Reviewerly/exHarmony huggingface dataset:
조선시대 대일사행과 울릉도⋅독도

This paper examines the records related to Ulleungdo and Dokdo in the records of diplomatic missions to Japan during the Joseon Dynasty. Records related to Ulleungdo and Dokdo were recorded in Busangnok written by Lee Kyung-jik, who visited Japan in 1617, SangHanSungSayeohyang, the Collection of written conversations and poems on diplomatic missions in 1719, Ilbonnok written by Sung Dae-Joong, and Hwagukji written by Won Joong-Ger on diplomatic missions in 1763, and It is confirmed in Sahwagiryak written by Pak young-hyo in 1882. Although this record is brief, it is a valuable record that can recognize both Japan's perception of Ulleungdo and Dokdo and both Japan's perceptions of the Ogan era.
End to End Chinese Lexical Fusion Recognition with Sememe Knowledge

In this paper, we present Chinese lexical fusion recognition, a new task which could be regarded as one kind of coreference recognition. First, we introduce the task in detail, showing the relationship with coreference recognition and differences from the existing tasks. Second, we propose an end-to-end joint model for the task, which exploits the state-of-the-art BERT representations as encoder, and is further enhanced with the sememe knowledge from HowNet by graph attention networks. We manually annotate a benchmark dataset for the task and then conduct experiments on it. Results demonstrate that our joint model is effective and competitive for the task. Detailed analysis is offered for comprehensively understanding the new task and our proposed model.
Teaching Speaking Experience Perceived By Sea Teacher Students Project of 11th Grade Students At Pangasinan State University (PSU) Integrated School-High School, Philippines: A Phenomenological Study

This research aims to investigate and describe the perceptions and problems faced by the student teachers who teach public speaking in grade 11, Pangasinan State University Integrated School-High School (PSU IS-HS), Philippines through SEA Teacher (Southeast Asean Teacher) program handled by Southeast Asean Minister of Education Organization (SEAMEO). The researcher employs descriptive qualitative method by using phenomenological approach. The technique of collecting data in this research uses semi-structured interview technique. The subjects of this research are four student teachers who teach public speaking in grade 11 of PSU IS-HS, Philippines. The objects of this research are the perceptions and challenges faced by the student teachers based on their experience while teaching in PSU IS-HS. The technique of analysis the data used in this research is hermeneutic phenomenology. The result of this research suggests that: 1) The student teachers’ perceptionin teaching through SEA Teacher project; 2) The student teachers’ challenges which had a limited time and minimum support of IT media in SEA Teacher program in PSU. Keywords: Teaching speaking, experience, student teacher’s perception, SEA Teacher
Towards a Better Contextualization of Web Contents via Entity-Level Analytics

With the abundance of data and wide access to the internet, a user can be overwhelmed with information. For an average Web user, it is very difficult to identify which information is relevant or irrelevant. Hence, in the era of continuously enhancing Web, organization and interpretation of Web contents are very important in order to easily access the relevant information. Many recent advancements in the area of Web content management such as classification of Web contents, information diffusion, credibility of information, etc. have been explored based on text and semantic of the document. In this paper, we propose a purely semantic contextualization of Web contents. We hypothesize that named entities and their types present in a Web document convey substantial semantic information. By extraction of this information, we aim to study the reasoning and explanation behind the Web contents or patterns. Furthermore, we also plan to exploit LOD (Linked Open Data) to get a deeper insight of Web contents.
Micro Terrain Construction of Service Facilities along the Expressway

Abstract The service facilities along the expressway are designed to meet the needs of drivers and passengers, which is directly related to the convenient travel of the general public. In order to break through the rigid construction mode of expressway service facilities, this paper analyses the micro terrain design and construction of different types of expressway service facilities, and discusses the micro terrain construction of expressway service facilities, so as to improve the view of the landscape and the sense of physical and mental pleasure of the drivers, passengers and service personnel, eliminate security risks and improve service efficiency.
Plotting Ternary Diagrams by R Library ggtern for Geological Modelling

