Reviewerly/exHarmony huggingface dataset:
Rapid Restart Hill Climbing for Learning Description Logic Concepts

Recent advances in learning description logic (DL) concepts usually employ a downward refinement operator for space traversing and hypotheses construction. However, theoretical research proved that ideal refinement operator does not exist for expressive DLs, including the language $$\mathcal {ALC}$$. The state-of-the-art learning framework DL-Learner suggests to use a complete and proper refinement operator and to handle infiniteness algorithmically. For example, the CELOE algorithm follows an iterative widening approach to build a search tree of concept hypotheses. To select a tree node for expansion, CELOE adopts a simple greedy strategy that neglects the structure of the search tree. In this paper, we present the Rapid Restart Hill Climbing (RRHC) algorithm that selects a node for expansion by traversing the search tree in a hill climbing manner and rapidly restarts with one-step backtracking after each expansion. We provide an implementation of RRHC in the DL-Learner framework and compare its performance with CELOE using standard benchmarks.
의식 고양 과제를 활용한 한국어 학습자 대상 ‘-다’체 교육 방안

The purpose of this paper is to propose a method for teaching ‘-다’ style to Korean learners by using “Consciousness-Raising Tasks”, which consists of requiring learners to solve grammar rules problems through information exchange and interaction among themselves. Among the typical sentence endings that Korean Language learners are exposed to, the ‘-다’ style generates continuous errors even at the intermediate level due to the complexity of the rules and lack of practice. The study reviewed the types of exercises and activities currently adopted by most textbooks and proposed teaching procedures and a series of tasks for Level 3 learners based on “Consciousness-Raising Tasks” as an alternative or supplementary student materials. (Korea University)
LUCAS Soil Texture Processing Scripts

This repository is a placeholder for the processing scripts of the soil texture data included in the LUCAS dataset [5]. This script is used for a study [3, 4] of 1D Convolutional Neural Networks (CNNs). The code of the 1D CNNs is published in [2].
Naïve Bayes Classification

The data mining methods we have been learning all favor numerical data: linear regression, LDA, k-means clustering, logistic regression, and K-NN. Naïve Bayes favors categorical data, however. Because of its simplicity, naïve Bayes data mining method is much more efficient compared to other data mining methods, while its performance can still match most other data mining methods.
IMPLEMENTING UNSTRUCTURED DATA SECURITY IN SMALL AND MEDIUM ORGANIZATIONS

Abstract : Protecting data from any organization is not an easy task. It requires immense knowledge and strategies on the part of data security engineers in information and communication technologies, because many times they require very high costs and resources. protect this very valuable asset for any line of business dealing with sensitive and confidential data and information such as credit card numbers, medical information, regulated contents of state or country secrets, many of which are confused between privacy policies and data protection policies, the two concepts relate to each other because they are based on confidentiality, availability and data integrity. In recent years, due to technological advances and the globalization of electronic commerce, government policies have also changed their data protection laws in favor of technologies, in a set of millions of files that can be found in a storage place like hard drives, pen drives and more makes it imperative to define what sensitive data is? Who has access to them? Who is using it and for what purposes? With these range of questions, you will be able to define appropriate policies for tracking and monitoring to provide reliable security suggestions. The framework used in this work analyzes the behavior of users and integrated systems that access data and can alert competent entities to bad breach behavior, and to protect files and email servers from cyber-attacks and internal threats by organizations' attempts to rule access control not allowed to sensitive data.
Procesos de enseñanza y aprendizaje guiados por pares

The following article is the result of an experience carried out at the School of Medicine of the University of Buenos Aires (UBA), related to peer-to-peer-mediated and near peer mediated learning. The learning and teaching process is analyzed in this context. Similarly, the perception of students and teachers is weighted. Certain tendencies to the adoption of the new tools are verified and also certain anachronisms product of learning processes that have begun very early in the school life of the students.
Research and Realization on Big Data of Science and Technology Resources Search Engine Based on SOLR

Since the reform and opening to the world, the development of science and technology has made great progress. A large amount of scientific and technological resources has been accumulated in China, the storage forms of which can be divided into two categories: structured data and unstructured data. SOLR playing an important role in this field is a means for us to obtain information and data fast and precisely. In this paper, we import the stored data to SOLR, and establish the index of relevant content. Then the retrieval results data could be presented quickly on the basis of index.
The Effect of Corruption on Economic Growth in Nigeria

