Reviewerly/exHarmony huggingface dataset:
Bent Identity-based CNN for Image Denoising

ABSTRACTIn the process of image acquisition and transmission, the image will be polluted by noise. Therefore, we propose a bent identity-based convolutional neural network (BICNN) model. The model is a full convolutional network model with a depth of 30 layers, consisting of six feature extraction modules (FEM) and skip connection. Skip connection combines the output features of the first convolution layer with the output features of each FEM in series to guarantee the full extraction of image’s features. Then we adopt the residual learning to alleviate the gradient disappearance and improve the convergence speed so as to ensure that the nonlinear mapping acquired by the trained denoising model is image noise. Bent identity is selected as the activation function, which has soft saturation and the output mean is close to zero, which can enhance the robustness of the model against input noise and accelerate the convergence of the model. Our extensive experiments demonstrate that our BICNN model can not only exhibit high effectiveness in several general image denoising tasks, but also make it highly attractive for practical denoising applications.
Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations

Recent research has established sufficient conditions for finite mixture models to be identifiable from grouped observations. These conditions allow the mixture components to be nonparametric and have substantial (or even total) overlap. This work proposes an algorithm that consistently estimates any identifiable mixture model from grouped observations. Our analysis leverages an oracle inequality for weighted kernel density estimators of the distribution on groups, together with a general result showing that consistent estimation of the distribution on groups implies consistent estimation of mixture components. A practical implementation is provided for paired observations, and the approach is shown to outperform existing methods, especially when mixture components overlap significantly.
Image Animation with Refined Masking

We present a novel approach for image-animation of a source image by a driving video, both depicting the same type of object. We do not assume the existence of pose models and our method is able to animate arbitrary objects without knowledge of the object's structure. Furthermore, both the driving video and the source image are only seen during test-time. Our method is based on a shared mask generator, which separates the foreground object from its background, and captures the object’s general pose and shape. A mask-refinement module then replaces, in the mask extracted from the driver image, the identity of the driver with the identity of the source. Conditioned on the source image, the transformed mask is then decoded by a multi-scale generator that renders a realistic image, in which the content of the source frame is animated by the pose in the driving video. Our method is shown to greatly outperform the state of the art methods on multiple benchmarks. Our code and samples are attached as supplementary.
모바일 기반 한자어 교육에 대한 연구

This study aims to present app development procedures and models for Mobile Assisted Sino Korean Vocabulary Learning. It is based on learning content through the principle of word formation. Using Keller''s ARCS Motivation model, the design of the Sino Korean Vocabulary learning mobile app, app flow chart, and app screens were created. The paper discusses mobile learning, which is connected to paper textbooks, and is useful in reinforcing a learners’vocabulary. Mobile Assisted Vocabulary Learning (MAVL) allows students to use mobile devices on their own for self-directed learning without any time and space constraints. This study will contribute to the development of mobile vocabulary learning apps in the future.(Sookmyung Women’s University)
GRCNN: Graph Recognition Convolutional Neural Network for Synthesizing  Programs from Flow Charts

Program synthesis is the task to automatically generate programs based on user specification. In this paper, we present a framework that synthesizes programs from flow charts that serve as accurate and intuitive specifications. In order doing so, we propose a deep neural network called GRCNN that recognizes graph structure from its image. GRCNN is trained end-to-end, which can predict edge and node information of the flow chart simultaneously. Experiments show that the accuracy rate to synthesize a program is 66.4%, and the accuracy rates to recognize edge and nodes are 94.1% and 67.9%, respectively. On average, it takes about 60 milliseconds to synthesize a program.
THE EFFECT OF APPLYING SCANNING AND SKIMMING STRATEGY ON THE STUDENTS’ READINGCOMPREHENSION OF GRADE EIGHT AT SMPNEGERI 3 MUARA IN ACADEMIC YEAR 2017/2018

