Reviewerly/exHarmony huggingface dataset:
Convolutional Neural Networks(CNN) based Eye-Gaze Tracking System using Machine Learning Algorithm

To avoid the rising number of car crash deaths, which are mostly caused by drivers' inattentiveness, a paradigm shift is expected. The knowledge of a driver's look area may provide useful details about his or her point of attention. Cars with accurate and low-cost gaze classification systems can increase driver safety. When drivers shift their eyes without turning their heads to look at objects, the margin of error in gaze detection increases. For new consumer electronic applications such as driver tracking systems and novel user interfaces, accurate and effective eye gaze prediction is critical. Such systems must be able to run efficiently in difficult, unconstrained conditions while using reduced power and expense. A deep learning-based gaze estimation technique has been considered to solve this issue, with an emphasis on WSN based Convolutional Neural Networks (CNN) based system. The proposed study proposes the following architecture, which is focused on data science: The first is a novel neural network model that is programmed to manipulate any possible visual feature, such as the states of both eyes and head location, as well as many augmentations; the second is a data fusion approach that incorporates several gaze datasets. However, due to different factors such as environment light shifts, reflections on glasses surface, and motion and optical blurring of the captured eye signal, the accuracy of detecting and classifying the pupil centre and corneal reflection centre depends on a car environment. This work also includes pre-trained models, network structures, and datasets for designing and developing CNN-based deep learning models for Eye-Gaze Tracking and Classification.
Cross-Partisan Discussions on YouTube: Conservatives Talk to Liberals but Liberals Don't Talk to Conservatives

We present the first large-scale measurement study of cross-partisan discussions between liberals and conservatives on YouTube, based on a dataset of 274,241 political videos from 973 channels of US partisan media and 134M comments from 9.3M users over eight months in 2020. Contrary to a simple narrative of echo chambers, we find a surprising amount of cross-talk: most users with at least 10 comments posted at least once on both left-leaning and right-leaning YouTube channels. Cross-talk, however, was not symmetric. Based on the user leaning predicted by a hierarchical attention model, we find that conservatives were much more likely to comment on left-leaning videos than liberals on right-leaning videos. Secondly, YouTube's comment sorting algorithm made cross-partisan comments modestly less visible; for example, comments from conservatives made up 26.3% of all comments on left-leaning videos but just over 20% of the comments were in the top 20 positions. Lastly, using Perspective API's toxicity score as a measure of quality, we find that conservatives were not significantly more toxic than liberals when users directly commented on the content of videos. However, when users replied to comments from other users, we find that cross-partisan replies were more toxic than co-partisan replies on both left-leaning and right-leaning videos, with cross-partisan replies being especially toxic on the replier's home turf.
3D Avatar Approach for Continuous Sign Movement Using Speech/Text

Sign language is a visual language for communication used by hearing-impaired people with the help of hand and finger movements. Indian Sign Language (ISL) is a well-developed and standard way of communication for hearing-impaired people living in India. However, other people who use spoken language always face difficulty while communicating with a hearing-impaired person due to lack of sign language knowledge. In this study, we have developed a 3D avatar-based sign language learning system that converts the input speech/text into corresponding sign movements for ISL. The system consists of three modules. Initially, the input speech is converted into an English sentence. Then, that English sentence is converted into the corresponding ISL sentence using the Natural Language Processing (NLP) technique. Finally, the motion of the 3D avatar is defined based on the ISL sentence. The translation module achieves a 10.50 SER (Sign Error Rate) score.
Key Generation Using Generalized Pell’s Equation in Public Key Cryptography Based on the Prime Fake Modulus Principle to Image Encryption and Its Security Analysis

Abstract RSA is one among the most popular public key cryptographic algorithm for security systems. It is explored in the results that RSA is prone to factorization problem, since it is sharing common modulus and public key exponent. In this paper the concept of fake modulus and generalized Pell’s equation is used for enhancing the security of RSA. Using generalized Pell’s equation it is explored that public key exponent depends on several parameters, hence obtaining private key parameter itself is a big challenge. Fake modulus concept eliminates the distribution of common modulus, by replacing it with a prime integer, which will reduce the problem of factorization. It also emphasizes the algebraic cryptanalysis methods by exploring Fermat’s factorization, Wiener’s attack, and Trial and division attacks.
The Impact of Using Augmented Reality as Teaching Material on Students' Motivation

