Reviewerly/exHarmony huggingface dataset:
Research on Model of Seismic Anomaly Data Mining Based on Neural Network

Data mining in the seismic anomaly database will be affected by the instability of the seismic monitoring system signal and the environment, so in the development of practice should be based on the existing technology to comprehensively explore, pay attention to gradually break through the limitations of traditional mining methods, in order to effectively solve the problems existing in the previous data mining. Under the background of new era, the neural network as a machine learning algorithm is the most common way of mining, need according to the related theory had a clear standard equation of the minimum mean square error values, thus to build optimized mining model, and then using the calculation data of database, the feature vector to construct the corresponding to the monitoring data are accurate judgment. On the basis of understanding the current development of seismic monitoring technology, this paper proposes a new optimization model based on the constructed seismic anomaly database, and verifies its application effect in practice.
Graph-Based Transform Based on Neural Networks for Intra-Prediction of Imaging Data

This paper introduces a novel class of Graph-based Transform based on neural networks (GBT-NN) within the context of block-based predictive transform coding of imaging data. To reduce the signalling overhead required to reconstruct the data after transformation, the proposed GBT-NN predicts the graph information needed to compute the inverse transform via a neural network. Evaluation results on several video frames and medical images, in terms of the percentage of energy preserved by a sub-set of transform coefficients and the mean squared error of the reconstructed data, show that the GBT-NN can outperform the DCT and DST, which are widely used in modern video codecs.
Anonymous Stochastic Routing

We propose and analyze a recipient-anonymous stochastic routing model to study a fundamental trade-off between anonymity and routing delay. An agent wants to quickly reach a goal vertex in a network through a sequence of routing actions, whereas an overseeing adversary observes the agent’s entire trajectory and tries to identify the agent’s goal among those vertices traversed. We are interested in understanding the probability that the adversary can correctly identify the agent’s goal (anonymity) as a function of the time it takes the agent to reach it (delay). A key feature of our model is the presence of intrinsic uncertainty in the environment, so that each of the agent’s intended steps is subject to random perturbation and thus may not materialize as planned. Using large-network asymptotics, our main results provide near-optimal characterization of the anonymity–delay trade-off under a number of network topologies. Our main technical contributions are centered on a new class of “noise-harnessing” routing strategies that adaptively combine intrinsic uncertainty from the environment with additional artificial randomization to achieve provably efficient obfuscation.
Considerations on the Construction of Future Financial Regulations in the Field of Initial Coin Offering

Abstract Legal issues of ICOs are significant in the contemporary financial world because this method of capital formation is becoming widespread. In spite of the significance of ICOs, there are no financial regulations in this field in the most important legal systems. Therefore, research concerning future legal provisions in the area of ICOs is needed. This paper concerns issues related to the scope and structure of future financial regulations applicable to ICOs. The author focuses on principles on which legal provisions in the field of ICOs should be based. National, European and international matters are discussed separately in this work. Matters regarding a future international organisation competent in issues of crypto-assets are elaborated. Furthermore, the author proposes to create a model convention on cryptocurrencies and bilateral agreements on the exchange of information in crypto-asset matters. Disclosure obligations, anti-manipulation provisions and anti-money laundering principles are also set out. Moreover, the author discusses issues regarding audit requirements, special methods of registration and innovative payment rules for crypto-asset purposes. Finally, recommendations concerning ICOs are made separately for national, European and international purposes. The author believes that the conclusions in this paper can be useful not only for legislators but also for international actors, European institutions and legal researchers.
Problems and Prospects of Cryptocurrency Development

The article is devoted to the prospects for the integration of cryptocurrencies into the national and world economy. The views of various researchers on the essence of cryptocurrency, its strengths and weaknesses are systematized, the impact of differences in state regulation of cryptocurrency circulation on the nature of its use and the risks of money laundering is assessed, the factors that complicate the implementation of the main functions of money are identified. It has been revealed that in a broad sense, cryptocurrency is a new transaction technology that embodies the properties of money, goods, property, and financial assets but does not fully correspond to any of them. A cross-country analysis of the impact of changes in the economic and political situation on cryptocurrency and financial assets has confirmed a growing interest in investing in cryptocurrency, including institutional investors, which opens up the opportunity to become not just a speculative but also a protective investment asset. Taking into account the identified trends in government regulation of cryptocurrencies creates new opportunities for economic growth.
Human Skeleton Motion Prediction Using Graph Convolution Optimized GRU Network

