Reviewerly/exHarmony huggingface dataset:
A Comprehensive Study of Camouflaged Object Detection Using Deep Learning

Various living organisms and objects around us often blend themselves with their environment and is thus invisible to the naked eye. Whether it is an organism like a pygmy seahorse, a warfare tank or a soldier, the Camouflage Object Detection Using Deep Learning overcomes these issues of objects hiding within the environment. In addition to object detection, we detect camouflage objects within an image using deep learning techniques. Deep learning is a subset of machine learning that is essentially a three-layer neural network. Over 6500 images that possess camouflage properties are gathered from various internet sources and divided into four categories to compare the result. Those images are labeled and then trained and tested using VGG16 architecture. The architecture is further customized using transfer learning. The purpose of this transfer of learning methodologies is to aid in the evolution of machine learning to the point where it is as efficient as human learning.
Thermogram-based Regions With Convolutional Neural Network (RCNN) and Facial Biometrics for Safe Driving

A significant number of wrongful death cases involve motor vehicle accidents. In most car accidents, the driver is somehow at fault. This can be due to a lack of visibility, slow decision-making, or bad weather, among other things. The proposed system aims to create a safe driving assist technology consisting of thermal camera-based object detection and intelligent vehicle anti-theft measures to assist in safe driving and provide vehicle security on top of the existing system. This proposed system not only can easily detect objects in low visibility under unsuitable weather conditions, with an average accuracy of 97% but also provides vehicle safety by using facial-biometrics-based vehicle authentication where the accuracy is 95%. This also has a 36 facial data saving capacity in the database at a time. The authorized user doesn't always require internet support to access the vehicle, whereas the unregistered user needs app-based permission from the user to access the car.
An interview with CUNY University Collaborative Systems Librarian and Alma specialist, Kristen Fredericksen

The author interviews City University of New York (CUNY) University Collaborative Systems Librarian Kristen Fredericksen about migrating an entire consortium to a new integrated library system during the COVID-19 pandemic with a focus on the development of training and documentation to support electronic resource practitioners in their work.
Blockchain Based Secured Refugee Identity Management by Using the Assistance Smart Contract

Refugee identity documentation is a sensitive process that a host country must go through to avoid unnecessary adversities. Flawless identity documentation is not only important to restrict unwanted population influx, but also to provide public and private services only to the rightful citizens of a country. The established refugee identity system process is faulty, inefficient, and resource intensive. The government spends an incredible amount of time and assets to document the refugees to incur a loss eventually in the inflow of illegal citizens. Our research is keen to change the current refugee identity documentation process. In this paper, we demonstrate a blockchain solution to enhance the effectiveness of the current identity management procedure. We clearly demonstrate the solution using a blockchain-based system where no refugee can become the host country's citizen. We also demonstrate the blockchain architecture along with the internal activities. We present some techniques used in a blockchain to enhance the system's data security and privacy.
Surface Damage Detection of Line Insulators Using Deep Learning Algorithms to Avoid Insulation Failure

The power system's reliability dramatically depends on the high voltage line insulators. However, the surface of these insulators is frequently damaged because of the outdoor environment, which includes complicated landforms and unpredictable weather. Damage to the insulator's surface can lead to short circuits, permanent damage to the transmission line, and even blackouts. To deliver quality service, it is essential to keep track of the condition of these insulators. As traditional fault-detection systems have become more time- and labor-intensive, a YOLOv4-based detection approach is proposed here to achieve fast and precise damage detection and classification of line insulators. YOLOv4 is a Deep Learning (DL) algorithm model that operates on the darknet framework. The research findings show that 97.711% is the maximum average, depending on detecting YOLOv4 for insulators. Insulator damage has a maximum AP value of 98.17%, and discolored Insulator has a maximum AP value of 97.07%. When the system is trained on the insulator data set, the overall m-AP (mean Average Precision) value is 97.65%. The detecting speed in virtual environments for YOLOv4s is 43 FPS, and it has a greater detection rate.
Toward a Transfer Learning Approach to Detect Face Mask Type in Real-time

