Reviewerly/exHarmony huggingface dataset:
A 3D Mathematical Breast Texture Model With Parameters Automatically Inferred From Clinical Breast CT Images

A numerical realistic 3D anthropomorphic breast model is useful for evaluating breast imaging applications. A method is proposed to model small and medium-scale fibroglandular and intra-glandular adipose tissues observed in the center part of clinical breast CT images. The method builds upon a previously proposed model formulated as stochastic geometric processes with mathematically tractable parameters. In this work, the medium-scale parameters were automatically and objectively inferred from breast CT images. We hypothesized that a set of random ellipsoids exhibiting cluster interaction is representative to model the medium-scale intra-glandular adipose compartments. The ellipsoids were reconstructed using a multiple birth, death and shift algorithm. Then, a Matérn cluster process was used to fit the reconstructed ellipsoid centers. Finally, distributions of the ellipsoid shapes and orientations were estimated using maximum likelihood estimators. Feasibility was demonstrated on 16 volumes of interests (VOI). To assess the realism of the 3D breast texture model, β and LFE metrics computed in simulated projection images of simulated texture realizations and clinical images were compared. Visual realism was illustrated. For 12 out of 16 VOIs, our hypothesis on clustering interaction process is confirmed. The average β values from simulated texture images (3.7 to 4.2) of the 12 different VOIs are higher than the average β value from 2D clinical images (2.87). LFE of simulated texture images and clinical mammograms are similar. Compared to our previous model, whereby simulation parameters were based upon empirical observations, our inference method substantially augments the ability to generate textures with higher visual realism and larger morphological variety.
A Study on Educational and Cultural Contents Revitalization of Sejong City Public Museum

The purpose of this study is to find a method for the Sejong City Public Museum to contribute to the development of local culture and the promotion of lifelong education. It can be said that the difference from existing research is that it focuses on the “Ordinance”, which is an institutional device for this purpose, and compares and analyzes it with the museum management ordinances of other local governments. As a result of the survey, out of the 23 local government's museum operation ordinances, five ordinances were the ordinances in which the purpose of the ordinance was to describe the development of local culture and the promotion of cultural rights of citizens. The Sejong City Ordinance did not include the purpose of such ordinances. And there are six ordinances with content on museum education, including Sejong City. Looking at the contents, only the contents related to most operation, registration fee, and recruitment of instructors are described. Therefore, in order for the public museum of Sejong City to lead the role of the regional cultural education institution, it is necessary to supplement the ordinance that becomes its institutional device. This re- search proposes that the “Sejong City Municipal Museum Operation Ordinance” should include content that enhances the historical value and self-esteem of the region, such as the development of regional culture and the promotion of citizens' cultural rights. In addition, it is proposed to include education contents that utilizes local culture and content that can form a committee in museum education.
A Study on the Design Principles of On-Off Blended Learning for Music practical Education

본 연구는 온·오프라인 혼합형 학습을 활용하여 온라인 음악 실기수업에 대한 학습자의 부정적인 인식을 개선하고 음악 실기 교수자가 어떠한 요소에 주목하여 온·오프라인 혼합형 음악 실기수업 설계원리를 개발할 것인가에 대한 목적으로 실행하였다. 이를 위해 설계·개발 연구방법을 적용하여 선행문헌 고찰과 현장 전문가 면담을 통해 음악 실기교육을 위한 온·오프라인 혼합형 수업의 초기 설계원리를 도출하였다. 또한, 내적 타당화를 위해 총 20명의 교육전문가를 대상으로 세 차례의 전문가 델파이를 실시하였고, 외적 타당화는 교수자 1명과 학습자 10명이 본 연구에 참여하였다. 그 결과 음악 실기교육을 위한 온·오프라인 혼합형 수업을 위한 최종 설계원리로 총 17개의 원리와 34개의 상세지침이 개발되었다. 개발된 설계원리를 적용한 결과, 온·오프라인 혼합형 수업은 실기실습 활동이 주를 이루고 있는 음악전공자에게 효과적인 수업의 방안이 될 수 있음을 확인하였다.This study was conducted to improve learners' negative perception of online and offline Blended music classes by using online and offline Blended learning, and to develop design principles for online and offline Blended music classes by paying attention to certain factors. To this end, the initial design principles of online and offline Blended classes for music practical education were derived through prior literature review and field expert interviews by applying design and development research methods. In addition, this study conducted internal verification on a total of 20 educational experts with three expert Delphis, and one instructor and 10 learners were participated for external verification. As a result, a total of 17 principles and 34 detailed guidelines were developed as the final design principles for online and offline Blended classes for music practice education. As a result of applying the developed design principles, it was confirmed that online and offline Blended classes could be an effective teaching plan for music majors who mainly engage in practical activities.
A Study on Peacebuilding through Music

