Reviewerly/exHarmony huggingface dataset:
Study on ‘Gei’(給) Constructions - Based on the Current 8 Types of Chinese Conversation Textbooks

This paper selected eight textbooks currently used in the class to find out how to explain the Gei’(給) Constructions and briefly suggested precautions and effective teaching methods when compiling textbooks through analysis. As a result of examining the emergence of S1, S2, S3, and S4 formats, textbook C was the only one to present all four formats. S1 and S4 formats can be found in all textbooks, and S2 formats can be found in the rest of the textbooks except for D textbooks. The S5 format was the only one that could be identified in the B textbook and was used in the passive sentence. Most of the eight textbooks presented various phrases in the text, but even if there was no explanation of the grammar or even if there was, the content was not sufficient. I hope that scholars will pay a lot of attention so that the results of the study can lead to education and develop textbooks and guidelines that help learners.
Convex Non-negative Matrix Factorization Through Quantum Annealing

In this paper we provide the quantum version of the Convex Non-negative Matrix Factorization algorithm (Convex-NMF) by using the D-wave quantum annealer. More precisely, we use D-wave 2000Q to find the low rank approximation of a fixed real-valued matrix <tex>$X$</tex> by the product of two non-negative matrices factors <tex>$W$</tex> and <tex>$G$</tex> such that the Frobenius norm of the difference <tex>$X$</tex> - XWG is minimized. In order to solve this optimization problem we proceed in two steps. In the first step we transform the global real optimization problem depending on <tex>$W, G$</tex> into two quadratic unconstrained binary optimization problems (QUBO) depending on <tex>$W$</tex> and <tex>$G$</tex> respectively. In the second step we use an alternative strategy between the two QUBO problems corresponding to <tex>$W$</tex> and <tex>$G$</tex> to find the global solution. The running of these two QUBO problems on D-wave 2000Q need to use an embedding to the chimera graph of D-wave 2000Q, this embedding is limited by the number of qubits of D-wave 2000Q. We perform a study on the maximum number of real data to be used by our approach on <tex>$\mathbf{D}$</tex> -wave 2000Q. The proposed study is based on the number of qubits used to represent each real variable. We also tested our approach on D-Wave 2000Q with several randomly generated data sets to prove that our approach is faster than the classical approach and also to prove that it gets the best results.
A data science approach to readability

In this paper we study and test a novel hypothesis: namely, sentence complexity is a measure of readability. Our goal is not to replace nor necessarily improve readability tests but to investigate an experimental, data science application of word complexity. Word complexity is the number of words needed to define a word according to a reference dictionary. Sentence complexity is the aggregation or reduction of word complexity for words in a sentence. With the aid of natural language processing, we test our hypothesis using a corpus of 24 classic fairy tales. We chose these stories for their accessibility, popularity, and brevity. The data shows their reading difficulty to be centered around the 6<sup>th</sup>-7<sup>th</sup> grade level. The data also show the Flesch-Kincaid test and sentence complexity are negatively correlated in reading ease (r=-0.31, P&#x003C;0.01) and positively correlated in grade level (r=0.46, P&#x003C;0.01), respectively. That the correlations are moderate suggests sentence complexity is related to Flesch-Kincaid but not a proxy for it. Finally, the data further show that sentence complexity is weakly correlated with maximum number of syllables in a sentence (r=0.12, P&#x003C;0.01), weakly correlated with maximum word length in a sentence (r=0.20, P&#x003C;0.01), and moderately correlated with the number of words in a sentence (r=0.52, P&#x003C;0.01). These findings support our hypothesis.
A Chaotic and Hyperchaotic Map based Image Encryption Protocol for High-End Colour density Images using enhanced S-box pixel permutator

The chaotic and hyperchaotic maps from Chaos theory which are both based on cryptographic standards are used in this article to present a novel technique based on repetitive permutations of pixel randomization. A unique picture encryption algorithm is being constructed that takes such meta-data into account and acts at the per pixel rotation level to boost the confusion matrix values, making decrypting the encrypted image extremely difficult for the attacker. With chaotic mapping schemes, an effective encryption key is created, which is a multifunctional encryption key used to jumble the pixel sequences to the point of picture encryption. The suggested protocol conducts both the encryption and decryption phases by increasing the finite fields padding of the corrupted pixels. Multiple levelled s-boxes that orient the pixels in the new encrypted image according to the minimum correlation coefficient are being used to induce pixel permutation and confusion. The experiment employed one of the most well-known photos, such as Lena, to test the quality of the encryption technique and the handling of the key generated thereafter. The outcome of the built protocol for image encryption shows promise in terms of security when compared to existing picture encryption approaches. Correlation coefficient and other parametric experiments indicate the validity of our approach. Because a larger image has more pixels, it has a greater impact on image encryption.
STGE: Sensor Topology and Graph Embedding Learning with Heterogeneous Smart Environment

