Reviewerly/exHarmony huggingface dataset:
A Tour to Exact Visualization of Search Engine in Unique Boundaries Facilitating User Expectations to Usage

In today’s world, everything is automated in every field which makes the user interaction with systems easier. The automation process depends on the user query given to the system and also on what basis the system understands the query; based on its intelligence, it produces the results. In majority cases, the results are accompanied by the noisy, dirty and redundant data as a system response. When the user gives an exact query, the system understands the query based on the data training given to it. To overcome this problem, this paper proposes advanced training to the system database with which it can differentiate the domain, based on the user query i.e., it categorizes the user data with the pre-defined classified data existing in the database with clarification and get trained with the proven result. Before navigating to the results, the system prompts to the user for the conformation of the domain, and based on the conformation it navigates to the database unit and fetches the exact data content with the relevant result required for the user query and confirms with the database managing unit and release the response. If the released response is not favouring the user, then the user attempts the same query with additional data. The managing unit retrieves the hierarchical data formed during the data processing, and allows user to choose the appropriate result. The same event is recorded by the data managing unit for further experimentation.
Question Answering System using NLP and BERT

This project proposes a simple and smart question answering system using Named Entity Recognition (NER) and Bidirectional Encoder Representations from Transformers (BERT) for communication purposes. Communication is a major factor that eases our day-to-day work. In this smart Question answering system, the system will create a customized dataset for the user based on the answers given by the user. Using NER, only the selective answers are taken, i.e., the most important answers, and then updated in the dataset. Based on that dataset, if a person then asks the system a question, the system will use BERT and CDQA (closed domain question answering) to fetch the best answer for the question asked. The resulting system will be an interactive one.
Implementation of Blur Image to Sharp Image Conversion using Laplacian Approach

Some parts among several digital photos are blurry because of motion blur or defocus. Finding and naming areas of an image that are blurry is a very important part of many media evaluation tasks. This research reveals a simple and effective way to find blurry images as well as categorise them. The suggested methodology in this research paper is that blurred areas of an image are first found by using Laplacian and Sobel visualisation to get information about each image pixel, and then the blur classifications (motion blur or defocus blur) are determined by calculating using SVM model training, which doesn’t require image de-blurring. A wide-ranging study is done on a large set of 100 digital images that included 50 blurry images and 50 clear images. The test as shown by this study is capable of distinguishing among various varieties of blurry images as well as finding the blur in them. The proposed technique could be used for other multiple sorts of multimedia analysis and classification, such as classifying and separating images.
An AI Tool for Emergency Medical Assistance system

This research work attempts to check the medical condition of human beings by incorporating a smart medical assistance center in public places such as pharmacies, shopping centers, transportation route stations, interstate highways, and areas, where clinical stores are limited. The major advantage of the proposed type of medical assistance model is that the people would be able to recognize their current health condition. Based on these findings, the developed machine recommends the medicine to the user’s registered mobile number. Today, with the increased availability of internet and other connectivity resources, people are frequently facing side effects without consulting with a Medical Practitioner. They basically self-medicate themselves, which either has no effect on the body or has too many consequences on the body causing early/unpredictable death(s). The main challenge or disadvantage of self-mediation is that even after the recovery, the individuals begin using that medication for the rest of their lives or the continuous unknown side effects can lead to unexpected health issues. To address this issue, this research study works on a Proof-of-concept approach, which will assist in proposing a useful medical assistance.
Solar Power Prediction using Machine Learning Algorithms: A Comparative Study

Energy industry has been revolutionized rapidly over past few decades. The consumption of electricity from the renewable sources is increasing speedily due to the low maintenance and environmental friendly nature. Now a days, installment of PV solar cells have captured the energy market in an unparalleled way. The big challenge behind integration of PV arrays to the grid is its unpredictable and fragmented variation in terms of having PV power output. Therefore, construction of an effective predications models are needed as a solution for planning and controlling of the power grid. In the past, ample of machine learning algorithms i.e. Artificial Neural Networks (ANN), Moving Average and Fuzzy logics etc. has been used to overcome the volatile nature of solar energy. The Challenges can be effectively met by choosing robust machine learning algorithm. In this paper, comparative analysis has been made in terms of prediction accuracy by using different machine learning algorithms. Recurrent Neural Networks (RNN), Support Vector Machine (SVM), Autoregression with exogenous variable (ARX) Feedforward Neural Network with gradient descent momentum (FFNN-gdx) and Least Absolute Shrinkage and Solution Operator (LASSO) have been adopted as a predictive tool for bringing efficacy and precision in the PV power output. The results confirms that non-linear techniques i.e. RNN and SVM outperforms very well as compare to other linear models for predicting solar power output. Moreover, a comparative study helps out to select an appropriate and effective solution for forecasting and prediction models.
Comparative Study On Breast Cancer Classification Using Multiple Convolution Neural Network Architectures

