Reviewerly/exHarmony huggingface dataset:
NATIONAL HIGHER EDUCATION TRANSFORMATION DURING THE COVID-19 PANDEMIC IN PERCEPTIONS OF STUDENTS AND TEACHERS

This article examines the response of national higher education to the COVID-19 pandemic in 2020. In the hard year of 2020, students and teachers faced a new challenge – the transition to digitally-based distance education. They were forced to master previously unknown educational platforms, change the format and methods of teaching, learning and exams. Therefore, students and teachers’ attitudes and experiences of teaching-learning in such an emergency is a source for decision-making to improve the digital transformation of national higher education. The research focuses on the study of experience and perception by teachers and students of distance learning. The study is based on a survey of students of the capital university and teachers of three national universities, which give a clear idea of ​​the rapid transition of higher education to a new format – to digitally-based distance learning in an emergency – pandemic COVID-19. The questionnaires were developed in Google Forms format and distributed selectively to students and academics online in 2020-2021. It is emphasised that the COVID-19 pandemic tested the autonomy of national universities, their ability to reorganise educational services into an online format in a short time based on various technical capabilities and financial resources. The findings suggest that the COVID-19 pandemic has accelerated the digitisation of higher education, both international and national. In addition, the survey results allow not only to identify positive and negative trends in the digital transformation of higher education but also to outline promising formats of education in perceptions of students and teachers in the future.
Modulation Recognition of Communication Signal Based on Convolutional Neural Network

In the noncooperation communication scenario, digital signal modulation recognition will help people to identify the communication targets and have better management over them. To solve problems such as high complexity, low accuracy and cumbersome manual extraction of features by traditional machine learning algorithms, a kind of communication signal modulation recognition model based on convolution neural network (CNN) is proposed. In this paper, a convolution neural network combines bidirectional long short-term memory (BiLSTM) with a symmetrical structure to successively extract the frequency domain features and timing features of signals and then assigns importance weights based on the attention mechanism to complete the recognition task. Seven typical digital modulation schemes including 2ASK, 4ASK, 4FSK, BPSK, QPSK, 8PSK and 64QAM are used in the simulation test, and the results show that, compared with the classical machine learning algorithm, the proposed algorithm has higher recognition accuracy at low SNR, which confirmed that the proposed modulation recognition method is effective in noncooperation communication systems.
Overview of Algorithms for Natural Language Processing and Time Series Analyses

A host of machine learning algorithms have been used to perform several different tasks in NLP and TSA. Prior to implementing these algorithms, some degree of data preprocessing is required. Deep learning approaches utilizing multilayer perceptrons, recurrent neural networks (RNNs), and convolutional neural networks (CNNs) represent commonly used techniques. In supervised learning applications, all these models map inputs into a predicted output and then model the discrepancy between predicted values and the real output according to a loss function. The parameters of the mapping function are then optimized through the process of gradient descent and backward propagation in order to minimize this loss. This is the main premise behind many supervised learning algorithms. As experience with these algorithms grows, increased applications in the fields of medicine and neuroscience are anticipated.
Practical Application of QR Code Electronic Manuals in Equipment Management and Training

Objective: To explore the application value of QR code electronic manuals in operating theater equipment management and training. Methods: The control group adopted the traditional management mode. Training was carried out before each device was put into use in the department. A unified operation process and a paper card manual for common faults were formulated and hung next to each device. For the observation group, the electronic manuals generated by the QR code were pasted on the operating theater equipment for management and training. The management and training effect was compared between the two groups. Results: The efficiency of equipment management and training using the QR code electronic manuals was significantly higher than that of traditional training, and the difference was statistically significant (P < 0.05). Conclusion: The QR code manuals are very effective in operating theater equipment management and training. They can be read and learned in time to improve the operating theater equipment utilization rate, accuracy rate, and equipment management and training quality.
Neighborhood micro-renewal: a perspective of gene in China

