Reviewerly/exHarmony huggingface dataset:
Methodsof analysis of multimodal data to increase the accuracy of classification

This paper proposes methods for analyzing multimodal data that will help improve the overall accuracy of the results and plans for classifying K-Nearest Neighbor(KNN)to minimize their risk. The mechanism of increasing the accuracy of KNN classification is considered. The research methods used in this work are comparison, analysis, induction, and experiment. This work aimed toimprove the accuracy of KNN classification by comparing existing algorithms and applying new methods. Many literary and media sources on the classification according to the algorithm k of the nearest neighborswere analyzed, and the most exciting variations of the given algorithm were selected. Emphasis will be placed on achieving maximum classification accuracy by comparing existing and improving methods for choosing the number k and finding the nearest class. Algorithms with and without data analysis and pre-processing are also compared. All the strategies discussed in this article will be achieved purely practically. An experimental classification by k nearest neighborswith different variations was performed. Data for the experiment used two different data sets of various sizes. Different classifications k and the test sample size were taken as classification arguments. The paper studies three variants of the algorithm k nearest neighbors: the classical KNN, KNN with the lowest average and hybrid KNN. These algorithms are compared for differenttest sample sizes for other numbers k. The article analyzes the data before classification. As for selecting the number k, no simple method would give the maximum result with great accuracy. The essence of the algorithm is to find k closest to the sample of objects already classified by predefined and numbered classes. Then, among these k objects, you need to count how often the class occurs and assign the most common class to the selected object. If two classes' occurrences are thelargest and the same, the class with the smaller number is assigned.
REAFFIRMATIONS IN MACHINE LEARNING IN CURRENT IT INDUSTRIAL SCENARIO

The digital revolution converts artificial intelligent in too many subsets like Machine Learning, Deep Learning etc. In some other words we can say AI is a science of machines which trains them to perform human tasks while Machine learning is make a system able to learn automatically. In this paper we try to recapitalize the practices of machine learning for a business environment. We also discuss some important points have to follow by a developer of generic algorithm for ML. A machine can be trained and obtained data by the technique used by machine leaning on experiences and examples Traditionally we have to write long codes for that but in Machine learning their algorithm feeds to base algorithm. These MLA or machine builds the logic based on the feed data instead of writing the lengthy code.
The effect of ubiquitous learning management system using imagineering to enhance learning achievement and multimedia creation skill for upper secondary school

The research objectives were: (1) to analyze the efficiency of the Ubiquitous Learning Management System using Imagineering; (2) to compare the student's learning achievement and student's multimedia creation skill between experimental group and control group.The samples consisted of 120 grade 11 students from three classrooms who enrolled in the Digital multimedia course, Liberal arts structure plan at Satri Samutprakarn School, in the second semester of the academic year 2019, selected by using cluster random sampling and randomizing the classrooms into 3 groups by lottery method.The first classroom was 40 students who were tested for efficiency of the Ubiquitous Learning Management System using Imagineering (E1/E2).The second was 40 students who were an experimental group.The third was 40 students who were a control group.The research instruments were 3 sets of:(1) the Ubiquitous Learning Management System using Imagineering;(2) the assessment form of multimedia creation skill; and (3) The achievement test.Statistics for data analysis included Mean, Standard Deviation and Hypothesis testing by One-way Multivariate Analysis of Variance (One-way MANOVA).The research results found that the efficiency of the Ubiquitous Learning Management System using Imagineering (E1/E2) was 81.00/ 80.15.The learning achievement and the multimedia creation skill of the experimental group yielded a higher score than the control group at the statistical significance level of .05.
Innovation to optimize the teaching and development of modern education in the field of culture and art: vectors of development

The essence of optimization is the creation of the most favorable conditions for obtaining the desired results without excessive use of time and reduction of physical activity. This aspect is an important component of training specialists in the field of culture and arts, as it allows to increase the efficiency of the educational process and reduce resources to achieve this result. It should be noted that the relevance of the proposed topic is undeniable -the search for optimal solutions to reform the educational process has been going on for more than a decade. Accordingly, the article aims to analyze innovative approaches to optimizing the teaching and development of modern cultural education. Both general theoretical methods and special art studies were used in the preparation of the work. In particular, the analytical method was used to determine the current state of teaching cultural and art disciplines, to characterize the content of curricula and programs. Among other methods we have used, we define synthesis, analysis, induction and deduction. Based on the research, it was found that in addition to traditional teaching methods, special ones should also be used, in particular, more attention should be paid to distance learning methods.
Research on construction technology of digital model of distribution network based on graph computing

