Reviewerly/exHarmony huggingface dataset:
A Study on the Development System of Coast Guard Officers: Focusing on recruiting beginner officers

With the advent of the era of maritime competition over territorial sovereignty and marine resources worldwide, the role of the Coast Guard, a maritime executive agency, is becoming increasingly important. There is a growing demand from the public that wants to provide the highest level of administrative services internally and demands rational leadership in terms of fairness and justice. Coast Guard officers carry out important tasks to manage the organization, deal with pending issues, and design the future with a basic spirit of service and democracy to the people. Therefore, it is an important and urgent matter to be competitive in the organization and provide the best service to the people through the training of competent executive personnel.&#x0D; Despite its long history, the Korean Coast Guard maintains the previous system in its officer development system. It has been maintained until today without significant changes in the basic framework of Police Executive Candidate, which is the initial system. As a result, the overall organizational competitiveness has been weakened externally, and it has not been able to keep pace with the rapidly changing marine environment. Internally, it is deriving various problems such as lack of leadership, pure bloodism, and failure to promote its own director.&#x0D; In order for the Korean Coast Guard to become an institution that leads the pending maritime issues and provides high-quality services, it is necessary to diagnose the problems of the officer development system and seek alternatives. In consideration of the constraints of the scope of the study, this study will focus on the recruitment system and education and training of beginner officers among various categories related to officer development.&#x0D; First, we will look at the recruitment system for beginner officers and the current status of education and training for advanced foreign and domestic similar institutions. Next, we will identify the problems caused by the Korea Coast Guard officer development system. Finally, I would like to present a reasonable alternative to the development plan of Korea Coast Guard officers by focusing on the recruitment of beginner officers.
A Study on the Effect of Periodic Auditor Designation on Audit Quality

[Purpose] This study’s purpose is to verify how the periodic auditor designation system of the newly implemented periodic auditor designation affects audit quality.&#x0D; [Methodology] Hypotheses were established by reviewing previous studies, and 493 samples from 2019 were selected for KOSPI－listed companies in Korea. The quality of accruals (McNichols, 2002) was used as the dependent variable, and the effect of whether or not the firm was designated as a periodic auditor and whether the auditor was part of the Big 4 was empirically analyzed.&#x0D; [Findings] As a result of the analysis and correlation analysis, a statistically significant difference was confirmed in the quality of accrual of the dependent variable and the independent variable auditor designation (PA) was found to be insignificant. We could not confirm the difference in audit quality according to the Big 4 auditors. However, a significant difference was only confirmed in ROA and loss in the control variables.&#x0D; [Implications] This study has significance in that it is a study of the effect of audit quality and the selection of periodic auditor designation companies introduced in 2019 using empirical data. However, since the periodic auditor designation system, which is the main subject of this study, was first implemented in 2019, not much related data has been accumulated, so there are limitations in the analysis of related studies.
Detecting Hate Speech on Social Media with Respect to Adolescent Vulnerability

Social media has become one of the biggest plat-forms for children and young adolescents to spend their free time. Due to the rising age gap between people using social media, tweens can be exposed to offensive and provocative posts made by others that are older. Twitter allows children aged 13 and up to create an account on their platform. While this is a common age restriction on most social media platforms, it can be damaging to young adolescents who are at a crucial time developing their morals and beliefs based on what they see online. Identifying hate speech within a timely matter is crucial for censoring hate speech for kids. This can impact the overall environment on social media to be less toxic and uplifting to all users. In this work, we propose to use multiple machine learning techniques: SVM, k-nearest neighbor, Naive Bayes, and soft-voting ensemble classifier.
Experimental Exploration of Evolutionary Algorithms and their Applications in Complex Problems: Genetic Algorithm and Particle Swarm Optimization Algorithm

