Reviewerly/exHarmony huggingface dataset:
USE OF LEGO TECHNOLOGY IN THE EDUCATIONAL SPACE OF THE NEW UKRAINIAN SCHOOL

The article examines the use of LEGO-technology in the educational space of the new Ukrainian school. It is determined that the New Ukrainian School as an institution of socialization will actualize the process of purposeful formation of the value movement of Ukraine to the European educational space. The positive aspects of the application of innovative technologies in the educational process of primary school students are analyzed. Today, LEGO constructors are an indispensable material for classes in educational institutions. For the pedagogical process, LEGO-technology is interesting because, based on integrated principles, it combines elements of play and experimentation. LEGO games are a means of exploring and orienting a child in the real world. LEGO kits can be used for integrated learning, literature reading, math classes, exploratory project activities, games and physical education, as a tool in the work of a practical psychologist. But the most positive aspect of using this technology is that kits can be used to effectively develop children's memory.Today, LEGO constructors are an indispensable material for classes with children in primary school. LEGO technology is interesting because it combines elements of play and experimentation. Every teacher can find something useful in it: music teachers will study the notes with the help of a designer, a physical education teacher can use bricks as non-standard equipment, a practical psychologist will conduct testing and relaxation exercises. In the course of the research it was found that the use of LEGO-bricks gives positive results in mastering educational material, helps to master the ability to accept and keep the goals and objectives of educational activities, finding ways to implement them, promotes learning creative and exploratory problems. It is determined that the use of LEGO in lessons in primary school is an important element of the learning process, through which you can help students develop mental and physical abilities: attention, motor skills, memory, speech.
The Use of Taking Notes on Report Text

Some problems of reading and writing incompetency are suffered by students commonly. It can become more difficult for them to understand what they are reading and writing. Additionally, it can become difficult to gain a deeper accepting of the text, something that requires assimilating competency, experience, and knowledge. This paper attempts to describe the use of taking notes on report text. Taking notes could help secondary level students to simplify the process of reading and writing report text. Students or learner who has decent skill of reading and writing and theory of taking notes will find less difficulties to write down report text as teacher requires. Therefore, teacher should equipped students by academic writing skill particularly about taking notes. Reading a lot and keep on writing are the best way to achieve those abilities. Keywords: reading, writing, taking notes, report text.
Mining diagnostic knowledge from spacecraft data based on Spark cluster

Compared with the data obtained from ground simulation experiments, the spacecraft telemetry data can better reflect the real working state of the spacecraft. How to effectively utilize telemetry data and extract effective information is an important issue. This paper uses diagnostic data from real spacecraft telemetry to mine diagnostic knowledge and build a diagnostic knowledge base. Compared with the traditional fault diagnosis method based on expert knowledge, the diagnostic knowledge mined can enrich the existing expert knowledge base. In this paper, the FP-Growth algorithm is used to mine the association rules of the parameters to obtain the diagnostic knowledge, and a satellite telemetry data diagnosis knowledge base is constructed. Mined diagnostic knowledge includes association rules among parameters, and the relationship between parameters and faults. In addition, due to the large number of telemetry parameters, the amount of data reaching TB level, the Spark distributed computing cluster is used to implement distributed and efficient computing of the algorithm. Finally, building a spacecraft telemetry data mining diagnostic platform with the Django architecture.
From remote sensing to bioeconomy: how big data can improve automatic map generation

&amp;lt;p&amp;gt;Recent years are experiencing an exponential increase of remote sensing datasets coming from different sources (satellites, airplanes, UAVs) at different resolutions (up to few cm) based on different sensors (single bands sensors, hyperspectral cameras, LIDAR, &amp;amp;#8230;). At the same time, IT developments are allowing for the storage of very large datasets (up to Petabytes) and their efficient processing (through HPC, distributed computing, use of GPUs). This allowed for the development and diffusion of many libraries and packages implementing machine learning algorithm in a very efficient way. It has become therefor possible to use machine learning (including deep learning methods such as convolutional neural networks) to spatial datasets with the aim of increase the level of automaticity of the creation of new maps or the update of existing maps.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Within this context, the Norwegian Institute of Bioeconomy Research (NIBIO), has started a project to test and apply big data methods and tools to support research activity transversally across its divisions. &amp;amp;#160;NIBIO is a research-based knowledge institution that utilizes its expertise and professional breadth for the development of the bioeconomy in Norway. Its social mission entails a national responsibility in the bioeconomy sector, focusing on several societal challenges including: i) Climate (emission reductions, carbon uptake and climate adaptation); ii) Sustainability (environment, resource management and production within nature and society's tolerance limits); iii) Transformation (circular economy, resource efficient production systems, innovation and technology development); iv) food; and v) economy.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The presentation will show obtained results focus on land cover mapping using different methods and different dataset, include satellite images and airborne hyperspectral images. Further, the presentation will focus related on the criticalities related to automatic mapping from remote sensing dataset and importance of the availability of large training datasets.&amp;lt;/p&amp;gt;
LBGP: Learning Based Goal Planning for Autonomous Following in Front

