Reviewerly/exHarmony huggingface dataset:
High Density Salt and Pepper Noise Removal using Clustering

영상 매체의 발전에 따라 다양한 영상 처리 기술이 사용되고 있다. 특히, 의료 영상 및 생물 영상 등 잡음에 취약한 분야에서 잡음 제거는 필수적으로 사용해야 한다. 점 잡음(Salt and Pepper)을 제거하기 위한 다양한 필터가 제안되었지만, 이들은 여전히 미흡한 성능을 나타냈다. 또한, 클러스터링에 잡음 제거를 반영한 기존 연구가 존재했지만, 이들은 클러스터링의 성능 향상을 목적으로 하였기 때문에, 잡음 제거 필터로는 적절하지 않았다. 따라서 본 논문은 고밀도의 점 잡음(Salt and Pepper) 제거를 위한 K-means 클러스터링 알고리즘을 제안하였다. 고밀도 잡음 영상으로 인한 필터링 시간을 줄이기 위해, 분산이 심한 비잡음 화소만을 데이터 세트로 사용하였으며, DBI를 클러스터링에 적용하여 원 영상과 유사한 유효 화소를 검출하였다. 유효 화소는 적응적 필터에 사용하였으며, 유효 잡음을 찾을 수 없는 패치에서도 필터링하였다. Various image processing technologies have been adopted with the emergence of digital video as an important medium. In particular, filtering out noise is often essential in applications such as capturing medical imagery or recording living organisms. Although various filters have been proposed to remove salt-and-pepper noise, their performance remains largely insufficient. Some works have also considered clustering reflecting noise filters. However, these methods were primarily developed to improve the performance of clustering techniques, and are not suitable for filtering noise. In this study, a k-means clustering algorithm is proposed to remove salt-and-pepper noise. To reduce the computational load for highly dense imagery, our approach includes only non-noisy pixels with high variance in the dataset, and the Davies-Bouldin index (DBI) is applied in clustering to detect effective pixels similar to the original image. Valid pixels are used in adaptive filters, and filtering is also performed in patches where no effective noise is found.
Effectiveness of Vision Transformers in Human Activity Recognition from Videos

Human Action Recognition (HAR) has got the attention of computer vision domain researchers due to its wide variety of applications like surveillance, behavior detection, sports action monitoring, and elderly monitoring. Due to the huge amount of data, the Deep Learning-based method is widely used in HAR compared to the Machine Learning-based approach. This study explored the various Deep Learning and pre-trained Deep Learning models in HAR. In the pre-trained model, we do not require to train the model from scratch, which is already trained on huge data. This study explored the recent pre-trained Deep Learning model to classify action accurately. This study helps the researcher to evaluate the benefit of the latest Vision Transformer model in the domain of HAR.UCF 50 action dataset is used in this study to examine the effectiveness of the Vision Transformer model in HAR. On UCF 50 action dataset, we have achieved 94.70% accuracy using the Vision Transformer model variant.
Requirement Traceability Matrix for SaaS Development

Very small entities (VSEs) have experienced numerous difficulties in the midst of the extremely competitive software market. They typically manifest as increased customer expectations, frequent changes in user needs, and the need for rapid deployment. The most popular choice for development architecture is software as a service (SaaS). It can replace the role of an outdated system and react immediately to user input. The service delivery standard ISO/IEC 29110-4-3 outlines the process for implementing services while guaranteeing their quality. In this study, the requirement traceability matrix was designed to solve the requirement of service problems.
Composition and element’s speciation in the spring’s waters of the southern part of Balashikh urban district (Moscow region)

This study was carried out to evaluate the content of major ions (Ca2+, Mg2+, Na+, K+, NН4+, Cl–, SO42–, НСO3–, NO3–) and trace elements (Sr, Ba, Fe, Mn, Co, Ni, Cu, Zn, Cd, Pb, Al, Ti, Rb, U, V, Cr, As, Se, Mo, Ag) in the waters of 12 springs in the of Balashikhа urban district of the Moscow region. The district is located within the Uchinsko-Balashikha hydrogeological block, confined to the western part of the Meshchersky hydrogeological region of the Moscow artesian basin. The macro- and micro-component composition of groundwater of various aquifers was characterized according to the sampling data of springs. It has been established that the waters are slightly acidic— near neutral (pH 5,3–6,9), with a mineralization of 130–670 mg/l. Water has highly variations in macrocomponent composition and predominantly high content of nitrate ion and ammonium ion. It is shown that the mean contents of trace elements in the waters of springs are at the level of concentrations characteristic of groundwater in the leaching zone of a temperate climate. Based on the thermodynamic calculation using the Visual-MINTEQ, it was found that the predominant dissolved forms of Ba, Sr, Fe, Mn, Zn, Cd, Ni, Co in the waters of the surveyed springs are free ions, and Cu and Pb are carbonate and organic complexes.
Evolution of Fuzzy Set Theory under Monotonic Constraints

