Reviewerly/exHarmony huggingface dataset:
Design and Build Mango E-Commerce Information System

The development of e-commerce today, especially agribusiness has very good prospects. Demand for ornamental plants and fruits is still very high, especially the demand for mangoes is very high at around 80%. The purpose of this research is to build an e-commerce information system for online mango fruit stores. E-Commerce System Mangga Mandalagiri online shop, designed to promote, sell and manage sales data, and can improve performance in the store sales business online Mandalagiri. The system development method that is used is the method for Object-Oriented namely RAD using UML tools. The results of this study are to help Mandalagiri's online stores sell, introduce, promote and manage mangoes in a manner.
An Efficient Approach for Traffic Monitoring System Using Image Processing

Traffic congestion has become a major problem in the world wide. So we need efficient system which monitors the traffic and updates the time setting in traffic signal. The cameras installed in the road junction will be used to capture the real time traffic and these images will be processed to count the number of vehicles in each lane. MATLAB Platform is used where it develops the various object detection algorithms for the combination of many image processing algorithms. The real time object detection and tracking will be generated by control signals where Arduino programming will provide an interfacing hardware prototype. The centroid value will be calculated in each lane. Based on the centroid values obtained from the system, the signals will be sent for the traffic pole as the output.
Multi-Loss Function Fusion for Face Recognition Based on the Convolutional Neural Network

In recent years, deep convolutional neural network techniques have been widely used in face recognition. Loss function is an important part of convolutional neural network for face recognition. At present, a variety of loss functions have been proposed. Each loss function has its own scope of application as well as advantages and disadvantages. In order to improve the robustness,a face recognition algorithm based on multi-loss function self-adaptive weighted fusion convolutional neural network is proposed. Its core is to learn the weight of each loss function automatically. Experimental results show the improved multi-loss function self-adaptive weighted fusion convolutional neural network can improve the recognition rate of the algorithm effectively.
Pelatihan Test of English Proficiency bagi Dosen di Universitas Prof. Dr. Hazairin, SH Bengkulu

Workshop of Test of English Proficiency on Lecturers of Universitas Prof. Dr. Hazairin, SH Bengkulu&#x0D; This community service was aimed to improve the ability of the test of English proficiency (TOEP) the lecturers of Prof. Dr. Hazairin, SH University by practicing listening and reading skills. In addition, this activity was provided participants with insight into easy ways or tips and tricks when dealing with listening and reading questions in the TOEP with the aimed of increasing the participant's TOEP score. The method used was a lecture, discussion/question and answer, and practice. Participants who were the target of this training were lecturers who had not yet passed the lecturer certification. Pre-test at the beginning of the activity and post-test at the end of the activity carried out as an instrument for evaluating community service activities. The results of these community service activities indicated that there were differences in the ability scores of the trainees before and after the training. The pre-test results showed that the maximum score for TOEP was 405, the average score was 266.5 and the minimum score was 205. While the post-test results showed that the maximum score for participants' TOEP was 465, the average score was 393.5 and the minimum score was 360. Increasing the participant's TOEP score showed that the training activity was successful.
EFFECT OF COGNITIVE APPRENTICESHIP INSTRUCTIONAL METHOD ON ACADEMIC ACHIEVEMENT AND RETENTION OF AUTO MECHANICS TECHNOLOGY STUDENTS IN TECHNICAL COLLEGES

The need to improve students’ academic achievement in auto mechanics technology necessitated this study. The study investigated the effect of cognitive apprenticeship instructional method on students’ academic achievement and retention in auto mechanics technology in technical colleges in Delta State. Three research questions guided the study and three null hypotheses were tested at 0.05 level of significance. The study adopted the quasi-experimental research design. Specifically, the pre-test post-test non-equivalent control group experimental design was used. Population of the study was 237 vocational II auto mechanic students in the six technical colleges in Delta State. A sample of 114 was used for the study. Instrument for data collection was Auto Mechanic Achievement Test (AMAT). The instruments was validated by three experts (two from the Department of Technology and Vocational Education and one from Measurement and Evaluation Unit of the Department of Educational Foundation all in NnamdiAzikiwe University Awka. The reliability coefficient of Auto Mechanics Achievement Test (AMAT) was established using Kunder Richardson 21 Formula and the reliability coefficients of 0.75 was obtained. Arithmetic mean was used to analyse data relating to research questions, while analysis of covariance ANCOVA was used to test the null hypotheses. Findings revealed that students taught auto mechanics technology using cognitive apprenticeship instructional method achieved and retained better than those taught with demonstration method. Conclussion was drawn that cognitive apprenticeship instructional method is an innovative and effective mode of instruction with capacity to improve students’ learning outcome. Consequently, it was recommended among others that technical teachers should use cognitive apprenticeship instructional method in teaching auto mechanics course so as to enhance students’ academic achievement and knowledge retention. Article visualizations:
Design of Travel Service System in Indonesia Based on Service-Oriented Architecture