This study is quantitative designed by experimental research design. The problem of this study is; Does scanning and skimming strategy significantly affect on the students’ reading comprehension of grade eight at SMP N.3 Muara in academic year 2017/2018? and the objective of the is; To find out whether scanning and skimming strategy is significantly effective on the students’ reading comprehension. The population of this research was all the eight grade of SMP Negeri 3 Muara in academic year 2017/2018. There were 86 of them as a total, the researcher took all them as the sample. Since this research is experimental design, the 86 students are divided into two groups, in which 43 as an experimental, and 43 as a control group. From the data and its analysis, in which the mean scores of pre-test and post-test of experimental group are 50.0465, and 70.8372, and the mean scores of control group both pre-test and post-test are 46.0930 and 49.3953. The standard deviation of experimental group of pre-test and post-test are 8.48515, and 8.67379, while the standard deviation of control group of pre-test and post-test are 5.80563, and 8.28118. The mean comparison of the experimental and control group is 70.8372 and 49.3953, and based on the t-test analysis,  it can be concluded that there is a significance effect of applying scanning and skimming reading technique on the students’ reading comprehension of grade eight at SMP Negeri 3 Muara in Academic Year 2017/2018. By understanding the technique, the writer is certain that the students are able to face the English reading test even they are not really good in English. In fact, dealing with a test, it is not how well a student master English, but how good the technique is used.
Sistem Informasi Inventaris Sekolah Menengah Atas Muhammadiyah Al-Kautsar Program Khusus Kartasura

Muhammadiyah Al-Kautsar Special Program (SP) Kartasura High School is a school under the auspices of the Muhammadiyah organization. At present, Muhammadiyah Al-Kautsar SP Kartasura High School does not yet have an inventory information system to manage inventory data in its school. Management of inventory data in Muhammadiyah Al-Kautsar SP Kartasura High School is still done manually by handwriting into a journal book and when the process of searching inventory data is still done by searching one by one for the inventory data in the journal book. This is certainly ineffective and inefficient in managing and in reporting inventory data. The purpose of this study is to develop an inventory information system to assist the process of entering or inputting inventory data, assisting admin in storing data so that data management becomes more effective, as well as providing more accurate and efficient inventory data reporting. The method used in developing this Inventory Information System is the waterfall method, which starts from system planning, system analysis, system design, system coding, program testing, system implementation, and system maintenance. The Inventory Information System is developed with the programming language, the PHP and MySQL and uses the PHP framework, CodeIgneter. The results of the black box test states that the inventory information system can run according to its function and based on Usability testing using the SUS (System Usability Scale) questionnaire method, obtaining an average yield of 75 which can be concluded that the system is in the good category and can be accepted by the user
[The Elucidation of Informational Basis of Functional Differentiations in Terms of a Theory of Constrained Self-Organization].

Focusing on the developmental process of the brain, we propose a neural network model of functional differentiation including functional parcellation. We explain the emerging process of functional elements, of the system through the constraints, which act on the whole network system. We explain several kinds of differentiation, such as the differentiation of neuronal cells, functional modules, and the sensory neurons, thereby proposing three hypotheses.
“Corona-Trigger”: Optimization Possibilities of the Teaching Algorithm in Higher Education Institutions

The epidemiological situation in the world in the spring of 2020 made the educational environment change overnight.Due to the lack of an adaptation period and time for the development of methodological recommendations, the abrupt transition to online education once again indicated the need to optimize the teaching algorithm in higher school, integrating traditional and distance forms of education.The purpose of this study is to describe the optimization possibilities of the teaching algorithm in higher education institutions in a changing education ecosystem, as well as to analyze some of the problems lurking in online education.The conducted survey of the control group made it possible to identify several problems that the participants of the educational process have faced when switching to online education: physical and psychological discomfort, lack of socialization, technical problems.The Corona-Trigger accelerated the understanding of the need for a fast revision of the teaching algorithm in higher education institutions, taking into account the technologies of distance education.
Research on Correlation between Ubiquitous Telecommunication Data and Earthquake Intensity

Abstract Based on a large number of literatures and reports, this paper takes communication systems in lifeline engineering as an example, collects as many disaster-related communication data as possible to form a training data set, and analyzes the relationship between disaster-related telecommunications data and earthquake intensity. Then, use visual means to preprocess the collected data set. This paper uses different classification algorithms in machine learning, such as Naive Bayes, K-nearest neighbors, logistic regression and support vector machines for classification training of the formed data set. Optimize it, and finally it proposes a reference model that can be used to predict earthquake intensity. Finally, experiments show that the model has good accuracy.
Employee Performance in the era of COVID-19: Implications for Public Sector’s Management in the United Arab Emirates

As the COVID-19 pandemic has taken the world by surprise, it has resulted in an alteration tohuman activities that have not never been seen before. Among the many areas that have been mostaffected is how organizations whether public or private maintain operations during lockdowns andother strict measures that governments around the world have resorted to. Employee performance hasrisen as the main concern in that context, this study is focusing on examining certain factors that mayinfluence employee performance which is derived from the literature to come out with a proposedconceptual framework for the future empirical undertaking. The intended context of this study willbe employees within the public sector in the United Arab Emirates and implications are thoroughlydiscussed as well.
Digitalizing language learning -A materials analysis of The Witcher 3: Wild Hunt

