Reviewerly/exHarmony huggingface dataset:
Analysis and Research on Provincial Postgraduate Cultivation Performance Based on BP Neural Network

According to relevant data, the comprehensive competitiveness and quality of graduate education in Henan Province are in the middle of the 31 provinces in China, and its graduate education has a great room for improvement. Through the research on the current situation of educational performance evaluation at home and abroad, and the analysis of the limitations in the evaluation process of postgraduate training in Henan province, we proposed a performance evaluation method based on BP neural network. This method uses the theory and technology of information theory, analytic hierarchy process, and BP neural network to establish an educational performance evaluation model. Through this model, the shortcomings of the existing performance evaluation methods of graduate student training are overcome, and performance evaluation methods that are closer to the actual situation are excavated, which provides a scientific decision-making reference for the performance evaluation of graduate education in Henan Province. This experiment uses the university education data of Henan Province as the experimental data test set. The experimental results show that this model has good generalization ability and can provide reference for the reform and construction of postgraduate education in Henan province.
Hermeneutical approach to the educational situation

The purpose of the study is to investigate the educating role of a real pedagogical situation, since a human act carries a possible expression of understanding of a particular text, its interpretation. The authors propose to address the conditions when students, together with the teacher, reconstruct a particular cultural norm, a text, putting all their experience in them, and this shows the variability of interpretations, thanks to which a hermeneutic approach to the educational situation is implemented. As a result, it was revealed that within the framework of this approach, the activity of the educator is carried out on the basis of a deep understanding, penetration into the specifics of thinking, feelings, experiences of the child (pupil). The decisive role in this case is played not so much by the appeal to pedagogical facts and phenomena, but rather to what is behind them - subjective meanings, unconscious motives of behavior.
FOREIGN EXPERIENCE AND DEVELOPMENT PROSPECTS STEM EDUCATION IN UKRAINIAN SCHOOLS

Modern education is updated every year, incorporates the latest methods and forms of learning. The introduction of the so-called STEM education in the educational process is considered to be a relevant and promising direction today. It is this area that builds research activities in learning, sets students up for teamwork, solves competency problems, and uses the acquired knowledge in practice. The purpose of the study is to formulate the author's understanding of the term STEM education based on the analysis of modern scientific publications; to determine the positive experience of STEM education outside Ukraine; to determine the prospects for the implementation of STEM education in Ukrainian schools. Research methods: systematic scientific-methodological analysis of scientific, psychological-pedagogical, and educational-methodical literature on the problem of research; systematization and generalization of the experience of domestic and foreign scientists; analysis of learning outcomes by the research problem; synthesis and generalization of theoretical positions revealed in scientific and educational literature; generalization of own pedagogical experience and experience of colleagues. Research results. The analysis of the world experience of STEM education provided an opportunity to formulate a single concept of this term, to consider the current prospects of domestic school education in this area. The expediency of introducing STEM education based on foreign experience is emphasized. We consider forms of STEM-study abroad and analysis of various STEM-programs that are freely available to Ukrainian students as promising areas of research. In our opinion, it is possible to introduce into the school curriculum of optional or group classes special courses with a focus on the STEM industry.
The Application of Knowledge Management in Higher Education Management

There is global change in the Higher education due to various aspects of pressure of society and need of the day in the past decades.The coordination and control and better action plans have developed the quality of the higher education by using knowledge management.The effective intellectual capital management uplift the total scenario globally of the university.The university at present cannot be escape with the implementation of knowledge management.To develop scientific, technical and administrative holdings of the university, it is quite necessary to implement e-learning and knowledge management in total in organisation.By the help administrative documents, courses, training pad, electronic books we establish the organisation
LSOP: Layer-Scaled One-shot Pruning

Neural network pruning is a technique that removes unnecessary weight parameters from a network to decrease its memory and computational requirements. Many different pruning techniques have been proposed to reduce networks with over 90% shrinkage in size while minimizing accuracy loss. This paper aims to establish a framework that can generalize the mechanism among various pruning techniques, which can be used to guide users to design better deep pruning methods in the future. With some basic concepts and findings from data matrix approximation, the framework can explain the success of the state-of-the-art methods as well as a more generalized pruning design (Layer-Scaled One-shot Pruning, LSOP) proposed in this work. After pruning with different algorithms and measuring their accuracies, the researcher also found that those methods or algorithms aligned with the proposed framework were more accurate at sparser networks (density &lt; 10%) than the methods that did not. This suggests that future research into neural network pruning can focus on the proposed framework, which has the potential to accelerate the development of pruning technology and adoption of more efficient neural networks.&#x0D; &#x0D; The LSOP framework’s capability to explain strong pruning performances implies that polynomial decay and Low-Rank Matrix Approximation techniques from the field of data science can provide support for neural network pruning.
The report of the rector’s administration of the State University of land use planning for 2021 and on tasks for 2022

