Reviewerly/exHarmony huggingface dataset:
ORGANIZATION OF COMMUNICATION SYSTEM IN THE EDUCATIONAL ENVIRONMENT WITH SIGNS OF OSTRACISM

The article examines the system of organizational principles of communication in the educational environment with manifestations of ostracism and the influence of ostracizing tendencies in school groups on the academic and social development of students and the professional achievements of teachers. The peculiarities of the communication system formation in educational institutions are analyzed. It is determined that the administration of the educational institution and the teaching staff acts as a catalyst both in the formation of cooperative relations and in the implementation of educational work of the school unit. The aim of the study was to carry out a theoretical analysis of organizational aspects of communication systems in the educational environment with signs of ostracism. The structure of conditions for ecological communication in the educational system was formed: participants feel safe, the presence of an atmosphere without grading during the educational process, a sense of warmth, active interest in students, the compassion of adults, no corporal punishment in case of violation of rule or other unacceptable behaviour. Such general scientific theoretical methods of information study as analysis, synthesis, abstraction, generalization, induction, deduction, explanation and classification were used to achieve the aim of the study. Risk factors that affect the success of interpersonal interaction are revealed: a number of external factors, such as natural phenomena or social events (conflict, change of collective, disturbance of the structure of educational space - change of leader), or internal factors, such as the behaviour of teachers and staff, number of students, group composition, entry of new persons into an already formed group, an accident in the group, etc. The classification of Marshall factors that affect communication during the learning space is included: the number and quality of interactions between adults and students; learning environment, including buildings, classrooms, available materials, as well as the perception of this space by teachers and students; student's success; the presence of an atmosphere of equality between all subjects of educational activity. The role of the management personnel in the process of creating an ecological educational space is indicated, because the communication skills of the administration of the institution largely determine the effectiveness of the educational space and the achievement of goals. The characterological features of the administration are reflected, which are a manifestation of a high level of emotional intelligence, as a consequence of possibility of building an effective system of communicative ties: self-awareness, empathy, social responsibility, focus on reality, sociability, control of impulses.
Local occluded face recognition based on 2D-DWT and sparse representation

Because most of the current face recognition systems do not consider the possible occlusion in the real environment, the traditional face recognition algorithm has poor recognition results. This paper proposes a face recognition method based on sparse representation of two-dimensional discrete wavelet transform in feature subspace for the presence of facial occlusion. Through two-dimensional discrete wavelet decomposition of the training samples, the high-frequency signals are filtered and the low-frequency signals are retained. The low-frequency signals are constructed by PCA, and then the test samples are sparse decomposed on the occlusion dictionary, and finally classified. Compared with other traditional face recognition methods, the proposed algorithm has better recognition results.
MODEL-BASED ADAPTIVE MONITORING: IMPROVING THE EFFECTIVENESS OF REEF MONITORING PROGRAMS

An abstract is not available for this content. As you have access to this content, full HTML content is provided on this page. A PDF of this content is also available in through the ‘Save PDF’ action button.
A Logarithmic Distance-Based Multi-Objective Genetic Programming Approach for Classification of Imbalanced Data

Standard classification algorithms give biased results when data sets are imbalanced. Genetic Programming, a machine learning algorithm based on the evolution of species in nature, also suffers from the same issue. In this research work, we introduced a logarithmic distance-based multi-objective genetic programming (MOGP) approach for classifying imbalanced data. The proposed approach utilizes the logarithmic value of the distance between predicted and expected values. This logarithmic value for the minority and the majority classes is treated as two separate objectives while learning. In the final generation, the proposed approach generated a Pareto-front of classifiers with a balanced surface representing the majority and the minority class accuracies for binary classification. The primary advantage of the MOGP technique is that it can produce a set of good-performing classifiers in a single experimental execution. Against the MOGP approach, the canonical GP method requires multiple experimental runs and a priori objective-based fitness function. Another benefit of MOGP is that it explicitly includes the learning bias into the algorithms. For evaluation of the proposed approach, we performed extensive experimentation of five imbalanced problems. The proposed approach’s results have proven its superiority over the traditional method, where the minority and majority class accuracies are taken as two separate objectives.
Artificial Intelligence Implementation In Omani Higher Education; Logistics Studies, Imcoi As A Case Study

