Famous Application Research Articles

1. THE MOST POPULAR ARTIFICIAL INTELLIGENCE APPLICATION USED BY AEROSPACE ENGINEERING STUDENTS’ TO LEARN MATHEMATICS

Artificial intelligence (AI) develops over time. Education is concurrently the AI evolution,sometimes in front, sometimes behind. Education underlies AI, and later on, AI is used in education. Therefore, the utilization of AI in education cannot be denied. There are many advantages, but also some limitations and ethical issues that arise from the use of AI in education. There are many applications of AI in mathematics scattered throughout society, but not all of them are useful enough. Thus, the purpose of this research is to gain the famous application AI in mathematics (app AI math) that is mostly used and recommended by the students’ experience in doing and learning mathematics. T-test independent samples were used to test the score of two groups, using the AI math app or not, in order to know whether it was affected or not. The result of this research is that the most popular app is Photomath, followed by ChatGPT and Mathway. But still, many students did not use this app. Relating to their score, the students who have used the AI math app to learn math before are not different from the students who do not use the AI math app.

A promising exponentially-fitted two-derivative Runge–Kutta–Nyström method for solving [formula omitted]: Application to Verhulst logistic growth model

Explicit exponentially-fitted two-derivative Runge–Kutta–Nyström method with single f-function and multiple third derivatives is proposed for solving special type of second-order ordinary differential equations with exponential solutions. B-series and rooted tree theory for the proposed method are developed for the derivation of order conditions. Then, we build frequency-dependent coefficients for the proposed method by integrating the second-order initial value problem exactly with solution in the linear composition of set functions eλt and e−λt with λ∈R. An exponentially-fitted two-derivative Runge–Kutta–Nyström method with three stages fifth order is derived. Linear stability and stability region of the proposed method are analyzed. The numerical tests show that the proposed method is more effective than other existing methods with similar algebraic order in the integration of special type of second-order ordinary differential equations with exponential solutions. Also, the proposed method is used to solve a famous application problem, Verhulst logistic growth model and the result shows the proposed method still works effectively for solving this model.

Towards global scale segmentation with OpenStreetMap and remote sensing

Land Use Land Cover (LULC) segmentation is a famous application of remote sensing in an urban environment. Up-to-date and complete data are of major importance in this field. Although with some success, pixel-based segmentation remains challenging because of class variability. Due to the increasing popularity of crowd-sourcing projects, like OpenStreetMap, the need for user-generated content has also increased, providing a new prospect for LULC segmentation. We propose a deep-learning approach to segment objects in high-resolution imagery by using semantic crowdsource information. Due to satellite imagery and crowdsource database complexity, deep learning frameworks perform a significant role. This integration reduces computation and labor costs. Our methods are based on a fully convolutional neural network (CNN) that has been adapted for multi-source data processing. We discuss the use of data augmentation techniques and improvements to the training pipeline. We applied semantic (U-Net) and instance segmentation (Mask R-CNN) methods and, Mask R–CNN showed a significantly higher segmentation accuracy from both qualitative and quantitative viewpoints. The conducted methods reach 91% and 96% overall accuracy in building segmentation and 90% in road segmentation, demonstrating OSM and remote sensing complementarity and potential for city sensing applications.

Does the physicians’ medical team joining behavior affect their performance on an online healthcare platform? Evidence from two quasi-experiments

With the growing interest in and use of online healthcare platforms (OHPs), online medical team (OMT) services have gradually been recognized by physicians as an important multi-channel initiative. However, only a handful of studies have explored the pros and cons of the OMT channel over the traditional individual online service channels in an OHP or examined the critical roles of physicians’ OMT joining behavior. To fill this gap, utilizing two sets of unbalanced panel data, representing high- and low-risk diseases, from 3,500 physicians in a leading OHP in China, we investigate the impacts of physicians’ OMT joining behavior on their online performance based on the trust-transfer theory. The moderating effect of physician’s team involvement is investigated based on data from a famous OHP application. Employing a combination of propensity score matching and difference-in-differences identification, the results indicate that physicians’ OMT joining behavior positively influences their online performance. Furthermore, for the physicians who deal with low-risk diseases, their OMT involvement weakens the positive relationship between their OMT joining behavior and online performance although it has the opposite impact in the case of high-risk diseases. Our subsample analyses provide further insights into how physicians’ OMT joining behavior influences their online performance based on their seniority levels. Specifically, for physicians with higher seniority levels, the team joining behavior positively influences their online performance whereas there is no significant effect for the physicians with lower seniority levels.

