Reviewerly/exHarmony huggingface dataset:
Cloud Formation (IaC): Deploying a Containerized Application on Cloud

Abstract: This study is a literature review on cloud computing trends as one of the Fastest growing technologies in the computer industry and their benefits and opportunities for all types of organizations.In addition, it addresses the challenges and problems that contribute to increasing the number of customers willing to adopt and use the technology. A mixed research study approach was adopted for the study, that is by collecting and analysing both quantitative and qualitative information within the sane literature review and summarizing the findings of previous (related) studies. Results highlights the current and future trends of cloud computing and exposes readers to the challenges and problems associated with cloud computing. The reviewed literature showed literature showed that the technology is promising and is expected to grow in the future. Researchers have proposed many techniques to address the problems and challenges of cloud computing, such as security and privacy risks, through mobile cloud computing and cloud-computing governance.
An Evaluation of Ranking-to-Learn Approaches for Test Case Prioritization in Continuous Integration

Continuous Integration (CI) environments is a practice adopted by most organizations that allows frequent integration of software changes, making software evolution more rapid and cost-effective. Such environments require dynamic Test Case Prioritization (TCP) approaches that adapt better to the test budgets and frequent addition/removal of test cases. In this sense, Ranking-to-Learn approaches have been proposed and are more suitable for CI constraints. By observing past prioritizations and guided by reward functions, they learn the best prioritization for a given commit. In order to contribute for improvements and direct future research, this work evaluates how far the solutions produced by these approaches are from optimal solutions produced by a deterministic approach (ground truth). To this end, we consider two learning-based approaches i) RETECS, which is based on Reinforcement Learning; and ii) COLEMAN, an approach based on Multi-Armed Bandit. The evaluation was conducted with twelve systems, three test budgets, two reward functions, and six measures concerning fault detection effectiveness, early fault detection, test time reduction in the CI cycles, prioritization time, and accuracy. Our findings have some implications for the approaches application and reward function choice. The approaches are applicable in real scenarios and produce solutions very close to the optimal ones, respectively, in 92% and 75% of the cases. Both approaches have some limitations to learn with few historical test data (a small number of CI Cycles) and deal with a large test case set, in which many failures are distributed over many test cases.
Jointly Defending DeepFake Manipulation and Adversarial Attack Using Decoy Mechanism

Highly realistic imaging and video synthesis have become possible and relatively simple tasks with the rapid growth of generative adversarial networks (GANs). GAN-related applications, such as DeepFake image and video manipulation and adversarial attacks, have been used to disrupt and confound the truth in images and videos over social media. DeepFake technology aims to synthesize high visual quality image content that can mislead the human vision system, while the adversarial perturbation attempts to mislead the deep neural networks to a wrong prediction. Defense strategy becomes difficult when adversarial perturbation and DeepFake are combined. This study examined a novel deceptive mechanism based on statistical hypothesis testing against DeepFake manipulation and adversarial attacks. First, a deceptive model based on two isolated sub-networks was designed to generate two-dimensional random variables with a specific distribution for detecting the DeepFake image and video. This research proposes a maximum likelihood loss for training the deceptive model with two isolated sub-networks. Afterward, a novel hypothesis was proposed for a testing scheme to detect the DeepFake video and images with a well-trained deceptive model. The comprehensive experiments demonstrated that the proposed decoy mechanism could be generalized to compressed and unseen manipulation methods for both DeepFake and attack detection.
Menumbuhkan Sikap Bangga Berbahasa Indonesia Sebagai Bahasa Nasional Dan Internasional

Over the years, Indonesian has developed into the state language and national language, but many Indonesians cannot be proud of it. When they talk about Indonesia without really understanding it, the fact is clear. One of the reasons for this lack of pride is the implementation of the educational process by the government, the community, and the teaching staff. Although there are several rules regarding the use of the Indonesian language, the government has not taken serious action against public violations. people are more proud to use a foreign language than Indonesian, and young people today are less interested in learning it so they cannot create pride in their language, namely Indonesian.
Music Note Feature Recognition Method based on Hilbert Space Method Fused with Partial Differential Equations

Hilbert space method is an old mathematical theoretical model developed based on linear algebra and has a high theoretical value and practical application.The basic idea of the Hilbert space method is to use the existence of some stable relationship between variables and to use the dynamic dependence between variables to construct the solution of differential equations, thus transforming mathematical problems into algebraic problems.This paper firstly studies the denoising model in the process of music note feature recognition based on partial differential equations, then analyzes the denoising method based on partial differential equations and gives an algorithm for fused music note feature recognition in Hilbert space; secondly, this paper studies the commonly used music note feature recognition methods, including linear predictive cepstral coefficients, Mel frequency cepstral coefficients, wavelet transform-based feature extraction methods and Hilbert spacebased feature extraction methods.Their corresponding feature extraction processes are given.
Fall Detection and Monitoring using Machine Learning: A Comparative Study

