Reviewerly/exHarmony huggingface dataset:
Mouse Cursor Movement and Control using Eye Gaze- A Human Computer Interaction

An emerging technology is human computer interaction (HCI). One of the most important HCI strategies is the eye gazing method, which enables the user to operate the display without using their hands. Direct eye movement detection, template, appearance, feature, hybrid, regression, clustering 3D methods are all ways to categorize eye gaze detection techniques. Deep learning, a technology that mimics human behavior and features like speech recognition, image recognition, and language translation can make this possible. A web camera was employed in this study to capture a frame of an eye frame for mouse cursor movement. In connection with the point previously mentioned, we must first concentrate on the function of our eye. We are employing a web camera for pupil identification, which can manage the computer’s cursor. For this paper, an Aspect Ratio Eye (EAR) is determined that corresponds to the blinks of eye’s (right or left) applying the library of Mediapipe which is open source and acts as a computer vision library. You can provide smart people with crippled limbs who are having trouble using computers a chance to express their opinions. Here, the method’s objective is improving the experience of using computer for physically disabled people by assisting them in overcoming challenges using a mouse.
Key Transposition Cipher and Decipher based on Genetic Algorithm

For increasing attackers and their risks in life, the two parties (sender and receiver) can be using a safe environment to send and receive the secret message by encrypting the message (unreadable message) and send to the receiver. The symmetric key algorithm is more efficient and very fast than an asymmetric key algorithm. This paper used a key transposition cipher for encrypting and decrypting messages, the key obtained by the genetic algorithm when the GA generated the best key after many iterations, and for each iteration treated the individuals by selection and crossover and mutation. The fitness function calculated the difference between letters occurrence between the plaintext and ciphertext. After testing, the proposed method’s success in passing six types of attacks when the traditional transposition fails for two attacks. Also, the proposed method is faster than traditional transposition and it is suited for use in online chatting and live communications.
Context-Based Visual Sentiment Analysis for Social Media Data

Social networking sites have recently grown in importance and popularity, so the field of textual sentiment analysis has emerged and attracted a great deal of research interest, additionally, sentiment analysis in images is still in its infancy and little research has been conducted in this area; listing text or visual content alone is insufficient to convey And the opposite of the feelings of the published content; therefore, it was proposed to analyze the visual feelings based on the content of the image. In this paper, a system was proposed to determine the polarity of posts and tweets on social networking sites using textual analysis and visual analysis. A system or model was proposed that integrates these different properties using a proposed neural network (DVSF) that integrates the text model and the visual model to provide a final decision to indicate the polarity of these posts. Twitter (MVSA) and Flickr(EmotionROI) datasets were utilized, and the results were encouraging overall.
Privacy Preserved Effective Bill Collection System Using Blockchain

Electronic invoices are generated using blockchain technology. Additionally, it has the potential to fundamentally alter how invoices are produced, transactions are validated, and payments are executed. These distributed ledgers continue to be the most well known and are regarded as the cornerstone of cryptocurrencies. They are composed of blocks, and each block contains a transactional record. On a decentralized blockchain network, a document that is accessible and editable by multiple users simultaneously keeps account of who made changes and when. It is impermeable and translucent. The proposed system is used for enforced bill collection system ensuring user privacy using the smart contract. Since every transaction is visible to both parties and every record or block is linked together and encrypted, there is no need for a middleman. It will be possible for users to make automatic payments to a service provider’s digital wallet by using an invoicing system built on the blockchain. Transactions can be easily tracked and monitored, and thanks to blockchain technology, a complete history of all transactions can be downloaded. This study suggests using smart contracts and accountable ring signatures to secure blockchain transactions.
Object Detection in Aerial Images : A Case Study on Performance Improvement

Object Detection (OD) in aerial images has gained much attention due to its applications in search and rescue, town planning, and agriculture yield prediction etc. Recently introduced large-scale aerial images dataset, iSAID has enabled the researchers to advance the OD tasks on satellite images. Unfortunately, the available OD pipelines and ready-to-train architectures are well-tailored and configured to be used with tasks dealing with natural images. In this work, we study that directly using the available object detectors, specifically the vanilla Faster RCNN with FPN is sub-optimal for aerial OD. To help improve its performance, we tailor the Faster R-CNN architecture and propose several modifications including changes in architecture in different blocks of detector, training & transfer learning strategies, loss formulations, and other pre-post processing techniques. By adopting the proposed modifications on top of the vanilla Faster-RCNN, we push the performance of the model and achieve an absolute gain of 4.44 AP over the vanilla Faster R-CNN on the iSAID validation set.
File encryption based on the process of comparing locations and values between images and a text file

