Reviewerly/exHarmony huggingface dataset:
Relevance of Article 21- Right to Life at Epidemic Like Covid 19

Right to Life is the most important human right envisaged by the international community as well as the constitution of India which means lock stock and barrel. Right to life concretes every other right to happen, because when the people have life then only, they can fight for other rights such as right to education, human dignity, freedom and nationality etc. This article sought to analyses the elements of article 21in the light of scenario of Covid 19 with an extended view. The entire planet has undergone the impacts of the havoc in everyday life owing to the rapid spread of the corona virus pandemic 2019(Covid 19) over the world. Many countries had to announced lockdown, command to shelter in place or issue stay at home order, to check the massive spread of the pandemic. Several arenas of the privacy of common people were also affected where the states had taken due care to release the personal data of people who had been quarantined and even the international community sought for it. The functioning of Arogya Sethu App in India hooks in to the ambit of right to privacy. Right guaranteed under Article 21A, government is under an obligation to make endeavor to impart educational facilities at all levels and under all circumstances. Indian population cannot pay to the basic technology which is currently being utilized to provide for the strayed school hours. The ones infected by Covid 19 as well as the one up and running on medical front are equally entitled under the right to health and medical care. Reflection upon the element of Right to food the State Government had taken time however assured constant availability of food stock to ration shops, but the migrant residents who did not have BPL cards were also considered by the government to avail food grains from ration shops. The effect of Covid 19 has called attention to the elasticity of the existing fundamental rights there by bringing forth prevailing loopholes of the system in to light.
Comparison Analysis of Prediction the Number of Covid-19 Cases Using Support Vector Regression and Long Short-Time Memory

Covid-19 emerged as a pandemic outbreak that spread almost worldwide at the end of December 2019. While this research was carried out, the Covid-19 pandemic was still ongoing. Many countries have made various attempts to overcome Covid-19. In Indonesia, the government and stakeholders, including researchers, have made many activities to reduce the number of positive patients. One of many activities that the government made is the vaccination program. The vaccination program is believed to be the most effective in reducing the number of positive cases of Covid-19. But nobody knows when the Covid-19 pandemic will end. Stakeholder has to know how the trend of Covid-19 cases in Indonesia to make a better decision for facing Covid-19 cases. This study aims to predict the number of positive Covid-19 cases in Indonesia by conducting a comparative analysis performance of Support Vector Regression (SVR) method and Long Short-Term Memory (LSTM) method in machine learning to the prediction of the number of Covid-19 cases. This study was conducted using the dataset Covid-19 in Indonesia from Control Team from 13 January 2021 until 08 November 2021 and with 300 records. The evaluation has been conducted to know the performance of the model prediction number of Covid-19 with Support Vector Regression method and Long Short-Term Memory method based on values of R-Square (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), the value of Mean Absolute Error (MAE) and Mean Square Error (MSE). The research found that the method Support Vector Regression has better performance than Long Short-Term Memory method for making a prediction of the number Covid-19 using Machine Learning model based on the value of accuracy and error rate based with the value of R-Squared, MAE, and MSE are consecutively 0.902, 0.163, and 0.072.
Research on Trading Strategy of Distributed Generation and User Power Based on Blockchain Platform

With the continuous promotion of the power system reform and the continuous improvement of the penetration rate of distributed energy, the power market is facing new opportunities and challenges. In this context, a research on the trading strategy of distributed power and user electric energy based on blockchain platform is proposed. This mechanism and model can effectively alleviate the computational pressure on traditional trading platforms and protect the privacy of user information. It can not only ensure the security time limit of P2P operation, but also improve the system and reduce the risk of information exposure of the transaction parties.
Cross-Modal Two-Stream Target Focused Network for Video Anomaly Detection

Video anomaly detection (VAD) is a challenging computer vision task that aims at identifying the events that do not conform to expected behaviors in a video. Training an unsupervised learning model on normal data is one of the most prevalent VAD paradigms owning to rare abnormal snippets. However, anomaly related and irrelated information sharing feature space will degrade the representation ability of the model for abnormal events. In this article, we propose a novel unsupervised video anomaly detection method, named Cross-Modal Two-Stream Target Focused Network (CMTSTFN) to focus on the anomaly related target and alleviate the impact of anomaly irrelated information on the model. Firstly, we use the global-local optical flow as a priori knowledge to blur the anomaly irrelated information in the image domain by Gaussian blur kernel. More importantly, conditioned on the blurred video frame, we then employ a dual cross-modal attention module to reconstruct the fused image feature map and optical flow feature map, respectively. Therefore, both spatial and temporal cues of abnormal events could further deteriorate the quality of the final reconstructed frame, making the anomalies more detectable. Experimental results on UCSD Pred1 and UCSD Pred2 demonstrate the effectiveness of the proposed CMTSTFN.
QoS Analysis for Serverless Computing Using Machine Learning

