Reviewerly/exHarmony huggingface dataset:
Training RNN-T with CTC Loss in Automatic Speech Recognition

The end-to-end model for automatic speech recognition (ASR) has recently gained significant interest in the research community.Standard RNN-T loss causes an unreasonable alignment of output and labels, such as the model outputting a sequence of labels at a single time frame and a single label can only be emitted by a single frame.We apply the CTC loss function to RNN-T model to address these problems.On the assumption that the output sequence is no longer than the input sequence we propose an improved forward-backward algorithm to calculate the loss.Experimental results for speech recognition are provided on the AISHELL-1 speech corpus.The results show that TCL converges faster without causing gradient explosion compared with RNN-T.TCL achieves a 9.5% character error rate on a test set of AISHELL-1 Chinese speech corpus, outperforming both the baseline RNN-T reported by Z. Tian etc. and standard CTC achieve11.82%and 10.8% respectively.RNN-T with CTC loss achieves a 19.63% relative reduction in character error rate compared with the standard RNN-T and a 12.04% relative reduction in character error rate compared with standard CTC on the test set.
Math Word Problem Generation with Memory Retrieval

The task of math word problem generation (MWPG), which generates a math word problem (MWP) given an equation and several topic words, has increasingly attracted researchers’ attention. In this work, we propose a memory retrieval model to better take advantage of the training data. We first record training MWPs into a memory. Later we use the given equation and topic words to retrieve relevant items from the memory. The retrieved results are then used to complement the process of the MWP generation and improve the generation quality. In addition, we also propose a low-resource setting for MWPG, where only a small number of paired MWPs and a large amount of unpaired MWPs are available. Extensive experiments verify the superior performance and effectiveness of our method.
Improved lower bounds for self-dual codes over $ \mathbb{F}_{11} $, $ \mathbb{F}_{13} $, $ \mathbb{F}_{17} $, $ \mathbb{F}_{19} $ and $ \mathbb{F}_{23} $

&lt;p style='text-indent:20px;'&gt;We construct self-dual codes over &lt;inline-formula&gt;&lt;tex-math id="M6"&gt;\begin{document}$ \mathbb{F}_{11} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, &lt;inline-formula&gt;&lt;tex-math id="M7"&gt;\begin{document}$ \mathbb{F}_{13} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, &lt;inline-formula&gt;&lt;tex-math id="M8"&gt;\begin{document}$ \mathbb{F}_{17} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, &lt;inline-formula&gt;&lt;tex-math id="M9"&gt;\begin{document}$ \mathbb{F}_{19} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; and &lt;inline-formula&gt;&lt;tex-math id="M10"&gt;\begin{document}$ \mathbb{F}_{23} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; which improve the previously known lower bounds on the largest minimum weights. In particular, the largest possible minimum weight among self-dual &lt;inline-formula&gt;&lt;tex-math id="M11"&gt;\begin{document}$ [n, n/2] $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; codes over &lt;inline-formula&gt;&lt;tex-math id="M12"&gt;\begin{document}$ \mathbb{F}_{p} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; is determined for &lt;inline-formula&gt;&lt;tex-math id="M13"&gt;\begin{document}$ (p, n) = (19, 24) $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; and &lt;inline-formula&gt;&lt;tex-math id="M14"&gt;\begin{document}$ (23, 28) $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;.&lt;/p&gt;
Research on Intelligent Anti-collision Device of Heavy Truck Based on Wireless Communication Technology

The increase in the number of vehicles, rapid development of road traffic, traffic accidents have become increasingly serious. However, these sensors have some limitations, such as single detection direction, detection blind area, and can not realize long-distance transmission. So they can not meet the current requirements. Based on this, the intelligent anti-collision device of heavy truck based on wireless communication technology is put forward. The intelligent anti-collision research of heavy truck is realized by designing the display interface of intelligent anti-collision device, constructing anti-collision early warning database and modulating and demodulating algorithm based on wireless communication technology. Tests show that the wireless communication module and the collision warning and display function of the intelligent anti-collision device can run successfully, and the overall performance of the device is reliable and stable.
A Blockchain-based Data Market for Renewable Energy Forecasts

This paper presents a data market aimed at trading energy forecasts data. The system architecture is built using blockchain as a service, allowing access to data streams and establishing a distributed settlement between stakeholders. Energy Forecasts data is presented as the commodity traded in the market, whose settlement is provided through the blockchain on the basis of the extracted value provided by market stakeholders. Our proposal allows market stakeholders to acquire energy forecasts and pay according to the data accuracy, solving the confidentiality problem of freely sharing data. A data quality reward is introduced, steering the compensation sent to market participants. The data market design is presented and an evaluation campaign is performed, showing that the data market produced functionally valid results in comparison with the results achieved with a central simulated approach. Moreover, results show that the data market architecture is able to scale.
The state of augmented reality technology in tourism cartography