Purpose Improvement of the environment quality and human development has become the main focus of modern urban development. Micro-renewal is a relatively people-oriented model of urban transformation compared with traditional renewal modes. To improve the theoretical system of neighborhood micro-renewal from a microcosmic perspective, a comprehensive analysis of neighborhood residents' cognition is needed. The purpose of the study is to explore the possibilities and methods of applying gene theory into the study of neighborhood micro-renewal. Design/methodology/approach According to the meme theory, the research explores the genetic analysis of neighborhood micro-renewal. The cross-over studies with “gene theory” from natural science to social science are analyzed and the neighborhood micro-renewal system was constructed from the perspective of micro-participants and micro-objects. Moreover, the concept of neighborhood “micro-renewal gene” was put forward. Finally, the authors show three application scenarios of public participation with a specific neighborhood micro-renewal project. Findings The cross research on urban studies with gene theory could be divided into three scales and four research fields. The characteristics of carrying out neighborhood micro-renewal in China could be summarized as micro-participants and micro-objects. Residents' cognition could be considered as “micro-renewal gene” in refer to meme theory. The application scenarios of introducing “micro-renewal gene” into the study of neighborhood renewal are of great potentialities. Originality/value Neighborhood micro-renewal system was constructed from the perspective of micro-participants and micro-objects. Moreover, neighborhood “micro-renewal gene” was proposed and applied into the study of this system in refer to meme theory.
IoT privacy preservation using blockchain

Internet of Things (IoT) security is a fundamental challenge for the enterprises working behind the buildup of the IoT, and it is related applications. In today’s ara, with IoT, another buzzword is a blockchain-based cryptocurrency and other blockchain applications. Blockchain technology has proven itself one of the most secure existing technology in trust and privacy. This paper discusses IoT and blockchain technology fundamentals, followed by a discussion regarding numerous consensus models. We also discuss mathematical foundations used by blockchain technology, and it is followed by the proposed privacy-preserving technique for IoT devices with better device identity management using blockchain technology. This paper presents a novel solution for privacy preservation and identity allocation using secure identity management through public key-based cryptography-based blockchain. Though we consider smart home management and smart health management’s ecosystem for applicability discussion of the proposed scheme, the proposed system can be applied to any IoT application for better privacy preservation. Finally, we conclude with the discussion regarding the futuristic scope of the proposed system.//
A Revisit Histogram of Oriented Descriptor for Facial Color Image Classification Based on Fusion of Color Information

Histogram of Oriented Gradient (HOG) is a robust descriptor which is widely used in many real-life applications, including human detection, face recognition, object counting, and video surveillance. In order to extract HOG descriptor from color images whose information is three times more than the grayscale images, researchers currently apply the maximum magnitude selection method. This method makes the information of the resulted image is reduced by selecting the maximum magnitudes. However, after we extract HOG using the unselected magnitudes of the maximum magnitude selection method, we observe that the performance is better than using the maximum magnitudes in several cases. Therefore, in this paper, we propose a novel approach for extracting HOG from color images such as Color Component Selection and Color Component Fusion. We also propose the extended kernels in order to improve the performance of HOG. With our new approaches in the color component analysis, the experimental results of several facial benchmark datasets are enhanced with the increment from 3 to 10% of accuracy. Specifically, a 95.92% of precision is achieved on the Face AR database and 75% on the Georgia Face database. The results are better more than 10 times compared with the original HOG approach.
Predictive Wireless Channel Modeling of MmWave Bands Using Machine Learning

The exploitation of higher millimeter wave (MmWave) is promising for wireless communication systems. The goals of machine learning (ML) and its subcategories of deep learning beyond 5G (B5G) is to learn from the data and make a prediction or a decision other than relying on the classical procedures to enhance the wireless design. The new wireless generation should be proactive and predictive to avoid the previous drawbacks in the existing wireless generations to meet the 5G target services pillars. One of the aspects of Ultra-Reliable Low Latency Communications (URLLC) is moving the data processing tasks to the cellular base stations. With the rapid usage of wireless communications devices, base stations are required to execute and make decisions to ensure communication reliability. In this paper, an efficient new methodology using ML is applied to assist base stations in predicting the frequency bands and the path loss based on a data-driven approach. The ML algorithms that are used and compared are Multilelayers Perceptrons (MLP) as a neural networks branch and Random Forests. Systems that consume different bands such as base stations in telecommunications with uplink and downlink transmissions and other internet of things (IoT) devices need an urgent response between devices to alter bands to maintain the requirements of the new radios (NR). Thus, ML techniques are needed to learn and assist a base station to fluctuate between different bands based on a data-driven system. Then, to testify the proposed idea, we compare the analysis with other deep learning methods. Furthermore, to validate the proposed models, we applied these techniques to different case studies to ensure the success of the proposed works. To enhance the accuracy of supervised data learning, we modified the random forests by combining an unsupervised algorithm to the learning process. Eventually, the superiority of ML towards wireless communication demonstrated great accuracy at 90.24%.
The role of historical and contextual knowledge in enterprise search

