Reviewerly/exHarmony huggingface dataset:
Manifold Density Estimation via Generalized Dequantization

Density estimation is an important technique for characterizing distributions given observations. Much existing research on density estimation has focused on cases wherein the data lies in a Euclidean space. However, some kinds of data are not well-modeled by supposing that their underlying geometry is Euclidean. Instead, it can be useful to model such data as lying on a {\it manifold} with some known structure. For instance, some kinds of data may be known to lie on the surface of a sphere. We study the problem of estimating densities on manifolds. We propose a method, inspired by the literature on "dequantization," which we interpret through the lens of a coordinate transformation of an ambient Euclidean space and a smooth manifold of interest. Using methods from normalizing flows, we apply this method to the dequantization of smooth manifold structures in order to model densities on the sphere, tori, and the orthogonal group.
Quantum software engineering. Pt. I: Quantum Circuit (Gate) Model based Computing – education Lectures and pedagogical workshop

All the quantum algorithms are based on a certain quantum computing model, varying from the quantum circuit, one-way quantum computation, adiabatic quantum computation and topological quantum computation. These four models are equivalent in computational power; among them, the quantum circuit model is most frequently used. In the circuit model, it has been proved that arbitrary single-qubit rotations plus twoqubit controlled-NOT gates are universal, i.e. they can provide a set of gates to implement any quantum algorithm. This article discusses the goal for this research: it is to given a lightning-fast (as-barebones-as-possible) definition of the quantum circuit model computing and leisurely development of quantum computation before actually getting around to sophisticated algorithms. In this article the main ideas of quantum software engineering is described.
플립드 러닝을 적용한 비즈니스 한국어 수업 모형 모색 -상대 높임법을 기반한 이메일 작성을 중심으로

This study aims to improve the ability of Korean learners in China to write business Korean E-mail using their grammar knowledge related to relative heights they studied in class. To do this, I would like to present a class model through Flippedlearning method to see if it is applicable in real-world classes. An emerging learning method, Flipped-learning, has gained much interest lately due to its process involving prior study followed by the students’ classroom involvement, which direction matches that of the current educational policy that emphasizes self-directed learning.Flipped-learning is not a model that has been recently developed, as it uses the format of blended learning with the introduction of a new concept of prior learning that allows students to learn in advance through online lessons and video clips related with the classroom content to be covered. During class time, individualized supplementary or in-depth study is conducted on the basis of the students'' prior learning.Studies that apply Flipped-learning in Korean language education and the development of Flipped-learning professors and learning models in the writing are also being carried out gradually, mostly for Korean learners. And if you look at the papers related to Flipped-learning, there are not many studies that explore the educational uses and models of Flipped-learning, which are effective in the Korean writing education field.As a result, in this paper we’ll study on the Applicability of Flipped-learning in Business Korean Class Model-base on Korean Hearer Honorifics by writing the E-mail.
공공 건설사업 CM at Risk 적용시 GMP 산출 프로세스

Recently the diversification of construction market and the continuous reduction of construction amount are raising the need of alternative delivery method in the construction industry. The foreign advanced companies actively adopted the CM at Risk delivery method where they perform the service of agent CM in the design phase, and agree GMP(Guaranteed Maximum Price) with the client at the time of 50~80% completion of design. Even in Korea they began to apply that method to pilot projects. In CM at Risk, through the early participation of builder, the level of design completion can be improved and the change order and construction period delay can be minimized. On the other hand, GMP is usually calculated when the design is about 80% complete, so there is uncertainty in the construction cost. Therefore, in this research, the increased amounts of construction cost are analyzed in a number of public construction projects, and GMP calculation process is proposed using the analysis results and CBR(Case-Based Reasoning) technique to reduce the construction cost increase in the construction phase.
Multi-Intelligent Vehicle Cooperative Formation Control Method based on Visual Navigation

