Reviewerly/exHarmony huggingface dataset:
A Verifiable Secret Sharing Scheme using Elliptic Curve Cryptography

One of the significant problems with communication technology is data privacy, which impacts our everyday activities. Sharing a secret is crucial for safe contact and has many uses in information security. Secret sharing allows a select group of authorised users to view private information. Few authorised members are given access to the secret key in various organisations, with a certain number of them being expected to figure out the secret. In secret sharing, all individuals are assumed to be trustworthy, but in reality, there is the chance of cheating. This study suggests a threshold method called verifiable secret sharing to detect a cheater and verify each individual’s honesty. The elliptic curve cryptosystem, hash function, XOR, and Lagrange interpolation technique do the sharing process. Only shadow shares, not the actual shares, are given to the participants, and before sending information, participants perform combiner authentication. Analysis of security and performance reveals that the proposed method is more effective.
Sistem Temu Kembali Informasi Pesawat Udara Militer Menggunakan Jaringan Syaraf Tiruan Back Propagation Network

Various war events in various parts of the world between two or more countries along with various high-tech war equipment such as military aircraft arouse curiosity to find out more about these war equipment, getting this information is still hampered by the lack of existing information, a lot of information is available in printed form, in museums managed by the Indonesian National Army (TNI), and in the form of books about military aircraft. The design of a Military Aircraft Information Retrieval System Using Machine Learning is aimed at providing information to the public about types of military fighter aircraft, this is due to the lack of information about fighter aircraft in the military world among the public.
Gateway-based Interoperability for Distributed Ledger Technology

Blockchain is a distributed ledger technology (DLT) to manage data in a decentralised way. During the last years, interoperability has become one of the main challenges within blockchain research as blockchains increasingly require integration between each other. Indeed, blockchains work by design in silos of information as interoperability is not a native feature. The main efforts in the field are focused on blockchains, such as Bitcoin and Ethereum. However, interoperability in DLT remains as an almost untouched area of work as they introduce additional requirements focusing on privacy and identity. Although there are some interoperability solutions for DLT, they are either high-level design proposals not providing concrete implementations or focus on interoperability issues between business applications and blockchain platforms. In this paper we propose a gateway-based platform-to-platform interoperability solution for DLT, which comprises a detailed solution design and a reference implementation. The proposal was assessed through the development of a social security case scenario and the evaluation through two interoperability frameworks. A reference implementation was built using two DLT: Hyperledger Fabric and Corda. The experimental results shows that it is possible to achieve technical interoperability between two heterogeneous DLT platforms using a gateway-based interoperability solution, relaxing decentralisation, data privacy, identity and authorisation management properties.
3D Human-Body Modeling for Medical Simulation: Usability and Robustness of Photogrammetry Techniques

Total skin electron therapy TSET is mainly used in the treatment of mycosis fungoides, a rare cutaneous T-cell lymphoma affecting the first few millimeters of skin. The Skinscan project aims to optimize irradiation by creating a 3D model of the patient’s body surface. This would avoid overdosing or underdosing in different skin areas compared to the prescribed dose and would reduce the inhomogeneity of dose distribution associated with current techniques used during TSET therapy. In SkinScan, we investigate the creation of 3D models of the human body, analyzing the compromise between usability of the software and robustness of the techniques. State-of-the-art techniques for the creation of the 3D models are RGBD and photogrammetry. The first ones require dedicated devices, the other are based on standard photographs. Commercial software to align the different views and to create the models are available, and they can be used by non-experts. The final application would indeed require the creation of ad-hoc models for a personalized medical intervention on the patients. Such models should be created in hospitals, without the intervention of computer scientists. Moreover, accurate 3D modeling often requires specific post-processing to remove noise without adding artifacts on the surfaces. We present the preliminary results to create the 3D models to be used as the input of the SkinScan simulation pipeline. We modeled a flat wall and a box composed of flat surfaces, to evaluate the amount of noise in the 3D reconstruction and the pre-processing of the images to remove it. We then modeled two different body parts, a hand and a shoulder, with the aim of understanding the best compromise between the accuracy of the model and its size, thus the simulation time.
Ensemble Learning Approaches for Fraud Detection in Financial Transactions

