Reviewerly/exHarmony huggingface dataset:
PENERAPAN MEDIA BELAJAR PUZZLE UNTUK MENINGKATKAN HASIL BELAJAR IPA PADA MATERI PENGGOLONGAN MAKHLUK HIDUP SISWA KELAS III SEKOLAH DASAR NEGERI MANDALASARI 2 PANDEGLANG

Learning games are used to create an exciting atmosphere and this will encourage the acceleration of learning. Specifically in science learning by using a constructivist approach encourages students to increase their courage and foster collaboration in trying to find solutions to their own ways and find complex information so that students automatically re-establish new understanding to overcome problems. Based on the results of research students' learning activities progressed in several activities namely asking in the first cycle 59% rose to 63.63%, answered from 68% to 72.72%, but in refuting activities 9% did not increase and the activity of expressing opinions increased from 13.63% to 18% however, still in the lack category. From the test results during the research it can be concluded that the application of puzzles with constructivist approaches in science learning can improve students' ability to understand the concept of classifying living things. This is evidenced by the two times the implementation or cycle obtained the average value of the test results of each cycle that always increases. The value obtained in the first cycle of pretest meetings reached an average of 48.4 then in the post-test it rose to 60.64, this indicates an increase of 12 points. Then the second meeting reached 63 pretest and posttest reached 70.20. This shows that all students who follow the second cycle have succeeded in exceeding the limits of mastery learning.
Transcribear – Introducing a secure online transcription and annotation tool

Abstract Reliable high-quality transcription and/or annotation (a.k.a. ‘coding’) is essential for research in a variety of areas in Humanities and Social Sciences which make use of qualitative data such as interviews, focus groups, classroom observations, or any other audio/video recordings. A good tool can facilitate the work of transcription and annotation because the process is notoriously time-consuming and challenging. However, our survey indicates that few existing tools can accommodate the requirements for transcription and annotation (e.g. audio/video playback, spelling checks, keyboard shortcuts, adding tags of annotation) in one place so that a user does not need to constantly switch between multiple windows, for example, an audio player and a text editor. ‘Transcribear’ (https://transcribear.com) is therefore developed as an easy-to-use online tool which facilitates transcription and annotation on the same interface while this web tool operates offline so that a user’s recordings and transcripts can remain secure and confidential. To minimize human errors, the functionality of tag validation is also added. Originally designed for a multimodal corpus project UNNC CAWSE (https://www.nottingham.edu.cn/en/english/research/cawse/), this browser-based application can be customized for individual users’ needs in terms of the annotation scheme and corresponding shortcut keys. This article will explain how this new tool can make tedious and repetitive manual work faster and easier and at the same time improve the quality of outputs as the process of transcription and annotation tends to be prone to human errors. The limitations of Transcribear and future work will also be discussed.
Improvement of Steganography Technique: A Survey

The improved technology in information security, now day has been being still developed.This, cause of the object called data which still has an important role in communications.There are two kinds of security technique, they are called steganography and cryptography.This paper will discuss growth of steganography technique from 2015 to 2019.The data obtained from journals and identified as a systematic literature review.The results are that improvement in steganography still developed by modifying the algorithm, combining the method and threating at parallel processing.Future, utilization of steganography is still needed, considered that communication channels are growing fast and sophisticated.
Readers’ Interest of Students of Pre-Revolutionary Russia

The article considers the reading activity and book priorities of students of the Kiev Polytechnic Institute of Emperor Alexander II in 1904—1912. The author studied the readers’ interest using the analysis of student questionnaires and library loan books of the early twentieth century, as well as reports on the activities of the Institute and memoirs of students and teachers. The fundamental library was created at the establishment of the technical University in 1898. Professors and students used loan services and reading room of the Institute library. Since 1905, the library began to keep statistical records of the loan books and magazines. The article presents the data on the loan books and magazines in 1905—1909. Requests for reading of books significantly exceeded the library’s capacity, so students independently organized the library-reading room, where the books and magazines were purchased taking into account the needs of students. The work of the student library was aimed to help students in solving problems of cultural leisure and intellectual self-improvement; especially young people in extremely difficult financial situation were in urgent need of loan services. The most popular among students were books about history, love and travel. Politically active young people were interested in literature on philosophy, theology, economics, and to a lesser extent, on specialized technical subjects. To better characterize the reading activity of students, it is important to understand their social status, which largely determines the worldview and aesthetic interests of young people. The author presents statistical data on the social and education status of students of the Institute for 1905 and 1907. In the Kiev Polytechnic Institute, in comparison with the capital’s technical universities, there were many Jewish students actively participated in the anti-government movement. They preferred to read literature on social, legal and philosophical issues, including illegal ones. Priorities in choosing genres and authors of works in many ways help modern researchers to understand the literary interests and worldview of young students of the early twentieth century, as well as to recreate the socio-cultural portrait of pre-revolutionary students.
Phononic bus for coherent interfaces between a superconducting quantum processor, spin memory, and photonic quantum networks

