Reviewerly/exHarmony huggingface dataset:
POSSIBILITIES OF USING VR IN EDUCATION AS A PEDAGOGICAL TOOL

The paper presents studies and experience of their application and impact of VR in education. Their influence on thinking and psychological state of a person is traced. Virtual reality is considered as a special information environment, in which all objects are presented in three dimensions, there is a wide animation, change of images in real time and experience of effective presence. Thus, it is reasonable to interpret virtual reality as a method, means and technologies of training. Also, some directions of education in which VR gives advantages over other means of visualization and accuracy of reproduction in the safe environment of a lesson are highlighted. Through the power of psychological influence, which is provided by means of virtual reality, depending on a kind of used gadgets, new opportunities and possibilities arise as well as requirements to the lessons and representatives of the sphere of education. New safety rules, lessons design have to consider new possibilities and peculiarities of virtual reality and technologies providing it.. Also it is important to take into consideration the issue of socialization in new conditions. This aspect must also be considered in the design of educational process and all its components. Because of enhanced visuality of information the risks of incorrect premises perception might appear as a result of misinterpretation of the information given by a teacher. That is why it is necessary to control the simplicity and rationality of the semantic charge both during theoretical and practical lessons. That is why it is impossible to fully change people for machinery as it is not able to understand wrong interpretation of information by a student whereas an experienced teacher has abstract thinking and life experience required for the task
The moral and psychological environment in the library studies in 1930s. On the occasion of the 120-anniversary of Yury Grigoryev

Professor Yury Grigoryev (1899–1973) is a prominent Soviet Library scientist; he made a valuable contribution into the studies of library collections and general studies of book culture. The author reviews just one episode in Grigoryev’s biography, i. e. the publication of his practical aid for librarians “Exhibition activities in libraries” (1931). The same 1931 year, N. T. Tol­kachev, post-graduate student of the Library Studies Institute, published the slashing review “The bourgeois librarianship disguised as Marxist”. Instead of objective academic criticism, he descended upon the author with the hard political blames using Josef Stalin’s concept of class struggle exacerbation during the USSR’s advancing towards socialism. Yury Stolyarovr includes this review in his paper in full, with his own notes and remarks. He argues that the above reviewer was incompetent in the issues he criticized. This publication characterizes the environment of the Big Terror political implacability. The author emphasizes that our contemporary librarians have to know what the moral and psychological conditions for even most prominent librarians of the past were.
The Impact of Internet Finance in an Open Economy

The issue of Internet finance system has been a matter of hot debate among academics and policymakers. The purpose of this paper is to explore the implications of Internet finance for the effects of an open economy. While lots of papers may be empirically more relevant, this paper seems to be theoretically more interesting. The small open economy model is applied in this study. The outcome shows that the widespread use of Internet finance has a positive increase effect on the national income in the open economy. Besides, it is also discovered that Internet finance has an ambiguous effect on the domestic interest rate and foreign exchange rate in the small open economy.
Preserving the distribution function in surveys in case of imputation for zero inflated data

Item non-response in surveys is usually handled by single imputation, whose main objective is to reduce the non-response bias. Imputation methods need to be adapted to the study variable. For instance, in business surveys, the interest variables often contain a large number of zeros. Motivated by a mixture regression model, we propose two imputation procedures for such data and study their statistical properties. We show that these procedures preserve the distribution function if the imputation model is well specified. The results of a simulation study illustrate the good performance of the proposed methods in terms of bias and mean square error.
A New DEC/TED Code for Fast Correction of 2-Bit-Errors

In this paper a new double-error-correction and triple-error-detection code as a modified BCH code is proposed. It is demonstrated by simulation experiments that the time delay for error correction of 2-bit errors is smaller for the proposed code as for a traditional BCH code with the same number of check bits and the same code distance 6. The new code is of interest for 2-bit error correction for memories.
Bayesian Inference of a Finite Population Mean Under Length-Biased Sampling

We present a robust Bayesian method to analyze forestry data when samples are selected with probability proportional to length from a finite population of unknown size. Specifically, by using Bayesian predictive inference, we estimate the finite population mean of shrub widths in a limestone quarry area with plenty of regrown mountain mahogany. The data on shrub widths are collected using transect sampling, and it is assumed that the probability that a shrub is selected is proportional to its width; this is length-biased sampling. In this type of sampling, the population size is also unknown, and this creates an additional challenge. The quantity of interest is the average finite population shrub width, and the total shrub area of the quarry can be estimated. Our method is assisted by using the three-parameter generalized gamma distribution, thereby robustifying our procedure against a possible model failure. Using conditional predictive ordinates, we show that the model, which accommodates length bias, performs better than the model that does not. In the Bayesian computation, we overcome a technical problem associated with Gibbs sampling by using a random sampler.
Projection‐transformation method with considering energy conservation