Cancer of the breast is one of the primary causes of mortality of women across the globe. Breast abnormalities may often be diagnosed and classified with the use of ultrasound imaging. To better pinpoint health issues, radiologist diagnosis have been supported by a variety of CAD technologies created in recent decades. Using Automatic Breast Ultrasound (ABUS) scans, this research employed CNN models to differentiate between benign tumor, malignant tumor, and Normal breast tissue. Pre-processing the data is crucial in data classification since it helps the learning model function more effectively. The implemented dataset contains unprocessed image data where classes have data imbalance issue. So, it is important to resolve this imbalance problem. To resolve this issue data augmentation approach is applied on the minority class. Before the learning models are employed to the preprocessed data, the data is first partitioned into two subsets: a training set and a test set. This study’s deep learning models for breast ultrasound classification are MobileNetV2, VGG16, AlexNet, ResNet, Inception V3, and Xception. Each model is trained and then evaluated at different epochs to keep track of its progress. The study found that as the epochs go higher, data loss lowers and accuracy rises. The accuracy of MobileNetV2, VGG16, AlexNet, ResNet, Inception V3, and Xception is 98.82%, 94.70%, 95.96%, 93.50%, 92.72%, and 93.65%, respectively. MobileNetV2 outperforms with 98.82% accuracy rate the comparative to other applied models. Finally, breast cancer is classified using the MobileNetV2 model with significant accuracy and precision.
Building a Backend-as-a-Service Platform in Kubernetes

Cloudbase(our Backend-as-a-service platform), provides a system of services that helps improve developer productivity and reduce technical debt. It offers out-of-the-box solutions to common problems faced by a development team working on a typical project. The aim is to reduce the man hours spent on menial tasks and instead focus on working on the core product. Therefore the end-user of our system is the developer or the development team. The core product of Cloudbase centers around Server-less computing. The theme of the entire service is that the user should not worry about the scaling and the management of the underlying infrastructure.
Comparative Analysis of Different Approaches to Object Detection: A Survey

Object detection has a pivotal role in the field of security. Often, a security breach occurs under cover of night when the visibility is reduced. Manual supervision is relatively difficult as visibility is drastically reduced and human error increases to a large extent. In recent times, automation in security has gained a lot of traction. There are various inventions made in the field of automated surveillance. There are various models to detect objects in a frame with high accuracy accurately. With the help of pre-defined datasets and exclusive object detection models following the concepts of deep learning, an automated surveillance system can be implemented with ease. This study discusses the impact of automated surveillance, both night vision and daytime surveillance, the pros and cons, and the development of automated surveillance models that lead to accurate results. The study mainly focuses on deep learning concepts used to improve object detection.
An Intelligent Breast Cancer Forecasting System using Optimized Elman Deep Neural Network

Breast cancer is one of the widely occurring cancer variety among the women and the mortality is assured by early detection. Computer Aided Design (CAD) and computational approaches has significance in the breast cancer detection. The mammogram images have much information including those which will not help in precise identification of region of interest. The feature extraction and selection are vital for any classification problems. The performance of the classifier is highly dependent on the optimal feature set selected for the classification. Hence to avoid high dimensionality data, Grey Level Co-Occurrence Matrix (GLCM) is used and feature reduction is done by Singular Value Decomposition (SVD) where the classification is done by optimized Elman Deep Neural Network (OELDNN). The main intent of optimizing the OELDNN is to enhance the accuracy of classification. It has been shown in the results that the classification accuracy of OELDNN classifier performs better by 6.43% than naive bayes, 3.61% than SVM and 3.11% than CNN.
Early Detection of Alzheimer’s Disease based on InceptionV3 and Machine learning Classifier using Neuroimaging

