Reviewerly/exHarmony huggingface dataset:
PERHITUNGAN UNIT COST PENGEMBANGAN SISTEM INFORMASI SEKOLAH DENGAN METODE ABC (ACTIVITY BASED COSTING)

In developing an information system the school must determine a strategy in its development. Look for managers who are reliable in making decisions that are oriented to reliable development. One of the proper management concepts is cost accuracy. Accuracy of charging costs on cost objects is very important for users of cost informationBeside the accuracy, it is also needed to find out the cost per unit needed in the information system. This is intended to have a price benchmark for the development of information systems in the future. In this study the unit cost calculation uses the ABC (Activity Based Costing) method which emphasizes the cost calculation for the activities involved. The allocation of costs is based on salary data from trusted institutions, namely the Pedoman Standard Minimal 2019 by INKINDO. The results of this study get a total cost value of  18.407.082,00 while the cost of each complexity is 5,301.00 with the standard cost of procurement of goods and services issued by the government and several survey institutions.
Статистическая обработка качественных (категориальных) данных в фармакологических исследованиях

The article deals with descriptive statistics of data measured using qualitative (categorical) scales and criteria for determining the statistical significance of differences between samples. Special attention is paid to the problem of multiple comparisons of data measured using nominal scales. For each method, examples of data processing obtained in pharmacological studies are given.
Data Cleaning Mechanism for Big Data and Cloud Computing

Data cleaning and data filtering mechanism are an important tool for IoT(Internet of Things) based Big Data and Cloud Computing technologies. In today's world, majority of the industries and sectors needs data's for processing and they find out potential customer for their businesses. With the exceptional growth in scale up of data's, increasing amount of training data set available for processing at Big Data and Cloud Computing side. In most of the data set, at least 10 % of data set are missing values, erroneous, in-complete, not suitable, noisy data or outliers. Data cleaning approach is used to remove outliers, missing values from the huge data set and correct information gathered. No data set provides right information with noisy or missing values. This paper deals with mechanism through programming concept to remove noisy or missing values in a heterogeneous data set under Big Data system and Cloud Computing. This paper deals with outlier analysis concept with respect of Big Data technologies.
A 2020 perspective on “A generalized stereotype learning approach and its instantiation in trust modeling”

Owing to the rapid increase of user data and development of machine learning techniques, user modeling has been explored in depth and exploited by both academia and industry. It has prominent impacts in e-commerce-related applications by facilitating users’ experience in online platforms and supporting business organizations’ decision-making. Among all the techniques and applications, user profiling and recommender systems are two representative and effective ones, which have also obtained growing attention. In view of its wide applications, researchers and practitioners should improve user modeling from two perspectives: (1) more effort should be devoted to obtain more user data via techniques like sensing devices and develop more effective ways to manage complex data; and (2) improving the ability of learning from a limited number of data samples (e.g., few-shot learning) has become an increasingly hot topic for researchers.
Fault Prediction for Network Equipment based on Oversampling Method in Imbalanced Dataset

Nowadays, there are two main problems in Network service. On one hand, Network customers require better service and experience. For another, the Network service providers prefer to improve users' quality of experience (QoE) in order to increase their revenues. In this paper, we combine the network parameters and user complaints to establish a fault prediction model. Firstly, we clean the data and use an improved SMOTE algorithm called K-SMOTE to balance the data. Then we use principal component analysis (PCA) to perform dimensionality reduction on the data, which is beneficial to algorithm efficiency. Finally, in order to get a more accurate model, we conduct our experiment based on some machine learning models in our dataset. Experimental results show that decision tree model using K-SMOTE algorithm has a better performance than other models.
Research on gesture recognition control of UAV based on CNN+FCN

Unmanned aerial vehicles (UAVs) are the focus of development and Cultivation in China, especially in artificial intelligence. Current UAV control mainly carries out related flight tasks through a certain professional training of pilots'hand-held remote controllers, which is undoubtedly not conducive to the popularity of UAVs. In recent years, the rapid development of artificial intelligence, especially in image recognition, has achieved good results. In many practical applications, its identification ability surpasses human beings and brings opportunities to the development of all walks of life. In order to popularize UAV better, this paper studies the introduction of gesture recognition control in UAV, and puts forward a scheme of camera mounted in UAV and human gesture shooting and recognition, which simplifies the cumbersome control of UAV.
Awards at Robotics and Artificial Intelligence 2020

From the effective fruition of a few International gatherings in the field of Robotics and Artificial Intelligence, the Conference Series is happy to incorporate another global meeting. The gathering is tilted as Progression on Robotics and Artificial Intelligence the meeting will be hung on July 27-28, 2020, 2020 at Chicago, USA.
A case study of institutional features and policy implications of school foreign language education in Hessen, Germany for the Korean foreign language policy

