Reviewerly/exHarmony huggingface dataset:
Development of Engineering Vocational Choice Systems in Higher Education

The high unemployment rate is a challenge for the world of education because not all graduates succeed in entering the world of work. Higher education is one of the largest contributors to unemployment in Indonesia. Likewise with the objectives of vocational education which are expected to be able to immediately work and create jobs. The problem is that not all majors are according to personality type. Therefore, researchers will develop a system aimed at recommending majors in higher education based on personality types. The method used is the Research and Development (R&amp;D) method using the Four D (4 D) model a. Defining stage (Define); b) Design Stage (Design); c) Development Stage (Develop); d) Stage of Spread (Disseminate). This research was only carried out to the extent of development and resulted in a valid system to be implemented based on the validator's assessment. The data collection instrument consisted of product and linguistic expert validation sheets. This study shows that the validation results have an average percentage of 92.5% with a valid category. Based on the results of the research and discussion, it can be concluded that the system is valid to be used as a reference in selecting majors
Distributed Reactive Navigation of Robotic Teams for Sweep Coverage of a Corridor Environment with an Obstacle Course

A team of mobile robots travels with an upper-bounded speed in a corridor environment, which contains obstacles. The team should sweep the corridor with a given speed, while autonomously lining up on a moving cross-section of the corridor and evenly distributing themselves over this section. The robots should also avoid collisions with one another, the obstacles, and the corridor sides. In the parts of the corridor containing obstacles, the requirement to achieve or maintain the even distribution is forcedly relaxed. However, the even distribution should be automatically restored after such a part is left behind. In its local frame, every robot has access to the corridor direction and to the relative coordinates of the objects that lie within a finite range of visibility The robots are unaware of the team size and the corridor width, are unable to distinguish between the peers or play different roles in the team. A computationally inexpensive and distributed control algorithm is presented that solves the mission. The performance of the proposed navigation law is justified by a mathematically rigorous global convergence result and is confirmed by computer simulation tests.
DETECTING THE SCHEMATIC SEMANTIC DOMAINS OF THE IMAGE “LABYRINTH” USING A MACHINE LEARNING ALGORITHM

The convergence of cognitive grammar and machine learning models to analyze fictional texts is a new synthetic approach in linguistic research.The presented article is grounded on a modern American dystopian text, "The Maze Runner" by D. Dashner.Dystopian literature is an instance of language use where the author manipulates lexical and syntactic choices to encode meanings.The paper aims to reconstruct the schematic semantic domains of the image "labyrinth" by discerning scaffolding lexemes and applying a deep learning model to extract word embeddings.The article studies the image "labyrinth" as a predication embodied by the complex conceptual matrix following the principles of cognitive grammar.The schematic semantic domains reflect essential experiences a person acquires while encountering new objects or concepts.Hence, external factors such as body changes, kinesthetic movements, and sensory exposures determine the lexemes that comprise the basic semantic domains of the image "labyrinth".The article identifies the main nominal semantic domains of the image "labyrinth" such as the Maze, the Box, the Glade, the Cliff, Thomas, and the Grievers.These terms build a mental map in the reader's mind and bind the prominent topological landmarks.By applying the deep learning algorithm word2vec, we further discern the top ten word embeddings that make up these domains.We analyze the grammatical categories that comprise each schematic semantic domain and check whether the model's output can contribute to the generalized inference of the basic domains that construct the image "labyrinth".The paper concludes by discussing the consistency of deep learning models with the expected output and limitations that can be traced by utilizing only a machine learning algorithm.We conclude that the deep learning approach can bring logical structure to the discerned schematic semantic domains of the image "labyrinth" and highlight their relations.However, the attained lexemes are sometimes not fully comprehensible and interpretable to rely on in constructing the senses of the image "labyrinth".
Information Teaching Management System of College Employment Guidance Course Based on Hybrid Architecture

