Reviewerly/exHarmony huggingface dataset:
SIDE EFFECTS OF HEMODIALYSIS IN CHRONIC KIDNEY DISEASE (CKD) PATIENTS WITH ALOE VERA GEL

Patients with chronic kidney disease or Chronic Kidney Disease (CKD) really need action that can help replace the role of the kidneys in the body. One of the actions that can be taken is hemodialysis therapy, where this therapy is able to replace kidney function by filtering blood using a special machine at an unaffordable cost. However, hemodialysis also has side effects, one of which is skin problems such as xerosis, dry skin or xerosis, which is a skin disorder caused by loss of lipids and natural moisturizing factors in the stratum corneum. Problems that arise as a result of hemodialysis actions, of course, also require treatment such as providing moisturizers for xerotic skin. The method used in this research is a literature review study. Database searches via Pubmed, Science Direct, Elsevier, Jama, and Google Scholar. Based on searches and case study results from 5 articles, the effectiveness of aloe vera gel has been proven to overcome skin problems through research. It has been proven that aloe vera gel can moisturize the skin because aloe vera gel contains water with a water content of 99% and, besides that, it also contains glucomannans, amino acids, lipids, sterols and vitamins. Aloe vera gel is indeed effective for treating skin problems such as dry skin without having to spend a lot of money, so it can minimize cost overruns in patients undergoing hemodialysis therapy on a regular or even long-term basis. Keywords: Chronic Kidney Failure; Hemodialysis; Xerosis; Aloe Vera Gel
Research on 3D human pose estimation technique based on multi-view information fusion

Traditional 3D human pose estimation algorithms are often influenced by the accuracy of 2D keypoint detection and camera calibration, and they struggle to handle low-resolution and occluded scenes. To address these challenges, we propose a multi-view 3D human pose estimation method that incorporates prior information. At the 2D pose extraction stage, we design a bottom-up detection network called HRPifPaf to achieve accurate human pose detection in low-resolution scenarios. It first constructs a high-resolution feature extraction module that combines features from different scales. Then, a joint prediction and association module combines confidence scores and scale factors with vector directions pointing to the main body parts of the joints. We also utilize a Kalman filter to optimize the final detection results. At the 3D pose synthesis stage, we propose a multi-camera parameter joint optimization calibration method that leverages prior information of the human skeleton to address challenges such as body occlusion and inaccurate camera intrinsic and extrinsic parameters, designing a comprehensive cost function based on reprojection error, and human body geometry constraints.
Qualitative perception of a deep learning model in connection with malaria disease classification

The most troublesome sickness for people of all ages is malaria, we may infer from this. Even if it is uncommon in areas with a moderate environment, malaria is nevertheless common in countries that are tropical or subtropical. Therefore, this research concludes that a microscopic red blood cell picture may be classed as an uninfected cell or parameterized cell, therefore providing a judgment as to whether the person was afflicted or unaffected by the malaria sickness. The key message is to demonstrate how a separable CNN performs better than a conventional convolution neural network that was built.
Comparison of Various Algorithms for Handwritten Character Recognition of Indian Languages

Abstract: In this paper, we present a comparison of various pre-processor, feature extraction methods and algorithms for handwritten character recognition of various Indian languages. Comparison of classifier, feature set and accuracy of offline handwritten character recognition of Gujarati, Devanagari, Gurmukhi, Kannada, Malayalam, Bangla and Hindi Indian languages. Comparison of classifier, feature set and accuracy of online handwritten character recognition of Assamese, Tamil, Devanagari, Malayalam, Gurmukhi, and Bangla Indian languages. Indian language wise best performance of each language is compared for both offline and online handwritten character recognition systems.
Enhancing the Reading Capabilities of Students with Learning Disabilities: Remedial Classroom Teaching &amp; Learning Skills

For a receptive human being, learning is a ceaseless process. However, learning methods at school are relatively vigorous. There can be more than one aspect which creates an impediment to the learning processes. Therefore, coping with learning difficulties can be extremely stressful for a student. The methodology that the present paper follows is an analysis of various books on dyslexia with an intention to offer incisive insights which can be adopted by teachers in the classroom for managing the educational needs of a disabled student. After a general discussion on the term ‘learning disabilities,’ the paper attempts to understand dyslexia, its symptoms and some techniques which can be adopted by teachers in their remedial classes in order to enhance the reading skills in students with such disabilities. In simple language, the term ‘Dyslexia’ can be understood as a difficulty in reading. A dyslexic student is commonly mistaken as a slow learner. However, the paper attempts to discuss that dyslexic students are not slow learners and by implementing certain strategies and remedial programmes, a dyslexic student can effectively excel in academics. The paper circumspectly knits together ideas and techniques for building the reading skills of children with difficulties and provides pragmatic strategies for teachers as well as parents to help students in developing their reading skills. The conclusion of this paper offers some tips for improving the reading skills of dyslexic students. It emphasizes on the fact that if these easy and understandable reading techniques are faithfully taught by a teacher such techniques would prove to be an aid to facilitate disabled students to strengthen their reading skills. Also, through the bibliography of this paper, a list of resources has been created for those who may wish to seek further guidance or assistance in this area of research.
The Impact of Corporate Social Responsibility on Environmental Performance: Evidence from less developed countries

