Reviewerly/exHarmony huggingface dataset:
Information and Computer Technologies as a Research Tool in the Works of Historians of Central Asia

The article considers the possibilities of using information and computer technologies in historical research in Central Asia. Today, when there are discussions among Asian historians about a differentiated approach to the study of history and a desire to preserve the traditional directions of historical science that were laid down in the last century, there is an urgent need to use an interdisciplinary approach. The development of historical geoinformatics at the present stage can be identified that is continuously associated with the level of informatization of society, when information and communication technologies have become an integral part of everyday life, a change in the theory and methodology of historical science. Revolutionary changes in the theoretical development of an understanding of the essence of historical processes naturally led to a change in the methodology of history.&#x0D; The problem of information and computer technologies efficiency, in particular geoinformation, in scientific historical research, from a theoretical and methodological point of view is being posed and solved.&#x0D; Since the mid-1990s, the main emphasis has been shifted to the use of multimedia tools and methods and the use of global communications - the Internet. For the preservation, presentation of cultural heritage is an urgent task, the solution of which in a century of rapidly developing information and communication technologies is impossible without the use of new information technology.
Light Pose Calibration for Camera-light Vision Systems

Illuminating a scene with artificial light is a prerequisite for seeing in dark environments. However, nonuniform and dynamic illumination can deteriorate or even break computer vision approaches, for instance when operating a robot with headlights in the darkness. This paper presents a novel light calibration approach by taking multi-view and -distance images of a reference plane in order to provide pose information of the employed light sources to the computer vision system. By following a physical light propagation approach, under consideration of energy preservation, the estimation of light poses is solved by minimizing of the differences between real and rendered pixel intensities. During the evaluation we show the robustness and consistency of this method by statistically analyzing the light pose estimation results with different setups. Although the results are demonstrated using a rotationally-symmetric non-isotropic light, the method is suited also for non-symmetric lights.
The implementation of quantum computing operations with phase modulated microwave pulses and high-spin ions at zero magnetic fields

New physical implementations of quantum computing elementary operations by pulse manipulations with electron spins of paramagnetic ions having two electrons and spin S=1 in a zero magnetic field are proposed. New type of microwave pulses with variable phases and different polarizations are able to induce different transitions between ion spin states, and those transitions are described by quantum computing operators. To describe the effects of such pulses, a new way to present exponential operators as polynomials of Pauli matrices is proposed.
Pengujian Black Box pada Aplikasi Perpustakaan Berbasis Web Menggunakan Teknik Equivalence Partitioning

In determining the level of quality of a software it is necessary to test the software. Tests tested can include specifications, design, and coding of the software. This test is conducted to test web-based library software. This test uses the Black Box Testing method using the Equivalence Partitioning technique. Black Box Testing is applied in order to find errors in software related to erroneous functions, errors in interface display, data structure errors or access rights in external databases, sub-optimal performance in a process, and errors in initialization or termination. The Equivalence Partitioning technique defines the cases to be tested by finding some errors and minimizing the number of cases that must be made. The Equivalence Partitioning test case design is based on the premise of the input and output of a component which is partitioned into classes according to the specifications of that component, which are treated equally (equivalence). It can also be assumed that the results of an input produce the same response. The expected result of testing this software is to ensure that this software is free from errors so that in its use there will be no obstacles that prevent the user from doing his work. And inform that the software is in accordance with predetermined specifications.
Formation and Development of Military Education and Military Training of Youth in Eastern Siberia

The article deals with the formation and development of military education in Eastern Siberia. The author identifies and аnаlyzes the рrоblems faced by military schools during the Great Раtriоtic War and the роstwаr рeriоd. It includes evacuation, unsettled living conditions, lack of sufficient material and technical base for training future officers, etc. The аuthоr cоmes tо the cоnclusiоn thаt, despite аll the difficulties in the trаining оf militаry рrоfessiоnаls in Eаstern Siberiа а lоt оf wоrk was done to trаin quаlified militаry рersоnnel. In the most difficult conditions of wartime, without sleep, without food,with a meager educational and material base, reduced to the limit terms of training military educational institutions, the front was provided with well-trained and politically mature commanders. In the post-war period, military education develops and undergoes changes that were caused by the scientific and technical revolution and the revolution in military Affairs. Today it is necessary to continue to improve the system of military education. The complex geopolitical situation in the world requires special attention to military education and military training of the youth.
Learning Management System - access and registering

Learning Management System. Our new Learning Management System is coming soon. In the meantime, please use our current system
Enriching Consumer Health Vocabulary Using Enhanced GloVe Word Embedding

