Reviewerly/exHarmony huggingface dataset:
ARTIFICIAL NEURAL NETWORK TECHNIQUES FOR HEALTHCARE SYSTEMS: FOCUSING ON HEART ATTACK BY INCORPORATING ‘INFECTED WITH CORONAVIRUS’ AND ‘CORONAVIRUS VACCINE’ AS ADDITIONAL CRITERIA

Artificial neural networks have revolutionized data-driven applications in the medical area.Machine learning is a type of artificial intelligence that is used to solve a variety of data science challenges.The prediction of a result based on existing data is a typical use of machine learning.Diagnosing heart disease is a difficult process that takes a great deal of expertise and knowledge.Traditional methods of forecasting heart illness include hospital examination or a variety of medicinal checks.The health-care business has a massive amount of health-care data, much of which are buried.This concealed knowledge can help you make better decisions.For the best outcomes, computer-based data are combined with modern data mining techniques.The neural network is a widely utilized tool for predicting the diagnosis of heart disease.There is no evidence that the COVID-19 vaccination increases the risk of heart attack.However, it is being used as "proof" that the vaccine kills in social media posts.As a result, we investigated this issue by incorporating 'infected with coronavirus' and 'coronavirus vaccine' as additional criteria for improved accuracy.This study describes a proposed artificial neural network model to predict a heart attack.The technology makes predictions based on eight medical characteristics.The current study's findings demonstrated that the model developed using the artificial neural network efficiently predicts a patient's heart attack.In order to evaluate the accuracy of the suggested model, the RandomForest technique was tested.
N-GRAMS ASSISTED YOUTUBE SPAM COMMENTDETECTION

This paper proposes a novel technique for detecting intrusive comments or spam on the video sharing website - YouTube. We describe spam comments as those which have persuasive intent or those who deem to be contextually irrelevant for a given video. The prospects of monetization through advertising on popular social media channels over the years have attracted an increasingly large number of users. This has in turn led to the growth of the malicious users who have begun to build up automated bots, which are proficient of large scale orchestrated distribution of spam messages across multiple channels simultaneously. These intrusive comments damage the fame of a channel and also the experience of regular users. YouTube themselves have embarked upon this issue with some finite methods which blocks unsolicited comments that consists of links. Those methods have proven to be exceptionally unproductive as spammers have found strategies to bypass such heuristics. Standard machine learning classification algorithms are operative but there is always a possibility for better accuracy with novel methods. In this work, we aim to identify such comments by implementing conventional machine learning algorithms such as Random Forest, Support Vector Machine, and Naive Bayes along with certain custom heuristics such as Count Vectorizer which have proven to be very effective in detecting and subsequently combating spam commentary.
Models of Relevant Arithmetic

It is well known that the relevant arithmetic R# admits finite models whose domains are the integers modulo n rather than the expected natural numbers. Less well appreciated is the fact that the logic of these models is much more subtle than that of the three-valued structure in which they are usually presented. In this paper we consider the DeMorgan monoids in which R# can be modelled, deriving a fairly complete account of those modelling the stronger arithmetic RM# modulo n and a partial account for the case of R# modulo a prime number. The more general case in which the modulus is arbitrary is shown to lead to infinite propositional structures even with the additional constraint that '0=1' implies everything.
System Identification of FIR Filters

Identification of Finite Impulse Response (FIR) filters refer to finding out the coefficients also known as the weights of its transfer function.Adaptive filtering using Least Mean Square (LMS) Algorithm is used to find the estimated weights of the transfer function, using ATMEGA16 processor.This method can be used to find the coefficients of complex resistive circuits.This is done by constantly comparing the FIR system with Adaptive filter until the difference signal is zero, both the systems are fed with same input signals.
Mother’s Knowledge in Providing Nutrition for Children in The Working Area of East Denpasar I Primary Health Care Centre

The nutritional problem in young children particularly nutrition deficiency and over nutrition are still exist. Nutritional intake is one of the most outstanding factors in determining children’s nutritional status. Providing adequate nutrition for children is influenced by their mothers’ knowledge. Hence, by giving health education to mothers was expected to increase mothers’ knowledge in providing adequate nutrition for their children. Regarding this, the information was delivered to mothers by using booklet as the health education media. The aim of present study was to analyze the influence of health education towards the mothers’ knowledge in providing nutrition for their children. This study was a pre experimental study with one group pretest-posttest design. The population of the present study were mothers who have children aged under five years old and living in working area of East Denpasar I primary health care center. In order to decide the location (banjar) of the study, the simple random sampling was used. Meanwhile, the total sampling was used to recruit the mothers in the selected location. The Wilcoxon Test was performed since the data not normally distributed (? = 0.05, CI = 95%). Finding showed that there was a significant difference in mothers’ knowledge before and after given health education about providing nutrition for their children.
Students’ Perception of Mental Load in Multidisciplinary Workshop during Pandemic Covid-19 Era (Case Study: Interior Design and Industrial Engineering Students of Universitas Kristen Maranatha, Bandung, Indonesia)