Banks must adapt to an ever-changing fraud landscape. This research employs ensemble learning techniques to increase the precision of fraud detection. This study demonstrates that the Random Forest ensemble method is effective at detecting fraudulent financial transactions. Using a large dataset and cutting-edge feature engineering, this demonstrates how Random Forest can be used to improve fraud detection by identifying subtle connections and patterns in transaction data. The proposed work findings indicate that ensemble learning is effective in this context, substantially enhancing model precision, recall, and overall performance. This research augments the antifraud resources already available to financial institutions. Using Random Forest as its foundation, the proposed work suggests a path forward for advancing fraud detection systems.
Unleashing the Power of Machine Learning: A Precision Paradigm for Breast Cancer Subtype Classification Using Open-Source Data, with Caution on Dataset Size and Interpretability

This research paper introduces an innovative approach that harnesses the power of machine learning algorithms and open-source data to enhance the precision and personalization of breast cancer treatment decisions. Leveraging the publicly available Breast Cancer Wisconsin (Diagnostic) Dataset. we explore the potential of machine learning in accurately classifying breast cancer subtypes. A wide range of machine learning algorithms, including Logistic Regression, Support Vector Machines, Decision Trees, Random Forest, and Gradient Boosting, are employed to classify breast cancer subtypes. Their performance is evaluated using key metrics such as accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). These metrics provide a comprehensive assessment of the algorithms' effectiveness in subtype classification. The results demonstrate the exceptional performance of the machine learning models, with Logistic Regression emerging as the top-performing algorithm, achieving an accuracy of 99.12%, precision of 98.61 %, recall of 100%, F1-score of 99.3%, and AUC-ROC of 99.87%. These remarkable metrics highlight the potential of machine learning in accurately identifying and distinguishing between malignant and benign breast cancer subtypes. The identified best-performing model holds promise for improving precision and personalized treatment decisions in breast cancer management.
Image Recognition and Similarity Retrieval with Convolutional Neural Networks

Image is a form of representation of data that is presented with visual beauty and has many patterns in it, in a database of irregular image datasets it is enough to make us humans who like structured, neat, and high readability data sets Therefore, the role of machine learning in helping to search for the same image will greatly help humans both in implementing search engines, tidying up image data structures automatically, etc. This research will focus on creating the same 5-image search system based on input from the user using the CNN algorithm & pretrained models such as ResNet with the expected results of model accuracy ranging from 80-95%. This was carried out using the CIFAR-10 dataset as a source. The CIFAR-10 dataset is a medium-scale image data set with a total of 60000 images, there are 10 categories in it, meaning each category contains 6000 images with the size of each image being 32x32. In general, a dataset has different image sizes, so this research study will first do an overview about whether the data for each image is enough with a size of 32x32.
Cyberbullying Avoidance and Impact from Online Tweets

Cyberbullying has created a negative impact on people which directly or indirectly caused psychological setback in mind thought, especially youth. This has led to investigations on a wider group of research communities to focus on this major issue. The problem identified is aimed at addressing harassment on social media and several other wider cyber perspectives. This research work proposes an advanced methodology to detect cyberbullying posts using machine learning algorithms. The proposed research uses the help of three algorithms namely Convolutional Neural Network (CNN), Naïve Bayes (NB), and Support Vector Machine (SVM). The observations recorded out of the study seems to be have strong influence and lead to gain in classifying cyberbullying using CNN, due to stronger influence on understanding the tweets as compared to NB and SVM. The algorithm proved its significance with accuracy level of 90.7% using CNN approach.
Forensic Video Watermarking

The aim of forensic watermarking is to trace out unauthorized users who are responsible for distribution of illicit digital content. Although there are many approaches for preventing of pirated video in the internet such as video watermarking the content but the real time implementation of the watermarked video and distributing in the streaming platforms is complex and also expensive. In this paper we are going to propose a solution by implementation of a low-cost server-side blind watermarking using a/b watermarking. Although there are traditional watermarking methods for distributing the video, we discussed about the real time watermark generation and embedding and distributing to the users during live streaming. Here we will be dividing live video stream content as A and B to the nginx server the nginx server embed the watermark using a script and store it as an input. When a user requests the live video the node.js script sequence a particular watermark content to the user so that for every frame sequence each user gets a different type of video. If suppose when the live video is pirated when can find the user by the sequence of the watermark which is sent to the user.
Quantum Attacks on Public Cryptosystems

