Reviewerly/exHarmony huggingface dataset:
Siswa Sd Menggunakan Smartphone dalam Pembelajaran Online

The use of smartphones at the age of elementary school (SD) students can have a negative effect on the growth of knowledge and learning activities. However, with the implementation of home learning and online learning in Indonesia today, elementary students have a lot of time using smartphones to study or using smartphones in a positive way. The purpose of this study was to determine the use of smartphones for elementary students in online learning. This study used a quantitative approach to survey methods and selected a random sample of 124 respondents who lived in urban areas, elementary school students in grades 3-6 and had given answers to a questionnaire given in the form of google form. The research instrument was adapted from several literatures. The instrument is made in 10 question items. The study was analyzed with quantitative statistical descriptions using SPSS version 24. The results found that 31 students had smartphones and 94 students used smartphones belonging to their parents, siblings and other people in online learning. The time spent studying is between 5-6 hours. Elementary students use smartphones in the "good" category to study, do and collect assignments, a tool to explore electronic and digital learning resources, are able to discuss with peers and all feel satisfied using smartphones in online learning process activities. Suggestions for future research are to use attitude, behavior and impact variables of smartphone use for students who have their own smartphones.
Trajectory Object Detection using Deep Learning Algorithms

Video surveillance data in smart cities needs to analyze a large amount of video footage in order to locate the people who are violating the traffic rules. The fact is that it is very easy for the human being to recognize different objects in images and videos. For a computer program this is quite a difficult task. Hence there is a need for visual big data analytics which involves processing and analyzing large scale visual data such as images or videos. One major application of trajectory object detection is the Intelligent Transport Systems (ITS). Vehicle type detection, tracking and classification play an important role in ITS. In order to analyze huge amount of video footage deep learning algorithms have been deployed. The main phase of vehicle type detection includes annotating the data, training the model and validating the model. The problems and challenges in identifying or detecting type of vehicle are due to weather, shadows, blurring effect, light condition and quality of the data. In this paper deep learning algorithms such as Faster R CNN and Mask R CNN and Frameworks like YOLO were used for the object detection. Dataset (different types of vehicle pictures in video format) were collected both from in-house premises as well as from the Internet to detect and recognize the type of vehicles which are common in traffic systems. The experimental results show that among the three approaches used the Mask R CNN algorithm is found to be more efficient and accurate in vehicle type detection.
Redesign University Operational Performance through Strategic indicators, and Employees' commitment

The new higher education systems have developed speedily with the standard and worldwide quality. The educational systems have endured long reformation and transformation processes from an old fashioned educational management model that deeply seated in the past to a strategic model. This study purpose is to identify the relationship among strategic planning (SP), organizational commitment (OC), and university operational performance (PERF). This relationship indicates the identifications of UAE universities. A quantitative research approach has followed in this study and survey-based questionnaire was appeared to collect data from universities academic staffs. All together 500 questionnaires was distributed among the staffs and 309 valid questionnaires were collected. Structural Equation Modelling (SEM) is conducted using data from 309 respondents. All study hypotheses were supported. SL is positively predicted OC and PERF. In addition OC mediates the relationship between SL and PERF. Managerial and theoretical implications are introduced.
Determining Employee Eligibility in Equalizing Staffing Status Using the Naïve Bayes

The term non-permanent employee first appeared in the rule of law, namely in Act Number 13 Year 2003 concerning Manpower.This Act has an impact on the emergence of clarity about staffing status so that the salaries obtained by employees do not match their workload.Therefore, this study aims to determine employees who are eligible to earn the same income at one of the private universities in Palembang based on university's strategic plan, namely class, employment status, membership, and education permit using the Naïve Bayes method. The results showed that the highest accuracy of predictive conclusions for non-permanent and permanent employees is 83.33%, while the lowest accuracy value is 50%.
Introduction to the Handbook on Think Tanks in Public Policy