The Ministry of Education and Culture Republic of Indonesia changed the higher education concept to achieve the world class education equality considerably at the same time with the outbreak of COVID-19. Therefore a sudden change in learning method change drastically whether it is using synchronous or asynchronous method due to time and capacity availability. Thus, student’s mental load arguably enhance in this current situation and therefore it is assumed that the evaluation of the new way of distance learning study never be done especially for collaborative study between department based on different faculty in Indonesia. This study used NASA-TLX paper based as the inventory instrument, evaluated and understood the current learning situation while the collaborative events with problem based assignment took place. All of the workshop activities were given and reported online with the duration of eight weeks from the beginning to the end. From the result, it was shown that the collaborative workshop implementation did not perform any different perception of the two departments who were originally came from science and social. The participants were equally assumed to participate seriously because they came from the 3rd semester’s mandatory subject. Other implication related to academic performance, reduces of team member which not fully cooperated, credit load taken in the current semester and the flexibility in doing individual or team assignment are discussed.
A Hybrid Feature Selection Model for Emotion Recognition using Shuffled Frog Leaping Algorithm (SFLA)-Incremental Wrapper- Based Subset Feature Selection (IWSS)

Emotion recognition method is required for therapy to recognize the emotions of patient and helps in treatment.Many computer science based emotion recognition works focused on facial expression, speech, body gesture and multi-modal based machine learning techniques.Existing methods have limitations of poor convergence and easily trap into local optima.In this research, the Shuffled Frog Leaping Algorithm (SFLA)-Incremental Wrapper-based Subset Selection (IWSS) hybrid method is proposed to improve the emotion recognition.The proposed method involves in analysis the emotion of user through video, audio, and text features and recommends the music to the users.The analysis shows that hybrid modality shows the higher performance in emotion recognition.AlexNet model is applied for the feature extraction in video data and Latent Dirichlet Allocation (LDA) is applied for text feature extraction.Multi-Class Support Vector Machine (MC-SVM) model is used for the classification.The proposed SFLA-IWSS method has 97.05 % accuracy and existing gSpan method has 90 % accuracy.
Studying Professional Reflexion of Teachers: A Comparative Analysis of Two Research Methods

The purpose of the research is to analyze two research methods.One of them was developed and tested to study the peculiarities of the professional reflexion of school teachers and the other is to study the peculiarities of the reflexion of students -future pedagogue, participants of the "I am a Professional" student competition.The method of the comparative analysis was used.The object for comparison was the system of elements constituting the research design: theoretical framework, goals and objectives of the research, subjects, methods used, design of the research technique, procedure of its implementation, methods of data processing, obtained results, opportunities and limitations in the use of methods.The analysis revealed similarities and differences in the way the research procedure was designed and implemented, related to the content of the goals and objectives for which these tools were developed.The limitations in the scope of application of both methods, due to the use of qualitative ways for data processing, are identified.It is noted that there is a possibility of applying these techniques to both categories of respondents: teachers and students.It is also possible to combine these techniques for a comprehensive study of pedagogical reflexion in correlation with the whole system of pedagogue's activity: value orientations, means and ways of activity, ideas about the object of activity.
An Effective Resource Matching Scheme Based on a Novel Unified Descriptive Model for Modern Manufacturing Industry Systems

In order to effectively solve the problem of heterogeneous design/manufacturing/service resources and isolation in the whole lifecycle and realize unified description of design/manufacturing/service resources and resource sharing across subjects and stages, this paper proposes a hierarchical and modularized ontology-based resource-unified descriptive model, according to the characteristics of design/manufacturing/service resources. We analyze all kinds of properties of the resources, design a specific descriptive model of ontology, function, and service, ensure the consistency and independence of resource descriptions, and use the OWL (Web Ontology Language) ontology descriptive language and Protégé tools to verify. Then, based on the unified descriptive model, a resource matching method based on multi-level tags is proposed, which matches the task request with the resources in the resource library, selects the resources that meet the task request, and guarantees the resource sharing across subjects and stages. The resource matching work first performs task description and decomposition, and uses information entropy and rough set theory to sort the importance of subtasks, then uses the semantic similarity algorithm to complete multi-level tags’ matching. Finally, two examples are used to prove the feasibility and effectiveness of the experiment.
Children’s Language Learning Vocabulary Through Mobile Applications: Duolingo

