Reviewerly/exHarmony huggingface dataset:
PENGARUH HEALTH LITERACY TERHADAP PENGETAHUAN PENDAMPING MENELAN OBAT PASIEN TUBERKULOSIS DI BENGKULU

Problems: The achievement of the prevalence of pulmonary TB in Bengkulu is not yet optimal, it is still a health problem for the community. Province ranks first with 17,419 cases, while Bengkulu City ranks first with 334 souls. The independence of companions to the treatment of tuberculosis patients is very important in the tuberculosis treatment and care program at home. The aim of the research to analyze the effect of health literacy on drug swallowing assistance (PMO) for tuberculosis patients. Research method: Research design "quasy experiment". The population was all PMO tuberculosis patients who underwent a 1-2 month treatment program at the Nusa Indah Public Health Center, Sukamerindu, Small Bridge, Muara Bangkahulu and Bengkulu City Fish Market as many as 80 people. Samples were taken using purposive sampling technique and were divided into treatment groups (40 people) and control groups (40 people). The independent variable is health literacy. The dependent variable is PMO independence. The instruments in the study were questionnaires and observation sheets. The first step is that the researcher obtains the identity of the respondent and then conducts a pre-test to assess the independence of the PMO before conducting health literacy. After that, according to the agreement, the researcher made a home visit for 1 time in the first month after that the treatment was for 1 week, then in the 3rd month the researcher conducted a post test. Research data processing using SPSS version 25. Previously, the instrument was tested on the modified PMO independence questionnaire with a reliability value of r table 0.220 and a Chronbach r alpha value of 0.764 which means reliable. The value of the r validity test is 0.771 where r is greater than r table so that the keyword is valid. Then analyzed using ANOVA at a significant level of 0.05. Results: showed that there was a significant effect of health literacy on PMO knowledge (p=0.000). Conclusion: knowledge is an important element in the independence of PMO and the success of tuberculosis patients in treatment. Future researchers are expected to conduct research with other variables such as quality of life of pulmonary tuberculosis patients and more samples.Keywords: health literacy, knowledge, tuberculosis
DEVELOPMENT OF DESIGN AND CREATIVE COMPETENCIES IN TEACHING KYRGYZ LITERATURE IN HIGH SCHOOL

The quality of students’ perception of a literary work is based on the analysis of the results of reading activity, while a number of issues are considered in the disclosure of creative competence. The design method develops activities aimed at the collective work of students and the development of their creative activity, thinking and creativity. Literary and creative competence - increases the integrative ability of a person to independently apply elements of different knowledge, skills and methods of action in a particular situation (educational, personal, professional). The role of competencies in their implementation in the learning process, the practical problems of teaching in ethno-pedagogical context is explained on the basis of the latest subject standards.The quality of students’ perception of a literary work is based on an analysis of the results of reading activity, while considering a number of issues in the disclosure of creative competence.
Cross Age Face Generator: A Generative Adversarial Networks (GANs) Based Approach

The interpretation of various features in the world can be carried out with the help of computer vision. Cross age face generation is one of the emerging field in computer vision with great significance to solve different identity related problems. Different deep learning approaches aids computers to yield effective results in the area of computer vision. This work focuses on generation of person’s face at different age in individual’s lifespan. Cross age face generation helps in solving various problems related to identity of an individual, such as the recognition of a missing person, identification of criminals, etc. Preservation of the identity of a person is very important and challenging task while creating images at different ages. This work proposes a system using Conditional Generative Adversarial Networks and Convolutional AutoEncoder model. The Convolutional AutoEncoder model is used for identity preservation. Conditional Generative Adversarial Networks are used for generating images of a person in different ages. The system is evaluated on the IMDB-Wikicleaned dataset with 120 K images and Cross-Age Celebrity Dataset with 163 K images. Various tests are performed to quantify the results. The proposed solution gives better results in terms of identity preservation, age estimation error and frechet inception distance parameters. Identity preservation with the proposed system. is 96.82% with 2.75 as age estimation error.
Regional Model of Foreign Students' Professional Socialization Based on the Dialogue of Cultures

The purpose of the study is to substantiate the model of foreign students' professional socialization taking into account social, cultural, material, educational, educational and professional factors at the regional level. The article describes the regional model of foreign students' professional socialization presented in three aspects axiological, theoretical and normative. The scientific novelty of the study is to determine the logic of pedagogical activity for the organization of foreign students professional socialization in a regional university. As a result of the research, the stages of foreign students professional socialization in the educational and socio-cultural space of the region, as well as the directions of educational and educational-professional activities that contribute to the inclusion of foreign students in the professional environment, are identified.
Data Center Monitoring Based on Equipment Clustering and Data Fusion

