Reviewerly/exHarmony huggingface dataset:
Organization of the educational process at the National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute» during COVID-19 quarantine

International scientific and practical conference «Pedagogy, psychology and teaching methods: international experience» : conference proceedings, July 16–17, 2021. Riga, Latvia : «Baltija Publishing», 2021. 295 pages.
Scientific comparative analysis of Professional Self-Development of future social pedagogue in Kazakhstan and abroad

The paper deals with the scientific analysis in the sphere of social and pedagogical education and with the professional self-development of future teachers. In the paper the structure of the professional development that introduces the interconnection of the four members is presented: self-identification, self-esteem, self-determination, self-government. Attention is focused on the integrity of the system of self-development of a social pedagogue as a competent specialist, who also possesses a set of general culture of personality with professionally significant qualities.The theoretical bases of social and pedagogical support, its essence and content are considered. The role of practice as an integrative component of the professional development of a specialist is emphasized. The necessity of introducing the self-development trajectory in the educational process for successful professional formation of a future social teacher is acknowledged. The paper is intended for researchers and educators involved in the training of future social pedagogues.&#x0D; &#x0D; Keywords: professional self-development, social pedagogue, self-development trajectory;
Neither Good nor Bad: A Large-Scale Empirical Analysis of HTTP Security Response Headers

HTTP security-focused response headers can be of great aid to web applications towards augmenting their overall security level. That is, if set at the server side, these headers define whether certain security countermeasures are in place for protecting end-users. By utilising the curated Tranco list, this work conducts a wide-scale internet measurement that provides timely answers to the following questions: (a) How the adoption of these headers is developing over time?, (b) What is the penetration ratio of each key header in the community?, (c) Are there any differences in the support of these headers between diverse major browsers and platforms?, (d) Does the version of a browser (outdated vs. new) affects the support rate per key header?, and (e) Is the status of a header (active vs. deprecated) reflected to its support rate by web servers? Setting aside the use of the more robust Tranco corpus, to our knowledge, with reference to the literature, the contributions regarding the third and fifth questions are novel, while for the rest an updated, up-to-the-minute view of the state of play is provided. Amongst others, the results reveal that the support of headers is somewhat related to the browser version, the penetration ratio of all headers is less than 17% across all platforms, outdated browser versions may be better supported in terms of headers, while deprecated headers still enjoy wide implementation.
Construction and 3D Simulation of Virtual Animation Instant Network Communication System Based on Convolution Neural Networks

In recent years, great progress has been made in 3D simulation modeling of instant network communication system, such as the application of virtual reality technology and 3D virtual animation online modeling technology. Facing the increasing demand of different industries, how to build an instant network communication system for 3D virtual animation has become a research hotspot. On this basis, the construction method of fast instant network communication system based on convolutional neural network and fusion morphological 3D simulation model is studied. This paper analyzes the research status of instant network communication system. The experiment optimizes and improves the shortcomings of the current research hotspot of virtual animation instant network communication system and takes the morphological 3D simulation model fusion as the core for in-depth optimization. Finally, the experimental results show that the fusion morphological 3D simulation model can reconstruct the standard 3D virtual animation model according to different needs and can quickly model the optimization strategy according to the local differences of different animations. The response accuracy of the network communication system reaches 97.7%.
RETRACTED ARTICLE: Internet of Things Based Network Security for Supply Chain Management in the Business Environment

Supply chain organizations operate in an increasingly complex and dynamic market environment in the current global surroundings. The business environment's risk factors include lack of quality customer service, supplier relationship, and unforeseen security delays are considered essential factors. In this paper, IoT-based Secured Decision-Making Management Approach (IoT-SDMMA) is proposed to enhance Customer service efficiency, partner with suppliers and unexpected delays, and manage supply chain activities most efficiently. The supply chain hierarchical approach is introduced to provide customer's unique solutions and make a difference entirely and offer customers products of high standards in a timely manner. Structural business modeling technique is integrated with IoT-SDMMA to know about materials validation and create a time to make sure everything runs smoothly. The proposed supply chain management system will overcome all traditional supply chain management network security challenges and provide a risk-free environment for quality processes. The simulation analysis is performed based on scalability, performance, and accuracy factors to prove the proposed framework's reliability.
Analisis Tren Perubahan Intensitas Hujan (Studi Kasus: Jakarta dan Bogor)