The scale of the power distribution system is becoming larger and larger, and the equipment of the power distribution network is developing rapidly. With the continuous growth of users and business complexity, traditional relational databases can no longer satisfy the data storage and analysis of distribution network. This paper firstly defines the model of distribution network graph data based on graph theory, and on this basis proposes an optimization strategy and analyzes the application of distribution network. Experiments are carried out according to real distribution network data, and the results show that the model of distribution network diagram data proposed in this paper can better store and analyze distribution network data, with integrity, consistency and efficiency.
Impact of Quizzes as Learning Media for Online Mandarin Courses during COVID-19

In learning foreign languages, especially Mandarin, a student needs an adaptation process to understand the vocabulary, grammar and culture in using the language being studied.Various kinds of components and elements of language need to be mastered by the students.Therefore, as a teacher, it is mandatory to prepare lesson plans with appropriate methods and media for the students.The Quizizz application media is one of the options in supporting an online Mandarin learning process.It is supported by the results of the research using descriptive research method, by obtaining the data though student responses in learning Mandarin online using Quizizz media, with the aim of investigating the role of Quizizz application media in supporting the achievement of learning objectives and competence in Mandarin.The results obtained are that Quizizz can provide benefits, a positive influence on learning Mandarin.However, there are several components that need to be improved or combined to make the online classes more attractive and reach the desired results.
Research of Infrared Image Segmentation of Composite Insulator Based on Feature Fusion

With the advantage of the encoding-decoding structure of U-net, this paper proposes a composite insulator infrared image segmentation method, which focuses on the feature fusing of the extracted image and temperature value features. Firstly, the infrared images of composite insulators are transformed to derive the temperature data matrix with the same size. Secondly, the feature extraction network, which is used as the encoder of U-net network, is adopted for the feature extraction of the infrared image and temperature data matrix respectively. Then, in the decoding process, the feature fusing is realized that the outputs of feature extraction networks are fused and the fused feature map is gradually restored to the original input size through up sampling. Finally, the experimental results show that the average intersection-over-union of proposed model is 74.09 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> and the precision is 84.33%, which are both better than the results of traditional U-net with one type of input.
The Future of Research on Social Interaction

Our daily lives involve many instances of social interactions, from parents talking with their children, friends catching up on each other’s news, to a customer interacting with a shop assistant. Throughout these instances of social interaction, human beings show a remarkable ability to compute ‘invisible’ mental states, allowing inference of what another person believes, can see, or has knowledge of. As highlighted throughout this book, these sociocognitive capacities are critical in determining successful social interactions, and thus furthering our understanding of social interactions in ‘real-world’ scenarios remains a key research topic. This chapter examines some of the cutting-edge methods used to study social cognition abilities, core research questions that are being explored within the field of social cognition (including ways to approach second-person neuroscience), and discusses factors that might influence social interaction abilities and how these could be used to develop intervention protocols for improving social cognition.
Automatic segmentation of breast cancer histological images based on dual-path feature extraction network

The traditional manual breast cancer diagnosis method of pathological images is time-consuming and labor-intensive, and it is easy to be misdiagnosed. Computer-aided diagnosis of WSIs gradually comes into people*s sight. However, the complexity of high-resolution breast cancer pathological images poses a great challenge to automatic diagnosis, and the existing algorithms are often difficult to balance the accuracy and efficiency. In order to solve these problems, this paper proposes an automatic image segmentation method based on dual-path feature extraction network for breast pathological WSIs, which has a good segmentation accuracy. Specifically, inspired by the concept of receptive fields in the human visual system, dilated convolutional networks are introduced to encode rich contextual information. Based on the channel attention mechanism, a feature attention module and a feature fusion module are proposed to effectively filter and combine the features. In addition, this method uses a light-weight backbone network and performs pre-processing on the data, which greatly reduces the computational complexity of the algorithm. Compared with the classic models, it has improved accuracy and efficiency and is highly competitive.
A New Approach Towards Development of News App

