Reviewerly/exHarmony huggingface dataset:
Case study: digital twin in cardiology

Even the skilled surgeons face difficulty in choosing the right treatment for their patients. Doctors and surgeons tailor their diagnosis and treatment patterns based on the patient&apos;s current situation. When doctors and surgeons are unaware of the underlying cause of the medical problem, they opt for broad-spectrum treatment and many times are left with unanswered questions which cost the life of a patient. With the advancement of technology and digitalization in every field of science, healthcare is not an exception. Healthcare evolved through various stages like surgery, biopsy, laparoscopy, and nano-robotic surgery to name quite a few. Cyber-physical systems came into existence with the concept of integrating computer systems and physical objects. Precision medicine is the new medical model being put into practice. It focuses on the lifestyle, genetics, and environmental conditions of a patient and even studies molecular diagnostics and imaging analysis to precisely diagnose a patient and choose the most suitable treatment.Then came the digital twins, an advancement of cyber-physical systems [1]. The digital twin is still thriving in many fields of science to help human in identifying, studying, and examining real-world objects which would not have been possible in the absence of its digital twin.
Developing a Hybrid Learning Model Based on Moodle E-Learning on Morphology Course

This study aimed to develop a hybrid learning model and design instructional material for the Morphology course at Makassar State University's Faculty of Languages and Literature in order to develop students' knowledge of linguistic theories and skills, in particular those pertaining to the English language. These improvements were intended to benefit students in the English department. Following a research design and development model that has been designed will be the development of a learning model and the design of instructional content. This study is an extensive process that designs the needs of students and develops the product or course material of morphology to increase the ability or skills of students in learning language through hybrid learning based on moodle. Specifically, this research focuses on the development of a hybrid learning environment (e-learning). Students enrolled in the English department of the Faculty of Languages and Literature at the State University of Makassar are the subjects of this study's population. The participants in the English Education and Literature Study Program would serve as the sample for this investigation. The findings of the study indicate that there may be a hint of a hybrid learning model based on student worksheet activities or exercises, Syllabus, design instructional material for a morphology course, and moodle e-learning. The findings of the study implicate on the pedagogical practice of Moodle e-learning within an EFL hybrid learning setting.
A Blockchain-based Framework for News Fine-grained Detection and Management

The development of the Internet has revolutionized the news industry. While bringing various advantages, it has also caused an overflow of fake and malicious news, which has damaged social and economic benefits. In recent years, new news detection schemes are constantly proposed. In the scheme based on artificial detection, blockchain has become an important infrastructure and tool. Because blockchain is open, transparent, tamper-resistant, and traceable, it has important use value in the scenario of artificial detection, which requires the consensus of multiple entities. However, most of the current blockchain-based solutions focus on ex-ante detection, and they do not implement fine-grained detection division. Obviously, one validator cannot detect the authenticity of all types of news, which leads to a reduction in the credibility of the detection results. In this paper, we propose a framework for news full lifecycle management. First, the news is matched with the professional validators through a fine-grained detection control strategy. Then, based on sealed bid auction and secret sharing technology, a Six-stage Submission Protocol is proposed, which places the validator's consensus process off-chain, saving transaction costs and ensuring the credibility of the detection results. We analyzed the cost and performance of the proposed framework, and the results proved its correctness and feasibility.
On model for estimation of integral characteristics of transport of a medicinal product in an organism

In this paper we introduce a model of transport of a medicinal product in a organism. The model based on estimation of integral characteristics. We introduce an analytical approach for analysis of the considered transport with account of changing of conditions. We consider a possibility to accelerate and decelerate of transport of the above medicinal product.
Integrating color deconvolution thresholding and weakly supervised learning for automated segmentation of neurofibrillary tangle and neuropil threads