Artificial intelligence (AI1) in education started over 30 years. Currently, artificial intelligence witnesses’ significant growth in the region to improve the productivity of learning quality. Previous studies revealed artificial intelligence in higher education in different aspects, majors, and countries. This study released the use of artificial intelligence in logistics studies in higher education institutions of Oman, where it took IMCO as a case study. The research paper thoroughly finds out the perfect use of artificial intelligence in logistics studies and the extent of its impact. The primary objectives of this study were to assess the use of AI in logistics study, the pros and challenges of using AI in logistics programs in higher education and suggest some recommendations to better use of AI in logistics study. The survey and interview were the principal methods used to collect primary data, while the secondary data were collected from the literature relevant to the study. The questionnaire was designed by using Office 365 form. The target population of this study was approximately 100 people that included teachers and students in International Maritime College Oman. The findings showed a slight variation in some of the responses, but most supported the previous studies that AI implementation in education is vital.
Building Campus Sports Information Learning Platform Based on Intelligent Sensor Network

In the present era of rapid network growth and in the face of growing national power rivalry, education, as the cornerstone of a nation and a national prosperity symbol, has been considered by countries around the world as an important indicator of comprehensive national power. From this perspective, education should keep pace with the times and integrate into the Internet era of “Internet+” and big data. Education computerization is a great product of education’s absorption into the Internet era and is an important content and an important symbol of education modernization. It can realize the modernization of education, and its construction can better reduce the teaching differences between regions, improve teaching quality, and balance resources in education. Moreover, it is conducive to the implementation of quality education and the cultivation of innovative talents, and it is an effective way to realize a learning society and build a lifelong education system. The experiment draws the results of the experiment by comparing the results of students receiving two different education methods. From the specific performance point of view, the average long jump score of boys in the experimental group was 4.40 m, while the average score of boys in the control group was 4.28 m, and the score of the experimental group was 0.12 m higher than that of the control group. The average long jump performance of the girls in the experimental group was 3.52 m, and the average performance of the girls in the control group was 3.30 m. In the long jump scores assessed in the second grade, the full score of boys is 4.35 m, and the score of girls is 3.49 m; the average scores of the experimental group students reached full marks. Compared with traditional teaching methods, information-based teaching facilitates the analysis of key and difficult points for teachers, greatly improves the quality and efficiency of teaching, and improves the teaching level of the classroom.
Topic Load Balancing in a multi IoT Gateways Scenario under Publish/Subscribe Paradigm

Publish/Subscribe paradigm can support multi-broker management to increase network reliability and support more traffic load. However, in this context, efficient load balancing techniques need to be designed to try benefit by multiple IoT brokers. In this paper a novel optimization problem to minimize the number of topic migration has been formulated and a novel greedy-based load balancing strategy has been proposed to find a solution to the formulated problem. Performance evaluation has been considered in terms of number of topic migrations, traffic load and variance.
A Survey on Web Service Reconfiguration

In order to satisfy and meet requirements of users who interact with Web services, it is often necessary to combine and reuse existing Web services to create new Web services. The invocation of such service may be affected by various unforeseen changes. In order to adapt the composite web service to user invocations, this latter has to be dynamically reconfigured. The aim of this paper is to present a survey of the most important works, which deal with Web services reconfiguration. A comparative study is also given in order to show strengths and weaknesses of each work.
Blockchain Technology in E-Healthcare

Blockchain is one of the technologies that is gaining significant attention within the healthcare industry. In order for blockchain to reach its full potential, it's essential that governments and public institutions map the blockchain ecosystem and establish a blockchain framework for coordinating early adopters. The authors have proposed a web-based application using blockchain with security algorithms to store patients' and other users' details who sign up on the website. Blockchain is highly secure as any tampering with data in any block is not possible. Blockchain attributes ensure smooth interaction between different applications and systems along the availability chain. They also reward the patient with their own implemented bitcoin on their transaction. Ethereum library Web3 is used to make a complete decentralized peer-to-peer chat service without any involvement of a third party. The application will be able to send encrypted messages both securely and anonymously. Blockchain technology has been seeing widespread interest as a means to ensure the CIA triad in fewer environments.
The Existence of the Pulpit of the Historic Jakarta Mosque