Modern devices like smart phones and tablets have become increasingly powerful in recent years. This generation of mobile technology is opening the gates to the android apps; the websites are being phased out and the mobile phones and apps are appearing very fast. As mobile devices are becoming more like PCs, they came to replace objects we tend to carry around, such as checkbooks, credit cards, planners, mp3-mp4 players, etc.; in short, we are all using them to accomplish our daily tasks and for learning. Our application that falls into this category is the ‘News App’. The OS (operating system) of this app is ‘Android’, which is most dominating operating system in modern day smart phones and tablets. The main motive of this work is to provide people a handy and simple android application through which they can access all type of news and information. This project (apk file) is developed by using the Java programming language and mark-up language XML (Extensible Markup Language) by using Android Studio, which contains a number of tools that help to develop mobile applications for the Android Platform.
Heralding Single Photons from a Photon Pair Source using a Superconducting Nanowire Detector

We experimentally demonstrate an improved heralded single photon source using a photon-number-resolving superconducting nanowire detector compared to that using a conventional bucket detector. This work delineates a path towards ideal single photon sources.
Efficient Scheduling of Jobs and Allocation of Resources in Cloud Computing

Due to the drastic utilization of clouds, a Proper and proficient allocation of resources in dynamically working environment of cloud systems turns into the challenging task. Different promising mechanisms have been created to work on the effectiveness of process of resource allocation. Yet at the same time there is some incompetency as far as resource allocation and job scheduling, when the systems become highly loaded. Hence, an effective algorithm for scheduling of jobs is needed to work on the proficiency of resource allocation activities. In this paper a advanced technique for scheduling of jobs is proposed for effective and unique process of allocation of resources in cloud computing. By making use of prediction-based techniques and mechanism of updating resource tables in dynamic manner, we achieve, better allocation of resources in the form of response time and completion of jobs. The experimental results demonstrate the effective outcomes compared to existing techniques, by achieving exactness in values for resource table updation.
ANALYSIS OF THE FEASIBILITY OF USING DATA OBTAINED FROM SOCIAL MEDIA TO BUILD ONTOLOGIES: A LITERATURE REVIEW

Social media has evolved from simple platforms for social interaction to a phenomena of dynamic global influence, which generates an immense quantity of data everyday.This event attracts the attention of institutions and entities from all fields, because this bulky and heterogeneous cluster of information can, if extracted and analysed correctly, provide singular perspectives.This article presents a Systematic Review, using the PRISMA protocol, with the intent on exploring the use of data from social media, together with Linked Open Data (LOD), for the development of ontologies.At the end of this research, it is noted that the mass of data from social media, if semantically enriched with LOD, can be used as a basis for ontology construction and that this is a recent application whose potential is still being expanded.
Shortest path type classification for real-time three-points Dubins problems

This paper addresses the three point Dubins problem using a classification approach, namely by only evaluating the relative initial and final configurations with the via point position using a suitable partition of the Cartesian plane. This allows to promptly choose the path type among the eight possible thus making the path planning for three points Dubins problems much faster and more suitable for real-time applications. Some examples are provided to show the efficiency of the proposed strategy.
Indic Visual Question Answering

Visual Question Answering (VQA) is a problem at the intersection of Computer Vision (CV) and Natural Language Processing (NLP) which involves using natural language to respond to questions based on the context of images. The majority of existing methods focus on monolingual models, particularly those that only support English. This paper proposes a novel dataset alongside monolingual and multilingual models using the baseline and attention-based architectures with support for three Indic languages: Hindi, Kannada, and Tamil. We compare the performance of traditional (CNN + LSTM) approaches with current attention-based methods using the VQA v2 dataset. The proposed work achieves 51.618% accuracy for Hindi, 57.177% for Kannada, and 56.061% for the Tamil model.
K-Means Clustering in Determining the Category of Stock Items In Angkasa Mart

A good and accurate stock management system is crucial in any institution or organization that conducts product buying and selling transactions. This is done to improve stock efficiency, reduce storage costs, be more effective, and meet customer expectations. This research produced three groups of the most desirable products for large stock quantities, moderate stock quantities for products of interest, and low stock quantities for products that were less or not in demand. Data was processed using the clustering method, specifically the K-Means method, which was based on historical sales data that includes the product code, number of transactions, and average sales. This study was carried out using web-based calculations. Testing with the rapid miner application. The research yielded three members of the product group for large stock, 238 large stock, 273 medium stock, and 25 low stock.
Sequential Search Algorithm Implementation on Web-Based EDocument Search Application