Recently think tanks have encountered significant challenges in carrying out their research and networking functions due in part to the expansion of populism in political, social and economic governance institutions around the globe. As a result of these and other pressures, including funding shortfalls at the organizational and project level, many have sought to secure more prominent roles in the marketplace of ideas. Their higher visibility has generated increased scrutiny from scholars, who have tried to come to terms with what think tanks are, the functions they perform and the strategies they employ to shape public opinion and public policy. While these explorations have raised the profile of think tanks in the scholarly literature, this volume seeks to fill many of the remaining gaps in the development of evaluation methodologies as they relate to their operations, ontologies and durability. The introduction outlines how the volume's contributors examine the evolution of think tanks and their influence on policy discourse and the body politic.
An implementation of a fault-tolerant database system using the actor model

Fault-tolerant systems are an important discussion subject in our world of interconnected devices. One of the major failure points of every distributed infrastructure is the database. A data migration or an overload of one of the servers could lead to a cascade of failures and service downtime for the users. NoSQL databases sacrifice some of the consistency provided by traditional SQL databases while privileging availability and partition tolerance. This paper presents the design and implementation of a distributed in-memory database that is based on the actor model. The benefits of the actor model and development using functional languages are detailed, and suitable performance metrics are presented. A case study is also performed, showcasing the system’s capacity to quickly recover from the loss of one of its machines and maintain functionality.
An Explanatory Strategy for Reducing the Risk of Privacy Leaks

As machine learning moves into high-risk and sensitive applications such as medical care, autonomous driving, and financial planning, how to interpret the predictions of the black-box model becomes the key to whether people can trust machine learning decisions.Interpretability relies on providing users with additional information or explanations to improve model transparency and help users understand model decisions.However, these information inevitably leads to the dataset or model into the risk of privacy leaks.We propose a strategy to reduce model privacy leakage for instance interpretability techniques.The following is the specific operation process.Firstly, the user inputs data into the model, and the model calculates the prediction confidence of the data provided by the user and gives the prediction results.Meanwhile, the model obtains the prediction confidence of the interpretation data set.Finally, the data with the smallest Euclidean distance between the confidence of the interpretation set and the prediction data as the explainable data.Experimental results show that The Euclidean distance between the confidence of interpretation data and the confidence of prediction data provided by this method is very small, which shows that the model's prediction of interpreted data is very similar to the model's prediction of user data.Finally, we demonstrate the accuracy of the explanatory data.We measure the matching degree between the real label and the predicted label of the interpreted data and the applicability to the network model.The results show that the interpretation method has high accuracy and wide applicability.
Islamic Boarding School Learning Organization: Analysis of Learning Dynamic, Organizational Transformation and Application of Technology

The advance of science and technology requires an organization to make changes in developing its organization. Learning organization is seen as the ability of an organization to develop and improve the knowledge and attitude of all human resources in responding to the various changes that arise. Islamic boarding school is the oldest educational institutions in the national history of Indonesia as the nation's assets in people's life. The objective of this study is to analyze learning dynamics, organizational transformation, and the application of technology in Islamic boarding school in facing the challenges of disruptive innovation. The results of the research showed that all personnel in Islamic boarding school form learning interaction model both individual and group. An Islamic boarding school that renews the vision of the organization is a form of transformation in response to the changes that occur. Furthermore, the use of information technology is implemented in learning activities.sed for the utilized as a piece of a diesel motor with diesel mixes.
Freehand Sketch-Based Authenticated Security System using Convolutional Neural Network

An Authenticated Security System is a highly desired feature. In this paper, a FreeHand Sketch-based Authentication Security strategy is proposed for authentication purposes by allowing a user to choose one label from a collection of different labels and asking him to sketch the corresponding image for the selected label for registration to avoid mischievous registration and the sketched image gets preprocessed using adaptive threshold with Gaussian mixture and then predicted with a trained Convolutional Neural Network(CNN) data model to generate the necessary image label. The produced image label will compare with selected image label. If both are same then the details will store in the system database. The user gets login with his/her authorized details with sketch based image password. The image password gets preprocessed using adaptive threshold with Gaussian mixture and then predicted with a trained CNN model to produce the image name. The produced image name will compare with the system database for authentication. The methodology is tested with some sample input image passwords and the performance calculation is carried out using metrics like Recall and Precision. The proposed work exhibits the accuracy of approximately 85% by ensuring the authentication for the user security.
Enhanced Object Detection using Generalized Hough Transform and RANSAC

