Reviewerly/exHarmony huggingface dataset:
Healthcare Framework for Privacy-Preserving Based on Hyperledger Fabric

The healthcare business deals with overly sensitive information that should be handled with care. Names, social security numbers, insurance numbers, addresses, and medical history are all stored in Electronic Health Records (EHRs). Patients, healthcare professionals, medical insurance companies, and research institutes all value such personal information. Patients and healthcare providers, on the other hand, face substantial privacy and security risks as a result of the public dissemination of this extremely sensitive personal data. When health information is transferred, EHR management systems necessitate the use of effective technology. Medical record storage systems are frequently exposed to typical security attack vectors due to current management techniques. As a result, we anticipate the need for new solutions to solve the security and privacy issues that private data poses in healthcare applications. Decentralized, anonymous, and secure EHR handling is possible with healthcare-focused blockchain systems. The needs for privacy and interoperability in healthcare data exchange are examined in this research, and a blockchain-based solution is offered. We used private blockchain technology to examine their suitability for healthcare applications in this paper. The permissioned blockchain structure of Hyperledger Fabric is used in the article. The proposed approach may effectively preserve patient details while maintaining anonymity. The findings of this study show that blockchain technology may be used to promote privacy while also improving interoperability in healthcare information management.
Data Analysis on ERP System of Easy-Pay Payment Services

Machine Learning is one of the leading technologies in this generation with unique techniques and problem-solving algorithms which are very helpful to predict the models accurately with higher positivity rates as compare to the less or false positives prediction of any given projects. In this chapter, we can assure that all of the proposed approaches are almost valid and able to detect fraud transactions. Through this chapter, our aim is to show and determine the possibility of balancing the ratio between fraudulent and non-fraudulent transactions. The methods and parameters which have been used to study the project has anticipated after a thorough research and every requirement have been fulfilled to obtain the desired output. Payment fraud detection is the solid example of classification. The datasets have been modelled after analyzing and pre-processing the existing datasets later to differentiate between fraudulent and non-fraudulent transactions. Such predictions can only be determined with the help of Machine Learning as we used along with the relevant algorithms and techniques which was required for the respective chapter. Numerous interpretations have been done, but acquiring 100% accuracy is still cannot be accomplished due to class imbalanced data related to the transactions. To monitor the usual transactions of people is a vast task and so is to track the fare and fraudulent transactions among billion of people. The main goal of this research is to prevent users from performing unusual transactions through unverified sources and to obtain the positive results in terms of data accuracy and data visualization.
Optimized Activation Function-Based SAR Ship Detection

The task of recognizing or locating an object or group of objects in an image or video sequence is the focus of object recognition in the field of computer vision. It's a problem of matching models from a database with image luminance data representations of those models. The paper presents the novel method for the detection of vessels in complex environments. The present work is inspired by cuckoo search algorithm which was used to optimize the results. Otsu thresholding method was used to binarize the object image and then adaptive cuckoo search algorithm was applied for the detection of vessels in an optimized manner. In this work, we are comparing the proposed results with existing accuracy and time with other methods. The proposed method gives a better accuracy (97.8%) and processing time (3.67 s) as compared to other previous methods used.
Cervical Cancerous Cell Detection Using Enhanced Classification and Embedded Deep Learning Method

The health of women is seriously threatened by cervical cancer, which is the second most common gynecological malignancy. More than 85% of fatalities from cervical cancer among women globally occur in underdeveloped nations, making it one of the top causes of early death among women. The rate of cervical cancer in Bangladesh is mostly 70%. Several imaging tests, such as a Pap smear, cervicography, and colposcopy, are used to check for this prevalent cancer. These tests are used to make judgments, but the structural intricacies of cervical cells may make the process more difficult. This study proposed an Improved Naive Bayes (INB) algorithm. This classification method INB provides better results in compared to support vector machine (SVM), random forest tree (RFT), and Naïve Bayes for cervical cancer. The accuracy of the proposed method becomes 95% which is in general 7% greater than all the previous algorithms used.
Cursor Motion Control Using Eye Tracking and Computer Vision