Research of Vulnerability Scanners of Web Applications of Intelligent Transport Systems

The intellectualization of transport systems is accompanied by the widespread use of web applications. The paper presents a system of criteria for evaluating the effectiveness of vulnerability scanners for web applications of intelligent transport systems, the features of the functioning of which impose additional requirements for the secure development of applications used in critical information infrastructure and systems interacting with it. A study was made of the most famous web application vulnerability scanners.

On the security of fully homomorphic encryption for data privacy in Internet of Things

SummaryTo achieve data privacy in Internet of Things (IoT), fully homomorphic encryption (FHE) technique is used to encrypt the data while allowing others to compute on the encrypted data. However, there are many well‐known problems with FHE such as chosen‐ciphertext attack security and circuit privacy problem. In this article, we demonstrate that a famous FHE application named Brakerski/Fan–Vercauteren scheme, a circuit privacy application based on fast private set intersection, and an encoding application that encodes integer or floating point numbers based on Microsoft Simple Encryption Arithmetic Library homomorphic encryption library, are insecure against chosen ciphertext attacks due to insecurity of the underlying fully homomorphic schemes. These results show that using cryptographic primitives even with security proofs causes serious security vulnerabilities on the applications themselves. The results also give evidences that the security of adopted cryptographic primitives in IoT should be proved in appropriate formal security models as well as proof of the scheme itself.

A new fingerprint definition for effective song recognition

Music and song recognition is an activity of wide interest for researchers and companies due to the intrinsic challenges and the possible economic profits it can give. Despite basic algorithms about song recognition are simple in principle, it is quite difficult to obtain an efficient and robust approach able to generate an effective algorithm for identifying songs on the fly. This statement is proved by the fact that there are very few companies in the world having their core business into this field, even if the potential market is very huge. In this paper, we propose a new approach for generating fingerprints from excerpts of songs that is the first step in implementing a complete algorithm of song recognition. Their generation is based on the Welch’s method for spectral density estimation, the use of a Mel filter bank and an exponential adaptive threshold curve in the frequency domain never used before. Even if the previous techniques are not new, at the best of our knowledge they are not used all together for fingerprint generation. Our main purpose is to show that the proposed fingerprint generation approach permits to obtain a very high accuracy in recognizing pieces of song and their position inside the song, as well as it appears robust compared to typical alteration of the audio signal. Specifically, the fingerprints we generate are highly insensitive to noise and audio lossy compression algorithms; moreover, we think the method is prone also to generate pitch insensitive fingerprints with a small modification. We show through an experimentation with a large database of songs the recognition accuracy obtained with our fingerprints is better than the landmark-based approach (already used by the famous Shazam application). This is not a negligible results because even small improvements means a very large number of more recognitions, with higher profit prospects in industrial applications. In order to better focus on the fingerprint structure and its generation algorithm, we don’t discuss any specific search algorithm, that is a subject of further work, and we use a linear search only in our experiments; in such a way, we think the goodness of the fingerprint as such is better evinced.

INCREASING READING COMPREHENSION AND SUMMARY WRITING ACHIEVEMENTS BY USING RAP STRATEGY WITH 100 FAMOUS STORIES APPLICATION

This study aimed to investigate the students’ writing achievement between before and after the students were taught by using RAP strategy with 100 Famous Stories application. It involved 68 students as pasrticipants and used quasi experimental research method. The result of this study were analyzed by using paired sample t-test and independent sample t-test with the SPPS Version 22.The result of the study for the experimental group showed that the significance level of p was 0.05. The critical value of t-table was lower than t-obtained (22.3332.036) for reading and (9.1622.036) for writing. In other words, there was a significance difference in reading comprehension and writing achievement before and after the treatments in experimental group. Furthermore, the result of the independent t-test showed that the critical value of t-table was lower than t-obtained (5.9361.997) for reading and (7.8521.997) for summary writing, that is to say there was a significance difference in reading comprehension and writing summary acheievement of the eleventh grade students of SMA Srijaya Negara who were taught by using RAP Strategy with 100 Famous Stories Application and those who were not. It concludes that RAP strategy with 100 famous stories application could improve the students’ reading comprehension and writing achievements.

Discursive Practice in Online Learning by Using Zoom Meeting on COVID-19

Since November 2019 COVID-19 virus spread, all activity chance totals especially in Education. Especially in Indonesia applied online learning, the famous application use is Zoom Meeting. I’m as researcher interested analyze how interaction and communication teacher and student in Zoom application, then want find out how Zoom application in learning media present the discursive practice especially between teacher and student, last how student and teacher construct discursive practice use Zoom application. This research aims to find out how discursive practice present in full online class, especially in English class by using Zoom application. This descriptive qualitative study used interview and observation methodology. Twenty six students from English Language Education Study Program, Magister Program of UNP academic year 2021 participated in this study. Based on observed conducted by researcher in class A and B that use Zoom platform in full online learning toward discursive practice. Researcher found discursive practice can present in the class, although in virtual class. Discursive practice in online class by using Zoom application beyond expectation lecturer and student communicate and interaction well with menu that provide by zoom. It can conclude by researcher that the discursive practice can present although in virtual class.