The article presents an overview of the state of augmented reality technology in tourist cartography. The use of augmented reality technology is a promising direction in tourist cartography, which allows you to discover new facets of tourist mapping, from the study of individual attractions to cartographic works designed for long journeys with the display of various reference information in the most understandable form for the user. The article analyzes and summarizes information about existing types and methods of augmented reality, considers the relationship of tourist cartography with the capabilities of augmented reality technology. A system of criteria for evaluating augmented reality software platforms has been developed and, on its basis, a summary table has been compiled for evaluating the most relevant software that allows developing augmented reality applications. Conclusions are made about the applicability of software libraries for creating augmented reality applications, taking into account the specifics of using geoinformation 3D models.
On construction of lightweight MDS matrices

MDS matrices are widely used in block ciphers. Constructing lightweight MDS matrices is one of the research focuses of lightweight cryptography. In this paper, we define a new operation called the Copy operation by using registers. It is a generalization of Type 3 elementary operations (add a row to another one multiplied by a nonzero number). It is shown that any nonsingular matrix can be obtained by Copy operations and Multiplication operations from the identity matrix $ I $ (a Copy Block Implementation of the matrix). Thus we introduce a new metric called gw-xor using Copy Block Implementations to construct lightweight MDS matrices with respect to low xor gates. Compared with sw-xor, the gw-xor count is a better approximation of the optimal implementation cost, and in particular it may be a better approximation of the optimal implementation cost than s-xor. By searching the potential paths of Copy operations that can obtain formal MDS matrices (i.e., matrices with indeterminate elements and each determinant of square submatrix of any order is a nonzero polynomial in these indeterminates), we find 52 classes $ 16\times 16 $ and $ 32\times 32 $ binary MDS matrices with 35 and 67 xor gates respectively, which are the best known results. Furthermore, by considering the depth of MDS matrices, we find more $ 4\times4 $ MDS matrices over $ \mathbb{F}_{2^n} $ with the lowest xor gates at depths 3, 4, 5.
Developing a Critical Success Factor Model for DevOps

DevOps has presently become a mainstream software development model in the software industry. DevOps is a software engineering paradigm which is adopted and implemented by various software organizations. There is a need for a model which could guide the professionals and practitioners to achieve organizations goals and performance. To address this study objective, we have developed an initial framework for the critical success factors models which will be validated by three research questions.
Improvement and extension of secure vector dominance protocol

The millionaire’s problem is the origin of secure multi-party computation. This problem has always been the focus of scholars' research, and it is also the design basis and important part of other secure multi-party computation protocols. An in-depth study of the millionaires’ problem is of great significance to promote the further development of secure multi-party computation. Based on millionaires’ problem and secure vector dominance protocol, we propose an improved protocol and an extended protocol. The improved protocol solves the problem of coarse granularity when comparing elements in the original protocol, and can realize that two parties can compare the relationships of greater than, equal to or less than between their elements by executing the protocol once. The improved protocol and the extended protocol are further applied to solve the problem of finding the cardinality of the intersection of the two-party sets in two cases. In addition, based on the integer division problem, we give a method to privately evaluate the greatest common factor of two integers.
Application of Passive IoT Technology in Digital Development of Distribution Network (CICED2022)

The integration of digital technology and power grid technology will facilitate the transformation and upgrade of the energy supply system and promote the high-quality development of the economy and society. Due to the limitations of power supply mode and communication distance, traditional communication technology seriously restricts the function of key information collecting and transmission in some power distribution network operation scenarios. Passive IoT technology combines Passive sensor IoT communication technology with cellular mobile communication network without the external power supply, and enables remote transmission of pre-stored identity, temperature, and humidity from sensors. It has two modes: label connection mode and limited sensor connection mode. It can be applied to the digital assets management of power distribution equipment and the operation status monitoring of distribution cables and other equipment to solve the shortage problem of power supply in the operation environment of power distribution equipment. The application design, which is based on the low-voltage distribution area and combined with Passive IoT technology, greatly enriches the ways of collecting the information from ends by the cloud center in the intelligent power distribution area system, addressing the weakness during the construction of digitalized and intelligent low-voltage power distribution system, and accelerate the digital transformation of power distribution equipment management.
The Dynamic Relation between Bitcoin Volatility and Stock Volatility: DCC-GARCH Approach

