Reviewerly/exHarmony huggingface dataset:
Image denoising based on improved RDN algorithm

In recent years, good results have been achieved in the research of convolution neural networks. However, the image denoising algorithm based on CNN still faces serious challenges, such as poor detail reconstruction ability, many parameters, difficult training, etc. On the basis of the traditional residual dense network (RDN), combined with attention mechanism (CBAM), it can not only effectively alleviate the gradient disappearance caused by the increase of network depth, but also provide a more dense connection mode to realize the reuse of features. Moreover, CBAM can effectively improve the connection of various features in channel and space, and use dense jump connection and residual structure to solve difficult training problems and low feature utilization in information extraction networks. In the experiment, we used aluminum pictures as the data set and added different noise levels to them, compared with the traditional filtering algorithm and deep learning filtering method DNCNN, and to verify its effectiveness. The results show that this method is superior to many other image denoising algorithms in terms of noise reduction and computational complexity.
How to pack for sampling Earth’s hottest pockets

When the Fagradalsfjall volcano in Iceland awoke in spring 2021 after 815 years of slumber, the geochemists and petrologists at the University of Iceland knew they wanted a sample. It was a rare opportunity to get their hands on fresh lava that usually stays close to the mantle. But first they needed suitable safety gear and a plan. Active volcanoes share some general safety hazards with one another, as well as with other fiery environments such as wildfires and burning vents. Yet each site is unique in its specific dangers. There is no international safety manual for eruptions, says Ed Marshall, a geochemist at the University of Iceland. Back in the 1970s, he adds, you would see a “guy in a T-shirt setting up his camp next to a vent of lava and just running in there with a scoop.” On the other end of the spectrum are people wearing
Intelligent Frame and Table Segmentation in Blueprint Documents: Method and Implementation

Blueprints are documents that contain the drawing of a design and the information that explains this design. Recently, the task to automatically recognize the information in the blueprint documents has become required. Segmenting the frame and tables in the blueprint is the first step of understanding the blueprint document. In this paper, we explain an automated method for frame and table segmentation. The proposed method processes the blueprint as an image and defines all the parts (pixels) of the blueprint that belongs to the frame or tables. It finds all the lines in the blueprint’s image and decide which combinations of lines construct frame and tables. It can process blueprints that have high resolution. The proposed method allows to isolate frame and tables in the blueprint document. We achieved an accuracy of 99% for excellent quality documents.
The Effects of Vertical and Horizontal Knowledge Inflows on Collaborative Innovation of Research Institutes

The concept of sustainable development implies a balanced development of society in environmental and economic aspects. The structure of information support for the process of vertical and horizontal inflow of knowledge and interconnections of management levels for the implementation of collaborative innovation of research institutes has been developed. A generalized model of information flow management has been developed, which is implemented in the decision support system. The development and application of basic universal mechanisms made it possible to create a unified software platform and the ability to use it in the information flow management system in the process of creating collaborative innovations to ensure sustainable development.
A Cryptocurrency for the Masses or a Universal ID?: Worldcoin Aims to Scan all the World's Eyeballs

In a college classroom in the Indian city of Bangalore last August, Moiz Ahmed held up a volleyball-size chrome globe with a glass-covered opening at its center. Ahmed explained to the students that if they had their irises scanned with the device, known as the Orb, they would be rewarded with 25 Worldcoins, a soon-to-be released cryptocurrency. The scan, he said, was to make sure they hadn't signed up before. That's because Worldcoin, the company behind the project, wants to create the most widely and evenly distributed cryptocurrency ever by giving every person on the planet the same small allocation of coins.
Up-To-Date Libraries for Handwriting Recognition

The article contains information and information about current libraries and their use in order to perform handwriting recognition tasks.
A Comparison Study of Fuzzy Transform based Approximation Methods

Fuzzy Transform based on fuzzy set is a method for data compression and function approximation. Least Sqaures Fuzzy Transform that applied L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> -norm to fuzzy transform has been proposed. Least Squares Fuzzy Transform shows good performance on function approximation, but is sensitive to outliers. Recently, Least Absolute Deviation Fuzzy Transform that applied L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> -norm to fuzzy transform which is robust to outliers and function approximation has been proposed. In this paper, we compare the performance of three methods in terms of function approximation and outlier robustness problems. Experiments show that the Least Absolute Deviation Fuzzy Transform outperforms the others in function approximation and outlier robustness problems.
A Comparative Analysis on Information Structure between the Cleft Construction ‘Shi…De’ and the Focus Construction with ‘Shi’

