Reviewerly/exHarmony huggingface dataset:
A Survey of High Demanding Recent and Future Technologies in Terms of Wireless Communication

A way of sharing knowledge via open space or Wi-Fi medium, wireless technology has a significant impact on the process in development. This essay introduces wireless technology while also attempting to concentrate on a few of the latest findings hotspots. After comparing the wireless and wired, this has been discovered that a wired connection seems to be more dependable than a wirelessly one. A wireless connection, meanwhile, is accessible and simple to set up. Furthermore, a thorough discussion of wireless communication development was included. According to research, it has been shown that by increasing the generation, the speed of the technology is also upgraded which is quite good. It has been noticed that gadget power utilization rises according to data growth. In addition, the writers examined a number of technological advances that are beneficial for wireless technology.
Introducing JavaFX

In Chap. 11 you learned how to create graphical applications using PythonPython and Tkinter. Now we are going to learn how to create similar programs using the latest of Java'sJava graphics libraries, JavaFX. We should mention here that since the release of JavaJava 11, JavaFX is not packaged with JavaJava and has to be downloaded and used as a separate moduleModules. This makes the process of creating JavaFX programs a little more complicated, but the various ways of doing this are clearly explained in the Appendix.
A timestamp-based covert data transmission method in Industrial Control System

Covert channels are data transmission methods that bypass the detection of security mechanisms and pose a serious threat to critical infrastructure. Meanwhile, it is also an effective way to ensure the secure transmission of private data. Therefore, research on covert channels helps us to quickly detect attacks and protect the security of data transmission. This paper proposes covert channels based on the timestamp of the Internet Control Message Protocol echo reply packet in the Linux system. By considering the concealment, we improve our proposed covert channels, ensuring that changing trends in the timestamp of modified consecutive packets are consistent with consecutive regular packets. Besides, we design an Iptables rule based on the current system time to analyze the performance of the proposed covert channels. Finally, it is shown through experiments that the channels complete the private data transmission in the industrial control network. Furthermore, the results demonstrate that the improved covert channels offer better performance in concealment, time cost, and the firewall test.
Maze Adventure: An Application of Maze Algorithm in Role-playing Game Development by Python

This paper talks about the development and algorithms of a 2D role-playing game coded in Python: Maze Adventure.The game is developed based on the Aldous Broder maze generation algorithm, a random walk algorithm, which is a way to produce uniform spanning trees.With the maze that is drawn by these uniform spanning trees, this paper introduces each part of code that has different functions, which works together to form a complete game.It summarizes the basic algorithms for not only the maze algorithm, but how to apply and link the function with the game's structure.It includes the generation and modification of different types of sprites, a Misty algorithm that hides most of the mazes for players to explore, and various ways of connecting collision detection along with the maze, in order to let players fight monsters or pick up treasures.It's an exploration of how to combine a traditional maze algorithm with new media like games, and by finishing Maze Adventure, it remarkably proved that Python language has strong fluidity, which is capable of using one of the newest media: Games.
Intercultural Dialogue in Social Processes: Lessons Learned from Local Wisdom

AbstractIntercultural dialogue is not always practiced through formal, structured, and mediated forms; it may also occur during the informal social processes succeeding naturally in social life. This article seeks to map the intercultural dialogue that takes place through cultural, structural, and agent mechanisms within a culturally and religiously diverse society. The article departs from a qualitative study, wherein data were collected through observations and in-depth interviews. Data analysis was conducted through data reduction, display, and interpretation, which was facilitated by QDAlite (Version 4.2). The article shows that intercultural dialogue occurs within social processes, including those used to maintain kinship and familiarity. Such cultural processes cannot be separated from the traditional values that are socialized, both formally and informally, by cultural leaders. The key limitation of this study is its scope, which is limited to the Nias and Minangkabau people. It is possible that further research, conducted within a broader scope, could provide useful comparison and contrast.
Password Management

In this chapter, we will look at the storage and management of passwords, both for our users and for accessing services like databases.
The Research and Exploration of Practical Teaching Based on Problem Based learning mode