Open-Access and Collaborative Consumer Health Vocabulary (OAC CHV, or CHV for short), is a collection of medical terms written in plain English. It provides a list of simple, easy, and clear terms that laymen prefer to use rather than an equivalent professional medical term. The National Library of Medicine (NLM) has integrated and mapped the CHV terms to their Unified Medical Language System (UMLS). These CHV terms mapped to 56000 professional concepts on the UMLS. We found that about 48% of these laymen's terms are still jargon and matched with the professional terms on the UMLS. In this paper, we present an enhanced word embedding technique that generates new CHV terms from a consumer-generated text. We downloaded our corpus from a healthcare social media and evaluated our new method based on iterative feedback to word embedding using ground truth built from the existing CHV terms. Our feedback algorithm outperformed unmodified GLoVe and new CHV terms have been detected.
Evaluation university ranking system using quacquarelli Symonds and integrated performance measurement system approach

Abstract Institutional assessment in the current era is indispensable because of increasing global competition and economic and information growth. Assessment efforts were also carried out on various factors including improving the quality of lecturers, meeting study programs that were in line with national development needs, developing educational curricula, improving the quality of community services and research, and adding internal and external collaboration. It aims to strengthen management and governance so that it has good performance and makes the university a World Class University (WCU). Fulfillment of WCU criteria in faculties or departments is one of the steps to determine the strategy for achieving a tertiary institution by giving more weight to faculty excellence to encourage the use of relevant new criteria. Also, in the ranking process, there are different criteria or indicators in several ranking techniques in the world as a manifestation of performance appraisal of the overall implementation of the organization. Quacquarelli Symonds (QS) is a rating agency that evaluates university weaknesses and strengths and produces assessments that show achievement ratings. The QS ranking also has six indicators that are believed to illustrate the value of the university.
무용분야 진로계발 연구의 성과와 과제: 1986-2019

The purpose of this study were to discover the trending topics in career development research in dance. From KERIS (Korea Education and Research Information Service), a total of 139 studies published in Korea between 1986 and 2019 were collected.The results of the review shows that the research of career development in dance area was rapidly increased since 2006s and more than 74% of the participants were university dance students. The types of methodology comprised of survey(79.9%), qualitative research including narrative and case study(9.3%), reviews(7.2%), delphi(2.2%), field experimental research(0.7%), and mixed research(0.7%). Major tools of data analysis were regression(16.8%), ANOVA(15.8%), frequency analysis (12.0%), correlation(10.0%), t-test(10.0%), and descriptive statistics(5.5%). Some traditional career topics are quite popular in career-decision making( 17.3%), career self-efficacy(14.1%), career preparation behavior (12.4%), career maturity(10.3%), career choice/exploration(8.1%), and career barriers(6.5%). Future research directions for career development in dance education were discussed and suggested with respect of the results.
Limit Behaviour of Upper and Lower Expected Time Averages in Discrete-Time Imprecise Markov Chains

We study the limit behaviour of upper and lower bounds on expected time averages in imprecise Markov chains; a generalised type of Markov chain where the local dynamics, traditionally characterised by transition probabilities, are now represented by sets of ‘plausible’ transition probabilities. Our main result is a necessary and sufficient condition under which these upper and lower bounds, called upper and lower expected time averages, will converge as time progresses towards infinity to limit values that do not depend on the process’ initial state. Remarkably, our condition is considerably weaker than those needed to establish similar results for so-called limit—or steady state—upper and lower expectations, which are often used to provide approximate information about the limit behaviour of time averages as well. We show that such an approximation is sub-optimal and that it can be significantly improved by directly using upper and lower expected time averages.
Extracting ice phases from liquid water: why a machine-learning water model generalizes so well

We investigate the structural similarities between liquid water and 53 ices, including 20 knowncrystalline phases. We base such similarity comparison on the local environments that consist of atoms within a certain cutoff radius of a central atom. We reveal that liquid water explores the localenvironments of the diverse ice phases, by directly comparing the environments in these phases using general atomic descriptors, and also by demonstrating that a machine-learning potential trained on liquid water alone can predict the densities, the lattice energies, and vibrational properties of theices. The finding that the local environments characterising the different ice phases are found in water sheds light on water phase behaviors, and rationalizes the transferability of water models between different phases.
Faster Online Computation of the Succinct Longest Previous Factor Array