Through corpus-based and theoretical analysis, it can be confirmed that the cleft construction ‘Shi…De’ has more restrictions than the focus construction with ‘Shi’. First, the cleft construction is used only for past events, whereas the focus construction can be used for past, present and future events. Second, in terms of information structure, the argument that follows the focus marker ‘Shi’ always functions as a unique focus in a cleft construction, while a focus construction with ‘Shi’ can have an argument-focus structure, a predicate-focus structure, or a sentence-focus structure. Third, an indefinite noun phrase cannot follow a verb in a cleft construction, but it can follow a verb in the focus construction with ‘Shi’. Fourth, there are restrictions on the situational types of verbs used in the cleft construction, but there are few restrictions on those of verbs in the focus construction with ‘Shi’. This study shows that the pragmatic functions of the cleft construction and the focus construction are totally different, and the view that the information structure of the two constructions is the same is not appropriate.
Analysis of Large-Scale Data Linked to Online Platforms Using Evolutionary Computing Mechanisms

Online learning in the present situation possesses a vital role linked to socio-economic aspects and helps our society to achieve its goal. Definitely it is beneficial in the sense to help in acquiring not only the knowledge but also the desired skills to learn independently with ample education resources available in the cloud. Somehow online learning has now been confined toward some specific domains in the course curriculum. Therefore, the major attention of the online mechanism should be more focused toward the development of multi-domain skills. Of course, sometimes the relevant data should be accumulated in this regard based on associated surveys. In this pandemic period, online learning is the better option as it gives the freedom of learning from anywhere and anytime. Considering the case of active learning where knowledge can be shared, the mechanism is associated with the reflection of the knowledge based on problem-solving through simulation and the development of attitudes toward active participation in the process. Toward obtaining optimality, it is essential to ensure the optimality on the completeness and communication aspects of the course curriculum and develop specific activities to relate the analysis and synthesis of the applications. Generally, in some peculiar situations, hurdles may be observed to focus on exact mechanisms due to the continual allocation of large scaled heterogeneous data. Therefore, it is very much required to analyze the specialties within the linked datasets. So to obtain the minimized cost factors preserving suitable decisions, it is also needed to acquire the responses. Accordingly, in this application, it is intended to accumulate a large-sized heterogeneous data implementing evolutionary computing mechanisms, especially genetic algorithm toward optimal analysis of data.
The Environmental Impact of Online Education

School and college education is moving from a teacher-centric traditional approach to online education, which is more learner-centric. It has many advantages compared to traditional face-to-face learning. Humanity has survived COVID-19 due to IT, which is using computers, the internet, multimedia, graphics, videoconferencing tools, etc. A SWOC analysis of online education is performed. The analysis of the environmental impact of online education is predicated.
PENERAPAN SISTEM PEMBELAJARAN SHIFT YANG IDEAL DI KABUPATEN SUMBA TIMUR

Learning from homeapplied in East Sumba is a distance learning Shift. This Shift learning system is done by dividing class levels. This research is R and D research, that focus on application of implementation, the application of the results of previous studies which is the development of the ideal distance learning model in East Sumba by using Addie model. The implementation of the Shift Research Results Guidelines was carried out in Kambera Public Middle School with quesioners, interview and FGD as data collection technique. The results of this study indicate that in: 1) preparation stage, the principal, parents and regional government has conducted a distance learning shift in accordance with the guidelines, while students and teachers have not implemented the preparation according to the guidelines because many students do not prepare health protocol devices while many teachers do not prepare props and video learning; 2) Implementation stage, students, teachers, principals and regional governments have conducted a distance learning Shift in accordance with the guidelines, while parents are not in accordance with the guidelines because parents do not accompany students in carrying out Shift learning at school; 3) Evaluation stage, students, teacher, principals, and local government has conducted a distance learning Shift in accordance with the guidelines, while parents are not in accordance with the guidelines because parents do not accompany students in taking the results of the evaluation of children in school and do not provide feedback to the assessment results.
Latent Factor Learning Under Multiple Rating Patterns for Undirected, Large-Scaled and Sparse Networks

