Reviewerly/exHarmony huggingface dataset:
Uncovering Learners’ Experiences to New Normal Education: Implications of Asynchronous Instruction in GE 5 Course Teaching

The new normal education policy in response to the pandemic crisis pushed institutions to shift from traditional face-to-face to asynchronous instruction that posed challenges particularly to science courses in higher education. The purpose of this study was to understand the learning experiences of the students and the implications of asynchronous teaching instruction in GE 5: Science, Technology, and Society course. This study utilized a parallel method of research employing descriptive-comparative and descriptive phenomenological research designs. There were 100 respondents for the quantitative phase and 12 participants for the qualitative phase. Based on the quantitative findings, the level of implementation of asynchronous instruction in the GE 5 course is “excellent” with no significant difference. Moreover, based on the qualitative analysis, the learning experiences of GE 5 students in asynchronous instruction were both positive and negative that implied two-way learning experiences. The general recommendation gleaned from the students was GE 5 asynchronous delivery improvement. The final output of this study was an activity proposal built from the findings and recommendations that covered teacher improvement, SIM improvement, and assessment tool improvement.
Archaeological survey and excavation of the Mingtepa Site in Andijan Region, Uzbekistan

Abstract Since 2012, the Institute of Archaeology of Chinese Academy of Social Sciences and Institute of Archaeology of the Academy of Sciences of Uzbekistan organized joint archaeological team and conducted five terms of archaeological survey and excavation to the Mingtepa Ancient City Site in Uzbekistan. The excavation showed that the Mingtepa Ancient City Site is a large-scale city site with nested inner and outer cities; confirmed the coexistence relationships among the architectural sites with high rammed-earth platform foundations, city walls, gates, roads and handicraft workshop remains, which are the scientific evidences for the in-depth researches on the layout and cultural connotations of the inner city; the burials found on the east wall of the outer city provided rare data of the terminus ante quem of the abandoning of the outer city.
Preliminaries

Abstract Blockchains are heavily based on mathematical concepts, in particular on algebraic structures. This chapter starts with an introduction to the main aspects in number theory, such as the divisibility of integers, prime numbers and Euler’s totient function. Based on these basics, it follows a very detailed introduction to modern algebra, including group theory, ring theory and field theory. The algebraic main results are then applied to describe the structure of cyclic groups and finite fields, which are needed to construct cryptographic primitives. The chapter closes with an introduction to complexity theory, examining the efficiency of algorithms.
THE IDEALITY AND ITS RELATIONSHIPS WITH THE MATERIALITY AS A CERTAIN MULTIPLICATIVE OPERATION

The idea is the philosophical issue that needs rethinking. Prejudices similar to geocentrism have influenced and continue to influence the views of theologians and philosophers on «ideas. » Main objective the aim of the work is to summarize what is known about ideal objects, highlighting some fundamental and special properties of the ideal. Research (at the experimental level) of culture (in its broadest sense) allowed relying on facts by induction, by analogy and deduction, using the concepts and means of formalization of set theory, general algebra and mathematical analysis to identify a separate intangible component in cultural substance (fundamental fact), to assume the presence and negativity of the ideal component in living substance, to identify the epicenter of bio and cultural creativity — the Creator. The most important result is the separation of substance and energy of the ideal, the metamorphosis of substance (idempotency of the atom in living and cultural substances), the flow of energy (± entropy) and the evolution of the ideal (creation), as well as identifying the epicenter of bio and cultural creativity. We emphasize that so far at the atomic level. Despite the obvious facts, they did not receive a clear wording. Having received this formulation, they become important in the logical construction of various scientific disciplines and worldview systems. Our statements can be easily verified. Verification methods provide an opportunity to verify the findings. Further research in this direction is urgently needed.
A Fast Audit Doubt Finding Model

Abstract In order to improve the audit efficiency and accuracy under the condition of big data, an audit doubt discovery model (CLOWF) based on an adaptive clustering outlier detection algorithm is proposed. The model first uses the slope ratio method to obtain the K value of the K-means++ algorithm, thereby effectively ensuring the clustering effect; At the same time, for the problem of slow pruning speed, a parallel calculation method for the data radius and centroid of each cluster is proposed and designed; Combined with the weighted local outlier factor detection algorithm to calculate the outlier factor, the data greater than the epsilon threshold of outlier is proposed as an indicator to determine the audit doubt. Through auditing application cases, comparing the local outlier factor detection algorithm and the cluster-based outlier detection algorithm, the results show that the execution time of the CLOWF model is the shortest and the accuracy rate is 97.3%.
Radio Modulation Recognition Based on Attention-based Convolutional Neural Network and Time-frequency Analysis