Previous empirical studies have appraised efforts of the government in fighting corruption in Nigeria. In spite of this, corruption has been identified as one of the hindering factors for attaining the desirable economic growth. This study therefore examines the effect of corrup-tion on economic growth in Nigeria between 1987 and 2017 using secondary time series data sourced from the Transparency Interna-tional, Central Bank of Nigeria (CBN) Statistical Bulletin and World Development Indicators (WDI). Data were analysed using Ordinary Least Squares (OLS) techniques and Barro model. The result shows a negative significant relationship between corruption and economic growth and recommends that the government should strengthen the anti-corruption agencies in order to comprehensively fight corruption in Nigeria.
On Discourse System for International Communication of Indigenous Culture in the Era of Omni media

Indigenous culture is the intelligence and spirit of Chinese people. As the development of economic, Chinese culture is also required to go back the center of world culture. The key strategy of Chinese culture â€œgoing globalâ€ is to construct proper discourse system to internationally communicate the indigenous culture, as well as an effective way to tell the good story of China. Therefore, this paper mainly investigates in the problems of indigenous culture in Shaanxi during the international communication, and then explores the ways to construct the discourse system with Chinese features under the circumstance of Omni media. Whatâ€™s more, the research on discourse system for international communication of indigenous culture can enlighten the Chinese traditional culture to transmit the history and customs, and build a bridge for Chinese culture â€œgoing global.â€
Construction and Practice of Characteristic Specialty in Universities

The article is aimed at the construction of national-level specialty of â€œMicroelectronics Science and Engineeringâ€ of Chongqing University of Posts and Telecommunications. According to the relevant policies and regulations of the Ministry of Education and the Chongqing Municipal Education Commission, the implementation opinions on the development of microelectronics science and engineering professional training programs are proposed, combined with the school background. Based on the school background and current situation, the research content, objectives and problems to be solved are put forward, and the expected benefits of this construction project are finally put forward.
Deep Generative Models for Semantic Text Hashing

As the amount of textual data has been rapidly increasing over the past decade, efficient similarity search methods have become a crucial component of large-scale information retrieval systems. A popular strategy is to represent original data samples by compact binary codes through hashing. A spectrum of machine learning methods have been utilized, but they often lack expressiveness and flexibility in modeling to learn effective representations. The recent advances of deep learning in a wide range of applications has demonstrated its capability to learn robust and powerful feature representations for complex data. Especially, deep generative models naturally combine the expressiveness of probabilistic generative models with the high capacity of deep neural networks, which is very suitable for text modeling. However, little work has leveraged the recent progress in deep learning for text hashing. Meanwhile, most state-of-the-art semantic hashing approaches require large amounts of hand-labeled training data which are often expensive and time consuming to collect. The cost of getting labeled data is the key bottleneck in deploying these hashing methods. Furthermore, Most existing text hashing approaches treat each document separately and only learn the hash codes from the content of the documents. However, in reality, documents are related to each other either explicitly through an observed linkage such as citations or implicitly through unobserved connections such as adjacency in the original space. The document relationships are pervasive in the real world while they are largely ignored in the prior semantic hashing work. In this thesis, we propose a series of novel deep document generative models for text hashing to address the aforementioned challenges. Based on the deep generative modeling framework, our models employ deep neural networks to learn complex mappings from the original space to the hash space. We first introduce an unsupervised models for text hashing. Then we further introduce the supervised models that utilize document labels/tags as well as consider document-specific factors that affect the generation of words. To address the lack of labeled data, we employ unsupervised ranking methods such as BM25 to extract weak signals from training data. We propose two deep generative semantic hashing models to leverage weak signals for text hashing. Finally, we propose node2hash, an unsupervised deep generative model for semantic text hashing by utilizing graph context. It is designed to incorporate both document content and connection information through a probabilistic formulation. Based on the deep generative modeling framework, node2hash employs deep neural networks to learn complex mappings from the original space to the hash space. The probabilistic generative formulation of the proposed models provides a principled framework for model extension, uncertainty estimation, simulation, and interpretability. Based on variational inference and reparameterization, the proposed…
Extractive Summarization by Rouge Score Regression Based on BERT

In this paper, we present BESRES, a BERT-based regression model for extractive document summarization. We mainly explore two techniques for employing the BERT model for extractive document summarization: (1) Token Score Prediction: we propose to decompose the sentence score prediction into token-level score prediction, and (2) Soft Label: we propose a new attempt of using uses Rouge scores as an index for scoring the importance of a sentence. From experiment results, our model incorporating the two proposed techniques advances the state-of-the-art result on CNN Dailymail dataset from 42.99 to 43.42 in terms of Rouge-1 score.
Necesidades formativas del profesorado universitario de Historia de Cuba Básica. Fiabilización y validación de un cuestionario para su diagnóstico