At present, the information-based teaching management system of college employment guidance course can not meet the urgent needs of students’ employment information. Therefore, this paper proposes an information-based teaching management system of College Employment Guidance Course Based on hybrid architecture. The business requirements of the employment management system are analyzed from the perspective of users, and the functional modules of the system are abstracted. The system architecture is designed by using the browser / server three-tier architecture mode, and the functions of the system are realized by using PHP technology, Java language and other computer programming technologies. The employment information related to students and enterprises is stored through mysql, and the employment status over the years is analyzed to provide an important reference basis for the employment guidance of the school. The experimental results show that this system is suitable for most networks and will not be hindered by the firewall in the process of use, so it has a wider scope of application and richer data collection.
Implementation of Natural Feature Tracking in Eclipse Applications Using Augmented Reality

An eclipse is a phenomenon that occurs when a celestial body moves into the shadow of another celestial body, be it a solar eclipse or a lunar eclipse. In the process of understanding this phenomenon, people do not understand what is meant by the eclipse phenomenon. Because of this, we need a teaching aid to help us understand the material. &#x0D; The application will apply Augmented Reality technology using the Natural Feature Tracking (NFT) method. The application in this research will be made in the form of a native website. With python 3.7.0 64-bit programming language using the IDE (integrated development engineer), namely Visual Studio Code. This application will take advantage of the flask frameworks. This website will be native. Application implementation using native website templates with HTML (Hypertext Markup Language), CSS (Cascading Style Sheets), and Javascript. This application will be intended for a platform website resolution of 16:9 with a breakdown of 1920 x 1080 pixels. However, this ratio will be able to adjust because it applies the bootstrap 4 framework. From some tests conducted, the percentage of accuracy of the percentage of 3D object appearance is 52% on objects performing in the correct position, 34% on objects performing in the wrong position, and 10.67% on objects not appearing. In terms of functions, it has been running and meets the requirements with black box testing with 100% accuracy which is carried out based on 9 functions in the use case diagram.
Personalized Recommendation System of College Students’ Employment Education Resources Based on Cloud Platform

In view of the current use of the linkage university student employment service platform, the recommendation method under the “double innovation” education perspective is generally based on the stand-alone mode, with limited processing capabilities and scalability, resulting in low system recommendation accuracy and recall rates. In order to solve this problem, a personalized recommendation system for college students’ employment education resources based on a cloud platform is designed. Use the Elastic Search open source search engine to build indexes so that users can quickly locate the resources they need from the search results. Build a talent training decision-making system platform to enable students to choose high-quality module resources that they are interested in. Design a hypertext service platform to obtain recommended paths. Establish a keyword index platform to index educational resources based on keywords. Construct a vector space model, describe the keyword vector, and determine the similarity between the relevance of the courseware and the category. Analyze queries and documents, remove stop words, extract stems, and vectorize documents. The document content is represented by the feature weight set, and a personalized recommendation process is designed. It can be seen from the experimental results that the highest recommendation accuracy rate of the system is 99%, and the highest recall rate is 98%, which has an efficient recommendation effect.
Affinity-Aware Relation Network for Oriented Object Detection in Aerial Images

Object detection in aerial images is a challenging task due to the oriented and densely packed objects. However, densely packed objects constitute a significant characteristic of aerial images: objects are not randomly scattered around in images but in groups sharing similar orientations. Such a recurring pattern of object arrangement could enhance the rotated features and improve the detection performance. This paper proposes a novel and flexible Affinity-Aware Relation Network based on two-stage detectors. Specifically, an affinity-graph construction module is adopted to measure the affinity among objects and to select bounding boxes sharing high similarity with the reference box. Furthermore, we design a dynamic enhancement module, which uses the attention to learn neighbourhood message and dynamically determines weights for feature enhancement. Finally, we conduct experiments on several public benchmarks and achieve notable AP improvements as well as state-of-the-art performances on DOTA, HRSC2016 and UCAS-AOD datasets.
Eliciting Organizational Requirements Using Stakeholder Prioritization

Different stakeholders come together to find the requirements of a software in an organization. While there are many contradicting requirements which can be provided by different stakeholders, they need to be prioritized properly in order to avoid the confusion in the development or requirements elicitation phase.
REQUIREMENTS GATHERINGS AND IMPLEMENT ISSUES