We introduce a method for high-fidelity quantum state transduction between a superconducting microwave qubit and the ground state spin system of a solid-state artificial atom, mediated via an acoustic bus connected by piezoelectric transducers. Applied to present-day experimental parameters for superconducting circuit qubits and diamond silicon vacancy centers in an optimized phononic cavity, we estimate quantum state transduction with fidelity exceeding 99\% at a MHz-scale bandwidth. By combining the complementary strengths of superconducting circuit quantum computing and artificial atoms, the hybrid architecture provides high-fidelity qubit gates with long-lived quantum memory, high-fidelity measurement, large qubit number, reconfigurable qubit connectivity, and high-fidelity state and gate teleportation through optical quantum networks.
Supporting Soccer Analytics through HyperKnowledge Specifications

An issue in semantic search is to infer contextual meaning from terms used in the search query. This is related to appropriately structuring and representing knowledge in a particular domain. In the case of soccer in the entertainment industry, imprecise resolutions when modeling the domain can introduce heavy processing and storage requirements due to the high volume of concepts and content replication demands (e.g., replays). In this work we propose a knowledge retrieval system based on a novel method for modeling the soccer domain: considering contextual meanings to isolate knowledge instances in containers, aiming to reduce the scope of semantic searches by avoiding content replication. It was done by exploiting a hybrid knowledge representation called hyperknowledge that allows the definition of spatiotemporal anchors, and by introducing a query language to ease mapping the contextual meaning into containers stored in hyperknowledge bases.
The Effects of Stress and Life Satisfaction on Smartphone Addiction in College Students

This study was conducted to examine the effects of stress and life satisfaction on smartphone addiction.To do this, 1,600 college students were surveyed and 1,509 people were analyzed using SPSS ver.23.0.The results of the study are as follows.The variables of stress and life satisfaction which affect the smartphone addiction of college students showed that 7 of the 20 variables had a significant effect on smartphone addiction, the explanatory power (R2) was 25.2% in total.In particular, stress of appearance (14.1%), college life satisfaction (4.4%), relationship stress with opposite sex (2.4%), academic problems stress (1.7%) and relationship stress with parent (1.0%) showed that the explanatory power is high.Based on these results, in order to prevent smartphone addiction among college students, we sought to reduce stress and increase life satisfaction, and suggested the necessity of follow-up study considering environmental factors.
THE IMPLEMENTATION OF WORD WALL STRATEGY IN TEACHING WRITING DESCRIPTIVE TEXT

Sometimes students experience writing problems, attempt to understand the text and get confused to begin describing because they lack vocabulary and concept. The study was directed at finding out the Word Wall approach introduced in teaching descriptive text to high school vocational learners and finding answers for learners in learning descriptive text to implement the Word Wall approach. The study was carried out at one of Bandung's vocational high schools. In this study, the descriptive qualitative method was used. The research participants are a first-grade teacher and five students. The information was acquired as the study tool by observation and interview. Word wall approach is one of the strategies that can be used in the learning process of teaching. Word Wall approach was a strategy consisting of a group of phrases that placed a distinct background color on the wall that corresponds to a distinct curricular goal. The teacher provides learners with important phrases or clues to readily create their thoughts. The outcome demonstrates that all respondents responded positively to the approach of Word Wall. Implementing Word Wall approach has been useful in teaching for high school vocational learners and makes writing descriptive text enjoyable for learning.
A SURVEY ON TOOLS USED FOR MACHINE LEARNING

In this paper, a brief introduction to Machine Learning and its Tools are studied.In the recent developments, most of the Machine learning tools are more advanced and efficient.The various tools learn the machine by using a training set, which predicts the output correctly and efficiently.Machine Learning is applied in different applications such as Agriculture, Data Quality, Information Retrieval, Financial Market Analysis etc.., In this paper, we have discussed few tools like Scikit learn, Pytorch, Tensor flow, Amazon Machine Learning, KNIME, Rapid Miner, Keras, and Shogun with its features and its advantages.
ElixirNet: Relation-aware Network Architecture Adaptation for Medical Lesion Detection