Selecting proper methods of data modelling is crucial in geosciences, as effective data visualization enables better understanding of complex geological phenomena: processes, structure and dynamics. Various approaches of the data analysis by R language include both traditional methods of linear charts and other approaches to data visualization: ternaries, circular and radar charts. Using ternaries for triple correlations between variable can be seen in applied geological analysis which proves it to be important method for data modelling.Visualizing geological variables by ternaries enables to highlight correlation between variables in a triangle, how data are dependent and affected. In this study, several geologic, tectonic and geomorphic variables, such as sediment thickness, tectonic plates, volcanic areas, steepness and depths, were tested using R based modelling in {ggtern} library. Other graphs include radar charts and circular diagrams. Visualizing attributes as a triple component correlation by ternaries gives a better insight to the geological factors. Traditional techniques for visualization of pairwise linear correlations are not sufficient to show triple variations. Ternaries approach identifies data correlations by triple factors. Additional graphical models include circular and Euler-Venn diagrams of quantitative and qualitative geospatial data modelling. The study is supported by 7 R code listings and 9 figures.
The impact of microwave radiation in the processes of carbon dioxide absorption by chlorophyll-producing microalgae

The influence of microwave radiation on the rate of СО2 uptake by chlorophyll-producing microalgae of the Chlorella Vulgaris has been established.Experimental dependencies of greenhouse gases absorption by microalgae depending on the impact of microwave electromagnetic (MEM) field have been obtained.The mathematical model of the dynamics of biomass growth of Chlorella Vulgaris microalgae depending on the time of electromagnetic radiation has been constructed.Based on the mathematical model solution and the experimental data obtained, a chart of the dependence of CO2 absorption by microalgae of the Chlorella Vulgaris provided that microwave radiation is present, has been constructed.The optimal value of microwave electromagnetic radiation for the design of technological schemes for the industrial purification of gas emissions from carbon dioxide by biological method has been established.
EDUCATION AND BUSINESS PARTNERSHIP: IN SEARCH OF NEW FORMS OF INTERACTION

The article analyzes modern approaches to the partnership interaction between education and business, characterizes the specific features and modern forms of partnership interaction between higher educational institutions and business corporations. The authors have established that the partnership between higher education and business is a very specific type of cooperation and involves a wide range of activities, multifaceted goals and multifunctional opportunities. The researchers emphasize that establishing a productive mechanism for cooperation between universities and business will increase the efficiency of educational activities and develop ways of technology transfer to production, because this partnership is mutually beneficial, since the university receives additional financial resources and practical focus of its activities and the company gets qualified professionals and advanced technologies. The authors have described the Master’s Educational and Professional Program «Corporate Education and Staff Development» in Specialty 011 «Educational, Pedagogical Sciences». The purpose of the educational program is to provide theoretical and practical training for the specialists capable of creating and operating alternative education systems in corporations and enterprises that provide training, advanced studies and continuous development of the staff in accordance with the strategic goals and objectives of these institutions. The authors have presented the content of the educational program, competencies and program outputs, a selective block of educational disciplines and prospects for employment of graduates.
Warne, James - Biographical entry - Index to the AMA archive

Warne, James - Biographical entry - Index to the AMA archive - NAME is a biographical, bibliographical and archival database of SUBJECT with links to related articles and images. ADD MORE DESCRIPTION AS REQUIRED
Evaluating the Effectiveness of the Standard Insights Extraction Pipeline for Bantu Languages

Extracting insights from data obtained from the web in order to identify people's views and opinions on various topics is a growing practice. The standard insights extraction pipeline is typically an unsupervised machine learning task composed of processes that preprocess the text, visualize it, cluster and identify the topics and sentiment in each cluster, and then graph the network. Given the increasing amount of data being generated on the internet in Africa today, and the multilingual state of African countries, we evaluated how well the standard pipeline works when applied to text wholly or partially written in indigenous African languages, specifically Bantu languages. We carried out an exploratory investigation using Twitter data and compared the outputs from each step of the pipeline for an English dataset and a mixed Bantu language dataset. We found that for Bantu languages, due to their complex grammatical structure, extra preprocessing steps such as part-of-speech tagging and morphological analysis are required during data cleaning, threshold values should be adjusted during topic modeling, and semantic analysis should be performed before completing text preprocessing.
Uniform State Abstraction For Reinforcement Learning