Analysis on the human motion can reveal patterns proven very useful in human-machine interactions, medical applications and ambient assisted living. One such analysis is human motion prediction consisting of predicting human pose in a set of time instances contained in constrained time window of up to 1 to 2 seconds. This prediction is done by analyzing previous motion, i.e. set of previous poses, within a selected time window. In this paper we propose to predict human motion using Gated Recurrent Unit (GRU) network, a variant of Recurrent Neural Network. The prediction is based on human skeleton model and joints position change in time. We further optimize the GRU by substituting the weighting of inputs and recurrent outputs with convolution utilizing the graph structure of the human skeleton. We validate our proposed network by testing it on publically available dataset and providing state of the art results in comparison with other popular methods.
Chapter 2. ZHEN

Most Chinese-English parallel corpora consist of English source texts translated into Chinese. This chapter discusses the need for corpora representative of the under-resourced Chinese into English translation direction. After a brief overview of the current Chinese-English translation scenario and an analysis of existing parallel corpora for this language pair, we discuss problems in mining contemporary Chinese to English translations and issues in Chinese to English parallel text alignment. We then introduce ZHEN, a corpus of circa one-million characters of contemporary simplified Chinese source texts from a range of text types aligned with authentic translations into English. Its aim is to contribute to our understanding of Chinese to English translation norms and of features of English translated from Chinese.
Linear and Nonlinear Controls of Wireline Logs on Automated Grain Size Estimation Using Machine Learning Approach

Abstract Wireline logs have been utilized to indirectly estimate various reservoir properties, such as porosity, permeability, saturation, cementation factor, and lithology. Attempts have been made to correlate Gamma-ray, density, neutron, spontaneous potential, and resistivity logs with lithology. The current approach to estimate grain size, the traditional core description, is time-consuming, labor-intensive, qualitative, and subjective. An alternative approach is essential given the utility of grain size in petrophysical characterization and identification of depositional environments. This paper proposes to fill the gap by studying the linear and nonlinear influences of wireline logs on reservoir rock grain size. We used the observed influences to develop and optimize respective linear and machine learning models to estimate reservoir rock grain size for a new well or targeted reservoir sections. The linear models comprised logistic regression and linear discriminant analysis while the machine learning method is random forest (RF). We will present the preliminary results comparing the linear and machine learning methods. We used anonymized wireline and archival core description datasets from nine wells in a clastic reservoir. Seven wells were used to train the models and the remaining two to test their classification performance. The grain size-types range from clay to granules. While sedimentologists have used gamma-ray logs to guide grain size qualification, the RF model recommended sonic, neutron, and density logs as having the most significant grain size in the nonlinear domain. The comparative results of the models' performance comparison showed that considering the subjectivity and bias associated with the visual core description approach, the RF model gave up to an 89% correct classification rate. This suggested looking beyond the linear influences of the wireline logs on reservoir rock grain size. The apparent relative stability of the RF model compared to the linear ones also confirms the feasibility of the machine learning approach. This is an acceptable and promising result. Future research will focus on conducting more rigorous quality checks on the grain size data, possibly introduce more heterogeneity, and explore more advanced algorithms. This will help to address the uncertainty in the grain size data more effectively and improve the models performance. The outcome of this study will reduce the limitations in the traditional core description and may eventually reduce the need for extensive core description processes.
CPscan: Detecting Bugs Caused by Code Pruning in IoT Kernels

To reduce the development costs, IoT vendors tend to construct IoT kernels by customizing the Linux kernel. Code pruning is common in this customization process. However, due to the intrinsic complexity of the Linux kernel and the lack of long-term effective maintenance, IoT vendors may mistakenly delete necessary security operations in the pruning process, which leads to various bugs such as memory leakage and NULL pointer dereference. Yet detecting bugs caused by code pruning in IoT kernels is difficult. Specifically, (1) a significant structural change makes precisely locating the deleted security operations (DSO ) difficult, and (2) inferring the security impact of a DSO is not trivial since it requires complex semantic understanding, including the developing logic and the context of the corresponding IoT kernel.
Risk Reduction for Autonomous Systems

