Reviewerly/exHarmony huggingface dataset:
Answering Topical Information Needs Using Neural Entity-Oriented Information Retrieval and Extraction

In the modern world, search engines are an integral part of human lives. The field of Information Retrieval (IR) is concerned with finding material (usually documents) of an unstructured nature (usually text) that satisfies an information need (query) from within large collections (usually stored on computers). The search engine then displays a ranked list of results relevant to our query. Traditional document retrieval algorithms match a query to a document using the overlap of words in both. However, the last decade has seen the focus shifting to leveraging the rich semantic information available in the form of entities. Entities are uniquely identifiable objects or things such as places, events, diseases, etc. that exist in the real or fictional world. Entity-oriented search systems leverage the semantic information associated with entities (e.g., names, types, etc.) to better match documents to queries. Web search engines would provide better search results if they understand the meaning of a query. This dissertation advances the state-of-the-art in IR by developing novel algorithms that understand text (query, document, question, sentence, etc.) at the semantic level. To this end, this dissertation aims to understand the fine-grained meaning of entities from the context in which the entities have been mentioned, for example, "oysters" in the context of food versus ecosystems. Further, this dissertation aims to automatically learn (vector) representations of entities that incorporate this fine-grained knowledge and knowledge about the query. This dissertation refines the automatic understanding of text passages using deep learning, a modern artificial intelligence paradigm. This dissertation utilizes the semantic information extracted from entities to retrieve materials (text and entities) relevant to a query. The interplay between text and entities in the text is studied by addressing three related prediction problems: (1) Identify entities that are relevant for the query, (2) Understand an entity's meaning in the context of the query, and (3) Identify text passages that elaborate the connection between the query and an entity. The research presented in this dissertation may be integrated into a larger system designed for answering complex topical queries such as dark chocolate health benefits which require the search engine to automatically understand the connections between the query and the relevant material, thus transforming the search engine into an answering engine. Awarded by: University of New Hampshire, Durham, USA on 1 September 2022. Supervised by: Laura Dietz. Available at: https://scholars.unh.edu/dissertation/2714/.
Report on the 16th Round of NII Testbeds and Community for Information Access Research (NTCIR-16)

This is a report on the NTCIR-16 conference held online in June 2022. NTCIR is a series of parallel and collective evaluation efforts designed to enhance research on diverse information access technologies, including, but not limited to, cross-language and multimedia information access, question-answering, text mining, and summarization. 53 active research groups from 20 countries/regions participated in one or more of the 10 different tasks in NTCIR-16. This report introduces the highlights of the conference and describes the scope and task designs of 10 tasks organized at NTCIR-16. Date: 14--17 June, 2022. Website: https://research.nii.ac.jp/ntcir/ntcir-16/.
Report on the 6th International and Interdisciplinary Perspectives on Children &amp; Recommender and Information Retrieval Systems (KidRec 2022) Workshop at ACM IDC 2022

We summarize the outcomes of the most recent KidRec Workshop (June 2022), which was held for the first time in a hybrid, synchronous mode. The focus of the workshop was to understand the impact of the COVID-19 era on the design, development, and evaluation of Information Retrieval Systems for which children are the primary stakeholders. Date: June 27, 2022. Website: https://kidrec.github.io/.
The Ethics of Artificial Intelligence: How Educators Can Adapt to Close the Bias Gap

With the rapid growth of artificial intelligence technologies, it becomes necessary to interrogate the way in which our society’s prejudices translate into the machine learning systems we are becoming increasingly dependent upon. The first part of this paper explores the lack of women and people of color in technology fields and points out the danger of this through the material implications of having biased machine learning algorithms in an increasingly digitized world. The second part argues that this bias is a result of our symbolic associations with artificial intelligence. To do so, it analyzes the racialized and gendered traits of robots appearing in popular culture and of household devices, as well as the ways in which rationality is fetishized and deemed as both white and masculine. Finally, the paper concludes with a proposed starting point for a shift in education that would address the exclusion of marginalized groups using Paulo Freire’s Pedagogy of the Oppressed as a model. This paper should be used as a guide for educators who hope to make their field more accessible. While employment of these strategies cannot eliminate the deeply socially engrained inequities that contribute to bias in artificial intelligence, the goal is for it to encourage a positive shift in our collective cultural character that is a pre-requisite to any form of structural change.
Applying Face Recognition on Smart Family Album Player