Technological developments have entered the era of the Industrial Revolution 4.0. The Industrial Revolution 4.0 is called the digital era. One of the keys that must be improved in this era is the ability to manage data that is safe and precise in accordance with applicable regulations. The first step taken in managing documents is converting the document into an e-document and saving it to an application. The e-document application that was built is a web-based application because of the ease in accessing e-documents, it can be done anywhere and anytime via internet access. Search is a fundamental process in processing large amounts of data. Search requires an algorithm. The sequential search algorithm is the simplest search model performed on a data set. The test results on the application show that the e-document application can run well on the web, the source code in the application and the sequential search algorithm logic path in the application are declared valid, and the data search process finds results that match the keywords.
Metaverse in Indian Health Sector – A Regime Shift

For a considerable amount of time, India's healthcare system has been struggling against a variety of challenges, such as a shortage of institutions and human resources that fall short of being appropriate.Primary, secondary, and tertiary care services were the three categories that comprised the Indian healthcare system's hierarchical structure, and there is an immediate need to concentrate on primary, secondary, and tertiary levels of medical treatment.Despite the fact that there are various challenges in the healthcare system, but primarily the attention is required to the following five "As": Awareness, Access, Absence, Affordability and Accountability of our healthcare system, because of these five issues people's health in India is at stake.We must prepare the healthcare system for a future that is both full of potential and full of unknown challenges, so let's be conscious for this and other problems and get ready to handle them, remembering that the fight against illness is the struggle against anything that is harmful for humanity [1,2].In healthcare, the Metaverse is becoming increasingly popular.A wide range of technologies are projected to revolutionise healthcare, including Artificial Intelligence (AI), Augmented Reality (AR), Internet of Things(IoT), Virtual Reality (VR), Quantum Computing, and robotic systems.The advancements in augmented reality and virtual reality over the last few years have been astonishing.With the use of these devices, doctors are now able to perform complex procedures with great precision.In order to improve the functionality of medical devices and equipment, these components are also included into software and hardware [3].The performed work relates to the study of the contribution of Metaverse in the health sector of India.
PELATIHAN PENGENALAN TEKNOLOGI CLOUD COMPUTING UNTUK MENINGKATKAN LITERASI DIGITAL GURU-GURU di SMK JP1 JAKARTA PUSAT

Cloud computing technology is one of the pillars of Information technology which is the foundation for the Industrial Revolution 4.0. Mastery of this technology is needed by prospective middle-class workers to be easily absorbed by the industry. SMK Jakarta Pusat 1, as a provider of human resources for software engineering skills, needs to update the knowledge and skills of teachers to meet these needs. The PKM team of Informatics Departemen of Universitas Trisakti held a cloud computing workshop to update the knowledge of SMK JP1 teachers. Pre-test and post-test were used to measure the knowledge level of participants. The training materials include various cloud computing services, the utilization of IaaS services, and the installation of e-commerce web applications on Digital Ocean's cloud services. The post-test results showed that the workshop participants could participate well in training. As many as 67% of the training participants gave a positive impression of this activity and expected more intensive training and a longer duration of time. The output of this training in the form of a training module is very relevant and can enrich the Cloud computing practice course at the Trisakti Informatics Engineering Department
Study on the Design of Agricultural Landscape at Jingshan Entrance of Hangzhou-Changxing Expressway

As a window for displaying the city's image, the landscape of the highway entrance has become the focus of urban construction.However, as urban construction is bound to occupy more cultivated land, the contradiction between urban construction and cultivated land protection has become increasingly prominent.Therefore, applying agricultural landscape at the highway entrance for creating a beautiful space with historical characteristics and rich farming culture is an effective way to solve this contradiction.Here, to explain the design concepts, ideas and principles of the application of agricultural landscape, Jingshan entrance of Hangzhou-Changxing Expressway was studied.The results showed that the tea hills at Jingshan Entrance created a rich rural cultural landscape of Zen-Tea culture, thus the city's entrance became the most distinctive first scenic line without changing the use of agricultural land, and the people who enter the toll station gradually immersed themselves in farming landscape and the mood of Zen-Tea culture.Considering that, the agricultural landscape is featured with production value, leisure value and aesthetic value.It can not only alleviate the contradiction between city construction and cultivated land, but also has a unique and profound aesthetic effect on the city entrance, and reconnects urbanites with rural.In this case, attention should be paid to agricultural landscape in the process of transportation planning and urban planning, and urban agriculture is worthy of further practice and research in urban construction, agriculture heritage protection, ecology, production, education,