Reviewerly/exHarmony huggingface dataset:
Customizing Super-Resolution Framework According to Image Features

As a popular research field of computer vision, super resolution(SR) has received more and more attention in recent years. Although the deep learning methods have achieved good results in SR, there are still some problems. For example, the previous models are often based on single depth mechanism. This means that the SR reconstruction problem of all images is regarded as of equal complexity. And we found that some images have more details and are suitable for recovering in complex models, while other images have less texture information and are suitable for recovering in simple models. At the same time, the size of the training set is too large, which creats a lot of resource overhead. To solve these problems, this paper proposes a new SR framework can be customized according to image features. We choose 3 representative models for testing and in test our framework can reduce the size of the training set by 41.9%. For MSRResNet, we can reduce the training time from 2517 minutes to 2449 minutes. The reconstruction quality of 61 % test images has been improved and the average perceptual index has dropped from 5.1912 to 5.155833, at the same time the reconstruction time has been optimized from 85 seconds to 59 seconds. For SRGAN, the training time can be reduced from 1920 minutes to 1812 minutes. The reconstruction quality of 58 % test images have been improved and the average perceptual index has dropped from 2.0869 to 2.0509, while the reconstruction time has been optimized from 82 seconds to 46 seconds. For ESRGAN, the training time can be reduced from 4368 minutes to 4249 minutes. The reconstruction quality of 78 % test images has been improved and the average perceptual index has dropped from 2.2041 to 2.0535. The reconstruction time has been optimized from 138 seconds to 95 seconds. Our framework can improve the effect of super-resolution models while reducing the resource overhead.
K-Nearest Neighbors Classification of Semantic Web Ontologies

The growing interest in the semantic web technologies in the past years has led to the increase in the number of ontologies on the web. This gives semantic web developers the opportunity to select and reuse these ontologies in new applications. However, none of the existing approaches has leveraged the power of Machine Learning to assist in the choice of suitable ontologies for reuse. In this paper, the k-Nearest Neighbors (KNN) algorithm is implemented to classify ontologies based on their quality metrics. The aim is to group the ontologies that display the same quality properties into classes, thereby, providing some insights into the selection and reuse of these ontologies using a Machine Learning technique. The experiments were carried out with a dataset of 200 biomedical ontologies characterized each by 11 quality metric attributes. The KNN model was trained and tested with 70% and 30% of the dataset, respectively. The evaluation of the KNN model was undertaken with various metrics including accuracy, precision, recall, F-measure and Receiver Operating Characteristic (ROC) curves. For the best value of k = 5 the KNN model displayed promising results with an accuracy of 67% and the average precision, recall, and F-measure of 69%, 67%, and 67%, respectively as well as an area under ROC curve of 0.78.
Research on Application of Chinese Natural Language Processing in Constructing Knowledge Graph of Chronic Diseases

Knowledge Graph can describe the concepts in the objective world and the relationships between these concepts in a structured way, and identify, discover and infer the relationships between things and concepts. It has been developed in the field of medical and health care. In this paper, the method of natural language processing has been used to build chronic disease knowledge graph, such as named entity recognition, relationship extraction. This method is beneficial to forecast analysis of chronic disease, network monitoring, basic education, etc. The research of this paper can greatly help medical experts in the treatment of chronic disease treatment, and assist primary clinicians with making more scientific decision, and can help Patients with chronic diseases to improve medical efficiency. In the end, it also has practical significance for clinical scientific research of chronic disease.
Research on Labor Education Reform Relying on Computer

Abstract With the development of science and technology, our education has begun to enhance the quality of teaching continuously, which combines new network technologies. After the new curriculum reform, our education pay more attention to the reform and innovation of labor education. It also combine modern technology to continuously improve the effectiveness of labor teaching. After combining modern science and technology, labor teaching makes the labor courses more perfect in theory and practice. According to the current trend of student growth and social development in our country. If we want education reform to satisfy the teaching needs, we must continue to combine the current Internet technology to improve the development of teaching.
Analysis of Client-Side Security for Long-Term Time-Stamping Services