Symmetric and nonnegative latent factor (SNLF) models have great capability of extracting useful information from symmetric, high-dimensional and sparse (SHiDS) matrix with positive constraints. However, due to inadequate consideration of LFs in a single rating pattern and in a certain scenario, most currently developed SNLF models still suffer from fairly poor performance on the prediction for missing data. To address this problem, a novel collective SNLF model is proposed in terms of the transfer learning method under multiple rating patterns, where rating patterns shall keep consistent while reflecting the special relationships between entities. Meanwhile, different LFs under various scenarios are introduced into the objective function to make a further extraction of latent knowledge from the known information. Furthermore, the alternation direction (ADI) method is adopted to design the algorithm, which effectively reduces the risk of getting into the local optima while guaranteeing convergence. Finally, experiments conducted on four real data sets show that the proposed model outperforms some state-of-the-art models on the estimation accuracy as well as rigidly guarantees the symmetry of the target matrix and the nonnegativity of LFs.
Simultaneous intensity projection for projector response computation with application to an active radiometric calibration

One of the most important sources of errors in digital fringe projection (DFP) systems is the nonlinearity in the response of the projector when it uses the three-step phase retrieval algorithm. Thus, it is necessary to increase the accuracy without affecting the efficiency. In this sense, the radiometric rectification methods are used. In this paper, an active radiometric rectification method for digital fringe projection is proposed. This proposal consists in two improvements of traditional active techniques: first, parallel intensity projection is used to obtain the projector response which requires only four dot patterns; and second, a method is provided for the calculation of the inverse polynomial that guarantees symmetry with respect to the response of the projector. Experimental results, in a digital fringe projection system, show that the root-mean-square phase error improves 6.3 times using this proposal.
Money Without Magic: Why Bitcoin and Precious Metals Actually Have Value

People have said for generations that money is based on "trust," or "shared fiction." But that doesn't explain why cultures all eventually come to value the same metals or items as money. Others have said that the value of money depends on the way it facilitates trade, but even that doesn't explain where the value emerges before people agree to use money to trade. In actuality, there's a real value to gold, silver, and other forms of money that makes them reliably gain worth to people, and will emerge eventually even if everyone in a society does not like or believe in those precious metals or possessions. This is because possessing scarce objects has real reputational value among people, since having those objects indicates that we can go outside the known territory, and we instinctively collect and want things that have known scarcity. When that scarcity combines with other traits like transferability, durability and so on, we can see that money naturally emerges in society. We can use this idea to also see the real value and desire that people have for new forms of money like cryptocurrencies, and to know what will or won't emerge and last as a form of money in the future.
Special Approaches to Recurrent Retrieval of Item Sets

High utility items (HUIs) mining is a formative stress in records mining that finds all items with absolute utility expressed by a client using the min_util minimal programming perspective. Visit sets see a basic part of two or three evaluations admirably choosing to look at interesting motivations behind attention from data sets, like association rules. Mining the rules of the association is one that you are told is basic payment. Exit mining from standard transactional data sets, when and where to see liberally additional facts in case of a leak a very emotionally basic type of nature to work with.In-visit matters will end after some time and accordingly cannot be ignored. For the duration of this paper, we observe the current situation by obtaining frequent itemized mining estimates. The framework signals a lack of open assessments for HUI and proposes transparent choices to defeat stigma.
Optical asymmetric double-image encryption and authentication in an interference-based scheme using sparse representation

We report an optical asymmetric scheme for double-image encryption and authentication based on interference using sparse representation. We employ sparse representation and interference to process the Fresnel spectra related with the two original images, and then respectively acquire two ciphertexts and two pairs of private keys. Each original image possesses its corresponding two private keys. Furthermore, the decrypted image is compared with its corresponding plaintext with the aid of a nonlinear correlation for authentication. In the proposed scheme, any information concerning each primary image and comprising its silhouette cannot be recognized even though one, two, or even three masks of the two ciphertexts and two private keys are utilized for decryption. The Fresnel spectrum functions which have different diffraction distances enhance the security of the proposal significantly. Moreover, the proposal also avoids the crosstalk problem. The effectiveness and security of this proposed method are demonstrated via numerical simulations.
Querying and retrieving data from graphs stored in distributed architecture (QRDA)

The ability to handle huge amount of data using big graphs and graph data structures plays a vital role in ever growing areas like IOT, social networking, e-commerce and bioinformatics applications. Querying graphs and extracting data in an effective manner is very crucial in big graph processing. This paper presents a framework that focuses on reassignment of vertices among partitions based on query graph so that entire query related information gets shifted to one partition to the extent possible leading to minimised query execution time. This technique first finds the partitions in which the query graph nodes are present and performs searching only in those partitions leading to minimised retrieval time. The proposed QRDA technique is compared with various state-of-art graph querying practices and the outcomes show that QRDA performance is better over other approaches.
IDEFE algorithm: IDE algorithm optimizes the fuzzy entropy for the gland segmentation