Fuzzy Set Theory (FST) has proved advantageous in various areas of research ever since it was proposed by Prof. Lotfi A Zadeh in 1965. The work on FST has grown manifolds since then resulting in many extensions of FST, one of them being the development of FST under Monotonic Constraints (MC). The notion of Monotonic Fuzzy Set Theory (M-FST) was first presented in 2000 by P Lindskog and L Ljung. It has since seen various augmentations of its own with expansions over various extensions of FST. The applications of M-FST include classification, regression, and prediction. In this paper, the evolution of FST under MC has been studied. First, the evolution of FST has been presented which is then extended to FST under MC. The benefits, challenges, and applications along with the future scopes of FST under MC have also been studied and presented.
Cloud Computing

To Reduce IT infrastructure cost there is use of cloud computing. The term cloud refers to a network or the internet. It Works in distributed computing environment and multiple users and applications can work more efficiently.
The Effect of Roundconsen (Roundtable-Concept Sentence) Learning Model on The Ability to Write Explanatory Texts for Eighth Grade Students of SMP Negeri 28 Jakarta

&#x0D; This study aims to find out the effect of the Roundconsen (Roundtable-Concept Sentence) learning model on the ability to write explanatory texts for eighth grade students of SMP Negeri 28 Jakarta. The study was conducted in the 2019/2020 school year, the second semester in May in class VIII E. The research method used in this study was an experimental method with a pre-experimental one group. pretest-posttest design with a sample of 27 students. The prerequisite test for data analysis was performed with the Liellifors test, obtained Lo = 0.0974 &lt;Lt = 0.1665 at the pretest with a significance level α = 0.05 and Lo = 0.0806 &lt;Lt = 0.1665 at the posttest the significance level α = 0,05, then the data are normally distributed. Homogeneity test results obtained by fisher test, = 1,08 and = 4.23, obtained that &lt; , this shows that the data used are homogeneous at the significance level α = 0.05. The results showed that there was an increase in the average value at the time before and after being given treatment, at the time of the pretest, the average value obtained was 47.96. Then at the posttest, the average value obtained was 83.03. Based on the results of calculating the hypothesis using the t-test, = 13.39 is greater than = 2.29 in the real level (α = 0.05). This indicates that there is an influence of the use of the roundtable concept concept on the ability to write explanatory texts in eighth grade students of SMP Negeri 28 Jakarta. This roundconsen learning model can also be used in learning to write exposition text. The use of this roundconsen model can be complemented by the use of interesting learning media.&#x0D; &#x0D; Keywords: roundconsen, roundtable, concept sentence, writing, explanatory text.
Trust Enhanced Collaborative Filtering Recommendation Algorithm

Recommender systems have been designed to suggest to users the suitable items based on the user profiles and therefore reduce the danger of information overload, however, the recommender systems are still prone to profile injection attacks which then exposes a user to a potential fraud, which leads to a sense of untrustworthiness and reduced accuracy due to malicious manipulations. In this research, we developed a model which should be embedded into the recommendation pipeline in order to improve trustworthiness of recommender system output. We extended the classical collaborative recommendation algorithm by adding a new trust parameter and then compare the prediction accuracy of the trust enhanced collaborative filtering algorithm against that of the classical collaborative filtering algorithm using Mean Absolute Error and Root Mean Square Error and then test the hypothesis using t-test. We found that the new trust parameter improves the accuracy of collaborating filtering algorithm significantly.
Research Paper on Artificial Intelligence

This branch of computer science is concerned with making computers behave like humans. Artificial intelligence includes game playing, expert systems, neural networks, natural language, and robotics. Currently, no computers exhibit full artificial intelligence (that is, are able to simulate human behavior). The greatest advances have occurred in the field of games playing. The best computer chess programs are now capable of beating humans. Today, the hottest area of artificial intelligence is neural networks, which are proving successful in a number of disciplines such as voice recognition and natural-language processing. There are several programming languages that are known as AI languages because they are used almost exclusively for AI applications. The two most common are LISP and Prolog. Artificial intelligence is working a lot in decreasing human effort but with less growth.
Expanding the Horizons of Situated Visualization: The Extended SV Model

To fully leverage the benefits of augmented and mixed reality (AR/MR) in supporting users, it is crucial to establish a consistent and well-defined situated visualization (SV) model. SV encompasses visualizations that adapt based on context, considering the relevant visualizations within their physical display environment. Recognizing the potential of SV in various domains such as collaborative tasks, situational awareness, decision-making, assistance, training, and maintenance, AR/MR is well-suited to facilitate these scenarios by providing additional data and context-driven visualization techniques. While some perspectives on the SV model have been proposed, such as space, time, place, activity, and community, a comprehensive and up-to-date systematization of the entire SV model is yet to be established. Therefore, there is a pressing need for a more comprehensive and updated description of the SV model within the AR/MR framework to foster research discussions.
TEACHING GRAPHOMOTOR SKILLS TO CHILDREN BY MEANS OF FINE ART

