Reviewerly/exHarmony huggingface dataset:
Facial Detection and Recognition Among Heterogenous Multi Object Frames

An individual's face plays a really important role in social interaction, conveying a person's identity and has a high degree of variability in its appearance.To solve the problem of this variance.Currently discovering different ways to find or identify facial features have been a really active research field.Different applications are being developed using different face detection techniques such as Convolution Neural Networks (CNN), Region Based Convolution Neural Networks (RCNN), You Only Look Once (YOLO), Single Shot Detection (SDD) can be used for applications such as live face filters, face recognitionbased screen unlocking systems, etc.These applications also use pattern recognition techniques such as Linear Discriminant Analysis (LDA), Principal component Analysis (PCA), Histogram Oriented Graph (HOG) can be combined with .Both face detection and face recognition techniques can be combined to efficiently detect faces and give really accurate and faster results which can be used in security purposes, interactive gaming, human computer interaction.
ANALISIS KOHESI GRAMATIKAL DALAM BERITA ONLINE CNNINDONESIA.COM PADA RUBRIK NASIONAL EDISI OKTOBER 2019

The research aims to expose grammatical cohesion in the online news cnnindonesia.com on the National rubric of October 2019 edition. This method of research uses a qualitative descriptive approach. The technique used for data retrieval in this research is the technique of reading and note-taking. The subject of this research is the discourse of online news cnnindonesia.com on the National rubric of October 2019 edition. This research is focused on the form of grammatically cohesion markers. The result of this research is that of the 10 news analyzed found 52 data. The grammatical markers contained in the data are references, substitutions, ellipses, and conjunctions. Conjunctions and references are the most widely found type of grammatical cohesion
Design of Student Worksheets Based on Augmented Reality

Design innovations on student worksheets need to be developed so that students are motivated to following the learning process. Many students difficult to understand an abstract concept of biology because they can not see directly. We can using Augmented Reality technology in the design of Student Worksheets as one of that innovation. Therefore a descriptive study was conducted to design a Student Worksheet based on Augmented Reality. The design process was carried out in two stages, namely analysis and design. The analysis phase was done by observing using questionnaires and the design stage was done by utilizing various computer software. The result of research is the design of Student Worksheets that is integrated with Augmented Reality, so the student can see the abstract object of biology by 3D AR directly with that technology. This result were expected to be developed and carried out further testing.
Interactive Storytelling: Bringing Personas to Life through an AR/VR Experience

Personas are an effective way to bring customers to life, enabling business owners and designers to have a better understanding of their audiences' needs, values and behaviors in detail. Personas help with building and improving product and brand experiences. There is a diverse set of tools and methods to create personas, and while most are successful in creating detailed stories, they're mostly limited in terms of creating an immersive experience or building a deeper level of empathy. Our AR/VR Persona Experience is a unique, dynamic persona engagement tool that uses best‐in class interactive storytelling methods and enables audiences to better visualize and connect with their personas' emotions, habits and aspirations. Based on in‐depth, on‐site interviews, the AR/VR experience creates an ethnographic experience for the audience in the sense that they are able to deeply acquaint themselves with people and the worlds in which they live, ultimately creating greater understanding and empathy.
Penerapan Algoritma C4.5 Untuk Prediksi Loyalitas Nasabah PT Erdika Elit Jakarta

C4.5 algorithm is a decision tree algorithm group. This algorithm has input in the form of training samples and samples. While samples are data fields which we will use as parameters in classifying data. From the variable transaction frequency the company can see which customers are loyal to the company based on historical customer transaction data, but there are still some variables that make customers loyal to the company. These variables are age, customer gender, company sales gender, educational background, customer transaction frequency. The company knows how to predict customers who will be loyal to the company based on the experience of some of the variables above, but the company does not know the most influential variable in the assessment of loyal customers because of some of the variables above are not interconnected and it is uncertain if one variable can make a decision whether the customer loyal. Based on the decision tree that has made the most influential attribute on customer loyalty is the educational background because it has the highest gain value of 1.545292721 and as the root of the decision tree while the client's gender does not significantly affect customer loyalty because it is always at the last node with the gain value which is 0.623919119.
ES2: Building an Efficient and Responsive Event Path for I/O Virtualization