This research describes an algorithm that performs cursor function while allowing humans and computers to connect without using their hands. It provides a better option to the standard cursor-controlled computer. By using computer vision to scan different facial expressions and matching them to pre-stored expressions, functions can be performed in responses to the movements. That technique is going to allow bodily challenged humans use their face and eye movements to perform cursor tasks. They can lefts-press, rights-press, higher scrolls, and moves pointer lefts and rights. Webcams, numpys, dlibs, but also some few others more fundamental study are all needed by the system.
Vulnerability Assessment of Cryptocurrency Wallet and Exchange Websites

With the great rise in Crypto, the future of blockchain has become susceptible to security threats. These security vulnerabilities allow attackers to compromise sensitive data and manipulate legitimate websites for malware attacks. In this paper, we observe threats and vulnerabilities in blockchain-based websites. By the use of security testing skills and software, we discover severe categories of security vulnerabilities in web applications. To carry out the security testing, we used tools such as, Nmap, Whatweb, and Uniscan, whereas the scans were carried in the OWASP’s Zed attack proxy and Nikto tools. After the scanning process, these tools identify the low, medium, high-level risks, and vulnerabilities and configure the overall security analysis for these web applications. This research puts forth the most common vulnerabilities faced by blockchain-based applications and contributes in reducing the attacks and risk in future.
ITiCSE 2023 Call for Papers

We are delighted to call for submissions to ITiCSE 2023, which will be held in Turku, Finland, from 7 July to 12 July 2023. ITiCSE is a computing education conference held annually in Europe, sponsored by ACM SIGCSE and supported by the ACM Europe Council and Informatics Europe. ITiCSE 2023 will be an in-person conference. All details are at the conference website, https://iticse.acm.org/2023/.
Security and Privacy Column

Differential privacy is nowadays considered the "gold standard" when releasing information, e.g., statistics, on sensitive data. To avoid leaking too much sensitive data noise-adding mechanisms may be used. ϵ-differential privacy measures the amount of privacy ϵ that such a mechanism ensures. Of course, adding too much noise results into useless, random information, while adding not enough may lead to privacy violations. This problem is known as the privacy-utility trade-off and raises a natural optimality question: how can we maximise utility for a given amount of privacy ϵ.
Sharia financial education using augmented reality technology to increase student motivation in distance learning

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Ismi Solikhatun, Ratna Candra Sari; Sharia financial education using augmented reality technology to increase student motivation in distance learning. AIP Conference Proceedings 14 February 2023; 2654 (1): 020008. https://doi.org/10.1063/5.0114754 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Implementation of behavior driven development (BDD) through test cases on Sentratekstil web application

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Sha Asa Ruth Shanda Ginting, Yudi Priyadi, Rosa Reska Riskiana; Implementation of behavior driven development (BDD) through test cases on Sentratekstil web application. AIP Conf. Proc. 14 February 2023; 2654 (1): 020025. https://doi.org/10.1063/5.0114708 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Building AHP – Promethee model for deciding level status floods disaster in edge computing

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Prabowo Wahyu Sudarno, Ahmad Ashari, Mardhani Riasetiawan; Building AHP – Promethee model for deciding level status floods disaster in edge computing. AIP Conf. Proc. 14 February 2023; 2654 (1): 020019. https://doi.org/10.1063/5.0117603 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
FINDING EQUIVALENCE LEVEL BETWEEN SOURCE LANGUAGE AND TARGET LANGUAGE

This research study aims to investigate and identify the equivalence level of translation from textbooks using a qualitative approach with content analysis. As qualitative research, this study presented an analysis of the strategy made by the translator on how English text (Pretext for Mass Murder) was translated into Indonesian. The research study was started through observation of the source language that involved reading, identifying, classifying and evaluating the data. The collected data were analyzed descriptively under four main headings of equivalence level proposed by Mona Baker, namely in word level, above word level, grammatical level, and textual level equivalence. The sampling data of this research study is 150 sentences taken randomly, which are classified as word level (N=58, 39%), above word level (N=45, 30%), grammatical level (N=35, 30%), and textual level (N=12,8%). The findings demonstrated that the translator used different strategies creatively to find out the closest meaning of translation between the source language and the target language. In addition, word level equivalence was found to be the most dominant data compared to others. Keywords: translation, equivalence, the source language and target language.
Performance Analysis of Artificial Neural Network - Chimpanzee Leader Election Optimization on Classification Case