Social commerce isn’t the cherry on the cake, its the new cake! How consumers’ attitudes and eWOM influence online purchase intention in China

PurposeIn the recent decade, social media popularity and growth have boosted the development of social commerce (SC). This study aims to explore the significant impact of guanxi (关系) elements (ganqing 感情, renqing 人情 and xinren 信任) on online purchase intentions. Furthermore, this study investigated the moderating role of social support between electronic word-of-mouth (eWOM) and online purchase intentions.Design/methodology/approachAn online survey was conducted on 309 consumers who had online purchasing experience through WeChat, a famous social media application in China. Structural equational modeling was used to test all hypotheses.FindingsThe results indicate that guanxi elements are positively related to eWOM, affective attitude and online purchase intentions in SC. Additionally, social support has a significant moderating role between eWOM intention and online purchase intention.Practical implicationsConsidering the effects of guanxi elements on eWOM affective attitude and online purchase intention in Chinese SC, online retailers are advised to carefully develop their marketing strategies to retain and attract new consumers. Furthermore, online retailers can use the findings from this study to understand the consequences when online purchase intention is strongly influenced by guanxi elements.Originality/valueThis research extends the current literature by applying the notion of guanxi components to the sense of SC and relating the notion of guanxi components and consumer decisions. The results show an understanding of the reality by which guanxi elements may affect the intention of eWOM sharing and boost online purchase intention.

Modified Fuzzy Approach to Automatic Classification of Cyber Hate Speech from the Online Social Networks (OSN’s)

The extensive use of online media and sharing of data has given considerable benefits to humankind. Sentimental analysis has become the most dynamic and famous application area in current days, which is mainly used in knowing the public's opinion. Most algorithms of machine learning are used as principle methods for sentimental analysis. Even though several methods are available for classification and reviews, all of them belong to a single class of classification which differs among several different classes. No methods are available for the classifying of multi-class instances. Therefore, fuzzy methods are used for classifying the instances depended on multi-class for achieving a clear-cut view by indicating suitable labels to objects during the classification of text. This paper includes the categorization of cyberhate information. If there is a growth in dislike speeches of the online social network may lead to a worse impact amongst social activities, which causes tensions among communication and regional. So, there is the most demand for cyberhate conversation detection automatically through online social media. Generally, an updated process of fuzzy words is designed that includes two stages of training for the classification of cyberhate conversation into 4 forms, race, disability, sexual orientation, and religion. Depended on the types of classification, experiments have been conducted on these four forms by gathering different conversations through online media. Systems based on rules of fuzzy approach have been used. This fuzzy with rule-based is for the classification of features using Machine Learning techniques such as the words that implants for future bag-of-words and extraction methods. In this, the cyberhate conversations are taken from OSN's depended on the attributes defined in a dataset using rule-based fuzzy.

A deep learning-based multi-turn conversation modeling for diagnostic Q&A document recommendation

Online healthcare communities (OHCs) have become producers of medical information. Solving the issue of how to effectively reuse such a large amount of medical data and discover its potential value is of the utmost importance for alleviating the shortage of medical resources. Online consultation has received widespread attention and population since its first appearance in 1999, and as a result, many diagnostic multi-turn questions and answers (Q&A) documents have become available. This type of document is formed by multiple rounds of patient questions and doctors’ diagnostic answers and contains massive medical knowledge and doctors’ diagnostic experience. Few studies concentrate on the modeling and recommendation of this type of document, yet making these documents convenient for reuse reduces the cost of medical consultation for patients and saves time addressing common diseases for doctors. In this paper, we focus on the modeling and understanding of diagnostic multi-turn Q&A records and propose a deep-learning recommendation framework based on patient medical information needs, the contents of Q&A records and doctor background information. With the evaluation based on a real dataset that contains pediatric consultation dialogues fetched from DingXiangYuan, a famous online consultation application in China, we found that the proposed model achieved a good performance on the recommendation of diagnostic multi-turn Q&A records and outperformed baseline models. In addition, we discussed a potential application scenario of the recommendation model, suggesting that the proposed model can promote the reduction of patient costs and doctors’ work pressure in countries or regions with insufficient medical resources.

