Reviewerly/exHarmony huggingface dataset:
Reliability-Aware: Task Scheduling in Cloud Computing Using Multi-Agent Reinforcement Learning Algorithm and Neural Fitted Q

Cloud computing becomes the basic alternative platform for the most users application in the recent years. The complexity increasing in cloud environment due to the continuous development of resources and applications needs a concentrated integrated fault tolerance approach to provide the quality of service. Focusing on reliability enhancement in an environment with dynamic changes such as cloud environment, we developed a multi-agent scheduler using Reinforcement Learning (RL) algorithm and Neural Fitted Q (NFQ) to effectively schedule the user requests. Our approach considers the queue buffer size for each resource by implementing the queue theory to design a queue model in a way that each scheduler agent has its own queue which receives the user requests from the global queue. A central learning agent responsible of learning the output of the scheduler agents and direct those scheduler agents through the feedback claimed from the previous step. The dynamicity problem in cloud environment is managed in our system by employing neural network which supports the reinforcement learning algorithm through a specified function. The numerical result demonstrated an efficiency of our proposed approach and enhanced the reliability
THE EXPANDING ROLE OF IMMERSIVE MEDIA IN EDUCATION

This paper seeks to understand the impact of immersive media, in particular augmented reality, within the education sector.A small experimental methodology was adopted alongside a current literature review.Three research questions were posited as follows: (1) What are the affordances of immersive media within education?(2) What are the barriers to immersive media in education?(3) How may immersive media be implemented?Affordances of immersive media include greater content customisation, increased creativity with less risk, promoting student interaction, enhanced motivation and engagement, and the chance for students to engage in extraordinary experiences.In 2020, many students around the world are staying home and studying in isolation, as a result of the COVID-19 pandemic.In this emergency context, mobile applications can offer interesting experiences that can make life and learning less difficult and more pleasant.The mobile web application Metaverse was used to exemplify in two different ways how immersive media can be appropriately used as a tool in a university level education setting.The first prototype was a short quiz on general knowledge about New Zealand, to help orient new international students.The second prototype was a quick poll to feel the pulse and understand the mood of students while at home and physically away from the campus and their lecturers.In summary, the authors present an updated review of this technology and present their own new examples of applications that serve additional purposes.
Image Extraction Using Image Attribute Conception Technique

Image extraction based on image attributes is one of the emerging fields in image processing. Today, most of the retrieval process is done with help of image attributes. Because of this popularity, today, most of the researchers are devoted the research activity in this domain. Through the help of image attribute, image extraction gives good results compared to text-based retrieval process and image-based retrieval gives good accuracy, and high amount of exact information is retrieved in short span of time. Through this image attributes, users can extract the need information from the stored data source. This is achieved in various techniques one of the emerging techniques through image color histogram value. This image attribute extraction technique not only gives the good result but also overcome the existing text-based retrieval. This paper brings the idea about retrieves information based on content-based using image attributes, and experimental output verifies the proposed technique gives the better performance.
Blockchain Use Cases for COVID-19: Management, Surveillance, Tracking and Security

Blockchain has become a key technology in building and managing healthcare systems. the distinguished attributes of the blockchain (e.g. security, decentralization, time stamping, and transparency) make it the best technology for managing COVID-19 pandemic in real-time. This chapter investigates five blockchain use cases for fighting against the COVID-19 virus spread. Finally, this chapter is ended with discussing the recent blockchain platforms that can be utilized for Managing epidemic diseases, HashLog, and XMED Chain.
Development of e-learning teaching material with augmented reality based on problem based learning for nature of chemistry and scientific methods topic as teaching material in Covid-19 pandemic

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Munzil, Puji R. A. Mentari; Development of e-learning teaching material with augmented reality based on problem based learning for nature of chemistry and scientific methods topic as teaching material in Covid-19 pandemic. AIP Conf. Proc. 2 March 2021; 2330 (1): 020029. https://doi.org/10.1063/5.0043248 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Systemogenesis of values of pedagogic activity within digitalisation of society

