Reviewerly/exHarmony huggingface dataset:
An Application of Information Granules to Detect Anomalies in COVID-19 Reports

The COVID-19 pandemic has affected almost every aspect of life. The patterns of interpersonal contacts, the ways of doing business and the methods of school education have changed. A significant part of worldwide business has migrated to the virtual world, and the global supply chains have been disrupted. On the other hand, this new situation created opportunities for a much faster development of some areas of business and science. For example, the observation and analysis of pandemic data has contributed to the development of new techniques for effective mathematical forecasting. It is worth noting that during a pandemic most political and economic decisions are based on official data on the number of new infections at the country level. Therefore, the quality of this data is very important for making difficult decisions, such as implementing new restrictions. In this study, we will focus on the problem of pandemic data quality and present a novel anomaly detection method based on information granules. In numerical experiments, data from several European countries were compared. The selection of data for analysis was based on the following information: the movement of people between countries, similar quality of medical care and the sanitary standards. An appropriate adaptation of the author’s anomaly detection method based on information granules allowed to identify potential anomalies in daily COVID reports.
A Fast Learning Algorithm for the Multi-layer Neural Network

In this paper, the computational improvement for the scaled Givens rotation-based training algorithms is presented. Application of the scaled rotations boosts the algorithm significantly due to the elimination of the computation of the square root. In a classic variant scaled rotations utilize so-called scale factors — $$\chi $$ . It turns out that the scale factors can be omitted during the computation which boosts the overall algorithm performance even further. This paper gives a mathematical explanation of how to apply the proposed improvement to the scaled variants of the training algorithms. The last section of the paper contains several benchmarks which prove the proposed method to be superior to the classic approach.
OESCPM: An Online Extended Spatial Co-location Pattern Mining System

In spatial data mining, co-location pattern mining is intended to discover the sets of spatial features whose instances occur frequently in nearby geographic areas. Co-location pattern mining is an important task in spatial data mining and has been applied in many applications. Although many spatial co-location pattern mining methods applied to point objects have been proposed, few mining approaches are designed for extended spatial objects in practical scenes. Unlike existing works, we develop OESCPM (Online Extended Spatial Co-location Pattern Mining System) to mine co-location patterns over extended objects. OESCPM decomposes extended instances into cells and counts the number of cells belonging to different features. With simple interaction on OESCPM, users can view co-location patterns over interested extended spatial datasets online.
Convergence of RBF Networks Regression Function Estimates and Classifiers

In the paper convergence of the RBF network regression estimates and classifiers with so-called regular radial kernels is investigated. The parameters of the network are trained by minimizing the empirical risk on the training data. We analyze MISE convergence by utilizing the machine learning theory techniques such as VC dimension and covering numbers and the error bounds involving them. The performance of the normalized RBF network regression estimates is also tested in simulations.
A Study on the Paradigm Shift of Multicultural Education in Mission: Focusing on the WCC’s Understanding of Mission

