Reviewerly/exHarmony huggingface dataset:
Investigation of Buoy Hydrodynamic Damping Based on Model Testing Data Series of Indonesia Tsunami Buoy

Abstract Due to the recent Tsunami event in Indonesia in 2018, the Ina-TEWS program has been started again by the Indonesian Government. A new design has been manufactured and already deployed to the Indonesian water. This design is named Buoy G – 3. The tsunami early warning system (TEWS) consists of a tsunami buoy, an ocean bottom unit (OBU), a satellite and a ground station. The changes of the water pressure due to the seismic movement prior to the tsunami event will be acquired by OBU and sent the reading to the tsunami buoy by acoustic signal. Then, tsunami buoy transmits the signal to the ground station via satellite. The continuously link between OBU – Buoy – Satellite makes the buoy motions especially roll/pitch has to be restricted. Because of these the hydrodynamic motion damping assessment of this tsunami buoy becomes so important. This paper describes a hydrodynamic damping analysis for roll/pitch of the tsunami buoy that based on model testing data series of Indonesian Tsunami Buoy G-1 to G-3. The data analysis will be presented on graphics that show the aspect ratio of buoy diameter over draft (D/T) and form ratio of buoy displacement over weight of the cylindrical submerged form (Δ/W) against damping ratio (β). This study finds that by using the linear damping approximation the higher the aspect ratio D/T the larger roll/pitch damping ratio β, but also the higher ratio of Δ/W the smaller roll/pitch damping ratio β.
The DeCAMFounder: nonlinear causal discovery in the presence of hidden variables

Abstract Many real-world decision-making tasks require learning causal relationships between a set of variables. Traditional causal discovery methods, however, require that all variables are observed, which is often not feasible in practical scenarios. Without additional assumptions about the unobserved variables, it is not possible to recover any causal relationships from observational data. Fortunately, in many applied settings, additional structure among the confounders can be expected. In particular, pervasive confounding is commonly encountered and has been utilised for consistent causal estimation in linear causal models. In this article, we present a provably consistent method to estimate causal relationships in the nonlinear, pervasive confounding setting. The core of our procedure relies on the ability to estimate the confounding variation through a simple spectral decomposition of the observed data matrix. We derive a DAG score function based on this insight, prove its consistency in recovering a correct ordering of the DAG, and empirically compare it to previous approaches. We demonstrate improved performance on both simulated and real datasets by explicitly accounting for both confounders and nonlinear effects.
Improving Machine Translation Using Parts-Of-Speech Tags and Dependency Parsing

Language is the most important tool for communication which in turn is necessary for growth. With diverse languages present in India, translation becomes an important tool. Current state-of-the-art models are mostly trained for foreign or well-known languages. These models require large data for training making it difficult to implement them for Indian dialects where sufficient literature is not available. Also, these models are not able to capture the rich morphology of Indian languages resulting in a reduced accuracy. We aim to increase the accuracy using syntactic features and also reduce the required dataset. We used the syntactic features: Parts-of-Speech tags and Dependency Parsing. We are able to achieve an incearsed BLEU score and generate target sentences that are more qualitative.
New practices of literature distribution for children and youth in Siberia and the Far East

The article describes the directions of distribution of books for children in the Eastern regions of Russia that have appeared in the last decade. Based on a combination of landscape-reconstructing principles, surveys, and comparative typological analysis, the article presents data that expand previously studied aspects of the functioning of the main channels for the sale of books for children in the Siberian-Far Eastern region. It is shown that books for children are the largest segment of the Russian book market, which has been growing since 2008. At the regional level, they are widely presented in all book distribution channels: bookselling networks and independent stores; online stores; book departments of supermarkets (non-core retail); kiosks and stalls; book fairs. The purpose of the article is to analyze the current trends in the distribution of books for children in the region. Positive trends typical for the children book market in the Siberian-far Eastern region are identified. They are: the activity of children book supermarkets, the appearance of independent small stores of club-backstage format, the organization of specialized children Internet projects; increasing the importance of regional book exhibitions, fairs, festivals and holidays in the distribution of books for children; projects support by major Russian patrons. The significance of the study of new practices for the distribution of literature for children and youth in the region is determined by their influence on the formation of a new conceptual model for the popularization of reading, based on the idea of culture as a powerful lever of socio-economic development of territories.
Tackling Noise in Active Semi-supervised Clustering

