Reviewerly/exHarmony huggingface dataset:
Security Enhancement using Image verification method to Secure Docker Containers

Now with the dawn of the internet, cloud computing has been reformed by opening new horizons at an inclusive level with auspicious opportunities. With the rise of opportunities, popularity, and public connectivity by the internet, it is the next foremost edge for Trojans, worms, viruses, hackers and cyber-attacks will broaden because a hacker would see this as a new edge to try to steal confidential interrupt, information, services and route damage to the originality cloud computing Docker system. Security in Docker container over the cloud is of great anxiety hence care needs to be taken to provide secure Docker containers and secure cloud services. Docker is usual within the software development community due to the portability, versatility, and scalability of containers. In this paper, we analyze the Docker storage-based security and by applying the methods of Docker image verification, checking capabilities, and storage selection, improve the security in Docker containers.
Missing Person Detection Using AI

The focus of the planned study is on finding lost people in crowded environments including public events, festivals, temples, and meetings. In today's busy environments, single-person identification is a challenging endeavor. This problem is addressed by applying a deep learning idea to arrive at a workable solution. Individuals are recognized through the use of a Convolutional Neural Network (CNN). Several face characteristics are used to positively identify the missing person. The use of Face Detection is crucial to the success of this endeavor. The ImageNet Large-Scale Visual Recognition Challenge participant AlexNet is used. The main takeaway is that the model's depth is crucial to its excellent performance, which is computationally expensive but is made possible by the use of graphics processing units (GPUs) during training. With sufficient training using a wide variety of images, it is possible to locate the sought-after object in the allotted space. The project's momentum is based on watching the live feed. Faces are extracted from the video and saved to a database. A collection of photos is available for use in making identifications. We use our own dataset to train the AlexNet's many layers. The images in the database are labelled according to whether or not they contain a person by utilizing the pretrained network. Additionally, the KLT method should be used to determine the person's location and enable real-time tracking.
Designing of an Efficient Model for Violence Detection Using Advance Deep Learning Techniques

In recent times the crime rate even in the presence of CCTV is high, and surveillance footage monitored manually is increasing the chances of suspicious behaviour going unnoticed. Thus there’s a need for real-time automated analysis of footage so that such activities can be tracked and the concerned government agencies can be notified immediately, The main focus of the paper is on immediate recognition of fight scenes in surveillance videos, For this purpose three models have been tested to classify videos into fight and non-fight, ConvLSTM and 2D CNN with LSTM and 2D CNN with Bi-LSTM, The models were tested on Movie, Hockey Fights and CCTV dataset, It was observed that CNN with Bi-LSTM worked consistently well with all three datasets obtaining competitive results: 99.4% on movies, 94.8% on hockey and 73% on CCTV dataset respectively.
GREAT REFORMERS OF RUSSIA: S. Y. WITTE, P. A. STOLYPIN

In our society a hundred years ago there was a problem of modernization of Russia. Then two people who became a symbol of the “perestroika” of Russia in the 19th and 20th centuries tried to solve such a difficult, perhaps thankless task: Sergei Yurievich Witte (1849-1915) and Pyotr Alexandrovich Stolypin (1862-1911). How this was implemented will be discussed in this article.
MentorBot: A Traceability-Based Recommendation Chatbot for Moodle

E-learning platforms are excellent educational exchange and sharing environments. They are accessible everywhere and offer several advantages such as flexibility, consistency, profitability, improvement, and the possibility of designing rich digital content. E-learning services are an interactive way of communication and are available to learners anytime and anywhere in the world using the internet. Nowadays, they have been more and more engaged, mainly due to the recent pandemic, and because of that, the number of resources and students is still increasing. Consequently, as these platforms aim to provide learners with more personalized individual learning, suggesting the most suitable items for learners is now a complicated task, even for experienced tutors; since each learner has its own learning style and interests, and the process of recommending resources needs a considerable amount of time. This paper introduces “MentorBot”; a recommendation chatbot prototype, which uses traceability (using personalized models) as its primary technique for computing suggestions. The prototype can provide a rich conversational experience, trace learners’ behaviors, and offer adequate academic resources in real-time. MentorBot is integrated with Moodle and can be accessed through a floating web widget, throughout the whole platform. Our goal is to reduce the workload and free tutors from repetitive tasks while promoting the use of chatbots in learners’ everyday life.
Novel Method for Detecting Content Based Image Retrieval Using OCR Tools