이 연구는 분쟁과 갈등을 겪은 지역의 평화구축에 있어 음악이 어떻게 적용될 수 있고 어떤 역할을 할 수 있는지에 대한 가능성을 제안한다. 이를 위하여 평화와 평화구축에 대한 개념과 정의를 검토하고, 평화의 문화 조성에 기여하는 예술에 대해 알아보며, 비폭력적인 기제로서의 음악의 가치와 역할 및 음악을 통한 평화구축에 있어 중요한 개념을 탐색하였다. 또한, 이스라엘과 아랍의 청년들로 구성된 서동시집 오케스트라(West-Eastern Divan Orchestra), 보스니아 헤르체고비나의 폰타니마 종교 간 합창단(Pontanima Inter-Religion Choir)과 2018년 한반도의 남북 예술단 교류 공연의 사례를 고찰하여 이들의 음악 활동의 의의를 평화구축 관점에서 논하였다. 연구결과, 음악은 평화구축 과정에 있어 관계 형성의 공간을 창출하고, 대화, 치유, 화해, 연대를 위한 역할을 할 수 있으며, 평화로운 공존의 모습을 은유적으로 형상화할 수 있다는 사실이 일부 도출되었다. 이 연구를 통하여 음악을 통한 평화구축에 대한 다양한 시각이 양산되어 음악학, 음악치료학, 예술학, 평화학, 사회학, 교육학 등 다양한 학문 분야에서의 융·복합적 접근과 연구가 촉발되기를 기대한다.This study proposes the possibility of how music can be applied and what role it can play in peacebuilding in regions that have experienced strife and conflict. To this end, the concept and definition of peace and peacebuilding were reviewed and arts that could contribute to the creation of a culture of peace were studied and the value and role of music as a nonviolent mechanism and important concepts in peacebuilding through music were explored. In addition, cases of the West-Eastern Divan Orchestra consists of Israeli and Arab youth, the Pontanima Inter-Religion Choir of Bosnia and Herzegovina and Inter-Korean Art Group Exchange Performances on the Korean Peninsula in 2018 were reviewed to discuss the significance of their musical activities from the perspective of peacebuilding. As a result of the study, it was partially derived that music could create a space for relationship formation and play a role for dialogue, healing, reconciliation, and solidarity and metaphorically embody peaceful coexistence in the peacebuilding process. It is hoped that various perspectives on peacebuilding through music will be produced through this study and convergence and complex approaches and research in various academic fields such as musicology, music therapy, art,peace studies, sociology, and education will be inspired by it.
A Study on the Relationship between Career Barriers and Career Decision Levels of Music Students: The Mediation Effects of Failure Tolerance and Career Decision-making Self-efficacy