Potential Based Reward Shaping combined with a potential function based on appropriately defined abstract knowledge has been shown to significantly improve learning speed in Reinforcement Learning. MultiGrid Reinforcement Learning (MRL) has further shown that such abstract knowledge in the form of a potential function can be learned almost solely from agent interaction with the environment. However, we show that MRL faces the problem of not extending well to work with Deep Learning. In this paper we extend and improve MRL to take advantage of modern Deep Learning algorithms such as Deep Q-Networks (DQN). We show that DQN augmented with our approach perform significantly better on continuous control tasks than its Vanilla counterpart and DQN augmented with MRL.
Instancenet: Object Instance Segmentation Using DNN

One-stage object detectors like SSD and YOLO are able to speed up existing two-stage detectors like Faster R-CNN by removing the object proposal stage and making up for the lost performance in other ways. Nonetheless, the same approach is not easily transferable to instance segmentation task. Current one-stage instance segmentation methods can be simply classified into segmentation-based methods which segment first then do clustering, and proposal-based methods which detect first then predict masks for each instance proposal. Proposal-based methods always enjoy a better mAP; by contrast, segmentation-based methods are generally faster when inferencing. In this work, we first propose a one-stage segmentation-based instance segmentation solution, in which a pull loss and a push loss are used for differentiating instances. We then propose two post-processing methods, which provide a trade-off between accuracy and speed.
Correction: A smart indwelling needle with on-demand switchable anticoagulant and hemostatic activities

Correction for ‘A smart indwelling needle with on-demand switchable anticoagulant and hemostatic activities’ by Yuanhao Wu <italic>et al.</italic>, <italic>Mater. Horiz.</italic>, 2020, DOI: 10.1039/c9mh01619h.
Existential analysis philosophy of art creation

The article is devoted to the principles of existential analysis and the possibility of its application, when we study the art creation. We present the methods of existential analysis: the selection of semantic matrices, the search for a rational grain, including in a situation of semantic uncertainty, which makes it possible to reveal the embedded meanings in any sign-symbolic systems, to analyze the reality created by a person/society. Existential analysis is particularly productive for the study of artistic creation. The authors demonstrate the possibilities of using existential analysis in the visual arts, tracing the evolution of Christian images. Three images of the Crucifixion are analyzed Dionysius, Matthias Grunewald and Alain Wilbox paintings from the collection of Robert Harris Rothschild.
National Translation Directive and Public-Sector Translation Standardization Policy

