Reviewerly/exHarmony huggingface dataset:
PENGEMBANGAN SISTEM INFORMASI SEKOLAH BERBASIS WEBSITE DI SMA NEGERI 1 MARGA

When the research was started, there was no information system of the high school of SMA Negeri 1 Marga that could be accessed online by the students, teachers and public users. At that time all the important announcements, including the information about students’ enrollment (PPDB) are announced conventionally, e.g. by using the announcement boards and loudspeakers at the school. The applicants had to visit the school and brought all the required documents with. The aim of this research was to develop a web-based information system for the SMA Negeri 1 Marga which will provide all the information about the school and provide a better and faster students’ enrollment system that is simple, accurate, yet appropriate for all the users. This research is done based on Research and Development model that is adapted from Borg & Gall and Sugiyono models, which consists of 8 stages, including: (1) potency and problem, (2) data gathering, (3) product design, (4) design validation, (5) design revision, (6) product testing, (7) product revision and (8) mass production. Based on the resulting system, everyone in the school were very excited, because: (1) the newly created system can be used and accessed anywhere and anytime, (2) the newly created system can be used by the school to deliver information faster and easier, and (3) for public users, it can be used to search for information about the school including the information about students and the enrollment system.
On direct distribution matching for adapting segmentation networks

Minimization of distribution matching losses is a principled approach to domain adaptation in the context of image classification. However, it is largely overlooked in adapting segmentation networks, which is currently dominated by adversarial models. We propose a class of loss functions, which encourage direct kernel density matching in the network-output space, up to some geometric transformations computed from unlabeled inputs. Rather than using an intermediate domain discriminator, our direct approach unifies distribution matching and segmentation in a single loss. Therefore, it simplifies segmentation adaptation by avoiding extra adversarial steps, while improving both the quality, stability and efficiency of training. We juxtapose our approach to state-of-the-art segmentation adaptation via adversarial training in the network-output space. In the challenging task of adapting brain segmentation across different magnetic resonance images (MRI) modalities, our approach achieves significantly better results both in terms of accuracy and stability.
TradeIX: Blockchain-Enabled Trade Finance in Global Supply Chains (Teaching Case)

In accordance with the announcement of its new strategy, Warehousing & Co—one of the largest logistics companies worldwide—began an initiative through Robert Barnes — CEO of TradeIX — to optimize its working capital management in 2017. However, the potential for improvement in the realm of trade finance was heavily restricted by not only a complex network of suppliers but also by siloed inter-organizational data flows.For this reason, Global 3PL initiated a pilot project with TradeIX, a Dublin-based startup that offered a blockchain-based platform managing trade finance, especially receivables finance. Essentially, the platform solution, called Marco Polo platform, connected bank and insurance to a secure network, enabling full transparency during interim financing of accounts receivables. Despite the success of a trial run of using the Marco Polo platform, it had to be assessed on a technological and strategic level, whether this proved a long-term sustainable solution for full operation across Global 3PL's global trade finance system landscape.In this decision-oriented teaching case based on a real situation, students are asked to put themselves in the role of the protagonist, Robert Barnes. They need to analyze the challenge faced and evaluate the solution pursued by the protagonist. The teaching case takes place in the field of trade finance. The key topics are trade finance, working capital management, supply chain management (SCM), data management, process optimization and blockchain technology (BCT).
APPLICATION OF PROJECT BASED LEARNING IN ENGLISH TEACHING DURING A PANDEMIC AT AL AZHAAR ELEMENTARY SCHOOL

On March 2020, director General of Wealth Health Organization establishes the corona virus outbreak as pandemic. This condition affects the world in all aspects, including in education. The schools close and students have to learn independently at home. This condition is not so problematic for developed countries but for developing countries as Indonesia, this condition brings enormous effect as most of teachers and students do not ready with this condition. They have to change the old system or face to face learning system or offline learning system into distance learning or online. Even there are so many applications that can be used to conduct online learning, they still do not familiar with them and cannot operate them. It makes teachers use familiar application to teach by project based learning
Perkembangan E-Learning dalam Pembelajaran di Dunia

E-learning is a learning that done using sophisticated electronic technology media whose learning is designed by facilitators such as teachers or lecturers without knowing distance and time which is often known as online learning. The development of e-learning started in the 1970s where learning was done remotely and assignments were coordinated and given via email. The Open University in the UK was the first college to implement distance learning due to geographical conditions and student time constraints. In Afrika, the development of e-learning began in 1886, when the electronic telecommunication cable connection was installed by the colonial authorities between Lagos and the colonial office in London to send and receive information. This case is purposed to know about how the development of e-learning in the world.
Converted measurements Bayesian extended target tracking applied to X-band marine radar data

