Reviewerly/exHarmony huggingface dataset:
Research on a Lightweight Panoramic Perception Algorithm for Electric Autonomous Mini-Buses

Autonomous mini-buses are low-cost passenger vehicles that travel along designated routes in industrial parks. In order to achieve this goal, it is necessary to implement functionalities such as lane-keeping and obstacle avoidance. To address the challenge of deploying deep learning algorithms to detect environmental information on low-performance computing units, which leads to difficulties in model deployment and the inability to meet real-time requirements, a lightweight algorithm called YOLOP-E based on the YOLOP algorithm is proposed. (The letter ‘E’ stands for EfficientNetV2, and YOLOP-E represents the optimization of the entire algorithm by replacing the backbone of the original model with EfficientNetV2.) The algorithm has been optimized and improved in terms of the following three aspects: Firstly, the YOLOP backbone network is reconstructed using the lightweight backbone network EfficientNet-V2, and depth-wise separable convolutions are used instead of regular convolutions. Secondly, a hybrid attention mechanism called CABM is employed to enhance the model’s feature-representation capability. Finally, the Focal EIoU and Smoothed Cross-Entropy loss functions are utilized to improve detection accuracy. YOLOP-E is the final result after the aforementioned optimizations are completed. Experimental results demonstrate that on the BDD100K dataset, the optimized algorithm achieves a 3.5% increase in mAP50 and a 4.1% increase in mIoU. During real-world vehicle testing, the detection rate reaches 41.6 FPS, achieving the visual perception requirements of the autonomous shuttle bus while maintaining a lightweight design and improving detection accuracy.
Order Space-Based Morphology for Color Image Processing

Mathematical morphology is a fundamental tool based on order statistics for image processing, such as noise reduction, image enhancement and feature extraction, and is well-established for binary and grayscale images, whose pixels can be sorted by their pixel values, i.e., each pixel has a single number. On the other hand, each pixel in a color image has three numbers corresponding to three color channels, e.g., red (R), green (G) and blue (B) channels in an RGB color image. Therefore, it is difficult to sort color pixels uniquely. In this paper, we propose a method for unifying the orders of pixels sorted in each color channel separately, where we consider that a pixel exists in a three-dimensional space called order space, and derive a single order by a monotonically nondecreasing function defined on the order space. We also fuzzify the proposed order space-based morphological operations, and demonstrate the effectiveness of the proposed method by comparing with a state-of-the-art method based on hypergraph theory. The proposed method treats three orders of pixels sorted in respective color channels equally. Therefore, the proposed method is consistent with the conventional morphological operations for binary and grayscale images.
An investigation of the impact of COVID-19 on health-related cryptocurrencies using time-varying parameters and impulse responses

This study examines the impact of COVID-19 on health-related cryptocurrencies. More precisely, we use the variable-Lag time-series (VLT) causality to test whether the pandemic caused the price performance and the volume of transactions of these cryptocurrencies. We then employ time-varying parameter (TVP) models to capture the sign of this effect (for the evidenced cases) and the impulse responses among the cryptocurrencies since they may affect one another. The results show there is no certain pattern, which means that for the years of the pandemic, COVID-19,has impacted cryptocurrencies differently, except for one case. Moreover, the results are very unstable during 2021, indicating time-varying characteristics for all cases, while during 2022, the impact of the pandemic on these cryptocurrencies was mostly negative. Similarly, during 2020 the price was negatively affected, but the transaction volume was mainly positively impacted. Spillovers are evidenced only for 2022, for certain cases both in the prices and volume of transactions. The results indicate that the pandemic affected cryptocurrencies heterogeneously, evidencing a different pattern among the three years examined. This finding should be taken into consideration in the adoption of relevant technological advancements in healthcare.
A refined method for investigating the morphological and optical properties of a biodegradable monocryl suture

An interferometric computed tomography (ICT) technique with a refined method is proposed and analysed for investigating the morphology of the Monocryl surgical sutures during the degradation process. This method includes four integrated stages. In the first stage, the two-beam Pluta polarizing interference microscope is equipped with the rotational device for repeatedly recording the 2D interference patterns of the degraded sample at various angular rotations. The second stage in which an artificial intelligence model is designed to detect and remove the noise classes in the interference patterns. In the third stage, the computational Phase-Shifting interferometric approach (CPSI) is employed for demodulating the phase maps of the tested sample. Finally, autocorrelation and the filtered back projection (FBP) algorithms are utilized to explore the 3D phase morphology of the tested sample. The presented method is useful for determining the refractive indices and the birefringence during the whole degradation duration. Collectively, the results of the proposed method reveal that ICT technology has the potential to investigate the degradation performances of this kind of biodegradable polymer effectively.
Users Gone Astray

