Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis on Public Transportation Using Different Tools and Techniques: A Literature Review

With the rapid increase use of WWW people from all age group shares their views, thought, ideas, suggestions on the internet. This leads the internet to become a hub for user-generated data. If this extracted data from the web is analyzed properly, then this may become the largest platform for making decisions for new product reviews, political issues, public issues, etc. But manually extracting and analyzing this content becomes a drastic task. So, for solving this problem a new area of research has been introduced that is Sentiment Analysis. It is the part of data mining where it extracts and analyzes the unstructured data. It is a domain where there is a huge scope of research. Various researchers already had researched different datasets with different Tools and Techniques on various domains like politics, sports, public issues and etc. This paper is focused on a literature survey for Sentiment Analysis on public transportation using different Tools and Techniques.
Multiclass Hierarchical Fuzzy Classification on Multi-labeled Data

Multiclass classification is dissimilar from binary classification based on the number of output classes. It can be implemented by using subsequent methods such as extended from the binary case, translate the multiclass into binary-class classification problems and hierarchical classification methods. In this paper, multiclass hierarchical fuzzy rule-based classification tree for using quantitative transactions for correlated pattern mining is proposed. It uses different fuzzy membership linguistic operator values for each fuzzy data item to generate fuzzy classification patterns from multi-labeled data set. The result of experiments shows that the MHFRB mining algorithm is used to generate fuzzy classification rules and efficiently classify infrequent patterns from multi-labeled data.
A Comparative Analysis of Event Detection and Video Summarization

In advancement of computer vision, intelligent video surveillance has led to rapid growth of video processing and providing effective requirement of advanced techniques for analysis of events in the video. Event detection plays phenomenon role in the smart and intelligent surveillance. Events such as traffic rule violations and misbehaving in common places are used detect automatically in time and must be reported as abnormal events. Abnormal event detection is a critical task in the video surveillance with challenging facts. Detecting the abnormal events and summarizing the key essential frames from surveillance video from multimedia repositories is very essential problem in video surveillance. Therefore, video summarization techniques are used for quick browsing for high quantity of video data for indexing. In these fact stockholders needs the video summarization of the abnormal event video as an abstract of the original video. In this paper, various video summarization techniques and methods are analyzed which can produce static or dynamic summaries. This analysis presents different methodologies and techniques in video summarization and also discussed some important datasets used in video summaries.
Past, Present, and Future of Vocational Training Teacher System

The purpose of this study is to respond to the changes in labor demand that have been occurring for about 50 years, beginning in 1967 when Korea’s vocational training teacher system was established, as well as to analyze the changing processes and propose future tasks. Based on the legal basis of the vocational training teacher system, the changes in the vocational training and vocational training teacher system, as a response to changes in the labor market for each period have been analyzed, and, based on this, tasks for the future will be discussed.
Classical Deep Learning Models

Deep learning has achieved a big success in computer vision, NLP, audio processing and machine translation. Accordingly, there have been a bunch of classical deep learning models designed for these tasks. In this chapter, convolutional neural network (CNN), LSTM, autoencoder (AE) and GAN are discussed briefly. These models are most efficient for processing image, time series (e.g., video, NLP) and image generation respectively, as the foundation of our proposed models in this book. Recently, more advanced deep learning models/principles have emerged, such as attention (e.g., non-local, squeeze and excitation (SE), global context (GC), and most popular transformer), graph convolution network (GCN), self-supervised learning and contrastive learning. They can further boost model performance, extend application filed and break the limits of lack of labelled data, noise data and etc.
Image stegoanalysis using deep neural networks and heteroassociative integral transformations

The problem of steganalysis of digital images is considered. The proposed approach is based on the use of deep convolutional neural networks with a relatively simple architecture, distinguished by the use of additional layers of special processing. These networks are trained and used for steganalysis of small fragments of the original large images. For the analysis of full sized images, it is proposed to carry out secondary post-processing, which involves combining the obtained classification results in blocks as a sequence of binary features according to the scheme of a naive Bayesian classifier. We propose to use integral heteroassociative transformations that provide the selection of the estimated and stochastic (masking) components on the processed image fragment based on the forecast model of one part of the fragment in relation to another to identify violations of the structural and statistical image properties after message embedding. Such transformations are included in the architecture of trained neural networks as an additional layer. Alternative versions of deep neural network architectures (with and without an integral layer of heteroassociative transformation) are considered. The PPG-LIRMM-COLOR images base was used to create data sets. Experiments have been carried out for several well-known stego algorithms (including the classic block and block-spectral algorithms of Kutter, Koha - Zhao, modern algorithms EMD, MBEP and algorithms for adaptive spatial steganography WOW and S-UNIWARD) and for the stego algorithms based on the use of heteroassociative compression transformations. It is shown that the accuracy of steganalysis obtained when implementing the proposed information processing schemes for large images with relatively low computational costs is comparable to the results obtained by other authors, and in some cases even exceeds them.
Breast tumor segmentation on medical images using combination of fuzzy clustering and threshold

