Reviewerly/exHarmony huggingface dataset:
Semi-supervised Learning of Galaxy Morphology using Equivariant Transformer Variational Autoencoders

The growth in the number of galaxy images is much faster than the speed at which these galaxies can be labelled by humans. However, by leveraging the information present in the ever growing set of unlabelled images, semi-supervised learning could be an effective way of reducing the required labelling and increasing classification accuracy. We develop a Variational Autoencoder (VAE) with Equivariant Transformer layers with a classifier network from the latent space. We show that this novel architecture leads to improvements in accuracy when used for the galaxy morphology classification task on the Galaxy Zoo data set. In addition we show that pre-training the classifier network as part of the VAE using the unlabelled data leads to higher accuracy with fewer labels compared to exiting approaches. This novel VAE has the potential to automate galaxy morphology classification with reduced human labelling efforts.
독도에 대한 문학적 관심과 그 실태 -『詩로 만나는 독도』를 중심으로-

Dokdo is a concern of all Koreans and is still in a confrontation with Japan due to territorial disputes. There are various ways to protect Dokdo in various fields of society. Then, there was a question as part of the preservation of Dokdo, what has been going on in literature? Most of the literary works on Dokdo in the meantime, were able to see in the fairy tales at the educational level or historical literary works mainly focused on academic level. In the case of fairy tales or novels published in this way, readers can meet within the limitations of the audience as a book with a purpose. However, in the case of poetry, which is a song of the pure mind of the artist, the interpretation can be different. This study examined the literary interest and actual condition of Dokdo based on the works of 􋺷Dokdo meeting with poetry􋺸 which is composed of poems written by the literary readers and professional poets. What can be seen through 􋺷Dokdo meeting with poetry􋺸 was the diversity of the artist and bias toward the theme. The authors who wrote poems about Dokdo include not only literary people (poets) who are active in the literary world but also young students to the 80s who have experienced liberation government. In addition, the authors can be seen that almost all the people belonging to every fields of society such as housewives, businessmen, workers, public officials, students, entertainers, artists, religious person, politicians and so on are participating. On the other hand, despite the diversity of the artist, it was found that most of the works were limited to the 'Patriotic'. he writing of slogan, 'Let's protect Dokdo, Dokdo is our land' has the utility of 'Dokdo love', but its utility is shown when the poem is popularized and enjoys the public. In other words, it is necessary for poets to make an effort of rediscovery on Dokdo as a literary material beyond the patriotic material in order for Dokdo poetry to become a 'popular poem'. This suggests that Dokdo is not enough to be a literary material, but it is also a characteristic of poetry and aesthetics of poetry. Therefore, it is expected that Dokdo will be an opportunity to inform Dokdo more diversely in terms of literature.
Quasirandom estimations of two-qubit operator-monotone-based separability probabilities

We conduct a pair of quasirandom estimations of the separability probabilities with respect to 10 measures on the 15-dimensional convex set of two-qubit states, using its Euler-angle parametrization. The measures include the (nonmonotone) Hilbert–Schmidt one, plus nine others based on operator monotone functions. Our results are supportive of previous assertions that the Hilbert–Schmidt and Bures (minimal monotone) separability probabilities are [Formula: see text] and [Formula: see text], respectively, as well as suggestive of the Wigner–Yanase counterpart being [Formula: see text]. However, one result appears inconsistent (much too small) with an earlier claim of ours that the separability probability associated with the operator monotone (geometric-mean) function [Formula: see text] is [Formula: see text]. But a seeming explanation for this disparity is that the volume of states for the [Formula: see text]-based measure is infinite. So, the validity of the earlier conjecture — as well as an alternative one, [Formula: see text], we now introduce — cannot be examined through the numerical approach adopted, at least perhaps not without some truncation procedure for extreme values.
Penerapan Algoritma Id3 untuk Mendukung Sosialisasi Penerimaan Mahasiswa Baru yang Tepat Sasaran di UIN Raden Fatah Palembang

UIN Raden Fatah Palembang has used information technology to socialize new students through social media. In addition to social media there are also directly to the location of socialization of cities/districts in South Sumatra. The location of the socialization is usually with the command of the leader produced in the leadership meeting. Utilizing the graduation data of the last three to dig up data with several criteria such as district origin, district origin, school origin, old study, and alumni gpa so as to produce a new knowledge that is useful to provide input to make socialization more targeted. One method in classification is the Iterative Dichotomiser 3 Algorithm. The results of this study produce an information pattern that supports the socialization of new students' admissions that are targeted and potentially produce the best students.
Using a Supervised Method without supervision for foreground segmentation

