Reviewerly/exHarmony huggingface dataset:
ADOPTION OF FRACTIONAL DIFFERENTIAL EQUATIONS UNDER IMPROVED VARIATIONAL ITERATIVE ALGORITHM COMBINED WITH DNA CODING ALGORITHM IN IMAGE ENCRYPTION

To explore the application of improved variational iterative method in solving fractional differential equations and the effect of DNA coding algorithm based on logistic chaos mapping in image encryption, combining Chebyshev polynomial with variational iterative method, a new algorithm for solving fractional differential equations is proposed. In order to solve the initial value problem, the simulation software is used to compare the approximate solution after the change of values of [Formula: see text] and [Formula: see text] in the solution process to investigate the influence of different parameters on the accuracy of fractional order system. Subsequently, the logistic chaos sequence is used to generate discrete binary sequences, and an image encryption algorithm based on DNA coding is proposed. In order to verify the encryption performance of the proposed algorithm, Matlab simulation software is used to simulate and verify the image encryption processing. The results show that when [Formula: see text] approaches 1, the solution is closer to the exact solution than that when [Formula: see text] approaches 0.5, 0.7, and 0.9; compared with 1, 3, 5, and 7, the constructed algorithm has the highest accuracy when [Formula: see text] value is 9. The simulation results of the fuzzy partial fractional order system show that the chaotic sequence is sensitive to the initial value, and the logistic mapping sequence is suitable for the information encryption of the secure communication system. The simulation results of the image encryption algorithm based on DNA coding show that the algorithm can effectively encrypt the image, and the image histogram after encryption is approximately a horizontal line; slight changes in the initial value will affect the decryption effect of the image; the correlation analysis results show that there is no correlation between adjacent pixels in the image after encryption, which indicates that combining Chebyshev polynomials with variational iteration method can reduce the computational burden and improve the computational accuracy, and the image encryption algorithm proposed in this study can improve the reliability and security of image encryption.
Domain-Robust Mitotic Figure Detection with Style Transfer

Recent studies for mitotic figure identification have shown performance comparable to that of human experts; however, the challenge to develop strategies invariant to image variance in different microscope slide scanners still remains. In this paper, we propose a method for domain generalization in mitotic figure detection by considering the scanner as a domain and the characteristic specified for each domain as a style. The method aims to make the mitosis detection network robust to scanner types by using various style images. To expand the style variance, the style of the training image is transferred into arbitrary styles by the proposed style transfer module based on StarGAN. Furthermore, we propose patch selection criteria to resolve the imbalance problem. Our model with the proposed training scheme has obtained satisfactory detection performance on the MIDOG Challenge containing scanners that have not been seen.
THE INFLUENCE OF AUDIO-VISUAL MEDIA ON STUDENTS 'LEARNING MOTIVATION DURING THE PANDEMIC TIME OF COVID-19 AT MI AL FAJAR PRINGSEWU: indonesia

AbstractThe Covid 19 outbreak has an impact on various areas of life, especially the world of education. Student learning is required to transform and adapt to the conditions of the 4.0 century. This is what motivates researchers to conduct research on children's learning motivation during the Covid-19 pandemic. The aim of this research is to determine the effect of using audio-visual media on student motivation during the Covid-19 pandemic in grade IV MI Al Fajar Pringsewu. This research is a quantitative study with data collection techniques using a questionnaire. The data were analyzed using simple linear regression test. The results of this study indicate there is an effect of audio visual media on student learning motivation during the Covid-19 pandemic with a known value tcount > ttable (4.118 > 2.056) then Ho was rejected and Ha accepted. The big influence of audio-visual media on student learning motivation namely 57,3 %Keywords : Covid-19, audio-visual media, motivation to learn
Motivational Strategies in Essay Writing: A Study at Indonesian EFL Context

This research aims to examine students’ perceptions of the implementation of motivational strategies, which include Creating Basic Motivational Condition (CBMC), Generating Initial Motivation (GIM), Maintaining and Protecting Motivation (MPM), and Encouraging Positive Retrospective Self-Evaluation (EPRS). The research participants were student volunteers of a private University in Central Java, Indonesia. This research used a mixed-methods design. The researchers used questionnaires to collect the quantitative data and interview as the qualitative data. The result showed that the students have good appraisal to the strategies. This study's implications are outside the local context, as they show how motivational strategies in essay writing course is perceived, and it is a part of the Indonesian national program for the university level. The lecturer also needs to set specific learning objectives and use approved methods to improve students' writing skills. The lecturer must focus not only on the structure and content of the essay but also on other writing elements such as grammar, vocabulary, and mechanics. The lecturer must also give a clear explanation of students' mistakes to improve their understanding of grammar to have good proficiency in writing.
Conference Report of Why R? Turkey 2021

