Reviewerly/exHarmony huggingface dataset:
MATHEMATICAL MODELING FORECASTING OF CONSEQUENCES OF DAMAGE BREAKTHROUGH

The article is devoted to the development of a mathematical model for preventing a breakthrough of a dam and predict its possible consequences. In this work, the task of developing a single integrated approach to ensuring the safe operation of hydraulic structures, based on the notification of interested bodies in real time, was solved. A mathematical model of the state of the reservoir is developed, on the basis of which a hardware-software complex for operational notification of interested organizations (akimats) and local emergency departments is implemented. A mathematical model of predicting the consequences of a dam break is proposed. An algorithm for calculating the maximum level of the breakout wave has been formulated, taking into account many parameters of the hydraulic structures. The convergence of the developed algorithm in the form of a theorem has been proved. This method has a large practical focus, compared with existing formulas. The Java language implements a hardware-software complex (PAC) for predicting the effects of a dam break, consisting of the following modules: 1) a module for receiving and transmitting current information about the water level, humidity and temperature on the crest of the dam; 2) a module for processing constant and operational information about the threat of dam breakthrough (server); 3) a module for predicting the effects of a dam break. Based on the solution of the model problem, the effectiveness of the developed hardware-software complex is shown. The practical basis for the model task was the events that took place in Kyzylagash village of Almaty region of the Republic of Kazakhstan.
Plagiarism Problem Solving Based on Combinatory Semantics

The paper is presenting an updated edition of the second version of Theory for Automatic Generation of Knowledge Architecture (TAPAZ-2) and proposes a new approach to solving the problem of automatically identifying the semantic equivalence of text documents and borrowing scientific ideas in order to curb the spread of plagiarism and prevent clogging the information space under the conditions of its globalization.
Learning Descriptors Invariance through Equivalence Relations within Manifold: A New Approach to Expression Invariant 3D Face Recognition

This paper presents a unique approach for the dichotomy between useful and adverse variations of key-point descriptors, namely the identity and the expression variations in the descriptor (feature) space. The descriptors variations are learned from training examples. Based on labels of the training data, the equivalence relations among the descriptors are established. Both types of descriptor variations are represented by a graph embedded in the descriptor manifold. Invariant recognition is then conducted as a graph search problem. A heuristic graph search algorithm suitable for the recognition under this setup was devised. The proposed approach was tested on the FRGC v2.0, the Bosphorus and the 3D TEC datasets. It has shown to enhance the recognition performance, under expression variations, by considerable margins.
Role of Education in the Fourth Industrial Revolution (Social-Philosophical Analysis)

The study aims to reveal social and historical features of the influence that industrial revolutions in general and the Fourth Industrial Revolution (Industry 4.0) in particular have on social functions of educational institutions within the framework of a social-philosophical analysis. The article summarises and analyses the social-philosophical forecasts characterising revolutionary shifts in the processes of acquiring, distributing and transferring knowledge through the education system in the context of such a phenomenon as the Fourth Industrial Revolution. Scientific novelty of the research lies in developing a concept of relationship between industrial revolutions and the education system. As a result, the main probabilistic fundamental technical and social events, which are of paramount importance for reproduction of the education system, are identified and characterised.
Banking Sector Competition and Financial Development in Sub-Saharan Africa

The study sort to establishing a relationship between banking sector competition and financial development in subSaharan n Africa. The study further disaggregated the data used into Francophone and Anglophone countries, and these were examined separated and compared. Using an annual data on banks across 37 countries in Sub-Saharan Africa spanning the period 2001-2016 and employing the Fixed Effects estimation technique, the study revealed that there is a positive and significant relationship between financial development (FDCredit) and competition (CR3) for the full sample and the anglophone samples. The study further revealed that banking sector stability is essential for the financial development of both Anglophone and francophone countries within the sub-region. Macroeconomic variables did not have any impact on financial development generally except in francophone countries where exchange rates were found to have an impact on financial development. Bank-level variables such as ZSCORE, non-performing loans, profitability, liquidity and capitalization, on the hand had little impact in Anglophone countries on financial development compared to francophone countries. The study found that larger banks contribute positively to the development of the financial sector and banks tend to be bigger in Anglophone countries, and their banking sector is also more competitive than the francophone countries for the period used. Less emphasis should be placed on bank-level variables as these do not have significant impacts on the financial sector for Anglophone countries. Francophone countries should, however, control bank-level variables to ensure that they achieve greater financial development.&#x0D; Citation: Ricky-Okine, C. K., Amankwaa, T.and Anane, E.Banking Sector Competition and Financial Development in Sub-Saharan Africa,2020; 5(3): 58-85.&#x0D; Received: July 18, 2020Accepted: September 30, 2020
Word Shape Matters: Robust Machine Translation with Visual Embedding

