Reviewerly/exHarmony huggingface dataset:
Excavating Multimodal Correlation for Movie Recommendation System

Recommendation mechanisms, which aid users in navigating the deluge of information at their disposal, have become more popular as e-commerce applications have grown. In streaming services for movies, where tailored content and services are crucial, these technologies are particularly significant. But these algorithms frequently run into the issue of scant data, which makes it challenging to formulate precise suggestions. This research presents a customized deep learning-inspired multi-modal movie referral system to solve this problem. This system basically enhances the correlation between different pairs of attributes to get the highly correlated multimodal features. The correlation-based mechanism mines the hidden properties of different attributes (movie_id, movie_title, movie_description, and movie_poster) individually and the properties based on the dependency of one attribute on another. These hidden properties give a high contribution to movie score prediction. The study in this paper justifies that the multimodal features based on the correlation between different attributes enhance systems that provide suggestions. This strategy may result in many relevant suggestions and enhance user interfaces, especially for movie streaming services where customers seek much relevant products and services. The system analyzes the performance of the proposed recommendation approach using the IMDB Vision and NLP datasets and the results outperform the existing methods.
Is Semantic Communication Secure? A Tale of Multi-Domain Adversarial Attacks

Semantic communication seeks to transfer information from a source while conveying a desired meaning to its destination. We model the transmitter-receiver functionalities as an autoencoder, followed by a task classifier that evaluates the meaning of the conveyed information. The autoencoder consists of an encoder at the transmitter that jointly models source coding, channel coding, and modulation, and a decoder at the receiver that jointly models demodulation, channel decoding, and source decoding. By augmenting the reconstruction loss with a semantic loss, this encoder-decoder pair is interactively trained with the semantic task classifier. This approach transfers compressed feature vectors reliably with a small number of channel uses while keeping the semantic loss low. We identify the multi-domain security vulnerabilities of using deep neural networks (DNNs) for semantic communications. Based on adversarial machine learning, we introduce test-time (targeted and non-targeted) adversarial attacks on these DNNs. As a computer vision attack, small perturbations are injected into the images at the input of the transmitter's encoder. As a wireless attack, small perturbation signals are transmitted to interfere with the input of the receiver's decoder. By launching these attacks individually or jointly (as a multi-domain attack), we show that it is possible to change the semantics of the transferred information (with larger impact than conventional jamming) and highlight the need of defense methods for the safe adoption of semantic communications.
Face Recognition through Landmarks to Find Accurate Distance Between Two Landmarks Using Pythagorean Algorithm

Being accurate and efficient and compact is crucial for a face recognition system for practical use. This technique relies on the fact that these features are relatively stable and unique for each individual, making them ideal for face recognition purposes. Face landmark detection involves identifying and localizing key points or landmarks on a person's face. These landmarks are typically areas such as the corners of the eyes and mouth, the tip of the nose, and the contour of the jawline. More concretely, we customize an end-to-end method to calculate the distances between two or more landmarks. The process of face recognition through landmarks involves various steps, including face detection, landmark detection, feature extraction, and matching. Feature extraction involves measuring the distance and angles between these landmarks to create a unique facial signature for each individual. This approach is independent of any face detection and landmark detection algorithms where the distances are to be calculated between the 468 3D landmarks using the proposed technique. The entire concept and the problems for each technique are highlighted in this paper
Hash based image indexing framework using deep earning

Convolutional neural networks (CNN) have demonstrated their effectiveness in extracting discriminative features from images, allowing for accurate image recognition and classification. Traditional search approaches, on the other hand, become computationally expensive and impractical as image databases expand. This research presents a system for indexing CNN characteristics in order to enhance the efficiency of large-scale image search. The necessity to develop indexing frameworks capable of effectively extracting relevant photos from enormous datasets motivates this research. This framework contributes to the improvement of the performance of content-based image retrieval systems by establishing an efficient indexing framework for CNN features. The system draws on existing cutting-edge techniques to create an indexing framework capable of organizing and retrieving images based on similarities in the complex and multidimensional CNN feature space. To lower the computational cost and storage needs associated with large-scale picture searches, the system makes use of image features and hashing methods.
Cross-Lingual Sentiment Analysis in Deep Learning: A Comparative Study of Multilingual Approaches

