Reviewerly/exHarmony huggingface dataset:
A Comparative Analysis of Image Captioning Techniques

Image captioning is the task of generating a textual description that accurately represents the content of an image. This task involves combining computer vision techniques, such as object recognition and scene understanding, with natural language processing to produce a human-like description of an image. Over time, various models have been introduced to perform image captioning, all aiming to accurately describe the content of an image. These models have practical applications such as improving the accessibility of multimedia content, assisting individuals with visual impairments, medical image captioning, and enhancing image search and retrieval. This paper explores some of the models and studies their efficiency using different evaluation metrics.
Image Classification using Quantum Convolutional Neural Network

The unprecedented progress in the domain of quantum computing in the last few years has influenced researchers around the globe to solve multitudes of problems in this promising computing technology. This power of the quantum computer has allowed multitudes of computationally hard problems to be sped up exponentially over their classical counterparts. Along with such power, another promising application of quantum computing has been found in image processing and machine learning. Researches in both quantum image processing and quantum machine learning are still in their infancy but promise exceptional power over its classical counterparts. In this thesis, neural networks will be trained to determine parameters for various parametric quantum circuits to perform important classification tasks, such as image classification. But for image classification, features from the images must also be extracted and epresented in terms of qubits, requiring convolutional layers tailored for quantum techniques. This thesis aims to find good quantum convolutional neural network architectures for image classification with higher accuracy. This is still challenging due to increased cost and error with a higher number of qubits within a system. This thesis is expected to be important in the future direction of the research of quantum CNN.
PSDFormer: A Pyramid Simple Detail Injection Transformer for Real Time Semantic Segmentation

One of the fundamental tasks in computer vision is semantic segmentation, which is increasingly being widely applied, for example in autonomous driving. However, past research has often resulted in large computational overhead and high latency. To address this issue, we propose a lightweight architecture called Pyramid Simple Detail Injection Transformer (PSDFormer). Experimental results demonstrate the superiority of our approach in lightweight semantic segmentation. On the Cityscapes dataset, PSDFormer achieves an mIoU of 66.78 while achieving 57.1 fps.
Highway Tunnel Environment Perception System Based on Internet of Things and Cloud Computing Technology

This paper studies the highway tunnel environment awareness system based on the Internet of Things and cloud computing technology. The system is based on Internet of Things and cloud computing technology. It can be used to detect objects, people and vehicles in highway tunnels. The system can also provide real-time information about traffic flow, road conditions and weather conditions in the tunnel. It provides decision data for government officials, engineers and maintenance personnel. The measured results show that the system has high integration, simple layout and stable operation, which makes up for the technical defects of single collection content of portable tunnel monitoring equipment, insufficient data accuracy and complex structure and decentralized layout of traditional fixed tunnel monitoring facilities.
Review on Dysgraphia Prediction using Handwriting Recognition

In the following paper, we present an overview of current work on handwriting recognition-based dysgraphia prediction. Our analysis reveals both promising insights and limitations. We suggest that to improve the effectiveness of proposed approaches, it is important to compare them with other state-of-the-art methods and expand datasets to include more diverse samples and inclined text lines. We also emphasize the need to consider existing challenges such as sensitivity to image quality and noise, and the importance of replicating studies with larger and more diverse sample sizes. Our findings provide valuable insights for researchers working in this field to guide future developments and improve the accuracy and applicability of handwriting recognition and analysis.
A Dual Attention Network for Multimodal Remote Sensing Image Matching

Multimodal remote sensing image matching is a crucial task for many remote sensing applications. Due to different imaging mechanisms and conditions, multimodal images often suffer from significant nonlinear radiation distortions (NRD). To address this issue, this paper proposes a robust feature matching framework, where the learnable descriptors can be invariant to contrast changes. Firstly, we created a multimodal remote sensing image dataset for training the algorithm, as there is a lack of datasets in this field. Secondly, we use the phase congruency algorithm to preserve the invariant features between multimodal image pairs and optimize the parameters to make them more suitable for multimodal remote sensing image matching tasks. Finally, we design a matching network that introduces both position attention and channel attention modules to model the spatial and dimensional dependencies. Experimental results show that our method exhibits significant advantages over popular algorithms on publicly available datasets.
Fault diagnostic of power transformer by CFS-GA Case-Based Reasoning model