There are many path information noises in the traditional multi-agent vehicle cooperative formation control method, which leads to poor dynamic stability. Use the image capture card in the visual navigation system to obtain the path information of multiple intelligent vehicles, enhance the image quality, extract the edge of the path, obtain the complete path feature, calculate the expected speed and expected position of the target intelligent car, and perform it on the original control protocol Improved to realize the cooperative formation control of multiple intelligent vehicles. The experimental results show that the designed multi-agent vehicle cooperative formation control method based on visual navigation has a timely and effective response to direction and acceleration, and the speed consistency is maintained at a high level, which shows that the dynamic steady-state performance of the control method has been improved.
Intelligent robust controller based on cognitive computing technologies. Pt. 1: cognitive Control models with THE BRAIN emotional learning

n on-linecontrol and decision-making systems, emotional brain training is a preferred methodology (compared to stochastic gradient-based and evolutionary algorithms) due to its low computational complexity and fast robust learning. To describe the emotional learning of the brain, a mathematical model was created —the brain emotional learning controller (BELC). The design of intelligent systems based on emotional signals basedoncontrol methods assoft computing technologies: artificial neural networks, fuzzy control and genetic algorithms. Based on the simulated mathematical model of mammals BEL, a controller architecture has been developed. Applied approachcalled “Brain Emotional Learning Based Intelligent Controller” (BELBIC) —a neurobiologically motivated intelligent controller based on a computational model of emotional learning in the mammalian limbic system. The article describes applied models of intelligent regulators based on emotional learning of the brain. BELBIC's learning capabilities;versatility and low computational complexity make it a very promising toolkitfor on-lineapplications.
Aspek Sosial Dalam Novel Tapak Jejak Karya Fiersa Besari Sebagai Bahan Ajar di SMA

This study aims to describe the builder elements and social aspects contained in Tapak Jejak, a novel by Fiersa Besari, and their relevance in the study of literature in high school. This study uses a qualitative descriptive approach in the form of data words, sentences, and discourse that contain in the novel Tapak Jejak by Fiersa Besari. The data collection techniques are reference study. The data analysis is dialectical techniques. The data validity technique uses the source triangulation technique. The results showed that the structure of the builder in Tapak Jejak novel by Fiersa Besari included themes, plot, characters (characterization), and settings. The novel's theme is the writer's adventure around Indonesia in 2013. The flow is flowing forward. The characters consist of Fiersa Besari as the main figure, and additional figures such as Swarandee, Sakti, Sarah, Desi, and Irfan. The place setting generally occurs in Eastern Indonesia. The time setting was in 2013. The social setting depicts the general social background of the situation of Eastern Indonesian citizens. The novel Tapak Jejak by Fiersa Besari contained social aspects in the form of cultural aspects, social norms of Fiersa Besari, social environment, economy and is relevant as teaching material in class XI high school in the 2013 curriculum under KD 3.11, analyzing messages from a fiction book that reads, and 4.11, compiling a review of messages from a fiction book that reads.
SUPPORT OF SELF-EDUCATION IN A DISTANCE RETRAINING PROGRAM OF TUTORS

Introduction. The paper addresses the issue of support of the students involved in a fully online retraining program, which imposes special requirements on the development of the self-education ability. The study aims to establish the conditions for organizing the support of self-education in a digital educational environment. Materials and methods. The study relies on the methodology proposed by G.N. Prozumentova for reconstructing the innovative experience to analyze the reflexive text materials of the Logbook of students of the retraining program; to identify the points of stress, misunderstanding, breaks, and transitions during training; and make an analytical generalization. Results of the study. Categories of difficulties encountered by students of the program in their self-educational activity were identified, and the conditions necessary to support self-education were established. These are points of self-determination, professional trials, points of reflection, and individual educational route. Conclusion. In the context of restrictive measures during the pandemic and the transition to the online educational process, self-skills related to self-education, i.e., self-organization, independence, and self-determination, become essential. These competences are an indispensable part of the work of a tutor. However, before the tutor can support someone, they need to build these self skills themselves. The established conditions in the retraining programs will facilitate this process. At the same time, additional studies are required to reveal in more detail the individual progress of a person under these conditions for building a model for supporting the development of self-education ability, and to determine the methods for tutors to provide the support of this progress. Keywords. Self-education, self-determination, individual educational route, professional retraining, tutor, digital environment, self skills.
“ THE EFFECT OF MIXED GAMIFICATION AND ACHIEVEMENT MOTIVATION ON CONCEPT COMPREHENSION AND CREATIVE THINKING SKILLS IN LEARNING SCIENCE.”

