Reviewerly/exHarmony huggingface dataset:
The Correlation Between Students’ Behavioral Engagement And Students’ Speaking Skill

The objective of the research is to find out whether there is any correlation between students’ behavioral engagement and students’ speaking skill. The research used quantitative approach and the method was correlational research. The population was the eighth-grade students of SMP Islam Malahayati Jakarta. The sample of 26 students were randomly selected. The data of the students’ behavioral engagement were collected by using the questionnaire which was adopted from Miserandino’s BEQ, while the data of students’ speaking skill were collected by using the speaking test which had been validated by the expert judgment. The result shows that there is a positive significant correlation between students’ behavioral engagement and their speaking skill. It can be proven from the result of rxy = 0.698 is higher than rtable = 0.388 and tcount = 4.770 is higher than ttable = 1.7109. Coefficient determination shows that (0.698)2×100% = 48.72% so that students’ behavioral engagement contributes 48.72% towards their speaking skill. It can be concluded that there is a positive significant correlation between students’ behavioral engagement and students’ speaking skill. It means that the students who engage behaviorally have good speaking skill and vice versa.
DESIGN OF MACHINE LEARNING DETECTION MASK USING YOLO AND DARKNET ON NVIDIA JETSON NANO

In the Covid-19 pandemic, wearing a mask has become the obligation of Indonesian citizen,for outdoor and indoor. Public awareness of wearing masks when leaving the house according to the government's appeal is still low. This can be seen from the many people who do not wear masks while on the public place or in other place. Technology that can help increase public awareness of using masks is a mask detection application. The application to detect use of masks is made using Artificial Intelligence and uses machine learning methods from a tool that will read every use of masks on the face automatically, either using masks appropriately, using inappropriate masks or not using masks. The method used in this research is to use the Research and Development method, namely research that can produce a product. Based on the test results using masks with facial motifs, there are different results from the two samples tested depending on the quality of the image on the mask Yolo (You Only Look Once) is an object detection system in real time. Darknet is an open source neural network framework written in the C and CUDA programming languages. The Jetson Nano is a System On Module (SoM) and developer kit from the Nvidia Jetson family.
Ontological automation of software essence kernel to assess progress of software project

Managing a software project with a large number of requirements is a challenging task, a lot of effort and time is required to measure the software’s progress and health. To tackle this, software development organizations look for different development processes that would best assist them. Recently, software essence is used to measure the progress and health of software development. However, these are applied manually to assess the development and fitness of the system. Therefore, it is very difficult for software organizations to access the required information quickly. The aim of this study is the automation of Software Essence Kernel through Ontology Development to quickly measure the progress and health of the system. Experimental work is done with a hypothesis that if software essence kernel is automated, then one can quickly measure the progress and health of the system. The results of this study argue that software health of the system can be quickly and easily measured if the ontology is automated.
Policy-Based Group Signature Scheme from Lattice

Although the existing group signature schemes from lattice have been optimized for efficiency, the signing abilities of each member in the group are relatively single.It may not be suitable for complex applications.Inspired by the pioneering work of Bellare and Fuchsbauer, we present a primitive called policy-based group signature.In policy-based group signatures, group members can on behalf of the group to sign documents that meet their own policies, and the generated signatures will not leak the identity and policies of the signer.Moreover, the group administrator is allowed to reveal the identity of signer when a controversy occurs.Through the analysis of application scenarios, we concluded that the policy-based group signature needs to meet two essential security properties: simulatability and traceability.And we construct a scheme of policy-based group signature from lattice through techniques such as commitment, zero-knowledge proof, rejection sampling.The security of our scheme is proved to be reduced to the module short integer solution (MSIS) and module learning with errors (MLWE) hard assumptions.Furthermore, we make a performance comparison between our scheme and three lattice-based group signature schemes.The result shows that our scheme has more advantages in storage overhead and the sizes of key and signature are decreased roughly by 83.13%, 46.01%, respectively, compared with other schemes.
A Variational Inequality Approach to Bayesian Regression Games

Bayesian regression games are a special class of two-player general-sum Bayesian games in which the learner is partially informed about the adversary’s objective through a Bayesian prior. This formulation captures the uncertainty in regard to the adversary, and is useful in problems where the learner and adversary may have conflicting, but not necessarily perfectly antagonistic objectives. Although the Bayesian approach is a more general alternative to the standard minimax formulation, the applications of Bayesian regression games have been limited due to computational difficulties, and the existence and uniqueness of a Bayesian equilibrium are only known for quadratic cost functions. First, we prove the existence and uniqueness of a Bayesian equilibrium for a class of convex and smooth Bayesian games by regarding it as a solution of an infinite-dimensional variational inequality (VI) in Hilbert space. We consider two special cases in which the infinite-dimensional VI reduces to a high-dimensional VI or a nonconvex stochastic optimization, and provide two simple algorithms of solving them with strong convergence guarantees. Numerical results on real datasets demonstrate the promise of this approach.
Manajemen Keuangan Sekolah Dalam Pemenuhan Sarana Prasarana Pendidikan di Bekasi