Bitcoin's performance during the COVID-19 pandemic has drawn a lot of attention, with many researchers wondering whether bitcoin can act as a hedge against the stock market, and how exactly the COVID-19 pandemic has changed bitcoin's connection to the world.This paper aims to investigate the dynamic conditional volatility correlation between bitcoin and stock markets before and after the COVID-19 outbreak.Bitcoin data and stock market data are collected from Coindesk and Invest, respectively.DCC-GARCH model is built to measure the dynamic correlation between bitcoin and stock markets.As a result, it shows the overall dynamic conditional correlations increase after the COVID-19 outbreak.Based on the dynamic correlations, this paper further conducts investment portfolio analysis for bitcoin and stock markets.The GARCH model is used to extract the volatilities of bitcoin and stock markets and calculate the dynamic optimal weight of bitcoin in the portfolio.Finally, this paper finds that the dynamic relation between bitcoin and the stock markets changes before and after the COVID-19 outbreak, and this change dramatically affects the optimal weight of bitcoin in the stock-bitcoin investment portfolio.
Problem of stability of modern cryptosystems against the background of the emergence of quantum computers

Every year, the production of quantum computers is becoming more productive and cheaper - for example, “The Tsuchongzi” computer uses 56 qubits and is able to solve problems that highlight the possibility of quantum acceleration within a few hours, while classical supercomputers require tens of thousands of years. Currently, IBM allows any Internet user to remotely connect and work on a real quantum computer, albeit with a power of several qubits. Modern cryptography is based on the fact that it is difficult to carry out factorization of integers or discrete logarithm by classical algorithms. But with the use of Shor's algorithm on a quantum computer, these difficulties are easily bypassed. Some of the most popular cryptographic systems - RSA (integer factorization), DH (discrete logarithm), and ECDSA (elliptic curves over finite fields) - will no longer be a reliable tool for data encryption with the advent of productive quantum computers. In this article, post-quantum cryptographic systems are studied and compared with the classical RSA system.
Research on university ranking based on literature big data and industry thesaurus - Taking double first-class universities as an example

Most of the current university rankings base on the academic system and lack the detailed classification. It is not that suitable for industry field to screen the universities for the potential corporation on research and development. In this paper, we adopt big data processing technology to form a structured process of university rankings based on the industrial system, with support on the massive literature data and the built industry thesaurus from our previsions study. It consists of four modules: resource download, text segmentation, data processing and result presentation. Results show that our new ranking can reach the precise divisions on subdivided industry, also in accord with other authoritative rankings in the similar divisions from both classifications. Our ranking can possibly help understand the research priorities of different universities in perspective, improve the efficiency of technology transfer from universities to industry and sketch the map of industry-university-research system.
Automated Attendance System Using Computer Vision

Automated attendance system using computer vision is a model based on real-time facial recognition which works on deep learning algorithms. This automated system efficiently manages time with excellent accuracy. To reduce human effort, our model will precisely recognise the image and according to the label assigned to that particular image, the attendance will be marked accordingly. The concerned faculty can also retrieve the database of present students on a particular day. This reduces the time required for managing the manual attendance and also eliminates the chances of proxy attendance. Moreover, our model uses OpenCV and Dlib as the major libraries and Principal component analysis (PCA) algorithm. Traditional facial recognition systems only recognise one face at a time, but this paper deals with the automated attendance system which could detect multiple faces at a time and mark their presence simultaneously. In our paper, we have done multiple experiments with our model and the results are excellent with accuracy ranging from 75 to 100 %.
Power Analysis Attack Based on Lightweight Convolutional Neural Network

Since the beginning of the 21st century, modern information technology and electronic integrated circuit technology have developed rapidly. In the chip industry, the ability to resist side-channel attacks has become an important indicator for international mainstream evaluation agencies to evaluate chip security. This paper proposes an improved method for side channel analysis based on the $${CNN}_{best}$$ model, incorporating a lightweight combined channel and space convolutional attention module, optimising the position of the attention module, improving the learning efficiency of key features of the power consumption curve, and effectively reducing the number of traces used by the attack model. The addition of dropout layer network structure solves the problem that the model is prone to rapid overfitting. The optimal value of drop rate is sought through comparative experiments to speed up the convergence of the model and reduce the number of traces required for a successful attack. The experimental results show that the number of traces required by the method in this paper for side-channel attacks is reduced by 88% compared with the original model, which significantly improves the attack performance and can meet the requirements of side-channel modeling and analysis.
Coarse Semantic Guided Alpha Matting via Simultaneous Foreground and Background Estimation