The Why R? Turkey 2021 as a three-day online conference was organized to bring togetherresearchers and professionals from Turkey on April 16-17-18, 2021. We hereby aimed to promote theR community in Turkey by bringing R users with different backgrounds such as genetics, sociology,finance, economy, bio-statistics. There were 8 thematic sessions and 18 invited speakers. In this article,it is aimed to describe the preparation phase, technical details, and the impact of the conference onaudience.
REVIEW OF THE STRENGTH OF FOX GLUE IN DEVELOPMENT BOOK VOLS TECHNOLOGY USING THE YOSHINO 602 ENGINE

Competition in the manufacture of raw materials for printing and binding purposes is undeniable, as is the hot glue required for binding on books. The method used in this writing is descriptive method, namely by presenting factual information obtained from observations obtained and seen in the Industrial Practices place. The binding technique without thread or perfect binding is used for binding books, magazines and others. The main ingredient in the perfect binding binding process is glue. Glue greatly affects the production result, if the adjustments are not done properly the temperature of the glue and the thickness of the glue. The purpose of writing this paper is to determine the adhesion strength of Fox brand glue and to find out the problems that occur in the gluing unit during the volume of the book "Technology Development in the Yoshino 602 machine. Fox glue has high adhesion strength and the adhesion dries quickly so as not to waste a lot of time. , Fox glue is also very suitable for binding paper types (coated and uncoated) with a temperature of 1650C-1800C and a side glue temperature of 1400C on the Yoshino 602 machine. So Fox brand glue is very suitable for the binding process of "Technology Development" books using a machine. Yoshino 602 at PT. Putra Nugraha Sentosa. The problems that occur in the gluing unit are the temperature of the glue, the thickness of the glue on the roller and the glue roller, due to the lack of accuracy of the machine operator.Keywords: Adhesion, Glue and Machine
Acciones y desafíos en la formación de docentes de matemáticas en el contexto de la pandemia en la Universidad Nacional - Costa Rica

As a result of the Covid-19 pandemic, many of the activities carried out in mathematics teacher training were affected. The Department of Mathematics at the Universidad Nacional in Costa Rica has addressed the challenges generated and proposed solutions to mitigate, as far as possible, the impact of the pandemic. In this Department, work has been done on the continuity of the courses and the link with the work context, on the follow-up of the final graduation projects (research carried out by the students) and on the research projects formally established at this university within the framework of mathematics education. The following pages illustrate the main factors that played a part in this adaptation process and how contingency actions have been planned and implemented, as well as some lessons learned that could be of interest to the national and international education community.
Autonomous Navigation Method for Mars Rover Using Distributed EKF-SLAM Assisted by Sun Sensor

Since the existing SLAM algorithm can not meet the actual demand of the Mars rover autonomous navigation problem in real-time and accuracy, a distributed EKF-SLAM algorithm is proposed based on heading assistance to achieve the rover's autonomous navigation. The solar azimuth is got by using the dual axis analog sun sensor, and then the rover heading information is calculated and added it to each subsystem of SLAM. Consequently, a SLAM model of EKF-SLAM system is built and the federal EKF is adopted to realize the state estimation of the distributed SLAM system. Finally, a whole astronomical heading assistant distributed system is constructed. The experiment test is performed in outdoor experimental environment using a mobile robot, and the experimental results demonstrate the accuracy and effectiveness of the proposed algorithm.
Framework to Understand and Deploy AI-Based Smart CRM in the Airline Industry

This is the age of AI. Soon what customers think will be understood by the smart applications on their mobile devices and the information—most of which will be pre-processed based on the customer personas—will be available at the blink of an eye. In this chapter a critical analysis of how AI bolsters CRM capabilities in the airline industry is conducted. To understand that, AI capabilities are surveyed and its transformational effects on CRM and its impact on customer acquisition, retention, loyalty, and experience are explored in depth. In the end, a customer journey-based deployment framework is presented that supports the finding of the AI-CRM implementation use cases.
An Introduction to Query Token Density (QTD) Search Algorithms

