Reviewerly/exHarmony huggingface dataset:
Hybrid Multi-User Based Cloud Data Security for Medical Decision Learning Patterns

Machine learning plays a vital role in the real-time cloud based medical computing systems. However, most of the computing servers are independent of data security and recovery scheme in multiple virtual machines due to high computing cost and time. Also these cloud based medical applications require static security parameters for cloud data security. Cloud based medical applications require multiple servers in order to store medical records or machine learning patterns for decision making. Due to high computational memory and time, these cloud systems require an efficient data security framework in order to provide strong data access control among the multiple users. In this paper, a hybrid cloud data security framework is developed to improve the data security on the large machine learning patterns in real-time cloud computing environment. This work is implemented in two phases, data replication phase and multi-user data access security phase. Initially, machine decision patterns are replicated among the multiple servers for data recovering phase. In the multi-access cloud data security framework, a hybrid multi-access key based data encryption and decryption model is implemented on the large machine learning medical patterns for data recovery and security process. Experimental results proved that the present two-phase data recovering and security framework has better computational efficiency than the conventional approaches on large medical decision patterns.
Image Correction Methods for Regions of Interest in Liver Cirrhosis Classification on CNNs

The average error rate in liver cirrhosis classification on B-mode ultrasound images using the traditional pattern recognition approach is still too high. In order to improve the liver cirrhosis classification performance, image correction methods and a convolution neural network (CNN) approach are focused on. The impact of image correction methods on region of interest (ROI) images that are input into the CNN for the purpose of classifying liver cirrhosis based on data from B-mode ultrasound images is investigated. In this paper, image correction methods based on tone curves are developed. The experimental results show positive benefits from the image correction methods by improving the image quality of ROI images. By enhancing the image contrast of ROI images, the image quality improves and thus the generalization ability of the CNN also improves.
Penerapan K-Means Clustering Untuk Pengelompokan Penyebaran Demam Berdarah Dengue (DBD) Di Kabupaten Deli Serdang

DBD is a disease that spread rapidly. Usually if there is an area affected by dengue fever, it is likely to spread to other people in the area. Due to the large number of DBD sufferers, so much data is collected and processing needs to be done on these data, such as the grouping of DBD sufferers data with the aim of focusing vector control in areas that are vulnerable to DBD. The area will be the main priority to carry out socialization related to the handling of DBD. Data mining is a series of processes to get useful information from large database warehouses. One function of the data mining process for finding groups or group identification is Clustering. There are two types of clustering methods, namely hierarchical clustering and non-hierarchical clustering, commonly called Kmeans. K-Means Clustering begins by determining the number of clusters first. The results obtained from grouping the data are in the form of areas that have the highest DBD potential in Deli Serdang. By using clustering method to do calculations, it can help solve problems in Deli Serdang Regency in classifying the data of DBD sufferers which is still done manually
Face Recognition System for Control Access to Restrictive Domain

Recent progress in computer vision applied to facial analysis has led to state-of-the-art face detection and facial feature extraction models. A cautious implementation of these models into face recognition pipelines can enable achieving superior performances and popularized daily applications of face recognition in a variety of domains. However, modern face recognition system is a multi-steps process including face detection, feature extraction and classification model. Developing a high-performance face recognition application generalizing on local data set remains challenging. In this paper, we present Deep learning based face recognition system employing MTCNN for face detection and FaceNet for feature extraction. We compare KNN and SVM classification models trained on the facial features extracted from prepared labeled faces. Both models demonstrated almost 100% accuracy on static test faces. Moreover, as face pose get more pronounced, far above 30°, both SVM and KNN models demonstrate efficient recognition rate of 95.95% and 96.67% respectively. Real-time evaluation shows less than 1% deviation from the static performances with both classifiers on less 30° tilted images.
A Deep Learning-Based Framework for Social Data Sensing and Fusion for Enterprise Management

How to effectively realize the perception and fusion of data in the enterprise management society is the core problem that must be solved by enterprise management. On the basis of defining the resource system of enterprise management society, a semantic-oriented metadata model is proposed, combined with the classification of user needs, to build a data fusion framework for enterprise management society based on multisource data. The study designs a multilayer convolutional neural network model to process the data and proposes a recommendation path for the implementation of user data services for the enterprise management society based on the data operation center of the enterprise management society. Finally, it proposes suggestions for the development of the data fusion of the enterprise management society by improving the data fusion standard of the enterprise management society from multiple sources, actively formulating the data opening policy, and exploring the personal data collection and storage protection scheme. Experiments show that the scheme designed in this study is 5% more accurate than the state-of-the-art scheme.
The Impact of the COVID-19 Pandemic on the Online Learning Process (Survey Perspective)

