Reviewerly/exHarmony huggingface dataset:
신경망기계번역 기술 진화와 번역품질 분석

There was a big technical progress in the research field of machine translation: the main approach has switched from statistical machine translation (SMT) to neural machine translation (NMT), leading to dramatic improvements in translation quality. Recently, another progress has been taking place from recurrent neural network(RNN)-based NMT to transformer-based NMT (T-NMT). As the performance of NMT has evolved, a lot of research papers for machine translation have been published in the field of interpretation and translation. Their main focus is on whether machine translation can replace human translation, and analyzing the quality of translation results.In this paper, we briefly explain the history of the machine translation research and review the mechanism of NMT. NMT is basically composed of three parts: encoder, attention mechanism, and decoder. Further we discuss the new transformer structure based on the encoder-decoder model.We also discuss the challenges in NMT and explain the research direction or solutions to the problems. Particular attention is given to the mistranslation of NMT, quality of the translation, and robustness against the noises in the training dataset as well as in the testing sentences. In order to test the performance of transformer-based NMT, we used the Google NMT (GNMT) service for 4 languages - Korean, English, German, and Japanese. We confirmed the robustness against sentences with noises. However, we found unexpected volatility of NMT models where the input sentence is semantically and syntactically correct, resulting in critical degradation of translation quality.
LibGuides: Pathways to Research Data Management series: Pathways to Research Data Management Series

Pathways to Research Data Management is a series of workshops developed by the University of Victoria Libraries to support students and faculty in developing new skills and knowledge to support the management of their research data
The new concept of laboratory support for educational institutions and scientific establishments

Purpose: To develop a new concept of laboratory support for learners at different levels of education to maintain a high education quality in conditions of limited opportunities, such as lockdown, lack of funding, etc. Design/methodology/approach: To create a new concept, the practical experience of school teachers, educators of technical colleges and universities, research workers, as well as experience of scientists in the educational field published in open information' sources was taken into account. The development of a new concept was carried out from 2014 to 2016, and its implementation, introduction into the educational process, and possible ways to solve technical and organizational difficulties from 2016 to the present. In the last year, special attention was paid to distance learning. Findings: The development and the first practical steps to implement the new concept of updating the laboratory support of educational institutions clearly demonstrate of its practical implementation' feasibility. Positive consequences from the implementation were identified as constant improvement of laboratory equipment, increase in the educators’ level, transformation of virtual distance learning based on lectures and videos into real learning with "live" visual demonstration of physical, chemical, biological and other processes and experimental research. As a recommendation, should wish that at the state level to recommend the implementation of this approach and to propose some legal mechanisms to overcome identified problems. Research limitations/implications: The present study was conducted for Ukrainian educational institutions with the involvement of foreign students from different countries studying in Ukraine. Practical implications: The introduction of a new concept into the educational process in countries with a weak economy will improve the educational quality and reduce the cost of creating laboratory support. Originality/value: Equipment should not only be a teaching tool, but also a teaching subject.
THE PROBLEM OF FORMATION OF PROFESSIONAL MOTIVATION OF FUTURE PSYCHOLOGISTS IN THE TRAINING PROCESS

The article considers theoretical approaches to the study of motivation during the training of future psychologists in higher education, the structure of professional motivation for learning. Emphasis is placed on the complexity of mastering the profession of a practical psychologist, which includes in addition to the acquisition of knowledge, practical skills and abilities, the implementation of important work on the formation of one's own personality.The effectiveness of professional training of future psychologists directly depends on the degree of formation of their values to future professional activities. After all, the training of the future profession is more productive in the presence of strong, bright motives that arouse the desire to actively engage in this activity.Values of professional activity - an integrated quality of personality, based on professional and moral self-awareness, the importance of which encourages activity in mastering professional knowledge, skills and abilities.The appropriate level of professional training of the future psychologist is possible only under the condition of development and self-realization of his personality, activation of educational and cognitive activity of students-psychologists.The problem of formation of professional motivation of the future psychologist during training in an institution of higher education is experimentally investigated.
Self-Supervised Visibility Learning for Novel View Synthesis