This study aims to analyze the financial management of schools in the fulfillment of educational facilities at SDN Wanasari 01, Cibitung, Bekasi. This research uses a qualitative case study approach. The data collection technique is done by using interviews, observation, and documentation. The data analysis technique uses data reduction, data display, and conclusion drawing/verification. Checking the validity of the data is done through; credibility, transferability, dependability, and confirmability. The results showed that school financial management in fulfilling educational facilities at SDN Wanasari 01 Cibitung Bekasi was carried out through school budget planning, school financial accounting, and school financial accountability.
Computer Vision and Neural Networks for Libras Recognition

In recent years, one can find several efforts to increase the inclusion of people with some type of disability. As a result, the global study of sign language has become an important research area. Therefore, this project aims at developing an information system for the automatic recognition of the Brazilian Sign Language (LIBRAS). The recognition shall be done through the processing of videos, without relying on support hardware. Given the great difficulty of creating a system for this purpose, an approach was developed by dividing the process into stages. In addition to dynamically identifying signs and context, neural network concepts and tools were used to extract the characteristics of interest and classify them accordingly. In addition, a dataset of signs, referring to the alphabet in LIBRAS, was built as well as a tool to interpret, with the aid of a webcam, the signal executed by a user, transcribing it on the screen.
The effective of augmented reality in the interactive print design

The research deals with the effectiveness of augmented reality in the design of interactive publications, as augmented reality is one of the newest and most widely used technology techniques.the latest popularity in print advertisements, and it supports advertising with sound and movement, which makes it more attractive than traditional advertisements, where advertisement is displayed on phone screens, which is a process of merging Virtual objects in a real environment in Real-time , augmented reality combines the real and the virtual.Augmented Reality aims to simplify a user's life by bringing virtual information not only to their immediate surroundings, but also to any indirect view of the real world environment, such as a live video broadcast. Augmented reality enhances the user's ability to visualize and interact with the Real world.
Elementary School Teachers’ Efforts in Enriching Elementary School Students’ Reading Literacy during the Pandemic Period through Literacy Reading Books

This study aims to determine the efforts of elementary school teachers in enriching the reading literacy of elementary school students during the pandemic through literacy reading books that have been compiled by Badan Pengembangan dan Pembinaan Bahasa in 2019.The study uses a qualitative method with a descriptive approach.Analysis of the data used in this study is to analyze the results of questionnaires from respondents using the Analysis Interactive Model from Miles and Huberman.Data was collected by filling out a questionnaire via Google Form.The sample of this research is fifty elementary school teachers in Jakarta.The results showed that literacy reading books have an important role and can be used as an alternative reading for elementary school students to enrich their reading literacy.The results of this study are expected to further enrich the reading literacy of elementary school students during and after the pandemic.
A CRITICAL COMPARISON OF BRAVE BROWSER AND GOOGLE CHROME FORENSIC ARTEFACTS

Digital forensic practitioners are tasked with the identification, recovery, and analysis of Internet browser artefacts which may have been used in the pursuit of committing a civil or criminal offence.This research paper critically compares the most downloaded browser, Google Chrome, against an increasingly popular Chromium browser known as Brave, said to offer privacy-by-default.With increasing forensic caseloads, data complexity, and requirements for method validation to satisfy ISO 17025 accreditation, recognising the similarities and differences between the browsers, developed on the same underlying technology is essential.The paper describes a series of conducted experiments and subsequent analysis to identify artefacts created as part of normal user browsing activity.Analysis of the artefacts found that Brave and Chrome share almost identical data structures, with on-disk artefact recovery successful, even for deleted data.The outcome of this research, based upon the results, serves to enrich understanding and provide best practice for practitioners and software developers, respectively responsible for examining Chromium artefacts for use in evidence production and developing new forensic tools and techniques.
Studi Pengenalan Teknologi Artificial Intelligence Bagi Guru dan Siswa SMK Nurul Islam Larangan

