Reviewerly/exHarmony huggingface dataset:
A Study on Priority Exploration of Elderly Poverty Response Policy: Application of Social Values

The purpose of this study is to establish a strategy to explore the priorities of elderly poverty policies by analyzing the core of the system related to elderly poverty and to derive the priorities of policies with strategic implementation ability. In other words, in order to combat poverty for the elderly, the cause of poverty was identified, and a solution was suggested by finding the priority of policies through the social security system (the 2nd basic social security plan task). As a result of the study, first, it was found that it was urgent to prepare a management plan for health hazards for the elderly. To solve this problem, it is necessary to minimize temporary or post-response methods and first establish a professional management system that finds and actively responds to health risk factors. Second, it is necessary to cultivate manpower and strengthen quality to provide social services. Third, system improvement should be prepared for this, such as increasing the income guarantee rate of the unemployment benefit system and eliminating blind spots of benefits. In addition, it is necessary to strengthen policies by applying policy priority search strategies at the same time as establishing institutional solutions for elderly poverty. First, application to resource concentration, second, emphasis on education and issue symbolism, and third, use of mass media.
Achievements and Tasks of Island Regeneration Project: Focusing on the ‘Islands that People Hope to Visit’ in Jeollanam province, Korea

Until recently, the island development effort has been primarily concerned with infrastructure. Jeollanam-do is leading the direction of the Korean island policy. Beginning in 2015, The Islands That People Hope to Visit was a strategy to construct a vision with three key promotion strategies: protection and recovery of biological resources, appealing island cultural tourist resources, and island development with citizens and local governments. This study intends to provide future solutions after reviewing the current status and performance of the ‘Islands that People Hope to Visit’ promoted by Jeollanam-do. As the ‘Islands that People Hope to Visit’ is led by a small number of leaders rather than by the entire population, conflicts between residents have arisen. In the place where the project started in the mid-range, issues of management and operation, and trust between residents and leaders, and villages and administration are being raised. Second, the premise is that certain island identities have been harmed as a result of village specialization and concept development. Third, the primary problem is that there is a lack of knowledge of the people' life since there are no cultural and welfare programs for the locals because it is established primarily for the tourism industry. Fourth, although the ratio of hardware and software expenses is stipulated in the ‘GuidelinesforIslands that People Hope to Visit’ as 8:2, in reality, it is often carried out at a ratio of 9:1. Furthermore, there are occasions when facilities cannot be developed or are installed illegally owing to issues with land ownership and land use planning during the promotion of hardware projects. Fifth, there are still tasks to be completed, such as dispute resolution, transparent project and budget execution, transparent administration and operation, the interaction between village residents and project entities, project assessment and rearrangement, and the establishment of follow-up projects.
Participatory Action Research on Class for Student Below the Basic Level in Middle School Using AI-based Adaptive Learning System Applying Personalized Instruction Strategy

Objectives The purpose of this study is to conduct action research on a mathematics class for students with below basic academic ability using an AI-based adaptive learning system to which a personalized instruction strategy is applied and to explore educational changes between learners and teachers in this process.&#x0D; Methods For this reason, teachers and researchers conducted math classes using an AI-based adaptive learning system that applied personalized instruction strategies derived for middle school students with the achievement of below basic proficiency level for about four months. Collecting main data was from observation logs, reflection logs, interview, learning data, and mathematical learning affective domain test, and the data were analyzed as the mixed research method.&#x0D; Results During research, a class for students with below basic academic ability was changed to a personalized class using the AI-based adaptive learning system, and in this process, planning, execution, and reflection of personalized class using AI-based adaptive learning were analyzed. The learners who participated in this study were diagnosed and recovered the prerequisite learning deficits and the cognitive domains generally increased, while no significant differences were found in the affective domains. Through qualitative analysis, we confirmed that mathematical interest and mathematical self-efficacy improved and learning satisfaction increased. Teachers realized personalized classes, experienced roles of new instructors, and discovered the possibility of personalized and customized instruction within the school.&#x0D; Conclusions Based on the results of this study, the implementation process, advantages, and limitations of classes using the AI-based adaptive learning system applied with personalized instruction strategies were reviewed, and measures were discussed to realize personalized instruction at schools.
Analysis of changes in media articles following the announcement of government press releases on the return of U.S. military bases