Neural networks are a powerful framework for foreground segmentation in video acquired by static cameras, segmenting moving objects from the background in a robust way in various challenging scenarios. The premier methods are those based on supervision requiring a final training stage on a database of tens to hundreds of manually segmented images from the specific static camera. In this work, we propose a method to automatically create an "artificial" database that is sufficient for training the supervised methods so that it performs better than current unsupervised methods. It is based on combining a weak foreground segmenter, compared to the supervised method, to extract suitable objects from the training images and randomly inserting these objects back into a background image. Test results are shown on the test sequences in CDnet.
Exposure Interpolation by Combining Model-driven and Data-driven Methods

Deep learning based methods have penetrated many image processing problems and become dominant solutions to these problems. A natural question raised here is "Is there any space for conventional methods on these problems?" In this paper, exposure interpolation is taken as an example to answer this question and the answer is "Yes". A framework on fusing conventional and deep learning method is introduced to generate an medium exposure image for two large-exposureratio images. Experimental results indicate that the quality of the medium exposure image is increased significantly through using the deep learning method to refine the interpolated image via the conventional method. The conventional method can be adopted to improve the convergence speed of the deep learning method and to reduce the number of samples which is required by the deep learning method.
Syntactic Cut-Elimination for Intuitionistic Fuzzy Logic via Linear Nested Sequents

This paper employs the linear nested sequent framework to design a new cut-free calculus ($$\mathsf {LNIF}$$) for intuitionistic fuzzy logic—the first-order Gödel logic characterized by linear relational frames with constant domains. Linear nested sequents—which are nested sequents restricted to linear structures—prove to be a well-suited proof-theoretic formalism for intuitionistic fuzzy logic. We show that the calculus $$\mathsf {LNIF}$$ possesses highly desirable proof-theoretic properties such as invertibility of all rules, admissibility of structural rules, and syntactic cut-elimination.
CLOUD BASED E-HEALTH CARE DATA SECURITY AND PRIVACY BY USING ADVANCED ENCRYPTION STANDARD AND WATERMARKING TECHNIQUE

In 21st century technological word now has bring a tremendous change to health care system is widely used by doctor patients and researchers. Patients use wearable device such as smart eyewear, foot wear, smart watches, which is use by both patient to wear and by doctor to monitor patient condition remotely on cloud. This provides time savage fastest treatment facility but as this data is huge in amount it’s very important to take care of security and privacy of the big data on cloud. Because the big data is not between patient and doctor it’s on cloud were several third parties like service providers and other unauthorized user are also present and they can easily fetch patient’s personal information for their own benefit. In this paper a novel approach based on advance encryption method and watermarking technique is presented to provide protection and security to e health data on cloud.
A Novel Memory-Efficient Deep Learning Training Framework via Error-Bounded Lossy Compression

Deep neural networks (DNNs) are becoming increasingly deeper, wider, and non-linear due to the growing demands on prediction accuracy and analysis quality. When training a DNN model, the intermediate activation data must be saved in the memory during forward propagation and then restored for backward propagation. However, state-of-the-art accelerators such as GPUs are only equipped with very limited memory capacities due to hardware design constraints, which significantly limits the maximum batch size and hence performance speedup when training large-scale DNNs. In this paper, we propose a novel memory-driven high performance DNN training framework that leverages error-bounded lossy compression to significantly reduce the memory requirement for training in order to allow training larger networks. Different from the state-of-the-art solutions that adopt image-based lossy compressors such as JPEG to compress the activation data, our framework purposely designs error-bounded lossy compression with a strict error-controlling mechanism. Specifically, we provide theoretical analysis on the compression error propagation from the altered activation data to the gradients, and then empirically investigate the impact of altered gradients over the entire training process. Based on these analyses, we then propose an improved lossy compressor and an adaptive scheme to dynamically configure the lossy compression error-bound and adjust the training batch size to further utilize the saved memory space for additional speedup. We evaluate our design against state-of-the-art solutions with four popular DNNs and the ImageNet dataset. Results demonstrate that our proposed framework can significantly reduce the training memory consumption by up to 13.5x and 1.8x over the baseline training and state-of-the-art framework with compression, respectively, with little or no accuracy loss.
Multi-label Causal Variable Discovery: Learning Common Causal Variables and Label-specific Causal Variables