Neural machine translation has achieved remarkable empirical performance over standard benchmark datasets, yet recent evidence suggests that the models can still fail easily dealing with substandard inputs such as misspelled words, To overcome this issue, we introduce a new encoding heuristic of the input symbols for character-level NLP models: it encodes the shape of each character through the images depicting the letters when printed. We name this new strategy visual embedding and it is expected to improve the robustness of NLP models because humans also process the corpus visually through printed letters, instead of machinery one-hot vectors. Empirically, our method improves models' robustness against substandard inputs, even in the test scenario where the models are tested with the noises that are beyond what is available during the training phase.
Meta-Learning for Domain Generalization in Semantic Parsing

The importance of building semantic parsers which can be applied to new domains and generate programs unseen at training has long been acknowledged, and datasets testing out-of-domain performance are becoming increasingly available. However, little or no attention has been devoted to learning algorithms or objectives which promote domain generalization, with virtually all existing approaches relying on standard supervised learning. In this work, we use a meta-learning framework which targets zero-shot domain generalization for semantic parsing. We apply a model-agnostic training algorithm that simulates zero-shot parsing by constructing virtual train and test sets from disjoint domains. The learning objective capitalizes on the intuition that gradient steps that improve source-domain performance should also improve target-domain performance, thus encouraging a parser to generalize to unseen target domains. Experimental results on the (English) Spider and Chinese Spider datasets show that the meta-learning objective significantly boosts the performance of a baseline parser.
Denoising Individual Bias for Fairer Binary Submatrix Detection

Low rank representation of binary matrix is powerful in disentangling sparse individual-attribute associations, and has received wide applications. Existing binary matrix factorization (BMF) or co-clustering (CC) methods often assume i.i.d background noise. However, this assumption could be easily violated in real data, where heterogeneous row- or column-wise probability of binary entries results in disparate element-wise background distribution, and paralyzes the rationality of existing methods. We propose a binary data denoising framework, namely BIND, which optimizes the detection of true patterns by estimating the row- or column-wise mixture distribution of patterns and disparate background, and eliminating the binary attributes that are more likely from the background. BIND is supported by thoroughly derived mathematical property of the row- and column-wise mixture distributions. Our experiment on synthetic and real-world data demonstrated BIND effectively removes background noise and drastically increases the fairness and accuracy of state-of-the arts BMF and CC methods.
Open Domain Suggestion Mining Leveraging Fine-Grained Analysis (Workshop Paper)

Suggestion mining tasks are often semantically complex and lack sophisticated methodologies that can be applied to real-world data. The presence of suggestions across a large diversity of domains and the absence of large labelled and balanced datasets render this task particularly challenging to deal with. In an attempt to overcome these challenges, we pro-pose a two-tier pipeline that leverages Discourse Marker based oversampling and fine-grained suggestion mining techniques to retrieve suggestions from online forums. Through extensive comparison on a real-world open-domain suggestion dataset, we demonstrate how the oversampling technique combined with transformer based fine-grained analysis can beat the state of the art. Additionally, we perform extensive qualitative and qualitative analysis to give construct validity to our proposed pipeline. Finally, we discuss the practical, computational and reproducibility aspects of the deployment of our pipeline across the web.
Coronal slice segmentation using a watershed method for early identification of people with Alzheimer's

One physical sign of a person who has Alzheimer's is the diminution of the area of the hippocampus and ventricles.A good quality magnetic resonance imaging (MRI) will provide a high-quality image so that the doctor will quickly analyze the abnormalities of the hippocampus and ventricle area.However, for low-quality MRI, this is difficult to do.This condition will be a significant problem for some regions in developing countries including Indonesia, where many hospitals have only low-quality MRI, and many hospitals do not have them at all.The primary purpose of this research is to develop simple tools to analyze morphological characteristics in Alzheimer's patients.In this paper, we focus only on coronal slice analysis.We will use watershed method segmentation, because of this method able to segment the boundaries automatically, so that parts of the hippocampus and ventricles can be identified in an MRI image.Analysis of morphological characteristics is also classified by age and gender.Then by referring to the value of the clinical dementia rating (CDR), the process of identifying between images with Alzheimer's disease (AD) and healthy models is done based on the morphological analysis that has been done.The results show this method has a better performance compared to the previously work.
Transaction Characteristics of Bitcoin