본 논문은 독일 헤센 주를 중심으로 독일의 학교 외국어교육이 지닌 구조와 특성 및 정책적 함의를 분석한 후, 한국 외국어교육에 유익하게 활용 가능한 시사점을 도출한다. 독일의 외국어교육은 다양한 외국어 선택기회를 부여하는 점, 언어교육과 역량교육을 유기적으로 결합하는 점 등이 주요 특징을 이룬다. 이에 본 연구는 외국어교육 정책의 차원을 언어위상 정책과 언어내적 정책으로 나누어 분석한다. 분석 대상은 외국어교육의 법적 근거, 학교유형별 교과 편성, 외국어교육의 내적 특성(목표설정, 역량기반, 성취기준, 주제영역) 등이다. 그리고 한국교육에 던지는 시사점으로 다언어주의, 잘 구조화된 역량교육, 역량에 따른 성취기준 제시, 교육과정 이념 개발 등을 다룬다.This research aims to draw on suggestions for foreign language education in South Korea by analyzing the structure and policy implications of foreign language education in schools in Hessen, Germany. The analysis will take place on two policy levels: status planning and corpus planning. The legal basis of foreign language education, class credit management by school forms, features of teaching and learning (e.g. competence-based learning) will be closely examined. Based on the results of the analysis, implicationsI will be discussed for Korean foreign language education such as multi-lingualism, structured competency education and dualization of curriculum documents.
System for Competences' Development to Form an Engineering Profile on Undergraduate Student

In this paper, we present the results of a study carried out with Engineering students studying Differential Equations at the Tecnologico de Monterrey and with Mechatronics students at the Tecnologico de Mexico in Hermosillo, Mexico. The purpose of this research was to determine the influences of various challenges posed in laboratory practices, an authentic evaluation system focused on the validation of competencies, and the subsequent analysis of students' academic performance to form an engineering profile that will benefit the students in their professional lives. The design of this research project was quasi-experimental, where we compared two different teaching methods applied in different semesters using control and experimental groups. The results of the research conclude that a methodology based on the implementation of laboratory practices is better than the traditional methodology for the development of the transversal and disciplinary competencies which form an engineering profile. The overall results of the research regarding the efficiency of knowledge acquired favor the experimental group, particularly when it comes to procedural, conceptual, and attitudinal competencies.
The PHOTON Wizard - Towards Educational Machine Learning Code Generators.

Despite the tremendous efforts to democratize machine learning, especially in applied-science, the application is still often hampered by the lack of coding skills. As we consider programmatic understanding key to building effective and efficient machine learning solutions, we argue for a novel educational approach that builds upon the accessibility and acceptance of graphical user interfaces to convey programming skills to an applied-science target group. We outline a proof-of-concept, open-source web application, the PHOTON Wizard, which dynamically translates GUI interactions into valid source code for the Python machine learning framework PHOTON. Thereby, users possessing theoretical machine learning knowledge gain key insights into the model development workflow as well as an intuitive understanding of custom implementations. Specifically, the PHOTON Wizard integrates the concept of Educational Machine Learning Code Generators to teach users how to write code for designing, training, optimizing and evaluating custom machine learning pipelines.
A class of goodness-of-fit tests for circular distributions based on trigonometric moments

We propose a class of goodness–of–fit test procedures for arbitrary parametric families of circular distributions with unknown parameters. The tests make use of the specific form of the characteristic function of the family being tested, and are shown to be consistent. We derive the asymptotic null distribution and suggest that the new method be implemented using a bootstrap resampling technique that approximates this distribution consistently. As an illustration, we then specialize this method to testing whether a given data set is from the von Mises distribution, a model that is commonly used and for which considerable theory has been developed. An extensive Monte Carlo study is carried out to compare the new tests with other existing omnibus tests for this model. An application involving five real data sets is provided in order to illustrate the new procedure.
Correction to: the Video Spatial Error Concealment Algorithm Using Separately-Directional Interpolation Technique

I am the author of this paper published in Journal of Signal processing Systems.
Pemanfaatan Media Belajar Ikan Mujair Pada Mata Pelajaran IPA Materi Organ Pernapasan pada Hewan Untuk Sekolah Dasar

The use of mujair fish learning media by teachers in the implementation of science learning activities in class V MI Ma'arif NU 2 Sanggreman, Rawalo District, Banyumas District, the results show the direction of increasing learning activities both in terms of activities and learning outcomes. The results of the implementation of science learning activities breathing material material on animals by utilizing tilapia fish learning media showed maximum results. The number of KKM students is 17 (89%) students and not yet KKM is only 2 (11%) students. And classical has reached classical completeness, namely the number of students who reach KKM is 84.21.
Reconstructing the Noise Manifold for Image Denoising

