Reviewerly/exHarmony huggingface dataset:
Application and Research of Curriculum Blended Learning Based on Big Data Platform

In the information age, not only can we perceive and analyze in real time many different types of transmission data of larger and smaller sizes and a greater number of different types, through the analysis and processing of these transmitted data, we can even tap the inherent technical information and more core technical values that may be contained in it. Among them, the education field is generally regarded as an important component of the application of big data technology in our country, and the integration of various deep technology applications in the field of big data technology and higher education is still the key, as well as the major practical application needs and future development trends of the development of our country’s higher education. This article uses literature review, questionnaire surveys and other methods, after collecting a large amount of data, and draws the following conclusions: 97% of students have a positive attitude towards the use of blended learning learning methods, and 92% of students said they can adapt to blending, 85% of students hope to use blended learning in the learning process of other courses.
Multi-system Platform Cooperative Electronic System

With the continuous progress of science and technology, it provides more possibilities for the development of electronic music. It is possible to make rational use of the cooperation of different software and hardware platforms to jointly complete the creation and practice of electronic music. This paper provides theoretical and practical analysis and support for the construction of multi system platform collaborative electronic music system by combing the relevant elements.
Study Books for Engineering Education of the Second Half of the XVIII Century

The work aims to examine the connection between theory and practice in the engineering education of the second half of the XVIII century. The paper is the first to consider almost two dozen original textbooks for engineering education by Russian authors, confirm I. Kartmazov’s authorship in relation to the 1765 textbook “The Introductory Foundations of Fortification…”. Scientific novelty of the research lies in identifying the methods of training material presentation for engineering education used by XVIII-century authors. As a result, the researchers have determined the ratio of theoretical material to problems in XVIII-century textbooks, demonstrated the arrangement of problems in a textbook, characterised the content of problems, ways of problem solving, use of interdisciplinary relationships, textbook design.
Gradient-Free Neural Network Training Based on Deep Dictionary Learning with the Log Regularizer

Gradient-free neural network training is attracting increasing attentions, which efficiently to avoid the gradient vanishing issue in traditional neural network training with gradient-based methods. The state-of-the-art gradient-free methods introduce a quadratic penalty or use an equivalent approximation of the activation function to achieve the training process without gradients, but they are hardly to mine effective signal features since the activation function is a limited nonlinear transformation. In this paper, we first propose to construct the neural network training as a deep dictionary learning model for achieving the gradient-free training of the network. To further enhance the ability of feature extraction in network training based on gradient-free method, we introduce the logarithm function as a sparsity regularizer which introduces accurate sparse activations on the hidden layer except for the last layer. Then, we employ a proximal block coordinate descent method to forward update the variables of each layer and apply the log-thresholding operator to achieve the optimization of the non-convex and non-smooth subproblems. Finally, numerical experiments conducted on several publicly available datasets prove the sparse representation of inputs is effective for gradient-free neural network training.
Optimal Bayesian Estimation of a Regression Curve, a Conditional Density, and a Conditional Distribution

In this paper, several related estimation problems are addressed from a Bayesian point of view, and optimal estimators are obtained for each of them when some natural loss functions are considered. The problems considered are the estimation of a regression curve, a conditional distribution function, a conditional density, and even the conditional distribution itself. These problems are posed in a sufficiently general framework to cover continuous and discrete, univariate and multivariate, and parametric and nonparametric cases, without the need to use a specific prior distribution. The loss functions considered come naturally from the quadratic error loss function commonly used in estimating a real function of the unknown parameter. The cornerstone of these Bayes estimators is the posterior predictive distribution. Some examples are provided to illustrate the results.
A Document-Level Machine Translation Quality Estimation Model Based on Centering Theory

Machine translation Quality Estimation (QE) aims to estimate the quality of machine translations without relying on golden references. Current QE researches mainly focus on sentence-level QE models, which could not capture discourse-related translation errors. To tackle this problem, this paper presents a novel document-level QE model based on Centering Theory (CT), which is a linguistics theory for assessing discourse coherence. Furthermore, we construct and release an open-source Chinese-English corpus at https://github.com/ydc/cpqe for document-level machine translation QE, which could be used to support further studies. Finally, experimental results show that the proposed model significantly outperformed the baseline model.
EmojiText: An Information Visualization Technique for Analyzing Phrases and Sentiments