Sentiment analysis is a rapidly growing field that aims to extract and analyze the opinions and emotions expressed in text. With the increasing popularity of social media and the availability of multilingual data, sentiment analysis on multilingual text has become an important research area. In this paper, we present a comprehensive review of recent advances in sentiment analysis on related languages like Hindi, English, Urdu, Punjabi text. We review the state-of-the-art research papers in this area. Our review includes a comprehensive analysis of the deep learning techniques used in these papers. Our analysis highlights the importance of considering linguistic and cultural differences when analyzing sentiment in multiple languages. Our review provides a comprehensive overview of the current state of research in sentiment analysis on multilingual text and can serve as a useful reference for researchers and practitioners in this field.
Performance Evaluation of Consensus Algorithms for Permissioned Blockchain Networks

The ever-evolving technological landscape demands systems that can ensure trust, security, transparency, and traceability. To meet these requirements, blockchain technology has emerged that uses decentralized ledgers to keep transaction records. It utilizes cryptography to interconnect devices, and once recorded, data stored on a blockchain cannot be altered by any unauthorized parties, thereby enhancing its security features. Consensus in blockchain refers to the process of achieving agreement among a distributed network of nodes on the current state of the blockchain ledger. No centralized body is in charge of validating and approving transactions in a decentralized blockchain network. To guarantee that every node in the network has a consistent and accurate copy of the ledger, the consensus mechanism is used. By simulating the time to attain consensus with the NS-3 network simulator, we assess the performance of the consensus algorithms Raft and PBFT in this paper. This paper considers the effectiveness of the Raft and PBFT consensus algorithms by simulating the time taken to reach consensus using the NS-3 network simulator. Based on the evaluation results, it was found that the performance of PBFT is on average 30% better than RAFT with varying message sizes, transaction speeds, and the number of nodes.
Nuclei Segmentation using EfficientNetV2 and Convolutional Block Attention Module

Cell nuclei segmentation is crucial for developing digital pathology and medical research. It is helpful in numerous applications, including illness diagnostics and other medical therapies. Unfortunately, due to the massive number of nuclei, manual analysis of these image slides takes time and effort. Morphometric appearances add to the complexity. Nevertheless, we upgraded the Nested UNet with an EfficientNetV2S backbone and added a CBAM module in the decoder layers to obtain state-of-the-art performance in medical imaging. This improved model is easier to train, and attention blocks help to tune the retrieved features for better performance. We tested our model using the CryoNuSeg dataset to see how well it performed. Our model scores 0.941 on Dice, 0.605 on AJI, and 0.614 on PQ.
Analyze Quantum Gate Involving Hidden Layers

In this work we have used CNOT gate to represent a quantum state and comparing with the help of NOT gate, here we use two qubit four layer neuron model is investigated. We have suggested a quantum back propagation algorithm to train qubit neural network and trained the network different parameter with the help of Matlab.
Face Detection and Recognition Using OpenCV and Vision Transformer

Face recognition technology is vital in the real world with diverse applications. It is primarily used for security, law enforcement, personalization, healthcare, and education. Face recognition systems use biometric features like facial landmarks, texture, and shape to identify and verify individuals. The suggested approach employs a transformer-based architecture that solely relies on self-attention and does not utilize Convolutional Layers. This design choice enables the model to be trained efficiently with minimal computational power and fewer parameters than a CNN. The application of Vision Transformer (ViT) in various computer vision tasks has been highly successful, making it a state-of-the-art approach. Given its superior performance, we are interested in exploring whether ViT can enhance the accuracy of sheep face recognition.In this paper, we show that ViT can be a useful technique for facial recognition. Since there was no predefined dataset for face recognition, a PCI dataset was built for this investigation. Along with the PCI dataset, two more well-known datasets, AT&T and 5_Celebrity, we used to examine performance. In our model was seen that ViT could identify human faces on the PCI dataset with a 99% accuracy rate and perform much better than other face recognition algorithms like Eigenface, FisherFace, and LBPH.
HSDH: Detection of Hate Speech on social media with an effective deep neural network for code-mixed Hinglish data