The past decade has seen large advances in technology allowing for technologies only available to governments and large scale companies to now be accessed by anyone with a phone. One such example is the access to facial recognition software with apps like Snapchat, Instagram, and TikTok identifying faces and applying filters based on the user’s request. These apps make it as simple as swiping a screen and immediately the user’s face is recognized and an additional layer is added on top. Facial recognition has capabilities beyond applying filters as it allows people to quickly sort and group images based on a person. Moreover, facial recognition has the flexibility of being added to any previous programming project from beforehand. Therefore, to give users an easy method of sorting images, we intend to create a simple graphical-user interface (GUI) that can sort through hundreds of images without human input. In this poster, we present the design and function of a GUI that would work on many different devices with little set-up. At this moment, the GUI is programmed with $\text{C}++$ and the facial recognition engine is programmed with Python. The graphical-user interface was already programmed in $\text{C}++$ on a Windows device from a previous project before being used for this one. Python was the chosen programming language for facial recognition software as there are large sources of troubleshooting information and it can be implemented in a C++ program without much difficulty. The program as a whole would work through the user specifying a folder of images they have and the program would go through each file in the folder, including files inside other folders, and cluster them based on the people identified. For instance, the folder chosen contains photos of a family of a dad, mom, and a child. From there, each photo is clustered together under the respective person. The libraries used in both the GUI and facial recognition program will be shown and explained on the poster. At the current stage, the facial recognition software is mostly complete as it is able to identify and recognize faces if given training data. In short, the program works by analyzing the commonalities of the training images and determining if the other images match with the data. Currently, the software is being worked on to allow for unsupervised machine learning, meaning the user doesn’t need to give training data from beforehand. Furthermore, integrating the current Python program into the GUI is being looked at. With this project, we hope that any user will find it simple and efficient to sort their memorable family photos without spending any of their own precious time.
Facial Recognition for Deepfake Detection

Facial recognition is a tool utilized by social media companies to assist in tagging people in photos. Facial recognition’s ability to detect modified images is useful for these companies to prevent privacy violations, or even online impersonation. For example, deepfakes are very realistic pictures and/or videos that are created by pasting a face onto someone else’s body, which are of concern to social media companies. Deepfakes are very dangerous media that could be used to hijack someone else’s identity for malicious purposes. In order to prevent this, facial recognition must be accurate in recognizing modified faces. By using the Python facial recognition library, we researched if facial recognition can be used to recognize when an image has been modified. To start off, we first checked if facial recognition can identify an alternate photo of someone, like if they are in a different position, wearing sunglasses, or of a different age. We did this by creating a folder of known celebrity faces, and an unknown folder with the alternate photos. Once we could do it manually, we automated the code to cycle through a folder of unknown images and compare each of them to a known target image, and count how many times a match was found in the folder. We are ultimately trying to use this library to check the accuracy of facial recognition in recognizing original and modified faces. Understanding the current effectiveness of facial recognition technologies against modified faces will enable us to improve these technologies, and in turn allow us to defend better against deepfakes, and even greater threats against privacy and security.
Coding Classical Logic Gates on the D-Wave Quantum Annealer