In a view of implementation of the concept of scientific and technological development in the Russian Federation, the problem of systemogenesis of pedagogue’s work and training is topical for modernisation of pedagogic education as an institution. Issues about origin and specifics of such values are still not quite clear in science, although they make the basis of goal-setting and updating of curriculums in pedagogic education. The digital transformation of the Russian society has generated new, "digital values" of pedagogic activities and has led to modification of traditional values in this area. Values as separate characteristics of pedagogic activities significant for a person are objective and do not depend on the person; instead, they influence people’s attitudes and actions. The values appearing and transforming due to digitalisation of the society and education require a revision of the theory and practice of organising educational activities. So it is of great importance, to identify traditional and post-traditional, the so-called "digital values" of pedagogic activities as well as mechanisms of formation and influence on the pedagogues’ training. These tasks are supposed to be solved by the scientific theory about transformation of the value content of pedagogic activities under digital modernisation of the society.
A New Method for Hiding Text in a Digital Image

Information is always being transmitted through the internet. Securing that information is an important issue that programmers need to take into consideration. There are several ways to protect digital images, one of which is steganography, a method of encryption that encrypts information inside another type of information such as images. In this research, we propose a new technique to protect information from hacking. We have used the process of hiding. There are many techniques to secure the image such as hiding information inside other information as in steganography. In this paper, we introduce a new method to protect the information from theft. We used encryption to encrypt the text by using the password between the sender and the receiver. After that we used the least significant bit technique to hide the text in the image. Finally, we applied the proposed method in the MATLAB program.
The relationship between educational informatization and regional economy and its coordinated development path in fragile ecological region

The rapid development of a new generation of information technology has caused major changes in teaching and learning methods, promoted the innovation and transformation of education informatization, and promoted the realization of education modernization. Education informatization has gradually become one of the core factors that determine the quality of education. There is a close connection with the regional economy. The western region of my country (mainly including the mountainous areas in the southwest and the arid areas in the northwest) shows regional characteristics such as fragile ecological environment, lagging economic development, and large gaps in infrastructure and public services. Informatization of education is urgently needed to promote regional economic development. By comparing the gross product and educational expenditure of Zhejiang province and Gansu Province, this paper concludes the interdependence and mutual promotion between the development of educational informationization and regional economic development. On this basis, this paper analyzes the necessity of the informationization of education and the coordinated development of regional economy in the ecologically fragile areas of western China. Finally, from five aspects, it puts forward an effective way to promote the development of education informatization and regional economy, aiming at realizing the coordinated development of education informatization and regional economy. Finally improve the social productivity of western region and our country's comprehensive national strength.
Data Quality and Privacy Enhancement

The large amount of synchrophasor data obtained by Phasor Measurement Units (PMUs) provides dynamic visibility of power systems. As the data is being collected from geographically distant locations facilitated by computer networks, the data quality can be compromised by data losses, bad data, and cybernetic attacks. Data privacy is also an increasing concern. This chapter, describes a common framework of methods for data recovery, error correction, detection and correction of cybernetic attacks, and data privacy enhancement by exploiting the intrinsic low-dimensional structures in the high-dimensional spatial-temporal blocks of PMU data. The developed data-driven approaches are computationally eﬃcient with provable analytical guarantees. For instance, the data recovery method can recover the ground-truth data even if simultaneous and consecutive data losses and errors happen across all PMU channels for some time. This approach can identify PMU channels that are under false data injection attacks by locating abnormal dynamics in the data. Random noise and quantization can be applied to the measurements before transmission to compress the data and enhance data privacy.
Image-Abstraction Framework as a Preprocessing Technique for Extraction of Text From Underexposed Complex Background and Graphical Embossing Images

Underexposed heterogeneous complex-background and graphical embossing text documents are treated using proposed preprocessing image-abstraction framework that can deliver the effective structure preserved abstracted output by manipulating visual-features from input images. Reading of the text character in such images is extremely poor; hence, the framework effectively boosted the significant image properties and quality features at every stage. Work effectively preserves the foreground structure of an image by comprehensively integrating the sequence of NPR filters and diminishes the background content of an image, and in this way, the framework contributes to separation of foreground text from image background. Effectiveness of the proposed work has been validated by conducting the trials on the selected dataset. In addition, user's visual-feedback and image quality assessment techniques were also used to evaluate the framework. Based on the obtained abstraction output, this work extracts text-character by wisely utilizing traditional image processing techniques with an average accuracy of 98.91%.
Digitalization of education: transformation of a natural science experiment

