Reviewerly/exHarmony huggingface dataset:
Research on Online Classroom Training Method Based on Educational Technology Analysis

With the continuous development of higher education, the teaching quality of island schools has attracted more and more attention, and college teacher training has gradually become an important content of teaching reform. Under the background of networking and informatization, online training has become an important choice for teachers’ training in Colleges and universities with its advantages of convenience, flexibility and rich resources. The improvement of teachers’ professional quality is the goal of online training, which should ensure the effectiveness of online training. Sex must promote the continuous improvement of teachers’ professional quality. Considering the characteristics of teachers’ online training and learning pay equal attention to the research process and results, and combine the theory of teachers’ professional quality structure and the particularity of online training. On this basis, it puts forward the evaluation criteria of the effectiveness of online training for college teachers.
(Semi) automatic Assertion Generation from Controlled Chinese Natural Language: A Practice in Aerospace Industry

Due to the effectiveness of assertion based verification (ABV) in programmable logic verification, and the error-proneness and time-consuming of the manual writing of assertions, the research on automatic generation of System Verilog Assertion (SVA) from English has developed rapidly in the past decade. The demand for high reliability verification in the aerospace industry urges us to explore the role of automatic assertion generation in Chinese. In this work, we proposed a controlled Chinese natural language (CCNL) framework to translate natural language assertions (NLA) into SVA. The step of capturing controlled Chinese NLAs from specification documents remains manually, which is the reason for so-called semi-automatic generation. CCNL framework consists of Chinese controlled word list, regular expression based semantic parser and assertion translator. We used this framework to extract assertions from requirements documents in Chinese, evaluated the framework, and proved its effectiveness.
Personas, the Pandemic, and Inclusive, Synthetic, Smart City Planning

In the era of COVID-19 planners, and more broadly, city administrators and policy makers, have learned to cope with the accelerated pace of change, the broad band of uncertainty, and the need for rapid decision-making strategies. This, in the context of ever more diverse communities and greater reliance on technology as an effective response to the social and public health challenges of the pandemic. “Smart” cities harness distributed communication and service delivery technologies to enhance the quality of urban life. The voices of citizens from marginalized and under-served populations, such as older adults and people with disabilities, are vital to the development of inclusive smart cities. In this paper expanding an inclusive policy design approach is proposed that uses ‘personas’ to actively engage those citizens.
Particle Swarm Optimization for Fixed-Wing UAV Path Planning

Path planning is the key to the realization of drone perception and action capabilities as a core technology. Particle swarm algorithm and its improved algorithm are widely used as one of the commonly algorithms to deal with path planning problems. Taking fixed-wing UAVs as the research object, combining the characteristics of UAV path planning, a mathematical model is established for the known three-dimensional global path planning problems of environmental models. This standard particle swarm algorithm path planning is analyzed and improved. Base on analysis, an improved PSO algorithm (GPSO) is proposed, and the effectiveness of the algorithm is verified through simulation experiments. And its speed and quality are better than PSO.
Sign Language Processing

The aim of the paper is to describe algorithms for automated Slovak words stacking, syntax readable for such algorithms and explain the need for these algorithms. Algorithm for Slovak nouns stacking serves to inflect all forms of Slovak nouns and together with designed syntax accelerate the process of writing them into system for sign language processing.
Accurate Pose Estimation Based on Multi-frame Cooperative Identification

As the field of robotics has become more and more widely used in medical, dock, rescue and other fields, positioning technology has become a hot field of robotics research. Among them, visual positioning technology has the advantages of low cost and good reliability, but the robustness of visual positioning is poor. Data jumps and other shortcomings, this paper designs a vision-based multi-frame cooperative logo accurate pose estimation method, this method can improve the measurement accuracy in the process of visual positioning, and provide nearby data in the case of complex jumps The measurement value with the smallest jump in the frame has been verified by experiments in this paper. This algorithm has good stability and data smoothness, and has important research value in the field of robot navigation and positioning.
Insurtechs: The Force to Reckon Within Insurance Sector

The touch of digitization has always accelerated and given a new height to every aspect. So why the insurance sector should stay away from that touch and so comes the new normal in the world of insurance INSURTECH.
Analysis of Prevalence and Effectiveness of Mobile Devices and Wearables in the Domain of Education

