designs and simulation patterns investigating deep code evaluation with the analytics of the respective performance. These 2performances used statistical simulation and are capable doing better for predictive analytics after multiple simulation attempts. To improve the software quality by one-fourth of the time taken, work is done to minimize the time taken to achieve accuracy compared with classical methods.
Enhanced Intelligence Architecture

Many smart sensors and actuators come together to make an Internet of Things (IoT) network. Artificial intelligence (AI) is a methodology that enables machines to analyze the received data. Based on the outcome, they instruct the devices to follow a set of instructions. Blockchain is the technology that brings trust to the person dealing with it. These different domains are combined with storage to enable a combo set of features. The previous methodology provides a reliable, secure system. The user has to feed his username and password to use the IoT devices. The data are transmitted to its peer devices using storage. A calculated outline of blockchain and artificial intelligence for IoT is where the IoT stage depicts a mix of six layers—physical, correspondence tier, connect control, administration, executives, and application tier. The physical distinguishes the information or data like temperature, area, contamination, climate, movement, and farming. The data are assembly from different sensor gadgets like RFID, barcode, and infrared. This region has various safety dangers and problems, for example, moving the data starting with one spot then onto the next. It makes it unstable for malevolent people. Blockchain and AI usage make the data or exchange of bitcoin. The gathered information moves to 220the correspondence layer. It is a model for moving data with one device then onto the next gadget. It finishes by actualizing a few cutting-edge innovations, for example, WiFi, Zigbee, radio, and infrared movement. This region has safety and protection problems, blockchain and AI innovation use in point-to-point organizations, and omnipresent broadband use for encoding and verification. The suggestion adds some of the features to the previous system and increases security by 7.37%, reliability by 1.83%, and speed by 8.053%.
Liver Tumor Segmentation Using Deep Learning Neural Networks

The second leading cause of cancer-related fatalities worldwide is liver tumor disorders, and its global incidence rate has been increasing, in contrast to other cancers' static or falling rates. Almost all highly malignant cancers are caused by hepatocellular carcinoma (HCC) and might result in a serious worldwide health issue. Early identification and diagnosis of HCC can help patients get treatment quicker and have a greater chance of surviving. Blood tests, imaging, and biopsies are traditionally used to diagnose liver cancer. Through MRI imagery, staging tests can be used to identify the extent and location of cancer. The proposed research uses CT imaging to segment liver cancers. The liver lesions in abdominal CT images are segmented using automatic segmentation techniques. The hybrid U-Net model is employed with using the Deep ResNet34 model architecture. Hyper parameters and epoch scores are utilized to determine the model's accuracy. Finally, the CT scans are employed to train the proposed model to segment multiple liver tumors and the performance is analyzed with the average losses and dice score. This will serve as the foundation for developing an automated liver tumor diagnosis system for clinicians with increased accuracy, reducing the risk of manual mistakes during diagnosis.
Contemporary Trends in the Early Detection and Diagnosis of Human Cancers Using Deep Learning Techniques

According to the World Health Organization (WHO), cancers are one of the leading causes of death and have accounted for nearly 10 million deaths worldwide in 2020. This number is only projected to increase in the future. Early detection and diagnosis of cancers would help curb the mortality rate by increasing chances of survival and allowing early interventions and the use of inexpensive treatment methods. Deep learning (DL) has been successfully used in the medical industry in areas such as oncology, radiology, cardiology, neurology, urology, and osteology for medical image analysis and object recognition. Recently, convolutional neural networks (CNNs), autoencoders, and artificial neural networks (ANNs) have begun to be utilized to develop highly accurate algorithms and have been coupled with computer-aided diagnostic techniques to aid clinicians in cancer diagnostics. This chapter analyzes the use of DL architectures for early detection and diagnosis of human cancer. The recent progress involving the use of different DL techniques for the early diagnosis of various cancers is discussed, the current limitations and challenges for the implementation of DL techniques are assessed, and solutions to some of the common challenges faced by researchers are presented. Finally, directions for future research are outlined.
Cryptoassets: An Overview

Bitcoin’s introduction as the first cryptoasset in 2009 ushered in a new era, representing a seismic shift in the financial markets. Since then, this evolving asset class has generated much interest, excitement, and growth. This chapter begins by providing a brief background of cryptoassets. It then discusses their main types (cryptocurrencies, security tokens, and utility tokens), users (individual investors, major financial institutions, endowments, and hedge funds), and benefits and drawbacks. Next, it sets forth the book’s purpose, distinguishing features, intended audience, and structure. The chapter provides a synopsis of each of the remaining 21 chapters. Although no single book can encompass all changes and iterations of these technologies as they emerge in the marketplace, this book brings together a broad collection of industry expertise and academic analysis to create a book helpful to researchers, academics, and practitioners.
Trade Settlements

The current security trade settlement life cycle presents several inefficiencies derived from intermediaries involved in the transaction between buyers and sellers. This chapter examines distributed ledger technology (DLT), the underlying technology of all blockchain applications, including trade settlements. It also reviews the implications of using blockchain in trade settlements for cryptoassets. Emerging blockchain technology provides investors, exchanges, regulators, and countless potential intermediaries with the most up-to-date technology with the highest efficiency, transparency, credibility, and automation enabled by smart contracts. Smart contracts allow an ecosystem to manage the process of trade settlements starting from execution to clearing and then settlement. These contracts reduce reconciliation and recordkeeping costs and streamline repetitive processes present in today’s trade settlement system. The chapter highlights the benefits of implementing DLT in financial markets globally in all trading aspects, including cryptoassets.
Determinants of Web-Based CSR Disclosure: Middle East Region Evidence