The article presents the main performance indicators of the Federal State Budgetary Educational Institution of Higher Education “The State University of Land Use Planning” for the calendar year of 2021. The University’s position in the international, national and branch ranking, the data of the monitoring of the University’s activity efficiency, conducted in 2021 by the Ministry of Education and Science of Russia, as well as the indicators of educational, scientific, international, sport, recreational, social, cultural, civil, patriotic, and economic activity have been estimated in this paper. The goals for the University staff to reach in 2022 have been formed as well.
Computer vision technologies for social distancing

Many technological applications have been developed and implemented in the last two years to fight the COVID-19 pandemic via social distancing. Despite its importance in response to the coronavirus, the remaining challenge is the limitation of human resources to monitor and provide timely warnings to maintain appropriate activities, such as keeping distance between each other, wearing face masks, or complying with curfew restrictions. To tackle this problem, computer vision researchers have proposed numerous approaches for autonomous object detection and distance measurement, which will be summarised in this chapter. First, vision-based applications in intelligent surveillance systems for social distancing monitoring as well as masked face detection, are introduced. Then, core classical and modern neural network-based methodologies for these applications are analysed. A simple masked face detection is developed to verify its effectiveness and limitations, followed up by remarks and discussions on open problems.
Proposed Model for Remote Maintenance of CCTV Cameras

A data center is a physical facility in which an organization stores its critical applications and data. In our paper, the researchers developed an Android model for surveillance CCTV system maintenance and fault identification. The descriptive analytic approach and object-oriented systems analysis and design approach were used. The Unified Modeling Language (UML) was used as a tool in analysis and design, and Java was used as a programming language to develop an Android model to maintain CCTV faults. Image freezing, incorrect time and date, and video delay were resolved. It has been developed an Android model that works on smart phones to monitor and maintain surveillance CCTV systems. This model allows users to view faults reports and fix camera faults, such as (image freezing, camera image delay, and incorrect time on the camera) in surveillance CCTV systems from anywhere that has Internet access.
A Survey on Chatbots in Healthcare Domain

The aim of the survey is to review the work done in field of chatbots for healthcare. According to research, 60% of medical visits are for minor ailments that may be treated at home with easy home remedies. Colds and coughs, headaches, and stomach pains are among the most frequent illnesses. They can be caused by changes in the weather, poor food, exhaustion, and other factors, and they can be treated without the help of a doctor. There are a variety of chatbots available. The Chatbot is a software application that uses natural language processing to engage with clients via text or text to speech. In today's world, one of the most significant difficulties that India has is providing high- quality, cheap healthcare to its rapidly rising population while being cost-effective. Nowadays, providing healthcare services is becoming increasingly challenging, as we saw in COVID-19 extreme conditions, when the situation in India was deteriorating due to a shortage of transportation, physicians, and hospitality. It can sometimes drive patients to postpone their treatment, as well as an increase in the number of deaths. According to this survey goal to create Conversational AI-powered Chatbot for Medical Diagnostics that employs Deep Learning and focuses on rural areas as well as the impoverished and needy people of our country. Such chatbot system is capable of comprehending the patient's symptoms and communicating with the patient (end-user) via web-UI. Index Terms: Chatbot, Healthcare Domain, ML (Machine Learning), NLG (Natural Language Generation), NLU (Natural Language Understanding), Smart Communication, Virtual Communicating Friend
Rough-Set-Based Real-Time Interest Label Extraction over Large-Scale Social Networks

Labels provide a quick and effective solution to obtain people interesting content from large-scale social network information. The current interest label extraction method based on the subgraph stream proves the feasibility of the subgraph stream for user label extraction. However, it is extremely time-consuming for constructing subgraphs. As an effective mathematical method to deal with fuzzy and uncertain information, rough set-based representations for subgraph stream construction are capable of capturing the uncertainties of the social network. Therefore, we propose an effective approach called RS_UNITE_SS (namely, rough-set-based user-networked interest topic extraction in the form of subgraph stream), which is suitable for large-scale social network user interest label extraction. Specifically, we first propose the subgraph division algorithm to construct a subgraph stream by incorporating a rough set. Then, the algorithm for user real-time interest label extraction based on upper approximation (RILE) is proposed by using sequentially characteristics of the subgraph. Empirically, we evaluate RS_UNITE_SS over real-world datasets, and experimental results demonstrate that our proposed approach is more computationally efficient than existing methods while achieving higher precision value and MRR value.
A Secure Decentralized Data Storage Framework Using Blockchain Technology

