Reviewerly/exHarmony huggingface dataset:
Prediction analysis of the length of time for changes in the status of positive patients for covid-19 delta and omicron variants using the markov chain model

In this research, a Markov chain model was constructed from the conditions that might be experienced by the people during the Delta or Omicron variant of the COVID-19 pandemic. This condition is divided into 3 states; "0" indicates a healthy condition, "1" indicates the state of being infected with COVID-19, and "2" indicates the death state. Furthermore, from the model obtained, a transition probability matrix is made to determine the transition probability value and calculate the average number of steps needed to get to the death state. From the results of the analysis, the probability of transition to a state of death is 1. This shows that a person will surely die from being healthy or positive for COVID-19 within a certain time. During the Delta variant of the COVID-19 pandemic, the average time that a person reaches a death state from a healthy state is 34.02 years. Meanwhile, the average time taken for someone infected with the Delta variant of COVID-19 to death is 33.79 years. During the Omicron variant of the COVID-19 pandemic, the average time that a person reaches a death state from a healthy state is 37.63 years. Meanwhile, the average time taken for someone infected by the Omicron variant of COVID-19 to death is 37.41. This shows that the average age of a person infected with the Delta variant of COVID-19 has decreased by 2.79 months, while the average age of a person infected with the Omicron variant of COVID-19 has decreased by 2.70 months.
Conceptual and linguistic changes: document, resource, and item

The bibliographic universe has radically changed in a short time; the major innovations, mainly related to the digital environment, have had consequences in terms of terminology. This contribution offers an analysis of three specific international cataloguing terms: document, resource, and item. The terms document and resource constitute an example of terminological evolution; item, introduced in cataloguing by FRBR as the fourth entity of Group I, highlights the difficulties of transposition from one linguistic system to another, and the multiple terminological meanings linked to different disciplinary contexts.
Research on the Application of Pigai AWES in College English Composition Writing

At present, the writing evaluation system based on cloud computing and corpus is becoming the focus of college English writing research. As an intelligent assisted English writing teaching system, the Pigai Automatic Writing Evaluation System came into being and is widely used in the teaching of College English writing. The Pigai AWES can effectively improve the students’ writing level and the teachers’ work efficiency. This paper analyzes the advantages in application of the Pigai AWES in College English writing, expounds the problems in detail in its practical application, and puts forward some suggestions and measures. Building a diversified feedback system for evaluation should be insisted on, only in that way can we get complementary and mutually reinforcing effects and promote innovation and development of College English writing.
A Python-Based Virtual AI Assistant

Artificial intelligence is a core specialization of making smart machines, especially computer programs. It relates to the familiar task of using computers to understand human intelligence. AI is broadly classified to study computations that permit perception, reason, and action. This paper gives an overview idea of a Python-based personal assistant for Windows-based systems. This paper got its motivation from popular AI assistants like Windows’s Cortana, Siri of iOS, Amazon’s Alexa, and Google Assistant of Google. It is designed in a good user-friendly interface to execute various types of tasks by just saying certain set of instructions. As an AI personal assistant, this paper focuses mainly upon the assistance of the users with their daily activities like asking questions on Google, staying updated with the latest news, video watching in YouTube, sending emails, live weather forecasts, simple calculations, Wikipedia-powered AI, utilization of Google Maps, keep tracks on time, operating system-based tasks like shutting down the PC and restarting the PC, plays songs, tells pyjokes, makes notes, and last but not the least, greets the user when called by name.
Stabilize, Decompose, and Denoise: Self-supervised Fluoroscopy Denoising

Fluoroscopy is an imaging technique that uses X-ray to obtain a real-time 2D video of the interior of a 3D object, helping surgeons to observe pathological structures and tissue functions especially during intervention. However, it suffers from heavy noise that mainly arises from the clinical use of a low dose X-ray, thereby necessitating the technology of fluoroscopy denoising. Such denoising is challenged by the relative motion between the object being imaged and the X-ray imaging system. We tackle this challenge by proposing a self-supervised, three-stage framework that exploits the domain knowledge of fluoroscopy imaging. (i) Stabilize: we first construct a dynamic panorama based on optical flow calculation to stabilize the non-stationary background induced by the motion of the X-ray detector. (ii) Decompose: we then propose a novel mask-based Robust Principle Component Analysis (RPCA) decomposition method to separate a video with detector motion into a low-rank background and a sparse foreground. Such a decomposition accommodates the reading habit of experts. (iii) Denoise: we finally denoise the background and foreground separately by a self-supervised learning strategy and fuse the denoised parts into the final output via a bilateral, spatiotemporal filter. To assess the effectiveness of our work, we curate a dedicated fluoroscopy dataset of 27 videos (1,568 frames) and corresponding ground truth. Our experiments demonstrate that it achieves significant improvements in terms of denoising and enhancement effects when compared with standard approaches. Finally, expert rating confirms this efficacy.
Transition to Adulthood for Young People with Intellectual or Developmental Disabilities: Emotion Detection and Topic Modeling

