Reviewerly/exHarmony huggingface dataset:
Augmented Reality for Media Promotion Research Findings.

Science &amp; Technopark (STP) was built to accelerate the process of transfer technology of research findings that have been discovered by LIPI. The research findings not only in the form of patents or products but also in the form of tools and buildings. To facilitate the understanding of stakeholders in the research findings an innovative media for the promotion of LIPI research findings was made using Augmented Reality (AR) media. This study uses a linear strategy method that combines text, posters, and videos into AR media. The AR application was tested on 25 LIPI stakeholders by distributing questionnaires using a Likert measurement scale. The results obtained are that AR not only effective in promoting commercial products but also very effective in promoting research products that have been found by LIPI at STP and making promotional activities more interesting and interactive.
THE ROLE OF LEADERSHIP STYLES IN ENCOURAGING AND IMPROVING TEAM PERFORMANCE IN ONE OF THE BIGGEST OIL &amp; GAS GROUP OF COMPANIES IN ABU DHABI, UAE

Abstract: The aim of the current study is to examine the impact of leadership styles such as transformational leadership, transactional leadership and participative leadership on the team performance of one of the biggest oil &amp; gas group of companies in UAE. The goal also includes the moderating role of perceived organizational culture on the links of transformational leadership and team performance, transactional leadership team performance, participative leadership, and team performance. The employees who have at least one immediate boss and currently working in one of the biggest oil &amp; gas group of companies are the respondent and data were gathered by using questionnaires and it was analyzed by using Smart-PLS. The results indicated that transformational leadership, transactional leadership, and participate leadership have positive nexus with team performance. The results also indicated that perceived organizational culture has positively moderates among the nexus of transformational leadership and team performance, transactional leadership team performance, participative leadership, and team performance. The regulators of this group of companies will be the foremost users who want to formulate policies regarding the leadership style that improves team performance.
Spatial distribution of Fe and Mn in paper mill effluent affected agricultural soils in Morigaon, Assam

The spatial distribution of heavy metals (Fe and Mn) in the paper mill contaminated area of Jagiroad, Assam, India were investigated using statistics, geostatistics and GIS techniques. The total concentration of Fe and Mn were determined for 188 samples collected from the contaminated area. The mean concentration of Fe (7629 mg kg1) was high. The highest and the lowest standard deviation were observed in the Fe (1749) and pH (0.81), respectively. Analysis of the isotropic variogram indicated that the Fe semivariogram was well described with the Gaussian model, with the distance of spatial dependence being 1354 m, while Mn was well described with the spherical model, with the distance of spatial dependence being 833 m. The ordinary kriging estimates of Fe and Mn maps showed that high concentrations of these metals occured in the low-lying areas like bils (lakes). For both the investigated heavy metals the prediction of goodness (G) value was greater than zero. This indicates that spatial prediction is better than assuming mean of observed value as the property value for any unsampled location. Thus the geostatistical method was spatial variability of Fe and Mn.
Wide-Area Search Tracking for Siamese Region Proposal Network

With the introduction of deep learning technology into the field of visual tracking, the accuracy and robustness of visual tracking have greatly improved. Recently, trackers based on Siamese region proposal networks have attracted a great deal of attention because of their favorable performance, especially when faced with challenges of heavy target deformation and out-of-plane rotation. However, strategies of local-area search and using a fixed template degrade the performance of such networks in the long-term tracking. In this paper, we focus on a wide-area search tracking approach with adaptive template updating for a Siamese region proposal network. By embedding a correlation filter module into the Siamese region proposal networks, a structure of moving anchors distribution is designed to make the anchor regression centered around the target. Through adaptive reliability evaluation and online template update, the discriminative performance of the model improves greatly. Furthermore, in order to address the problem of being misled by false positives, a multi-trajectory tracking mechanism is joined to consistently boost the classification ability. Experiments on OTB100 and VOT short-term benchmarks and UAV123 long-term benchmark show that our tracker significantly outperforms the original algorithm and achieves comparable performance compared with other state-of-the-art trackers.
Multi-text classification of Urdu/Roman using machine learning and natural language preprocessing techniques