This research is motivated by the poor comprehension and creativity of elementary school students in science lessons. This study aimed to determine gamification's effect on improving learning outcomes in concept comprehension and creative thinking skills in students studying science. Achievement motivation is suspected of being involved as a variable that interacts with gamification in improving learning outcomes. The data analysis technique used the Multivariate Analysis test. The research subjects were 73 students in the control class and the experimental class. This research is a quasi-experimental research, with a 2x2 factorial, and pretest-posttest non-equivalent control group design. The experimental class uses mixed gamification, while the control class uses content gamification. The difference between the two lies in the gamification elements used. The study results showed that 1) there was no significant difference in the learning outcomes of conceptual comprehension between groups of students who used mixed gamification and content gamification; 2) there was no significant difference in the learning outcomes of conceptual comprehension between groups of students who have high achievement motivation and low achievement motivation; 3) there was no interaction between gamification (mixed gamification and content gamification) and achievement motivation on learning outcomes comprehension the concept; 4) there was no significant difference in the learning outcomes of creative thinking between groups of students who use mixed gamification and content gamification; 5) there was no significant difference in the learning outcomes of creative thinking between groups of students who have high achievement motivation and low achievement motivation; 6) there was no interaction between gamification (mixed gamification and content gamification) and achievement motivation on learning outcomes to think creatively. Achievement motivation is not the only factor that affects the learning outcomes of concept comprehension and creative thinking. Future research is important to consider the different moderator variables from achievement motivation and other gamification elements.
Pulse of the Pandemic: Iterative Topic Filtering for Clinical Information Extraction from Social Media

The rapid evolution of the COVID-19 pandemic has underscored the need to quickly disseminate the latest clinical knowledge during a public-health emergency. One surprisingly effective platform for healthcare professionals (HCPs) to share knowledge and experiences from the front lines has been social media (for example, the "#medtwitter" community on Twitter). However, identifying clinically-relevant content in social media without manual labeling is a challenge because of the sheer volume of irrelevant data. We present an unsupervised, iterative approach to mine clinically relevant information from social media data, which begins by heuristically filtering for HCP-authored texts and incorporates topic modeling and concept extraction with MetaMap. This approach identifies granular topics and tweets with high clinical relevance from a set of about 52 million COVID-19-related tweets from January to mid-June 2020. We also show that because the technique does not require manual labeling, it can be used to identify emerging topics on a week-to-week basis. Our method can aid in future public-health emergencies by facilitating knowledge transfer among healthcare workers in a rapidly-changing information environment, and by providing an efficient and unsupervised way of highlighting potential areas for clinical research.
ICF-Based Classification to Bridge the Gap Between End-Users and AAL Solutions

MAESTRO (Sustainable Reference Framework evaluating equipment and services for seniors) is a web-based ICT multi modal platform providing a broad range of services and benefits in the domain of monitoring and self-monitoring systems for well-being and health-related information acquisition. Specifically, MAESTRO aims at realizing an innovative framework to facilitate the interaction and communication between producers and consumers at different levels, taking into account needs of end-users and features of products. This paper provides an overview of the key concepts and capabilities of MAESTRO focusing on the use of the International Classification of Functioning, Disability and Health (ICF) to build profiles of end-users and discover and rank products that meet their health-related needs. Additionally, the paper shows the results of a study aimed at evaluating the classification capabilities of MAESTRO and the integrated ICF-based taxonomy with respect to products and services developed within AAL (Active Assisted Living) Projects. The evaluation points out more recurrent health-related needs of end-users of AAL projects focus and underscore some limits and possible enhancements of the integrated ICF-based taxonomy.
Rate-Distortion Optimization for 360-degree Image Considering Visual Attention

Nowadays, 360-degree image is emerging as a new way of experiencing immersive life. It offers users a free viewpoint experience. At the same time, the huge data amount of 360-degree image and video brings the need of efficient coding for this kind of content, in order to achieve real-time transmission, particularly for virtual/augmented reality application. Since only part of the scene is watched by the observer, compressing the whole scene with equal quality is probably inefficient. In this paper, visual attention is considered for 360-degree image coding. By referring to the saliency map of the 360-degree image, the distortion for the rate-distortion optimization (RDO) is modified to ensure better visual experience. The experimental results show that the viewport with higher interest will be rendered with higher quality, and significant bitrate reduction is achieved when the quality measurement is performed in these regions.
Shadow Image Enlargement Distortion Removal