The article is devoted to the study of the role and content of ISO tools in teaching graphomotor skills. Which in turn affect the activation of development. The principle of developing writing skills through drawing is based on visual analysis and subsequent copying as the only way to develop the mental function of translating visual information into a motor-graphic analogue. Drawing allows startibg the process of setting graphomotor skills at a younger age, starting with the simplest elements - lines, as well as with acquaintance with visual activity. In the development of graphomotor functions, it is worth noting the development offine motor skills, which occurs gradually in the process of various types of artistic activity. Graphomotorics by means of iso positively affects the imagination, attention, memory, speech and general coordination of the child. Graphomotor skills influence the formation of mental processes. Hence the relevance of visual drawing techniques are gaining particular popularity in correctional techniques.
WMO: an ontology for the semantic enrichment of wetland monitoring data

Rich observation data generated by ubiquitous sensors are vital for wetland monitoring, spanning from the prediction of natural disasters to emergency response. Such sensors use different data acquisition and description methods and, if combined, could provide a comprehensive description of the wetland. Unfortunately, these data remain hidden in isolated silos, and their variety makes integration and interoperability a significant challenge. In this work, we develop a semantic model for wetland monitoring data using an agile and modular approach, namely, wetland monitoring ontology (WMO), which contains five modules: wetland ecosystem, monitoring indicator, monitoring context, geospatial context, and temporal context. The proposed ontology supports the semantic interoperability and integration of wetland monitoring data from multiple sources, domains, modes, and spatiotemporal scales. We also provide two real-world use cases to validate the WMO and demonstrate the WMO’s usability and reusability.
QoS-Centric Diversified Web Service Recommendation Based on Personalized Determinantal Point Process

With the popularity and widespread adoption of the SOA (Service-Oriented Architecture), the number of Web services has increased exponentially. Users tend to use online services for their daily business and software development needs. With the large number of Web service candidates, recommending desirable Web services that meet users’ personalized QoS (Quality of Service) requirements becomes a challenging research issue, as the QoS preference is usually difficult to satisfy for users, i.e., the QoS preference is uncertain. To solve this problem, some recent works have aimed to recommend QoS-diversified services to enhance the probability of fulfilling the user’s latent QoS preferences. However, the existing QoS-diversified service recommendation methods recommend services with a uniform diversity degree for different users, while the personalized diversity preference requirements are not considered. To this end, this paper proposes to mine a user’s diversity preference from the their service invocation history and provides a Web service recommendation algorithm, named PDPP (Personalized Determinantal Point Process), through which a personalized service recommendation list with preferred diversity is generated for the user. Comprehensive experimental results show that the proposed approach can provide personalized and diversified Web services while ensuring the overall accuracy of the recommendation results.
PROBLEMS OF PROFESSIONAL TRAINING OF TEACHERS-ARTISTS IN CHINA AND RUSSIA

The problem of professional training of art teachers is acute both in China and in Russia, and in order to solve it, attention should be paid to the following aspects of education: the organization and content of training, the methodological support of the educational process and the coordination of target guidelines that make it possible to significantly improve the quality of professional education of future teachers of fine arts.
Performance Analysis of UAV Control Architectures Over Urban Environments with Degraded GNSS Accessibility

Unmanned Aerial vehicles are currently becoming a widely used technology that provides support to a set of different applications. However, specific levels of confidence and robustness in the performance of these devices are required for the safe integration of these technologies within the National Airspace. This paper describes efforts towards the development of flight control architectures based using precision and way-point control strategies for autonomous operations. Moreover, a performance analysis of the integration of the specified control architectures over UAV vehicles being operated within urban canyons is presented based on simulations results obtained by using a virtual environment built in Gazebo. The simulation environment uses a built GNSS model with typical signal degradation sources for GNSS accessibility such as muti-path, signal obstruction and shadowing, evaluated within the virtual world. Results are presented in terms the Cross track errors, probabilities of collisions within the defined mission parameters as well as GPS metrics as it is the Dilution of Precision (DOP), along its distribution within the 3D map, in order to correlate the impact of GNSS degradation into each control strategy and therefore, the navigation performance and operation safety.
An Empirical Study on Sentimental Analysis using Deep Learning

