Reviewerly/exHarmony huggingface dataset:
Depression Discovery in Covid-19 Communities Using Deep Learning

: Seemingly, in the contemporary era, data is the new currency. Facing the exponential explosion of data through its various online sources, there arises a need for the industry to tap into this source. The art of ascribing sentiment to a piece of text is Sentiment Analysis making it relevant to a wide array of fields. The world has witnessed assortments of a wide family of coronaviruses, 229E and NL63 being the alpha coronaviruses and OC43 &amp; HKU1 being the beta coronaviruses. A version of coronavirus MERS-CoV seemed to be identified in Saudi Arabia. This work focuses on The Deep Neural Network and Natural Language processing to detect depression in COVID-19 patients, and the model is trained with the depression labeled tagged dataset with an accuracy of 98%. The foundation of this work shall be instrumental in aiding the doctors for the timely diagnosis and treatment of their patients.
Human Centered Protocols in Transformative Computing

This paper presents new protocols dedicated to transformative computing technology. The main idea is based on human centered solutions characteristic for individual human perceptual and decision-making processes. Proposed solutions will be based on perception, recording and description processes leading to the meaningful data analysis. Evaluation of proposed techniques will be presented, as well as possible application for such protocols in transformative computing areas.
Chaotic encryption system based on pixel value and position transformation for color images

In this paper, a full image encryption system is proposed. The proposed system depends on changing the value and coordinates of the pixels at the same time. The system has the ability to be applied on all types of images with any dimensions. The results of the system were tasted and compared with the traditional algorithms. The results that have been obtained from the simulation show that the proposed system has higher degree of security and faster encryption time.
Study on Image Enhancement Method for Low Illumination Target Recognition

Low-illumination target recognition is a popular topic in computer vision. This study investigated the issue of the low accuracy target recognition caused by image aliasing and noise, and which happened in the low-illumination environment. In this paper, we first analyzed the imaging mechanism. Then, a national standard image data collecting environment and a collecting standard procedure was set up. Based on this, we extracted the characteristics of low-illumination image and determined the relationship between imaging environment, sample collection and Mask R-CNN target recognition. Finally, we proposed an image enhance algorithm for low-illumination target recognition which improved the robustness and recognizing accuracy significantly. This algorithm also extended the application context of the target recognition.
Confidence-Guided Learning For Breast Density Classification

Assessment of breast density is an important part of breast cancer screening. Dense tissue is a risk factor and increases the chance of findings being occluded and missed by a radiologist. Density is an inherently subjective assessment, and the inter-rater variability between radiologists has been reported at 30-40%. The subjectivity of breast density assessment means that there is no “gold standard” label for training an algorithm. To address this issue, we created a multi-annotator dataset, where each case is labeled multiple times independently, along with annotator confidence. To utilize this information during algorithm training, we introduce a confidence-guided method that adjusts batch distribution, batch size and loss calculations based on consistency and confidence of the labels. Using this approach, we achieve breast density classification that is interchangeable with the radiologist annotations on a test set from an independent held-out site, simulating a deployment of the algorithm in a real-world scenario.
Readiness of teachers of agricultural universities to manage the process of distance learning

The electronic educational environment is now being actively implemented in the field of vocational education all over the world. The concept of digital competence and the readiness of a university teacher to use the Moodle distance learning management system are considered. The study involved 45 respondents - teachers of the humanities at a technical university, 23 - with an academic degree, 21 - higher education without an academic degree. The described study based on the analysis of questionnaire answers reveals significant differences in the components of digital competence of teachers of different generations.
Secure Information Migration Using Mignotte Sequence with Integral Transform

This paper describes enhancing secure communication-based on secret sharing using Chinese remainder theorem and integral transform. Some of the secure electronic devices are trying to combine a different kind of services through this providing a secure communication between nodes by securing the information which is sent by the sender to the receiver for better communication. For the better communication, it uses secure key, and for a secure key, it uses an efficient algorithm for encrypting and decrypting the message. Integral transform used of encryption of plain text using symmetric encryption key used both sender and receiver shared among user by Chinese remainder theorem. For decryption process, it uses inverse integral transform. So the key factors behind the secure information communication are algorithm used for encrypting and decrypting the data and the key used in the algorithm. To provide a secure communication of information, we need a good encryption algorithm and the strong secret key sharing technique.
Monitoring the quality of e-learning implementation in educational institutions

