Reviewerly/exHarmony huggingface dataset:
The potential of professional thinking among the students of socionomic group of profession

The issues of development of professional thinking as a cognitive resource in the learning process in the institutes of higher education have been discussed in the article. Gnostic and transformative types of professions have been taken as an example. The study engaged one hundred and two respondents of various targeted directions of socionomic group of professions – the students of psychology and management, being either at initial or final stages of study in the institutes of higher education. It has been defined that the gnostic type does not possess any changes at the componential-elemental level, while the transformative type (the students of management) has some partial readjustments emerged in the psycho-linguistic characteristics, and presents the reflection of important aspects of development of potential of professional thinking of a manager, related to efficiency in decision-making and the role of meta-emotions. The partial interconnection of component-subsystems of professional thinking in the process of studying at higher education establishments has been identified – progressive integration of cognitive and metacognitive subsystems and transformation of correlation between personal and cognitive and metacognitive subsystems, which obtains reverse character. It has been demonstrated that actualisation of the potential of thinking as the system within the socionomic group of professions depends on the stages of education and the goal of training and professional activity.
Demo: Attacking LiDAR Semantic Segmentation in Autonomous Driving

As a fundamental task in autonomous driving, LiDAR semantic segmentation aims to provide semantic understanding of the driving environment.We demonstrate that existing LiDAR semantic segmentation models in autonomous driving systems can be easily fooled by placing some simple objects on the road, such as cardboard and traffic signs.We show that this type of attack can hide a vehicle and change the road surface to road-side vegetation.
Metaverse: A New Tool for Real-Time Monitoring of Dynamic Circumstances in the Physical Production System

The notion of metaverses facilitates online communities where individuals may connect and socialise, highlighting the growing importance of online interactions in people’s daily lives. The digital assets and values that the metaverse offers are useful in many fields since it is a simulation of the actual world with added enhancements.In this paper, we analyze the concept of metaverse in detail. Along with this, we analyze the evaluation of metaverse technology over the years. The results show that metaverse have a significant impact on the physical production system. Metaverse represent a major tool for real-time monitoring of dynamic circumstances in the physical environment. In addition to this, it is also evident that the in metaverse environment real-time monitoring of dynamic circumstances in the physical asset is possible.
Multistage Spatial Context Models for Learned Image Compression

Recent state-of-the-art Learned Image Compression methods feature spatial context models, achieving great rate-distortion improvements over hyperprior methods. However, the autoregressive context model requires serial decoding, limiting runtime performance. The Checkerboard context model allows parallel decoding at a cost of reduced RD performance. We present a series of multistage spatial context models allowing both fast decoding and better RD performance. We split the latent space into square patches and decode serially within each patch while different patches are decoded in parallel. The proposed method features a comparable decoding speed to Checkerboard while reaching the RD performance of Autoregressive and even also outperforming Autoregressive. Inside each patch, the decoding order must be carefully decided as a bad order negatively impacts performance; therefore, we also propose a decoding order optimization algorithm.
Classification of Different Participating Entities in the Rise of Hateful Content in Social Media

Hateful content is a growing concern across different platforms, whether it is a moderated platform or an unmoderated platform. The public expression of hate speech encourages the devaluation of minority members. It has some consequences in the real world as well. In such a scenario, it is necessary to design AI systems that could detect such harmful entities/elements in online social media and take cautionary actions to mitigate the risk/harm they cause to society. The way individuals disseminate content on social media platforms also deviates. The content can be in the form of texts, images, videos, etc. Hence hateful content in all forms should be detected, and further actions should be taken to maintain the civility of the platform. We first introduced two published works addressing the challenges of detecting low-resource multilingual abusive speech and hateful user detection. Finally, we discuss our ongoing work on multimodal hateful content detection.
Soil erosion forecasting of substations based on ELM and environmental factor

The intelligent forecasting and early warning of soil erosion with high precision is necessary for the safe construction and operation of substations. However, there are few researches on the intelligent forecasting of soil erosion based on machine learning algorithms and environmental factors. A soil erosion forecasting method based on extreme learning machine (ELM) and environmental factor was proposed in this paper. Firstly, the correlation between the soil erosion and the rainfall was analyzed comprehensively. Then, the forecasting model was built based on historical soil erosion and rainfall, using ELM algorithm. Finally, the forecasting model was validated with the online monitoring data of soil and water conservation in the substation. The accuracy of the soil erosion forecasting method is verified to be high, and the mean absolute percentage error is 4.07%. It is proved that the intelligent forecasting model of soil erosion based on ELM and environmental factor is effective.
Intrusion Detection Based on Data Privacy in Cloud-Edge Collaborative Computing Using Federated Learning

