Reviewerly/exHarmony huggingface dataset:
MediChain: Medical Record Management System

Abstract: Currently, Electronic Health Records are managed using a client-server architecture by which healthcare providers retain data stewardship. But this strategy has problems with security and privacy, a single point of failure, fragmented data, and susceptibility. Blockchain is a brand-new, developing technology for distributed databases that uses cryptography to ensure the security, incorruptibility, and integrity of data. These characteristics allow secure and reliable data storage. Blockchain-based record management system uses smart contracts to ensure the privacy of patients. This health record management platform will store the user’s hospital, lab, pharmacy, and insurer records. It will increase the speed of information storing and exchange So that all patients can easily access individual medical records in different hospitals and clinics. This will help doctors and patients with diagnoses and also various insurance companies to check the medical records by getting access to records from the platform and also this will help the researchers and scientists.
Imperceptible Technique to Secure Digital Audio Signals while Attaining Trade-Off among the Parameter of Magic Triangle

Abstract: This paper outlines an audio encryption method that is used during audio compression. Security for the multimedia data is provided by encryption methods. Here, audio encryption is a method used to communicate secret information by subtly altering an audio stream. It is a technique that guarantees secure data transfer between parties that often interact online. Here, we offer a fresh method for dealing with issues raised by audio encryption's substitution mechanism. We employ an enhanced RSA encryption technique, which is incredibly difficult to crack, to encrypt messages in the first level of protection. The encrypted message bits are embedded into random LSB layers to boost robustness against purposeful assaults, in which the hackers constantly attempt to reveal the hidden message, as well as some inadvertent attacks, like noise addition. Here, GA operators are utilised to lessen distortion. The major goal of this work is to propose a good, effective way for hiding data from hackers and sending it to the target in a safer manner by maintaining randomization in message bit insertion into audio data.
Extracting Temporal Relationships in EHR: Application to COVID-19 Patients

Association rules are one of the most used data mining techniques. The first proposals have considered relations over time in different ways, resulting in the so-called Temporal Association Rules (TAR). Although there are some proposals to extract association rules in OLAP systems, to the best of our knowledge, there is no method proposed to extract temporal association rules over multidimensional models in these kinds of systems. In this paper we study the adaptation of TAR to multidimensional structures, identifying the dimension that establishes the number of transactions and how to find time relative correlations between the other dimensions. A new method called COGtARE is presented as an extension of a previous approach proposed to reduce the complexity of the resulting set of association rules. The method is tested in application to COVID-19 patients data.
DeepTSE: A Time-Sensitive Deep Embedding of ICU Data for Patient Modeling and Missing Data Imputation

Missing data is a common problem in the intensive care unit as a variety of factors contribute to incomplete data collection in this clinical setting. This missing data has a significant impact on the accuracy and validity of statistical analyses and prognostic models. Several imputation methods can be used to estimate the missing values based on the available data. Although simple imputations with mean or median generate reasonable results in terms of mean absolute error, they do not account for the currentness of the data. Furthermore, heterogeneous time span of data records adds to this complexity, especially in high-frequency intensive care unit datasets. Therefore, we present DeepTSE, a deep model that is able to cope with both, missing data and heterogeneous time spans. We achieved promising results on the MIMIC-IV dataset that can compete with and even outperform established imputation methods.
Extended Reality Is Underutilized in Medical Device Training: A Descriptive Literature Review

This descriptive review provides a synthesis of existing literature about the use of extended reality (XR) including virtual (VR) and augmented reality (AR) technology solutions for competence assurance, training and orientation regarding digital skills and medical device training. From the literature, only few original studies were recognized with a study question or aim to assess medical device training as the target of virtual training modalities. XR methods could provide potential useful solutions to improve medical device competence. Based on the literature, it was evident that further studies are required to research the possibilities of XR technologies to improve medical device training.
Interpretable EEG-Based Emotion Recognition Using Fuzzy Cognitive Maps

The brain is one of the most complex parts of the human body, consisting of billions of neurons and it is involved in almost all vital functions. To study the brain functionality, Electroencephalography (EEG) is used to record the electrical activity generated by the brain through electrodes placed on the scalp surface. In this paper, an auto-constructed Fuzzy Cognitive Map (FCM) model is used for interpretable emotion recognition, based on EEG signals. The introduced model constitutes the first FCM that automatically detects the cause-and-effects relations existing among brain regions and emotions induced by movies watched by volunteers. In addition, it is simple to implement and earns the trust of the user, while providing interpretable results. The effectiveness of the model over other baseline and state-of-the-art methods is examined using a publicly available dataset.
The strategies of politeness In AL- Sistani 's advice &amp; Guidance to the Fighters in the Jihad arenas A Discourse analysis