The detection of falls has emerged as an important topic for the public to discuss because of the prevalence and severity of unintentional falls, particularly among the elderly.A Fall Detection System, known as an FDS, is a system that gathers data from wearable Internet-of-Things (IoT) device and classifies the outcomes to distinguish falls from other activities and call for prompt medical aid in the event of a fall.In this paper, we determine either fall or not fall using machine learning prior to our collected fall dataset from accelerometer sensor.From the acceleration data, the input features are extracted and deployed to supervised machine learning (ML) algorithms namely, Support Vector Machine (SVM), Decision Tree, and Naive Bayes.The results show that the accuracy of fall detection reaches 95%, 97 % and 91% without any false alarms for the SVM, Decision Tree, and Naïve Bayes, respectively.
Adaptive Rectified Linear Unit (Arelu) for Classification Problems to Solve Dying Problem in Deep Learning

A convolutional neural network (CNN) is a subset of machine learning as well as one of the different types of artificial neural networks that are used for different applications and data types.Activation functions (AFs) are used in this type of network to determine whether or not its neurons are activated.One nonlinear AF named as Rectified Linear Units (ReLU) which involves a simple mathematical operations and it gives better performance.It avoids rectifying vanishing gradient problem that inherents older AFs like tanh and sigmoid.Additionally, it has less computational cost.Despite these advantages, it suffers from a problem called Dying problem.Several modifications have been appeared to address this problem, for example; Leaky ReLU (LReLU).The main concept of our algorithm is to improve the current LReLU activation functions in mitigating the dying problem on deep learning by using the readjustment of values (changing and decreasing value) of the loss function or cost function while number of epochs are increased.The model was trained on the MNIST dataset with 20 epochs and achieved lowest misclassification rate by 1.2%.While optimizing our proposed methods, we received comparatively better results in terms of simplicity, low computational cost, and with no hyperparameters.
A Biologically Inspired Appearance Modeling and Sample Feature-based Approach for Visual Target Tracking in Aerial Images

Visual tracking in uncrewed aerial vehicles is challenging because of the target appearance.Various research has been fulfilled to overcome appearance variations and unpredictable moving target issues.Visual saliency-based approaches have been widely studied in biologically inspired algorithms to detect moving targets based on attentional regions (ARs) extraction.This paper proposes a novel visual tracking method to deal with these issues.It consists of two main phases: spatiotemporal saliency-based appearance modeling (SSAM) and sample feature-based target detection (SFTD).The proposed method is based on a tracking-by-detection approach to provide a robust visual tracking system under appearance variation and unpredictable moving target conditions.Correspondingly, a semi-automatic trigger-based algorithm is proposed to handle the phases' operation, and a discriminative-based method is utilized for appearance modeling.In the SSAM phase, temporal saliency extracts the ARs and coarse segmentation.Spatial saliency is utilized for the object's appearance modeling and spatial saliency detection.Because the spatial saliency detection process is timeconsuming for multiple target tracking conditions, an automatic algorithm is proposed to detect the region saliences in a multithreading implementation that leads to low processing time.Consequently, the temporal and spatial saliencies are integrated to generate the final saliency and sample features.The generated sample features are transferred to the sample feature-based target detection (SFTD) phase to detect the target in different images based on samples.Experimental results demonstrate that the proposed method is effective and presents promising results compared to other existing methods.
A Survey of Forensic Analysis and Information Visualization Approach for Instant Messaging Applications

Instant messagingapplications, including WhatsApp, Viber, and WeChat, are moving beyond text messages to videos and voice calls, which are proportioned to current media, files, and locations.In this study, we surveyed existing forensic visualization and forensic analysis techniques for instant messaging applications, with the aim of contributing to the knowledge in the discussion of these research issues.A total of 61 publications were reviewed after searching various academic databases, including the IEEE, ACM Digital Library, Google Scholar, and Science Direct during the last five years.Our observation from research trends indicates that both forensic analysis and information visualization are relatively mature research areas.However, there is a growing interest in forensic visualization and automated IM forensic analysis.We also identified the lack of discussion on forensic selection criteria in existing forensic visualization works and the needs of benchmarking the evaluation method of automate forensic analysis tools.
Design of an English Web-based Teaching Resource Sharing Platform based on Mobile Web Technology