Deep Convolutional Neural Networks (CNNs) have been successfully used in many low-level vision problems like image denoising. Although the conditional image generation techniques have led to large improvements in this task, there has been little effort in providing conditional generative adversarial networks (cGAN)[42] with an explicit way of understanding the image noise for object-independent denoising reliable for real-world applications. The task of leveraging structures in the target space is unstable due to the complexity of patterns in natural scenes, so the presence of unnatural artifacts or over-smoothed image areas cannot be avoided. To fill the gap, in this work we introduce the idea of a cGAN which explicitly leverages structure in the image noise space. By learning directly a low dimensional manifold of the image noise, the generator promotes the removal from the noisy image only that information which spans this manifold. This idea brings many advantages while it can be appended at the end of any denoiser to significantly improve its performance. Based on our experiments, our model substantially outperforms existing state-of-the-art architectures, resulting in denoised images with less oversmoothing and better detail.
The Research on Method and Difficulty Analysis of Automatic Word Segmentation in Yi Language for Information Processing

Yi word automatic segmentation is a basic Yi language information processin research. The article first analyzes the characteristics of Yi language. Second, the concept of word of Yi and application, and a variety of Yi segmentation algorithm has done a systematic introduction. Then combined with the characteristics of Yi automatic segmentation to achieve the difficulties faced where the word Yi pointed out the future direction of technology.
Attention-Based Top-Down Single-Task Action Recognition in Still Images

Human action recognition via deep learning methods in still images has been an active research topic in computer vision recently . Different from the traditional action recognition based on videos or image sequences, a single image contains no temporal information or motion features for action characterization. In this study, we utilize a top-down action recognition strategy to analyze person instances in a scene respectively, on the task of detecting determine persons playing a cellphone. A YOLOv3 detector is applied to predict the human bounding boxes, and the HRNet (High Resolution Network) is used to regress the attention map centered on the area of playing a cellphone, taking the region of given human bounding box as the input. Experimental results on a custom dataset show that HRNet can reliably represent a person image to a heatmap where the region of interest (ROI) is highlighted. The accuracy of the proposed framework exceeds the performance of all the evaluated naive classification models, i.e., Densenet, inception_v3 and shufflenet_v2.
IGTKI Website Development as Kindergarten Teachers' Digital Media Literacy in Central Java Province

The objectives of this study are to find out how the development of the IGTKI website and to analyze how the level of digital literacy understanding of kindergarten teachers in Central Java Province. The method used in this study is research and development (R&D) by adopting a model of Thiagarajan development. This method is used because the researcher develops a product, namely the Central Java Province IGTKI website, whereas to find out the level of digital literacy of kindergarten teachers, the data collection was conducted by doing a survey to the kindergarten teachers in Central Java Province. The result shows that the development of the Central Java Province IGTKI website is carried out through several stages, they are: 1) define namely content determination, 2) design namely website design, 3) develop namely developing websites (domain determination, website installation, website content management, validation test, and trials), and 4) disseminate namely website dissemination. Kindergarten teacher understanding in digital literacy on the other hand, can be divided into the ability to find information, to evaluate information, to use information, to make information and to utilize information. It can also be seen from the indicators of technical dimension, cognitive dimension, and social emotional dimension.
Pfeiffer Library: Cyber Security: Publications

Resources in the field of Cyber Security Provides links to specific print and electronic publications purchased by Pfeiffer Library.
Improved Pearson Similarity for Collaborative Filtering Recommendation System

Recommendation systems are used in many applications such as social media, e-commerce web sites, movies, books, search engines. Recommendation system filters out the information in which user is interested from a huge amount of data. For example, Amazon recommends items to the user according to their interest, from millions of users and items. Various recommendation system techniques have been proposed since mid-1990s, some of them are content based, collaborative, demographic, and hybrid. Among these techniques, collaborative filtering is popular due to its effectiveness (recommend similar items to the user according to its own interest). User based collaborative filtering and item based collaborative filtering are two generalized approaches under collaborative filtering. In this report, we propose a Weighted Pearson similarity metric using mahout that increases the similarity of items or users. This similarity is used for predicting the unknown ratings of users. Improved similarity of items or users helps in increasing the prediction (finding unknown ratings) accuracy. Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) are used to evaluate prediction accuracy. The proposed Weighted Pearson similarity gives less MAE and RMSE error rate than the traditional Pearson similarity.
M-learning Applications