A new class of search algorithms and metrics based on an idea called Query Token Density (QTD) has been created and implemented in the Lexis Nexis search engine. QTD is based on the notion that dense, high-frequency co-occurrence of query terms within a document is a proxy for high user relevance. QTD has proven to be highly successful at improving Discounted Cumulative Gain (DCG) based relevance precision for multi-token Boolean and Natural Language queries, though results are significantly better for Natural Language searches. We discuss the computation and testing of the QTD algorithm including the initial implementations, testing of the algorithm via human subject matter expert relevance testing completed on thousands of queries and results, end-customer facing A/B testing, and performance testing in a cloud-based environment. We also discuss some additional notes on implementation, possible extensions for various content types, algorithm implementation improvements, and some variants of the algorithm to be explored in future work.
Survey Paper on Applications of Cloud Computing

Cloud computing is an architecture for facilitating computing service through the internet on requirement and pay per use access to a group of shared resources namely networks, storage, servers, services and applications, without physically acquiring them Cloud DBMS is a distributed database that gives computing as a service. It is sharing of web infrastructure for resources, software and information over a network. The cloud is used as a storage location and database can be accessed and computed from anywhere. In this paper I have discussed about cloud and its use. How we can implement cloud for better performance and different benefits and drawbacks of cloud which we can improve in future Cloud computing has received increasing interest from enterprises since its inception. With its innovative information technology (IT) services delivery model, cloud computing could add technical and strategic business value to enterprises. However, cloud computing poses highly concerning internal (e.g., Top management and experience) and external issues (e.g., regulations and standards). This paper presents a systematic literature review to explore the current key issues related to cloud computing adoption. This is achieved by reviewing 15 articles published about cloud computing adoption. Using the grounded theory approach, articles are classified into eight main categories: in-ternal, external, evaluation, proof of concept, adoption decision, implementation and integration, IT governance, and confirmation. Then, the eight categories are divided into two abstract categories: cloud computing adoption factors and processes, where the former affects the latter. The results of this review indicate that enterprises face serious issues before they decide to adopt cloud computing. Based on the findings, the paper provides a future information systems (IS) research agenda to explore the previously under-investigated areas regarding cloud computing adoption factors and processes. This paper calls for further theoretical, methodological, and empirical contributions to the research area of cloud computing adoption by enterprises.
Providing security to usage of long-term keys in case of semi-trusted secure environment

The task of enabling usage of cryptographic software (especially, for working with digital signature) with general-purpose mobile devices (e.g., smartphones with iOS or Android operation systems) is inevitably discussed during all conference discussions dedicated to mass-market cryptography. Users are used to performing their everyday operations with smartphones. Lower level of trust to such devices as environment for cryptographic software must always be kept in mind while developing systems involving them in processes; nevertheless, integrated additional security measures have made possible significant increase of security on such devices and, therefore, certification and usage of cryptographic software for mobile devices. At the same time, the question of providing secure and convenient ways of using long-term keys in hardware tokens with mobile devices still requires further research. In the current paper, we do a review of existing ways of using cryptographic tokens with long-term keys via mobile devices and develop approaches for solving this task in real-world scenarios based on recently developed protocols for password-based authenticated key establishment with proven security.
Modern Trends and Specifics of Additional Professional Education of University Teachers

The subject of consideration in this article is additional professional education as a pedagogical system, an integrated set of methods, for,ms and means of obtaining, expandi,ng and deepening the competencies, which have already been formed earli andas acquiring new ones. Nowadays, the development and further improvement of additional professional education have become actual in recent decades. In Russia, this is carried out in the context of a profound restructuring of society. Therefore it has its own characteristic features due to socio-economic transformations and corresponding changes in the labour market. It is stated that additional professional education of teachers acquires a special social status and is distinguished by its motives and values, goals and objectives, forms of organization and content, teaching technologies and methods of control and assessment. The goals and objectives of additional professional education of teachers have been represented, including the formation of their motivation for professional growth, advanced training, professional retraining, as well as the prevention of professional and the strain of personality, overcoming professional and personal crises. The result of studying this issue was the identification of modern trends and directions for the further development of the system of additional professional education for teachers.
Watermarking for Video Inter-Frame Tamper Detection

Technology advances at the same rate as human beings. Technology comes with both pros and cons. As difficult as it may be to create a video, modifying it is a piece of cake. Today's culture has easy access to video editing tools, making it possible to make changes to video footage. Video fraud cannot be detected by the sagacity of a person. Tampering is the act of making unauthorised changes or additions to video content by tampering with the video's source code or other code. As a result of their frequent use as evidence, digital videos are indispensable in the courtroom. This method uses Particle Imaging Velocimetry (PIV) to locate areas in the video that have been tampered with. Video manipulation may be detected in a variety of ways using this technique.
The Importance of Cyber Threats Identification and Countermeasures During the Covid-19 Pandemic Havoc in the U.S.