The D-Wave Quantum Annealer is a machine that uses qubits in place of bits since they have special behaviors desirable for solving optimization problems. These machines are not able to natively replicate logical gates (AND gate). Logical gates process binary information in order to output processed information in binary too. Without the basic function of these gates, larger circuits, such as a multiplication circuit, are impossible to replicate. This project was conducted in order to build and write a program that would allow the quantum annealer to replicate the behavior of classical logic gates. Given a certain input, the quantum computer should be able to realize the correct outputs based on the behavior of the gate that is being encoded. The technical report “Boosting Integer Factoring Performance via Quantum Annealing Offsets” was used in order to formulate a binary quadratic model which replicates the behavior of the AND gate, and the half-and full-adder circuits. These models use the Ising penalty model, where constraints are set in order to achieve the wanted behavior; these models are encoded using binary, or in this case, the Ising model. This project took place over the course of 5 months, under the mentorship of Professor Terrill Frantz and Alex Khan from Harrisburg University. In the report, the binary formulation is used to encode the constraints. In practice, it is more practical to use the Ising formulation, which encodes 0 as -1, and 1 as 1. In this binary quadratic model, two dictionaries are required as inputs: the linear dictionary (for the values of qubits) and the quadratic dictionary (for the connections between these qubits). These values were taken from the charts and graphics from the “Boosting” report and then implemented in a Python program. The results showed inputs and outputs that correspond with the expected values from an AND gate, and the half and full-adders. The results of the project were posted in full detail in an article on Medium.com (https://tinyurl.com/yj55fc7m). The program successfully replicates the AND gate and other small circuits (the half and full-adders). Continuing on the success in encoding logical gates (such as the AND gate), more complex and useful circuits can be created to solve problems never done before on a quantum annealer.
Learning Performance of Real-Time Online Classes Using PBL for Clothing and Textiles Majors in College

최근 코로나19 사태로 인해 온라인 매체나 수업 도구의 활용이 점차 증가하고 특히 고등 교육에서의 전반적인 교육 방향이강의 중심형 수업에서 활동적이고 학생 중심적인 학습이 가능한 수업으로 이동하면서 학습자의 적극적⋅능동적인 참여를 유도할수 있는 수업의 전략적 요소로서 온라인 학습 환경의 제공이나 다양한 학습자 주도형 교수⋅학습법이 주목받고 있다. 이에본 연구는 문제 중심 학습법(PBL)을 적용한 실시간 온라인 수업에서 학습자의 주요 학습효과 변인인 자기주도적 학습 능력, 협력학습 능력, 문제해결 능력이 궁극적으로 학습자가 인지하는 학업성취도에 영향을 미치는지 검증하였다. 이를 위해 충청도에소재한 S대학교의 의류학 전공 수업 중 3학년 35명의 재학생을 대상으로 이론과 실습이 병행되는 ‘패션마케팅과 머천다이징’ 교과목에 4주 간에 걸쳐 온라인 PBL을 적용하였으며 학습자는 온라인 실시간 강의를 수강 후, 동료 학습자와 소집단을 구성하여주별 학습 내용과 연관된 문제를 해결하는 활동을 수행하였다. 학습자의 원활한 문제해결을 위해 교수자는 과제수행서와 다양한학습 도구를 제공하였고 학습자는 소집단별로 Zoom 소회의실에서 집단 토의 활동을 통해 정보의 공유와 분석, 해결안 도출, 결과물 정리를 하였다. 실시간 온라인으로 진행된 PBL 수업의 학습 효과를 알아보기 위해 학습자의 학업성취도를 종속 변인으로, 학습자의 자기주도적 학습 능력, 협력학습 능력, 문제해결 능력을 예측 변인으로 설정하여 학업성취도에 영향을 미치는 요인을파악하였으며 그 결과, 자기주도적 학습 능력, 협력학습 능력, 문제해결 능력, 학업성취도 간에 모두 유의한 정적인 상관관계가나타났고 온라인 PBL 수업을 통한 학업성취도에 협력학습 능력, 자기주도적 학습 능력, 문제해결 능력 순서로 모두 유의한정적 영향을 미치는 것으로 확인되었다. 실시간 온라인 PBL 수업에서 학습자가 자기주도적인 개별 학습은 물론 협력학습을통해 최적의 결과를 도출할수록 전공 지식을 적절하게 확산⋅적용할 수 있으며 학업 성취감을 느낄 수 있음을 알 수 있었다.The aim of this study is to identify the learning performance of online classes using problem-based learning(PBL) for clothing and textiles majors in college with the increased use of online learning tools after the COVID-19 pandemic. In order to achieve this goal, the PBL was developed and applied to the ‘Fashion Marketing and Merchandising’ class conducted in real-time online at University in North Chungcheong Province, Korea for four weeks. After a four-week PBL class, a survey was conducted on 35 students in the ‘Fashion Marketing and Merchandising’ class and the 35 completed questionnaires were used for analysis. The measurement tools of this study were self-directed learning, cooperative learning ability, problem-solving ability, and learning achievement regarded as an important learning effect in PBL class. In addition, students’ self-reflective essays were also analyzed to examine the educational effect of PBL applying online classes. As a result of this study, bivariate correlations among the four variables, students' self-directed learning, cooperative learning ability, problem-solving ability, and learning achievement were significantly positive. Furthermore, the results of multiple regression analysis showed that the three independent variables had significant effects on students’ perceived learning achievement, in the order of cooperative learning ability, self-directed learning, and problem-solving ability. The students’ self-reflective essays indicated that problem-based learning worksheet was helpful for identifying problems, and clarifying what they already and what they need to study more. Based on this study, it could be recommended that online class applying PBL could contribute to the improvement of student's learning performance.
Estimation of Impact of Innovations on the Quality of Tertiary Education