The article analyzes some aspects of quality control and efficiency of electronic educational and methodological complexes of educational disciplines usage in the educational process. Analysis of the implementation of e-learning and of user work allows to build a model of student’s work, identify the disadvantages of both organizational and technological plan and take timely steps to eliminate them. The solution to this problem should include a comprehensive approach to ensure an objective result. Google Analytics tools, Moodle Learning Management System (LMS) tools, the teacher’s surveys to monitor the quality of e-learning in an institution proves its efficiency and allows to see the full advantages and disadvantages of ongoing e-learning in an institution, plan and implement further development and improving e-learning. The analysis of the effectiveness of e-courses usage should be carried out in a comprehensive manner, using the monitoring tools of the educational process adopted in the educational institution. Such as surveys for teachers and students, analysis of success, etc. The specifics of e-learning, the Moodle LMS tools and third-party services for collecting statistics and analyzing user activity in the LMS should be taken into account.
A Roc Curve Based K-Means Clustering for Outlier Detection Using Dragon Fly Optimization

Outlier detection is an essential step in the data mining process. Its main purpose to remove the incompatible data from the original data. This process helps in the removal of data which are necessary for carrying out to speed up the applications like classification, data perturbation and compression. It plays an important role in the weather forecasting, performance analysis of sports person and network intrusion detection systems. The outlier for the single variable can be easily observed but for the n-variable it become a tedious process. To enhance the performance of outlier detection in n-variable or attributes several methods were proposed. Some of the existing methods are statistical approaches, proximity-based measures, classification approaches, and index-based approaches and optimization based approaches. The first four approaches were not able to classify the data when there is an imperfection in the labels. But, the optimization based approach is able to overcome this problem even there is an imperfect labelling. One of the existing optimization approach is K-means and K-median based approach. The existing method failed to process the larger records and smaller attributes. To overcome this problem a dragon fly based K-means clustering and multi-layer feed forward neural network is proposed. This objective is achieved with the help of ROC curve (negative ratio) as objective function. The performance is evaluated using detection rate. The proposed method is tested on datasets like Arrhythmia, Diabetics and Epileptic seizure which has larger attributes and larger records. The proposed method outperforms the existing approach with high detection rate of 0.95 for arrhythmia dataset and 0.96 for Epileptic dataset.
Plenary: Real-Time Transformation of the freshmen Mathematics Engineering Courses during COVID-19 Outbreak

Due to the circumstances associated with the COVID-19 pandemic, the academic year 2020 represented a challenge for engineering education around the world. In Guatemala, the first case of COVID-19 was reported on Friday, March 13, 2020, and as a consequence, all higher-level educational institutions were closed. Before the pandemic, in Universidad Galileo, all of the different engineering mathematics courses were taught traditionally following a face-to-face format. As a result, the pandemics' challenge consists of, not only the fact of having to transform the teaching and learning format but also in making the transition in the shortest amount of time possible. During the pandemic, the class sessions continued with the schedule established at the beginning of the semester, but with the help of video conferencing tools. It is important to mention that we used a set of distance teaching tools and techniques that help us fulfilling the main objective of the course: students' learning while enhancing their motivation. A test designed specifically for the pandemic situation was used to measure the students' motivation. Such a test includes sub-scales associated with the students' feelings about the current situation and it also assesses the course structure, flexibility, and content delivery in the virtual format. In this plenary, we present our experience and the main lessons learned from such a transition from traditional to virtual teaching.
Analysis and Excavation of the Design Concept of Special Sea Atlas

Abstract Atlas design is a creative mental processing, with few fixed ideas and patterns to follows. Taking“XXX Company’s Global Maritime Trade Achievements” as an example, this paper explores and mines the formation of design concept as well as the complementation process of design scheme of the special marine atlas, which can provide reference for the design of the special sea atlas latter.
Arithmetical Completeness and BHK Semantics

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Fall Detection for Pedestrians in Video-Surveillance