Time-stamping services produce time-stamp tokens as evidences to prove that digital data existed at given points in time. Time-stamp tokens contain verifiable cryptographic bindings between data and time, which are produced using cryptographic algorithms. In the ANSI, ISO/IEC and IETF standards for time-stamping services, cryptographic algorithms are addressed in two aspects: (i) Client-side hash functions used to hash data into digests for nondisclosure. (ii) Server-side algorithms used to bind the time and digests of data. These algorithms are associated with limited lifespans due to their operational life cycles and increasing computational powers of attackers. After the algorithms are compromised, time-stamp tokens using the algorithms are no longer trusted. The ANSI and ISO/IEC standards provide renewal mechanisms for time-stamp tokens. However, the renewal mechanisms for client-side hash functions are specified ambiguously, that may lead to the failure of implementations. Besides, in existing papers, the security analyses of long-term time-stamping schemes only cover the server-side renewal, and the client-side renewal is missing. In this paper, we analyse the necessity of client-side renewal, and propose a comprehensive long-term time-stamping scheme that addresses both client-side renewal and server-side renewal mechanisms. After that, we formally analyse and evaluate the client-side security of our proposed scheme.
Multi‐node Expectation–Maximization algorithm for finite mixture models

Abstract Finite mixture models are powerful tools for modeling and analyzing heterogeneous data. Parameter estimation is typically carried out using maximum likelihood estimation via the Expectation–Maximization (EM) algorithm. Recently, the adoption of flexible distributions as component densities has become increasingly popular. Often, the EM algorithm for these models involves complicated expressions that are time‐consuming to evaluate numerically. In this paper, we describe a parallel implementation of the EM algorithm suitable for both single‐threaded and multi‐threaded processors and for both single machine and multiple‐node systems. Numerical experiments are performed to demonstrate the potential performance gain in different settings. Comparison is also made across two commonly used platforms—R and MATLAB. For illustration, a fairly general mixture model is used in the comparison.
Collocating News Articles with Structured Web Tables✱

In today’s news deluge, it can often be overwhelming to understand the significance of a news article or verify the facts within. One approach to address this challenge is to identify relevant data so that crucial statistics or facts can be highlighted for the user to easily digest, and thus improve the user’s comprehension of the news story in a larger context. In this paper, we look toward structured tables on the Web, especially the high quality data tables from Wikipedia, to assist in news understanding. Specifically, we aim to automatically find tables related to a news article. For that, we leverage the content and entities extracted from news articles and their matching tables to fine-tune a Bidirectional Transformers (BERT) model. The resulting model is, therefore, an encoder tailored for article-to-table match. To find the matching tables for a given news article, the fine-tuned BERT model encodes each table in the corpus and the news article into their respective embedding vectors. The tables with the highest cosine similarities to the news article in this new representation space are considered the possible matches. Comprehensive experimental analyses show that the new approach significantly outperforms the baselines over a large, weakly-labeled, dataset obtained from Web click logs as well as a small, crowdsourced, evaluation set. Specifically, our approach achieves near 90% [email protected] as opposed to baselines varying between 30% and 64%.
Asymptotics of Sequential Composite Hypothesis Testing Under Probabilistic Constraints

We consider the sequential composite binary hypothesis testing problem in which one of the hypotheses is governed by a single distribution while the other is governed by a family of distributions whose parameters belong to a known set <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Gamma $ </tex-math></inline-formula> . We would like to design a test to decide which hypothesis is in effect. Under the constraints that the probabilities that the length of the test, a stopping time, exceeds <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> are bounded by a certain threshold <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> , we obtain certain fundamental limits on the asymptotic behavior of the sequential test as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> tends to infinity. Assuming that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Gamma $ </tex-math></inline-formula> is a convex and compact set, we obtain the set of all first-order error exponents for the problem. We also prove a strong converse. Additionally, we obtain the set of second-order error exponents under the assumption that the alphabet of the observations <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {X}$ </tex-math></inline-formula> is finite. In the proof of second-order asymptotics, a main technical contribution is the derivation of a central limit-type result for a maximum of an uncountable set of log-likelihood ratios under suitable conditions. This result may be of independent interest. We also show that some important statistical models satisfy the conditions.
Face Recognition of Intelligent Building based on Super-Resolution Reconstruction of Visual Image