K-팝의 대명사 방탄소년단과 영화 ‘기생충’의 선전은 그동안 일부 지역 및 국가에 국한되어 온 한류의 존재감을 세계 대중문화의 본산으로 꼽히는 프랑스와 미국으로까지 확대시켰다. 한국의 문화콘텐츠 및 방문관광에 대한 외국인들의 수요 역시 크게 늘고있는 상황에서 외국인들에게 적절한 문화관광콘텐츠를 선별 제공하고 이를 수준 높은 번역을 통해 전달하는 일이 제대로 이루어지고 있는가를 다시 한번 평가할 시점에 이르렀다. 본 논문에서는 그동안 학계와 행정부·국회 등에서 필요성이 꾸준히 제기되어 온 ‘국가차원의 번역 표준화’ 사업의 일환으로 제정된「공공용어의 영어 번역 및 표기 지침(‘문체부 훈령’ 제279호)」을 중심으로, 도입 이후 이루어지고 있는 문화관광콘텐츠 용어 번역표기 현황을 살펴보고자 한다. 구체적으로는 이 훈령의 제정과정과 의의, 내용, 포괄적 문제점을 우선 소개한 다음, 서울특별시, 한국관광공사, 한국학중앙연구원의 3개 유관기관에서 제공하고 있는 온라인 용어사전의 번역표기 용례를 대상으로 이 훈령의 준수 현황과 문제점을 살펴볼 것이다. 실제 분석을 통해 훈령 제정관련 협의체에 참여한 기관에서조차 훈령에 부합되지 못하는 번역대안을 제시하고 있는 현실을 보고한 후, 본 연구가 향후 ‘공공번역’이 나아갈 방향, 번역표준화, 국가차원의 번역정책에 제기하는 함의를 제시할 것이다.In 2019, the global popularity of a 7-member boy band BTS, a symbol of K-POP strength, and the movie Parasite were met with aggressive global enthusiasm, and the number of foreigners visiting Korea surpassed the 17 million mark. These phenomena combined give rise to a social need to objectively evaluate the current whereabouts of public-sector translation practice with a view to delivering appropriate cultural contents to foreigners in reliable translations. Against these backdrops, this paper aims to introduce and look at Korean Ministry of Culture, Sports and Tourism (MCST) Directive No. 279, the first-ever Korean government-level regulation aimed toward public-sector translation standardization. Specifically, efforts will first be made to introduce the directive by briefly discussing its enactment procedure, scopes and contents covered, and its roles and significances. Then an analysis will follow of specific translation cases found in three on-line dictionaries respectively operated by three representative translation-related government organs in Korea: Seoul Metropolitan City, Korean Tourism Organization, and the Academy of Korean Studies. Conclusions will summarize the findings of the analysis, together with some suggestions for future direction of public-sector translation in Korea. That way, public-sector translation in Korea will be able to prove instrumental in making Korea more attractive to the eyes of foreigners with needs for Korean cultural and tourism contents, and, thus, enhancing the national image and competitiveness of Korea on the global scene.
CURL: Contrastive Unsupervised Representations for Reinforcement Learning

We present CURL: Contrastive Unsupervised Representations for Reinforcement Learning. CURL extracts high-level features from raw pixels using contrastive learning and performs off-policy control on top of the extracted features. CURL outperforms prior pixel-based methods, both model-based and model-free, on complex tasks in the DeepMind Control Suite and Atari Games showing 1.9x and 1.2x performance gains at the 100K environment and interaction steps benchmarks respectively. On the DeepMind Control Suite, CURL is the first image-based algorithm to nearly match the sample-efficiency of methods that use state-based features. Our code is open-sourced and available at this https URL.
Pengaruh Model Problem Based Learning dengan Menggunakan Media Puzzle Terhadap Hasil Belajar Peserta Dididk pada Pokok Bahasan Sistem Peredaran Darah Kelas XI di SMA Negeri 2 Tondano

The teaching and learning process with the use of media and learning models has not been maximized. The activeness of students during the learning process is still lacking, students have not been trained to find solutions to solve problems, students are indifferent to the subject matter delivered by the teacher. This studied aims to determine the effect of differences in Problem Based Learning models by using media Puzzles on learning outcomes of students on the subject of the circulatory system of class XI in SMA Negeri 2 Tondano in the academic year 2019/2020. This type of research is experimental research. The sample used in this study is class XI IPA 1 consisting of 22 students as the control class and XI IPA 2 consisting of 22 students as the experimental class. Data collection techniques using learning outcomes tests with test instruments in the form of multiple choice questions. The results showed that the average experimental class learning outcomes = 82.04 while the average control class learning outcomes = 62.72. ata were analyzed using the T-test statistic at a significance level of 0.05 and the results obtained were Tcount = 9.41> Ttable = 2.02. From these results it can be concluded that there is a difference in the Problem Based Modeling Learning Model using the media of Puzzles on student learning outcomes.  Keywords: learning outcomes, puzzle media, problem based learning
THE PURSUIT FOR NOVEL ACTIVATION FUNCTION

