Reviewerly/exHarmony huggingface dataset:
Pengembangan Augmented Reality Menggunakan Metode AGILE Sebagai Media Pembelajaran Wisata Religi

Knowledge of Cirebon cultural heritage that can be used as tourist attractions with religious nuances includes the Kesepuhan Palace, Sunyaragi Water Park, At-Taqwa Mosque and Belawa Cirebon, This tourist attraction introduces a cultural history that has an Islamic nuance because Cirebon is the first place for the teachings of Islam in the West Java area, but the management is not optimal, it requires information facilities using augmented reality technology to increase tourist interest in visiting, The purpose of this study is to apply augmented reality to introduce and provide information about cultural heritage in Cirebon, which is presented attractively in 3 dimensions and in real time that combines real content with virtual oriented real world that can be seen directly by the user, This study uses the agile method in the development of augmented reality-based systems because this method has no limits in iterating and making the system carried out in stages and focuses on producing quality products. The results of functional testing of the augmented reality application features of religious tourism can run well and users state that they are satisfied with the appearance and workings of this application so that it can help increase public interest in religious and historical tourism in the city of Cirebon
Penerapan Klasifikasi Untuk Kelayakan Hasil Produksi Jam Tangan dengan Menggunakan Algoritma K-Nearest Neighbor

Industrial companies in Indonesia are one of the sectors that require development in carrying out their contribution in the manufacturing world. Market needs become the main guideline for the industry to create new ideas and improve the quality of production. Most industries, including digital watch electronics companies, have a need to implement technology at every stage of manufacturing their goods, such as at the stage of filtering raw materials to checking quality control before the goods reach consumers. Filtering and quality control are carried out to maintain the quality of each product produced by the company. The problem discussed in this study is how to apply technology in detecting the quality of watch production using the K-Nearest Neighbor algorithm so that it can increase the quality of production, consumer confidence and increase profits for the company. The results of this study are that the model built using the K-Nearest Neighbor method can increase the accuracy for detecting quality control on watch products with an accuracy value of 92% for k = 3
Effectiveness of banking card security in the Ethiopian financial sector: PCI-DSS security standard as a lens

Purpose This study is aimed at assessing the information security management practice with a focus on banking card security in selected financial institutions in Ethiopia, using an international information security standard as a benchmark. It is to identify the gaps and recommend best security practices to help financial institutions meet the required security compliance. Design/methodology/approach Two financial sectors were purposively selected. A total of twenty-five respondents (IT executives and IT staff) were included in the study. Quantitative data was collected using the PCI-DSS (Payment Card Industry Data Security Standard) security standard questionnaire. In addition, observation and document analysis were made. Findings The result shows that most of the essential security management activities in the financial sectors do not comply with the international security standard. Similarly, the level of most of the indispensable security requirements that should be in place is found to be below the acceptable level. The study also revealed major security factors that prohibit the financial sectors from PCI-DSS security standard compliance. Originality/value This study assessed the information security management practice with a focus on banking card security and tried to figure out the limitations of security practices of the organizations surveyed based on the standard adopted. The topic has not been well explored especially in the Ethiopia context. Hence, the result can positively influence security policies, particularly in the banking sector.
Pengembangan Billing Online Sistem dengan Menggunakan Metode Rapid Application Development

Billing management system and services at PT. Media Prima Jaringan is still not properly and efficiently recorded. Data management and recording still uses manual processes and paper media so that errors can occur in calculating Customer Service bills. So that customer complaints often occur due to inappropriate bill values, late payments until service termination occurs. In addition to complaints, company customers will also experience losses from dissatisfied customers who switch to other providers because they stop subscribing to the service. With the development of an online billing system, it is hoped that it will make it easier for companies to manage invoices and customer data and avoid billing errors. In developing a system, systematic steps are needed to produce a good and quality information system that can be used by users. The fundamental stages in the System Development Life Cycle (SDLC) are the planning stage, the analysis stage, the design stage, the implementation stage, the testing stage and the maintenance stage. One of the SDLC development models is Rapid Application Development (RAD) which prioritizes a relatively short development time of around 60-90 days. In addition to using the RAD model for system development, the author also uses the Unified Modeling Language (UML) as a tool in the design stage. The programming language used to build this information system uses PHP (Hypertext Pre-processor) by implementing the Laravel framework and MySQL as the database. The implementation of the RAD method for the development of an online billing system takes 45 days of development. The intensity of the discussions that were carried out during development helped the programmer team complete the program design more quickly. The increase in company revenue increases after the system is running and customer complaints are also reduced.
Evaluasi Pengalaman Pengguna Aplikasi SIMARIS UPN “Veteran” Jawa Timur Menggunakan Metode UEQ