Cloud storage is one of the leading options to store massive data, however, the centralized storage approach of cloud computing is not secure. On the other hand, Blockchain is a decentralized data storage system that ensures data security. Any computing node connected to the internet can join and form peers network thereby maximizing resource utilization. Blockchain is a distributed peer to peer system where each node in the network stores a copy of blockchain thus making it immutable. In the proposed system, the user’s file is encrypted and stored across multiple peers in the network using the IPFS (InterPlanetary File System) protocol. IPFS creates hash value. The hash value indicates the path of the file and is stored in the blockchain. This paper focuses on decentralized secure data storage, high availability of data, and efficient utilization of storage resources. Here is our take on using Block Chain technology as a decentralized data storage system. Wherein we use resiliency attacks to display the benefits of Block chain based decentralized data storage by performing key operations on our proposed decentralized structure.
Identifying the Genuineness of the Product using Blockchain Technology

The global development of a product or technology always comes with risk factors such as counterfeiting and duplication. Which can affect the company's name, revenue and customer review. Blockchain technology is the distributed, decentralized, and digital ledger that stores transactional information in the form of blocks in many databases which is connected with the chains. Blockchain technology is secure technology therefore any block cannot be changed or hacked. By using Blockchain technology. Customers or users do not need to rely on third-party users for confirmation of product safety. This web application uses Blockchain. Blockchain technology helps to solve the problem of counterfeiting a product and will be reliable. This web application uses SHA-256 hashing algorithm (Secured Hashing Algorithm), where every piece of data produces a unique hash that is thoroughly non- duplicable. SHA- 256 is applied to identify the genuineness of the product.
An exact quantum hidden subgroup algorithm and applications to solvable groups

We present a polynomial time exact quantum algorithm for the hidden subgroup problem in $\Z_{m^k}^n$. The algorithm uses the quantum Fourier transform modulo $m$ and does not require factorization of $m$. For smooth $m$, i.e., when the prime factors of $m$ are of size $(\log m)^{O(1)}$, the quantum Fourier transform can be exactly computed using the method discovered independently by Cleve and Coppersmith, while for general $m$, the algorithm of Mosca and Zalka is available. Even for $m=3$ and $k=1$ our result appears to be new. We also present applications to compute the structure of abelian and solvable groups whose order has the same (but possibly unknown) prime factors as $m$. The applications for solvable groups also rely on an exact version of a technique proposed by Watrous for computing the uniform superposition of elements of subgroups.
AI Based Self Learning Intelligent Information Leak Protection System for TI Companies using LSTM

Preventing the leak of sensitive information, also popularly known as data leak or data loss to an unauthorized recipient, is the primary goal of an organization's information security system. A data leak can occur through multiple channels. While it may not always be possible to prevent it entirely, measures can be taken to minimize the possibility of the occurrence. Like all other financial institutions, TI companies collect sensitive personal information of their customers for business purposes. This information is often categorized into three primary types; NPI, PII, and PI are the designated types in the descending order of sensitivity. The detection of sensitive documents and redaction of sensitive information is required if it is needed to be shared. Inspection of such digital documents to find any sensitive information is by far a human-driven process, and thus time-consuming and costly. An and costly. An intelligent and robust system is required where the content is analysed by state-of-the-art data mining, statistical and machine learning techniques from various data dimensions. An AI based self-learning Intelligent Information Leak Protection System using LSTM is proposed in the project that mines and extracts information and categorizes the document images, to SD or NSD, based on the presence of NPI and PII semantic signatures without any explicit rule configuration. The system is designed to be used proactively as an early warning system to tag the SD images while resting in the data store. It can also act as a real-time checkpoint for the information loss by the documents in transit or use. The proposed model prescribes an information loss protection mechanism using a binary classifier based on the state-of-the-art LSTM technique within the paradigm of Artificial Intelligence.
The concept of “education quality” and evaluation as an element of quality management