This paper aims to study the relationship between corporate social responsibility and environmental performance in Yemeni small and medium industrial enterprises. Based on a research framework, a self-questionnaire was used to collect data from a sample of 220 owners/managers of small and medium enterprises. The empirical results reveal that there is a significant positive relationship between four dimensions of corporate social responsibility (suppliers, environment, human resource management, and society) and the environmental performance of Yemeni SMEs, in contrast, there is one dimension of corporate social responsibility (investors), and its results showed no relationship with environmental performance.
Digital Learning Management as an Option for Future Learning

IEOM Society Publication Database: A dedicated hub for knowledge and insights in the field of Industrial Engineering and Operations Management. Explore research, articles, and resources that exemplify our commitment to advancing IE & OM globally, fostering critical thinking, and serving humanity.
The Urgency of Higher Education Human Resource Management and Application of Theory Z as a Development Strategy

IEOM Society Publication Database: A dedicated hub for knowledge and insights in the field of Industrial Engineering and Operations Management. Explore research, articles, and resources that exemplify our commitment to advancing IE & OM globally, fostering critical thinking, and serving humanity.
Analyze Netizen Sentiment on the Shopee Application

Internet users in Indonesia continue to grow yearly, with the number of users in 2020 reaching 160 million. Most people access social media, primarily buying and selling e-commerce platforms. Based on data from the Play Store in 2020, Shopee is ranked first as the most frequently used e-commerce application. Seeing this phenomenon, a goal was formed to examine the sentiment of netizens towards the Shopee application. Researchers chose qualitative methods with extensive data analysis assisted by NVIVO applications to achieve this goal. This study uses secondary data by looking at the research unit in the form of comments in the application comment column on Google Play by text mining. The results of this study are that 52.39% of Shopee users give negative comments, 41.26% positive comments, and 6.35% neutral comments, so it can be concluded that Shopee still has many shortcomings that must be corrected to provide the best service to the user community.&#x0D; Keyword: Netizen sentiment; big data; text mining
Classifying BISINDO Alphabet using TensorFlow Object Detection API

Indonesian Sign Language (BISINDO) is one of the sign languages used in Indonesia. The process of classifying BISINDO can be done by utilizing advances in computer technology such as deep learning. The use of the BISINDO letter classification system with the application of the MobileNet V2 FPNLite SSD model using the TensorFlow object detection API. The purpose of this study is to classify BISINDO letters A-Z and measure the accuracy, precision, recall, and cross-validation performance of the model. The dataset used was 4054 images with a size of consisting of 26 letter classes, which were taken by researchers by applying several research scenarios and limitations. The steps carried out are: dividing the ratio of the simulation dataset 80:20, and applying cross-validation (k-fold = 5). In this study, a real time testing using 2 scenarios was conducted, namely testing with bright light conditions of 500 lux and dim light of 50 lux with an average processing time of 30 frames per second (fps). With a simulation data set ratio of 80:20, 5 iterations were performed, the first iteration yielded a precision result of 0.758 and a recall result of 0.790, and the second iteration yielded a precision result of 0.635 and a recall result of 0.77, then obtained an accuracy score of 0.712, the third iteration provides a recall score of 0.746, the fourth iteration obtains a precision score of 0.713 and a recall score of 0.751, the fifth iteration gives a precision score of 0.742 for a fit score case and the recall score is 0.773. So, the overall average precision score is 0.712 and the overall average recall score is 0.747, indicating that the model built performs very well.
Blockchain Technology in Enhancing Health Care Ecosystem for Sustainable Development