In the field of image processing content based image retrieval is having research scope to retrieve the images which is stored in any location on the network. In the content based image retrieval system to check images which are deposited on the network based on image attributes like image size, name of the image, shape and color of the image. This work gives a novel method for system retrieve the image based on the text query given by the user. The scope of the paper also extends the scope to finding the similar combined images containing text and also graphical shapes. In the proposed system we can retrieve the images by using Optical Character Recognition system. The important scope of the proposed methodology is to produce accurate result even in the case of invariance to difficult graphics and background features, text design and different font size. The approach has been tested for retrieving images of English text based on the provided text. We put forth a brand-new method for creating a content-based picture retrieval system based on locating images with the same textual information within an image.
Performance Analysis of CHAID Algorithm for Accuracy

Decision tree algorithm belongs to the machine learning method of supervised learning, and it is one of the common techniques of data mining. CHAID algorithm is one of the common decision tree algorithms. The CHAID algorithm can be used for prediction as well as for classification. It is mainly used in market analysis, risk prediction and other fields. The second part tells the core idea of CHAID algorithm and the specific steps of the classification process, as well as the principle formula of chi-square detection and the specific steps of the calculation mainly used by CHAID algorithm. The third part describes the branch principle of CHAID algorithm and other decision tree algorithm, and analyses the accuracy of CHAID algorithm. Let us choose a relatively good decision tree algorithm according to the specific data situation, and apply the CHAID algorithm can also make some countermeasures according to some factors affecting the accuracy, which is more conducive to get a more accurate and better result.
Semantic line breaks

Line breaks usually have no semantic meaning within a Markdown paragraph. However, using line breaks to mark the end of a sentece can help with productivity for various reasons.1 Documents with one sentence per line are also called “ventilated prose”, and the Write the {Ascii}Docs website has a good article on that topic. A question came up in a Slack channel, asking whether it was possible to convert existing Markdown docs to this style. Naturally, the answer is “pandoc can do that”, but that...
A Searchable Power Engineering Design Document Distribution Model based on Alliance Blockchain

The design document is an important basis for the establishment, construction and production of power engineering construction. After the power engineering design documents are completed by the design unit, they are usually distributed to other units in the form of printing paper documents and making blueprints. The current method of document distribution has resulted in higher costs of document printing and storage, and the efficiency of document distribution is low. In this paper, a searchable power engineering design file distribution model based on the Alliance chain is stated. It uses the main feature of the blockchain's data tamper-proof technology, and combines the searchable encryption technology to realize the distribution and access management of digital power design files, eliminating the problems of inconsistent file versions and malicious tampering of file contents in the existing design file distribution process. In this model, the feature information ciphertext and keyword index ciphertext of the design file are written into the block data of private chain of design unit, and private chain send transaction to alliance blockchain. At the same time, the design documents are stored in distributed file systems, and the feature information of the documents is compared during the access of the design documents, so that the design documents can be tamper-proof after distribution, and the solution of replacing the paper blueprints with digital design drawings is realized. The model can make the engineering documents have the ability of tamper-proof, and can trace the flow process of the documents.
Stellarium Assisted Celestial Coordinate Learning to Encourage Students’ Concept Comprehension and Digital Literacy

The aim of this work is to determine the understanding of concepts and digital literacy of students using the Stellarium application in learning astronomy, specifically the celestial body coordinates. The study used pre-experimental one-group pretest-posttest design. The sample consists of physics students at UNNES who were taking the Astronomy course. Data was collected through testing using a concept comprehension test and a digital literacy questionnaire. Due to the non-normal distribution of the data, hypothesis testing was conducted using the Wilcoxon test, which showed that the increase in students' concept comprehension was not significant (z-score = -1.43, p-value = 0.076). This result was confirmed by the N-gain of 8.71% (low). However, there were three indicators of the seven concept comprehension indicators tested, that showed a significant improvement, i.e. interpreting, exemplifying, and inferring. Rasch model analysis of the digital literacy questionnaire showed that students' digital literacy skills were very good and consistent with the model's prediction. Thus, the use of Stellarium in learning celestial coordinates can partially improve students' concept comprehension and digital literacy skills.Keywords: Stellarium, Celestial Body coordinates, concept comprehension, Digital Literacy.
EVALUATION TOOLS IN THE PREPARATION OF BACHELORS - LANDSCAPE ARCHITECTS

The article discusses the experience of using evaluation tools during the entrance, current, intermediate and final control of knowledge, skills and abilities of students in the field of training 35.03.10 Landscape architecture (bachelor’s level). The most popular assessment tools are tests, case studies, situational tasks. It is shown that it is desirable to combine the considered evaluation tools with traditional forms and methods of testing knowledge, skills and abilities.
On the Multidimensionality of Scientific Rationality and Scientific Progress

