Reviewerly/exHarmony huggingface dataset:
Eye Gaze Correction System

The process of gaze correction involves manipulating the eye region of an individual in order to redirect their gaze towards the camera. This method, which is a particular kind of image resynthesis, may be used to take pictures where the subject appears to be looking straight at the camera, among many other practical uses. Using an inpainting model, we present a novel method for accomplishing gaze correction in this research. With our method, we train a face-to-face learning model on photos, then manually fill in the blanks where the eyes should be. A benefit of our approach is that it does not necessitate labelled training data that provides information about the exact location of the head and the orientation of the eyes. As a result, we had a rather easy time amassing the training data we needed. To retain the eye area identification information in the original input, we present a self-guided pretrained model that can learn angle-invariant properties. Our tests show that, when applied to a dataset gathered from multiple websites, our approach produces incredibly convincing gaze-corrected results. In our study, we go into detail about this dataset.
Design of an SLA Violation identification framework via Iterative Learning under Temporary Peak loads for Cloud deployments

Service Level Agreements (SLAs) are essential for seamless cloud operations but can get violated due to misconfigurations and network faults. Existing SLA violation identification mechanisms rely on static reconfiguration, which limits their scalability under high-speed scenarios. Additionally, most of these models are either highly complex or exhibit low efficiency under real-time cloud scenarios. To address these challenges, a novel SLA violation identification framework is proposed in this research paper. The framework uses iterative learning via a customized 1D Convolutional Neural Network (CNN) and Q-Learning to identify SLA violations and recommend mitigation strategies via incremental learning operations. The proposed model is able to learn from previous responses and improve its performance incrementally, making it useful for a wide variety of real-time use cases. The proposed model reduces SLA violations by 8.3%, improves deadline hit ratio by 1.2%, and reduces scheduling delay by 4.5% under real-time cloud traffic scenarios
A Comparative Sentiment Analysis on ChatGPT Reviews using Machine Learning Models

Sentiment analysis is a field within natural language processing that seeks to recognize and extract personal and emotional data from textual information, including feelings, viewpoints, and attitudes. Reviews have now turned into an indispensable data source for both consumers and businesses. Analyzing the sentiment of such reviews can offer valuable ideas into customers' likes, contentment, and discontentment. In recent times, Chat Generative Pre-trained Transformer (ChatGPT) has paved new paths in the area of Artificial Intelligence and Natural Language Processing. In our work, sentiment of people is analyzed through reviews taken from people of various age groups from different educational backgrounds to understand the efficiency of ChatGPT. A comparative analysis was carried out by using four different machine learning models and two hybrid models using SVM + Decision Tree and Random Forest + Logistic Regression. The survey also analyzed various effects of using ChatGPT and how it contributes in making people lethargic and decreases logical thinking ability and problem-solving skills.
Detecting Public Hate Sentiment Using Transformers

This research explores the application of deep learning techniques, specifically BERT and DistilBERT models, for detecting public hate sentiment in text data. Through a systematic analysis of the literature and a thorough understanding of the subject matter, we developed a groundbreaking method that leverages the power of these advanced models. Extensive experimentation and evaluation were conducted using a carefully curated dataset, employing techniques such as tokenization, padding, and truncating for preprocessing. The results demonstrate the efficacy of our approach, achieving high accuracy and precision in identifying and classifying hate sentiment. This research contributes to the field of natural language processing and provides valuable insights for effectively addressing and mitigating hate speech in online platforms.
Self-Improved Namib beetle optimization Trained Hybrid Model for Human Activity Recognition

Human Activity Recognition is a recognition technique which identify and name the human activities by AI. It collects activity data from devices such as wearable sensors and smart devices etc. Literally, it classifies human activities on the basis of interaction, motion and action. Nowadays, its usability is spread in various applications so, the research on Human Activity Recognition is still ongoing with viable enhancements. Though, this technique needs to solve limitations such as collection and processing of data, stability of hardware and approaches, complexity in activity detection and messed up activities. This work is based on Human Activity Recognition which is trained by the hybrid model of classifiers such as LSTM and Bi-GRU via proposed Self-Improved Namib Beetle Optimization model (SINBO). The activity recognition is detected by two stages: (1) Preprocessing, (2) Feature Extraction and (3) Classification. The extracted features areImproved bag of visual words, Local texton XOR pattern and SLIF are given as an input in classification stage. The hybrid model of LSTM and Bi-GRU is enhanced by optimal tuning of weights via SINBO algorithm.
Audio Based Facial Expression Generation On Ar Applications

