Reviewerly/exHarmony huggingface dataset:
MULTIMODALANALYSIS OF VIDEO WITH NEURAL NETWORKS

The cyberspace technology in age of social-media, has introduce an innovative and ravishing way of passing the information in form of online streaming videos. Interestingly, videos are able to communicate a complex message in some seconds of duration involving all auditory and visual senses. Hence, somewhere it is needed to monitor the videos, its shared context and its affect over viewers. Thereby, it becomes imperative to test the negative, positive and neutral sentiments hidden within the video which is also termed as sentiment analysis of videos. For this text, audio and visuals are known modalities in consideration to relation with utterances. This paper presents the contextual multimodal sentiment analysis using the technique of neural network using attention mechanism.
Leprosy Screening Based on Artificial Intelligence: Development of a Cross-Platform App (Preprint)

<sec> <title>BACKGROUND</title> According to the World Health Organization, achieving targets for control of leprosy by 2030 will require disease elimination and interruption of transmission at the national or regional level. India and Brazil have reported the highest leprosy burden in the last few decades, revealing the need for strategies and tools to help health professionals correctly manage and control the disease. </sec> <sec> <title>OBJECTIVE</title> The main objective of this study was to develop a cross-platform app for leprosy screening based on artificial intelligence (AI) with the goal of increasing accessibility of an accurate method of classifying leprosy treatment for health professionals, especially for communities further away from major diagnostic centers. Toward this end, we analyzed the quality of leprosy data in Brazil on the National Notifiable Diseases Information System (SINAN). </sec> <sec> <title>METHODS</title> Leprosy data were extracted from the SINAN database, carefully cleaned, and used to build AI decision models based on the random forest algorithm to predict operational classification in paucibacillary or multibacillary leprosy. We used Python programming language to extract and clean the data, and R programming language to train and test the AI model via cross-validation. To allow broad access, we deployed the final random forest classification model in a web app via shinyApp using data available from the Brazilian Institute of Geography and Statistics and the Department of Informatics of the Unified Health System. </sec> <sec> <title>RESULTS</title> We mapped the dispersion of leprosy incidence in Brazil from 2014 to 2018, and found a particularly high number of cases in central Brazil in 2014 that further increased in 2018 in the state of Mato Grosso. For some municipalities, up to 80% of cases showed some data discrepancy. Of a total of 21,047 discrepancies detected, the most common was “operational classification does not match the clinical form.” After data processing, we identified a total of 77,628 cases with missing data. The sensitivity and specificity of the AI model applied for the operational classification of leprosy was 93.97% and 87.09%, respectively. </sec> <sec> <title>CONCLUSIONS</title> The proposed app was able to recognize patterns in leprosy cases registered in the SINAN database and to classify new patients with paucibacillary or multibacillary leprosy, thereby reducing the probability of incorrect assignment by health centers. The collection and notification of data on leprosy in Brazil seem to lack specific validation to increase the quality of the data for implementations via AI. The AI models implemented in this work had satisfactory accuracy across Brazilian states and could be a complementary diagnosis tool, especially in remote areas with few specialist physicians. </sec>
Perbandingan Berbagai Model Peramalan Indeks Harga Saham Gabungan (IHSG) Di Masa Pandemi Covid-19

The COVID-19 pandemic has a negative impact on various sectors, including the stock market where many people are hesitant to invest in stocks, especially in Indonesia. This condition is due to anxiety and the inability to control random influences in the field of buying and selling shares. However, investors can still get benefits in investing in stocks if every decision is made by using the best model to predict the future trend and determine their optimized portfolio. Therefore, this study aims to compare several models that can forecast the Composite Stock Price Index (IHSG). The results confirmed that the Prophet model is the best. These results are confirmed by observing the MAE, MAPE, MASE, SMAPE, RMSE, and R- squared.
Students’ Perception in Learning English through Communicative English Club (CEC) at IAIN Salatiga

