Reviewerly/exHarmony huggingface dataset:
An application on Consent Based Monitoring of Healthcare statistics with Blockchain

The organized provisions of medical care to an individual or a community is already a difficult task. In that, providing Healthcare for the elderly, people above the age of 60 is a tough nut to crack. The current situation depicts that elderly people prefer independent lifestyles free from outside intrusion which complicates monitoring their physical health at regular intervals. Physical intervention after the onset of COVID-19 is a possibility but should be avoided unless absolutely required due to the virus’s aggressive nature. Considering all these problems, we devise a conceptual application framework of Consent Based Monitoring of Elderly with Blockchain. The application acts as a method by which we can monitor the patients at regular intervals in a secure and unbreachable manner. The application takes the consent of the patients on what data they want the application to collect and relay, and the users who can access the data including the medical professional. After this, the medical professional can analyze the data to find any anomalies and provide a diagnosis. Only with the aid of Google Authorization may the patient update the consent-based data. The novelty of the project lies in the usage of Blockchain to maintain anonymity and integrity of the consent based data. The consent based access control lists and policies are stored in Blockchain in a comprehensive schema. The Blockchain is a privately running test only Blockchain specifically used to implement decentralized application development. The backend of the application is solely responsible for uploading and querying the data while communicating with the private Blockchain. The application framework assumes that the patient’s data has already been transmitted via smaller edge computing nodes. The application works completely separate from the aforementioned nodes.
Conversion of American Sign Language to Text Using Deep Learning for Feature Extraction and Naive Bayes for Classification

Sign language serves as a bridge between persons who are physically impaired and those who are not. It is necessary to increase the consciousness about sign language in public, cause people with abnormalities loose many opportunities of growth, recognition in life due to lack of communication abilities. Hence, sign language plays a vital role in human life. It also improves Human computer interaction (HCI) . This paper aims to describe a vision-based system of approach for recognition of people’s hand gestures in order to help them communicate more effectively with normal people. This method aims to recognize American Sign Language (ASL) hand gestures and translate them into Text. After a thorough analysis, the best potential technique to converting real-time American Sign Language into text with the approach of Transfer Learning using Alexnet for feature extraction and with classification algorithms like KNN, SVM, Naive Bayes in MATLAB software are used. Among the three Classifiers, the Naive Bayes achieved highest accuracy of 97.92%.
Comparison of Different DNN Models for Classification of Different Sports Images

In today’s world, sports have become an important part of almost everyone’s life. Any kind of sports, not only it entertains people but also helps to maintain physical and mental health of oneself. In this fast-growing world, sports industry is also evolving with same pace. Due to development in the sports field, various new sports are being introduced, which most of the people are not aware about. Also, there are few sports which are getting newly introduced, but have very similar features like other sports which already exist. So, classifying such sports activity through images can be very confusing. But with the help of deep learning techniques, it has become easy to understand what is happening inside an image. It has now possible to extract the features from complex images by using Deep Neural Network (DNN) techniques. So, using DNN techniques different sports recognition and classification is possible. In this paper, we have proposed a comparative analysis of different DNN algorithms for recognition and classification of different sports images and coming to a conclusion about which model is best suited for deployment on the embedded systems. We have compared ResNet-50, Efficient Net , and MobileNet-V2 models. Based on the parameters like accuracy, model size, parameters generated, and F1 score. In this paper, F1 score obtained for different models like ResNet-50, Efficient Net , and MobileNet-V2 are 97%, 95% and 94% respectively. Different models can be chosen according to the requirements. The Efficient Net model gave the best accuracy among all and the MobileNet-V2 model consumed the least space. Details of the models and comparative study are discussed further in the paper.
Scalable and Secure Architecture for Smart Home using Blockchain with data analysis for security risk