Most advances in medical lesion detection network are limited to subtle modification on the conventional detection network designed for natural images. However, there exists a vast domain gap between medical images and natural images where the medical image detection often suffers from several domain-specific challenges, such as high lesion/background similarity, dominant tiny lesions, and severe class imbalance. Is a hand-crafted detection network tailored for natural image undoubtedly good enough over a discrepant medical lesion domain? Is there more powerful operations, filters, and sub-networks that better fit the medical lesion detection problem to be discovered? In this paper, we introduce a novel ElixirNet that includes three components: 1) TruncatedRPN balances positive and negative data for false positive reduction; 2) Auto-lesion Block is automatically customized for medical images to incorporate relation-aware operations among region proposals, and leads to more suitable and efficient classification and localization. 3) Relation transfer module incorporates the semantic relationship and transfers the relevant contextual information with an interpretable the graph thus alleviates the problem of lack of annotations for all types of lesions. Experiments on DeepLesion and Kits19 prove the effectiveness of ElixirNet, achieving improvement of both sensitivity and precision over FPN with fewer parameters.
Non-Lambertian photometric stereo vision algorithm based on a multi-scale convolution deep learning architecture

As a form of fine 3D measurement technology, photometric stereo vision is widely used in 3D reconstruction, defect detection, biomedicine, and other fields, but the traditional reflection model has limited ability to reflect the real physical properties of materials and is not suitable for non-Lambertian surfaces with nonlinear light reflection characteristics such as bright metal, greatly limiting broader application of the technology. This paper proposes a photometric stereo vision algorithm based on a multi-scale convolution deep learning architecture that implements high precision reconstruction of a nontransparent material surface under arbitrary illumination conditions. The algorithm implements a multi-scale convolution structure in the deep network. The small-scale convolution kernel strengthens the expression of photometric physics that gives the model superior detail prediction performance. The large-scale convolution kernel encourages deep networks to take advantage of neighborhood features and enhances the ability of models to overcome shadows and distinguish multiple materials. To further address the problem of arbitrary illumination conditions, the algorithm employs a multi-resolution polar coordinate division method for the illumination vector space that integrates the input image information and fully utilizes the multi-scale convolution. It is experimentally demonstrated that the multi-scale convolution deep learning architecture effectively integrates the advantages of both photometric stereo and deep learning and greatly improves the adaptability to non-Lambertian surfaces while retaining the ability to perform fine reconstruction. It provides powerful support for a wide range of applications in photometric technology.
BUILDING THE PREDICTIVE SCALE FOR THE PSYCHOLOGICAL EFFICIENCY OF THE ATHLETES STUDENTS AT AL-FURAT AL-AWSAT TECHNICAL UNIVERSITY

The objectives of the research focused on the preparation of a questionnaire to build a predictive measure of psychological efficiency for the students of the athletes of the University of the Middle Euphrates technical, as well as “the recognition of predictions of psychological efficiency, and applied research on a group of athletes at the University of the Euphrates Middle Tech for the period of 22/11/2017 and The researcher used the descriptive method in the survey method. The most important steps of the research are to limit the number of athletes in Al-Furat Al-Awsat Technical University from the students. The main questionnaire was composed of (48) Building a collective and individual scale, as required (325) questionnaires were returned (301), (24) of which were excluded due to lack of complete answer.
DAMN: Defeasible Reasoning Tool for Multi-Agent Reasoning

This demonstration paper introduces DAMN: a defeasible reasoning platform available on the web. It is geared towards decision making where each agent has its own knowledge base that can be combined with other agents to detect and visualize conflicts and potentially solve them using a semantics. It allows the use of different defeasible reasoning semantics (ambiguity blocking/propagating with or without team defeat) and integrates agent collaboration and visualization features.
An experimental Tagalog Finite State Automata spellchecker with Levenshtein edit-distance feature