Summary of the Research Research goal is better understanding and learning by using old and new techniques for resolve problems and empty blank spaces which are mostly facing during requirement gathering phase to development phase. This paper mainly purpose gathering research on Software requirements gathering and their effect on all incoming challenges and cost, benefits. Requirements is one in technology application style and development. . Most of the projects are fail due to gathering the poor requirement gathering. Many of the software based projects acquired by one to one the interview for requirement gathering. During last few years new software development approaches were introduced for increase software product which are helpful for reduce cost, time, and effort. For all procedure Software Requirement gathering is the one of the special key.
Requirement introspection and involvement of stakeholder

In this paper, we clarify how to fulfil the need of client. When a client has giant idea and he don’t know how to describe it to others than the requirement engineers approach this technique. The involvement of stakeholder to inspect the need of client and give a hint near to his requirement. How the client introduces his idea to company and how the requirement engineer will work on it will be explain in this paper. Client attitude may also a reflection of his requirement. Introspection is basically the self-perception. An organization is responsible for taking care of stakeholder. Once they show their involvement in any organization, it will be grateful for both parties i.e. their business will grow on the power of two. Once the communication is started requirement engineer will use some techniques for gathering requirement. As the stakeholders duties is to operate the organization and they are all connected virtually to each other. So during using techniques, introspection is also very important and without it requirement gathering process won’t be complete. Some organization use groupware tools for gathering requirement. Elicitation technique is also use in this process. Elicitation technique is also known as appropriate and qualitative technique. No attribute is considered in introspection.
Non-Functional Requirements (Usability, security)

Ideally the use of software is determined by the performance of both its functionality, such as its usefulness, flexibility, functionality, synergy, security. However, there has been a third-party emphasis on software performance, even if the functionality is not helpful or used without he necessary non-functional features. we discuss the state of the art in the design of non-functional requirements (hereafter, NFRs), while providing hope for some future directions.
A Study on the Efficiency Analysis of Korean Courier Companies

This study has analyzed the efficiency of courier companies in Korea in order to provide direction for growth in the fierce competitive market. Based on a reliable source of data, 15 companies are selected as Decision Making Units. In terms of the analysis, four models are adopted. For static analysis, CCR, BCC, and super-efficiency levels are suggested. For dynamic analysis, Malmquist productivity index are analyzed. The result of the CCR and BCC analysis suggest that 8 companies that found a niche market are found to be efficient. They had competitive edge on specialized areas of business sectors. Super efficiency analysis also showed 12 companies to be efficient which means that in the times of Covid19, companies enjoyed the opportunity of growing cargo demands from Covid19 are found to be operated efficiently. From malmquist analysis, there are companies found to be improved and degraded in terms of efficiency levels. It is obvious that traditional logistics companies with size and network still remain competitive while others either enjoy or struggle to compete and survive in the Korean market.
Topic Modeling of Conflict Reports in National Daily Newspapers

This study aims to examine the subject and characteristics of conflict reports in major national daily newspapers in Korea. This study is intended to understand which aspects of the news media coverage focuses on between the positive function and the negative function of the conflict. Specifically, conflict-related news articles reported in major national daily newspapers from 2010 to 2022 were analyzed using topic modeling. From the results, major topics such as policy/diplomatic conflict, government and local government conflict, international trade conflict, and political conflict were derived. The conflict reports in major national daily newspapers focused on the government's internal and external conflict and political conflict. There was also a characteristic of reporting on specific conflict events by year. This reveals that news reports on conflict emphasize the negative function of conflict that can produce social division and confrontation. In conclusion, this study notes that it is time to consider and improve the role of the news media in preventing conflict and aiming for social integration.
A Study on the Relationship Among Global Corporate Hyundai Motor Company Stock Price, Hyundai Motor Company Stock Futures Price and Internet Search Trends