본 연구는 음악대학에 재학 중인 성인진입기 대학생 209명을 대상으로 이들이 지각하는 ‘진로장벽’과 ‘진로결정수준’ 사이의 관계를 개인 내적 변인인 실패내성과 진로결정 자기효능감이 매개하는지 살펴보고자 하였다. SPSS 25.0을 활용하여 주요 변인 간의 상관분석을 실시하였고, Process macro 4.0으로 변인 간의 관계를 확인하였다. 이후 Bootstrapping을 실시하여 매개효과의 유의성을 검정하였다. 연구결과는 다음과 같다. 첫째, 주요 변인 간 상관분석을 실시한 결과, 주요 변인 간의 상관이 모두 유의하게 나타나 변인 간 밀접한 관련성이 있음을 확인할 수 있었다. 둘째, 독립 및 매개 변인들이 진로결정수준을 예측하는지 확인한 결과, 진로장벽은 진로결정수준을 부적으로 예측하는 것으로 나타났다. 실패내성은 진로결정수준을 유의하게 예측하지 못했지만, 진로결정 자기효능감은 진로결정수준을 부적으로 예측하는 것으로 나타났다. 셋째, 연구모형 검정을 위해 Bootstrapping을 실시한 결과, 진로장벽과 진로결정수준 간의 관계에서 실패내성의 단순매개효과는 통계적으로 유의하지 않았으나, 진로결정 자기효능감의 단순매개효과는 유의하였다. 특히, 진로장벽이 실패내성과 진로결정 자기효능감을 순차적으로 거쳐 진로결정수준에 이르는 이중매개가 유의함을 확인하였다. 본 연구는 음악대학생의 진로결정수준을 높이기 위한 심리 내적 기제로 동기적이고 인지적인 특징을 가진 두 매개 변인을 확인하였다는데 의의가 있다.The study aimed to examine whether failure tolerance and career decision self-efficacy mediate the relationship between the‘career barrier’and the‘career decision level’among 209 music college students. Correlation analysis was conducted with SPSS 25.0, and the relationship between variables was verified with Process macro 4.0. The significance of the mediating effects were then tested using bootstrapping. The findings indicated that while the mediation via career decision self-efficacy was significant, the simple mediating effect of failure tolerance was not. Importantly, the serial mediation effect through failure tolerance and career decision self-efficacy in regards to the relationship between career barriers and career decision levels was significant. Focusing on an individual’s cognitive-motivational characteristics, the present study identified the underlying psychological mechanisms for music students’unusally low level of career decision-making despite their years of involvement and dedication.
Evaluación de las estrategias formativas digitales para profesionales sanitarios implementadas durante la pandemia por COVID 19

Objective: to evaluate the implementation of the microlearning educational strategy through the use of a mobile application for the healthcare professionals of a university hospital during the COVID-19 pandemic. Methods: a survey was conducted on hospital staff. The visualizations of knowledge pills were monitored. The questionnaire allowed to collect information about the sociodemographic profile of participants, the use of educational mobile applications, and of the pills created ad hoc during the COVID-19 pandemic. The number of users registered in the application was of 4,572. The data obtained were analysed through descriptive analysis, using the Student’s T test and the ANOVA test for bivariate analyses. Results: the study included 633 professionals, with a mean age (SD) of 44.8 (11.1) years; 81.7% (n= 517) were female and 93.5% (n= 592) were healthcare professionals. Nurses represented 41.2% (n= 261) of the sample, and doctors 22.3% (n= 141). During the pandemic, 55.8% (n= 353) of the study subjects had worked providing direct assistance to COVID (+) patients; 94% (n= 595) visualized videos for training / getting information for their professional setting; and 94.9% reported awareness of the knowledge pills by the Formación SanidadMadrid App. The 22 knowledge pills received 25,539 visualizations in total. There was high satisfaction by participants regarding the use of this method for their training during the pandemic. Conclusions: the microlearning educational strategy, based on knowledge pills available at a mobile application, demonstrated being a key element for the development of new digital training methodologies in the hospital setting during a pandemics scenario.
Fine Registration of SAR and Optical Image Based on Improved Hausdorff Distance

The registration of SAR and optical image is a core step of multi-source image fusion. The quality of registration restricts the effect of multi-source image fusion. Due to the importance of SAR and optical image registration, we analyze the difficulties of SAR and optical image registration first. And we propose a fine registration method for SAR and optical image based on improved Hausdorff distance. Firstly, we correct the nonlinear distortion of SAR image in range direction by interpolation. Secondly, we use the genetic algorithm to search the mapping parameters that minimize the Hausdorff distance between the images to be registered. These parameters are used for affine transformation of optical images to achieve coarse registration. Finally, we use the Fourier operator to calculate the residual translation between SAR and optical image. Experiments on real data show the effectiveness of the proposed method, and the offset between the corresponding images is less than 2 pixels.
Conclusions

I discuss the general implications of the rule-combining approach to morphotactics developed in the course of the foregoing chapters. I summarize the numerous superficially problematic phenomena that the rule-combining approach resolves and I relate these phenomena to the variety of ways in which rule combinations may deviate from the canonical characteristics of a language’s morphotactics. Finally, I synopsize the set of formal definitions on which the rule-combining approach is based.
Rule Combinations and Morphological Simplicity