Smart homes are becoming increasingly popular as they offer convenience, energy efficiency, and security to homeowners. However, there are several risk factors associated with smart homes that should be considered before investing in them. One of the main risk factors is security. Smart homes rely on internet connectivity and connected devices, which may be vulnerable to hacking and cyber-attacks. This can lead to breaches of personal data and privacy, which can have serious consequences for homeowners. As we increase the scale of devices in the smart home homeowners will get a chance to build a highly connected and efficient environment that serves their unique requirements and preferences through steady expansion and customization of their smart home system over time. In this article, we have performed an analysis on the dataset of smart homes that was obtained from Kaggle. The purpose of this analysis is to give all of the readers an idea of which parameters are included in the collapse of security risk. To minimize this risk, it is important to carefully research devices before purchasing them and to ensure that they are compatible with other devices in the home. Power outages can also disrupt the functionality of smart homes, leading to potential security risks or inconvenience. Homeowners should consider investing in backup power sources, such as generators or battery backup systems, to ensure that their smart home remains operational during power outages. We need to make use of ECC in order to protect a person from these dangers. Also, smart houses should include the appropriate safety features. In this investigation, we will focus on bringing to light all of the significant aspects of ECC as well as other characteristics of blockchains that have potential applications in smart homes. After that, we will present the data analysis of the open-source smart home dataset that can be found online. It will give the readers an idea of what are all the possible risks that come with having a smart house.
Recommendation System using NLP and Collaborative Filtering

A significant amount of online shopping has been observed during the pandemic. Surveys suggest that 20-30% more businesses are moving online in response to the pandemic. The goal was to develop an apparel e-commerce website that is also tailored to the individual using Natural Language Processing and Collaborative Filtering algorithms, thereby recommending items based on what they choose. In the current system, the market uses a collaborative filtering and content-based filtering, hybrid model. We aimed to resolve the cold start problem that persists in many of the current e-commerce websites. The recommendation engine is based on the input given by the pre-trained Natural Language Processing model, where the items from the dataset are segregated into broad themes based on the product description. These themes are then put into clusters based on their similarity. The recommendation engine uses collaborative filtering and picks up similar products from the clusters formed to provide the users with appropriate recommendations.
Weighted Pooling RoBERTa for Effective Text Emotion Detection

Textual emotion detection is a classification problem that assigns different emotions to a given text input. It reveals the writer’s mental state. Its diversity and uncertainty make it a challenging task. The existing methods in machine learning can be used for emotion detection; however, it fails in processing very long passages. In this work, we employ weighted pooling pretrained RoBERTa model for effective textual emotion detection. To perform experiments, we use two data sets, ISEAR and tweets, with 7516 and 21048 records, respectively. Precision, recall, F1-score, and classification accuracy are used to assess the models. Experimental results indicate that the weighted pooling RoBERTa model outperforms the deep learning models on both datasets with significant improvement in accuracy.
Smart Toll Booth System Using Smart Contract

The present toll collection system has a number of problems, including varying toll rates on different roadways, heavy traffic, etc. Long wait times, theft from toll plazas, and other issues are problems that the toll collection system, particularly in India, encounters. Only 300 automobiles may be serviced by these systems each hour, and if more cars arrive at that area than that, severe traffic Jams could happen. For toll collection, the present toll methods, including Radio Frequency Identification (RFID), still require a lot of time. To address the above mentioned issues, this study discusses a GPS-based toll collection system. Every time a vehicle switches from a highway to a service road, this system would track the coordinates of the moving object. As a result, the user can pay the toll without being pulled over anywhere along the freeway. This system will leverage blockchain technology to construct a decentralized and intelligent mechanism for toll collection and tracking records because it has recently become one of the most well-liked technologies and has the potential to change the field of intelligent transportation systems. The technology uses blockchain smart contracts for the maintenance of security and integrity of records. Thus, presenting a strategy for putting into place a seamless toll collection system with the most transparency possible.
Signboard Detection using YOLOv5 with Transfer Learning

With the ever increasing popularity of autonomous driving technology, effective and precise detection and identification of traffic signs is critical for subsequent decision-making and control actions. The huge elevation in technology has made it more feasible to classify and detect images. The precision and inference time are the key factors for object detection in real-time. The introduction of high-performance computing resources, cutting-edge deep learning, and machine learning algorithms offer real-time solutions to the above issues. After a detailed survey and study that exhibited different methods to address these issue, we have proposed a deep learning model which is based on one of the recent versions of YOLO. In this paper, we have proposed YOLOv5 with transfer learning as an optimum solution to address the mentioned issue. The proposed method has various potential parameters that turn out better than the other models of deep learning including the older versions of YOLO. The dataset is taken from ’roboflow’ platform consisting of 19 different classes of signboards, each of 1272 images. The proposed and implemented model gives overall high performance and detection accuracy. This model gives 96% accuracy and 0.93 mAP. The model can be equipped with ADAS for better navigation and safety.
Image Caption Validation for Public Complaints on Social Media

