Reviewerly/exHarmony huggingface dataset:
The Use of Cohesive Devices in Descriptive Text by English Training Participants at PST”

Today, many media to large companies are starting to invest English skills in their employees, including Pura Smart Technology (PST). This research aims to find out the use of cohesive lexical devices, the use of cohesive grammatical devices, the correctness of cohesive device uses, and the correlation of cohesive device use and descriptive text writing skills of the participants. The researcher took eleven employees as the participants. The researcher analyzed the data with Halliday &amp; Hasan's theory, Coh-Metrix 3.0, and Spearman Correlation. The results were repetition with 54.92%, synonym with 6.81%, hyponym with 33.71%, meronym with 3.40%, and collocation with 1.14%. In terms of grammatical cohesion, the researcher found a reference with 29.54% and conjunction with 69.31%. The result also showed that the participants' cohesive device use excellence was 39.65%, with a very strong correlation, (rs) = 0.95. The researchers found that the English training participants had low mastery of cohesive device use quality. The researchers also found that the participants' cohesive devices and writing skills had a strong, significant, and immense correlation. The researchers suggest that future researchers upgrade the study's object and involve participants from different and complex backgrounds. From the pedagogical perspective, cohesive device uses are important to create a good image and judge an excellent educational background between the writer and the reader while understanding the meaning. Thus, writers can produce texts without any hoax information nowadays. Practically, these results help people use text appropriately in formal and non-formal settings.
Adapting Reinforcement Learning Treatment Policies Using Limited Data to Personalize Critical Care

Reinforcement learning (RL) demonstrates promise for developing effective treatment policies in critical care settings. However, existing RL methods often require large and comprehensive patient data sets and do not readily lend themselves to settings in which certain patient subpopulations are severely underrepresented. In this study, we develop a new method, noisy Bayesian policy updates (NBPU), for selecting high-performing reinforcement learning–based treatment policies for underrepresented patient subpopulations using limited observations. Our method uses variational inference to learn a probability distribution over treatment policies based on a reference patient subpopulation for which sufficient data are available. It then exploits limited data from an underrepresented patient subpopulation to update this probability distribution and adapts its recommendations to this subpopulation. We demonstrate our method’s utility on a data set of ICU patients receiving intravenous blood anticoagulant medication. Our results show that NBPU outperforms state-of-the-art methods in terms of both selecting effective treatment policies for patients with nontypical clinical characteristics and predicting the corresponding policies’ performance for these patients.
Development and modeling of combined components of the information environment

The relevance of the study is due to the problem of forming the concept of the information environment from the point of view of management. The theoretical foundations for the construction and practical application of the information environment of educational institutions are given, a model of an individual educational trajectory is designed, and the methodology for modeling the information environment of educational institutions based on systems theory is described. This is important, since the information environment helps to improve the efficiency and quality of the educational process through the implementation of information technology capabilities. An individual educational trajectory is considered as a process of making a decision by a student based on a system of individual values and personal meanings; contains a qualification model of a specialist in the world of professions and opportunities for the implementation of vital plans. The qualification model of a specialist includes a qualification portrait of a specialist, regulatory requirements – a set of linguistic assessments and a set of numerical assessments that meet the requirements of employers. As a result of the study, a systematic approach was applied, which makes it possible to determine the limitations of the system's functioning, the procedure for planning activities and the system for stimulating elements of the organization. A detailed review of the main models of interaction between the elements of the organization is made: bureaucratic, democratic participation and parity compromise. The bureaucratic model is characterized by a clear hierarchy of rules and decisions, strict regulation of job descriptions. The model of democratic participation is focused on the development of creative abilities of an employee, when, performing a particular function, he expects to achieve personal goals, directing his efforts to achieve the goals of the organization. The results of the study are aimed at an effective choice of interaction models depending on the main elements of systemic activities in educational institutions related to improving the quality of work and education
Optimization of the Index System of the Sports Social Organization Development Ability Relying on Image Optimization, Recognition, and Simulation