Constraint-based clustering leverages user-provided constraints to produce a clustering that matches the user's expectation. In active constraint-based clustering, the algorithm selects the most informative constraints to query in order to produce good clusterings with as few constraints as possible. A major challenge in constraint-based clustering is handling noise: the majority of existing approaches assume that the provided constraints are correct, while that might not be the case. In this paper, we propose a method to identify and correct noisy constraints in active constraint-based clustering. Our approach reasons probabilistically about the correctness of the user's answers and asks additional constraints to corroborate or correct the suspicious answers. We demonstrate the method's effectiveness by incorporating it into COBRAS, a state-of-the-art method for active constraint-based clustering. Compared to COBRAS and other active-constraint-based clustering algorithms, the resulting system produces better clusterings in the presence of noise.
Inverse Problems in Physics

First, we consider inverse problems in physics. In fact, inverse problems are at the heart of revolutionary development in physics. What does it mean to solve an inverse problem? What is the meaning of the phrase “machine learning is good at solving inverse problems”? You will gain a comprehensive perspective and significance in applying machine learning to theoretical physics.
Sub-band invariants of handwritten texts fuzzy fragments

Abstract Socio-economic processes informatization is largely implemented by using various documents electronic storage, including scanned handwritten texts or their fragments images, in particular, in the form of officials’ original signatures. Among the very diverse tasks of handwritten documents scans electronic repositories computer analysis, the direction of automatic search for images fragments, that contain outlines of key words of interest, is quite relevant. The search for such fragments is significantly hampered by the variability of the letterforms, even by the same author. Therefore, it is advisable to use a fragment from the analyzed text as an initial sample. Thus, we are talking about the fragments precedent identification. In this case, a complicating factor is the impossibility of using many identical objects in training, and only one is available. Therefore, the problem of forming an artificial sample, which elements preserve some common characteristic property, arises. In this work, the authors substantiated the application adequacy of the original sub-band approach, which is based on the concept of the data segment Euclidean norm sub-band part and the mathematical apparatus of sub-band matrices obtained on this basis. Decision procedures for the handwritten text fragments sub-band identification were developed, including training on a single precedent.
Experimental research of reagent capsulation process at various methods of carbon dioxide supply

Abstract The article considers some features of the reagent encapsulation method at low temperatures. A setup layout has been created to measure temperature of the encapsulated material while reagent encapsulating. During the experiment, the duration of maintaining the temperature for preparing the next portion was determined. Methods for supplying carbon dioxide to the unit during reagent encapsulation were compared. As a result of the experiment, a more profitable way of supplying carbon dioxide to the unit was determined.
Label Noise Filtering Based on Data Distribution and Relative Density

Label noise is a non-negligible problem in supervised and semi-supervised machine learning. Current methods to deal with label noise are mainly algorithm-level robust modeling and data-level noise filtering. However, these methods suffer from poor or inefficient filtration. In this paper, a label noise filtering method based on data distribution and relative density (DD-RDF) is proposed. Firstly, according to the sample distribution of the data set, the regions where each sample is located and divided. Secondly, different filtering rules are adopted for noise removal in different areas. Compared with the Data Distribution Filtering (DDF) algorithm, the DD-RDF algorithm adopts relative density filtering rules to increase neighborhood information in the low-density mixed label area, which makes the algorithm more robust. Experimental results on 12 UCI standard multi-classification data sets show that the noise filtering effect of the DD-RDF algorithm is better than that of the comparison algorithms under different noise ratios.
TEADS: A Defense-Aware Framework for Synthesizing Transient Execution Attacks

Since 2018, a broad class of microarchitectural attacks called transient execution attacks (e.g., Spectre and Meltdown) have been disclosed. By abusing speculative execution mechanisms in modern CPUs, these attacks enable adversaries to leak secrets across security boundaries. A transient execution attack typically evolves through multiple stages, termed the attack chain. We find that current transient execution attacks usually rely on static attack chains, resulting in that any blockage in an attack chain may cause the failure of the entire attack. In this paper, we propose a novel defense-aware framework, called TEADS, for synthesizing transient execution attacks dynamically. The main idea of TEADS is that: each attacking stage in a transient execution attack chain can be implemented in several ways, and the implementations used in different attacking stages can be combined together under certain constraints. By constructing an attacking graph representing combination relationships between the implementations and testing available paths in the attacking graph dynamically, we can finally synthesize transient execution attacks which can bypass the imposed defense techniques. Our contributions include: (1) proposing an automated defense-aware framework for synthesizing transient execution attacks, even though possible combinations of defense strategies are enabled; (2) presenting an attacking graph extension algorithm to detect potential attack chains dynamically; (3) implementing TEADS and testing it on several modern CPUs with different protection settings. Experimental results show that TEADS can bypass the defenses equipped, improving the adaptability and durability of transient execution attacks.
Using the power of pixels in the physical world