The current COVID-19 pandemic has impacted the economy worldwide and forced employees to shift to a remote workforce. Work from home also emphasized how organizations must effectively protect against cyber threats. Active or persistent threats have now increased and targeted more home users. The increase in cyber-attacks such as DDoS and Phishing, whereas ransomware shows a slight decline. Malicious users have spread misinformation about COVID-19 awareness, which led to many phishing attacks. Users must understand and recognize social engineering attacks and their respective countermeasures. There is a clear correlation between the rise of online users and the increase in cyberattacks, as expressed in the Routine Activity Theory. Crime will essentially increase because motivated criminals can access probable victims, and security is not relatively present. Cyber vigilance is indispensable and critical during the pandemic. Best security practices are necessary to provide a secure communication environment. No single network effectively promotes full-proof protection against sophisticated cyber attackers. Cybersecurity is yet again the most important factor during these unprecedented times.
Multivariate Stochastic Volatility Modeling of Neural Data

Because multivariate autoregressive models have failed to adequately account for the complexity of neural signals, researchers have predom- inantly relied on non-parametric methods when studying the relations between brain and behavior. Using medial temporal lobe (MTL) recordings from 96 neurosurgical patients, we show that time series models with volatility described by a multivariate stochastic latent-variable process and lagged interactions between signals in different brain regions provide new insights into the dynamics of brain function. The implied volatility inferred from our process positively correlates with high-frequency spectral activity, a signal that correlates with neuronal activity. We show that volatility features derived from our model can reliably decode memory states, and that this classifier performs as well as those using spectral features. Using the directional connections between brain regions during complex cognitive process provided by the model, we uncovered perirhinal-hippocampal desynchronization in the MTL regions that is associated with successful memory encoding.
IMPROVING READING ABILITY OF CLASS II STUDENTS ELEMENTARY SCHOOL TIPAR IN LEARNING LANGUAGE ABILITIES THROUGH IMAGE MEDIA

This study aims to explain about learning to read beginners using picture media classically and in groups and the process of increasing students' reading skills. Achieve this goal, classroom action research was conducted, with assistance of classroom teachers and 30 students of SDN TIPAR. Research data obtained by qualitative methods, observation and assisted by a recording aid in form of a camera. This research was conducted with two cycles of action. First cycle tends to increase participation and reading ability in word card and picture matching games. and the second cycle on reading improvement. Each cycle consists of stages: preparation, action, action, observation, analysis and reflection. The results of this study indicate that learning to read beginning with picture media classically students who lack courage tend to be passive so that when teacher assigns students to match word cards with pictures the students do not want to and the attention of some students is less focused in this learning and their reading ability is still dominated by active students. Learning the ability to read using picture media in groups, students are invited to play a game of matching word cards with pictures and their own friends and teacher acts as a facilitator and motivator. Student creativity, student shyness can be overcome and students' reading ability can be improved and the learning atmosphere is more attractive.
Mixture-Based Feature Space Learning for Few-Shot Image Classification

We introduce Mixture-based Feature Space Learning (MixtFSL) for obtaining a rich and robust feature representation in the context of few-shot image classification. Previous works have proposed to model each base class either with a single point or with a mixture model by relying on offline clustering algorithms. In contrast, we propose to model base classes with mixture models by simultaneously training the feature extractor and learning the mixture model parameters in an online manner. This results in a richer and more discriminative feature space which can be employed to classify novel examples from very few samples. Two main stages are proposed to train the MixtFSL model. First, the multimodal mixtures for each base class and the feature extractor parameters are learned using a combination of two loss functions. Second, the resulting network and mixture models are progressively refined through a leader-follower learning procedure, which uses the current estimate as a network. This target network is used to make a consistent assignment of instances to mixture components, which increases performance and stabilizes training. The effectiveness of our end-to-end feature space learning approach is demonstrated with extensive experiments on four standard datasets and four backbones. Notably, we demonstrate that when we combine our robust representation with recent alignment-based approaches, we achieve new state-of-the-art results in the inductive setting, with an absolute accuracy for 5-shot classification of 82.45 on miniImageNet, 88.20 with tieredImageNet, and 60.70 in FC100 using the ResNet-12 backbone.