Rainfall intensity and drainage channel capacity are the main inputs in flood management infrastructure planning. The Intensity-Duration-Frequency (IDF) curve in a region has a different pattern from other regions. This study examines the temporal distribution pattern of rain and the IDF curve with case studies of the Jakarta and Bogor, represented by Kemayoran Rainfall Station and Citeko Rainfall Station, respectively. This study compared data analysis from the above stations with the ABM Method, Modified Mononobe, Indonesian National Standard for Calculating Flood Discharge, and Indonesian National Standard for Road Surface Drainage Design. The analysis results showed that the rainfall event in the Jakarta and Bogor areas was dominated by events of â‰¤ 4 hours with the highest percentage of rainfall volume at the beginning of the event (at the first two hours of the event). The observed hourly rainfall data has a significant difference pattern with the rainfall distribution calculated using the ABM and Modified Mononobe methods. The IDF curve obtained from the Base Curve on the Indonesian National Standard for Road Surface Drainage Design gives higher results than the IDF curve of the observation data. Meanwhile, the IDF curve calculated using the Mononobe Equation gives lower results than the IDF curve of the observation data. This study shows that standard design of rainfall duration and rainfall intensity for drainage infrastructure planning and flood management need to be continuously developed based on the latest observational data with better temporal and spatial resolution. Continuous effort in improving codes and design standard needs to be conducted to respond to climate change challenges, dynamic regional developments, and the increasing trend of flood events in Indonesia.
Model Kepuasan Orang Tua/Wali Siswa Berdasarkan Peningkatan Kualitas Pelayanan Sekolah Secara Berkelanjutan (Studi Kasus TK. Bina Mulia)

kindergarten. Bina Mulia Cibitung is a school engaged in early childhood education Customer satisfaction survey is certainly needed to know the response of parents / guardians of students to the quality of service. But during this kindergarten. Bina Mulia has not conducted a survey on the satisfaction of parents of students to the services performed by teachers and administrative personnel. In anticipation of things that the school did not want during this pandemic, the survey was done not directly but online. Respondents to this survey are all parents / guardians of students both still active in school and who have graduated. This research is planned on an ongoing basis with the aim of knowing the level of satisfaction of parents / guardians to school services. In addition, this research aims to produce an online survey application that is integrated with the survey management information system, while the CSI method to measure customer satisfaction. Analysis of survey results is calculated using customer satisfaction index (CSI) method. CSI is a quantitative analysis in the form of a percentage of the number of parents / guardians of students to this service satisfaction survey.
Intuitionistic Fuzzy Cognitive Map Based Analysis of Supply Chain Risks

Supply chains (SCs) are network-like structures and gigantic systems. Such systems are complex and multifaceted. Therefore, they contain many components and risk factors that are directly or indirectly related to each other. The importance of SCs, which has always been important for companies, is increasing day by day. Therefore, it is vital to focus on supply chain management (SCM) and the risk factors encountered in this management process. Plus, the investigation of the cause-effect relationships, evaluation, and analysis of these factors hold a critical role for SCM. Accordingly, this study aims to reveal the risk factors that should be considered in SCM and analyze these factors. Moreover, the causal relationships of these risk factors are aimed to be revealed. For this purpose, Cognitive Map (CM) approach is utilized. The classical CM approach is expanded to the intuitionistic fuzzy (IF) environment and used as the IFCM approach to eliminate the uncertainty and intuition involved in real-life problems and decision-making processes. SC risk factors’ importance degrees and causal relations between the factors are displayed and visualized.
Usefulness of Augmented Reality as a Tool to Support Online Learning

The global crisis following the outbreak of the Covid-19 epidemic has had an impact on the teaching and learning process (PdP). The main problem with PdP during the Covid-19 epidemic was the limitation in conducting face-to-face activities in the classroom. Therefore, a learning aid is needed to enable PdP to run optimally even though there is no face-to-face interaction between teachers and students. The research contribution is to highlight the application of Augmented Reality to support distance learning in the Covid-19 epidemic situation, specializing in Wood Carving Art for the subject of Visual Arts Education Form 4. The AR Wood Carving Art mobile application uses the ADDIE design model based on five phases, namely Analysis, Design, Development, Implementation, and Testing. The AR Wood Carving Art mobile application is evaluated based on its usefulness. The AR Wood Carving Art mobile application was evaluated among 27 students from 4 of SMK Pasir Gudang (Johor, Malaysia) and registered to Visual Arts. Based on the result, 80% of respondents strongly agree that the AR Wood Craving Art mobile application help respondents be more effective. It helps users to be more productive and giving ideas to users to be creative and innovative. One hundred percent of respondents strongly agree that the AR Wood Craving Art mobile application makes things that users want to achieve easier to do, and the AR Wood Craving Art mobile application does what users want. Eighty percent of respondents strongly agree that the AR Wood Craving Art application is useful and the application saves time when users use it. Therefore, the AR Wood Craving Art application is effectively used in learning which makes users more productive, creative, and innovative. In addition, the AR Wood Craving Art mobile application makes it easy for users to understand wood carving topics in visual arts subjects, and users can carry out educational and teaching activities like in a classroom.
Keynote Lecture : Gradient compression for efficient distributed deep learning