Mapping values is part of the craftmanship of tangible interaction design. It is easy to imagine but hard to put in words and even harder to code. This swatch presents ways that might allow us to move faster from idea to experience. Through direct manipulation the experience becomes some sort of 'clay' that we can experience and manipulate simultaneously. This is done by using pixels to control the physical world and allows the use of software in the graphical domain to be used in the physical computing domain.
Margin CosReid Network for Pedestrian Re-Identification

This paper proposes a margin CosReid network for effective pedestrian re-identification. Aiming to overcome the overfitting, gradient explosion, and loss function non-convergence problems caused by traditional CNNs, the proposed GBNeck model can realize a faster, stronger generalization, and more discriminative feature extraction task. Furthermore, to enhance the classification ability of the softmax loss function within classes, the margin cosine softmax loss (MCSL) is proposed through a boundary margin introduction to ensure intraclass compactness and interclass separability of the learning depth features and thus to build a stronger metric-based learning model for pedestrian re-identification. The effectiveness of the margin CosReid network was verified on the mainstream datasets Market-1501 and DukeMTMC-reID compared with other state-of-the-art pedestrian re-identification methods.
ECOSYSTEM OF MATH EDUCATION YESTERDAY, TODAY AND TOMORROW. ISSUES, HISTORY, TEACHING METHODS, EDUCATION IDEAS

This article examines some of the problems that are at the intersection of the sets of issues formulated in the ecology of personality, pedagogy, methods of teaching mathematics. It is proposed to consider the pedagogy not as experimental knowledge, but nevertheless as applied philosophy, following S.I. Gessen. It is also suggested to understand the methodology of teaching mathematics not only as a system of knowledge that has an independent history of development, but as a complex system associated with cultural traditions, psychophysical capabilities of an individual existing in the ecoenvironment, an integral part, which is the micro- and macroenvironment of the individual. The authors consider that the interaction of environments can be formally described in some cases by mathematical methods, and, therefore, build mathematical models of pedagogical activity. In particular, the compared in learning process, vocabulary complexes of students give a lot of information to the teacher and provide an opportunity to control the flow of educational information.
Influencing Factors of Continuous Use of New Agricultural Information Platform

Abstract The Chinese government strongly supports the construction of data information sharing platforms, focusing on the domestic scientific research industry. The construction of agricultural scientific data sharing platform will help users more easily obtain agricultural knowledge. This removed the obstacles to agricultural development and had a major impact on the modernization and construction of agricultural practices. However, at the current level of science and technology, it is difficult to collect all data. Sources of agricultural scientific data are more extensive, and more data are generated from agricultural scientific research. The purpose of this article is to study the factors that influence the continued use of new agricultural information platforms. In terms of methods, this article first conducts exploratory and confirmatory research on the scale, uses Kaiser’s point of view, and uses eigenvalues greater than 1 as the basic principle for extracting common factors. Perform factor analysis, reliability analysis, validity analysis, correlation analysis. In terms of surveys, the survey methods used are field questionnaires, on-site interviews, and interviews with people. The main survey objects are agricultural producers, farmers, government employees of townships and villages, and employees of agricultural enterprises and cooperative organizations. A total of 300 questionnaires were distributed and 268 were recovered, of which 226 were valid, with a recovery rate of 89.3% and an effective rate of 84.3%. It is concluded that the main factors affecting the continued use of the new agricultural information platform are farmers’ demand for agricultural market information, learning knowledge, policies and regulations, and soil and fertilizer.
Evaluating Super-Resolution of Satellite Images: A Proba-V Case Study

Super-resolution reconstruction is a process aimed at enhancing image spatial resolution. To evaluate the quality of super-resolution, the reconstruction outcome is compared with a ground-truth reference image, and the dissimilarity between them is commonly treated as a determinant of the reconstruction quality. While this is straightforward for simulated data, it becomes more challenging for real-world scenarios, in which reference images and the reconstruction inputs are acquired using different imaging sensors. In such cases, the dissimilarity also results from other factors concerned with different sensor characteristics. In a recently organized Proba-V Super Resolution Challenge, the reconstruction quality was assessed using a modified peak signal-to-noise ratio which compensates for small shifts and global changes in the brightness. In the study reported here, we investigate a number of image similarity metrics to verify their robustness against different levels of distortions applied to Proba-V images. We expect that the reported results will help in choosing appropriate metrics while developing new super-resolution solutions aimed at real-world scenarios.
Visual Context Aware Ship Detector for High-Resolution SAR Imagery

