Reviewerly/exHarmony huggingface dataset:
IMAGE DENOISING VIA EXACT MINIMUM RANK APPROXIMATION WITH RELATIVE TOTAL VARIATION REGULARIZATION

To Image denoising is one of the classical problems in image processing. Such denoising by minimum rank approximation via Schatten p-norm minimization is prone to cause over-smoothing. Intricate and irregular image structures cann't be distinguished dramatically by Schatten p-norm minimization. A flexible and precise model named weighted Schatten p-norm minimization (WSPM) with relative total variation regularization (RTV-WSPM) was proposed in this study to address this issue. The proposed RTV-WSPM not only had an accurate approximation with a Schatten p-norm but also considered the prior knowledge where different rank components have different importance by relative total variation. Moreover, the alternating direction method of multipliers was introduced to solve the proposed RTV-WSPM model. Experiments on Gaussian white noise and salt-and-pepper noise demonstrate that the proposed technique outperforms other state-of-the-art methods, especially under degradation for high-density image noise. In terms of peak signal-to-noise ratio evaluationthe proposed RTV-WSPM achieves significant improvements over the conventional WSPM under salt-and-pepper noise. Therefore, the RTV-WSPM exerts a good effect to restore the image structure and smoothness and improves denoising performances. Keywords: Weighted Schatten p-norm minimization; image denoising; low rank matrix approximation; RTV norm
Management Information System of the Billing Subsystem: A Prototype Design

Abstract— Prof. Soeparwi Veterinary Hospital is one of veterinary medical service providers in Yogyakarta ad Central Java areas in which the transaction and the documentation managing process is still done manually. Therefore, Prof. Soeparwi VH needs a management information system that facilitate them in managing process and documentation of transactions, one of which is through billing or billing subsystem. This subsystem was designed using UML and was developed in a form of web-based prototype using PHP and HTML languages as well as CSS with CodeIgniter framework and MySQL for database. The development of this billing subsystem applied a Rapid Application Development (RAD) model process that focused on the working model and obtained feedback from users to improve the system. Results of this system's development were evaluated with a system feasibility test and functionality test. A system feasibility test was administered by distributing Likert scale questionnaire and analysed them based on a summated rating scale method which showed a result of 85.4%. That result indicates that users strongly agree that the system has met their needs. The system functionality was tested using a black box method and the result was that the system properly functioned. This billing subsystem could process transaction bills automatically included in the calculation. This system produced bills and reports that could be printed and exported. In addition, data was stored in a database so that it supported paperless documentation.
An Enhanced Weighted Associative Classification Algorithm without Preassigned Weight based on Ranking Hubs

Heart disease is the preeminent reasons for death worldwide and in excess of 17 million individuals were kicked the bucket from heart disease in the past years and the mortality rate will be increased in upcoming years revealed by WHO.It is very tough to diagnose the heart problem by just observing the patient.There is a high demand in developing an efficient classifier model to help the physician to predict such threatening disease to recover the human life.Now a day, many researchers have focused novel classifier model based on Associative Classification (AC).But most of the AC algorithm does not consider the consequence of the attribute in the database and treat every itemsets equally.Moreover, weighted AC ignores the significance of the itemsets and suffering the rule evaluation due to support measure.In this proposed method we have introduced attribute weight, which does not require manual assignment of weight instead the weight would be calculated from link based model.Finally, the performance of the proposed algorithm is verified on different medical datasets from UCI repository with classical associative classification.
Development of web-based for egg surveillance to maintain egg distribution

Recently there was news that the price of eggs increasing, and one of the problems is caused by uneven distribution. A system for particular interest about the data and information for eggs distribution is urgently needed to solve this problem. The objective of this research is to design a system that is able to visualize egg distribution in Bogor through WEB GIS. Development of this information system is Web-based Development Life Cycle (WDLC). This method consists five steps, there are web development planning, engineering web requirements, web application design, web technical implementation, and usage web system. As the result, a prototype design of web GIS has been developed to be able to visualize eggs distribution in Bogor. This system can be used as a tool to determine egg distribution of a location either in Bogor or other location in Indonesia. It is recommended that the government ca use this system to support even eggs distribution in Indonesia.
Bayesian distance metric learning and its application in automatic speaker recognition systems