We address the problem of novel view synthesis (NVS) from a few sparse source view images. Conventional image-based rendering methods estimate scene geometry and synthesize novel views in two separate steps. However, erroneous geometry estimation will decrease NVS performance as view synthesis highly depends on the quality of estimated scene geometry. In this paper, we propose an end-to-end NVS framework to eliminate the error propagation issue. To be specific, we construct a volume under the target view and design a source-view visibility estimation (SVE) module to determine the visibility of the target-view voxels in each source view. Next, we aggregate the visibility of all source views to achieve a consensus volume. Each voxel in the consensus volume indicates a surface existence probability. Then, we present a soft ray-casting (SRC) mechanism to find the most front surface in the target view (i.e. depth). Specifically, our SRC traverses the consensus volume along viewing rays and then estimates a depth probability distribution. We then warp and aggregate source view pixels to synthesize a novel view based on the estimated source-view visibility and target-view depth. At last, our network is trained in an end-to-end self-supervised fashion, thus significantly alleviating error accumulation in view synthesis. Experimental results demonstrate that our method generates novel views in higher quality compared to the state-of-the-art.
Bitcoin burglaries and the Theft Act 1968

Discusses the growing trend for criminals to burgle the homes of bitcoin holders and force them to disclose the password to their cryptocurrency. Reviews the operation of crypto asset systems, and considers, with reference to three hypothetical scenarios, how such rubber hose attacks might be prosecuted, including whether they constitute property offences for the purposes of theft and burglary convictions under the Theft Act 1968.
Three-class Overlapped Speech Detection using a Convolutional Recurrent Neural Network

In this work, we propose an overlapped speech detection system trained as a three-class classifier. Unlike conventional systems that perform binary classification as to whether or not a frame contains overlapped speech, the proposed approach classifies into three classes: non-speech, single speaker speech, and overlapped speech. By training a network with the more detailed label definition, the model can learn a better notion on deciding the number of speakers included in a given frame. A convolutional recurrent neural network architecture is explored to benefit from both convolutional layer's capability to model local patterns and recurrent layer's ability to model sequential information. The proposed overlapped speech detection model establishes a state-of-the-art performance with a precision of 0.6648 and a recall of 0.3222 on the DIHARD II evaluation set, showing a 20% increase in recall along with higher precision. In addition, we also introduce a simple approach to utilize the proposed overlapped speech detection model for speaker diarization which ranked third place in the Track 1 of the DIHARD III challenge.
A Theoretical Model For Artificial Learning, Memory Management And Decision Making System

Human beings are considered as the most intelligent species on Earth. The ability to think, to create, to innovate, are the key elements which make humans superior over other existing species on Earth. Machines lack all those elements, although machines are faster than human in aspects like- computing, equating etc. But humans are still more valuable than machines, due to all those previously discussed elements. Various models have been developed in the last few years to create models that can think like human beings, but are not completely successful. This paper presents a new theoretical system for learning, memory management and decision making that can be used to develop highly complex systems, and shows the potential to be used for development of the systems that can be used to provide the essential features to the machines to act like human beings.
A Layered Approach for Merging Application Ontologies: A Case Study from Agriculture Domain

Ontology is a machine interpretable way of representing knowledge in a precise and complete way favorable to solve many problems in the field of knowledge engineering. Different knowledge areas evolve with the time and the applications which use ontologies should be updated with new knowledge accordingly. It is more effective to combine the ontologies with new knowledge with the existing application ontologies rather than designing a new ontology from scratch. When combining ontologies, keeping the original usability of the initial ontologies and the heterogeneity of the components of the ontologies are the main obstacles. As there are no universal standard for naming of ontology components, this is a major reason for the heterogeneity problem. Methods for overcoming these problems are needed. In this research, we have proposed two algorithms to overcome the aforementioned problems. These algorithms for finding correspondences of ontology concepts and merging domain specific application ontologies keeping the original usability of the initial ontologies are the main outcomes of this research. The proposed algorithms are evaluated in terms of accuracy by comparing the resultant ontology merged using the proposed algorithm and the resultant ontology merged by an expert. The evaluation results prove that the proposed methodology merges the domain specific application ontologies very similar to the ontology merged by human intervention.
FONTNET: On-Device Font Understanding and Prediction Pipeline

Fonts are one of the most basic and core design concepts. Numerous use cases can benefit from an in depth understanding of Fonts such as Text Customization which can change text in an image while maintaining the Font attributes like style, color, size. Currently, Text recognition solutions can group recognized text based on line breaks or paragraph breaks, if the Font attributes are known multiple text blocks can be combined based on context in a meaningful manner. In this paper, we propose two engines: Font Detection Engine, which identifies the font style, color and size attributes of text in an image and a Font Prediction Engine, which predicts similar fonts for a query font. Major contributions of this paper are three-fold: First, we developed a novel CNN architecture for identifying font style of text in images. Second, we designed a novel algorithm for predicting similar fonts for a given query font. Third, we have optimized and deployed the entire engine On-Device which ensures privacy and improves latency in real time applications such as instant messaging. We achieve a worst case On-Device inference time of 30ms and a model size of 4.5MB for both the engines.
Active training-as a requirement of educational reform