Thanks to the booming technology of computers and multimedia, student-centered online teaching resource platforms have become an important way for students to learn.However, English teaching resource platforms at the present stage fail to effectively integrate the massive and scattered learning resources.Based on this, the study proposes an English online teaching resource sharing platform based on mobile Web technology, using the SOAP protocol to deploy heterogeneous data resources as Web services to achieve interchangeability between heterogeneous resources.In addition, to enhance the efficient use of learning resources by students, the study proposes a hybrid algorithm based on collaborative filtering algorithm and sequential pattern mining algorithm to achieve personalized sequential recommendation for students.The results show that the platform created by the study exhibits excellent performance in terms of resource transfer capability, achieves efficient teaching resource sharing in a short response time and also shows that the proposed recommendation algorithm is highly accurate.
R-Diffset vs. IR-Diffset: Comparison Analysis in Dense and Sparse Data

The mining of concealed information from databases using Association Rule Mining seems to be promising.The successful extraction of this information will give a hand to many areas by aiding them in the process of finding solutions, economic projecting, commercialization policies, medical inspections, and numbers of other problems.ARM is the most outstanding method in the mining of remarkable related configurations from any groups of information.The important patterns encountered are categorized as recurrent/frequent and non-recurrent/infrequent.Most of the previous data mining methods concentrated on horizontal data set-ups.Nevertheless, recent studies have shown that vertical data formats are becoming the main concerns.One example of vertical data format is Rare Equivalence Class Transformation (R-Eclat).Due to its efficacy, R-Eclat algorithms have been commonly applied for the processing of large datasets.The R-Eclat algorithm is actually comprised of four types of variants.However, our work will only focus on the R-Diffset variant and Incremental R-Diffset (IR-Diffset).The performance analysis of the R-Diffset and IR-Diffset algorithms in the mining of sparse and dense data are compared.The processing time for R-Diffset algorithm, especially for sequential processing is very long.Thus, the incremental R-Diffset (IR-Diffset) has been established to solve this problem.While R-Diffset may only process the nonrecurrent itemsets mining process in sequential form, IR-Diffset on the other hand has the capability to execute sequential data that have been fractionated.The advantages of this newly developed IR-Diffset may become a potential candidate in providing a time-efficient data mining process, especially those involving the large sets of data.
Implementation of Big Data Privacy Preservation Technique for Electronic Health Records in Multivendor Environment

Various diagnostic health data formats and standards include both structured and unstructured data.Sensitive information contained in such metadata requires the development of specific approaches that can combine methods and techniques that can extract and reconcile the information hidden in such data.However, when this data needs to be processed and used for other reasons, there are still many obstacles and concerns to overcome.Modern approaches based on machine learning including big data analytics, assist in the information refinement process for later use of clinical evidence.These strategies consist of transforming various data into standard formats in specific scenarios.In fact, in order to conform to these rules, only de-identified diagnostic and personal data may be handled for secondary analysis, especially when information is distributed or transferred across institutions.This paper proposes big data privacy preservation techniques using various privacy functions.This research focused on secure data distribution as well as security access control to revoke the malicious activity or similarity attacks from end-user.The various privacy preservation techniques such as data anonymization, generalization, random permutation, kanonymity, bucketization, l-diversity with slicing approach have been proposed during the data distribution.The efficiency of system has been evaluated in Hadoop distributed file system (HDFS) with numerous experiments.The results obtained from different experiments show that the computation should be changed when changing k-anonymity and l-diversity.As a result, the proposed system offers greater efficiency in Hadoop environments by reducing execution time by 15% to 18% and provides a higher level of access control security than other security algorithms.
Equally Spread Current Execution Load Modelling with Optimize Response Time Brokerage Policy for Cloud Computing

Cloud computing is one of the significant technologies that is used to provide seamless internet surfing for large-scale applications and data storing purposes.The cloud is described as a large platform that enables users to access data from the internet without needing to buy storing space in their equipment such as a computer.Many studies have analysed the load-balancing technique on the cloud to distribute tasks equally between servers using the Equally Spread Current Execution (ESCE) algorithm.ESCE, which is a dynamic load balancer, has quite a few problems such as average level performance and too long of response time which affected the Quality of Services.This research has simulated a cloud computing concept using the ESCE Load Modelling technique with the CloudAnalyst simulator for three servers of Data Center (DC) locations.The ESCE was simulated to enhance its algorithm's performance as a load balancer and higher throughput in the cloud environment.The result shows that ESCE average overall response time is shortest when the DC is located at R0 with response times of 15.05s, 13.05s with 10 VMs, and 8.631s with the Optimize Response Time brokerage policy.This research is significant to promote notable load-balancing technique testing for virtualized cloud machines data centers on Quality of Services (QoS) aware tasks for Internet of Things (IoT) services.
A Visual Target Representation using Saliency Detection Approach

