Reviewerly/exHarmony huggingface dataset:
Model Pelatihan Berbasis Produk Untuk Meningkatkan Kinerja Penelitian dan Publikasi Karya Ilmiah

This paper aims to develop a product-based training model to improve the performance of research, and publication of scientific work. This research was carried out with R &amp; D covering two stages. In the preliminary study phase, a literature review and field survey were conducted on the training models used so far. At the development stage, drafting the model, validation carried out expertand limited field testing were. Data analysis techniques using thedifferent test techniques Mann-Whitney U test. The R &amp; D results show findings: 1) the training model is carried out through analysis, planning, development, implementation, and evaluation; 2) the validity level of the model is in the good category; and 3) the products produced by lecturers, namely the results of research and publication are higher than before using the model.
Improving Object Detection with Inverted Attention

Improving object detectors against occlusion, blur and noise is a critical step to deploy detectors in real applications. Since it is not possible to exhaust all image defects through data collection, many researchers seek to generate hard samples in training. The generated hard samples are either images or feature maps with coarse patches dropped out in the spatial dimensions. Significant overheads are required in training the extra hard samples and/or estimating drop-out patches using extra network branches. In this paper, we improve object detectors using a highly efficient and fine-grain mechanism called Inverted Attention (IA). Different from the original detector network that only focuses on the dominant part of objects, the detector network with IA iteratively inverts attention on feature maps and puts more attention on complementary object parts, feature channels and even context. Our approach (1) operates along both the spatial and channels dimensions of the feature maps; (2) requires no extra training on hard samples, no extra network parameters for attention estimation, and no testing overheads. Experiments show that our approach consistently improved both two-stage and single-stage detectors on benchmark databases.
Assessing Students’ Understanding Towards Critical Reading and Its Impacts Towards Their Lives

ABSTRACTCritical reading was not a new term for university students but the levels to how students understand it and how it could help them develop in many aspects had seldom been studied. Therefore, this study tried to explore the levels of students’ understanding towards the meaning of critical reading, what possible problems could be present in the implementation, what activities could be carried out for practicing critical reading, and what benefits critical reading could bring to students’ lives. This study; conducted by survey questionnaire, was focused on IT students who had just finished a Reading course and were indicated not in favor of reading. From data analysis, it was found that the IT students did not know well that critical reading required interpreting and criticizing, did not realize potential problems in implementing critical reading and was not aware that critical reading practices could help improve their academic achievements. ABSTRAKMembaca kritis bukanlah sebuah istilah baru untuk mahasiswa tetapi masih jarang dilakukan penelitian terhadap tingkat pemahaman mahasiswa terhadap makna dari membaca kritis dan bagaimana dengan membaca kritis dapat membantu mahasiswa dalam banyak hal. Oleh karena itu, penelitian ini dilakukan untuk melihat tingkat pemahaman mahasiswa terhadap makna membaca kritis, terhadap masalah yang mungkin muncul ketika melakukan kegiatan membaca kritis, terhadap contoh kegiatan yang dapat mengembangkan kegiatan membaca kritis, dan terhadap manfaat dari membaca kritis terhadap kehidupan mahasiswa. Kajian ini, yang dilakukan dengan metode survei kuesioner, difokuskan kepada mahasiswa IT yang baru saja menyelesaikan mata kuliah ‘Membaca’ dan yang diidentifikasi kurang berminat dalam membaca. Dari kajian yang dilakukan ditemukan bahwa mahasiswa IT tersebut tidak mengetahui dengan baik bahwa membaca kritis membutuhkan kemampuan untuk menafsirkan dan mengkritik, tidak menyadari berbagai macam tantangan yang muncul dalam pelaksanaan kegiatan membaca kritis, dan tidak menyadari bahwa dengan membaca kritis mahasiswa dapat meningkatkan prestasi akademik mereka. How to Cite: Silalahi , R. M. (2018). Assessing Students’ Understanding Towards Critical Reading and Its Impacts Towards Their Lives. IJEE (Indonesian Journal of English Education), 5(2), 191-203. doi:10.15408/ijee.v5i2.9532.
A Systematic Model of Big Data Analytics for Clustering Browsing Records into Sessions Based on Web Log Data