Systems with a physical instantiation which can cause harm to people have well-established methods to ensure their safe use. These rely on the system being reliable, reproducible and having predictable behaviour. This is no longer the case if it has machine learning processes which can change its behaviour during use. This non-deterministic behaviour introduces new technical, legal and financial risks into every aspect of concept, design, manufacture and use. Three critical questions are raised from a technologist’s perspective: what is the relevant legal framework; how to model non-deterministic behaviour; and how actions are authorized. Addressing them shows that there must be a separation of ML-based decisions and authorizations to act. System design must inherently limit its behaviour so that it is legal, safe and effective. It is shown that the real-time control architecture 4D/RCS can provide this assurance in both civil and military systems. The principle risk areas are identified, showing that organisations and individuals will have to accept new responsibilities. It will need the technical and legal professions to collaborate to understand and define acceptability both within a country and internationally before there can be widespread use of machine learning methods after the initial design is approved for general use.
Dash Bootstrap Components

This chapter covers Dash Bootstrap Components, a Python library from the Plotly family that enables key Bootstrap functionalities on a Dash web app, thus simplifying the web app development. Bootstrap borrows from HTML, CSS, and JavaScript. Figure 6-1 illustrates the building blocks of Bootstrap, thus dash_bootstrap_components.
Psychological and Pedagogical Aspects of Distance Learning

The article gives the psychological and pedagogical characteristics of distance learning, reveals its advantages and disadvantages from the point of view of the educational process subjects. The research was carried out at Mari State university with 2–4th year students of intramural and extramural forms of study. In total, 100 students of the psychological and pedagogical direction of training took part in the study. There were noted such problems of distance learning that worried respondents, such as insufficient technological preparation of teachers for the use of distance learning tools, insufficient control of the level of knowledge assimilation and development of skills, incomplete correspondence of distance learning programs to the educational needs of students, and low quality of educational material. From the psychological point of view, students are worried about the situation of uncertainty in the knowledge assessment system, the decrease in contacts with the teacher and fellow students, the unpreparedness of a number of teachers to use digital technologies in the educational process. It was emphasized that distance learning requires the formation of students’ responsibility, dedication, independence, attention, memory, thinking, etc. It is concluded that distance learning is somewhat inferior in its psychological and pedagogical characteristics to blended learning, which is preferred by students. However, a serious analysis of a variety of experimental data on distance learning will increase its effectiveness and minimize negative risks.
Siamese ELECTRA Network Combined with BERT for Semantic Similarity

Semantic similarity has always been a difficult problem in NLP task. Learning the deep meaning of text and comparing the similarity between two texts are the link between text representation and upper-level application. In this study, a Siamese ELECTRA Network combined with BERT which named SENB was proposed to solve the semantic similarity problem. The model uses the Siamese Network structure to extract the difference information of sentence pairs, and uses ELECTRA as the encoding layer of the Siamese network to improve the computational efficiency and accuracy. The difference information obtained from the Siamese Network is combined with the interaction features obtained from the BERT to determine the semantic text similarity. Experimental results on TwitterPPDB data set show that the accuracy of the method proposed by SENB method is 82.8%, and the F1-score is 84.1%. The results are superior to other methods, which proves the effectiveness of the method.
Analysis of multivariate non-gaussian functional data: A semiparametric latent process approach

Commonly assumed for multivariate functional regression models are normality and structural dependence, which, however, may not hold in practice. To relax these restrictions, we propose a new semiparametric transformation latent process functional regression model for multivariate functional data. Our model does not require normality assumptions or any specific dependence structures among multivariate response curves or intra-individual variability across time. We propose a combined likelihood- and estimating equation-based method to estimate parameters, transformation functions and covariance structures. We establish theoretical properties, including n−consistency and asymptotic normality, for the proposed estimators. The utility of the method is illustrated via extensive simulations and analyses of an elderly cognitive evolution dataset, which yield a better fit than the other competing methods and some interesting findings.
Optimized Duplicate Question Detection in Programming Community Q&amp;A Platforms using Semantic Hashing

Duplicate Question Detection (DQD) in Programming Community Question & Answer (PCQA) platforms has been a highly prominent area of research in the recent past. A lot of studies use Semantic Text Similarity (STS) as a key mechanism for this concept. Yet, the use of STS introduces one major drawback, fast retrieval of data with efficient use of computational resources. The drawback is a cause of iteratively comparing a given query question with all the questions within the data source. This research paper presents a novel concept named StackO-DQD that combines STS with hashing to overcome the abovementioned. At the benchmarking stage, the results show an average increase of 1.73%, 6.52%, and 7.22% over the previous work in recommending the precise similar question within the top 5, top 10, and the top 20 results each.
Emotions and learning styles making an online research master's degree