This paper presents a proposal for text visualization with a focus on the representation of sentences and sentiments. It is noteworthy that traditional text visualization techniques, such as wordcloud, do not visually present relations between words, making it challenging to analyze textual data more deeply through visualization. EmojiText uses a directed graph, where the nodes represent the words and the edges represent the sequence in which the words appear in the sentences. Furthermore, the area of each node is related to the frequency of the represented word, and each node is associated with an emoji that represents the most frequent sentiment associated with the word. Finally, the edges also allow identifying the sentiment at the sentence level. The tool also includes zoom interactions, filters, details on demand, visual mapping configuration, and temporal analysis functionality through animations and multiple views for comparisons. The tool was developed in a javascript programming language using the d3.js framework and is available at http://emojitext.roberto.eti.br/.
Analyzing mixed-type data by using word embedding for handling categorical features

Most of real-world datasets are of mixed type including both numeric and categorical attributes. Unlike numbers, operations on categorical values are limited, and the degree of similarity between distinct values cannot be measured directly. In order to properly analyze mixed-type data, dedicated methods to handle categorical values in the datasets are needed. The limitation of most existing methods is lack of appropriate numeric representations of categorical values. Consequently, some of analysis algorithms cannot be applied. In this paper, we address this deficiency by transforming categorical values to their numeric representation so as to facilitate various analyses of mixed-type data. In particular, the proposed transformation method preserves semantics of categorical values with respect to the other values in the dataset, resulting in better performance on data analyses including classification and clustering. The proposed method is verified and compared with other methods on extensive real-world datasets.
A Fast Method for Extracting Parameters of Circular Objects

A region-based method is proposed for extracting parameters of circular objects in images. The Hough space is defined by a 2-dimensional accumulator with less columns. By analysing the voting values distribution in each column of the accumulator, a quadratic function, which denotes the relationship between the voting value and the voting distance, is deduced. Regrading all columns, a linear function formula is deduced by analysing the relationship between mean voting distances and the voting angles. After all region pixels have voted for 2D accumulator, a quadratic function is fitted in each column. The circle radius is calculated based on the fitted coefficients. Then a linear function is fitted to mean voting distances corresponding to every voting angles. The circle center coordinates are computed based on the fitted coefficients. Synthetic images and real-world images are used to test the proposed region-based method. Experimental results show the proposed method is fast and accurate even in the presence of contour defects and outliers.
SVM Based EVM for Open Space Problems

In this paper, we consider a multi-class classifier for problems where unknown classes are included in testing phase. Previous classifiers consider the “closed-set” case where the classes used for training and the classes used for testing are the same. A more realistic case is the “open-set” recognition, in which only a limited number of classes appear at training time, and unknown classes appear during testing. To handle such problems, we need classifiers that accurately classify data belonging to not only known classes but also unknown classes. In this paper, We introduce a Support Vector Machine (SVM) based Extreme Value Machine (EVM) to determine a compact class region. Any data outside of such class regions is rejected as being in unknown classes. To construct a class region, we approach the class decision boundary found by SVM towards the samples, by removing some support vectors close to the boundary. This SVM based EVM resolves the three problems that EVM possesses: unfair size of class regions, excessive sensibility to certain points and fragmentation of a class region.
BASIC METHODS OF PEDAGOGICAL MONITORING: FEATURES OF APPLICATION

The purpose of this article is to analyze existing approaches to assessing the quality of knowledge at a university, to identify their advantages and disadvantages. The author notes the relevance of obtaining objective information about the state of the quality of the educational process and the search for means to ensure effective management of its quality. Attention is drawn to the fact that monitoring can become such a means, since it provides ample opportunities for managing the quality of education, and it is pedagogical monitoring, which includes didactic and educational elements. The article presents various interpretations of the concept of «educational monitoring». The main methods of pedagogical monitoring are highlighted, the advantages and disadvantages of testing, portfolio and rating system of accounting for educational achievements of students as methods of pedagogical monitoring are revealed. The analysis carried out can contribute to the optimal choice of methods for assessing the quality of the educational process.
Adaptive layer-two dispute cutoffs in smart-contract blockchains