The main objective of video surveillance in public places is to ensure public safety. This can be accomplished by continuously analyzing video streams in order to detect any abnormal event. Human fall automatic detection is also of great interest in video surveillance, especially for elderly people where accident falls are common and can lead to severe injuries. In this paper, we introduce a new approach to detect pedestrian fall event in video surveillance. The proposed method integrates visual human tracking using an active contour and human velocity estimation to detect motionless pedestrians. We start tracking moving pedestrians using an active contour that evolves and follows the pedestrian shape boundaries in each frame of the video streams. We analyze the velocity of moving pedestrians based on the displacement of their interest points. Once the velocity is determined to be null, we analyze the body boundaries of this person to determine if they are motionless during several frames. If it is the case then a red curve surrounding the pedestrian is displayed in the video alarming the operator system that an urgent intervention is needed. The presented fall detection method is tested under real traffic scenarios. Experimental results show that our new approach achieves a good performance and it is efficient in detecting a true pedestrian fall.
Key Frame Extraction of Evidence Video Based on YOLO Algorithm

For the massive forensic video data, if we watch and intercept the key information manually, it will take a lot of time and delay the opportunity of obtaining useful evidence. For real-time processing the data with high accuracy, the computer automatically extracts the video frames with high-value content (including the image frames of specified types of aircraft, ships, and military uniforms in our experiments) through automatic video analysis. This method achieves the preliminary screening requirements for the key-frames, thus greatly reducing the time required for manually processing a large number of images. In this work, we propose a new method to utilize YOLO algorithm to conduct key-frame extraction in processing the video data.
Digital informational and educational environment as a means of educating a competitive graduate of a technical university (on the example of penza state technological university)

The problem of this study is the implementation of a competence-based approach to the information support of the educational work of a technical university, by introducing into practice a complex of elements of the digital informational and educational environment (DIEE), which serve as a systematic methodological support of the educational process, which is a single information professional field for teachers, an individual educational space for students, and a means of assessing the educational level of graduates by future employers. The purpose of this study is to improve the complex of methods, forms, and means of education, which allow to compare the results of the development of the personality of students (personal growth) with their achievements in educational and practical activities with the level of competence formation. From the point of view of a scientific approach, the research is aimed at identifying and disclosing contradictions between: the high demands of employers to the quality of competitive graduates of technical universities, who possess not only professional, but also general cultural competencies that ensure successful social activity in society, on the one hand, and the imperfection of information technology applied in education, on the other. To assess the results of the study, the following methods were used: diagnostics, comparison of standard and current indicators, analysis of the relationship of quantitative and qualitative indicators. The study involved 3,500 students annually. The practical significance of the study lies in the developed set of DIEE elements, which are a set of information, educational, telecommunication resources and technologies that form a single educational system of the university.
A unified probabilistic factor model with social regularization for point of interest recommendation

Point of Interest (POI) is an important task in Location Based Social Networks like, Gowalla, Foursquare and Brightkite etc as it guides user in decision making process. Although, ‘‘Probabilistic Factor Model’’ (PFM), is a promising method applied in various recommender systems, it is not effective in sparse data situation. Moreover, traditional recommendation methods do not consider the social feature of consumption, where a reliable friend suggests a popular item that does not pair with our preferences. This paper contributes a state-of-the-art solution for a personalized recommendation tool, which suggests interesting and new locations to users by bridging preference-aware and social-based recommendations. To demonstrate the effect of social context of user, the social regularization term, is unified into the Poisson Factor Model. Experiments are performed using the Gowalla and Brightkite dataset. The experiment reveals that, the new framework achieves higher accuracy than the traditional recommendation algorithm.
Automatic Facial Emotional Valence Detection System Modelling Using Crafted Features and Deep Neural Network

This work provides a method that merges handcrafted and automatic feature extraction with deep convolutional neural network (D-CNN) and visualization bagging (VB) to acquire effectual and improved results from traditional facial expression recognition (FER) results. To acquire features, pre-trained model with D-CNN architecture is experimented with Dense and over sparse network. After extracting merged information, it is imposed over every featured image to protect structural information of all textured image. Therefore, dimensionality reduction of implicit feature extraction is done with maximal pooling approach. At last, deep CNN is used to recognize class label only for testing images that was trained and classified using soft-max classifier. To validate robustness for recognizing facial expression under complex setup, simulation was performed by merging available dataset with online dataset. Experimental results demonstrate that anticipated model can acquire recognition rate with training speed on training image is approximately two times faster than that of prevailing model. The anticipated model shows better trade off in comparison with prevailing models. Simulation is done with MATLAB simulation environment.
Fish swim water bulk displacement visualization with digital holographic interferometry