Abnormally phosphorylated tau proteins are known to be a major indicator of Alzheimer’s Disease (AD) with strong association with memory loss and cognitive decline. Automated generation of pixel-wise accurate neurofibrillary tangles (NFTs) and neuropil threads (NTs) segmentation is a challenging task, due to lack of ground truth segmentation data of these abnormal tau pathology. This problem is most prominent in the case of segmenting NTs, where the small threadlike morphology makes pixel-wise labeling a laborious task and unrealistic for large-scale studies. Lack of ground truth data poses a significant limitation for many learning-based methods to generate accurate segmentations of NFTs and NTs. This work presents an automated pipeline for pixel level segmentation of NFTs and NTs that does not rely on ground truth segmentation data. The pipeline is composed of four main steps: (1) color deconvolution is used to separate histopathology images into staining channels (DAB, Hematoxylin, and Eosin), (2) Otsu’s thresholding is used on the DAB stain channel to generate pixel level segmentation of abnormal tau proteins staining, (3) a weakly-supervised learning paradigm (WildCat), using only global descriptors of images, is used to generate density maps of potential regions of NFTs and NTs, and (4) density maps and segmentations are then integrated using connected component analysis to localize NFTs and NTs in the detected tau segmentations. Our results show high global classification accuracy for NFTs (Acc:0.96) and NTs (Acc:0.91), and statistically significant distinctions when evaluating the percent area occupied of the detected NTs relative to expert ratings of NTs severity. Qualitative assessment of the NFTs and NTs results showed accurate pixel-level segmentations of the NFTs, while modest performance for NTs.
Deep Learning Based Automotive Requirements Analysis

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Automotive system functionalities spread over a wide range of sub-domains ranging from non-driving related components to complex autonomous driving related components. The requirements to design and develop these components span across software, hardware, firmware, etc. elements. The successful development of these components to achieve the needs from the stockholders requires accurate understanding and traceability of the requirements of these component systems. The high-level customer requirements transformation into low level granularity requires an efficient requirement engineer. The manual understanding of the customer requirements from the requirement documents are influenced by the context and the knowledge gap of the requirement engineer in understanding and transforming the requirements. The manual way of understanding the requirements of the automotive systems always involve a certain amount of ambiguity, misunderstanding, bias etc. in analyzing the functionality of the requirements. The complex automotive system, which is solely developed based on human understanding always causes some violations in transforming the actual requirements from the stockholders in a product functionality. Hence, to mitigate this human influence on this aspect of requirement understanding, an intelligent system, which either to assists the manual requirement analysis or completely analyze the requirements alone is required. In this regard, an intelligent system is proposed here to analyze the automotive requirements efficiently by reducing human conflict, manual efforts, and to improve design and execution performance of an automotive component. The proposed system uses deep learning based Natural Language Processing (NLP) based algorithms to analyze and understand the requirement corpus from a set of platform requirements. The training of the deep learning CNN algorithms is performed on a huge set of pre-implemented platform requirements. The inference of the new customer requirements is done using the trained deep learning-based models to classify the requirements into one of the pre-defined platform requirement classes, thereby assisting the manual analysis using its intelligent component by also providing the traceability.&lt;/div&gt;&lt;/div&gt;
Building an Image Enhancer using Deep Learning and SICE Techniques

Most of the images captured on digital devices like cameras, mobiles are often under exposed or over exposed to light due to inappropriate lighting conditions, which is a cause of loosing detailing of the picture.To adjust the lighting in the outcome, there are various techniques like single image contrast enhancement which are trained on a single image to spotlight the defects in the image and correct it wherever needed.This could solve the irregularities to some extent, but may not give satisfactory results in all possible scenarios.Hence, we need to train a memory (algorithm in this case) on multiple images, which could memorise a defect and its corresponding resolution tactics.All of this knowledge could be used at once to identify multiple blemishes in the input and corresponding fixes could be made for each of them.For this purpose of knowledge extraction, Convolutional neural networks (CNN) are employed on the dataset which will study and identify the problems like darkness, over exposure, blurred images and apply the remedies on them.Low Light Image/ Video enhancing (LOL) dataset is used for this purpose which has 500 pairs of defective and corresponding corrected images.CNN is trained easily on the dataset to provide significantly better results over the existing SICE techniques.
Opinion Mining on Twitter Data Using Machine Learning

Twitter is a social media platform, where we can express our opinions about various events happening worldwide.Researchers are concentrating on opinion mining due to the exponential rise of electronic media-based society and online social networks.A method for figuring out how individuals feel about an event is called opinion mining, often known as sentiment analysis.This is done by using various machine learning algorithms.With the help of these algorithms, we can do sentiment analysis without reading tweets manually.The outcome of these models could help government and industry experts socialize their policies and products.This study investigates how emoji characters are used on social media and how that affects opinion mining.We used three machine learning models to determine the polarity of tweets, such as whether they were positive, negative, or neutral.After building the models, this method combines voting-based models, and additional comparison metrics are determined for each model.We applied the proposed ensemble model to a few tweets to show the emoji's usefulness.
Recognizing Human Activity Using Hybrid Models of CNN and LSTM in Deep Learning