This paper proposes state-of the-art Automatic Speaker Recognition System (ASR) based on Bayesian Distance Learning Metric as a feature extractor. In this modeling, I explored the constraints of the distance between modified and simplified i-vector pairs by the same speaker and different speakers. An approximation of the distance metric is used as a weighted covariance matrix from the higher eigenvectors of the covariance matrix, which is used to estimate the posterior distribution of the metric distance. Given a speaker tag, I select the data pair of the different speakers with the highest cosine score to form a set of speaker constraints. This collection captures the most discriminating variability between the speakers in the training data. This Bayesian distance learning approach achieves better performance than the most advanced methods. Furthermore, this method is insensitive to normalization compared to cosine scores. This method is very effective in the case of limited training data. The modified supervised i-vector based ASR system is evaluated on the NIST SRE 2008 database. The best performance of the combined cosine score EER 1.767% obtained using LDA200 + NCA200 + LDA200, and the best performance of Bayes_dml EER 1.775% obtained using LDA200 + NCA200 + LDA100. Bayesian_dml overcomes the combined norm of cosine scores and is the best result of the short2-short3 condition report for NIST SRE 2008 data.
Blockchain Based Secured E-voting by Using the Assistance of Smart Contract

Voting is a very important issue which can be beneficial in term of choosing the right leader in an election. A good leader can bring prosperity to a country and also can lead the country in the right direction every time. However, elections are surrounds with ballot forgery, coercion and multiple voting issues. Moreover, while giving votes, a person has to wait in a long queue and it is a very time consuming process. Blockchain is a distributed database in which data are shared with the participant of the node and each participant holds the same copy of the data. Blockchain has properties like distributed, pseudonymous, data integrity etc. In the paper, a fully decentralized evoting system based on blockchain technology is proposed. This protocol utilizes smart contract into the evoting system to deal with security issues, accuracy and voters privacy during the vote. The protocol results in a transparent, non editable and independently verifiable procedure that discards all the intended fraudulent activities occurring during the election process by removing the least participation of the third party and enabling voters right during the election. Both transparency and coercion are obtained at the same time.
Twelve Ways to Reach for a Star: Player Movement Strategies in a Whole-Body Exergame

This paper describes findings from a laboratory-based assessment of a balance-training exergame developed by the authors. The exergame was designed to elicit specific body movements during gameplay involving weight-shift (transfer of body weight from one foot to the other), which is considered beneficial for training balance. The body movements observed during gameplay were analyzed using a modified Labanotation. The study was conducted with twelve able-bodied adults. All users played the exergame successfully, but contrary to our expectations, only 23% of the observed player movements corresponded to the specific body movements the exergame intended to elicit. Despite the low number of intended movements, 49% of the observed movements involved weight-shift. The exergame was therefore appropriate for balance training, although users moved differently than what we had designed for. Regarding balance-training exergames, our findings suggest that designers need worry less about “puppeteering” able-bodied players with a strict choreography to elicit specific body movements. Instead we recommend that designers embrace a less rigid design approach, where the goal is to elicit desired movement characteristics (e.g., weight-shift) through a more open and playful behavior.
Peran Dukungan Sosial Orang Tua terhadap Prestasi Akademis melalui Mediasi Motivasi Belajar Intrinsik