The implementation of information and communication technology in a university is increasing according to the needs of each university. Research and community service activity carried out by lecturers at UPN "Veteran" East Java have been accommodated by an application called SIMARIS. In its implementation, this application still has several complaints related to the user experience felt by lecturers, such as difficulties when viewing proposal details, limitations for uploading files, no preview feature to view files, and the reviewer assessment menu doesn’t work for lecturers. In this study, the user experience of SIMARIS application was evaluated using the User Experience Questionnaire (UEQ) method. The questionnaire data obtained will be analyzed using UEQ Data Analysis Tool. The measurement results show that each scale has a high mean value with the scale of Attractiveness 1.61, Perspicuity 1.52, Efficiency 1.64, Dependability 1.44, Stimulation 1.51, and Novelty 1.25. The consistency level of all scales shows that Cronbach's Alpha value &gt; 0.70. This means a high level of consistency is owned by all scales.
How digital activities become (im)possible in Swedish school-age educare centres

This study explores how digital tools play a part in the practices of Swedish school-age educare centres (SAEC). The aim is to contribute knowledge about opportunities and/or obstacles in and with digital activities in SAEC practices. Data is produced using observations and conversations at five SAEC centres. The SAEC practice is found to be characterized by three different approaches to digital tools and their use: 1) A permeating practice, where digital tools are an integrated part of the whole day, 2) A happening practice, where digital tools are present on special occasions, and 3) A neglecting practice, where digital tools are absent. These differences can be connected to how teachers interpret their assignment but also to differences in competence, access, and interest in relation to digital tools. This entails that SAEC pupils are given unequal opportunities to develop digital skills.
Deep learning for content-based image retrieval in FHE algorithms

Abstract Content-based image retrieval (CBIR) is a technique used to retrieve image from an image database. However, the CBIR process suffers from less accuracy to retrieve many images from an extensive image database and prove the privacy of images. The aim of this article is to address the issues of accuracy utilizing deep learning techniques such as the CNN method. Also, it provides the necessary privacy for images using fully homomorphic encryption methods by Cheon–Kim–Kim–Song (CKKS). The system has been proposed, namely RCNN_CKKS, which includes two parts. The first part (offline processing) extracts automated high-level features based on a flatting layer in a convolutional neural network (CNN) and then stores these features in a new dataset. In the second part (online processing), the client sends the encrypted image to the server, which depends on the CNN model trained to extract features of the sent image. Next, the extracted features are compared with the stored features using a Hamming distance method to retrieve all similar images. Finally, the server encrypts all retrieved images and sends them to the client. Deep-learning results on plain images were 97.87% for classification and 98.94% for retriever images. At the same time, the NIST test was used to check the security of CKKS when applied to Canadian Institute for Advanced Research (CIFAR-10) dataset. Through these results, researchers conclude that deep learning is an effective method for image retrieval and that a CKKS method is appropriate for image privacy protection.
A novel method for designing indexes to support efficient substring queries on encrypted databases

Recently, the queries techniques on encrypted character data in database have been interested which concentrate to searching keywords. In this paper, we propose Double Indexes Query - Searchable Symmetric Encryption (DIQ-SSE) schema which is performed in Proxy model. This schema allows effectively querying any substring on encrypted character field on outsource databases. The idea of this schema is that each plaintext As at the safety zone of Proxy is encrypted with traditional encryption algorithms. In this time, two special index sets of Index1 and Index2 are built for supporting search. The tuple of (cipher, Index1, and Index2) that corresponding As are stored on outsource database. To perform the queries, 4 phases are used. Firstly, the query command is transformed. Secondly, the query is performed on Index1. Then, the task is continued of querying on Index2 for data taken from phase 2. In the last phase, data is decrypted, and query on plaintext is performed. Beside of proposing algorithm of building indexes and queries on 4 phases, the paper also focuses on the protection analysis as well as experimental evaluations.
High availability of kernel-based virtual machine using nested virtualization