The pulpit which existed at the time of the Prophet, then it became a sunnah that was respected, so that in generations after the Prophet the existence of the pulpit was still maintained. This study attempts to describe the existence of the pulpit at a historic mosque in Jakarta. This research uses a qualitative research approach with analytical descriptive method. The research location takes place in historic mosques in Jakarta. Data collection techniques using interviews, observation, documentation. The results of this study indicate that historic Jakarta mosques still use pulpits with various models so that their existence is still maintained today.
3D mesh reconstruction of indoor scenes from a single image in-the-wild

3D reconstruction from a single RGB image is making some progress especially with the advent of deep learning in recent years. In this paper, we focus on 3D reconstruction of indoor scenes taking as input a single image, without point clouds, multi-view images, depth or masks. Our mesh reconstruction is based on an encoder-decoder framework of mesh deformation. Features extracted by the encoder from the input image will be used as supervision for mesh deformation. We propose an inter-point interaction attention in the decoder to exploit the vertexes’ influence on each other. We also use a smooth loss to generate smoother surface for objects. We have evaluated the proposed framework on the Pix3D dataset, and state-of-the-art performance has been achieved with visually appealing 3D geometry.
Analysis of animation videos in online learning in class IV students of MI Imam Syafi'i

This research aims to find out how to use video in online learning and find out obstacles in using animated videos in class IV MI Imam Syafi'I. Researchers used a qualitative approach with the teachers' assistance and fourth-grade students as subjects in the study. In collecting data using techniques, including observation techniques, interviews, questionnaires, and also documentation. For data analysis techniques used in researchers such as data reduction, data display, conclusion/drawing. Animated videos used by teachers when online learning are animated GIF types. The benefits of animated videos are to improve student understanding and increase student attractiveness during learning. Based on the research results, the use of media can help teachers to increase creative activities when carrying out learning activities so that students are not easily bored, lazy and students become excited and active when learning takes place online. Educators can see the understanding and enthusiasm of students through a questionnaire distributed by the homeroom teacher in the form of a link. Meanwhile, the delivery of material in online learning using animated videos has several obstacles during learning, such as inadequate signals and sometimes the student quota is also limited, so that making animated videos does not work well and the delivery of material in learning activities is also less than optimal
Context-Based Quote Generation from Images

Photographs are compact stores of special moments of our lives. Due to the convenience of photography tools such as mobiles and cameras, we can capture each and every moment of celebration. We often share those moments with our friends and colleagues and sometimes put them on our social media profiles. To make our social media posts attractive, we usually try to associate them with some inspirational quotes. Those quotes are either self-created or searched from the Internet. Searching and selecting the right quote from the Internet for our photograph is tedious as well as uninspiring at the same time. We have to first think about the theme which our image is depicting and then we have to search quotes related to that theme. After this, we have to filter out the most probable quotes for our image from about thousands of quotes. Our application aims to ease the process of searching and associating the right quote for the image. It accepts a picture as an input and after processing it, suggests relevant quotes for the image to the user. To achieve this functionality, we have used the computation power of Convolution neural networks, the concept of Long Short Term Memory and Similarity measures for suggesting the suitable caption for the image which are then further utilized to render quotes to the user. In this paper, we not only represent the method of conversion of image to quote but also a comparative study on performance measures (BLEU scores and validation loss in this work) of various pre-trained CNN models, and based on the comparative study, the best performing CNN model is chosen (MobileNetV2 in our study).
A Multi-precision Quantized Super-Resolution Model Framework

Equipment’s computing capability has been greatly enhanced at present, which helps deep learning achieve excellent results in various applications, such as super-resolution. However, for higher performance, lower model size and faster computing speed, model compression is widely applied to accomplish the goal. For instance, model quantization is a typical compression method, such as quantization aware training and etc. Quantization aware training can take more quantization loss due to data mapping in model training into account, clamping and approximating the data representation range when updating parameters, which introduces quantization errors into loss function. In the quantization process, we used a quantization strategy that we quantized the model in different stages of combination, and found that some stages of the two super-resolution models’ generators based on SRGAN and ESRGAN showed sensitivity to quantization during the process, which greatly reduced the performance. Therefore, according to the quantization sensitivity, we use higher bits integer quantization for the sensitive stage, and get the multi-precision quantized model. For quantizing the SR model automatically, we propose a multi-precision quantization framework in this paper according to the ratio of input and output channels in every stage in the model. We also have our work tested on eight classical data sets of super-resolution. Generally speaking, both the two models’ PI values approach the original model’s respectively.
Comparative Analysis of Nature-inspired Optimization Techniques for Data Analytics