We consider the problem of computing online the Longest Previous Factor array LPF[1, n] of a text T of length n. For each $$1 \le i \le n$$ , LPF[i] stores the length of the longest factor of T with at least two occurrences, one ending at i and the other at a previous position $$j<i$$ . We present an improvement over the previous solution by Okanohara and Sadakane (ESA 2008): our solution uses less space (compressed instead of succinct) and runs in $$O(n\log ^2n)$$ time, thus being faster by a logarithmic factor. As a by-product, we also obtain the first online algorithm computing the Longest Common Suffix (LCS) array (that is, the LCP array of the reversed text) in $$O(n\log ^2n)$$ time and compressed space. We also observe that the LPF array can be represented succinctly in 2n bits. Our online algorithm computes directly the succinct LPF and LCS arrays.
Cognitive Modeling and Model-Based Performance Evaluation on Collaborative Decision and Control Task

This paper designs a three positions collaborative decision making and control task for the purpose of investigating the collaboration performance. To build cognitive model for the collaborative decision-making task, the model and cognitive parameters are abstracted from realistic control task. Based on cognitive architecture, the cognitive model for the collaborative task are built up and verified by simulation. Depending on the cognitive workload quantifying criteria, the cognitive workload is obtained respectively. Finally, by comparing the cognitive process or tasks, the performance for the collaborative decision making and control tasks is evaluated.
Theory and Experiments for a Practical Usecase

In many aerial robot applications such as search and rescue, the task consists on providing assistance in hostile environments such as mountains or civil areas after natural catastrophes. In this scenarios it is very likely that the terrain is not flat, making the landing and takeoff maneuvers of the aerial robot very complicate and unsafe. In contact-free conditions, the complexity of the task is increased by the underactuation of standard unidirectional-thrust aerial vehicle. In this chapter we shall show that the use of physical interaction, and in this case exploiting the tether, a unidirectional-thrust aerial vehicle can perform the task in a much robust and reliable way. In this chapter we will provide a formal study of the problem, proving the superiority of the tethered system. We shall then how how the results of Chap. 4 have been exploited to perform the task. A simple but effective trajectory generator is also derived for the particular task. Real experiments are presented validating the proposed method.
Towards a Computational Framework for Automating Substance Use Counseling with Virtual Agents

Motivational interviewing is a counseling technique that involves the in-depth exploration of a person's reasons for and against changing their behavior, and is particularly effective for substance use counseling. We are developing a computational framework that uses techniques from motivational interviewing to conduct substance use counseling sessions by simulating face-to-face interactions with a virtual agent. We evaluated the feasibility of using a virtual agent system that uses a constrained-input modality and dialogue trees to automate parts of motivational interviewing, and report the results conducted with patients at two substance use treatment facilities. We are extending this prototype to encompass all of motivational interviewing by processing information from unconstrained user speech. To that end, we report results from training a dialog act prediction model on 132 transcripts of patient-provider counseling sessions. Our best model realized an F1 score of 0.62, recall of 0.61, precision of 0.65 and accuracy of 0.6 across five classes. This indicates reasonably good performance, highlighting the potential of this approach.
Development Of Pesoktren's Business Architecture

Abstrac-Technology is a thing that is needed for now. One of them is technology that is also needed in Islamic boarding schools. Islamic boarding schools currently do not have a clear architecture for developing technology. In an institution, it needs a mature planning for architectural development. To support the success of Islamic boarding school, a good business process is needed. To make this happen thepesantren business architecture was created using the TOGAF ADM method. In making this business architecture refers to 52 boarding school standards. Of the 52 boarding school standards, each SOP will be made and then grouped each SOP using the TF-IDF algorithm and articulation to facilitate the process of grouping SOPs so that an organizational structure of the boarding school is obtained
Personalized and Contextualized Persuasion System for Older Adults’ Physical Activity Promoting

Aging often involves a significant change in roles and social positions. The greatest health risk for seniors is the adoption of a sedentary lifestyle that causes isolation, depression, and many diseases. However, convincing an older adult to regularly do physical activities is not generally a simple mission. This paper proposes a personalized and contextualized persuasion system to promote physical activities for older adults. In fact, our approach considers the personal and health profile of the older adult. It also considers different context parameters (context-awareness). This intelligence is guaranteed thanks to the use of the semantic modeling and reasoning, which from different types of information would be able to decide the best moment to trigger notifications from our persuasive system to the participating older adults.
REVIEW, STUDY &amp; MODELLING OF NEURO-FUZZY SYSTEM, IT'S PERFORMANCE COMPARISION USING VARIOUS SPEECH DATABASES FOR RECOGNITION OF EMOTIONAL STATES

Objective Study and modelling of various Neural Network & Neuro Fuzzy System to determine best design solution for recognition of emotional states through human speech.Methods By performing test experiment on 3 different models using MFCC and other speech extraction technique on multiple databases. Database must include native language speech samples as well. Results As per above mentioned objective and method 76% (approx.) overall average recognition rate was achieved. Some significant experimental finding and observations were also recorded for further and future research.
DanHAR: Dual Attention Network For Multimodal Human Activity Recognition Using Wearable Sensors