From the very start, activation functions in the neural network have been a key research area. More and more people are trying to come up with the state of the art activation functions for various purposes. These functions play an important role in deciding how good the model performs.Deep Learning allows us to create various complex learning models that are developed by algorithms capable of multi-level representation. It uses representation learning, which allows it to learn from the raw data and is capable of performing tasks like detection and classification. The functions play an important role in deciding the performance of a simple MLP to a very complex Deep Neural Networks. With the invention of every new activation function, we have seen a performance boost in various neural network architecture ranging from object detection to various Natural language applications too. The aim of the invented activation function is also focused on trying to further boost the performance of these Deep Neural networks, thereby providing a better user experience with increased performance in various Deep Learning-based applications.
Entanglement in Shor's Factoring Algorithm

Quantum algorithms are well known for their higher efficiency compared to their classical counterparts.However, the origin of the speed-up offered by quantum algorithms is a debatable question.Using entanglement measure based on coefficient matrix, we investigate the entanglement features of the quantum states used in Shor's factoring algorithm.The results show that if and only if the order r is 1, the algorithm generates no entanglement.Finally, compare with published studies results (
Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspective

This paper studies federated learning (FL) in a classic wireless network, where learning clients share a common wireless link to a coordinating server to perform federated model training using their local data. In such wireless federated learning networks (WFLNs), optimizing the learning performance depends crucially on how clients are selected and how bandwidth is allocated among the selected clients in every learning round, as both radio and client energy resources are limited. While existing works have made some attempts to allocate the limited wireless resources to optimize FL, they focus on the problem in individual learning rounds, overlooking an inherent yet critical feature of federated learning. This paper brings a new long-term perspective to resource allocation in WFLNs, realizing that learning rounds are not only temporally interdependent but also have varying significance towards the final learning outcome. To this end, we first design data-driven experiments to show that different temporal client selection patterns lead to considerably different learning performance. With the obtained insights, we formulate a stochastic optimization problem for joint client selection and bandwidth allocation under long-term client energy constraints, and develop a new algorithm that utilizes only currently available wireless channel information but can achieve long-term performance guarantee. Further experiments show that our algorithm results in the desired temporal client selection pattern, is adaptive to changing network environments and far outperforms benchmarks that ignore the long-term effect of FL.
Active Recursive Bayesian Inference with Posterior Trajectory Analysis Using α-Divergence.

Recursive Bayesian inference (RBI) provides optimal Bayesian latent variable estimates in real-time settings with streaming noisy observations. Active RBI attempts to effectively select queries that lead to more informative observations to rapidly reduce uncertainty until a confident decision is made. However, typically the optimality objectives of inference and query mechanisms are not jointly selected. Furthermore, conventional active querying methods stagger due to misleading prior information. Motivated by information theoretic approaches, we propose an active RBI framework with unified inference and query selection steps through Renyi entropy and $\alpha$-divergence. We also propose a new objective based on Renyi entropy and its changes called Momentum that encourages exploration for misleading prior cases. The proposed active RBI framework is applied to the trajectory of the posterior changes in the probability simplex that provides a coordinated active querying and decision making with specified confidence. Under certain assumptions, we analytically demonstrate that the proposed approach outperforms conventional methods such as mutual information by allowing the selections of unlikely events. We present empirical and experimental performance evaluations on two applications: restaurant recommendation and brain-computer interface (BCI) typing systems.
Training few-shot classification via the perspective of minibatch and pretraining

Few-shot classification is a challenging task which aims to formulate the ability of humans to learn concepts from limited prior data and has drawn considerable attention in machine learning. Recent progress in few-shot classification has featured meta-learning, in which a parameterized model for a learning algorithm is defined and trained to learn the ability of handling classification tasks on extremely large or infinite episodes representing different classification task, each with a small labeled support set and its corresponding query set. In this work, we advance this few-shot classification paradigm by formulating it as a supervised classification learning problem. We further propose multi-episode and cross-way training techniques, which respectively correspond to the minibatch and pretraining in classification problems. Experimental results on a state-of-the-art few-shot classification method (prototypical networks) demonstrate that both the proposed training strategies can highly accelerate the training process without accuracy loss for varying few-shot classification problems on Omniglot and miniImageNet.