In every society, education has served ensuring the interests of the people as a social process. This is so because man is not for education, education is for man. The education reform implemented in the country is aimed at creating a National Education System and is based on the most preferred principles of democratization, humanization, integration, differentiation, individualization and a number of principles in modern times. The implementation of these principles implies that the student becomes a key participant in the educational process, forming him as a personality. As time changes, so does the education system. Curricula, the content of education and training methods must also change. Active training methods are one of the most important training methods in the field of education in our globalized world. Key words: active training, globalizing world, education reform, cooperation, individualization
Obstacles In Learning Speaking Faced By The 8th Grade Students In Smp Muhammadiyah 01 Kartasura

The purpose of this study is to identify the obstacles faced by the 8th grade students in SMP Muhammadiyah 01 Kartasura in learning speaking and describe how the teachers deal with the obstacles faced by the students. This is a descriptive qualitative research in which the techniques of data collection in this study are observation, interview, and documentation. In this case the researcher use an Indonesian language to interview with the respondents in order to get information clearly. The subject of this research are 20 students and two English teachers. To analyze the data, the researcher applies Miles and Huberman’ theory of analyzing qualitative data involving three steps: data reduction, data display, and data verivication. The theoretical foundation used is Dewey's (1938) theory of learning by doing theory and Tanveer (2007). Based on the data analysis, it can be concluded that the obstacles faced by the 8th grade students in SMP Muhammadiyah 01 Kartasura in learning English, especially speaking, are divided into two factors, namely internal factors, and external factors. The first is the internal factors. In internal factors, there are six obstacles faced by the students in learning speaking such as mother tongue, lack of motivation, fear of mistake, shyness, anxiety and lack of confidence. Next is the external factor. External factors are divided into four, namely, unsupportive environment, limited learning time, there are too many students in one class and different class characters. The methods that teachers used to overcome these obstacles are by using repetition, memorization, using listening, and jigsaw learning method.
Research on robot optimal path planning method based on improved ant colony algorithm

To overcome the problem of poor convergence and obstacle avoidance when traditional methods are used to plan the optimal path of robot, a new optimal path planning method based on improved ant colony algorithm is proposed. Firstly, the odometer model, ultrasonic sensor model and robot motion model are constructed to obtain the environmental information and robot motion state information. Then, according to the adaptive transformation heuristic function of the target point and the principle of wolf swarm assignment, the pheromone is refreshed. On this basis, the core parameters of the improved ant colony algorithm are optimised by particle swarm algorithm, so as to complete the optimal path planning of the robot. The experimental results show that the overall mean value of collision avoidance of the proposed method is 0.97, and the planning performance is significantly better than that of similar planning methods, with considerable application value.
Analisis Keamanan Jaringan Pada Pay2home Menggunakan Metode Penetration Testing

Penetration Testing is a method used in finding loopholes in the system, in order to minimize attacks and breaches in a company website network. In tests carried out on the pay2home network at shremit.p2hdevworks.com using several penetration testing applications to test network security including Nessus, Acunetix, W3af, and Nmap. Where each of these applications functions to test attacks and look for security gaps on the network and system, the variables that are the reference in this trial are the gaps that are obtained, where the results of the research show that the pay2home.com network website has a fairly high security.
Cultivating and Promoting a Culture of Reading Among Fathers of Men

William Wordsworth has rightly said that the child is father of the man. The whole story of human progress is based on the creative spirit of mankind. Hence it is essential for us to empower the child with critical thinking, decision making, and problem solving skills from his childhood. School libraries and public libraries have a pivotal role to play in this regard, but that is possible only when these libraries themselves are properly developed. School libraries are the weakest link in library and information infrastructure in India. This paper highlights the factors responsible for a declining culture of reading among children in India and also suggests the ways to promote the reading culture from the early days of childhood.
THE PECULIARITIES OF ACADEMIC PROCRASTINATION OF STUDENTS-FUTURE POLICE OFFICERS