Second-layer or off-chain protocols aim to increase the throughput of permissionless blockchains by enabling parties to lock funds into smart-contracts and perform payments through peer-to-peer communication, only resorting to the smart-contracts for protection against fraud. Current protocols have fixed periods during which participants can dispute any fraud attempts. However, current blockchains have limited transaction processing capacity, so a fixed dispute period will not always be sufficient to deter all fraudulent behaviour in an off-chain protocol. In this paper we present a novel mechanism for adaptive dispute cutoffs (ADCs) which ensure that users retain the opportunity to dispute fraudulent behaviours despite blockchain congestion, while increasing second-layer protocol efficiency by reducing dispute period lengths when the number of disputes is low. We present a non-interactive argument system for setting adaptive dispute periods under the current Ethereum Virtual Machine, and describe how to efficiently integrate built-in support for adaptive dispute periods in any blockchain using binary-indexed trees. We empirically demonstrate that an ADC-enabled second -layer protocol can handle a larger number of disputes and prevent more fraud than its non-adaptive counterparts even when users are slow to issue disputes, due to denial of service or blockchain congestion.
An artificial bee colony algorithm with an improved updating strategy

The artificial bee colony (ABC) algorithm shows a relatively powerful exploration search capability but show convergence rate, especially on unimodal functions. In this paper, an improved artificial bee colony algorithm is introduced to shorten its computation time. In the proposed algorithm, two novel update equations, utilizing the social experience of the whole population, are proposed to boost the performance of employed bees and onlooker bees respectively. The effectiveness of our algorithm is validated through the basic benchmark functions. Furthermore, a model of feed-forward artificial neural network is also employed to verify the effectiveness of our algorithm. The experimental results show the IUABC algorithm achieves better performance than the other compared algorithms.
Development of an Augmented Reality Application as Support in Antenna Characterization

This document presents the design and implementation of a mobile application aimed at visualizing the characteristic parameters of antennas. The principal objective is to use augmented reality to observe the radiation pattern on real antennas, considering how difficult it can be for a student to imagine this parameter. Common antenna designs are considered in academic and work environments. All antennas were designed to work at a frequency band of 2.4 GHz. Characteristic parameters were obtained by simulation in ANSYS HFSS (High-Frequency Structure Simulator) software because it allows to design and simulate high-frequency antennas. In addition to the radiation pattern, parameters such as return losses, Voltage Standing Wave Ratio, impedance, and directivity are observed on the mobile device screen. Detection of the antenna is carried out by previously established identifiers such as QR codes. The established process to visualize the parameters is detailed, thus allowing the application to be replicated in other areas within the academic field as support to the learning process.Jara-Quito, Tatiana A. Duy-Romero, Hernán Guerrero-Vásquez, Luis F. Chasi-Pesantez, Paul A. Ordoñez-Ordoñez, Jorge O.
Towards an immersive visualization of consumer-level simulations of vehicular traffic

Since the beginning of the 2010 decade, many improvements have been made in terms of Virtual Reality (VR) hardware technologies and software platforms. Such trend is expected to continue, pushing the penetration rate of VR headsets. In the meantime, two research questions emerge: (a) how 3D-based immersive environments may get to be used, and with which benefits, in place of 2D ones, and, (b) how to support such integration. We focus on the latter, taking as a case study SUMO, a well-known PC-based vehicular traffic simulation tool. To this aim, we adopt an approach based on the OpenGL intercept technique, with the implementation of the OGL2VR middleware, which has been extended to remove graphical artifacts and to include the on/off toggling of dynamic elements in a 3D scene. We here describe the solutions that have been embraced, as well as the challenges that still remain along this path, to render dynamic traffic animations in an immersive setting to support the use of a blended 2D/3D environment.
Mixed Reality in an Operating Room Using Hololens 2—The Use of the Remote Assistance from Manufacturers Techinicians during the Surgeries

The aim of this work is that the participants, using HoloLens 2 and Dynamics 365 Remote Assistance, can receive all the training and information necessary for the correct application of prosthesis and medical devices remotely, from a support center of the manufacturers, avoiding the displacement and presence of these technicians during surgeries. After implementing this method, an analysis will be made on its impact, avoiding displacement and the presence of technicians during surgery, in terms of increasing satisfaction and improving the experience of the participants, reduction of various risks (including the risk of infection) and on reduction of some economic and environmental costs.
Modified linear congruential generator to secure random number generation