In the polemic with T.D. Sokolova’s article the issue is discussed addressing the question whether there is anything new that we can get from the methodology of interdisciplinarity and trans-disciplinarity for determination of scientific rationality and scientific progress. The solution of this question is connected with the intensions of a historical and social-cultural epistemologies. These intentions consist in a complex or “multidimensional” approach to the creation of conceptual designs that define the application of these concepts. None of the "measurements” (methodological, psychological, social, etc.) provides them with a universal definition, but a “stereometric” image “restored” from them is capable to approximate to real use of these concepts of an epistemology and philosophy of science. It is necessary to remember that progress in science in one dimension can be regression in another just as the scientific rationality in a sociological dimension can be in no correspondence with the interpretation of rationality given to it by a certain methodological conception. Philosophy of science must avoid a dogmatic universalization of the criteria of rationality as well as the extremes of of relativism in this question.
Recurrence and transience of quantum Markov semigroups constructed form quantum Bernoulli noises

Quantum exclusion semigroup constructed form quantum Bernoulli noises can be written on diagonal and off-diagonal operators space, respectively. The diagonal parts describe a classical Markov process. In this paper, we first produce a decomposition into the sum of irreducible components determined by the class states of the associated classical Markov process and prove that each irreducible component is recurrent or transient if and only if its diagonal restriction is recurrent or transient. We then give conditions for existence of invariant states and study properties of invariant states such as detailed balance, uniqueness and convergence towards as time approaches infinity.
Self-supervised Anomalous Sound Detection for Machine Condition Monitoring

Automatic detection of anomalous sounds is very important for industrial equipment maintenances. However, anomalous sounds are difficult to collect in practice, and self-supervised methods have received extensive attentions. It is well-known that the self-supervised methods show poor performances on certain machine types. To improve the detection performance, in this work, we introduce other types of data as targets to train a general classifier. After that, the model has certain prior knowledge, and then we fine tune the parameters of the model for a specific machine type. We also studied the impact of input features on performance, and it is shown that for machine types, filtering out low-frequency noise interference can significantly improve model performance. Experiments conducted using the DCASE 2021 Challenge Task2 dataset showed that the proposed method improves the detection performance on each machine type and outperforms the DCASE 2021 Challenge first-placed ensemble model by $$8.73\%$$ on average according to the official scoring method.
Developing of Drama Teaching Materials with Benime Assisted for High School Students

This research and development is to find out the product development process and feasibility, as well as to find out the effectiveness of the drama material development product using the Benime application for class XI students at SMA Negeri 1 Hamparan Perak. The process of developing drama material uses the Benime application in the form of audiovisual teaching materials using the stages of the R&amp;D method by Borg and Gall with 8 research stages covering 1) potential and problems, 2) data collection, 3) product design, 4) design validation, 5) design revision, 6) small-scale trials, 7) product revisions, and 8) mass production. From the validation results obtained a score calculation from material experts of 82% very good "and media experts of 97% "very good". The results of the validation assessment of product feasibility have very good quality to use. one group pretest-posttest design 20 multiple-choice test questions. Data analysis used the Normalized Gain Test. The results showed that the total pretest score of all students was 1760 and the total posttest score of all students was 2485. The results were processed using the formula N-Gain and achieved a value of 0.58 with the interpretation of "Medium" this result there is a significant increase in student achievement by using Benime-based teaching materials in class XI students.
Two-Phase Virtual Machine Placement in Cloud Computing Data Centers Using Extreme Learning Machine Prediction-Based Triggering Method

Background: Two-phase Optimization of Virtual Machine Placement (VMP) Problem considers both the Online Incremental VMP (iVMP) phase in which the new arrival of dynamic requests of Virtual Machines VMs are attended to and the Offline VMP reconfiguration (VMPr) phase that performs placement recalculation. In the two-phase scheme, the first part of the two-phase approach is the iVMP, where virtual machines (VMs) can be built, changed, or destroyed at runtime. While the second phase focuses on raising the standard of solutions produced by the iVMP, several studies have been done in different literature to solve the VMP problem. However, the methods used tend to be over-forecast and have long runs of a linear trend. This affects the prediction and produces a less optimal solution. Objective: The following four objective functions are optimized using the proposed Extreme Learning Machine Prediction-Based Triggering Method for Virtual Machine Placement in Cloud Computing Datacenters in Two-Phases, which combines the advantages of both online (dynamic) and static (offline) VMP formulations: the length of the reconfiguration process, the amount of energy used, the way resources are used, and the financial expenses. This study suggests a brand-new strategy for deciding when to start the VMP reconfiguration phase. Results: The Method provides more accuracy to the predicted requests as well as reduces the total economic penalties for service Level Agreement(SLA) violations. An experimental comparison with the existing approach is conducted utilizing 400 cases. Conclusion: The results demonstrated that, in comparison to the benchmark approach, the proposed work obtained a minimum cost function with a 10.5% improvement
TEACHERS’ NON-VERBAL COMMUNICATION INFLUENCING STUDENTS’ MOTIVATION IN LEARNING ENGLISH FOR THE YOUNG LEARNERS