This research article aims to investigate how the influence of Duolingo can help improve children’s vocabulary. The data were obtained from the 10 children of 7th graders as the sample that was collected in 1 main place that was the researcher’s house to make it easier to collect and observe the data. This study used the theory of language learning proposed by Halliday (1978). This study used a pre-experimental research design with a pre-test and post-test. The 10 children as the sample were practicing using Duolingo apps for 10 days, during which they had to answer 1 lesson consisting of 10 to 15 exercises every day. The collected data in this study were quantitatively analyzed by observing the children that consisted of some procedures. This study used two research instruments, namely, tests and questionnaires to answer the research questions. How can Duolingo help children improve their vocabulary? Furthermore, to present the data, this study used the tables and charts which were elaborated with symbols and numbers, to ensure validity and reliability, and described the ethical consideration in terms of the involvement of the respondents. The result of this study showed that by using Duolingo as a learning medium, children can master most of the vocabulary from all the given exercises. The scores they obtained for the post-test were much higher than the scores for the pre-test.
PENGEMBANGAN BUKU PINTAR CEPAT BACA UNTUK MENINGKATKAN KEMAMPUAN MEMBACA ANAK KELAS 1 SEKOLAH DASAR

This research is aims to develop teaching materials that meet the needs of students. This teaching material as a learning support to improve students' reading skills so that students are able to read and write. This textbook is in the form of “Quick Read Smart Books”. The procedure for developing this fast-reading smart book consists of three stages, namely Define, Design, Develop. This smart book was assessed by two validators, namely a media expert validator and a material expert validator to determine the feasibility of the product. Then carried out the observation of the implementation of learning to obtain data and determine the practicality of the product. Furthermore, there are pretest-posttest questions and student response questionnaires to determine the effectiveness of the product.
Features of Reaction in a Conflict Situation Among Students-Defectologists with Different Levels of Reflexivity

The article presents an analysis of the problem of the peculiarities of reaction in a conflict situation among students of defectology with different levels of reflexivity. It is noted that in the mechanisms of response in a conflict situation, a significant place is occupied by personal factors that determine the predisposition of the subject to the choice of a behavior strategy. Various typologies of conflicts are presented, which are developed on the basis of different approaches to understanding this social phenomenon. A number of definitions of behavior strategies in a conflict situation are given based on the analysis of foreign and domestic works. The concept of reflexivity in relation to various psychological characteristics is defined. The results of an empirical study of the peculiarities of reaction in a conflict situation among students of defectology with different levels of reflexivity are presented, according to which students of defectology with a low level of reflexivity tend to defend their own interests without taking into account the interests of the opponent, students of defectology with a low level of reflexivity are characterized by the lack of a tendency to find solutions to the problem and defend their own interests, which manifests itself in ignoring the conflict situation., students of defectology with an average level of reflexivity tend to search for solutions to the problem taking into account their own interests and the interests of the opponent.
Diagnosis Accuracy of Fine Needles Aspiration Assessment in Bone Tumors in the Department of Anatomical Pathology FKUI/RSCM 2015-2019

BackgroundFine needle aspiration biopsy (FNAB) is a safe and rapid technique and has an important role in making the preoperative diagnosisof bone tumors because it can help clinicians determine treatment for patients. This retrospective study aims to assess the accuracyof the FNAB cytological examination compared with the histopathological examination in preoperative diagnosis of bone tumors.MethodsThe data comes from the archives of the Department of Anatomic Pathology, FKUI/RSCM 2015-2019 in the form of a cytologicalexamination of FNAB lesions on bone. Then the histopathological examination data were searched for each cytology preparation.There were 133 pairs of cytology, and histopathology preparations. The preparations were reviewed by the investigator andsupervisor, assessed for the suitability of the cytologic diagnosis, and compared with the histopathological diagnosis.ResultsThere were 84 (63.2%) cases of malignant tumors, 44 (33.1%) cases of benign tumors, and 5 (3.7%) cases of non-tumors. Thediagnostic test results showed an accuracy rate of 86.5% with a sensitivity of 85.9%, a specificity of 100%, a positive predictivevalue (PPV) of 100%, and a negative predictive value (NPV) of 21.7%.ConclusionFNAB cytology examination of bone tumors that have adequate sample quality, supported by complete clinical information andinformative radiological images can produce an accurate preoperative diagnosis
Assessment of the Quality of Mastering Natural-Science Concepts Using Mental Modeling