Rule combination can contribute to morphological simplicity. Synchronically, rule combinations (like word combinations) are sometimes stored as formulaic units, and this fact contributes to a morphological system’s processing simplicity, since accessing a stored rule combination directly is simpler than decomposing that combination into its component rules for separate lookup. Stored, formulaic rule combinations may also contribute to diachronic simplifications of a language’s morphology, since they are the locus of reanalyses that may eventuate in “affix telescoping,” the development of a rule combination into a simple rule. But affix telescoping is not a monolithic phenomenon; it involves the reduction of a rule combination’s combinatory transparency along at least four dimensions. Thus, it is possible to find rule combinations that are progressing toward reanalysis as simple rules without yet having reached the point of reanalysis.
Separate Changeable Code Using Factories

A developer’s end goal is to make an application that meets the requirements of the customer. But the code must be easily extensible, maintainable, and stable enough to meet future needs too. It is tough to write the best version of a program on the very first attempt. You may need to analyze the code and refactor it several times. In this chapter, you will see such a scenario and learn how to use factories. To simplify the discussion, I start with a small program. We’ll keep analyzing the program and modifying it accordingly.
Wise Use of Code Comments

Comments help you understand other people’s codes. They can describe the logic behind the code. But expert programmers are particular about comments and do not like to see unnecessary comments. This chapter provides case studies to help you decide whether to put a comment in your application.
Multi-layer Wavelet Transformations for Image Super-Resolution: Applications to Voxel-Based Deep Learning and Areal Density Maps of Carbon Nanotube Sheets

Miralon sheets are an advanced carbon-based product which enables environmentally resistant solutions for some of the toughest industrial problems. One of the challenges for quality control is to ensure the uniformity of density distribution. However, current areal density maps obtained from a beta transmission equipment are insufficient to identify variation and defects at the required level. As areal density maps can be interpreted as images, image super-resolution convolutional neural networks (CNNs) promise to reconstruct high-resolution areal density maps with fine texture details. However, for this promise to be realized two challenges need to be overcome. First, generating areal density maps is expensive, and only limited training data is available. Second, current 2D CNNs cannot provide sufficient accuracy in depth. Therefore, we propose a 3-layer voxel representation to adapt 2D CNNs to the particular application of Miralon sheets. Moreover, we also study the transform domains to provide more distinguishable features for density distribution patterns. Our results demonstrate that appropriate data augmentation is essential to achieving accurate results and transform domains could provide special information based on image contents. Using our proposed techniques, a high-resolution areal density map, which is rich in 3D information, can be obtained by leveraging 2D image super-resolution CNNs.
A Comprehensive Analysis of Aspect-Oriented Suggestion Extraction from Online Reviews

In the world of business, products need to evolve adjusting to the needs of the customer to ensure customer satisfaction. The abundance of opinionated text on the internet contains suggestions made by users that can be used to improve products. These suggestions are important to multiple stakeholders; as product improvements for businesses or as tips and advice to consumers. Until now, the extraction of suggestions has usually been defined as a problem of classifying sentences into suggestion and non-suggestion classes. No work has attempted to differentiate suggestions on the basis of intended receiver. Extracting these suggestions with respect to the aspects that reviewers are not satisfied with could be used as a potential solution to improve products. To address these shortcomings, this study proposes a novel task decomposition called Aspect-Oriented Suggestion Extraction to identify product improvements. It contains three main subtasks: suggestion classification, beneficiary classification and aspect extraction. The proposed approach proved to be very effective in determining suggestions from non-suggestions, outperforming state-of-the-art methods on suggestion mining across the domains of hotel, electronics, travel and software achieving F-scores of 91%, 76%, 75% and 85%, respectively. Experimental results of extracting aspect-oriented suggestions from hotel reviews show the effectiveness of the techniques. The proposed framework, being the first of its nature, yields promising results.
Next Job Application Prediction by Leveraging Textual Information, Metadata, and Personalized-Attention Mechanism