This study aimed at finding out the types of teachers’ non-verbal communication in English classes and the types of teachers’ non-verbal communication the most and less affecting young learners’ motivation in English classes. This study applied a mixed-method research design. The participants were two English teachers; the third and fourth-grade students, third grade consisted of 20 students while the fourth grade consisted of 7 students at SDIT At-Tibyan Al-Auliya Bajeng selected by total population sampling. An observation checklist was used to find out the types of teachers’ non-verbal communication in English classes and a questionnaire was used to find out the types of teachers’ non-verbal communication the most and less affecting young learners’ motivation in English classes. The findings of this study showed types of teachers’ non-verbal communication in English classes were divided into two parts they are body languages, which consisted of facial expressions, body gestures and movements, eye contact and touch and the use of space, and paralanguage which consisted of intonation, pitch, rhythm, volume, speed, accent and emphasis and types of teachers' nonverbal communication that most affected young learners’ motivation in English classes was body gesture and movement, based on the result of questionnaire 85% of the students very agree that the teachers' body gesture and movement affected their motivation in learning English, and 15% of them agree that they get more motivation if the teacher uses their body gestures in the learning process, while types of teachers’ non-verbal communication that less affected young learners’ motivation in English classes was touch and the use of space, based on the result of questionnaire only 19% of the students very agree and also 19% of students felt agree that the teachers' body gesture and movement affected students’ motivation in learning English.
INVESTIGATING THE EXPERIENCES OF STUDENTS WITH DISABILITIES WITH E-LEARNING DURING THE COVID-19 PANDEMIC IN VIETNAMESE HIGHER EDUCATION

This study uses a mixed-methods approach to investigate the experiences of Vietnamese university students with disabilities (visual/mobility impairments) with e-learning as a consequence of emergency remote teaching during the COVID-19 pandemic. An analysis of the ideas of 20 surveyed students with disabilities at eight universities in Ho Chi Minh City and six students interviewed afterward shows that students can change their study habits to adapt to e-learning and to enjoy this model of learning. However, the participants revealed that they also want to experience face-to-face learning so that they can interact with their lecturers and peers more effectively and in more diverse ways, as well as assimilate lectures more easily. Furthermore, the research shows that various adjustments should be made by system designers, universities, and lecturers to make e-learning friendlier to disabled students. The recommended adjustments include designing easy-to-use learning tools and platforms, providing lecturers with the necessary tools and facilities to design lessons appropriate for all students, providing psychological and technical support for disabled students, choosing user-friendly learning applications and platforms, providing students with suitable learning resources, and modifying testing and assessment methods.
The Recursive Algorithm for Filling Areas of Recognized Objects

Purpose of research. The recursive algorithm for painting recognized areas on a binarized image and extracting the contours of a color mark, in order to find the minimum time for counting the number of filled pixels in a recognized mark, was developed in the article. Methods. The region filling algorithm in the image is based on the recursive method. The algorithm begins the work from the central point of the image and analyzes the presence of unshaded pixels in adjacent cells. In the article is considered the definition of the shortest time for filling the recognized area in eight directions: east-south-west-north, east-north-west-south, southeast-northwest, southwest-northeast, west-south-east-north, west-north-east-south, north-east-south-west, north west south east. The algorithm contains several stages: checking the exit from the recursion provided that all areas are filled, filling the initial cell, filling the elements in four directions from the starting point, counting the number of filled elements. Results. A recursive algorithm for filling recognized areas on a binarized image with the possibility of highlighting the contours of a recognized mark has been developed. The direction of the optimal bypass, which has the shortest time in relation to other considered directions, is determined. The testing was carried out for cycles with 10, 50 and 100 iterations. According to the presented algorithm, a specialized software model was created. The number of the certificate of state registration of the computer program "Program for filling a recognized mark" is 2023612631. Conclusion. The results of experimental studies showed that for a cycle value of 10 iterations, the best time to fill the area is 12762 msec, for a cycle value of 50 iterations, the best time is 76008 msec, for a cycle value of 100 iterations, the best time is 160568 msec. The minimum average time to complete the filling operation was 84357 msec, therefore, the best of the eight pass combinations turned out to be the direction - north-east-south-west.