연구 목적 본 연구의 목적은 다문화 담론의 맥락에서 선교의 개념 변화를 WCC의 공식문서를 중심으로 성찰하는 것이었다. 연구 내용 본 연구는 세 가지의 주제를 탐색하였다. 첫째, 다종교 시대에서의 다문화 선교의 의미와 함께 다문화교육이론을 살펴보았다. 둘째, 선교의 개념이 WCC의 역사를 통해 어떻게 변화되어 왔는지 살펴보았다. 셋째, 본 논의가 한국교회의 다문화 선교교육에 어떤 시사점을 찾을 수 있을지 탐색하였다. 결론 본 연구의 결론은 다음과 같다. 첫째, 교회에서의 다문화 논의는 기독교의 복음 선교와 밀접히 연결되어 있음이 발견되었다. 따라서 한국교회가 다문화 선교교육을 부차적인 교회 활동으로 간주하는 것은 큰 오류이며, 오히려 교회의 본질은 선교에 있다는 인식하에 한국교회는 다문화 선교교육에 더욱 큰 관심을 둘 필요가 있다. 둘째, 다문화 선교는 곧 종교 간의 대화와 긴밀히 연결되어 있음으로, 다문화 선교교육은 상호문화주의 및 종교간교육의 맥락에서 더욱 확대될 필요가 있다. 셋째, 선교의 의미가 에큐메니컬 운동의 전개와 더불어 다양한 의미로 확대되고 있음이 확인되었다. 특히 ‘하나님의 선교’ 개념이 등장한 이래로 그 의미는 가난한 자의 우선적 선택, 주변성의 선교, 그리고 생태환경을 포함한 온 생명의 살림 등 다양한 의미로서 더욱 풍성해지고 있다. 따라서 한국교회의 다문화 선교교육은 이러한 의미들을 모두 포함하는 보다 통전적인 방향으로 나갈 필요가 있다. 넷째, 한국교회는 남북한의 분단과 이주민 및 가나안 신자의 폭발적인 증가 등을 고려하여 환대의 영성에 기반한 다문화 선교교육이 필요하다.PURPOSE The purpose of this study is to reflect the conceptual change of mission in the context of multicultural discourse under WCC’s official documents. CONTENTS To accomplish this, this study explores three different subjects. First, this study observes the significance of multicultural education in mission. Second, it narrates how the concept of mission has changed throughout the history of WCC. Third, it explores how the discourse could be implied in the multicultural education in mission of Korean Church. RESULTS The results of this study are as follows. First, multicultural discourse of church is connected to the mission. Therefore, it is an error for Korean church to regard multicultural education in mission as a secondary activity of church. Second, as multicultural mission is closely related to interreligious dialogue, multicultural education in mission must be expanded in the context of interreligious education. Third, the significance of mission is expanding into different dimensions along with the development of ecumenical activities such as a preferential option for the poor, marginality, recovery of all living things including environment, and more. Fourth, Korean church needs multicultural education in mission based on spirituality of hospitality in consideration of the division of Korean peninsula and rapid growth of immigrants and non-churchgoers.
A Study on Methodology of Catholic Religious Education for Citizenship Education: Focusing on Thomas Groome’s Theory of Education

연구 목적 본 연구의 목적은 토마스 그룸의 교육론을 중심으로 가톨릭 종교교육 방법론을 고찰하여 시민교육을 주제로 하는 학교 종교교육과정에 대해 시사점을 제안하는 데 있다. 연구 내용 첫째, 종교교육 맥락에서 시민과 시민교육, 시민교육의 목표 역량에 대해 고찰한다. 둘째, 가톨릭 종교교육에서 접근할 수 있는 시민교육 주제로서 ‘종교 이해를 통한 상호존중’, ‘문화간 대화 교육’, ‘형제적 인류애’에 대해 고찰한다. 셋째, 토마스 그룸의 교육론과 시민교육에 적용할 수 있는 가톨릭 종교교육 방법론을 고찰한다. 결론 토마스 그룸 교육론으로부터의 주요 시사점은 첫째, 가톨릭 인간론에 기초하여 학습자 삶과의 관련성을 통한 참여 촉진, 성찰로 이끄는 수업 활동, 존중과 도전이 부여되는 수업 환경 조성이다. 둘째, 가톨릭 사회론에 기초하여 교육자가 공동선에 기여하겠다는 인식과 대화를 통한 수업 운영이다. 셋째, 정의에 대한 이해에 기초하여 정의에 대한 교육자의 열정, 질문 그리고 비판적 성찰 활동이다. 넷째. 가톨릭의 보편성에 대한 이해에 기초하여 다양한 의견에 대한 존중과 환영이다. 결국 종교교육에서 시민교육은 인간화 교육과 다르지 않으며, 인간화 교육의 교수법은 학습자를 참여시키기, 주제에 관심을 기울이게 하기, 표현하게 하기, 성찰하게 하기, 접촉하게 하기, 내면화하게 하기, 그리고 결정하게 하기이다. 이와 함께 수업을 실행하면서 인간화 교육의 교수법에 대한 의식적인 노력을 지속하고 헌신하는 것이 필요하다.PURPOSE To suggest implications for school religious curriculum with the theme of civic education based on Thomas Groome's theory of Catholic education. CONTENTS 1. Civics, civic education, and civic education competencies in the context of religious education. 2. ‘Mutual Respect through Understanding of Religion’, 'Educating for Intercultural Dialogue', and ‘Fraternal Love for Humanity’. 3. Thomas Groome's educational theory and Catholic religious education methodology applied to civic education. RESULTS Facilitating learner engagement through reflective classroom activities and a respectful and challenging classroom environment. Commitment to the common good and teaching through dialogue. Encourage enthusiasm, questioning, and critical reflection on justice based on their understanding of justice. Based on the meaning of Catholic, it is to respect and welcome diverse opinions. After all, civic education in religious education is not different from character education, and its teaching method is to engage learners, pay attention to the subject, express, reflect, contact, internalize, and help them make decisions.
Usability Testing Methods and Usability Laboratory Management