The phenomenal rise of social media platforms like Twitter, Facebook, Instagram, and Reddit has led to the blending of native languages or regional tongues with English for the purpose of improving communication in linguistically open geographic regions around the world. There are many ways in which Holocaust denial can lead to an increase in violence, from direct assault to purging out of compassion. Online, people are very hostile to one another. Distinguishing between language that incites hatred and language that is disparaging is a fundamental challenge in the categorization and tracking of extremely toxic lexical features. Our research focuses on identifying harmful tweets composed in Hinglish, a fusion of Hindi and the Roman alphabet. We propose a system in this paper for classifying tweets as either abusive, neutral, or offensive. The help of Hindi-English offensive tweet dataset is comprised of tweets written in the code-transferred language of Hindi and is further subdivided into three groups: neutral, abusive, and hateful. We studied the abusive and hate speech dataset with transfer learning and pre-trained the proposed model on Hinglish-processed English tweets. With our proposed model, we were able to improve accuracy to 98.54 percent.
A Sequential Model of Neural Networks for Low Light Image Enhancement

Enhancing the quality of low light images is a critical area of research, and the recent advancements in this field offer significant potential for enhancing the standard of low light images and their subsequent processing. However, most of these approaches do not consider the case of spatially uneven dark areas with backlit illumination. To address this issue, a pipeline of processes is proposed, where the sky region is segmented first, as most of the source of backlit illumination is a direct result of the presence of sky region. For noise removal and adjustment of exposure without color distortion, conversion of the remaining region from RGB to Luminance Chrominance color space is proposed. The enhancement is done on the Luminance component alone which is then combined with the chroma components and the segmented sky region. Furthermore, the results show that the proposed framework is promising and outperforms the Zero Deep Curve Estimation (DCE) model.
Experimental Approach Toward Training and Analysing Siamese Deep Neural Network for Sentence with No Repeated Expressions

Matching the context of the sentences is a very common yet complex problem in the field of Natural Language Processing. Many aspects such as lexical-semantic, compositional semantic, feature granularity, etc have to be taken under observation to receive an accurate output. For Semantic Sentence Matching there are numerous models, in this paper we try to re-create and train a model in such a way that it provides a positive response in differentiating two statements with no similar words. We have used Deep Siamese Neural Network, determined the best thrush hold then trained the model on five different datasets, and then edited the dataset such that we receive a positive response even when no word is common between the two statements.
FPGA Implementation of Elliptic Curve Point Multiplication for a 256-bit Processor on NIST Prime Field

Elliptic curve cryptography (ECC) has long been a prevailing technique among security researchers for developing security architectures of digital devices to their hardware levels. With the proliferation of advanced technological ecosystems, the necessity for secured information transfer has become even more pronounced. Elliptical Curve Cryptography can substantially outperform other cryptographic schemes in addressing the security concerns of such intricate digital hardware environments. An influential and inalienable part of the ECC architecture is the Group Operation part. In this work, an area & time-efficient Point Doubling and Point Addition architectures have been implemented by combining modular arithmetic units for Elliptic Curve Group Operation. Koblitz curve in Jacobian coordinates have been used to reduce the cost of group operation. Two architectures have also been proposed to compute point doubling & point addition using equations for the Koblitz Curve for 256-bits. For minimizing area and reducing time spent for computing, we have used an interleaved modular multiplier in this work. An area & time-efficient Elliptic Curve Point Multiplication (ECPM) module have been designed. The designed ECPM module for 256 bits has been observed to require 0.53 ms for execution, which is a significant improvement compared to other contemporary studies.
Implementation of AWS Cloud Infrastructure

Using the internet, many users can access different resources as needed through the cloud computing paradigm. In a dynamic environment, these resources and the uses of heterogeneous systems are rapidly changing. But there are some scientific challenges that cloud computing must overcome. Highly scalable and accessible web hosting may be a challenging and expensiv. Traditional scalable web designs needed precise traffic forecasts in addition to complicated solutions to achieve high levels of dependability and customer service. During peak hours, there is a high volume of traffic, and there are large fluctuations in traffic patterns, which leads to low hardware utilization rates. As a consequence, keeping idle gear in operation results in high running expenses. The most demanding online applications may access a trustworthy, scalable, secure, and high-performance infrastructure with Amazon online Services (AWS). This infrastructure instantly correlates IT spending with customer traffic patterns. The cloud infrastructure architecture that we will use to deliver the web application has to be designed. Small companies may benefit from using AWS on their websites since it gives them access to a cloud where they can store their data and save time and money. The finest aspect about AWS is the free tier. Customers may get a free tier of AWS services from Amazon in order to use the platform.
A Comparative Study of Ethereum-Based and Web2.0-Based Personal Health Record Systems

