Reviewerly/exHarmony huggingface dataset:
Coaching instruments in teaching general English

The most important feature of education in the information society, according to the laws of which humanity lives today, is lifelong learning, and therefore the ability to learn becomes one of the key competencies. Today, the ability to acquire knowledge on one’s own, without the help of a teacher, is of particular importance, which creates the need to form students’ cognitive interest and self-education skills. This is facilitated by the coaching approach to learning, which is a new step in the methodology of teaching foreign languages, and involves a partnership between the teacher and the student in the educational process, the student’s awareness of the personal need for mastering knowledge and the need to take responsibility for the result. The purpose of the research is to describe the tools of the coaching approach to teaching general English. The research showed that the creation of a relationship of trust and partnership in the educational process is facilitated by: meeting the student’s need for meaningful learning, understanding and recognition of the student’s values, active listening, developing feedback, and effective questions. The use of these technologies is intended to provide students with access to self-education in the future. The study concludes that the use of coaching approach tools in teaching general English contributes to the maximum disclosure of learners’ internal resources and capabilities.
Dynamic Residual Filtering With Laplacian Pyramid for Instance Segmentation

Various studies have been conducted on instance segmentation and made great strides over the past few years. Most recently, instance-specific mask generation via dynamic kernel predictions has shown the significant performance improvement even without bounding boxes as well as anchors. However, this scheme still does not fully consider dynamic properties since the size of the receptive field is not enough to cover the spatially-meaningful range due to memory limitations. Furthermore, the single-fused feature often fails to grasp complicated boundaries for objects of different sizes. In this article, we propose the dynamic residual filtering method with the Laplacian pyramid, which separately restores the global layout and local boundaries of instance masks. Specifically, we firstly apply the Laplacian pyramid-based decomposition scheme to features encoded from the backbone and subsequently restore sub-band mask residuals from coarse to fine pyramid levels. To do this, we design spatially-aware convolution filters to progressively capture the residual form of mask features at each level of the Laplacian pyramid while holding deformable receptive fields with dynamic offset information. This is fairly desirable since global and local properties of mask features can be accurately restored with keeping the spatial flexibility through the invertible process of the Laplacian reconstruction. Experimental results on the COCO dataset demonstrate that our proposed method achieves the state-of-the-art performance, i.e., 42.7% AP. The code and model are publicly available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/tjqansthd/LapMask</uri> .
Design of Diversified Evaluation System of College Teaching Quality Based on Deep Data Mining

At present, the mainstream teaching evaluation is dominated by students, supplemented by experts in or out of school. Under this evaluation method, only simple quantitative evaluation results can be obtained, and the evaluation data can not be analyzed. Therefore, it is proposed to design the diversified evaluation system of college teaching quality based on deep data mining. In the hardware design of the diversified evaluation system of college teaching quality, the ARM microprocessor based on diversified evaluation is designed, and the data manager based on data mining is designed. In the software design of the diversified evaluation system of college teaching quality, the diversified evaluation model of teaching quality is established, Design evaluation model database. The test shows that the evaluation accuracy coefficient of the system is more than 95, which is much higher than the traditional two systems.
Operator-Potentials in Symbolic Search: From Forward to Bi-directional Search

Symbolic search using binary decision diagrams is a state-of-the-art technique for cost-optimal planning. Heuristic search in this context has been problematic as even a very informative heuristic can be detrimental in case it induces difficult-to-represent state partitionings. It was recently shown that operator-potential heuristics can address this issue in forward search by computing a numeric potential for each operator corresponding to the change of the heuristic value induced by that operator. Forward search is, however, not the best known variant of symbolic search. Here we investigate the integration with backward and bi-directional search instead. We prove that forward search (distance-to-goal) operator-potential heuristics can be turned into backward search (distance-to-initial-state) heuristics elegantly in this context, by summing the backward search path operator-potentials with the initial state goal-distance estimate. We run exhaustive experiments on IPC benchmarks, showing that significant performance improvements can be obtained over symbolic forward search and other state-of-the-art techniques.
Nonlinear Self-Calibration Method Based on Phase Registration in Phase-Shifting Profilometry