This paper investigates a hybrid solution which combines deep reinforcement learning (RL) and classical trajectory planning for the following in front application. Here, an autonomous robot aims to stay ahead of a person as the person freely walks around. Following in front is a challenging problem as the user's intended trajectory is unknown and needs to be estimated, explicitly or implicitly, by the robot. In addition, the robot needs to find a feasible way to safely navigate ahead of human trajectory. Our deep RL module implicitly estimates human trajectory and produces short-term navigational goals to guide the robot. These goals are used by a trajectory planner to smoothly navigate the robot to the short-term goals, and eventually in front of the user. We employ curriculum learning in the deep RL module to efficiently achieve a high return. Our system outperforms the state-of-the-art in following ahead and is more reliable compared to end-to-end alternatives in both the simulation and real world experiments. In contrast to a pure deep RL approach, we demonstrate zero-shot transfer of the trained policy from simulation to the real world.
Stable descriptors in image recognition tasks

Objective . The objective of the study is to determine various stable characteristics of images (semi-invariants and invariants) as descriptors necessary for the formation of a feature space of standards intended for recognizing images of different nature belonging to different classes of objects. Methods . The authors propose metrics for evaluating the proximity of the recognized image to a given standard in the space of covariance matrices, based on the obtained descriptors as a methodological basis for constructing image recognition methods. Results . The content of the main stages of selecting descriptors for a given class of objects is developed, taking into account the different illumination of the recognized images. The effectiveness of the results obtained is confirmed by experimental studies related to the solution of the problem of recognition of special images - facies. Conclusions . The definition of stable image descriptors as invariants or semi-invariants to zoom and brightness transformations allows solving the problems of facies classification in conditions of the unstable shooting of recognized images. The images can be rotated and shifted in any way. In general, the proposed approach allows developing an effective image recognition system in the presence of various types of interference on the recognized images.
Variational Bayes Neural Network: Posterior Consistency, Classification Accuracy and Computational Challenges

Bayesian neural network models (BNN) have re-surged in recent years due to the advancement of scalable computations and its utility in solving complex prediction problems in a wide variety of applications. Despite the popularity and usefulness of BNN, the conventional Markov Chain Monte Carlo based implementation suffers from high computational cost, limiting the use of this powerful technique in large scale studies. The variational Bayes inference has become a viable alternative to circumvent some of the computational issues. Although the approach is popular in machine learning, its application in statistics is somewhat limited. This paper develops a variational Bayesian neural network estimation methodology and related statistical theory. The numerical algorithms and their implementational are discussed in detail. The theory for posterior consistency, a desirable property in nonparametric Bayesian statistics, is also developed. This theory provides an assessment of prediction accuracy and guidelines for characterizing the prior distributions and variational family. The loss of using a variational posterior over the true posterior has also been quantified. The development is motivated by an important biomedical engineering application, namely building predictive tools for the transition from mild cognitive impairment to Alzheimer's disease. The predictors are multi-modal and may involve complex interactive relations.
Penerapan Metode Demonstrasi Untuk Meningkatkan Hasil Belajar Materi Cahaya Pada Siswa SD Kelas V

The purpose of this study was to determine the implementation of learning using demonstration methods to improve learning outcomes about light in grade V SDN Pakong 3 Pamekasan. Student learning outcomes data were taken using observation sheets, field notes, and written tests. Analysis of the data from this study using qualitative descriptive means by analyzing student development data from cycle 1 to the next cycle. The results of the implementation of learning using demonstration methods can improve student learning outcomes. In cycle 1, the percentage of students who achieved the KKM score was 76.67%. This result said that the students had not finished learning classically. In cycle 2 there was an increase, the percentage of students who reached the KKM score of 96.67% so that the fifth grade students of SDN Pakong 3 Pamekasan were said to have studied classically
Korelasi antara persepsi pengelolaan dan layanan pustaka dengan motivasi belajar di digital library UNY