Abstract In this paper, a projection‐transformation method that considers the conservation of energy during transformation is proposed. To consider energy conservation, an area‐integration function of the input image is introduced. By conducting the projection‐transformation of this function, energy is conserved during transformation. The pixel value of the transformed image is calculated by integrating the transformed function. The Gauss–Legendre numerical integration scheme is used for the integration calculation. Moreover, the pixel area change during the transformation is considered by using the Jacobian factor. To demonstrate the projection‐transformation, standard images of the SIDBA and SCID datasets are used. Also, the ISO 12233 Test Chart is used for evaluation using high‐frequency images. The quality of the transformed images is evaluated using the structural similarity index and peak signal‐to‐noise ratio values. Because the image quality is affected by the color space, the evaluations are conducted using RGB and YUV color spaces. The results show that the proposed methods produce the highest quality values regardless of the type of image or color space. Moreover, the quality of the transformed image is improved when considering the Jacobian of the pixel area change. The results demonstrate that the proposed method is effective in improving the quality of the projection‐transformed image. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.
A Note on the Zero-Difference Balanced Functions with New Parameters

As a generalization of perfect nonlinear (PN) functions, zero-difference balanced (ZDB) functions play an important role in coding theory, cryptography and communications engineering. Inspired by a foregoing work of Liu et al. [1], we present a class of ZDB functions with new parameters based on the cyclotomy in finite fields. Employing these ZDB functions, we obtain simultaneously optimal constant composition codes and perfect difference systems of sets.
Object extraction in the context of an image registration workflow

With the advent of affordable drone systems, imagery from airborne sensors has become available for addressing many different tasks in various fields of application. For some of these tasks the imagery has to come with a georeference satisfying certain accuracy requirements. If we want to perform such a task and the accuracy of GPS and INS sensors onboard the sensor platform cannot match accuracy requirements or location information is faulty or unavailable, we need to establish a georeference or improve the inaccurate existing one. We do this with our image registration workflow. It matches the contours of objects present both in the imagery and a reference image which comes with a georeference satisfying the accuracy requirement of the task to be performed. This approach has proven to be both feasible and robust to appearance unsimilarity between the image and the reference image, enabling to use a reference that is rather unsimilar in appearance to the image. The workflow comprises four steps, namely extracting the objects, extracting their contours, reducing the amount of contour points and finally matching them. To improve the performance of our workflow, we aspire to improve the performance of each of the four steps individually. While previous work has focused on the finetuning of the three latter steps keeping the object extracting method and thus step one fixed for the time being, the scope of this work was the implementation of a novel object extraction method and its evaluation in the context of the workflow. Long line shaped objects such as road networks are likely to be present both in the image and the reference despite their possible unsimilarity in appearance. The method extracts such objects after growing them by merging smaller individual line-shaped objects if certain merge criteria is met.
Improving Robustness of Shoulder Gesture Recognition Using Kinect V2 Method for Real-Time Movements

Shoulder motion acknowledgment is a vital point in human–PC collaboration. Notwithstanding, a large portion of the current strategies are muddled and tedious, which constrains the utilization of hand motion acknowledgment conditions progressively. In this paper, we propose an information combination based shoulder motion acknowledgment demonstrated by melding profundity data and skeleton information. In light of the exact division and following Kinect V2, the system working can accomplish ongoing execution, which is quicker than a portion of the best in class techniques. Dynamic Region Segmentation is presented. This paper deals with the recognition of shoulder movements. This guarantees its utilization in various certifiable human–PC cooperation errands and improves the use in real time without any restrictions in terms of distance.
Using Convolutional Neural Networks with Direct Acyclic Graph Architecture in Segmentation of Breast Lesions in US Images