This study is an empirical study on the relationship among global corporate Hyundai Motor company stock price, Hyundai Motor company stock futures price and internet search trends. In this study we used 1807 daily data of the global corporate Hyundai Motor company stock price, Hyundai Motor company stock futures price and internet search trends from January 4, 2016 to December 6, 2022. We try to analyze the mutual influence and the causality between Hyundai Motor company stock price, Hyundai Motor company stock futures price and internet search trends. We wish to analyze the extent of cross-influence. We employ variance decomposition function based on VAR model as well as impulse response after scointegration test and unit root test. An important result of this study are summarized as follows: First of all, raw time series data of global corporate Hyundai Motor company stock price and Hyundai Motor company stock futures price has unit roots. Secondly, the first differential data of the global corporate Hyundai Motor company stock price, Hyundai Motor company stock futures price and internet search trends has no unit roots. Third, there is at least one cointegration among the first differential data of global corporate Hyundai Motor company stock price, Hyundai Motor company stock futures price and internet search trends. Finally, global corporate Hyundai Motor company stock price, Hyundai Motor company stock futures price and internet search trends granger causes global corporate Hyundai Motor company stock price, Hyundai Motor company stock futures price and internet search trends.
The Effects of Maker Education on Prospective Early Childhood Teacher’s STEAM Literacy Indexes and Empathic Ability

This study was conducted to find out the effect of maker education applied in children's literature classes on the cultivation of convergence talents and empathic ability of prospective early childhood teachers. To this end, maker education was applied to a total of 23 freshmen students taking early childhood education and children's literature classes at a College in Korea, and its impact was verified through pared t-tests on convergence talent cultivation and empathic ability. First, the study showed that maker education for pre-child teachers had a positive effect on fostering convergence talent. All four areas of convergence, creativity, consideration, and communication showed statistically significant results. Second, maker education for pre-childhood teachers was found to have a positive effect on empathy. Among the two areas of cognitive empathic ability and affective empathies, cognitive empathy showed statistically significant results, but affective empathy did not. Through this study, it was confirmed that maker education has an important educational value to promote convergent knowledge necessary for the future society, and implications were drawn that it is a meaningful educational method for teachers who educate future generation.
Movie Box Office Prediction at the Distribution Stage Using Text Mining of Movie Reviews

Most movie box office predictive models use various variables related to movie production, audience, and movie screening. However, one of the most important variables in the distribution stage when a movie is released is the movie review related to the audience, that is the subject of word of mouth. In most predictive models in which various variables have been used so far, movie reviews have been substituted into emotional indices for additional analysis. In this case, it can be said that it has a limitation in that it is difficult to specifically grasp the contents of word of mouth about the movie. In this paper, only words constituting movie reviews on online portal sites were used as independent variables in the movie box office predictive model, and the relationship with movie box office performance was identified using the LASSO model. Through this, the predictive power of movie reviews on movie box office success was evaluated, and the words in movie reviews that affect movie box office were investigated. As a result of the analysis, the LASSO regression coefficients of words had a correlation with the success of the movies. The LASSO regression coefficients increased as the words appeared in reviews of successful movies, but decreased as they appeared in reviews of unsuccessful movies. As a result, it can be said that movie box office prediction using movie reviews can be helpful for planning additional investment strategy at the distribution stage.
Ensemble Model for Multiclass Imbalanced Data Using Cluster Computing of Spark

Big data analysis using machine learning has become a challenging problem today.Classification problems become more challenging when class distribution is imbalanced.In this paper, we propose a distributed ensemble model with an intelligence technique based on Particle Swarm Optimization to overcome the imbalanced problem.For compensating the class imbalance, first SMOTE is used to balance the minority class samples, and then sampling based on Particle Swarm Optimization is applied.Here, to perform fast processing, the whole model is implemented using spark-cluster computing, which uses the underlying concept of parallel programming of spark RDD.Results of the proposed system have shown consistent improvements on several evaluation metrics and overall processing time.Evaluation of the proposed system has been done using different performance metrices also comparison between sequential and distributed ensemble models.Most of the existing techniques show different performances for different datasets, while the proposed method has shown better generalization property, which improves the data-model dependency issue.The proposed model has been evaluated using KDD-CUP'99 intrusion detection and insect sensor datasets.For the datasets, it shows better improvement over traditional sampling techniques.F-Measure value is 99% for KDD'cup dataset and 92% for insect dataset.