Reviewerly/exHarmony huggingface dataset:
A Novel Review on Healthcare Data Encryption Techniques

Sharing of personal digital health information is an arising idea of changing health statistics for research and different functions. Confidentiality, besides for legal users, and access auditability are sturdy safety requirements for health statistics. This paper will examine those requirements and advise a review for healthcare companies as a good way to assist in securely storing and sharing of affected persons' statistics they host. It should additionally allow the best legitimate users to get entry to portions of the facts' statistics they are permitted to. The recognition can be on these precise protection troubles of modern-day encryption techniques utilized in health care and the way encryption can help in addressing healthcare regulatory necessities. This paper provides an overview of encryption and decryption procedures, highlighting their security foundations, implementation regions, and strengths and limitations in the early stages of operation. Finally, the study pinpoints the existing gap based on the findings of the analysis, with a focus on a set of rules that are most acceptable for commercial use, given current cryptography trends that are moving closer to quantum computing. The focus of this study then shifts to the genuine need for a set of rules that offers no trade-off between encryption and decryption speeds, has low computation overhead, and is resilient to quantum method attacks.
Time interval uncertainty-aware and text-enhanced based disease prediction

Deep learning methods have achieved success in disease prediction using electronic health records (EHR) data. Most of the existing methods have some limitations. First, most of the methods adopt a homogeneous decay way to deal with the effect of time interval on patient's previous visits information. However, the effect of the time interval between patient's visits is not always negative. For example, although the time interval between visits for patients with chronic diseases is relatively long, the importance of the previous visit to the next visit is high, and we may not be able to consider the effect of the time interval as negative at this point. That is, the effect of the time interval on previous visits is exerted in a nonmonotonic manner, and it is either positive, negative, or neutral. In addition, the effect of text information on prediction results is not taken into account in most of methods. The text in EHR contains a description of the patient's past medical history and current symptoms of the disease, which is important for prediction results. In order to solve these issues, we propose a Time Interval Uncertainty-Aware and Text-Enhanced Based Disease Prediction Model, which utilizes the uncertain effects of time intervals and patient's text information for disease prediction. Firstly, we apply a cross-attention mechanism to generate a global representation of the patient using the patient's disease and text information from the EHR. Then, we use the key-query attention mechanism to obtain the two importance weights of the two visit sequences with and without time intervals, respectively. Furthermore, we achieve disease prediction by making slight adjustments to the encode part of the Transformer, a deep learning model based on a self-attention mechanism. We compare with various state-of-the-art models on two publicly available datasets, MIMIC-III and MIMIC-IV, and select the top 10 diseases with the highest frequency in the dataset as the target diseases. On the MIMIC-III dataset, our model is up to three percent higher than the optimal baseline in terms of evaluation metrics.
Image Enhancement Under Gaussian Impulse Noise for Satellite and Medical Applications

Remote sensing technologies such as hyperspectral imaging (HSI) and medical imaging techniques such as magnetic resonance imaging (MRI) form the pillars of human advancement. However, external factors like noise pose limitations on the accurate functioning of these imaging systems. Image enhancement techniques like denoising therefore form a crucial part in the proper functioning of these technologies. Noise in HSI and MRI are primarily a mixture of Gaussian and impulse noise. Image denoising techniques designed to handle mixed Gaussian-impulse (G-I) noise are thus an area of core research under the field of image restoration and enhancement. Therefore, this chapter discusses the mathematical preliminaries of G-I noise followed by an elaborate literature survey that covers the evolution of image denoising techniques for G-I noise from filtering-based to learning-based. An experimental analysis section is also provided that illustrates the performance of several denoising approaches under HSI and MRI, followed by a conclusion.
AI-Based Interview Agent

Selection of an unbiased and most suitable candidate for the post is a tedious job in the traditional recruitment process. Human resource management department faces time constraints. Issues while screening resumes and then analyzing shortlisted Candidates. An AI-based interview agent is an unbiased, partially automated, time-saving system for the selection of candidates. It includes three main modules—Resume classification, Face recognition and Face feature Analysis. The final results of the system guarantee scientific search, elimination of human errors and automation of repetitive tasks. However, the system still requires manual intervention, i.e., actual human interviewer for completion of the process.
Smart Surveillance System Using Deep Learning Approaches