Objectives: This research presents multi-text classification from the news text dataset.The main purpose of this work is to classify multi-text for Urdu and Roman language using Natural Language processing and Machine Learning classification models.Methods/Statistical analysis: In this research, online news data has been collected through beautiful soup web scraping tool.In order to analyze the model accuracy news data is divided into six categories which has been composed from various online newspaper platforms.The main news corpus data consists of 10500 news in Urdu and Roman Urdu language including, Accidental, Education, Entertainment, International, Sports and Weather news have been primarily focused in the proposed research study.Furthermore, preprocessing is performed on text corpus using Natural Language Processing technique; for example, data cleaning, data balancing, and stop word removal.For feature extraction count vector, TF-IDF and Chi2 are employed as word filtering.For multi-text classification the Machine Learning classification schemes have been implemented namely, Naive Bayes Classifier, Logistic Regression, Random Forest Classifier, Linear SVC, and K-Neighbors Classifier.After comparative analysis results showed that Linear Support Vector Classifier provided 96% accuracy among other tested methods.Findings: Multi-Text classification of Urdu Roman language having different writing styles, word structure, irregularities, grammar, and combined corpus is a challenging task.For this purpose, we implemented different Machine Learning algorithms with Natural Language preprocessing technique which provided optimal results in classification of multi-text news data.
Embedded AutoEncoders: A Novel Framework for Face De-identification

Recent advances in deep learning and big data have greatly promoted the development of image recognition technology. In the meantime, however, it also makes it more challenging to protect human identify information. In this paper, we propose a novel framework called Embedded AutoEncoders to address face de-identification problem in deep learning. The structure of our framework contains two parts: a Privacy Removal Network and a Feature Selection Network. The main objective of our framework is to ensure that the Privacy Removal Network is capable of discarding information involving privacy and retaining desired information for certain image recognition applications. In order to achieve this goal, the design of the Privacy Removal Network is crucial. Specifically, we employ two different autoencoders, one of which is embedded within the other. We evaluate the proposed framework through extensive experiments, which show that the Embedded AutoEncoders framework can not only effectively retain data utility, but also protect personal identity information.
Contextual Teaching Learning (CTL) Approach towards Students’ Self Confidence in Learning English; does it have any Effect?

In teaching English Language, self-confidence is one psycological aspect that play crucial role for the students. Self-confidence is the positive power or willingness that has a good effect for students during conversation or discussion. Willingness is created when students clearly comprehend what to do and why. Optimism is a mental attitude, optimism is a form of positive thinking that includes the belief that you are responsible for your own happiness (Keller; 2010). To lift the students’ self-confidence in learning English, English language materials that based on the context or real world need to be used in the teaching and learning process. This paper is aimed at knowing the effect English language material which is based on contextual teaching learning towards students’ self confidence in learning English. To achieve this purpose, the students were taught using English language material which is based on contextual teaching learning before the speaking test was taken at the end. To analyze the data t-test was applied. The data analysis shows that students’ self confidence in learning English is improved but it doesn’t give significantly affect towards the students’ self-confidence.
Credit Card Fraud Detection using Machine Learning Methods

A major problem which is affecting growth in financial services is "CREDIT CARD FRAUD". Many organizations lost their amount due to these frauds. Even though many research studies are made on fraud detection, they lack on analyzing data extracted from actual transaction, due to privacy issues. Here, certain machine learning algorithms are used to detect fraudulent transactions. First, the Standard methods are applied. Then, in combination hybrid methods such as AdaBoost and majority voting are applied. A publicly available datasets are used so that model efficacy can be evaluated. Later, a real-world credit card data set is analyzed which is taken from certain financial institution. Further, to estimate the robustness of the algorithm, noise is added to the samples. By comparing various machine learning algorithms, the main aim is to find the best in those to detect the fraudulent transactions to avoid credit card fraud. The experimental results indicate that the hybrid methods such as majority voting efficiently provides nearly best accuracy for detecting fraudulent transactions of Credit cards.
Effective Access Control in Shared-Operator Multi-tenant Data Stream Management Systems

The proliferation of stream-based applications has led to the widespread use of Data Stream Management Systems (DSMSs), which can support the real-time requirements of these applications. DSMSs were developed to efficiently execute continuous queries (CQs) over incoming data. Multiple CQs can be optimized together to form a query network by sharing operators across CQs. DSMSs are also required to enforce access controls over operators according to data providers’ policies. In this paper, we propose the first solution to satisfy access control policies at run-time in shared-operator networks in an non-disruptive, efficient manner. Specifically, we propose a new set of low overhead streaming operators, coined as Privacy Switches (PrSs), which are strategically placed in the operator network to dynamically allow or deny the flow of data in certain branches of the network based upon the current state of access control permissions. Our experimental evaluation confirms that our approach introduces low overheads in the shared operator networks while achieving high savings in the overall network performance.
A Comparative Analysis of Mobile Advertising from the Perspective of Visual Grammar—Take Huawei Mate 30 Series and iPhone 11 for Example

