Reviewerly/exHarmony huggingface dataset:
HAND GESTURE RECOGNITION USING MACHINE LEARNING ALGORITHMS

Communication between people is an integral part of life. In the process of communication, people convey their emotions, thoughts, desires to each other. People with disabilities, such as deaf and dumb people, experience various difficulties in the process of communication [1]. Today, 5% of the world's people (more than 430 million), and in Kazakhstan more than 18 thousand people suffer from deafness. By 2050, this it is assumed will reach 700 million people around the world. Deaf or mute people use hand gestures to communicate with others, to express themselves correctly. People who speak a natural language do not always understand their actions. To understand this, we need sign language interpreters. Their number is very small, and most of them work in large cities or regional centers.&nbsp; The solution to these problems can be found by human computer interaction. Today, a lot of research is being carried out in this direction. To solve this problem, a lot of research is currently underway. But the program that provides the perfect two-way translation has not yet been created. Especially for people who speak in Kazakh language. In this paper will be considered classical algorithms for gesture recognition. TRANSLATE with x English Arabic Hebrew Polish Bulgarian Hindi Portuguese Catalan Hmong Daw Romanian Chinese Simplified Hungarian Russian Chinese Traditional Indonesian Slovak Czech Italian Slovenian Danish Japanese Spanish Dutch Klingon Swedish English Korean Thai Estonian Latvian Turkish Finnish Lithuanian Ukrainian French Malay Urdu German Maltese Vietnamese Greek Norwegian Welsh Haitian Creole Persian &nbsp; // &nbsp; TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster Portal Back // &nbsp; &nbsp; Язык этой страницы: Английский &nbsp; Перевести на Русский &nbsp; &nbsp; &nbsp; &nbsp; Азербайджанский Албанский Амхарский Английский Арабский Армянский Африкаанс Бенгальский Бирманский Болгарский Валлийский Венгерский Вьетнамский Греческий Гуджарати Датский Иврит Индонезийский Исландский Испанский Итальянский Казахский Каннада Каталанский Китайский (традиционный) Китайский (упрощенный) Корейский Креольский (гаити) Курманджи Кхмерский Лаосский Латышский Литовский Малагасийский Малайский Малаялам Мальтийский Маори Маратхи Немецкий Непальский Нидерландский Норвежский Панджаби Персидский Польский Португальский Пушту Румынский Русский Самоанский Словацкий Словенский Тайский Тамильский Телугу Турецкий Украинский Урду Финский Французский Хинди Хорватский Чешский Шведский Эстонский Японский &nbsp; Всегда переводить Английский на РусскийPRO Никогда не переводить Английский Никогда не переводить jpcsip.kaznu.kz
Conditional generative adversarial network (cGAN) for synthesis of digital histologic images from hyperspectral images

The aim of this study is to develop a method to transform hyperspectral images of H&E-stained slides to natural-color RGB histology images for easy visualization. Hyperspectral images were obtained at 40× objective magnification and downsampled by various factors to generate data equivalent to different magnifications. High-resolution digital histologic RGB images were cropped and registered to the corresponding hyperspectral images as the ground truth. A conditional generative adversarial network (cGAN) was trained to output natural color RGB images of the histological tissue samples. The generated synthetic RGBs have similar color and sharpness to real RGBs. Image classification was implemented using the real and synthetic RGBs, respectively, with a pretrained network. The classification of tumor and normal tissue using the HSI-synthesized RGBs yielded a comparable but slightly higher accuracy and AUC than the real RGBs.
The Effect of Blockchain Technology on Hospital Supply Chain Performance

There is an ongoing challenge in the healthcare sector to meet increased demand.Deployment of innovative technologies is one of the ways to enhance the level of service level in this sector.Blockchain technology has several advantages, such as immutability, security and transparency, which can address major issues in the healthcare supply chain.This research investigates the impact of blockchain technology on hospital supply chain performance in the Kingdom of Saudi Arabia.Underlined by task-technology fit theory, a conceptual framework of blockchain technology utilisation and its impact on hospital performance is developed.Responses are collected from managers working in both public and private hospitals.A fuzzy-set qualitative comparative analysis (fs-QCA) is used to analyse the impact of blockchain technology on hospital supply chain performance.The results revealed that causal conditions task characteristics (TC), blockchain characteristics (BC), Individual characteristics (IC), task-technology fit (TTF) and blockchain utilisation (BU) are necessary and sufficient for higher levels of hospital performance.The study findings also contribute to an in-depth understanding of how blockchain technology can enhance the healthcare supply chain system by improving its privacy, safety and transparency.
A Siamese Network based Writer Independent Offline Signature Verification

