Reviewerly/exHarmony huggingface dataset:
ANALISIS KETERAMPILAN MENULIS HURUF TEGAK BERSAMBUNG MELALUI PENGGUNAAN BUKU TULIS HALUS KELAS IIA SDN KEBONSAWAHAN 02 JUWANA

Abstract Writing upright letters is one form of writing skills with regard to rules and aesthetic values that combine letters that are connected with each other in a rounded shape. The skill of writing upright letters on class II A students at SDN Kebonsawahan 02 Juwana is quite good, but most students write with no regard for accuracy. There are still students who experience confusion when writing upright letters, the use of soft writing aids is used by students to make writing letters and writing easier. This study aims to describe writing letters upright through the use of fine line books in class IIA SDN Kebonsawahan 02. This type of research is qualitative descriptive. Data collection in this study was conducted by methods (1) observation, (2) interviews, and (3) questionnaires. The results of the study showed that all students of class IIA SDN Kebonsawahan 02 Juwana were able to write upright letters. The use of fine line books is very useful to facilitate and facilitate students when writing upright letters so that the writing looks neat and beautiful. The number of correct procedure formulations when writing connecting letters in fine notebooks, resulting in not all students knowing thoroughly about these procedures. This creates confusion when writing for some students so that many errors arise in writing connect letters in fine line books. Of the 21 students of class IIA SDN Kebonsawahan 02 Juwana, only one student was found whose results did not experience any errors at all. All other students can only write upright letters in a fine line book without including the correct steps in writing. Keyword: Writting skills, upright letters,fine line books
UAV Flight State Recognition Using AC-GAN Based Method

Unmanned Aerial Vehicle (UAV) has become a widely used tool in both military and civilian applications. As the UAV capabilities and technology complexity rapidly increase, UAV reliability and security are particularly essential. In order to improve the operational security, fault diagnosis, flight quality assessment and fuel consumption assessment are implemented. However, under different flight states, such as takeoff, climbing, level flight, etc. the possible failures, the meaning of flight quality and fuel consumption levels are different, so flight state recognition is the basis of the above work. Aiming at the problem that the flight time of various flight states is different, which leads to the imbalance of recognition samples, this paper proposes an Auxiliary Classifier Generative Adversarial Network (AC-GAN) based method. The method supplements samples by generating samples that enjoy the same distribution with the real data, thereby improving the accuracy of the recognition. It also segments the time series by sliding window method, to solve the classification problem of unequal time series. The performance of the proposed method is illustrated using actual UAV sensor data. The results show that the proposed method can effectively improve the recognition accuracy.
Statistically driven model for efficient analysis of few-photon transport in waveguide quantum electrodynamics

Understanding transport properties in quantum nanophotonics plays a central role in designing few-photon devices, yet it suffers from a longstanding extensive computational burden. In this work, we propose a statistically driven model with a tremendously eased computational burden, which is based on the deep understanding of the few-photon spontaneous emission process. By utilizing phenomenological, statistically driven inter-photon offset parameters, the proposed model expedites the transport calculation with a three-order-of-magnitude enhancement of speed in contrast to conventional numerical approaches. We showcase the two-photon transport computation benchmarked by the rigorous analytical approach. Our work provides an efficient tool for designing few-photon nano-devices, and it significantly deepens the understanding of correlated quantum many-body physics.
Self-adapting Goals Allow Transfer of Predictive Models to New Tasks

A long-standing challenge in Reinforcement Learning is enabling agents to learn a model of their environment which can be transferred to solve other problems in a world with the same underlying rules. One reason this is difficult is the challenge of learning accurate models of an environment. If such a model is inaccurate, the agent’s plans and actions will likely be sub-optimal, and likely lead to the wrong outcomes. Recent progress in model-based reinforcement learning has improved the ability for agents to learn and use predictive models. In this paper, we extend a recent deep learning architecture which learns a predictive model of the environment that aims to predict only the value of a few key measurements, which are indicative of an agent’s performance. Predicting only a few measurements rather than the entire future state of an environment makes it more feasible to learn a valuable predictive model. We extend this predictive model with a small, evolving neural network that suggests the best goals to pursue in the current state. We demonstrate that this allows the predictive model to transfer to new scenarios where goals are different, and that the adaptive goals can even adjust agent behavior on-line, changing its strategy to fit the current context.
Visual Narrative Technology of Paintings Based on Image Objects