This research aimed to empirically examine the effect of parents’ social support on academic achievement through the mediation of intrinsic motivation to learn. It was hypothesized that parents’ social support positively affect academic achievement through the mediation of intrinsic motivation to learn. Researcher adapted Gordon’s Parent Subscale Social Support Questionnaire for Children Rahmi’s Parental Support Scale and Rahmi’s Intrinsic Motivation to Learn Subscale along with weighted summative asessment tests to measure academic achievement. Participants were 84 students of grade 8 from three different junior high schools in Yogyakarta. Path analysis was used to investigate the hyphotesis. Results indicated that parents’ social support was a significant predictor of intrinsic motivation to learn, b = 0.55, (p &lt; 0.05), and that intrinsic motivation to learn was a significant predictor of academic achievement, b = 0.47, (p &lt; 0.05). These results supported the mediational hypothesis. Parents’ social support was no longer a significant predictor of academic achievement after including the mediator, parents’ social support, b = 0.16, (p &gt; 0.05), consistent with full mediation.
A Proposal of Music-Based Personal Authentication System

This paper describes a concept of music-based personal authentication. In recent years we frequently need to use personal authentication system to use the Internet and so on. Password is a popular way for personal authentication. However, long password is not good for remembering, while short password is not good for protecting. In the proposed method, we pay attention to the feature of music and discuss the potential of music for personal authentication system.
Website-based High School Management Information System

Technology development has a great benefit in improving the quality of a school, for example the utilization of technology for value management.Many schools have not benefited yet from the current technological developments.Academic data in the form of value and attendance reports are still manually managed (written), therefore the data storage and management are inefficient.The solution for the related problems is inputting an existing archive into a website-based information system that enhances the efficiency of data management, especially in managing student value information.As the result, school management information system application is created, where it has a feature that can improve school's working quality.The results can be concluded that it has been successfully applied based on the tests that have been conducted, such as testing application directly, conducting an analysis and analyzing the progress of the applied school.
THE IDENTIFICATION OF DETERMINANT PARAMETER IN FOREST FIRE BASED ON FEATURE SELECTION ALGORITHMS

This research conducts studies of the use of the Sequential Forward Floating Selection (SFFS) Algorithm and Sequential Backward Floating Selection (SBFS) Algorithm as the feature selection algorithms in the Forest Fire case study. With the supporting data that become the features of the forest fire case, we obtained information regarding the kinds of features that are very significant and influential in the event of a forest fire. Data used are weather data and land coverage of each area where the forest fire occurs. Based on the existing data, ten features were included in selecting the features using both feature selection methods. The result of the Sequential Forward Floating Selection method shows that earth surface temperature is the most significant and influential feature in regards to forest fire, while, based on the result of the Sequential Backward Feature Selection method, cloud coverage, is the most significant. Referring to the results from a total of 100 tests, the average accuracy of the Sequential Forward Floating Selection method is 96.23%. It surpassed the 82.41% average accuracy percentage of the Sequential Backward Floating Selection method.
Discussions on Capturing a Watermarked Image on an LCD Monitor

Digital image watermarking technology has been developed for years as a tool of digital protection. Depending on the application field, a watermarking method faces specific kinds of challenges. It may be robust, fragile, reversible, etc. In this paper, the issues of capturing a watermarked image on an LCD monitor were discussed. It is similar to the print-and-scan watermarking in some sense. However, it suffers from a few additional problems. A few experiments were conducted to illustrate the severity of the issues. An additional source of distortion was also identified during the experiments. The results showed the challenges of this type of applications.
Adversarial Example in Remote Sensing Image Recognition

