Reviewerly/exHarmony huggingface dataset:
Research Status and Trends of Panoramic Video in Education: Based on Social Network Analysis and Co-word Analysis Method

Panoramic video, as an emerging media presentation technology with low cost and low technical requirements, has the potential of wide application. However, the current empirical studies on the application of panoramic video in education are relatively scattered, lacking systematic analysis of literature in this field, and it is difficult to understand the research focus and trend. Therefore, this study used EBSCO, Web of Science, Elsevier Science Direct Online and Springer database as data sources. A total of 58 high-quality English empirical research articles published between 2011 and 2021 on panoramic video in education were selected as the research samples to conduct co-word analysis and social network analysis for keywords. The results show that the research emphases and trends of the application of panoramic video in education mainly focus on the development and application of panoramic video based on virtual reality, panoramic video technology and the optimization of learner experience. Finally, some suggestions for future research are proposed.
Thalamus: a brain-inspired algorithm for biologically-plausible continual learning and disentangled representations

Animals thrive in a constantly changing environment and leverage the temporal structure to learn well-factorized causal representations. In contrast, traditional neural networks suffer from forgetting in changing environments and many methods have been proposed to limit forgetting with different trade-offs. Inspired by the brain thalamocortical circuit, we introduce a simple algorithm that uses optimization at inference time to generate internal representations of the current task dynamically. The algorithm alternates between updating the model weights and a latent task embedding, allowing the agent to parse the stream of temporal experience into discrete events and organize learning about them. On a continual learning benchmark, it achieves competitive end average accuracy by mitigating forgetting, but importantly, by requiring the model to adapt through latent updates, it organizes knowledge into flexible structures with a cognitive interface to control them. Tasks later in the sequence can be solved through knowledge transfer as they become reachable within the well-factorized latent space. The algorithm meets many of the desiderata of an ideal continually learning agent in open-ended environments, and its simplicity suggests fundamental computations in circuits with abundant feedback control loops such as the thalamocortical circuits in the brain.
Flouting of Conversational Maxims In “Avengers: Endgame” Movie

To achieve the goals of communication, it is important for the participants in every conversation to obey the cooperative principle in order to avoid misunderstanding of interpretation between them. This research deals with the study of flouting maxim in a superhero movie entitled Avengers: Endgame movie. This research focuses on analysing the types of flouting maxim in Avengers: Endgame movie flouted the maxims. The researcher used the theory of cooperative principle proposed by Grice (1975) to analyse the types of flouting maxim. The researcher used descriptive qualitative method to describe the data to answer the problems of the study. The result of the study shows that there are 20 data of flouting maxim in Avengers: Endgame movie. The highest occurrence of flouting maxim in this movie is flouting maxim of quantity and quality with 7 data (35%). Then followed by flouting maxim of relevance with 4 data (20%). Flouting maxim of manner is the lowest occurrence of flouting maxim with 2 data (10%).
Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines

Strong inductive biases give humans the ability to quickly learn to perform a variety of tasks. Although meta-learning is a method to endow neural networks with useful inductive biases, agents trained by meta-learning may sometimes acquire very different strategies from humans. We show that co-training these agents on predicting representations from natural language task descriptions and programs induced to generate such tasks guides them toward more human-like inductive biases. Human-generated language descriptions and program induction models that add new learned primitives both contain abstract concepts that can compress description length. Co-training on these representations result in more human-like behavior in downstream meta-reinforcement learning agents than less abstract controls (synthetic language descriptions, program induction without learned primitives), suggesting that the abstraction supported by these representations is key.
On Sparsity Measures In Deep Subspace Clustering

&lt;p&gt;This work introduced an auto-encoder-based deep learning architecture consisting of a self-expressive layer for the deep subspace clustering task&lt;/p&gt;
DIALOGICAL INTERACTION AS A MEANS OF SOLUTION CONFLICTS IN HIGHER EDUCATION INSTITUTIONS

The given article considers the concept npedagogical conflict” in a higher school, its negative and positive functions in pedagogical interaction are examined. The author of the article emphasizes the necessity of formation at the students of dialogical interaction skills as an original mean and purpose of the decision of the pedagogical conflict.
Analisis Forensik pada Email Menggunakan Metode National Institute of Standards Technology