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Recent successful results in the field of artificial intelligence and machine learning are achieved with deep learning models that contain a large number of parameters and are trained using a massive amount of data. Training such deep networks in a single machine (given a fixed set of hyperparameters) can take weeks. An answer to this problem is data-parallel distributed training, where a deep model is replicated into several computational nodes that have access to different chunks of the data. This approach, however, entails high communication rates and latency because of the computed gradients that need to be shared among nodes at every iteration. We will elaborate on various gradient compression strategies proposed to address this bottleneck within distributed training, including gradient sparsification, quantization, and entropy encoding. We will also discuss error correction techniques that compensate for the errors introduced by gradient compression. Furthermore, we will present new communication strategies that explore the correlation of gradients across distributed nodes to achieve further improvements in reducing the communication rate and latency.
A Set Automaton to Locate All Pattern Matches in a Term

Term pattern matching is the problem of finding all pattern matches in a subject term, given a set of patterns. Finding efficient algorithms for this problem is an important direction for research [21]. We present a new set automaton solution for the term pattern matching problem that is based on match set derivatives where each function symbol in the subject pattern is visited exactly once. The algorithm allows for various traversal patterns over the subject term and is particularly suited to search the subject term in parallel.
Knowledge-aware Prompt-tuning with Synergistic Optimization for Relation Extraction

Recently, prompt-tuning has achieved promising results on some few-shotclassification tasks. The core idea of prompt-tuning is to insert text pieces,i.e., templates, into the input and transform a classification task into amasked language modeling problem. However, as for relation extraction,determining the appropriate prompt template requires domain expertise. Singlelabel word handcrafted or auto-searched is cumbersome and time-consuming toverify their effectiveness in non-few-shot scenarios. Further, there existabundant semantic knowledge among the entities and relation labels which cannotbe ignored. To this end, we focus on incorporating knowledge into prompt-tuningfor relation extraction and propose a Knowledge-aware prompt-tuning withsynergistic optimization (KNIGHT) approach. Specifically, we inject entity andrelation knowledge into prompt construction with learnable virtual templatewords and answer words and jointly optimize their representation with knowledgeconstraints. Extensive experimental results on five datasets with standard andlow-resource settings demonstrate the effectiveness of our approach.
Classified Optimum Real Time Recognition And Extraction Of Multiple Object Through Surveillance Monitoring System

In this paper we propose different cameras utilizing continuous following for observation and security framework. It is widely utilized in the examination field of PC vision applications, similar to that video observation, verification frameworks, mechanical autonomy, pre-phase of MPEG4 picture pressure and client bury faces by signals. The key parts of following for observation framework are separating the component, foundation subtraction and recognizable proof of extricated protest. Video reconnaissance, question identification and following have drawn a fruitful expanded enthusiasm for ongoing years. A question following can be comprehended as the issue of finding the way (i.e. direction) and it tends to be characterized as a technique to recognize the diverse places of the protest in each casing of a video. In light of the past work on single identification utilizing single stationary camera, we stretch out the idea to empower the following of different question discovery under numerous camera and furthermore keep up a security based framework by various camera to track individual in indoor condition, to recognize by my proposition framework which comprise of various camera to screen a man. Present investigation for the most part plans to give security and distinguish the moving item continuously video arrangements and live video spilling. In light of a powerful calculation for human body identification and following in recordings made with help of different cameras.
Alternatives to Google Suite Applications

Librarians adopted and utilized web-based Google suite applications as a method of collaborating with each other on projects, research, and professional association membership duties. However, as cybercriminals have begun to exploit these tools to infect healthcare networks with ransomware, many hospital IT departments have blocked access to Google applications. This paper provides a background on security risks to healthcare institutions and possible alternatives to Google applications hospital librarians can use to continue collaborating.
Efficient Learning of Generic Grasping Functions using a Set of Local Experts