Blockchain has been considered as an important technique to enable secure management of virtual network functions and network slices. To understand such capabilities of a blockchain, e.g. transaction confirmation time, demands a thorough study on the transaction characteristics of the blockchain. This paper presents a comprehensive study on the transaction characteristics of Bitcoin -- the first blockchain application, focusing on the underlying fundamental processes. A set of results and findings are obtained, which provide new insight into understanding the transaction and traffic characteristics of Bitcoin. As a highlight, the validity of several hypotheses/assumptions used in the literature is examined with measurement for the first time.
Institutional Features of the Development of Competitive Cryptocurrency

The aim of the article is to clarify the basics of the digitalization strategy of the competitive businesses and identify features of the institutional environment that ensure the development of cryptocurrency as a new asset (IT product) of the modern economy, analyze the methods of implementing the cryptocurrency business models. The relevance of the research paper is determined by the need to develop a competitive Russian cryptocurrency (including the cryptoruble) with the growing private, state and cross-national cryptocurrencies. The scientific novelty of the study implies clarifying the informal and formal rules of the institutional environment and related methods ensuring the development of a competitive cryptocurrency. The authors consider the following methods to implement the institutional features of the cryptocurrencies business model development: logic and blockchain algorithm that establish trust and collaboration between cryptocurrency developers; logic and blockchain consensus algorithm ensuring that all the parties of the blockchain network come to a common agreement (consensus); logic and blockchain algorithms that form cryptocurrency transactions and control its turnover by generating blocks of cryptocurrencies, by forming the structure of blocks and transactions of cryptocurrencies, by storing cryptocurrencies’ keys and providing security, by mining (forging) cryptocurrency, etс.The results of the study provide a basis for identifying the institutional features and the corresponding methods providing a competitive cryptocurrency development with a detailed analysis of the blockchain consensus algorithms that ensure the competitiveness of the cryptocurrency. The conclusions show that the most promising are the hybrid consensus algorithms which may include both the logic of two or more known algorithms and the original logic of a new algorithm. The authors recommend defining the logic of the blockchain consensus algorithm as a priority when developing a cryptocurrency to ensure reliability of the transactions in the blockchain network, thus increasing the competitiveness of the cryptocurrency.
Covert speech communication through audio steganography using DWT and SVD

In this paper, a covert speech communication technique through a semi-blind audio steganography algorithm using discrete wavelet transform (DWT) and singular value decomposition (SVD) is proposed. This method securely transmits the speech signal by embedding its singular values (SV) in a cover audio. The cover audio is decomposed using fifth level DWT and embedding is performed in SV matrix of approximate coefficients to achieve good imperceptibility and robustness to the attacks. The performance of the proposed algorithm is evaluated by conducting imperceptibility and robustness tests on various speech signals. The experimental results show that the secret speech is reconstructed with high SNR, correlation and Perceptual Evaluation of Speech Quality (PESQ) score even the stego audio is subjected to various attacks. It is observed that under no attack conditions the correlation coefficient (NCC) is 1.0, SNR and PESQ of reconstructed secret audio is 70.58 and 4.49, respectively.
Imagining the Future of Longitudinal HCI Studies: Sensor-Embedded Everyday Objects as Subjective Data Collection Tools

Automated data collection has a significant role in collecting reliable longitudinal data in human–computer interaction (HCI) studies that involve human participants. While objective data collection can be obtained by and mediated through personal informatics, subjective data is mostly collected through labour-intensive tools. The potential of sensor-embedded everyday objects as subjective data collection tools is underexplored. Hence, in this chapter, we investigate the use of such products for subjective data collection purposes in longitudinal studies. First, we demonstrate current practices on subjective data collection tools and examine the aforementioned research gap. Following that, we discuss the results of three discussion sessions in which we collected insights from six expert researchers on the enablers and barriers of using sensor-embedded everyday objects as subjective data collection tools. We present our insights with use-case scenarios to communicate what possible roles sensor-embedded everyday objects could have in collecting subjective data in future longitudinal HCI studies and discuss how they could be further developed within the field.
Digitalisation of Education: Myth of Multitasking