Significant developments in the field of wireless communication, networking and semiconductors have paved the way for ubiquity of mobile technology. Being lightweight, portable and affordable, it is used by a large number of students and academicians worldwide. This has given rise to MOOCs- a method of disseminating knowledge that is almost entirely reliant on the use of mobile technologies. This study involves a survey of 120 people to document the prevalence and use of various mobile devices used by students, and the extent of challenges faced by them in doing so. The data is analyzed using certain Python libraries. 42.5% of the participants had completed their senior secondary schooling. 53.3% participants were graduates. Despite being owned by an equal number of respondents, laptops fared better than smartphones for their role in education by a margin of 29.2%. The market share of wearable devices is significantly low as compared to other mobile devices. Among those who owned wearables, only 3.3% used them for educational purposes. Although a remarkable 81.7% have used MOOCs at least once, only 57.6% admit that MOOCs have helped them excel in their career. As affirmed by the survey, smartphones and laptops have become crucial means to impart knowledge. Wearable technology is a relatively new and developing field with untapped scope in education. While MOOCs face challenges due to the massive participation in these courses, they still act as stepping stones for students to improve their skills and augment their knowledge.
Medical Image Segmentation Using Transformer

For the past few years, the U-Net structure shows strong performance in the field of medical image segmentation. However, due to the inherent locality of convolution operations, U-shaped structures are often limited in modeling long-range dependencies. Transformer, a global self-attention mechanism designed for sequence-to-sequence prediction, has been successfully used in the field of computer vision. In this paper, we propose a novel network, named TransHarDNet. HarDNet, which is a low memory traffic CNN. We combine it as backbone with Transformer. Our network enables the global semantic context information and low-level spatial details of the input image to be captured more effectively. We evaluate the effectiveness of the proposed network on five medical image datasets.
“Aspects of Freedom” a Case of Design and Making of AR App for Interactive Communication in the Field of 3D Animation Production in Culture

This research aims to provide the users with an experience of learning about tangible heritage items-as symbols of freedom through interactive mobile communication AR app. Some of the 3d models are the exact replicas of the genuine museum items, made with photogrammetry, from the National Historic Museum and the War Museum of Athens and its department in Tripoli. These three museums have collaborated with the University of West Attica for the digitalization of some interesting but unknown to public cultural heritage items. It is conducted in three parts, a preliminary stage of research and item collection, the stage of image target design and the scenario of the usage in many different printed materials as well as the making of AR app and the final stage of launching it. Also, specific material such as worksheets and table games were created as to supplement the learning experience. So, in this paper, there is a synopsis of the concept, the major issues are addressed and the significance of the work is highlighted. Also, the theoretical and methodological approaches pursued are explained and finally data are presented and analyzed.
Democratic Aftermarket for Domain Names

In this growing age of the Internet, people want to have a domain name relevant to their work. In scenarios of already taken domain names, the widely adopted technique is to buy from someone through the secondary market and brokers. However, the existing solutions for the secondary market of domain names are centralized and demand huge commission fee. This paper presents a secure, decentralized, and cheaper platform for buying and selling aftermarket domain names using Blockchain Technology. The work focuses on growing a secondary market of domains and entities involved with legacy Domain Name System (DNS) architecture but currently shares nothing with Blockchain-based DNS architecture. With that said, the idea is to implement a Blockchain-based service on top of the existing DNS to allow Domain owners (potential sellers) and buyers to interact directly without any middleman in between the two parties involved.
Personalised recommendation method of English micro-lectures teaching resources based on internet of things platform

In order to overcome the problems of low accuracy and long time-consuming in traditional teaching resource recommendation methods, a personalised recommendation method of English micro course teaching resources based on internet of things platform is proposed. Collect the internet of things platform server agent, server and client for user interest resource data, preprocess user interest resource data. The user interest model is constructed by using the obtained user interest resource data, and the English micro class teaching resource model is constructed by vector space model. This paper combines the user interest model with the English micro class teaching resource model, and makes personalised recommendation of English micro class teaching resources. The experimental results show that: the accuracy rate of the proposed method is as high as 98%, and the recommendation time is less than 6 s, which can realise the personalised recommendation of English micro class teaching resources.
Research on RPG Game Recommendation System