The integrated technology of Augmented Reality (AR) in education is expanding rapidly due to the advancement of smartphones and fast internet access. Therefore, this study aims to examine the effect of the use of Augmented Reality learning materials on student motivation based on four motivational constructs, which are attention, relevance, confidence, and satisfaction. This research is conducted using the quantitative approach, and questionnaire instruments were applied to collect the research data. The respondents are from the first semester students that took the Basic Electrical and Electronic Course at Bagan Datuk Community College. The data were analyzed by descriptive and inference to determine the level and difference of the students' motivation before and after using Augmented Reality as teaching material. The results showed that the motivation initially at a moderate level has increased to a high level after using Augmented Reality learning materials. The study found that the use of Augmented Reality learning materials has a positive effect on student motivation based on the significant difference test results, and the percentage value of each motivational construct increased.
An Efficient Image Classification of Malaria Parasite Using Convolutional Neural Network and ADAM Optimizer

Machine learning can be a technique of nursing lysis that automatically develops an analytical model. It is a branch of synthetic intelligence that believes that systems are going to learn information, determine patterns of information and decide with degraded human intervention. Machine learning addresses the question of how computers can be constructed that improve mechanically through knowledge. It lies at the intersection of technology and statistics and at the center of artificial data and information science, one in all the quickest increasing technical fields of nowadays. Recent advances in machine learning were driven by the event of latest learning and theories also as by the constant explosion. The event of latest learning algorithms and also theory and the in-progress growth within the accessibility of on-line information also as low-priced computation crystal rectifier to recent progress within the field of machine learning. Additional evidence-based decision-making could be carried out in science, technology and trade, including healthcare, production, education and monetary modelling, enforcement and promotion, with adoption of mechanical learning techniques based on data-intensive methods. The results are also available. The infection can be a life-threatening disease. The bite of a nursing partner is often transmitted in dipterous Anopheles. In infected mosquitoes, plasmodium parasite is a gift. The parasite is discharged into your blood after you bite this dipterous insect once it bites you. Once your body is composed of the parasites, they mature into the liver. The mature parasites enter the blood for several days when red blood cells start to infect. In red blood cells, parasites increase over 48-72 hours, causing infected cells to divide. The parasites still infect red blood cells, which last 2 to 3 days in cycles. This paper is used for observation of protozoan infection with a deep learning idea.
On the Evaluation of Vision-and-Language Navigation Instructions

Vision-and-Language Navigation wayfinding agents can be enhanced by exploiting automatically generated navigation instructions.However, existing instruction generators have not been comprehensively evaluated, and the automatic evaluation metrics used to develop them have not been validated.Using human wayfinders, we show that these generators perform on par with or only slightly better than a template-based generator and far worse than human instructors.Furthermore, we discover that BLEU, ROUGE, METEOR and CIDEr are ineffective for evaluating grounded navigation instructions.To improve instruction evaluation, we propose an instruction-trajectory compatibility model that operates without reference instructions.Our model shows the highest correlation with human wayfinding outcomes when scoring individual instructions.For ranking instruction generation systems, if reference instructions are available we recommend using SPICE.
Hierarchy of quantum correlations under non-Markovian dynamics

We investigate the dynamics of quantum correlation (QC) under the effects of reservoir memory, as a resource for quantum information and computation tasks. Quantum correlations of two-qubit systems are used for implementing quantum teleportation successfully, and for investigating how teleportation fidelity, violation of Bell-CHSH inequality, quantum steering and entanglement are connected with each other under the influence of noisy environments. Both Markovian and non-Markovian channels are considered, and it is shown that the decay and revival of correlations follow the hierarchy of quantum correlations in the state space. Noise tolerance of quantum correlations is checked for different types of unital and non-unital quantum channels, with and without memory. The quantum speed limit time $$(\tau _{QSL})$$ is investigated from the perspective of memory of quantum noise, and the corresponding dynamics is used to analyze the evolution of quantum correlations. We establish the connection between information backflow, quantum speed limit time and dynamics of quantum correlations for non-Markovian quantum channels.
An Empirical Comparison of Instance Attribution Methods for NLP

Widespread adoption of deep models has motivated a pressing need for approaches to interpret network outputs and to facilitate model debugging.Instance attribution methods constitute one means of accomplishing these goals by retrieving training instances that (may have) led to a particular prediction.Influence functions (IF; Koh and Liang 2017) provide machinery for doing this by quantifying the effect that perturbing individual train instances would have on a specific test prediction.However, even approximating the IF is computationally expensive, to the degree that may be prohibitive in many cases.Might simpler approaches (e.g., retrieving train examples most similar to a given test point) perform comparably?In this work, we evaluate the degree to which different potential instance attribution agree with respect to the importance of training samples.We find that simple retrieval methods yield training instances that differ from those identified via gradient-based methods (such as IFs), but that nonetheless exhibit desirable characteristics similar to more complex attribution methods.Code for all methods and experiments in this paper is available at:
“Hey, voice assistant!” How do users perceive you? An exploratory study