The study aims to substantiate illusory, mythological nature of the idea of multitasking, rooted in mass consciousness, as a kind of special cognitive ability formed through the use of information technologies, as a positive aspect of clip thinking. The analysis of the Russian and foreign published works, which mention multitasking, has revealed that the authors attribute different content to this term: while foreign sources understand multitasking as a mode of operation with many distractions, the Russian sources treat it as a person’s ability to perform several operations simultaneously. The work shows that the idea of multitasking as a positive cognitive ability, formed on the basis of a child’s early introduction to simultaneous use of various electronic devices, is one of social myths generated by informatisation and digitalisation of the society. The study has identified the origins of mythologised ideas of the phenomena associated with digitalisation of education. Scientific novelty of the research lies in substantiating the necessity to dispose of mythologised ideas associated with informatisation in order to bring about positive changes in the Russian education system. As a result of the research, the author comes to the conclusion that the idea of multitasking is of illusory nature as a personality trait associated with the use of information technologies; that the main disadvantage of using the multitasking mode for educational purposes is its negative impact on formation of a person’s cognitive sphere, personal knowledge. Simultaneous use of several devices results in meditative thinking development inhibition, therefore, it prevents actualisation of a person’s creative potential.
LTSpAUC: Learning Time-Series Shapelets for Partial AUC Maximization

Shapelets are discriminative segments used to classify time-series instances. Shapelet methods that jointly learn both classifiers and shapelets have been studied in recent years because such methods provide both interpretable results and superior accuracy. The partial area under the receiver operating characteristic curve (pAUC) for a low range of false-positive rates (FPR) is an important performance measure for practical cases in industries such as medicine, manufacturing, and maintenance. In this article, we propose a method that jointly learns both shapelets and a classifier for pAUC optimization in any FPR range, including the full AUC. In addition, we propose the following two extensions for shapelet methods: (1) reducing algorithmic complexity in time-series length to linear time and (2) explicitly determining the classes that shapelets tend to match. Comparing with state-of-the-art learning-based shapelet methods, we demonstrated the superiority of pAUC on UCR time-series data sets and its effectiveness in industrial case studies from medicine, manufacturing, and maintenance.
TwitchChat: A Dataset for Exploring Livestream Chat

Most natural language processing research focuses on modelling and understanding text formed of complete sentences with correct spelling and grammar. However, livestream chat is drastically different. Viewers are typically writing short messages while responding to in-stream events, often with incorrect grammar and many repeated tokens. Additionally, tokens that are commonly used in livestream chat are unknown to traditional language understanding efforts that focus on prosaic text. To advance and encourage further research in terms of livestream chat understanding, in this work, we present a large-scale dataset of video game livestream chat, consisting of over 60 million tokens. As livestreaming becomes more popular it is also increasingly pertinent to study, though chat analysis, the way in which the audience is engaging with the stream. However, this is not a straightforward task, livestream chat is a rich and complex domain, far removed from often studied prosaic text. Additionally. we provide a case study analysis of word vector methods applied to the dataset, showing that the vector space is strangely shaped but clusterable and that the resulting clusters correlate with features such as streamer popularity. Furthermore, human relatedness tests highlight the difference that this domain poses with respect to prosaic text. It is hoped the livestream chat dataset, the discussion of its unique features, and the challenges highlighted for future work will invigorate the research community into further study of livestream chat.
Rapid Domain Adaptation for Machine Translation with Monolingual Data

One challenge of machine translation is how to quickly adapt to unseen domains in face of surging events like COVID-19, in which case timely and accurate translation of in-domain information into multiple languages is critical but little parallel data is available yet. In this paper, we propose an approach that enables rapid domain adaptation from the perspective of unsupervised translation. Our proposed approach only requires in-domain monolingual data and can be quickly applied to a preexisting translation system trained on general domain, reaching significant gains on in-domain translation quality with little or no drop on general-domain. We also propose an effective procedure of simultaneous adaptation for multiple domains and languages. To the best of our knowledge, this is the first attempt that aims to address unsupervised multilingual domain adaptation.