Quantum Key Distribution (QKD) networks have emerged as a revolutionary technology in ensuring secure communication in the digital age. By leveraging the principles of quantum mechanics, QKD allows two parties to establish a secret key securely, which can subsequently be used for encrypting and decrypting sensitive information. While QKD holds immense promise for enhancing cybersecurity, it also presents several challenges that necessitate further research to achieve its full potential. The research presented here examines the development of QKD networks, their real-world use, as well as the general QKD network architecture and the most recent advancements in QKD network deployments.
A Study on the Application of optimized K-Means Algorithm in Intelligent Hospital Procurement Supplier Management

In the procurement supplier management system of intelligent hospitals, the K-means clustering algorithm is widely used for clustering analysis. However, the K-means algorithm is sensitive to the selection of initial clustering centers and may generate local optimal solutions or fall into local minima. So this research improved the traditional K-mean clustering algorithm by combining the effectiveness evaluation index of K-mean clustering algorithm with particle swarm algorithm. And on the basis of the improved K-mean clustering algorithm, this research designs the supplier selection method of raw material intelligent procurement system. The experimental results show that the improved K-mean clustering algorithm has the highest initial profile coefficient of 0.51, and its profile coefficient is the highest when the number of samples reaches 300, which is 0.79. The improved K-mean clustering algorithm’s initial Dunn’s index and its final Dunn’s index are higher than the other three comparative algorithms, which are 59 and 67, respectively, and its Calinski-Harabasz index is the highest on average at 37.6, ranging from 3.4 to 11.2 higher than the other three compared algorithms. The optimized K-mean clustering algorithm has a larger Calinski-Harabasz index, which indicates that the inter-cluster variance is larger, the intra-cluster variance is smaller, and the clustering results are relatively better. This study is effective for the design of intelligent hospital purchasing supplier management system based on optimized K-mean clustering algorithm, which can be used as a technical reference for the customer segmentation problem of clustering algorithm.
Image Dehazing Using a Simple Convolutional Autoencoder

In this work, we introduce an end-to-end autoencoder-based convolutional neural network designed to effectively remove haze from hazy images. Our proposed approach leverages a supervised learning methodology, utilizing a dataset that encompasses both haze and haze-free images. Unlike traditional methods relying on atmospheric scattering models, our method aims to directly capture the intricate relationship between hazy and haze-free images. To train our model, we used the RESIDE dataset, consisting of diverse indoor and outdoor images, all with a size of 512$\times$ 512 pixels. Although our technique stands out for its simplicity and lightweight nature, it achieves competitive effectiveness according to evaluation metrics, properly establishing the correlation between haze and haze-free images.
Voting Decentralized Application Using Blockchain

The democratic practise of voting is one of the most important. The most reliable method for guaranteeing that everyone's right to vote is protected, in the opinion of many experts, is to use paper ballots. Meanwhile, this tactic is open to misuse and blunders. To address the problems with paper ballots, many countries now use digital voting procedures. Digital voting machines are prone to mistakes that could result in widespread vote rigging. Voting procedures for elections must be authorised, precise, secure, and practical. Acceptance may be constrained, though, by problems with digital voting procedures. Blockchain technology was created to solve these issues due to its end-to-end verification capabilities. To ensure We have employed blockchain technology. We implemented our protocol on the blockchain-based Ethereum 2.0 platform, which uses the Solidity programming language To test it, it is necessary to create smart contracts. Smart contract technology offers a safe way to validate voters, ensure the validity of the results, make the voting process public, and thwart fraud. Based on security and gas costs, we examined how well the system performed. It becomes more secure and costs less to maintain the required infrastructure.
Human Face Mask Recognition using ResNet 152 Model

The global health crisis, and the enforcement of mask-wearing protocols has become crucial for public safety. To automate the monitoring of these protocols, this study proposes a robust human face mask recognition system utilizing the ResNet 152 model, a deep convolutional neural network known for its accuracy in image classification tasks. The system inputs an image and processes it through successive convolutional layers- Conv 1 through Conv 4-with varying filter sizes and strides to extract detailed feature maps. These layers are followed by a max pooling operation and a fully connected layer with 1000 dimensions to synthesize the extracted features into a comprehensive representation. The output is a binary classification that determines the presence or absence of a face mask. This study implemented skip connections characteristic of ResNet architectures to enable training of deeper networks without the hindrance of vanishing gradients. Preliminary results demonstrate a high accuracy rate in face mask detection, indicating that the ResNet 152 model is a promising for enhancing public health measures through automated face mask recognition.
Elevating Cybersecurity in Defence: A Collaborative and Technologically Driven Approach