Signatures are frequently used for both personal authorization and verification.The signatures on numerous papers, including legal agreements and bank checks, must be verified.Offline signature verification is a type of authentication that examines the physical action of signing while measuring the characteristics of an user's handwriting.In the present paper, an offline signature verification method was developed utilising Convolutional Siamese Network of Deep Learning [4].Convolutional Siamese networks, which are dual networks that shares parameters, can be developed to acquire a feature map.Thus, Convolutional Siamese network is employed to verify the person's signature to a input signature that is saved in the database.Research has been conducted with a dataset of signatures that includes 750 signatures from 30 different individuals.An Accuracy of 91.6% was attained by the proposed solution.
Unlocking the Potential of the Metaverse

This chapter explores the potential of the metaverse as an enabling technology for the development of intelligent libraries. The metaverse has provided new opportunities for growth and challenges to the competencies of intelligent librarians. The chapter evaluates the advantages of the metaverse and its supporting technologies, emphasizing its central role in propelling libraries towards complete intelligence. The impact of the metaverse on various aspects of library operations, including collection management, reading spaces, cultural promotion, reading experiences, and special user services, is analyzed. The chapter concludes that intelligent libraries must embrace this opportunity to pioneer a new form of immersive experiences, catalyzing a transformative breakthrough in the evolution of these institutions, driven by the diverse digital technologies of the metaverse. This exploration of the transformation of the intelligent library service model under the perspective of the metaverse is significant in driving the development of these institutions and enhancing their services.
Knowledge management: Implications to SMEs performance in Nigeria

Purpose: The purpose of this research is to determine the effect of knowledge management on firm’s performance in selected manufacturing small and medium enterprises (SMEs) in Southeast Nigeria.&#x0D; Design/Methodology/Approach: The data is gathered from different manufacturing SMEs in Southeast Nigeria for this study. The sample size was 196 gathered from the population size of 800 selected manufacturing SMEs. The convenience sampling is used for the study. Descriptive statistics (frequency tables and percentages) was adopted for data presentation, however, Pearson correlation co-efficient along with regression analysis was applied for hypotheses testing.&#x0D; Results: The results exposed that there is an encouraging positive significant association between knowledge management along with SMEs performance. The outcome expressed that there is an encouraging significant relationship between the knowledge management dimensions (knowledge sharing, knowledge acquisition in addition to knowledge protection) and firm performance dimensions (market share, sales growth as well as customer satisfaction).&#x0D; Conclusion: The study concluded that SMEs which acquire new knowledge tend to satisfy their customers more than those with outdated knowledge, and those who share knowledge with employees tend to gain larger market share than those who do not share with employees.&#x0D; Contribution to Literature: This study contributes to the existing literature in two ways: first, this utilized the knowledge management variables which were not utilized in previous studies. Second, this study decomposed firm performance by using three different indicators.
ENGLISH SPEAKING ANXIETY AMONG INDONESIAN JUNIOR HIGH SCHOOL STUDENTS: UNVEILING CAUSES AND SOLUTIONS

An important aspect that may hinder a foreign language learning is anxiety. This qualitative study attempts to examine the phenomena of speaking anxiety and clarify its causes and treatments. Thirty junior high school students of grade IX (nine) participated in this study. Interviews and Foreign Language Classroom Anxiety Scale (FLCAS) questionnaires were used to collect the data. The findings of this study reveal that the anxiety the students experience when speaking English falls into three distinct categories: low, moderate, and high, with the majority of the students had a high level of anxiety. Furthermore, it was also found that a lack of provisions and preparations to learn English, the fear of making pronunciation and grammatical errors, and feeling insecure around interlocutors who are more proficient in the language are the factors that cause students to feel anxious when speaking English.. In light of the findings of this study, students should continue to practice speaking English, attempt to reduce feelings of insecurity, maintain a positive outlook, and improve their drive to learn to speak English.
Automatic Identification Method of HPLC Platform Topology Based on Characteristic Data Extraction

The topological structure of distribution network system is complex, and the operation state changes frequently, so the obtained distribution network topological information has a high error. Therefore, this article proposes a clustering feature extraction method of load curve based on singular value decomposition. The load curve is given by the invariance of singular vectors to improve the generalization ability of feature processing. On the basis of considering the weight of load feature, the data integrity is ensured by singular value curve, and the clustering accuracy is high, which makes the load feature have practical physical significance. The experimental results show that this method can achieve good clustering effect, reduce the clustering time, improve the reliability of data transmission and communication coverage, and meet the communication access requirements of the power Internet of Things sensing layer.
VOICE ENBALED Q &amp; A SYSTEM