- KVM (Kernel-based Virtual Machine) is one of the popular, free and open-source virtual machine managers for Linux distributions. Many people or companies use plain KVM virtualization, but in many cases, they have no measures in place for ensuring availability if a host fails. The existing method to set up a high availability cluster using basic KVM virtualization is a difficult procedure for someone unfamiliar with the process. A growing number of powerful strategies are furnished with network interconnection and are related to the Internet of Things (IoT). In this research work proposes a simple method for ensuring the high availability of KVM hypervisor with the help of the nested virtualization technique. In the proposed work, the KVM hypervisor will run on another high availability enabled hypervisor. For the performance evaluation, the parameters like CPU performance, I/O performance and memory performance were evaluated. That got the better results in the proposed system than others. The proposed evaluation demonstrates that the overhead in relations of network throughput and CPU accomplishment is low so it related that the proposed concept is a evocative step in the direction of additional resilient forthcoming IoT devices.
On the deletion of notes and whales

Click to increase image sizeClick to decrease image size Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsArlan VriensArlan Vriens is a Doctor of Musical Arts candidate at the University of Toronto. His research and performance aim to uncover lost violin performance techniques and bring timely new sounds to the concert stage.
Pengembangan Media Pembelajaran Powerpoint Interaktif dalam Pembelajaran Tematik untuk Meningkatkan Hasil Belajar Kognitif Siswa di Kelas V Sekolah Dasar

This study aims to develop interactive powerpoint learning media in thematic learning for class V elementary school. The research subjects were in class V SDN Karang Anyar 03 Pagi with a total of 31 students. Validation was carried out by experts (design experts and material experts), class teachers, and individual students (3 students), small groups (10 people), and field trials. This study uses research and development (R&amp;D) methods with the Dick &amp; Carey model which includes 10 steps. The results of this study were design experts 89.4%, material experts 84.6%, class V teachers 82.4%, and students 93.5% with a very decent category. And students' cognitive learning outcomes can be seen from the results of the pre-test and post-test, where the average value of the test results has increased with a pre-test result of 60.6 and a post-test result of 80.3. Based on these data it can be concluded that interactive powerpoint learning media is feasible and effective for use in thematic learning of class V elementary schools.
The Effect of Knowledge Based Feature Extraction on Failure Detection of Control Surface Failures of Fighter Aircraft

While the area of maintenance is developing from scheduled maintenance toward the condition based maintenance also the failure detection that utilizes system operational data becomes more important. The failure detection from system data can be done in many manners but a process of feature extraction is present more or less almost when the system data is high dimensional, that is the case also with fighter aircraft systems. In this study we examine an effect of system knowledge based feature extraction on the further performance of an algorithmic tasks of failure detection on the operational flight data of a fighter aircraft. The failures for validating the results are several flight control surface failures from the flight data. The feature extraction has been done by using system knowledge of fighter aircraft experts. The failure detection algorithms are comprehensive set of algorithms from the field of anomaly detection, novelty detection, one class classification and unsupervised machine learning. This study demonstrates that some specific failure detection algorithms are more robust for feature extraction and can perform well even with low level of feature extraction when detecting the flight control surface failures. This result can be further used for selecting algorithms for failure detection tasks for other subsystems of aircraft in cases when the system knowledge and expertise are lacking and thus the feature extraction that can be done is little.
An Aspect-Based Semi-supervised Generative Model for Online Review Spam Detection

With the continuous development and popularization of Internet technology and the normalization of epidemic prevention and control, people’s life mode is gradually changed by the network. More and more people consume food, clothing, housing and transportation through the Internet, and the online reviews left by people have become valuable information resources. However, the authenticity of online reviews is worrying. The proliferation of review spams has become one of the most urgent security problems to be solved in the Internet economy. Review spams will lead users to make suboptimal purchase strategies and have a negative impact on the credit of the online review platform. Most of the existing studies are manual feature extraction, rule making and labeling training samples, which are usually complex and time-consuming. We propose a new semi-supervised review spam detection model for online reviews. We first compute the attention distribution over different aspects of a product in a review as the aspect coefficient. We then use the conditional variational auto-encoder (CVAE) to learn the review styles of fake and non-fake reviews for different aspects of a product, utilizing the aspect and aspect coefficients as conditions. Finally, CVAE model can distinguish fake reviews from non-fake reviews according to the review style. The model is evaluated based on restaurant reviews on yelp website, and the experimental results show the superior performance of this method.
Classification of Slow and Fast Learners Using Deep Learning Model

Cognitive learning strategies are focused on the improvement of the learner’s ability to analyze information more deeply, efficiently handle new situations by transferring and applying the knowledge. These techniques result in enhanced and better-retained learning. To cater to the needs of different students having different levels of cognitive learning, it is very important to assess their learning ability. In this paper, a method based on deep learning is presented to classify the earners based on their past performance. This technique is taking the student's past semester marks, their total failures in subjects/passing heads, and their current semester attendance. The proposed method classifies the learners into three categories, namely slow, fast, and average learners. A deep learning classifier with multilayer perceptron-based nodes is built for the classification. The proposed method is fully automatic and robust. A final accuracy of 90% is achieved in the classification of the learners in their cognitive learning level.
Microcalcification Detection Using Ensemble Classifier