Causal variables in Markov boundary (MB) have been widely applied in extensive single-label tasks. While few researches focus on the causal variable discovery in multi-label data due to the complex causal relationships. Since some variables in multi-label scenario might contain causal information about multiple labels, this paper investigates the problem of multi-label causal variable discovery as well as the distinguishing between common causal variables shared by multiple labels and label-specific causal variables associated with some single labels. Considering the multiple MBs under the non-positive joint probability distribution, we explore the relationships between common causal variables and equivalent information phenomenon, and find that the solutions are influenced by equivalent information following different mechanisms with or without existence of label causality. Analyzing these mechanisms, we provide the theoretical property of common causal variables, based on which the discovery and distinguishing algorithm is designed to identify these two types of variables. Similar to single-label problem, causal variables for multiple labels also have extensive application prospects. To demonstrate this, we apply the proposed causal mechanism to multi-label feature selection and present an interpretable algorithm, which is proved to achieve the minimal redundancy and the maximum relevance. Extensive experiments demonstrate the efficacy of these contributions.
한국어 억양 교육용 애플리케이션 개발 방안 -섀도잉(Shadowing) 학습을 활용하여-

The main aim of this research is to suggest a method of an application development for Korean intonation education based on ‘Shadowing learning method’ which is recognized as effective for improving Korean intonation. Therefore, at first, we organized the definition of mobile learning and shadowing learning method and designed development plan of Korean intonation education application using ADDIE education model. Specifically we confirmed the need of an application development through analyzing learner’s need and organized contents composition plan using shadowing learning method after setting up learner and aim. In addition, we presented a model of Korean intonation education application by showing an example of how application development is implemented.
Cultural Change in the Context of Implementing Online Learning in Higher Education

This paper will describe efforts to change the culture of online learning in higher education in Indonesia amid the Covid-19 epidemic which includes approaches to cultural change that need to be carried out by all academic communities in higher education.An Approach to cultural change is done through a behavioral approach, structural approach and technological approach.As for the success of the change can be achieved if there is a need to use online learning, support ideas to solve problems online learning the adoption and implementation process that requires lecturers and students to do online learning with support of the availability of modules / tutorials and the freedom to determine online media used in learning with adequate resource support.
Penentuan Jadwal Overtime Dengan Klasifikasi Data Karyawan Menggunakan Algoritma C4.5

Technological development and scientific advancement are very important and influential parts of all fields. With the development and advancement of technology, being experts in a field is a must because it is required to know more and learn about the technology that is currently developing. Information technology and informatics are needed to support performance. Large and small companies also need fast and accurate information to make easier decisions making. Therefore, data mining classification techniques are needed to solve these problems. The classification used in data mining is a Decision tree because it is a technique that is widely used and produces output with existing rules so that it can present employee data to determine the overtime schedule. This study uses the C4.5 algorithm to determine the overtime schedule. The test results of the overtime schedule with the C4.5 algorithm with the Confusion matrix have good accuracy, precision, and recall values, namely 91% accuracy, 86.05% precision, and 92.5% recall.
EKSTRAKTO A tool to reconstruct Dedukti proofs from TSTP files (extended abstract)

Proof assistants often call automated theorem provers to prove subgoals.However, each prover has its own proof calculus and the proof traces that it produces often lack many details to build a complete proof.Hence these traces are hard to check and reuse in proof assistants.DEDUKTI is a proof checker whose proofs can be translated to various proof assistants: Coq, HOL, Lean, Matita, PVS.We implemented a tool that extracts TPTP subproblems from a TSTP file and reconstructs complete proofs in DEDUKTI using automated provers able to generate DEDUKTI proofs like ZenonModulo or ArchSAT.This tool is generic: it assumes nothing about the proof calculus of the prover producing the trace, and it can use different provers to produce the DEDUKTI proof.We applied our tool on traces produced by automated theorem provers on the CNF problems of the TPTP library and we were able to reconstruct a proof for a large proportion of them, significantly increasing the number of DEDUKTI proofs that could be obtained for those problems.
Road Surface Reconstruction by Stereo Vision

Abstract This paper covers the problem of road surface reconstruction by stereo vision with cameras placed behind the windshield of a moving vehicle. An algorithm was developed that employs a plane-sweep approach and uses semi-global matching for optimization. Different similarity measures were evaluated for the task of matching pixels, namely mutual information, background subtraction by bilateral filtering, and Census. The chosen sweeping direction is the plane normal of the mean road surface. Since the cameras’ position in relation to the base plane is continuously changing due to the suspension of the vehicle, the search for the base plane was integrated into the stereo algorithm. Experiments were conducted for different types of pavement and different lighting conditions. Results are presented for the target application of road surface reconstruction, and they show high correspondence to laser scan reference measurements. The method handles motion blur well, and elevation maps are reconstructed on a millimeter-scale, while images are captured at driving speed.
Dual Membership Fuzzy Support Vector Machine Algorithm Based on SVDD