The shortcomings of 3D face technology in faithfully recreating human facial expressions, particularly for nuanced emotions, are examined in this research paper. The authors use augmented reality (AR) technology to give enhanced visual experience of the generated voice-over-character animation with expressions. The system receives user-provided audio speech as input, predicts the user’s facial expression from its text, and then displays it on a 3D augmented reality face. The authors also plan to use machine learning algorithms to analyze the emotion of the user’s voice, which could lead to a more personalized experience for the user and enhance engagement and satisfaction in scenarios such as virtual therapy or job interviews, where emotions play a crucial role in communication.
A Comparative Study of Video Watermarking using Various Machine Learning Algorithms

Watermarking refers to the process of inserting data or information into your content for the purpose of identification or protection to maintain its authenticity and copyrights. Watermarking is like stenography and is used in many places such as in photos, videos, money, and stamp to assist in identifying counterfeiting. There are many techniques developed for watermarking in which some are wavelet based and some are transform based. Watermarking is a two-way process in which first the watermark is embedded over the data by using embedding techniques such as two-way dimensional discrete wave transform and watermark extraction techniques such as the extreme learning machines, neural networks etc. To provide the clear overview of the existing algorithms in combination with extreme learning machine we went through existing studies from 2014-2022. This review gives an exhaustive study of existing methods and suggests some recommendations for further enhancements to be done by using the various deep learning and machine learning techniques.
Cyberbullying Text Classification for Social Media Data Using Embedding And Deep Learning Approaches

With the world moving towards an almost digital lifestyle, especially after the covid-19 pandemic, people are extensively using social media platforms to express their views. The comments and chats on these platforms are often observed to be raw and direct, to the extent that it can become an act of bullying. Increase in suicide attempts, anxiety and depression cases reported on account of cyberbullying highlights the importance of developing automated systems for identifying cyberbullying oriented content. The proposed work deals with the classification of comments on social media oriented as cyberbullying or not. The work explores various deep learning classifiers in combination with varied word embedding inputs on five different social media datasets for classification. The model evaluation reported that Glove embeddings when input to an attention based Bi-LSTM classifier with GRU was the best performer across most of the experimented datasets with a micro-average F-measure ranging from 0.8 to 0.97.
Post Quantum Blockchain Based Lightweight Edge Server Handover Authentication Key Agreement Protocol for Vehicular Communication System

We have designed a safe and portable vehicle to infrastructure handover authentication using blockchain technology. In this protocol, blockchain technology provides benefits for data sharing since it can guarantee data quality, transparency, and provenance. To get information for handover authentication, road side units can act nodes of the consortium blockchain. Furthermore, transactions on the blockchain cannot be falsified since they include an road side unit signature and cannot be updated due to blockchain technological features. As a result, other road side units may verify the authenticity of vehicles by utilizing the data stored on the blockchain. For authentication phase, we have used lattice based cryptography that plays a very important role in authentication and key exchanges that protects against the threat of quantum attacks. The proposed protocol ensures both securities against quantum attacks and efficiency due to simple algebraic operations that are polynomial addition and multiplications.
UVM Components for the Secure Hash 512 Algorithm

The Secure Hash Algorithm (SHA-512), which offers data integrity and security, is a well-liked cryptographic hash function. As algorithms' complexity increases, it becomes increasingly crucial to ensure that they are correct and covered within the verification process. This work presents a detailed methodology for certifying the SHA-512 algorithm using the Universal Verification Methodology (UVM). Complex designs may be efficiently verified using UVM's standardised architecture for building extendable and reusable verification environments. The SHA-512 algorithm is divided into modular components that each handle a specific function as the initial stage in the verification process. UVM component-based verification technique is used for the modelling and verification of each individual component. The UVM component architecture promotes modular design while allowing for independent verification and reuse.A functional coverage model captures the key traits, behaviours, and unique situations of the SHA-512 algorithm. The UVM testbench is linked with the coverage model to track the development and effectiveness of the verification efforts. Coverage-driven verification techniques are employed to guarantee thorough evaluation of key algorithmic components. A number of UVM analysis tools are also used to extensively evaluate the SHA-512 algorithm. These analysis components acquire and review data during the simulation, providing insight into the algorithm's behaviour and aiding in the identification of any issues or irregularities. The proposed UVM-based verification approach may be used to efficiently and systematically verify the SHA-512 algorithm. UVM components are appropriate for complex algorithms like SHA-512 verification due to their adaptability, reuse, and scalability. Functional coverage and analytical elements are combined to ensure. The validation of the UVM components performed by the functional coverage and the UVM environment covered 83.33% of the possible outcomes.
Exploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study