A collimated transmission beam interferometer is used to measure the water motion provoked by the fish swimming through it. An indirect measurement of the fish motion impact in the water contained in a home-type aquarium is detected. Measurements of the whole aquarium are possible due to a large diameter collimated laser beam in the interferometer's object arm. This beam goes through the aquarium, and any perturbation inside it deflects the collimated beam. The interferometer detects a phase difference, i.e., the beam through the disturbed water undergoes different optical paths. This optical phase change was first demonstrated by means of a simple test using spherical steel marbles placed in a cuvette. For this, the small water movements for a single steel marble are detected with the acquired optical phase. Next, the aquarium optical phase results show water movements according to the fishes' size and swimming speed. It is worth mentioning that no additives were added to the aquarium's fresh water during the tests, so the water was crystal clear.
SOME RESULTS OF RESEARCH IN THE FIELD OF DIGITAL COMPETENCE FORMATION OF GENERAL EDUCATIONAL INSTITUTIONS TEACHERS

The article considers and summarizes some research results in the field of digital competence formation of secondary school teachers. Theoretical sources on the research problem are presented and analyzed, the concept of digital competence in the works of domestic and foreign researchers is analyzed. Modern trends and problems of development of teachers’ digital competence in Ukraine are considered. The possibility of the digital competence framework DigCompEdu utilization as the background for Ukrainian education is demonstrated. The following levels of teachers’ digital competence are determined: Newcomer (A1), Explorer (A2), Integrator (B1), Expert (B2), Leader (C1), Pioneer (C2). The main peculiarities of each level are described from the perspective of teachers’ knowledge and skills in the sphere of digital technologies utilization in the educational process. Based on DigCompEdu analysis the survey for determination of secondary school teachers’ digital competence level is developed and conducted with the involvement of teachers of two schools in Kryvyi Rih. The levels of digital competence of the respondents are determined, the results of the complex survey are presented and the guidelines on the teachers’ digital competence development system for the school level are given. The assumption was made that most teachers and secondary schools in Ukraine have approximately the same situation with the levels of teachers’ digital competence according to DigCompEdu gradation. Thus, the provided recommendations will be useful for school management representatives to provide teachers’ digital competence development at school level. The processed methodological guidelines on teachers’ digital competence development management for school management representatives are described.
Introducing the Privacy Aspect to Systems Thinking Assessment Method

Systems thinking is a valuable skill that may be required for an individual to be promoted in the business arena to managerial or leading positions. Thus, assessing systems thinking skills is an essential transaction for decision makers in the organization as a preceding step to the promotion decision. One of the well-known and validated tools for this task is a questionnaire. However, because some of the questions invade the employee or candidate’s privacy, the answer may be biased. In this paper, we consider this potential bias, a phenomenon that is becoming more and more significant as privacy concerns and awareness continuously increase in the modern digital world. We propose a formal methodology to optimize the questionnaire based on the privacy sensitivity of each question, thereby providing a more reliable assessment. We conducted an empirical study (n=142) and showed that a systems skills questionnaire can be enhanced. This research makes a significant contribution to improving the systems skills assessment process in particular, and lays the foundations for improving the evaluation of other skills or traits.
Design and Development of Hybrid Algorithms to Improve Cyber Security and Provide Securing Data Using Image Steganography With Internet of Things

Internet of things (IoT) is a typical thing (object) in this day and age, which fills in as a component of our standard life exercises. In spite of the fact that it benefits the private region in a few different ways, different difficulties, for example information classification and protection, are made. Web of things (IoT) is all over the place and utilized in a lot more advantageous functionality. It is utilized in our homes, clinics, fire counteraction, and announcing and controlling of ecological changes. Information security is an urgent prerequisite for IoT since the number of late advances in various spaces is expanding step by step. Different endeavors have been set to sate the client's expectations for greater security and protection.
The Problem of Named Entities Unification based on Geographical Ontologies

The subject of this research is to develop a system for extracting knowledge from both semi-structured and unstructured data and filling with this system a knowledge base that would provide support for decision-making on any problematic issues. The article deals with the problem of unification of named entities based on geographical ontologies.