The Swedish Ministry of Education has in recent years implemented revisions with aims tomake students more digitally competent. This implementation demands that teachers adapttheir lesson planning ...
Learning deep video stabilization without optical flow

Learning the necessary high-level reasoning for video stabilization without the help of optical flow has proved to be one of the most challenging tasks in the field of computer vision. In this work, we present an iterative frame interpolation strategy to generate a novel dataset that is diverse enough to formulate video stabilization as a supervised learning problem unassisted by optical flow. A major benefit of treating video stabilization as a pure RGB based generative task over the conventional optical flow assisted approaches is the preservation of content and resolution, which is usually obstructed in the latter approaches. To do so, we provide a new video stabilization dataset and train an efficient network that can produce competitive stabilization results in a fraction of the time taken to do the same with the recent iterative frame interpolation schema. Our method provides qualitatively and quantitatively better results than those generated through state-of-the-art video stabilization methods. To the best of our knowledge, this is the only work that demonstrates the importance of perspective in formulating video stabilization as a deep learning problem instead of replacing it with an inter-frame motion measure
Fine grain sentiment grading of user-generated big data using contextual cues

With a tremendous growth in the use of online resources, micro-blogging website like Twitter offers a vast amount of user-generated knowledge about marketing. This knowledge can be used for sentiment analysis and finds wide application in both public and private sectors. A novel two-tier classification framework using machine learning algorithms and lexicon-based algorithm is proposed to extract sentiment of tweets and assign a fine grain grade to the tweets among one of the seven classes namely: high negative, moderate negative, low negative, neutral, low positive, moderate positive and high positive. Context is also used by forming SentiCircles for the task of sentiment classification. The evaluation of the study shows that the proposed method provides fine grain grading to tweets with accuracy of 61.84%. The hybrid machine learning and lexicon-based processing technique with the usage of contextual cues is an effective and practical method for sentiment analysis.
Gamma-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction

We introduce the $\gamma$-model, a predictive model of environment dynamics with an infinite probabilistic horizon. Replacing standard single-step models with $\gamma$-models leads to generalizations of the procedures central to model-based control, including the model rollout and model-based value estimation. The $\gamma$-model, trained with a generative reinterpretation of temporal difference learning, is a natural continuous analogue of the successor representation and a hybrid between model-free and model-based mechanisms. Like a value function, it contains information about the long-term future; like a standard predictive model, it is independent of task reward. We instantiate the $\gamma$-model as both a generative adversarial network and normalizing flow, discuss how its training reflects an inescapable tradeoff between training-time and testing-time compounding errors, and empirically investigate its utility for prediction and control.
PENGEMBANGAN MEDIA PEMBELAJARAN BIOLOGI BERBASIS AUGMENTED REALITY PADA SUB MATERI JARINGAN PENYUSUN ORGAN TUMBUHAN UNTUK KELAS XI SMA

This study aimed to develop a valid, practical and effective biology learning media so that it could be used when learning in class using Augmented Reality based on media in the sub component of tissue compilation of plants on XI grade of senior high school classes. Augmented Reality (AR) is an interactive technology that is able to project objects in the form of two dimensions or three dimensions and then bring them up in real time. This research was a Research and Development by adapting from the 4D model. This research was conducted in 4 stages, namely: 1) Defining (curriculum analysis, student analysis and material analysis); 2) Designing (designing storyboards and research instruments); 3) Development (applications, product trials, effectiveness using pre-test and post-test as well as students interests in using media); and 4) Disseminate. Biology learning media based on Augmented Reality is validated by material experts and media experts with “very valid” results. The results of the assessment of the practicality of the media by teachers and students obtained “very practical” results. Meanwhile, the effectiveness assessment consists of cognitive results and student interest in learning before and after the use of media with “very effective” results. From these results, the based Augmented Reality learning media on plant tissue topic is declared valid, practical and effective for use by class XI SMA students.
Limits on Testing Structural Changes in Ising Models

We present novel information-theoretic limits on detecting sparse changes in Ising models, a problem that arises in many applications where network changes can occur due to some external stimuli. We show that the sample complexity for detecting sparse changes, in a minimax sense, is no better than learning the entire model even in settings with local sparsity. This is a surprising fact in light of prior work rooted in sparse recovery methods, which suggest that sample complexity in this context scales only with the number of network changes. To shed light on when change detection is easier than structured learning, we consider testing of edge deletion in forest-structured graphs, and high-temperature ferromagnets as case studies. We show for these that testing of small changes is similarly hard, but testing of \emph{large} changes is well-separated from structure learning. These results imply that testing of graphical models may not be amenable to concepts such as restricted strong convexity leveraged for sparsity pattern recovery, and algorithm development instead should be directed towards detection of large changes.
Internet and social media development in Somalia