In recent years, the Smart Environment(SE) supported by the Internet of Things(IoT) shows great ability to acquire and apply knowledge about an environment. Typical tasks in such environments are intended to monitor and predict the behavior patterns of residents. It would be helpful to these tasks if we can model the rich multi-model perceptional context and select the right combinations of sensory resources. In this study, we propose STGE, a novel sensor topology learning and graph embedding technique. STGE consists of two phases. “ST” applies data-driven adjacency learning methods to acquire sensor logical topology, which helps to determine the combination of sensors. “G E” learns representations of sensory resources based on the sensor topology, and leverages ideas of a novel graph embedding model called struct2vec. Through experiments with Smart Home(SH) data-sets, we demonstrate that STGE can achieve explainable and computable sensor's logical topology and their latent representation across different smart homes. The learned embedding is compatible with machine learning techniques, and can optimize the performance of downstream tasks such as human activity recognition.
ASTC: An Adaptive Gradient Compression Scheme for Communication-Efficient Edge Computing

In edge computing, though distributed training of Deep Neural Network (DNN) is expected to exchange massive gradients between the parameter servers and workers, the high communication cost constrains the training speed. To break this limitation, the gradient compression algorithms employ the ultimate compression ratio at the expense of the accuracy of the trained model. Therefore, new gradient compression techniques are necessary to ensure both communication efficiency and model accuracy. This paper introduced a novel technique - an Adaptive Sparse Ternary Gradient Compression (ASTC) scheme to enhance the communication efficiency and training accuracy during the DNN training. Specifically, ASTC firstly establishes the model compression selection criterion by the amount of gradients to compress the network layer that meets the criterion. Secondly, ASTC evaluates the gradients&#x0027; importance based on entropy to adaptively perform sparse compression. Finally, ASTC performs ternary quantization compression and a lossless code scheme to further reduce the communication cost. Numerical results demonstrate that the average iteration time of ASTC can be reduced largely compared to the state-of-art benchmark schemes. Furthermore, ASTC improves an average of about 1.9&#x0025; in training accuracy.
Identifying Patients With Hypoglycemia Using Natural Language Processing: Systematic Literature Review (Preprint)

<sec> <title>BACKGROUND</title> Accurately identifying patients with hypoglycemia is key to preventing adverse events and mortality. Natural language processing (NLP), a form of artificial intelligence, uses computational algorithms to extract information from text data. NLP is a scalable, efficient, and quick method to extract hypoglycemia-related information when using electronic health record data sources from a large population. </sec> <sec> <title>OBJECTIVE</title> The objective of this systematic review was to synthesize the literature on the application of NLP to extract hypoglycemia from electronic health record clinical notes. </sec> <sec> <title>METHODS</title> Literature searches were conducted electronically in PubMed, Web of Science Core Collection, CINAHL (EBSCO), PsycINFO (Ovid), IEEE Xplore, Google Scholar, and ACL Anthology. Keywords included &lt;i&gt;hypoglycemia&lt;/i&gt;, &lt;i&gt;low blood glucose&lt;/i&gt;, &lt;i&gt;NLP&lt;/i&gt;, and &lt;i&gt;machine learning&lt;/i&gt;. Inclusion criteria included studies that applied NLP to identify hypoglycemia, reported the outcomes related to hypoglycemia, and were published in English as full papers. </sec> <sec> <title>RESULTS</title> This review (n=8 studies) revealed heterogeneity of the reported results related to hypoglycemia. Of the 8 included studies, 4 (50%) reported that the prevalence rate of any level of hypoglycemia was 3.4% to 46.2%. The use of NLP to analyze clinical notes improved the capture of undocumented or missed hypoglycemic events using International Classification of Diseases, Ninth Revision (ICD-9), and International Classification of Diseases, Tenth Revision (ICD-10), and laboratory testing. The combination of NLP and ICD-9 or ICD-10 codes significantly increased the identification of hypoglycemic events compared with individual methods; for example, the prevalence rates of hypoglycemia were 12.4% for International Classification of Diseases codes, 25.1% for an NLP algorithm, and 32.2% for combined algorithms. All the reviewed studies applied rule-based NLP algorithms to identify hypoglycemia. </sec> <sec> <title>CONCLUSIONS</title> The findings provided evidence that the application of NLP to analyze clinical notes improved the capture of hypoglycemic events, particularly when combined with the ICD-9 or ICD-10 codes and laboratory testing. </sec>
Assessing the Implementation of Digital Innovations in Response to the COVID-19 Pandemic to Address Key Public Health Functions: Scoping Review of Academic and Nonacademic Literature (Preprint)