Outlining lesion contours in Breast Ultrasound (BUS) images is an important step in breast cancer diagnosis. Malignant lesions infiltrate the surrounding tissue, generating irregular contours, with spiculations and angulated margins, while benign lesions produce contours with a sharp outline and elliptical shape. In breast imaging, the majority of the existing publications focus on using Convolutional Neural Networks (CNNs) for segmentation and classification of lesions in mammographic images. In this study we propose CNNs with direct acyclic graph (DAG) architecture for breast lesion segmentation in US images. We also compare the performance of these two proposed architectures with a series architecture. The best results were obtained with the DAG architecture. The following mean values were obtained for the metrics used to evaluate the segmented contours: Global accuracy = 9593%; IOU = 87.92%; BF score = 68.77%; and Dice coefficient = 89.11%.
Predicting Popular News Comments Based on Multi-Target Text Matching Model

With the development of information technology, there is explosive growth in the number of online comment concerning news, blogs and so on. Good comments can improve the experience of reading, but the massive comments are overloaded, and the qualities of them vary greatly. Therefore, it is necessary to predict popular comments from all the comments. In this work, we introduce a novel task: popular comment prediction (PCP), which aims to find out which comments will be popular automatically. First, we construct a news comment corpus: Toutiao Comment Dataset, which consists of news, comments, and the corresponding label. Second, we analyze the dataset and find the popularity of comments can be measured in three aspects: informativeness, consistency, and novelty. Finally, we propose a novel multi-target text matching model, which can measure these three aspects by referring to the news and surrounding comments. Experimental results show that our method can outperform various baselines by a large margin on the new dataset.
Resepfinder as an iOS-Based Local Cooking Recommendation

The process of finding recipes is needed in the world of cooking. This is because recipes play an important role in the process of making dishes such as helping to prepare cooking needs and giving steps in order to produce delicious dishes. The objective of this research is to design a mobile application to help the Indonesian people to access recipes throughout Indonesia. ResepFinder is an application that recommends recipes based on available ingredients and provides solutions to find recipes by using the smartphone camera to detect an object so that the process of finding becomes easy and appealing. Besides that, ResepFinder also allows its user to communicate with others when they encounter a problem. The system design was done by designing the initial framework or work flow in the form of UML diagram and mockup. While the process of conducting research was carried out by spreading several questions through google form and doing some research on study literature to find out users interests toward cooking. The result of this research is an iOS-based mobile application.
Electronic Literary Text in the Framework of Post-Structuralism Textology

The article features the phenomenon of electronic literary text. The research objective was to identify the structural and semantic features of electronic literary texts within the framework of post-structuralism. The electronic literary text resulted not only from the development of information technology: it is also the product of the development of philosophical and linguistics ideas of post-structuralism. The post-structuralism perspective was not repeated exactly on the technological level of the electronic text representation. However, the post-structuralist text theory was reflected in the electronic literary text structure, i.e. its rhizomatic, decentered, fragmentary, intertextual, and simulation character. In particular, the attempt to build cohesion of semiotically diverse components in the electronic environment reflects the post-structuralists idea of chaos and disorganization. The attempt to provide the navigation in this multi-component unity by the key-word hyperlinks reflects the idea of the total interconnectivity of all structural components, i.e. intertextuality. The phenomenon of intertextuality defines the culmination of decenterment and nomadism in the text theory. It is connected with the rhizomatic concept and hypertextuality. The research used electronic literary texts based on Adobe Flash and HTML. The research employed general scientific methods, such as monitoring and description, in conjunction with the method of comparative linguistic analysis. The author concludes that the text perception and electronic virtual world immersion become much more important than the artifacts in the electronic literature of XXI century. The electronic literary text became the poststructuralist concept of the new esthetic object, lost its integrity and composition stability, and opened itself to external input.
Online Payment Network Design

Payment channels allow transactions between participants of the blockchain to be executed securely off-chain, and thus provide a promising solution for the scalability problem of popular blockchains. We study the online network design problem for payment channels, assuming a central coordinator. We focus on a single channel, where the coordinator desires to maximize the number of accepted transactions under given capital constraints. Despite the simplicity of the problem, we present a flurry of impossibility results, both for deterministic and randomized algorithms against adaptive as well as oblivious adversaries.
Deep Diffusion Autoencoders

Extending work by Mishne et al., we propose Deep Diffusion Autoencoders (DDA) that learn an encoder-decoder map using a composite loss function that simultaneously minimizes the reconstruction error at the output layer and the distance to a Diffusion Map embedding in the bottleneck layer. These DDA are thus able to reconstruct new patterns from points in the embedding space in a way that preserves the geometry of the sample and, as a consequence, our experiments show that they may provide a powerful tool for data augmentation.
An Efficient Framework by Topic Model for Multi-label Text Classification