SMK Nurul Islam is a vocational high school located in Larangan sub-district, Brebes district, which was founded by the Nurul Islam Slatri educational foundation. Based on the results of an initial survey conducted at partners, that students, teachers, and employees (+- 80%) have heard of the industrial revolution 4.0, but only 30% have heard of Artificial Intelligence. And from the survey, it was found that only 2% have understood and understood Artificial Intelligence technology, which has been applied in almost all sectors of life. The survey results also show that all teachers, employees, and students are familiar with the use of information technology, such as: laptops, smartphones, and the internet. However, the current trend of information technology development is that many do not understand, especially regarding the application of the concept of Artificial Intelligence technology, such as: Face Recognition, Speech Recognition, Expert System, Chatboot, Image Detection, and others. Therefore, community service activities were carried out with the theme Introduction to Artificial Intelligence Technology. The methods of presentation, dialogue and demonstration are carried out in the implementation of these community service activities. Based on community service activities and the comparison between the results of the pre-test and post-test, it is obtained that the knowledge and understanding of partners, namely: students and teachers of SMK Nurul Islam Prohibition on Artificial Intelligence is increasing.
PREDICTIVE MODEL FOR CHILD DELIVERY

Antenatal care is an essential period in which medical experts examines pregnant women to prepare them for proper child delivery. Choosing or knowing the likely mode of child delivery is essential both for the mother and the medical team. It helps in proper preparation for labour and any possible complication that could arise. There are also chances of reducing maternal or child mortality. However, the decision on which of the options is appropriate is sometimes difficult due to several parameters and variable. Analyzing Obstetric and mode of delivery for pregnant woman is tedious, therefore, this work used data from three medical facilities in Katsina State and apply three machine leaning algorithms to predict the most appropriate mode of child delivery. The work was implemented using python programming language software. The result of the work has shown that random forest algorithm performs better with accuracy result as precision was 0.918, recall of 0.715 and 0.896 for Spontaneous Vagina delivery and 0.716 for precision, 0.929 for recall and 0.896 for Caesarian section mode
Optimization of the implementation of an electronic educational environment in an educational institution

The article considers the conceptual and practical approaches to the implementation of the direction of the national educational policy in terms of the implementation of the tools of the electronic educational environment. Shown high efficiency in the implementation of the information system in the organization of the educational process. The characteristics of the implemented EIOS are given on the example of the Voronezh Institute of High Technologies. It is noted that the introduction of the "Elinos" system made it possible to reduce the costs of implementing the educational process using e-learning and distance technologies in online and mixed modes, to transfer almost all students and pedagogical workers to the use of a cloud solution without using additional software The article also describes in detail technical means of implementation and the external interface of the developed system. The functional of managing educational content, building educational trajectories, conducting various forms of testing is described. Comparative values of cost reduction in the implementation of commercial, free and specially developed software electronic environments are given. Demonstrated EIOS tools, allowing to reduce the complexity of mastering by students, as well as by the teaching staff. As a feature of the system, it is noted the presence of cloud storage located on the servers of the university and supporting the placement of all educational materials and students' files. It is noted that it is possible to connect a two-factor authorization mechanism based on students' mobile devices. The practical efficiency of the applied solutions is shown.
Assessment of Physicochemical Properties of Groundwater near Oil Well Pads in Lokichar Basin, Turkana County, Kenya

Oil mining wastes such as Produced water, drilling muds and cuttings may lead to contamination of groundwater.The study investigated whether Produced water from the oil well pads in south Lokichar basin, Turkana County, Kenya, leads to contamination of the neighboring groundwater resources.The specific objectives of the study were to 1) determine the physicochemical properties of Produced water and 2) determine the physicochemical properties of boreholes water near oil well pads.Produced water was found to be highly contaminated since all the selected physicochemical parameters exceeded the set drinking water quality standards.The computed Water Quality Index of 4030 found for the Produced water indicated that it was unsuitable for drinking purpose.Produced water heavy metals and salts levels that were found to be predominant exceeded acceptable water quality standards as follows: lead by 15,680%, zinc by 22%, iron by 16,567%, chlorides by 8128%, fluorides by 2500%, and nitrates by 480%.This was attributed to the usage of production chemicals to enhance oil recovery through hydraulic fracturing.Out of 11 groundwater samples analysed, eight were found contaminated with high levels of heavy metals and salts exceeding set drinking water quality standards.Nalemsekon borehole iron levels exceeded allowable standards by 250%, chloride 10.4% and fluoride 148%.RCEA borehole lead levels exceeded standards by 1500%, while fluoride 56%.Nakwakipi borehole lead levels exceeded by 5180%, nitrate levels 287%.Nakukulas 10 borehole zinc levels exceeded by 51%, nitrate 111%.Nakwakitela borehole fluoride levels exceeded by 36%, nitrate levels 428%.Nitrates levels at Irir 1, Chinese 1 and Nawoyatira boreholes exceeded standards by 604%, 463% and 322%, respectively.The high levels of heavy metals and salts observed in these boreholes was as a result of oil mining wastes contamination since these boreholes were in close proximity to the oil well pads.
BCSM: A BlockChain-based Security Manager for Big Data