In phase-shifting profilometry, the nonlinearity of the projector is the main error source to reduce the 3D reconstruct accuracy. Most calibration algorithms mainly focus on calibrate the nonlinear responses and compensate the related errors. However, it requires a tedious pre-calibration process. In this paper, a phase registration algorithm based on Gray code guidance is proposed, majority vote and Hough transform are performed to accuracy locate the boundary of Gray code, thus select correct phase matching pairs. In addition, outlier detection technology is used to iteratively fit phase matching pairs to eliminate mismatched points, and improve the accuracy of fitting. Finally, the error is compensated point by point using the fitting curve. Experimental and simulation results demonstrate the effectiveness of the proposed method. It has the advantages of simple deployment, no photometric pre-calibration, high accuracy, strong robustness, and can handle the projector's nonlinear scene that changes with time.
Effect of IoT Application and Smart Cities Using Deep Learning Techniques

This effort is an attempt to provide some insight into the future degree, possible plausible consequences, and challenges that may arise. The Internet of Things and how it may impact the life of the general public sooner rather than later in relation to the creation of smart urban areas in India are important questions to consider. The Internet of Things (IoT) can be defined as a network of physical or virtual articles or things that have been implanted with hardware, sensors, and programming projects that have scheduled availability, enabling these items to gather and exchange information, thereby providing availability to anything and everyone at any time, from any location, at any time. Articles may be recognized and controlled remotely using the Internet of Things (IoT), which makes use of an existing system foundation. The Internet of Things allows items to be detected and controlled remotely over an existing web-based network infrastructure, opening the door to more straightforward coordination between the genuine physical world and computer-based virtual environments, and resulting in increased productivity, accuracy, and financial advantage for businesses. Everyone and everything would be able to communicate with one another via the use of an implanted figuring structure that exists inside the Internet foundation, which would enable them to be easily distinguished. As a consequence of the growth of the 100 Smart urban community’s effort in India, the Internet of Things as a concept seems to be incredibly promising. The Internet of Things (IoT) has a particularly promising future in India, thanks to the government’s emphasis on, support of, and experimentation with efforts to develop a better framework. Even businesses are developing innovative products, and businesses are aware of the advantages that the Internet of Things brings to the table, which is why they are embracing it. In the event that research and assets are further developed, the Internet of Things might become significant on a global basis. If such a development becomes a reality, it is possible that the aspirations for IoT use in India would be realised as predicted, all things considered.
An Access Control Method with Secret Key for Semantic Segmentation Models

A novel method for access control with a secret key is proposed to protect models from unauthorized access in this paper. We focus on semantic segmentation models with the vision transformer (ViT), called segmentation transformer (SETR). Most existing access control methods focus on image classification tasks, or they are limited to CNNs. By using a patch embedding structure that ViT has, trained models and test images can be efficiently encrypted with a secret key, and then semantic segmentation tasks are carried out in the encrypted domain. In an experiment, the method is confirmed to provide the same accuracy as that of using plain images without any encryption to authorized users with a correct key and also to provide an extremely degraded accuracy to unauthorized users.
Meta-Learning based Heterogeneous Graph Attention Network for Top-N Review Recommendation

User-generated content (UGC) has become more and more popular on the web and the published review is an essential type of UGC. Nevertheless, the explosion of reviews brings a problem of severe information overload. Therefore, most web services supply review recommendations for users. Traditionally, reviews of an item could be exhibited in chronological or popularity order without personalization. However, some researchers are aware of the significant role of the personalized review recommendation, which focuses on discovering users’ personalized preferences so that recommended reviews could match users’ preferences better. Unfortunately, it is hard to obtain users’ feedback on reviews due to the privacy protection and trade secrets. Furthermore, the difficulty in capturing varying patterns of users’ preferences and the sparsity of interactions between users and reviews are also challenging. To address these problems, we first formally define the top-N review recommendation problem and construct two categories of datasets based on a public dataset. Secondly, we propose a meta-learning based heterogeneous graph attention network incorporating multiple relationships among the users, items and reviews to model personalized users’ preferences and cope with the sparse situation. Moreover, to accelerate the message propagation computation, a method of the substructure-oriented local graph construction is proposed and is fused into the meta-learning framework based on a pair-wise ranking. For the top-N review recommendation, experiments are conducted on the two categories of a real-world dataset. Compared with the state-of-the-arts, the results validate the effectiveness of our model for review recommendation.
Face Mask Detection Alert System Using CNN