Purpose of the paper:The increasing consumer adoption of voice-based artificial intelligence technologies is starting to catch the attention of researchers.This study fits into the nascent marketing literature on user perceptions of interactions with voice assistants (VAs) by exploring perceived VA anthropomorphism and benefits.We also seek to identify millennial clusters based on perceptual differences.Methodology: Quantitative exploratory research was conducted based on questionnaires (N=337) administered to millennials.The data were analysed through exploratory factor analysis.Subsequently, to identify clusters, we performed K-means cluster analysis.Findings: The EFA indicated a four-factor solution: "utilitarian and hedonic benefits", "symbolic benefits", "human-like voice" and "human-like presence".The K-means cluster analysis identified three clusters: "useful and pleasant", "human" and "status symbol".Research limits: This paper is not exempt from limitations, especially those related to the exploratory nature of the analysis techniques adopted.Practical implications: Indicating the main perceptual dimensions of VA anthropomorphism and the benefits associated with user-VA interaction, our results provide marketers with important strategic implications for designing the VA interaction experience.The cluster analysis offers companies the possibility of selecting a target and addressing it by creating a specific value proposition.Originality of the paper: This study contributes to the existing partial and fragmented knowledge by offering an overall integrated interpretation of consumer perceptions related to VA interactions.Our findings are the first to jointly reveal user perceptions of the human voice and of the VA as a human interlocutor.Moreover, we contribute to the literature on anthropomorphism by conceptualising the human-like voice construct.
Robust segmentation method for noisy images based on an unsupervised denosing filter

Level-set-based image segmentation has been widely used in unsupervised segmentation tasks. Researchers have recently alleviated the influence of image noise on segmentation results by introducing global or local statistics into existing models. Most existing methods are based on the assumption that the distribution of image noise is known or observable. However, real-time images do not meet this assumption. To bridge this gap, we propose a novel level-set-based segmentation method with an unsupervised denoising mechanism. First, a denoising filter is acquired under the unsupervised learning paradigm. Second, the denoising filter is integrated into the level-set framework to separate noise from the noisy image input. Finally, the level-set energy function is minimized to acquire segmentation contours. Extensive experiments demonstrate the robustness and effectiveness of the proposed method when applied to noisy images.
Multi-Class Probabilistic Atlas-Based Whole Heart Segmentation Method in Cardiac CT and MRI

Accurate and robust whole heart substructure segmentation is crucial in developing clinical applications, such as computer-aided diagnosis and computer-aided surgery. However, the segmentation of different heart substructures is challenging because of inadequate edge or boundary information, the complexity of the background and texture, and the diversity in different substructures' sizes and shapes. This article proposes a framework for multi-class whole heart segmentation employing non-rigid registration-based probabilistic atlas incorporating the Bayesian framework. We also propose a non-rigid registration pipeline utilizing a multi-resolution strategy for obtaining the highest attainable mutual information between the moving and fixed images. We further incorporate non-rigid registration into the expectation-maximization algorithm and implement different deep convolutional neural network-based encoder-decoder networks for ablation studies. All the extensive experiments are conducted utilizing the publicly available dataset for the whole heart segmentation containing 20 MRI and 20 CT cardiac images. The proposed approach exhibits an encouraging achievement, yielding a mean volume overlapping error of 14.5% for CT scans exceeding the state-of-the-art results by a margin of 1.3% in terms of the same metric. As the proposed approach provides better results to delineate the different substructures of the heart, it can be a medical diagnostic aiding tool for helping experts with quicker and more accurate results.
Probabilistic robustness estimates for feed-forward neural networks

Robustness of deep neural networks is a critical issue in practical applications. In the general case of feed-forward neural networks (including convolutional deep neural network architectures), under random noise attacks, we propose to study the probability that the output of the network deviates from its nominal value by a given threshold. We derive a simple concentration inequality for the propagation of the input uncertainty through the network using the Cramer–Chernoff method and estimates of the local variation of the neural network mapping computed at the training points. We further discuss and exploit the resulting condition on the network to regularize the loss function during training. Finally, we assess the proposed tail probability estimates empirically on various public datasets and show that the observed robustness is very well estimated by the proposed method.
Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain

Abstract It is 50 years since Sieveking et al. published their pioneering research in Nature on the geochemical analysis of artefacts from Neolithic flint mines in southern Britain. In the decades since, geochemical techniques to source stone artefacts have flourished globally, with a renaissance in recent years from new instrumentation, data analysis, and machine learning techniques. Despite the interest over these latter approaches, there has been variation in the quality with which these methods have been applied. Using the case study of flint artefacts and geological samples from England, we present a robust and objective evaluation of three popular techniques, Random Forest, K-Nearest-Neighbour, and Support Vector Machines, and present a pipeline for their appropriate use. When evaluated correctly, the results establish high model classification performance, with Random Forest leading with an average accuracy of 85% (measured through F1 Scores), and with Support Vector Machines following closely. The methodology developed in this paper demonstrates the potential to significantly improve on previous approaches, particularly in removing bias, and providing greater means of evaluation than previously utilised.
BPS: Blockchain Based Decentralized Secure and Versatile Light Payment System