Radio modulation recognition is the key link of modern electronic warfare. This paper applies the idea of deep learning to radio modulation recognition. Since the modulation type is the most important information about the subtle features of the radio signal, we use short time Fourier transform (STFT) to convert the radio signal into a time-frequency distribution map to form a joint distribution of frequency and time. The attention-based convolutional neural network is used to classify and recognize the modulation types of time-frequency distribution maps. The simulation results show that time-frequency analysis can improve the accuracy of recognition, and adding attention can effectively improve the performance of the network.
Assessing Low Frequency Variations in Solar and Wind Power and Their Climatic Teleconnections

Electrical grids around the world are being increasingly exposed to climatic and synoptic variability due to the rapid addition of climate-dependent renewable capacity. Low frequency climate variations, nevertheless, are often poorly captured in the measuring campaigns of individual wind and solar facilities, even though they might condition their economical flows and risks. In this study we analyzed the co-occurrence of low frequency variations in the production of wind and solar energy over Argentina, and their link with large-scale climate oscillations. We found low complementarity between periods of high and low availability of wind and solar resources defined using the variable threshold method. We found a negative simultaneous relationship between wind energy production and the Antarctic Oscillation (AAO) during spring and summer. Solar energy production seems to be related to oceanic oscillations both in the Pacific and Atlantic basins, with the former dominating on the interannual time scale and the tropical Atlantic dominating on the longer time scales. We found a simultaneous negative relationship with the Multivariate El Niño Index (MEI) during autumn, winter and spring. We also found positive relationships with the Atlantic Multidecadal Oscillation (AMO) and the Tropical South Atlantic (TSA) indexes.
Conclusions

In this concluding chapter, I return to address the two questions which this book sought to answer: 1. how can qualitative methods be used in the development of QoL measures for use with youth in sub-Saharan Africa? 2. How can young people’s accounts of their goals be used to develop a measure of their QoL? I relate these questions to the earlier discussion of universal versus locally-grounded measurement of youth QoL, exploring the contribution of the approach advanced for the design of more locally grounded measures. The strengths and limitations of adopting qualitative methods, as applied in my six-step process, and the implications of the approach for policy and research are also discussed. I conclude that locally grounded measures could help us better understand the QoL of youth in specific contexts and enable the design of policies that are more responsive to their needs.
Dimensionality Reduction

Learning good representation for images is always a hot topic in machine learning and pattern recognition fields. Among the numerous algorithms, dictionary learning is a well-known strategy for effective feature extraction. Recently, more discriminative subdictionaries have been built by Fisher discriminative dictionary learning with specific class labels. Different types of constraints, such as sparsity, low-rankness and locality, are also exploited to make use of global and local information. On the other hand, as the basic building block of deep structure, the auto-encoder has demonstrated its promising performance in extracting new feature representation.
An intelligent Command and Control decision-making support model based on big data mining

The decision-making process of traditional assistant decision-making model has the disadvantages of long decision-making time and slow convergence. Big data mining technology has the characteristics of extracting or mining hidden valuable information from complex and multi-scale massive data, and its convergence speed is fast based on artificial intelligence algorithm. Based on the original “six database” decision model and the characteristics of data mining, this paper designs a big data decision support model based on deep learning algorithm, and deeply expounds its working principle and operation process. This process provides an intelligent and information-based auxiliary means for commanders’ decision-making, and improves the real-time and accuracy of command and control decision-making. In addition, the further application of data mining and deep learning algorithm in command and control information system is also discussed.
Artifacts 1. Definition

In this paper I present a definition of artifact based on cases of philosophical and scientifical use: anthropogenic abiotic virtual or real object with meaning and/or function. This definition is proposed in a new dimension: real–virtual, which purports to replace the classical opposition material–ideal as a better way of defining what an artifact is. I consider as virtual here not only digital simulations, but all sign forms. I show that my definition works better in explaining artifacts. I follow empirical science as a technique of studying artifacts, and subscribe to the anthropological paradigm.
The Future of Assessment for Personalized Naturalistic Learning