Practical teaching and theoretical teaching are two basic ways of teaching. The relationship between practical teaching system and constituent elements is complex. Practice teaching is the basis of improving students' practical ability. There is a new concept proposed in this paper that is the Research and Exploration of Practical Teaching based on Problem Based learning mode. The characteristics of shaping students' comprehensive quality are highlighted, and the reform scheme is designed based on PBL teaching mode to ensure the effective achievement of practical teaching objectives.
Activities

Activities represent user interface entry points of your app. Any app you need to interact functionally with the user in a direct way, by letting the user enter things or telling the user graphically about the functional state of an app, will expose at least one activity to the system. We say functionally because telling the user in a notifying way about events can also happen by notifications via Toasts or the status bar, for which an activity is not needed.
The role of quantum imaging in C.T. scans for COVID-19 management

Quantum computing and its advancement seem to be dominating technology in building the future of the world. The increasing need to extract information from images and videos led to quantum image processing which emerged from a branch of science to a mature research field. Here we provide an overview, how quantum image processing advances can be made in health applications like C.T. scans to enhance methods for obtaining faster results. We have focused on building a concept of using quantum image processing for enhancing the C.T. scans technique used for detection of the COVID 19 virus and lead to its contribution in the field of the health industry. Along with the detection of the virus, it is relevant to capture and store all the data for further research work, and thus building a management system with quantum use is suggested in the paper. The quantum image processing, with its high speed and capabilities, can help in reducing the time required to detect the virus and help in storing the data with its mechanism. This paper specifies how quantum image processing can be seen as a path towards a solution in this time of the present state of the COVID situation and the use of this technology for future enhancement in the health industry.
A Bilateral Reduction Sentence for Modulation

The theory of mental models considers sentential connectives to refer to semantic possibilities. The theory also proposes that the possibilities can be amended by virtue of modulation processes, that is, processes in which semantics or pragmatics can have an influence. The definition of modulation the theory of mental models gives has been deemed as a scientific definition within Carnap’s framework. This is because it is easy to find reduction sentences corresponding to it. What seems to be harder is to think about bilateral reduction sentences for that definition. This paper addresses this issue. It shows that at least a bilateral reduction sentence is possible for modulation.
Non-intrusive Load Disaggregation Based on Fully Convolutional Denoising Encoder and Self-attention Mechanism

The non-intrusive load disaggregation (NILD) technique based on low-frequency data has a low initial investment cost, a highly promotion value, and a wide range of applications among residential customers. A non-intrusive load disaggregation model based on a fully convolution denoising encoder and self-attention mechanism (FCN-ATT) is proposed in this study. The model uses the one-dimensional aggregation power consumption as the input, the self-attention mechanism is used to improve the network's capacity to extract and apply load features, and the fully convolutional denoising encoder architecture to successfully minimize the model's number of parameters. Finally, the suggested model is tested on the publicly available UK-DALE dataset. The proposed model outperforms the three benchmark models in terms of load disaggregation and significant decreases the number of model parameters.
Development of remote learning system in higher educational institutions of the Russian Federation: from local practice to integration into the digital university model

The experience of remote learning during the pandemic COVID-19 significantly accelerated the process of digitalisation of the higher education in Russia and intensified preparations for the transition to a model of the Digital University. However, even those educational institutions that successfully applied remote technologies in the educational process faced the problem of effectively managing the entire remote learning system. The aim of the study is to identify the management potential of remote learning at universities in Russia from the point of view of the methodology of active management systems. Models of the remote learning system in institutions of higher education relevant for different stages of development have been created; recommendations are made to integrate the proposed models into the Digital University model. Research results contribute to the development of the theory and practice of active management systems in terms of modelling remote learning in institutions of higher education.
Optoelectronic Implementation of Compact and Power-efficient Recurrent Neural Networks

Optoelectronic implementation of artificial neural networks (ANNs) has been attracting significant attention due to its potential for low-power computation at the speed of light. Among the ANNs, adopting recurrent neural network (RNN) is a promising solution since it provides sufficient inference accuracy with a more compact structure than other ANNs. This paper proposes a novel optoelectronic architecture of RNN. The key idea is to implement the vector-matrix multiplication optically to exploit the speed of light and implement the activation and feedback electronically to exploit the controllability of electronics. The electronics part is composed of an electrical feedback circuit with a dynamic latch to synchronize the recurrent loops with a clock signal. Using a commercial optoelectronic circuit simulator, we confirm the correct behavior of the optoelectronic RNN. Experimental results obtained using TensorFlow show that the proposed optoelectronic RNN achieves more than 98% inference accuracy in image classification with a minimal footprint without sacrificing low-power and high-speed nature of light.
Offline Data Access