The online learning platform is used as an object of research because it is one of the best ways that are widely used by students in helping to learn outside of school to deepen the knowledge that has been provided by the school. In this pandemic situation, the online learning platform is immensely helpful for students who do not understand the learning materials provided by the school because of the limitations when online classes make students bored. The research is focused on the use of online learning platforms among students. Research data to be analyzed in this study were collected by survey method, using a questionnaire. The questionnaire itself is divided into three main sections. From the results of the questionnaires that have been distributed to 100 respondents, users gave positive responses and accepted that they were satisfied with the online learning platform application services, the average online learning platform users were aged 16-18 who live in Greater Jakarta. This study investigates the independent and dependent variables from the online learning platform application. Keywords-- COVID-19, Online learning platform, online learning
Students’ and Parents’ Perception on Online English Learning During Pandemic

Covid-19 pandemic is a situation that has impacted people’s life and all sectors especially Education. This pandemic situation forced the restrictions to do online learning in the teaching activities, including English learning. It raises pro contra between the implementation of online learning. The study aimed at analyzing the students’ and parents’ perception on English learning via online during pandemic situation. The study employed quantitative approach with survey design. The instrument was questionnaire. The sample involves 102 eighth graders and 101 parents. The results showed that students had a positive perception toward online learning in general, but parents had the negative perception toward online learning in general. It could be proved by the result of statistics analysis that revealed 63.7% students give positive response with a positive category while 61.4% of parents give negative response which categorized as negative perception based on table qualification. Therefore, it is suggested for the government to use this result as a reference to evaluate the online learning specifically for teaching and learning English for junior high school students.
Teachers’ Perspectives on Foreign Language Acquisition and Mobile or Computer Assisted Language Learning: A Qualitative Study

The study underscores issues of teachers' perspectives, their understanding, awareness, utilization, and effectiveness of technological mediated mobile capabilities in foreign language acquisition (FLA). Seven teachers (FLA) from five nationalities were interviewed online, and the data collected and compiled along with notes were descriptively analyzed. The findings illustrate that although FLA teachers are ready to embrace changes and accept the usage of technology-mediated computer and mobile-assisted language learning (C/MALL) in online classes; however, this adaptability posits some challenges, which need technical support, training, interactions on its usage for language acquisition. The argument emphasizes the discourses between FLA teachers, parents, and institutions for creating a conducive environment and technological affordances for executing computer and mobile-assisted language learning in significant ways. Besides, it highlights the contradiction between the usage of Smart Mobile Devices (SMDs) for language acquisition and lack of efficiency and effective use of such devices by teachers, and the formalities of mobile use for teachers to include student-centered activities and students’ immersion. Finally, the study presents implications and prospects for future studies based on the perception of FLA teachers, and parents towards the use of the computer or SMDS viewed as CALL or MALL.
Face recognition based on Siamese convolutional neural network using Kivy framework

Human face recognition is a vital biometric sign that has remained owing to its many levels of applications in society. This study is complex for free faces globally because human faces may vary significantly due to lighting, emotion, and facial stance. This study developed a mobile application for face recognition and implemented one of the convolutional neural network (CNN) architectures, namely the Siamese CNN for face recognition. Siamese CNN can learn the similarity between two object representations. Siamese CNN is one of the most common techniques for one-shot learning tasks. Our participation in this study determined the efficiency of the Siamese CNN architecture with the enormous quantity of face data employed. The findings demonstrated that the suggested strategy is both practical and accurate. The method with augmentation produces the best results with a total data set of 9000 face images, a buffer size of 10000, and epochs of 5, producing the minimum loss of 0.002, recall of 0.996, the precision of 0.999, and F1-score of 0.672. The proposed method gets the best accuracy of 98% with test data. The Siamese CNN model is successfully implemented in Python, and a user interface and executables are built using the Kivy framework.
The main pillars of Agile consolidation in newly Agile teams in Agile software development

