Reviewerly/exHarmony huggingface dataset:
Fast and Accurate Deep Bidirectional Language Representations for Unsupervised Learning

Even though BERT achieves successful performance improvements in various supervised learning tasks, applying BERT for unsupervised tasks still holds a limitation that it requires repetitive inference for computing contextual language representations. To resolve the limitation, we propose a novel deep bidirectional language model called Transformer-based Text Autoencoder (T-TA). The T-TA computes contextual language representations without repetition and has benefits of the deep bidirectional architecture like BERT. In run-time experiments on CPU environments, the proposed T-TA performs over six times faster than the BERT-based model in the reranking task and twelve times faster in the semantic similarity task. Furthermore, the T-TA shows competitive or even better accuracies than those of BERT on the above tasks.
The Influence of Library and Internet Utilization of Student Achievement Index

Abstract The Use of library and internet on the index of student achievement is important because by using libraries and internet students can earn a high achievement Index student. Achievement Index student is used as a measure of the academic intelligence of a person. This study aims to determine the influence of partially and jointly use the library and internet for achievement index students of the study program economic studies of STKIP Muhammadiyah Sampit in the Academic Year of 2014/2015. The approach in this study is a causal correlation with the population of 271 students and a sample of 68 students were taken by proportional stratified random sampling technique. The Data analysis technique used is multiple linear regression. Results showed there is an influence of partially or simultaneously the use of libraries and internet to students of achievement index students of the study program economic studies of STKIP Muhammadiyah Sampit in the Academic Year of 2014/2015 with a percentage contribution of 9.8%.
A Frame Work for Classification of Multi Class Medical Data based on Deep Learning and Naive Bayes Classification Model

From the past decade there has been drastic development and deployment of digital data stored in electronic health record (EHR).Initially, it is designed for getting patient general information and performing health care tasks like billing, but researchers focused on secondary and most important use of these data for various clinical applications.In this paper we used deep learning based clinical note multi-label multi class approach using GloVe model for feature extraction from text notes, Auto-Encoder for training based on model and Navie basian classification and we map those classes for multi-classes.And we perform experiments with python and we used libraries of keras, tensor flow, numpy, matplotlib and we use MIMIC-III data set.And we made comparison with existing works CNN, skip-gram, n-gram and bag-of words.The performance results shows that proposed frame work performed good while classifying the text notes.
A Text-based Deep Reinforcement Learning Framework for Interactive Recommendation

Due to its nature of learning from dynamic interactions and planning for long-run performance, reinforcement learning (RL) recently has received much attention in interactive recommender systems (IRSs). IRSs usually face the large discrete action space problem, which makes most of the existing RL-based recommendation methods inefficient. Moreover, data sparsity is another challenging problem that most IRSs are confronted with. While the textual information like reviews and descriptions is less sensitive to sparsity, existing RL-based recommendation methods either neglect or are not suitable for incorporating textual information. To address these two problems, in this paper, we propose a Text-based Deep Deterministic Policy Gradient framework (TDDPG-Rec) for IRSs. Specifically, we leverage textual information to map items and users into a feature space, which greatly alleviates the sparsity problem. Moreover, we design an effective method to construct an action candidate set. By the policy vector dynamically learned from TDDPG-Rec that expresses the user's preference, we can select actions from the candidate set effectively. Through experiments on three public datasets, we demonstrate that TDDPG-Rec achieves state-of-the-art performance over several baselines in a time-efficient manner.
Back Cover: Machine Learning Transfer Efficiencies for Noisy Quantum Walks (Adv. Quantum Technol. 4/2020)

Quantum walks is a tool for studying various phenomena in quantum systems, including quantum transport in complex networks. In article number 1900115, Alexey A. Melnikov and co-workers suggest how to improve our understanding of noisy quantum walks in networks with computer vision. The cover gives a pictorial view of a unique eye that learns to observe differences between quantum and classical effects in the space of surrounding networks.
PELATIHAN PENGGUNAAN SCAFFOLDING TALK DALAM PENGEMBANGAN INSTRUKSI PEMBELAJARAN BAHASA INGGRIS BAGI GURU PAUD INTI KOTA BENGKULU

This paper aims to discuss the training on the use of scaffolding talk in developing English language learning instruction for kindergarten teachers of Bengkulu City. The purpose of the training was to enhance the quality of the kindergarten teachers’ professional development.Followed by 20 kindergarten teachers of English, the training used the inductive participative training method was held in PAUD Intan Insani in Muara Bangkahulu of Bengkulu city on August 3, 2019. The training consisted of three phases: presenting the training materials, developing lesson plans, and practicing using the lesson plans in the classroom. This training was also the application of the books of Kiddos: An Integrated, Communicative and Character-based Material for Kindergarten Students, the outcome of the national research grant in 2005-2016. In this training, the teachers were trained to use the scaffolding talk in developing the instruction for learning English. The result show that the teachers are able to develop the lesson plans based on the chosen topics in the books but there is no video of the teaching practices in the classroom were submitted by the teachers yet. It could probably caused by many administrative tasks and activities they have to fulfil at schools. In addition, there is also the tendency among the teachers that they prefer a short training instead of the one which take three phases and long time to finish. Therefore, it is suggested to conduct a short training which takes one short time only and has not had many phases to be fulfilled.
The Recognition of Persian Phonemes Using PPNet.