The move from traditional healthcare to smart healthcare systems is greatly aided by current technology. It is now necessary that the patient retains ownership of his or her personal medical data, organizes it securely, and shares it with those who remain anonymous in the system. Transparency, security, and privacy of health data are three major drawbacks of conventional healthcare systems. Utilizing blockchain technology, which has several uses in the healthcare industry because of characteristics like decentralization (no need for a central authority), immutability, accountability, and openness, is one potential remedy for this. The proposed system allows patients to enter their medical data and access the doctor's prescription that has been uploaded to the platform. Registered doctors can access patient reports and provide feedback as necessary. To maintain privacy, both patients and doctors will remain anonymous to each other, and all data and activity will be encrypted and unchangeable. This ensures that the information remains secure and cannot be tampered with. In this study, we developed a blockchain-based system using Ethereum for storing personal health records and conducted a comparative analysis with SQL-based health record systems. Our experimental results demonstrate that the Ethereum-based system offers superior security and data privacy compared to traditional SQL-based systems. Moreover, the blockchain transactions were found to be 87% faster on average than transactions processed through traditional SQL databases according to our study.
Secured Authentication System for Product Verification: Integrating Blockchain and Secure Data Management

Counterfeit items are a serious hazard to consumers and businesses, resulting in financial losses, brand reputation harm, and severe health and safety issues. In answer to this issue, we provide a revolutionary counterfeit detection system based on blockchain technology. Our project combines Kotlin, Android, Hyperledger Fabric, and FirebaseDB to create an Android app that enables users to authenticate items via barcodes, QR codes, and serial numbers. Users receive immediate feedback on the legitimacy of the goods after scanning or inputting the product details into our app. This data is collected from a blockchain-based logging system that securely records and monitors each product’s status changes. We secure the immutability and transparency of the logged data by employing blockchain technology, prohibiting unauthorized duplicates or changes of product information. Our project’s implementation addresses the shortcomings of existing anti-counterfeiting techniques by offering a user-friendly and efficient means for customers to check the authenticity of their acquired items. Furthermore, the incorporation of Hyper-ledger Fabric, a permissioned blockchain platform, improves the system’s security and privacy. Our research emphasizes the benefits of using Kotlin and Android development for mobile-based verification systems, as well as the potential of blockchain technology in combatting counterfeit items. Our project’s results demonstrate the efficacy and feasibility of the suggested solution, presenting a potential method for protecting consumers and companies from the hazards connected with counterfeit items.
Vision Controlled Matter Smart Home Solution for Dysdiadochokinesia Patients

Smart home products are means of comfort for many, but they are still of dire need for a few. Dysdiadochokinesia (DDK) is a type of disease which results in difficulty in performing fast, consecutive action, most probably because of a prevailed issue. Matter is constructed with an aim of making smart home products interoperable, easy to use and smooth without compromising on security. By stacking the same on top of Internet Protocol (IP) layer, Matter will make communication possible across smart home products, mobile-based applications, and cloud-based services, and demonstrate a specific set of IP networking technologies for the certification of smart products. Development of an independent smart home solution for people suffering from Dysdiadochokinesia is proposed in this work. The system proposed enables them to travel indoors using a vision-controlled wheelchair and establish control over home appliances via vision commands by leveraging Matter Smart Home Protocol. Eye gaze movements and eye blinks are used as controlling parameters which are fed to machine learning models such as SVM and KNN which produces a classification label. Controller designed is tested for the movements of wheelchair in either of the four directions and also for controlling Matter enabled light and door lock producing promising results.
Hand Gesture and Audio Recognition System Using Neural Networks

This paper presents a system that combines hand gesture and audio recognition using deep learning techniques. Convolutional Neural Networks (CNN) are used by the system to recognise hand movements, while an architecture based on CNN + RNN and the CTC Loss algorithm is used to recognise audio. The proposed system is capable of recognizing sign language gestures and individual characters in speech, which can be helpful for individuals with hearing and speech impairments. The system is trained and tested on publicly available datasets, achieving high accuracy rates in both gesture and audio recognition. This work could advance the field of human-computer interaction and increase accessibility for people with hearing or speech problems. The outcomes show how well the suggested method works and illustrate its potential for use in practical situations. The results demonstrate that our model reliably recognises audio signals at a state-of-the-art level when tested against a public dataset.