X-band marine radar systems are flexible and low-cost tools for monitoring multiple targets in a surveillance area. They can provide high resolution measurements both in space and time. Such features offer the opportunity to get accurate information not only about the target kinematics, as other conventional sensors, but also about the target size. In this paper we exploit the random matrix framework to track extended targets. Proper measurement models to deal with the radar’s measurement noise and its conversion into Cartesian coordinates are presented here. Benefits of the proposed extended target tracking using converted measurements can be mainly related to the problem of the targets’ size estimation, while advantages on estimation of the targets’ kinematic features can be considered negligible. The validity of the proposed approach has been demonstrated by using both simulated and real data. Gains up to 70% for the targets’ width estimation accuracy and around 65% for the length are observed on real data. The integration of the proposed model into the gamma Gaussian inverse Wishart probability hypothesis density tracker is also provided and tested on real data.
CS-ONPP: Class Similarity Based ONPP for the Modular Facial Expression Recognition

Facial expression recognition is a big problem in the field of Human behavioral analysis. Much work has been done in this field where local texture, features have been extracted and used in the classification. Due to the very local nature of this information, the dimension of the feature vector achieved for the full image is very high, posing computational challenges in real-time expression recognition. In recent times, Dimensionality Reduction methods have been successfully used in image recognition tasks. Though being high dimensional data, natural images such as face images lie in low dimensional subspace, and Dimensionality Reduction methods try to learn this underlying subspace to reduce the computational complexity involved in classification stage of image recognition task. This paper proposes the Orthogonal Neighborhood Preserving Projection with Class Similarity-based neighborhood for expression recognition. The extensive experiment has been performed on three well-known Facial Expression Databases. The proposed techniques achieve similar recognition performance at very low dimensions compared to local feature extraction based methods.
A Conversation Diary: A Teaching and Learning Speaking Media

This research aims to analyze the use of conversation diary as a teaching and learning speaking media at ASMI Citra Nusantara Banjarmasin. This research is done in Classroom Action Research (CAR) within 3 cycles. Each of the cycle is started by assessing classroom problems, and continues with planning, acting, observing, and reflecting. Each cycle has 5 hours with 17 student as participant.  In the first cycle, there’s less than 50% student passing at passing grade, according to many students who are weak in mastering grammar and vocabulary and not confident in speaking. But the result more better in the next cycle, because students began to enjoy the activities of convertible diary even though there were still passive students.   In conclusion, this study proved that Conversation Diary could improve the students’ speaking skill better and help decreasing the students’ anxiety. With enough length of time, it would gain better result.
PELATIHAN PENINGKATAN KEMAMPUAN PENGOLAHAN NILAI SISWA BERBASIS MICROSOFT EXCEL KEPADA GURU SEKOLAH DASAR GUGUS IV KABUPATEN FAKFAK

Technology in Indonesia has experienced rapid development in various fields including computer technology, which directly requires the world of education to act significantly in adjusting itself to technological developments. This is a prerequisite for implementing the 2013 curriculum, where teachers are required to have knowledge and skills, including skills in utilizing information technology in carrying out learning planning activities, administering student personal data, to calculating student average scores and determining student rankings. Even though in Fakfak training for teachers is quite frequent, they talk more about the competence of the learning process in the classroom and very rarely to touch the administrative skills of teachers, including how to process student scores. Therefore, this computer training activity was initiated to improve the abilities and skills of teachers in order to have job support skills, especially being able to process student grade data based on Microsoft Excel. So inputting, calculating grades as an indication of student achievement, as well as creating a simple student score database can be done by the teacher more precisely, accurately, and efficiently. The target to be achieved from this activity is that thethe teachers are able to operate the Microsoft Excel program, especially in processing student scores data, and applying an assessment method as the main indicator for measuring student ability and achievement
Determinants of pro poor growth and its impacts on income share: evidence from ethiopian time series data.