In modern days, CCTVs are being used for monitoring, and most shops have surveillance cameras up and running during the night times, but still, robberies are happening since the surveillance footage is being checked only after a robbery on the next day. To overcome the problems of having manual security and cost wastage along with automating the monitoring of the surveillance during the night times once the shops are closed, the authors propose the smart surveillance system. Deep learning algorithms and computer vision techniques are used to detect the presence of humans/intruders in a given video. The smart surveillance system along with the reduction in the cost of manual securities also provides robust nighttime monitoring, and it provides immediate notification to the authority as soon as it spots the intruder in the specified monitoring time, thereby reducing the robberies and the business impact caused.
Implementation of Anomaly Detection Using Unsupervised Machine Learning

Anomaly detection is a strenuous and complex task to achieve. With the increase in the generation of live-streamed big data, detecting anomalous data and alerting the concerned party are crucial. Although many algorithms have been developed by researchers to detect anomalies from a data set, choosing the right fit is significant. Anomaly detection is a key issue when dealing with the enormous amount of continuous data. This paper presents different unsupervised learning algorithms and how well they fare when applied to huge multidimensional data for anomaly detection. As a sample data set, we have selected Weather Records of Goa, India from 1st July 2016 to 31st July 2019 from Kaggle. We have applied three algorithms to this data set, viz. Isolation Forest, K-Means and Density-Based Spatial Clustering Application with Noise (DBSCAN) and have compiled their respective observations for comparison. As this data set was obtained from Kaggle, there it was no need of data pre-processing.
Fall Detection Using Transformer Model

Falls are exceptional activities that put one’s health in danger. To limit the effect of falls, it is necessary to build fall detection and prevention systems. The goal of emerging technology is to create such systems that will improve people’s quality of life, especially for the elderly. To limit the danger of injury, a fall detection system detects the fall and generates an assistance signal. The suggested system detects falls by classifying various behaviors as fall or non-fall activities and alerting those who are affected in the event of an emergency. To calculate characteristics, the dataset SisFall is used, which contains a variety of actions performed by numerous people. The machine learning methods XGBoost and LightGBM are used to identify falls based on calculated characteristics. Using the XGBoost algorithm, the system achieves ROC-AUC scores of up to 97.64%. Our proposed solution is based on a transformer model, which is then tailored to produce the best outcomes, with an accuracy of approximately 95.7%.
Evolution of Cryptocurrencies and Sentiment Analysis: A Correlation Analysis

A cryptocurrency is an arguable virtual asset intended to work as a standard of transformation that makes practice of robust cryptography to protect monetary communications, switch the introduction of supplementary devices, and confirm the transmission of belongings. Cryptocurrencies are extraordinarily unstable. Four years of precariousness within the stock marketplace can be blanketed in 30 days of valuing moves inside the cryptocurrency marketplaces. Twitter is an Internet information and community networking facility on which handlers submit and engage with posts referred to as “tweets”. It is similarly the key network of conversation for cryptocurrencies. Numerous crucial newscast is retweeted with the aid of heaps of consumer and might spread a very massive viewers. As a case, John McAfee, who has 828,338 admirers in the intervening time, is a well-known cryptocurrency person. His posts can have an effect on the path of a cryptocurrency active to more than 100% because of his enormous target viewers. There are numerous further prominent Twitter operators. The intention of this paper is to suggest a methodology to envisage the association among cryptocurrencies charge in USD and a mark founded on the sentiment investigation of posts, the quantity of supporters of the consumer, the wide variety of retweets, and the range of likes. In this paper, an historic evaluation of the correlation is finished. In this paper, a version is proposed for the real-time visualization of the advancement of tweets scores with cryptocurrency’s costs in USD.
A dynamic topography for visualizing time and space in fictional literary texts

Abstract This paper presents research on creating interactive prototypes for visualizing temporal spatial relationships in fictional literary texts. Developed within the context of the Chronotopic Cartographies project, a practice-led inquiry yielded dynamic visualizations from literary texts, the research explores the development and application of interactive three-dimensional environments illustrating the ‘chronotopic’, time-space relationships across a series of fictional literary texts. Expert feedback highlights the potential of the interaction model as a useful visual paradigm for supporting methods of reflective inquiry hypothesis making. The work also represents a potential model for creating interactive temporal visualizations which support hypothesis making across a broader sphere of the humanities.
Seeing what is not shown