Education in general has transcended boundaries of a physical classroom and given rise to the phenomenon of ubiquitous learning (u-learning) and the ability to access knowledge on-demand. To understand the effect of learning as it is evolving, the present chapter puts forth a framework of formal, non-formal, and informal virtual learning environments discussed on the basis of nine components. As the learning environment changes, the role of assessment within this new learning paradigm must be reconsidered. The chapter concludes with a discussion of integrating assessment into intelligent tutoring systems and the importance of designing such systems as open architecture for accommodation of a variety of domains.
Challenges and Solutions for Addressing Software Security in Agile Software Development

There has been a surge in the number of software security threats and vulnerabilities in recent times. At the same time, expectations towards software and data security are growing. Thus, there is a need to ensure that security-related tasks are effectively integrated in the software development processes. However, integrating security practices with agile software development is not trivial due to, for instance, differences in process dynamics and the concentration on functional vs non-functional requirements. In this article, the authors present a literature review on the challenges and solutions when adopting security in an agile software development context. Their findings suggest that there are ongoing efforts to integrate security-practices in agile methods, but more research is needed to make the processes more optimized and simpler for developers. A rigor and relevance assessment on primary studies highlights a need for improving the manner in which studies on the topic are performed as well as reported.
Biometric Image Security Using Chaos Algorithm

In this world of Advanced Technology, the Biometrics are proved to be a significant method for user identification. However, the use of biometric is not new, but these days, with the increase in multimedia applications, it has gained its popularity in analysing human characteristics for security purposes. Biometric Encryption using Chaos Algorithm is a technique used to make it more convenient to the user and to provide high level security. The most prominent physical biometric patterns investigated for security purposes are the fingerprint, hand, eye, face, and voice. In the proposed image encryption scheme, an external secret key of 160-bit is used. The initial conditions for the logistic map are derived using the external secret key. The results obtained through experimental analysis provide an efficient and secure way for real-time image encryption and transmission.
Determining Attributes of Encrypted Data Traffic using Feature Selection Method

Encrypted packages such as banking and e-commerce are widely used in various fields because it is advantages in terms of data security. However, the problem occurs when checking attributes package to determine if it is a safe packet instead of malware. The purpose of this study is to get the best attributes using feature selection processes by ranking.The results of this study found that from the two best methods of IG and One R, in average IG better than One R. If based on the results of the response, the data produced for the estimated data of TLS V1.0 IG method has better accuracy compared to the One R method, on the contrary inTLS V1.2 One R data is better than IG.
Application of Artificial Intelligence Technology in Social Training

Social training is a combination of the number of systems and the number of processes, which can ensure easy business management. The effective application of artificial intelligence can be combined with the business field and the official field. This hypothesis represents the official assessment of human beings. Employees can meet many of the requirements of their field of work by meeting their requirements, and can follow their carrier conditions. It can be monitored by the camera and saved to the external memory, because of this situation it can be controlled by the FPGA controller. Can monitor their performance, health, skill development and their training time. This process involves input that can pass through the camera in the form of video. The output can be visualized by a computer system; this process can continue to run until the goal is achieved. The field programmable gate array (FPGA) contains artificial neural network (ANN) algorithms. The existing algorithms are the support vector machine algorithm (SVM) and the convolutional neural network algorithm (CNN) algorithm. By using this algorithm, the efficiency can be achieved a lot, and the process can be very easy.
An Analysis of Errors in Google Neural Machine Translation

Google Neural Machine Translation employs an encoder-decoder framework with an attention mechanism and it has resulted in a tremendous increase in efficiency. However, the quality of its translations is not yet comparable to that of human translations. In order to discover the specific problem of GNMT, the study analyzes the accuracy rate and types of errors in Google Neural Machine Translation by means of case studies. Based on the data collected, it is found that errors at the lexical level are most significant. The sparse data for certain terms in the Google Translate database, such as buzzwords, make undertranslation, a subcategory at the lexical level, the most significant translation error. These results proved that the users and developers may pay greater attention to the lexical level and try to match the selflearning capability of machine translation to the speed of partial vocabulary updates.
University Management System Engineering based on BAN PT Accreditation Criteria Two using SysML and Semantic Approach