The Internet of Things has been integrated into every aspect of our modern life, intelligent IoT services and applications are booming, and massive amounts of data are generated every day, many of which contain private information. However, due to limited resources and limited computing power, IoT networks are vulnerable to various types of attacks. Therefore, it is crucial to protect the IoT network from adversarial attacks. In today's technology, applying deep learning to classify traffic is a very effective method. It also brings a problem. In a general cloud server architecture, training data needs to be transmitted to the cloud for processing and model training. The massive data transmission overhead will bring delays in transmission and response, as well as privacy leakage issues. Federated learning (FL) based on cloud-edge collaborative networks has received considerable attention, an emerging framework for training deep learning models from decentralized data. The system sends deep learning algorithms to all edges (data sources) at the same time, trains partial models at each edge, and aggregates these partial models into a learned overall model. User information is not uploaded to the cloud during the entire process. This paper adopts the federated learning framework to detect and classify network traffic attacks, and effectively protect user privacy data.
Server Load Balancing Using Linux

Linux virtual server (LVS) uses load balancing technology to connect several servers into a virtual server, which can provide a convenient, practical and low price processing measure to meet the rapidly increasing network usage. Based on the full consideration of enterprise services, this design discusses how to build a highly available load balanced enterprise server cluster environment under Linux. In the design, the enterprise's web server adopts the lamp environment. Kept and LVS can realize the enterprise's highly available load balancing web server cluster, use MySQL dual host to realize the automatic backup of the database, and realize the automatic jump of the database in the case of single point of failure of the database through mysql-ha cluster.
A Phishing Website Detection Method Based on Multi-layer Perceptron with Mutual Information Feature Selection

With the rapid development and wide application of the Internet, security issues have become increasingly prominent. Various Internet security incidents emerged in an endless stream. Of all the cyber threats, phishing websites are one of the biggest threats to users. By spoofing well-known websites, attackers lure users into entering their private information, which can then be used to commit other illegal and criminal activities, harming the Internet environment. Traditional methods that are black and white list-based defenses have shown a significant loss in accuracy in the face of increasingly diverse phishing sites. To solve the problem, a phishing detection method based on multi-layer perceptron with feature selection is proposed, of which architecture is a typical three-layer structure. The procedure of the method includes feature selection and model training. In the feature selection stage, more relevant features to the label are screened according to the mutual information value; in the training stage, the multi-layer perceptron model is trained by using the generated new dataset. Experiments show that the proposed method can effectively improve the accuracy of phishing detection.
Design of a Programmable Touch Keyboard

In order to further improve the utilization rate of equipment, reduce the dependence on other countries' components, realize the independent control of equipment, and further enhance the scientific and technological strength. This paper proposes a programmable touch keyboard design scheme, which realizes that the display console can carry out different command control according to different scenarios and different application environments. The programmable touch keyboard device can support a variety of mainstream communication protocols, support USB, NET, RS232 and other interfaces, can communicate with the computer through each communication port, process the data of the computer and display it on the programmable touch keyboard interface, and feedback the data processing results to the computer. Programmable touch keys have 100% self-study, programmable display, multi-touch, high resolution, low power consumption, resistance to harsh environments, strong stability and other notable features. Compared with the traditional keyboard that can only display fixed text and a single interface, the programmable touch keyboard proposed in this paper can redefine each key many times according to the scene, and can display multi-interface commands to meet different command requirements.
Interval-valued intuitionistic fuzzy linear transformation

In this paper we introduce the concept of interval-valued intuitionistic fuzzy relations(in biefly IVIFR) and composition of IVIFR-equations. Then we continued it to interval-valued intuitionistic fuzzy linear transformation(in brief IVIFL-transformation) and discussed its properties. Also introduced the concept of composition of IVIFL-transformations.
INDEPENDENT WORK OF STUDENTS AS A BASIS FOR QUALITY QUALIFICATION TRAINING