This story, which begins from the widely deplored look of the graphing in Microsoft’s spreadsheet program, Excel, and extends back some seventy-five years, is about code that enables users to create visualizations without enough craft—at least in the eyes of their critics. It investigates two facets of data visualization since World War II: iterative analysis through graphical means and the making of business charts concerning numerical data. These two efforts automate some human skills. Both are seen as aberrant, dangerous, and in bad taste when they become too automatic, as users fail to reflect upon defaults. Both activities challenge the binary division between a “nonalgorithmic” culture of human judgments and a contemporary world subjected to hard, unaccountable logics. Telling a story of everyday cultures deemed to have gone bad, this article offers a history of envisioned users shaped through tools that could amplify virtues—and also vices—in thinking, depicting, and acting.
Multi-Disease Prediction Techniques using Machine and Deep Learning

With the development of technologies, the progress of life, and environmental changes, the number of diseases is increasing day by day. Disease prediction and analysis are important research topics in the medical field. Different techniques and methods are introduced for prediction and analysis. The development of artificial intelligence and its integration into the health sector have produced huge changes in the prediction and analysis of disease. The subset of artificial intelligence known as machine learning and deep learning techniques has contributed different methods and analysis systems for better prediction and classification of diseases. In this article, we present the different machine and deep learning techniques and provide a comprehensive review for the prediction and classification of multi-disease in the healthcare sector. Particularly, this article presents four main contribution summaries for multi-disease prediction, such as statistical, machine, and deep learning techniques for prediction and analysis of multi-disease, different dataset summary presentations for multi-disease prediction and analysis, metrics and evaluation parameters for prediction and classification, and the future direction of research for classification and prediction of multi-disease prediction.
AGYLE Methodology Applied in Supply Chain Management

Nowadays, the uncertainty is known as a common rule within the social, economic and political areas. In respect with this, the present paper emphasizes the benefits of using AGILE methodology in the logistics field, in order to reduce the negative effects of the unpredictable events. More precisely, it is an interdisciplinary case study which highlights the way a software development method - Agile is suitable for the supply chain management. The article addresses three main topics: the theoretical approach of the AGILE methodology, the supply chain management framework and the applicability of the AGILE within the fuel supply.
A Deep Learning-based Convolutional Neural Networks Model for White Blood Cell Classification

White blood cells, or leukocytes, are indispensable for the optimal functioning of the immune system. They play a critical role in protecting the body against infections, diseases, and other foreign invaders by identifying and fighting harmful bacteria and pathogens that can cause illness. Additionally, they contribute to the elimination of dead and damaged cells from the body and facilitate tissue healing and repair processes. The absence of white blood cells would render the body defenceless against infections and diseases, exposing it to a variety of harmful pathogens. This could result in significant health issues and potentially even lead to death in severe instances. White blood cell classification is an important task in medical diagnosis and treatment because healthcare professionals diagnose and treat a variety of immune system-related diseases and conditions, including autoimmune disorders, infections, and cancers by identifying the structure, characteristics and functions of white blood cells. In this work, a convolutional neural network (CNN) model has been trained to classify white blood cells. The proposed model has achieved an accuracy of 88.78%, which has been identified as the highest among all the models implemented by various authors in the literature review. This implies that the proposed model has correctly classified white blood cells in almost 9 out of 10 cases. Moreover, the error rate of the model is only 0.108967 which indicates that the model is very reliable and consistent in its predictions. Additionally, this work shows the promising result for white blood cell classification using deep learning techniques. Furthermore, with improvements and refinements in the future, it can be possible to achieve higher levels of accuracy and precision, which could have a significant impact on medical diagnosis and treatment.
Real-Time Yoga Pose Detection Using OpenCV and MediaPipe

In this era of advanced technology, where everything is going online, from shopping to education, there may be ways to get an automated, personalised trainer and mentor for yoga and meditation. Yoga is one of the best exercises for maintaining good physical and mental health. But what if yoga is done incorrectly? It can be harmful to our bodies, so there is a need for a personalised yoga trainer and mentor who is available 24 x 7 to assist you with the poses and provide you with immediate feedback in order to maintain a healthy and wealthy body. In this model, we are coming up with the idea of providing real-time posture detection using deep learning and computer vision that will guide the user to the correct postures after comparing them with the standard yoga postures and also help them track their gym activities. Our research also aims to integrate all of the features from various platforms related to posture detection applications on a single platform rather than switching between multiple platforms. In order to make it more user-friendly, we are giving users the option of selecting between an AI tutor and a physical trainer based on their needs. One of the objectives of the model is to display information about the pose, such as the benefits of performing it and how to incorporate it into routine exercises that the trainee can simultaneously practise to increase its effectiveness.
Design of FIR filter with Fast Adders and Fast Multipliers using RNS Algorithm