With the wide application of remote sensing technology in various fields, the accuracy and security requirements for remote sensing images (RSIs) recognition are also increasing. In recent years, due to the rapid development of deep learning in the field of image recognition, RSI recognition models based on deep convolution neural networks (CNNs) outperform traditional hand-craft feature techniques. However, CNNs also pose security issues when they show their capability of accurate classification. By adding a very small variation of the adversarial perturbation to the input image, the CNN model can be caused to produce erroneous results with extremely high confidence, and the modification of the image is not perceived by the human eye. This added adversarial perturbation image is called an adversarial example, which poses a serious security problem for systems based on CNN model recognition results. This paper, for the first time, analyzes adversarial example problem of RSI recognition under CNN models. In the experiments, we used different attack algorithms to fool multiple high-accuracy RSI recognition models trained on multiple RSI datasets. The results show that RSI recognition models are also vulnerable to adversarial examples, and the models with different structures trained on the same RSI dataset also have different vulnerabilities. For each RSI dataset, the number of features also affects the vulnerability of the model. Many features are good for defensive adversarial examples. Further, we find that the attacked class of RSI has an attack selectivity property. The misclassification of adversarial examples of the RSIs are related to the similarity of the original classes in the CNN feature space. In addition, adversarial examples in RSI recognition are of great significance for the security of remote sensing applications, showing a huge potential for future research.
“Efektifitas Metode Funcalistung Dalam Menumbuhkan Minat dan Kegemaran Mengenal Huruf Dan Angka (Studi Kualitatif Di Kelompok B TK Cahaya Indonesia Cimahi )”.

Early Childhood Education (ECD) is a development effort aimed at children from birth to the age of six, which is done through the provision of educational stimulus to help the growth and development of children physically and mentally in order to have the readiness to enter further education.The cognitive aspect is one of the basic competence is an aspect that should be developed at the age of 5-6 years because it is the basis of pre-reading and pre-count ability which have an important role in continuing to pursue elementary school (SD).In reality the early childhood parents have a claim to the managers and early childhood teachers to be able to teach the material to read, write, count on their children.This becomes a challenge for early childhood teachers to use the proper method in presenting the material reading and math fun for children so that children do not feel compelled to follow the learning.To that end, the researchers intend to conduct research on the extent to which the effectiveness of the method Funcalistung in growing interest and fondness recognize letters and numbers in groups of Cahaya Indonesia kindergarten.This study is a descriptive study using a qualitative approach.Researchers attempt to describe the results of observations in terms of specific strategies, the role of teachers, instructional media and the atmosphere created when learning activities using Funcalistung in group B at Cahaya Indonesia kindergarten, and interviewed principals, teachers and parents group B students.Result: The first: a special strategy is to use the material in accordance with the objectives, both: the role of teachers who participate actively, third: the media varied with special tricks in use, the fourth: creating an atmosphere of enthusiastic students in the following study.Implementation: The first: the teacher can implement specific strategies to motivate students, second: more teachers improve their skills in planning and learning strategies as well as actively participate in the teaching, the third: learning media made it interesting, varied in use and can be played by students, fourth: creating a learning environment that is fun and enjoyable and students were enthusiastic in participating in the study.
Utilization of Augmented and Virtual Reality Technologies in Geoscience and Mining

This study reviewed application cases of augmented reality (AR) and virtual reality (VR) in the fields of geoscience and mining and evaluated their future prospects. Numerous papers and reports were reviewed closely. AR has been applied in the field of geoscience for the purposes of geological surveying, geoscience education, underground facility management, and geotourism. There were relatively few cases in which AR was used in the mining sector, but some cases could be classified as mine development enhancement and mine reclamation support. Conversely, VR has been applied to education and geotourism in the field of geoscience. VR has been applied in the mining sector since the 1980s for mining development simulation, training for worker safety, and mining software control. These technologies will be useful for geological surveying and stability improvement in the fields of geoscience and mining.
Robust speaker recognition using unsupervised adversarial invariance