Breast tumor segmentation and boundary detection are crucial stages in breast cancer therapy and follow-up. Radiologists can minimize the high workload associated with screening for breast cancer by automating this complex process. This article proposes a system for segmenting breast and unaffected areas (breast) tumors on medical images using a combination of fuzzy clustering and thresholding (FCMT) tools. This computer diagnostic method works with each section of the mammary gland without learning segmentation and definition of boundaries. As part of the approbation, two databases of breast mammogram images were used. To increase the image quality, we used pre-processing techniques such as contrast augmentation before applying the FCMT for segmentation. To assess the effectiveness of the devised approach, the Mean Square Error, dice coefficient, Structured Similarity Index, Peak Signal-to-Noise Ratio, accuracy, and sensitivity were computed. Using the proposed FCMT segmentation technique, a mean intersection over union (IoU) of 93.85 was attained. The presented approach is more resilient and accurate in segmenting tumor progression on medical images, according to the findings of the experiments.
Review of Data Storage and Security in Cloud Computing

With the rapid growth in data there is a problem occurs to store the generated data, to solve this problem there is a solution to store the data over the cloud via cloud computing. Cloud computing is a computing strategy where a huge group of systems are connected to private or public networks, where data owners can store their data on remote systems, free themselves from the burden of storage, and use on-demand data anywhere. Cloud computing is a functional paradigm that is evolving and making it easier for consumers to use IT (Information Technology) every day. Cloud computing provides users with standardized applications online and in a way that can be accessed on a regular basis. These applications can be accessed by as many people as necessary within a company without having to bother with the maintenance of such an application. Since a user of cloud data does not have direct control over his data, security is one of the few problems that need to be tackled. This paper presents a survey on data storage and security issues in cloud computing.
A Uav Patrolling Path Problem Considering Distributed Recharging Facilities and Control Signal

Unmanned aerial vehicles (UAV) are an emerging technology for security patrolling in urban cities, and a UAV-based patrolling system is proposed by utilizing communication base stations to provide both recharging and remote-control services through mobile signals. Owing to the limited battery capacity and signal-covering range, the routing of the UAV needs to be well-designed to maintain a highly reliable operation. This study introduces a UAV routing problem that considers the sequence of patrolling tasks, allocates facilities, and arranges recharging activities. Motivated by the vehicle location-routing problem and aircraft maintenance routing problem, a mixed-integer programming model was developed. Inspired by PageRank, a rate-based heuristic method was proposed. Experiments, including one using a real-world scenario, show that when time is a consideration, the heuristic method outperforms the exact method using a commercial solver.
IMPROVING THE FUNCTIONAL CAPABILITIES OF THE EPIDEMIC DISEASE PREDICTION MODEL

Сonsidered the problem of predicting the epidemic development of diseases. It is proposed to use a basic model for predicting epidemic diseases and an optimization method in order to bring the epidemic development forecast closer to real parameters. The improvement uses least squares methods to improve the accuracy of the prediction results. The results of modeling are presented in the Matlab application package on the basis of statistical data on the COVID-19 disease in the United States of America are presented.
Distributed Ledger Technology Based Common GIS Map for All Domains in a Smart City

By allowing multiple departments within Smart City to work on a common GIS map brings lots of benefits on their plate along with few challenges. One of the major challenges is in exchanging and contributing information among various departments. Distributed Ledger Technology (DLT or Blockchain) permits multiple stakeholders (departments) to own, manage and share geospatial data with other stakeholders (departments) almost in real-time. Each stakeholder has their own copy of the data and all changes done with respect to data are recorded on blockchain along with a timestamp. Stakeholders can trace the history of the updated data. A holistic vision provided by DLT based common GIS map would help in planning & coordinating various activities of stakeholders. Various stakeholders within a smart city like Civil Department, Drainage Department, Water Department, Electricity and Public Lighting Department, Metro, Roads Departments, Education department can work together on DLT based common GIS map which would make existing processes fast, more effective and efficient. It would also help in disseminating information for benefit of the public at large and in making various reports like land plot report, zone confirmation report available to the public quickly and efficiently. This paper not only proposes DLT based system but also describes a prototype which was developed using Hyperledger Fabric.
Performance Interpretation of Supervised Artificial Neural Network Highlighting Role of Weight and Bias for Link Prediction