The primary driving force behind the creation of this work was to provide the design and implementation of a 4-tap, 8-tap, 16-tap, 32-tap, and 64-tap RNS (Residue Number System) based on efficient and excessive-overall performance FIR filter. RNS mathematics is a prized tool for theoretical investigation of the speed limitations of rapid mathematics. Some suggested solutions also include a few addition operations; however, using conventional adders will slow down operation and add to the amount of logic gates. So, to address the aforementioned concerns, Kogge-Stone Adder and Brent Kung Adder are being used to reduce delay and area and enhance performance as a whole. First, the multiplier is created using the RNS methodology. In which the Vedic multiplier's power dissipation is also minimized while the latency is shortened from 70% to 90%. In order to assess the findings, we are also using a simple adder and a simple multiplier. Using the Quartus 9.0 Simulation Tool, the combination of those methods results in a completely new structure with an excessively high speed and a small implementation area for the FIR filter.
CB-YOLOv5 Algorithm for Small Target Detection in Aerial Images

Aerial images are often plagued by background interference, and small targets with indistinct features, leading to low accuracy, high false detection rates, and high miss detection rates. To address these challenges, a small target detection algorithm based on YOLOv5, Coordinate-attention and Bidirectional-feature-pyramid-network YOLOv5 (CB-YOLOv5), is proposed in this paper. Considering the small number of pixels occupied by small targets and their indistinct features, a fourth target detection layer is added by concatenating the feature map from quadruple down-sampling during feature extraction with the feature map output from 8-fold up-sampling during feature fusion. Additionally, a coordinate attention mechanism is introduced during the feature extraction stage to improve small target localization and enhance detection accuracy. Finally, the original Path Aggregation Networks (PANet) structure is replaced with a weighted Bidirectional Feature Pyramid Network (BiFPN) structure during the feature fusion stage to improve the network’s ability to fuse feature maps of different scales. The simulation results demonstrate that the CB-YOLOv5 improves mAP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</inf> by 9.4%, mAP <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">75</inf> by 9.7%, and mAP50:95 by 7.8% compared to the original YOLOv5s model. Thus, the effectiveness of the CB-YOLOv5 algorithm for detecting small targets in aerial images is validated.
An Overview of Protocols-Based Security Threats and Countermeasures in WLAN

WLAN security protocols are the most important section for wireless networks which controls all the security-related issues by securing the network with some pre-defined rules made by wireless-protected access organizations, further which makes it the main target of hacking and threats by interested parties and to be safe from such threats. There should be countermeasures for such threats where there will be a solution for any threat encountered and to mention. WIFI or wireless local area Network is a cost-effective easy deployment Network that makes users take advantage of vast data shared on the internet and by the network without the need for any cable for connections. Here in this overview paper by reviewing many researchers’ techniques and works on the WLAN security topic, there are shared threats to WLAN and the ways of taking precautions against those threats as countermeasures and for which the tables are used. For comparison of techniques used by authors and some famous and highly dangerous threats are also defined further. WPA3’s new security protocol is also defined with its newly found vulnerabilities and countermeasures except there is a conclusion on the downgrade attack problem with WPA3. Further, there are also countermeasures for that as well, nowadays people think the network is secured with standards defined by the IEEE organization but some know this is not the case so it makes it a more important area for research.
NOx emissions estimation of boiler based on mutual information feature reconstruction and optimization of extreme learning machine

Abstract The measurement of NOx emissions in the selective catalytic reduction (SCR) system of boilers has problems with poor real-time performance and abnormal measurements during purging. It is necessary to accurately estimate NOx emissions. For this reason, the NOx emissions prediction method of boiler based on mutual information feature reconstruction and optimization of extreme learning machine (ELM) is proposed: firstly, delay estimation and data space reconstruction of input features are performed based on mutual information; Then the conditional mutual information based on greedy selection strategy is adopted to rank and choose the input features; Finally, the hybrid quantum sparrow search algorithm (QSSA) was proposed by combining Lévy flight strategy and quantum strategy in the sparrow search algorithm, and QSSA is used to optimize the weights and biases of the ELM. Taking the operation data of the SCR system of a 1000 MW thermal power unit as an example for verification. The results show that the proposed method can effectively improve the accuracy and generalization ability of the ELM, and provide a new method for NOx emissions estimation of boilers.
Analysis of Live Video Object Detection using YOLOv5 and YOLOv7