Usability testing of software products is of key importance nowadays. In order for usability testing to have the desired effect, the appropriate testing methodology must be chosen. Usability testing can be carried out using qualitative or quantitative methods. A widely used qualitative method today is the heuristic evaluation proposed by Jacob Nielsen. However, there are other testing methods such as cognitive walkthrough or collaborative testing (proposed by Josef Pavlicek and R. Bock). Although heuristic analysis has generally received a lot of attention in the current literature, it is important to put the other methods in the right light. These provide a significantly better view of the user’s passage through the interface under test. The methods better simulate the environment in which the final UI will operate. The user experience (UX) is then significantly better measurable if the participant goes through the test scenario in a cognitive (i.e., mental model-defined) way, rather than by mere heuristic evaluation. A significant milestone is then the cognitive-collaborative passage, where the collaborative element of the evaluators contributes to the evaluation of the solution.
Self Balancing Quad-Copter Using PID Controller

Unmanned Aerial Vehicle (UAV) technology is considered one of the important technologies at present, which is believed to solve many problems in the future, such as delivering small-weighted goods, agriculture, and civil fields. This research paper presents the mathematical model of a quadcopter that was developed as an example of an unmanned aerial vehicle. The MATLAB and Simulink environment create the model simulation and controller design. The present study proposes a cost-effective and easily deployable control system with a comparison that shows the difference between the controller made in this paper and the literature. The mathematical model represented has been linearized, and the transfer function of the brushless DC motors responsible for quadcopter motion is derived using system identification techniques to construct the attitude controller. The controller is evaluated, and the simulation results are described. It presents the design and development of a remote-controlled quadcopter utilizing a PID (Proportional Integral Derivative) cost-effective controller implemented using an Arduino UNO board.
Theory-Driven Statistics for the Digital Humanities: Presenting Pitfalls and a Practical Guide by the Example of the Reformation

The Digital Humanities face the problem of multiple hypothesis testing: Evermore hypotheses are tested until a desired pattern has been found. This practice is prone to mistaking random patterns for real ones. Instead, we should reduce the number of hypothesis tests to only test meaningful ones. We address this problem by using theory to generate hypotheses for statistical models. We illustrate our approach with the example of the European Reformation, where we test a theory on the role of opinion leaders for the adoption of Protestantism with a logistic regression model. Given our specific setting, including choice of data and operationalisation of variables, we do not find enough evidence to claim that opinion leaders contributed via personal visits and letters to the adoption of Protestantism. To falsify or to support a theory, it has to be tested in different settings. Our presented approach helps the Digital Humanities bridge the gap between the qualitative and quantitative camp, advance understanding of structures resulting from human activity, and increase scientific credibility.
Value-semantic phenomena and paradoxes of optimistically oriented pedagogy: An existential-humanistic approach