This paper presents a systematic model of big data analytics for clustering browsing records into sessions based on the web log data.With the rapid development of the Internet and World Wide Web technologies, the behavior of web users becomes more and more complicated.The analysis on web log data may reveal some hint at the browsing behavior of web users.Since the information of browsing sessions has a great impact on the effectiveness of analysis on web log data, especially in the precision of describing the behavior of web users, this motivates our work in developing a systematic model of clustering browsing sessions.First, we present a five-phase architecture that we develop for big data analytics.We have built a computing environment with the architecture, and we have implemented a few methods of big data analytics with such an architecture.Then, we propose the new systematic model, called EDCP model, of big data analytics for clustering browsing records into sessions based on the web log data.Since the analysis on the web log data with various goals may pose distinct criteria for clustering browsing records into sessions, the design of EDCP model allows simple adaption for the distinct criteria in order to meet the need of various goals.We demonstrate the application of EDCP model with the session criteria given by a research group in the tourism and recreation area.We present the experiments of applying EDCP model on the web log data from the official web site provided by Taiwan Tourism Bureau with a goal of clustering the browsing sessions for the web users of 2018 Taiwan Lantern Festival.As a summary, we have a total of 344,963,578 browsing records in the web log data, and we find 55,318,326 records among them are related to 2018 Taiwan Lantern Festival.Our systematic model successfully clusters the records into 307,154 browsing sessions, as a result.
Operation-aware Neural Networks for User Response Prediction

User response prediction makes a crucial contribution to the rapid development of online advertising system and recommendation system. The importance of learning feature interactions has been emphasized by many works. Many deep models are proposed to automatically learn high-order feature interactions. Since most features in advertising system and recommendation system are high-dimensional sparse features, deep models usually learn a low-dimensional distributed representation for each feature in the bottom layer. Besides traditional fully-connected architectures, some new operations, such as convolutional operations and product operations, are proposed to learn feature interactions better. In these models, the representation is shared among different operations. However, the best representation for different operations may be different. In this paper, we propose a new neural model named Operation-aware Neural Networks (ONN) which learns different representations for different operations. Our experimental results on two large-scale real-world ad click/conversion datasets demonstrate that ONN consistently outperforms the state-of-the-art models in both offline-training environment and online-training environment.
Neural source-filter waveform models for statistical parametric speech synthesis

Neural waveform models such as WaveNet have demonstrated better performance than conventional vocoders for statistical parametric speech synthesis. As an autoregressive (AR) model, WaveNet is limited by a slow sequential waveform generation process. Some new models that use the inverse-autoregressive flow (IAF) can generate a whole waveform in a one-shot manner. However, these IAF-based models require sequential transformation during training, which severely slows down the training speed. Other models such as Parallel WaveNet and ClariNet bring together the benefits of AR and IAF-based models and train an IAF model by transferring the knowledge from a pre-trained AR teacher to an IAF student without any sequential transformation. However, both models require additional training criteria, and their implementation is prohibitively complicated. We propose a framework for neural source-filter (NSF) waveform modeling without AR nor IAF-based approaches. This framework requires only three components for waveform generation: a source module that generates a sine-based signal as excitation, a non-AR dilated-convolution-based filter module that transforms the excitation into a waveform, and a conditional module that pre-processes the acoustic features for the source and filer modules. This framework minimizes spectral-amplitude distances for model training, which can be efficiently implemented by using short-time Fourier transform routines. Under this framework, we designed three NSF models and compared them with WaveNet. It was demonstrated that the NSF models generated waveforms at least 100 times faster than WaveNet, and the quality of the synthetic speech from the best NSF model was better than or equally good as that from WaveNet.
Multi-scale Cross-form Pyramid Network for Stereo Matching

Stereo matching plays an indispensable part in autonomous driving, robotics and 3D scene reconstruction. We propose a novel deep learning architecture, which called CFP-Net, a Cross-Form Pyramid stereo matching network for regressing disparity from a rectified pair of stereo images. The network consists of three modules: Multi-Scale 2D local feature extraction module, Cross-form spatial pyramid module and Multi-Scale 3D Feature Matching and Fusion module. The Multi-Scale 2D local feature extraction module can extract enough multi-scale features. The Cross-form spatial pyramid module aggregates the context information in different scales and locations to form a cost volume. Moreover, it is proved to be more effective than SPP and ASPP in ill-posed regions. The Multi-Scale 3D feature matching and fusion module is proved to regularize the cost volume using two parallel 3D deconvolution structure with two different receptive fields. Our proposed method has been evaluated on the Scene Flow and KITTI datasets. It achieves state-of-the-art performance on the KITTI 2012 and 2015 benchmarks.
Bitcoin and Blockchain: Security and Privacy

A cryptocurrency is a decentralized digital currency that is designed for secure and private asset transfer and storage. As a currency, it should be difficult to counterfeit and double-spend. In this paper, we review and analyze the major security and privacy issues of Bitcoin. In particular, we focus on its underlying foundation, blockchain technology. First, we present a comprehensive background of Bitcoin and the preliminary on security. Second, the major security threats and countermeasures of Bitcoin are investigated. We analyze the risk of double-spending attacks, evaluate the probability of success in performing the attacks and derive the profitability for the attacker to perform such attacks. Third, we analyze the underlying Bitcoin peer-to-peer network security risks and Bitcoin storage security. We compare three types of Bitcoin wallets in terms of security, type of services and their trade-offs. Finally, we discuss the security and privacy features of alternative cryptocurrencies and present an overview of emerging technologies today. Our results can help Bitcoin users to determine a trade-off between the risk of double-spending attempts and the transaction time delay or confidence before accepting transactions. These results can also assist miners to develop suitable strategies to get involved in the mining process and maximize their profits.
Deep Constrained Dominant Sets for Person Re-identification.