Abstract Under the conditions of digitalization of education, a natural science experiment is analyzed on the example of the development and transformation of a chemical experiment. The influence of the development of information and communication technologies and their implementation in education on the transformation of a natural science experiment and the effectiveness of using various forms of education, such as real and virtual experiments, in digital laboratories is examined.
RETRACTED ARTICLE: Cloud enabled robust authenticated key agreement scheme for telecare medical information system

We, the authors, Editors and Publisher of the journal Connection Science, have retracted the following article:Tian Liu, Xuchong Liu, Xiong Li, Ruhul Amin, Wei Liang & Meng-Yen Hsieh (2021) Cloud enabled robust authenticated key agreement scheme for telecare medical information system, Connection Science, DOI: 10.1080/09540091.2021.1901072Since publication, the authors identified some technical issues that they had overlooked. In general, biometrics are data that owns fuzzy properties but the authors in this paper have neglected this technical point and the biometric information was authenticated using a hash function. Since the output of a hash function is sensitive to the input it cannot be applied to verify the validity of fuzzy data such as biometrics. Since this scheme is based on a hash function for biometric verification, it must not be used in real applications. The authors alerted the issue to the Editor and Publisher and all have agreed to retract the article to ensure the integrity of the scholarly record.We have been informed in our decision-making by our policy on publishing ethics and integrity and the COPE guidelines on retractions.The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as ‘Retracted’.
Estimation of the Number of Filters in the Convolution Layers of a Convolutional Neural Network Using a Fuzzy Logic System

In this paper, we propose to search for the best number of filters in the convolution layer of a convolutional neural network, we used a fuzzy logic system to find the most suitable parameters for the proposed case study. In addition to this we make use of the Fuzzy Gravitational Search Algorithm method to find the parameters of the fuzzy system memberships.
Prediction of News Popularity via Keywords Extraction and Trends Tracking

In the last years, news agencies have become more influential in various social groups. At the same time, the media industry starts to monetize online distributed articles with contextual advertising. However, the efficiency of online marketing highly depends on the popularity of news articles. In our work, we present an alternative and effective way for article popularity forecasting with two–step approach: article keywords extraction and keywords-based article popularity prediction. We show the benefits of this technique and compare with widely used methods, such as Text Embeddings and BERT–based methods. Moreover, the work provides an architecture of the model for dynamic keyword tracking trained on the newest dataset of Russian news articles with more than 280k articles and 22k keywords for the popularity of forecasting purposes.
Communication Modulation Recognition Method Based on Clustering Algorithm

Abstract The modular identification of wireless communication signals is widely used in civil and military fields. With the rapid development of modern communication technology in China, signal modulation becomes complex and diverse. How to accurately identify communication signal modulation methods has become a research hotspot. Among them, multi band quadrature amplitude modulation (MQAM) is widely used in microwave communication, satellite communication and network communication. It has the advantages of high frequency band utilization and flexible modulation. Therefore, the recognition of MQAM modulation mode is of great significance. Traditional clustering algorithm has some limitations when it is used to identify MQAM signal modulation, such as too many iterations and long recognition time. For high-order modulated signals, a modulation recognition method based on Constellation Clustering is proposed. The constellation is used as the feature of modulation recognition, and the constellation of received signal is reconstructed in groups. The simulation results show that the collected characteristic parameters have strong interference ability to multi-channel, and the recognition rate reaches 97.6%. It has important engineering application value for signal modulation recognition in low SNR environment.
Selection of Classifiers for Hand Gesture Recognition Using Analytic Hierarchy Process: A Systematic Literature Review

This article presents a systematic study forNogales, Ruben the selection of classifiers for hand gesture recognition by electromyography signals.Benalcazar, Freddy The selection of a classifier can be determined using an arbitrary search criterion or employing an Analytic Hierarchy Process (AHP).Guilcapi, Jaime The classifiers are determined as alternatives, which for the study are K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machines (SVM), and Linear Discriminant Analysis (LDA).Vargas, Javier Each of these alternatives is selected through search criteria, which are average training accuracy, average testing accuracy, sampling fee, average acceptance rating, and scrap rating average. These criteria and alternatives allow through a systematic study and the AHP model to select each search factor of the analysis of the systematic mapping (SMS) quantitatively. The results determine the KNN and SVM classifiers as the most used for research projects in electromyography signal recognition (EMG).
WITHDRAWN: Blockchain technology: Road to transform the Indian banking sector