Məqalədə “təhsilin keyfiyyəti” anlayışı və qiymətləndirmənin keyfiyyətin idarə olunmasının elementi kimi araşdırılır. Təhsilin keyfiyyəti və qiymətləndirilməsi problemi pedaqoji nəzəriyyə və praktikada ən mühüm məsələlərdən biridir. Bu problemin həlli pedaqoji innovasiyaların və texnologiyaların effektivliyini qiymətləndirmək üçün lazımdır. Pedaqoji hadisələrin mürəkkəbliyi, pedaqoji prosesə və onun nəticələrinə təsir edən çoxlu sayda amillərin, o cümlədən təsadüfi amillərin olması ona gətirib çıxarır ki, bu prosesləri tam deterministik hesab etmək olmaz. Qiymətləndirilmə prosesinin ən mükəmməl təşkili ilə biz hər bir fərdi şagird üçün təlim nəticələrinin nə olacağını birmənalı olaraq proqnozlaşdıra bilmərik. Bu baxımdan müasir təhsil sistemi təhsilin keyfiyyəti və qiymətləndirilmə ilə bağlı tələb qoyur. Hər bir müəllim qiymətləndirmənin obyektivliyini artırmağa, ənənəvi nəzarət vasitələri və pedaqoji elmin innovativ nailiyyətləri ilə təhsilin keyfiyyətini artırmağa çalışmalıdır. Açar sözlər: təhsilin keyfiyyəti, qiymətləndirmə, şagird, müəllim, idarə olunma, element Mujgan Ali Huseynova The concept of “education quality” and evaluation as an element of quality management Abstract The article examines the concept of "quality of education" and assessment as an element of quality management. The problem of quality and evaluation of education is one of the most important issues in pedagogical theory and practice. The solution to this problem is needed to assess the effectiveness of pedagogical innovations and technologies. The complexity of pedagogical events, the presence of a large number of factors affecting the pedagogical process and its consequences, including random factors, leads to the fact that these processes can not be considered completely deterministic. With the most perfect organization of the assessment process, we cannot unequivocally predict what the learning outcomes will be for each individual student. In this regard, the modern education system requires quality and evaluation of education. Every teacher should try to increase the objectivity of assessment, to improve the quality of education with traditional means of control and innovative achievements of pedagogical science. Key words: quality of education, assessment, student, teacher, management, element
Parallel line lidar with frequency domain filtering

Simple, low-cost, rangefinders using parallel beams and a single camera are often used for underwater ranging, but they have limited performance in turbid operating environments such as those associated with coastlines and harbors. A new approach to parallel beam ranging is described which incorporates a combination of simple receiver optics and image processing to significantly reduce the effects of scatter, thereby extending and improving performance. The technique is demonstrated using commercial-off-the-shelf (COTS) cameras and experimental results are presented comparing the new approach to the traditional parallel beam rangefinder. Improvements of multiple attenuation lengths are reported.
A Novel Approach to Explainable AI using Formal Concept Lattice

Current approaches in explainable AI use an interpretable model to approximate a black box model or use gradient techniques to determine the salient parts of the input. While it is true that such approaches provide intuition about the black box model, the primary purpose of an explanation is to be exact at an individual instance and also from a global perspective, which is difficult to obtain using such model based approximations or from salient parts. On the other hand, traditional, deterministic approaches satisfy this primary purpose of explainability of being exact at an individual instance and globally, while posing a challenge to scale for large amounts of data. In this work, we propose a deterministic, novel approach to explainability using a formal concept lattice for classification problems, that reveal accurate explanations both globally and locally, including generation of similar and contrastive examples around an instance. This technique consists of preliminary lattice construction, synthetic data generation using implications from the preliminary lattice followed by actual lattice construction which is used to generate local, global, similar and contrastive explanations. Using sanity tests like Implementation Invariance, Input transformation Invariance, Model parameter randomization sensitivity and model-outcome relationship randomization sensitivity, its credibility is proven. Explanations from the lattice are compared to a white box model in order to prove its trustworthiness.
Students' perceptions toward online learning in EFL Speaking

Although there have been numerous studies on online learning in language classes, fewer studies focus on EFL speaking. This qualitative case study aimed at describing students’ perceptions toward the implementation of online learning in speaking classes. The participants were EFL students taking a speaking course in an Indonesian university. Open-response questionnaires and semi-structured interviews were employed. The results showed that students had positive attitudes toward online learning through a Zoom application. It was also found that some challenges arose toward the implementation of online learning. It is suggested that the lecturers should emphasize more on collaborative and blended learning in teaching EFL speaking.