Human Activity Recognition (HAR) is a time series categorization challenge that requires data from a number of timesteps in order to correctly classify the activities that are carried out.In recent times, the usage of image datasets for activity recognition has increased, however good classification cannot be done with just one frame.To increase recognition accuracy, multiple frames of data and the context of environment are required.It is known that a video is made up of a number of still images (frames) that are quickly updated to create the illusion of motion.The hybrid models of Deep Learning (DL) algorithms like Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) are proposed for recognising the human activity from video dataset.The hybrid models, Convolutional Long Short-Term Memory (ConvLSTM) and Long-term Recurrent Convolutional Network (LRCN) are introduced to improve the accuracy of HAR on video dataset.The models will be evaluated on standard video datasets, and the outcomes will show how HAR has the potential to significantly influence a number of industries.It has many applications in the fields of security, sports, and healthcare.
Ask-Me – A BnB’s Hybrid Chat bot using RASA

Chat bots are computer programs that can communicate with humans just like humans use Natural Processing functions (NLP) in communication. The main goal of this project is to design the Hotel Chat bot called Ask-Me Bot. Ask-Me Bot provides all the basic features required for hotels like booking rooms, breakfast convenience, FAQs, booking or canceling hotel or restaurant reservations, etc. To interact on communicating platforms with many customers, they can write different programs that converse just like humans known as Chat bot. In this, we make Ask-Me Bot for hotels. Key Word: Chat bot, Rule-based, Artificial Intelligence, Natural Language Processing,.
FEATURES OF THE SPEECH CULTURE OF YOUNGER GENERATION AND THE INFLUENCE OF THE INTERNET LANGUAGE ON ITS FORMATION

People are increasingly using the Internet as a number one means of communication in the current world. It is used by every second person and is where they spend the majority of their time. The Internet and other global computer networks, as well as the ability to communicate in a variety of chat rooms, forums, and blogs, all contributed to the fast-paced improvement of information technology and laid the groundwork for the emergence of Internet language, which is now used outside the world web and is a big part of younger generation‘s speaking culture. Being a unique medium for communication, the Internet has given rise to unique forms of discourse, linguistic devices, and behavioral stereotypes. As a result, it is impossible to avoid seeing how the Internet influences the speech of those who use it: spelling errors, numerous typos, and a disregard for punctuation standards. This article is devoted to identifying the features of the speech culture of modern youth and the influence of the Internet language on its formation. The purpose of the paper is to find the features of the formation of the speech culture of modern youth and to determine the role of the Internet language in this process. Following our goal, we have identified the following tasks: To identify the origin and characteristics of the Internet language as a means of communication for the younger generation; Determine the reasons for the use of the Internet language by modern youth outside the Internet; Consider slang expressions frequently used by young people that originate on the net; To identify the features of the influence of the Internet language on the literacy of today's youth and their speech culture in general.
A domain density peak clustering algorithm based on natural neighbor

Density peaks clustering (DPC) is as an efficient algorithm due for the cluster centers can be found quickly. However, this approach has some disadvantages. Firstly, it is sensitive to the cutoff distance; secondly, the neighborhood information of the data is not considered when calculating the local density; thirdly, during allocation, one assignment error may cause more errors. Considering these problems, this study proposes a domain density peak clustering algorithm based on natural neighbor (NDDC). At first, natural neighbor is introduced innovatively to obtain the neighborhood of each point. Then, based on the natural neighbors, several new methods are proposed to calculate corresponding metrics of the points to identify the centers. At last, this study proposes a new two-step assignment strategy to reduce the probability of data misclassification. A series of experiments are conducted that the NDDC offers higher accuracy and robustness than other methods.
Cloud computing task scheduling problem by Nondominated Sorting Genetic Algorithm II (NSGA-II)

Cloud computing is applied in wireless sensor networks (WSNs) by dispersing large-scale computations across multiple distributed resources.Cloud computing task scheduling has been a long-standing issue in ascertaining the quality, accessibility, accuracy, and capability of WSNs.In order to achieve a well-organized solution to the biobjective time-constrained task scheduling problem, the Nondominated Sorting Genetic Algorithm II (NSGA-II) has been developed.Additionally, The NSGA-II addresses the problem of task scheduling by experimenting.The study results demonstrate that NSGA-II can successfully achieve this study's objective.
Predicting the Number of Emergency Department Patients with Forecasting Models Based on Machine Learning