Politeness is a social phenomenon that can be investigated in each language withinside the world. Every language has its own way of expressing through the speakers relying on their tradition and consequently politeness principle suffers from shortcomings that decrease its performance in the analysis of the act of communication. For example, the term 'universalism' is not illustrated properly through Brown and Levinson, similarly to Leech who confines himself to cope with particular speech acts.&#x0D; It is hypothesized that politeness strategy used is positive politeness whilst the negative is not used right here withinside the coming data, and it could be considered as a type of etiquette which is not the same as one society to another.&#x0D; The current study examines the strategies of politeness in Al- Sistani 's advice &amp; Guidance to the Fighters in the Jihad arenas.&#x0D; There are two types of politeness strategies (positive and negative). Both types of politeness strategies contain preserving and readdressing the social distance among the speaker and the hearer.&#x0D; The goal of the study is to analyze the types of politeness strategies might be proposed by Brown and Levinson (1987), the use of discourse analysis which is found out that politeness is used in lots of approaches to construct and maintain the friendly mood.&#x0D; The outcomes proved that the positive politeness is the most effective method used and the negative strategy is not employed at all.
Pelatihan Aplikasi Learning Tools untuk Peningkatan Kualitas Pembelajaran Hybrid di Sekolah Dasar

Teachers must master information and communication technology (ICT) because the lack of it will cause them to fall behind, making the teaching profession challenging. The training activity focuses on three learning tools, namely word wall, peardeck, and storyline. The steps involved in this training include: (1) Providing initial understanding of the importance of learning tool applications, (2) giving examples of how to use learning tools in teaching, (3) practicing the utilization of learning tools, (4) providing mentoring and direct practice in the use of learning tools in teaching, and (5) distributing questionnaires about the training implementation to the participants. The training program provides many benefits, such as: (1) increasing the effectiveness of teaching, (2) optimizing students' learning experience, (3) improving teachers' digital competence and creativity, (4) helping to overcome obstacles in using technology for teaching, (5) ultimately improving student learning outcomes.
Physicochemical Characteristics and Heavy Metals Assessment in Water and Sediments of Obodo Oilfield in Warri South Local Government Area of Delta State, Nigeria

This study investigated the physicochemical characteristics and heavy metals levels in water and sediments of Obodo Oilfield in Warri South Local Government Area of Delta State using standard laboratory-approved APHA and ASTM methods. Results indicated traces of heavy metals present in the water and sediments. Temperature and TDS in the water samples were within the DPR established limits while TSS, Chemical Oxygen Demand, Salinity and Biochemical Oxygen Demand were higher than the control and well above the DPR established limits. The heavy metals present in the soil were all still within the permissible DPR limits, except for Cadmium which was higher than the control and DPR established limit. The results showed that the water and soil within the environment may have been considerably polluted and degraded by the operations of oil and gas production in the region. The study recommends that the activities of the oil and gas production companies be checked to ensure they are operating within the environmental standard regulations of the country and that the Government and necessary bodies should be proactive in checkmating illegal bunkering activities as they have become the major source of oil spills in the region.
IMPLEMENTASI METODE SPIRAL PADA PENGEMBANGAN APLIKASI SIMPAN PINJAM BERBASIS ANDROID

Currently, it is hoped that information technology will provide many conveniences for consumers. Because of its ease of use, information technology makes it easier to process data that was previously done manually to become systematic. A newer feature is the use of applications that have been systemized in Android on smartphones. Mitra Karya Cooperative is one of the cooperatives that performs data processing using the Microsoft Excel application, so there are still problems that occur regarding the completeness and security as well as the data distribution process. Building an Android application to process customer data, savings data, and online lending data at the Mitra Karya Cooperative is the goal of this research. The spiral system development methodology, which includes phases of customer communication, planning, risk analysis, engineering, construction, and disposal as well as customer evaluation, is used in this study. While the model used by the author to visually design the application uses the UML (Unified Modeling Language) model. The Kodular framework and Airtable database are used in the development of Android-based applications. The outcome of the research is an android-based application that can be used to assist Mitra Karya Cooperative in resolving issues related to the cooperative data processing process, which consists of processing customer data, processing deposit data, and processing customer loan data online using an Android device.
PENERAPAN FAILOVER NETWORK MENGGUNAKAN JARINGAN VPN DAN JARINGAN WIRELESS POINT-TO-POINT PADA DISTANCE BUILDING DI PT. TITIPAN KILAT RIAU