In RPG Games, how to make players quickly adapt to the game and like the game has always been the focus of game merchants. Good props recommendation can not only improve the game experience of players, but also bring benefits to game providers. RPG games are different from other types of games in that the player level changes faster, and there is a great correlation between levels and equipment. Therefore, we need to recommend corresponding props in real time according to player’s levels. In the previous recommendation systems, offline features are often used, which leads to the inability to capture the level changes of players in real time, and finally affects the recommendation results; Similarly, in the process of offline sample splicing, there will be inconsistency between the characteristics used in online services and those used in model training samples. To solve the above problems, we use real-time computing to replace the previous offline framework. By real-time changing features, we avoid the problem of outdated features, and use real-time sample splicing instead of offline splicing to directly save the samples requested and purchased by users to HBase, to solve the problem of inconsistency between online and offline features. The experimental results show that after using the above scheme, the real-time recommended exposure ARPU effect is 14.75% higher than the offline effect. Finally, by solving the problem of data skew, the overall performance of the system is improved by more than 50%. The experimental results prove that the model can well capture the player’s status changes after the features are real-time, and the use of real-time technology to ensure the consistency of online and offline features significantly improves the prediction effect of the model.
A Dual Regression Scheme to Improve GAN in Low-Dose CT Scan Restoration

The application of Super Resolution (SR) technology on recovering low-dose medical image is gaining its importance nowadays. Because it can provide as many contextual details as high-dose CT for physi-cians without harming the patient or spending more on devices. Usage of Deep Learning (DL) on SR tasks has been developed many years, and Generative Adversarial Network (GAN) plays a leading role among them. In this paper, we find that using a special closed-loop scheme, dual network, can strongly improve the performance of GAN model, with about 1.2 point of improvement in PSNR. And the best way to implement this dual network is also evaluated.
Generating Stylistically Similar Vernacular Language Fonts Using English Fonts

This paper aims to explore neural network-based models to generate fonts for vernacular languages, given a family of English font. Compared to the amount of English language fonts that are in existence, the number of fonts available for regional languages (such as Tamil, Telugu, and Malayalam) is very little. Creating new fonts for such languages is an expensive and time-consuming process as it requires hiring specialized artists for the purpose. Also, unlike English which has only 26 base characters, Indian languages tend to have a much higher number (anywhere from 50 to 200+). If the huge amount of existing English language font data can be leveraged to synthetically generate similar looking characters in regional languages, it would be a great breakthrough indeed. So far, there have only been a few attempts to generate Japanese characters in this manner. Following the exploration, this work implements two deep learning models: an encoder-decoder model and a multidecoder model to accomplish this task for Indian vernacular languages.
Security Analysis of a Cryptosystem Based on Subspace Subcodes

In 2019, Berger et al. introduced a code-based cryptosystem using quasi-cyclic generalized subspace subcodes of Generalized Reed-Solomon codes (GRS). In their scheme, the underlying GRS code is not secret but a transformation of codes over $${\mathbb {F}}_{2^m}$$ to codes over $${\mathbb {F}}_2$$ is done by choosing some arbitrary $${\mathbb {F}}_2$$ -subspaces $$V_i$$ of $${\mathbb {F}}_{2^m}$$ and by using the natural injection $$V_i\subset {\mathbb {F}}_{2^m} \hookrightarrow {\mathbb {F}}_2^m$$ . In this work, we study the security of the cryptosystem with some additional assumption. In addition to the knowledge of the GRS code, we introduce a new kind of attack in which the subspaces are corrupted. We call this attack “known subspace attack” (KSA). Although this assumption is unrealistic, it allows us to better understand the security of this scheme. We are able to show that the original parameters are not secure; in practice this however does not break the original proposal. In this paper, we provide new parameters for Berger et al.’s scheme which are secure against the known subspace attack.
A New Two-Axis Solar Tracker Based on the Online Optimization Method: Experimental Investigation

This article presents a new two-axis solar tracker based on an online optimization algorithm so as to track the position of the sun. In this research, four well-known optimization algorithms are employed to find the two unknown parameters named azimuth and zenith angles. These two parameters are vitally important for many practical solar applications such as solar plants. In this paper, first, a brief description of the mathematical equation governing the movement of the sun and the proposed intelligent solar tracker is introduced. Afterward, the optimization algorithms are briefly reviewed. These optimization algorithms are used to find the position of the sun (zenith and azimuth angles of the tracker) online without using the model of sun movement. Accordingly, the magnitude of the sunray is considered as the cost function of all algorithms. Then, several experiments are carried out to find the best optimization algorithm with optimal population size, number of iterations, and also the best initialization method. Finally, the experimental and comparative results clearly show that particle swarm optimization algorithm with a population size of 15 and 7 iterations using uniform initialization method has better performance than the other algorithms. In addition, the outcomes reveal the feasibility of using online optimization algorithms in an effort to bypass the complex mathematical model of mechatronic systems and the movement of the sun.
The Construction and Practice of Precision Teaching Model of “Smart Class” based on “Internet + Education”