The full-fledged return of U.S. military bases began in 2004 when U.S. military bases scattered across the country were relocated to Pyeongtaek and other areas under the YRP (Yongsan Relocation Plan), LPP (Land Partnership Plan), which were ratified by the National Assembly. The relocation is expected to be completed by the end of this year. In addition, the U.S. Military Jeonpyeong Housing Support Act, a special corporation enacted for the relocation of U.S. military bases to Pyeongtaek, is also expected to achieve its purpose and be abolished in 26. At this point, the newspaper article according to the government's press release when returning the U.S. military base would like to find out what interests are through text mining and topic modeling, and analyze the differences in these key words at the time of base return announcement. In addition, it presents a methodology for post-examination and analysis after the release of the government press release. Through this study, it was recognized that it was necessary to fully consider whether there was a difference in opinions between stakeholders and whether the opinions of policy beneficiaries were sufficiently reflected when writing a government press release. In addition, it is necessary to monitor follow-up newspaper articles to see how people's interests change and whether government press releases provide sufficient information to resolve conflicts. This can be used as a tool or methodology to resolve conflicts between stakeholders in advance for large-scale national projects to be carried out in the future.
Application and Evaluation of The Blended learning-based Disaster Severity Classification(Triage) Education Program at a University

Objectives This study is the application and evaluation of the blended learning education program for paramedic students at a university. The purpose of this study is to understand how the education program about disaster severity classification(triage), which combines blended learning-based distance education and simulation education, affects the educational satisfaction and knowledge understanding of students.&#x0D; Methods This study is one group, post-test design. A blended learning-based disaster severity classification education program that combines distance education and simulation was implemented for 20 first and second-year emergency rescue students at a university. The difference in knowledge comprehension and education satisfaction was measured as an evaluation.&#x0D; Results As a result of applying and evaluating the blended learning for college students in the emergency rescue department for 105 minutes, the satisfaction with the web environment was the highest (M=9.47), immediately after online class and online class. And after face-to-face class, knowledge of disaster site severity classification increased statistically significantly before (M=7.40), after online class (M=8.20), and after online and face-to-face class (M=9.80) (χ2=13.48, p=.001).&#x0D; Conclusions The blended learning-based education program that mixes distance learning and simulation in disaster response can not only contribute to improving students' educational satisfaction but also expand their understanding of knowledge. In particular, simulation training is effective in the psychomotor domain. Accordingly, it is possible to increase the effectiveness of education by applying a blended learning-based education program for students majoring in health according to the subjects.
Few-Shot Learning based Anomaly Detection in Security Applications

One of the most important areas of Computer Science is network security. Due to the high volume of data being transferred, the network is now open to different security-related attacks. As a result, it is now of utmost importance to detect network based attacks. In this paper, we propose a simple, adaptable and general framework aiming to detect Anomaly in Security Applications using Relation Network based Few-Shot Learning (RNFSL) model, which is cheaper to compute and needs less data compared to the traditional Machine Learning (ML) and Deep Learning (DL) models that are data hungry. We perform extensive experiments on a publicly available dataset where RNFSL on 1% data resulted in a loss value of 0.032 and also outperformed traditional ML and DL models.
Understanding catastrophic forgetting for adaptive deep learning

Deep learning is still limited in practice tho it has progressed state of the art over the past few years. Current deep learning algorithms are rigid and static once trained and can’t adapt to new data when deployed for inferencing. In this paper, we analyze the catastrophic forgetting phenomenon and show that the EWC algorithm for overcoming the same is not commutative for given tasks. And we also propose that learning to context switch between different sets of weights might overcome catastrophic forgetting.
Emulation Tool Diversity for Future Engineering Education

Engineering academia makes use of emulation and simulation i.e software tools. These software tools should evolve to meet the new demands of provisioning of quality engineering education. This requires additional research consideration and is addressed in the presented research. The research proposes the evolution of simulation and emulation tools and describes a model wherein the intelligence is integrated in future emulation and simulation tools used in engineering education and research. The incorporation of intelligence in this case leads to a notion of emulation and simulation tool diversity. In this case, the diversity implies the choice of simulation and emulation tool that meets the learning preferences of an individual. In addition, the research presents a framework wherein the proposed solution executes its function within the environment of a higher education institution. This is done for the cases of a constrained and non-constrained engineering faculty in a higher education institution.
A Study on the Design of Classes to Discover Cultural Contents in the Campus Town Area