Large-scale computing systems are becoming more popular as the need for computing power increases every year. Serverless computing has emerged as a powerful and compelling paradigm for the hosting services and applications because of the rapid shift in business application architectures for containers and microservices. Further, Serverless computing offers economical services and scalability to fulfil the growing demand of computing in a timely manner. Therefore, it is important to analyse the Quality of Service (QoS) of Serverless Computing systems to monitor its performance. In this chapter, we used the latest machine learning models to predict system configurations in Serverless computing environments. Knowing about system configurations in advance helps to maintain the performance of the system by analysing QoS. Further, a no-cost model is proposed to examine and compare different configurations of workstations in serverless computing environments. To achieve this, we deployed Theoretical Moore’s, Fitted Moore’s, 2-D poly regression and 3-D poly regression machine learning models following Graphics Processing Unit (GPU) requirements and compare the results. The experimental results demonstrated that Fitted Moore was the best model with an R2 score of 0.992.
Comparison Analysis on Techniques of Preprocessing Imbalanced Data for Symbolic Regression

Symbolic regression is an important research field which aims to find a symbolic function to fit a given data set. Since real-world data sets usually have imbalanced characteristic, preprocessing imbalanced data sets is an important technique to improve the performance of symbolic regression techniques. In this paper, we compare and analyze the performance of six preprocessing imbalanced data techniques for symbolic regression. Specifically, three of them are data weighting techniques, including proximity weighting, remoteness weighting, and nonlinearity weighting. The fourth is data compression technique, which removes redundant data according to the weight to improve the fitting accuracy and efficiency, while the last two techniques are data sampling techniques, namely, the synthetic minority over-sampling and the K-medoids clustering undersampling.
Dense or Sparse: Crowd Counting with Binary Supervision

Dense crowd counting is one of the challenging problems where creating large labeled datasets turns out to be difficult. Typical crowd images have thousands of people positioned close to each other and annotating the locations of every person is tedious. Add to these the growing need to include crowds from as many diverse scenarios as possible for better generalization. In this context, labeling every head for various settings under consideration is not scalable and directly affects the performance of deep models on account of limited data. We mitigate this issue with a new binary labeling scheme. Every image is simply labeled to either dense or sparse crowd category, instead of annotating every single person in the scene. This leads to dramatic reduction in the amount of annotations required and becomes proportional to the number of images rather than the crowd count. For training counting models, we create noisy density maps directly from the edge density of the images, which are then improved through rectifier networks. There are separate rectifier networks for dense and sparse categories, trained in an unsupervised fashion. The proposed counting model is composed of a self-supervised backbone feature network and a regressor head. The ground truth density maps are generated using the binary labels and the rectifier networks for training the regressor. Experiments show that the proposed architecture achieves competitive performance than existing models at an extremely low annotation cost.
Categorisation of random images into fog and blur based on the statistical analysis

Noisy images are a bottleneck to solve the image processing problems. The present paper aims to classify images as different types of foggy and blurry images. A feature based classifier called FB classifier has been proposed. Given an image the classifier is able to tell whether the image is clear or unclear, which type of distortion is there, either foggy or blurry and also the categories of different types of blur and fog. The quality of the images taken through any equipment depends on few factors: 1) medium in which the photograph is taken; 2) the movements of either the camera or the object or movement of both; 3) the quality of the equipment that is used for capturing. All the algorithms of classification or the removal of distortions are made to handle the above three scenarios. The three factors encompass all types of foggy or the blurry images. The images viewed are given different threshold values according to their properties and finally the cumulative threshold value decides which type of the image is it. The algorithm is simple to implement yet it is comparable to the state of art methods.
Facial Beauty Prediction using AlexNet Model

Facial beauty prediction (FBP) is a rising topic in the area of evaluating attractiveness, with the goal of making assessments which are more human-like. Since FBP is a regression problem, there are many data driven ways to determine the relationships among face traits and beauty ratings. Deep learning approaches have shown their incredible potential for feature representations and analysis of the data. Convolutional Neural Network (CNN) has demonstrated extremely outstanding performance in facial identification and understandings, and have been proven to be an effective way for exploring facial features. Recently, a lot of well-designed networks with efficient topologies have been researched for improved representation of ability. However, these models focus more on the efficient block but do not create an efficient data transmission route, resulting in a feature representation capacity which is sub-optimal. In this paper, we have taken the Alex Net model into consideration which is a deep convolutional network which has been created to consider a large range of colorful images (224x224x3). It has a range of 62 million parameters which are trainable. For better performance, an intricate network architecture for the FBP issue is given in this research.
Detection of Hate Posts and Tweets in the Social Network Society