Information security is one of the most important things in the science of security, and data and information encryption are essential parts of it. In this paper, we will talk about a new type of encryption called Using Images to Encrypt Text file (UIFTF). The basis of its work depends on two images, where this algorithm will compare the text with the first image in bytes, then it takes the location of the byte that is similar to the message text, and after that the same location in the second image, we will take its value and it will be the text Our encoder. We will also mention the types of encryption and some of the algorithms used in them to be a comparison with our work and what are the differences between them.
Indian Sign Language Recognition Using MobileNet

Sign Language is one of the most widely used methods of communication by the specially-abled, primarily the hearing and speech impaired. Millions of people in India and around the world use this language made out of gestures daily. They find it difficult to express themselves and understand others without the condition. This paper presents a lightweight application that uses a Convolutional Neural Network (CNN) and the popular Mobile Net Classification Model for recognizing Indian Sign Language using OpenCV. The model is created using the TensorFlow library with the Keras API as the Neural Network model-building framework. The model is then deployed in a python application which shows a user-friendly GUI for people who wish to learn the basic alphanumeric characters of Indian Sign Language (ISL). The application takes in dynamic frames in which one shows the gestures and gets the corresponding alphabet on the same window. This model could be used in schools that require the teaching of basic characters for the specially-abled or those who would like to learn the ISL.
DoodSearch - OpenCV with Image Recognition

Hand recognition using OpenCV is a burgeoning field with several applications, including virtual environment control, sign language translation, robot remote control, and musical composition. One such application is gesture recognition, which allows users to practically create anything on the screen by simply using their hands. Another exciting function that is prevalent is searching by image. The concept behind searching by image is to take a picture as an input and generate metadata from it that may be used later. In this paper, integration of both the aforementioned methodologies is done to produce an application in which a user may draw any image using numerous hand motions that are captured by the user's webcam. This image is then processed by an image recognition algorithm, which generates metadata that may be used to search for the entity depicted in the image.
Improving and Analyzing the Movie Sentiments Using the SVM Approach

Sentiment analysis is a tool that assists in better understanding the sentiment behind the reviews of an individual and allows an organization to make improvements to its goods and services in response to the feedback. An automatic model is required to classify the sentiments as positive and negative. Thus, the paper proposes a sentence-level sentiment analysis approach to classify whether a movie review is positive or negative. The experiment is performed on the IMDb dataset using three supervised machine algorithms, Linear Support Vector Machine, Logistic Regression and Multinomial Naïve Bayes. Each have been tuned to have the best settings of hyperparameters to achieve the best possible results. The results obtained by these models were then compared based on their Accuracy Score, F1-Score and AUC Score. This approach obtained an F1-Score of 0.914 and an AUC-ROC score of 0.97 after 10-fold Cross-validation. There was an improvement in the evaluation of the F1 score and AUC-ROC score compared to other state-of-the-art models.
Into the matrix: collecting psychometric data from consumers immersed in virtual worlds

Consumer-oriented virtual reality (VR) brand experiences are becoming increasing popular despite the paucity of information about marketing best practices. This research describes a technique for embedding traditional psychometric measures into virtual environments that allows for the collection of data from consumers immersed in a simulated experience. Additionally, we provide proof-of-concept of this methodology by extending prior research findings in the embodied cognition literature to virtual environments. Specifically, our results demonstrate that temperature-related embodiment can be experienced by consumers immersed within a virtual world. The methodological, theoretical, and marketing implications of this research are discussed.
Impact of e-Learning in Education Sector: A Sentiment Analysis View

The COVID-19 condition had a substantial impact on the education sector, corporate sector and even the life of individual. With this pandemic situation e-learning/distance learning has become certain in the education sector. In spite of being beneficial to students and teachers, its efficacy in the education domain depends on several factors such as handiness of ICT devices in various socio economic groups of people and accessible internet facility. To analyze the effectiveness of this new system of e learning Sentiment Analysis plays a predominant role in identifying the user's perception. This paper focus on identifying opinions of social media users i.e. Twitter on the most prevailing issue of online learning. To analyze the subjectivity and polarity of the dynamic tweets extracted from Twitter the proposed study adopts TextBlob. As Machine Learning (ML) models and techniques manifests superior accuracy and efficacy in opinion classification, the proposed solution uses, TF-IDF (Term Frequency-Inverse Document Frequency) as feature extraction technique to build and evaluate the model. This manuscript analyses the performance of Multinomial Naive Bayes Classifier, DecisionTreeClassifier, SVC and MLP Classifier with respect to performance measure as Accuracy.
Hardware and Software Development of a Small Scale Driverless Vehicle