Nowadays developments in information technology are growing rapidly, especially in email. Email became one that almost the whole world had. Email is one of the results of developments in information and communication. Email is widely used to exchange information by sending and receiving data, such as document files, pictures, letters, and others. So much for the crimes that often occur in emails. Email crimes that often occur among them are email spoofing. Email spoofing is a forgery that occurs in the header of the email. So, the email is sent as if it were a valid email. Email spoofing is often used in spamming activities. Crimes committed by cybercrime must leave evidence such as IP Address, sender's email, and time of sending the email. This research will do forensics on email spoofing. The research uses the Live Forensics method, where the computer is used in a powered-on state. The research also uses the NIST (National Institute of Standards Technology) research flow. The email that will be analyzed is in the email header section using 3 tools, namely tracer email analyzer, email dossier, and mail header analysis. This analysis will compare and check the accuracy of the email headers using these tools. Emails suspected of email spoofing will be proven using tools. Based on the 'form' received' and 'Message-ID' headers. Based on the results, the tool that meets the value after the analysis is tracer email analysis.
Explaining Causal Models with Argumentation: the Case of Bi-variate Reinforcement

Causal models are playing an increasingly important role in machine learning, particularly in the realm of explainable AI. We introduce a conceptualisation for generating argumentation frameworks (AFs) from causal models for the purpose of forging explanations for the models' outputs. The conceptualisation is based on reinterpreting desirable properties of semantics of AFs as explanation moulds, which are means for characterising the relations in the causal model argumentatively. We demonstrate our methodology by reinterpreting the property of bi-variate reinforcement as an explanation mould to forge bipolar AFs as explanations for the outputs of causal models. We perform a theoretical evaluation of these argumentative explanations, examining whether they satisfy a range of desirable explanatory and argumentative properties.
Resource Allocation in Cloud: History Kerberos based Approach

Cloud computing is one of the remarkable approaches for massive computation.The cloud has solved many computational issued faced in the past.It has brought more benefits for the individuals those cannot offer the necessary computation power for their work and research.In this aspect, several cloud-based organizations have bloomed to server for the needs.As the demand of the cloud computation increases it is necessary to cope with the resources and efficient resource allocations.It is practically to have enormous resources to serve each and individual request.Instead, the best optimal solution is to have minimal resources with optimal resource scheduling algorithms.This research article proposed a history Kerberos based resource allocation techniques.
Preparation of future education managers for professional activities

In this article, the following objectives were carried out: analysis of the essence, content and structure of the managerial culture, identification of the features of the managerial culture development, working out and testing of the model and the program for the managerial culture development of future education managers. The analysis of various aspects of the manager’s activity, the requirements for the professional performance and the specifics of managerial culture in the educational sphere made it possible to state that managerial culture is both a quality of the individual and a property of the managerial environment of the organization. In this context, the following pattern is observed: the managerial culture of the organization determines the managerial culture of its employees, and at the same time, there is an opposite trend, showing that managerial culture of an individual employee affects managerial culture of the organization.
TO THE PROBLEM OF PROFESSIONAL FORMATION COMPETENCIES OF FUTURE TEACHERS-PHILOLOGISTS1B BY MEANS OF DIALOGICAL SITUATIONS

This article considers the problem of the formation of the professional competence of a teacher in the context of making the training more dialoguing.
AI is the New DJ

Artificial intelligence in the realm of digital music platforms is changing the cultural ecosystem. Technological developments and music streaming platforms, aided by machine learning and algorithms, influence cultural trends, listener engagement, and music creation on a global scale. With references to Marshall McLuhan and his exponents, this article discusses the medium of music in past, current, and future contexts, and considers the impact of assigning AI as the cultural curator.
Extension of FBSE-EWT for Complex Signal Analysis

In this letter, a new framework based on Fourier- Bessel series expansion (FBSE)-empirical wavelet transform (EWT) is proposed with the aim of analyzing complex signals. The critical part of this framework is to separate the real- time equivalent positive and negative frequency components of complex signals using suitable filters and then decompose both the real-time signals into their corresponding intrinsic mode functions (IMFs) using the FBSE-EWT technique. After that, the obtained IMFs are represented in a set of complex IMFs (CIMFs) using the Hilbert transform. In the end, joint time-frequency distribution (TFD) is obtained using Hilbert spectral analysis of CIMFs. Two synthetic multicomponent complex signals are considered for simulation purposes, along with a real-world wind signal. The results of proposed framework compared with complex empirical mode decomposition (CEMD) and complex iterative eigenvalue decomposition of Hankel matrix (CIEVDHM) and found to be providing a better result.
The game of Cops and Robber on geodesic spaces

The game of Cops and Robber is traditionally played on a finite graph. The purpose of this paper is to introduce and analyse the game that is played on an arbitrary geodesic space (a compact, path-connected space endowed with intrinsic metric). It is shown that the game played on metric graphs is essentially the same as the discrete game played on abstract graphs and that for every compact geodesic surface there is an integer $c$ such that $c$ cops can win the game against one robber, and $c$ only depends on the genus $g$ of the surface. It is shown that $c=3$ for orientable surfaces of genus $0$ or $1$ and nonorientable surfaces of crosscap number $1$ or $2$ (with any number of boundary components) and that $c=O(g)$ and that $c=Ω(\sqrt{g})$ when the genus $g$ is larger. The main motivation for discussing this game is to view the cop number (the minimum number of cops needed to catch the robber) as a new geometric invariant describing how complex is the geodesic space.
PERSONALLY FOCUSED LEARNING ON COURSES OF QUALIFICATION OF TEACHERS AS A MEANS OF CREATIVE FORMATION PERSONALITIES OF THE TEACHER