The amount of data generated globally is increasing rapidly.This growth in big data poses security and privacy issues.Organizations that collect data from numerous sources could face legal or business consequences resulting from a security breach and the exposure of sensitive information.The traditional tools used for decades to handle, manage, and secure data are not suitable anymore in the case of big data.Furthermore, most of the current security tools rely on third-party services, which have numerous security problems.More research must investigate protecting user-sensitive information which can be abused and altered from several sides.Blockchain is a promising technology that provides decentralized backend infrastructure.Blockchain keeps track of transactions indefinitely and protects them from alteration.It provides a secure, tamper-proof database that may be used to track the past state of the system.In this paper, we present our big data security manager based on Hyperledger Fabric, which provides end-to-end big data security, including data storage, transmitting, and sharing as well as access control and auditing mechanisms.The manager components and modular architecture are illustrated.The metadata and permissions related to stored datasets are stored in the blockchain to be protected.Finally, we have tested the performance of our solution in terms of transaction throughput and average latency.The performance metrics are provided by Hyperledger Caliper, a benchmark tool for analyzing Hyperledger blockchain performance.
Deep reinforcement learning approach to predict head movement in 360&amp;#xB0; videos

The popularity of 360°videos has grown immensely in the last few years.One probable reason is the availability of lowcost devices and ease in capturing them.Additionally, users have shown interest in this particular type of media due to its inherent feature of being immersive, which is completely absent in traditional 2D videos.Nowadays such powerful 360°videos have many applications such as generating various content-specific videos (gaming, knowledge, travel, sports, educational, etc.), during surgeries by medical professionals, in autonomous vehicles, etc.A typical 360°video when seen through a Head Mounted Display (HMD) gives an immersive feeling, where the viewer perceives standing within the real environment in a virtual platform.Similar to real life, at any point in time, the viewer can view only a particular region and not the entire 360°content.Viewers adopts physical movement for exploring the total 360°content.However, due to the large volume of 360°media, it faces challenges during transmission.Adaptive compression techniques have been incorporated in this regard, which is in accordance with the viewing behaviour of a viewer.Therefore, with the growing popularity and usage of 360°media, the adaptive compression methodologies are in development.One important factor in adaptive compression is the estimation of the natural field-of-view (FOV) of a viewer watching 360°content using a HMD.The FOV estimation task becomes more challenging due to the spatial displacement of the viewer with respect to the dynamically changing video content.In this work, we propose a model to estimate the FOV of a user viewing a 360°video using an HMD.This task is popularly known as the Virtual Cinematography.The proposed FOVS-electionNet is primarily based on a reinforcement learning framework.In addition to this, saliency estimation is proved to be a very powerful indicator for attention modelling.Therefore, in this proposed network we utilise a saliency indicator for driving the reward function of the reinforcement learning framework.Experiments are performed on the benchmark Pano2Vid 360°dataset, and the results are observed to be similar to human exploration.
Caricature Face Photo Facial Attribute Similarity Generator

Caricatures can help to understand the perception of a face. The prominent facial feature of a subject can be exaggerated, so the subject can be easily identified by humans. Recently, significant progress has been made to face detection and recognition from images. However, the matching of caricature with photographs is a difficult task. This is due to exaggerated features, representation of modalities, and different styles adopted by artists. This study proposed a cross-domain qualitative feature-based approach to match caricature with a mugshot. The proposed approach uses Haar-like features for the detection of the face and other facial attributes. A point distribution measure is used to locate the exaggerated features. Furthermore, the ratio between different facial features was computed using different vertical and horizontal distances. These ratios were used to calculate the difference vector which is used as input to different machine and deep learning models. In order to attain better performance, stratified k-fold cross-validation with hyperparameter tuning is used. Convolution neural network-based implementation outperformed the machine learning-based models.
New Reversible Data Hiding Algorithm Based on Edge Detection and PVO Mechanisms

Reversible data hiding technique is able to restore the cover data exactly after data extraction. This paper presents a new reversible data hiding scheme based on the combination of edge detection and pixel value ordering mechanism is proposed. In the proposed scheme, the cover image is divided into smooth or rough areas by Canny edge detection algorithm. Then, the secret data is embedded into pixels in the smooth areas based on pixel value ordering algorithm to obtain better image quality of marked images. The experimental results demonstrate that our scheme achieves better visual quality than those of state-of-the-art schemes while guaranteeing the reversibility.