In this paper, a novel approach is proposed for the recognition of Persian phonemes in the Persian consonant-vowel combination (PCVC) speech dataset. Nowadays, deep neural networks (NNs) play a crucial role in classification tasks. However, the best results in speech recognition are not yet as perfect as human recognition rate. Deep learning techniques show outstanding performance over many other classification tasks, such as image classification and document classification. Furthermore, the performance is sometimes better than a human. The reason why automatic speech recognition systems are not as qualified as the human speech recognition system, mostly depends on features of data which are fed to deep NNs.In this research, first, the sound samples are cut for the exact extraction of phoneme sounds in 50 ms samples. Then, phonemes are divided into 30 groups, containing 23 consonants, 6 vowels, and a silence phoneme.The short-time Fourier transform is conducted on them, and the results are given to PPNet (a new deep convolutional NN architecture) classifier and a total average of 75.87% accuracy is reached which is the best result ever compared to other algorithms on separated Persian phonemes (like in PCVC speech dataset).This method not only can be used for recognizing mono-phonemes but it can also be adopted as an input to the selection of the best words in speech transcription.
Semi-automatic Assessment of I/O Behavior by Inspecting the Individual Client-Node Timelines—An Explorative Study on $$10^{6}$$ Jobs

HPC applications with suboptimal I/O behavior interfere with well-behaving applications and lead to increased application runtime. In some cases, this may even lead to unresponsive systems and unfinished jobs. HPC monitoring systems can aid users and support staff to identify problematic behavior and support optimization of problematic applications. The key issue is how to identify relevant applications? A profile of an application doesn’t allow identifying problematic phases during the execution but tracing of each individual I/O is too invasive. In this work, we split the execution into segments, i.e., windows of fixed size and analyze profiles of them. We develop three I/O metrics to identify three relevant classes of inefficient I/O behaviors, and evaluate them on raw data of 1,000,000 jobs on the supercomputer Mistral. The advantages of our method is that temporal information about I/O activities during job runtime is preserved to some extent and can be used to identify phases of inefficient I/O. The main contribution of this work is the segmentation of time series and computation of metrics (Job-I/O-Utilization, Job-I/O-Problem-Time, and Job-I/O-Balance) that are effective to identify problematic I/O phases and jobs.
From Drab to Fab: Teaching Visualization via Incremental Improvements

In our September 2014 column, “Five concrete reasons your students should be learning to analyze data in the reproducible paradigm,” we said that “[a reproducible] workflow establishes the norm tha...
SMART CLASSROOM IMPROVEMENT USING WEB APPLICATIONS

This paper presents the development of web applications to support smart classroom. This research describes the web application which teachers and students can use on their mobile devices such as smartphones, tablets or laptops. The core idea behind this study is that web application motivates students to study in the classroom. The population of our research is 50 Computer Engineering students. The survey shows that the ease of using the application scores 4.40. The functions of the application score 4.44. The run time performance scores 4.32. Finally, user experience scores 4.48. and User Interface scores 4.44.
EFFICIENCY ASSESSMENT OF THE ADDITIONAL DRINKING WATER TREATMENT WITH HOUSEHOLD CARTRIDGES

The purpose of this study was to rate the performance of cartridges for commercially available pitcher filters. The design features and composition of cartridge loadings of industrial pitcher filters were studied and a comparative study of their performance in terms of water purification from chlorine, hardness ions and organic compounds was made. It is established that all the studied industrial cartridges are physicochemical analogues and differ only in design parameters, and the main factor that influences the degree of water purification from pollutants is the contact time of water with the cartridge loading. It was also found that the information provided by the industrial cartridge manufacturers about the effectiveness of water purification from impurities is untrue and in fact is overstated. The analysis of information on the work of industrial cartridges showed the possibility of a significant increase in their efficiency by creating optimal operating conditions. Requirements have been formulated, that have to be met by the filter cartridge with the optimal properties and the main variable and constant factors that affect the performance of the cartridges are identified.
Rounding Meshes in 3D

