Reviewerly/exHarmony huggingface dataset:
Deep Learning Based Sentiment Analysis of COVID-19 Tweets via Resampling and Label Analysis

Twitter has emerged as a platform that produces new data every day through its users which can be utilized for various purposes. People express their unique ideas and views on multiple topics thus providing vast knowledge. Sentiment analysis is critical from the corporate and political perspectives as it can impact decision-making. Since the proliferation of COVID-19, it has become an important challenge to detect the sentiment of COVID-19-related tweets so that people’s opinions can be tracked. The purpose of this research is to detect the sentiment of people regarding this problem with limited data as it can be challenging considering the various textual characteristics that must be analyzed. Hence, this research presents a deep learning-based model that utilizes the positives of random minority oversampling combined with class label analysis to achieve the best results for sentiment analysis. This research specifically focuses on utilizing class label analysis to deal with the multiclass problem by combining the class labels with a similar overall sentiment. This can be particularly helpful when dealing with smaller datasets. Furthermore, our proposed model integrates various preprocessing steps with random minority oversampling and various deep learning algorithms including standard deep learning and bi-directional deep learning algorithms. This research explores several algorithms and their impact on sentiment analysis tasks and concludes that bidirectional neural networks do not provide any advantage over standard neural networks as standard Neural Networks provide slightly better results than their bidirectional counterparts. The experimental results validate that our model offers excellent results with a validation accuracy of 92.5% and an F1 measure of 0.92.
Entanglement enhancement from single-port and two-port feedback optical parametric amplifiers

Coherent feedback control has been proved to be an effective approach in entanglement enhancement of the entangled states produced by a nondegenerate optical parametric amplifier (NOPA). However, the present research mainly focuses on a two-port feedback NOPA (TFPA). In this study, a scheme of single-port feedback NOPA (SFPA) is proposed and investigated. For the ideal case, both SFPA and TFPA have the potential to realize an infinite degree of entanglement. For the practical case with feasible physical parameters of realistic systems, the features of entanglement enhancement for two schemes are compared. The effect of entanglement enhancement for SFPA is relatively weaker than that of TFPA, but has a loose phase-locking accuracy requirement. Our results may provide useful references on the control of nonclassical light fields.
Removing the Black-Box from Machine Learning

We discuss an algorithm which allows us to find the algebraic expression of a dependent variable as a function of an arbitrary number of independent ones where data may have arisen from experimental data. The possibility of such approximation is proved starting from the Universal Approximation Theorem (UAT). As opposed to the neural network (NN) approach to which it is frequently associated, the relationship between the independent variables is explicit, thus resolving the “black box” characteristics of NNs. It implies the use of a nonlinear function such as the logistic 1/(1 + e-x). Thus, any function is expressible as a combination of a set of logistics. We show that a close polynomial approximation of logistic is possible by using only a constant and monomials of odd degree. Hence, an upper bound (D) on the degree of the polynomial may be found. Furthermore, we may calculate the form of the model resulting from D. We discuss how to determine the best such set by using a genetic algorithm leading to the best L∞-L2 approximation. It allows us to find the best approximation polynomial consisting of a fixed number of monomials and yielding the degrees of the variables in every monomial and the associated coefficients. Furthermore, we trained a multi-layered perceptron network to determine the most adequate number of such monomials for a set of arbitrary data. We discuss how to analyze the explicit relationship between the variables by using a well known experimental database. We show that our method yields better quantitative and qualitative measures than those of the human experts.
Improved loss and feature fusion for water surface object detection

Water surface object detection is one of the essential techniques for carrying out water-related tasks and assisting in the conduct of vessels. However, water surface object detection algorithms currently have low-precision problems detecting small and irregular objects, and difficult samples are hard to detect in harsh environments. To this end, an improved loss and enhancement feature fusion method is proposed to optimize the YOLOX object detection algorithm for water surface target detection. YOLOX, as an anchorless frame algorithm, has better detection capability for irregular objects. Improved loss improves the focus on difficult samples and treats positive samples asymmetrically based on EIoU loss. The Lite-RFP enhanced feature fusion mechanism enables the network to recursively pass contextual information recursively. It enables the shallow network to integrate deep semantic information better, improving the performance of small target detection and maintaining a lighter network structure. The experimental results show that the improved algorithm based on YOLOXs improves the mAP value by 4.11 percentage points compared to the original YOLOX-l. At the same time, a real-time detection speed of 56.534 FPS can be achieved, and the detection problems caused by different water surface environments are improved.
Attribute-related graph visual dependencies for pedestrian attribute recognition