To address the problems that knowledge rules are difficult to obtain and inadequate use of fault case experience in the process of transformer fault diagnosis, a CFS-GA case-based reasoning model is proposed. The model uses the correlation- based feature selection (CFS) method to extract key attribute features with strong correlation in power transformer fault cases, and solves the feature redundancy in case searching caused by excessive test data of transformers. Using the reduced attribute features, a genetic algorithm (GA) method is employed to optimize the feature weights that are difficult to determine manually and assign them to the K Nearest Neighbor (KNN) network to realize the diagnosis and final classification of power transformer fault category. The feasibility of the method is verified by experiments. The overall recognition accuracy of fault diagnosis is over 70% and it can reach 84% with the selected attribute feature set and optimized weights. This method also
Crime Evaluation Using Neural Networks by GPS Trajectory Dataset

One of the many irregularities of the world includes crime. It clumps in some areas and is absent in others. Various daily activities require this knowledge. They seek out particular locations while avoiding others. Their decisions on neighborhoods, schools, stores, roadways, and recreational opportunities are influenced in part by the knowledge that Some of these locations have higher victimization rates than others. These choices further create a reason of developing crime concentrated areas also known as hot spots. Thus, a need to analyze the crime levels and to determine the causes of such concentrations is really useful. Tracking every route of the area with possible reason is only possible by using Global Positioning Systems (GPS) which makes the data availability easier and more accurate on minute level. A study is done this research which finds the best features that might explain the possible criminal activities in a region by building a machine learning neural network (NN) model and minimizing the errors. By the end of this study the top features that came out are ‘accidents’, ‘traffic’, ‘tourist attraction, ‘length of the route’, ‘duration of the route’ which majorly effect the crime rates of an area.
Reactive-Optimized Sentence Detection In Kubernetes Using OpenNLP And Native GraalVM Image With Framework Metric Comparison

Java was chosen over Python for data science endeavours since it is a widely-used programming language. The default JVM was GraalVM, an industry-leading and high-performance JVM, rather than Oracle’s default. GraalVM has several advantages, including native image generation, which pre-compiles Java code into an executable, resulting in a smaller footprint and better performance. Apache OpenNLP, a Java-based natural language processing framework, was utilized for sentence detection. OpenNLP has pre-built models for language recognition, sentence detection, etc. The default OpenNLP code was made completely reactive and functional by modifying it. To achieve the goals, Spring WebFlux was leveraged and the advanced capabilities of Java 17, including lambda and streams. WebFlux, an event-driven asynchronous framework, can provide backpressure-capable, non-blocking clients and servers. Additionally, some of the most widely-used Java frameworks were employed, namely Micronaut, Quarkus, Helidon, and Spring Boot, to develop the code. Two codes were written for each framework: one utilizing traditional Java code and the default OpenNLP sentence detection implementation. At the same time, the other was entirely reactive and functional, with the OpenNLP implementation modified to be reactive and functional. Docker images were created for each and deployed in a Kubernetes environment using Minikube. Finally, concurrent load-testing of the endpoint for 1-1000 users was conducted using an Apache JMeter to assess the performance. Vital metrics such as CPU, memory, docker image size, executable size, executable start-up time, throughput, and error per cent were collected. It was found that reactive-functional programming with Spring Boot resulted in superior scalability, responsiveness, and fault tolerance.
E-mail Spam Detection Using Machine Learning

Spam e-mail detection involves applying algorithms and rules to locate and delete unwanted or unwelcome e-mails. These algorithms and guidelines frequently look at an e-mail's content, the sender's reputation, and other factors to determine if it is likely to be spam. For users' inboxes to be free of unwanted or potentially hazardous information, the ability to recognize spam e-mails is essential. Spam e-mail detection is critical for protecting customers from unsolicited and potentially harmful information that can clog their inboxes and compromise their security. Mails are Categorized as 'Spam' or 'Ham' in the dataset. By performing analysis and applying machine learning algorithms for building the predictive system, the model will predict the upcoming mail as spam or ham according to the research on the data. Various machine learning classification algorithms are used, out of which the Multi-Layer Perceptron (MLP) gives the most accurate results and prediction over the data with about 98% accuracy.
Optimised Architecture of Shadow Lightweight Cipher

Due to its expanding user base and range of potential uses, the IoT is often regarded as the next big social revolution. It's impossible to name a single institution that hasn't been touched by the IoT's pervasive influence. Therefore, proper precautions must be taken to secure our data while transmission over the internet. Since our data is vulnerable while in transit, cryptography is necessary to encrypt it at the sender's end and decode it at the receiver's end. A cipher is used during encryption and again during decryption. As a result of technological advancements, the Internet is increasingly being utilised in a wide variety of applications that have low resource (monetary, electrical, and physical) requirements. When dealing with constraints like those imposed by power and memory, we resort to "Lightweight Cryptography". Shadow Cipher, a Feistel structure-based cipher, is the subject of this paper. In order to improve the frequency and throughput of standard Shadow based implementations, a 2-Clock Loop Unrolled Implementation of the lightweight cryptographic algorithm has been presented and its performance has been simulated on the Spartan and Virtex families of FPGA platform. An increase in throughput of 41.8% compared to the baseline implementations are observed.
Clustering the Various Categorical Data: An Exploration of Algorithms and Performance Analysis