Hypervisor intervention in the virtual I/O event path is a main performance bottleneck for I/O virtualization because of the incurred costly VM exits. The shortcomings of prior software solutions against virtual interrupt delivery, a major source of VM exits, promoted the emergence of the hardware-based Posted-Interrupt (PI) technology. PI can provide non-exit interrupt delivery without compromising any virtualization benefit. However, it only acts on the half of the event path, i.e., the interrupt path, while guests I/O requests may also trigger a large amount of VM exits. Additionally, PI may still suffer a severe latency from the vCPU scheduling while delivering interrupts. Aiming at an optimal event path, we propose ES2 to simultaneously improve bidirectional I/O event delivery between guests and their devices. On the basis of PI, ES2 introduces hybrid I/O handling scheme for efficient I/O request delivery and intelligent interrupt redirection for enhanced I/O responsiveness. It does not require any modification to guest OS. We demonstrate that ES2 greatly reduces I/O-related VM exits with the exit handling time (EHT) below 2.5 percent for TCP streams and 0.1 percent for UDP streams, increases guest throughput by 1.9x for Memcached and 1.6x for Nginx, and keeps guest latency at a low level.
GSA to Obtain SVM Kernel Parameter for Thyroid Nodule Classification

Support Vector Machine (SVM) is one of the most popular methods of classification problems due to its global optima solution. However, the selection of appropriate parameters and kernel values remains an obstacle in the process. The problem can be solved by adding the best value of parameter during optimization process in SVM. Gravitational Search Algorithm (GSA) will be used to optimize parameters of SVM. GSA is an optimization algorithm that is inspired by mass interaction and Newton's law of gravity. This research hybridizes the GSA and SVM to increase system accuracy. The proposed approach had been implemented to improve the classification performance of Thyroid Nodule. The data used in this research are ultrasonography image of Thyroid Nodule obtained from RSUP Dr. Sardjito, Yogyakarta. This research had been evaluated by comparing the default SVM parameters with the proposed method in term of accuracy. The experiment results showed that the use of GSA on SVM is capable to increase system accuracy. In the polynomial kernel the accuracy rose up from 58.5366 % to 89.4309 %, and 41.4634 % to 98.374 % in Polynomial kernel
Analisis Klasifikasi Kanker Payudara Menggunakan Algoritma Naive Bayes

Breast cancer is one of the highest causes of death among women, this disease ranks second cause of death after lung cancer. According to the world health organization, 1 million women get a diagnosis of breast cancer every year and half of them die, in general this is due to early treatment and slow treatment resulting in new cancers being detected after entering the final stage. In the field of health and medicine, machine learning-based classification has been carried out to help doctors and health professionals in classifying the types of cancer, to determine which treatment measures should be performed. In this study breast cancer classification will be carried out using the Naive Bayes algorithm to group the types of cancer. The dataset used is from the Wisconsin breast cancer database. The results of this study are the ability of the Naive Bayes algorithm for the classification of breast cancer produces a good value, where the average percentage of correctly classified data reaches 96.9% and the average percentage of data is classified as incorrect only 3.1%. While the level of effectiveness of classification with naive bayes is high, where the average value of precision and recall is around 0.96. The highest precision and recall values are when the test data uses a percentage split of 40% with the respective values reaching 0.974 and 0.973.
Analysis of Multi-Element Blended Course Teaching and Learning Mode Based on Student-Centered Concept under the Perspective of “Internet+”

The integration of Internet and education has changed students’ learning environment and affected their learning behavior, which poses a greater challenge to the traditional teaching mode. Through the SWOT analysis of the “student centered” multi-element blended teaching mode in the era of “Internet + education”, it is concluded that the adaptability of learners themselves and the mismatch between teachers’ educational ideas and this teaching model delay the development of education to a certain extent. Some suggestions are put forward, such as strengthening the supervision and guidance, implementing the teaching and learning model scientifically, improving teachers’ ideology and comprehensive quality, and making full use of the characteristics of Internet opening, sharing and collaboration to construct the public service system and platform of national educational resources.
MODERN APPROACHES TO THE PROBLEMS OF PROFESSIONAL TRAINING OF STUDENTS AT PEDAGOGICAL UNIVERSITY

Professional training of future teachers is one of the urgent problems of training specialists at Pedagogical University, especially in the context of changing concepts of pedagogical profession and pedagogical activity. Professional training is aimed to form a system of professional values and attitudes that regulates teachers’ relations with socio-cultural environment, their inner selves and teaching activities. In addition, it is oriented at the development of an active professional and subjective position of future teachers. The authors of the article analyze the problems of professional training of students at Pedagogical University, identify contradictions, showing different aspects of the problem. The purpose of the article is to determine the main approaches to the modern understanding of the problems of professional training of future teachers at university, the description of educational space at a pedagogical university with its possibilities for students’ professional development. To achieve this goal, the authors use the methods of analysis and systemize the data obtained in previous studies on this problem. The characteristic of the conditions necessary to organize professional training of future teachers at university were formulated. The results can be used to design educational materials and to organize the education process for Bachelor students, Master students, and the professionals upgrading their qualification at advanced professional training courses.
Survey of User to User Recommendation System in Online Social Networks