Hate speech is a prevalent issue on social media platforms that has recently been a cause for concern due to its detrimental effects on individuals and society. The development of effective hate speech detection procedures and algorithms is crucial to address this issue. However, the existing natural language processing (NLP) algorithms and machine learning models face several challenges in accurately identifying and categorizing hate speech. These challenges include the ambiguity and variability of language use, the lack of standardized definitions and guidelines for hate speech, and the rapid evolution of new and creative forms of hate speech. In this paper, we propose a technique that leverages classic machine learning and deep learning methods to locate and categorize hate speech in social media. Our approach involves the use of Support Vector Machines (SVM) and Long ShortTerm Memory (LSTM) networks for classification. We evaluate the performance of our model on a hate speech dataset and compare it with a deep learning-based model. Our results show that the SVM model outperforms the deep learning-based model in accuracy and efficiency. Our approach offers a promising solution to the challenges posed by hate speech detection on social media and contributes towards building a safer and more welcoming online community. Keywords—Hate speech, Social Networks, NLP, LSTM, Transformers
Yoga Pose Detection

Abstract: This paper describes a real-time yoga pose detection system that can accurately classify and detect yoga poses in images using Convolutional Neural Networks (CNNs) and OpenPose. By using OpenPose, the system generates a 3D joint map of the person's body, which is then used as input for linear regression to detect the individual yoga pose. The system is suitable for real-time applications, and is expected to be used in fitness centers, yoga studios, and even for personal use. Additionally, the system can also be used to track the progress of yoga practitioners, allowing them to analyze their performance and improve their practice. Furthermore, the proposed system is expected to benefit the yoga industry by providing a low- cost, efficient, and accurate means to detect poses.
Effect of Screentime during School From Home

The COVID-19 pandemic has significantly changed people's lives around the world. Thousands of millions of people around the world must stay at home to protect and prevent Covid-19 transmission. Covid-19 cases in children have also increased even though the prevalence rate is still lower than adult cases.The government through the Ministry of Education and Culture made recommendations during the Covid-19 pandemic to conduct distance / online learning, so that the teaching and learning process can still take place even though you don't have to come to school. This requires a separate adaptation process for the continuity of online learning to run well, with digital media as a learning tool. Screen time or staring at the screen for children has a recommended time limit, because if it is used beyond the safe time it will have a serious impact on the child's health and development. It takes the role of parents, teachers / schools, the community, and policy makers to oversee the process of using digital media in distance learning during the Covid-19 pandemic.In conclusion, the use of digital media during the pandemic is increasing, especially for children, apart from being a medium for distance learning as well as a medium for entertainment. This must be properly monitored by parents, teachers / schools, the community, and policy makers so that the adverse effects of using digital media do not occur according to WHO / IDAI recommendations.
Leveraging High-Fidelity Datasets for Machine Learning-based Anomaly Detection in Smart Grids

Data-driven anomaly detection systems are increasingly becoming essential for protecting critical cyber-physical system (CPS) infrastructure, such as the power grid, against the growing number of sophisticated cyber-attacks. The development of such tools is reliant on the availability of high-fidelity cyber-physical datasets that cover a diverse variety of potential cyber events. In this work, a co-simulation smart grid platform is utilized to develop a realistic dataset, which is used to train and test a machine learning-based anomaly detection system (ADS). The evaluation of the developed ADS shows robust performance even when tested with statistically diverse test data not used in training. This work is a preliminary step towards the development of a cyber-resilient middleware framework, which will serve as a testbed for the development and evaluation of cybersecurity solutions and CPS datasets.
Design and implementation of millimeter wave radar target detection algorithm based on SYS_BIOS

Aiming at the complexity and difficulty of millimeter wave radar target detection algorithm, SYS/BIOS is used to design and implement the algorithm. Due to the large amount of computation and tasks, the algorithm of millimeter wave radar target detection is implemented by SYS/BIOS embedded operating system, rather than developing based on bare computer. The operating system is modular, so it is convenient to manage and develop programs, and effectively solves the difficult problem of millimeter wave radar target detection algorithm. The feasibility of the algorithm is proved by practical experiment.
The Interplay between Artificial Intelligence and Innovation and its impact on B2B Marketing Performance

