Reviewerly/exHarmony huggingface dataset:
Penerapan Metode MFCC dan Naive Bayes untuk Deteksi Suara Paru-Paru

Lung disease is an unpleasant illness that can be dangerous if not treated properly. This is because lung disease can infect others. The lungs are an important part of the human organ that distributes oxygen throughout the body, so this lung disease needs to be treated with proper procedures. Lung disease problems can be solved using an expert system. Expert systems can help doctors work to provide an early diagnosis of ear diseases. The method used in this study is the MFCC method, which provides compelling information about lung disease and provides treatment solutions based on the symptoms of each existing disease. This system is performed by calculating the symptom weights of the disease, which is obtained from expert experience, and produces the optimum value in the form of the maximum value. The highest value data provides the results of the disease diagnosis at the level of confidence in the form of percentage values. Based on the test results, the goodness of fit between the system diagnostics using the system validation test method and the expert diagnostic results is 90%. The results of the tests performed show that the system is operating normally according to its capabilities.
Classification of White Blood Cells using Convolutional Neural Network with Data Augmentation

Nowadays, along with the development of science and technology, the classification of white blood cells (WBC) can assist in the diagnosis of leukemia.Manual blood cell classification is often not very accurate, time-consuming, laborious, and costly.Moreover, the risk of manual blood collection can be the transmission of infectious diseases such as HIV/AIDS, which causes unnecessary harm.Therefore, developing an automated system for blood cell classification will improve safety and save time and money.In this work, we proposed the classification method of white blood cells using Convolutional Neural Network (CNN) with Data Augmentation.To test the effectiveness of the proposed white blood cell classification system, a total of 10,299 white blood cells images from CellaVision was used.The experimented results of the proposed method have achieved 96% accuracy and compared to other published methods.
DeMRC: Dynamically Enhanced Multi-hop Reading Comprehension Model for Low Data

Multi-hop reading comprehension requires the aggregation of multiple evidence facts to answer complex natural language questions, and the answer should be avoided when there is no answer. Training a model that can handle such difficult tasks requires a large number of data sets to support, but the labeling of data sets is very expensive and time-consuming, so it is very important to explore reading comprehension models suitable for low data, and external data related to large-scale tasks. It will also effectively improve the performance of the model. This paper proposes a two-stage model with dynamically context-enhanced method for multi-hop reading comprehension tasks under low data called DeMRC. The first stage sentence filtering model filters the top k sentences that are strongly related to the question, and the second stage answer prediction model dynamically constructs the training set every time during training to expand the data set, and uses sentences selected by sentence filtering model as input to reduce the interference of irrelevant sentences to the model during inference. In addition, the self-training method is used to pseudo-label the external data and use it as an auxiliary data set to improve the performance of the model. We conducted experiments on the multi-hop reading comprehension data set of the Chinese “CAIL 2020" Judicial Artificial Intelligence Challenge Reading Comprehension Track and English cross-document level data set called HotpotQA, which are 3.5% and 21.3% higher than the powerful baseline model, showing the effectiveness of the method.
Mahasiswa Cerdas Tangkal Berita Hoaks di Era Disrupsi melalui Literasi Digital

The student body is one of the most important assets in a nation, especially in an era where information is disseminated very easily and quickly through the internet or digital media. But unfortunately, that ease has brought not only positive but also negative effects, such as the hoax easily spread especially when it is not accompanied by an understanding of digital literacy. It is a challenge that will then require students not only to grow with the times, but it must be wise not to become one knowingly or unknowingly spreading the hoax. In conclusion, in the use of social media, critical behavior becomes one of the principal things to prevent unnecessary communication from reading information and its origins. Hence in this post, the author mentions some things or attitudes that students can do to be clever and wise in digital media to counter the spread of hoaxes that are so dangerous to the nation, based on digital literacy.
Improving Student's Writing Skills Through the Application of Content Structure Strategy

The purpose of this study was to determine the effect of content structure strategy on improving student's writing skills and to determine whether or not the content structure strategy was effective in increasing student's writing skills. This study used a cluster random sampling method consisting of 28 students taken as the research sample from three classes. The results showed that there was a significant difference between student scores before and after treatment. Student writing recount text increases after use. In addition, the calculated t-test match result is higher than the matched t-test result table coefficient. In conclusion, there was a significant different between the student's achievement before and after the treatment. The student's mean score on the post-test was higher than the mean score on the pre-test. This shows that the content structure strategy is effectively used in learning recount text in Al-Ikhlas Lubuklinggau Junior High School. &#x0D; Keywords: Ability, Content Structure, Strategy, Writing
Multiple Statistical Analysis of the Influencing Factors of the Retail Price Index