The distance education model has insufficient consideration of the emotional aspects of the students, where the training action takes place without personal interactions, causing it to be very difficult to feel like a member of an educational community. The research aimed to observe the evolution of emotions and learning styles during the Master’s Degree in Research in Teacher Training and ICT. A quasi-experimental, pretest-posttest, single-group research design was employed, using descriptive and inferential data analysis. On the one hand, the questionnaire to measure emotions towards the elements of online education. On the other hand, the REATIC (Relation of Learning Styles with ICT, in Spanish) questionnaire. A significant increase in boredom has been observed and the predominant learning styles are pragmatic and theoretical.
Mining of High Utility Itemsets for Incremental Datasets

High Utility Itemset Mining aims at extracting high utility patterns from a given database. Most of the mining algorithms are designed for static datasets. However, the real-life datasets continuously evolve with incremental data. In this paper, we present a mining algorithm which uses efficient data structures and pruning conditions to mine and maintain high utility patterns across incremental datasets. Several experiments are conducted to show the efficacy of the proposed algorithm.
Distributed Radar PRI Sequence Classification using K-medoids Algorithm and Feedforward Neural Networks

Pulse repetition interval (PRI) is an important feature of distributed radar signals. PRI feature extraction and PRI sequence classification are meaningful in modern electronic warfare (EW). This article recommends applying the k-medoids algorithm and feedforward neural networks (FNN) to classify PRI sequences. The proposed approach first calculates the first-order difference of PRI sequences, then uses the k-medoids algorithm to cluster the elements in each PRI sequence and its difference vector, and extracts the centroids, and feeds the centroids into the FNN classifier to categorize the sequences. Simulation results show the proposed approach achieves excellent performance and has strong robustness.
A Preliminary Study on the Recognition of Speech Acts Performed by International Chinese Teachers in Class Based on Deep Learning

Teachers’ speech acts play an important role in the teaching process in class. The teaching speech given by international Chinese teachers in class, which contains both linguistic learning and cultural knowledge, is not only a tool for organizing teaching but also a template for students to learn Chinese. This paper provided a literature review at home and abroad of the classic classification labeling system of teachers’ speech acts in class, based on which we analyzed the characteristics of international Chinese language classes and teacher’s speech acts in the teaching process. We classified teachers’ speech acts according to their function to construct a classification labeling system of teachers’ speech acts in international Chinese class. We processed the teaching videos of actual international Chinese classes and labelled teaching speech text as corpus by using the classification labeling system we have built. Finally, we trained a text classification model, and initially realized the automatic recognition of international Chinese teachers’ speech acts in class with a maximum test value of class speech classification up to 78%.
An Adaptable LSTM Network Predicting COVID-19 Occurrence Using Time Series Data

As the COVID-19 pandemic progresses, it has become critical for policymakers and medical officials to understand how cases are trending. Machine learning models, particularly deep learning LSTM (Long Short-Term Memory) models, may hold immense value to forecast changes in COVID-19 cases. In this paper, a novel LSTM-based architecture is proposed, developed and trained on human logistics data that includes travel patterns, visits to commercial properties, as well as historical cases, demographic, and climate data. This data includes both time series and static data allowing the LSTM to be used in both classification and regression tasks to predict COVID-19 occurrence trends. For classification, the problem is modeled as a multiclass supervised learning classification problem with varying granularity. The proposed LSTM network achieves an 81.0% F1-score outperforming conventional machine learning model benchmarks (such as the random forest model with an F1 score of 58.9 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ) and is comparable in performance to a time series forest model. Additionally, the LSTM model is adaptable to perform regression and predict a 14-day sliding window based on currently observed data with a mean absolute error of 0.0026. This research serves as a foundation for future work in the forecasting of COVID-19 and other similar disease outbreaks using similar temporal and static data.
Information Fusing Algorithm in Inertial/Geomagnetic Navigation System

To improve the position precision of inertial/geomagnetic navigation system, information fusing algorithm based on simplified UKF was presented. Firstly the approach took INS error as state equation, geomagnetic as the measurement, and the geomagnetic field model as the observation equation indirectly. Then simplified UKF was designed and implemented to estimate the error in INS. The simulations show that simplified UKF algorithm can significantly improve the navigation precision compared with EKF algorithm; meanwhile navigation precision of simplified UKF is in accordance with UKF algorithm, but the calculation amount greatly reduces; it is not limited by the maneuverability of the aircraft, and especially suitable for the aircraft with varying flight height such as ballistic missile.