In this paper, we address the problem of speaker recognition in challenging acoustic conditions using a novel method to extract robust speaker-discriminative speech representations. We adopt a recently proposed unsupervised adversarial invariance architecture to train a network that maps speaker embeddings extracted using a pre-trained model onto two lower dimensional embedding spaces. The embedding spaces are learnt to disentangle speaker-discriminative information from all other information present in the audio recordings, without supervision about the acoustic conditions. We analyze the robustness of the proposed embeddings to various sources of variability present in the signal for speaker verification and unsupervised clustering tasks on a large-scale speaker recognition corpus. Our analyses show that the proposed system substantially outperforms the baseline in a variety of challenging acoustic scenarios. Furthermore, for the task of speaker diarization on a real-world meeting corpus, our system shows a relative improvement of 36\% in the diarization error rate compared to the state-of-the-art baseline.
RSM-GAN: A Convolutional Recurrent GAN for Anomaly Detection in Contaminated Seasonal Multivariate Time Series

Robust anomaly detection is a requirement for monitoring complex modern systems with applications such as cyber-security, fraud prevention, and maintenance. These systems generate multiple correlated time series that are highly seasonal and noisy. This paper presents a novel unsupervised deep learning architecture for multivariate time series anomaly detection, called Robust Seasonal Multivariate Generative Adversarial Network (RSM-GAN). It extends recent advancements in GANs with adoption of convolutional-LSTM layers and an attention mechanism to produce state-of-the-art performance. We conduct extensive experiments to demonstrate the strength of our architecture in adjusting for complex seasonality patterns and handling severe levels of training data contamination. We also propose a novel anomaly score assignment and causal inference framework. We compare RSM-GAN with existing classical and deep-learning based anomaly detection models, and the results show that our architecture is associated with the lowest false positive rate and improves precision by 30% and 16% in real-world and synthetic data, respectively. Furthermore, we report the superiority of RSM-GAN regarding accurate root cause identification and NAB scores in all data settings.
Analysis Of Change In Innovation School Curriculum Institution

본 연구는 신제도주의의 제도 변화 이론을 적용하여 혁신학교의 교육과정을 교육과정의 개념, 지원 구조, 실행 방식의 요소로 분석하고 학교교육과정의 변화 특성을 연구하였다. 공식적&#8228;비공식적 제도를 포괄하는 신제도주의 관점에서 볼 때 교육과정 제도는 해석과 실천에서 재량권이 크므로 비공식적 제도(문화나 관행)의 변화가 중요하고, 실천적 변화는 교사들이 공유하는 인식과 행위 논리가 함께 변할 때 가능하다. 연구 결과, 제도로서의 혁신학교 교육과정은 변화하였는데 첫째, 교사들은 교육과정을 ‘학교에서 학생 성장 발달을 위해 이루어지는 모든 교육활동’으로 폭넓게 해석하는 관점을 공유하고 교육과정 지원 구조(조직과 거버넌스)를 변화시켜 학교를 학습조직으로 전환시켰으며, 교육과정 실행(표준운영절차)방식도 함께 변화시켰다. 둘째, 학교교육과정의 성격이 실적중심 교육과정에서 학생 성장발달 중심 교육과정으로 변화되었다. 셋째, 교육과정 제도화 방식이 “교육과정 → 교과서 → 수업”에서 “교육과정 → 학교교육과정 → 교육활동과 수업”으로 변화되었다.This study applied institutional change theory of new institutionalism to analyze the curriculum of innovative school by the concept of curriculum, support structure, and execution method of the curriculum and researched the change characteristics of the school curriculum. In terms of the new institutionalism that includes formal/informal institutions, the curriculum system has a great deal of room for interpretation and practice and discretion, so it is important to change the informal institution (culture or practice), and the practical change is possible when awareness and behavioral theory that teachers share gets changed together. As a result of the study are as follows: First, teachers share the view that the curriculum is broadly interpreted as ‘all the educational activities that take place in the school for student growth and development . According to this concept, teachers changed the curriculum support structure, transformed the school into a learning organization, and also changed the way of curriculum execution. Second, the nature of school curriculum changed from performance-oriented curriculum to students growth development-centered curriculum. Third, the method of curriculum institutionalization changed from “curriculum → textbook → class” to “curriculum → school curriculum → education activity class”.