Human activity recognition (HAR) in ubiquitous computing has been beginning to incorporate attention into the context of deep neural networks (DNNs), in which the rich sensing data from multimodal sensors such as accelerometer and gyroscope is used to infer human activities. Recently, two attention methods are proposed via combining with Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) network, which can capture the dependencies of sensing signals in both spatial and temporal domains simultaneously. However, recurrent networks often have a weak feature representing power compared with convolutional neural networks (CNNs). On the other hand, two attention, i.e., hard attention and soft attention, are applied in temporal domains via combining with CNN, which pay more attention to the target activity from a long sequence. However, they can only tell where to focus and miss channel information, which plays an important role in deciding what to focus. As a result, they fail to address the spatial-temporal dependencies of multimodal sensing signals, compared with attention-based GRU or LSTM. In the paper, we propose a novel dual attention method called DanHAR, which introduces the framework of blending channel attention and temporal attention on a CNN, demonstrating superiority in improving the comprehensibility for multimodal HAR. Extensive experiments on four public HAR datasets and weakly labeled dataset show that DanHAR achieves state-of-the-art performance with negligible overhead of parameters. Furthermore, visualizing analysis is provided to show that our attention can amplifies more important sensor modalities and timesteps during classification, which agrees well with human common intuition.
Nature-Inspired Biogeography-Based Optimization for Estimation of GPS Receiver Position in Low Latitude Regions of the Indian Subcontinent

The concept of biological species distribution in a geographical area laid a path to develop biogeography-based optimization (BBO) in order to solve real-time optimization problems. BBO is implemented on GPS receiver data that belongs to low latitude region of the Indian subcontinent in order to estimate the receiver position. 3D positional coordinates of the receiver obtained by using BBO algorithm are compared with the surveyed location coordinates to analyze the performance of BBO algorithm. From the estimated mean position errors, BBO is observed to be a better evolutionary computing algorithm in the estimation of any GPS receiver position which is located in low latitude regions of the Indian subcontinent. This algorithm can also be used for QZSS and IRNSS satellite systems.
Policy-GNN: Aggregation Optimization for Graph Neural Networks

Graph data are pervasive in many real-world applications. Recently, increasing attention has been paid on graph neural networks (GNNs), which aim to model the local graph structures and capture the hierarchical patterns by aggregating the information from neighbors with stackable network modules. Motivated by the observation that different nodes often require different iterations of aggregation to fully capture the structural information, in this paper, we propose to explicitly sample diverse iterations of aggregation for different nodes to boost the performance of GNNs. It is a challenging task to develop an effective aggregation strategy for each node, given complex graphs and sparse features. Moreover, it is not straightforward to derive an efficient algorithm since we need to feed the sampled nodes into different number of network layers. To address the above challenges, we propose Policy-GNN, a meta-policy framework that models the sampling procedure and message passing of GNNs into a combined learning process. Specifically, Policy-GNN uses a meta-policy to adaptively determine the number of aggregations for each node. The meta-policy is trained with deep reinforcement learning (RL) by exploiting the feedback from the model. We further introduce parameter sharing and a buffer mechanism to boost the training efficiency. Experimental results on three real-world benchmark datasets suggest that Policy-GNN significantly outperforms the state-of-the-art alternatives, showing the promise in aggregation optimization for GNNs.
Improving The Students’ Vocabulary Mastery Through Make a Match Method at The Second Grade Students of SMP Negeri 19 Poleang Barat

This research was designed to improve the students’ vocabulary mastery through Make a Match Method at the second grade of SMP Negeri 19 Poleang Barat. The problem of research is “How can Make a Match Method improve the students’ vocabulary mastery at the second grade students of SMP Negeri 19 Poleang Barat in each cycle?” . The objective of the research was to describe the improvement of students’ vocabulary mastery at the second grade students of SMP Negeri 19 Poleang Barat by using Make a Match Method in each cycle. The design of this research was collaborative Classroom Action Research (CAR). It means that the researcher collaborate with one of the English teachers of SMP Negeri 19 Poleang Barat. The research was conducted at the second grade students of SMP Negeri 19 Poleang Barat which were registered in academic year 2017/2018 with number of students were 24. This research was conducted in two cycles by following the procedure of classroom action research such as: preliminary research, planning, implementing, observing, analysing and reflecting. The data was collected by using vocabulary test and observation sheets which were taken during the implementation of the action. The result of this research showed that Make a Match Method can improve the students’ vocabulary mastery at the second grade of SMP Negeri 19 Poleang Barat.