AbstrakPenelitian ini bertujuan untuk: (1) mengetahui tingkat pengelolaan pustaka di Digital Library, (2) mendeskripsikan tingkat layanan pustaka di Digital Library, (3) menginvestigasi tingkat motivasi belajar mahasiswa FIP terhadap Digital Library, (4) mengetahui korelasi antara pengelolaan pustaka dengan motivasi belajar di Digital Library, dan (5) mengetahui korelasi antara layanan pustaka dengan motivasi belajar di Digital Library. Jenis penelitian yang dilakukan dalam penelitian ini adalah kuantitatif korelasional, menggunakan metode korelasi product moment. Sampel dalam penelitian ini adalah pemustaka (mahasiswa) yang sedang menempuh gelar sarjana S1 dengan target presentase 15% dari program studi yang ada di Fakultas Ilmu Pendidikan UNY. Hasil penelitian menunjukkan bahwa: (1) tingkat pengelolaan pustaka di Digital Library senilai 3,2 kategori sangat tinggi, (2) tingkat layanan pustaka di Digital Library senilai 3,1 termasuk dalam kategori sangat tinggi, (3) tingkat motivasi belajar mahasiswa FIP terhadap Digital Library senilai 3,1 kategori sangat tinggi, (4) korelasi pengelolaan pustaka dengan motivasi belajar berdasarkan teori Pearson Corelation nilai significancy menunjukan (p = 0,000 &lt;0,05). Hal ini membuktikan bahwa hubungan kedua variabel adalah positif. (5) korelasi layanan pustaka dengan motivasi belajar berdasarkan teori Pearson Corelation nilai significancy menunjukan (p = 0,000 &lt;0,05), dengan demikian hal ini membuktikan bahwa hubungan kedua variabel adalah positif. Abstract This study aims to: (1) determine the level of library management in the Digital Library, (2) describe the level of library services in the Digital Library, (3) investigate the motivation level of student learning towards the Digital Library, (4) find out the correlation between library management and motivation of learning in the Digital Library, and (5) knowing the correlation between library services with motivation of learning in the Digital Library. This type of research conducted in this study is quantitative correlational, using the product moment correlation method. The sample in this study is the user (student) who is pursuing a bachelor's degree with a target percentage of 15% of the existing study programs at the Faculty of Education UNY. The results showed that: (1) the level of library management in the Digital Library worth 3.2, categories is very high, (2) the level of library service in the Digital Library worth 3.1 is included in the very high category, (3) the level of students’ motivation of learning towards Digital Library worth 3.1 categories is very high, (4) correlation of library management with learning motivation based on the Pearson Corelation theory significance value shows (p = 0,000 &lt;0.05), this proves that the relationship between the two variables is positive, (5) correlation of library services with motivation of learning based on the Pearson Correlation theory significance value shows (p = 0,000 &lt;0.05), thus this proves that the relationship between the two variables is positive.
FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) methods for learning domain invariant representations have achieved remarkable progress. However, most of the studies were based on direct adaptation from the source domain to the target domain and have suffered from large domain discrepancies. In this paper, we propose a UDA method that effectively handles such large domain discrepancies. We introduce a fixed ratio-based mixup to augment multiple intermediate domains between the source and target domain. From the augmented-domains, we train the source-dominant model and the target-dominant model that have complementary characteristics. Using our confidence-based learning methodologies, e.g., bidirectional matching with high-confidence predictions and self-penalization using low-confidence predictions, the models can learn from each other or from its own results. Through our proposed methods, the models gradually transfer domain knowledge from the source to the target domain. Extensive experiments demonstrate the superiority of our proposed method on three public benchmarks: Office-31, Office-Home, and VisDA-2017.
GAMBARAN FUNGSI KOGNITIF PADA LANSIA DENGAN PEMBERIAN TERAPI PUZZLE

&lt;p align="center"&gt;&lt;strong&gt;&lt;em&gt;Description Of Cognitive Function In Elderly By Puzzle Therapy &lt;/em&gt;&lt;/strong&gt;&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;&lt;p align="center"&gt;&lt;strong&gt;Abstract&lt;/strong&gt;&lt;/p&gt;&lt;p align="center"&gt;&lt;strong&gt; &lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;Decreased cognitive function in the elderly will affect their daily activities and make them depend on others. The solution to overcome the problems is improving the elderly cognitive function. Puzzle Therapy is a non-pharmacological therapy used to prevent cognitive decline. This research aimed to discover the Description Of Cognitive Function In Elderly By Puzzle Therapy .This was Quasy Experimental research with a Pre and Posttest with Control Group approach. There were 36 elderly taken as the research sample using a total sampling technique. The research instrument used was MMSE. Data obtained were analyzed using Mann Whitney. &lt;/em&gt;&lt;em&gt;The results showed of&lt;/em&gt;&lt;em&gt; majority of the respondents were women. The average age of respondents was in the range of 60-74 years. Most of them did not go to school or only graduated from primary school.. Based on the Mann Whitney test result, the Asymp.Sig value was 0,000 (P value 0.05) there was an effect of Puzzle Therapy on the cognitive function of the elderly in Sudagaran Old Age Home, Banyumas. While based on the Shapiro-Wilk test, the p value was 0.05.&lt;/em&gt;&lt;/p&gt;&lt;p align="center"&gt;&lt;strong&gt; &lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Keywords&lt;/em&gt;&lt;/strong&gt;&lt;em&gt;: &lt;/em&gt;&lt;em&gt;Puzzle Therapy; elderly; cognitive function. &lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt; &lt;/em&gt;&lt;/p&gt;
Building an IRIS Trust Framework