Neuroimaging is a term referring to the techniques that efficiently expose facts well about cellular structure of the human brain. These techniques have been used in clinical condition, medicinal operations, and scientific work. The challenge of classifying neuroimages is among the most important stages that healthcare experts need to take in evaluating and treating their sufferers effectively by evaluating the symptoms of different types of neuroimaging. This may be achieved by analyzing the multiple types of neuroimaging. The symptoms of Alzheimer's disease need to be recognized in their earliest stages in order to prevent the quality of medical services from deteriorating further. In the current investigation, a novel method was devised by making use of a digitally subtracted angiography scan. This method has all of the required qualities of a novel indicator for blood flow to the brain and contributed to its development. In addition, samples of normal participants and digitally removed angiograms of patients with Alzheimer's disease are included in the database that is being exploited. Due to the fact that each scan had a large number of packets for the ICAs on both the left as well as the right, from before the approaches were utilized in order to get the information ready for the future stages of object classification. The multiple frames of the scan were translated from real space to DCT space so that noise could be removed from the image. After that, the median picture was reprojected into interplanetary world, and the two halves were afterwards filtered by Meijering and blended into a single image. The suggested method extracts the features using a variety of pre-trained networks, such as InceptionV3 and DenseNet201, among others. After that, using the PCA method, it was feasible to select the attributes that had an estimated deviation ratio of 0.99. These were the attributes that could subsequently be merged and used in classification algorithm.
Recommendation system based on Tamil-English code-mixed text analysis

The cinema industry has always been popular since its inception many years ago and is a preferred pastime of many people. It can be observed that even though online movie applications are popular in multilingual society, English is the preferred language. Naturally, people of other languages mix their native language with English during communications resulting in an abundance of multilingual data called code-mixed data, available in today's world. This research focuses on the movie recommendation system whose primary objective is to make a recommender system through Natural Language Processing (NLP) Tools for Tamil-English Code-mixed (Tanglish) Comments. Our recommendation system will be a filtering scheme whose primary objective is to predict a viewer's rating or preference towards a movie or web series.
An Artificial Intelligence and Computer Vision Based EyeWriter

The authors in this paper devise a low-cost EyeWriter that can be used as an assisting aid for patients. The proposed system aims to give voice to the voiceless persons who have been living as an isolated section of the society due to non-existence of any efficient system. In the proposed system, the user communicates by writing the alphabet by alphabets in order to form words and sentences through his/her eyes. The proposed system uses artificial intelligence, OpenCV, Python, Python frameworks, dlib, neural networks, and computer vision. The suggested system is also cost-effective as it does not require any specialized hardware. The proposed model is also simulated and the obtained results are quite encouraging and hence the proposed system can be considered for its widespread deployment in the real world.
Intelligent IoT-Based Healthcare System Using Blockchain

It has seen an inescapable interest in medical services issues and quicker and more secure assistance for patients. Using the new pattern innovations in the healthcare area could offer other option courses in dealing with the patients' healthiness records and furthermore improve the health quality. Researchers are looking for permanent and easy ways for monitoring patient’s records remotely by means of a patient monitor system. The utilization of the Internet of Things (IoT) is one of these methods where remote patient monitoring by healthcare providers is possible. However, issues in privacy and security have arisen due to the rise in the number of IoT devices. Discloser of patient data is another privacy issue. In various studies, it was presented that blockchain technology is a trustworthy network that ensures the privacy and security issues of patient information transferred through IoT devices. Subsequently, this segment traces the IoT advancement in the healthcare segment as a rising exploration and beneficial trend these days. This investigation endeavors to introduce another outline that works with the restoration and transferring of patient information using blockchain through Django by consolidating healthiness records with a patient monitoring system that exchanges data within multiple peers through a smart contract.
Efficient federated learning under non-IID conditions with attackers

Federated learning (FL) has recently attracted much attention due to its advantages for data privacy. But every coin has two sides: protecting users' data (not requiring users to send their data) also makes FL more vulnerable to some types of attacks, such as targeted attacks and untargeted attacks. Many robust FL algorithms have therefore been proposed, in order to ensure training accuracy under such attacks. Some of the existing solutions assume that data conforms to the independent and identically distribution (i.i.d), so as to simplify the problem. But, limiting the data distribution to i.i.d hinders the practical application of FL, and FL under non-i.i.d conditions is more general. However, designing efficient robust algorithm for FL under non-i.i.d faces two additional challenges: identifying malicious clients and guaranteeing model accuracy. To tackle these challenges, we propose a new FL workflow named Cominer which consists of a Label Cluster process and a Vertical Comparison (VC) process. LC solves the problem of declining accuracy by supporting non-iid data diversity by classifying all clients into multiple clusters, then VC identifies and eliminates malicious clients within each cluster. We verify the improvement in accuracy achieved by Cominer in a series of experiments, and show that under Non-IID conditions, Cominer not only improves the accuracy of the federated model over previous algorithms by up to 24.85%, but also enjoys high resilience to different kinds of attacks while maintaining accuracy over 80%.
Computing offloading of multi-MEC nodes in blockchain-based parked vehicle edge computing