Image Caption Validation is the task of validating whether a caption matches the image provided. The result of this task can reduce distress by validating complaints conveyed from the local community. In this paper, we introduce a custom data set of Indonesian public complaints that consists of floods and littering complaints from Twitter. We used a fine-tuned VGG16 model for image classification and an IndoBERT Model (Indonesian Bert Model) for text classification. Both of these models were combined to into a system that validates whether a given caption accurately describes the image. The experimental result showed that the fine-tuned VGG16 had an accuracy score of 93%, the IndoBERT Model had an accuracy of 89%, and the Validation Output Prediction Model had an accuracy of 56%.
"Talking Books" : A Sinhala Abstractive Text Summarization Approach for Sinhala Textbooks

The ability for books to talk would be an exciting concept, and this research discussion paves the path for an identical approach. The research objectives discussed in this paper address several burning problems, solve them and adapt them to future technological enhancements from a Sri Lankan context. Burning problems include reducing printing costs for textbooks, addressing students’ health, promoting green technology, and identifying a suitable summarising approach to the native language, Sinhala resulting in students’ learning ease. Other symptoms for the betterment indicate paths taken to reduce the weight of school bags carried by students, reduce paper usage by the government on printing textbooks, and spread technological awareness to teenagers regarding e-Learning. Textbooks issued by the government will be digitized and centralized into a single system that the government officials themselves can administer. The paper discusses limited hindsight literature and proposes 2 new algorithms for abstractive and extractive summarization for Sinhala text. The 2 algorithms are compared against one another in terms of performance, efficiency, precision and accuracy. Experts in the education domain have verified the derived summary of both algorithms. The deliverable artefacts are the mobile application, a RESTful auto-summarization plugin service, and new data sets extracted to train the GPT-3 models.
Design A Smart Pillow for Detection and Management of Snoring

Snoring is one of the most common disorder and yet there is no proper solution for this problem. Polysomnography test is required to have before using any kind of snoring devices. So, the work is focused on a system which help to detect snoring and to manage snoring by alerting that patient. A multi-sensor system is designed so that it will sense, record and alert by using an Internet of things platform. Thus system helping patients to have a good quality of sleep because it has inbuilt music system which play relaxing music when required, meanwhile the patient snores then the system will alert that patient. One of the effect of snoring is neck pain, so an attempt is made to provide neck pain solution into pillow by considering some biological aspects. This system also gives support to neck , back and the head of the patient.
XNLI 2.0: Improving XNLI dataset and performance on Cross Lingual Understanding (XLU)

Natural Language Processing systems are heavily dependent on the availability of annotated data to train practical models. Primarily, models are trained on English datasets. In recent times, significant advances have been made in multilingual understanding due to the steeply increasing necessity of working in different languages. One of the points that stands out is that since there are now so many pre-trained multilingual models, we can utilize them for cross-lingual understanding tasks. Using cross-lingual understanding and Natural Language Inference, it is possible to train models whose applications extend beyond the training language. We can leverage the power of machine translation to skip the tiresome part of translating datasets from one language to another. In this work, we focus on improving the original XNLI dataset by re-translating the MNLI dataset in all of the 14 different languages present in XNLI, including the test and dev sets of XNLI using Google Translate. We also perform experiments by training models in all 15 languages and analyzing their performance on the task of natural language inference. We then expand our boundary to investigate if we could improve performance in low-resource languages such as Swahili and Urdu by training models in languages other than English.
Community-Based Multi-layer Analytics Architecture for Civic Violations

Civic violations such as public drinking and public smoking are common in the streets and have become a norm in the community. Recent efforts fail to automatically detect, alert, and make analytics for civic violations, specifically public smoking and alcohol drinking, for computing architectures adapted for local communities. This paper proposes a community-based multi-layer analytics architecture for civic violations. The multi-level civic violation recognition is implemented in a mist-fog-edge architecture. The architecture implements decentralization to support fault-tolerance in a community-based environment. The system implements a series of deep learning models for human clustering and scenario isolation using YOLOv5 with intersection of union (IoU) and non-maxima suppression (NMS) algorithms, and violation recognition using Vision Transformers with violation amplifiers. Experimental results show that the human clustering and scenario isolation has a mAP of 0.46 using IoU as 0.3 and the violation recognition with an accuracy score of 86.96 %. The overall architecture has a total latency of 3.06 seconds for end-to-end AI inference operation.
Face Gesture Based Virtual Mouse Using Mediapipe