With the improvement of the income level of people, our country's economic level has continued to improve.However, due to the differences in geographical and historical conditions in various regions, there is a large gap in the level of economic development in various regions of our country, and there are also significant differences in the prices of different commodities.This article uses the multiple linear regression and Principal Component Analysis (PCA) by SAS to explore the impact of the Retail Price Index (RPI) and different factors, such as the Producer Price Index (PPI) of agricultural production materials and the PPI of industrial production, the Purchasing Price Index (PRI) of industrial producers, and the Price Index of Investment in Fixed Asset.
A RAW Burst Super-Resolution Method with Enhanced Denoising

Deep learning-based burst super-resolution (SR) approaches are extensively studied in recent years, prevailing in the synthetic datasets and the real datasets. However, the existing networks rarely pay attention to the enhanced denoising problem in raw domain and they are not sufficient to restore complex texture relationships between frames. In this paper, we propose a new framework named A RAW Burst Super-Resolution Method with Enhanced Denoising (EDRBSR), which solves the BurstSR problem by jointly denoising structure and reconstruction enhancement structure. We adopt a Denoising Network to further improve the performance of noise-free SR images. Also, we propose a Reconstruction Network to enhance spatial feature representation and eliminate the influence of spatial noise. In addition, we introduce a new pipeline to compensate for lost information. Experimental results demonstrate that our method over the existing state-of-the-art in both synthetic datasets and real datasets. Furthermore, our approach takes the 5th place in synthetic track of the NTIRE 2022 Burst Super-Resolution Challenge.
THE SYLLABLE STRUCTURE IN NAGPURI (SADRI)

With the aid of certain specific instances, this study aims to offer a quick overview of the syllable structure in Nagpuri (Sadri). There is also a small remark about Nagpuri (Sadri). This language belongs to the Indo-Aryan family, according to Grierson's Linguistic Survey of India. The description in this paper is based on the background research limited to fieldwork in the districts of Ranchi, Palamu, Lohardaga (where the language is predominantly spoken). There are also some books, essays, articles and thesis accessible in the language. Around 5,108,691 people who were native speakers of Nagpuri (Sadri) were counted in the 2011 Census. The purpose of this study is to explore the syllabic structures of Nagpuri (Sadri). The Nagpuri (Sadri) language has a hexasyllabic word structure which is further divided into two sorts of syllables: open syllables and close syllables.
CIPSEC-Enhancing Critical Infrastructure Protection with Innovative Security Framework

of the Information and Communication Technologies (ICT) field.This rapid adaptation however, was performed without thorough evaluation of its impact on CIs' security.It resulted into leaving CIs vulnerable to a new set of threats and vulnerabilities that impose high levels of risk to the public safety, economy and welfare of the population.To this extend, the main approach for protecting CIs includes handling them as comprehensive entities and offer a complete solution for their overall infrastructures and ICT systems (IT&OT departments).However, complete CI security solutions exist, in the form of individual products from IT security companies.These products, integrate only in-house designed and developed tools/solutions, thus offering a limited range of technical solutions.The main aim of CIPSEC is to create a unified security framework that orchestrates state-of-the-art heterogeneous security products to offer high levels of protection in IT (information technology) and OT (operational technology) departments of CIs, also offering a complete security ecosystem of additional services.These services include vulnerability tests and recommendations, key personnel training courses, public-private partnerships (PPPs), forensics analysis, standardization activities and analysis against cascading effects.
Human Inspired Robot Learning to Search Objects in Cluttered Environments Using Maximum Information-based Method

Searching for target objects in clutter widely exists in our daily life. However, it is very challenging for robots because of the uncertainty and unobservability caused by cluttered environments. Therefore, this work proposes a method that can imitate the human searching process, spreading the clutter to get more unseen information, which addresses the un-observability problem. This work provides an objective function to conduct the imitation. Due to the uncertainty of cluttered environments, it is hard to obtain environments models. This work uses soft actor-critic, a model-free reinforcement learning method, to address this problem and learn a search policy based on the proposed objective function. We evaluate the training efficiency and search success rate of the policy. The experiments indicate that the proposed objective function can accelerate the training process. The success rates of our method and baseline methods are compared in different difficulty settings. Our method has a higher success rate in highly cluttered environments, including more than 60 objects.
HUBUNGAN PEMANFAATAN MEDIA PEMBELAJARAN PAI DENGAN MOTIVASI BELAJAR SISWA DI SMA N UNGGUL DHARMASRAYA KECAMATAN KOTO BARU KABUPATEN DHARMASRAYA