I. Baranauskienė1, O. Bohucharova2, A. Kovalenko3 1Klaipėda University (LITHUANIA) 2E.O. Didorenkо State University of Internal Affairs of Luhansk, Sievierodonetsk (UKRAINE) 3Taras Shevchenko National University of Kyiv (UKRAINE)
Improving Reading Comprehension By Using Internet Of The Fourth Semester Students Of Stiba Persada Bunda

The goal of this research was to get information whether the students’ reading comprehension could be improved through Internet using at the Fourth Semester Students of School of Foreign Language at Persada Bunda. The cases were some students’ reading comprehension still low or below standard passing of test. The research was classroom action research. The participants of this research were the Fourth Semester Students of School of Foreign Language at Persada Bunda. They are consisted of 12 students. This research was held into two cycles, each cycle consisted of three meetings. The research data were divided into two major types namely, quantitative and qualitative data. The quantitative data were taken through reading comprehension test. The qualitative data were collected through observation, and field notes. The result of the analysis showed that the students grades improved after internet using. The improvement happened on each cycle at the research. The improvement of students’ reading comprehension scores were influenced by factors such as motivation, interest, and classroom atmosphere. Thus, it was concluded that internet using can improve students’ reading comprehension. The internet using also increases students’ speed in reading comprehension that can be used by students of School of Foreign Language at Persada Bunda. Therefore, the internet using is recommended to be used by the students and lecturer in teaching and learning reading comprehension at the School of Foreign Language of Persada Bunda.
Fourier Transform based Digital Watermarking Scheme for Biometric Images

With the advancement of digital world, security and privacy of bio-metric data is of paramount importance, especially during the transmission in erroneous communication net-works as well as for storage and processing in large databases. The proposed zero-watermarking technique solves the problem of bio-metric data security without distorting the bio-metric data. In the proposed zero-watermarking algorithm which uses the simplistic Fourier transform based feature extraction in conjunction with the standard singular value decomposition (SVD) from bio-metric images, a digital operation has been performed with user details to generate the unique identification key. On the other hand, this key is used to extract the user details following the approach similar to key generation thereby ensuring data security without distorting the bio-metric image. The experimentation have been performed on various real-life bio-metric images making it suitable for digital watermarking in practical scenarios. The pro-posed scheme achieves unique identification and authentication of bio-metric images and robustness against various image processing attacks.
Design Of Mobile-Based Decoration Service E- Marketplace Application System

Nowadays, people tend to search for goods or services through their mobile device using an electronic marketplace. In Bali, a decoration business for weddings, religious events, and other events grow rapidly. This business provides services that rents various kinds of decorations such as tents, chairs, tables, stages and others that are commonly used in ceremonial events. However, unfortunate for decoration service entrepreneurs who have high prospects, many of them are using the traditional method of marketing and sales such as word-of- mouth. Moreover, many of them not catching up with the current development of the e-market. By listing the services on the e-marketplace, easier for the customer to find the desired decoration services that fit their needs and budget. Also, by utilizing the e-marketplace will save the customer time, because the customer can just search, compare, and book to rent decorations without leaving home. The purpose of this research project is to create a mobile-based application of market place that accommodates entrepreneurs renting decorations. Our purposed mobile-based marketplace uses the prototyping method to build this mobile-based application.
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning

With the ubiquitous graph-structured data in various applications, models that can learn compact but expressive vector representations of nodes have become highly desirable. Recently, bearing the message passing paradigm, graph neural networks (GNNs) have greatly advanced the performance of node representation learning on graphs. However, a majority class of GNNs are only designed for homogeneous graphs, leading to inferior adaptivity to the more informative heterogeneous graphs with various types of nodes and edges. Also, despite the necessity of inductively producing representations for completely new nodes (e.g., in streaming scenarios), few heterogeneous GNNs can bypass the transductive learning scheme where all nodes must be known during training. Furthermore, the training efficiency of most heterogeneous GNNs has been hindered by their sophisticated designs for extracting the semantics associated with each meta path or relation. In this paper, we propose WIde and DEep message passing Network (WIDEN) to cope with the aforementioned problems about heterogeneity, inductiveness, and efficiency that are rarely investigated together in graph representation learning. In WIDEN, we propose a novel inductive, meta path-free message passing scheme that packs up heterogeneous node features with their associated edges from both low- and high-order neighbor nodes. To further improve the training efficiency, we innovatively present an active downsampling strategy that drops unimportant neighbor nodes to facilitate faster information propagation. Experiments on three real-world heterogeneous graphs have further validated the efficacy of WIDEN on both transductive and inductive node representation learning, as well as the superior training efficiency against state-of-the-art baselines.