Abstract In the case of excessive overlap between positive and negative samples in data set, the deviation in the category of reconstructed sample points will lead to unsatisfactory discrimination of SVM, no matter what methods are used to reconstruct the sample set. A dual membership fuzzy support vector machine algorithm based on support vector data domain description was thus proposed, followed by a simulation analysis of common data set. Experimental results show that the proposed algorithm can work well in classification when the sample set is overlapped.
Information System for Evaluating Specific Interventions of Stunting Case Using K-means Clustering

One of the causes of death of children under five is chronic malnutrition or stunting. The government has made a policy as an effort to reduce stunting. Specific intervention nutrition programs are activities that directly address the occurrence of stunting. Evaluation of edit interventions that have currently been carried out but have not separated the process based on intervention indicators so that it results in a long processing time and large exit costs so we need a system that can accelerate the process of evaluating specific interventions. Information systems for evaluating specific interventions can be a solution to this problem. In this study, an information system for evaluating specific interventions of stunting cases was developed web-based to facilitate real-time operation. The evaluation information system is designed to collect national data and carry out a clustering process using the k-means method and evaluation stucture cluster using silhouette method. In this study an evaluation value of 34 provinces was obtained which was divided into several clusters as a result of evaluation process in the information system.
EDITOR: an Edit-Based Transformer with Repositioning for Neural Machine Translation with Soft Lexical Constraints

We introduce an Edit-Based Transformer with Repositioning (EDITOR), which makes sequence generation flexible by seamlessly allowing users to specify preferences in output lexical choice. Building on recent models for non-autoregressive sequence generation (Gu et al., 2019), EDITOR generates new sequences by iteratively editing hypotheses. It relies on a novel reposition operation designed to disentangle lexical choice from word positioning decisions, while enabling efficient oracles for imitation learning and parallel edits at decoding time. Empirically, EDITOR uses soft lexical constraints more effectively than the Levenshtein Transformer (Gu et al., 2019) while speeding up decoding dramatically compared to constrained beam search (Post and Vilar, 2018). EDITOR also achieves comparable or better translation quality with faster decoding speed than the Levenshtein Transformer on standard Romanian-English, English-German, and English-Japanese machine translation tasks.
ANALISIS TRANSFORMASI KALIMAT AFFIRMATIF KE BENTUK KALIMAT NEGATIF DAN TANYA (INTERROGATIVE) DALAM TENSES‘SIMPLE PRESENT TENSE’TERHADAP SISWA KELAS VIII SMP NUSA DUA BALI

The purpose of this study is to determine the mistakes made by students in converting affirmative sentences into negative and interrogative sentence in the forms ofsimple present tense. The subject of this research is the second grade of SMP Nusa Dua. This research was conducted in class X.1, the writer analyzed 35 students. In this study, the writer used the method of observation, collecting tests, analyzing, and interpreting data. In collecting the data the author asks students to answer questions that focus on changing affirmative sentences into negative and interrogative forms in simple present tense. This test contains 20 questions. The results showed that as many as 65% of students made mistakes in usingdon't or don't in negative or interrogative sentences. They are confused how to use don't or don't. Therefore the writer suggest to teachers to deepen the explanation of how and when to use it.
朝鲜汉文小说写本≪一乐亭记≫俗字研究

“East-Asia codicology”is a new research area in recent years.The Chinese novel manuscripts which left over from the history of korea are important basis for the study of “East-Asia codicology”. Yiletingji is a Chinese novel in the Korean era,the reliable text is hidden in Kuizhang Pavilion. Collected the folk characters from the three transcripts of Yiletingji for comparing and describing the common folk characters and different folk characters.Meanwhile, according to the historical literatures in korea to find the source of the folk characters in Yiletingji by using comparative analysis method.
Implementasi Usability Testing dalam Evaluasi Website Sekolah

One of the products of information technology that is common today is the school website. Where the school website is the spearhead for schools in disseminating information to related parties. The most important aspect of a website is the usability aspect because it greatly affects visitor convenience. The use of the website for disseminating information is also used by SMK Negeri Sumsel and the school's main portal on the internet. For that to see the importance of the usability aspect, this study aims to see the level of usability of the SMK Negeri Sumsel website. In measuring the level of usability, usability testing techniques are used with the stages of preparation, selecting respondents, data collection and data analysis. The test results show that the usability aspects on the website such as errors, learnability, memorability and satisfaction get good ratings while the efficiency aspects are not categorized as good.