Prediction of the next job application is one of the most important use-cases of job recommender systems. This work proposes to use next-item recommendation methods to model job seekers’ career preferences to more accurately discover the next job postings they may apply for. Our proposed model, Personalized-Attention Next-Application Prediction (PANAP), consists of three modules. The first module learns job posting representations from textual content and metadata attributes in an unsupervised way. The second module learns job seeker representations from job application records and metadata attributes. It includes a personalized-attention mechanism that adapts the importance of each job in the learned career preference representation to the profile of a specific job seeker. The third module then models the Next-Application Prediction task as a top-K search process based on the similarity of representations. Text content plays a major role in describing job postings, and how the job content is encoded affects the model performance. Therefore, we explore the utility of different content encoding methods. In addition, geographic location is another essential factor influencing the preferences of job seekers in the recruitment domain. We also explore the influence of geographic location on the model performance from the perspective of negative sampling strategies.
Matlab-Simulink Based Comparative Simulations of Different Neuron Models

Different types of neuron models have been developed using various mathematical equations to understand how the brain works. In this study, Hodgkin-Huxley, Morris- Lecar, Adaptive Exponential Integrate Fire, Hindmarsh-Rose and Izhikevich spiking neuron models, which are widely used in the literature, were compared in Simulink environment. As a result of the comparisons, two neuron model categories were created. These categories are (Hodgkin-Huxley Morris-Lecar) and (Adaptive Exponential Integrate Fire, Hindmarsh Rose and Izhikevich) neuron models categories. The first created category neuron models were compared in terms of the formation of intracellular and extracellular ion densities on Action Potentials. In the second category, neuron models were compared in terms of which neuronal behaviors seen in the cerebral cortex could produce and which ones could not.
A Prototype Design on Privacy-Preserving Outsourced Bayesian Network

Recently, cloud computing acts as a significant service for outsourcing some heavy-weight tasks. It also can take care of machine learning, where a trained model can be outsourced to the server, and then a user can enjoy the benefits of such the model by simply submitting queries. In this paper, we focus on a basic structure of Bayesian network prediction, and consider a simple scenario of model outsourcing with privacy preservation. In addition, we also require the query data privacy. The main technique is to build secret sharing-based protocols for the model owner, user, and server. Moreover, a round trick is proposed to overcome calculation of probability over our secret sharing-based protocol. Finally, the experiments show the performances of the proposed scheme.
An Active User-Side Detector for Evil Twins

This paper proposes an active user-side solution, called Air Wireless Packet Detector (AWPD), which will solve the notorious evil twin problem. The evil twin problem can result in diverse security problems, such as fraud, identity theft, and man-in-the-middle attacks. We utilize the packet retransmission rules of TCP/IP protocol and packet forwarding behaviors created by evil twins to detect evil twins at public hotspots or unencrypted WLANs. Thus it does not need any data or assistant devices, and will be with a good performance.
Anomaly Detection in Time Series using Unsupervised Machine Learning Approach

Anomaly detection is the process of identifying data points, observations or events which deviate from normal behavior.To detect anomalies, we have used the PyCaret machine learning library.PyCaret is a low code, open-source library that automates our machine learning processes and helps us detect outliers/anomalies Its Anomaly detection and Regression modules contain various machine learning algorithms and frameworks such as XGBoost, CatBoost, Isolation Forest, DBSCAN, etc.It is a deployment-ready library that is easy to use and helps users to perform end-to-end experiments efficiently.In this paper, we applied clustering and regressionbased methods on the NAB Twitter dataset for time series anomaly detection.In the regression method, we predicted the tweets, calculated the difference by comparing them with actual tweets, and used the thresholding technique for anomaly detection.
Learnability Evaluation Model for Android Mobile Applications

Educational mobile applications have become an important tool in education.In addition, its spread played a major role in this field.In order to use it efficiently, we must adopt the concept of learnability.By applying some standards, learnability characterizes how easy it is to use a certain application.In other words, it is the answer to whether the users can complete simple tasks on their first attempt.This paper proposes a comprehensive evaluation model for learnability that can help define clear learnability dimensions based on several approaches to measure learnability.Hence, improves users" learnability while using a mobile application targeting Android system mobile applications.The research approach will follow these steps: integration of multiple resources, building a framework, testing, and reporting results.We proposed a new learnability evaluation model for Android mobile applications.We will use this model as a basis for our framework to measure how learnable any Android mobile application is.Moreover, generates results using three integrated learnability methods: Performance measurement learnability curve as a logarithmic approximation, Petri-net approach through fitness value, and analytics through logs as well as other related measurements.The results of this study offer clear statistics for measuring Learnability and assets to improve the approach to designing a mobile application to be more learnable for users.