Abstract Higher education organizations need to implement a management system. However, there has not been a study that designed the management system based on the latest BAN PT Accreditation Criteria, specifically criterion number 2. The design uses the Model Based System Engineering approach with SysML Language. Translation of accreditation standard sentences is done using the Semantic approach. Trough this research, obtained a design that can be applied at the University in order to maintain the quality of education.
Education and the Future

How can educators prepare their students for the many changes brought about by the information explosion? It is taking longer for students to finish their undergraduate degrees; more women are successful with earning their degrees that are men. Occupations are disappearing and new ones appearing at increasing speed. Education is one approach, but a changed and more responsive education system will be necessary. Students must learn how to be lifelong learners and discover their interests and talents through aptitude testing followed by counseling. Though of great age, the lecture remains one of the best ways to transmit information and will likely last, but in a modified form. These changes will profoundly affect politics, economics, the environment, and other areas of our lives.
Research on Extraction and Translation of English Public Signs in Tourist Attractions Based on Machine Vision

There are many English public signs in tourist attractions. Using digital imaging technology and computer technology to extract and translate these English public language is helpful for tourists to understand or warn the surrounding environment. This study constructs the recognition system based on machine vision technology and convolutional neural network (CNN) to extract, recognize and translate English public signs. The results show that the average recognition accuracy of the system is 98%; the average accuracy of translation is 96.5%. The above results show that the recognition system can effectively extract and translate English public signs, which is helpful for tourists to understand the information conveyed by scenic spots.
Teaching Supercomputers

The paper presents results of semantic search analysis of the key educational concepts and their conceptual relations presented in the course “Supercomputers I and II” taught at the School of Media, Arts and Technology at the Solent Southampton University during the 2015–2019 academic years. The study uses Big Data Analytics approaches to extract the domain key concepts and to outline their pedagogical interconnections which reflect the structure of course lectures, so to teach ‘supercomputers’ as an university subject with its specificity and to allow students to acquire that knowledge comprehensively by having the insight of its key concepts as well as of its practical applications. The study uses the Sketch Engine software’s statistical approaches, so to extract the key terms (related to the key concepts in the field) from the course texts lectures and to compare their semantic content with that produced by the students (feedback) in the related courses taught at some other universities in UK. It, also, outlines the importance of the ‘activity-based learning approach’ used for supporting the teaching process, so to improve the acquired knowledge by the use of Solent Online Learning system allowing interactive and distant web-based learning.
Lossy Compression Algorithms

In this chapter we examine compression algorithms such that recovered input data cannot be exactly reconstructed from the compressed version. This is termed “loss”. What we have, then, is a tradeoff between efficient compression versus a less accurate version of the input data. This tradeoff is captured in the Rate-Distortion Theory. Most of the loss occurs in quantization, and we introduce both Uniform and Nonuniform Scalar Quantization, and then Vector Quantization. Transform Coding, especially the Discrete Cosine Transform (DCT), is the main step in JPEG compression. We study the DCT in great length and provide several examples. JPEG 2000 and some video compression schemes are supported by Wavelet-Based Coding so we introduce this method here and go on to study Wavelet Packets, the Embedded Zerotree of Wavelet Coefficients, and SPIHT
Multi-window Drift Detection Method Based on Integrating Outlier Identification and Input/Output Space Information with Its Application

In soft sensing application of actual industrial process, it is necessary to accurately identify samples that represent the working condition drift. Therefore, a multiwindow concept drift detection method is proposed in this paper, which integrates outlier identification and input/output space information. Firstly, a principal component analysis (PCA) model and a decision tree (DT) model are built by using historical data. Then, in the first window, the process data are standardized on the basis of 3σ criterion to remove outliers. In the second window, the T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and SPE of the new sample are calculated to determine whether it is a drift sample in the input space. In the third window, the prediction error of the new sample is calculated to determine whether it is a drift sample in the out space. Finally, we take the union of the concept drift samples detected in the input and output space to obtain the new updating sample set. At the same time, the PCA model and the decision tree model are updated after the number of the updating samples reach a pre-set threshold. The experimental results verify the effectiveness of the proposed algorithm in the actual industrial datasets.