Reviewerly/exHarmony huggingface dataset:
The Application of Big Data Based Statistical Technology to Improve the User Experience of Products

The advent of the era of big data has become the consensus of the whole society. Only by mastering the analysis skills of big data can we make intelligent matches with demands and enhance the user experience in products. The purpose of this paper is to study the application of improving the user experience degree of a product based on big data statistical technology. Using review data from the F brand car website as the research object, the big data statistical technique is constructed in the face of the characteristics of Internet text data. This is followed by an introduction to the strategies used in this study for Chinese text mining, the development tools and the parameter optimisation of the SVM algorithm for big data statistical techniques. The testing process and results of SVM classification models generated by different kernel functions are presented, analysed and evaluated. The tests show that the SVM algorithm using Gaussian kernel functions enables optimal sentiment classification accuracy in this study.
Gilead Scis., Inc. v. United States

Biotechnology Law ReportVol. 42, No. 1 Case in PointGilead Scis., Inc. v. United StatesUnited States Court of Federal Claims, 2022 No. 20-499C, 2022 WL 17335933Edited for Biotechnology Law Report by Christopher M. HolmanEdited for Biotechnology Law Report by Christopher M. HolmanChristopher M. Holman is a Professor of Law at the University of Missouri-Kansas City School of Law; a Senior Scholar at the Center for the Protection of Intellectual Property at the Antonin Scalia Law School, George Mason University; and the Executive Editor of Biotechnology Law Report.*The synopsis is original content and the decision itself has been condensed for ease of readability.Search for more papers by this authorPublished Online:15 Feb 2023https://doi.org/10.1089/blr.2023.29298.cipAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 42Issue 1Feb 2023 InformationCopyright 2023, Mary Ann Liebert, Inc., publishersTo cite this article:Edited for Biotechnology Law Report by Christopher M. Holman.Gilead Scis., Inc. v. United States.Biotechnology Law Report.Feb 2023.48-63.http://doi.org/10.1089/blr.2023.29298.cipPublished in Volume: 42 Issue 1: February 15, 2023Online Ahead of Print:February 7, 2023PDF download
MATH METHODS AND MODELS OF PRODUCTS KNOWLEDGE MANAGEMENT

The purpose of this research work is to review the existing literature on methods and solutions to the problem of efficient storage and processing of semi-structured semantic information, including in the field of product knowledge management. At the beginning of the article, the rationale for the relevance of the study is given, then it discusses possible ways to build an ontology of semantic networks, various types of knowledge representation, a stack of possible technologies on which such networks can potentially be implemented. An explanation of the semantics, ways to search for information in such systems, including an overview of the semantic data query languages used, as well as ready-made implementations of knowledge bases, is given. The result of the research work was the creation of an extensive database of analyzed sources, which raises the problem of processing semi-structured heterogeneous data, as well as searching for information on them. In addition, as a result of the study, the most effective solution to the above problem was derived - the construction of an ontology of knowledge, the representation of knowledge within the ontology, semantic networks and their architecture, and implementation. Finally, the author managed to prove a high degree of relevance of further qualitative and in-depth scientific research on the problem considered in the research work.
Enhanced Hand Gesture Recognition In Augmented Reality Using Genetic Algorithm

Hand Gesture recognition is technology which interpret human gestures using various algorithms.Interpreting human hand gestures has various challenges and issues such as image noise,visibility and orientation. There are various kinds of computer based algorithms have been proposed in the literature to overcome these limitations and still needs improvement. Hence in this research work a new Hand Gesture recognition system for augmented reality using Genetic Algorithm and Artificial neural network algorithm has been proposed.This shows that the experiment is successful and gesture recognition system is vigorous against various changes that are made in illumination changes and background changes. Experimental results show that the extracted features are effective, robust, and can cover the entire feature space of the selected gestures. This method satisfactory performance when compared with convensional methods
EVALUATION OF APPLICABILITY OF YOKOHAMA SYSTEM FOR REPORTING OF BREAST FINE NEEDLE ASPIRATION CYTOLOGY: A RETROSPECTIVE STUDY IN A TERTIARY CARE CENTER