In this work, we propose an end-to-end constrained clustering scheme to tackle the person re-identification (re-id) problem. Deep neural networks (DNN) have recently proven to be effective on person re-identification task. In particular, rather than leveraging solely a probe-gallery similarity, diffusing the similarities among the gallery images in an end-to-end manner has proven to be effective in yielding a robust probe-gallery affinity. However, existing methods do not apply probe image as a constraint, and are prone to noise propagation during the similarity diffusion process. To overcome this, we propose an intriguing scheme which treats person-image retrieval problem as a {\em constrained clustering optimization} problem, called deep constrained dominant sets (DCDS). Given a probe and gallery images, we re-formulate person re-id problem as finding a constrained cluster, where the probe image is taken as a constraint (seed) and each cluster corresponds to a set of images corresponding to the same person. By optimizing the constrained clustering in an end-to-end manner, we naturally leverage the contextual knowledge of a set of images corresponding to the given person-images. We further enhance the performance by integrating an auxiliary net alongside DCDS, which employs a multi-scale Resnet. To validate the effectiveness of our method we present experiments on several benchmark datasets and show that the proposed method can outperform state-of-the-art methods.
Algorithm for Persian Text Sentiment Analysis in Correspondences on an E- Learning Social Website

By 2000, sentiment analysis had been only studied based on speech and changes in facial expressions. Since then, studies have been focused on text. Concerning Persian text mining, studies have been conducted on the methods for extracting properties for classification and examination of opinions on social websites with an aim to determine text polarity. The present research was aimed to prepare and implement an algorithm for Persian text sentiment analysis based on the following six basic emotional states: happiness, sadness, fear, anger, surprise, and disgust. In this research, sentiment analysis was carried out using the unsupervised lexical method. Lexicons are divided into four categories, namely the emotional, boosters, negation, and stop lists. The algorithm was written in six different ways using different properties. In the first method, the algorithm was capable of identifying an emotional word in a sentence. The sentiment of the sentence was determined based on the given emotional word. However, it should be noted that the text itself is also important for sentiment analysis because in addition to the emotional words, other factors (such as boosters and negating factors) are also present in the sentence and affect the text sentiment. Hence, the algorithm was enhanced in the subsequent methods to detect the boosters and negating words. Results of running the algorithm using different methods indicated that the algorithm accuracy increased with an increase in the number properties involved. In the sixth method, an algorithm capable of identifying emotional, boosters and negative words was applied to two data samples including sentences written by typical users and sentences written by university students on an electronic learning social website. The accuracy of the algorithm with 100 data samples from typical users and 100 data samples from university students was 80% and 84%, respectively.
Factors that Influence Workers’ Participation in Unhygienic Cyber Practices: A Pilot Study from Nigeria

Participation or engagement in unhygienic cyber practices could ultimately harm an organization's information and communication technologies, if unchecked. This present study used concepts from the theory of planned behavior and organizational control theory to examine the effects of factors such as attitude, subjective norms, organizational facilitators, monitoring, and self-efficacy on workers' participation in unhygienic cyber practices. A cross-sectional survey of Nigerian professionals was used to test the formulated hypotheses. Partial least squares technique of structural equation modeling (SEM) was used for data analysis. The results indicate that attitude toward cyber hygiene has a negative effect on worker's participation in unhygienic cyber practices; similarly, subjective norms have a negative effect on engagement in such acts. The data did not show that organizational facilitators, self-efficacy, and monitoring had a meaningful impact on Nigerian workers' participation in unhygienic cyber practices. Implications of the study were discussed and contribution to the extant literature noted.
The Us of Dice Game in Teaching Vocabulary for Young Learners

Dice games is a cube (often with corners slightly rounded), Marked on each of its six faces with a different number of circular patches or Pits called pips. Based on the purpose of this research to find out whether the use of Loaded Dice Game can increase or not the students’ Vocabulary mastery at the fourth-grade students of SDIT Anak Sholeh Mataram in the school year 2016/2017. In this research, the writer used quasi-experimental research (pre-test and post-test design) with the population was 111 of 4 classes, and the sample was 58 students by using cluster sampling. The writer took two classes as samples, they are Malik class as an experimental and Khuzaifah class as a control group. To collect the data, the writer used multiple choices items with 20 numbers items. The finding of this research from the value of t-test was 5.01 higher than the value of t-table in the significance level 95% (1.67) and at the confidence level 99% (2.39). The value of t-test is higher than t-table, it means that the hypothesis alternative (Ha) is accepted. So, the writer can conclude that the use of Loaded Dice game can increase the students’ vocabulary mastery at the fourth-grade students of SDIT Anak Sholeh Mataram in the school year 2016/2017.
UNIVERSITY STUDENTS’ SKILLS IN USING THE INTERNET FOR EDUCATIONAL PURPOSES AND THE DIGITAL DIVIDE