Painting images contain many meanings in the images. Especially when the painting is a cultural asset, it expresses the life form and the event of the time, so it has great historical and cultural value. However, there are not many ways to visually communicate the contents of paintings.In this paper, we introduce a Visual Narrative technique that can describe the meaning of painting image sequentially using MVS (Mobile Visual Search) technology and scene graph, based on objects in the image.In the future, it is necessary to construct a large-scale image database of cultural assets of painting and analysis of the proposed methodology using this database
Texture image retrieval based on statistical feature fusion

In view of the fact that multiple complementary feature representation can effectively improve the performance of image retrieval, this paper proposes a new texture image retrieval method based on statistical distribution feature fusion in dual-tree complex wavelet transform domain. Firstly, the statistical distribution energy of the coefficients is calculated in the low frequency subband. Then, in the high frequency complex subbands, the magnitude coefficients are modeled as the Weibull distribution and the relative phase coefficients are modeled as the von Mises distribution. Furthermore, the distribution energy and the estimated model parameters are fused into new features. Finally, the similarity measurement adopting optimal weighted sum is used to retrieve the texture images in the VisTex database. The experimental results show that, compared with the existing texture image retrieval approaches, the proposed method has a higher average retrieval rate.
LAKI-LAKI ATAU PEREMPUAN, SIAPA YANG LEBIH CERDAS DALAM PROSES BELAJAR? SEBUAH BUKTI DARI PENDEKATAN ANALISIS SURVIVAL

This study aims to compare the level of intelligence between male and female, especially in the learning process in college. Someone who could complete their undergraduate study with a shorter period of time is assumed to have a higher level of intelligence. This research was conducted on Syiah Kuala University students who graduated on the second period of 2017 with a population of 758 people. The number of samples used was 262 people that were determined through Slovin formula with a margin error of 5%. The sampling technique used was Stratified Random Sampling with scientific disciplines as the basis for population stratification. The data analysis method used was the Cox Proportional Hazard (Cox PH) model to find a graduation hazard ratio based on gender and scientific disciplines variables. This study showed that female students have 125.5% potential to graduate faster compare to male. More detail, female students have the potential of 127% to graduate faster on the field of social science, economics and humanities, and 123.9% on the field of sciences and technology. Survival analysis also showed that female student has higher probability to be able to complete their studies faster. Accordingly, it can be concluded that female have a higher level of intelligence than male, especially in the learning process in college.
Preserving IPR Using Reversible Digital Watermarking

The spreading wings of the Internet are facing significant obstacles for preserving the intellectual property of individuals and organizations. Threats to digital data and the rights of its creator are always an issue of concern as copying of digital data is easy. The ease of dissemination of digital data leads to penetration in intellectual property rights. Here, rights mean financial and moral interests of the author. The authors have faced risk in protecting their unique ideas, products and/or services. It can be achieved by embedding some copyright information with digital data. Digital watermarking allows the authentication of the data over the intrusion of the attackers. In this paper, we will consider a reversible technique for digital watermarking where the original message or cover is recovered after the extraction of hidden data. A digest of the hidden data is created and embedded with the cover that can be extracted on the receiver end using the extracting algorithm. In addition, the use of private key increases the security level. The cover image can be retrieved in lossless manner and can be reused for further moderations.
SSE: Semantic Sentence Embedding for learning user interactions

Semantics is an integral component in NLP. Semantics provides a meaningful view about the meaning of the language. The meaning of the text is susceptible to the negative words present in them. Thus the impact of semantics in NLP in turn increases the impact of negative words in the sentence. It plays crucial role in shaping the semantics of any sentence. Although all the sub-areas of natural language processing try to find out the impact of negative words on their implementation, the effect of these words are not considered in the word embedding process. Being the foremost step in NLP the omission of these words in embedding can affect the representation of the language. The aim is to propose a model which produces word embeddings by considering the negative words. Although most NLP implementing systems consider all negative words this paper focuses on the word `not' which is followed by an adjective. The antonym for the adjective is identified and is then replaced in the corpus. The model is achieved by replacing the corpus where negative words and this corpus is fed to a three layer neural network to form the embeddings. The proposed technique proves an accuracy of 93.6% in retrieving the relevant answer to the user.
Localisation of spliced region using pixel correlation in digital images