An emergency department (ED) is a specialized area where patients usually arrive in critical condition and require immediate care.Medical staff know that in these cases reacting quickly is crucial for saving patients' life.Since supplies, personnel and infrastructure are limited resources, the efficiency in their use is fundamental to operate adequately.Therefore, planning well in advance is fundamental to optimize the use of the existing resources.This research presents an interesting approach to forecast the number of patients received at the ED of a public hospital by means of implementing and comparing model bases in machine learning algorithms.This investigation was carried out following a 4-phase methodology: analysis, design, development, and validation.During the phase of analysis, the ED database with recorded collected during 2020 was reviewed and preprocessed.Data were prepared and organized to show number of patients visiting ED every day.During the phase of design, machine learning algorithms for forecasting were analyzed and compared.Among others: linear regression, artificial neural network, machine support vectors, and Gaussian methods.The development and the validation phases were carried out entirely using the data processing software WEKA 3.9.6 with the forecasting package version 1.1.27.In total, over seventy thousand records corresponding to ED visits occurred during 2020 were used in the investigation.Data were divides in two datasets: one for building forecasting models and another one for comparing predicted and actual values.Six forecasting horizons were study: 60, 45, 30, 15, 10, and 6 days.For each horizon four machine learning algorithms were used to predict the number of ED visits.To evaluate and compare predictions, the usual error metrics were considered: MAE, MSE, RMSE, and MAPE.Forecasting results revealed notorious differences in the accuracy of the predicted values.Although no big differences are noticeable when forecasting short periods, the prediction error is considerable when forecasting a long period of 60 days.In this case, MAPE fluctuated between 39% and 16% depending on the algorithm.When forecasting short periods, for instance 5 days, MAPE varied between 17% and 14%.In conclusion, experimental results showed that ML-based forecasting algorithms can be used to predict the number of ED visits with accuracy even when with long forecasting horizons.The application of such approach might be of great help to estimate resource requirements and to support decision making.
Deep Facial Diagnosis: Deep Transfer Learning from Face Recognition to Facial Diagnosis

One of the most crucial uses for facial analysis is facial diagnosis.Practitioners of traditional medicine have used their expertise to assess a person's health state for tens of thousands of years.Since then, a lot has changed, and this long-standing tradition has been supplanted by computerised face diagnosis.The proposed method of utilizing deep transfer learning from face recognition for face illness diagnosis with computer assistance is an interesting approach that can potentially provide a low-cost and non-invasive way for disease screening and detection.The objective of this research endeavor is to investigate whether techniques based on deep learning may be employed to detect malignancies through uncontrolled 2D face images.By leveraging the pre-trained face recognition models, the system can effectively learn relevant features from the face images and use them to diagnose different diseases.The proposed system will be using data augmentation to handle the imbalance of data in the system and reduce over-fitting.By artificially generating new data from the existing dataset, the system can increase the amount of data and diversity of the samples, leading to better generalization and improved accuracies.
A Systematic Approach to Detect Spliced and Forged Images using Deep Learning Technique

The widespread accessibility of image editing tools has made it simpler to alter the contents of digital figures as multimedia technology has advanced.Moreover, photographs are more susceptible to counterfeiting when they are distributed through an open channel using information and communication technology (ICT).Due to the flaws in the telecommunications infrastructure, it is possible for hackers to make subtle but deceptive alterations to picture databases.If edited with a malicious intent, the phoney photographs might create serious social and legal problems.The use of sophisticated tools built through deep learning techniques can accurately detect changes to the mathematical image is required for the discovery of image forgeries.The sensitivity in photos is typically concealed through splicing forgeries.Extreme contrast is introduced by splicing in the edges, smooth areas, and corners.In-depth Learning
Image Steganography Using Modified DWT Technique

The modern computing world revolves around the word "DATA", but just what is so intriguing about it?In today's world, data is the power and business start realizing it because data can predict customer trends potentially, get increased sales, and help the organization to achieve newer heights.The technology has become so advanced and our topmost priority is to secure data.Here in this project, the high frequency coefficients produced by the discrete wavelet transform contain hidden messages.To enhance the quality of the images, low frequency sub-band coefficients are kept intact.Before embedding, some elementary mathematical operations are performed on the secret messages.These operations prevent messages from being stolen or destroyed by unauthorized internet users, and they do so while also providing adequate security.