Transportation is the most basic thing for people to migrate, especially when Indonesia is an archipelago country. That is why; transportation is absolutely needed for Indonesian. It also helps the tourism activities in Indonesia. Therefore it is necessary to have a service that provides a one-stop travel solution to accommodate tourism activity in Indonesia that provides end to end services based on Service Oriented Architecture (SOA). This research method using literature review and observe the phenomenon. The result of this research is the design of a travel information services system that integrates frontend service and backend service using Service Oriented Architecture. The rich picture consists of 13 entities that integrate with each other to build Jalan.com.
A Neutrosophic based Non-Local Means Filter for Despeckling of Medical Ultrasound Images

In this paper, a new weight function based on neutrosophic logic is presented for improving the performance of non-local means (NLM) filter to deal with speckle noise in ultrasound (US) images. In neutrosophic domain, each pixel is characterized by three components including truth membership T, indeterminacy membership I and falsity membership F. In our proposed method, according to the nature of noise in US images, modified functions are introduced for obtaining neutrosophic components. Then, we apply these components for measuring the similarity between pixels and designing a proper weight function to improve despeckling performance of NLM filter. The evaluations on synthetic and real US data show superiority of our proposed method compared to other state-of-the-art techniques.
HEVC Double Compression Detection with Non-Aligned GOP Structures Based on a Fusion Feature with Optical Flow and Prediction Units

Video double compression detection is an important part of video forensics. Optical flow method can effectively reflect the periodic anomaly caused by Relocated I-frame (which is also called I-P frame) inside double compression video. In this paper, a fusion feature OFRB-NIPU is extracted to detect double compression video under high definition HEVC coding standard, far more with group of pictures (GOP) estimation. Firstly, optical flow residual of background area and the number of intra prediction units are extracted from each frame of input video respectively. A background subtraction algorithm is used to reduce the impact of moving objects. Finally, Support Vector Machine (SVM) classifier for double compression and Periodic Analysis (PA) method for GOP size estimation are adopted respectively. Experimental results have demonstrated better performance of the proposed method in comparison to the state-of-the-art methods.
DENCLUE-DE: Differential Evolution Based DENCLUE for Scalable Clustering in Big Data Analysis

In data analysis, clustering is one of the important tasks. In this context many clustering methods are proposed in literature for big data analysis. Density based clustering (DENCLUE) is one of the powerful unsupervised clustering methods for the huge volume of data sets. In denclue, hill climbing plays important role to find the density attractor. In this paper, we apply Differential evolutionary algorithm in the place of hill climbing to find the global optimum solution. In this model, we propose Gaussian based mutation function in DE to improve the accuracy and execution time on spark platform. We test this approach on big data sets presented in literature. Experimental results shows that the proposed approach outperforms other variants in terms of execution time.
Comparative Analysis of Lightweight Cryptography Algorithms on Resource Constrained Microcontrollers

This paper studies the performances of lightweight cryptographic algorithms on microcontrollers with limited resources, namely, computing power and memory. STM8 Microcontroller platform has been chosen as the test platform owing to its sub-20 cent market price and increasing popularity. Algorithms were selected from a broader list of widely used architecture- independent cryptography algorithms and have been coded in C language, fully conforming to their definitive documents. Tests have been carried out on ST’s widely available STM8S-Discovery boards. All outputs have been measured employing electronic instruments calibrated by the accredited agencies. Results tabulated in the paper reveal the performance differences and resource requirements of the algorithms and highlights the influence of the optimization level chosen on the results.
A Visualization Jump Lists tool for Digital Forensics of Windows

In this paper, a visualization digital forensics tool, called JumpList Analyzer, is implemented.The tool can analyze the complicated Jump Lists files, and then the results are demonstrated by visualization.To compare the proposed tool with the other Jump Lists tools, the proposed tool is the only one can display the analyzed results by visualization.The visualization will help the investigators more easily to find the evidence than the other tools showing the analyzed results by texts only.In the experiment, the proposed JumpList Analyzer is demonstrated its convenience at identifying artifacts for doing digital forensics in a financial fraud case.In addition, the proposed tool can also be used to reveal the computer user's behavior or background.
Zone-Based Resource Allocation Strategy for Heterogeneous Spark Clusters