In this paper object detection process has been implemented using the combination of some robust algorithms. The algorithms used are SIFT, GHT and RANSAC. Two models have been proposed which using two of the above mentioned algorithms. In model one features extraction by SIFT has been processed using Generalized Hough Transform (GHT) , where in other model improved RANSAC has been used to eliminate the mismatches in order to achieve better recognition resolution. Overall GHT is found to be superior technique to achieve object detection efficiently.
Performance Analysis of Heap Sort and Insertion Sort Algorithm

In the era of new technology, we have huge amount of data to deal with arranging the huge amount of data has remained a big challenge. This research paper includes two types of sorting algorithm, Heap Sort and Insertion Sort and also their performance analysis on thebasis of running time along with their complexity. This paper includes the algorithms and theirimplementation in Java programming language. For theresults of this research study,the comparison ofthese two sorting algorithms with different type of the data at running time such as Large, Average, and Small. In Large,data pass100 integers in the array. For Average data pass 50integers in the array and for Small data pass10 integers in the array. It checks that,which sorting technique is efficient according to the input data. Then identifiesthe efficiency of these algorithms according to this data three cases used that is Best, Average and Worst Case. The result of this analysis is showing with the help of graphs to show that how much time both algorithms take while given the desired output
Security Techniques for Protecting Data in Cloud

Cloud computing can store and manage the large amount of data. Storing the data on to cloud is widespread among companies additionally as private users. It permits its users to access the cloud services from the different locations. It wants solely a working internet connection to access the cloud services. A lot of attention is gained by cloud still there are some problems that need to be taken in considerations(i.e. data security, privacy and reliability)in which data security is one in all the most problems. The biggest challenge in the cloud is to maintain the integrity and confidentiality of data. Many techniques are urged for data protection in cloud. This paper focuses on the present security techniques for shielding the data in cloud. The paper has been carried out on the basis of cryptography, intrusion detection, attacks solutions, Data integrity and privacy as well as authentication and identity.
Exploring the Quality Improvement in Small Scale Project Using Aspect-Oriented Design

Aspect-oriented software development (AOSD) seeks to renovate the software quality by use of alteration to the design in object-oriented based systems. Each system component has been divided into numbers of modules and arranges a design in a way that the module has not crosscut to each other. A module is a collection of block of codes, whose role is to restrict and conceal the design definition. This type of behavior is known as the Separation of Concerns (SoC). AOP helps programmers for separating crosscutting concerns. It can be helpful at better modularizing system, the problem with complex system is that the component require different, crosscutting descriptions at different levels and need maintenance too. In this paper, a study was conducted to analyses quality in aspect-oriented software design. This paper also explores varieties of existing metrics related to AOP also and will discuss in detail with comparison of OOPs using small scale projects that how the new programming paradigm design effects on quality of the software.
Creating a portable, high-level graph analytics paradigm for compute and data-intensive applications

HPC offers tremendous potential to process large amounts of data often termed as big data. Distributing data efficiently and leveraging specialised hardware (e.g., accelerators) are critical in order to best utilise HPC platforms constituting of heterogeneous and distributed systems. In this paper, we develop a portable, high-level paradigm for such systems to run big data applications, more specifically, graph analytics applications popular in the big data and machine learning communities. Using our paradigm, we accelerate three real-world, compute and data intensive, graph analytics applications: a function call graph similarity application, a triangle enumeration subroutine, and a graph assaying application. Our paradigm utilises the MapReduce framework, Apache Spark, in conjunction with CUDA and simultaneously takes advantage of automatic data distribution and accelerator on each node of the system. We demonstrate scalability and parameter space exploration and offer a portable solution to leverage almost any legacy, current, or next-generation HPC or cloud-based system.
Solar Tracker with a Self-Deploying System

This article provides a brief overview of the state of solar energy in the world. Disadvantages of fossil energy sources are discussed. Low efficiency is considered the main disadvantage of renewable energy sources. A possible way to improve efficiency of solar modules through the use of solar tracking systems (STS) is analyzed. STS is a special device with two drives for horizontal and vertical guidance to ensure that solar modules face the highest light flux source and thereby increase generation of electricity using a special algorithm. The theoretical increase in production for systems under ideal conditions is calculated; working values thereof are given. The disadvantages of the considered type of renewable energy generators are listed. The calculation of wind loads on the STS was carried out according to the presented method. Examples of destruction of solar power plants by natural disasters are given. A new type of solar trackers with a self-deploying system is proposed; these can protect the installation from unfavorable weather conditions. We analyzed accuracy of the positioning algorithm developed for this device. The conclusion contains possible ways of using the installation, its disadvantages, as well as plans for future research.
Face Recognition using Cascade Algorithm