— The aim of question answering is to respond to queries that are expressed in natural language (QA). Question answering systems offer automated ways to locate solutions to queries posed in natural language. One important development in information can be seen in the concept of question-answering systems particularly in its capacity to retrieve information resources in a natural way, retrieval technologies method through effective wordfor-word querying and retrieval of the appropriate responses. The development of machine learning algorithms like text summarizing, which can automatically shorten lengthier texts and extract summaries of individual sections of text without losing the meaning, is another result of advances in NLP. Text summary is a cutting-edge method of information processing in a time-constrained and efficiency-obsessed culture. It shortens the amount of time needed to read and makes finding, analyzing, and assimilation of relevant information simpler. In order to answer the questions that users have asked and to provide a concise overview of the same context, a novel method for extracting text from a picture and converting it into text files has been developed. In order to validate the proposed methodologies, a manual evaluation of the quality of the responses was also carried out.
Relationships Among the Changes of Leisure Activity, Leisure Satisfaction, and Smartphone Overdependence of Middle School Students Under the COVID-19 Pandemic

The purpose of this study was to investigate the relationships among leisure activities, leisure satisfaction, and smartphone overdependence under the COVID-19 pandemics in adolescents. A survey was conducted using a convenient sample drawn from 267 students from junior high school in Gyunggi area and data were analyzed. The results showed that leisure activities before and after COVID-19 pandemics showed the significant differences depending on gender. After COVID-19, most of adolescent participated in hobbies and entertainment activitie. Participants showed the lowest leisure satisfaction in physical part both in male and female adolescents. In addition, the smartphone usage time of female students was significantly higher than that of male students during week days and weekends. Changes of leisure activities before and after COVID-19 did not affect leisure satisfaction and smartphone overdependence but did increase smartphone usage time and potential risk of smartphone overdependence. Therefore, after the end of the COVID-19 pandemics, families, schools, and youth organizations should work together to develop various leisure activity programs and to encourage participation of adolescents for their physical, mental, and social health.
Analysis of Question Types and Expressions in Discussion Scripts: Focused on the Discussion Scripts of the Korean Language Education Conference

This study is aimed at analyzing question types of expert group in the context of discussion. This is to examine expert group how to raise questions and how to ask background information on the content of the presentation in formal discussion contexts and to help international students to ask questions properly to develop into discussion in academic contexts. This study analyzed 60 discussion scripts of academic conferences in 3 Korean language education academic associations and typed questions and extracted the frequency of them. The types of questions are ‘knowledge’, ‘comprehension’, ‘application’, ‘analysis’, ‘synthesis’, ‘evaluation’. While the frequency of ‘evaluation’ and ‘synthesis’ are high, the frequency of ‘knowledge’ was the lowest. This study showed typical examples for each question type and examine characteristics and expressions of question types. It is expected that the results of this analysis will be used as basic materials for the educational content of questioning as a debater in Korean language education for academic purposes.
Experimental investigation on computational volumetric heat in real time neural pathways

This research presents an adaptive intelligence internet approach to the reverse heat flow issue of simultaneously identifying the thermally internal heat of vaporization and thermal diffusivity functions of a solid material. A reverse issue was created using the BICOND heat transfer characteristic measuring technique. Internal specific heat and thermal transfer vs temperature characteristics can always be calculated using the velocity and temperature histories of two devices. This work used an analytical simulation of the evet heat flow issue to construct noiseless and noisy fake observations. This reverse issue was handled using a multi-layer graze classifier with a rear method and a stochastic gradient descent type classifier using a complete historical classification algorithm. In light of the results, feedforward deep networks seem to be essential tools for non-iteratively solving component prediction opposite heat flux issues, and being extremely efficient in assessing real temperature variation past to all ascertain fluid flow specific heat and ductility as an arbitrarily defined function of pressure. Furthermore, whenever the temperature spectrum of observation changes, there is no need to retrain the networks.
Use of Waste Plastics as Building Materials- A New Step in Sustainable Construction Technology

The dangerous problem in the contemporary world is plastic trash. This problem represents the biggest danger to civilization. These plastic wastes have a negative impact on the aquatic life, and they also cause the sea water's quality to decline. We look for practical answers to the problem of plastic pollution. Consequently, action is done to create the plastic bricks from waste plastic. It is the most economical option presently being used in the construction industry. It is also advantageous for the environment. In our research, we gathered waste plastic from the neighbourhood municipality, mixed it with the appropriate amount of sand (0.8 kg of plastic and 3.2 kg of sand), and burned it correctly in a pit so that the plastic melted easily. We then poured this mixture into a brick mould and let it sit for a few hours. It is demoulded and kept available for laboratory tests after three to four hours. The water absorption test result and compressive strength result given in Table 2 indicates that the plastic brick is definitely an alternative solution of clay brick and from Table 3 it is also concluded that plastic bricks are economical for use.
Augmented Reality and Metaverse in Aesthetic Education Discourse in Indonesia for Future