The analysis and interpretation portion of the study concentrated on managing blockchain technology and on comprehending the rate of technological advancement in actions that support the development of the sustainability and healthcare ecosystems. The study reports results of a survey from 50 respondents who were directly handling activities in the healthcare industry. The study's discussion and findings section also included information on the analysis of the survey data that was acquired. This information is crucial for raising the healthcare ecosystem's to sustainability level. Blockchain technology is one of the most significant breakthroughs of recent times. By consistently changing the healthcare industry, it advances the entire field. It is seen as a series of building blocks that preserves interpersonal trust and covers vital information. The rapid development of blockchain technology is enabling a wide variety of uses across many industries. The potential of blockchain technology will be revolutionised by the systematic production of literature evaluations. One illustration of how swiftly the world has gone digital is the emergence of countless electronic records in the healthcare industry. Blockchain technology has made it possible to eliminate third-party administration's risks in the medical industry.
SafeTalk: Pengembangan Sistem Informasi Pelaporan Kasus Pelecehan Seksual dan Bullying untuk Mengatasi Perilaku Kekerasan di Sekolah

The best place for children to learn and explore their creative potential is in the classroom, allowing them to interact positively with their surroundings. Education differs from sex education in that it refers to the process by which people acquire knowledge and skills that can help them realize their potential and live a better life. Sex education is still seen as a taboo and receives little praise in society. The increase in cases of sexual violence and bullying on campuses and in schools shows that this problem is still poorly understood and there is a lack of resources to help affected individuals. In this work, the author tries to develop an information system that can function as a system that helps victims of violence by providing sexual and anti-bullying education. A total of 37 students and instructors based in Makassar became the sample for this study. Information gathering conducted using a survey distributed via Google Form and literature review as a method of data collection. Black Box Testing is used to test the designed system, checking the functionality of each feature. According to research findings, SafeTalk is quite useful and has a positive impact on its users
The landscape of the Russian Magistracy: DEANS models

The book covers the results of a ve-year study of Russian master's education by the authors of the Higher School of Economics. The empirical background of the book is the project «Landscape of the Russian master's education», implemented by the authors in 2021-2023. It is emphasized that the master education in Russia has a con icting function for di erent subjects of the educational process. To substantiate the conclusions drawn, the authors illustrate in detail the following topics: the motives for choosing educational and professional trajectories, skills building (special attention is paid to digital ones), salary expectations and professional plans of master students, the relationship of professional plans with the region of study, academic mobility, assessment of quality and formats of master's education. The book contains a set of master education (programs) models that have emerged because of the reforms of the last few decades in Russia. The authors proposed ve DEANS' models of Russian master's programs: dummy. 2. elementary (secondary school for adults). 3. academic. 4. night school (evening department / second higher education). 5. Supplementary EDU. The DEANS models made describes the diversity of the master's education landscape in Russian universities. The diversity means high level of the di erentiation in content, forms and practices of master's programs and their e ect on graduates at the Russian Universities.
MC$^{3}$Net: Multimodality Cross-Guided Compensation Coordination Network for RGB-T Crowd Counting

Owing to the expansion in processing of industrial information through advances in machine learning, the demand for accurate crowd counting in various applications is increasing. We propose a multimodality cross-guided compensation coordination network (MC <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{3}$</tex-math> </inline-formula> Net) for accurate red–green–blue and thermal (RGB-T) crowd counting. The network includes modules of intricate interactive fusion, feature difference compensation, and complementary attention enhancement. We use ConvNext as the backbone and process the three streams from RGB, thermal, and spliced RGB-T inputs. The multimodality data are sequentially guided and fused hierarchically, fully combining features extracted from the RGB and thermal images. Thereafter, difference compensation is applied to compress fusion and splicing features. Redundant information is removed. Then, feature mismatch is mitigated to enhance complementary information, reduce the loss of details, and finally obtain crowd statistics. Results from extensive experiments on the RGBT-CC dataset indicate the robustness and effectiveness of MC <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^{3}$</tex-math> </inline-formula> Net, which also achieves high performance on the DroneRGBT dataset and ShanghaiTechRGBD dataset, outperforming existing crowd counting methods. The code and models are available at: https://github.com/WBangG/MC3Net.
A Generalized Nesterov-Accelerated Second-Order Latent Factor Model for High-Dimensional and Incomplete Data

High-dimensional and incomplete (HDI) data are frequently encountered in big date-related applications for describing restricted observed interactions among large node sets. How to perform accurate and efficient representation learning on such HDI data is a hot yet thorny issue. A latent factor (LF) model has proven to be efficient in addressing it. However, the objective function of an LF model is nonconvex. Commonly adopted first-order methods cannot approach its second-order stationary point, thereby resulting in accuracy loss. On the other hand, traditional second-order methods are impractical for LF models since they suffer from high computational costs due to the required operations on the objective's huge Hessian matrix. In order to address this issue, this study proposes a generalized Nesterov-accelerated second-order LF (GNSLF) model that integrates twofold conceptions: 1) acquiring proper second-order step efficiently by adopting a Hessian-vector algorithm and 2) embedding the second-order step into a generalized Nesterov's acceleration (GNA) method for speeding up its linear search process. The analysis focuses on the local convergence for GNSLF's nonconvex cost function instead of the global convergence has been taken; its local convergence properties have been provided with theoretical proofs. Experimental results on six HDI data cases demonstrate that GNSLF performs better than state-of-the-art LF models in accuracy for missing data estimation with high efficiency, i.e., a second-order model can be accelerated by incorporating GNA without accuracy loss.
Think Holistically, Act Down-to-Earth: A Semantic Navigation Strategy with Continuous Environmental Representation and Multi-step Forward Planning