Background:Breast cancer is most common cancers in reproductive women and also the leading cause of cancer deaths worldwide. First line of invasive investigation of breast lump is Fine needle aspiration cytology (FNC) to categorized the lesion as benign or malignant. Recently, an expert panel published [International Academy of Cytology](IAC) Yokohama System for reporting and classification of breast lump cytopathology. The aim of the present study was to evaluate the applicability of Yokohama system of classification and reporting. Materials and Methods: In this retrospective studya total of 612 breast FNC cytology specimens from 1st January 2021 to 31st October 2021 were obtained. These were reclassified according to Yokohama system of reporting. The Risk of malignancy (ROM), sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV) and diagnostic accuracy of breast FNC were calculated. Result: Out of 612 cases histo-pathological correlation were available in 355 cases. ROM is calculated for each category of Yokohama system. ROM for each category was 10% for category 1, 2.5% for category 2, 44.4% for category 3, 100% for category 4 and 5. Sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy of FNAC were respectively 94.3%, 96.2%, 89.1%, 98.4% and 99.1% , respectively. Conclusion: In conclusion, the IAC Yokohama System for reporting Breast cytopathology is simple but it simultaneously enables greater diagnostic accuracy, consequently leading to better patient care.
RaiseAuth: a novel bio-behavioral authentication method based on ultra-low-complexity movement

Abstract Authentication plays an important role in maintaining social security. Modern authentication methods often relies on mass data datasets to implement authentication by data-driven. However, an essential question still remains unclear at data level. To what extent can the authentication movement be simplified? We theoretically explain the rationality of authentication through arm movements by mathematical modeling and design the simplest scheme of the authentication movement. At the same time, we collect a small-sample multi-category dataset that compresses the authentication movement as much as possible according to the model function. On this basis, we propose a method which consists of five different cells. Each cell is matched with a custom data preprocessing module according to the structure. Four cells are composed of neural network modules based on residual blocks, and the last cell is composed of traditional machine learning algorithms. The experimental results show that arm movements can also maintain high-accuracy authentication on small-sample multi-class datasets with very simple authentication movement.
MATHEMATICAL AND COMPUTER MODELS OF THE COVID-19 EPIDEMIC

The COVID-19 epidemic has gone down in history as an emergency of international importance. Currently, the number of people infected with coronavirus around the world continues to grow, and modeling such a complex system as the spread of infection is one of the most pressing problems. Various models are used to understand the progress of the COVID-19 coronavirus epidemic and to plan effective control strategies. Such models require the use of advanced computing, such as artificial intelligence, machine learning, cloud computing, and edge computing. This article uses the SIR mathematical model, which is often used and simple to model the prevalence of COVID-19 infection. The SIR model can provide a theoretical basis for studying the prevalence of the COVID-19 virus in a specific population and an understanding of the temporal evolution of the virus. One of the main advantages of this model is the ease of adjusting the sampling parameters as the study scale increases and the most appropriate graphs between the data and the resulting assumptions. Computer models based on the mathematical SIR model of the spread of the COVID-19 epidemic make it possible to estimate the number of possible deaths in the future. In addition, on the basis of the proposed models, it will be possible to assess the effectiveness of measures taken to prevent infection by comparing published data with forecasts. Computer models in Python are created on the basis of the proposed mathematical apparatus of SIR. The following libraries were added in the Python high-level programming language for the numerical solution of the system of differential equations for the SIR model: NumPy, Matplotlib PyPlot and the Integrate module from the SciPy library.
URL and Domain Structure

This chapter covers proper URL structures and the way URL structures can affect SEO. Search engines use a page's URL as a unique identifier for each page. That means every signal a search engine finds about a page is attached to that distinct URL. However, there are many possible URL variations, especially where parameters are concerned, and those different variations can lead search engines to identify one page under multiple URLs. This creates problems that disrupt SEO performance. A key step in improving a website's SEO performance is making sure that URLs are properly structured so that search engines can crawl, index, and rank each page correctly.
Sitemaps

This chapter reviews XML and HTML sitemaps. Sitemaps provide a list of URLs, and that list can help search engine robots discover new content on a website. Along with listing HTML pages, XML sitemaps can also list other files, like images or videos. However, it is important that XML sitemaps are carefully maintained and only list the pages on the website that robots should rank in search results.
Explainability for Deep Learning Models

Deep learning models are becoming the backbone of artificial intelligence implementations. At the same time, it is super important to build the explainability layers to explain the predictions and output of the deep learning model. To build trust for the deep learning model outcome, we need to explain the results or output. At a high level, a deep learning layer involves more than one hidden layer, whereas a neural network layer has three layers: the input layer, the hidden layer, and the output layer. There are different variants of neural network models such as single hidden layer neural network model, multiple hidden layer neural networks, feedforward neural networks, and backpropagation neural networks. Depending upon the structure of the neural network model, there are three popular structures: recurrent neural networks, which are mostly used for sequential information processing, such as audio processing, text classification, etc.; deep neural networks, which are used for building extremely deep networks; and finally, convolutional neural network models, which are used for image classification.
Explainability for Linear Supervised Models