In this article, we presented the concept of generic grasping functions that can be used to find grasping choices for arbitrary objects in the environment Examples of these functions actions can be collected from sensory data and approximated using artificial neural networks. We tested the application of a modular neural architecture to the approximation of some simulated grasping functions. A modified architecture in which the gating network output rule was replaced by a competitive output rule gave better results. We also found that applying an incremental learning process would significantly enhance the learning rate and the approximation accuracy.
Health Risk Detection and Classification Model Using Multi-Model-Based Image Channel Expansion and Visual Pattern Standardization

Although mammography is an effective screening method for early detection of breast cancer, it is also difficult for experts to use since it requires a high level of sensitivity and expertise. A computer-aided detection system was introduced to improve the detection accuracy of breast cancer in mammography, which is difficult to read. In addition, research to find lesions in mammography images using artificial intelligence has been actively conducted in recent days. However, the images generally used for breast cancer diagnosis are high-resolution and thus require high-spec equipment and a significant amount of time and money to learn and recognize the images and process calculations. This can lower the accuracy of the diagnosis since it depends on the performance of the equipment. To solve this problem, this paper will propose a health risk detection and classification model using multi-model-based image channel expansion and visual pattern shaping. The proposed method expands the channels of breast ultrasound images and detects tumors quickly and accurately through the YOLO model. In order to reduce the amount of computation to enable rapid diagnosis of the detected tumors, the model reduces the dimensions of the data by normalizing the visual information and use them as an input for the RNN model to diagnose breast cancer. When the channels were expanded through the proposed brightness smoothing and visual pattern shaping, the accuracy was the highest at 94.9%. Based on the images generated, the study evaluated the breast cancer diagnosis performance. The results showed that the accuracy of the proposed model was 97.3%, CRNN 95.2%, VGG 93.6%, AlexNet 62.9%, and GoogleNet 75.3%, confirming that the proposed model had the best performance.
Investigating the use of feature selection techniques for gender prediction systems based on keystroke dynamics

Biometric-based solutions keep expanding with new modalities, techniques and systems being proposed every so often. However, the first ones that were used for authentication, such as handwritten signature and keystroke dynamics, continue to be relevant in our digital world, despite their analogical origin. In special, keystroke dynamics has had an increase in popularity with the advent of social networks, making the need to continue to authenticate in desktop or game-based user verification more prevalent and this became an open door to risky situations such as paedophilia, sexual abuse, harassment among others. One of the ways to combat this type of crime is to be able to verify the legitimacy of the gender of the person the user is typing with. Despite the fact that keystroke dynamics is well accepted and reliable, this technique can have far too many attributes to be analysed which can lead to the use of redundant or irrelevant information. Therefore, propose a comparative study between two features selection approaches, hybrid (filter + wrapper) and wrapper. They will be tested by using a genetic algorithm, a particle swarm optimisation, a k-NN, a SVM, and a Naive Bayes as classifiers, as well as, the Correlation and Relief filters. From the results obtained, it can be said that the two proposed hybrid approaches reduce the number of attributes, without negatively impacting the accuracy of the classification, and being less costly than the traditional PSO.
La pantalla que nos separa: docentes y estudiantes en tiempos de pandemia, conectados/as y desconectados/as

This paper integrates opinions and reflections on the complexity of the educational processes in the context of the Covid-19 sanitary contingency. It examines how, since it was declared a pandemic, Covid-19 has revealed great deficiencies in the Mexican educational field, as is the case of the newly implemented ways that are meant to continue with the development of school activities, in all educational levels. This is how we observe, clearly, how difficult the conditions are for teachers and students to conclude their educational tasks in front of a computer screen, which on one hand allows them to feel close among themselves, connected to a network that lets them hear and see each other, and on the other hand, by being so far away from a process that demands a face- to-face relationship, it allows us to study how integral the face-to- face dynamic is to the teaching process.&#x0D;
Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning

Motivated by suggested question generation in conversational news recommendation systems, we propose a model for generating question-answer pairs (QA pairs) with self-contained, summary-centric questions and length-constrained, article-summarizing answers. We begin by collecting a new dataset of news articles with questions as titles and pairing them with summaries of varying length. This dataset is used to learn a QA pair generation model producing summaries as answers that balance brevity with sufficiency jointly with their corresponding questions. We then reinforce the QA pair generation process with a differentiable reward function to mitigate exposure bias, a common problem in natural language generation. Both automatic metrics and human evaluation demonstrate these QA pairs successfully capture the central gists of the articles and achieve high answer accuracy.