The primary aim of this study was to experiment and compare the empirical evidence of evolutionary algorithms and their applications using the Genetic Algorithm (GA) and Particle Swamp Optimization (PSO) algorithm. The objectives of the study included reviewing recent scholarly scientific research papers and practices, and algorithms with existing genetic algorithm techniques, identifying the need for lessons learned and leveraging best health practices garnered from the existing problems of optimization, reviewing and identifying the technological tools used in the healthcare domain, algorithms, and the effectiveness of GA and PSO techniques and to develop and analyse GA algorithms using datasets to predict and determine which of the two algorithms performs better. Evolutionary algorithms which are normally viewed as components of both evolutionary computing and bio-inspired computing use mechanisms inspired by nature and are indeed capable of solving problems through processes that emulate the behaviours of living organisms. Genetic algorithms (GAs), evolution strategies (ESs), differential evolution (DE), and particle swarm optimization (PSO) are typical examples. Therefore, evolutionary algorithms are typically used to provide good, estimated solutions to problems that cannot be solved easily using other techniques, and as such numerous optimization problems fall into this category. This work aims to provide a systematic evaluation of the literature on contemporary PSO methods for knowledge discovery in the area of illness detection. Although the major objective is to present recommendations for future enhancement and development in this field, the systematic analysis reveals the possible study topics of PSO techniques as well as the research gaps. The essential ideas, theoretical underpinnings, and traditional application domains are covered in this study. It is fervently anticipated that the findings will help researchers to thoroughly examine PSO algorithms for disease diagnosis. According to the current state of PSO strategies in healthcare, several difficulties that can be taken on to advance the sector are mentioned.
An Approach to Convert Sign to Text for Impaired People

Human Computer Interface is the study of how humans and computers interact. Hand gestures are a great way to communicate with people when they don’t understand exactly what we are saying. Understanding hand gestures is essential to make sure the listener understands what we are saying. The main idea of our project is to try different approaches to hand gesture recognition. This proposed work first with radar data and then with camera sensor to achieve hand gesture recognition. First, we tried to build hand gesture recognition using radar data, and since most people don’t know sign language and few interpreters, we developed an approach to real-time approach for American Sign Language based on neural networks finger spelling followed by another model with Media Pipe. We propose a complex neural network method to detect hand gestures of human behaviour from camera recorded images. The hand gesture first goes through the filter and after applying the filter the gesture goes through a classifier that predicts which type of hand gesture it is. In an existing system radar unable to detect static gestures in our approach, a deep learning-based image captioning algorithm captures both static and dynamic gestures through Media Pipe.
Developing Comic Strips in Teaching Vocabulary for EFL Students

There are various ways that can be taken to improve the vocabulary of EFL students. The use of media, as well as teaching methods. This research focuses on developing Comic Strips as a supporting medium for vocabulary teaching. Based on the sections from Comic Strips, students can choose what vocabulary they want to use to complete the available bubbles so that they form sentences and in the end a coherent story. Lecturers can also use Comic Strips to facilitate students in developing ideas in their writings by asking some stimulating questions. In line with the usefulness of Comic Strips to improve the vocabulary of EFL students, the researchers developed comic strip media with certain themes to be used in vocabulary courses for 7 meetings. The researcher believes that the development of Comic Strips media can be effective because it can make students enjoy the teaching and learning process.
Measuring Human Capital: from WorldCat Identities to VIAF

This note is a summary of how we can measure scholars’ human capital after the retirement of theWorldCat Identities project on March 23, 2023. WorldCat Identities was previously providing us measures of scholars’ output and recognition.
Security Analysis of Medical Image Encryption using AES Modes for IoMT Systems

The Internet of Medical Things (IoMT) driven health is the most advantageous aspect of technology in the global healthcare industry.Securing medical images in IoMT systems is a great challenge to protecting medical privacy.Many cryptographic schemes that provide the confidentiality and integrity of these images over IoMT devices are proposed.The Advanced Encryption Standard (AES), is one of the most standards used in digital image security systems, it employs five encryption modes.In this paper, the security analysis of medical images encryption was described for IoMT Systems using the AES modes.For the evaluation and analysis of security performance, several tests were employed such as the quality of the encrypted images, the Peak Signal to Noise Ratio (PSNR), the histogram analysis, the correlation between the adjacent pixels, and the encryption/decryption times for the five modes.
Information Security Policy Implementation Assessment in Libyan Telecommunications Companies