Many scientific and engineering issues can be solved with the right tools, expressed as optimization problems with complex nonlinear constraints. Highly nonlinear issues frequently necessitate advanced optimization techniques, and standard algorithms may struggle to handle them. Because of their versatility and effectiveness, nature-inspired algorithms are getting more popular. This study provides an in-depth examination of a number of current nature-inspired algorithms, with MATLAB implementation for solving sphere function. The various algorithms used for the comparison are social group optimization, particle swarm optimization (PSO), simulated annealing, genetic algorithm (GA), cuckoo search, artificial bee colony (ABC), farmland fertility optimization, firefly optimization, water cycle optimization (WCO), teaching–learning-based optimization (TLBO), and antlion optimization. Through experimental results, we will show the best optimization technique for a given number of iteration and for a given function (here, it is sphere function).
An image segmentation method of pulmonary nodules based on IAC-FCMSPCNN

Medical image segmentation plays an increasingly important role in the whole field of image processing. Among them, the method of tumor segmentation has been paid more attention because of its special clinical significance. To solve the problems of traditional pulse-coupled neural network (PCNN) in the field of medical image processing, an internalactivity-changed FCMSPCNN (IAC-FCMSPCNN) is proposed to segment pulmonary nodules. This method further optimizes and improves the synaptic weight matrix, link strength and dynamic threshold, and reduces the number of model iterations. Experimental verification on five images in PET-CT lung cancer image library shows that the proposed method has good segmentation effect and is more suitable for clinical medical image segmentation.
Document Classification Using Genetic Algorithm

Document classification involves categorization through labelling of documents based on the contents. In this paper, a genetic algorithm-based document classification approach is proposed. It is assumed that there is a predefined group of documents and a set of keywords corresponding to each group. Whenever a new document is taken for classification, a new gene is created consisting of one-third of the unique words in file, followed by crossover and mutation. Finally, a function is used to find the appropriate group for a document based on gene matching. The experiment is conducted using NetBeans IDE 7.3. It has been found that the proposed approach could classify all the files under consideration, with utmost accuracy.
Real Time Image Captaioning

Image caption generator means it will generate a description for the images. It will predict what is happing in the images. We make our model using a hybrid CNN-RNN model in which in the CNN part of the model we use inception model for transfer learning and RNN is majorly used for language modeling. We use Flickr8k Dataset for training and testing the model. We use LSTM model in RNN to avoid the problem of vanishing or exploding gradient in the training phase.
A family of asymptotically independent statistics in polynomial scheme containing the Pearson statistic

Abstract We consider a polynomial scheme with N outcomes. The Pearson statistic is the classical one for testing the hypothesis that the probabilities of outcomes are given by the numbers p 1 , …, p N . We suggest a couple of N − 2 statistics which along with the Pearson statistics constitute a set of N − 1 asymptotically jointly independent random variables, and find their limit distributions. The Pearson statistics is the square of the length of asymptotically normal random vector. The suggested statistics are coordinates of this vector in some auxiliary spherical coordinate system.
Discovering Healthcare Data Patterns by Artificial Intelligence Methods

Abstract The variety of the artificial intelligence and machine learning methods are applied for data analysis in various areas, including the data-rich healthcare domain. However, aiming to improve health care efficiency and use the captured information to improve treatment methods is often hampered by poor quality of medical data collections, as high percent of health data are unstructured and preserved in different systems and formats. In addition, it is not always agreed which methods of artificial intelligence and machine learning perform better in different problem areas, and which computer tools could make their application more convenient and flexible. The chapter provides essential characteristics of methods, traditionally applied in statistics, such as regression analysis, as well as their advanced modifications of logit, probit models, K-means, and Neural networks. The performance of the methods, their analytical power and relevance to the healthcare application domain is illustrated by brief experimental computations for investigation of stroke patient database with the help of several readily available software tools, such as MS Excel, Statistica, Matlab, Google BigQuery ML.