With the development of the Internet of Vehicles (IoV), Parked Vehicle Edge Computing (PVEC) has gradually attracted people's attention. Parked vehicles (PVs) can be exploited as a supplementary computing resource to Mobile Edge Computing (MEC). Using the onboard resources of parked vehicles can effectively improve the security of the blockchain-based internet of vehicles system. However, there are multiple MEC nodes belonging to different service providers and multiple parked vehicles in an area. Due to individual rationality, parked vehicles and MEC nodes will not provide services for free. Therefore, in this paper, we study the interaction of MEC nodes and parked vehicles in blockchain-based parking vehicle edge computing and model it as a two-stage Stackelberg game to optimize the utility of MEC nodes and Parked vehicles. Specifically, we treat the parked vehicle as a leader, set the price of computing resources, treat the MEC node as a follower, and determine the demand for computing resources through the price of the leader. We use ADMM to optimize their utility function to obtain a computing offloading scheme that maximizes system utility. Simulation results show that our scheme can maximize the utility of MEC nodes and Parked vehicles and maximize social welfare.
Active learning by query by committee with robust divergences

Active learning is a widely used methodology for various problems with high measurement costs. In active learning, the next object to be measured is selected by an acquisition function, and measurements are performed sequentially. The query by committee is a well-known acquisition function. In conventional methods, committee disagreement is quantified by the Kullback–Leibler divergence. In this paper, the measure of disagreement is defined by the Bregman divergence, which includes the Kullback–Leibler divergence as an instance, and the dual $$\gamma $$ -power divergence. As a particular class of the Bregman divergence, the $$\beta $$ -divergence is considered. By deriving the influence function, we show that the proposed method using $$\beta $$ -divergence and dual $$\gamma $$ -power divergence are more robust than the conventional method in which the measure of disagreement is defined by the Kullback–Leibler divergence. Experimental results show that the proposed method performs as well as or better than the conventional method.
TrafficAdaptor

One of the most popular location privacy-preserving mechanisms applied in location-based services (LBS) is location obfuscation, where mobile users are allowed to report obfuscated locations instead of their real locations to services. Many existing obfuscation approaches consider mobile users that can move freely over a region. However, this is inadequate for protecting the location privacy of vehicles, as their mobility is restricted by external factors, such as road networks and traffic flows. This auxiliary information about external factors helps an attacker to shrink the search range of vehicles' locations, increasing the risk of location exposure.
Geospatial accessibility and inclusion by combining contextual filters, the metaverse and ambient intelligence

Accessibility and inclusion in information systems provide guidelines and methods for building applications and devices that work for everyone, including for people with disabilities and other diverse characteristics. Systems that apply accessible and inclusive design (AID) provide an easy way to adapt features for people with different capabilities. In geospatial applications and wearable technology, aspects of location and time should be taken into account as well. In this paper we present a visionary approach of applying contextual filters to the input, output and the underlying dataset of geospatial applications, to examine adaptation of systems, devices and applications to different populations and for verification of accessibility and inclusion. The goal of AID is to help developers add accessibility features to their systems and provide transparency regarding lack of accessibility when needed, e.g., make it clear if there are features that prevent usage by people who lack certain abilities. We explain how to apply AID in physical and virtual worlds, like the metaverse, and we describe how ambient intelligence, accessibility maps and virtual worlds can assist in building accessible and inclusive geospatial applications. We present our vision and elaborate on related challenges and research directions.
Quantum classifiers for remote sensing

Hybrid classic-quantum systems utilized existing quantum hardware for machine learning (ML) by running pre- and post-processing on classic hardware to overcome the limitations of today's quantum computers. In this work, hybrid systems with several pre-processing techniques and two circuit architectures are evaluated by classifying remote sensing (RS) imagery. The potential of quantum machine learning (QML) for RS is investigated and particularly autoencoder methods are found to be suitable for pre-processing. The code is published in an open repository: https://github.com/tumbgd/qc4rs.