Abstract Critical studies of data visualization often highlight how the reductive nature of visualization methods excludes data limitations and qualities that are crucial to understanding those data. This case study explores how a data visualization could express contingent, situated, and contextual facets of data. We examine how such data limitations might be surfaced and represented within visualizations through an interplay between the critique of an existing data visualization and the development of alternative designs. Based on a case study of urban tree data, we interrogate data limitations in relation to four different types of missingness: Incompleteness, Emptiness, Absence, and Nothingness. Our study enables reflections on how data limitations can be investigated using visualizations and considers the development of a critical visualization practice.
Three-string inharmonic networks

This paper studies the resonant frequencies of three-string networks by examining the roots of the relevant spectral equation. A collection of scaling laws are established which relate the frequencies to structured changes in the lengths, densities, and tensions of the strings. Asymptotic properties of the system are derived, and several situations where transcritical bifurcations occur are detailed. Numerical optimization is used to solve the inverse problem (where a desired set of frequencies is specified and the parameters of the system are adjusted to best realize the specification). The intrinsic dissonance of the overtones provides an approximate way to measure the inherent inharmonicity of the sound.
Tamil Question Answering System Using Machine Learning

Tamil question answering system (QAS) is aimed to find relevant answers in in the native language. The system will help farmers to get information in Tamil related to the agriculture domain. Tamil is one of the morphologically rich languages. As a result, developing such systems that process Tamil words is a difficult task. The list of stop words in Tamil has to be collected manually. Parts of speech (POS) tagging is used to identify suitable POS tag for a sequence of Tamil words. The system employs Hidden Markov Model (HMM)-based viterbi algorithm, a machine learning technique for parts of speech tagging of Tamil words. The analyzed question is given to the Google search to obtain relevant documents. On top of Google search, locality sensitive hashing technique (LSH) is utilized to retrieve the five relevant items for the input Tamil question. Jaccard similarity is used to obtain the response from the retrieved document items. The proposed system is modelled using a dataset of 1000 sentences in the agriculture domain.
The Benefits of Technology in Healthcare Management

Technology adoption can improve healthcare delivery, outcomes, and cost-efficiency. Using the COVID-19 pandemic as a foundation, this chapter discusses technology's role during the crisis and explores the barriers to successful healthcare management strategies due to inadequate or inappropriate technology adoption. This chapter also explores the pros and cons of technology and provides tools to help determine when adoption will benefit healthcare providers, systems, and consumers. The author discusses operational effectiveness and efficiency, effective communication, team coordination, health outcomes, data collection and utilization, and risk management in this chapter.
Conceptual Design Principles for Data-Driven Clinical Decision Support Systems (CDSS)

In an era where health organizations are constantly striving to increase the quality of care amidst significant challenges, such as the recent pandemic, the development of clinical decision support systems (CDSS) can make contextually relevant predictions that can contribute to more efficient and safe health systems. This chapter outlines the conceptual design principles for data-driven clinical decision support systems. It starts by explaining to the reader the user setting and healthcare data use characteristics to discuss 11 principles and considerations for designing contextually data-driven models for clinical decision-making. The chapter was written to be comprehensive to a wide range of audiences and is meant to be enjoyable for readers without an extensive background in data science.
Automatic Image Captioning Using Different Variants of the Long Short-Term Memory (LSTM) Deep Learning Model

Today's world is full of digital images; however, the context is unavailable most of the time. Thus, image captioning is quintessential for providing the content of an image. Besides generating accurate captions, the image captioning model must also be scalable. In this chapter, two variants of long short-term memory (LSTM), namely stacked LSTM and BiLSTM along with convolutional neural networks (CNN) have been used to implement the Encoder-Decoder model for generating captions. Bilingual evaluation understudy (BLEU) score metric is used to evaluate the performance of these two bi-layered models. From the study, it was observed that both the models were on par when it came to performance. Some resulted in low BLEU scores suggesting that the predicted caption was dissimilar to the actual caption whereas some very high BLEU scores suggested that the model was able to predict captions almost similar to human. Furthermore, it was found that the bidirectional LSTM model is more computationally intensive and requires more time to train than the stacked LSTM model owing to its complex architecture.
Scripting, space design and user movement in room-scale VR: Storytelling strategies to direct gesture and gaze.