This research is constrained by students with experiencing the lack of student learning motivation caused by the use of learning media by the PAI teacher. Thus the students tend to lack the motivation to learn. Many students are less interested in the media used by teachers, there are even some students who do not understand the material that the teacher delivered, there are also unpregive, sleepy, boredoms at the time of following the PAI learning. The purpose of this study is to know if there is a relationship between the utilization of the PAI media with student learning motivation and to know how much the media endicule relationship is with student motivation at SMA N Unggul Dharmasraya. This research is a correlational quantitative type study. While the data used is primary data obtained by questionnaire using a kind of scale likes spread to 28 high school students Unggul Dharmasraya. Data analysis techniques in research are descriptive statistical analysis, normality test, simple linear regression test, product moment correlation test. The result of hypothesis test has a variable relationship of the media equivalent of media equivalent with student learning motivation. Thus it is stated that Ha accepted Ho rejected. Indicates that the proposion of the variable relationship of the media equivalent of media studies with student learning motivation is equal to 0.336 or 33.6%, while the remaining 66.3% is influenced by other variables that are not in the research that the authors do.
Predicate Logic Network: Vision Concept Formation

Although deep learning has shown good performance in many fields, it still lacks the most basic human intelligence, which we often called the ability to draw inferences about other cases from one instance. Therefore, how to empower model with logical reasoning ability has received much attention. Thus, we propose neural predicate networks, a model that combines deep learning methods with first-order logic. It converts visual tasks into first-order logic problems by deconstructing them into objects, concepts and relations. Then, achieve first-order logic differentiable by learning logical predicates as neural networks. Finally, the differentiable model can be trained by back propagation to simulate the formation of concepts in the human brain and solve the problem. Experimental results on two image concept classification datasets demonstrate the effectiveness and advantages of our approach.
Message Passing Graph Neural Networks for Software Security Vulnerability Detection

With the booming development of deep learning and machine learning, the use of neural networks for software source code security vulnerability detection has become a hot pot in the field of software security. As a data structure, graphs can adequately represent the complex syntactic information, semantic information, and dependencies in software source code. In this paper, we propose the MPGVD model based on the idea of text classification in natural language processing. The model uses BERT for source code pre-training, transforms graphs into corresponding feature vectors, uses MPNN (Message Passing Neural Networks) based on graph neural networks in the feature extraction phase, and finally outputs the detection results. Our proposed MPGVD, compared with other existing vulnerability detection models on the same dataset CodeXGLUE, obtain the highest detection accuracy of 64.34%.
PEMANFAATAN FITUR TRACK CHANGE PADA PERKULIAHAN EDITING DI PERGURUAN TINGGI

In the current digital era, learning in every subject must be based on the use of Information and Technology (IT). It is not limited to lectures. The IT feature used in this research is the Track Change feature in Microsoft Word. The purpose of this research is to use the Track Change feature in lectures. The research method used is qualitative with a descriptive analysis approach. The subjects of this study were B2 class students, batch 2019 of the Indonesian Language and Literature Education Study Program, IKIP Siliwangi. The data are in the form of observations and questionnaires on student responses to manuscripts and the results of student edits. Through a problem-based editing model assisted by the Track Changes feature, students not only edit or edit simply, but every language error is edited and explained according to the error. The results showed that the use of the Track Changes feature in editing lectures went well. This is indicated by the data from observations on the activities of lecturers and students, student response questionnaires and the assessment of the results of editing student manuscripts.
Analysis of the Effectiveness of Flash Floods Disaster Mitigation in Java Island

Flash flood is one of the natural disasters that currently happens a lot in Indonesia. Java Island is one of the largest archipelagoes in Indonesia and has the highest incidence of flash floods. Several efforts were conducted to anticipate and mitigate flash floods in Java Island, including an early warning system, preparing safety equipment, building evacuation route signs, and monitoring watersheds. Through the dataset of Village Potential 2018, this study aims to explore the effectiveness of flash flood mitigation in Java Island using the R programming language. The stages of research carried out in this study are data preprocessing, including selecting, recoding the variables, exploratory univariate, bivariate, and multivariate data analysis. The results showed that the fatalities of flash floods often occurred in areas with plains surface, especially in West Java and East Java, followed by the topography of Central Java on the hills and the valleys in Banten. In addition, the most effective disaster mitigation established in Java Island is safety equipment and the construction of evacuation route signs compared to other disaster anticipation efforts.
Determinants for the utilisation of blockchain technologies for accounting in Kuwait

This paper investigates the major determinants influencing accountants' intentions to utilise blockchain technologies (BCTs) in conducting accounting practices in Kuwait. A variant of the technology acceptance model (TAM) is used to assess the intention to use the technology. A survey based on a sample of 714 accountants and accounting faculty revealed that around 636 have the willingness to adopt BCT in a classroom context. Using hierarchical regression analysis, results reveal that unexpectedly the perceived ease of use does not have the anticipated impact on the intention to use BCT, whereas usefulness, compatibility, subjective norms and attitudes have a direct influence to the intentions to use BCT for academic activities. Findings of the study show that TAM can be customised and used in a non-conventional setting such as BCT. This study is one of the rare attempts to analyse factors influencing accountants' adoption of BCT in a developing economy.
Implementasi XGBoost Pada Keseimbangan Liver Patient Dataset dengan SMOTE dan Hyperparameter Tuning Bayesian Search