A study of a person's attitude in terms of using several unstructured texts is denoted as Sentimental analysis or opinion mining. Opinion mining or sentimental analysis distinguishes as the degree of polarity discover. The estimation of tweet review topics and a product is a high-grade sentimental analysis. Natural language understanding was essential for such data; many challenges were present in the natural language processing field for sentimental analysis. Nowadays, many pieces of research consider deep learning-based techniques for sentimental analysis in the natural language processing field. In this study, 25 papers were reviewed through deep learning-based approaches. Measures, as well as achievements attained by various methods, were simplified. The survey described the improvements and a limitation of each method as well as it regards the challenges and future potential research which is to acquire high accuracy and precision in sentimental analysis. Taxonomy represents the study gap and it elaborates on the various approaches.
Read/write factorizable programs

Abstract In the cons-free programming paradigm, we eschew constructors and program using only destructors. Cons-free programs in a simple first-order language with string data capture exactly P, the class of polynomial-time relations. By varying the underlying language and considering other data types, we can capture several other complexity classes. However, no cons-free programming language captures any functional complexity class for fundamental reasons. In this paper, we cleanly extend the cons-free paradigm to encompass functional complexity classes. Namely, we introduce programs with data that can either only be destructed or only be constructed, which we enforce by a type system on the program variables. We call the resulting programs read/write - (or RW -)factorizable, show that RW-factorizable string programs capture exactly the class FP of polynomial-time functions, and that tail-recursive RW-factorizable programs capture exactly the class FL of logarithmic-space functions. Finally, we state and solve the nontrivial problem of syntactic composition of two RW-factorizable programs.
A Comparative Analysis of Image Classification Classifiers

The most significant and difficult issue in computer vision is classification. The characterization, structure, or likeness of objects is used to classify them. The categorization of images into one of several designated groups is known as image classification. An image is expressed by units called pixels. A method called image classification interprets an image and derives data from it that could be applied to other tasks. Using Machine Learning (ML) techniques to address the complicated problem of image classification presents a challenge in today’s environment. The K-Nearest Neighbor (KNN), Artificial Neural Networks (ANN), Convolutional Neural Network (CNN), Support Vector Machine (SVM), ISODATA and Random Forest classifier methods are used to tackle these objectives in the study. In addition to outlining each technique’s benefits and drawbacks, this overview offers theoretical background on a range of classification methodologies. This study can aid in leveraging the efficiency of neural networks for classifying tasks that call for non-binary classifications, which is a common requirement when actual statistics is taken into account.
Usage of the Kullback–Leibler divergence on posterior Dirichlet distributions to create a training dataset for a learning algorithm to classify driving behaviour events

Information theory uses the Kullback–Leibler divergence to compare distributions. In this paper, we apply it to bayesian posterior distributions and we show how it can be used to train a machine learning algorithm as well. The data sample used in this study is an OCTOTelematics set of driving behaviour data.
ORGANIZATION OF INDEPENDENT WORK ON THE DISCIPLINE «PHYSICAL CULTURE AND SPORT» IN THE IMPLEMENTATION OF TECHNOLOGY FOR THE FORMATION OF STUDENTS' PROFESSIONAL WORLDVIEW

The author analyzes the specifics of the modern organization of independent work in the implementation of the technology of forming a professional worldview in physical education and sports classes at the university. Special attention is paid to the content of each stage of the technology. The conclusion is made that the content of the term «professional worldview», which includes the structural elements «Knowledge», «Experience of activity», «Experience of creative activity», «Implementation of an emotional-value relationship», is the basis for the organization of independent work of students.
Using infrared to improve face recognition of individuals with highly pigmented skin

Face recognition is widely used for security and access control. Its performance is limited when working with highly pigmented skin tones due to training bias caused by the under-representation of darker-skinned individuals in existing datasets and the fact that darker skin absorbs more light and therefore reflects less discernible detail in the visible spectrum. To improve performance, this work incorporated the infrared (IR) spectrum, which is perceived by electronic sensors. We augmented existing datasets with images of highly pigmented individuals captured using the visible, IR, and full spectra and fine-tuned existing face recognition systems to compare the performance of these three. We found a marked improvement in accuracy and AUC values of the receiver operating characteristic (ROC) curves when including the IR spectrum, increasing performance from 97.5% to 99.0% for highly pigmented faces. Different facial orientations and narrow cropping also improved performance, and the nose region was the most important feature for recognition.
The Effect on the Size of Virtual Hand Joints in Immersive Virtual Environments

Recent sensing devices for haptic interaction in immersive virtual reality (IVR) environments have become much more feasible for sufficient experience. This paper investigates the effect on the size of virtual hand joints for collision detection with virtual objects related to natural interaction in IVR. We compare and explore interaction quality according to change in size of hand skeleton joints, in our study, we found effective configuration of the size of each virtual hand joint was set to a 3mm when interacting a 8mm virtual cube. With our approach, when interacting with virtual objects, it would be helpful to relatively apply to hand joint changes depending on the size of virtual objects for improved interaction quality.