Higher education plays an essential role in providing professional training of competent and competitive specialists for the health care system.Nowadays, the educational process mainly focuses on active teaching methods and independent work of higher education students.Employers are only sometimes satisfied with the quality of training of higher education institution graduates.When it comes to improving the quality of education of higher education students, one of the main focus areas is developing students' skills of independent work, which necessitates teaching them to autonomously process, analyze, structure, and effectively use the information students need for their utmost self-fulfillment and contribution to society.The modern higher education system seeks to intensify the cognitive activity of students by independently finding answers to questions.Reducing the number of in-class lessons requires organizing the independent study of topics and providing proper methodological support for students' independent work.The efficiency of students' independent work depends on planning.When planning this type of work, it is necessary to consider students' ability to work on topics independently, i.e. the material's complexity and volume.Naturally, a teacher plays the primary role in organizing higher education students' independent work.Evaluating the quality of independent work requires a qualitatively new format of conducting contact hours of a teacher with higher education students.Based on the experience of the Department of Experimental and Clinical Pharmacology, the most effective methods include round table discussions, Internet reviews, computer presentations, case studies, conferences, debates, and role-playing games.Thus, using active learning methods and new monitoring forms in managing the independent work of higher education students significantly improves the quality of training of future doctors.
VoxSRC 2022: The Fourth VoxCeleb Speaker Recognition Challenge

This paper summarises the findings from the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC-22), which was held in conjunction with INTERSPEECH 2022. The goal of this challenge was to evaluate how well state-of-the-art speaker recognition systems can diarise and recognise speakers from speech obtained "in the wild". The challenge consisted of: (i) the provision of publicly available speaker recognition and diarisation data from YouTube videos together with ground truth annotation and standardised evaluation software; and (ii) a public challenge and hybrid workshop held at INTERSPEECH 2022. We describe the four tracks of our challenge along with the baselines, methods, and results. We conclude with a discussion on the new domain-transfer focus of VoxSRC-22, and on the progression of the challenge from the previous three editions.
Multisensor Detection Alliance Based on Improved Elephant Herding Optimization Algorithm

Aiming at the problems that the elements of the traditional Multi-sensor Detection alliance are not fully considered in the construction of the model, and the population optimization algorithm applied in the detection process of the alliance has many control parameters, weak global optimization ability and slow convergence speed, a detection alliance mathematical model considering the target threat, sensor detection accuracy, sensor energy consumption and constraints is proposed in this paper, Then, basic Elephant Herding Optimization algorithm is improved by alternately using Opposition-based learning strategy (OBL) and sine cosine algorithm (SCA), and applied to the target detection process of sensor detection alliance. Finally, simulation experiments show that the algorithm significantly enhances the global search ability and convergence speed, and verifies the feasibility of the alliance model.
Representing a method to identify and contrast with the fraud which is created by robots for developing websites’ traffic ranking

Representing a method to identify and contrast with the fraud which is created by robots for developing websites’ traffic ranking
CONTRIBUTION OF THE UNIVERSITY LIBRARY IN THE FORMATION AND DEVELOPMENT OF A COMFORTABLE INFORMATION ENVIRONMENT OF SCIENCE AT THE UNIVERSITY. 1

The definitions and notions of the “information environment”, “comfort of the information environment” and the interpretation of the “information environment of science” are considered, the characteristics of the comfortable information environment of science are presented. In this connection, such concepts as “information resources” and “information infrastructure” are analyzed ex necessitate. A “pragmatic” understanding of the information environment is formulated in relation to its practical application to the library. The advantages of setting by a university library such a goal as forming the university information environment of science in comparison with the traditional task of providing information services to the users are substantiated.
REVISING REFERENCE LISTS OF SCIENTIFIC WORKS ON USER REQUESTS: EXPERIENCE OF THE CENTRAL SCIENTIFIC LIBRARY OF THE NATIONAL ACADEMY OF SCIENCES OF BELARUS

The article deals with one of the popular paid services of the Central Scientific Library n.a. Y.Kolas of the National Academy of Sciences of Belarus - the revision of the lists of references for scientific works. The peculiarities of compiling and editing the bibliographic lists, according to the established regulations, are noted.
CONTRIBUTION OF THE UNIVERSITY LIBRARY IN THE FORMATION&#x0D; AND DEVELOPMENT OF A COMFORTABLE INFORMATION ENVIRONMENT OF SCIENCE AT THE UNIVERSITY. 2