Liver disease is a disorder of liver function caused by infection with viruses, bacteria or other toxic substances so that the liver cannot function properly. This liver disease needs to be diagnosed early using a classification algorithm. By using the Indian liver patient dataset, predictions can be made using a classification algorithm to determine whether or not patients have liver disease. However, this dataset has a problem where there is an imbalance of data between patients with liver disease and those without, so it can reduce the performance of the prediction model because it tends to produce non-specific predictions. In this study, classification uses the XGBoost method which is then added with SMOTE to overcome class imbalances in the dataset and/or combined with Bayesian search hyperparameter tuning so that the resulting model performance is better. From the research, the results obtained from the XGBoost model get an AUC value of 0.618, for the XGBoost model with Bayesian search the AUC value is 0.658, then for the XGBoost SMOTE model the AUC value is 0.716, then for the XGBoost SMOTE model with Bayesian search the AUC value is 0.767. From the comparison of the four models, XGBoost SMOTE with Bayesian search obtained the highest AUC results and has an AUC difference of 0.149 compared to the XGBoost model without SMOTE and Bayesian search.
Is Bitcoin a Good Investment Asset?

This paper aims to analyze the consequences of adding Bitcoin to an investment portfolio.The main methodology used is the Mean-Variance model combined with the Monte Carlo Simulation.Results show that Bitcoin can improve the Sharpe Ratio of an already diversified portfolio, however the inclusion of Bitcoin has to be done in proportions averaging 3.83 percent of the portfolio's weight.This paper also found that Bitcoin does not seem to behave as a safe haven/hedge asset during the Covid-19 pandemic.A CRYPTOCURRENCY, as defined by the Merriam-Webster dictionary, is "any form of currency that only exists digitally, that usually has no central issuing or regulating authority but instead uses a decentralized system to record transactions and manage the issuance of new units, and that relies on cryptography to prevent counterfeiting and fraudulent transactions".When Nakamoto (2008) first introduced Bitcoin, the goal was to create a virtual, alternative currency that would be completely independent from any financial institution.In 2009 Bitcoin was launched, a cryptocurrency based on a peer-to-peer network that relies on a public ledger of every transaction, called Blockchain. 1 Considered by many as breakthrough technology, Blockchain allows every user to check the balance and the transactions of any Bitcoin wallet, thus providing full transparency.It is not completely anonymous, but rather pseudo-anonymous, in the sense that a person is linked to their Bitcoin address (the pseudonym) but not to their name or home address (Moreno & Shivangee, 2011).Bitcoins are created by "miners", people that use their Central Processing Unit (CPU) power to validate transactions, thus generating blocks, that are added to the blockchain.The supply of bitcoin is capped at 21 million and its rate is predetermined: in 2140, the last bitcoin will be mined.
Use of simplified bowed string model in physics education: A laboratory experiment

This paper proposes an educational science experiment on elastic string vibration where the string is excited by bowing. A custom-built monochord is used to study the resulting vibration experimentally. The mono-chord employs the Faraday's law of induction to measure the string displacement. The proposed experiment is accompanied by numerical simulations based on the integration of a nonlinear 1D damped wave equation with an external forcing term. The simulations give students the ability to compare the theory with practice. The study of bowed string dynamics aims to make the proposed science experiment more meaningful and engaging for students, since many string instruments, such as violin, cello, and other viol and viola family instruments, are played in such a manner. The proposed experiment can be used by teachers teaching BSc and MSc level students in acoustics, physics, applied mechanics, applied mathematics, and other related fields of science.
The Confluence of AI and Blockchain in Tackling the COVID-19 Pandemic

COVID-19 came with a sudden surge, harshly affecting the day-to-day lives of the entire world. Social and economic problems affected almost every nation, wreaking havoc on people’s health, society, and economy everywhere. Although the pandemic is currently in control, the emergence of another pandemic is not unlikely. As technological breakthroughs accelerate, the possibility of controlling virological dangers becomes highly plausible. Better virus containment is attainable with the confluence of technologies such as Blockchain and AI. The newly growing fields and application cases of futuristic technologies for tackling upcoming pandemics are emerging. Several researchers are contributing to COVID-19 management with current and futuristic technologies, and such tools have room for additional improvement. This paper extensively highlights the work done in tackling COVID-19 using Blockchain and AI, illustrating the role of this collaborative approach in dealing with biological threats. We also discuss the prospects and obstacles in combining these technologies to tackle COVID-19-like situations.