The data center is the infrastructure of the modern information society, it is vitally important to continuous monitor the data center's equipment for the stable operation. Real-time performance and low resource consumption are required for monitoring by larger and larger data centers. To meet this demand, this paper proposes a data center monitoring method based on equipment clustering and data fusion. Optimize cluster monitoring of data center equipment based on network management costs and equipment running characteristics, then data collection and data fusion are performed by the selected cluster head. This saves time and network bandwidth resources for the entire monitoring solution. The simulation experiment results show the effectiveness of the proposed method in this paper.
VC.LAB (Virtual Chemistry Laboratory): DESIGN AND BUILD AN ANDROID-BASED ACID-BASE TITRATION GAME APPLICATION AS A CHEMISTRY INTERACTIVE LEARNING MEDIUM IN HIGH SCHOOL

Acid-base titration material is difficult for most students because it is abstract. According to Sheppard [17], students describe the neutralization process as the physical mixing of acids and bases that do not produce products and do not have a reaction equation. From these problems, researchers designed the VC.LAB (Virtual Chemistry Laboratory) application: Design and Build an Android-Based Acid-Base Titration Game Application as a Chemistry Interactive Learning Medium in High School. This application can make it easier for students to understand the acid-base titration material. In addition, the existence of various cool features available can attract students to learn it. The method used in this study is the prototype method. This method is carried out in stages, namely by developing a simple prototype first and then being developed from time to time until the software is finished being developed [16]. The results of the design of making this game are carried out by creating a model that explains the material and practical simulation activities in the form of a special smartphone application to make it easier for users to carry out individual learning virtually.
Delay-aware evolutionary optimization of digital circuits

In the recent years, machine learning techniques have successfully been applied in various fields of digital circuit development including logic synthesis. One of the approaches is evolutionary resynthesis. It is based on the idea of iterative local optimization of parts of the original circuit. The main advantage is the possibility to mitigate various scalability issues connected with the usage of evolutionary algorithms. However, success of this method depends heavily on the ability to identify suitable candidates for local optimization. Despite that, it has been shown that the local optimization produces significantly compact solutions compared to the evolutionary optimization performed at the level of the original circuit. In this paper, we analyze how the local optimization affects the delay of the circuit and propose a modified approach to the optimization of digital circuits. Compared to the existing techniques, the proposed method allows the presence of the non-uniform delay at the inputs of the circuits selected for the local optimization. This modification enables to maintain the delay of the optimized circuit at a reasonable level without a significant overhead. The evaluation done on a set of non-trivial highly optimized benchmark circuits representing various real-world circuits demonstrated that our method is able to remove a significant number of gates while preserving the delay at the original value.
Manipulation Planning for an Mobile Manipulator with Positioning Error

We proposed the mobile manipulation capability map(CM) and pose planning method based on multivariate Gaussian distribution in this paper. According to the theory of manipulator workspace operation capability map, we design a planning and control method for separating the mobile platform from the manipulator, to achieve high efficiency and high-success rate grasping. Solve the grasping problem of uncertain positioning of a mobile manipulator in chaotic and changing environments. The experimental results show that the filtering method based on the confidence interval of the multi-dimensional Gaussian distribution can greatly shorten the calculation time and ensure a reasonable calculation result error when the parameters are reasonably selected. Through the object grasping experiment in the actual environment, it is verified that the operation capability map proposed in this paper can improve the success rate of grasping in the cluttered environment.
Server-Side Action: Node and NPM

Welcome to the book! I hope you’ve got a comfy chair under you, a tasty drink on the table next to you and perhaps a light snack (may I suggest some dark chocolate biscotti?), and your brain ready to soak up a ton of knowledge on modern web development, ’cause that’s what the show is all about and the curtains are about to be drawn!
A Hybrid Image Enhancement Framework for Underwater 3D Reconstruction

Underwater images captured by optical cameras are usually degraded due to the absorption and scattering effects. This kind of light attenuation often leads to deteriorated visual image quality. In this paper, a hybrid underwater image enhancement framework is proposed, which focuses on correcting color distortion and removing haze for degraded underwater images. The proposed framework consists of color restoration and haze removal. We first restore the color distortion based on combining underwater white balance and contrast enhancement, and then take a further step to remove the haze and enhance the details using the haze-line technique. Experimental results show that the proposed framework outperforms several state-of-the-art methods on two publicly available datasets. Additionally, we conduct the real-world underwater 3D reconstruction using the processed underwater images. The results show that the 3D reconstruction result using the processed images by our proposed framework is able to display more three-dimensional details.
IoMT Enabled Prototype Edge Computing Healthcare System for Isolated Patients