Real-time database for geochemical earthquake precursory research

In the present research for the first time, an effort has been made to create an online real-time database for geochemical earthquake precursory research. The manually operating real-time database has been improved for earthquake precursory studies by changing the operating system from manual to automatic. We have used an open-source programming languages "R" and python for the data computing. Both are free programming languages software for statistical computing and graphics. To upgrade our working procedure to integrate our data with the popular and famous open source web application solution stack "AMP" (Apache, MySQL, and PHP) has been used. In this real-time database, continuous radon data from our monitoring stations, whereas, seismic and metrological data from Central Weather Bureau have been uploaded. It enables us to see the changes in the radon time series before and after the seismic events. Now, we could analyze the data in better ways before the seismic events using mathematical tools. This system would be very helpful in increasing the efficiency of earthquake prediction studies. It is also useful to have a better understanding of the physical processes involved in the more advanced statistical tools that allow the prognostic of earthquakes by the use of the data from a network of continuous monitoring stations.

Improving Cost for Data Migration in Cloud Computing Using Genetic Algorithm

Cloud computing is gradually increasing its popularity in enterprise-wide organizations. Information technology organizations e.g., IBM, Microsoft, and Amazon have already shifted towards Cloud computing. Cloud-based offerings such as Software as a Service, Platform as a Service and Infrastructure as a Service (IAAS) are the most famous offerings. Most of the existing enterprise applications are deployed using an on-premise model. Organizations are looking for Cloud based offerings to deploy or upgrade their existing applications. SAP, Microsoft Dynamics, and Oracle are the most famous ERP or CRM application OEMs. These enterprise applications generate lots of data are hosted in an organization or on client data centers. Moving data from one data center to the Cloud is always a challenging tasks which cost a lot and takes much effort. This study proposes an efficient approach to optimize cost for data migration in cloud computing. This study also proposes the approach to optimize cost for data collection from multiple locations which can be processed centrally and then migrate to Cloud Computing.

Spatial adiabatic passage of massive quantum particles in an optical Lieb lattice

Quantum interference lies at the heart of quantum mechanics. By utilizing destructive interference, it is possible to transfer a physical object between two states without populating an intermediate state which is necessary to connect the initial and final states. A famous application is a technique of stimulated Raman adiabatic passage, where atomic internal states can be transfered with high efficiency regardless of lossy intermediate states. One interesting situation is a case where the initial and final states are spatially well separated. Quantum mechanics allows a particle to move without practical possibility of being found at the intermediate area. Here we demonstrate this spatial adiabatic passage with ultracold atoms in an optical lattice. Key to this is the existence of dark eigenstates forming a flat energy band, with effective transfer between two sublattices being observed. This work sheds light on a study of coherent control of trapped cold atoms.

Spatial disparities in accessibility to recreational amenities: the case of Pokémon GO

In this study, we analyze spatial disparities in access to particular recreational amenities. We focus on the famous application for smart phones: Pokemon GO. The aim of this location-based augmented reality application is to capture and collect virtual creatures called Pokemon, using a cell phone. Collecting Pokemon is made possible by using certain items only available in particular places, called Pokestops, that the player has to visit in order to obtain them. Pokestops are also very useful to players because the chances of finding Pokemon within its vicinity are higher as opposed to elsewhere. Because these Pokestops are not randomly located, some spatial disparities have been observed by players. Our objective is to explain these spatial disparities in French cities. We focus on the French department of Loire-Atlantique and Nantes metropolis that permit use, to compare situations and determinants in urban and rural neighborhoods. Results of different spatial econometric models reveal strong disparities between neighborhoods located in the Nantes metropolis, and others. It appears that determinants of these disparities may vary according to whether an individual is located in a rural or urban area. Location of Pokestops does not seem to be correlated with population characteristics, but rather with local amenities such as historical monuments, shops, or green spaces.

A Generic Review of Messenger Application: Wechat And Whatsapp

Over the Internet today, communication environment is more easy and interesting to the citizen. There are much interesting in modern technology when they provided a good substrate for creating large-scale data sharing, content distribution, and application-level multicast applications. The important objective of this article is getting information about comparison of two distributed system in messenger application. Messenger application is known as a tool communication that can get any information. The distributed system is one of the independent and single coherent systems it was built an application that will be informed that section is being fixed or replaces to serve more applications or users. The distributed system for this article about the comparison of two distributed system in messenger application for WeChat and Whatsapp, it is also known as the famous application for this generations nowadays.

