Reviewerly/exHarmony huggingface dataset:
Medchain: Securing Electronic Medical Records with a Peer to Peer Networks and Distributed File System

Though the advances in technology have changed every aspect of our lives, it has barely budged the way we think about our medical records being stored across various platforms. This electronic document talks about the problem that has always existed in the medical field but has failed to be solved; Privacy in the current electronic medical record systems. This paper discusses the failure of traditional medical record systems in terms of security and sustenance to ease of use and provides a possible solution to use today’s technology to address the disadvantages.
Expert system development for homeopathy medicine

Abstract Health is the most critical thing in life. It is invaluable for every individual because health is a prosperous state of soul, body, and social that enables everyone to live a good and productive life. Homeopathy is an alternative treatment that has a fundamental theory that sick people can carry by using the effect of the reflection of substance or energy in the body that produces symptoms in healthy people. This study aims to assist the public in obtaining information from doctors or homeopathic medicine experts based on symptoms, complaints, illnesses, and suggested drugs. The method used is the Expert System Development Life Cycle (ESDLC) with expert system reasoning using forward chaining inference techniques. This research resulted in an application to help expert homeopathic medicine experts with pediatric patients who can facilitate the process of diagnosing the disease along with the recommended medicine. In conclusion, the implementation of an expert system created can help diagnose the condition with homeopathic treatments. This research also expected to contribute to facilitate the community in diagnosing and alternative medicine.
Ontology as a Term System of BPMN Notation

Introduction. Business Process Model and Notation (BPMN) is widely used standard for modeling business processes and supported by the Object-Oriented Technology Specification Management Group (OMG). However, the BPMN 2.0 specification is quite lengthy and contains over 500 pages. Item definitions are scattered across different sections of the specification and sometimes conflict. In addition, the structure of elements and their relationships are described in the form of metamodel, but further syntactic rules are defined within the natural language text. The purpose. This article proposes an ontology that formally represents the BPMN specification. The item description is consolidated within the appropriate class, and further explanations are provided in the annotations. The taxonomic structure in an ontology provides navigation through concepts in is-a relationships. Methods. A possible way to organize terminological vocabulary is the construction of ontologies, terminological systems and the creation of terminological data banks. The open source Protégé ontology editor was chosen to form the ontology. The correctness of the resulting terminology is partly ensured by the Reasoner included in Protégé. Results. The ontology of BPMN 2.0 notation can be used in the educational process as reference, as well as to control knowledge. In addition, ontology can be used as tool for solving semantic search problems. A potentially interesting result can also be an automated synthesis procedure based on the proposed ontology of questions for tests in the LMS Moodle system or similar.
How to Teach School Students to Produce Creative Writing in English

The relevance of the article is conditioned by the insufficient effort taken at school to teach students to produce different types of writing in English with the exception of the informal letter and essay in the State Exam format. The article deals with the issue of training students to create a simple coherent text (story). The authors define the concept and specify the structure of a simple coherent text (story). The problems that students face are thoroughly analyzed and the authors suggest effective ways of coping with the problems that students experience while writing stories in English. The article describes teaching methods that assist students to excel at creative writing. The aim of the article is to make school teachers aware of the necessity to develop students’ writing skills in terms of storytelling.
Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video Processing

Labeled data is a critical resource for training and evaluating machine learning models. However, many real-life datasets are only partially labeled. We propose a semi-supervised machine learning training strategy to improve event detection performance on sequential data, such as video recordings, when only sparse labels are available, such as event start times without their corresponding end times. Our method uses noisy guesses of the events’ end times to train event detection models. Depending on how conservative these guesses are, mislabeled samples may be introduced into the training set. We further propose a mathematical model for explaining and estimating the evolution of the classification performance for increasingly noisier end time estimates. We show that neural networks can improve their detection performance by leveraging more training data with less conservative approximations despite the higher proportion of incorrect labels. We adapt sequential versions of CIFAR-10 and MNIST, and use the Berkeley MHAD and HMBD51 video datasets to empirically evaluate our method, and find that our risk-tolerant strategy outperforms conservative estimates by 3.5 points of mean average precision for CIFAR, 30 points for MNIST, 3 points for MHAD, and 14 points for HMBD51. Then, we leverage the proposed training strategy to tackle a real-life application: processing continuous video recordings of epilepsy patients, and show that our method outperforms baseline labeling methods by 17 points of average precision, and reaches a classification performance similar to that of fully supervised models. We share part of the code for this article at the following repository: fpgdubost/CIFAR-10-Sparsely-Labeled-Sequential-Data.
Optimization of the Classic Transfer-Stacking Model Migration Algorithm: A Way to Solve Time-Varying Performance Degradation of Acute Kidney Injury Clinical Prediction Model