This text draws on the experience of developing and exhibiting two room-scale VR artworks in order to discuss the relationship between scripting, virtual space design, and interactive design with the audience’s movements and expected position in the VR and physical space. Different design and scripting strategies allow or impede to anticipate the specific position of the audience at every given time, and this anticipation is key to articulate the virtual elements in a way that responds to not just the gaze but also the overall movement of the person wearing the headset. Both artworks present different approaches from this point of view. In comparing them, two main design choices are shown to particularly affect this possibility of anticipation: simplicity vs. complexity of visual and interactive elements, and sequencing vs. simultaneity. While none of the design strategies is more desirable than the other per se, these conclusions shed some light on how to script and design for VR experiences in terms of deciding how much the artist wants to control, or anticipate, the body movements of their audience when experiencing their piece.&amp;nbsp;
Automatic Speech Recognition Models, Tools, and Techniques

Automatic speech recognition (ASR) has gained wide popularity in last decade. Various devices like mobile phones, computers, vehicles, and audio/video players are now being equipped with ASR technology. The increasing use and dependence on ASR technology leads to research enhancements and opportunities in this domain. This chapter provides a detailed review of various advancements in ASR systems development. It highlights history of speech recognition followed by detailed insight into recent advancements and industry leaders providing latest solutions. ASR framework has been discussed in detail which includes feature extraction techniques, acoustic modeling techniques, and language modeling techniques. The chapter also lists various popular data sets available and discusses generation of new data sets. This work will be helpful for the researchers who are new to this field and are exploring development of new speech recognition techniques.
Reconnoitering Generative Deep Learning Through Image Generation From Text

A picture is worth a thousand words goes the well-known adage. Generating images from text understandably has many uses. In this chapter, the authors explore a state-of-the-art generative deep learning method to produce synthetic images and a new better way for evaluating the same. The approach focuses on synthesizing high-resolution images with multiple objects present in an image, given the textual description of the images. The existing literature uses object pathway GAN (OP-GAN) to automatically generate images from text. The work described in this chapter attempts to improvise the discriminator network from the original implementation using OP-GAN. This eventually helps the generator network's learning rate adjustment based on the discriminator output. Finally, the trained model is evaluated using semantic object accuracy (SOA), the same metric that is used to evaluate the baseline implementation, which is better than the metrics used previously in the literature.
COVID-19 Success Story of the United Arab Emirates (UAE)

The case illuminates the success story of the UAE's Federal Government, Ministry of Health and Prevention, and Dubai Health Authority in handling COVID-19, which launched a comprehensive approach to assess business readiness across all critical functions and country locations through the lens of a global pandemic. The strategy reviews several areas: operational readiness, medical staff mobility, infrastructure resiliency, and information security. The case is a descriptive or observational study describing the series of occurrences concerning health disorders and aftermath activities in March and April of 2020. The author noticed the happenings in his fortnight quarantine stay and witnessed how efficiently, with significant efficacy, the entire medical and admin activities are planned and delivered to provide accurate and dedicated services to all COVID-19 patients. These activities mostly relate to leadership, crisis management, strategic initiatives, human resources, team building, customer experience, and positive psychology.
Remote Literacy Interventions With Children During COVID-19

The outbreak of COVID-19 pandemic resulted in large-scale educational disruptions for school-going children around the world. India was among the worst hit, as multiple lockdowns put a sizeable percentage of Indian children, out of school which resulted in accumulated learning gaps, particularly for children coming from marginalized socio-economic backgrounds. To confront this learning crisis and curricular deficits, Room to Read India, a global organization working towards a world free of illiteracy and gender inequality, adopted multiple remote literacy interventions to reach out to children from vulnerable families. The study recommends that a blended hybrid mode of education, incorporating both online and offline mediums of learning, is significant in bridging the digital divide for marginalized children, who do not have adequate access or knowledge about digital resources. It was inferred that successful digital learning for children cannot take place in isolation and a dynamic partnership between teachers and parents is essential to reach out to vulnerable children.
Mixture of Networks for Clustering Categorical Data: A Penalized Composite Likelihood Approach

One of the challenges in clustering categorical data is the curse of dimensionality caused by the inherent sparsity of high-dimensional data, the records of which include a large number of attributes. The latent class model (LCM) assumes local independence between the variables in clusters, and is a parsimonious model-based clustering approach that has been used to circumvent the problem. The mixture of a log-linear model is more flexible but requires more parameters to be estimated. In this research, we recognize that each categorical observation can be conceived as a network with pairwise linked nodes, which are the response levels of the observation attributes. Therefore, the categorical data for clustering is considered a finite mixture of different component layer networks with distinct patterns. We apply a penalized composite likelihood approach to a finite mixture of networks for sparse multivariate categorical data to reduce the number of parameters, implement the EM algorithm to estimate the model parameters, and show that the estimates are consistent and satisfy asymptotic normality. The performance of the proposed approach is shown to be better in comparison with the conventional methods for both synthetic and real datasets.