The article deals with the preference of the personal and orientative teaching during the process of pedagogical training. Favourable psychological climate in any pedagogical collective is a necessary condition in forming o f creative teacher’s personality.
Image to Text Processing Using Convolution Neural Networks

Captcha technology is used to discriminate between humans and robots. For websites, captcha technology is used to avoid automated harmful programming attacks. Captcha technology has become popular as the internet has grown. This work proposes the implementation of extracting a text from a captcha image. Information is taken from an image file and then stored as a text file. Thus, this work proposes the model based on convolutional neural network, recurrent neural networks, and CTC loss function. The convolutional neural network is utilized to extract the features in sequence from the given image, and the recurrent neural network is utilized to propagate that information in the same sequence extracted from the convolutional neural network. At the end, the output sequence contains five-character captcha. The problem with the naïve neural network is solved by incorporating a connectionist temporal classification output layer in the model.
VERBAL I NON-VERBAL METHODS TEACHING THE DISCIPLINE "DECORATIVE-PH PHASE ART"

The question of choice of methods of teaching for the high-quality mastering of theoretical knowledge’s, acquisition of practical abilities and skills is explored by the students of educational establishments of artistically-pedagogical education on a course "Decoratively-applied art”.
Editable Image Generation with Consistent Unsupervised Disentanglement Based on GAN

Generative adversarial networks (GANs) are often used to generate realistic images, and GANs are effective in fitting high-dimensional probability distributions. However, during training, they often produce model collapse, which is the inability of the generative model to map the input noise to the real data distribution. In this work, we propose a model for disentanglement and mitigating model collapse inspired by the relationship between Hessian and Jacobian matrices. This is a concise framework for producing few modifications to the original model while facilitating the disentanglement. Compared to the pre-improvement generative models, our approach modifies the original model architecture only marginally and does not change the training method. Our method shows consistent resistance to model collapse on some image datasets, while outperforming the pre-improvement method in terms of disentanglement.
Building the educational system of higher educational institution in the digital educational environment

Modern digital space is an unconditional daily accompaniment of human life activity, a condition for professional growth and self-actualization, interpersonal communication, as well as personal development. Formation of the younger generation cannot be considered without digital technology and social networks, which are the natural environment for their functioning and cognition, significantly affect the formation of self-awareness and personal qualities. The entire organizational-institutional structure and methodological approaches to the process of education and upbringing of personality are being transformed in today's educational system in the light of its active digitalization. The issue of improving the entire educational process by integrating the potential of the digital educational environment into all spheres, including educational activities, is very topical in the system of professional education. Actualization of educational and pedagogical technologies in cyberspace is the key problem of modern research in general and this study in particular.
Perspective Chapter: The Importance of Pipeline in Modern Cryptosystem

In this digital world, all the digital information transmitted through the wireless channel has a threat to its security. Along with security, encryption speed is also a significant factor in transmitting the data as fast as possible. The pipeline is the technique used to improve the throughput of the encryption process so that the amount of data encrypted per unit time will be increased. In this chapter, the design of the modern SMS4-BSK cryptosystem is briefed, various pipeline designs of SMS4 algorithms are surveyed and the pipeline implementation on SMS4-BSK cryptosystem is analyzed. The SMS4-BSK cryptosystem is robust, fast and has a throughput of 7.4 Gbps. This modern cryptosystem can resist all kinds of cryptanalysis attacks. The pipelining technique is implemented in this cryptosystem to improve the throughput further. The pipelining method is applied in the encryption architecture of the cryptosystem. The pipelined design is implemented in Kintex-7 FPGA. The design achieved a throughput of 9.9 Gbps. The pipeline implementation can be extended to the key scheduling architecture also as both the encryption and the key scheduling use the same architecture. As per the SMS4-BSK algorithm, the keys are generated in the host system to improve the throughput.
QAOA-in-QAOA: solving large-scale MaxCut problems on small quantum machines