Acute Kidney Injury (AKI) is one of the most common acute and critical illnesses in general wards and intensive care units.Its high morbidity and high fatality rate have become a major global public health problem.There are often serious lags in clinical diagnosis of AKI.Early diagnosis and timely intervention and effective care become critical.The use of electronic medical record data to build an AKI risk prediction model has been proven to help prevent the occurrence of AKI.However, in actual clinical applications, the distribution of historical data and new data will continue to vary over time, resulting in a significant decrease in the performance of the model.How to solve the problem of model performance degradation over time will be a core challenge for the long-term use of predictive models in clinical applications.Aiming at the above problems, this paper studies the classic Transfer-Stacking model migration algorithm.Aiming at the lack of this algorithm, such as the loss of a large amount of feature information of the target domain and poor fit when integrating the model of the target domain, the Accumulate-Transfer-Stacking algorithm is proposed to improve it.Improvements include: 1) Optimize the input vector and model integration algorithm of Transfer-Stacking's target domain model.2) Optimize Transfer-Stacking from a single-source domain model to a multi-source domain model.The experimental results show that for the improved algorithm proposed in this paper when the data is sufficient and insufficient, the average AUC value of the model on the data of subsequent years is 0.89 and 0.87, and the average F1 Score value is 0.45 and 0.36.Moreover, this method is significantly better than the unimproved Transfer-Stacking algorithm and baseline method, and can effectively overcome the problem of data distribution heterogeneity caused by time factors.
The Concept and Formula of Happiness: Historical and Contemporary Criteria

The article presents a historical and philosophical analysis of happiness as a socio-cultural phenomenon, considers the proposed criteria and components of the state of happiness as well as proposes the author's integrative definition of happiness. The relevance of this study is determined by the importance of the actual humanitarian indicators in assessing socio-political progress. As a result of the analysis of how the concept of happiness was historically formed and developed, it was shown that (a) the concept of happiness became widespread in the humanitarian discourse relatively late, (b) its predecessors are the ancient Greek concepts of agathon (the good) and eudaimonia (welfare, prosperity, supreme good), (c) in Western European philosophy, the understanding of good is contradictory and includes a wide range of meanings, from utilitarian to axiological, (d) the content of the concept of happiness includes two aspects: one is in the person's power and the other is determined by external factors. The article analyzes modern theories of happiness and feelings of life satisfaction as well as scientific studies of the components of the state of happiness. As a result of the analysis of these approaches, it is shown that in most modern studies, the emphasis in understanding happiness is placed on either socio-biological or socioeconomic factors. The author examines the criteria for the state of happiness. The author quotes other researchers and proposes certain steps as an algorithm for determining happiness, i.e., a formula for happiness. The importance of harmony of the following components of the human personality and life is substantiated: the belief system (and living in accordance with one's beliefs), abilities (and the possibility of their implementation in professional, cognitive activity), geographical location of residence, as well as social circle (and the balance of socio-psychological proximity and distancing).
Blockchain Technology for Managing COVID-19 Pandemic: Challenges and Solutions

COVID-19 has affected almost 213 countries and territories worldwide with total number of 6,064,289 confirmed cases with death toll of 367,475 deaths till May 30, 2020. COVID-19 outbreak affected the global healthcare system worldwide. COVID-19 pandemic has brought with it lot of challenges like contact tracing of infected patients, lack of data integrity, data privacy and security issues, data transparency issues, linguistic and cultural barriers, cross-border infection spread, drug finding and research. Furthermore, lack of available resources for treatment resources, drugs and supplies, healthcare staff requirement as well as credibility and authenticity of the information are other major problems that require responsiveness. The procedure of permitting data sharing of COVID-19 patients among numerous parties still requires complete transparency and regulation. The above-mentioned issues can be resolved by exploiting the potential of blockchain technology, which works on the foundation of immutability, security and faster transactions with complete transparency to the stakeholders. In this paper, authors have discussed various challenges faced by the healthcare sector in times of COVID-19 pandemic and how blockchain technology can be harnessed to find solutions to overcome them. Blockchain technology has immense potential to contribute to the healthcare sector in ways by addressing issues such as certifiability, traceability, accessibility, reachability, verifiability and real-time monitoring.
Exploiting Transfer Learning Ensemble for Visual Sentiment Analysis in Social Media