Artificial neural network (ANN) modeling has a long history of modeling and long-standing model modifications. In addition, various ways researchers and programmers do improve ANN performance. Although many developments have been carried out on ANN, this modeling continues to be explored further. This paper aims to combine ANN modeling and one of the population-based metaheuristic models, namely the chimpanzee leader election optimization (CLEO) algorithm. The CLEO algorithm is an optimization algorithm that models male chimpanzees as the leader in their community. The CLEO algorithm has the ability that it does not use hyper tuning in its application. It is because the CLEO method only relies on random values on its variables. The CLEO method in hybrid modeling functions in finding solutions to the ANN weight values. This study observes the performance of ANN-CLEO for classification tasks. The observation results show that the best classification accuracy for the results of this hybrid method depends on the domain of finding the solution or the given upper and lower limit values.
Signtific Translator: (The Automated Sign Language Recognition)

People with hearing loss or other limitations frequently communicate using sign language. Sign languages have been used by humans since the beginning of time. The learning curve for sign language is rather low. Signs and symbols were used by early people to communicate. Our goal is to develop a sign language translator with voice help that can translate sign language into text. By using our detection model, anybody who is deaf and has no knowledge of sign language may easily communicate with them. In this essay, we've covered key approaches, strategies, and associated details concerning our model's operation.
Visual Cursor Movement for Physically Disabled Person

Computers have advanced ever since the beginning and are available to a level where we will work remotely in numerous ways but these inventions are for people who are sound and don't have any physical disability. People with physical sicknesses require an elective approach to create their computers more open and helpful. In this research, a novel algorithm for utilizing iris movement to control the movement of a visible display unit pointer is presented. The foundation of the attention tracker is the use of images captured by a modified webcam to measure attentional movements. The visual display unit then graphs these eye movements to place the mouse pointer appropriately. The algorithm allows physically disabled people to control the movement of the computer cursor to the left, right, up, and down by precisely detecting the position of the iris within the eye and mapping that to a specific position on the monitor. By using this algorithm, the person can also open and close folders, files, or applications through a clicking mechanism. There are many people whose illnesses require them to move around with the aid of some article means. Additionally, by implementing a controlling mechanism that enables physically disabled people to utilize the computer independently is really beneficial. Not just in the long run of natural input, but more significantly for the handicapped and disabled people, the idea of eye control is very helpful.
G-GET: All In One Donation App

Poverty stands as a barrier in one's life in achieving their dream and making life hard. Our application is designed to help the people who are struggling in their life due to poverty and many other issues. This app focuses on forming a bridge between the needy and those who are willing to help, so that both are satisfied. Basic needs of a person like food, medical and education can be resolved by using this app. The application has two major divisions: WANT HELP and WANT TO HELP. Those who are willing to help others by providing food (e.g. for orphanages), donating blood, donating money, etc come under WANT TO HELP. Those who get help from the donors come under WANT HELP. The donors and needy can get connected by registering in this app.
A Method of Geographic Vector Data Collection Based on Web Map

Web map can provide huge amounts of present spatial data promptly, and users can embed the web map into web applications through APIs easily. However, current mainstream web maps as Google Maps, Gaode Maps, Baidu Maps, etc, are incompatible of spatial data service publish standard that are published by most GIS desktop systems, so they cannot be integrated by GIS desktop systems directly. This paper raised a web map integration mode based on web embed, which integrated web map into GIS desktop systems as reference map layer. By means of the development of vector data acquisition tool, we added geographic information data that collected by web maps into vector data layers of GIS desktop system and conduct a study of the interaction between web map and GIS system. Because most web maps encrypted their position coordinates, so the data collected by web maps has accuracy errors. This paper rectified the coordinate errors through position rectification method, and improved the accuracy of collected vector data position. Finally, based on the geographic information vector data collection system, we implemented the vector data collection tool based on web map, and collected the settlement place data of Nai Dong county in Shan Nan area by the tool, which verified the view and method raised in this paper.
Research on a panoramic video transmission scheme based on multi-view switching within the human eye's field of view