As the world enters the 21st century, the imperative of safeguarding cybersecurity has intensified. Considering the expanding dependence on information technology (IT) systems for a broad spectrum of military operations, the stakes have never been higher for thwarting successful cyberattacks. Conventional security protocols have become increasingly precarious in the face of evolving cyber threats. This necessitates the adoption of avant-garde and nascent technologies to bolster the military's cybersecurity stance. The paper delves into an array of incipient technologies that proffers the prospect of enhancing cybersecurity in the realm of defence. These include artificial intelligence (AI), machine learning (ML), blockchain, and quantum computing. AI can be leveraged to automate numerous cybersecurity functions, such as threat detection and response. ML can augment the precision of threat detection and response systems. Blockchain, a distributed ledger technology, can safeguard data and transactions. Quantum computing, a nascent technology, harbours the potential to revolutionize computation. It could be utilized to breach present-day encryption standards, thereby rendering data vulnerable to attackers. Moreover, the paper appraises the exigencies and possibilities in defence cyber security and scrutinizes the policy and practice ramifications of these emergent technologies and best practices.
6D Pose Estimation for Vision-guided Robot Grasping Based on Monocular Camera

Enhancing the intelligence of grasping operations through visual empowerment is currently a hot research topic in the field of robotics, where pose estimation is an important component. However, existing methods that solely rely on RGB images mostly employ direct methods or indirect methods using two-stage/multistage network predictions. Estimating the translation and rotation components of pose solely through a single network model does not yield optimal solutions. In this paper, different networks were used to predict the translation and rotation components, and convolutional block attention module (CBAM) and Pyramid Pooling Module (PPM) were introduced to address the issues that features decay in the backbone residual network after multiple layers of convolution and the insufficient utilization of feature information. Furthermore, in the rotation prediction branch, the problem of inaccurate recognition caused by weakened details after multiple layers of convolution was addressed by incorporating pyramid pooling into the network to capture richer detail and contextual information. Experimental tests were conducted on the publicly available Linemod dataset and in real environments. The results demonstrate that the proposed network exhibits a significant improvement of 2.76% in the most common ADD metric compared to a coordinate-based disentangled pose network (CDPN). It also achieves a 1 % improvement in the 5°, 5cm metrics and comparable performance in the Proj.2D metric with a 0.2% increase.
Comparing TensorFlow.js and TensorFlow in Python: An Accessibility and Usage Analysis

TensorFlow, a popular machine learning library released by Google, has gained significant traction among developers in creating ML models. It provides support for both Python and JavaScript, with TensorFlow.js, the JavaScript version. This paper presents a comparative analysis of these frameworks, focusing on accessibility factors like ease of learning, available content and tutorials, minimum required knowledge, and hardware requirements. The study highlights that TensorFlow for Python offers a comprehensive suite of tools and abundant resources, making it a robust choice for experienced developers. Tensorflow.js, on the other hand, stands out for its beginner-friendly characteristics. The research findings show that Tensorflow.js is very easy to deploy on the web and is capable of handling graphic intensive models (like face recognition using camera) on entry-level computers as well. This study looks at the advantages and disadvantages of TensorFlow in Python and TensorFlow.js in order to help new programmers choose the best tool for their machine learning projects.
Research on Computational Mechanics Construction of Bridge Civil Engineering Construction Based on Computer Big Data Technology

In this paper, a 3D visualization system for bridge construction management is established based on VRP virtual platform. The bridge construction monitoring data management system is designed on the platform of Access database and the object processing technology of VRP. Starting from the basic theory of construction monitoring of cable-stayed bridge, the system realizes the problem of construction error identification in the monitoring process and using the recognition results to compensate and modify the finite element model. The system mainly realizes the functions of data editing, query, data analysis, data visualization, bridge information introduction and so on. It solves the key problem of using relational database to form various reports quickly and intelligently.
Cultivating Resilience: Advancing Indian Agriculture through Decentralized Blockchain Crop Insurance

This research study delves into the concept of resilience in the Indian agricultural sector, with a focus on the potential for improvement through the implementation of decentralized blockchain technology for crop insurance. Resilience is a critical aspect of agricultural systems, as they face numerous challenges, including climate change, market fluctuations, and pests. The introduction also introduces the idea of utilizing decentralized blockchain technology to enhance crop insurance practices. Decentralized blockchain has the potential to revolutionize the insurance industry by providing transparency, security, and efficiency. The exploration of these concepts in the context of Indian agriculture aims to shed light on the potential benefits and challenges of adopting this innovative approach.