This research is intended to build a network that that can connect branch offices with main office buildings in a company. This is done in order to create all performance connected to a company network that can improve and run well, so as to be able to support the company's business processes. At the stage of meeting company needs and in an effort to overcome problems in the network used technology that can always be online with the expectation of quality of services (QoS) approaching 100%.. The technology applied uses two communication lines, the first connection line is point-to-point wireless and the second connection line applies a Virtual Private Network (VPN) with the use of the internet. Handles failover on both network communication connections used by the mikrotik router. This allows if one connection is lost, the other connection line will automatically become a backup line. In this failed network simulation, it shows satisfactory results, as can be seen from the communication performance that runs without feeling tired of traffic. The application of this method can also operate without involving humans. In addition, the results of the measurement of the time lag required for switching connection lines have an average delay value of not more than 4.97 seconds.
Analysis of the Character of the Insured by Policy Holders of Private Insurance Companies in Indonesia with the Rough Set Method

Insurance provides services in the form of services in facilitating a disaster that may occur based on a history of problems that have occurred. One of the private insurance companies in Indonesia has provided an insurance service program to guarantee employee welfare for policy-holding companies. The purpose of this research is to find out the pattern of association rules from the character of the insured by the policyholder so that it is expected to be able to make valuable information as input or consideration for the policymakers of private insurance companies in Indonesia. This study uses the rough set method as one of the efficient data analysis methods or techniques in database mining or knowledge discovery in relational databases. Rough sets provide algorithms to quickly and easily find hidden patterns in data. The results of the pattern of association rules for the character of the insured by the policyholder have formed as many as 14 rules. The certainty value is intended as a proportionate evaluation amount in order to find out the record of insurance claims that can be chosen by the insured. At the same time, the coverage value is intended as an evaluation amount to produce a decision for the insured to submit a claim record. The probable percentage of all events that are most recommended is seen from the highest coverage value related to record indicators, namely for administration by 45.9%, inspection by 44.1%, others by 40.8%, operation by 48.8%, financing by 46.3%, maintenance by 42%, and compensation of 65.9%.
State ordering and classification for analyzing non-sparse large Markov Models

Abstract Markov chains and Markov decision processes have been widely used to model the behavior of computer systems with probabilistic aspects. Numerical and iterative methods are commonly used to analyze these models. Many efforts have been made in recent decades to improve the efficiency of these numerical methods. In this paper, focusing on Markov models with non-sparse structure, a new set of heuristics is proposed for prioritizing model states with the aim of reducing the total computation time. In these heuristics, a set of simulation runs are used for statistical analysis of the effect of each state on the required values of the other states. Under this criterion, the priority of each state in updating its values is determined. The proposed heuristics provide a state ordering that improves the value propagation among the states. The proposed methods are also extended for very large models where disk-based techniques are required to analyze the models. Experimental results show that our proposed methods in this paper reduce the running times of the iterative methods for most cases of non-sparse models.
Hybrid Method to Enhance Effectiveness and Efficiency for Information Retrieval in Web Search Engines

Abstract Search engines are very important tool today, most of people depend on it to achieve the information they need it. The main issue of search engine is ranking algorithms because it provides top relevant web pages related to the search operation. İn this paper will describe the Page ranking algorithm, as an important part of information retrieval and we modified pagerank algorithm we called our proposed algorithm smart clustering pagerank (SCPR) after that we made hybrid between our proposed algorithm SCPR and BM25 algorithm . Also we compared between them according to time response, efficiency, effectiveness, importance and limitation, the evaluation results show that the our proposed method had better effectiveness and reduced computational time.
Learning to Research: Learning to Ranking the Similar Papers via BERT Fine-Tuning

Information retrieval has always been an important research topic in the era of big data. How to accurately retrieve the references needed by scholars from a large number of papers, sort them by relevance, and screen out valuable information to recommend to scholars is an important research demand at present. There has not yet been a model that can capture user attention in academic scenarios based on search results. At the same time, the construction resources of this dataset are limited, and there is a lack of a benchmark dataset that can be used for training search learning models. Therefore, this article constructs a dataset that can be used for academic literature relevance ranking search and a learnable search ranking model. The test results indicate that the model has different advantages in disciplinary fields, confirming that this system can be well applied in academic scenarios and improving the efficiency of scholars in literature search and selection.
Overview of Deep Learning Methods for Sentiment Analysis