Artificial intelligence includes various technologies: machine learning, neural network, deep learning, robotic, etc. Machine learning (ML) is a part of artificial intelligence in which machine is trained to learn without being explicitly programmed, and artificial neural network (ANN) is a popular model that is used for machine learning. The balanced combination of weight and bias plays a vital role in artificial neural network for error prediction. This paper is intended to provide a detailed study of different supervised artificial neural networks: feedforward ANN, multilayer perceptron, PatternNet, cascade feedforward network based on the different combination of weight and bias for link prediction. A comparative study has been done which highlights that the performance of ANN gets better as weight and bias initialization changes.
Customizable Text-to-Image Modeling by Contrastive Learning on Adjustable Word-Visual Pairs

Co-creation with AI is trending and AI-generation of images from textual descriptions has shown advanced and attractive capabilities. However, commonly trained machine-learning models or built AI-based systems may have deficient points to generate satisfied results for personal usage or novice users of painting or AI co-creation, maybe because of deficient understanding of personal textual expressions or low customization of trained text-to-image machine learning models. Therefore, we assist in creating flexible and diverse visual contents from textual descriptions, by developing neural-networks models with machine learning. In modeling, we generate synthesized images using word-visual co-occurrence by Transformer model and synthesize images by decoding visual tokens. To improve visual and textual expressions and their relevance with more diversities, we utilize contrastive learning applying on texts, images, or pairs of texts and images. In the experimental results of a dataset of birds, we showed that the rendering quality was required of models with some scale neural-networks, and necessary training process with fined training by applying relatively low learning rates until the end of training. We further showed contrastive learning was possible for improvement of visual and textual expressions and their relevance.
Linear Transformations

In Chapter 1, we introduced three applications. In Chapter 2, we considered vector spaces of objects to be radiographed and their respective radiographs, vector spaces of images of a particular size and vector spaces of heat states. In these particular applications, we see that vectors from one vector space are transformed into vectors in the same or another vector spaces.
Personally Identifiable Information (PII) Detection and Obfuscation Using YOLOv3 Object Detector

We live in an era of smart phones, and the number of smart phone users is growing by the day, resulting in a rapid increase in the number of people with access to the internet and social media. According to reports, the average person spends about five hours per day on his or her mobile phone. This extensive use of phone and internet had a significant impact on the amount of data exchanged on social media platforms such as Instagram, Facebook, Whatsapp, Snapchat, and others. Images are one type of data. Every day, millions and billions of images are shared, and many of them may contain information that compromises an individual’s privacy. Hackers can use such information for malicious purposes and personal gain. They can even use this person’s personal information to blackmail or threaten them. Cybercriminals can also use that information to open a bank account in the victim’s name, create a forged driving licence, and other forms of identification. Although many researchers have addressed this issue by using tools such as Tensorflow and OpenCV to detect and obfuscate sensitive information in images. In this paper, we will use the YOLOv3 object detector to solve this problem. First, we’ll label the data that needs to be blurred, and then we’ll train our object.
Using Services

Services are objects that provide common functionality to support other building blocks in an application, such as directives, components, and pipes. What’s important about services is the way that they are used, which is through a process called dependency injection. Using services can increase the flexibility and scalability of an Angular application, but dependency injection can be a difficult topic to understand. To that end, I start this chapter slowly and explain the problems that services and dependency injection can be used to solve, how dependency injection works, and why you should consider using services in your own projects. In Chapter 18 , I introduce some more advanced features that Angular provides for service. Table 17-1 puts services in context.
6 Self-supervision by dynamics-based layers

This dissertation deals with machine learning techniques for inverse dynamics of human motion. Inverse
Study of Machine Learning Techniques to Mitigate Fraudulent Transaction in Credit Cards