In the time of 2019–20, a new virus surprised the world and there was war kind of situation where each individual facing a need to face COVID-19 virus. The virus spreads with air and cough and sneeze. Thus, as a primary concern, everyone needs to wear face mask and hand sanitizer. The problem starts when after sometime people won’t follow the rules and the difficulties gets increase. The mandatory part is everyone should wear face mask everywhere. The schools, private institutes, colleges, and offices should follow this rule strictly. There should be a person at the doorstep of office who will check that each person is wearing mask or not. This manual job can be automated. Here comes the machine learning and artificial intelligence into picture. The machine learning can help the system to identify if that person is wearing mask or not. Furthermore, sometimes data got from survey is stale and that could cause wrong information about the severity of COVID-19 in particular city and area. This model can then help the authorities to manage the proper discipline and can help in planning distribution of masks, medicines, and vaccines in that city. This is nothing but real-time object detection, and we are detecting the mask as our preferable object. Then to get alert for the same an email will get send to the administration with his/her face as an attachment of that email if the person is not wearing mask. The real-time object detection techniques are now evolving in great amount with time. The existing algorithms are not that much up to the level with accuracy. This causes mis confusion for the machine and can predict wrong information with the available data. When it comes to real-time algorithm, CNN is best of them.
A Study of Satisfaction for the Talent Enticement Policy in Guangdong-Hong Kong-Macau Greater Bay Area—with Zhuhai as an Example

Under the circumstances of the rapid development of knowledge based economy and artificial intelligence, talents are one of the most important resources not only for the local economic and social development, but also of great significance to the development of the region. Since the establishment of the special economic zone in Zhuhai, many policies concerning talents have been implemented, and some of the policies have achieved desired results. However, in the face of the current new situation, new opportunities, and new challenges, especially when the total number of talents is much lower than the demand, the existing policies in the Bay Area cities, Hong Kong, Shenzhen, Guangzhou, and other places, still need to be continuously improved based on the overall strategies and actual needs. Using the current situation of the talent enticement policy in Zhuhai City as the starting point, taking the enticement convenience, material benefits, incentive policies and flow mechanisms as measurement factors, combined with the data collection and analysis results, to explore the current situation of the talent enticement policy in Zhuhai City and its main stakeholders' satisfaction, and propose corresponding improvement countermeasures.
On Incompleteness in Abstract Argumentation: Complexity and Expressiveness

One of the recent trends in research about abstract argumentation is the study of how incomplete knowledge can be integrated to argumentation frameworks (AFs). In this paper, we survey main results on Incomplete AFs (IAFs), following two directions: how hard is it to reason with IAFs? And what can be expressed with IAFs? We show that two generalizations of IAFs, namely Rich IAFs and Constrained IAFs, despite having a higher expressive power than IAFs, have the same complexity regarding classical reasoning tasks.
APPLICATION OF DIGITAL EDUCATIONAL PLATFORMS IN THE STUDY OF ECONOMIC DISCIPLINES

The forced mass transition to distant work due to coronavirus infection has become a kind of challenge and at the same time a starting point for the application of new educational technologies and the creation of new educational resources. The process of digital transformation of education is not just the creation of computer classes and connection to the Internet, but the formation and distribution of new work models in educational organizations. Digitalization in general is considered as a process of changing social, economic, and technical structures into those that are mediated by digital products and relationships with new embedded digital capabilities. Digitalization of the education system involves the formation of an educational environment that implies the construction of an interactive system with feedback between teachers and trainees. The relevance of our research consists in the studying and analysis of the practice of forming digital skills and competencies among students as a result of the use of digital educational platforms during the study of economic disciplines at a technical university. The authors of the article proceed from the position that digital skills are a set of possibilities for using digital devices, communication applications and networks to access and manage information. The formation of digital competencies is considered as the process of developing students' individual ability to use and combine their knowledge, skills and personal attitude connected with three related areas of competence – technology, cognitive and social sphere – for the analysis, selection and critical evaluation of information. The article lists the main services that can be used during the process of interactive work with students in the classroom. A study in which students were offered various environments for preparing reports for seminars was conducted. The specific pedagogical practice of implementing Mentimetr and Kahoot digital educational platforms in the study of economic disciplines allows us to figure out the following conclusions: - the use of interactive platforms has a positive effect on students' learning and forms the competencies necessary for the successful application of the acquired economic knowledge in life; - the process of creating reports and tests becomes not a routine activity, but a fascinating way to understand the theme and to approach the topic in a non-trivial way; - in the modern approach, the concept of "digital educational environment" implies the multifaceted use of well-known services to form a separate virtual space with a simplified model of studying the material.
Detection of outlier information using linguistically quantified statements – the state of the art

