Reviewerly/exHarmony huggingface dataset:
A Novel Q-Learning Algorithm Based on the Stochastic Environment Path Planning Problem

In this paper, we proposed a path planning algorithm based on Q-learning model to simulate an environment model, which is suitable for the complex environment. A virtual simulation platform has been built to complete the experiments. The experimental results show that the algorithm proposed in this paper can be effectively applied to the solution of vehicle routing problems in the complex environment.
Cross-Lingual Voice Conversion using a Cyclic Variational Auto-encoder and a WaveNet Vocoder

We propose a novel, cross-lingual voice conversion (VC) method using a cyclic variational auto-encoder (CycleVAE). Voice conversion is the transformation of the voice of one speaker into the voice of another speaker, while cross-lingual VC performs voice conversion between speakers who speak different languages. When using VC methods based on parallel learning, it is necessary to prepare accented speech uttered by the source or target speaker, using the pronunciation system of the speaker’s mother tongue. On the other hand, VC methods which use a non-parallel learning approach can utilize the natural speech data of both the source and target speakers, produced in their own native languages. It then becomes necessary, however, to deal with the issues of time-alignment and language mismatches. To address these issues, we apply CycleVAE to cross-lingual VC as a sophisticated, non-parallel method of VC. We also apply the WaveNet vocoder in the waveform generation process of CycleVAE-VC to improve overall conversion quality. Our objective and subjective experimental results when performing cross-lingual VC from a native English speaker to a native Japanese speaker confirm that the proposed method achieves a higher level of naturalness and speaker similarity than a conventional RNN-based parallel VC method using accented speech.
Students’ Perceptions of Reciprocal Teaching in Reading Comprehension Class

This research aimed to find out the difficulties and the benefits of reciprocal teaching perceived by the students in reading comprehension class, and the strategy in reciprocal teaching that the students prefer the most. This research was qualitative research applying phenomenological approach. The participants of this study were 5 students majoring in English Education Department of Institute Parahikma Indonesia. The data was collected by using semi-structured interview and it was analyzed by using thematic analysis adapted from Given (2018). The result of this study indicated that the difficulties faced by the students are less teacher’s monitoring, students’ interest in learning reading comprehension which makes them bored, self-competence of the students in understanding the text, and the difficulty in unifying their perceptions. The benefits of it that the students perceived are that they become more creative and imaginative, and it is an encouraging method and effective. Furthermore, it can boost their enthusiasm and create collaborative engagement. However in term of the most preferred strategies in reciprocal teaching, most of the students perceived that questioning is the best.
THE STRATEGIES OF LEARNING ENGLISH FOR YOUNG LEARNERS AT ENGLISH COURSE

English courses generally have a same goal in providing learning material for their participants. Moreover, teaching English to children is a difficulty for teachers, even though teaching in an informal environment at this English course, as a teacher should know and have learning strategies that are very important in learning in order to create a pleasant atmosphere for participants in learning process. This study aims to know learning strategies for young learners in learning English as a foreign language; especially regarding the most frequently used strategies and knowing the supporting and inhibiting factors in implementing of learning English at AB course Banjarmasin.Based on that phenomenon, the researcher was interested in investigating about the strategies of learning English using unique methods/ techniques that applied for young learners in AB course Banjarmasin. The researcher used descriptive qualitative as the research method of the study. Data analysis of descriptive qualitatif is generally taken by the researcher by doing three steps: data reduction, data display, and conclusion drawing. The data of the research are gathered through observation and interview. The results of this study showed that English learning strategies used by teacher to young learners are easy to understand and can help improve their achievement in English and finds out more about the factors that support and inhibit learning process at AB course Banjarmasin.
Adversarial Problems for Generative Networks

We are interested in the design of generative networks. The training of these mathematical structures is mostly performed with the help of adversarial (min-max) optimization problems. We propose a simple methodology for constructing such problems assuring, at the same time, consistency of the corresponding solution. We give characteristic examples developed by our method, some of which can be recognized from other applications and some are introduced here for the first time. We compare various possibilities by applying them to well known datasets using neural networks of different configurations and sizes.
Comparison some of kernel functions with support vector machines classifier for thalassemia dataset

&lt;span id="docs-internal-guid-9a30056f-7fff-8ff1-59e1-69f89f4280bd"&gt;&lt;span&gt;In the medical field, accurate classification of medical data is really important because of its impact on disease detection and patient’s treatment. Technology, machine learning, is needed to help medical staff to improve accuracy to classify disease. This research discussed some kernel functions, such as gaussian radial basis function (RBF) kernel, Polynomial kernel, and linear kernel with support vector machine (SVM) to classify thalassemia data. Thalassemia is a genetic blood disorder which is also one of the major public health problems. In this paper, there is an explanation about thalassemia, SVM, and some of the kernel functions that serve as a comprehensive source for the next research about this topic. Furthermore, there is a comparison result from three kernel functions to find out which one has the best performance. The result is gaussian RBF kernel with SVM is the best method with an average of accuracy 99,63%. &lt;/span&gt;&lt;/span&gt;
Nello spazio di senso di "La baracca dei saltimbanchi" (traduzione di Claudia Criveller)