Continuous patient care and the use of multiple medical machines are two challenges facing today's healthcare sector in terms of patient’s healthcare. During the pandemic situation, many people isolated in their home, such as covid-19 positive patients, elderly people living away from their families, bedridden patients, etc., need regular health checks and controls, but during this pandemic is lacking. Recent advances in the Internet of Medical Things (IoMT) has been able to give good results in collecting health data of patients at home environment. Deep learning (DL) applications can able to run on edge nodes, it locally processes, computes and analyzes data from IOMT devices to make inferences on patient health information. This ensures the privacy and security of the patient's physiological information and also and allows patient health information to remain at the patient's side. Send all this information to healthcare professionals and relatives of patients. This framework will provide safety for isolated patients and a health support systemas a whole.
An Empirical Study on Wrongly Written or Mispronounced Characters in Hanyu Shuiping Kaoshi Compositions from the Perspective of Readability Research

In the study of Hanyu Shuiping Kaoshi (HSK) composition, there is a lack of research on the quantitative form of wrongly written or mispronounced character (WWMC). This paper chooses the readability perspective to quantitatively study the influence of WWMCs in HSK compositions. Through the construction of HSK composition readability corpus, they are composed of 5498 HSK compositions carefully rated by experts. Through quantitative research, it is found that the lower the grade is, the more the number of compositions without WWMCs; With the increase of text readability, the proportion of WWMCs in the text decreases gradually, while the number of articles with WWMCs increases. With the help of the formula of HSK composition readability grade constructed by multiple linear regression, it is found that the WWMC's weight is -0.0250014, which has little effect on the readability grade of HSK composition. Although the WWMC feature does not play an important role in the readability formula, we can't ignore it in the reading and writing process of HSK compositions.
An Image Feature Mapping Model for Continuous Longitudinal Data Completion and Generation of Synthetic Patient Trajectories

Longitudinal medical image data are becoming increasingly important for monitoring patient progression. However, such datasets are often small, incomplete, or have inconsistencies between observations. Thus, we propose a generative model that not only produces continuous trajectories of fully synthetic patient images, but also imputes missing data in existing trajectories, by estimating realistic progression over time. Our generative model is trained directly on features extracted from images and maps these into a linear trajectory in a Euclidean space defined with velocity, delay, and spatial parameters that are learned directly from the data. We evaluated our method on toy data and face images, both showing simulated trajectories mimicking progression in longitudinal data. Furthermore, we applied the proposed model on a complex neuroimaging database extracted from ADNI. All datasets show that the model is able to learn overall (disease) progression over time.
Sentiment Analysis of Opinions over Time Toward Saudi Women’s Sports

The examining and promoting women’s health is essential to the general well-being of society. As sports activities are important for maintaining physical, psychological, and social health, female participation in sports is one of the significant factors in achieving the goals of Saudi Arabia’s Vision 2030. In this paper, sentiment analysis was used to compare the society opinions pre and post permit-ting Saudi women’s sports, between 2017 and 2021. To identify the sentiment of a given tweet, a lexicon for the Saudi dialect was developed. In total, 12,000 tweets were collected and prepared. After data preparation, the tweets were reduced to 1,999 across all selected hashtags for this the initial study. We used four different hashtags related to Saudi women’s sports, namely, (#Officially_female_sports_in_schools) represented as a Pre-Hashtag, whereas (#Women_Sport) and (#Female_Sport) as Pre and Post Hashtags, and (#Tahani _Alqahtani) as Post-Hashtag. The data in each hashtag were classified as positive, negative, or neutral. To build the sentiment classifier model, A Support Vector Machine (SVM) classifier was applied. The highest average accuracy was for the Pre-Hashtag with a score of 91%, followed by the Pre and Post Hashtag with a score of 85%. Finally, the Post Hashtag has the lowest score of 72%. The results show that 81% of the sample are positive. Accordingly, women have been becoming more motivated to engage in sports participation, as well as Saudi society is being more encouraging.
Approach to Sector-Specific Cybersecurity Schemes: Key Elements and Security Problem Definition

