Reviewerly/exHarmony huggingface dataset:
Real-Time Face Mask Detector with Multi-class Classification Using Deep Neural Networks

For the last three years, the coronavirus pandemic has created havoc in peoples’ lives and has taken many lives. Due to the airborne transmission of the coronavirus, as a non-pharmaceutical intervention, facemask wearing has been mandatory in public places, as it is a non-surgical and low-cost method to reduce mortality and morbidity from respiratory infections. Despite the mandatory rule, some people disregard this, oblivious to the hazard of probable virus transmission. To enforce the rule, models have been developed to detect whether a person has put on the face mask or not. But the drawback of these models is that they do not consider the case of an incorrectly worn face mask, which equally contributes to the onward transmission of the virus. The proposed model detects and classifies the different ways a person wears the face mask. As the initial step, the model compared different architectures of the Convolutional Neural Networks (CNN), namely Inception v3, ResNet 50 and VGG-16. It was found that the Inception v3 model outperformed the other architectures with an accuracy of 90.58%. A model like this would identify individuals, in a confined area, who are not complying with the government rule of face masks and are posing a threat in spreading the coronavirus and other airborne diseases.
Using Blockchain for Fast Re-Authentication in 5G Cellular Network

In order to serve more users and to provide higher bandwidth, 5G cellular network supports deployment of small cells like femtocells, picocells and microcells. However, a user equipment in 5G cellular network may experience frequent handover, when the subscriber moves from the coverage area of one cell to another. Therefore, for seamless user experience, it is necessary that the re-authentication procedure during handover is made faster. In this paper, we propose a fast re-authentication scheme for handover in 5G cellular network based on blockchain technology. We have considered security requirements like forward secrecy, backward secrecy, anonymity, etc. The scheme shows it is robust against various kinds of security attacks by performing formal and informal security analysis. Through performance analysis, it is found that the protocol achieves its goals with minimum communication and computation overhead.
Accurate Cervical Tumor Cell Segmentation and Classification from Overlapping Clumps in Pap Smear Images

Cervical tumors impact women of all ages from the world's enormous female population in a variety of ways. Therefore, the majority of researchers, pathologists, and many collegiate students have provided greater resolutions to identify this malignancy from the test photographs of Pap smear viewing tests. However, their results only partially indicate whether a person has cancer or not, and they do not specifically highlight the exact characteristics and brutality of cancer. It is suggested in this study that pictures from Pap smear type tests can be used to accurately identify cancer and classify its stages. Segmentation, improvement, feature removal, and categorization processes are the foundation of the suggested work. Results show the mean value and area values based on the classification of the normal and affected cells and also recognize the stage of cervical cancer. The outcomes would be more useful for healthcare pathologists to decrease their workload and decrease human error while enhancing the accuracy rate of diagnosis. In this research we will be using the following algorithms namely Support Vector Machine (SVM), Random Forest (RF), and Enhanced Convolution Neural Network (ECNN) in terms of accuracy. From the outcomes its proved that proposed ECNN produces accuracy of about 92% which is higher than RF accuracy which is 87%, and SVM accuracy which is 90%, respectively.
Hardware Vulnerability: Meltdown

Since the dawn of the digital era, the security of the network system and its components has been solely dependent on the security of the software. The hardware was trusted blindly. Initially, the software vulnerabilities were discovered. These vulnerabilities were easy to detect and could be mitigated by modifying the code. The emerging hardware attacks have shattered this trust. Fundamentally, the security of the computer is dependent on memory isolation. This means that the kernel address ranges are not accessible to the user. Hardware attack is not just a code that runs but the hardware itself is modified to perform a specific task. This modification can be carried out at any stage of the hardware manufacturers, such as during design or fabrication. This paper contains a prediction of meltdown. Meltdown reads arbitrary kernel memory regions, including private information such as cryptographic keys and passwords, by taking advantage of the consequences of out-of-order execution on contemporary processors. A revolutionary performance feature found in a wide variety of contemporary CPUs is out-of-order execution. By processing the instruction set simultaneously, out-of-order execution speeds up calculation. This paper introduces and describes the working countermeasures to be implemented for meltdown.
Survey on Virtual Paint Board Using Computer Vision