Teaching Cuban history is a matter of top priority in the context of the current educational transformations that the country is experiencing. In this process, an element of vital importance is the increase of virtualization in Cuban higher education. For this purpose, it is essential to determine the level of preparation that professors have of this subject to face these changes. The article describes the design and validation of a questionnaire to diagnose the training needs of university professors who teach Basic History of Cuba in the blended learning modality with the use of Information and Communication Technologies. The validation process of the instrument has been developed through consultation of experts and the Exploratory Factorial and Confirmatory Factorial analyses. The reliability of the questionnaire is determined using the Cronbach Alpha and McDonald Omega coefficients.
Evolving Fuzzy Models of Mechatronics Applications : Plenary Talk

Evolving fuzzy models are characterized by the continuous online rule base learning. The structure and parameters of these fuzzy models in their Takagi–Sugeno or Takagi–Sugeno–Kang (TSK) forms are obtained by several online identification algorithms. The online identification algorithms continuously evolve the parameters of the fuzzy models, which are built online by adding new or removing old local models; this process is referred to as the adding mechanism.According to the classification proposed by Dovžan, Logar and Skrjanc in 2015, three categories of online identification algorithms are considered, (1), (2) and (3), and they have specific features described as follows. (1) Adaptive algorithms start with the initial TS fuzzy model structure given by other algorithms or by user experience; the number of space partitions/clusters does not change over time, and these algorithms adapt just the parameters of the membership functions and the local models. (2) Incremental algorithms implement only adding mechanisms. (3) Evolving algorithms, which besides the adding mechanism they also implement removing and a part of them merging and splitting mechanisms as well.The presentation is organized to highlight a part of the recent results obtained by the Process Control group of the Politehnica University of Timisoara, Romania, related to the application of incremental online identification algorithms that belong to the category (2) mentioned above. A convenient implementation of these algorithms is given in two versions that depend on how the estimation of parameters in TS fuzzy model rule consequents is performed. The identification algorithms produce sets of TS fuzzy models of some representative mechatronics applications in the laboratories of the Process Control group, where the experimental validation is guaranteed in order to next enable the development of model–based fuzzy control system structures: pendulum-crane systems, twin rotor aerodynamic systems, magnetic levitation systems, and anti-lock braking systems.
The Influence Of User Training And Information System Development Formalization On Accounting Information System

This study aims to determine the influence of User Training, and Formalization of The Development Information System towards performance of Accounting Information System. This research uses quantitative methods using primary data in the form of questionnaires. The analytical method used in this study is the method of multiple linear regression with Rstudio software.The results showed (1) User Training has no effect on the Performance of Accounting Information Systems; (2) Formalization of Information System Development has no effect on Accounting Information System Performance; (3) User Training, and Formalization of Information System Development simultaneously affect Accounting Information System Performance.
THE OCCURRENCE OF CLASTIC SUCCESSIONS BELOW UC-3 UNCONFORMITY IN SOUTH MALAY BASIN: A NEW INSIGHT FROM PETROGRAPHY, XRD AND SEM ANALYSES

Summary This report presents the results of a multidisciplinary sedimentological studies for eight wells, located within South Malay Basin offshore Terengganu, Malaysia. The study intervals focus on the Pre-Tertiary - Early Tertiary aged successions below the UC-3 unconformity. A multidisciplinary study has been conducted on selected cutting samples from different wells, drilled from this region to analyze the reservoir potential and to confirm the occurrences of clastic successions below the UC-3 unconformity. To achieve this, a total of forty samples were selected from eight wells namely Well A, Well B, Well C, Well D, Well E, Well F, Well G and Well H.
A comparative study of vocabulary in secondary English textboks in North Korea and South Korea: A corpus-based aproach

In 2013, North Korea publicized a new middle school National Curriculum. This new curriculum brought about many changes within the North Korean education system. This current study examined the new North Korean middle school English textbook, comparing it with comparable textbooks of South Korea to assist in the understanding of current ELT practices. Corpus-based analysis was conducted in order to compare English textbooks from the first grades of middle school in both North Korea and South Korea. The research outcomes revealed similarities between the textbooks from North Korea and South Korea in many regards. Both the North Korean English textbook and South Korean English textbooks included words related to the use of computers and technologies. Moreover, both North Korean and South Korean English textbooks had vocabulary relating to the conventional lives of middle school students. The North Korean textbook, however, included limited types of verb inflections, when compared to South Korean textbooks. Meanwhile, only 12% of the words in the North Korean English textbook were inclusive of the basic word list at the middle school level in the 2009 Revised National Curriculum in South Korea. The results of this research are expected to contribute not only to the development of a unified curriculum for South and North Korea, but also to supporting North Korean defectors in South Korean Schools at a future point in time.
Innovation Research on Financial Reimbursement of Colleges and Universities Based on Big Data Technology