This paper documents the approach to define cybersecurity certification schemes as candidate methods for sector cybersecurity product certification as part of the EU Cybersecurity Certification Framework being prepared by ENISA. Indeed, it is a very recent area of research within the EU landscape. Our work was undertaken within H2020 ECHO project ( www.echonetwork.eu ) and it is reported in detail in its deliverables. This document is completing the research reported in our previous publication, which had complete references to the existing state of the art about the certification topic in EU. Our work started with the identification of the sector-specific needs to be addressed for specific critical sectors. The mandatory Key Elements of a certification scheme, as described in the EU Cybersecurity Act, have been customized and the sector specific analysis allowed to define a Security Problem Definition baseline to be used to quickly draft a Protection Profile of an asset category of the considered sectors. Security needs have been identified using also the sectoral risk assessment guidelines provided by ENISA for certification purposes. It has also been developed an inter sector risk scenario to highlight the most important security needs to mitigate cross-sector security failures. Finally, Cyber Range technologies have been leveraged for the Conformity Assessment activities of two Maritime and a Healthcare product prototypes, for which the substantial assurance level certification has been simulated for the sake of validation of our approach.
Analysis of Cisco and huawei computer network simulation software shells

The paper is devoted to comparison of computer network simulation and design software for two large companies in the info-communication sphere, Cisco company with Cisco Packet Tracer software and Huawei company with eNSP software. The article will tell and show the difference between these two programs from the user's point of view, the pros and cons, the convenience of setting up, the possibilities and methods of using both systems in teaching students.
Interactive Proofs for Rounding Arithmetic

Interactive proofs are a type of verifiable computing that secures the integrity of computations. The need is increasing as more computations are outsourced to untrusted parties, e.g., cloud computing platforms. Existing techniques, however, have mainly focused on exact computations, but not approximate arithmetic (e.g., floating-point or fixed-point arithmetic). This makes it hard to apply them to certain types of computations (e.g., machine learning or financial applications) that inherently require approximate arithmetic. In this paper, we present an efficient interactive proof system for arithmetic circuits with rounding gates that can represent approximate arithmetic. The main idea is to reduce the rounding gate into a small sub-circuit without rounding, and reuse the machinery of the Goldwasser, Kalai, and Rothblum’s protocol (also known as the GKR protocol) and its recent refinements. Specifically, we shift the algebraic structure from a field to a ring to better deal with the notion of “digits”, and generalize the original GKR protocol over a ring. Then, we reduce the rounding operation to a low-degree polynomial over a ring, and develop a novel, optimal circuit construction of an arbitrary polynomial to transform the rounding polynomial to an optimal circuit representation. Moreover, further optimization on the proof generation cost for rounding is presented employing a Galois ring. Our experimental results show the efficiency of our protocol for approximate arithmetic, e.g., the implementation performed two orders of magnitude better than the existing system for a nested 128 by 128 matrix multiplication of depth 12 on the 16-bit fixed-point arithmetic.
The Implications of ‘Soft’ Requirements

A new focus for RE is investigated as ‘soft’ requirements which extends non-functional requirements/soft goals with a collection of people-oriented phenomena: values, motivations, emotions, and other socio-political issues that may influence the requirements specification. The convergence of RE with user experience (HCI) and technology acceptance from the information systems literature is reviewed from a temporal perspective: pre-use, through initial to longer-term use. A taxonomy of soft requirements is proposed that extends non-functional requirements and soft-goal concepts to direct attention towards user characteristics and beliefs that may have implications for functional as well as system support requirements, such as training, help, explanation and trust in software. A timeline model of soft and hard (functional) requirements is presented with a focus on customization, adaptation and other soft requirements to, improve product acceptance and persuade users to take appropriate action. The paper concludes with a research agenda for soft requirements to improve the probability of system acceptance and the effectiveness of applications that aim to influence people’s decisions and behaviour in internet apps and other discretionary-use applications.
BERT-LSTM for Fake News Detection on Facebook Using SVD

Millions of individuals throughout the world consider several social networking. The Network sites/apps, such as WhatsApp, Instagram, Twitter, and Facebook, are essential information sources due to their vast user bases. Social networking platforms provide hands-on connectivity with others and ease of use to the down-market that radio and television broadcasters are unable to provide - multi-way communication. By modifying the original content or a full parody presented as fact, negative influencers can take advantage of the freedom provided by social networks and wilfully spread any misinformation. The spread of false information can happen in the blink of an eye, potentially deceiving the general public and spreading to other communities. Despite this awareness, social media platforms continue to spread dubious material. Even though many of them are still passed off as fact, hoaxes continue to be a major problem. To get around this problem, this study proposed a potent technique called BERT-LSTM to identify false information on Facebook. The accuracy of the suggested BERT-LSTM approach is 0.828.