Random numbers are important tools for generating secret keys, encrypting messages, or masking the content of certain protocols with a random sequence that can be deterministically generated. The nonattendance of demand about the self-decisive numbers conveyed can make genuine harm cryptographic shows, impelling weaknesses to be mishandled by the aggressors. In this paper, another pseudo - optional number generator calculating that utilizes dynamic design clock changed over to Epoch Timestamp as PRNG seed was made. The figuring utilizes a Linear Congruential Generator (LCG) tally that makes a get-together of pseudo - randomized numbers that performs numerical tasks to change numbers that has all the stores of being disconnected to the Seed. Multiplication result shows that the new PRNG calculation doesn’t convey emphasized self-emphatic numbers subject to the rehash of highlight, a decent pointer that the key for flighty numbers is guaranteed about. Mathematical evaluation utilizing NIST Test Suite results focused to optional movements made flighty numbers has a completely conventional of 0.342 P-Value. For a P-Value≥0.001, a social event would be viewed as abstract with an affirmation of 99.9%. This shows that strength and weirdness were developed. Accordingly, It is astoundingly deterministic in nature and has a benevolence of Pseudo-Random Numbers. It is hence a pleasant wellspring of a social event key age for encryption, equal in the assertion plans and other cryptographic assessment restricts that improve and secure information from a security assault.
On the bias of the score function of finite mixture models

We characterise the unbiasedness of the score function, viewed as an inference function for a class of finite mixture models. The models studied represent the situation where there is a stratification of the observations in a finite number of groups. We show that, under mild regularity conditions, the score function for estimating the parameters identifying each group's distribution is unbiased. We also show that if one introduces a mixture in the scenario described above so that for some observations, it is only known that they belong to some of the groups with a probability not in $\{ 0, 1 \}$, then the score function becomes biased. We argue then that under further mild regularity, the maximum likelihood estimate is not consistent. The results above are extended to regular models containing arbitrary nuisance parameters, including semiparametric models.
Development of a Token Design Framework

Apart from the financial success of Bitcoin, blockchain technology has not yet made the leap from emerging technology to mainstream application. The introduction of blockchain tokens could now offer a major contribution to making this happen. Blockchain tokens can be representations of real-life assets or rights and are, therefore, central for connecting blockchains to real world applications. Despite their pivotal role, their mainstream adoption is still lagging behind. One reason for this is that companies struggle to develop relevant use cases. They are hampered by the rapid development of the complex technology and the lack of development processes. Existing development processes no longer apply to the new decentralized structure of token use cases. As existing research on token systems is either too technical by focusing on a very detailed description of a single use case only or too general by providing token typologies without implementation guidance, companies have no choice but trial and error. It turned out that there is a lack of structured approaches or best practices for the development of token systems. To solve this problem, we follow a design science research approach and develop a framework that guides the design of token use cases. It provides decision support for all steps of the design process and further comprises basic token types which can be used to develop standardized modules. This approach leads to a faster and cheaper development process. We build this framework based on an extensive literature review, enhance it with expert interviews and finally validate its applicability by using it to implement a decentralized parking system based on tokens.
Elimination of Difficulties in Teaching Primary School Students by Means of Controlling Metasubject Achievements

The article highlights the possibilities of pedagogical control, which makes it possible to eliminate difficulties in the process of formation and&#x0D; development of universal educational actions in younger schoolchildren, which contributes to the achievement of metasubject learning outcomes. Examples of test and diagnostic tasks are given, with the help of which the level of formation of students’ cognitive, communicative, regulatory universal educational actions, which determine the degree of readiness to solve problems of a metasubject nature, is checked. It is emphasized that the collective analysis of the independent learning activity of younger schoolchildren provides not only the removal of difficulties and mistakes in the performance of tasks, but also their prevention.
A survey of semisimple algebras in algebraic combinatorics

This is a survey of semisimple algebras of current interest in algebraic combinatorics, with a focus on questions which we feel will be new and interesting to experts in group algebras, integral representation theory, and computational algebra. The algebras arise primarily in two families: coherent algebras and subconstituent (aka. Terwilliger) algebras. Coherent algebras are subalgebras of full matrix algebras having a basis of 01-matrices satisfying the conditions that it be transpose-closed, sum to the all 1’s matrix, and contain a subset $$\Delta $$ that sums to the identity matrix. The special case when $$\Delta $$ is a singleton is the important case of an adjacency algebra of a finite association scheme. A Terwilliger algebra is a semisimple extension of a coherent algebra by a set of diagonal 01-matrices determined canonically from its basis elements and a choice of row. We will survey the current state of knowledge of the complex, real, rational, modular, and integral representation theory of these semisimple algebras, indicate their connections with other areas of mathematics, and present several open questions.