Artificial intelligence has the potential to greatly improve the existing economy’s efficiency. It might, however, have a more profound impact by serving as a brand-new, all-purpose “method of invention” that can change the organization innovation processes. A wide spectrum of organizations is finding benefit in artificial intelligence (AI), which is upending the marketing and global economic markets. Business researchers and practitioners are showing an increasing interest in AI, according to recent highly referenced studies. For established and startup businesses employing big data and developing new, innovative strategies and business models, even in a B2B setting, AI is both an opportunity and a limitation. This study employs quantitative methodology and data analysis methods based on structural equation modelling. using the program SmartPLS 4.0. employing practical random sampling procedures to select a sample. The replies of 191 employees of financial organizations situated in Dubai, United Arab Emirates, who completed online surveys were evaluated.
Empowering Donors: How Blockchain Technology Can Help Ensure Their Contributions Reach the Right Recipients

Charity giving is a fundamental aspect of society, but there are concerns about the accountability and transparency of the donation process. Blockchain technology has emerged as a potential solution, offering a secure and auditable platform for managing charitable contributions. In this paper, we explore the concept of empowering donors through blockchain technology and examine how it can help ensure that contributions reach the right recipients. We review existing literature on the topic and identify key areas for future research, including the development of hybrid blockchain solutions along with the implementation of novel consensus mechanisms. Moreover, providing the idea of decentralized autonomous organizations, self-executing smart contracts, and integration of blockchain with artificial intelligence and Internet of things. By providing a comprehensive overview of the current state of the field and outlining a roadmap for future research, we hope to stimulate further exploration of the potential of blockchain technology to revolutionize charity giving, and create a more accountable and transparent system for managing charitable funds. Some research questions are designed for each novel idea. Our analysis suggests that blockchain has the potential to empower donors and ensure that their contributions make a positive impact in the world, but further research is needed to fully realize its potential.
Social Distance and Face Mask Detection using vision-based sensors and IP cameras

Healthcare systems all over the world are strained as the COVID-19 pandemic's spread becomes more widespread. The only realistic strategy to avoid asymptomatic transmission is to monitor social distance, as there are no viable medical therapies or vaccinations for it. A unique computer vision-based framework that uses deep learning is to analyze the images that are needed to measure social distance. This technique uses the key point regressor to identify the important feature points utilizing the Visual Geometry Group (VGG19) which is a standard Convolutional Neural Network (CNN) architecture having multiple layers, MobileNetV2 which is a computer vision network that advances the-state-of-art for mobile visual identification, including semantic segmentation, classification and object identification. VGG19 and MobileNetV2 were trained on the Kaggle dataset. The border boxes for the item may be seen as well as the crowd is sizeable, and red identified faces are then analyzed by MobileNetV2 to detect whether the person is wearing a mask or not. The distance between the observed people has been calculated using the Euclidian distance. Pretrained models like (You only look once) YOLOV3 which is a real-time object detection system, RCNN, and Resnet50 are used in our embedded vision system environment to identify social distance on images. The framework YOLOV3 performs an overall accuracy of 95% using transfer learning technique runs in 22ms which is four times fast than other predefined models. In the proposed model we achieved an accuracy of 96.67% using VGG19 and 98.38% using MobileNetV2, this beats all other models in its ability to estimate social distance and face mask.
Privacy Preserving Algorithm for Blockchain-Based IOT System

In the field of medicine, both machine learning and blockchain technology have been investigated for their prospective uses, but thus far, the results have been somewhat disappointing. The investigation of how these technologies may be combined with other encryption strategies that protect users’ privacy in order to improve their usefulness has been the primary focus. It appears that this combination, when applied to federated learning, is the key to successful applications of these technologies to rapidly advance evidence-based medicine. This combination makes it possible to execute functions and conduct analyses remotely, without the need to move highly regulated personal health information. In this paper, RSA technique is used to improve delay time for encryption in privacy preserving blockchain system for number identification.
Multi-qubit BEC trap for atomic boson sampling

Abstract We suggest a new platform for examination of quantum statistical phenomena in many-body interacting systems – a multi-qubit Bose-Einstein-condensate (BEC) trap. Ideally, it is suitable for demonstrating manifestations of quantum supremacy of such systems through atomic boson sampling of excited-state occupations. This is wholly different than the linear-interferometer platform which uses Gaussian boson sampling of non-interacting non-equilibrium photons rather than interacting atoms in equilibrium. We present a basic model of the multi-qubit BEC trap and illustrate its essential properties by means of numerical simulations.