This paper aims to examine the factors influencing the extent of the web-based CSR disclosure in the Middle East countries.This study uses multiple regression models to examine the effect of some companies' characteristics (company size, profitability, financial leverage, industry profile and company age) on the extent of online societal disclosure.The study was conducted on 200 listed companies in seven Middle East countries (the United Arab Emirates, Saudi Arabia, Kuwait, Bahrain, Jordan, Qatar and Turkey).The website content was analyzed during the period January-February 2020.The results reveal that the most important factors influencing the level of web-based CSR disclosure are company size, profitability, company's leverage and the type of industry.The results of the study have important implications for different stakeholders.It will help regulators to formulate policies about the online CSR disclosure as they offer insights into the characteristics of those companies which do and do not engage in these practices.Thereby, the regulators might expect that the Middle East companies that present CSR information, on the websites, to be larger, have higher performance and leverage levels and belonging to manufactory sector.This paper attempts to fill some of the gap in the knowledge of the online CSR disclosure practices in the Middle East region.It investigates the factors determining the web-based CSR disclosure practices and it was not limited to describe the online CSR disclosure practices like most previous studies that are conducted in this region which allows us to have a clearer idea on these practices in this region.
Challenges, Effects, Coping Strategies, and Measures of Financing Agriculture: Socio-dynamics of Conventional and Islamic Sources of Finance in Kaduna State, Nigeria

Kaduna State in Nigeria is a predominantly agricultural society with active farmers within the median age group of between 35-54 years who are into food crop production with maize as the major food crop and ginger as the major cash crop produced in Kaduna State.Their business activities are spread almost evenly across the geographical zones in Kaduna State.The main aim of this paper is to examine the causal relationships of the effects of the challenges of financing agriculture on the challenges of financing agriculture and also evaluate the effects of the measures to mitigate the challenges of financing agriculture on the coping strategies adopted in financing agriculture in Nigeria.Data were collected using the questionnaire as the instrument of data collection in a quantitative method based on the cluster sampling technique, 663(50%) questionnaires were administered across 17 different clusters across the three (3) geographical zones in Kaduna State and some centralized offices of financial institutions within Abuja and 562 responses were received with a sample error margin of -17.9%.An Ordinary Least Square (OLS) linear regression test conducted revealed the effects of challenges of financing agriculture on the lack of collateral to obtain a loan with a B .820 and managerial/government bureaucracy in financing agriculture coming second with a B .638.Privatization of energy is a major challenge to agro manufacturing and the value chain with B .585 is in third place while lack of finance for mechanization and irrigation technology is in fourth place with B .572.In addition, the study also revealed the impact of the measures on the coping strategies is strongest on Zakat with a B 1.679.The effect of the measure on contribution from family and friends as a coping strategy comes in second place with a B .912.The study also revealed insecurity and infrastructural development as other impediments to financing agriculture in Nigeria based on the explored open-ended question.It is recommended that a central zakat fund be established to provide stable noninterest finance to smallholder farmers.
A Study on the Empowerment of University Students as Village Teacher

In this study, in the process of forming a village education community in cooperation between University A and local government S, university students became the main actors and developed a 'Democratic Citizenship Education Program.' In addition, the process of university students exercising their power as teachers in the process of working as village teachers was studied. The process of developing and implementing the program consisted of planning-execution-reflection-replanning-re-execution-re-reflection. As for the collection of data, literature data, transcripts of club activities, result reports, and introspection journals collected in the course of the implementation research were analyzed. As a result, college students were able to confirm the process of building their power as instructors in the process of developing and implementing democratic citizenship education programs. As a result of the study, it was found that the power of the research participants as village teachers was built through the process of discovering, reinforcing, infusing, and expanding the power of each research participant.
Arriving at the Results by Comparing with Traditional Approach to My Approach in Deriving Function Points for ETL Operations

It can be hard to guess how much data will need to be put into the data warehouse when the whole history of the transaction system is moved there. This is especially true when the transfer process could take weeks or even months. The ETL system's parts must be broken down into its three independent stages, nevertheless, when estimating a big starting load. Data extraction from source systems, Creating the dimensional model from data, Loading the data warehouse and timing estimates for the extraction process. Surprisingly, data extraction from the source system may take up the majority of the ETL procedure. Online transaction processing (OLTP) systems are simply not built to return those massive data sets from the data warehouse's historic load, which extracts a tremendous quantity of data in a single query. However, the daily incremental loads and the breath-of-life historic database loads are very different. In any case, fact-table filling requires data to be pulled in a different way than what transaction systems are able to do. ETL extraction procedures frequently call for time-consuming techniques like views, cursors, stored procedures, and correlated subqueries. It is essential to anticipate how long an extract will take to begin before it does. Calculating the extract time estimate is challenging. Due to the hardware mismatch between the test and production servers, estimates based on the execution of the ETL operations in the test environment may be greatly distorted. Sometimes working on certain projects where an extract task would run continuously and until it eventually failed, at which point it would be restarted and run once more until it failed. Without producing anything, days or even weeks passed. One must divide the extract process into two simpler steps in order to overcome the challenges of working with large amounts of data. Response time for queries. the interval between when the query is conducted and when the data starts to be returned. It is pertinent that effort arrival for ETL Operations for Data Marts and DWH projects in terms of Function Points which is a scientific way is essential. In the last paper, I have talked about general System Characteristics to arrive at Value Adjustment Factor. In this paper, I came up with results. I compared my findings with the conventional FPA on industrial projects in order to evaluate the Function Point Analysis’s suitability for Data Mart projects. I outline the strategy, implementation, and outcomes analysis of this validation in this section.