The purposes of this study were (1) to know how is perceptions of student in learning English through Communicative English Club (CEC) IAIN Salatiga and (2) to know how is the student’s perception of the stimulus for the English material given by the Communicative English Club (CEC) IAIN Salatiga. This study uses qualitative research to find out the purposes. Sample is chosen by the researcher or called a purposive sample. Six students who joined in Communicative English Club (CEC) were interviewed. From the results of this study, it is concluded that students who join the Communicative English Club (CEC) Student Activity Unit (UKM) had a positive perception of learning English at CEC and the materials that were given. CEC activities or work programs improve their English language skills, especially speaking skills. Although not aiming to learn English, students got an experience of learning English at CEC that is different from learning in the classroom. They got the opportunity to practice English more often. In addition, CEC provides environmental and mental support for learning English. Students consider English material to be not just written grammar material, but English material can be obtained through an environment, discussion, and experience that can improve their English language skills. Keywords: students’ perception, learning, English, English club
Research on Rapid Dynamic Rendering and Modeling Technology of Water Landscape Based on 3D Image Technology

With the development of computer graphics technology, the application of 3D visualization simulation technology has become increasingly widespread. This paper develops a three-dimensional visualization simulation system for water bodies by studying the general model of regional landscape space and water quality purification. This paper mainly does research on rapid dynamic rendering and modeling technology of water landscape based on 3D image technology and uses the 3ds Max Material Editor to map the enhanced 3D landscape design to complete the design, and the designed landscape feature data will be saved in the system data statistics unit for later query. This article also expounds the entire process of alternative simulation construction and confirms the feasibility and superiority of alternative modeling methods through multiple case comparisons. Alternative modeling methods are used as the main way to construct waterfall scenes, that is, simple models are combined with texture mapping, particle effects, and other forms to replace complex models, while ensuring the simulation effect, and the complex models are presented in the most simplified way to make the virtual scene. It is well performed in terms of authenticity and real time. The research results show that the designed system design renderings are extremely clear and of good quality; they have a high score in the water landscape design. Moreover, the system application cost is low, energy consumption is low, and operation efficiency is high.
Human-Feedback Adaptive Learning Using Interpretable and Interactive Intelligent Systems

Machine learning (ML) is coming out from the research laboratories to the hands of the human users owing to the increasing availability of software tools with ML capabilities integrated into them. Humans and machines have their unique strengths, and collaboration between these two has the potential to improve ML systems further. For any such collaboration, human users must be able to interpret the behavior of ML systems. Moreover, human users should have a provision to give feedback to ML systems based on their domain knowledge. Consequently, apart from accuracy, human interpretability and the ability to interact with human experts are becoming crucial parameters for ML systems. During recent years, a renewed interest in conferring interpretability has been observed among the ML community. Several model-specific and model-agnostic approaches have been proposed, but the field is still evolving. Interest in conferring ability to interact with ML systems has also started catching up. However, there is a lack of consensus in definitions, standard practices, and evaluation metrics. This chapter reviews the state of the art in the field of interactive ML systems. The objectives of the proposed work include extracting principles and guidelines for the design of interactive ML systems, algorithmic description, and graphical representation of the workflow involved, identifying metrics for the evaluation, and proposing a human-feedback adaptive learning algorithm that adapts itself to incorporate the human expert feedback. The proposed approach is capable of reporting any conflict between human feedback and data along with an interpretable explanation. Moreover, it is capable of accommodating human users with different expertise levels of the domain. Establishing principles and guidelines for the design of interactive ML systems will help in standardization and building a consensus. A basic algorithm for the design of interactive ML systems gives a common starting point for further improvements. A set of commonly agreed-upon metrics will help the evaluation of interactive ML systems. Having an interface that can capture feedback from human experts with different levels of expertise will help make human experiments economical and enable leveraging of masses in improving or verifying interactive ML systems.
KAJIAN SISTEM ALIRAN PADA DAERAH ALIRAN SUNGAI (DAS) BENDUNG KOTA PALEMBANG

Bendung River is one of the nine Musi River’s streams located in Palembang. Its estuary is located on Jalan SlametRiyadi 10 ilir, Ilir Timur II Palembang. This river is affected by the annual cycle of the rainy season on September until April. The width of the drainage area of this Bendung river 15,4km2 and it is one of the rivers where flood often occurs. Based on the analysis which was done in the area of Kuto Bendung river in Palembang, the velue of the planning rain debit for ten years was 1,683m3/sec and the largest existing debit was 2,330m3/sec for S1 and S2 waterways, and 1,432m3/sec for S3 and S4 waterways. Thus, there should be further studies to solve the problem well or the correct way by making the dimension becomes larger or fixing it.
Analysis of publications on natural language processing indexed in RSCI