Sports social organizations refer to nonprofit, nongovernmental, and group organizations that achieve specific service goals and common aspirations and spontaneously form various sports and fitness activities in accordance with the rules. This article aims to study how to optimize the index system of the capacity developments by relying on image optimization and recognition simulation. This article puts forward the relevant theoretical knowledge of image optimization and recognition and capacity developments and proposes two algorithms based on image optimization and recognition. In 2019, the percentage of growth of sports social organizations rose from 6.7% to the final 27%; in 2020, the growth rate of sports social organizations is getting higher and higher, from 9% at the beginning to 34% at the end. It can be seen that people’s love for sports activities is getting higher and higher, and the state is paying more and more attention to sports social organizations. The factors that affect the development ability of sports social organizations include the quality, education, and management capabilities of the members of the organization, and the country and the government should also take corresponding measures. The improvement of the development ability of sports social organizations is of great significance to the development of sports social organizations.
Analysis of Students' Sports Exercise Behavior and Health Education Strategy Using Visual Perception–Motion Recognition Algorithm

This study aims to explore the future development path of the college health education and health education's impact on students' sports exercise. Specifically, artificial intelligence (AI) algorithm is combined with intelligent robotics technology to acquire and analyze students' sports exercise behaviors. As a result, a new development model is formulated for college health education. First, it explores students' sports exercise and health education situation in Chinese higher institutions and uncovers the underlying problems. Then it puts forward the corresponding modification suggestions. Second, the AI algorithm and the Kinect sensor-mounted intelligent robot capture the human skeleton features to obtain smooth skeleton joint points data. At the same time, a visual perception human motion recognition (HMR) algorithm is established based on the Hidden Markov Model (HMM). Afterward, the proposed HMM-based HMR algorithm is used to recognize students' sports exercise motions by analyzing human motion skeleton images. The experimental outcomes suggest that the maximum reconstruction error of the HMR algorithm is 10 mm, and the compression ratio is between 5 and 10; the HMR rate is more than 96%. Compared with similar algorithms, the proposed visual perception HMR algorithm depends less on the number of training samples. It can achieve a high recognition rate given only a relatively few samples. Therefore, the proposed (AI + intelligent robot)-enabled HMM-based HMR algorithm can effectively identify the behavior characteristics of students in sports exercise. This study can provide a reference for exploring college students' health education development path.
Evaluation of Labuhanbatu Regency Government Website based on Performance Variables

Since the establishment of the Labuhanbatu Regency Government website, no efforts have been made to evaluate its performance. This is important to do considering that the Labuhanbatu Regency Government website is a medium of public information needed by the community. The website is also an indicator of the quality assessment of the government as an effort to improve services to the community. This study aims to analyze the website of the Labuhanbatu Regency Government using the evaluation method. The evaluation process is carried out with the help of the GTmetrix measuring tool by measuring website performance through performance metrics such as: First Contentful Paint (FCP), Speed ​​Index (SI), Large Contenful Paint (LCP), Time to Interactive (TTI), Total Blocking Time ( TBT), and Cumulative Layout Shift (CLS). The test results show that the Grade of the website is at level D, with 61% Performance, and 78% Structure. The test results on performance metrics at two test times show a downward trend, where the best FCP is achieved within 2100 milliseconds, SI 6500 milliseconds, LCP 4300 milliseconds, TTI 2100 milliseconds, and TBT 0 milliseconds, although the FCP, SI, and LCP metrics are still in the non-recommended category. The results showed that the GTmetrix measuring instrument could be used to evaluate the website of the Labuhanbatu Regency Government with comprehensive evaluation results. The thing that must be evaluated is the “Reduce initial server response time” aspect which makes the website's initial page load slow. As a solution to these problems, the Labuhanbatu Regency website manager must make improvements to the program code and database queries, implement caching on the server, and perform periodic upgrades to Server hardware, CPU, and Memory.
A three-dimensional Josephson parametric amplifier

Abstract A Josephson parametric amplifier (JPA) is executed in a three-dimensional (3D) microwave cavity by coupling it to a superconducting quantum interference device (SQUID) that is embedded in a two-dimensional resonator. The JPA is activated in a three-wave mixing configuration by injecting ac magnetic flux, at twice the 3D cavity frequency, into the SQUID. An 8.3 GHz cavity is measured in a non-degenerate phase-insensitive configuration which yields gains in excess of 40 dB, where a 20 dB gain results in an operational bandwidth of 0.4 MHz, a 1 dB compression point of −115 dBm with half a quantum of added noise.
Implementation of PhET simulation with discovery learning model to improve understanding of dynamic electricity concepts