The telecommunications industry is a critical infrastructure that is essential for the functioning of modern society.It is also a lucrative target for cyber attacks.A successful attack on a telecommunications network could potentially expose the information of millions of customers.This study focuses on the information security policy of telecommunications companies in Libya.The study aims to assess the hypothetical risks of implementing an information security policy, as well as to examine the vulnerabilities and effectiveness of such a policy.The interview technique was selected to collect data and conduct the necessary analysis to verify the existence of any gaps.The study found that several vulnerabilities exist in telecommunications information security policies.It is therefore important for telecommunications companies to take steps to mitigate these vulnerabilities and protect their networks from cyberattacks.Overall, the study provides valuable insights into the importance of information security in the telecommunications industry.The study findings can help telecommunications companies to develop and implement effective information security policies that can help to protect their networks from cyber attacks.
Building an Information Security Awareness Program for a Private Financial Organization: Case from Libya

The protection of assets of organizations became one of the major aspects that organizations have to deal with.The issue is too serious when it comes to financial organizations due to their sensitivity to data security attacks.Security breach incidents can happen in an organization no matter how well the technology is protected.The main reason for this is employees' improper conduct or lack of action that leads to the majority of information security incidents.Educating employees to improve information security awareness plays a significant part in securing organizational environments.Although, best practice, standards provide a set of minimum information security awareness controls that should implement.This study proposes an information security awareness training program to improve knowledge of information security and improves employee behavior for a private financial organization in Libya.
Implementation of Text Similarity using Cosine Similarity Method in Python

The goal of this research is to develop a text similarity program using cosine similarity method in Python.The steps of text similarity process are: preprocessing text, word-tokenization, creating list of words, creating bag of words, calculating word frequency, and calculating cosine similarity.The developed program was examined on two experimental texts from Wikipedia.The program successfully performed the steps of text similarity and provided the required results.
Consequences of Violating Database Integrity Rules in Data Management Systems

Nowadays every action we do produces data like most computer applications.To this point moreover, some of these are reduced to managing and consulting data: our bank accounts, registrations, insurance, etc.This data is stored in what is called a file and is structured into records containing details such as our surnames, first names, date of birth, blood type, etc.More complex applications require data whose structure is also complex.The data is classified in several files according to the objects they describe: customer file, product file, order file, invoice file, etc.There are links between the files that reflect the relationships between the objects described.It also appears that the data necessary for an application could be useful to other applications, or even to other users.This data then constitutes what is called a database.Managing such data is no longer within the reach of elementary software.Guaranteeing the quality of the recorded data (can we find what was recorded?),their consistency (is the customer of each order listed?), protecting them in the event of an incident, allow several users to access them simultaneously, while strictly controlling access to confidential data, offering good access performance to all applications, in particular those that are interactive, are functions that require powerful and complex software like the database management systems, or DBMS such as SQL SERVER, MYSQL, ORACLE SQL.This type of software is one of the fundamental tools for the development of large computer applications thanks to these rules of integrity, namely the integrity of entities and referential integrity which allow the solidity and robustness of data, But these rules which must guarantee the essential security of the database for our various organizations are often violated by inattention or ignorance of these rules by the new developers and even by the old ones thus being able to involve many consequences on the whole of direct or indirect actors
A Review Article on Detection of Fake Profile on Social-Media

Nowadays, practically everyone, from a youngster to an adult, spends more time on online social media platforms, connecting with and exchanging information with individuals all over the world. The social network is becoming a popular means to communicate with people who live in different parts of the world. Because of the tremendous interconnectedness and information sharing enabled by the internet, social media platforms. This highlights the importance of establishing a system capable of detecting fake profiles on social media networks. There has been a lot of study done in this area utilising machine learning algorithms to identify fake profile, duplicate, spam, and bot accounts, and most of the fake profile accounts were effectively recognised using machine learning algorithms. This study discusses fake profile detection on social networks using machine learning.
Learn What Is Possible, Then Choose What Is Best: Disentangling One-To-Many Relations in Language Through Text-based Games

Language models pre-trained on large self-supervised corpora, followed by task-specific fine-tuning has become the dominant paradigm in NLP. These pre-training datasets often have a one-to-many structure--e.g. in dialogue there are many valid responses for a given context. However, only some of these responses will be desirable in our downstream task. This raises the question of how we should train the model such that it can emulate the desirable behaviours, but not the undesirable ones. Current approaches train in a one-to-one setup--only a single target response is given for a single dialogue context--leading to models only learning to predict the average response, while ignoring the full range of possible responses. Using text-based games as a testbed, our approach, PASA, uses discrete latent variables to capture the range of different behaviours represented in our larger pre-training dataset. We then use knowledge distillation to distil the posterior probability distribution into a student model. This probability distribution is far richer than learning from only the hard targets of the dataset, and thus allows the student model to benefit from the richer range of actions the teacher model has learned. Results show up to 49% empirical improvement over the previous state-of-the-art model on the Jericho Walkthroughs dataset.
Blockchain technology‐based medical information sharing management