Purpose The purpose of the study is to examine enterprise searching practices across different work areas and work tasks in an enterprise search system in an international biotechnology company. Design/methodology/approach A mixed-method approach studying employees' authentic search activities during a 4-month period by log data, questionnaire survey and interviews. The log data analysed the entire active searcher group, whereas the questionnaire and interviews focused on frequent searchers. Findings The three studies provided insight into the searching activities and an understanding of the way searchers used the enterprise search system to search for information as part of their work tasks. The data identified three searcher groups, each with specific search characteristics. Four work task types were identified, and for all four types the searchers applied a tracing searching technique with use of contextual and historical relationships as paths. Practical implications The findings point to the importance of knowledge on historical and contextual relations in enterprise search. Originality/value The work sheds new light on enterprise searchers' information search practices. A significant contribution is the identification of a tracing search method used in relation to four essential work task types. Another contribution is the importance of historical and contextual knowledge to support the tracing search and decide what paths to follow.
Simultaneous Segmentation of Four Cardiac Chambers in Fetal Echocardiography

Accurate segmentation of cardiac chambers is helpful for the diagnosis of Congenital Heart Disease (CHD) in fetal echocardiography. Previous studies mainly focused on single cardiac chamber segmentation, which cannot provide sufficient information for the cardiologists. In this paper, we present an instance segmentation approach capable of segmenting four cardiac chambers accurately and simultaneously. A novel object proposal recovery strategy is further deployed to retrieve possible missing objects. To alleviate the shortage of medical data and further improve the segmentation performance, we utilize a rotation and distortion method for data augmentation. Experiments on a fetal echocardiography dataset of 319 fetuses demonstrate that the proposed approach can achieve superior performance according to common-used evaluation metrics.Clinical relevance—This can be used to help the cardiologists to better analyze the structure and function of the fetal heart.
Analyzing transfer learning impact in biomedical cross-lingual named entity recognition and normalization

Abstract Background The volume of biomedical literature and clinical data is growing at an exponential rate. Therefore, efficient access to data described in unstructured biomedical texts is a crucial task for the biomedical industry and research. Named Entity Recognition (NER) is the first step for information and knowledge acquisition when we deal with unstructured texts. Recent NER approaches use contextualized word representations as input for a downstream classification task. However, distributed word vectors (embeddings) are very limited in Spanish and even more for the biomedical domain. Methods In this work, we develop several biomedical Spanish word representations, and we introduce two Deep Learning approaches for pharmaceutical, chemical, and other biomedical entities recognition in Spanish clinical case texts and biomedical texts, one based on a Bi-STM-CRF model and the other on a BERT-based architecture. Results Several Spanish biomedical embeddigns together with the two deep learning models were evaluated on the PharmaCoNER and CORD-19 datasets. The PharmaCoNER dataset is composed of a set of Spanish clinical cases annotated with drugs, chemical compounds and pharmacological substances; our extended Bi-LSTM-CRF model obtains an F-score of 85.24% on entity identification and classification and the BERT model obtains an F-score of 88.80% . For the entity normalization task, the extended Bi-LSTM-CRF model achieves an F-score of 72.85% and the BERT model achieves 79.97%. The CORD-19 dataset consists of scholarly articles written in English annotated with biomedical concepts such as disorder, species, chemical or drugs, gene and protein, enzyme and anatomy. Bi-LSTM-CRF model and BERT model obtain an F-measure of 78.23% and 78.86% on entity identification and classification, respectively on the CORD-19 dataset. Conclusion These results prove that deep learning models with in-domain knowledge learned from large-scale datasets highly improve named entity recognition performance. Moreover, contextualized representations help to understand complexities and ambiguity inherent to biomedical texts. Embeddings based on word, concepts, senses, etc. other than those for English are required to improve NER tasks in other languages.
Digital Repository of the Southern Federal University in the Scientific and Educational Space of the University

The article reveals the experience of creating a Digital Repository by the Zonal Scientific Library of the Southern Federal University. It is organized and technically supported as the central repository of the SFedU intellectual property objects. Having studied the experience of other universities, it was decided to develop its own software platform built in Python. As a result, a high level of service was provided for the use of the repository not only by librarians and users, but also by other structural divisions of the university. The repository contains the intellectual products of the university, provides access to the results of scientific research of the university, has the ability to exchange metadata through the API interface, has a customized search interface, ensures the safety of content, and minimizes the labor costs of users and service personnel.
Solar Energy Potential Evaluation. Case of Study: M'Sila, an Algerian Province