Distributed Ledger and Blockchain technologies have grown greatly to provide solutions for diverse sectors, in particular the financial industry. Banks that are the county's financial pillar are obliged to boost the economic position of society as a whole. Banks' governance is made imperative by receiving and deploying huge amounts of un-collateralized public funds and by leveraging funds in a fiduciary capacity by creating credit. If any instability arises due to unethical management of one bank there can be adverse and harmful effect on the entire financial system which may result into early liquidation and as a consequence obstruct the smooth operation of the payment system. Therefore, governance of the banking sector is not just a formality but a dire need of society. Internal governance must be formulated and implemented in a way that the banking sector can work efficiently. In such situation, accepting new technologies provides alternative platform or possibly complementary method to monitor governance structure in the Indian banking sector. “The Blockchain technology is the distributed, decentralised ledger that records all the transactions”. The technology helps in keeping track of all transactions with transparency and the ledger does not have to depend on the intermediaries to inspect whether the loaned digital-asset has reached the anticipated participant. The research attempts to examine the existing mechanisms and the flaws of governance structure and provides an insight to how blockchain can restructure governance in the Indian banks.
Online Disposition Data Based Management System

The development of technology nowadays is so fast, technology has become part of the people's lifestyle. The presence of technology in human life can facilitate and accelerate all activities carried out so as to increase work effectiveness. Including services via smartphones and the internet. This has prompted many companies to develop their network and technological facilities. To be able to continue to develop in this increasingly rapid technological era, a company needs to present a data management system, namely a database system that provides computerized services and facilities. The main principle of a database system is to organize a set of interrelated data, while the main purpose of a database is the ease and speed of retrieving data to solve problems in systems that use a file-based approach. The result of this research is to observe the online disposition system data management.
Three Third Generation Attacks on the Format Preserving Encryption Scheme FF3

Format-Preserving Encryption (FPE) schemes accept plaintexts from any finite set of values (such as social security numbers or birth dates) and produce ciphertexts that belong to the same set. They are extremely useful in practice since they make it possible to encrypt existing databases or communication packets without changing their format. Due to industry demand, NIST had standardized in 2016 two such encryption schemes called FF1 and FF3. They immediately attracted considerable cryptanalytic attention with decreasing attack complexities. The best currently known attack on the Feistel construction FF3 has data and memory complexity of $${O}(N^{11/6})$$ and time complexity of $${O}(N^{17/6})$$ , where the input belongs to a domain of size $$N \times N$$ . In this paper, we present and experimentally verify three improved attacks on FF3. Our best attack achieves the tradeoff curve $$D=M=\tilde{O}(N^{2-t})$$ , $$T=\tilde{O}(N^{2+t})$$ for all $$t \le 0.5$$ . In particular, we can reduce the data and memory complexities to the more practical $$\tilde{O}(N^{1.5})$$ , and at the same time, reduce the time complexity to $$\tilde{O}(N^{2.5})$$ . We also identify another attack vector against FPE schemes, the related-domain attack. We show how one can mount powerful attacks when the adversary is given access to the encryption under the same key in different domains, and show how to apply it to efficiently distinguish FF3 and FF3-1 instances.
Consistency- and Inconsistency-Aware Multi-view Subspace Clustering

Multi-view subspace clustering has emerged as a crucial tool to solve the multi-view clustering problem. However, many of the existing methods merely focus on the consistency issue when learning the multi-view representations, failing to capture the latent inconsistency across different views (which can be caused by the view-specificity or diversity). To tackle this issue, we therefore develop a Consistency- and Inconsistency-aware Multi-view Subspace Clustering for robust clustering. In the proposed method, we decompose the multi-view representations into a view-consistent representation and a set of view-inconsistent representations, through which the multi-view consistency as well as multi-view inconsistency can be well explored. Meanwhile, our method aims to suppress the redundancy and determine the importance of different views by introducing a novel view weighting strategy. Then a unified objective function is constructed, upon which an efficient optimization algorithm based on ADMM is further performed. Additionally, we design a new way to compute the affinity matrix from both consistent and inconsistent perspectives, which makes sure that the learned affinity matrix comprehensively fit the inherent properties of multi-view data. Experimental results on multiple multi-view data sets confirm the superiority of our method.