In recent years, more and more countries and regions have embarked on the road of modernization with the increasing competition in the field of high-tech. The development of science and technology has brought great convenience to people's life, and our living environment has been improved to a large extent. Face recognition technology integrates computer, communication, optics, electronics, machinery and other multidisciplinary technologies, and has broad application prospects in the field of control and intelligent building. This paper focuses on face details, through face super-resolution reconstruction technology for face recognition, to provide more information for many real scene applications such as face recognition. Experimental results show that the proposed method has high recognition rate and speed.
MCSEC: Secure Coded Matrix Multiplication Scheme for Edge Computing with Minimum Communication Cost

Edge computing is an emerging technology that enables real-time and high-efficiency data processing by utilizing the storage, computing and communication capabilities of edge devices. Data security of computing tasks is the most important consideration in edge computing. Ensuring data security will bring additional communication load. Many researchers have proposed effective solutions to reduce the communication load. However, they did not further consider the communication cost in edge computing, i.e., the sum of the cost of each data transmission over the network, which includes communication load (the total amount of data transmission over the network). In this paper, we use communication cost instead of communication load and take matrix multiplication as the computing tasks to study the problem of secure matrix multiplication in edge computing with minimum communication cost (SMC). Specifically, we first theoretically analyze the security and decodability conditions of SMC to get the maximum number of matrix multiplication tasks stored on each edge device. Then we propose a scheme, i.e., Minimum Communication cost Secure coded Edge Computing (MCSEC), which adopts dynamic allocation and linear coding methods to allocate the matrix multiplication tasks to multiple edge devices and complete the coding design to satisfy the requirement of information-theoretic security (ITS), respectively. Moreover, we prove that the MCSEC achieves minimum communication cost by using constructive and logical analysis methods. Finally, we conduct extensive simulation experiments to evaluate the efficiency of MCSEC. The experiment results demonstrate that the MCSEC effectively reduces the communication cost (up to 71%) compared with other schemes.
Construction of Video Management System Based on Remote Education

For effective remote education using multimedia, it is necessary to develop efficient management techniques of video information. This requires real-time processing of video information which should be managed and retrieved in a compressed forms. The main technology of compressing video is currently MPEG-2. This implies that it is very important to manage and retrieve video compressed in MPEG-2, and then to process the video in real-time for the remote education environment using multimedia. This paper is to develop the management system of video information which is one of the most critical requirements in remote education systems for managing and retrieving MPEG-2 video.
Method for Organizing Network Requests in iOS Applications

This work includes a project to develop a method that provides an advantage to the developer in three areas. The first is to improve the usability of developing iOS applications. The second is to reduce labor costs when developing and finalizing iOS applications. The third is to improve the user experience of using applications. A reactive approach is required to prioritize requests and to avoid erroneous duplicate requests. The developed method implements the reactive programming approach. The authors of the organization make use of the framework as the main data flow tool for organizing network requests in an iOS application. This ensures that you can opt out of all third-party technical solutions. The proposed method can be proposed in the development and refinement of applications for iOS, iPadOS, TVOS and macOS.
Quantum steering based on cascaded four-wave mixing processes

Multipartite quantum steering is an important quantum resource and the basis of secure quantum communication network. Multipartite quantum steering can be generated by beam splitter networks, optical frequency comb systems and nonlinear processes. Different types of quantum steering will be produced by different projects. In this paper, we design two different schemes, i.e. series cascaded four-wave mixing and hybrid cascaded four-wave mixing, and based on these two schemes tripartite quantum steering and quinquepartite quantum steering are generated respectively. The steering characters among different users are quantified based on the covariance matrix. In theory, we investigate steering parameters among different modes created by two schemes versus the amplitude gain of four-wave mixing process. We find that one mode can steer the other two modes separately, but the other two modes cannot steer the one mode simultaneously. By comparing the steering characters of joint multimodes to a certain single mode with the individual mode to the single mode respectively, it can be seen that the steerability of the former is stronger than the latter in the whole gain region, and there exists only the steering of joint multimodes to a single mode in the partial gain region. More importantly, the steerability of joint multimodes to a single mode can be enhanced with the quantity of joint multimodes increasing. The results show that multiple types of quantum steering can be realized by using these two schemes, which are helpful in understanding the distribution of quantum steering in multipartite system and have important significance in practical secure quantum communication and quantum secret sharing.
Scientific and methodological approaches to the study of pedagogical personality