With the development of The Times, the financial reimbursement business volume of colleges and universities increases year by year. The traditional financial reimbursement has many repetitive work, low efficiency and complicated approval procedures. The current financial management concept of colleges and universities cannot keep up with the pace of big data technology development. This paper studies the innovation of big data technology on financial reimbursement of colleges and universities, discusses the positive impact of big data technology on financial reimbursement of colleges and universities, as well as new policy suggestions on financial reimbursement of colleges and universities, in order to solve the contradiction between the two sides of financial reimbursement, and the smooth development of financial reimbursement. With the promotion of the new big data technology, colleges and universities should also strengthen the construction of the talent team of financial informatization and open up the path of financial development featured by information.
Study on the English Language Education in China

The study picturized the English language education in China, especially in the mainland. After briefly tracing the history of ELE in China, the overview of the Chinaâ€™s ELE present situation is generalized and the possible outlook of Chinaâ€™s ELE future is depicted. Finally, it states that the study is helpful and significant for English language education practitioners, especially for English major college students and teachers.
Visualizing the Deep Carbon Cycle

Geoscientists have created animations to help visualize different components of Earth’s carbon cycle.
The Intelligent Composition Evaluation System Based on Anomaly Detection

Compositions evaluation is one of the main tasks in Chinese teaching, how to evaluate compositions quickly and efficiently is an important assignment of language intelligence research. A method combining artificial intelligence and language intelligence is proposed to solve this problem, constructing a compositions evaluation system based on anomaly detection which belongs to unsupervised learning algorithm, applying Gaussian distribution theory to distinguish these abnormal characteristics from normal ones in a batch of compositions, according to which method could quickly identify anomalies in the compositions lead to low scores and ultimately achieve the goal of evaluating compositions accurately and efficiently.
Data-driven Methods in Inverse Problems

In this thesis on data-driven methods in inverse problems we introduce several new methods to solve inverse problems using recent advancements in machine learning and specifically deep learning. Th ...
An Improved Particle Swarm Optimization for History-Matched reservoir Parameters

Summary In this work, we propose an improved PSO algorithm that uses an averaging filter to reduce the number of function evaluations required to locate an optimal point. The method is applied to estimate history-matched reservoir parameters on two test problems and comparison is made with the standard PSO and the distance-weighted exponential natural evolutionary strategies (dxNES) (Fukushima et al. 2011). Results from the comparisons show that the proposed algorithm outperforms the other algorithms.
Challenges and Opportunities for Developing Communicative Competence of Future Teachers

This paper deals with the challenges of developing communicative competence representing a fundamental competence in the framework of future teacher professional training. The authors believe, that the above competence is underpinned by the speech mastery of a teacher and effective communication practice. The authors identified contradictions in the existing system of communicative training and outlined the way forward for developing communicative competence of students in pedagogical universities in the context of implementation of the new Federal State Standard of Higher Education. Communicative competence is viewed as a universal transprofessional meta-competence, which should be developed, first of all, through subject areas with philological focus, offering special assignments aimed at developing practical communicative skills: language, speech and rhetorical skills necessary for further professional activity of a teacher. The research carried out by the authors, supported by theoretical analysis, synthesis, classification and generalization, and method of pedagogical design, made it possible to refine and adjust the variable part of curricula, to make substantial changes in the design of programs and classes for students seeking Bachelor’s degree in the field of training Pedagogical Education, specialties Primary Education; and Pre-school Education. Primary education. The presented methodological experience indicates possible ways to eliminate the identified contradictions in the context of raising a future teacher.
Diagnosis and Optimization of Teachers' Educational Behavior in Kindergarten Living Activities

In the process of organizing and implementing kindergarten living activities, the teachers' inappropriate educational behaviors will cause the educational value of living activities not to be effectively exerted. This article analyzed and diagnosed the teacher's educational behavior during the organization and implementation of kindergarten living activities through some practical cases, and pointed out the main problems. Combined with the guiding principles that teachers should pay attention to in daily living activities, specific improvement suggestions and implementation strategies were put forward.