The effects of innovations on the quality of tertiary education have been examined across countries. As an indicator of the quality of tertiary education in the country was taken the number of published articles in basic science and engineering, which were indexed in Web of Science. Econometric models describing the impact of the export and import of intellectual property on the corresponding indicator of the quality of tertiary education for Azerbaijan, Austria, Germany, Russia, Kazakhstan, Iran and Turkey, have been constructed. For these countries, the coefficients of elasticity of the number of articles on export and import of intellectual property are calculated.
Development and Validation of a Student Satisfaction Survey Instrument on Problem-Based Learning in Higher Education

본 연구는 대학에서 문제중심학습(PBL) 수업에 대한 학생 만족도 조사도구를 개발하고 타당화 하는 것을 목적으로 수행되었다. 연구 목적 달성을 위해 PBL 관련 선행연구와 학생 만족도 관련 선행연구를 검토하여 PBL 수업 학생 만족도 조사를 위한 구인과 문항을 도출하고, 전문가 델파이를 거쳐 내용타당도를 검증하였다. 또한 PBL 수업을 수강한 학생들을 대상으로 표본조사, 예비조사, 본조사를 실시함으로써 구성타당도를 검증하여 PBL 만족도 조사 구인과 문항을 최종적으로 도출하였다. 연구 결과, PBL 수업 학생 만족도 조사 구인으로 수업목표 기대수준, 팀 활동, 퍼실리테이팅, 평가, 학습량 및 난이도, 학습성과, 전반적 만족도의 7개의 요인과 33개의 문항을 선정하였다. 본 연구 결과는 PBL 수업의 특성을 반영하여 학생 만족도를 파악할 수 있는 타당화 된 조사도구를 제시한다는 점에서 의의가 있다. PBL 학생 만족도 조사도구는 수업의 개선 활동을 위한 질 관리 측면에서 활용될 수 있으며, 더 나아가 대학에서 변화하는 환경에 맞추어 학습자중심수업을 확대해나감에 있어 정책 방향을 결정하기 위해서도 활용할 수 있다.The purpose of this study was to develop and validate a student satisfaction survey instrument for a problem-based learning (PBL) course in higher education. In order to achieve the research purpose, prior studies related to PBL and student satisfaction were reviewed to derive constructs and items for measuring PBL-course student satisfaction, and content validity was verified through expert Delphi. In addition, by conducting a sample survey, preliminary survey, and main survey on students who took PBL courses, construct validity was verified, and the final constructs and items for the PBL satisfaction survey were selected. As a result of the study, seven constructs (course goal expectation level, team activity, facilitation, evaluation, learning quantity and difficulty, learning performance, and overall satisfaction) and 33 survey items were derived. The contribution of this study is that it presents a valid instrument to measure learner satisfaction for PBL courses. The survey items reflect the main characteristics of PBL. Thus, these survey items can be used to deal with quality management for PBL courses, and furthermore, they can be used to determine policy directions when expanding learner-centered courses in line with the changing environment in higher education.
The Relationship Between Peers' Learning Attitudes and Academic Achievement in Online Classes in Response to COVID-19