The article presents a synthesized analysis of scientific and methodological approaches to the study of pedagogical personalities - famous teachers, scholars, public figures who have made a significant contribution to the development of education and pedagogical science of Ukraine and the world. The essence of scientific and methodological approaches is characterized according to their division into two main conditional groups: «traditional» that were used in historical and pedagogical studies in the second half of the XX century (historical and logical; semantic and formal; personal and active; quantitative and qualitative; culturological and its varieties, etc.) and «innovative» , which began to be actively used as a research tool in the late 1990s (anthropological, axiological, phenomenological, hermeneutic, intellectual and biographical, paradigmatic, synergetic, etc.).&#x0D; The peculiarities and difficulties of using scientific and methodological approaches to the study of pedagogical personality are clarified. They are connected to the fact that, first, this issue is mainly considered in the context of the study of individual biographies, which determines the brevity, abstractness of its understanding or does not take into account the specifics of biographical studies. Secondly, due to the «multi-layered» methodology of pedagogical science and the subjective reflections of scholars, there is an arbitrary interpretation and use of these methodological approaches. Third, in specific biographical studies, scientists operate with a relatively narrow range of methodological approaches, which limits the cognitive possibilities and general research results.
ADAS algorithm for creating a wider view of the environment with a blind spot display for the driver

Examining the blind spot of the vehicle while driving using the vehicle mirror and looking over the shoulder causes a critical time when the driver does not have an overview of the environment in front of the vehicle, which can be the cause of traffic accidents. For that reason, this paper presents the implementation of the solution for stitching multiple frames with the overlap area, obtained by vehicle cameras with the aim of increasing the visibility of the environment behind the vehicle and eliminating the need for a classic view over the shoulder. A real ADAS (Advanced Driver-Assistance System) development board and the three real automotive cameras were mounted on a small vehicle model and were used to create test videos. After frame stitching, correcting the brightness of the stitched frame is also implemented to achieve a higher quality of final output frame. The performed subjective quality evaluation results using 10 neutral viewers confirm that the proposed solution achieves high performance in terms of the quality of the output image. However, the results related to the measured execution time of the proposed solution show that it cannot achieve a real-time performance in its current form when implemented on real ADAS board. Therefore, for the proposed solution the intention is to use the Vehicle-to-Infrastructure (V2I) communication and remote data processing center which should process more demanding tasks of the solution.
Development report of the next generation of civil engineering

The development of civil engineering technology is a major issue related to the national economy and people’s livelihood, especially in the modern era where new technologies are emerging in all walks of life. Combining with the current development status of civil engineering technology in the world today, the author combed the new technologies closely related to the development of civil engineering technology in recent years. These new technologies mainly include: assembly structure, intelligent manufacturing, 3D printing, and industrial digitization. The existing high-tech and civil engineering technology developments are combined. Based on the current domestic and foreign technological development environment, the author puts forward the development forecast of the next generation of civil engineering technology and the company’s countermeasures. These predictions and suggestions are of great significance for the national government to formulate policies or the guidance of modern enterprises to formulate development strategies.
New Subclass Framework and Concrete Examples of Strongly Asymmetric Public Key Agreement

Strongly asymmetric public key agreement (SAPKA) is a class of key exchange between Alice and Bob that was introduced in 2011. The greatest difference from the standard PKA algorithms is that Bob constructs multiple public keys and Alice uses one of these to calculate her public key and her secret shared key. Therefore, the number of public keys and calculation rules for each key differ for each user. Although algorithms with high security and computational efficiency exist in this class, the relation between the parameters of SAPKA and its security and computational efficiency has not yet been fully clarified. Therefore, our main objective in this study was to classify the SAPKA algorithms according to their properties. By attempting algorithm attacks, we found that certain parameters are more strongly related to the security. On this basis, we constructed concrete algorithms and a new subclass of SAPKA, in which the responsibility of maintaining security is significantly more associated with the secret parameters of Bob than those of Alice. Moreover, we demonstrate 1. insufficient but necessary conditions for this subclass, 2. inclusion relations between the subclasses of SAPKA, and 3. concrete examples of this sub-class with reports of implementational experiments.