This project aims to adopt preprocessing operations to get less distortions for shadow image enlargement. The preprocessing operations consists of three main steps: first enlarge the original shadow image by using any kind of interpolation methods, second apply average filter to the enlargement image and finally apply the unsharp filter to the previous averaged image. These preprocessing operations leads to get an enlargement image very close to the original enlarge image for the same shadow image. Then comparisons established between the adopted image and original image by using different types of interpolation and different alfa values for unsharp filter to reach the best way which have less different errors between the two images.
PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators

We consider offline reinforcement learning (RL) with heterogeneous agents under severe data scarcity, i.e., we only observe a single historical trajectory for every agent under an unknown, potentially sub-optimal policy. We find that the performance of state-of-the-art offline and model-based RL methods degrade significantly given such limited data availability, even for commonly perceived "solved" benchmark settings such as "MountainCar" and "CartPole". To address this challenge, we propose PerSim, a model-based offline RL approach which first learns a personalized simulator for each agent by collectively using the historical trajectories across all agents, prior to learning a policy. We do so by positing that the transition dynamics across agents can be represented as a latent function of latent factors associated with agents, states, and actions; subsequently, we theoretically establish that this function is well-approximated by a "low-rank" decomposition of separable agent, state, and action latent functions. This representation suggests a simple, regularized neural network architecture to effectively learn the transition dynamics per agent, even with scarce, offline data. We perform extensive experiments across several benchmark environments and RL methods. The consistent improvement of our approach, measured in terms of both state dynamics prediction and eventual reward, confirms the efficacy of our framework in leveraging limited historical data to simultaneously learn personalized policies across agents.
Data breach stymies firms

Specialty chemical makers Momentive and Hexion, both controlled by the private equity firm Apollo Management, recently experienced what they call a “network security incident” that shut access to corporate computer systems. Hexion says the incident interrupted email and other systems. Momentive experienced similar issues. Press reports suggest that Hexion and Momentive were victims of LockerGoga, a ransomware program designed to block access to a computer system until a sum of money is paid. The virus also hit the aluminum maker Norsk Hydro at about the same time, reports say, compromising manufacturing operations. On March 26, a week after the attack, Norsk Hydro estimated the financial impact at $40 million. Both Hexion and Momentive say their manufacturing operations were mostly unaffected and continue to operate safely. Neither chemical firm tallied the cost of the attack. However, according to the online publication Motherboard, an internal email from Momentive CEO Jack Boss said
Learning Dense Correspondences via Local and Non-local Feature Fusion

We present a learning-based method for extracting distinctive features on video objects. From the extracted features, we are able to derive dense correspondences between the objects in the current video frame and in the reference template. We train a deep-learning model with non-local blocks to predict dense feature maps for long-range dependencies. A new video object correspondence dataset is introduced for training and for evaluation. Further, we propose a new feature-aggregation technique that is based on the optical flow of consecutive frames and we apply it to the integration of multiple feature maps for alleviating uncertainties. We also use the local information provided by optical flow to evaluate the reliability of feature matching. The experimental results show that our local and nonlocal fusion approach can reduce unreliable correspondences and thus improve the matching accuracy.
European Experience of the Use of Information and Communication Technologies In Initial Education of the English Language Teachers

The article deals with the analysis of the peculiarities of the use of information and communication technologies in the process of initial education of future English language teachers in the countries of the European Union. The topicality of the problem is based on the current trends in the development of education, in the particular extensive use of online technologies and tools in the process of English language learning and English language teacher education.The purpose of the article is to study the European experience of the use of information and communication technologies in the initial education of the English language teachers and single out the most important and topical of them in order to outline the possibilities of their use in the initial education of teachers of English in a broader context.The methodology of the research is realized with the use of such methods as analysis, synthesis, generalization, analogy and interpretation of empirical data and theoretical concepts; observation and study of practical experience of higher educational institutions in Poland and Portugal as well as an interview with teachers and students of the above-mentioned institutions.As a result, the importance of the development of information and technological competencies in future teachers of English is grounded, the role of information and communication technologies in initial teacher education is determined. Various information and communication technologies used in the educational process, in particular offline technologies and tools as well as online technologies and resources, are presented and analyzed. Attention is also given to the use of E-learning and the implementation of massive open online courses into the initial teacher education of the English language teachers.The conclusion about the variability of information and communication technologies is made.