The task of saliency detection is to identify the most important and informative part of a scene.Saliency detection is broadly applied to numerous vision problems, including image segmentation, object recognition, image compression, contentbased image retrieval, and moving object detection.Existing saliency detection methods suffer a low accuracy rate because of missing components of saliency regions.This study proposes a visual saliency detection method for the target representation to represent targets more accurately.The proposed method consists of five modules.In the first module, the salient region is extracted through manifold ranking on a graph, which incorporates local grouping cues and boundary priors.Secondly, using a region of interest (ROI) algorithm and the subtraction of the salient region from the original image, other parts of the image, either related or nonrelated to the interested target, are segmented.Lastly, those related and non-related regions are classified and distinguished using our proposed algorithm.Experimental result shows that proposed salient region accurately represent the interested target which can be used for object detection and tracking applications.
Clustering of massive datasets using an Adaptive and efficient K-Means approach

In today’s technology-driven and Internet-obsessed society, it can be challenging to go through huge amounts of information and find relevant knowledge for various educational contexts. Simple, fast, and adaptable machine learning algorithms make such tasks easier to complete. K-means is the most effective unsupervised learning technique for classifying data into meaningful groups. K-means groups data by shared characteristics. K-means clusters are determined by k. Unfortunately, standard k-means requires a lot of math. Scholars have suggested strategies to improve k-means grouping. This work recommends computing initial centroids and establishing a distance between data points that are unlikely to change their cluster in subsequent iterations and those that are extremely likely to do so to lessen the load of k-means clustering for very large data sets. This piece will find information digits whose cluster is statistically likely to alter in the following few cycles. After processing several datasets, it is compared to other K-Means methods
Multiclass Classification of Road Scene Traffic Images Using Semantic Segmentation

In the recent years Autonomous Vehicles (AVs) are going to be most foreseen development in the automotive industry. It requires Decision Making System (DMS) which will enable AVs to intuitively interpret the scene recognition on streets. Extracting relevant semantics from the scene is crucial task. So, multiclass image classification & object detection techniques using Deep Convolutional Neural Networks [DCNN] are going to play vital role in every other methodology designed for scene semantics extraction [4]. This research paper focuses on multiclass classification & semantic extraction from road scene images using image segmentation techniques, where large set of labelled road scene images of urban & semi urban areas in Indian subcontinent is built. The objective of this study is to develop a more accurate and efficient approach to detect and classify objects in road scenes, such as cars, pedestrians, trucks, riders, motorcycles, traffic signs & 7 other types of objects. To achieve this, a deep learning-based segmentation models have been proposed and trained on a labelled dataset scene images. The results show that the proposed approach outperforms existing methods in terms of accuracy and speed, and can be used in a wide range of applications, including autonomous driving and traffic monitoring.
Design And Development of Help Patient Data Management Information System at Sinergi Atap Negeri Foundation Web-Based Using Codeigniter

The Sinergi Atap Negeri Foundation is a legal and independent non-profit social foundation with the goal of becoming a halfway house facilitator for needy patients and families. The purpose of this study is to create a data management information system for assisted patients to aid in data management, patient submissions, and data transactions at the Sinergi Atap Negeri Foundation, thereby making work more effective and efficient. The waterfall method is used in software development to collect data from previous studies through observations, interviews, and literature studies. The Sinergi Atap Negeri Foundation's patient data management system, according to the analysis, is still manual, whereas previously data management could only be managed by one foundation administrator and accessed by anyone. Given these concerns, researchers are encouraged to develop a web-based application using PHP, the Codeigniter 3 framework, and Mysql as a database. It is hoped that this information system will improve the performance of foundation employees by making data entry easier and faster, as well as avoiding data loss errors from a security standpoint.
Classification of The Risk of Comorbid Covid-19 Patient at Bengkalis Hospital Using Bayesian Binary Logistics Regression

COVID-19 is an infectious disease caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2). This viral infection causes illness with symptoms ranging from mild to severe. The number of deaths from this disease is increasing day by day. A person who is most easily infected with the COVID-19 virus is a person who has a comorbid disease, because the body's immunity decreases due to the impact of a previous illness. The purpose of this study was to determine the comorbid factors that trigger a person's death due to COVID-19. This research uses binary logistic regression with Bayes method parameter estimation. In this study, the predictor variables used were in the form of categories. The results showed that the factors that influence the death of a person on the death of COVID-19 in comorbid diseases are Diabetes Mellitus and Pneumonia.