&lt;span&gt;Many software companies and teams use Agile methods as their main development approach. These methods promise higher team productivity, faster product delivery, a more flexible development process, and greater customer satisfaction. Nevertheless, a review of the literature shows that adapting to these methods, known as Agile transition, is not as easy as expected. However, several frameworks and models have been proposed to facilitate the Agile transition process. The challenging issue after the transition to agility is the behavior of companies and teams after the Agile transition and how to maintain agility in the long run. Very little research has been done on this issue, which has largely expressed concern. The present study tries to explore the hidden aspects of the transition to agility and provide a solution for Agile consolidation in newly Agile software teams. In this regard, using the grounded theory approach, the basic theory of Agile consolidation in these teams has been presented. Preliminary findings of the study indicate important factors that play an important role in Agile consolidation. Identification of challenges, facilitators, organizational culture structure, and human roles in Agile consolidation is the most important initial findings of this study.&lt;/span&gt;
Active Verb Spell Checking Mem- + P in Indonesian Language Using the Jaro-Winkler Distance Algorithm

The Indonesian language is used as a means of communication, including written communication. Unfortunately, many mistakes are found in Indonesian language writing, such as the writing of active verbs with the prefix mem- followed by the letter P. This problem can be addressed with the spell-checking method. Spell checking is a process in computer programs to check the spelling of each word in electronic text or documents in the correct order. To better the active verb this study used the Jaro-Winkler Distance algorithm. Meanwhile, for system development, the Iterative Waterfall method was used. The system output is active verbs of mem- + P which is standardized according to the Great Dictionary of the Indonesian Language of the Language Center (KBBI) and the morphological rules of the Indonesian Language Spelling System. The accuracy value of the algorithm was 80% for common mistakes, 95% for specific mistakes, and 80% for correctable mistakes with word suggestions.
Enhancing extreme learning machines classification with moth-flame optimization technique

Extreme Learning Machine (ELM) algorithm assigns the input weights and biases in a “one-time stamp” fashion, this method makes the algorithm to be ill-conditioned and reduces its classification accuracy. The contribution of this work is the enhancement of the performance of ELM with the Moth-Flame Optimization (MFO) algorithm to improve classification accuracy. A hybrid of the Moth-Flame Optimization and Extreme Learning Machine (MFO-ELM) algorithm is implemented in MATLAB. MFO ensures a concurrent simulation of exploration and exploitation of the search space to select an optimum candidate solution. The candidate solution is reshaped into input weights and biases for ELM classification. The hybrid algorithm is validated on five life-selected datasets. The performance improvement of MFO-ELM is compared with ELM-optimized Particle Swarm Optimization (PSO-ELM) and Competitive Swarm Optimization (CSO-ELM) algorithms. The improvement rates are qualitatively and quantitatively evaluated to show the improvement of MFO-ELM on ELM and the other meta-heuristic algorithms. MFO-ELM improved the accuracies of the basic ELM in all 100% of the simulations and performed better than the other meta-heuristic algorithms in 80% of the simulations. The performance of MFO-ELM is more competitive, and it is recommended for solving classification problems.
Level of Cybercrime Threat During the Outbreak of COVID-19 Pandemic: A Study in Malaysia

Cybercrime threat has shown a sudden increase during the enforced stay-at-home order due to the outbreak of the COVID-19 pandemic.Thus, empirical research was conducted in providing substantial evidence regarding the level of cybercrime threats in Malaysia during the outbreak of the COVID-19 pandemic.A quantitative approach was used to collect data among 332 respondents across Malaysia.The finding stipulated that a large number of respondents have experienced a high level of online phishing/malware distribution (72.9%), a moderate level of online fraud (69.2%), and a moderate level of online sexual harassment (87.7%).The move to explore the level of cybercrime threat experienced by the public during the outbreak of the COVID-19 pandemic is essential to detect, disrupt, and mitigate cybercrime threats during unprecedented situations.
Flow incorporated neural network based lightweight video compression architecture