Determinants of pro poor growth and its impacts on income share: evidence from ethiopian time series data. - AbstractThe
Research on Evaluation of Smart Learning Environment in Universities Based on AHP-FCE: A Case Study of Central China Normal University

With the rapid development of the Internet of Things (IoT), big data, Artificial Intelligence (AI) and other emerging technologies, the Smart Learning Environment (SLE) has emerged. This paper takes Central China Normal University as an example to evaluate SLE in universities. Taking the physical, resource and social of SLE as the first-level indexes of the evaluation index system, an index system with 3 first-level indexes and 26 second-level indexes was constructed by referring to relevant literature and the actual needs of teachers and students. According to this evaluation index system, a questionnaire was designed to collect data. Analytic Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE) were used to analyze the sample data and evaluate SLE, which can provide reference for the subsequent improvement of SLE.
Semi-supervised Corrupted Face Classification via Graph Learning

Semi-supervised learning aims to training model with both of labeled and unlabeled data by exploring the relationships among them.Graph-based semi-supervised learning is an classical representative method that learning the class indicator matrix by propagating the similarity within the well designed graph constructed by data.However, for face data, they often happen to pixel missing or occlusion, which will degrade the graph learning performance, leading awful semi-supervised classification results.To address this problem, a novel semi-supervised corrupted face classification method via graph learning is proposed, in which the dynamic graph is learned by the completion face data recovered from the low-rank subspace.In our proposed method, the robust data representation and graph learning are implemented alternatively to obtain the overall optimal solutions.Experimental results demonstrate that our proposed method outperforms comparison methods on both of classification accuracy and robustness.
Approach to Automatic Segmentation of Atherosclerotic Plaque in B-images Using Active Contour Algorithm Adapted by Convolutional Neural Network to Echogenicity Index Computation.

The presented paper is dedicated to image processing ofultrasound B-images in neurosonology. Atherosclerotic plaquesin in-vitro B-images are analyzed in this study. The content isdivided into two core parts. The first one is focused oncomputing Echo-Index value inside the atherosclerotic plaque asa defined Region of Interest. From achieved results is obviousthat the Echo- Index is well-reproducible value in general.Totally of 278 images were analyzed by two non-experiencedobservers and by an experienced sonographer to validate theresult. Basic statistical descriptors were calculated to judgethe level of agreement. In this part, the ROI were selectedmanually. The second part is focused on approach to automaticselection of the ROI. Manual drawing of the border istime-consuming. Our idea is to use active contour algorithm(ACM; Active Contour Model) to eliminate the black backgroundfrom the displayed plaque. Using ACM can be useful way toselect ROI automatically. The main issue is to separate theshape of the plaque from neighbor structures, especially fromthe bottom of the tube. To adapt ACM, the principle of theconvolutional neural network can be used to extract the featureof the shape to select a correct ROI. Thus, CNN can be trainedand learnt to adapt number of iterations of the ACM (or anotherparameter) based on supervised learning from properly borderedexamples. The main disadvantage is time-consuming process andhigh performance to be needed to train and learn the CNN. Infact, in this study, there is no real design of CNN but theprimary goals are defined to realize in future
Open and Closed Strings

In addition to the theory of closed strings just described, we may construct another consistent theory by considering world-sheets Σ with boundaries. Such a theory necessarily contains open strings. In this case, we can insert open string states to the boundaries and closed string states to the bulk of Σ as in Fig. 4.1.
Immersive Technologies Will Reshape Consumer Experiences

An ultimate tool for marketers and a new opportunity for modern consumers to really experience the brand which will enhance the participation and drives the consumer involvement and interest to a greater extent. This paper will be a conceptual and do not involve any primary data. The literature based analytical approach method will be used to explore the possible impacts of immersive experiences the potential consumers will get.
The relationship between word error rate and perceptual judgment

The aim of the present study is to examine the relationship between word error rate (WER) from an automatic speech recognition system and perceptual judgments (foreign-accentedness, fluency, and comprehensibility) from human raters. In a previous study, Franco et al. (1997) used HMM-derived scores based on posterior probabilities of phone segments, and Deville et al. (1999) used an HMM/ANN recognition approach to show how the results of automatic speech recognition can be used for perceptual judgments. Park and Culnan (2019) showed the possibility to assimilate human raters' perceptual judgments by using neural network models only with the speech signal, and suggested that the model worked better on accentedness judgments than fluency judgments. In this study, we will examine whether WERs of English sentences produced by three language groups (American, Korean, Chinese) are significantly different, and if there is any difference, we will analyze the correlations between WER and perceptual judgments. The perceptual data used in Park and Culnan (2019) will be used for the analysis. The preliminary results of this study will be used to find important features to build more accurate automatic proficiency judgment models.
Pengaruh Model Problem Based Learning terhadap Keterampilan Menulis Teks Eksposisi dari Siswa Sekolah Menengah