Let $${\mathcal {P}}$$ be a set of n polygons in $${\mathbb {R}}^3$$, each of constant complexity and with pairwise disjoint interiors. We propose a rounding algorithm that maps $${\mathcal {P}}$$ to a simplicial complex $${\mathcal {Q}}$$ whose vertices have integer coordinates. Every face of $${\mathcal {P}}$$ is mapped to a set of faces (or edges or vertices) of $${\mathcal {Q}}$$ and the mapping from $${\mathcal {P}}$$ to $${\mathcal {Q}}$$ can be done through a continuous motion of the faces such that: (i) the $$L_\infty $$ Hausdorff distance between a face and its image during the motion is at most 3/2, and (ii) if two points become equal during the motion, they remain equal through the rest of the motion. In the worst case the size of $${\mathcal {Q}}$$ is $$O(n^{13})$$ and the time complexity of the algorithm is $$O(n^{15})$$ but, under reasonable assumptions, these complexities decrease to $$O(n^{4}\sqrt{n})$$ and $$O(n^{5})$$. Furthermore, these complexities are likely not tight and we expect, in practice on non-pathological data, $$O(n\sqrt{n})$$ space and time complexities.
A Database for Printed Takri Class of North-West Indian Regional Scripts

The foremost step towards research in natural language processing is the availability of benchmark databases for various scripts or languages used worldwide. It was observed that no benchmark database has been developed so far for Takri class of scripts which is known to have abundant data lying unrecognized in North-west India. Thus, the paper presents a standard database for Printed Takri script collected manually from various sources and pre-processed for binarization, noise removal and skew correction. The database contains 272 text images in single font as no fonts have been developed so far in Takri and the metal fonts developed earlier for this century-old script are also of the same size. The database could be used for segmentation and recognition of Takri characters using various machine learning approaches. We believe that the database developed will encourage more researchers to work in this direction. The database is freely available to all the researchers on demand.
Graph-Theoretic One-Class Collaborative Filtering using Signed Random Walk with Restart

Graph-theoretic one-class collaborative filtering (gOCCF) has been successful in dealing with sparse datasets in one-class setting (e.g., clicked or bookmarked). In this paper, we point out the problem that gOCCF requires long processing time compared to existing OCCF methods. To overcome the limitation of the original gOCCF, we propose a new gOCCF method based on signed random walk with restart (SRWR). Using SRWR, the proposed method accurately and efficiently captures users' preferences by analyzing not only positive preferences from rated items but also the negative preferences from uninteresting items. Through extensive experiments using real-life datasets, we verify that the proposed method improves the accuracy of the original gOCCF and requires processing time less than the original gOCCF.
The Development of Financial Statement Analysis Learning Media to Enhance Student Analysis Abilities

&lt;p class="JurnalASSETSABSTRAK"&gt;ABSTRACT&lt;/p&gt;&lt;p&gt;This study aims to develop financial report analysis learning media based on mobile learning; by testing the feasibility level of the developed learning media based on validation/assessment that carried out by the expert teams and students. The Validation of Learning Media that carried out by the Media Expert team (Lecturer) and as well as testing the student analytical abilities were tested towards 40 students accounting program of State University of Medan 2018. The data analysis method used is a descriptive analysis of the percentage. In general, the media validation results fall into the category very viable whereas the result of field trial assessment also categorized as very viable.&lt;/p&gt;&lt;p class="JurnalASSETSABSTRAK"&gt;&lt;em&gt;ABSTRAK&lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;Penelitian ini bertujuan untuk mengembangkan media pembelajaran analisis laporan keuangan berbasis mobile learning; dengan menguji tingkat kelayakan media pembelajaran yang dikembangkan berdasarkan validasi/penilaian dari tim ahli dan mahasiswa serta pengujian kemampuan analisis mahasiswa. Validasi media dilakukan oleh Tim Dosen Ahli Media dan diujicobakan kepada 40 mahasiswa program studi Akuntansi Universitas Negeri Medan stambuk 2018. Teknik analisa data yang digunakan adalah teknik deskriptif presentase. Secara garis besar, hasil validasi aspek media tergolong kategori sangat layak dan hasil penilaian ujicoba lapangan juga tergolong kategori sangat layak.&lt;/em&gt;&lt;/p&gt;
The language attitude of Islamic boarding school students toward English

Language attitude study has proven to reveal students’ success and failure in learning language. Attitude is not innate, but learned. Students learn to grow and improve their positive language attitudes towards foreign languages through certain programs at schools. The aim of this research is to investigate the language attitude of students in an Indonesian Islamic boarding school towards English as a foreign Language and to describe why they have the attitude. The respondents were 49 students of 2nd grade of senior high school classes from HAC (High-Achieving Class) and LAC (Low-Achieving Class). The design of this research was mixed method using explanatory sequential design. To obtain the data, the instrument used was a questionnaire consisting of cognitive, affective and conative components of language attitudes. Interview was carried out to acquire the reason the students had the attitudes. The finding was both HAC and LAC students’ language attitudes were mostly positive (91.83%) in cognitive, affective and conative language attitudes. Some (8.17%) had different language attitudes. They showed positive language attitudes, but acquired low scores and showed negative language attitudes, but acquired high scores. The interview revealed this phenomenon.
ST$^2$: Small-data Text Style Transfer via Multi-task Meta-Learning