The widespread use of online social networks (OSN) and their applications by users lead to the lack of knowledge identification of their needs across the vast amount of data, which made the need to create systems that help people to solve the problems and make decisions with more accuracy, an example of these systems is the Recommendation system (RS), which helps users to make decision and save time in search on a commercial or personal level, one of the most critical types of recommendation systems is the friends recommendation system (FRS) .In this survey, several studies have been suggested to solve the problem of FRS and its mechanism, techniques, and algorithms used to create them Also, the RS types and techniques, a variety of dataset that deals with a specific system, are explained.Moreover, the challenges they face to determine the needs of people in terms of the choice of items or at the level of social networks are included.
Information Technology (IT) and Business: Two Unavoidable Paths to Africa’s Future

The role of information technology (IT)
MACHINE LEARNING TECHNIQUES FOR SPAMMER IDENTIFICATION: STATE OF THE ART AND ANALYSIS

Internet offers seamless communication with a high consumption rate. With the increase in the widespread utilization of internet tools such as e-mails, the spammers started to exploit the e-mail network to effect malicious and hazardous activities. As the e-mail spam detection systems became more sophisticated and accurate, the attention of spammers has now turned to the recently emerged social networks due to massive usage and popularity among the people. A range of online social networks (OSN) such as Facebook, Twitter, Instagram are available with each of them offering unique services to the account owners and benefits both professionally and personally. Besides these advantages, the network also houses illicit accounts that disturbs the user experience and destroy the ultimate objective of social networking. The existing techniques employed by these OSN to detect such illicit users are not effective and accurate and also demands manual approaches to spot them. Hence, a number of methods and algorithms were proposed in the literature to identify the spammers concealed in these networks. This work attempts to provide a detailed review of state of the art techniques and methodologies employed in the spam account detection problem along with future research directions.
Detection Method of Data Integrity in Network Storage Based on Symmetrical Difference

In order to enhance the recall and the precision performance of data integrity detection, a method to detect the network storage data integrity based on symmetric difference was proposed. Through the complete automatic image annotation system, the crawler technology was used to capture the image and related text information. According to the automatic word segmentation, pos tagging and Chinese word segmentation, the feature analysis of text data was achieved. Based on the symmetrical difference algorithm and the background subtraction, the feature extraction of image data was realized. On the basis of data collection and feature extraction, the sentry data segment was introduced, and then the sentry data segment was randomly selected to detect the data integrity. Combined with the accountability scheme of data security of the trusted third party, the trusted third party was taken as the core. The online state judgment was made for each user operation. Meanwhile, credentials that cannot be denied by both parties were generated, and thus to prevent the verifier from providing false validation results. Experimental results prove that the proposed method has high precision rate, high recall rate, and strong reliability.
Implementasi High Availability Cluster Web Server Menggunakan Virtualisasi Container Docker

The increasing demand for information on the internet causes the traffic load on the web server to increase. Therefore it can cause the workload on a web server service to be overloaded (request), so that the server is down (overloaded). Based on previous research the application of load balancing can reduce the burden of traffic on the web server. This research method uses load balancing on servers with round robin algorithm and least connections as well as a single server as a comparison. The parameters measured are throughput, responses time, requests per second, CPU utilization. From the test results obtained Haproxy load balancing system, the least connection algorithm is superior to the round robin algorithm. Generated per-second request value of 2607,141 req / s and throughput of 9.25 MB / s for the least connection, while 2807,171 req / s and 9.30 MB / s for round robin.
Sistem Informasi Monitoring Perjanjian Kerja Sama Berbasis Web Pada PT Dayamitra Telekomunikasi Jakarta

The development of the telecommunications world is developing very rapidly throughout the world and is no exception in Indonesia. The telecommunications business mostly involves companies providing equipment and providing telecommunications infrastructure, in this case, towers. PT Dayamitra Telekomunikasi or also known as Mitratel is a subsidiary of PT Telkom Indonesia. Tbk, which is engaged in providing telecommunications infrastructure in Indonesia. In carrying out its business activities, PT Dayamitra Telekomunikasi has collaborated extensively with external parties in terms of land use for the construction of telecommunications towers. Land use is currently divided into two land categories, namely private land and government land. The current condition in managing cooperative assets is still done manually or not systematically so that it is not effective and a monitoring information system is needed to monitor the use of assets listed in this cooperation document. The monitoring information system that will be created is web-based which will facilitate the user in monitoring, updating the status of the monetize point quota listed in the employment agreement document with the government. The software used in making this monitoring information system uses the PHP and MySQL programming languages.
ANALISIS PENGARUH APLIKASI PEMBELAJARAN AKSARA SUNDA TERHADAP PEMAHAMAN SISWA DENGAN METODE TAM