Natural Language Рrocessing (NLP), defined, as the general direction of artificial intelligence and mathematical linguistics, is an important tool for understanding and processing a gigantic amount of unstructured data.Representing theoretical and practical basis for solving a large number of problems, NLP is being actively studied by scientists around the world, since it is one of the most popular areas of data science and is used in many fields of human activity. The purpose of this bibliometric analysis is to identify the main research centers, research trends, the rating of leading Russian scientists in the field of NLP, as well as to show the picture of thematic fields’ distribution of publications. The results of the analysis will allow us to determine the dynamics of NLP field development in the domestic science and will provide scientists and specialists working in the field of NLP with up-to-date information on various aspects of the considered research area.Russian Science Citation Index (RSCI) – the main scientometric database in our country was used as the data source for scientific publications in the field of NLP search. The RSCI platform contains information on current research areas, allows you to assess the productivity of scientists, scientific communities, etc.The presented work will be interesting to researchers in the field of NLP, since it contains actual data on the development dynamics and structure of the documentary and information flow in the field of NLP in Russia, its distribution by branches of knowledge. Besides, the conducted bibliometric analysis of documents allows us to gain information about the authors most productively working in the studied area and their affiliations, as well as about the most cited articles – this is the review of the 30 most cited publications of Russian scientists and rating of TOP-10 scientific organizations of the Russian Federation by the number of publications reflecting the main problems and scientific achievements in the field of NLP.
CityLabs: Making Cities, Making Futures

This chapter is based on an anthropological research project initiated at the Catholic University of Leuven with a view to exploring the impact of so-called makerspaces and hackerspaces on the urban environment of which they form part. We describe the genesis of the project, and end by setting out five arenas, or heuristic devices, which we think are crucial when investigating the potential of these creative hubs in shaping their urban environment and in designing the future.
Analysis of the development of cryptocurrency instruments

Objective. The aim of the work is to determine the prospects for the development of digital financial instruments in the management of social and economic systems. Method. The comparative analysis of the results of an anonymous survey using interactive multi-user social networks was used as research methods in the work. Result. The factors and key parameters influencing the development of cryptocurrency mechanisms are revealed, the advantages and disadvantages of using digital tools are identified. Suggestions for the management of cryptocurrency instruments and recommendations for improving the reliability and development of digital instruments in the financial and digital environment are given. The results of the analysis of a survey of 1121 respondents on the topic of using cryptocurrencies are presented. The results of the study showed that the most commonly used cryptocurrencies are Bitcoin and Ethereum, with a significant proportion of respondents preferring digital activities such as mining and trading. At the same time, a significant number of respondents believe that the future development of digital financial instruments is directly dependent on the legitimization of cryptocurrencies by the state and improvement of blockchain technology. Conclusion. The development of cryptocurrencies cannot be assessed unambiguously. With the development of cryptocurrency instruments, a number of threats arise, which include significant volatility, the presence of legislative restrictions on their use, and the inability to withdraw operations. The further development of cryptocurrencies should be associated with the legal regulation of their distribution, both at the national and international levels, which will ensure the determination of their legal status among other monetary units. It is also necessary to carry out a phased introduction of cryptocurrencies into payment systems for cash settlements, which should be carried out on the basis of the organization of the official circulation of cryptocurrencies and blockchain technology.
Development of Intelligent SPARQL Query Editor

The paper discusses the development of SPARQL query editor. This is an actual question because of the growth of Semantic Web data – the data presented in RDF/OWL formats. A comparative analysis of different types of editors and their main features is provided. In this paper, we propose a description of a SPARQL editor that combines three most useful features: intelligent completions, query visualization comparison of query results provided by different reasoners or without them. The editor provides SPARQL support as LSP service, this approach is considered a modern way to implement language support. This paper also presents the editor’s screenshots.
The Progress of Quantum Communication