The sudden surge in the video transmission over internet motivated the exploration of more promising and potent video compression architectures. Though the frame prediction based hand designed techniques are performing well and widely used but the recent deep learning based researches in this domain provided further directions of pure deep learning based next generation codecs. As the bandwidth over the internet is varying, adaptive bit rate representation is more suitable for video quality adjustment in tune with bandwidth variation. The proposed architecture comprises of end to end trainable video compression network consisting of majorly three modules namely-motion extension network, flow autoencoder and frame autoencoder. Frame autoencoder generates the individual compressed frames, flow autoencoder is used for optical flow based motion compensation chore and next frame is predicted by the motion extension network. The network is designed and evaluated in incremental manner. The analysis of the outcomes demonstrates the promising performance of the network quantitatively and qualitatively. Moreover, the results reveal that inclusion of optical flow based motion compensation network to the MotionNet architecture has enhanced the performance.
Optimization of Three-dimensional Face Recognition Algorithms in Financial Identity Authentication

Identity authentication is one of the most basic components in the computer network world. It is the key technology of information security. It plays an important role in the protection of system and data security. Biometric recognition technology provides a reliable and convenient way for identity authentication. Compared with other biometric recognition technologies, face recognition has become a hot research topic because of its convenience, friendliness and easy acceptance. With the maturity and progress of face recognition technology, its commercial application has become more and more widespread. Internet finance, e-commerce and other asset-related areas have begun to try to use face recognition technology as a means of authentication, so people’s security needs for face recognition systems are also increasing. However, as a biometric recognition system, face recognition system still has inherent security vulnerabilities and faces security threats such as template attack and counterfeit attack. In view of this, this paper studies the application of threedimensional face recognition algorithm in the field of financial identity authentication. On the basis of feature extraction of face information using neural network algorithm, K-L transform is applied to image high-dimensional vector mapping to make face recognition clearer. Thus, the image loss can be reduced.
Survey on Generative Adversarial Behavior in Artificial Neural Tasks

GANs (generative opposing networks) are a technique for learning deep representations in the absence of a large amount of annotated training data. This is accomplished through the use of a competitive technique that employs two networks to generate background signals. GANs can use learned representations for a variety of applications, including image synthesis, semantic imaging, style transfer, super magnification, and segmentation. Images can be utilized in a variety of ways. Generative Adversarial Networks (GANs) are a unique class that has recently received a lot of interest due to the popularity of deep generative models. GANs implicitly distribute complex and high-resolution images, sounds, and data. However, due to inadvertently built network architecture, objective function usage, and optimization algorithm selection, significant difficulties such as mode collapse, inconsistencies, and instability develop while training GANs. In this paper, we conduct a thorough examination of the developments in GANs design and optimization strategies presented to address GANs difficulties. We provide intriguing study possibilities in this rapidly evolving area. GANs are a popular study topic due to their ability to generate synthetic data and the benefits of representations that can be understood regardless of the application. While various reviews for GANs in the image processing arena have been undertaken to date, none have focused on the review of GANs in multi-disciplinary domains. As a result, the utilization of GAN in interdisciplinary applications fields and its implementation issues were investigated in this survey by doing a thorough search for journal/research article connected to GAN.
Modular Domain Adaptation

Off-the-shelf models are widely used by computational social science researchers to measure properties of text, such as sentiment.However, without access to source data it is difficult to account for domain shift, which represents a threat to validity.Here, we treat domain adaptation as a modular process that involves separate model producers and model consumers, and show how they can independently cooperate to facilitate more accurate measurements of text.We introduce two lightweight techniques for this scenario, and demonstrate that they reliably increase out-ofdomain accuracy on four multi-domain text classification datasets when used with linear and contextual embedding models.We conclude with recommendations for model producers and consumers, and release models and replication code to accompany this paper.
FRONT-END APPLICATION FOR MULTIPLE STOREFRONTS ECOMMERCE USING CROSS-PLATFORM TECHNOLOGY

Cross-platform framework is becoming more and more popular. Many giant tech companies have their own offering with different programming languages. With so many choices, sometimes it is difficult for a start-up developer to choose which framework they will use for their project. Therefore, this research aims to give insight for start-up developers on which cross-platform framework is better between Flutter and React Native by comparing their performance. This research achieved its goal by creating two identical mobile applications using Flutter and React Native and then comparing their performance using a theoretical framework that measures load time, average frame rate, and memory usage. The result of this testing is that Flutter has a shorter load time at 1.69 second compared to React Native at 4.26 second and lower memory usage at 6.52MB compared to React Native at 27.6MB. Their average frame rate is very comparable hovering around 60 frames per second. With faster load time and lower memory usage Flutter has better performance wise compared to React Native.
Correction to “A Classification of Arab Ethnicity Based on Face Image Using Deep Learning Approach”

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