A supervised learning model is a model that is being used to train an algorithm to map the input data with the output data. A supervised learning model can be of two types: regression and classification. In a regression scenario, the output variable is numerical, and in the case of classification, the output variable is binary or multinomial. A binary output variable has two outcomes, such as true and false, accept and reject, yes and no, etc. In the case of multinomial output variables, the outcome can be more than two, such as high, medium, and low. In this chapter, we are going to use explainable libraries to explain a regression model and a classification model, while training a linear model.
Explainability for Nonlinear Supervised Models

A supervised learning model can one of two types, either regression or classification. In a regression scenario, the output variable is numerical, and in the case of classification, the output variable is binary or multinomial. A binary output variable has two outcomes, such as true and false, accept and reject, yes and no, etc. In the case of a multinomial output variable, the outcome can be more than two, such as high, medium, and low. In this chapter, we are going to use explainable libraries to explain a regression model and a classification model, while training a linear model. A nonlinear model is something where either the input variables are transformed using nonlinear transformations or the function to model the input and output is nonlinear.
Mobility

In this chapter, we design a calculus of truly concurrent mobile processes (πtc) following the way paved by π-calculus for bisimulation and our previous work on truly concurrent process algebra CTC and APTC.
Digital Archiving Policies of Central European Journals Registered in the Directory of Open Access Journals

Abstract This paper examines digital archiving policies of open access (OA) electronic scholarly journals in Central Europe. The research sample consists of 1589 journals from nine Central European countries registered in the Directory of Open Access Journals (DOAJ). Results show that of the 1589 journals, 69% of them have an archiving policy. In terms of the number of indexed journals, Poland ranks first with 592 journals. Switzerland is at the forefront of digital archiving with 89% of archived journals. It was also found that Portico and national libraries are the most widely used archives, and that 19% of the preserved journals are archived in more than one archive. A preliminary analysis was also conducted to investigate the possible existence of a relationship between journal rank and digital archiving. The results show a higher number of JCR indexed titles, as well as a higher ranking, among archived journals. The paper also compares the status of archiving policies among Asian and Central European countries. The results reveal that archived journals are significantly more represented in Central European countries.
Selection Structures

Selection structures are explained in this chapter using flowcharts, pseudocode, and the corresponding Java code. The if–then, if–then-else, and nested if structures, including if–then-else-if and if–then-if structures, are introduced. The dangling-else problem is also discussed. Logical operators are presented followed by the introduction of the case structure. Two complete programs are provided: one with objects and one without.
Recursion

This chapter examines recursion using contour diagrams. The power function and Fibonacci numbers are used as examples. In addition to contour diagrams, stack frames and a tree of calls are shown as alternative ways of visualizing the recursive process. As with other chapters, a complete program is provided.
Strings

This chapter discusses string variables and the String class. In addition to the concatenation of strings, various methods defined in the String class such as the length, indexOf, and substring methods are examined. The toString method which returns a string representation of the properties of an object is also shown along with a complete program implementing String objects.
Towards Lightweight and Automated Representation Learning System for Networks

We propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LightNE 2.0</small> , a cost-effective, scalable, automated, and high-quality network embedding system that scales to graphs with hundreds of billions of edges on a single machine. In contrast to the mainstream belief that distributed architecture and GPUs are needed for large-scale network embedding with good quality, we prove that we can achieve higher quality, better scalability, lower cost, and faster runtime with shared-memory, CPU-only architecture. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LightNE 2.0</small> combines two theoretically grounded embedding methods NetSMF and ProNE. We introduce the following techniques to network embedding for the first time: (1) a newly proposed downsampling method to reduce the sample complexity of NetSMF while preserving its theoretical advantages; (2) a high-performance parallel graph processing stack GBBS to achieve high memory efficiency and scalability; (3) sparse parallel hash table to aggregate and maintain the matrix sparsifier in memory; (4) a fast randomized singular value decomposition (SVD) enhanced by power iteration and fast orthonormalization to improve vanilla randomized SVD in terms of both efficiency and effectiveness; (5) Intel MKL for proposed fast randomized SVD and spectral propagation; and (6) a fast and lightweight AutoML library FLAML for automated hyperparameter tuning. Experimental results show that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LightNE 2.0</small> can be up to 84× faster than GraphVite, 30× faster than PBG and 9× faster than NetSMF while delivering better performance. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LightNE 2.0</small> can embed very large graph with 1.7 billion nodes and 124 billion edges in half an hour on a CPU server, while other baselines cannot handle very large graphs of this scale.
Cell Nucleus Detection in Oral Cytology Using Artificial Intelligence