Pedestrian attribute recognition has attracted extensive attention in surveillance scenarios. Many researchers treat it as a multi-label classification problem. However, they ignore the relations among attributes. For example, long hair and female often appear together at the semantic level, and there exists an overlap between their attribute-related regions at the visual level, e.g., female-related regions including long-hair-related head area and skirt-related areas. There is a correspondence between the semantic level and the visual level. However, existing relation-based methods which explore relations among attributes mostly ignore the correspondence between semantic and visual levels in relations among attributes. Our main contribution lies in proposing a novel end-to-end trainable Attribute-related Graph Visual Dependencies (AGVD) framework that can learn the dependencies of attributes based on extracted attribute-related visual features. Moreover, we propose a Graph Fusion mechanism to guide the attribute-related graph generation and pruning via semantic relations innovatively. We conduct experiments on three large-scale pedestrian attribute datasets. The experiments on PETA, RAP, and PA-100k have demonstrated that the AGVD outperforms the previous state-of-the-art methods.
HYPERTENSIVE EMERGENCIES AND BLOOD PRESSURE CONTROL DURING THE COVID-19 PANDEMIC

Objective: To assess the quality of adequate control of hypertension, during the pandemic and in the year after it in 100 previously treated patients as well as the percentage of hypertensive emergencies (HE). Design and method: From July 2020 to July 2021, we studied 100 patients with hypertension, 60 women and 40 men with an age of 72±8 years, who have previously been diagnosed and treated for hypertension. In the first consultation after the pandemic and at 6 months, they underwent an AMPA and a study with home control to all of them. Subsequently, they were asked at 12 months to perform a new home control. The following parameters were measured: systolic and diastolic blood pressure (TAS, TAD), treatment completion (Ctto), control by family doctor (Cmap), the number of times they have gone to the emergency room for uncontrolled tension. Results between the first measurement and 6 months later were compared. Results: The BP per home control at baseline was 148±6/98±4 compared at 6 months was 135±2/84±3 (p&lt;0.05). The baseline ABPM: 152±4/89±3 and at 6 months were 135±2/79±3 (p&lt;0.05). The completion was assessed using the Morisky-Green method and basally we found that 45% of patients were compliant, while, at one year it was 100% (at 6 months of the study 68%). Regarding the number of patients who were controlled by their MAP in these first months was only 5% while at the end of the study they were 55% (80% telephone). Of these P, 18 have gone to the emergency room for an HE (18%). Conclusions:
Segmentation of leukemia in blood cell image

Leukemia is an abnormal proliferation of white blood cells in bone marrow and blood. The abnormal increase of immature white blood cell count and the decrease of other blood cell counts may be signs of leukemia. Usually, pathologists diagnose leukemia by observing blood smears under a microscope. Bone marrow examination can be recommended to confirm and identify the specific type of leukemia. These routine methods are time-consuming and may be affected by the skills and expertise of the medical staff involved in the diagnostic process. Image processing based methods can be used to analyze microscopic smear images to automatically and quickly detect the incidence rate of leukemia. Image segmentation is one of the important tasks in processing and analyzing medical images. In this paper, we try to review the relevant works in the medical image processing field of blood smear images, focus on the automatic detection of leukemia, and classify and discuss the works in the relevant fields according to the segmentation methods used.
Skin injury data expansion based on improved generative adversarial network

Early detection of skin cancer is the key to increase the chance of successful treatment. Deep learning model has significant effect in automatic classification of skin lesions, but deep learning model requires a large number of data sets, and the availability of annotated images in the medical field is more limited. Data expansion can expand the training data set by transforming the input image. For medical images, the traditional data expansion can only be done by cutting, rotating and other simple operations that do not change the original image features. Therefore, this paper uses generative adjunctive network (GAN) to synthesize medical images of skin lesions for data expansion, so as to obtain more high-quality images with richer content and improve the efficiency of data expansion accordingly. In this paper, the channel focus (ECA) module is added to DCGAN model to improve model performance. Avoid shrinking channel dimensions and reducing the number of references when learning channel attention information. With the addition of a few parameters, the module can obtain obvious performance gain and make the expanded image clearer. The nature of the data allows us to compare directly with the expanded image. Finally, the classification accuracy of the proposed method in ResNet reaches 99.5%, which is 1% and 15.4% higher than that before improvement and without expansion.
SF-VIO: a visual-inertial odometry based on selective feature sample using attention mechanism