Introduction . The article discusses project activities in the development of a professional development program for advanced training of a computer science teacher. Purpose setting . The purpose of the article is to study the experience of organizing project activities in the system of professional development of teachers. In the process of implementing project activities, the project competence of teachers is formed. Methodology and methods of the study . The methodological basis of the research is project activity as a joint educational creative activity of the course participants, having a common goal and agreed methods to achieve it. Research methodology is the analysis of literature and contemporary views on the design and development of an additional professional development program in the educational process and the impact of ICT on the involvement of students in the educational process. Professional development is redirected in an online format. For this, it is necessary to create professional development programs on digital educational platforms. Various information systems are convenient for all participants of the educational process. Results . The empirical research was conducted by research methods (questionnaire, discussion, interview). In this article, we focus on the understanding of pedagogical situations and the acquisition of experience by teachers in the pedagogical process when taking advanced training courses in online format. Our article discusses project activities and the use of ICT in the educational process. Conclusions . It is concluded that project activity occupies an important place in the system of professional development. Its implementation allows you to radically change the entire learning process, make it active, creative and independent. Continuing education is characterized by flexibility in time, place, content, learning technologies and many other features that contribute to the best training of teachers in modern conditions. Project activity is currently one of the most effective ways to train employees of educational organizations.
Microsoft Teams Supports Authentic Assessment of Learning

In computer technology, statistics, and business courses that I have taught over the past decade, students have worked in groups/teams. I assumed students communicated with teammates within the Learning Management System (LMS). In 2017, I was surprised to discover students were using GroupMe. To help me understand the attraction, I signed up for a GroupMe account. I was underwhelmed with its purpose but understood the ease of the application. I was also learning more about Slack, thinking it was a much better tool for communication and collaboration, and realizing that communicating within the LMS was not ideal, even awkward, and not real world. However, Slack was not readily available for student use, so when our institution introduced Microsoft Teams in 2020, I began exploring using Microsoft Teams for teamwork/projects because it is a real-world tool that students will likely use/encounter after college. I have been using Microsoft Teams consistently since Fall 2020. Using Microsoft Teams moves students from conceptual to applicable knowledge related to teamwork, communication, and even leadership skills. Microsoft Teams can supplement any content/course project. Requiring students to use Microsoft Teams as it would be used in the workplace allows teams to choose when they meet to collaborate, manage their “channel,” and ultimately create their project. All the work leading up to the final deliverable is archived in one space accessible by instructor (manager) as would be in the real world. The assessment(s) takes on a level of authenticity that is missing with a traditional LMS. In this reflective essay, I will show how Microsoft Teams supports authentic assessment while engaging students in a real-world technology that adds to their post-undergraduate toolkit and future success.
A statistical evaluation of the preprocessing medical images impact on a deep learning network’s performance

The aim of this paper is to explore the efficiency of preprocessing medical images before applying a deep learning algorithm to classify the data. The study uses a statistical framework that establishes the fact that depending on the dataset used, image preprocessing indeed decreases the computational time, without having a dropdown in performance. The dataset used in this study regard colon cancer, lung cancer, and fetal brain ultrasound scans. The study proposes a statistical performance that studies the performances of the ResNet50 deep learning network in different preprocessing scenarios.
Automatic Summarization of Critical Threat Intelligence Using Transfer Learning

The advancement of hacking techniques has extended the sophistication of cyberattacks. Facing evolving cyberattacks, security officers need to acquire information about cyberattacks to gain visibility into the fast-evolving threat landscape. This research proposes a novel threat intelligence summarization system that extracts critical information and produces a summary report. This study combines BERT and BiLSTM and proposes a hybrid word embedding model to capture the critical information in the corpus. The evaluation results show that the proposed system could summarize reports effectively.
Prediction of Middle-Aged Unhealthy Facial Skin Using VGG19 and Support Vector Machine Models

In human communication, the resource of primary information can be read from a human’s face. Health problems occur in line with age, and one way to detect health issues is through changes in facial skin. People typically pay less attention to initial facial skin changes, even though the changes might be linked to a particular disease, such as Lupus. Treatment for Lupus takes time and is costly. Cutting-edge technology and Artificial Intelligence (AI) bring a new horizon to the medical field where a disease can be detected or predicted early. This paper presents the classification of training images for the early detection of middle-aged unhealthy facial skin. The Multi-Task Cascaded Convolutional Neural Networks (MTCNN) technique provides the face boundary box and the performance of face detection uses VGG19 architecture. The images dataset was divided into data training and data testing with a ratio of 80%: 20%, respectively, and the healthy or unhealthy face image was determined with a machine learning approach. The experimental results showed that the accuracy of the proposed Support Vector Machine classifier model was 92.2%.
Adiabatic Quantum Computation for Cyber Attack and Defense Strategies