The Candoglia Marble and the “Veneranda Fabbrica del Duomo di Milano”: A Renowned Georesource to Be Potentially Designed as Global Heritage Stone

Marbles from Alpine area have been widely employed to build and decorate masterpieces and buildings which often represent the cultural heritage of an area (statuary, historic buildings and sculptures). Candoglia marble, object of the present research, is one of the most famous and appreciated marbles from Alpine area; it has been quarried since Roman times in the Verbano-Cusio-Ossola (VCO; Piemonte—NW Italy) extractive area. Candoglia Marble outcrops are present as lenses within the high-grade paragneisses of the Ivrea Zone, a visible section of deep continental crust characterised by amphibolite- to granulite-facies metamorphism (Palaeozoic period). Candoglia calcitic marble (80–85% CaCO3 and the 15–20% other minerals) shows a characteristic pink to gray colour and a coarse-grained texture (>3 mm): frequent centimetre-thick dark-greenish silicate layers (mainly represented by diopside and tremolite) characterize the texture of the marble. It has been largely used in local rural constructions and historical buildings, but its most famous application has been (and still is) for the “Duomo di Milano” construction (fourteenth century). The Veneranda Fabbrica del Duomo di Milano carried out the anthropogenic activities dealing with the Candoglia marble exploitation; it has to be highlighted that the company have managed the Marble exploitation during the last seven centuries and that the quarry itself is a tangible sign of the development of extraction and heritage in the VCO area. Candoglia marble can be recognized as a significant example of a “Global Heritage Stone Resource”: its exploitation from quarry to building (the Duomo di Milano) well represents the close correlation between stone and cultural heritage, between georesources and humankind development.

駁斥「無風險利率也就是風險中立機率」與王詩韻的偽證 (Rebuttal to 'Risk-Free Rate IS Risk-Neutral Probability' and Shin-Yun Wang’s Perjury)

Chinese Abstract: 衍生性金融商品的基礎訂價理論，是透過假設商品的預期報酬率等於無風險利率，報酬率的隨機分布模式被稱作風險中立機率。以上被稱作「資產訂價基本定理」，它是根基於假設金融市場具有完美性、完全性、不存在套利機會而獲致的結果，最知名的應用是選擇權訂價公式，學者Merton與Scholes因此獲得1997年諾貝爾經濟學獎。遺憾的是，台灣的教科書都遺漏陳述「市場不存在套利機會」是「風險中立訂價」的必要條件，劉任昌（2016）評論此現象，也揭發東華大學財金系王詩韻（2014）持續傳遞錯誤觀念，甚至扭曲為「無風險利率也就是風險中立機率」的荒謬。劉任昌的評論，導致王詩韻向廖四郎賠償，也引發王詩韻誣告劉任昌。本文旨在說明劉任昌揭發行為的公益性與正當性，以及揭發王詩韻的偽證細節。 English Abstract: The fundamental pricing theory of derivatives is conducted under the assumption that the expected return rate of assets is equal to the risk-free rate, and its probability distribution is called the risk-neutral measure. This principle is called the Fundamental Theorem of Asset Pricing, which assumes perfect, complete and no-arbitrage markets. The most famous application of this theorem is the option-pricing formula, whose contributors, Merton and Scholes, were rewarded the Noble Economic Prize in 1997. Unfortunately, Chinese textbooks on financial derivatives in Taiwan never specify the fact that no arbitrage is a necessary condition for risk-neutral pricing. Liu (2016) criticized this phenomenon and showed how the falsehoods were lifted and amplified by Wang (2014) from Liao and Wang (2012). Wang (2014) specified that a risk-free rate is a risk-neutral probability in an attempt to escape an accusation of copyright infringement. Liu (2016) identified these falsehoods, which lead to Wang’s compensation to Liao. Consequently, Wang sought retribution by intimidating and defaming Liu. This article explains the reasons for Liu's actions and the outcomes that followed. In particular, this article details Wang’s perjury to the Investigation Bureau and the District Attorney.

Proteção e tratamento de dados sob o prisma da legislação vigente

O presente estudo tem como objetivo analisar os dados coletados pelos principais serviços de aplicação de internet à luz das legislações de proteção à dados pessoais.Partindo de análise histórica do surgimento da internet e das gerações de leis de proteção de dados, passaremos à análise do Marco Civil da Internet e da Lei Geral de Proteção de Dados (ainda não em vigor), bem como de seus alcances e efetiva proteção aos titulares de dados.Em seguida, passaremos à análise dos principais serviços de aplicação de internet e análise de seus termos de uso, concluindo pela ampla coleta de dados por mencionados serviços e a necessidade de aprofundamento legislativo no tocante ao processamento e compartilhamento de referidos dados.