Text style transfer aims to paraphrase a sentence in one style into another style while preserving content. Due to lack of parallel training data, state-of-art methods are unsupervised and rely on large datasets that share content. Furthermore, existing methods have been applied on very limited categories of styles such as positive/negative and formal/informal. In this work, we develop a meta-learning framework to transfer between any kind of text styles, including personal writing styles that are more fine-grained, share less content and have much smaller training data. While state-of-art models fail in the few-shot style transfer task, our framework effectively utilizes information from other styles to improve both language fluency and style transfer accuracy.
A Consensus Approach to Improve NMF Document Clustering

Nonnegative Matrix Factorization (NMF) which was originally designed for dimensionality reduction has received throughout the years a tremendous amount of attention for clustering purposes in several fields such as image processing or text mining. However, despite its mathematical elegance and simplicity, NMF has exposed a main issue which is its strong sensitivity to starting points, resulting in NMF struggling to converge toward an optimal solution. On another hand, we came to explore and discovered that even after providing a meaningful initialization, selecting the solution with the best local minimum was not always leading to the one having the best clustering quality, but somehow a better clustering could be obtained with a solution slightly off in terms of criterion. Therefore in this paper, we undertake to study the clustering characteristics and quality of a set of NMF best solutions and provide a method delivering a better partition using a consensus made of the best NMF solutions.
Aiming for operability by habit training in the baseball and all sports

Authors and colleagues have been involved with rehabilitation, sports medicine, Masters athlete, pole exercise, baseball and so on.The author Moriyasu was an excellent batter and won the leading hitter prize in the high school.He is now a physiotherapist, sports trainer and emphasizes the operability factor in a sports textbook "You can see in the video!Habit training for baseball skills".Its characteristic points are i) a variety of useful photos, ii) 131 QR codes connecting to videos, iii) observing videos by smartphone anytime, anywhere.
CREATION AND VERIFICATION OF DEEP-LEARNING-BASED DESIGN AI

In recent years, there has been a growing interest in the application of Deep Learning to architecture and urbanism. This research is focused on content generation AI using Deep Learning. Despite claims that replacing creativity-related work with machines is difficult, the use of generative adversarial networks (GANs) is becoming more popular in various fields. The objective of this research is to develop an AI-supported design or a co-creation between humans and AI through the application of GANs. The primary goal of this work could be interpreted as repurposing existing concepts to create new designs through the combination of multiple design sources. Therefore, the purpose of this research is the creation of AI that emulate and support the design process.
Bit Commitment for Lottery and Auction on Quantum Blockchain

This paper propose a protocol for lottery and a protocol for auction on quantum Blockchain. Our protocol of lottery satisfies randomness, unpredictability, unforgeability, verifiability, decentralization and unconditional security. Our protocol of auction satisfies bid privacy, posterior privacy, bids' binding, decentralization and unconditional security. Except quantum Block-chain, the main technique involved in both protocols is quantum bit commitment. Since both quantum blockchain and quantum bit commitment can be realized by the current technology, our protocols are practically feasible.
Visibility of Learning Media of a Culinary-Based Electronic Magazine in Blended Learning

This study aims to produce media products that use culinary-based electronic magazines on blended learning. The method used in this study was research and development. The procedure of media development used the Four-D model to produce learning media based on blended learning. The stages of the study consisted of define, design, development, and dissemination. The stages of development used alpha testing for product validation by learning experts, material experts, and media experts. After that the stages of dissemination, beta testing was performed by testing the product on users in small groups. The subjects of this study were students in foodservice expertise. The results of this study showed that the alpha tests on aspects of media and materials and learning both obtained very high criteria. Furthermore, the average score of all aspects was more than 89% with reasonable criteria. while the beta test results indicated high criteria with the average of students’ reached 86%, categorized as feasible.
COLAM: Co-Learning of Deep Neural Networks and Soft Labels via Alternating Minimization

Softening labels of training datasets with respect to data representations has been frequently used to improve the training of deep neural networks. While such a practice has been studied as a way to leverage “privileged information” about the distribution of the data, a well-trained learner with soft classification outputs should be first obtained as a prior to generate such privileged information. To solve such a “chicken-and-egg” problem, we propose COLAM framework that Co-Learns DNNs and soft labels through Alternating Minimization of two objectives—(a) the training loss subject to soft labels and (b) the objective to learn improved soft labels—in one end-to-end training procedure. We performed extensive experiments to compare our proposed method with a series of baselines. The experiment results show that COLAM achieves improved performance on many tasks with better testing classification accuracy. We also provide both qualitative and quantitative analyses that explain why COLAM works well.