코로나-19에 대응하여 비대면 교육이 확대됨에 따라 학생들의 부정적 수업태도와 학력 격차 및 저하에 대한 우려가 증가하고 있다. 이에 본 연구는 또래의 영향력에 주목하여 비대면 수업에서 학생과 또래의 수업태도 변화가 학력 격차 및 저하 현상을 유발하는 메커니즘이 될 수 있다는 관점을 견지하며, 코로나-19 전후 수업태도의 변화와 학업성취와의 관계를 분석하였다. 먼저 코로나-19 이후 교내 또래의 수업태도와 학생 의 수업태도 및 학업성취도 간의 관계 변화를 실증적으로 검토하였으며, 학생의 성적에 따라 교내 또래의 수업태도 영향력이 차별적인가를 확인하였다. 분석을 위해 부산교육종단연구(Busan Educational Longitudinal Survey, BELS)의 1-6차 자료를 활용하였으며, 코로나-19의 영향을 받은 처치집단과 영향을 받지 않은 통제집단의 동질성을 확보하기 위해 경향점수 매칭 후 영과잉 순서형 프로빗(Zero-inflated ordered probit)모형과 삼중차분(Difference-in-Difference–in-Differences) 모형을 적용하였다. 분석결과, 비대면 수업은 학생들의 직접적 수업 방해행위(떠들기, 말과 행동으로 방해하기)와는 유의한 부적 관계가 있으며, 간접적 수업 방해행위(다른 숙제하기)와는 유의한 정적 관계가 있는 것으로 나타났다. 또한, 또래의 직접적 방해행위는 학생 의 직접적 방해행위(떠들기)와 정적 상관이 있었으며, 또래의 간접적 방해행위는 학생 의 직접적 방해해위(떠들기)와 부적 상관이 있었다. 마지막으로, 비대면 수업에서 또래의 간접적 수업 방해행위는 학생 의 학업성 취도와 유의한 부적 상관이 있었으며, 이러한 경향은 학업성취도가 높은 집단에서만 뚜렷하게 나타났다. 이를 바탕으로 본 연구는 향후 디지털 전환 교육에서 수업의 효과성을 제고하기 위한 정책적 시사점을 도출하였다.As online learning expands in response to COVID-19, concerns about students' negative learning attitudes in online classes and educational gaps are increasing. Therefore, this study focuses on the influence of peers on changes in students' learning attitudes in online classes and academic achievement, which can be a mechanism that caused educational gaps during COVID-19. First, we empirically examined the change in the relationship between the learning attitudes of peers in school and individual students' learning attitudes and academic achievement. We also verified whether the influence of learning attitudes of peers in school differs depending on students' achievement levels. For the analysis, we used the first through sixth wave data of the Busan Educational Longitudinal Survey (BELS) and exploited propensity score matching methods to ensure homogeneity between the treatment and control groups. Afterward, the zero-inflated ordered probit model and the difference-in-difference-in-differences model were applied. We found that shifting face-to-face classes to online classes had a significant negative relationship with students' direct disruptive behavior (talking, disrupting with words and actions) and a significant positive relationship with indirect class disruptive behavior (doing other homework). Also, in online classes, peers' direct disruptive behavior in school was positively correlated with students' direct disruptive behavior (making noises), and peers' indirect disruptive behavior in school was negatively correlated with students' direct disruptive behavior (making noises). Finally, peers' direct disruptive behavior in school significantly negatively correlated with students' academic achievement, but this tendency was evident only in high academic achievers. Based on this, this study derived policy implications for improving the effectiveness of classes in digital conversion education in the future.
Redevelopment and Validation of Online and Offline Instructional Analysis and Consulting Tools