The article presents the analysis of the research of evaluation of the level of mastering of natural-science concepts by pupils of the middle school by means of mental modeling.It touches upon the problems of "evidence-based" pedagogics and, in particular, those aspects which are connected with the general structure of pedagogical research design, the use of mathematical apparatus and the integration of pedagogical research as an element of educational quality evaluation system which allows making managerial decisions on the basis of analysis of quantitative indices.The article presents the results of a local study of the quality of mastering of natural-science concepts, which involved 165 pupils in the middle school at the age of 14.The study used a multilevel diagnostic tool, which allows one to give not only a qualitative but also a quantitative assessment of the formation of the functional level of mastering a concept.The materials, modes and regulations of the conducted experiment can be used as a tool for assessing the quality of education in educational organizations.In addition, the results of this experiment can be used to design educational systems both at the level of an individual school (both private and public) and at the level of municipal organizations to provide additional education for schoolchildren, as well as for all educational organizations practicing modular education formats.
Enabling Industrial Mixed Reality Using Digital Continuity: An Experiment Within Remanufacturing

In the digitalisation era, overlaying digital, contextualised information on top of the physical world is essential for an efficient operation. Mixed reality (MR) is a technology designed for this purpose, and it is considered one of the critical drivers of Industry 4.0. This technology has proven to have multiple benefits in the manufacturing area, including improving flexibility, efficacy, and efficiency. Among the challenges that prevent the big-scale implementation of this technology, there is the authoring challenge, which we address by answering the following research questions: (1) “how can we fasten MR authoring in a manufacturing context?” and (2) “how can we reduce the deployment time of industrial MR experiences?”. This paper presents an experiment performed in collaboration with Volvo within the remanufacturing of truck engines. MR seems to be more valuable for remanufacturing than for many other applications in the manufacturing industry, and the authoring challenge appears to be accentuated. In this experiment, product lifecycle management (PLM) tools are used along with internet of things (IoT) platforms and MR devices. This joint system is designed to keep the information up-to-date and ready to be used when needed. Having all the necessary data cascading from the PLM platform to the MR device using IoT prevents information silos and improves the system’s overall reliability. Results from the experiment show how the interconnection of information systems can significantly reduce development and deployment time. Experiment findings include a considerable increment in the complexity of the overall IT system, the need for substantial investment in it, and the necessity of having highly qualified IT staff. The main contribution of this paper is a systematic approach to the design of industrial MR experiences.
Potential security and privacy issues in zero UI touchless technology

Abstract Touchless technology often called Zero User Interface (UI) has begun to permeate every aspect of our lives as its use became necessary for hygiene measures in public places. The evolution of touchless technology replacing touchscreen interaction started as a luxury concept to give a fancier look to digital interactions, but now it has gained real value as a health-oriented interaction method. Switching to a touchless interface reduces common touchpoints, which help to safeguard against the spread of pathogens. Although the evolution of touchless technology is not new, its use massively increased due to its inherent hygienic nature during the COVID-19 pandemic. However, this investment in a new form of digital interaction has several privacy and security issues that need attention, in order to allow for safe human–machine interaction to cope with security breaches and cyber-attacks to protect our credentials. This paper outlines the potential security and privacy issues concerning Zero UI adoption in various technologies that need to be considered if one wishes to adopt responsible technology practices with this technology.
Data mining security for big data

With the digitalization of the world, data is growing rapidly. A large amount of data is accumulated in all areas. Organizations store this data in a database as big data in a cluster. However, these clusters do not guarantee the security and confidentiality of the stored data. In recent years, data mining for privacy protection has become a popular research area for the protection of data stored as big data. There are many algorithms to keep data mining safe. This study focuses on knowledge mining by including security measures that must be applied at different stages of data mining. The different stages including data source, data miner and end-user are stored in the target database. Each step describes the risks involved in handling large amounts of data and the corresponding measures for data protection at each step.
MAGIC AND SOCIETY: HARRY POTTER’S EFFECT ON READING THROUGH THE EMPOWERMENT OF LEARNERS AND OF EDUCATORS

Harry Potter is a blockbuster. Reading the books from the series, one can find challenges and problems every adolescent confronts with. Harry faces real-world challenges, such as: racism, status in society, corruption. Reading about Harry Potter’s experiences, the choices he faces and the dilemmas he has empowers the reader (the learner) to find a way through his/her own choices and dilemmas. Education through active citizenship means empowering the learners to take part actively in society and to think critically about certain issues in society. Harry Potter books are a good resource for educators in attaining active citizenship skills.