This study describes the implementation of learning using PhET simulation with a discovery learning model, the effect on increasing understanding of dynamic electricity concepts in science subjects, and student responses after participating in the lesson. This type of research uses a pre-experiment in the form of a one-group pretest-posttest design. The research was conducted on class IX IJHS students at Al-Islahiyah junior high school. Collecting data using the test method, questionnaire method, and observation method, with instruments in the form of test sheets, student response questionnaire sheets, and learning implementation observation sheets. The results of this study are 1) the implementation of learning using PhET simulation media with discovery learning model at first to fourth meetings is categorized as very good with percentages of 95.05%, 94.78%, 99.47%, and 100%. 2) The average n-gain value of all sub-concepts in class IX-A and class IX-B of 0.68 and 0.66, respectively, in the medium category indicate an increase in concept understanding. 3) The students show a positive response to the implementation of learning with PhET simulation with a discovery learning model with an average percentage of 74.28%. Thus, PhET simulation learning with a discovery learning model can improve students understanding of concepts.
Dynamic Range Compression Self-Adaption Method for SAR Image Based on Deep Learning

The visualization of synthetic aperture radar (SAR) images involves the mapping of high dynamic range (HDR) amplitude values to gray levels for lower dynamic range (LDR) display devices. This dynamic range compression process determines the visibility of details in the displayed result. It therefore plays a critical role in remote sensing applications. There are some problems with existing methods, such as poor adaptability, detail loss, imbalance between contrast improvement and noise suppression. To effectively obtain the images suitable for human observation and subsequent interpretation, we introduce a novel self-adaptive SAR image dynamic range compression method based on deep learning. Its designed objective is to present the maximal amount of information content in the displayed image and eliminate the contradiction between contrast and noise. Considering that, we propose a decomposition-fusion framework. The input SAR image is rescaled to a certain size and then put into a bilateral feature enhancement module to remap high and low frequency features to realize noise suppression and contrast enhancement. Based on the bilateral features, a feature fusion module is employed for feature integration and optimization to achieve a more precise reconstruction result. Visual and quantitative experiments on synthesized and real-world SAR images show that the proposed method notably realizes visualization which exceeds several statistical methods. It has good adaptability and can improve SAR images’ contrast for interpretation.
A novel enhanced algorithm for efficient human tracking

&lt;p&gt;&lt;span&gt;Tracking moving objects has been an issue in recent years of computer vision and image processing and human tracking makes it a more significant challenge. This category has various aspects and wide applications, such as autonomous deriving, human-robot interactions, and human movement analysis. One of the issues that have always made tracking algorithms difficult is their interaction with goal recognition methods, the mutable appearance of variable aims, and simultaneous tracking of multiple goals. In this paper, a method with high efficiency and higher accuracy was compared to the previous methods for tracking just objects using imaging with the fixed camera is introduced. The proposed algorithm operates in four steps in such a way as to identify a fixed background and remove noise from that. This background is used to subtract from movable objects. After that, while the image is being filtered, the shadows and noises of the filmed image are removed, and finally, using the bubble routing method, the mobile object will be separated and tracked. Experimental results indicated that the proposed model for detecting and tracking mobile objects works well and can improve the motion and trajectory estimation of objects in terms of speed and accuracy to a desirable level up to in terms of accuracy compared with previous methods.&lt;/span&gt;&lt;/p&gt;
Engineered Josephson Parametric Amplifier in quantum two-modes squeezed radar

Josephson parametric amplifier (JPA) engineering is a significant component in the quantum two-mode squeezed radar (QTMS), to enhance, for instance, radar performance and the detection range or bandwidth. In this study, we apply quantum theory to a research domain focusing the design of QTMS radar. We apply engineered JPA (EJPA) to enhance the performance of a quantum radar (QR). We investigate the correlation between the signal and idler using and we propose strategies for maintaining entanglement at room temperature. We define the quantum signal-to-noise ratio (SNR) and detection range equations of the QTMS radar. The engineering JPA, leads to a remarkable improvement of the quantum radar performance, i.e. a large enhancement in quantum SNR of about 6 dB, a substantial improvement in the probability of detection through far fewer channels, and a huge increase in QTMS radar range, from half a meter in the conventional JPA to 482 m in the current study.
PSCNET: EFFICIENT RGB-D SEMANTIC SEGMENTATION PARALLEL NETWORK BASED ON SPATIAL AND CHANNEL ATTENTION