Clustering is a method of grouping data based on similarities, and is an unsupervised technique for discovering patterns in data. In this research paper, various clustering algorithms such as k-Means, DBSCAN, Spectral Clustering, Gaussian Mixture, and Agglomerative Clustering are compared and evaluated on Amazon Prime Video Movies and TV Shows, Netflix Movies and TV Shows, and Disney+ Movies and Tv Shows datasets. The results of the study indicate that the k-Means algorithm performed well in clustering the data for all datasets, with an overall high level of performance. Additionally, the study provides valuable insights into the genre distribution of the data, and highlights the advantages and limitations of each clustering algorithm.
Augmented Reality Snipping Tool

Augmented Reality is a mixture of the actual and computer-generated worlds. It is accomplished by complementing computer-generated graphics with real-world photographs. AR is frequently characterized as a framework that integrates the physical and virtual worlds, allowing for interaction between virtual and physical items in real-time and precise 3D placement. This is a program that allows you to clip and paste things from your environment into your system. The trim operation removes the specified data from its initial position, the clipboard data is then incorporated whenever a paste command is executed. The information is still available to a program that supports the highlight, allowing for easy information interchange across apps. With recent advances in machine learning, it is now feasible to recognize people and things in our surroundings, Delete the background automatically and deliver the output to the user's workstation. AR has so far been used to bring enhanced images into the real world. The AR Snipping Tool, on the other hand, brings real objects into the digital environment by reversing the process.
Multi-objective Hybrid Optimization-based Feature Selection for Sentiment Analysis

Reviews, ratings, and texts that customers have made online on items they have purchased online. These assessments assist business owners to assess their strengths and flaws and take action. The company owner cannot track each review due to the large volume of reviews. Recently, sentiment analysis for review summarization has demanded techniques from massive datasets. The sentiment analysis from the raw texts becomes a challenging task that needs to optimized feature selection approach. Using Natural Language Processing, the pre-processing step must eliminate the undesirable material from the text reviews (NLP). The extraction of more significant and reliable features for accurate sentiment analysis was the focus of the feature extraction phase. To that end, we present the Hybrid Multi-objective Optimization (HMO) method, which combines Particle Swarm Optimization (PSO) with the Krill Herd Algorithm (KHA). The HMO selects features from the supplied pre-processed reviews. The classification is performed by using different machine learning classifiers. The simulation results prove the efficiency of the proposed model compared to the existing solutions in terms of precision, recall, F1 score, and execution time.
Body measurement estimations using 3D scanner for individuals with severe motor impairments

In biomechanics, a still unresolved question is how to estimate with enough accuracy the volume and mass of each body segment of a subject. This is important for several applications ranging from the rehabilitation of injured subjects to the study of athletic performances via the analysis of the dynamic inertia of each body segment. However, traditionally this evaluation is done by referring to anthropometric tables or by approximating the volumes using manual measurements. We propose a novel method based on the 3D reconstruction of the subject’s body using the commercial low-cost camera Kinect v2. The software developed performs body segment separation in a few minutes leveraging alpha shape approximation of 3D polyhedrons to quickly compute a Montecarlo volume estimation. The procedure was evaluated on a total of 30 healthy subjects and the resulting segments’ lengths and masses were compared with the literature.
Raitu Vrudhi – An Android based Mobile Application for Agro-Marketing

The proposed work is a mobile application that helps farmers perform agro-marketing, leading to success and increasing their living standards. This work provides a solution for farmers in vain during the pandemic as they cannot sell the crop yields due to a lack of buyers and market agents who didn't buy the crop yields even at a quarter of the market price. Many farmers end up getting little pay for their hard work due to the ever-extending mediators in the value. The intuition of this novel real-time application is to improve communication between farmers and buyers easily with a constructive solution to revolutionize agricultural products for more returns on their yields. Considering the stated for this problem, an Android-based mobile agro-marketing application is developed that serves as a way for the farmers to sell their crop yields helps them to have more confidence in their sales, and will make farming more valuable by surpassing the mediator-dominated farming value chain. The mediators are replaced with a virtual system that provides direct interaction between farmers and markets or buyers with multiple options. A pre-requisite is just knowledge about the mobile and how to use the app to gain more insights into the farmer's side. This application addresses the communication gap faced by farmers during the pandemic despite having a high yield of crop harvest, but unable to sell it as they couldn't find any buyers and agents, to get market price and thereby increase their accumulation and growth. Thus, the mobile app offers a single platform that makes communication between the farmer and buyer straightforward and efficient aiming to help farmers grow economically.
Cover slip handling and mounting media dispensation for reliable automated cover slipping of large tissue sections