Face masks are considered protective equipment that has the ability to safeguard humans from vulnerable situations. Although there exists a wide range of masks specifically designed for diverse purposes, there is a terrible lack of concern regarding proper usage. Consequently, the generalization of their usage can cause many life-threatening problems. As a result, a system that can detect the type of face mask can play a life-saving role to ensure the proper usage of these safety gear. With this aim, a custom dataset was built by manually labeling face mask images which include 8 classes. Scratch CNN and four transfer learning models have been implemented and their performance was thoroughly evaluated and assessed on multiple criteria to select the best one. Based on the investigation, it is found that SSD MobNet V2 achieved the highest accuracy of 83%. The developed system takes real-time video stream input from the camera and can detect the type of mask in different conditions.
Detection of Homophobia &amp; Transphobia in Malayalam and Tamil: Exploring Deep Learning Methods

The increase in abusive content on online social media platforms is impacting the social life of online users. Use of offensive and hate speech has been making so-cial media toxic. Homophobia and transphobia constitute offensive comments against LGBT+ community. It becomes imperative to detect and handle these comments, to timely flag or issue a warning to users indulging in such behaviour. However, automated detection of such content is a challenging task, more so in Dravidian languages which are identified as low resource languages. Motivated by this, the paper attempts to explore applicability of different deep learning mod-els for classification of the social media comments in Malayalam and Tamil lan-guages as homophobic, transphobic and non-anti-LGBT+content. The popularly used deep learning models- Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) using GloVe embedding and transformer-based learning models (Multilingual BERT and IndicBERT) are applied to the classification problem. Results obtained show that IndicBERT outperforms the other imple-mented models, with obtained weighted average F1-score of 0.86 and 0.77 for Malayalam and Tamil, respectively. Therefore, the present work confirms higher performance of IndicBERT on the given task in selected Dravidian languages.
Pixel Attention Based Deep Neural Network for Chest CT Image Super Resolution

The High-Resolution chest CT scan images help to diagnose lung related diseases accurately. In general, the more advanced hardware used in CT Scan machines, the more high resolution images will be generated. But it is a costlier approach. This limitation can be overcome with the post processing of the images generated from the CT machine. Even when the image is upscaled, the quality of the image should be retained. So, the process of reconstructing the High-Resolution images from the Low-Resolution images is known as Image Super-Resolution. The recent advancements in hardware and Super Resolution deep neural networks enabled reconstructing High-Resolution images in an efficient way. The objective quality metric Peak-Signal-to-Noise-Ratio evaluates the performance of a SR deep model. In this paper, proposed a pixel attention based deep neural network, MediSR for chest CT scan medical image Super-Resolution. The model is trained with two chest CT datasets and the experimental results showed an improvement of 1.78% and 18.23% for the 2 $$\times $$ and 4 $$\times $$ scale factors over the existing literature.
Discontinuous Combinatory Constituency Parsing

Abstract We extend a pair of continuous combinator-based constituency parsers (one binary and one multi-branching) into a discontinuous pair. Our parsers iteratively compose constituent vectors from word embeddings without any grammar constraints. Their empirical complexities are subquadratic. Our extension includes 1) a swap action for the orientation-based binary model and 2) biaffine attention for the chunker-based multi-branching model. In tests conducted with the Discontinuous Penn Treebank and TIGER Treebank, we achieved state-of-the-art discontinuous accuracy with a significant speed advantage.
Sample Size Estimation for Effective Modelling of Classification Problems in Machine Learning

High quality and sufficiently numerous data are fundamental to developing any machine learning model. In the absence of a prior estimate on the optimal amount of data needed for modeling a specific system, data collection ends up either producing too little for effective training or too much of it causing waste of critical resources. Here we examine the issue on some publicly available low-dimensional data sets by developing models with progressively larger data subsets and monitoring their predictive performances, employing random forest as the classification model in each case. We try to provide an initial guess for optimum data size requirement for a considered feature set size using Random Forest Classifier. This sample size is also suggested for other machine learning (ML) models, subject to their trainability on the dataset. We also investigate how the data quality impacts its size requirement by introducing incremental noise to original class labels. We observe that optimal data size remained robust for up to 2% class label errors, suggesting that ML models are capable of picking up the most informative data instances as long as there are sufficient number of objects to learn.
YOLOv4 Vs YOLOv5: Object Detection on Surveillance Videos

Now-a-days, Object detection algorithms becomes more popular because of their significant contribution to the field of computer vision. Object detection algorithms are divided into two approaches i) region-based approach and ii) region-free approach. In this paper, we implemented YOLOv4 and YOLOv5 techniques of region-free approach due to their high detection speed and accuracy. The objective of this paper is to identify the relevant and non-relevant parts of surveillance videos as watching entire video footage is a time-consuming process. The use case for this research work is ATM surveillance footage, where the dataset is publicly not available, to train the network we developed our data set and then train and compare the proposed models. After experimental results, it is observed that YOLOv5 archives 84% accuracy and give better results than YOLOv4 which achieve 56% accuracy only.
Image Classification with Information Extraction by Evaluating the Text Patterns in Bilingual Documents