Transition to Adulthood is an essential life stage for many families. The prior research has shown that young people with intellectual or development disabil-ities (IDD) have more challenges than their peers. This study is to explore how to use natural language processing (NLP) methods, especially unsupervised machine learning, to assist psychologists to analyze emotions and sentiments and to use topic modeling to identify common issues and challenges that young people with IDD and their families have. Additionally, the results were compared to those obtained from young people without IDD who were in tran-sition to adulthood. The findings showed that NLP methods can be very useful for psychologists to analyze emotions, conduct cross-case analysis, and sum-marize key topics from conversational data. Our Python code is available at https://github.com/mlaricheva/emotion_topic_modeling.
Digital Transformation of the Health Sector During the Covid-19 Pandemic in Saudi Arabia

Background: In March 2020, the first case of COVID-19 in Saudi Arabia was confirmed. In reaction to the outbreak, Saudi Arabia ordered the closure of the majority of governmental and private services. Technology and digital solutions have made it possible to provide essential services after the implementation of these severe protection requirements. Objective: This study aims to show how Saudi Arabia used digital technology during the COVID-19 epidemic in the fields of public health and health care services, and shedding insight on the efficacy of digital transformation in Saudi Arabia for preventing the spread of Covid-19. Methods: Using public announcements, press briefings and releases, news clips, published statistics, peerreviewed literature, and professional debates, we documented Saudi Arabia's usage of digital technologies throughout the epidemic. Results: The government and business sectors of Saudi Arabia jointly created and released around 19 applications and platforms that support public health duties and offer health care. Detailed descriptions of each are given. Companies in the telecommunications industry collaborated well and took steps to assist continuing efforts. Using social media, websites, and SMS text messages, risk communication initiatives adhered to best practice guidelines. Conclusions: An important step toward digital transformation has been taken by Saudi Arabia's Vision 2030 framework. This shift was made possible by COVID-19. For future epidemics in Saudi Arabia, artificial intelligence might be used to integrate data from several sources. Reducing the number of applications and combining their operations may also boost and make it easier for people to utilize them.
Stochastic Weight Perturbations Along the Hessian: A Plug-and-Play Method to Compute Uncertainty

An uncertainty score along with predictions of a deep learning model is necessary for acceptance and often mandatory to satisfy regulatory requirements. The predominant method to generating uncertainty scores is to utilize a Bayesian formulation of deep learning. In this paper, we present a plug-and-play method to obtain samples from an already optimized model. Specifically, we present a simple, albeit principled methodology, to generate a number of models by sampling along the eigen directions of the Hessian of the converged minimum. We demonstrate the utility of our methods on two challenging medical ultrasound imaging problems - cardiac view recognition and kidney segmentation.
Digital Platform as an Object Modern Educational Space

The article discusses the scientific and practical problem of improving the practice of online learning using digital educational platforms – objects of educational space that fully provide educational and administrative processes. At the same time, commercial activities for the development of digital platforms require a scientific and pedagogical analysis of innovations in this area. The purpose of the article is to consider the potential of digital educational platforms to improve the quality of teachers’ educational activities, not only for the efficiency and automation of the educational process, but also to increase the productivity of students’ educational activities, for the implementation of deep learning. Methodology and research methods: the research was carried out in the context of a cognitive approach that considers global digitalization not only as a means of solving a variety of practical problems, but also a threat to human existence, focusing on the humanization of digital/information technologies; taking into account the concept of educational space, representing educational space as an object world and the subject of subjective activity. To prepare this article, we used methods of establishing causal relationships, which allowed us to identify an objective relationship between the key concepts of the problem under consideration; a web analytics tool (Google Trends); methods of analyzing and processing bibliographic sources. In conclusion, the directions of effective use of the digital platform in the educational space are proposed: the creation of a special management system, the organization of scientific and pedagogical support for the development and implementation of digital platforms, advanced training of teachers and students in the field of digital technologies and digital hygiene. The prospects of using digital educational platforms in higher education are outlined: educational analytics and forecasting of educational programs, the functioning of a flexible educational and administrative infrastructure in online offices, the integration of information, informal and formal strategies for self-education of students, teachers and staff.
Privacy-preserving Anomaly Detection in Cloud Manufacturing via Federated Transformer