Visual-inertial odometry is widely used in robot motion estimation due to its excellent performance in dealing with scale ambiguity. In this paper, an end-to-end monocular visual inertial odometry is proposed, and its innovation lies in its feature selectivity to extract key features to estimate motion, hence we call it SF-VIO. The most significant difference between our proposed SF-VIO and other methods is that SF-VIO uses key features to estimate robot motion more accurately after feature selection. The feature selection is divided into two steps. Firstly, SF-VIO implements the Attention Mechanism in the image process module Locator to filter useless image information. Secondly, SF-VIO implements a soft fusion mechanism in the feature fusion module Fusion, which effectively integrates visual features and inertial features. The experimental results on the KITTI dataset show that the SF-VIO algorithm outperforms similar methods such as DeepVO and ESP-VO in accuracy.
A Study on Writing Education that Criticizes Characters Based on the Acceptance of Gurwon(屈原) by Medieval Literary Person of Nobility

This study analyzed the aspects of how Korean medieval literary person of nobility accepted and evaluated China's character ‘Gulwon(屈原)’ and sought to be applicable to current character criticism writing education. There were three main aspects of character acceptance in the Gulwon(屈原) criticism: first, comparison with old characters, second, citation of others' evaluation, and third, urging the formation of a consensus. This can be said to be the principle of character criticism writing, and based on this, a total of six specific goals of character criticism writing education could be presented at the bottom of the current curriculum 'Reading and Writing' achievement standards.
Ten Years Bibliometric Analysis of New Public Management on Government Research Based on The Scopus Database

The public sector is often condemned for being costly, unproductive, low-quality, and without creativity or innovation in its organizations. In response to many concerns about government agencies, the New Public management was reform to modernize institutional public sector management. This research use Scopus database, RStudio, biblioshiny, and Vosviewer are the bibliometrics tools used. This research aims to put a numerical value on the growing body of literature on public management. Topic evolution, co-authorship, citation networks, and the correlation of keywords are all explored in this analysis. By examining the literature's structure, frequency of publication, journals, and area of research, we can demonstrate its significance. From 2013 to 2022, researchers from 169 different institutions across 13 countries produced 76 individual publications in the field of public administration. The term new public management becomes the most often used keyword for writing about this subject. Articles in the International Review of Administrative Sciences can be found on various topics. A bibliometric assessment advancement in new public management is major predicted outcomes of this paper.
A Study on the Quality Management Method of University Education through the Class Improvement Plan (CQI) Perception Survey: Centering on S Universities

The purpose of this study is to explore the structure and related guidelines and regulations of the CQI, which is being implemented for quality management of education at S University, and to investigate the perception of professors on the CQI. Based on the results, it provides practical help in managing the quality of education. The subjects of the study were 129 professors working at S University, and a questionnaire was constructed after reviewing theories and previous studies. The research questions were, first, to explore the guidelines and regulations related to CQI, second, to analyze the current status of CQI by college and professor position at S University in the first semester of the 2022 academic year, and third, to analyze the differences between the results of the questionnaire and professor characteristics, resulting in the following research findings. It was confirmed that various measures to induce active participation, such as excellent lecture workshops and reflection of teacher performance evaluations, which are used as external motivations to increase professors' participation rates, are needed.
A Study on the Improvement of College Students' Literacy by Using Dasan's Chaucer Reading Method

Currently, college students are evaluated as lacking in proper literacy despite having been taught reading and writing in the Korean language curriculum in middle and high schools. It is very important to cultivate proper literacy for university students who enter the academic world, explore their majors, and are in the preparation stage as members of society. This study utilized Dasan's Chaucer reading method to improve the literacy of college students. The Chaucer reading method is an active reading and writing learning v method that allows readers to expand their thoughts and deep thinking training by having them establish their subjectivity and have a conversation with the author. As a result of practicing by applying the Chaucer reading method, the learners tried to understand the text contents accurately by inducing intrinsic motivation for reading. In addition, while recording the contents that brought about the change of thinking, they were able to expand their consciousness and express their opinions with new meaning construction.
A Study on Children's Literature by Yoon Se-pyeong during the Period of North Korea: Focusing on the legends published in Children's Literature (Joseon Writers' Alliance Publishing Company, 1957)

This study examined the structure and characteristics of Yoon Se-pyeong's two legends, ‘Neungrado and Juamsan’ and ‘Hulryeongdosa,’ while examining the patterns of children's literary activities during the period of North Korea. Yoon Se-pyeong contributed to the expansion of the acceptance of children's literature by leading the “Literature Newspaper” from the period when the controversy over North Korean children's literature heated up. Neungrado and Juamsan, published above Yoon Se-pyeong's participation in children's literature, conveys the interest of legends according to historical records and awakens the dignity of the socialist capital plain. On the other hand, the legend of “Hulryeongdosa,” based on the Imjin War, added a heroic narrative of the Imjin War along with historical records. Through this, it has a sense of purpose to resist the anti-foreign power combined by the people. In the future, I hope that research will continue to enrich the connotation of North Korean children's literature.
ANALISA KEBUTUHAN SISTEM INFORMASI AKADEMIK SEKOLAH DASAR