With the growing popularityChowdhury, Etu of social media platforms, everyday millions of users are sharing their emotions, views, and information about notable events through visual contents especially images.Chy, Abu Nowshed Analyzing the sentiments of these user-generated images provide prevalence opportunities to various organizations for decision making.Chowdhury, N. M. Istiak Most of the prior studies focused on hand-crafted features and traditional deep learning-based approaches. Conversely, a little effort has been employed to exploit the state-of-the-art transfer learning methods. In this paper, we propose a visual sentiment analysis system adopting the strengths of the ensemble of transfer learning models. We employ three pre-trained deep convolutional neural network (CNN)-based feature extractors including VGG16, Xception, and MobileNet to train the corresponding base classifier. Finally, a majority voting-based fusion approach is employed to determine the sentiment label. Experiments on a benchmark Twitter image dataset indicate that our method achieves top-notch improvement compared to other state-of-the-art methods.
Metaheuristics in Modeling Humanoid Robots: A Literature Review

MetaheuristicsMetaheuristics are designed to find, generate, or select a heuristic that can provide a sufficiently good solution to a complex optimization problem, especially with incomplete, imperfect, vague and imprecise information. Fuzzy set theory is an excellent tool to capture this kind of information. Metaheuristics can be used as important building blocks in humanoid robotsHumanoid robots together with fuzzy set theory. In this chapter, we present a literature review on metaheuristics used in modeling robots.
A Data Analysis Study on the Current Situation of Policy Cognition of Free Orientation Rural Primary School General Normal Students

In the new era, it is important to build and strengthen the rural teachers' team, form excellent talents to contribute to rural education, and realize the good vision of “going down, staying and doing well”. The policy guidance is very important to the degree of recognition. The survey results show that the general normal students of free directional primary school do not have a high degree of in-depth understanding of the policy, and there are differences in their gender, source of students, parents' education level, family economic level and so on. To strengthen the awareness of the policy, we can start from the overall design of top-level policy, strengthen publicity, flexible mechanism, professional development and so on.
Diagnostics of the Level of Development of Metasubject Skills of Primary School Graduates (Based on the Subjects "Literary Reading" and "Literature")

The article describes a system of diagnostic techniques for identifying the level of development of metasubject skills of students during the transition from primary school to the general school (based on the subjects "Literary reading" and "Literature"). An analysis of the diagnostic results shows that the proposed diagnostic tasks and methodological approaches to their assessment help to determine the grounds for differentiating education in both primary and secondary schools, to identify the relationship between meta-subject and subject results in the process of teaching literary reading and literature.
Development Presensi PTI based on android with Quick Response code

Abstract Presensi PTI (PTI Presence) is an android-based application with the use of QR Code which can be used as a record of the attendance list of students in each lecture that is carried out by the schedule and contract of college and student lectures. The purpose of developing this application is to provide a more effective, efficient, and accurate presence system according to the facts. The process of making this application uses the waterfall method which starts from the analysis, design, coding, and testing of functional suitability, performance efficiency, portability, and usability. The results of application testing on the characteristics of Functional Suitability, Performance Efficiency, and Portability with a percentage of 100% which means the application is good. As for Usability, it can be said that the application is feasible to use with a percentage of more than 70%.
Analysis and Development of Information Security Framework for Distributed E-Procurement System

This paper proposes an information security framework for distributed E-Procurement system in Indonesia. E-Procurement in Indonesia has been implemented since 2008, and has provided many benefits. However, there are also information security issues in the use of IT. Developing an information security program is needed to overcome the issues. We compare and analyze the LPSE and ISO 27001 Standards to develop framework. The results show there are some gaps between LPSE Standard and ISO 27001. By implementing the proposed framework, LPSE as a provider of distributed E-Procurement system can be easier to implement the LPSE and ISO 27001 Standards simultaneously as an obligation to comply with government regulations.
Behavioral Analysis of Transformer Models on Complex Grammatical Structures

State-of-the-art neural MT, e.g. Transformer, yields quite promising translation accuracy. However, these models are easy to be interfered by noises, causing over- and undertranslation issues. This paper presents a behavioral analysis of Transformer models in translating complex grammatical structures, i.e. multiple-word expressions and long-distance dependency. Results consistently show that the more complex structures, the less translation accuracy the models yield. We imply that as phrase structures become more complex, the focus patterns learned by the attention mechanism may get erratically sporadic due to the issue of data sparseness. We suggest the use of locality penalty and the increase of attention heads to mitigate the issue, but their trade-offs should also be aware.
Minimum Defect Spline Multiwavelets and Parallel Computing for Big Data Compressing in Cyber-Physical Systems