Image forgery has become the main concern of the society over the past many years due to an increase in the number of false cases of image manipulation leaving a severe impact on the life of a victim. Therefore, it has become necessary to solidly build forgery detection method to overcome image forgery problem. This necessity led to research in this field of image processing and provide detection techniques to check the integrity of images. In this paper, a method for localization of such tampered region is proposed. The proposed method uses unsupervised learning approach to localize the region of image and find whether the region is forged or not. It uses GLCM, a textural feature descriptor that uses pixel correlation in image to extract features from it. Further, for feature matching, Euclidean and Hellinger distance is used and finally, the localization of tampered region is performed. Euclidean and Hellinger distance is calculated separately on each image for comparison of the results. The experimental results showed that Hellinger outperformed Euclidean distance in feature matching and gave better localization results.
Performance Evaluation of User-Behaviour Techniques of Web Spam Detection Models

Web spam detection is a critical issue in today's rapidly growing usage of the Internet and the World Wide Web.The upsurge of web spam has significantly deteriorated the Quality of Services (QoS) of the World Wide Web.The degeneration of the quality of search engine results has given rise to researches on the detection of spam pages efficiently and accurately.Existing user-behaviour oriented web spam detection models employed the content-based, link-based and other features of webpages for classification of web spams.These user-behaviour techniques either implemented singly or combined has achieved good detection performance.However, the effectiveness of these features in identifying Web spams correctly needs to be determined.In this study, predictive web spam detection models that employed all related user-behaviour features of webpages were developed and evaluated.The content, link, and obvious-based features datasets were collected from an online repository.Relevant features were extracted using an improved Filter-based method.Six user-behaviour related features extracted from the datasets were used to combine the datasets to generate all possible subset of feature space required, such that 7 new datasets were generated for the study.Multi-Layer Perceptron (MLP) approach was adopted as a classifier for each of the identified features.Python Machine Learning Library was used to simulate the models using percentage splits of 60/40%, 70/30% and 80/20% ratio for training/testing dataset and the performances were evaluated using accuracy, True Positive (TP) rate, False Positive (FP) rate and precision as metrics.The result showed that for the majority of the datasets the formulated models have shown an increase in efficiency after feature selection.The MLP classifier was able to achieve the best result of 66.0% accuracy when the link-based dataset was used with feature selection.The study concluded that link-based features of a user is sufficient and effective for the detection of web spams.
IMPLEMENTING THE HIDDEN CURRICULUM IN ENGLISH AS FOREIGN LANGUAGE (EFL) CLASSROOM

This research aimed to determine the implementation of the hidden curriculum in EFL learners’ interest. This research was conducted in AL-FITYAN Senior Hight School Gowa. Qualitative approach employed in this study. More specifically, this research employed the descriptive qualitative method. Data collection procedures in this study were the observation, recoding, and interview. Based on the research, it can be seen that the spiritual-cultural aspect of hidden curriculum was implemented in the first class. The findings show that the teacher employed some elements in implementing the hidden curriculum in term of spiritual-cultural aspect. (1) Giving advice and guidance, (2) providing inspiring story and utterance, and (3) applying exemplary. Furthermore, the effects of hidden curriculum implementation can emerge learners’ awareness so that they can be happy, enthusiastic, focus, and avoid their resistance in learning. The result of that, can stimulate the learners’ interest and then construct their attention and motivation.
Penerapan blended learning berbasis student centered learning pada mata kuliah matematika ekonomika bagi generasi Z

The aim of this study is to analyse the impact of blended learning applied in mathematics economics courses for students in higher education. The Blended Learning method requires students to be actively involved in groups to understand the material and complete the assignment. This study uses two approaches, observation and student perception questionnaire. The objects in this study is student of Study Program of Economics students that taking Mathematics fo Economics subject in academic year 2018/201. The learning methods developed in this program is combination of face-to-face lectures, the provision of instructional videos and the introduction of task completion online sites. The results of measuring the achievements of the programs success indicators are the ability of students to understand and correlate assignments, quizzes and competency tests with a final score of more than 60 or get C as 67%. For each learning objective (LO), the level of learning achievement of LO 1, LO 2 and LO 3 is 70.95%, 46% and 47% respectively. The results of the questionnaire to students showed 60% of students were helped by blended learning system. Based on the results achieved, the proposed learning method can be continued for the next period, but a review of the curriculum, course learning outcomes and the provision of more systematic learning videos are needed.
Pengembangan Multimedia Interaktif Pada Mata Pelajaran Jaringan Dasar