With wide use of IoT devices across all the applications has generated huge amount of image data. With the envision of smart city project in India has provisioned for collecting collating and analysing of traffic video stream data for the smooth flow of traffic. In the process of decision making how effective to identify the identity of an offender in traffic violation. The paper is mainly focused on evaluating two most prominent object detection methods i.e. YOLO (You Only Look Once). For training these models, we have considered common Objects in Context (COCO) dataset. The YOLOv5 make use of anchor boxes to detect the objects in the image and it is successful in prediction based on probabilities.The YOLOv5 has some limitation like misclassification when the objects are small in size, fails in angle deflection and has a limited scalability. The YOLOv7 is a real time object detection model uses instance segmentation. The implemented model of YOLOv7 with a live video to detect whether a two wheeler rider is wearing a helmet or not, as wearing a helmet is one of the safety measure while driving on busy roads in India. The YOLOv7 has achieved higher mean average accuracy than YOLOv5.
Security Analysis of Cryptographic Algorithms in Cloud Computing

As cloud computing is increasingly embraced by people and organizations for information storage and processing, security is a major concern. Cryptographic algorithms are utilized to guarantee the privacy and integrity of information in cloud computing. This paper presents a comprehensive security analysis of cryptographic algorithms in cloud computing. The paper talks about the different sorts of cryptographic algorithms, their strengths, and shortcomings, and their suitability for use in cloud computing. The paper also analyzes the threats to cryptographic algorithms in cloud computing and proposes a few mitigation strategies.
Blockchain Applications in Disaster Management Systems

This project presents a novel blockchain-based disaster management system designed to streamline and optimize the response process to natural disasters. The system’s front-end is built using React, while its back-end runs on Flask. It enables different stakeholders, including aid organizations, government agencies, and relief workers, to access different parts of the system, enhancing collaboration, and increasing efficiency. The system comprises four main categories: missing people log, aid package distribution, critical areas, and refugee-relief camp mapping, each having multiple modules. The blockchain-based system allows for secure and immutable record-keeping of all critical data, enhancing transparency, accountability, and trust. Smart contracts are utilized to automate various aspects disaster management, enabling the rapid and efficient delivery of aid packages to affected areas. The decentralized nature blockchain allows for the efficient coordination of resources and effective management of aid distribution, reducing waste and ensuring that resources are delivered to those in need. The system’s unique features, such as tamper-proof data storage, automated processes, and decentralized coordination, provide an innovative solution to the challenges faced in disaster management and relief efforts.
Voice command-integrated AR-based E-commerce Application for Automobiles

This research work aims to create an Augmented Reality (AR) based android app that can project the dimensions of an automobile in the real world and recognize voice commands to operate functions like opening car doors and changing colors. The app uses a combination of augmented reality, machine learning technology, Unity game engine, C# script, Google speech recognition API and Vuforia SDK to superimpose images of the car in the real world and allow control through voice commands. The initial focus is on cars, but the solution can also be used to create AR-enabled brochures for marketing companies to enhance sales and provide customers with a better understanding of the product before purchase.
Revolutionizing E-commerce with 3D Visualization: An Experimental Assessment of Behavioural Shopper Responses to Augmented Reality in Online Shopping

Augmented Reality (AR) has shown great potential to improve consumer purchasing decisions and experiences. By adding more information and functionality to mobile shopping, AR enables customers to browse and test goods virtually. The objective of this paper is to explore the use of augmented reality in e-commerce to enhance customers' experiences and to analyze customer behavior and satisfaction with product purchases. The methodology used in this paper is a form of markerless AR, enabling the 3D models to be placed freely in environments for all product categories with the ability to resize, rotate, and navigate as per customer needs. The study also finds how significantly the use of AR will affect the e-commerce sector in terms of purchase decisions and return rates in the future. The novelty of this work lies in providing a new AR feature to various product categories in e-commerce stores, thereby enhancing the product experience and improving the purchase decision of the customers.
Resume Classification Using ML Techniques

In today’s world, a typical job ad on the web attracts a massive number of applications in a short period of time. Manual screening of these resumes is not only time-consuming but also very expensive for the hiring companies. To address these challenges, this research paper proposes a solution that aims to automatically classify resumes to their corresponding suitable positions. To find the best possible solution, different ML techniques like Decision Tree, Random Forest, KNN, Support Vector are researched and the most accurate one is chosen. This approach has the potential to revolutionize the hiring process by reducing costs, saving time, and ensuring fairness.