A virtual paint board is a method in which one can draw, write, and erase on the screen by selecting different colors. By removing the background and concentrating on the human’s hand movements, a virtual paint board makes it easy to identify hand gestures. The design of the virtual paint board demonstrates how it understands hand motions and chooses the appropriate mode for drawing, writing, or erasing on the screen. Numerous scholars investigated and advanced numerous virtual writing strategies. In contrast to other studies, distinct virtual writing approaches were employed. The methods that have been suggested include You only look once (YOLO), Faster Regions with Convolutional Neural Networks (R-CNN), MediaPipe, and OpenCV. These methods are deep learning techniques. Detecting spatial and temporal features in a virtual paint board is required for object detection. Based on how well it identifies human hand motions, the virtual writing or erasing process is deemed effective. Numerous uses exist for virtual whiteboards, including online education and hearing-impaired communication.
Detection and Counting of Connected Lentil Grains Using Convex Deficiency for Quality Estimation

Proposed here is a simple and effective method for identification of connected grains in an image. The grains considered here are the whole lentil grains which are circular in shapes. These circular shapes can be considered as convex polygons. When two or more such convex polygons are connected or partially overlapped, there are concave regions along the boundary line of the connected region. Such concave regions form the convex deficiencies for the convex hull of the connected or overlapped grains. Finding the number convex deficiencies in the convex hull of connected object boundaries allows us to identify whether connected component is formed by connecting two or more objects or grains. Also, it allows us to exactly count the number of connected objects and simplifies the finding the segmentation boundaries. The method is simple, fast and gives good accuracy of counting connected grains without actually segmenting the connected grains.
Digital Bill: A Proposition for Minimizing Unlawful Practices and Overcoming Limitations of Crypto Currency

This research paper introduces a comprehensive approach aimed at addressing key concerns related to money laundering, illicit activities, and the limited acceptance of cryptocurrency in widespread usage. The proposed solution entails the development of a digital representation of a bill of exchange referred to as the "Digital Bill (DB)". This innovative currency encompasses a range of distinctive features, including robust cyber security measures, resistance to inflation, elimination of mining requirements, secure private key regeneration, prevention of DEX hacking, insurance for depository holdings, trust guarantees, and real-time currency value adjustments. By establishing an ecosystem centered around the "Digital Bill (DB)" network, this approach lays the groundwork for a more secure and reliable form of cryptocurrency. The paper also highlights the potential global benefits that can arise from the incorporation of these cryptocurrency features. It is anticipated that this approach will enhance public trust in cryptocurrency, alleviate concerns regarding investment losses, and facilitate wider adoption and implementation of blockchain technology. While further advancements are necessary, this proposed approach provides a glimpse into the promising potential of a secure and reliable cryptocurrency ecosystem.
Comprehensive Study on Edge Detection

Edge detection or edge finding is the way of finding out and detecting sharp breaks in an image. The breaks are unpredicted modifications in a pixel intensity scene. Traditional or customary approach of facet finding contains convolving an image through operators which is built to be superficial to huge inclines.Edge detectors shape a group of very essential local picture processing approach to find sharp adjustments within the depth characteristic. Edge detection is a vital method in which a number of picture processing packages together with item recognition, movement analysis, pattern reputation, medical image processing, and so forth. This paper presents several aspects of edge detection strategies with advantages and disadvantages of the chosen algorithms, i.e., Canny, Sobel, Robert, Laplacian of Gaussian (LoG), etc. An element discussion related to advantages and disadvantages of each algorithm also are discussed.
Eliminating Environmental Context for Fall Detection Based on Movement Traces

Falls are a prominent cause of mortality and severe injury among the elderly. It can be prevented by tracking them and providing prompt treatment. Current fall detection systems use data from sensors or cameras in various ways. False positives are common in sensor-based systems, and operating system constraints make privacy a major concern in vision-based systems. This paper proposes a technique for detecting falls from RGB images using a convolutional neural network (CNN) utilizing movement trace characteristics generated by a modified structural similarity index (SSIM) that can be integrated into resource-constrained devices for in-house monitoring. The proposed approach uses a camera system and is tested against the UR Fall detection (URFD) dataset, outperforming previous fall detection systems. Our method achieves 99% accuracy. The model's dependence on readily available sensors and superior performance on the URFD dataset makes it a viable option for reliable fall detection in the real world.
Bidirectional Gated Recurrent Unit (BiGRU)-Based Bitcoin Price Prediction by News Sentiment Analysis

Media news constantly changes the sentiment of traders and investors in the cryptocurrency market and impacts their decisions. Data collected from various news aggregators aid in the early indication of the cryptocurrency market movement. The sentiment and public mood states can be uncovered using different techniques such as data mining, natural language processing, and opinion mining. Bitcoin plays the most dominant role in the global economy, and it is extremely important to understand the correlation between news sentiment and price volatility of Bitcoin. This paper assesses news collections and interprets them to forecast the market movement of Bitcoin. Sentiment analysis and deep learning methods, namely long short-term memory (LSTM), gated recurrent units (GRUs), bidirectional long short-term memory (BiLSTM), bidirectional gated recurrent units (BiGRUs), were used for prediction modeling of Bitcoin prices. Each model was considered on its ability to forecast the Bitcoin early market movement using news sentiment in the Covid-19 era. BiGRU prediction model is developed which achieved an overall accuracy of 70% and showed better performance than the state-of-art cryptocurrency price prediction models based on sentiment analysis.
Decentralized Blockchain-Based Infrastructure for Numerous IoT Setup