The rapid development of digital data in today’s world needs highly efficient and automatic N-lingual image document classification and information extraction systems. Several text-based image processing systems have been introduced to date for different applications. However, they are limited to character recognition, word extraction, profiling, and script and language recognition. The proposed document classification and information extraction system categorizes the bilingual English-Hindi images into mutually exclusive categories by using SVM and random forest classifiers at the character-level and document-level recognitions, respectively. It used two pseudo-thesaurus, first to store the pre-defined English and Romanized Hindi characters and second to store the keywords of the pre-defined categories. It discriminates between the languages by determining the absence or presence of the shirorekha for the English or Hindi word images, respectively, and applies the minimum distance computing method to obtain the constructed words from the mapped English and Romanized Hindi characters. This system achieved promising results in extracting the information and classifying the bi-lingual image documents. It illustrates a case study to explain the working of the system.
How Can We Explain Different Number Systems?

InNumber system this and following chapters, we study applications to teaching. In this chapter and in the next chapter, we show how general decision making techniques can help us decide on what to teach. In Chap. 22 , we will analyze how to teach. And finally, in Chap. 23 , we will analyze how to grade, i.e., how to gauge the results of teaching.
A Study on Drama Content Production Using 360-Degree Video

360-degree images began with panoramic cameras and have evolved with the development of camera technology and projection equipment such as planetariums. As of 2022, the emergence of action cameras that can capture 360-degree images has made it possible for anyone to easily take pictures. Most of the 360-degree images produced today are environmental images, and the number of drama contents with a storyline is small. This is because existing drama shooting methods cannot be used for 360-degree images. For this reason, 12 drama contents using 360-degree images were produced in a project exercise at the Graduate School of Tokyo Metropolitan University. The camera work of these drama contents was analyzed by focusing on camera movement, camera position in the shooting space, and scene transitions. In this paper, we discuss the camera work of 360-degree images based on the analysis results of the camera work.
Robust State Estimation via Maximum Correntropy EKF on Matrix Lie Groups With Application to Low-Cost INS/GPS-Integrated Navigation System

This article focuses on solving state estimation problems of nonlinear systems whose states evolve on matrix Lie groups and measurements live in the Euclidean space under non-Gaussian measurement noise. Building upon the maximum correntropy criterion (MCC) and the invariant Kalman filtering theory, a generalized robust extended Kalman filtering (EKF) algorithm on matrix Lie groups is derived from maximizing a novel optimization cost function. This cost function is introduced by considering the correntropy and the system geometry structure. Then, two alternative versions of the proposed filtering algorithm are provided according to the distinct processing approaches of the nonlinear measurement function in the cost function. A Gaussian–Newton method on Lie groups and a fixed-point iterative method are applied to update the posterior estimation. In addition, sufficient conditions are given to ensure the convergence of fixed-point iterative steps. Extensive Monte-Carlo simulations on an inertial navigation system/global position system (INS/GPS) integrated navigation system and experiments on open-source datasets validate the theoretical analysis and illustrate the effectiveness and robustness of the proposed filter.
Blockchain Based E-Voting System

The process of establishing democracy in a country is defined by election. Elections might just be a significant event in today’s democracy, however many segments of population throughout the world lack faith in their electoral system, which is a major source of concern for democracy. Even the world’s most powerful democracies, such as the Republic of India, the United States, and Japan, have a flawed legal system. To eliminate these drawbacks of the electoral system, Blockchain Technology is considered as an ultimate solution. With the present surge in sales and use of blockchain technology for a number of purposes, including banking, medical, and identity, a lot of attention has been focused on the legal concerns rather than its practical uses in administration. In this paper, we discuss the concept of Blockchain in a detailed manner making the reader understand the working of blockchain, its characteristics etc. everything from the scratch, as well as how this concept can be implemented as an efficient solution for public voting and how it is more beneficial from the traditional voting methods, with the goal of eliminating the drawbacks of India’s current electoral system while also providing a better, more trustable, safe, and transparent means of public governance. Blockchain is really an emerging technology that promises to improve the resiliency of electronic voting systems. This method offers a way to profit from blockchain’s advantages, such as cryptological foundations and transparency, to achieve an efficient theme for the e-voting system.
Building a Multi-class Prediction App for Malicious URLs