The author’s vision of the problems of mathematical modeling of cyber-physical systems in transport as a three-level hierarchical structure, including environmental sensors at the lower level, data processing centers (DPC) at the middle level, and a single data storage center for developing management decisions at the upper level is considered. Prospects for big data compressing based on Haar-like multiwavelets of zero degree splines are explored. In contrast to the use of wavelet transforms based on Hermitian spline-multiwavelets, the proposed approach does not require the calculation of approximate values of derivatives. On the other hand, the parallelization effect is not rigidly related to the degree of a spline. This can lead to a significant acceleration of calculations due to parallel processing of the measured values by several (instead of one) filters. The results of numerical experiments are given in comparison with the known analogs. The presented algorithm can be used in the planning of road repairs, in the analysis of road accidents, in the processing of applications of road users, etc.
ATTITUDE TOWARDS STRESS AMONG FIRST YEAR MEDICAL STUDENTS

The results of modern research show that first-year students, in particular students of medical universities, experience severe stress due to the changed conditions of their life, heavy loads and physiological limitations. The consequences are quite serious, among those a decrease in academic achievement, lowered satisfaction with the educational situation and the chosen profession as a whole, an increase in psycho-emotional stress, the maximum number of expelled students, including expelled at their own free will, falls on the first year. An empirical study will identify problem areas and outline a set of measures to adapt freshmen. The attitude to stress in the context of the subjective approach can be considered through the analysis of interrelated cognitive, emotional and behavioral components. The study was conducted among first-year medical students of various specialties. The results show their extremely negative attitude towards stress. In first-year students' perception, stress is closely related to learning situations and living in a dormitory; they experience negative emotions in stress situations and primarily use avoidance as a coping strategy. We can say that they practically don’t notice the mobilizing possibilities of stress, which raises the question of the need for their psychological support, in particular, training in more adaptive strategies for coping with stress.
Twitter Sentiments: A Machine Learning Approach

Sentiment evaluation is the problem of examining texts,critiques, mind and conditioned emotional response published with the aid of various users in microblogging systems. Twitter is one of the maximum extensively used micro running a blog systems and has proved to be the biggest source of statistics.Twitter can give a clear cut view about the current trends. A big dataset of tweets is used to perform sentiment evaluation. The tweets are labeled into two classes, positive and negative. There are various techniques which can be utilised to perform this task. In this paper we aim to perform a comparison between a multilayer perceptron and a factorization machine for classification of tweets in the Sentiment 140 dataset. We apply this approach using two different lexical resources namely AFFIN dictionary and SentiWordNet. The generated models are compared based on the lexicon used as well as the classifier adopted (multilayer perceptron or factorization machine) for their accuracy and training time.
Development of a Automated Environmental Monitoring System with Forecasting

All environmental and monitoring systems are most often based on the accumulated statistics of various parameters of temperature, harmful emissions, humidity, carbon dioxide. This paper describes methods of implementing machine learning algorithms for forecasting problems within a specifically developed ASMOS system in Russia. The proposed algorithm makes it possible to consider the change in temperature for several hours in advance, as well as days. Later this algorithm can be implemented in the system for assessing hazardous environmental situations, where a comparison of hazardous factors affecting the environment carried out.
SPMNet: A light-weighted network with separable pyramid module for real-time semantic segmentation

Real-time semantic segmentation aims to generate high-quality prediction in limited time. Recently, with the development of many related potential applications, such as autonomous driving, robot sensing and augmented reality devices, semantic segmentation is desirable to make a trade-off between accuracy and inference speed with limited computation resources. This paper introduces a novel effective and light-weighted network based on Separable Pyramid Module (SPM) to achieve competitive accuracy and inference speed with fewer parameters and computation. Our proposed SPM unit utilises factorised convolution and dilated convolution in the form of a feature pyramid to build a bottleneck structure, which extracts local and context information in a simple but effective way. Experiments on Cityscapes and Camvid datasets demonstrate our superior trade-off between speed and precision. Without pre-training or any additional processing, our SPMNet achieves 71.22% mIoU on Cityscapes test set at the speed of 94 FPS on a single GTX 1080Ti GPU card.