With the rapid development of cloud manufacturing, industrial production with edge computing as the core architecture has been greatly developed. However, edge devices often suffer from abnormalities and failures in industrial production. Therefore, detecting these abnormal situations timely and accurately is crucial for cloud manufacturing. As such, a straightforward solution is that the edge device uploads the data to the cloud for anomaly detection. However, Industry 4.0 puts forward higher requirements for data privacy and security so that it is unrealistic to upload data from edge devices directly to the cloud. Considering the above-mentioned severe challenges, this paper customizes a weakly-supervised edge computing anomaly detection framework, i.e., Federated Learning-based Transformer framework (\textit{FedAnomaly}), to deal with the anomaly detection problem in cloud manufacturing. Specifically, we introduce federated learning (FL) framework that allows edge devices to train an anomaly detection model in collaboration with the cloud without compromising privacy. To boost the privacy performance of the framework, we add differential privacy noise to the uploaded features. To further improve the ability of edge devices to extract abnormal features, we use the Transformer to extract the feature representation of abnormal data. In this context, we design a novel collaborative learning protocol to promote efficient collaboration between FL and Transformer. Furthermore, extensive case studies on four benchmark data sets verify the effectiveness of the proposed framework. To the best of our knowledge, this is the first time integrating FL and Transformer to deal with anomaly detection problems in cloud manufacturing.
Research on the Development Status and Approaches of Smart Library

The library is an important place to provide social public service. But with the rapid development of current information technology, the way of people access information has changed greatly. This encourages libraries to actively carry out the construction of smart libraries, and increase efforts to carry out service model innovation, build a more comprehensive and multi-level service platform, and thus improve the experience of users and readers. And wisdom library in service to adhere to the people-centered principle, collection and release in for all kinds of resources in the process of deepening with the connection between the user, in order to ensure the application value of the resources, fully meet the needs of readers, the intelligent library is the current our country the important content of library construction and theoretical study. In this paper, the development of wisdom library and the future development of the way to carry out research are summed up and its optimization of the path to launch in-depth exploration.
Real-time and high precision feature matching between blur aerial images

When aerial cameras get aerial remote sensing images, the defocus will occur because of reasons such as air pressure, temperature and ground elevation changes, resulting in different image sharpness of continual aerial remote sensing images. Nowadays, the rapidly developing feature matching algorithm will rapidly reduce the registration rate between images with different image sharpness. Therefore, in order to enable aerial cameras to get image sharpness parameters according to the locations of aerial image feature points with inconsistent sharpness, this paper proposes a feature matching algorithm between aerial images with different sharpness by using DEM data and multiple constraints. In this paper, the feature matching range is extended according to the modified aerial imaging model and the nonlinear soft margin support vector machine. Then the relative moving speed and its variation of the feature points in the image are obtained by using the extended L-k optical flow, and finally the epipolar geometric constraint is introduced. To locate the feature points is obtained under multiple constraints, there is no need to calculate the feature point descriptors, and some mismatched point pairs are corrected, which improves the matching efficiency and precision. The experimental results show the feature matching precision of this algorithm is more than 90%, and the running time and matching precision can meet various application needs of aerial cameras.
Controller Synthesis for Timeline-based Games

In the timeline-based approach to planning, originally born in the space sector, the evolution over time of a set of state variables (the timelines) is governed by a set of temporal constraints. Traditional timeline-based planning systems excel at the integration of planning with execution by handling temporal uncertainty. In order to handle general nondeterminism as well, the concept of timeline-based games has been recently introduced. It has been proved that finding whether a winning strategy exists for such games is 2EXPTIME-complete. However, a concrete approach to synthesize controllers implementing such strategies is missing. This paper fills this gap, outlining an approach to controller synthesis for timeline-based games.
An ensemble learning with active sampling to predict the prognosis of postoperative non-small cell lung cancer patients