&lt;abstract&gt; &lt;p&gt;Breast cancer occurs in the epithelial tissue of the gland, so the accuracy of gland segmentation is crucial to the physician's diagnosis. An innovative technique for breast mammography image gland segmentation is put forth in this paper. In the first step, the algorithm designed the gland segmentation evaluation function. Then a new mutation strategy is established, and the adaptive controlled variables are used to balance the ability of improved differential evolution (IDE) in terms of investigation and convergence. To evaluate its performance, The proposed method is validated on a number of benchmark breast images, including four types of glands from the Quanzhou First Hospital, Fujian, China. Furthermore, the proposed algorithm is been systematically compared to five state-of-the-art algorithms. From the average MSSIM and boxplot, the evidence suggests that the mutation strategy may be effective in searching the topography of the segmented gland problem. The experiment results demonstrated that the proposed method has the best gland segmentation results compared to other algorithms.&lt;/p&gt; &lt;/abstract&gt;
Professional Career Planning and Decision Making Based on SQL Server Data Mining Techniques

The world today is facing the fourth industrial revolution – a “revolution of skills.” The demand for completely new sets of skills is unprecedented, with the advent of and rapid change in digital technologies. On the other hand, a large population of graduates and professional are struggling to empower themselves with newer skillsets and develop capabilities to achieve competitive edge in building a successful career. There is a need for a platform to bridge the gap between these two market forces so that “the supply meets the demand.” This chapter seeks to provide a predictive solution using SQL server data mining techniques that implements Microsoft decision tree data mining algorithm.
Security Enhancement in Cloud Computing Using CBC Technique

The data protection mechanism in cloud storage systems is based on two-way factored inversion. With the authors' solution, users can store encrypted communications in the cloud and distribute them to their intended recipients. There is no requirement for the sender to have any other information (public key, certificate, etc.) except from the name of the receiver. There are two things the decoder needs in order to decipher the ciphertext. Your computer's private key is the first. The second is a one-of-a-kind computer-based security system for the individual. The ciphertext cannot be cracked without both keys. The loss or theft of your security device will also cause it to become inoperable. In other words, it can't be utilised to read encrypted messages. A cloud server is an option for this. The sender will not be aware of any of this activity. In the same vein, cloud server reads encrypted Chrome content. The system's practicability is supported by analyses of its safety and efficiency.
Mortality Rate Prediction in the Critically Ill Patients by Using Apache-II Scoring Tool

OBJECTIVES The study’s objective was to implement a methodological approach, "Acute Physiological and Chronic Health Evaluation II (APACHE-II),” to classify critically ill patients based on severity. METHODOLOGY A retrospective study design was conducted at Shaukat Khanum Memorial Cancer Hospital Lahore, Pakistan, from May 2019 to May 2020. A pilot study of 6 months was conducted by reviewing the medical record of 30 adult patients following convenient sampling after the approval of the proposal by ASRB/IRB of Shaukat Khanum hospital. The record of both male and female patients was studied, while no record of paediatric or adult patients outside the ICU was studied. Each patient’s severity score was obtained using the patient’s parameters with the help of the APACHE-II table. RESULTS Among the patients, three out of 30 scored 25, 29 and 30 on APACHE-II. These patients later passed away in ICU. This indicates that the mortality rate increases with an increase in the APACHE-II score. Thus, the scoring system is very beneficial in predicting adult ICU patients' mortality rate. CONCLUSION It is concluded that APACHE II is one of the best severity scoring systems in predicting the critical condition of patients.
A Machine Learning-Based Image Segmentation for Real-Time Images in Smart Intelligent Transportation Systems

In the ever-evolving world of computer vision, image recognition is critical task including using image processing to solve real-world problems like reducing human involvement in the art of driving. We face many challenges in completing this mission, including object detection and segmentation. By integrating Keras and TensorFlow with instance segmentation and binary masks, the challenges are effectively overcome by the method proposed in this chapter. The technique instantaneous segmentation is adopted for separating and detecting each individual object of interest in an image based on their pixel characteristics. The Mask RCNN model outperforms the current CNN models in terms of object detection accuracy and performance. Also, the objects of interest are classified using regional proposal networks (RPN) instead of selective search algorithm by CNN. The instance segmentation system is conceptually clear, versatile, and general. The method successfully finds items in acquired input and also produces good results by masking for each case.
Wavelets for nonuniform non-stationary MRA on $H^s(\mathbb{K})$

In this paper, we are defined the nonuniform non-stationary multiresolution analysis (NUNSMRA) on Sobolev space over local fields ($H^s(\mathbb{K})$) and with help of NUNSMRA orthonormal wavelets are constructed.