As a primary big data processing framework, Spark can support memory computing to improve the computation efficiency. However, Spark cannot handle the situation of a heterogeneous cluster, in which the nodes have different structures. Specifically, a primary problem in Spark is that the resource allocation strategy based on the number of homogeneous processor cores cannot adapt to the heterogeneous cluster environment. To solve the above-mentioned problem, we propose a zone-based resource allocation strategy based on heterogeneous Spark cluster (ZbRAS) and implement such a strategy to improve the efficiency of Spark. We compare the proposed strategy with the native resource allocation strategy of Spark, and the comparison results show that our proposed strategy can significantly enhance the execution speed of Spark jobs in a heterogeneous cluster.
Pengaruh Augmented Reality sebagai Media Pembelajaran terhadap Minat Belajar Siswa Mata Pelajaran IPA SD Kota Bengkulu

This study aims to determine the effect of the utilization of Augmented Reality as a medium of learning on student interest in science subjects of Class IV SD in Bengkulu City. This research uses quantitative approach with experimental research type. The research method used is quasi experimental method with research design of maching only pretest-posttest control group design. The population in this study is all students of grade IV of SD Kota Bengkulu using KTSP curriculum. The sample in this research is the students of grade IVB SDN 82 amounting to 23 students is the experimental class and the IVA class of 24 students is the control class. The instrument used in this research is a questionnaire that uses Likert scale given through pretest that is before the learning takes place and posttest after the learning takes place. Data from this study were analyzed using descriptive analysis, and prerequisite test with normality test, homogeneity test, and inferential analysis. From result of research known result of t-test with tcount value of 10,52 and ttable value equal to 1,673. So with tcount (10,52)&gt; ttable (1,673) at 5% significant level, it can be concluded that there is influence of utilization of Augmented Reality as a learning media to student's interest in science class IV SD Bengkulu City.
Adaptive Neuro Fuzzy Inference System Based Obstacle Avoidance System for Autonomous Vehicle

Adaptive Neuro Fuzzy Inference System (ANFIS) is a well proven technology for predicting the output based on the set of inputs. ANFIS is predominantly used to track the set of inputs and output in order to achieve the target. In this paper, authors have proposed a Nonlinear ANFIS algorithm to track the distance between the autonomous vehicle and the obstacle while vehicle is moving and the brake force required. By tuning neuro fuzzy algorithm, accurate brake force requirement has been achieved and the results are captured in this paper. Back propagation algorithm based neural network & Sugeno model based Fuzzy inference system have been used in the proposed technique. Matlab/Simulink software platform is used to implement the proposed algorithm and proven the expected results.
DEVELOPING SOCIAL COMPETENCIES OF PUPILS THROUGH WORKSHOPS IN PHYSICAL EDUCATION CLASSES

The aim of the study is to determine and develop the social competences of elementary school age pupils in physical education classes, through workshops. The center of the author's interest is an introduction with opinions and behavioral patterns according to differences, based on defined criteria and indicators of measurement. It is pointed out that there is the necessity of taking active roles by all participants of the school community in acquiring new competencies in order to cultivate and respect the principles of differences in classes of physical education. The study offers new experience about utilization of the workshop method, forms, and techniques, as well as the possibilities of raising awareness and behavior change when it comes to discriminatory behavior of pupils through physical culture. This also provides the opportunity for further examination of the workshop method in order to implement it in the teaching of physical education.
Comparitive Analysis on Transformation based Watermarking

Steganography is used to hide information, such as an image into an image. Secrecy is one of the moral concepts in the medical field for securing patient records. This paper presents a comparison based on image transformation in digital steganography using medical images. Discrete wavelet transform and Ridgelet transformation are the two transformation techniques being compared where both the transformations use singular value decomposition for embedding the secret image into the cover image. Arnold transformation is applied to patient record (secret image) in order to scramble the image before transformation. The results show the comparative analysis of both transformation based watermarking schemes.
The Effects of Worker’s Behavior Changes on Batik Equipment in Traditional Batik Process of Oey Soe Tjoen

Batik Tulis is one of the world's worth preserving intangible heritage objects from Indonesia.Preservation of the batik industry also includes the conservation of the manufacturing process.Oey Soe Tjoen Peranakan Batik is one of the batik workshops that still maintains the traditional batik tulis process for almost a hundred years.This batik workshop located in the Kedungwuni area, Central Java, is now managed by Widianti Widjaja as the third generation.The intergenerational period causes differences in behavior in everyday life, including the process of making batik tulis.In the second and third generations, postural limitations founded, which also affected the equipment used in the process of making batik.The inconvenience factor in the process of making traditional batik tulis is one of the reasons that trigger changes in the equipment used.The purpose of this study is to examine changes in equipment used in the process of making traditional batik tulis in the Oey Soe Tjoen workshop.Through in-depth interviews, observations, and documentation in a certain period, it can be concluded that the influence of habits and comfort factors at work increased the work platform on some equipment used in making traditional batik tulis, especially at this workshop.
Catalyst and Instrument of National Growth: Interrogating the Significance of Entrepreneurship in Nigerian Economic Development