Abstract. RGB-D semantic segmentation algorithm is a key technology for indoor semantic map construction. The traditional RGB-D semantic segmentation network, which always suffer from redundant parameters and modules. In this paper, an improved semantic segmentation network PSCNet is designed to reduce redundant parameters and make models easier to implement. Based on the DeepLabv3+ framework, we have improved the original model in three ways, including attention module selection, backbone simplification, and Atrous Spatial Pyramid Pooling (ASPP) module simplification. The research proposes three improvement ideas to address these issues: using spatial-channel co-attention, removing the last module from Depth Backbone, and redesigning WW-ASPP by Depthwise convolution. Compared to Deeplabv3+, the proposed PSCNet are approximately the same number of parameters, but with a 5% improvement in MIoU. Meanwhile, PSCNet achieved inference at a rate of 47 FPS on RTX3090, which is much faster than state-of-the-art semantic segmentation networks.
ONTOLOGICAL MODEL OF A DATABASE OF INFORMATIONEDUCATIONAL PORTAL OF THE UNIVERSITY

According to the state program “Informational Society 2030”, it is planned todevelop education on the basis of new progressive concepts of introducing the latestinformation technologies and scientific and methodological achievements into the educationalprocess. In order to achieve goals, it is necessary to implement mobilization and effective use ofstaff and property and technical resources of the university. It is possible if there is purposefulmodeling of the information and educational portal of the university.The choice and design of teaching technology are primarily determined by the type ofstudents’ competencies and the characteristics of the planned learning outcomes for eachlevel of competence (knowledge, skills, and experience). Constant improvement of EP contentand educational technologies as a key factor of education services quality is a vital demand.The article considers the issues of the concept of ontology, IDEF-technologies and, basedon the ontological model proposed in the article, the architecture of the database of theinformation and educational portal of the university is developed and classes and propertiesfor the implementation of this model are defined, and a functional model of the university inIDEF0 is developed, which covers all types of university activities, integrates all informationflows and forms a single information space.
Django , The Python Web Framework

Academic technological implementations have aided students and professionals in a variety of ways. The availability of all educational resources is extremely beneficial to students in their academic endeavors. The paper depicts a website concept that allows students to obtain class notes, previous year question papers, and syllabuses, as well as sell their old books, all from the same digital platform. The function of software engineering in project development is also discussed in the article. The project is built on the Django Framework, with the backend written in Python, Jinja2, and Mysql.HTML, CSS, and Java make up the frontend. In the development process, appropriate SDLC Model and Testing procedures were employed. Each step of the SDLC Model (Iterative Model) is clearly discussed, with accompanying ER Diagrams and Flow Charts. The project that was created is extremely efficient, user-friendly, and straightforward. Software Engineering, Django, HTML, CSS, SDLC, Python, Testing are all terms that can be found in the index.
AN EXPLORATION ON STUDENTS’ PUBLIC SPEAKING ANXIETY: STIFIn PERSPECTIVE

The purpose of this research is to examine the level of public speaking anxiety among students based on the differences in human intelligence machine division: STIFIn (Sensing, Thinking, Intuiting, Feeling, and Insting). This study involved 51 Management students who took the English for Specific course on their semester credits. This study employs a case study that is qualitative in nature. The data was gathered using a semi-structured approach. The data collected was then processed by using two methods: statistical calculations for quantitative data related to the results of filling out 17 Public Speaking Anxiety Scale (FLAS) questionnaires by students concerning their level of anxiety when performing a public speaking and constructive analysis for qualitative data related to the results of comparing respondents' responses. The results shows that students in the Insting personality have a higher average of public speaking anxiety: 51.60. Students who are Sensing have an average anxiety level of 49.25, whereas students who are Thinking have an anxiety level of 47.50. In comparison, Intuiting students have an average anxiety level of 48.66, while Feeling students have an average anxiety level of 50.17. Consider the implications, some suggestions are discussed to overcome the challenges.
Decision-making Improvement in Dynamic Environments Using Machine Learning

The proliferation of ubiquitous computing with smartphones makes context models and information extremely rich and dynamic on account of highly dynamic environments. However, defining rules at design time may impair their efficiency and the decision-making process at runtime. Therefore, it is important to address decision-making problems leveraged by dynamic environments and context-model evolution. In this sense, a solution that could emerge is the continuous rule knowledge base evolution at runtime. In this paper, we propose a decision adaptation component that relies on generating new rules due to changes occurring around users at runtime. This component aims to support decision-making in dynamic environments and to alleviate the human effort needed to infer new rules. A case study was conducted to illustrate the implementation of the proposed component for the rule knowledge database enrichment and the decision-making improvement. Moreover, an experimental evaluation is provided to assess the effectiveness of the proposed component. The results show that this component exhibits better effectiveness than other well-known algorithms and state-of-the-art approaches. Doi: 10.28991/HEF-2022-03-01-04 Full Text: PDF