Breast cancer diagnosis and classification utilizing a CAD system with mammography pictures as input remains a difficult task in healthcare management systems. Microcalcification (MCs) is a microscopic deposit of calcium particles that acts as an early indicator of breast cancer. Without the assistance of an expert radiologist, automatic recognition of the MC region is a difficult task for a CAD system. MCs appear as bright small spots embedded in normal tissues on mammography images, and classification into normal, benign, and malignant is difficult. Clinical investigations demonstrate that benign zones are much denser than malignant regions, and that the malignant are more clustered than the benign. In this paper, we offer an entropy technique-based framework for autonomously identifying cancer regions. Fractal, topological, and statistical properties are retrieved to classify cancer as benign or malignant. An ensemble classifier comprising a combination of K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB) classifiers is introduced for successful output prediction. Our suggested model’s performance was evaluated using several metrics such as accuracy, specificity, sensitivity, precision, and recall, and it was found to be superior to existing techniques.
Plactic key agreement (insecure?)

Abstract Plactic key agreement is a new type of cryptographic key agreement that uses Knuth’s multiplication of semistandard tableaux from combinatorial algebra. The security of plactic key agreement relies on the difficulty of some computational problems, particularly the division of semistandard tableaux. Tableau division can be used to find the private key from its public key or to find the shared secret from the two exchanged public keys. Monico found a fast division algorithm, which could be a polynomial time in the length of the tableaux. Monico’s algorithm solved a challenge that had been previously estimated to cost 2 128 steps to break, which is an infeasibly large number for any foreseeable computing power on earth. Monico’s algorithm solves this challenge in only a few minutes. Therefore, Monico’s attack likely makes the plactic key agreement insecure. If it were not for Monico’s attack, plactic key agreement with 1,000-byte public keys might perhaps have provided 128-bit security, with a runtime of a millisecond. But Monico’s attack breaks these public keys’ sizes in minutes.
Monitoring Senior Citizens Using IoT and ML

Everyone wants their elder parents or guardians to be taken care of, it is very hard task to take care of them as they may require emergency assistance at any given point of time. So, in this Paper, a system is developed with the help of IOT sensors and python programming to monitor them closely and to take care of them. In this paper it is tried to achieve some level of monitoring of senior citizens. OpenCV library is used for the object tracking and fall detection. If it is seen that person in the view of camera fell for any reason it gives message as fall detected. It can really be useful in emergency situations, if for any miss happening like a fall emergency treatment can be provided so the damage to the person can be very less compared to not attending them for a long period of time. It can also be life saver for some situations. For this initial work of research webcam of the laptop is used to detect any fall and it can be used in various other situations to track children fall detection. This is done on python and OpenCV.
Performance Analysis of QuickMerge and QuickInsertionMerge Algorithms

An algorithm is a procedure for solving a problem using a finite sequence of actions. A sorting algorithm is applied to arrange the given array or list of elements into ascending or descending order. In computer science, sorting is a critical issue. Many researchers are working on sorting algorithms, because the best sorting technique enables working of other algorithms to be optimized. This paper proposed a novel algorithm which integrates quicksort, insertion sort, and merge sort and breaks the input dataset into different sub-lists. The suggested hybrid algorithm outperforms the original quicksort algorithm.
A Feature-Based Recommendation System for Mobile Number Portability

Mobile phones have become an inherent part of people’s lives in contemporary days. Mobile numbers have become the digital identity of a person these days so, changing phone number requires one to change their phone number at all its registered places. Mobile number portability (MNP) enables users to switch their network operator without changing the phone number. After the emergence of MNP services in India, the number of MNP requests has risen significantly since the last decade. This provides with the exemption from changing their number at every registered place. Despite such benefits of MNP service, people still face problems while switching to a new mobile network operator (MNO). Network strength varies from MNO to MNO and from area to area, making it difficult for users to choose MNO. With the diversity of plans in terms of daily data, price, and additional benefits, users are confounded and it becomes difficult to go through all the plans and decide the one that fulfills all their requirements. Thus, authors came up with a k-medoids clustering-based recommendation system that takes users’ requirements as input and recommends plans to choose from.