We describe the detection of cell nuclei in oral cytology using artificial intelligence (AI).We focused on the detection of cell nuclei because the ratio of cell nuclei to cytoplasm increases with increasing cell malignancy.As an initial step in the development of AI-assisted cytology, we investigated two methods for the automatic detection of cell nuclei in blue-stained cells in cytopreparation images.We evaluated the usefulness of the sliding window method (SWM) and the mask region-based convolutional neural network (Mask-RCNN) method in identifying cell nuclei in oral cytopreparation images.Thirty cases of liquid-based oral cytology were analyzed.First, we performed the SWM by dividing each image into 96 × 96 pixels.Overall, 591 images with or without blue-stained cell nuclei were prepared as the training data and 197 as the test data (from among 1576 images in total).Next, we performed the Mask-RCNN method by preparing 130 images of Class II and III lesions and creating mask images showing cell regions based on these images.By the SWM method, the highest detection rate for blue-stained cells in the evaluation group was found to be 0.9314.For Mask-RCNN, 37 cell nuclei were identified, and 1 cell nucleus was identified as a non-nucleus after 40 epochs (error rate: 0.027).Mask-RCNN was shown to be more accurate than SWM in identifying the cell nuclei.If the bluestained cell nuclei can be correctly identified automatically, the entire cell morphology can be grasped faster, and the diagnostic performance of cytology can be improved.
Associations between subjective neuropsychological symptoms and the usage of digital technologies

Agnė Staniūtė1, Rebeka Širmulevičiūtė1, Greta Pšemeneckienė2 1Lithuanian University of Health Sciences, Medical Academy, Faculty of Medicine, Kaunas, Lithuania 2Lithuanian University of Health Sciences Kaunas Clinics, Department of Neurology, Kaunas, Lithuania Abstract Introduction. Extensive screen time can have harmful effects (attention deficit, impaired emotional and social intelligence, technology addiction, social isolation, disrupted sleep). Multitasking can negatively impact cognitive functions (worse memory, impaired attention), psychosocial behaviour (increased impulsiveness, social anxiety, and depression) and neural structure (decreased anterior cingulate cortex volume). Aim: To evaluate the associations between subjective neuropsychological symptoms and the usage of digital technologies Methodology. 276 people aged 20-50 years old participated in a survey, which was an anonymous questionnaire, that was spread through social medias. The questionnaire included questions about demographical characteristics, usage of digital technologies, lifestyle, neuropsychological symptoms (fatigue, sleep, memory, attention impairment, pain), fatigue was evaluated using Fatigue Severity Scale. Results. People using social media for more than 3 hours a day more frequently complained about having headaches 5-6 times/week (p=0.018), impaired attention (p=0.009), severe fatigue (FSS score more than 4) (p=0.008). People, who multitasked for more than 1 hour/day, more frequently complained about shoulder pain (p=0.046), severe fatigue (p=0.014), inability to concentrate (p=0.014). People who used two screens simultaneously for 4-6 hrs, more frequently complained about their long-term memory than those who did it for shorter time (p<0.05). Respondents who used social media for more than 1 hr/day, more frequently felt inexplainable sadness (p<0.05). Conclusions. Long time spent on social media is related with frequent headaches, attention deficit and severe fatigue. Multitasking is related to shoulder pain, severe fatigue and inability to concentrate. Keywords: digital technologies, multitasking, neuropsychological symptoms, social medias. Full article https://doi.org/10.53453/ms.2023.1.10
[An Intelligent Method for Optimizing Large-volume Medical Image Transmission].

In order to alleviate the conflict between medical supply and demand, and to improve the efficiency of medical image transmission, this study proposes an intelligent method for large-volume medical image transmission. This method extracts and generates keyword pairs by analyzing medical diagnostic reports, and uses a 3D-UNet to segment original image data into various sub-area based on its anatomy structure. Then, the sub-areas are scored through keyword pairs and preset scoring criteria, and transmitted to user frontend in the order of prioritization score. Experiments show that this method can fulfill physicians' requirements of radiology reading and diagnosis with only ten percent of data transmitted, which efficiently optimized traditional transmission procedures.
Multiple information cognition of interrupted sampling repeater jamming in complex scenes

The dense and varied jamming signals in complex electromagnetic environment pose a great threat to radar detection, especially coherent repeater jamming affects anti-jamming ability seriously. The sufficient cognition of jamming signals is of great significance for effective countermeasures. This paper proposes a multiple information cognition framework and method for interrupted sampling repeater jamming(ISRJ) in complex scenes. Firstly, two one-dimensional convolutional neural networks(1D-CNN) are used to identify jamming types and repeater strategies hierarchically. The various jamming types in complex scenes are identified coarsely, and the specific repeater strategies of ISRJ are identified in detail. Secondly, a one-dimensional parameter estimation deep complex-valued convolutional network (PE-DCCN) is applied to estimate the slice width of ISRJ. Simulation results prove the proposed method can obtain multiple cognition results of ISRJ with high recognition precision and low estimation error.