This article focuses on digital skills competence of students in higher education. It is considered to be one of the most prominent scientific research topics in recent decades since it leads to the digital divide. Based on the theory of Bourdieu, this research describes quantitative data with the aim to investigate the presence of the digital divide in higher education. The findings show that the students’ socioeconomic status influences the level of skills development. The relationship between SES and students’ skills shows the presence of the digital divide in higher education that increases learning inequalities. It concludes that the bridging of the digital divide has to be a priority for policies in order to implement new digital teaching and learning processes at all levels of education. New practices also have to be applied in higher education institutions and support students digitally promoting them to acquire sufficient skills and to make more effective use of the Internet. Article visualizations:
Horseshoe Regularization for Machine Learning in Complex and Deep Models

Since the advent of the horseshoe priors for regularization, global-local shrinkage methods have proved to be a fertile ground for the development of Bayesian methodology in machine learning, specifically for high-dimensional regression and classification problems. They have achieved remarkable success in computation, and enjoy strong theoretical support. Most of the existing literature has focused on the linear Gaussian case; see Bhadra et al. (2019b) for a systematic survey. The purpose of the current article is to demonstrate that the horseshoe regularization is useful far more broadly, by reviewing both methodological and computational developments in complex models that are more relevant to machine learning applications. Specifically, we focus on methodological challenges in horseshoe regularization in nonlinear and non-Gaussian models; multivariate models; and deep neural networks. We also outline the recent computational developments in horseshoe shrinkage for complex models along with a list of available software implementations that allows one to venture out beyond the comfort zone of the canonical linear regression problems.
Semiotic Advertising by International Firms: A Comparative Study of Premium Smart Phone Brands in China, South Korea and USA

This paper presents a theoretical discussion regarding how semiotic advertising is adopted by international firms. The semiotic advertising concept as explained in this study is defined as an advertising approach or method based on the use of signs and symbols for the purpose of communicating with a target audience. The signs and symbols act as indirect communicators of messages that firms try to convey to consumers. Since this method of advertising is focused and based on the use of different symbolic features or signs, it was found to be beneficial for cross-cultural advertising by international firms. Therefore, this study evaluated the use of semiotic advertising by two (2) premium smartphone brands in three countries, i.e. South Korea, USA and China. Analysis of different advertisements adopted by these firms in the three countries revealed that there were two underlying factors regarding the use of semiotic advertising. The first factor was definitely the cultural factor, where it was found that Apple’s hidden messages in the advertisements were highly aligned to each country’s cultural aspects and other factors. The other factor established by Samsung was the use of signs to create a special perception about the brand, thus, leading to the development of better relations between the brand and the customer. The findings showed that the use of semiotic advertising by international premium smartphone brands was definitely different across countries.
IMPLEMENTASI DATA MINING UNTUK MENGETAHUI MANFAAT RPTRA MENGGUNAKAN METODE K-MEANS CLUSTERING

The vacant land in DKI Jakarta is increasingly reducing due to the construction of houses, queues, factories, which are increasingly rapid. Making less green open space and water catchment areas. That way, the park of the children to play even decreases, as well as the DKI Jakarta Provincial Government, decides to make a Child-Friendly Integrated Public Room or what we know as RPTRA with facilities that can help the community around it. With this RPTRA, the public especially children can play and interact with each other. Using the K-Means Clustering method can help the government or officers in each RPTRA more easily see how useful this RPTRA and the government also facilitates some of the rooms contained in this RPTRA namely the hall, library, and playroom. The results of this research are the hall that has the highest value for visitors who come where the value obtained is 1319 visitors.
The Use of Head-Worn Augmented Reality Displays in Health Communications.

The health industry is always seeking innovative ways to use technology to create or improve the experiences of their professionals. Such improvements are seen in a variety of areas including the analysis of relevant health data and the establishment of new ways of communicating medical education and training. Advancements in head-worn augmented reality displays (HWDs), such as the Microsoft Hololens, present a unique opportunity to leverage technology in the ongoing challenge of creating meaningful and novel educational experiences. This paper will review contemporary HWD technologies, how these technologies are being used to enhance the work-training environment, and how these technologies might enhance the communication of health professionals.