For thousands of IoT devices anticipated to be distributed in the coming years, its management is an essential problem to address and although the unified approach to cloud storage may not be sufficient to this large degree in recent decades. The use of distributed blockchain technologies has resulted in a plethora of theories to address the issue of scalability. This article provides the blockchain architecture to re-configure huge IoT devices in a modular way. To reduce resource usage, a publishing/subscription framework based on REST API is employed to separate IoT devices from blockchain activities. Furthermore, the blockchain-based upgrade procedure is supported by smart agreements, reconfiguration workflows. The actual evidence testing platform has been created to determine the feasibility and reliability of the planned architecture. Experimental findings demonstrate that the proposed design can offer a modular option for on-demand improvements to the setup, with a marginal impact on resource usage on IoT applications.
Detection of Phishing Website Using Intelligent Machine Learning Classifiers

As a result of the rapid development of the Internet, customers’ tendency has shifted from standard shopping to e-commerce. Nowadays, the criminal party is no longer bank/shop theft, but trying to find their victims on the Web with the purpose of theft. Attackers use the hidden advances of the Internet to propose new advances, such as phishing, and deceive misfortune by using false language conditions, and accumulate their sensitive data, such as account IDs, customer names, passwords, and so on. Knowing whether a Web site page can be authenticated or phishing is an incredible problem, because its semantic-based attack structure fundamentally exploits the mistakes of the PC client. Despite the detail that product organizations dispatch new enemy of phishing items, which use heuristics, visual, and AI-based methodologies, these items can’t forestall all of the phishing assaults. In this article, an anti-phishing framework uses five different classification techniques (logistic regression, K-nearest neighbor, decision tree classifier, random forest classifier, and support vector machine) ​​to predict phishing sites. In order to investigate the demonstration of the framework, a new dataset was collected, and preliminary results were set in it. According to the test and comparison results of the ranking estimation, the random forest calculation gives the best presentation, and the confirmation accuracy of the phishing URL is 97.10%. For validation of this outcome, confusion matrix, precision matrix, recall matrix, and ROC curve have been drawn.
Relevance Judgment Convergence Degree—A Measure of Assessors Inconsistency for Information Retrieval Datasets

The quality of training/testing datasets is critical when a model is trained and evaluated by the annotated datasets. In Information Retrieval (IR), documents are annotated by human experts if they are relevant or not to a given query. Relevance judgment of human assessors is inherently subjective and dynamic. However, a small group of experts’ relevance judgment results are usually taken as ground truth to “objectively” evaluate the performance of an IR system. Recent trends intend to employ a group of judges, such as outsourcing, to alleviate the potentially biased judgment results stemmed from using only a single expert’s judgment. Nevertheless, different judges may have different opinions and may not agree with each other, and the inconsistency in human relevance judgment may affect the IR system evaluation results. Further, previous research focused mainly on the quality of documents, rather on the quality of queries submitted to an IR system. In this research, we introduce Relevance Judgment Convergence Degree (RJCD) to measure the quality of queries in the evaluation datasets. Experimental results reveal a strong correlation coefficient between the proposed RJCD score and the performance differences between two IR systems.
Studies on Neural Networks as a Fusion Method for Dispersed Data with Noise

In this paper, the issues of classification based on dispersed data are considered. For this purpose, an approach is used in which prediction vectors are generated locally using the k-nearest neighbors classifier. However, in central server, the final fusion of prediction vectors is made with the use of a neural network. The main aim of the study is to check the influence of noise intensity, various data characteristics (the number of conditional attributes, the number of objects, the number of decision classes) and the degree of dispersion on the quality of classification of the considered approach. For this purpose, 270 data sets were generated that differed by the above factors. It was found that each of the examined factors has a statistically significant impact on the quality of classification. The main conclusions are as follows. For dispersed data, multidimensionality is very good. The greater the dispersion in data, the worse the quality of classification. Only when the noise intensity significantly increased, we can observe a significant increase in the classification error in comparison with the lower noise level. This means that the classification method for dispersed data with neural network is immune to noise to some extent.
Object Detection Using Mask R-CNN on a Custom Dataset of Tumbling Satellite