This study aims to identify the need for an easy-to-implement academic information system in elementary schools so that its use can be optimal for school development. This research is a survey research and interviews conducted with school principals and elementary school teachers in Sumedang District, data were collected through questionnaires and in-depth interviews. One of the applications of management information systems in elementary schools is through an academic system to create elementary schools that are able to provide quality academic services to their stakeholders. However, the application of information systems with the design of academic information systems has not been fully implemented in elementary schools. With this research, it is hoped that it will bring up a recommendation for an elementary school academic information system which can later be used as the basis or basis for designing an academic application that can be used by elementary schools to provide quality services for all stakeholders.
Optimasi Akurasi Klasifikasi Pada Prediksi Smokte Detection dengan Menggunakan Algoritma Adaboost

The problem of fire is a threat to nature and the environment. To deal with fire incidents, a smoke detector was created and developed in combination with an IoT device so that incident data can be recorded properly where the recorded data will be used as a reference for increasing the accuracy of early detection. Increasing the accuracy of smoke detectors so that they can be combined with artificial intelligence technology. This research proposes prediction optimization using the adaboost algorithm combined with the naïve Bayes classification algorithm with a measurement matrix based on accuracy, recall, and precision. The results showed that using the adaboost algorithm could increase the resulting accuracy value with a value of 0.987. If you look at the evaluation from the precision side, it also shows that the use of the adaboost algorithm can increase the precision value with a value of 0.971. But the recall evaluation showed that without boost it got a better score with a value of 0.995
Development of a heavy truck reversing safety system based on pedestrian detection and tracking using binocular vision stitching

The large blind region behind the huge body of a truck is the primary reason for reversing collisions. However, there are few truck reversing auxiliary approaches to avoid reversing accidents. In this paper, we present a novel heavy truck reversing safety approach based on pedestrian detection and tracking using binocular vision stitching. The proposed system contains three primary steps: binocular vision stitching, pedestrian detection and tracking, and heavy truck reversing speed control method. First of all, a binocular camera system based on vision stitching is used to perceive the reversing environment of the heavy truck. Secondly, the framework of YOLOv3 and discriminative correlation filter based tracking method is used to detect and track the pedestrian in real time. The pedestrian feature is extracted with improved MobileNetV2. Finally, a heavy truck reversing speed control method which can automatically control the reversing speed to avoid collisions improves the safety of truck reversing. This system has been tested on a van truck. Experiments demonstrate the viability of the proposed heavy truck reversing safety system.
Importance resampling based on surfels with dynamic lighting

DDGI, GIBS, and other similar dynamic global illumination algorithms solve the irradiance integral by shortening and reusing the light path, obtaining the multi-bounce diffuse reflection effect without recursion. However, these algorithms may perform poorly in scenes with complex lighting. GIBS introduced a simplified path guiding algorithm, which uses lighting information from history, and is limited by the resolution of radiance maps, causing inaccurate sample pdf under dynamic lighting. We propose an importance resampling method based on surfels that is more adaptable to scenes with dynamic lighting and provides more converged diffuse integral results. First, we derive the radiance composition under next event estimation(NEE) framework, then gather visibility maps and calculate the direct irradiance additionally to obtain the target pdf. Finally, we build reservoirs for each ray used for diffuse integral, and generate ray samples by reservoir resampling. We also provide a feasible temporal reuse strategy for single and persistent reservoirs based on surfels. Experimental results demonstrate that our method can generate higher quality samples, improving the accuracy and stability of diffuse global illumination under dynamic lighting.
Cognitive wireless communication network information sharing method based on blockchain technology

Cognitive Wireless Network based on Blockchain Technology (CWN-BT) has been proposed in this paper. Blockchain strategies can provide encrypted communication networks for closed wireless transactions due to their multiple advantages, such as transaction speed, confidentiality, better security, increased performance, etc. Since each device in a blockchain depends on the data stored in a successive unit, it ensures secured transactions and resources in the wireless cognitive network. The outcome shows that blockchain helps to enhance wireless network security in different ways. Besides, integrating blockchain into a cognitive wireless communication network will make optimal capital and efficient network monitoring. The numerical findings demonstrate that the proposed CWN-BT model improves the data security level by 98.3%, the data transmission rate of 94.3%, an efficiency ratio of 96.7%, the response time of 95.6% and reduces the latency rate of 20.2%, delay rate of 17.2% compared to other popular models.