A disabled person’s life is always dependent on someone else who needs aid with mobility or any other task. Individuals with disabilities may face challenges when using computers. The most common way of interacting with computers is using a mouse and keyboard. It is difficult for people with physical disabilities to use them. Facial movements are one of the best possible actions by physically disabled individuals, By recognizing and responding to these movements, it is possible for them to operate the computer using only their facial expressions. Face recognition is a contemporary approach to interaction between humans and computers (HCI) i.e., The proposed system can easily control the computers by using face gesture recognition. It can be a viable replacement for traditional HCI tools in the future. This research outlines the techniques utilized in the design, implementation, and evaluation of the experiments conducted and presents the results obtained.
Transfer Learning Approach to Multilabel Biomedical Literature Classification using Transformer Models

The recently developed transformer models have a significant influence on Natural Language Processing (NLP) research. Transformer-based models have attained innovative results on numerous NLP benchmarks in linguistics. Researchers have also investigated transformer models for a variety of applications in the biomedical field. To facilitate information classification, this work aims to thoroughly investigate four state-of-the-art transformer-based models (BERT, Roberta, SciBERT, and ClinicalBERT). Large language model training, however, requires a lot of time and processing resources. As a result, pre-trained language models like BERT are advantageous because they (1) offer cutting-edge performance, (2) The knowledge gained can be used to do several tasks, such as categorization, and (3) free up practitioners from having to gather enough resources (including the hardware, data and time) to train models. However, pre-trained models possibly will perform poorly in some domains since they are generic. Here, we inspect the case of multilabel classification for biomedical Literature, which is a domain that has received relatively minor attention in the literature assessing pre-trained language models. Based on our study, using SciBERT as the baseline model gave the best results compared to the rest of the models with an 89.35% micro F1 score. In the context of classifying Biomedical Literature, domain-specific pre-trained models tend to perform better compared to generic models.
Static Code Analyser to Enhance Developer Productivity

One of the main metrics important to software development is productivity. The measure of productivity helps a organization/ individual or team identify how well their employees/ software runs and how they could improve. In this research paper, a new software is proposed along with a background study of how improving code quality will improve developers productivity and efficiency. The software proposed to aid developer productivity is a custom built static code analyser using python and it’s rule set has been tailored through carefully selected research papers and with feedback from experts of the software industry.
Paraphrase Detection in a Low Resourced Language: Kannada

Paraphrase detection is a Natural Language Processing classification problem whose main goal is to determine if two sentences are synonymous. The research undertaken in this paper strives to implement the same in Kannada, a low resourced language. Kannada is a South-Indian language that originates from the Dravidian language. On comparison of many appropriate state-of-the-art classifiers for this problem, the decision tree gave the best results due to the non-linear aspect of the corpora. Such a system would be best suited to implement a plagiarism detection system for Kannada documents that are in the official dialect of the Kannada language.
Real Time Virtual Yoga Tutor

Articulated pose estimation in computer vision is the field of research that deals with the development of algorithms and systems that can determine the position and orientation of an articulated body, such as a human body, in 3D space. It has a wide range of applications, including motion capture, human-computer interaction, and robotics. In the context of the project, the goal is to estimate the position of human body parts and joints in each image, using a pre-trained pose estimation model, MediaPipe. The data obtained from this model is then used to explore various applications, with the focus being on building a yoga monitoring system. The system analyzes and tracks the user’s movements and postures, identifying any errors in their yoga routine. This allows the system to provide real-time feedback to the user, pointing out any inaccuracies in their body pose, so they can correct their mistakes.
Noise Cancellation by Fast Fourier Transform for Wav2Vec2.0 based Speech-to-Text System

Speech-To-Text (STT) systems are a part of the Speech Recognition domain in which speech is given as input, and it generates the transcript. The input speech sometimes disrupts the STT system and generates incorrect transcripts because of background noise. In this work, we have discussed a Fast Fourier Transform (FFT) based noise cancellation method for Hindi words with background noise and performed speech to text conversion using a fine-tuned and pre-trained Wav2Vec2.0 model. The background noise added to the audio samples is Gaussian white noise with three different intensity levels, 0.01, 0.03, and 0.05 units, indicated by the Gaussian distribution’s standard deviation (STD). The model has been trained on the OpenSLR Hindi dataset. The proposed system is evaluated by the metric Character Error Rate (CER). The testing of the model is done using 20 Hindi words in both clean and noisy conditions. The results obtained proved that the noise cancellation was found effective in terms of CER, and on first level noise with an STD of 0.01, the CER is better after noise cancellation than its noisy counterpart.