Along with the times and the lack of cultural preservation, traditional culture is starting to be displaced by western culture.For example, Sundanese script culture, many people, especially Sundanese who do not know about Sundanese script, which is one of Indonesia's cultural heritage.To safeguard and preserve the culture of a Sundanese script in modern times, it is needed away and learning media that is appropriate to the current era.This research aimed to find out how much influence the Androidbased Sundanese alphabet learning application program has on Sundanese script student's understanding of high school students.This research used the Technology Acceptance Model method.The sample in this study were high school students at Senior High School 1, Singaparna.This study used a purposive sampling method.The results of this study indicated that the Android-based Sundanese alphabet learning application program has a positive influence on the understanding of Sundanese script learning in Singapore High School 1 Singapore.
Neural Adaptive Transport Framework for Internet-scale Interactive Media Streaming Services

Network dynamics, such as bandwidth fluctuation and unexpected latency, hurt users' quality of experience (QoE) greatly for media services over the Internet. In this work, we propose a neural adaptive transport (NAT) framework to tackle the network dynamics for Internet-scale interactive media services. The entire NAT system has three major components: a learning based cloud overlay routing (COR) scheme for the best delivery path to bypass the network bottlenecks while offering the minimal end-to-end latency simultaneously; a residual neural network based collaborative video processing (CVP) system to trade the computational capability at client-end for QoE improvement via learned resolution scaling; and a deep reinforcement learning (DRL) based adaptive real-time streaming (ARS) strategy to select the appropriate video bitrate for maximal QoE. We have demonstrated that COR could improve the user satisfaction from 5% to 43%, CVP could reduce the bandwidth consumption more than 30% at the same quality, and DRL-based ARS can maintain the smooth streaming with <; 50% QoE improvement, respectively.
Image Haze Removal Using Dark Channel Prior Technology with Adaptive Mask Size

Image dehazing is a crucial technique in the study of computer vision.The most widely used image dehazing approach is the dark channel prior (DCP) method proposed by He et al. [IEEE Trans.Pattern Anal.Mach.Intell.33 (2011) 2341].Because a DCP-based method generates halo artifacts under certain conditions, this study aims to solve this problem and propose a DCP-based method that uses a mask with an adaptive size.The proposed method is based on the inverse ratio of the gradient of a hazy image and calculates the corresponding mask size.A small mask size is used for regions with a large gradient to solve the halo problem and a large mask size is used for regions with a small gradient to achieve the dehazing effect.Subsequently, the gradient was smoothened and the γ function was corrected using a Gaussian filter to obtain a more favorable nonlinear relationship.Finally, the ant colony optimization (ACO) algorithm was employed to determine the optimal parameters for the Gaussian filter and γ function.A new dehazing performance index (DPI) was also proposed in this study as the cost function for the ACO algorithm.The experimental results of this study verified that the proposed method can effectively minimize the effect of halo artifacts without compromising the dehazing performance and color distortion.
Data-Driven Load Data Cleaning and Its Impacts on Forecasting Performance

Massive and various bad data may be introduced to load profiles in the process of data acquisition, transmission, and storage deliberately or accidentally such as cyber attacks and equipment failures. The bad data may result in bias for load forecasting and other data analytic applications. This paper proposes a novel bad data identification and repairing method for load profiles. In the first stage, Singular Value Thresholding (SVT) algorithm is applied to complete the missing data and detect the anomaly spikes roughly. In the second stage, quantile regression with lag value is performed to detect local fluctuation. How the data cleaning influences the forecasting performance is also investigated. Case studies on load of Fujian Province, China are conducted to verify the effectiveness of the proposed method.
Emotion Recognition Using Eye Gaze Based on Shallow CNN with Identity Mapping

Machine recognition of human emotions has attracted more and more attention for its wide application in recent years. As a spontaneous signal of human behavior, eye gaze is utilized for emotion recognition. Compared with electroencephalogram (EEG) signals, eye gaze data is more available, and it can be used in many practical applications, such as virtual reality (VR). In this paper, a new method of human emotion recognition based on eye gaze is proposed. Firstly, a new set of eye gaze features is proposed which consists of eye gaze sequences, extracted statistical feature sequences and spectral feature sequences. Then, the eye gaze feature set is input into the convolutional layer to extract high-level features. Finally, these high-level features and the eye gaze feature set are combined to complete the mapping of features to human emotions. Experiments on MAHNOB-HCI dataset demonstrate the effectiveness of this method.