Formula Student Driverless is a competition that encourages engineering students to design autonomous racing vehicles to compete in international competitions. This paper proposes a software stack for an FSAE Driverless vehicle prototype. The software stack is divided into five subsystems. The perception subsystem detects the landmarks, the localization subsystem tracks the vehicle's position, the mapping subsystem creates a global map of landmarks observed, and the planning and controls subsystem decides how the vehicle navigates through the map. A LiDAR was used to detect waypoints on the map, and EKF-SLAM was implemented for mapping. PID and Pure Pursuit were utilized for control of the vehicle. This paper also discusses a scaled model for testing the said software stack. The design parameters of the small-scale prototype were replicated and scaled down from our own FSAE Electric Vehicle, Lemnos.
Research on Insulator Defect Detection System Based on Edge Intelligence

Abstract The paper aims at the problems of the high occupancy rate of communication resources and heavy server load caused by the image data taken during the inspection of the substation and sent to the remote server for processing. An edge intelligent inspection data processing architecture is constructed, and an insulator defect detection model based on transfer learning and YOLO v5 is proposed. The model can meet the training of a small sample network model in the case of scarcity of power equipment defect data. The proposed algorithm is compared with typical target detection algorithms, and the experimental results show that the proposed algorithm has an excellent performance in model recognition accuracy and recall rate. The trained insulator defect detection model is further deployed on the developed edge intelligent terminal for testing, and its image processing speed can meet the image processing requirements for intelligent inspection of substations. The system has good application value.
Natural Language Query in Bengali to SQL Generation Using Named Entity Recognition

Various search strategies are used to search the data from the database. Adapting the searching language and grasping its numerous syntaxes are the key hurdles that a user encounters when accessing these data. Thus, we propose a system that translates natural language queries into Structured Query Language (SQL) queries and retrieves the relevant data from a database. This proposed system allows inexperienced users to access a database without prior knowledge of query languages. The current approach applies machine learning and rule-based approaches because the machine learning approach gives better results for large-size data, whereas the rule-based approach performs well in small-size datasets. This system receives health queries in Bengali. Tokenization is applied to the user's query. The Bengali Natural Language Processing (BNLP) toolkit removes punctuation marks from the token list. After removing punctuation marks, the proposed system uses a predefined Bengali stop words list to provide a score for each token. The score facilitates the finding of nominal words. The stemming method is performed to obtain the nominal root word. The pattern is created to generate all possible nominal compounds in Bengali. A new set of proposed rules and named entity recognition module of the BNLP toolkit is utilized to predict entities and attributes using the pattern. The proposed system maintains a healthcare database. Finally, the SQL is formed using entities, and attributes and the relevant result is obtained from the database.
Representation of Status of Buildings under PMAY Scheme

The Pradhan Mantri Awas Yojana (PMAY) was established with the goal of providing affordable homes to the poorer sectors of society. That mainly include Groups like Middle Income (MIGs), Economically Weaker Section (EWS). The main aim of this scheme is to construct 20 million houses at an affordable price by March 31,2022. As the time is running out due to insufficient funds from government only up to certain level of building construction was completed. Previously it is very difficult for beneficiary's to track the progress of the building constructed. We come up with the idea that can be implemented on national scale. It is mostly based on Machine Learning techniques (Data processing) and representation of housing data on map with the help of GIS coordinates of the building. It might allow for frequent updates and cost-effectiveness analysis That might be useful in a variety of situations. For this study, we have taken the areas where the PMAY scheme is implemented.
Emotable - Emotion Detection Based Social Media Application Using Machine Learning And Deep Learning

Social media analytics and emotion recognition have gained immense popularity in recent years. Emotion recognition is a technique for identifying and detecting human emotions while utilizing technical capabilities. This paper presents emotion detection of text posted by users on a social media application and evaluates the performances of various machine learning models, individually and combined. We applied several preprocessing techniques before training the models, which helped us understand and analyze the data. This study concluded that combining all four approaches increased accuracy compared to using each separately.
Cross-Validated Ensemble ARDenseNet for Human Activity Recognition