The ever increasing resolution of synthetic aperture radar (SAR) images have made accurate scene understanding possible. However, the rich visual context information in SAR images is not yet well explored for model construction, which limited the performance of traditional target detectors. In this paper, we propose a visual context aware approach to mimic the human visual perception mechanism for ship detection from high-resolution SAR imagery. Rather than considering target as a group of individual scatters, we adopt object-level selective search to efficiently locate sparse targets from wide SAR scenes. To verify the effectiveness of the proposed method, we test it along with the Hebbian neural network approach on several high-resolution SAR images. Experimental results show that the proposed approach is quite robust under different imaging conditions and can correctly detect ship targets in a rapid manner.
Convolutional Recurrent Neural Networks for Earthquake Epicentral Distance Estimation Using Single-Channel Seismic Waveform

This paper proposes a deep learning method for epicentral distance estimation using a single-channel seismic waveform. The model is based on a convolutional recurrent neural network structure to extract spatial and temporal features. Since the proposed model needs only single-channel data, it can also perform the distance estimation even when some channels of the sensor are adversely disabled. To evaluate our approach, we conduct distance estimation experiments with the Korean peninsula earthquake database from 2016 to 2018, which include microearthquakes and distant earthquakes. The epicentral distance estimation by the proposed method show an absolute mean error of 0.50 km with 9.16km standard deviation of error distribution, which shows the best estimation result among the competing model structures. The promising result indicates that the proposed approach can be deployed for epicentral localization task as part of realizing a robust earthquake monitoring system.
Improving the Individual Verification of Estonian Internet Voting Scheme

Estonia has been using the Estonian Internet Voting (EIV) scheme for state-wide legally binding general elections. Due to the so-called "Student Attack" mounted in 2011, an optional individual vote verification mechanism was added to EIV scheme. Then, Mus et al. has shown that this mechanism may be used for revealing the cast vote if the device, on which individual vote verification is running, is compromised. However, their practical solution is broken afterwards. The scheme has been updated with respect to the security analysis, since 2017 the framework labeled as IVXV is in use. In this study, we note that this vote privacy issue still exists in the currently used IVXV framework and suggest two counter-measures to eliminate this attack. First one is to utilize screenshot prevention methods in the individual verification application. Second one is to apply Mus et al.'s proposition in a user-friendly checking mechanism that does neither induce vote privacy leakage nor add new attack surface to the framework.
Clinical Nursing Risk Assessment and Early Warning System based on Support Vector Machine

Clinical nursing has many risks. In the actual evaluation of early warning, the threshold set by the system is fuzzy, which leads to too long response time when the early warning system is actually running. In response to this shortcoming, a clinical nursing based on support vector machine is designed. Risk assessment and early warning system. Design the risk evaluation signal formation hardware, combine the requirements of the early warning system, design the hardware connection circuit, use the C/S network architecture to obtain clinical care risk data, calculate the clinical care risk value, use support vector machines to set different levels of early warning thresholds, and finally Complete the design of the system. Two traditional risk assessment and early warning systems and the designed assessment and early warning system are used for experiments. The results show that the response time of the designed early warning system is the shortest.
Wise optimisation: deep image embedding by informative pair weighting and ranked list learning

Deep image embedding learns how to map images onto feature vectors. Image retrieval performance is often used to evaluate embedding quality. In this study, the authors proposed a wise deep image embedding optimisation (WDIEO) algorithm based on informative pair weighting and ranked list learning (IPWRLL) for network optimisation of fine-grained image retrieval. First, a hard sample mining method Top-k is proposed to select positive and negative samples. Then, for the selected query sample, a ranking list is obtained by comparing the similarity between samples in the data set and the query sample, and the sample is labelled according to the similarity. Finally, for positive samples, two optimisation rules with different functions are used, while ensuring two key issues of instance weighting and intra-class data distribution. For negative samples, different from the widely adopted methods based on the weight of sample information, the authors’ algorithm's weights are set according to the ranking list, which keeps the inter-class data distribution and the optimisation direction consistent with the loss reduction direction. The WDIEO-IPWRLL model is an end-to-end optimisation that can share parameters in the testing process. Experiments show that their proposed model achieves the state-of-the-art performance on the benchmark data set.
Exploration and construction of comprehensive training course based on project oriented

Automation is an engineering specialty, which requires strong practical and application ability. Practical teaching has great influence on the comprehensive quality of students. This paper introduces Automation comprehensive training method based on project oriented. The subject competition projects, the enterprise practical innovation projects and the teachers’ scientific research projects are developed as a comprehensive training program, so that it has more practicability and advanced nature. The practical teaching reform greatly improves the width and thickness of the professional practice teaching. Through comprehensive professional training, students can effectively improve the ability to analyze and solve problems as well as practical operation ability, exercise innovative thinking.