본 연구는 수업의 질적 개선을 목적으로 활용하는 수업분석 및 컨설팅 도구를 재개발하고 타당화하는데 있다. 이를 위해 기존의 수업분석 도구(조 벽(2012))를 기초로 검토, 분석하고, 이를 개선하기 위하여 Delphi 조사방법을 사용하였다. 교내·외 교수자와 수업전문가를 대상으로 약 6개월 동안 3차에 걸친 내용타당도 검토 및 자문과정을 거쳐 수업분석 및 컨설팅 도구를 재개발하고 타당화 하는 과정을 거쳤다. 연구결과, 수업분석 및 컨설팅에 필요한 온/오프라인 수업용 수업자가진단 도구, 수업설문지, 수업계획서 분석 도구, 수업분석 및 컨설팅 도구, 수업분석 평정표를 재개발하였다. 본 연구의 과정과 결과를 기초로 수업의 목적과 특성, 그리고 수업환경에 적합한 수업분석 및 컨설팅 도구를 마련하여 활용하는데 도움이 되길 기대한다.The purpose of this study is to redevelop and validate instructional analysis and consulting tools used for improving the quality of instruction. To do this, the existing instructional analysis tools were reviewed and analyzed, and in order to improve them, instructional analysis and consulting through three content validity reviews and consultations with on-campus instructors and off-campus instructional experts was conducted using the Delphi survey method. As a result of this study, an online and offline instructional self-diagnosis tool, an instructional questionnaire, a syllabus analysis tool, an instructional analysis (consulting) tool, and an instructional analysis rating table have been redeveloped. We expect that this will be helpful in preparing and utilizing instructional analysis and consulting tools suitable for the purpose, characteristics, and environment of instruction based on the process and the results of this study.
Qualitative Data, Quantitative Data, Gradation Scales and Initial Structures of Statistical Data

This article demonstrates how qualitative data, quantitative data, grading scales and initial structures of statistical data can be organised in a relational way in order to understand the data as a whole, which can overcome the duality that has so far existed between the qualitative and quantitative aspects of science, This allows in the first instance the overcoming of the duality in the interpretation of research results, as well as the development of a complex statistical disciplinary model, which goes beyond the ultra-specialised conception of statistics within the disciplines of scientific knowledge.
New Year’s Prospects for the Korean Economy (2023)

At the outset of 2022, the South Korean economy sought to start anew, aiming to finally surmount the hurdles of COVID-19. Unfortunately, however, the economy is now confronted with an unprecedented level of uncertainty, as one unforeseen risk after another emerged over the course of 2022. These risks include the Russian invasion of Ukraine, the abrupt turn in US monetary policy, China’s only recently-abandoned zero-COVID-19 policy and draconian lockdowns, the intensifying conflict over economic hegemony between the world’s great powers, and the growing financial insecurity of emerging economies. The Korean economy has thus been seeing clear signs of slowdown since late 2022, especially in the manufacturing sector. The extreme difficulties posed by external factors compound the many domestic problems Korea has yet to tackle, and in addressing both the Korean economy is now confronting a distinct new challenge. This analytical brief summarizes the challenges ahead for Korea and the world in 2023.