Somalia’s telecommunication experience is relatively limited and humble compared to various countries on different continents, particularly those that have enjoyed political stability and generous governmental support. Nevertheless, the country enjoys more affordable and competitively priced telecommunication services, which are efficient and fast, than neighboring countries, and information and communications technologies (ICTs) and innovations have flourished in recent years. In particular, the Internet, social media, and smartphones have become a fundamental part of the public’s daily routines. The country enjoys vigorous and growing telecoms, offering mobile phone subscriptions and telephone and Internet access at reasonable prices. Several data-driven organizations have reported on the Internet and social media penetration in Somalia, often with contradictory statistics. However, these statistics all show a growing penetration nationwide, with Facebook and Twitter as the most prevalent social media platforms. The exponential growth of websites has also been observed. Hundreds of websites, the majority owned and managed by Somalis in the diaspora, appeared during the early years of the new millennium. Though the penetration is not yet high, the Internet and social media play an indispensable role in both political and social life in the country. The public depends on these platforms for numerous activities, such as news-seeking, entertainment, education, online shopping, and keeping up to date on current affairs. A communications act was recently adopted to regulate the telecommunication industry; the key objectives of which include managing ICT-related technologies such as telecoms and Internet providers.
군집분석을 이용한 영어 능숙도별 영어 쓰기 불안감 분석

The present study aimed to investigate learners’ different levels of second language (L2) writing anxiety across proficiency levels. Thirty-five Korean students learning English as a foreign language (EFL) participated in this study. Their proficiency levels were categorized using a cluster analysis in which the learners were categorized into two clusters: a high group and a low group. The learners’ L2 writing anxiety was assessed using Second Language Writing Anxiety Inventory (Cheng, 2004), consisting of three components of L2 writing anxiety, i.e., somatic anxiety, avoidance behavior, and cognitive anxiety. The analysis for the two groups showed differential writing anxiety across the three anxiety components. The results were then discussed, and relevant pedagogical implications provided.
Towards a Combinatorial Characterization of Bounded-Memory Learning

Combinatorial dimensions play an important role in the theory of machine learning. For example, VC dimension characterizes PAC learning, SQ dimension characterizes weak learning with statistical queries, and Littlestone dimension characterizes online learning. In this paper we aim to develop combinatorial dimensions that characterize bounded memory learning. We propose a candidate solution for the case of realizable strong learning under a known distribution, based on the SQ dimension of neighboring distributions. We prove both upper and lower bounds for our candidate solution, that match in some regime of parameters. In this parameter regime there is an equivalence between bounded memory and SQ learning. We conjecture that our characterization holds in a much wider regime of parameters.
Latent World Models For Intrinsically Motivated Exploration

In this work we consider partially observable environments with sparse rewards. We present a self-supervised representation learning method for image-based observations, which arranges embeddings respecting temporal distance of observations. This representation is empirically robust to stochasticity and suitable for novelty detection from the error of a predictive forward model. We consider episodic and life-long uncertainties to guide the exploration. We propose to estimate the missing information about the environment with the world model, which operates in the learned latent space. As a motivation of the method, we analyse the exploration problem in a tabular Partially Observable Labyrinth. We demonstrate the method on image-based hard exploration environments from the Atari benchmark and report significant improvement with respect to prior work. The source code of the method and all the experiments is available at this https URL.
Mobile Learning in the ELT Classroom: How to Improve Teaching English as a Foreign Language in a Vocational School

The main aim of this action research was to investigate the possibility of the improvement of teaching English as a foreign language in a vocational school in Croatia. The main research questions focused on discovering what could enhance student motivation while learning a foreign language, how a teacher could facilitate student ownership of learning, and what were the possibilities of using a mobile application and the topic of cultural heritage in order to foster learning. Previous studies have failed to address innovative foreign language teaching and learning approaches in secondary vocational schools. The action research methods included an anonymous survey, teachers' observation and written interviewing. The collected data was analysed quantitatively and qualitatively. Conclusions that could be drawn encompass the positive students' feedback and practical implications for the vocational school English language curriculum. Students' participation at all stages of the action research made them active partners and thus owners of their learning.