In this paper, we present an experimental development of a spell checker for the Tagalog language using a set of word list with 300 random root words and three inflected forms as training data and a two-layered architecture of combined Deterministic Finite Automaton (DFA) with Levenshtein edit-distance. A DFA is used to process strings to identify if it belongs to a certain language via the binary result of accept or reject. The Levenshtein edit-distance of two strings is the number (k) of deletions, alterations, insertions between two sequences of characters. From the sample trained wordlist, results show that a value of 1 for the edit-distance (k) can be effective in spelling Tagalog sentences. Any value greater than 1 can cause suggestion of words even if the spelling of words is correct due to selective and prominent usage of certain characters in the Tagalog language like a, n, g, t, s, l.
DRST: Deep Residual Shearlet Transform for Densely Sampled Light Field Reconstruction

The Image-Based Rendering (IBR) approach using Shearlet Transform (ST) is one of the most effective methods for Densely-Sampled Light Field (DSLF) reconstruction. The ST-based DSLF reconstruction typically relies on an iterative thresholding algorithm for Epipolar-Plane Image (EPI) sparse regularization in shearlet domain, involving dozens of transformations between image domain and shearlet domain, which are in general time-consuming. To overcome this limitation, a novel learning-based ST approach, referred to as Deep Residual Shearlet Transform (DRST), is proposed in this paper. Specifically, for an input sparsely-sampled EPI, DRST employs a deep fully Convolutional Neural Network (CNN) to predict the residuals of the shearlet coefficients in shearlet domain in order to reconstruct a densely-sampled EPI in image domain. The DRST network is trained on synthetic Sparsely-Sampled Light Field (SSLF) data only by leveraging elaborately-designed masks. Experimental results on three challenging real-world light field evaluation datasets with varying moderate disparity ranges (8 - 16 pixels) demonstrate the superiority of the proposed learning-based DRST approach over the non-learning-based ST method for DSLF reconstruction. Moreover, DRST provides a 2.4x speedup over ST, at least.
Possibilities of Formation, Diagnostics and Monitoring of Axiological Competences of Technical University Students

The article highlights the need to supplement the basic parameters of engineering education with an axiological component. The authors emphasize that the main problem of the current higher technical education is not the quality of professional competencies, but low humanization, which results in an underestimated social significance of engineering professions, as well as a focus on extracting benefits without taking into account the consequences of man-made impacts. The process of humanization of engineering education is possible with its value modification through the development of axiological competencies. The authors substantiate the structure of axiological competences, give the results of expert analysis of their content features in relation to higher technical education, define safety as a key concept in the interpretation of the essence of axiological competences of the engineering professional field. The analysis of domestic and foreign studies of the concept of «safety» as a value, system competence and purpose of engineering education is given, the term «save-attitude» is highlighted. The process of formation of axiological competences in higher education is a longitudinal educational work, an example of which is the educational program «Life-orientation workshop» approved by the authors. The article suggests possible means of diagnostics of axiological competences components and ways of estimation of their development, describes a pedagogical experiment, the results of which allow them to draw a conclusion about the effectiveness of both the educational program and monitoring forms of diagnostics of axiological competencies.
Fast achievement of quantum state transfer and distributed quantum entanglement by dressed states*

We propose schemes to realize quantum state transfer and prepare quantum entanglement in coupled cavity and cavity–fiber–cavity systems, respectively, by using the dressed state method. We first give the expression of pulses shape by using dressed states and then find a group of Gaussian pulses that are easy to realize in experiment to replace the ideal pulses by curve fitting. We also study the influence of some parameters fluctuation, atomic spontaneous emission, and photon leakage on fidelity. The results show that our schemes have good robustness. Because the atoms are trapped in different cavities, it is easy to perform different operations on different atoms. The proposed schemes have the potential applications in dressed states for distributed quantum information processing tasks.
Automatically Labelled Software Topic Model

Public software repositories (SR) maintain a massive amount of valuable data offering opportunities to support software engineering (SE) tasks. Researchers have applied information retrieval techniques in mining software repositories. Topic models are one of these techniques. However, this technique does not give an interpretation nor labels to the extracted topics and it requires manual analysis to identify them. Some approaches were proposed to automatically label the topics using tags in SR, but they do not consider the existence of spam-tags and they have difficulties to scale to large tag space. This article introduces a novel approach called automatically labelled software topic model (AL-STM) that labels the topics based on observed tags in SR. It mitigates the shortcomings of manual and automatic labelling of topics in SE. AL-STM is implemented using 22K GitHub projects and evaluated in a SE task (tag recommending) against the currently used techniques. The empirical results suggest that AL-STM is more robust in terms of MAP and nDCG, and more scalable to large tag space.