Cyber security has become increasingly important in today’s world widely connected by the Internet. Many important problems in the cyber security domain can be framed as optimization problems. Recently there has been much progress in applying quantum annealing to solve optimization problems in various application domains. In this article quantum annealing is shown to solve hard problems in cyber security from the perspectives of both attack and defense. In the process a few new techniques for QUBO construction are also illustrated and can potentially be used to solve problems in other application domains.
Anti-screenshot Watermarking Algorithm About Archives Image Based on Deep Learning Model

In recent years, there are an increasing number of incidents in which archives images have been ripped. Leak tracking is possible by adding an anti-screenshot digital watermark to an archive image. However, because an archives image's texture is single, there is a problem of low detection rate of watermark with the existing algorithm. So in order to improve the robustness of archives image anti-screenshot, we propose an anti-screenshot deep learning model (DLM): ScreenNet. It aims to enhance the background and enrich the texture with style transfer. Firstly, a preprocessing process based on style transfer is added before the archives image is input into the encoder. Secondly, the ripped images usually have moiré, so we generate a database of ripped archives images with moiré by means of a moiré network. Lastly, by improving the Stagstamp model, the watermark information is encoded/decoded through the improved ScreenNet model using the ripped archives image database as the noise layer. The experiment proves that the algorithm is able to resist anti-screenshot attacks and achieve the ability to detect watermark information to leak trace of ripped images.
Reshaping Today’s Education with Social Media

Abstract. The government's move to suppress the spread of the covid-19 virus in Indonesia by limiting the meeting of a number of people has an impact on many lines of life including higher education institutions. Starting from 2020, the learning system undergoes the most fundamental change in the history of the transformation of the Education system. Through government program policies, learning that was initially held face-to-face then replaced with virtual meetings, the transformation of learning media is also massive, where there is a discussion of online learning media that support the implementation of virtual lectures becomes the main prerequisite. The development of communication technology plays an important role to be an option in an effort to provide learning media that can be utilized. Social media that appeared to support the learning management system. Using social media in order to trigger the quality of student learning by utilizing all the ease of communicating and sharing information owned by the media for the education or learning process becomes more effective and efficient and accelerates information as a whole. The method of the research is Mixed method research, were to combine the quantitative and qualitative methods used in this study with the aim of identifying students' digital literacy competencies related to learning media by filling out questionnaires to a total of 120 students. All students who filled out the questionnaire were students who had participated in online learning and practicums on campus. and also conducting in-depth interviews on students to find out their opinions regarding social media to support their learning process. The collected data was analyzed using SPSS and analyzed using descriptive methods.
Homomorphic Encryption-Friendly Privacy-Preserving Partitioning Algorithm for Differential Privacy

This study addresses the privacy problems of data provided by multiple data owners for range query processing on the cloud. Although existing methods preserve data privacy against the cloud or data analysts who receive query responses, protecting data privacy from both remains a challenge. Combining differential privacy (DP) and homomorphic encryption (HE) to construct differentially private outputs over encrypted raw data is a promising way to avoid data privacy leakage to the cloud with encryption while protecting data privacy from data analysts with DP. Although DP adopts several partitioning algorithms to achieve small noise, partitioning cannot be executed once the data is encrypted. In this paper, we propose a new HE-friendly privacy-preserving partitioning algorithm satisfying DP. Although HE enables operations over encrypted data, the execution time of such primitive arithmetic operations is approximately 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">9</sup> times slower than without encryption. Therefore, it is mandatory to reduce the calculation complexity. The proposed partitioning method, which only compares every next-to-each-other data to merge, achieves $\mathcal{O}\left( n \right)$ calculation complexity, where n is the domain size of the input histograms, whereas the greedy algorithm requires $\mathcal{O}\left( {{2^n}} \right)$. The experimental evaluation showed that the execution time of the proposed algorithm for 4,096-domain-size data was approximately 4 h and 35 min, which was acceptable when creating a data summary for the range query processing system and not targeting on-the-fly adoption of DP. Additionally, we confirmed that the accuracy of the proposed algorithm was equivalent to that of the state-of-the-art partitioning algorithm.