Good quality cover slipping is essential for a reliable histopathological diagnosis and achieving this requires precise placement of the cover glass or cover slip over the tissue specimen mounted on a glass slide, without forming air bubbles. However, manual cover slipping relies heavily on the expertise of the histologist and can be time-consuming, while commercially available automated cover-slippers are limited to handling only 1” x 3” slides suitable for small tissue sizes. For large tissue sections extracted from brain, liver, and other organs, handling large-format slides, cover slips, and dispensing the optimal amount of mounting media can be challenging. To address this challenge, we developed SLIDE PROTEKT, an automated cover-slipper system that can handle slides of sizes 6” x 8”. The reliability of the system to effectively handle large cover slips and dispense the mounting media was validated and the results are presented in this paper. The cover slip pickup mechanism had an 84% accuracy in picking cover slips without misalignments or deformations. The pressure required to pick and hold the cover slip effectively was found to be 15 kilopascals. For large format 6” x 8” slides, the optimal mounting media volume required was 5 milliliters which the system consistently dispensed with a mean error of 4.2%. The validation results highlight the potential impact of SLIDE PROTEKT in histology, particularly in the reliable cover slipping of large tissue sections extracted from the brain and other organs.
Integral Calculations of the Forces Between Parallel Rectangular Permanent Magnets

The main purpose of this study is to find a general analytical relation that can be used to calculate the force between two permanent magnets. The relation will depend on the dimensions of the magnets and distance between them. These will be based on the Coulomb equations for the scalar electrical potential, that will be applied in the magnetic field to calculate the scalar magnetic potential.
Experimental Results on Synthetic Data Generation in Unreal Engine 5 for Real-World Object Detection

This paper investigates a new method for generating high-quality synthetic training data for object detection (OD) algorithms. Utilizing photogrammetry, we transform real-world objects into 3D digital replicas in Unreal Engine 5 (UE5). We then simulate diverse scenarios, capturing images of these objects at varying angles, backgrounds, and textures. To evaluate the data quality, we experiment with multiple configurations and train OD models using YOLOv8. Our findings highlight the effectiveness of the proposed image generation approach in UE5 and establish its potential as a cost-efficient solution for generating extensive volumes of training data for OD algorithms.
The Importance of Data Quality in Training a Deep Convolutional Neural Network

It is an acknowledged fact that good quality and varied training data samples are essential for achieving high rates of success in the operation of a convolutional neural network. The performance of the trained network is highly dependent on the training data being as diverse and representative as possible and each learnable feature being present in a large number of training data samples. There are situations when it is not possible to accrue a sufficient number of original samples and artificial sample generation is needed. The current work studies the impact on performance of a convolutional neural network of original training data supplemented with artificially generated samples. Multiple batches of training data consisting of varied combinations of fractions between original and artificial samples have been used to train a convolutional neural network in order to determine the percentage of original data needed to achieve minimal loss of performance.
A Method Based on Ellipse Fitting for Automatic Morphometric Parameter Measurements of Fish Blood Cells

Thanks to their sensitivity to different changes affecting the environment and capability of demonstrating the impacts of various pollutant types, fishes are considered valuable and suitable species to be used as bioindicators for the water body pollution. Measurement of Morphometric parameters of Fish Erythrocytes (MFE) has been considered a valuable approach for analysis of health status of fishes. Conventional MFE measurement methods have several limitations, including poor measurement reproducibility and reliability, since they rely mainly on the human expertise, as well as required intensive highly time-consuming work to take measurements manually. To address these issues, this work presents a solution based on image processing and ellipse fitting technique to automatically measure MFE. The proposed method first applies customized image processing steps to locate cells in the image, segments nucleus and border of cells, and then uses the ellipse fitting algorithm to fit ellipse on detected cell/nucleus. The effectiveness of the proposed method has been validated through the comparison of results carried out by two expert biologists. The experimental results show that the proposed method properly fit the ellipse on both fish blood cell and its nucleus, indicating its potential as a promising tool for biomonitoring applications.