The page that houses a malicious snippet that could misuse a user's computing resources, steal confidential data, or carry out other forms of assaults is known as a malicious host URL. They are generally distributed across the world wide web under various usage categories like spam, malware, phishing, etc. Although numerous methods or fixes (to identify URLs) have been developed in recent years, still cyberattacks continue to occur. This study contributes towards implementing three tiers of the system for detection and protection from harmful URLs. The first tier focuses on evaluating the performance of discriminative features in model creation. Discriminative features are derived from URL details and “Whois” webpage information that helps in improving detection performance with less latency and low computational complexity. The influence of feature variation on Parametric (neural network) and non-parametric classifier detection results are assessed to narrow down to the most prominent features to be adapted in the best model for the task of identifying URLs with multi-categorization. The study reveals that non-parametric ensemble models like Light GBM, XGBoost, and Random Forest performed well with a detection accuracy of over 95%, which facilitated building a real-time detection system and differentiating multiple attack types (such as Malware, Phishing, and spam). The second tier focuses on validation with a global database to know, if entered URL is reported as suspicious by various detection engines already. If not, it enables the user in updating the global database with URL details that are new and not reported yet. Finally, the two modules are integrated to create a web application using Streamlit that provides full system protection against malicious URLs.
How We Can Control Group Decision Making by Modifying the Proposed Options

In summary, this chapter explains how we can control group decision making by modifying the proposed options. This chapter contains both an empirical dependence—and our explanation of this dependence. This is the first chapter that contains completely new results.
DISASTER: Blockchain-Enabled Token Trading Game for Supply Chain Management

This paper describes the Blockchain-Enabled Token Trading Game for Supply Chain Management, a web-based supply chain simulation. The game uses blockchain technology’s concepts to create virtual markets for supplier capacity trading among retailers, where participants take on the role of a retailer, have different valuations for products, and submit an order before knowing their demand; after demand realization, participants trade tokens (claims on the supplier's capacity) among themselves using virtual markets to maximize their profits. The game provides participants with first-hand experience with how blockchain technology can be used in practice, thus serving as an interactive pedagogical tool. More generally, the game is intended to help supply chain management and blockchain technology students and executives understand the challenges of serving uncertain customer demand and the role of virtual markets in providing an effective remedy for reconciling supply and demand, thus improving supply chain performance.
A New Approach to Traffic Accident Anticipation With Geometric Features for Better Generalizability

Traffic accident anticipation is essential for successful autonomous and assistive driving systems. Existing accident anticipation algorithms that mostly rely on visual features of the accident related objects involved provides both high AP (Average Precision) and TTA (Time to Accident). Despite a spatiotemporal relationship with the visual features of the accident related objects involved, these methods are often biased and therefore not well generalizable. In this paper, firstly we discuss dataset biases and then show that those high AP and TTA results came mainly from visual biases. Secondly, to overcome some of the visual biases, we propose a novel deep learning framework that uses both visual and geometric information of the accident-related objects captured in dash cam videos. Thirdly, we show effectiveness of the proposed method in terms of generalization capability compared to existing approaches with several open datasets from actual accident videos.
Improved Helmet Detection Model Using YOLOv5

This report is about detecting motorbike riders without a helmet and also the pillion rider with the use of YOLO object detection algorithm. We introduced the updated approach for helmet detection. This approach is an upgradation of YOLO object detection algorithm which detects not only the rider’s helmet but also the helmet of the pillion rider. Primary objective is Detection of helmet of rider and pillion rider in that targeted image and Increase the accuracy of the YOLOv5 algorithm by adding one layer for detection of small details in an image. In this proposed model a new layer has been added for detection of smaller objects having smaller features. This has been done by changing the configuration of YOLOv5 architecture. The helmet detection using this proposed model has been carried out for a dataset containing images with maximum 3 people, with no helmets, 1 helmet each or 2 wearing it.
How People Combine Utility Values

To make a decision, people need to estimate the value (utility) of each alternative. In real life, alternatives are complex, they include several different items. So, to estimate the value of an alternative, we need to estimate the value of each item, and then combine these estimates into a single number characterizing the alternative as a whole. In the previous chapters, we described how people estimate the value of each item. In this chapter, we will analyze how they combine these values into a single alternative-wide value.