With the development of information, advertising discourse is no longer a single text mode, but is gradually replaced by multimodal discourse which integrates image, text, color, voice and so on.Mobile advertising has become a new favorite of the advertising industry.Because it can not only show the picture in a limited time, but also produce interactive significance, so as to achieve the publicity effect of the enterprise.Huawei Nate 30 series and iPhone 11 were released in the same period.The mobile phones of these two brands are very popular among the public, and their products are often compared.Therefore, this paper will focus on two advertisements from the perspective of visual grammar, and analyze the discourse behind the advertisements.
Sifter: A Hybrid Workflow for Theme-based Video Curation at Scale

User-generated content platforms curate their vast repositories into thematic compilations that facilitate the discovery of high-quality material. Platforms that seek tight editorial control employ people to do this curation, but this process involves time-consuming routine tasks, such as sifting through thousands of videos. We introduce Sifter, a system that improves the curation process by combining automated techniques with a human-powered pipeline that browses, selects, and reaches an agreement on what videos to include in a compilation. We evaluated Sifter by creating 12 compilations from over 34,000 user-generated videos. Sifter was more than three times faster than dedicated curators, and its output was of comparable quality. We reflect on the challenges and opportunities introduced by Sifter to inform the design of content curation systems that need subjective human judgments of videos at scale.
Octanove Labs’ Japanese-Chinese Open Domain Translation System

This paper describes Octanove Labs' submission to the IWSLT 2020 open domain translation challenge.In order to build a high-quality Japanese-Chinese neural machine translation (NMT) system, we use a combination of 1) parallel corpus filtering and 2) back translation.We have shown that, by using heuristic rules and learned classifiers, the size of the parallel data can be reduced by 70% to 90% without much impact on the final MT performance.We have also shown that including the artificially generated parallel data through back-translation further boosts the metric by 17% to 27%, while self-training contributes little.Aside from a small number of parallel sentences annotated for filtering, no external resources have been used to build our system.
CASIA’s System for IWSLT 2020 Open Domain Translation

This paper describes the CASIA's system for the IWSLT 2020 open domain translation task.This year we participate in both Chinese→Japanese and Japanese→Chinese translation tasks.Our system is neural machine translation system based on Transformer model.We augment the training data with knowledge distillation and back translation to improve the translation performance.Domain data classification and weighted domain model ensemble are introduced to generate the final translation result.We compare and analyze the performance on development data with different model settings and different data processing techniques.
THE CHALLENGES OF LEARNING ENGLISH AS A FOREIGN LANGUAGE AMONG UNDERGRADUATE STUDENTS

As one of the foreign languages in Indonesia, English must be learned from junior high school to undergraduate level. As a foreign language, challenges arise both in terms of the teacher as the one who give instruction or students as objects as well as subject learners. Various responses can be found in class related to these problems, especially in the attitude of students during the learning process, student learning outcomes, and participation in carrying out group activities. This study aims to explore the obstacles faced by students in learning English and its causal factors in the environment of Karimun University. This study uses a qualitative approach with a questionnaire, observation, and video recording instrument. Analysis of research data is done in three ways, namely, data reduction, data presentation, and drawing conclusions and verification. The results showed that the research subjects had various learning difficulties in English. This happens due to the different levels of English mastery between individuals.
Multi-Task Neural Model for Agglutinative Language Translation

Neural machine translation (NMT) has achieved impressive performance recently by using large-scale parallel corpora. However, it struggles in the low-resource and morphologically-rich scenarios of agglutinative language translation task. Inspired by the finding that monolingual data can greatly improve the NMT performance, we propose a multi-task neural model that jointly learns to perform bi-directional translation and agglutinative language stemming. Our approach employs the shared encoder and decoder to train a single model without changing the standard NMT architecture but instead adding a token before each source-side sentence to specify the desired target outputs of the two different tasks. Experimental results on Turkish-English and Uyghur-Chinese show that our proposed approach can significantly improve the translation performance on agglutinative languages by using a small amount of monolingual data.
LANGUAGE POLITENESS OF STUDENTS AND TEACHERS: AN ETHNOGRAPHIC STUDY