Two very important aspects that associate with the growth of human civilization are consumerism and the urge of minimizing human efforts, not only physical but also intellectual, for regular functioning. Due to the substantial growth of consumerism, a significant increase in credit card transactions can be observed in recent years and that certainly comes with rampant credit card fraud issues as well. This study suggests popular machine learning methods to mitigate fraudulent credit card transactions and explores the effectiveness of it. Six strategically different and popular classifiers, namely—random forest, XGBoost, decision tree, K-nearest neighbors, Naïve Bayes approach, and logistic regression, are employed to study their applicability in the credit card fraud detection problem. Out of the 6 algorithms taken into consideration for this particular problem, random forest classifier has turned out to be the best one and Naive Bayes has turned out to be the worst one in prediction according to conventional parameters such as accuracy, recall, specificity, and precision. This paper also intends to present a basic framework to select the best among state-of-the-art models to address the credit card fraudulency problem.
Components of the pedagogical model of professional education of students

An important role in the professional education of students is played by the process of becoming-assimilating a student-pupil to a teacher-educator. The problems of pedagogical interaction between the teacher-educator and the student-pupil include various aspects of the educational process, on the one hand, the current level of compliance of the content of the educational program with modern achievements in science and technology, on the other hand, the level of pedagogical skill of the teacher-educator. Despite the numerous publications on this issue, there are practically no studies on the professional education of students in the context of the future professional activities of graduates of the educational program for the bachelor’s degree in “Theology”, the problem in this formulation is considered for the first time. The purpose of this research is to analyze the content and interconnection of the components of the professional education of students, the features of the professional education of students in the context of the practical familiarization of students in the direction of preparation of bachelor’s degrees “Theology”, to the activities of the clergy. The research was conducted in the course of professional and practical training of students enrolled in the educational program “Theology”. The study involved 51 teachers and 69 students. As a result of the study, the components of professional education were identified, the behavior and actions of the student-pupil as a result of his upbringing; forms of student motivation for success in educational and future professional activities; a portfolio complex of a student-pupil and a teacher-educator. The results of the study open up a new theoretical direction in the professional education of students, in which practical training occupies a special place in the theoretical education of students. The results of the analysis of the components of the professional education of students are presented and a conclusion is made about the importance of motivation and discipline-obedience in the system of higher spiritual education as effective means of organizing the professional education of students.
Trace Element Composition of Base Metal Sulfides from the Sediment-Hosted Dolostone Ore Formation (Dof) Cu-Co Deposit in Northwestern Namibia: Implications for Ore Genesis

The Dolostone Ore Formation (DOF) copper-cobalt mineralization is a recently discovered sediment-hosted deposit in Neoproterozoic low-grade metamorphosed carbonates and fine-grained siliciclastics in the Pan-African Kaoko Belt in Namibia. Six mineralization styles have been identified in the DOF. They show both brittle and ductile textures and include sulfides occurring in disseminations, clusters, nodules, pressure shadows, veins and the locally termed “DOF event”. Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) in situ trace element analyses of base metal sulfides (sphalerite, chalcopyrite, pyrite and pyrrhotite) show distinct groupings of the mineralization styles. Sphalerite and chalcopyrite from the dissemination, cluster and nodule mineralization styles show similar trace element composition trends (e.g., Fe, Co, Ni, Se, Cd for sphalerite and Co, Ni, Se and Bi for chalcopyrite) that differ from vein and pressure shadow sphalerite and chalcopyrite which group together. Pyrite compositions in cluster and nodule types show a similar trace element grouping in elements such as Co, Ni, As, Se, Sb, Tl and Pb. In contrast, pyrite from pressure shadows and veins show a decoupling in elements such as Se, Sb, Tl and Pb, that grouped together in the aforementioned pyrite mineralization styles. Ge-Ga-In-Mn-Fe in sphalerite (GGIMFis) geothermometry indicates that the sphalerite (and associated sulfides) from all mineralization styles crystallized at temperatures at or above 310 ± 50 °C. This temperature range corresponds with the regional metamorphism induced by the Damaran orogeny. Thus, the DOF copper-cobalt mineralization is related to, or at least strongly overprinted by, Damaran metamorphism and deformation. Trace element differences in the sphalerite-chalcopyrite groupings reveal that the vein and pressure shadows mineralization styles were formed at lower temperatures than the other ones but still above the GGIMFis closing temperature of 310 ± 50 °C. Evidence is vacant for a typical low-temperature, early sediment-hosted copper mineralization. This work demonstrates the strength of using trace elements for deducing the ore-forming system of polyphase sulfide deposits.