When building a mobile application, there is no guarantee that you will always have a stable Internet connection. It is best to build your application with the expectation that the connection will be severed, and it should seamlessly transition between offline mode and online mode. Since more and more devices are becoming more portable such as hybrid laptops that act as tablets, our applications need to handle this on other platforms than just mobile.
Prediction of Element Component Content Based on Mechanism Analysis and Error Compensation

To solve the difficulty of rapid and accurate detection of component content in the rare earth extraction process, a component content modeling method combining mechanism model and error compensation model based on just-in-time learning (JITL) was proposed. Considering the different dynamic characteristics of each section, the extraction section is simplified using the segmented-aggregation method, and the mechanism model of the rare earth extraction process based on material balance is established; in view of the error caused by the simplification of the model and the characteristics of some rare earth solutions with color features, the color features of rare earth solution samples are extracted by machine vision technology, and the error compensation model of the mechanism model is established by the just-in-time learning algorithm. Through the experimental verification of the field sample data of the praseodymium/neodymium (Pr/Nd) extraction process, the results show that the modeling method proposed in this paper is suitable for rapid and accurate detection of elemental component content in the rare earth extraction process with ionic color features.
Identifikasi Topeng Bali Dengan Metode KNN (K Nearest Neighbor)

Mask art is art with the form of a face covering that has various forms such as humans and animals and is an important element in dance and mask drama. Unfortunately, many tourists and also the public cannot know the name of the Balinese mask they see. To overcome this problem, a system was built to help people who want to know information about Balinese masks but do not know the names or types of Balinese masks. In developing a system that able to identify Balinese masks, KNN classification method is implemented as a method that helps identify Balinese masks. The image entered by the user is processed until a classification result is obtained which is then sent back to the system so that it can be displayed to the user. The results of the system evaluation show the percentage accuracy of the KNN algorithm is 85% with value of k = 3.
Developing DIGCONAR (Digital Comic Narrative) As An Alternative Media for Teaching Writing Narrative Text in Pandemic Covid - 19 Period

This research was to develop DIGCONAR learning media. R&amp;D was the design of this research, which aims to develop educational products. In this research, there are five steps, namely need analysis, studying recent theories of education product development, developing the educational product, validating educational product, and field-testing educational product. In addition, a quasi-experimental design was used in this research to obtain data. The sample used was students X grade of SMA Negeri 2 Trenggalek 2020/2021 academic year, 36 students of MIPA 2 and X MIPA 3. The instruments that use in this research are questionnaire, interview, pre-test and post-test. To obtain the data, researchers used instruments in the form of questionnaires, interviews, pre-test and post-test. Data processing is carried out qualitative and quantitative way. The result of need analysis score was 4112. Then, the result of field-testing was 3341 it shows that students strongly agree with the product of DIGCONAR. The result of t-test; the sig.(2-tailed) showed 0.000 &lt; 0.05 in post-test result, so it can be conclude that Ho is rejected meanwhile Ha is accepted. From description above, it means that DIGCONAR was an effective learning media for teaching writing narrative text in pandemic Covid-19 period
A New Hybrid LSTM-RNN Deep Learning Based Racism, Xenomy, and Genderism Detection Model in Online Social Network

Hate speech, which is a problem that affects everyone in the world, is taking on new dimensions and becoming more violent every day. The majority of people’s interest in social media has grown in recent years, particularly in the United States. Twitter placed 5th in social media usage figures in 2022, with an average of 340 million users globally, and human control of social media has become unfeasible as a result of this expansion. As a result, certain platforms leveraging deep learning approaches have been created for machine translation, word tagging, and language understanding. Different strategies are used to develop models that divide texts into categories in this way. The goal of this research is to create an effective a new hybrid prediction model that can recognize racist, xenophobic, and sexist comments published in English on Twitter, a popular social media platform, and provide efficient and accurate findings. 7.48 percent of the data were classified as racist, genderist, and xenophobic in the used dataset. A new hybrid LSTM Neural Network and Recurrent Neural Network based model was developed in this study and compared with the most popular supervised intelligent classification models such as Logistic Regression, Support Vector Machines, Naive Bayes, Random Forest, and K-Nearest Neighbors. The results of these several models were thoroughly examined, and the LSTM Neural Network model was found to have the best performance, with an accuracy rate of 95.20 percent, a recall value of 48.94 percent, a precision of 60.95 percent, and an F1 Score of 51.32 percent. The percentage of test data was then modified, and the comparison was made by attempting to get various findings. With a larger dataset, these deep learning models are believed to produce substantially better outcomes.
Self-correcting Neural Networks for Safe Classification