<sec> <title>BACKGROUND</title> Digital technologies have been central to efforts to respond to the COVID-19 pandemic. In this context, a range of literature has reported on developments regarding the implementation of new digital technologies for COVID-19–related surveillance, prevention, and control. </sec> <sec> <title>OBJECTIVE</title> In this study, scoping reviews of academic and nonacademic literature were undertaken to obtain an overview of the evidence regarding digital innovations implemented to address key public health functions in the context of the COVID-19 pandemic. This study aimed to expand on the work of existing reviews by drawing on additional data sources (including nonacademic sources) by considering literature published over a longer time frame and analyzing data in terms of the number of unique &lt;i&gt;digital innovations&lt;/i&gt;. </sec> <sec> <title>METHODS</title> We conducted a scoping review of the academic literature published between January 1, 2020, and September 15, 2020, supplemented by a further scoping review of selected nonacademic literature published between January 1, 2020, and October 13, 2020. Both reviews followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach. </sec> <sec> <title>RESULTS</title> A total of 226 academic articles and 406 nonacademic articles were included. The included articles provided evidence of 561 (academic literature) and 497 (nonacademic literature) unique digital innovations. The most common implementation settings for digital innovations were the United States, China, India, and the United Kingdom. Technologies most commonly used by digital innovations were those belonging to the high-level technology group of &lt;i&gt;integrated and ubiquitous fixed and mobile networks&lt;/i&gt;. The key public health functions most commonly addressed by digital innovations were communication and collaboration and surveillance and monitoring. </sec> <sec> <title>CONCLUSIONS</title> Digital innovations implemented in response to the COVID-19 pandemic have been wide ranging in terms of their implementation settings, the digital technologies used, and the public health functions addressed. However, evidence gathered through this study also points to a range of barriers that have affected the successful implementation of digital technologies for public health functions. It is also evident that many digital innovations implemented in response to the COVID-19 pandemic are yet to be formally evaluated or assessed. </sec>
Deep Learning Based Application To Detect Face Masks For Health Protocol Surveillance During The Covid-19 Pandemic

As part of preventing the rapid spread of the COVID-19 virus, masks should be used besides physical distancing, avoiding crowds, setting good ventilation, cleaning hands, and more. In fact, there are still many people who do not use masks in public places. Security officers usually carry out the supervision system for the use of masks in public places. Direct interaction between security officers and many visitors in the long term will trigger a high risk of being infected. It is better if there is a system that can monitor the use of masks automatically and continuously. In this study, a deep learning algorithm with YOLOv3 architecture is applied to a web-based mask detection system. YOLOv3 is one of the best algorithms that has been widely applied and optimized by researchers for object detection. This system has been tested in two scenarios using different data sets, namely without augmentation and with augmentation. For each scenario, three experiments were carried out using varying amounts of training data, namely 10, 100, and 800. The best mAP and confidence values were obtained from experiments using 800 training data with augmentation. This mAP value is 0.99 with a confidence value range between 0.9 to 1.
Embankment Protection - React Native Application Cross-Platform Application for protection of embankments by crowd sourced data

Developing mobile application compatible for both Android and iOS, hence a cross-platform development approach, as developers faced a challenge previously learning development specific language for Android and iOS. Compared to other hybrid mobile application frameworks, React Native has faster development time, wide market search and easy third party integration. It is also time and cost efficient for single codebase nature. Getting to the bottom of the solution for the underlying problem, this paper utilizes React Native framework to create an efficient hybrid mobile application &#x201C;Embankment Protection App&#x201D; capable of provisioning crowd sourced solutions pertaining to embankment surveys. The framework has been created for Android and iOS, the produced results reflects adequate experience for users on both the platforms. The framework develops truly native apps and does not compromise much with user experiences regardless of the platform. The programming language used for the solution of this research paper is a combination of Javascript.
Review on Human Computer Interaction Intelligent Learning for Improved Identification Procedure