In today's Internet era, people have increasingly higher requirements for video clarity and breadth of vision. The scene information that can be expressed by traditional video is limited, and it is difficult to meet people's needs anymore, and panoramic video has emerged as the times require. Panoramic video can bring people an immersive and interactive audio-visual experience. However, due to its own characteristics, panoramic video with ultra-high resolution and ultra-high bit rate has a high demand for network bandwidth during network transmission. This paper compares the existing panoramic video transmission schemes, analyzes the advantages and disadvantages of each scheme, and proposes a transmission scheme based on multi-view switching within the human eye’s field of view, and then verifies the effectiveness of the scheme in saving network bandwidth by building a live platform.
Time-Optimal Trajectory Planning of Hoisting Manipulator

Aiming at the problem of time-optimal trajectory planning for hoisting manipulator, the improved particle swarm optimization algorithm is used to optimize the trajectory of 3-5-3 polynomial interpolation planning. First, based on the established kinematics model of hoisting manipulator, reducing the track running time, polynomial interpolation is used to plan the joint space trajectory. Secondly, the improved particle swam optimization algorithm with dynamically changing learning factor is used to optimize the joint space trajectory, which improves the work efficiency while meeting the joint angular velocity and acceleration constraints. Finally, Matlab simulation experiments are carried out, and the results verify the feasibility and effectiveness of the optimization algorithm.
Multivariate nonlinear time series prediction by chain-structure echo state network

For multivariate nonlinear time series, most existing prediction models are directly established based on the historical information of variable itself or some related variables. However, it is generally challenging to fully map and mine the internal characteristics, due to the strong coupling existing among multiple variables. In addition, when the system information is only partially known, it is also very difficult to develop a more accurate prediction model, under the condition of specific prediction technology and limited data sets. In this paper, a chain-structure echo state network (CESN) is developed to enhance the data mining ability and prediction accuracy of traditional models. A CESN consists of multiple series ESN modules, which are responsible for portraying complicated dynamics of nonlinear systems. In CESN, the predicted output of a certain ESN module is concatenated with external input to form the new input signals for the sequentially connected module. Compared with classical prediction models, more related information is therefore mined and adopted by CESN. The network structure, mathematical model and stability analysis are studied for CESN, respectively. Two different simulation examples are given to verify the effectiveness of CESN, and simulation results demonstrate that CESN outperform than traditional ESN models.
Analytical and Theoretical Perspectives on Sport-Related VR Research: Spatial Presence Manipulation Guidelines

As more scholars incorporate VR into their research, theoretical frameworks for empirical investigation and methodological guidelines for manipulation become more crucial. Because VR is a relatively new instrument and validating the implementation of VR can be challenging, scholars need to exercise caution when manipulating stimuli and measuring experimental conditions. Thus, the aim of the current essay is twofold: (a) address various methodological challenges (e.g., manipulation check) in recent VR studies and (b) discuss potential theoretical frameworks for future VR research in sport.
The Management of a Private Peruvian University at Pandemic Times: Assessment of Decisions and Implications on the Key Indicators

The management of a private Peruvian university along the period 2020-2021 is presented. Special attention has been paid on the qualitative angle in the sense that were described the main actions by which have had as central point the continuity of academic activities without the breaking of communications in the form top-down between the general manager and students. Essentially all actions have targeted to keep the quality of education in all the levels. The paper presents the general methodology that has consisted in the support of university to the universe of students in order that most of them continue in their program. Therefore, the management was not only the one applied to the university apparatus itself but also in to surpass the big obstacles such as the massive desertion of students in a critic epoch where Lima city has exhibited large peaks of infections as well as fatalities at the first and the second wave of pandemic at the first semester of 2021. Noteworthy actions have been taken by the general manager to conserve the desired harmony at the university even when communications have been done through the remote mode. The conclusion from the analysis of this paper is finally given.
Encouraging Creativity: Reading and Writing Short Stories

This article focuses on the use of short stories in the process of learning English for grade 6 students at Al Fath School Indonesia, especially in imrpoving reading and writing skill. Every year, Al Fath students take special training in writing short stories. They study plot structure diagrams and compare them with the short stories they read, then they write their own short stories. The results of the short stories that the children wrote were then analyzed and collected into an anthology book with ISBN. This article is expected to provide an overview and example for schools to encourage students' creativity in reading and writing activities