This survey paper presents a brief overview of recent research on graph data augmentation and few-shot learning.It covers various techniques for graph data augmentation, including node and edge perturbation, graph coarsening, and graph generation, as well as the latest developments in few-shot learning, such as meta-learning and model-agnostic meta-learning.The paper explores these areas in depth and delves into further sub classifications.Rule based approaches and learning based approaches are surveyed under graph augmentation techniques.Few-Shot Learning on graphs is also studied in terms of metriclearning techniques and optimization-based techniques.In all, this paper provides an extensive array of techniques that can be employed in solving graph processing problems faced in low-data scenarios.
An Interactive Interview Bot for Human Resource Interviewing

It has been difficult for recruiters from various organizations to inform all of their candidates about the interview procedure, which makes holding interviews a bother. Additionally, engaging with a big number of applicants and carrying out additional screening tasks add to the recruitment issues when there are several applicants. A human interviewer’s bias or weariness throughout the interview process could have an impact on the overall selection procedure. The interview process is typically used to screen applicants for open positions. This procedure differs from business to business. However, the technical interview and the human resource interview continue to be the two principal approaches. The conventional interviewing process is labor- and resource-intensive. By utilizing a chatbot in place of a human interviewer, we can automate this procedure. The interview process will be simplified using an interactive interview bot. It’s a robot that can interview candidates and assess their communication abilities, facial expressions, and other traits. The majority of companies rely on their staff to communicate, which could lead to diminished consequences for the business. As a result of its capabilities, Interview Bot significantly accelerates the entire interviewing procedure, streamlining the process and saving valuable time for both recruiters and candidates. With its advanced features and automated functionalities, Interview Bot expedites the overall workflow, making it more efficient and productive.
Consumer Complaints Classification using Deep Learning &amp; Word Embedding Models

The goal of Text classification is to categorize a document into predefined categories. Various supervised and unsupervised classifiers can be used to achieve this. In the research work proposed, SOTA (State of the Art) deep learning models and embedding techniques are used to classify consumers' complaints, which is in form of text, into 6 classes. Words and documents are represented as numeric vectors through embedding, enabling vector representations for related words. These representations are easily ingested by NLP algorithms. The classes represent departments where complaints are routed. Deep learning models like LSTM, Bi-LSTM, GRU and 1D CNN are used, along with word embedding techniques like Word2Vvec, Fasttext, Bert and Distilbert to represent text. The experimental results indicate that DistilBert and CNN achieved a 93% F-score.
A Deep Learning Approach for Predicting Personality Traits

One of the most effective science for determining a person's strengths and shortcomings is graphology, or handwriting analysis. Handwriting is a reflection of a person's personality. It reveals the writer's thoughts, emotions, and behaviors as well as the personality qualities one has and doesn't have. The personality can be profiled using Graphology in terms of traits like anger, self-esteem, pessimism, fear, honesty, work habits, social abilities, etc. Line spacing, margins, slant, letter size, text density, baseline inclination, and other handwriting elements are based on personality factors.The present paper proposes a deep convolution neural network (CNN) classifier for distinguishing eleven different lower case 't' alphabet patterns in graphology, each indicating different psychological attributes as willpower, self-esteem, motivation, and emotional stability. The suggested approach contributes to the area of graphology by providing a quick and accurate way to examine handwriting samples and identify the aforementioned characteristics. Eleven personality qualities are mapped onto all the characteristics. When evaluated on fresh, unseen handwritten documents, the suggested system had a categorization accuracy of at least 94%.
XGBoost Classification of XAI based LIME and SHAP for Detecting Dementia in Young Adults

As technology progresses on a fast pace, it is imperative that shall be used in the field of medicine for the early detection and diagnostics of dementia. Dementia affects humans by deteriorating the cognitive functions, and as such many algorithms have been used in the detection of the same but all these algorithms remain a black box to the medical fraternity which is still dubious about the nature and credibility of the prediction. To ease this issue, the use of explainable artificial intelligence has been proposed and implemented in this paper, which makes it easy to understand why and how the model is giving a particular output. In this paper the XGBoost classification algorithm has been used which give an accuracy of 93.33% and to understand these predictions, two separate algorithms namely Local Interpretable Model-agnostic Explanations (LIME) and Shapely Additive Explanations (SHAP) have been used. These algorithms are compared based on the type of explanation they provide for the same input and thus the weakness of LIME algorithm has been found out at certain intervals based on the clinically important features of the dataset. On the other hand, both the algorithms make it easy for medical practitioners to understand the dominating factors of a predicted output thereby helping to eliminate the black-box nature of dementia detection.
Real-time Hand Gesture Recognition Using MediaPipe and Artificial Neural Networks