With the development and improvement of Web 2.0, user-driven Internet products are rapidly increasing, generating huge amounts of data, especially text data. Sentiment analysis can extract and analyse sentiment tendencies from text data, and is one of the most valuable research directions in natural language processing. In recent years, deep learning has been widely applied to sentiment analysis tasks, using advanced model architectures to achieve better results than ever before. In this paper, we review various deep learning methods and their extensions for sentiment analysis tasks in recent years synthetically, especially the Transformer models and Pre-trained models. In addition, the advantages and disadvantages of these models as well as the limitations of their use are illustrated. Finally, the paper summarized the main challenges of the current sentiment analysis tasks and possible future research directions.
Implementation of the K-Mean Algorithm to Determine the Level of Student Satisfaction with the Online Learning Uhamka System (OLU)

The Covid-19 pandemic has had a negative impact on humans not only in health but also in the economy, social and education. Schools and colleges were closed so that learning that was originally carried out face-to-face was shifted to long distance learning (LdL). LdL implementation can be carried out synchronously and asynchronously. There are obstacles in learning using the Online Learning Uhamka (OLU), namely the effectiveness of using the Online Learning Uhamka (OLU) and the application of the k-means algorithm to determine the level of student satisfaction with the Online Learning Uhamka (OLU) system and the reliability of the k-means algorithm in clustering .One technique to measure the level of satisfaction is to use clustering techniques. The advantage of the clustering technique is that it is easy to adapt, imply and execute and is commonly used in various fields. One of the clustering techniques that is often used is the k-means algorithm. There are 2 clusters used in the k-means algorithm. Clustering results using the Kmeans algorithm showed that 309 respondents belonged to cluster 1, namely satisfied, and 94 respondents belonged to cluster 2, namely dissatisfied. The indicators used to assess satisfaction are usability, content quality, interaction quality. Of the three assessment indicators that have the lowest score is the interaction quality indicator with the centroid value in cluster 1, namely 19.33980583 and the centroid value in cluster 2, namely 14.08510638. The results of the Kmeans algorithm reliability test by calculating the Davies Bouldin index value are good enough in clustering data. The Davies Bouldin index value is 0.3806830859.
Emerging trends: Risks 3.0 and proliferation of spyware to 50,000 cell phones

Abstract Our last emerging trend article introduced Risks 1.0 (fairness and bias) and Risks 2.0 (addictive, dangerous, deadly, and insanely profitable). This article introduces Risks 3.0 (spyware and cyber weapons). Risks 3.0 are less profitable, but more destructive. We will summarize two recent books, Pegasus: How a Spy in Your Pocket Threatens the End of Privacy, Dignity, and Democracy and This is How They Tell Me the World Ends: The Cyberweapons Arms Race . The first book starts with a leak of 50,000 phone numbers, targeted by spyware named Pegasus. Pegasus uses a zero-click exploit to obtain root access to your phone, taking control of the microphone, camera, GPS, text messages, etc. The list of 50,000 numbers includes journalists, politicians, and academics, as well as their friends and family. Some of these people have been murdered. The second book describes the history of cyber weapons such as Stuxnet, which is described as crossing the Rubicon. In the short term, it sets back Iran’s nuclear program for less than the cost of conventional weapons, but it did not take long for Iran to build the fourth-biggest cyber army in the world. As spyware continues to proliferate, we envision a future dystopia where everyone spies on everyone. Nothing will be safe from hacking: not your identity, or your secrets, or your passwords, or your bank accounts. When the endpoints (phones) have been compromised, technologies such as end-to-end encryption and multi-factor authentication offer a false sense of security; encryption and authentication are as pointless as closing the proverbial barn door after the fact. To address Risks 3.0, journalists are using the tools of their trade to raise awareness in the court of public opinion. We should do what we can to support them. This paper is a small step in that direction.
Unearthing Hidden Research Opportunities Through Bibliometric Analysis: A Review

Bibliometric analysis is vital for identifying gaps and potential research directions. The importance of bibliometric analysis in identifying research gaps and prospective future research endeavours is addressed in this study. This study aims to provide insight into the crucial role that bibliometric analysis plays in identifying research gaps and potential future research orientations. This study demonstrates how bibliometric analysis can assist researchers in identifying new study prospects, avoiding duplication of effort, and ensuring effective resource utilisation. This study also offers future bibliometric analysis modifications to overcome its limitations, including cross-disciplinary indicators and data-sharing standards. Several previous studies adopted bibliometric analysis to identify developing issues and trends, directing future research and pointing to places where new insights and contributions were also emphasised. Based on this study, bibliometric analysis is an effective tool for identifying research gaps and prospective new areas for future research. Adding to this, researchers must be conscious of its limitations and apply other methodologies to support their more profound understanding of a particular topic that they have researched. In conclusion, this study has comprehensively enhanced our understanding of the crucial facets of bibliometric analysis in scientific research.