This research is based on the discovery of students and teachers at SMAN 01 Logas Tanah Darat, Kuantan Singingi, Riau, who spoke impolitely. As people working in the world of education, students and teachers must certainly pay attention to politeness in language. For this reason, this research needs to be conducted to see the extent of the politeness of students and teachers in communicating in the realm of education. Also, this study not only looked at the politeness of students and teachers but also want to see the factors causing the impoliteness of students and teachers in speaking. This research uses a qualitative approach with ethnographic methods. Sources of data in this study were students and teachers at SMAN 01 Logas Tanah Darat, Kuantan Singingi, Riau. The data in this study are the speech of students and teachers containing six maxim of politeness raised by Leech. The results of this study indicate that students and teachers at SMAN 01 Logas Tanah Darat, Kuantan Singingi Riau can already be categorized politely in speech. This can be seen from the 101 utterances found that only 38 utterances are categorized as impolite or violate the six maxims of the politeness principle stated by Leech.
A Teamwork as a Pedagogical Problem

In the article the problem is based on the analysis of the scientific literature on the fragmentation of the phenomenon of ‘a teamwork’ as a pedagogical problem. It is established that the team in the context of organizing student work in a small grope of student’s created by self-organization or indirect guidance of teachers to solve educational, research, socially significant tasks based on common goals, interaction, mutual responsibility, mutual trust, mutual support, complementarity, openness and clearness leader. It is determined that teamwork is research, social goals in the process of joint work. The organization of students in a team is a mutually agreed activity of a team of students aimed at achieving educational, presented as the interaction of students and teachers, aimed at stimulating the indirect management of educational (research, social) activities of students to achieve goals. The organization of the educational process on a team basis should ensure the formation of each student as an active subject of this process and the effective formation of a set of competencies necessary for life in the XXI&nbsp;century.
Explainability in Autonomous Pedagogical Agents

The research presented herein addresses the topic of explainability in autonomous pedagogical agents. We will be investigating possible ways to explain the decision-making process of such pedagogical agents (which can be embodied as robots) with a focus on the effect of these explanations in concrete learning scenarios for children. The hypothesis is that the agents' explanations about their decision making will support mutual modeling and a better understanding of the learning tasks and how learners perceive them. The objective is to develop a computational model that will allow agents to express internal states and actions and adapt to the human expectations of cooperative behavior accordingly. In addition, we would like to provide a comprehensive taxonomy of both the desiderata and methods in the explainable AI research applied to children's learning scenarios.
Impact of Large Cooling Tower on Atmospheric Dispersion of Effluent from Coastal Nuclear Power Plant

Wang, X.; Wei, G.; Wang, S.; Yang, Y.; Du, F., and Wang, B., 2020. Impact of large cooling tower on atmospheric dispersion of effluent from coastal nuclear power plant. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 474–478. Coconut Creek (Florida), ISSN 0749-0208.In order to improve the simulation quality of atmospheric dispersion on coastal nuclear power plant, computational fluid dynamics (CFD) method was used to simulate the impact of large cooling tower on the diffusion of gaseous effluent from coastal nuclear power plant. The simulation results show that when the release point is directly in front of the cooling tower, with considering the natural ventilation effect of the cooling tower, the axis concentration of the effluent is significantly reduced by about 1 to 2 orders of magnitude. Compared with different ambient wind speeds, the cooling tower has a more significant effect on the diffusion under low wind speed. For the ground release, atmospheric stability conditions have little effect on the concentration distribution. On this basis, a correction of the Gaussian model for the cooling tower effect is proposed. The atmospheric diffusion parameters in the Gaussian plume model can be given by three distance segments, which are 0-240 m, 240-270 m and above.
On the Difficulty of Hiding Keys in Neural Networks

In order to defend neural networks against malicious attacks, recent approaches propose the use of secret keys in the training or inference pipelines of learning systems. While this concept is innovative and the results are promising in terms of attack mitigation and classification accuracy, the effectiveness relies on the secrecy of the key. However, this aspect is often not discussed. In this short paper, we explore this issue for the case of a recently proposed key-based deep neural network. White-box experiments on multiple models and datasets, using the original key-based method and our own extensions, show that it is currently possible to extract secret key bits with relatively limited effort.