The design of fast algorithms for combinatorial optimization greatly contributes to a plethora of domains such as logistics, finance, and chemistry. Quantum approximate optimization algorithms (QAOAs), which utilize the power of quantum machines and inherit the spirit of adiabatic evolution, are novel approaches to tackle combinatorial problems with potential runtime speedups. However, hurdled by the limited quantum resources nowadays, QAOAs are infeasible to manipulate large-scale problems. To address this issue, here we revisit the MaxCut problem via the divide-and-conquer heuristic: seek the solutions of subgraphs in parallel and then merge these solutions to obtain the global solution. Due to the $\mathbb{Z}_2$ symmetry in MaxCut, we prove that the merging process can be further cast into a new MaxCut problem and thus be addressed by QAOAs or other MaxCut solvers. With this regard, we propose QAOA-in-QAOA ($\text{QAOA}^2$) to solve arbitrary large-scale MaxCut problems using small quantum machines. We also prove that the approximation ratio of $\text{QAOA}^2$ is lower bounded by 1/2. Experiment results illustrate that under different graph settings, $\text{QAOA}^2$ attains a competitive or even better performance over the best known classical algorithms when the node count is around 2000. Our method can be seamlessly embedded into other advanced strategies to enhance the capability of QAOAs in large-scale combinatorial optimization problems.
Bayesian error propagation for neural-net based parameter inference

Neural nets have become popular to accelerate parameter inferences, especially for the upcoming generation of galaxy surveys in cosmology. As neural nets are approximative by nature, a recurrent question has been how to propagate the neural net's approximation error, in order to avoid biases in the parameter inference. We present a Bayesian solution to propagating a neural net's approximation error and thereby debiasing parameter inference. We exploit that a neural net reports its approximation errors during the validation phase. We capture the thus reported approximation errors via the highest-order summary statistics, allowing us to eliminate the neural net's bias during inference, and propagating its uncertainties. We demonstrate that our method is quickly implemented and successfully infers parameters even for strongly biased neural nets. In summary, our method provides the missing element to judge the accuracy of a posterior if it cannot be computed based on an infinitely accurately theory code.
STRUCTURE OF FUTURE READYNESS FOREIGN LANGUAGE TEACHERS TO PERSONAL ORIENTED LEARNING OF SENIOR STUDENTS

The article deals with the structure of the readiness of future teachers of the foreign language for personality-oriented teaching of high school students. The role of each component in the formation of this phenomenon is revealed.
Ensemble-based Methods for Multi-label Classification on Biomedical Question-Answer Data

Background: Question-answer (QA) is a popular method to seek health-related information and biomedical data. Such questions can refer to more than one medical entity (multi-label) so determining the correct tags is not easy. The question classification (QC) mechanism in a QA system can narrow down the answers we are seeking. Objective: This study develops a multi-label classification using the heterogeneous ensembles method to improve accuracy in biomedical data with long text dimensions. Methods: We used the ensemble method with heterogeneous deep learning and machine learning for multi-label extended text classification. There are 15 various single models consisting of three deep learning (CNN, LSTM, and BERT) and four machine learning algorithms (SVM, kNN, Decision Tree, and Naïve Bayes) with various text representations (TF-IDF, Word2Vec, and FastText). We used the bagging approach with a hard voting mechanism for the decision-making. Results: The result shows that deep learning is more powerful than machine learning as a single multi-label biomedical data classification method. Moreover, we found that top-three was the best number of base learners by combining the ensembles method. Heterogeneous-based ensembles with three learners resulted in an F1-score of 82.3%, which is better than the best single model by CNN with an F1-score of 80%. Conclusion: A multi-label classification of biomedical QA using ensemble models is better than single models. The result shows that heterogeneous ensembles are more potent than homogeneous ensembles on biomedical QA data with long text dimensions. Keywords: Biomedical Question Classification, Ensemble Method, Heterogeneous Ensembles, Multi-Label Classification, Question Answering
SOME ASPECTS OF THE CONDUCT CREDIT-MODULAR LEARNING SYSTEMS

In article the didactics opportunities of introduction of elements of credit modular training in vocational training the teacher of music are considered; mechanisms of integration the general-didactic and individual-methodical are isolated. Didactics opportunities of a method of testing are analyzed during an estimation of results of performance of educational tasks.
Learner Performance-based Behavior Optimization Algorithm: A Functional Case Study

Abstract A novel algorithm called learner performance-based behavior algorithm (LPB) was proposed for single and multi-objective by Chnoor M. Rahman and Tarik A. Rashid in 2021. LPB proved its ability to deal with complex optimization problems compared to the dragonfly algorithm (DA), genetic algorithm (GA), and particle swarm optimization ( PSO ). This paper presents and explains the implementation of the LPB algorithm, and it applies it as a model in a case study to maximize a fitness function. As a result, the LPB algorithm is successfully improved the initial population and achieved the optimal solution.
DIDACTIC CONDITIONS OF CREATIVE FORMATION ACTIVITIES OF FUTURE TEACHERS IN THE SYSTEM PERSONALLY ORIENTED LEARNING

The article exposes the didactic conditions according to which the process of forming students ’ creative activity in personal-oriented system of education at higher pedagogical school.