Over the past decade, outlier detection has become a prominent area of research. Much of the current literature on Knowledge Discovery in Databases (KDD) pays particular attention to the identification of rare, unusual data items or surprising observations that differ substantially from the majority of the data. In Data Mining (DM), outlier detection refers to the identification of unusual data records that might be interesting in the context of data pattern discovery or may represent potential data errors that require further investigation. Recent research has emphasized the need for KDD / DM systems capable of processing information given in a linguistic form and communicating the result to the user in a natural language. Such systems represent a valuable tool for intelligent data analysis tasks, including outlier detection. The paper provides an overview of the current state of research on the detection of outlier information using linguistically quantified statements based on diverse variants of fuzzy linguistic summarization. It pays special attention to the use of monotonic and non-monotonic quantifiers defined on interval-valued fuzzy sets. The outlier detection procedure using linguistic summaries is described in detail.
THE SYSTEM OF “OPERATIONAL DEVELOPMENT" THE SCHEDULE OF CLASSES FOR EDUCATIONAL INSTITUTIONS

An approach to automating the process of extracting information, forming and analyzing the schedule of training sessions in educational institutions is presented, where the existing "working" schedule of classes for the same period of the previous year is used as an initial option. The article describes the capabilities of the developed software, which facilitates and accelerates the user's work, eliminating the possibility of unnoticed errors in the newly compiled schedule.
Polymorphism

Ask a developer, “What are the fundamental characteristics of object-oriented programming (OOP)?” You will hear an immediate reply saying that classes (and objects), inheritance, abstraction, encapsulation, and polymorphism are the most important characteristics of OOP.
A Survey on Recognition of Ancient Tamil Brahmi Characters from Epigraphy

The system which involves in character identification of Brahmi letters from epigraphy and converts to current Tamil character format.Identification of earliest Tamil characters is one of the hardest part.If the letters are on the walls, it is still more difficulty in recognizing the characters.Character recognition has reached near perfection in English and other language text.Identification of epigraphy brahmi characters is very difficult because brahmi characters has been used from 3rd Century BCE to 4th Century CE.The accuracy level is not high in Brahmi letters because of these reasons-First the consonants are similar to consonantal vowel characters, second the author doesn't inscribe the Brahmi letterings correctly and the fonts are inscribed in various styles and strokes.Only few people are known with ancient character if this remains, all the valuable data given by our forefather will not be identified by the future generation.In this survey paper, we describe and compare many techniques of identification through table.
Discussion on Dance Teaching Innovation Strategy under Internet Background

The "Internet Plus" model is an important manifestation of the new era.The widespread popularity of the network and the application of information technology make the whole era show a new form of development, and also change the operation mode of various industries to a large extent.The field of education also began to conform to this development trend, strengthen innovation in the carrier of education, began to pay attention to the innovation of teaching mode.In the field of education, dance is an important curriculum content.In order to comprehensively improve the overall teaching efficiency, the school needs to focus on strengthening the application of the "Internet Plus" model technology to create a modern dance teaching environment, so that students can gain more professional knowledge and promote the effective improvement of students' dance skills.In view of this, in the context of the "Internet Plus" model, dance teaching opportunities are analyzed, and the specific innovation path is effectively analyzed.
Swarm Intelligence Algorithm in the Evaluation System of English Reading Teaching Quality

The teaching of English reading in colleges and universities is a topic that both students and teachers are concerned about. To improve students' English proficiency, the quality of teaching must be improved. Therefore, it is necessary to establish a teaching quality evaluation system (TQES). The system evaluation method allows teachers to obtain relevant information in time through information feedback after the evaluation, find teachers' problems in the teaching process, adjust the teaching mode in time, continuously correct mistakes and deficiencies in the teaching process, improve grades, and raise the quality of teaching to a higher level. This paper introduces the swarm intelligence algorithm (SIA) when designing the English reading TQES, the purpose is to improve the calculation speed and accuracy of the scoring results, and verify the feasibility of the system by testing the performance of the system.