As one of the most successful application of Image processing, face recognition has received attention for quite a long time. Whether being used in CCTV cameras or home security, face recognition is an important application in today’s metropolitan era. The face recognization strategy is implemented by using Haar cascade algorithm. Both are used differently and the results are then compared to know which one works better or is more accurate. In this paper, we have used Python programing language, since this is only a basic Face recognition system there is no database used but for future extension, we can add a database to expand this method to a larger scale.
A Novel Approach of Symmetric Key Cryptography using Genetic Algorithm Implemented on GPGPU

The world of digital communication consists of various applications which uses internet as the backbone for communication. These applications consist of data related to the users of the application, which is confidential and integrity needs to be maintained to protect against unauthorized access and use. In the information hiding field of research, Cryptography is one of the wide techniques used to provide security to the internet applications that overcome the challenges like confidentiality, integrity, authentication services etc. In this paper, we present a novel approach on symmetric key cryptography technique using genetic algorithm that is implemented on CUDA architecture.
SISTEM INFORMASI PENDAFTARAN ONLINE SISWA BERBASIS WEB PADA SEKOLAH MENENGAH KEJURUAN (SMK) PELAYARAN JAKARTA RAYA

Online New Student Admission (PPDB) as a method or method that aims to register online for new prospective cadets at SMK Pelayaran Jakarta Raya. With the development of Information Technology (IT) today, information about schools to prospective cadets without being geographically restricted must be conveyed through the website so that the public can easily access all school information. Admission of new cadets at the beginning of a new academic year, in which in practice the admission of new cadets is still done manually so that it cannot be done efficiently. With the existence of a web-based computerized information system, it is hoped that it can help prospective cadets in registering and getting accurate information so they can find out the effectiveness of this system. This writer applies the waterfall design to the building sketch of the information system for new student admissions, the waterfall method is a method that describes the design of the System Development Life Cycle (SDLC), where the preparation begins with analysis, design, coding, and testing. Data collection techniques by conducting field observations, interviews, and literature studies. This new student admission information system was built using the PHP programming language using the MySQL database as the database server. The result of this writing is a web-based student admission information system that has effectiveness in providing convenience to parents of new cadets to receive all information regarding new student admissions and carry out the online registration process.
An efficient collaborative recommender system for textbooks using silhouette index and K-means clustering technique

A recommender system provides a great platform for filtering of information and various knowledge-based management systems. They provide very good recommendation to the users so that they are able to predict the quality of the product in e-commerce. In today's research, it is very difficult to predict the accurate information regarding online products. In this research, we are going to introduce textbook-based recommender systems which uses the silhouette index and k-means clustering technique to predict the ratings of the textbooks available online based on its previous data. Initial positions of clusters are obtained and classifying the clusters by similarity of users are done by k-means clustering techniques. Our proposed recommender systems are able to predict much improved results rather than the other available state-of-the-art methods. Efficiency and performance of this newly developed recommender systems is enhanced when compared with existing systems. All experiments are done using publicly available BX-Book-Ratings datasets and achieved the MAE of 0.63 which is best among other state-of-the-art recommendation systems.
Performance Analysis of the Modified Vigenere Algorithm to Secure Data

The Vigenere algorithm is simple but considered unbreakable.However, the repeating key is its weakness and is still widely developed by researchers to increase the key length of the algorithm.This study aims to test and evaluate the security performance of the modified Vigenere cipher algorithm to address its vulnerability.The results show that the modified algorithm effectively overcomes Kasiski attack.Moreover, the frequency analysis test shows that the frequency of occurrence of alphabetic letters I and O obtained 9.0 curve value whereas M and V have 6.7 curve value.This frequency analysis result indicates that the enhanced Vigenere algorithm has reduced the effect of cryptanalysis.The modified algorithm has also passed the randomness ciphertext test when the frequency (monobit) test was performed.The result shows that the p-value computed is 0.841 which is higher than 0.01; hence, the generated ciphertext is considered random.