Italian translation of V smyslovom prostranstve Balagancika by Zara Minс.Translated by Claudia Criveller (Universita degli Studi di Padova)
Implementation of Compression and Decompression using LZW Algorithm

Abstract Many compression techniques are used to minimize the amount of memory that is to be used to store the data. One such compression technique is by using LZW algorithm. The purpose of this is to present a data compression and decompression software to reduce the amount of memory used for storing data. By using data compression, stream of symbols is transformed into codes. For effective compression, the resultant stream of codes will be smaller than the original symbol. LZW is a dictionary-based compression algorithm. Here, to encode a substring, only a single code number is used, corresponding to the substring's index in the dictionary, which will be written to the output file. LZW algorithm finds its application in data compression and performs best on files with repeated substrings, such as text files. Keywords: LZW, compression, decompression, index, string
Goal-Driven Imitation Learning from Observation by Inferring Goal Proximity

Humans can effectively learn to estimate how close they are to completing a desired task simply by watching others fulfill the task. To solve the task, they can then take actions towards states with higher estimated proximity to the goal. From this intuition, we propose a simple yet effective method for imitation learning that learns a goal proximity function from expert demonstrations and online agent experience, and then uses the learned proximity to provide a dense reward signal for training a policy to solve the task. By predicting task progress as the temporal distance to the goal, the goal proximity function improves generalization to unseen states over methods that aim to directly imitate expert behaviors. We demonstrate that our proposed method efficiently learns a set of goal-driven tasks from state-only demonstrations in navigation, robotic arm manipulation, and locomotion tasks.
PENGARUH METODE THINK PHARE AND SHARE DENGAN ALAT PERMAINAN EDUKATIF ULAR TANGGA TERHADAP MOTIVASI BELAJAR ANAK SEKOLAH DASAR (SD)

Games as tools in education is one of the media-based simulation designed to stimulate the existing problems, in order to obtain the essence of science that can be used to solve the problem.  This research was conducted with the aim of increasing the learning motivation of elementary school (SD) children by utilizing educational games. The results of this study are expected to provide an alternative activity to play in learning process and cultivate children to skillfully handle the problem independently, encouraging teachers to be more creative in educating the children with media interest and in accordance with the children's development. This study used a quasi-experimental design. The data collection technique used was the test and observation guidelines. Implementation of research had been done in class III SD Plus Tigabalata Simalungun district. The research result showed that the activity of students in the learning was 84 which is very active. The result of learning process skills was 81, while learning completeness score was 88. The test results influenced the activity of the students’ learning motivation as big as 0,684. There was still 0,352 of variance which was influenced by other variables. The influence of skills in the learning process of students to the students test scores was 0,616. It means, there are other variables which influence the test scores as big as 0,384. Comparative tests between pretest and post-test (t test) was t = 12,2*, means that there was motivation to learn outcomes before and after implementation.
Deep Ensemble Kernel Learning

Gaussian processes (GPs) are nonparametric Bayesian models that are both flexible and robust to overfitting. One of the main challenges of GP methods is selecting the kernel. In the deep kernel learning (DKL) paradigm, a deep neural network or ``feature network'' is used to map inputs into a latent feature space, where a GP with a ``base kernel'' acts; the resulting model is then trained in an end-to-end fashion. In this work, we introduce the ``deep ensemble kernel learning'' (DEKL) model, which is a special case of DKL. In DEKL, a linear base kernel is used, enabling exact optimization of the base kernel hyperparameters and a scalable inference method that does not require approximation by inducing points. We also represent the feature network as a concatenation of an ensemble of learner networks with a common architecture, allowing for easy model parallelism. We show that DEKL is able to approximate any kernel if the number of learners in the ensemble is arbitrarily large. Comparing the DEKL model to DKL and deep ensemble (DE) baselines on both synthetic and real-world regression tasks, we find that DEKL often outperforms both baselines in terms of predictive performance and that the DEKL learners tend to be more diverse (i.e., less correlated with one another) compared to the DE learners.
Recurrent Neural Network Architecture based on Dynamic Systems Theory for Data Driven Modelling of Complex Physical Systems

While dynamic systems can be modelled as sequence-to-sequence tasks by deep learning using different network architectures like DNN, CNN, RNNs or neural ODEs, the resulting models often provide poor understanding of the underlying system properties. We propose a new recurrent network architecture, the Dynamic Recurrent Network, where the computation function is based on the discrete difference equations of basic linear system transfer functions known from dynamic system identification. This results in a more explainable model, since the learnt weights can provide insight on a system's time dependent behaviour. It also introduces the sequences' sampling rate as an additional model parameter, which can be leveraged, for example, for time series data augmentation and model robustness checks. The network is trained using traditional gradient descent optimization and can be used in combination with other state of the art neural network layers. We show that our new layer type yields results comparable to or better than other recurrent layer types on several system identification tasks.
공공재로서 무용의 지식 교육적 접근 방법