In view of the pursuit of increasing the capacity of channels and confidential communication, quantum communication has been developed in recent years. No-Cloning Theorem signifies that bugging can be identified during quantum communication. BB84 protocol provides the changed emissive photon to the receiver and the difference of signals would appear if there has any eavesdropper, due to quantum cannot be copied but intercepted and random selection of measuring modes by the eavesdropper. Although quantum communication still has room for growth, significant advantages of this technique make it a great candidate for future communication methods.
“<i>Dhangdhang Bang Bunghalan Tattwa</i>” Text in the Development of Emotional Intelligence in the Global Era

This study aims to describe the emotional intelligence contained in the Dhangdhang Bang Bunghalan Tattwa Text.The discussion includes the development of people's emotional intelligence in the global era which can be done through the study of the Dhangdhang Bang Bunghalan Tattwa Text.The theories used in this study were the narrative discourse, semiotic and reception.The study uses Hermeneutic approach.The results show that the Dhangdhang Bang Bunghalan Tattwa Text contains an appeal for the development of emotional intelligence.Emotional intelligence can be developed through giving examples, appeals for togetherness behavior, polite language and habituation in problem solving.Through the implementation of the teachings contained in the text, it can direct Hindus in particular to always be wise, patient, and ethical in their behavior in the global era.This can be done through Pandita (high priest) who transfers the teachings contained in the Dhangdhang Bang Bunghalan Tattwa Text.
English Department Students’ Stress Levels and Their Responses Towards Online Learning

This study examined whether stress level varied with gender and year of enrolment, whether there is a correlation between students’ stress level and their responses towards online learning, and examined students’ responses towards online learning in general. A total of 92 students were recruited for this study. Statistical analyses indicated that there was a significant difference in the stress level of male and female students, in that females had a significantly higher stress level than males (p=0.009). No significant differences were observed in stress level based on the academic years (year of enrolment). However, a significant negative correlation was observed between students’ stress level and their responses towards online learning
Formative Evaluation of College English Autonomous Learning Based on Whale Optimization Algorithm

With the advent of the Internet age, online autonomous learning as a new learning method is being accepted by more and more students. With the passage of time, the insufficiency of the network self-learning evaluation mechanism has become more and more obvious, which has affected the creation of teachers’ learning resources and the improvement of students’ learning effects. This article aims to study the formative evaluation of college English autonomous learning based on whale optimization algorithm. Based on the analysis of the whale optimization algorithm, the characteristics of network autonomous learning, the functions of formative evaluation and the tools of formative evaluation, it is necessary to understand formative evaluation. In the application effect of college English network autonomous learning, this paper selects students from a class of a certain university as the research object, and conducts three-month network autonomous learning in this class. The experimental results show that formative assessment can improve students’ autonomous learning ability, and it is also helpful to apply the learned knowledge in practice.
COVID-19 Surveillance Enhanced by an Integrated Electronic Infectious Diseases Information System in Albania

boarded every state to submit laboratory results to this systemthe first of its kind in the US.We set out to evaluate the quality of data collected by CELR. Methods & Materials:We compared jurisdiction-level data collected through CELR and published by HHS to the testing data published by jurisdictions on their health department webpages.Because jurisdictions define their testing data differently, we anticipated some differences from federal testing data.However, jurisdictions also tend to prioritize their dashboard reporting-since it is what is used for policy decisions like reopening-so we hypothesized that differences from federal data absent a definitional explanation could point to problems with federal data.Where we found differences between jurisdictional and federal data, we conducted interviews with public health officials to understand their cause.Results: Of the 56 states and territories, as of April 2021 (the first month when all states were onboarded to CELR), 38 had federal total data that diverges from state data by more than 5%.Of those states, the differences of 27 could not be explained by definitional factors.Based on our interviews, we identified three problems: non-electronic reporting streams, out-of-date surveillance systems, and deduplication of laboratory data. Conclusion:The federal testing dataset displays major unresolved quality problems, and because states present testing data so differently, state-published data forms a poor alternative to federal datasets.The federal government, which is uniquely positioned to provide testing data on infectious diseases, must work to improve the quality of laboratory data submissions by states.To support better national laboratory data, the United States should invest in updating state and laboratory data surveillance infrastructureincluding updates to state surveillance systems and laboratory system updates to eliminate outdated reporting methods like faxesand in creating more national laboratory data infrastructure.