The expansion of mobile technologies and their wider usage are transforming the process of e-learning into m-learning. The purpose of the article is to analyze the popular m-learning applications, to highlight their basic features and to outline the basic requirements for their development. Based on the cur-rent state of mobile phone market share the object of the research is Android learning apps. The applied approach includes analysis of the ten most popular m-learning applications. The results of the study show that all of the reviewed applications have a version for Android and for iOS operating systems. Howev-er, most of them are not typical m-learning applications, but rather they are e-learning libraries. In this regard, there is a need to find a way that more quickly introduce new learning approaches including the active use of mobile devices and applications in the learning process. First step must be to define a compre-hensive framework for development of m-learning applications.
Kesiagaan Pustakawan Dalam Menghadapi Bencana (Disaster Planning) Di Perpustakaan Institut Seni Indonesia Surakarta

The purpose of this study is to find out how librarians are prepared to deal with disasters (Disaster Planning) from biological factors (termites) and fire disasters in the Library of Indonesia Institute of the Arts of Surakarta. Various library threats or library assets from damage can be threats from natural disasters such as earthquakes, volcanic eruptions, floods, tsunamis, fires, and so on. Other damage threats can be caused by biological and chemical and human factors, such as theft, vandalism, and the like. This study uses a qualitative research approach, namely research that describes the phenomenon, digging data by interviews, observation, and documentation. Problems in qualitative research are still temporary, tentative, and will develop or change after the researcher is in the field. Qualitative research requires data sources from informants. The use of triangulation techniques is done to test the credibility of the data by checking the data to the same source with different techniques. For example, data obtained by interview, then checked by observation, documentation, or questionnaire. Good planning in dealing with disasters will reduce the impact, including preparedness and long-term risk reduction measures. Matthews (2009) states that there are several steps or stages in disaster planning, including 1) prevention, 2) responses. The results of this research could theoretically contribute to scientific treasures for librarians in dealing with disasters (Disaster Planning) in the library. Besides, it is an input or recommendation and thought for the Indonesian Institute of the Arts of Surakarta.Keywords: librarian’s alert, disaster planning, biological (termite), fire
Data Retrieval from Printed Image Using Image Features and Data Embedding

Data retrieval methods from printed images have recently been proposed. However, these methods have problems in terms of data capacity and flexibility. We propose a method of solving these problems using image recognition based on image features and a data-embedding method in the frequency domain. This makes it possible to include different information in the same image and greatly increase the amount of information that can be acquired. In the experiment, we show the effectiveness of the proposed method.
Demystifying Batch Normalization: Analysis of Normalizing Layer Inputs in Neural Networks

Batch normalization was introduced as a novel solution to help with training fully-connected feed-forward deep neural networks. It proposes to normalize each training-batch in order to alleviate the problem caused by internal covariate shift. The original method claimed that Batch Normalization must be performed before the ReLu activation in the training process for optimal results. However, a second method has since gained ground which stresses the importance of performing BN after the ReLu activation in order to maximize performance. In fact, in the source code of PyTorch, common architectures such as VGG16, ResNet and DenseNet have Batch Normalization layer after the ReLU activation layer. Our work is the first to demystify the aforementioned debate and offer a comprehensive answer as to the proper order for Batch Normalization in the neural network training process. We demonstrate that for convolutional neural networks (CNNs) without skip connections, it is optimal to do ReLu activation before Batch Normalization as a result of higher gradient flow. In Residual Networks with skip connections, the order does not affect the performance or the gradient flow between the layers.
Social Content Mining in Social Networks

The rapid increase in user-generated social contents posted on social networks causes big data analysis issues. For the huge social contents intangibly interpreted by machines, we propose a system to transform unstructured data into structured data, and then to mine them further. The system interprets user-generated social contents by annotating semantic terms on them to help users or applications explore further social information in social networks. First, it collects news from the New York Times and through external knowledge bases DBpedia and WordNet, it builds global ontology. By this, the system identifies social contents and generates a social content repository annotating semantic terms. Finally, the Twitter-based social content mining system is implemented and used to search relevant social contents on Twitter.
VALIDITAS DAN RELIABILITAS INSTRUMEN PENILAIAN MEMBACA SHORT FUNCTIONAL TEXT PADA SISWA SMP KELAS VIII

The objective of this study is description validity and reliability on instrument assessment of reading short functional text eight grade junior school. This research method is quantitative description, it have described validity and reliability of instrument. The research subjects were in 100 students of eight grade junior school. Data collection in this study is validation sheet to expert judgment and result of the field for students. This research data analyze is content validity by expert judgment using Aiken's V formula and reliability instrument by using Two Way Anova helping SPSS software than the results computing by Ebel formula. Research finding are: the validity of instrument 0.8 is said valid, the reliability instrument 0.731 is said reliable the reliability coefficient of instrument 0.920 can be interpreted that instrument assessment of short functional text has consistency. The instrument assessment of reading short functional text can be concluded valid and reliable.