Object detection is one of the most important aspects in remote applications like space docking, uncooperative satellite locating, etc. Most of the technologies employ this task using various machine learning as well as deep learning models. The main criteria that differentiate these models are their performance, accuracy, and doing the prediction successfully. Some of the widely used models for object detection are CNN-based, like ResNet, YOLO model, etc. Object detection plays a key role in locating satellite objects in images and thereby helping in the estimation of their relative pose. The main objective of this paper is to explore the object detection operation using a region-based convolutional neural network (R-CNN) model called Mask region-based convolutional neural network (Mask R-CNN) on a custom dataset. Our dataset consists of tumbling satellite images, captured using a 3-axis motion simulator. The detection result is then utilized by the SORT object tracker to track the tumbling satellite in the video frames. Here, we are exploiting the masked output from the model and then performing corner detection to trace them in continuous frames using SORT tracker.
Rainbow UDA: Combining Domain Adaptive Models for Semantic Segmentation Tasks

In this paper, we propose Rainbow UDA, a framework designed to address the drawbacks of the previous ensemble-distillation frameworks when combining multiple unsupervised domain adaptation (UDA) models for semantic segmentation tasks. Such drawbacks are mainly resulted from overlooking the magnitudes of the output certainties of different members in an ensemble as well as their individual performances in the target domain, causing the distillation process to suffer from certainty inconsistency and performance variation issues. These issues may hinder the effectiveness of an ensemble that includes members with either biased certainty distributions or have poor performance in the target domain. To mitigate such a deficiency, Rainbow UDA introduces two operations: the unification and the channel-wise fusion operations, to address the above two issues. In order to validate the designs of Rainbow UDA, we leverage the GTA5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\to$</tex-math></inline-formula> Cityscapes and SYNTHIA <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\to$</tex-math></inline-formula> Cityscapes benchmarks to examine the effectiveness of the two operations, and compare Rainbow UDA against a wide variety of baseline approaches. We also provide a set of analyses to show that Rainbow UDA is effective, robust, and can evolve with time as the ensemble grows.
Development of Character Recognition Model Inspired by Visual Explanations

Deep neural networks (DNNs) currently constitute the best-performing artificial vision systems. However, humans are still better at recognizing many characters, especially distorted, ornamental, or calligraphic characters compared to the highly sophisticated recognition models. Understanding the mechanism of character recognition by humans may give some cues for building better recognition models. However, the appropriate methodological approach to using these cues has not been much explored for developing character recognition models. Therefore, this paper tries to understand the process of character recognition by humans and DNNs by generating visual explanations for their respective decisions. We have used eye-tracking to assay the spatial distribution of information hotspots for humans via fixation maps. We have proposed a gradient-based method for visualizing the reasoning behind the model's decision through visualization maps and have proved that our method is better than the other class activation mapping (CAM) methods. Qualitative comparison between visualization maps and fixation maps reveals that both model and humans focus on similar regions in character in the case of correctly classified characters. Whereas, when the focused regions are different for humans and model, the characters are typically misclassified by the latter. Hence, we propose to use the fixation maps as a supervisory input to train the model which ultimately results in improved recognition performance and better generalization. As the proposed model gives some insights about the reasoning behind its decision, it can find applications in fields such as surveillance and medical applications where explainability helps to determine system fidelity.
Two-Level Classifier for Detecting Categories of Offensive Language

We explore the task of offensive content classification on the HASOC 2021 shared task dataset. Our approach is based on two-level classification scheme (corresponding to Subtasks 1A and 1B respectively). The first level is a binary classification (offensive or not). The second level further classifies only the offensive instances. The classifier at each level is a fine-tuned transformer model. Our model on the English dataset achieves an overall best macro F1 score of 0.831 and 0.666 on Subtasks 1A and 1B, respectively. The model performance on Hindi and Marathi is competitive: macro F1 score of 0.778 for Hindi Subtask 1A and 0.553 for Hindi Subtask 1B (fourth place on the leaderboard), and a macro F1 score of 0.847 for Marathi Subtask 1A (13th on the leaderboard).
Polar Data Forum IV – An Ocean of Opportunities

This paper reports on the Hackathon Sessions organised at the Polar Data Forum IV (PDF IV) (20&ndash;24 September 2021), during which 351 participants from 50 different countries discussed collaboratively about the latest developments in polar data management. The 4th edition of the PDF hosted lively discussions on (i) best practices for polar data management, (ii) data policy, (ii) documenting data flows into aggregators, (iv) data interoperability, (v) polar federated search, (vi) semantics and vocabularies, (vii) Virtual Research Environments (VREs), and (viii) new polar technologies. This paper provides an overview of the organisational aspects of PDF IV and summarises the polar data objectives and outcomes by describing the conclusions drawn from the Hackathon Sessions.