Entrepreneurship as it is widely believed is a fueling developmental hub and cause of financial progression of any given economy.The general Entrepreneurial activities have been found to be proficient of creating optimistic and progressive impacts towards nation's economy and improved class of life expectancy of the people.This study based on this, sourced to look at the entrepreneurship as a catalyst and instrument of progress and improvement in Nigeria economy.The study make used of the secondary data and the outcome signposts that entrepreneurship has immensely contributed significantly to the employment generation and growth of Nigerian economy.Some of the major observation is that entrepreneurship development has massively enhanced job creation.And, however consequently lead to economic development.It is recommended that Nigerian should advance more especially some other economic opportunities while government should ensure the enabling environments for purposeful development of entrepreneurship practices.
Model selection for relational data factorization

As a fundamental problem in relational data analysis, model selection for relational data factorization is still an open problem. In our work, we propose to estimate model order for mixed membership blockmodels (MMSB) within the generic allocation framework of Bayesian allocation model (BAM). We describe how relational data is represented as Poisson counts of the allocation model, and demonstrate our results both on synthetic and real-world data sets. We believe that the generic allocation perspective promises a generalized model selection solution where we do not only select the model order, but also choose the most appropriate factorization.
A PV Hosting Capacity Assessment Method with Spatial Correlation

This paper proposes a novel photovoltaic (PV) hosting capacity assessment method considering the spatial correlation of PV outputs. Based on the empirical PV output distribution for one location and the cross-correlation between PV output data at all location pairs, a copula is used to represent the dependence between locations. We show that the model realistically reproduces the spatially distributed PV output over a set of sites, based on comparisons with measured data. Combining the sampling data with a linear hosting capacity assessment model, we also demonstrate that the hosting capacity with the spatial correlation, differ substantially from those of a nonspatial approach. The results show that spatial correlation needs to be incorporated in the hosting capacity assessment scheme in order not to overestimate the grid impacts of PV systems.
A Multi-agent Default Theory of Permission

This paper formulates a topology of permission by extending Horty’s prioritized default theory, in order to study permissions with different defeasible manners in a reason-based reasoning. To do so, we reconstruct the prioritized default theory augmented with permissive norms and agents, so as to examine the possible relationships between permissions and defeasible norms in a agent-relative feature. This, we argue, brings about two essential elements the variety of permission and the multi-agent interaction in analyzing two noticeable pieces in Chinese and German legislations.
PENERAPAN METODE CLUSTERING K-MEANS UNTUK PENGELOMPOKAN KELULUSAN MAHASISWA BERBASIS KOMPETENSI

The difficulty often comes every year, in choosing graduation as a student by an employeeperengkrutan partners with AMIK Labuhanbatu. Competence of each student graduation takinginfluence the employee in question, so as not to disappoint both sides. In this study, the authors try toclassify the data using a competency-based graduation one simple data mining techniques, namelyclustering technique (clustering) two-dimensional, which means the two variables that will be used ingrouping the graduation GPA and the value of student competencies that. Algorithms used in thegrouping (clustering) using the K-Means algorithm that starts with a random selection of K, which isthe number of clusters to be formed from the data to be in the cluster. This testing is done manuallyin addition also performed with the RapidMiner data mining application of 46 records. From theresults of a study of 46 records were done manually or using RapidMiner data mining applicationshave 3 groups of competency-based graduation with similar results between the two tests, namelyCluster 1 consists of passing information to the student with IPK of 30.0 to 31.7 and 62.50 toCompetency with 71,50 the number of members of cluster 13. Cluster 2 consists of graduation studentswith IPK of 30.0 to 33.5 and 81.00 to 89.00 competencies that number 18 cluster members. Cluster 3consists of graduate students with IPK of 30.0 to 33.8 and 72.50 to 79.00 competencies that number15 cluster members.
Bitcoin: An Impossibility Theorem for Proof-of-Work Based Protocols

Bitcoin's main innovation lies in allowing a decentralized system that relies on anonymous, profit driven miners who can freely join the system. We formalize these properties in three axioms: anonymity of miners, no incentives for miners to consolidate, and no incentive to assuming multiple fake identities. This novel axiomatic formalization allows us to characterize which other protocols are feasible: Every protocol with these properties must have the same reward scheme as Bitcoin. This implies an impossibility result for risk-averse miners: no protocol satisfies the aforementioned constraints simultaneously without giving miners a strict incentive to merge. Furthermore, any protocol either gives up on some degree of decentralization or its reward scheme is equivalent to Bitcoin's.
Hardware acceleration of convolutional neural networks on FPGA

With the evolution of machine learning algorithms they are seeing a wider use in traditional signal processing applications. One of these areas is in radios for improved signal identification algor ...