This study proposes a process of developing subjects and conducting classes in connection with university classes to form the cultural industry in the campus town area. Culture-related parts of the campus town project can be a good program for college students to create culture and discover new cultural industries and cultural contents in connection with the local community. This study dealt with the entire process of designing classes to connect these universities and communities and conducting classes based on them. In the course of class design, it was not easy to coordinate the level of education required by universities and the level of education required by the campus town project. However, the results of the students who completed the class according to the class plan were sufficient to achieve the purpose of this study. Students' achievements will be responsible for the net function of expanding and reproducing through government agencies, along with the local community and related academic fields. As a result of this study, it is expected that college students will find and develop local cultural contents through a combination of interest in local culture and creative ideas to achieve positive results in promoting cultural prosperity in the campus town.
Internet of Things in Smart and Intelligent Healthcare Systems

Independent and convenient healthy living is the prime goal of everyone. Unfortunately, there are limitations to this idea due to various factors such as age, illness, and pandemics. Through health-monitoring systems, people can easily and safely monitor their vital health parameters. Modern technologies have enabled people to live a healthy and independent life. Internet of things (IoT)-based health-monitoring systems have evolved to enable healthcare providers and patients to connect and coordinate with each other, allowing patients to monitor their vital health conditions and make informed decisions. IoT technology combined with various other technologies such as blockchain, cloud computing, artificial intelligence, and so on is gradually increasing the grip of smart systems on several other tasks in the healthcare sector. Modern smart healthcare systems are becoming more prevalent due to the emergence of smart home technologies. The healthcare module of the smart home automation system will allow people to receive better care even at home, minimizing the loneliness that often accompanies hospital visits. Also, such systems have facilitated remote healthcare governance with utmost accuracy. Also, in light of COVID-19, the need for such systems is becoming higher. The reality is that such systems are taking the load off medical personnel and providing a safer contact-free environment. Also, when it comes to clerical and administrative tasks, IoT-based smart systems have proved their mettle over the years. Even though such technology is in a stage of rapid growth, the reach is still minimal due to certain challenges that persist when it comes to the implementation side. The future is expected to be controlled by such modular technologies, and thus, extensive research both from the side of academia and industry is expected to revolutionize the technology even more soon.
Can you help me scale? A Systematic Analysis of Scaling Capabilities of Conversational Agents for Digital Services

Today’s servitized and digitalized society has significant implications for the economic system as value creation increasingly shifts towards digital services. Organizations are increasingly relying of artificial intelligence, which enables them to operate with novel technological artifacts like conversational agents (CA). These human-like AI-driven artifacts open new possibilities and channels for service providers to scale up their business even further and to create a business value or even to make processes more efficient. While we witness the results of that each day, we do not fully understand how such CAs can contribute to scaling up a business. Therefore, we conduct an analysis of service research and popular CAs to determine the status quo and highlight opportunities for scaling services with CAs. We contribute to theory by clarifying the meaning of scalability in combination with CAs and support practitioners by providing an overview about how organizations successfully scale their CAs.
Data privacy with heuristic anonymisation

Abundance of data makes privacy more vulnerable than ever as it increases the attackers' ability to infer confidential data from multiple data sources. Anonymisation protects data privacy by ensuring that critical data are non-unique to any individual so that we can conceal the individual's identity. Existing techniques aim to minimally alter the original data so that either the anonymised data or its analytical results (e.g., classification) will not disclose certain privacy. Our research aims both. This paper presents HeuristicMin, an anonymisation approach that applies generalisations to satisfy user-specified anonymity requirements while maximising data retention (for analysis purposes). Unlike others, by exploiting monotonicity property of generalisation and simple heuristics for pruning, HeuristicMin provides an efficient exhaustive search for optimal generalised data. The paper articulates different meanings of optimality in anonymisation and compares HeuristicMin with well-known approaches analytically and empirically. HeuristicMin produces competitive results on the classification obtained from the anonymised data.
Object Detection in Images by Verifying Corners at Predicted Positions