The Object goal Navigation (ObjectNav) task requires an agent to navigate through a previously unknown domestic scenario using spatial and semantic contextual information, where the goal is specified by a semantic label (e.g., find a TV). Such a task is especially challenging as it requires formulating and understanding the complex co-occurrence relations among objects in diverse settings, which is critical for long-sequence navigational decision-making. Existing methods learn to either explicitly represent co-occurrence relationships as discrete semantic priors, or implicitly encode them from raw observations, thus can not benefit from the rich environmental semantics. In this work, we propose a novel Deep Reinforcement Learning (DRL) based ObjectNav strategy by actively imagining spatial and semantic clues outside the agent’s Field of View (FoV) and further mining Continuous Environmental Representations (CER) using self-supervised learning. Additionally, the illusion of spatial and semantic patterns allows the agent to perform Multi-Step Forward-Looking Planning (MSFLP) by considering the temporal evolution of egocentric local observations. Our approach is thoroughly evaluated and ablated in the visually realistic environments of the Matterport3D (MP3D) dataset. The experimental results reflect that our method combining CER and imagination-based MSFLP facilitates learning complicated semantic priors and navigation skills, thus achieving state-of-the-art performance on the ObjectNav task. In addition, adequate quantitative and qualitative analyses validate the excellent generalization ability and superiority of our method.
Cellular Neural Networks Templates Learning Approach Based on Mutual Information and Firefly Algorithm for X-Ray Images de-noising

The Cellular Neural Network is a 2D array of analog processors which forms a parallel computing framework. Themain key factors in this model are the values of the neighborhood of each cell, which are called templates. These templates are usually set by a domain expert in this framework to determine the optimal values of the templates. In this paper, a novel approach was proposed to discover the templates of the cellular neural networks based on mutual information and firefly optimization. The mutual information discovers the hidden pattern in the templates by measuring the similarities among cells. The firefly algorithm navigates the search space to find the optimal values of the templates. The benchmarking and validation have been performed on the ChestX-ray8, which is a real-world X-ray images dataset. The proposed method achieved significant results when compared to other meta-heuristics algorithms such as Genetic Algorithm and Particle Swarm Optimization.
TRUST IN THE EDUCATIONAL ENVIRONMENT OF THE CHELYABINSK REGION TO THE HIGHER EDUCATION INSTITUTIONS

Trust is a necessary condition that becomes a factor in the formation of an eff ective environment necessary for the eff ective development of any subsystem. The authors proceed from the hypothesis that trust can be “nurtured” and this should be done in an educational environment. The purpose of the study is to determine the confi dence factors of the subjects of the higher education system of the Chelyabinsk region (students, faculty and university staff ); quantify the level of institutional trust in the educational environment; prove the degree of infl uence of the found factors on the level of trust. The research materials were collected by interviewing students and universities employees in the Chelyabinsk region. The number of respondents was 382 people. The materials were processed by grouping, graphic illustration, ranking of responses. Integral trust indicator is proposed. The authors conducted a correlation analysis on two samples: students and university employees. According to the same samples, two regression models were built to assess the elasticity of the studied factors. The authors found three key factors that form the level of trust. This is the level of quality of the educational environment, the level of importance of higher education, the level of information transparency. The authors identifi ed two forms of trust — a priori and conditioned. The authors proved the hypothesis: the closer the trust object is to the trust subject, the higher the level of the latter. Students to a greater extent trust the faculty of the university, somewhat to a lesser extent the administration of the university, to an even lesser extent the federal institutes of the higher education system. The analysis made it possible to quantitatively measure the infl uence of the three established factors on the level of trust among the subjects of the educational environment of the Chelyabinsk region in the higher education system, as well as to establish the degree of their infl uence on the level of trust. The closest relationship, typical for both categories of respondents, is noted between the quality of the educational environment and the level of trust in it. A weak connection has been established between information transparency and the level of trust in the educational environment. The results of the study can be used in the development of support measures from the regional authorities of the Chelyabinsk region, aimed at creating a favorable environment that forms the basis of trust in the higher education system.