The purpose of this study was to discuss the effect of the Problem Based Learning (PBL) model on writing skills of exposition text for middle school students. The method used is an experiment inquiry to find the effect of certain treatments on others under controlled conditions. The research procedure which is natural setting and descriptive pay more attention to data processing and analysis, tend to be inductive, and search for suitable meaning. The data collection instrument was a performance test in the form of writing skills on exposition text. The results showed that the application of the PBL model increased the scores of the students' exposition text writing results with an average score of 58%. In addition, the application of the PBL model also deepens students' understanding of the structure, language rules, diction, language style, and speeling contained in the exposition text.
PENGARUH DIMENSI FRAUD DIAMOND TERHADAP PERILAKU KECURANGAN AKADEMIK MAHASISWA JURUSAN AKUNTANSI

This study aims to determine the influence of pressure, opportunity, rationalization, ability on academic cheating behavior of students majoring in accounting both partially and simultaneously. This research uses quantitative research methods with the type of survey research. The analysis technique used in this study is the data quality test, the classic assumption test, multiple linear regression and hypothesis testing using the t test to test the effect of variable X with partial Y variable and F test to test variable X on variable Y simultaneously with significant values 0.05% (5%). Based on the results of the study indicate that the value of the independent variables simultaneously (together) affect the dependent variable, namely academic cheating behavior (Y). Partially, the pressure variable does not have a significant effect on academic cheating, opportunity has a significant effect on academic cheating, rationalization has a significant effect on academic cheating, ability has a significant effect on academic cheating behavior.
(Invited Paper) Real-Time Implementation Aspects of Large Intelligent Surfaces

With the potential to provide a clean break from massive multiple-input multiple-output, large intelligent surfaces (LISs) have recently received a thrust of research interest. Various proposals have been made in the literature to define the exact functionality of LISs, ranging from fully active to largely passive solutions. Nevertheless, almost all studies in the literature investigate the fundamental spectral efficiency performance of these architectures. In stark contrast, this paper investigates the implementation aspects of LISs. Using the fully active LIS as the basis of our exposition, we first present a rigorous discussion on the relative merits and disadvantages of possible implementation architectures from a radio-frequency circuits and real-time processing viewpoints. We then show that a distributed architecture based on a common module interfacing a smaller number of antennas can be scalable. To avoid severe losses with analog signal distribution, multiple common modules can be interconnected via a digital nearest-neighbor network. Furthermore, we show that with such a design, the maximum backplane throughput scales with the number of served user terminals, instead of the number of antennas across the surface. The discussions in the paper can serve as a guideline toward the real-time design and development of LISs. (Less)
Ultra-bright Source of Indistinguishable Single Photons

We demonstrate an ultra-bright source of coherent single photons with an average end-to-end efficiency of 55%. By the virtue of our record efficiency, a 20-photon quantum protocol will run seven orders of magnitude faster than the state-of-the-art.
Mining Patterns in Open Source Software using Software Metrics and Neural Network Models

As the complexity of a system increases, the need to develop a prototype for the problems becomes more and more prominent. To take care of different plan issues, it is seen that structure design finds a superior answer for a large number of repetitive plan issues. For the most part, design-level documents are indicated utilising semi-formal documentation, for example, unified modelling language (UML) diagrams. In any case, these kinds of semi-formal documentation lead to ambiguities and irregularities. In this paper, design level documents are retrieved by using the idea of program metrics. The proposed methodology retrieves the reusable documents from the open-source software such as PMD and JUnit. The presented method uses three kinds of neural network models to analyse the effectiveness of the pattern retrieval approach.
Variations on the common electronic speckle pattern interferometer

Electronic speckle pattern interferometry is often used to visualize deflection shapes of vibrating objects. We discuss variations on this common laboratory technique, emphasizing unique applications as well as methods for reducing the cost and complexity of theexperimental arrangement.
Chatman’s theory of life in the round applied to the information seeking of small populations of ethnic minorities in China

Introduction. We explore Chatman’s theory of life in the round, that people living in small worlds will not cross their world’s boundaries to seek information, and examine its application to small populations of China’s ethnic minorities. Method. A qualitative investigation was conducted on information seeking, related to the daily lives of China’s ethnic minorities, and data were collected through in-depth interviews and observations of seventy-eight participants. Analysis. The data were processed by three levels of qualitative data coding. Results. In China, it seems that ethnic minorities with small populations prefer not to cross ethnic borders when seeking information, as they may not want to deviate from their traditions and customs, lifestyles or ethnic identities. Information is sought across borders when the information is general knowledge and important, or in an emergency. Conclusion. It seems that China’s ethnic minorities’ information seeking is consistent with Chatman’s fifth proposition of the theory of life in the round and that this behaviour may be influenced by their traditions and customs, lifestyles and ethnic identities. By applying Chatman’s theory to previously unstudied populations, this research enhances the understanding of ethnic characteristics and traditional culture, as contextual factors that can influence information-seeking behaviour.