Solar energy is the most important green energy source that may be used to address the problems caused by conventional energy sources. The research in this paper aims to evaluate the solar energy potential of M'Sila, an Algerian internal region, and to determine which technology, among those currently available on the market, is adequate for harnessing that potential for electricity production (PV and CSP). This assessment was based on data (daily temperature and daily solar irradiation) collected at M'Sila weather station for one year and from the NASA site for 34 years from 1984 to 2017. These data were treated with excel 2016 software to determine the total horizontal, diffuse and direct normal solar irradiation. During the study period, the total solar irradiation falling on a horizontal surface in M'Sila (GHI) was estimated to be 1.79 MWh/m2/year. The diffuse solar irradiation incident on a horizontal surface (DHI) and direct normal irradiation, respectively, were estimated to be 594.88 kWh/m2/year, 2117.93 kWh/m2/year. M'Sila is a favourable land for the installation of concentrating solar power plants and photovoltaic panels due to its significant potential energy. The parabolic trough system is recommended. While polycrystalline PV modules should be promoted and installed in M'Sila.
Situated Cognition and How to Overcome It

The first half-century of American psychology is often represented nowadays as a dark age of rat-running and rote verbal-learning experiments, which ended only when it was overwhelmed by the rise of the new cognitive psychology that started in the 1950s. Environmental transformation and expertise are to be found in every society, but there is a third way of transcending biological givens that is much less common and that represents a far more radical departure from the kinds of cognitive adaptations shared with other species. Situated cognition researchers have contributed substantially to our understanding of the relations between Worlds 1 and 2, arguing that these are much more directly and intimately connected than previous cognitive theories had supposed. Situativity theorists might concede that something like this continuum of abstraction exists, but they would argue that the more abstract kind of mathematical activity is just as situated as the more concrete.
MITM Attack on Modification of Diffie-Hellman Key Exchange Algorithm

After going through past research, it is observed that the security of data depends on five important key features such as authentication, data Integrity, mutual authentication between two parties, non-repudiation, and key exchange algorithm. Even if one of these features fail then it may breach security in real-world scenarios. The Diffie-Hellman Key Exchange (DHKE) algorithm allows securing one feature called key exchange in the cryptography field. However, this key exchange algorithm is unable to provide authentication. As a result, this key exchange scheme is prone to a hostile attack named as Man-In-The-Middle Attack (MITM). To solve this type of attack, a novel algorithm has been proposed called modification of the DHKE algorithm. In this algorithm, they claim that the MITM attack on classical DHKE can be prevented by applying the necessary functionalities like encryption and Zero-Knowledge Proof (ZKP). Although, after going through result analysis and working techniques, it is observed that the modified DHKE algorithm is still suffering from MITM attack. In this paper, the main objective is to develop an algorithm for finding out this type of security flaw in the existing modified DH algorithm which includes the concept of required authentication and encryption functions and able to perform the MITM attack on the existing modified DHKE algorithm.
Solving one-dimensional acoustic systems using the impedance translation theorem and equivalent circuits: A graduate level homework assignment

The natural frequency resonances and sound radiation from one-dimensional acoustic systems are of great interest in the study of musical instruments, human vocal tract effects on speech, automotive exhaust pipes, duct systems for temperature control in buildings, and more. The impedance translation theorem is an approach that may be used to solve for the input impedance and therefore the resonance frequencies of one-dimensional systems. Equivalent circuits offer another approach to solving one-dimensional systems, though with equivalent circuits you can also solve for the response at any location in the system, including the radiated sound pressure. At Brigham Young University, there are two graduate level courses that teach these two techniques. One of the most challenging and memorable homework assignments from these courses is based on using one of these techniques to analyze a particular acoustic system and compare its response with the real thing. This paper discusses the basics of these two techniques and applies them to an analysis of phonemes produced by altering the human vocal tract. Details about the homework assignments are also given.
Siamese Network Object Tracking Algorithm Combining Attention Mechanism and Correlation Filter Theory

Aiming to solve the problem of tracking drift during movement, which was caused by the lack of discriminability of the feature information and the failure of a fixed template to adapt to the change of object appearance, the paper proposes an object tracking algorithm combining attention mechanism and correlation filter theory based on the framework of full convolutional Siamese neural networks. Firstly, the apparent information is processed by using the attention mechanism thought, where the object and search area features are optimized according to the spatial attention and channel attention module. At the same time, the cross-attention module is introduced to process the template branch and search area branch, respectively, which makes full use of the diversified context information of the search area. Then, the background perception correlation filter model with scale adaptation and learning rate adjustment is adopted into the model construction, using as a layer in the network model to realize the object template update. Finally, the optimal object location is determined according to the confidence map with similarity calculation. Experimental results show that the designed method in the paper can promote the object tracking performance under various challenging environments effectively; the success rate increases by 16.2%, and the accuracy rate increases by 16%.