Under the background of “Internet + Education” era, the innovation and reform of education is facing new opportunities and challenges. Based on the description and analysis of the connotation of “smart class” and its basic characteristics, this paper constructs a data-driven precision teaching model of the smart class, and applies this model to practical English teaching. From numerous angles including the smart learning platform settings, students' study analysis based on data stored in the smart campus system, teaching goals setting, classroom teaching activities design and precise teaching evaluation, this paper states and analyzes how this model adopts the new generation of information technology to realize the customized, intelligent, and adaptive learning requirements and programs for different students, and implement ubiquitous learning that runs through the whole process (pre-, in-, and post-class), and promote the students' learning motivation and the development of independent learning capability in order to achieve desired learning effect.
Research on Automatic Marking Method of 3D Model Based on Geometric Feature Similarity

Model Based Definition (MBD) models are widely used in various manufacturing processes. The traditional automatic labeling methods still need to use pre-indexing and other preprocessing, which will reduce the efficiency and effect of labeling. Aiming at this problem, an automatic annotation method for 3D models based on similar geometric features is proposed. The Freeman chain code is used to describe the geometric shape and the contour information of the front line as the basis for geometric shape matching. The longest common subsequence is used to search and match the geometric features to determine the label object. A labeling and detection method based on case clustering is proposed, which uses case structure and information for labeling constraints and completeness detection. This paper starts from the case of car front hook cover, compares the matching results of geometric features, which turn out to be correct. The feature comparison results are very different, and the efficiency is high. Compared with traditional automatic labeling methods, the labeling time is shortened, the accuracy rate is high, and the efficiency is increased by 40%. The experimental structure shows that the method is feasible and effective, and has strong versatility, and can realize efficient automatic labeling in a short time.
A Novel Transmitter Recognition Model with Fish-SVC Clustering Method

Transmitter recognition with clustering method is an efficient spectrum sensing method to classify different devices and protect the spectrum space security. However, it is hard for the conventional clustering methods to recognize multiple transmitters in non-cooperative communication because some of the methods only process specific spectrum with narrowband. Besides, parameter setting and tuning strategies of the algorithms can be pretty challenging. We propose the recognition model with Fish-SVC clustering method to solve the problems above. We collect wideband spectrum trace data and extract reasonable statistical and physical features to conduct feature vectors of each signal. Our algorithm searches the optimal Gaussian kernel parameter and form reliable clusters. Therefore, we gather the signals that belong to the same transmitters together and recognize the devices. The experiment results show that Fish-SVC can recognize transmitters over wideband spectrum in a non-cooperative communication environment with a nearly 97% accuracy rate.
Set the configuration for the heart of the OS

This paper presents a study on the practicality of operating system (OS) kernel debloating, that is, reducing kernel code that is not needed by the target applications. Despite their significant benefits regarding security (attack surface reduction) and performance (fast boot time and reduced memory footprints), the state-of-the-art OS kernel debloating techniques are not widely adopted in practice, especially in production environments. We identify the limitations of existing kernel debloating techniques that hinder their practical adoption, such as both accidental and essential ones. To understand these limitations, we build an advanced debloating framework named Cozart that enables us to conduct a number of experiments on different types of OS kernels (such as Linux and the L4 microkernel) with a wide variety of applications (such as HTTPD, Memcached, MySQL, NGINX, PHP, and Redis). Our experimental results reveal the challenges and opportunities in making OS kernel debloating practical. We share these insights and our experience to shed light on addressing the limitations of kernel debloating techniques in future research and development efforts.
One-Shot Approach for Multilingual Classification of Indic Scripts

The use of multiple languages with different scripts is a common theme in India. There is an emerging need to digitise documents that may be handwritten or available solely as images. This necessitates a system for multilingual classification of different Indic scripts and the subsequent character recognition into digitised standards such as Unicode. However, a learning system for various languages with multiple character combinations can be computationally expensive and prove arduous with a dearth of available data. In this paper, the one-shot learning approach to the optical character recognition of different languages is explored, where there is a need to accurately classify the character given only one example of every additional class introduced. Siamese neural networks are used for learning and to tune a network to work with entirely new, unseen data. Compelling results are attained in the classification of characters in nine different Indian languages using this approach with an accuracy ranging from 77.72 to 91.83 across the Indic languages in the best case.