In the presentage, online paymentsystem is a very simple practice.But manypeople use this system to manipulate people’s money. Many are trying for finding a variety of solutions. Butthereis no way to stopthatcrime.Blockchain’syoke is a blessing.Usingblockchainisaveryeasywaytocompletea paymentwithoutmakingany mistakes.Hackerwill never find a way to do theirworkin this kindof system. OurSystem is full workedwith Blockchain.Basically, we choose blockchainas our projectbecauseit is the most secureway to do a transaction in everyonlinesystem.Thecentralbusinessmodelis basedon a database management system. Once accomplishedthe security of the transaction can no longer be guaranteed. On the otherhand, itis really expensivetoresolvepossiblefraudtransactionsby a middle man.Aiming at solving issues concerningsecurityand worthlessness,there is a proposalof a model which is completely madeofblockchainsystem.InOursystemtherearemany blocksof information of eachandeverytransaction. Wehave proposedan algorithm.The algorithm will make consumersable to transact through cryptocurrency in blockchainnetworks.It is totally different from the fiat system where consumers will be able to transact without the help of thirdpartiesand vendors can also be relievedwith theirtransaction. Thistypeof transaction will be very comfortablefor both consumersand vendors. Consumers along with vendorscan see the whole transaction date, time and everythingthatthey dealtwith when the transaction was held.
Determining Optimum Tilt Angle for 1 MW Photovoltaic System at Sukkur, Pakistan

Solar energy is directly converted into electrical energy by using photovoltaic (PV) panels. The efficiency of PV panel varies with its orientation and tilt angle with the horizontal plane. In this paper, we investigate the optimum tilt angle of solar panels installed at Sukkur IBA University. The optimum angle for tilted surfaces varying from 0° to 90° in steps of 1° was calculated for the values of which the daily total solar radiation was maximum for a specific period. It was found that the optimum tilt angle changed between 0° and 61.1° throughout the year in Sukkur IBA University, Sindh Pakistan ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mtext>latitude</mtext> <mo>=</mo> <msup> <mrow> <mn>27.7268</mn> </mrow> <mrow> <mo>°</mo> </mrow> </msup> </math> N, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mtext>longitude</mtext> <mo>=</mo> <msup> <mrow> <mn>68.8191</mn> </mrow> <mrow> <mo>°</mo> </mrow> </msup> </math> E). For calculating irradiance, optimal fixed (15 and 29.5 degrees) and variable tilt angles are used for every month of year 2019. The irradiance calculated at 15 degrees tilt angle is compared with the fixed angle of 29.5 and variable angles. It was found that optimal tilt angle for the region of Sukkur located in northern Pakistan is to be 29.5 degrees.
SENTIMENT ANALYSIS OF PRODUCT REVIEWS IN THE ABSENCE OF LABELLED DATA USING SUPERVISED LEARNING APPROACHES

With the growing pace of internet usage, there is a vast variety of diverse individual opinions and thoughts avail-able online. Consumer reviews can act as a feedback and as well as a pool of ideas for which they can be of immense importance to any business. With the growth and popularity of opinion-rich resources such as online review sites and personal blogs, people now can and do, actively use information technology to seek out and un-derstand the opinions of others to decide whether to buy a product or not. Social media websites such as Facebook, Twitter, and e-commerce websites such as eBay, Amazon, etc. are being widely used to communicate viewpoints effectively. Assigning a positive or a negative sentiment to these reviews can help companies understand their users and also help users to make better decisions. Sentiment analysis being a challenging task can be tackled using supervised machine learning techniques or through unsupervised lexicon based approaches if labelled data is unavailable. In this study, we show that in absence of labelled product reviews of a particular website, labelled product reviews from a different website can be effectively used to train the supervised techniques to achieve a comparable performance to the unsupervised lexicon based approaches. This approach also benefits by covering all of the product reviews which the lexicon based approaches fail to do so. We deduce this by comparing five su-pervised approached and three lexicon based approaches on iPhone 5s reviews gathered from Amazon, Facebook, and Reevoo blog. Furthermore, we also found that unigram features combined with bigram features give the best results, and the effect of varying the training data size on the performance of ML classifiers in some cases was significant whereas in other cases it did not have any effect. Our results also suggest that reviews from Amazon are easiest to classify, followed by reviews from Reevo, and Facebook reviews are the hardest to classify.