IRIS is the co-ordinating body of a UK science eInfrastructure and is a collaboration between UKRI-STFC, its resource providers and representatives from the science activities themselves. We describe an ongoing project to build a security policy trust framework suitable for use across the IRIS community using the AARC Policy Development Kit. Derived from existing practice, the AARC PDK aims to assist in efficiently bootstrapping a Research Infrastructure in the operation of an authentication and authorisation infrastructure in line with the AARC Blueprint Architecture, making resources accessible to researchers in an easy and secure fashion. We document the experience gained to date by the IRIS community in adopting component policies of the AARC PDK to build a trust framework to form a foundation for resource sharing, access and trust for a national infrastructure collaboration.
CENTAURUS: A Dynamic Parser Generator for Parallel Ad Hoc Data Extraction

It is important to handle large-scale data in text formats such as XML, JSON, and CSV because these data very often appear in data exchange. For these data, instead of data ingestion to databases, ad hoc data extraction is highly desirable. The main issue of ad hoc data extraction is to serve both the programmability to allow handling various types of data intuitively and the performance for large-scale data. To pursue it, we develop CENTAURUS, a dynamic parser generator library for parallel ad hoc data extraction. This paper presents the design and implementation of CENTAURUS. The experimental results on ad hoc data extraction have demonstrated that CENTAURUS outperformed fast dedicated parser libraries in C++ for XML and JSON, and achieved excellent scalability with actions implemented in Python.
Exploring the Relationship of Individual Indicator as the Critical Factor in Information Security Awareness

Awareness is a starting point for employees to perform well within the organization because they can anticipate numerous problems or threats that might occurred, which often disrupt the flow of activity.At certain occasion, organization have been made guidelines and policy to help achieving the objectives and monitor the employee performances.However, the degree of successes cannot be guaranteed by them due to intervention, changes approach, realization, and constraint lead to certain employees do not comply respectively.Therefore, understanding what factors motivate employees in increasing their awareness of information security (IS) is important to help IS managers diagnose deficiencies in their IS management.It also can provide clear direction to align with training programs and workshop with organization requirement.Thus, this study wants to measure the role of individual antecedent in influencing IS awareness using quantitative method by identifying several critical factors and its relationship.Meanwhile, this study uses one moderating factor namely intention comply, five exogenous constructs consist of selfattitude (AT), self-behaviour (BV), self-cognitive (CT), self-motivation (SM), and self-responsibility (SR); and one endogenous construct namely Information Security Awareness (ISA).The results indicated two factors have significance influence to the outcome positively which are AT with t-value 1.671 (p=0.095) and CT with t value 2,264 (p=0.024) while IC as moderating factor have significant influence to BV, CT, SM, and SR positively and AT negatively as exogenous constructs.
The Influence of Scientific Theories on Musical Form in Contemporary Instrumental and Electroacoustic Works

This article highlights the contradiction between theories of form in musicology (originating in the mechanistic and organicist definitions of the eighteenth and nineteenth centuries), and new forms created by contemporary composers since circa 1970. The initial discussion introduces various “traditional” definitions of form in aesthetics, semiotics and musicology. Following this, a critique of the mechanistic approach, drawing on the work of André Souris, is presented. The third part discusses some recent scientific theories which composers have drawn from. The remainder of the article provides examples of the manifestation of new musical forms based on scientific theories such as the spiral, morphogenesis, fractal geometry, psychological-literary analysis, and L-systems.
What's New? Summarizing Contributions in Scientific Literature

With thousands of academic articles shared on a daily basis, it has become increasingly difficult to keep up with the latest scientific findings. To overcome this problem, we introduce a new task of disentangled paper summarization, which seeks to generate separate summaries for the paper contributions and the context of the work, making it easier to identify the key findings shared in articles. For this purpose, we extend the S2ORC corpus of academic articles, which spans a diverse set of domains ranging from economics to psychology, by adding disentangled "contribution" and "context" reference labels. Together with the dataset, we introduce and analyze three baseline approaches: 1) a unified model controlled by input code prefixes, 2) a model with separate generation heads specialized in generating the disentangled outputs, and 3) a training strategy that guides the model using additional supervision coming from inbound and outbound citations. We also propose a comprehensive automatic evaluation protocol which reports the relevance, novelty, and disentanglement of generated outputs. Through a human study involving expert annotators, we show that in 79%, of cases our new task is considered more helpful than traditional scientific paper summarization.