Most existing multi-label text classification (MLTC) approaches only exploit label correlations from label pairwises or label chains. However, in the real world, features of instances have much importance for classification. In this paper, we propose a simple but efficient framework for MLTC called Hybrid Latent Dirichlet Allocation Multi-Label (HLDAML). To be specific, the topics of text features (i.e., a concrete description of documents) and the topics of label sets (i.e., a summarization of documents) can be obtained from training data by topic model before building models for multi-label classification. After that, hybrid topics can be used in existing approaches to improve the performance of MLTC. Experiments on several benchmark datasets demonstrate that the proposed framework is general and effective when taking text features and label sets into consideration simultaneously. It is also worth mentioning that we construct a new multi-label dataset called Parkinson about diagnosing parkinson disease by Traditional Chinese Medicine.
Improved Quality Estimation of Machine Translation with Pre-trained Language Representation

Translation quality estimation (QE) is a task of estimating the quality of translation output from an unknown machine translation (MT) system without reference at various granularity (sentence/word/phrase) levels, and it has been attracting much attention due to the potential to reduce post-editing human effort. However, QE suffers heavily from the fact that the quality annotation data remain expensive and small. In this paper, we focus on the limited QE data problem and seek to find how to utilize the high level latent features learned by the pre-trained language models for improving QE. Specifically, we explore three strategies to integrate the pre-trained language representations into QE models: (1) a mixed integration model, where the pre-trained language features are mixed with other features for QE; (2) a direct integration model, which regards the pre-trained language model as the only feature extracting component of the entire QE model; and (3) a constrained integration model, where a constraint mechanism is added to optimize the quality prediction based on the direct integration model. Experiments and analysis presented in this paper demonstrate the effectiveness of our approaches on QE task.
ICNet: Incorporating Indicator Words and Contexts to Identify Functional Description Information

Functional description information refers to the texts that describe the functionality or performance characteristics of a certain object. This type of information is of great potential value for the field of intelligence discovery. Thus automatically and accurately identifying this information from large amounts of texts on the web is very important. In this paper we reduce the functional description problem to a binary classification task deciding whether the input sentence is a functional description sentence or not. However, there exist lots of comment texts in the web data, which are semantically very similar to description texts, making our task quite difficult. Also, existing methods only provide general sentence representation models, which can't lead to targeted ways to solve our problem. Therefore, to address the problem, we not only exploit contexts, like many other previous work did, but also introduce indicator word information to learn rich representations. And in order to incorporate them both, we propose two models, namely ICNet(multi-tasks) and ICNet(ensemble). ICNet(multitasks) exploits them jointly in a integrated process of learning representations, while ICNet(ensemble) exploits them by two respective but concatenated sub-models. Experimental results on the collected real-world dataset indicate that both ICNet(multitasks) and ICNet(ensemble) achieve higher F1 scores compared with FaxtText, CNN, RNN, LSTM and Bi-LSTM, QuickThought models on this task.
RevCuT Tree Search Method in Complex Single-player Game with Continuous Search Space

Monte-Carlo Tree Search (MCTS) has achieved great success in combinatorial game, which has the characteristics of finite action state space, deterministic state transition and sparse reward. AlphaGo Zero combined MCTS and deep neural networks defeated the world champion Lee Sedol in the Go game, proving the advantages of tree search in combinatorial game with enormous search space. However, when the search space is continuous and even with chance factors, tree search methods like UCT failed. Because each state will be visited repeatedly with probability zero and the information in tree will never be used, that is to say UCT algorithm degrades to Monte Carlo rollouts. Meanwhile, the previous exploration experiences cannot be used to correct the next tree search process, and makes a huge increase in the demand for computing resources. To solve this kind of problem, this paper proposes a step-by-step Reverse Curriculum Learning with Truncated Tree Search method (RevCuT Tree Search). In order to retain the previous exploration experiences, we use the deep neural network to learn the state-action values at explored states and then guide the next tree search process. Besides, taking the computing resources into consideration, we establish a truncated search tree focusing on continuous state space rather than the whole trajectory. This method can effectively reduce the number of explorations and achieve the effect beyond the human level in our well designed single-player game with continuous state space and probabilistic state transition.