Due to advances in hardware embedding technologies, human activity identification utilizing sensors is a challenging study area. These have difficulty identifying actions that are significantly connected, such as a forward fall and a fall from a chair. A novel ensemble deep learning network has been proposed that incorporates training at several levels of abstraction. The UniMiB HAR dataset's fall signals are taken into account when evaluating the proposed network. The Synthetic minority oversampling technique (SMOTE) also helps to balance out the data size inconsistencies. The proposed deep learning network has been shown to have an overall accuracy of 96.3%.
Typical Hypergraph Computation Tasks

Abstract After hypergraph structure generation for the data, the next step is how to conduct data analysis on the hypergraph. In this chapter, we introduce four typical hypergraph computation tasks, including label propagation, data clustering, imbalance learning, and link prediction. The first typical task is label propagation, which is to predict the labels for the vertices, i.e., assigning a label to each unlabeled vertex in the hypergraph, based on the labeled information. In general cases, label propagation is to propagate the label information from labeled vertices to unlabeled vertices through structural information of the hyperedges. In this part, we discuss the hypergraph cut on hypergraphs and random walk interpretation of label propagation on hypergraphs. The second typical task is data clustering, which is formulated as dividing the vertices into several parts in a hypergraph. In this part, we introduce a hypergraph Laplacian smoothing filter and an embedded model for hypergraph clustering tasks. The third typical task is cost-sensitive learning, which targets on learning with different mis-classification costs. The fourth typical task is link prediction, which aims to discover missing relations or predict new coming hyperedges based on the observed hypergraph.
Human Activity Recognition with smartphone sensors data using CNN-LSTM

Human activity recognition (HAR) utilizing wearable inertial sensors has nowadays become a new research hotspot due to various advancements in sensor technology. Recently, methods based on deep learning (DL) have been successfully used to evaluate time series data recorded by wearable sensors and smartphone to predict a variety of human activity. Furthermore, the majority of HAR methods relied on traditional feature engineering. In this study, a convolutional neural network along with, a long short-term memory network, that is deep learning architecture for activity recognition is proposed here (CNN-LSTM). With minimum data pre-processing, the proposed model CNN-LSTM network extracts feature from raw sensor data automatically. By using testing data, the trained model is then used for the recognition of various activity. The performance of the model is examined using the benchmark dataset WISDM. The results of the experiment show how the suggested approach outperforms alternative strategies and how adaptable it is to be implemented in sensor-based healthcare systems for recognition of human activity. The research findings show the proposed model outperforms the other approaches that were compared by achieving 98.04% accuracy and a F1 score of 98.04%, which is better than the similar existing models.
Sentiment Analysis on Twitter Data of Omicron (B.l.l.529) using Natural Language Processing

The COVID pandemic is causing outrageous interference in everyday life and financial activity. Close to two years after the presence of COVID, WHO allotted the variety B.l.l.529 a variety of concern, named ‘Omicron’. Online diversion data assessment is created and transformed into a more renowned subject of investigation. In this paper, a sizably voluminous heap of appraisals and assessments are culminated with online redirection information. The evaluations and appearances of Twitter electronic diversion stage clients are summarised and researched by considering sentiment analysis by utilising various natural language processing techniques based on positive, negative, and neutral tweets. All potential outcomes are considered for investigating the feelings of Twitter clients. For the most part, tweets are assessed clearly, and this assessment ensures the headway of this investigation study. Different kinds of analyzers are utilised and measured. The ‘TextBlob Sentiment Analyzer’ has given the highest polarity score based on positivity, negativity, and neutrality rates in terms of inspiration, pessimism, and impartiality. A total dataset is fully determined and classified with all the analyzers, and a comparative result is also measured to find the ideal analyzer. It is intended to apply boosting machine learning methods to increase the accuracy of the proposed architecture before further implementation.
A 3-Lock based Password Hashing Algorithm

Password has become a predominating method for user authentication to gain access to restricted services. Most people use human-memorable passwords that are likely words in dictionaries or certain combinations of these words, which are easy to crack. The biggest problem with passwords is its strength. We introduce a 3-lock based password hashing algorithm which minimizes the fraction of password that would be cracked by an offline attacker without increasing computing time for a legitimate authentication server. It strengthens the user's weak password, by improving the character set from which password is selected. 3-lock based password hashing algorithm uses 3 locks consisting of various printable ASCII characters and an integer provided by the user. This integer is never stored in server's database. Finally, we analyze 3-lock based password hashing algorithm using RockYou password dataset. Our analysis shows that the proposed algorithm can reduce (up to 25%) fraction of password cracked by an offline attacker.