This study aims to develop interactive multimedia based on the talking stick learning model approach on the basic network subject for student of tenth grade at Vocational School High NW Kumbung and to find out the feasibility of interactive multimedia based on the assessment of material expert, media expert, and student opinion.This study wasstudy or development (S&amp; D) study adapted from the waterfal development model. There are 5 stages, namely: Communication Phase, Planning Phase, Modeling Phase, Construction Phase, Deployment Phase. The validation was carried out by material expert, media expert. The developed media was tested on 25 students of tenth grade at Vocational High School NW Kumbung. Based on the study that had been done, the result can be obtained from, (1) The validation result conducted by the validator team was media expert validation with an average score of 72% with good category and material expert with an average score of 86,841 with very good category, ( 2) The result of student responses to interactive multimedia material was very feasible with an overall percentage of 83.2%. So, it can be concluded in this study, the development of interactive multimedia using the waterfall model was said to be feasible to use for basic network learning.
Subject-dependent and -independent human activity recognition with person-specific and -independent models

The distinction between subject-dependent and subject-independent performance is ubiquitous in the Human Activity Recognition (HAR) literature. We test the hypotheses that HAR models achieve better subject-dependent performance than subject-independent performance, that a model trained with many users will achieve better subject-independent performance than one trained with a single user, and that one trained with a single user performs better for that user than one trained with this and other users by comparing four algorithms' subject-dependent and -independent performance across eight data sets using three different approaches, which we term person-independent models (PIMs), person-specific models (PSMs), and ensembles of PSMs (EPSMs). Our analysis shows that PSMs outperform PIMs by 3.5% for known users, PIMs outperform PSMs by 13.9% and ensembles of PSMs by a not significant 2.1% for unknown users, and that the performance for known users is 20.5% to 48% better than for unknown users.
Review of Data Visualisation: A Handbook for Data Driven Design <b>Data Visualisation: A Handbook for Data Driven Design</b> , by Andy Kirk, LA, Sage publications, 2019, 312 pp., $106.93 (hardcover), ISBN: 978-1-5264-6892-5

In a world headed up with visual information, it becomes relevant to understand the principles of data visualization so that it is done elegantly and efficiently. It is equally important to analyze...
A Study on Korean Emotion Index Using Cluster Analysis

Based on a study of the wheel of emotions of American psychologist Plutchik (1980). The emotional wheel of Plutchik’s has seven adjectives: surprised, sad, happy, fearful, disgusted, bad, angry. And the emotions were grouped in a hierarchical manner. We have reconstructed the English - based emotional wheel with Korean language processing and use it for the analysis of the emotion of the netizen in various media based on text. Media on the web environment was collected from textual informal data such as Internet news, article comments, blogs, and social network services (SNS) content (Facebook, Twitter, Instagram, YouTube, etc.). We collected the emotion words from the hash tags of SNS and constructed the Korean emotion dictionary based on the seven emotion sets of Flickr. In this study, we have obtained over 150,000 Korean emotional words in seven dimensions then calculated the weight of emotional words in each dimension. In other words, we want to find emotion index through cluster analysis. PVClust is an R package for evaluating uncertainty in hierarchical cluster analysis. For each cluster of hierarchical clustering, the quantity called p-value is computed through multi-scale bootstrap resampling. The p-value is a value between 0 and 1, and it is possible to check how strong the data is supported in the cluster, and it is expected to be utilized as a basic research on building three-dimensional emotional DNA through emotional word ranking in each emotional dimension.
Pattern of E-marketplace Customer Shopping Behavior using Tabu Search and FP-Growth Algorithm

Pattern of customer shopping behavior can be known by analyzing market cart. This analysis is performed using Association Rule Mining (ARM) method in order to improve cross-sale. The weakness of ARM is if processed data is big data, it takes more time to process the data. To optimize the ARM, we perform merging algorithm with Improved Tabu Search (TS). The application of Improved TS algorithm as optimization algorithm for preprocessing datasets, data filtering, and sorting data closely related products on sales data can optimize the ARM processing. The method of Association Rule Mining (FP-Growth) to determine frequent K-itemset, Support value and Confidence value of data which is already sorted on TS is based on patterns which often appear in the dataset so it generates rules as reference of decision making for company. To measure the level of power of rule which has been formed, the Lift Ratio value was calculated. Based on the calculation of 97 rules produced, the lift ratio produces values &gt; 1 of 82.54% and based on processing time, it produces the fastest data search in 1.66 seconds. When compared with previous research that uses the hybrid method, for data retrieval based on processing time, it produces the fastest data search within 12.3406 seconds, 150 seconds and 50 seconds. Previous studies have only compared the processing time of data searching without regard to validation / accuracy of data search. The test results in this study obtained more optimal results than when compared with the results of previous studies, namely in time efficiency and data mining in real time and more accurate data validation. As a conclusion, the resulting rule can be used as a reference in understanding shopping behavior patterns customer on the E-Marketplace.