Hand gestures are an important form of communication, especially for individuals who use American Sign Language (ASL) to communicate. This study explored the use of Hand Gesture Recognition (HGR) using a dataset of 135,000 images, with 27 classes representing the letters A to Z and the space character. The MediaPipe framework and an Artificial Neural Network (ANN) with four hidden layers were used for building a gesture recognition system. Also to prevent overfitting, two dropout layers. In the input and hidden layers, the Rectified Linear Unit (ReLU) activation function was utilized, and the softmax function was used in the output layer to predict probabilities for each class and obtained an accuracy of 99.34%, indicating the effectiveness of this approach for HGR. This study has important implications for improving communication for individuals who use ASL and may lead to the development of more advanced gesture recognition systems which will be beneficial to those who are deaf or dumb.
Software Defect Prediction Model Using AdaBoost based Random Forest Technique

Software testing is a crucial step in the development procedure. Typically, the defects or errors caused by developers are often dealt with a while later in software development, leading to an increased effect or influence of these flaws. In order to stop this from happening, issues must be identified early in the software development process allowing for the effective use of testing resources. As part of the defect prediction process, software modules are categorized as either defect-prone or non-defect-prone. To automate and enhance the prediction of defective software modules a key criterion for the analysis, numerous methods have been anticipated. In this paper, we create a combined model that assesses the presence of defects in a software module. The proposed work has been based on pre-processing, feature dimensionality reduction and classification. The model is tested through a publicly available NASA dataset. We have applied principal component analysis (PCA) for feature dimensionality reduction, which reduces the dimension of the feature vector. During the prediction task, the boosting technique called AdaBoost has been applied to a random forest named ABRF to determine the prediction rate. Several performance metrics which include accuracy, sensitivity, specificity, F1 score and MCC are measured to validate the planned model. The results of the PCA+ABRF method show that the model has an average accuracy of 0.9862 for the KC2 dataset. From the experimental results, it has been observed that the proposed model provides better defect prediction accuracy as compared to other existing models.
RISET: Secure Eye-Tracking Password Authentication with Random Image Sequence

The development of authentication systems plays a crucial role in ensuring security and user privacy across various domains. One such authentication mechanism is Transparent Image Moving (TIM), which enables users to position their password image correctly on a grid. However, TIM fails to mitigate shoulder surfing attacks, where attackers record or capture passwords for comparison. To address this issue, we propose Random Image Sequence Eye Tracking (RISET), a solution that conceals both the image's initial position and its final location by leveraging on-screen eye tracking. This approach necessitates user calibration to enable eye detection on the grid. Through trials, we demonstrate that the RISET scheme presents significant challenges for attackers. In adequate video conditions, attackers only achieve an average effectiveness of 7.01% without successfully obtaining the complete password. Experimental participants also reported a strong sense of security when employing this scheme, with a satisfaction rate of 93.75%. Overall, RISET provides users with a highly secure environment and makes it challenging to guess passwords without high-quality eye and screen videos.
An In-Depth Review of Blowfish Encryption Algorithm: Security, Performance, and Application

The important components in the information protection is network securitywhich is reasonable for protecting all information which are passing through networked systems. Through this paper we comprehensively analyzes and survey the Blowfish, DES, AES, and other algorithms exploring its design principles, encryption process, security analysis, and practical implementations. It investigates the algorithm's strengths, weaknesses, and potential applications in contemporary cryptographic scenarios. Recent developments and alternatives in the field are discussed, offering insights into the algorithm's relevance and prospects. This review paper aims to provide researchers, practitioners, and cryptography enthusiasts with a thorough understanding of the Blowfish algorithm and its significance in the realm of symmetric-key cryptography.
A Personalized Similarity Learning Method for Service Recommendation in Web

Web services (WS) that are functionally equivalent to the number of clients using them are becoming increasingly prevalent as more organizations supply services electronically. Therefore, most information is user-generated making it challenging for customers to find the right WS. Hence, there is a need for a recommended solution that will evaluate the WS quality feature. Although there are many ways to select and recommend Collaborative Filtering (CF) based WS, few have considered the impact of personalization on customers and WS quality features. The personalization similarity learning approach (PSLM) for WS recommendation is presented in this study. A vital factor of PSLM is computing a measure of similarity between the customer and quality features of the WS. The mechanism of PSLM implements the analysis of customer-products of WS relationship computation for personalized service recommendation utilizing the similarity learning computation model. A series of tests carried out on actual WS datasets show how much the concept has improved.