This study aims to conduct a systematic literature review on the concept of Augmented Reality (AR), which adds virtual objects into the real world and affects the computer vision field, which is the starting point for the metaverse concept of aesthetic education in Indonesia. This content analysis study systematically analyzes how AR impacts aesthetic education
De-Biasing Rating Propensityalgorithmin Group Recommendation

In recent years, group recommendation systems have gradually attracted attentionwith the increasingphenomenon of people's group activities.Nonetheless, most research focuses on optimizing machine learning models to fit user behavior databetter.However, user behavior data is observational rather than experimental. Due to the different psychological benchmarks of user ratings, the training data evaluated by the algorithm cannot fully represent the real preferences of the target group. A De-Biasing Rating Propensity Algorithmin group recommendation is proposed. The proposed algorithmidentifies user groups with similar behavior preferences through the Predict &amp; AHC algorithm based on cosine similarity, and calculates user bias information by groupand user preference tendency for different user groups. The De-Biasing Proportionon different items is used to build a rating bias consistency model, which effectively adjusts the user's predicted rating.The experimental results show that the algorithm can significantly improve the quality and fairness of recommendation.
A Study on the Effects of Early Childhood Teachers" Job Autonomy and Teacher Efficacy on Job Crafting and Creative Role Performance

Early childhood is the most important period in human development. It is a period in which the foundations of life are laid through interaction with the externally provided environment, and the influence of main caregivers and early childhood teachers, who are human capital, accounts for the greatest part. Therefore, this study presented job autonomy, teacher efficacy, and job crafting as determinants of early childhood teachers
Knowledge Management and Academic Service Quality on Organization Performance

Knowledge management (KM) in education is considered an approach that enables people to collect information, share and use knowledge to improve organizational performance through educational programs, student outcomes, and effective decision-making of work processes. Organizational performance in the university includes student satisfaction, research productivity, curriculum, academic staff responsiveness, and research rankings. KM through the adoption of advanced technology enables organizations to implement better processes, strong and good collaborative structures, knowledge and information sharing, and problem-solving tools. However, limited studies examine the determinants of the KM process and their relationship to university performance through academic service quality from an educator's perspective. In this study, we proposed a theoretical research model to investigate the determinants of the KM process and examine its relationship to university performance through academic service quality. Data collected from 71 academic staff in universities were used to test the hypotheses with structural equation modelling – Partial Least Square. The results showed information technology and culture have a positive impact on the KM process. Quality of academic services partially mediates knowledge management and university performance. KM process and quality of academic services determine and influence organizational performance substantially (f-square=77.6 %). Finally, among the KM processes, knowledge discovery was found to have the highest effect size on organizational performance. However, KM enablers consisting of rewards &amp; incentives, and organizational support were not proven to influence the KM process. Both culture and IT capabilities must be considered by universities in supporting the KM process and building a culture that supports sharing and utilizing knowledge in every task. Additionally, this capability can help improve the quality of academic services which leads to increased organizational performance.
A Logical Account of Subtyping for Session Types

We study the notion of subtyping for session types in a logical setting, where session types are propositions of multiplicative/additive linear logic extended with least and greatest fixed points. The resulting subtyping relation admits a simple characterization that can be roughly spelled out as the following lapalissade: every session type is larger than the smallest session type and smaller than the largest session type. At the same time, we observe that this subtyping, unlike traditional ones, preserves termination in addition to the usual safety properties of sessions. We present a calculus of sessions that adopts this subtyping relation and we show that subtyping, while useful in practice, is superfluous in the theory: every use of subtyping can be "compiled away" via a coercion semantics.
An Enhanced Approach Based on PCA and ACO Methods for Facial Features Optimization

An automatic system for facial expression analysis consists generally of three main phases: detection, feature extraction and classification. In this study, we focus on the extraction of face characteristics (feature extraction) as well as the optimization of the obtained results. The objective is to reduce the number of facial features by removing noisy and redundant data in order to ensure an acceptable facial recognition accuracy while guaranteeing an optimal selection of distinctive facial information. For this purpose, we suggest a new approach based on the combination of principal component analysis (PCA) and ant colony algorithm (ACO). The study was conducted by exploiting the database of the Olivetti Research Laboratory (ORL).