To address the "color spill" problem in image matting, an algorithm of simultaneously predicting the foreground, background and alpha matte is proposed, and an automatic matting network combined with coarse semantics is designed. The algorithm method includes two stages: coarse semantics generation and matting via simultaneous foreground and background prediction. In the first stage, a semantic segmentation network is used to obtain intermediate results, and then a coarse semantic information fusion module is applied to roughly estimate the semantic information on multiple scales. In the second stage, an encoder-decoder is used to refine the semantic information and obtain the prediction of foreground, background and alpha matte. Foreground objects can be extracted more accurately through this two-stage network. Also, the obtained alpha matte can be directly used for downstream tasks such as image and video processing. The experimental results on Adobe dataset and the Distinction-646 dataset show that the sum of absolution difference is 42.5 and 50.3, the gradient error is 27.1 and 28.0, respectively. And the details are also more accurate.
LEARNING MEDIA PHYSICAL EDUCATION BASED REVOLUTION 4.0 TO IMPROVE STUDENT LEARNING OUTCOMES THE COVID-19 PANDEMIC

Education 4.0 is an education characterized by the use of digital technology in the learning process, therefore android-based learning media can improve thematic learning outcomes carried out online. Android-based learning media is an application that contains material used by students in terms of learning. Researchers collected data through interview observations, surveys and blended learning methods. This research is the fifth grade students of SD Negeri 106790 Sei Menistrim for the Academic Year 2021/2022. Sampling was done by purposive sampling technique. That is by taking two classes taught by the same teacher. The initial conditions were obtained from daily test 1 with an average value of 75.00 as the control class using powerpoint learning media. While the average value of 87.00 as an experimental class using android-based learning media. By mastering android-based learning media, elementary school teachers can present Android-based learning as attractively as possible, which is related to the material to be studied and is the best solution when learning is carried out online and in accordance with the 4.0 revolution.Keywords: Learning Media, Revolution 4.0, Covid-9
MMISeg: A Semi-supervised Segmentation Method Based on Mixup and Mutual Information for Cardiac MRI Segmentation

Since the task of annotating medical image labels is pixel-level and needs to be depicted by trained experts, there are few large-scale medical image datasets with annotations. Semi-Supervised Learning (SSL) has become the focus of research for medical image segmentation tasks. The key techniques for our Segmentation method are Mixup and Mutual Information (MMISeg), which involve consistency-based regularization and unsupervised representation learning. On the one hand, we utilize an interpolation-based method to mix unlabeled data, and minimize consistency regularization. On the other hand, by taking the feature of the encoder stage as global feature and the feature of the decoder stage as local feature, we maximize mutual information of global and local features which are from two different transformations of the same image, respectively. Experimental results show that MMISeg outperforms existing semi-supervised methods.
The State and Features of the Media Culture of Modern Youth

The relevance of the topic “media culture of youth” is due to the insufficiency of special studies of the problem from the point of view of library activities. The article presents the results of a sociological study of the media culture of young people, conducted on the basis of the Chelyabinsk Regional Library for Youth. It is shown that it is characterized by a certain number of features of media consumption, where “getting information about what is happening in the world” is indicated as the most important by respondents. The problems of defining the concept of “media culture of youth” as a particular variant of the term “information culture”, and a comparison of the common grounds between information and media culture are raised, as well as the difference introduced by the emergence of the concept of “media culture”. Personality characteristics associated with a developed media culture are determined. The author cites the data of some sociological and statistical studies reflecting the peculiarities of the media culture peculiar to young people. The functions of the media and their influence on youth, the specifics of the existence of youth in the media environment are considered. The author highlights the peculiarity of youth activity in the media environment, including the specifics of information retrieval, the organization of communication through its interactive forms, the importance for the development of personal media culture of the phenomenon of social networks, its impact on changing the traditions of writing. The problem of network threats to users and the role of media culture formation in order to minimize such threats are considered separately. The role of the Internet in the formation of a synthesized youth subculture, which includes special etiquette, slang and hierarchy in the process of communication, is noted. The author provides data on the most typical properties of the media culture of young people, based on both existing and own research. An attempt is made to systematize these properties – the priority of communication through social networks, listening and browsing media files, turning to online games, running a network business, are called the most characteristic ones. The presented information and the results of the author’s own research are relevant for the development of strategies and tactics of libraries to form the media culture of young users.