We propose a new geometric descriptor for objects that can be identified by their corners. The proposed descriptor for an object includes reference triangles and the distances of the object’s corners to these triangles. We propose a point-set representation for objects where only corner points are considered. In detecting a given object in an image, first a triangle of corners in the searched image similar to a triangle in the queried object is found. Next, by using the geometric descriptor for the queried object, positions of predicted corner points in the searched image are calculated and verified. Results in our method are invariant under scaling, rotating, and mirroring of the queried object. It finds all matches including the partial (occluded) matches under certain conditions. The initial test results show that our algorithm is fast and accurate. Its time requirement and accuracy can be controlled by changing several parameters.
Software Defect Prediction using Deep Learning by Correlation Clustering of Testing Metrics

The software industry has made significant efforts in recent years to enhance software quality in businesses. The use of proactively defect prediction in the software will assist programmers and white box testing in detecting issues early, saving time and money. Conventional software defect prediction methods focus on traditional source code metrics such as code complexities, lines of code, and so on. These capabilities, unfortunately, are unable to retrieve the semantics of source code. In this paper, we have presented a novel Correlation Clustering fine-tuned CNN (CCFT-CNN) model based on testing Metrics. CCFT-CNN can predict the regions of source code that contain faults, errors, and bugs. Abstract Syntax Tree (AST) tokens are extracted as testing Metrics vectors from the source code. The correlation among AST testing Metrics is performed and clustered as a more relevant feature vector and fed into Convolutional Neural Network (CNN). Then, to enhance the accuracy of defect prediction, fine-tuning of the CNN model is performed by applying hyperparameters. The result analysis is performed on the PROMISE dataset that contains samples of open-source Java applications such as Camel Dataset, Jedit dataset, Poi dataset, Synapse dataset, Xerces dataset, and Xalan dataset. The result findings show that the CCFT- CNN model increases the average F-measure by 2% when compared to the baseline model.
Writing Strategy: an Analysis of Successful Malahayati English Council Learners in Writing

Writing Strategy: an Analysis Of Successful Malahayati English Council Learners in Writing. The purpose of this study is to discover what kinds of learning strategies are used by successful learners to improve their English writing skills. This study employed an explanatory mixed method. A questionnaire is an instrument used to collect data. The population in this study was Malahayati English Council students enrolled in writing classes, and the sample consisted of students who earned an A (86-100) in the final writing subject. The data was analyzed using a simple statistic and the cumulative frequency distribution method. Based on the result of this study, it was concluded that first, Writing is a continuous process of thinking, organizing, rethinking, and reorganizing. It requires command of vocabulary, spelling, grammar, punctuation, appropriate content, and word selection for the audience, topic, and occasion. A writer must be able to think and express ideas in well-organized sentences with high coherence. Second, successful learners applied the writing strategies those are suggested by the expert. They used those strategies to solve all problems and to get the best results in writing. Third, the strategy employed is to divide the writing process into four stages: prewriting, drafting, revising, and editing
The Evaluation of the Emergency Remote Teaching Process in Higher Education During Pandemic: TRNC Sample

The objective of this study is to define the factors that hinder an effective online education through the opinions of the instructors regarding the transition process to emergency remote education due to the COVID-19 global pandemic as well as to determine the measures that are possible to be taken for the enhancement of the qualities in the field of education. As a qualitative research method, a phenomenological pattern is applied to the study for a detailed version of the teachers’ experiences throughout the study. While composing the study group, the criterion sampling method is applied accordingly. The study group of this work is composed of 13 instructors who are determined from 7 universities which are actively functioning within TRNC. A semi-structured interview form with four questions, observation, and document analysis are used to collect data in order to increase the credibility of this study and to provide a thorough review of the issue. The outcome of this work indicates that problems occurred due to technical substructure inadequacies, thus the instructors encountered problems with the lessons that required application. It is found that the instructors need training for technology utilisation in teaching. It is considered that this work is to contribute to higher education institutions for the measurements to be taken for the betterment of the emergency remote teaching process and to enhance the quality of the teaching.