This paper introduces the application of blockchain technology in the field of IoT medical information, proposes a secure and trustworthy framework for storing and sharing medical information based on blockchain, containing functions such as tamper‐proof storage of IoT medical data, desensitization processing of sensitive information and sharing of medical information security, and predicts the future market state transfer and the construction time of the model framework from the perspective of economics.
Phantom Embeddings: Using Embedding Space for Model Regularization in Deep Neural Networks

The strength of machine learning models stems from their ability to learn complex function approximations from data; however, this strength also makes training deep neural networks challenging. Notably, the complex models tend to memorize the training data, which results in poor regularization performance on test data. The regularization techniques such as L1, L2, dropout, etc. are proposed to reduce the overfitting effect; however, they bring in additional hyperparameters tuning complexity. These methods also fall short when the inter-class similarity is high due to the underlying data distribution, leading to a less accurate model. In this paper, we present a novel approach to regularize the models by leveraging the information-rich latent embeddings and their high intra-class correlation. We create phantom embeddings from a subset of homogenous samples and use these phantom embeddings to decrease the inter-class similarity of instances in their latent embedding space. The resulting models generalize better as a combination of their embedding and regularize them without requiring an expensive hyperparameter search. We evaluate our method on two popular and challenging image classification datasets (CIFAR and FashionMNIST) and show how our approach outperforms the standard baselines while displaying better training behavior.
A Multi-Modal Entity Alignment Method with Inter-Modal Enhancement

Due to inter-modal effects hidden in multi-modalities and the impact of weak modalities on multi-modal entity alignment, a Multi-modal Entity Alignment Method with Inter-modal Enhancement (MEAIE) is proposed. This method introduces a unique modality called numerical modality in the modal aspect and applies a numerical feature encoder to encode it. In the feature embedding stage, this paper utilizes visual features to enhance entity relation representation and influence entity attribute weight distribution. Then, this paper introduces attention layers and contrastive learning to strengthen inter-modal effects and mitigate the impact of weak modalities. In order to evaluate the performance of the proposed method, experiments are conducted on three public datasets: FB15K, DB15K, and YG15K. By combining the datasets in pairs, compared with the current state-of-the-art multi-modal entity alignment models, the proposed model achieves a 2% and 3% improvement in Top-1 Hit Rate(Hit@1) and Mean Reciprocal Rank (MRR), demonstrating its feasibility and effectiveness.
Weighted quantile estimators

In this paper, we consider a generic scheme that allows building weighted versions of various quantile estimators, such as traditional quantile estimators based on linear interpolation of two order statistics, the Harrell-Davis quantile estimator and its trimmed modification. The obtained weighted quantile estimators are especially useful in the problem of estimating a distribution at the tail of a time series using quantile exponential smoothing. The presented approach can also be applied to other problems, such as quantile estimation of weighted mixture distributions.
Exemplar-Based Texture Synthesis Using Two Random Coefficients Autoregressive Models

Example-based texture synthesis is a fundamental topic of many image analysis and computer vision applications. Consequently, its representation is one of the most critical and challenging topics in computer vision and pattern recognition, attracting much academic interest throughout the years. In this paper, a new statistical method to synthesize textures is proposed. It consists in using two indexed random coefficients autoregressive (2D-RCA) models to deal with this problem. These models have a good ability to well detect neighborhood information. Simulations have demonstrated that the 2D-RCA models are very suitable to represent textures. So, in this work, to generate textures from an example, each original image is splitted into blocks which are modeled by the 2D-RCA. The proposed algorithm produces approximations of the obtained blocks images from the original image using the generalized method of moments (GMM). Different sizes of windows have been used. This study offers some important insights into the newly generated image. Satisfying obtained results have been compared to those given by well-established methods. The proposed algorithm outperforms the state-of-the-art approaches.