Classifiers learnt from data are increasingly being used as components in systems where safety is a critical concern. In this work, we present a formal notion of safety for classifiers via constraints called safe-ordering constraints. These constraints relate requirements on the order of the classes output by a classifier to conditions on its input, and are expressive enough to encode various interesting examples of classifier safety specifications from the literature. For classifiers implemented using neural networks, we also present a run-time mechanism for the enforcement of safe-ordering constraints. Our approach is based on a self-correcting layer, which provably yields safe outputs regardless of the characteristics of the classifier input. We compose this layer with an existing neural network classifier to construct a self-correcting network (SC-Net), and show that in addition to providing safe outputs, the SC-Net is guaranteed to preserve the classification accuracy of the original network whenever possible. Our approach is independent of the size and architecture of the neural network used for classification, depending only on the specified property and the dimension of the network’s output; thus it is scalable to large state-of-the-art networks. We show that our approach can be optimized for a GPU, introducing run-time overhead of less than 1 ms on current hardware—even on large, widely-used networks containing hundreds of thousands of neurons and millions of parameters. Code available at github.com/cmu-transparency/self-correcting-networks .
Classification of documents as a theoretical problem in office work and in the archive

For archival and documentary studies, classification issues are of key importance for the development of the methodology of scientific research, the results of which should be applied in practice. The issues of document classification were raised by scientists and specialists in the field of working with documents throughout the Soviet period. K.G. Mityaev paid a lot of attention to the problem of document classification. He was the first to draw attention to the fact that it is necessary to achieve conjugation in the classification of documents in office work and archive. In archival science and archival work, the question of the classification of documents has traditionally been associated with the development of lists of documents with an indication of storage periods. The purpose of the selection of documents for archival storage determined the approach to the classification of documents as the formation of complexes of document systems and subsystems formed as a result of documenting various areas of society. In document science, the problem of document classification resulted in the development of the concept of "document type" and the construction of a specific classification scheme. The problem of classification was actualized in connection with the development of unified documentation systems and raised in the scientific research of A.N. Sokova on document systematics. Her attempt to deepen the classification scheme to the level of a document was unsuccessful and in document science the concept of "document type" was fixed in the meaning of the classification unit of documents with a common self-designation, functional purpose and design features. Modern possibilities of using information technologies in working with documents allow us to take a different look at solving the problem of document classification. It is necessary to abandon the idea of constructing a universal hierarchical classification, which is a flat, linear model. It is necessary to create an integral classification structure, which is based on numerous internal relationships between meanings. The model of such an integral classification is the database of the information system. The classification scheme of the database is based on specific measurements (details, attributes, indicators), the meanings of which are determined using the reference book. At the same time, the composition of classification measurements for all documents in one information system will be the same, but the values of these measurements for each document will be different. Practical possibilities for classifying documents are associated with the need to solve a number of theoretical problems. It is necessary to determine the composition of measurements for constructing a classification scheme and to establish those that are uniform for information systems in management and in the archive.
An Efficient Drift Detection Module for Semi-supervised Data Classification in Non-stationary Environments

In the data stream (DS) context, data is received at high speed, and it must be processed as soon as possible. Furthermore, it is not possible to guarantee that all data is labelled. Consequently, semi-supervised learning (SSL) becomes an efficient attempt to build an effective model in this context. Dynamic Data Stream Learning (DyDaSL) is a framework that uses a SSL algorithm to build a model able to classify instances in a data stream context. In this paper, an extension of the DyDaSL drift detection module is proposed. Its main aim is to make drift detection more flexible and, in turn, to improve the whole data stream process. An empirical analysis is conducted using real and synthetic datasets. The proposed approach achieved better results than the original one and some state-of-art drift detection methods.