Every day, new systems and devices are being developed, and research in this human and computer interaction field has increased dramatically over the previous several decades. Research into how computers impact people's lives, organizations, and society as a whole is done via the field of Human-Computer Interaction (HCI). This involves not just the simplicity of use but also innovative interaction approaches for supporting user activities, offering better access to information through new HCI principle, and establishing more powerful forms of communication for the user. After contemplating its potential, HCI is currently a popular new study topic among academics with feedback and recommendation by yes or no. To make HCI an effective tool, it must learn from the new principles of HCI that is planned here for future and reconsider some of its fundamental concepts.
Foundation and Operation of Private Secondary Schools Under Japanese Colonial Rule - Focused on Korean-established Schools -

The Foundation of private secondary schools under Japanese colonial rule had an identity as an educational institution for Koreans. However, there were differences in the foundation’s operating structure and composition depending on the subject of the school establishment. Sookmyung Girls’ School, a school related to the royal family of the Korean Empire, had a broad stipulation on the role of the foundation, and the foundation was operated by Japanese officials. Choongang Academy Foundation was run by Kim Sung-soo and his close aides. However, Koreans tried to keep school operators in check based on the perception that private secondary schools are public institutions in the Korean community. On the other hand, Osan School, established with the cooperation of local residents, allowed a large number of people in the local community to participate in the operation of the school through the council.
Bloom Filter Index

Within the big data ecosystem, highly performant workloads and queries are critical for keeping customers happy and businesses running successfully. Within the SQL database realm, indexes support query performance optimization opportunities quite gracefully. Within the Data Lakehouse, there have been limited methods of applying indexes to Delta Tables. Bloom Filter Indexes are space-efficient data structures that enable Data Skipping on chosen columns. It operates by stating that data is definitively not in the file, or that it is probably in the file, with a defined false positive probability (FPP). Bloom Filter Indexes are critical to building highly performant Delta Lakehouses. In this chapter, we will address the question of how to get started with Bloom Filter Indexes.
The correlation between students’ vocabulary mastery and their reading comprehension

The goal of this research is to find the objective condition from the correlation between students’ vocabulary mastery and students’ reading comprehension which was observed and analyzed from SMA N 1 Bandar Lampung. Moreover, the purpose of this study is also to get the information and the empirical data about the correlation of students’ vocabulary mastery and their reading comprehension. The populations of the research are 178 students and 36 students were being the sample of the research. All of the objects are from the second semester of IPA 5 SMA N 1 Bandar Lampung. This research was using correlation through the application of quantitative methods by collecting the data from the test. According to the result of the analysis and statistical calculation, it is found that rxy is 0.678 and rt is 0.329 and 0,339 with the degree of freedom ( df=N-nr) is 34. By comparing the values of rxy and rt, it means that rxy is bigger than rt and the statistic hypothesis states: If ro&gt;rt significance: there is correlation and Ha accepted. If ro&lt;rt non-signification: there is no correlation and Ha is rejected. The result of the hypothesis is rxy bigger than rt which means the alternative hypothesis is accepted. Based on this finding from this study, it can conclude that having more vocabulary or vocabulary mastery significantly affects the comprehension of the students reading activity. Moreover by having many vocabularies the students’ can easily get the information and fun story written in English which finally in can enrich their knowledge by reading a lot of books.
Improving students’ ability in writing descriptive text through guiding question at SMPN 13 Bandar Lampung

The aims of the research were to find out whether there was an improvement of the students’ writing ability in descriptive text and students’ perceptions after the the implementation of Guiding Question. The research was a quantitative-qualitative research. The design used in this research was one group pretest and posttest. The subjects were 24 students of class VIII E of SMP Negeri 13 Bandar Lampung. The instruments were writing test and interview protocol. The data were in the form of scores and transcripts taken from the pretest and posttest and those were analyzed by using Paired Sample t-test. The results showed that students’ writing score was gained 13.75 points from 50.16 in pretest to 63.91 in posttest and the statistical result showed that the significance score was 0.00 it means that there is a significant improvement on students’ ability in writing descriptive text since 0.00 &lt; 0.05. Furthermore, the result of the interview showed that students found it easy and enjoyed while learning descriptive text through Guiding Questions so it can be concluded that students had positive perceptions during the implementation of guiding questions technique in the class. The result of the interview also shows that most students did not feel any difficulties, stressed or frustrated and they were also interested and motivated when this media was applied. It can be concluded that Guiding Questions provides a positive impact for the students when it was applied in the class as a teaching media.
THE USE OF AN INFORMATION EDUCATIONAL PLATFORM DURING LABORATORY WORK

The article provides an example of conducting laboratory work using an information educational platform. The developed branched methodology of conducting a laboratory workshop with syn-chronous testing is presented. The evaluation of the block-modular technology of teaching labora-tory work is given. Recommendations are given on the further application of the presented meth-odology for related disciplines studied by students.