Abstract Background Lung cancer is the leading cause of cancer death worldwide. Prognostic prediction plays a vital role in the decision-making process for postoperative non-small cell lung cancer (NSCLC) patients. However, the high imbalance ratio of prognostic data limits the development of effective prognostic prediction models. Methods In this study, we present a novel approach, namely ensemble learning with active sampling (ELAS), to tackle the imbalanced data problem in NSCLC prognostic prediction. ELAS first applies an active sampling mechanism to query the most informative samples to update the base classifier to give it a new perspective. This training process is repeated until no enough samples are queried. Next, an internal validation set is employed to evaluate the base classifiers, and the ones with the best performances are integrated as the ensemble model. Besides, we set up multiple initial training data seeds and internal validation sets to ensure the stability and generalization of the model. Results We verified the effectiveness of the ELAS on a real clinical dataset containing 1848 postoperative NSCLC patients. Experimental results showed that the ELAS achieved the best averaged 0.736 AUROC value and 0.453 AUPRC value for 6 prognostic tasks and obtained significant improvements in comparison with the SVM, AdaBoost, Bagging, SMOTE and TomekLinks. Conclusions We conclude that the ELAS can effectively alleviate the imbalanced data problem in NSCLC prognostic prediction and demonstrates good potential for future postoperative NSCLC prognostic prediction.
Understanding NFT Price Moves through Tweets Keywords Analysis

Non-Fungible Token (NFT) is evolving with the rise of the cryptocurrency market and the development of blockchain techniques, which leads to an emerging NFT market that has become prosperous rapidly then followed by a cooldown. Nevertheless, the overall rise procedure of the NFT market has not been well understood. To this end, we consider that social media communities evolving alongside the market growth, are worth exploring and reasoning about, as the mineable information might unveil the market behaviors. We explore the procedure from the perspective of NFT Twitter communities and its impact on the NFT price moves with two experiments. We perform a Granger causality test on the number of tweets and the NFT price time series and find that the number of tweets has a positive impact on (Granger-causes) the price or reversely for larger part of the 19 top authentic projects but seldom copycat projects. Besides, to investigate the price moves predictability, we experiment on predicting Markov normalized NFT price (representing the direction and magnitude of price moves) given tweets-extracted word features and interpret the feature importance to find insights. Our results show that social media words as the predictors result in all 19 top projects having a testing accuracy evidently above the baseline. Based on the feature importance analysis, we find that both general market-related words and NFT event-related words have a markedly positive contribution in predicting price moves. We summarize the characteristics including categorization and sentiment for the words with the top and least feature importance.
Method for studying the dependence of the visibility of artifacts in an image on its physical dimensions

An actual size of screen proposed displays video sequence influence the process of creating subjective quality evaluation of multimedia content perception on various devices. The question of display size and resolution impact on improvements in perceived visual quality still remains unclear. Any decisions on display size must demand understanding of physical, perceptual and cognitive opportunities of each user, also physical working/operational environment constrains. In proposed paper presented a method allowing to dynamically change the visual quality of a typical media content depending on the physical size of the display. The unique contribution of proposed study for the first time represents method ology of the degraded video images association with streaming video and their effects on various display sizes with fixed screen parameters. Method can be used for scientific purposes related to practical applications for visualization technologies, signal reception and processing in information technology devices, also in related areas such as marketing, advertising, film industry.
Multidimensional Quantitative Measurement Analysis of Big Data Application Ability based on Mathematical Modeling Path

Based on the multi-dimensional quantitative measurement of the application ability of big data modeling ideas based on the mathematical modeling path, this paper briefly explains the importance of the fusion of the two, and deeply analyzes the difficulties faced by the current fusion, and proposes a feasible fusion path. In order to provide suggestions and references for higher mathematics educators. Seven quantitative measures of IPC number, patent grant rate, H index, G index, patent effectiveness, and patent implementation rate. Conduct empirical research on the technological innovation capabilities of Chinese universities. Through SPSS software analysis, it is verified that there is a strong system among the indicators. Sexuality and relevance can objectively quantify the technological innovation capabilities of colleges and universities.
Signal Denoising of MEMS Vector Hydrophone Based on Optimized VMD, Compressed Sensing, and Wavelet Threshold

With the noise in underwater acoustic signal extracted from ocean background, the denoising algorithm based on the Variational Mode Decomposition (VMD) optimized by improved Grasshopper Optimization Algorithm (IGOA), the compressed sensing (CS) and wavelet threshold (WT) is proposed in this paper, named by IGOA-VMD-CS-WT, where VMD optimized by IGOA is utilized to perform sign composition and the obtained Intrinsic Mode Functions (IMF) are divided into effective components and noise components using cross-correlation coefficient of each IMF. CS is performed on sparse representation of noise components and the obtained sparse coefficients are processed with WT for the filters. The effective components and the denoised components are reconstructed to the denoised signal by the Orthogonal Matching Pursuit. The experiments show that IGOA-VMD-CS-WT has the highest signal-to-noise ratios and the least root mean square errors under different noise levels and has the better denoising effect on the denoising of the actual data.