The main activities of the Scientific Library of Belarusian National Technical University (BNTU) aimed at the formation and development of a comfortable information environment for the University’s academic activities are described, including: organizing of redundancy of content and relative limitation of volume in the presented documentary flow, organizing of assistance in creating documents and promoting them to the “intellectual market”. Among the concrete forms of activities there are selection of serial publications for reading and submission of articles; development of the journals published by BNTU; creation and maintenance of open access resources; advisory work on registration in various systems of identification of author profiles; various bibliometric studies. The issue of the coincidence of the implementation of the principles of “open science” with the conditions for the formation of a comfortable information environment of science is touched upon.
Ensemble Kernel Learning Model for Prediction of Time Series Based on the Support Vector Regression and Meta Heuristic Search

Ensemble Kernel Learning Model for Prediction of Time Series Based on the Support Vector Regression and Meta Heuristic Search
Blockchain

Blockchain is another innovation with solid ramifications for the eventual fate of how we trade data and money as a comprehensively organized society. It is new to the point that there is moderately minimal scholastic work done on it, yet this is evolving rapidly. For this writing survey, we have started by gathering an example of principally peer-inspected sources, and additionally an educational diagram of articles from different channels. Our determination of articles enables us to give an agent perspective of three essential points. In the first place, a portion of the essential current themes being talked about with respect to blockchain innovation. Second, the agent classifications of said points. Third, the potential fate of blockchain improvement alongside its effect on society and innovation.
A reliable matching algorithm for heterogeneous remote sensing images considering the spatial distribution of matched features

Owing to the differences in sensor types, resolutions, and imaging conditions of heterologous remote sensing images, the matching results of remote sensing images, such as low accuracy, few matched pairs, and low distribution quality, are not ideal, which makes precise registration between heterogeneous images difficult. To mitigate this, we propose a reliable matching algorithm for heterogeneous remote sensing images that considers the spatial distribution of the matched features. First, feature-based matching algorithms such as the scale-invariant feature transform (SIFT) algorithm or the speeded-up robust features algorithm are used to match images to obtain an initial set of matched pairs and a set of candidate features. Then, according to the stability of the spatial distribution of locally correctly matched features, the distance and angular proximity between matched features and their neighbours are calculated to obtain the accuracy of the matched pairs and remove incorrectly matched pairs. Finally, the random sample consensus (RANSAC) algorithm was used to fit the transformation model between images, and the final matched feature selection algorithm and automatic transformation error algorithm were used to detect candidate features to increase the number of matched pairs. Experimental analysis of heterogeneous multiscale and multitemporal optical remote sensing images demonstrates the superior capability of the proposed algorithm over commonly used algorithms, including SIFT, RANSAC, locality preserving matching, learning a two-class classifier for mismatch removal, and linear adaptive filtering algorithms. In particular, when the precision of the initially matched pair is low, the proposed algorithm can achieve excellent results.
Analysis and Classification of Security Mechanisms on the Cloud for Digital Healthcare

Cloud computing provides an inexpensive solution to digital problems. In Cloud Computing, security is a major issue and this paper looks into several security mechanisms. In many ways, cloud computing-based healthcare has revolutionized the picture of healthcare. The key benefits of the cloud in healthcare include scalability of the needed service and the flexibility to increase or decrease data storage, as well as collaborative AI and machine learning. In the present scenario, securing data, availability of data, data integrity, as well as confidentiality of information and network security are the most pressing concerns in cloud security. APIs weren't the only way to secure cloud data; cryptography, authentication, and classification were also common practices. Encryption is a useful tool for securing information storage and transmission. Regardless of the several benefits of cloud-computing programmers for healthcare to be evaluated and addressed, In this research, we examined the state-of-the-art and current literature on the several methods utilized in digital health to ensure patient confidentiality. A few of these methods have advantages and disadvantages. Following the choice of original articles, a literature assessment was conducted, and numerous methods used in their solutions were discovered and analyzed.
The Application of HTML3.0 in the Comprehensive Quality Analysis System for Professional Arts Talents

With the continuous development of education and the continuous expansion of the scale of higher education, how to improve the comprehensive quality of students and how to scientifically evaluate the comprehensive quality of college students have become the focus of university managers. The scientific evaluation of College Students' comprehensive quality is not only related to the development of college students themselves, but also related to the cultivation of high-quality talents in Colleges and universities. This paper introduces the application of decision tree algorithm in the comprehensive quality analysis of art professionals. The purpose of applying this technology is to determine the most important factors affecting the quality of works of art. The results show that there are many factors affecting quality, and it is impossible for only one factor to be responsible for all these effects. However, by using the decision tree algorithm, we can determine which factors are important in determining the quality level.