Contemporary dance is recognized as a space of sharing in which numerous people directly participate as a form of human understanding. Contemporary dance, described using bidirectional relationships, is created and distributed by knowledge structures in which experiences and knowledge are connected. In this context, the contents of this study, which attempted to explain the accessibility of the public good of dance through knowledge education, can be presented as follows.The role of contemporary dance, in which communication takes place through the body as a subject of awareness, has become an investment of life that directly participates in dance with a change of consciousness (an expansion of the mind) and it functions as a public good that communicates with participants through a dance knowledge structure. Dance knowledge structures that are connected among and related to participants possess structures that systematically expand knowledge through themselves.Spaces that participate in dance are relational spaces in which connected relationships between people and dance and between people and people are established and knowledge is created through participant activities (experiences).Dance knowledge education is a form of imagination that approaches knowledge systems formed within history and culture based on background knowledge (sensory data) without depending on any particular knowledge and it recognizes universal lives common to mankind along with individually unique lives and contributes to awakening self-management abilities within them.Integrated artistic dance and dance literacy in contemporary dance knowledge education discovers the creation origins of knowledge from the human spirit of culture and history as public goods and knowledge is created through imagination learning that forms relationships with other things.Dance literacy, which reasons and infers from what has been, makes reading the world possible withing diverse relationships and integrated artistic dance, which broadens the ranges of pluralistic thinking through various sensory data (forms), amplifies the possibilities of self-expression. This signifies that they can possibly serve as containers in which dance knowledge structures are shared as dance knowledge education strategies.
Learning What Not to Model: Gaussian Process Regression with Negative Constraints

Gaussian Process (GP) regression fits a curve on a set of datapairs, with each pair consisting of an input point 'x' and its corresponding target regression value 'y(x)' (a positive datapair). But, what if for an input point 'x¯', we want to constrain the GP to avoid a target regression value 'y¯(x¯)' (a negative datapair)? This requirement can often appear in real-world navigation tasks, where an agent would want to avoid obstacles, like furniture items in a room when planning a trajectory to navigate. In this work, we propose to incorporate such negative constraints in a GP regression framework. Our approach, 'GP-NC' or Gaussian Process with Negative Constraints, fits over the positive datapairs while avoiding the negative datapairs. Specifically, our key idea is to model the negative datapairs using small blobs of Gaussian distribution and maximize its KL divergence from the GP. We jointly optimize the GP-NC for both the positive and negative datapairs. We empirically demonstrate that our GP-NC framework performs better than the traditional GP learning and that our framework does not affect the scalability of Gaussian Process regression and helps the model converge faster as the size of the data increases.
Library Research Guides: Collaborative Archive & Data Research Environment (CADRE) : Home

CADRE is a cloud-based platform that provides access to a standardized version of the Web of Science and Microsoft Academic Graph datasets. CADRE includes GUI data querying, analysis, storage, and visualization capabilities.
PSYCHOLOGICAL COUNSELING OF FREELANCE DESIGNERS WITH VARIOUS LEVELS OF PROFESSIONAL ADAPTATION

Introduction. This study examines the problem of professional adaptation of freelance designers, which is of particular relevance due to the insufficient coverage of this issue, the annual increase in the number of freelancers in the design environment, the specifics of professional adaptation of freelancers engaged in interior design, and the lack of specially developed programs for psychological counseling of freelance designers with different levels of professional adaptation. This work aims to study the professional adaptation of freelance designers and to build and test a psychological counseling program for freelance designers with various levels of professional adaptation. Methods and materials. The study involved several groups of participants. For each group, an individual psychological counseling program was developed and implemented. The program contains modeling, developing, training, and analytical methods. The paper presents the program implementation results, which show the change in the indicators of professional adaptation of freelance designers with different levels of professional adaptation. Results. Analysis performed in this study has confirmed the assumption that professional adaptation of the freelancers at issue has various levels (high, medium, and low). In this work, each level is assigned a certain characteristic with corresponding indicators. Conclusion. The findings of the study indicate that traditional methods cannot affect the professional adaptation of freelancers. In this regard, psychological counseling is one of the most effective approaches. Keywords: professional disadaptation, job satisfaction, professional stress, psychological counseling program, adaptability of freelancers.
Determinants of Ethiopian Commodity Exchange Participants Membership Category

Purpose: The Ethiopian Commodity Exchange (ECX) was established to revolutionize Ethiopian agriculture and transform the economy through introducing a dynamic, efficient and transparent marketing system.Properly implemented and regulated, commodity exchanges can contribute greatly to the achievement of a country's economic and developmental goals and strengthen the bargaining power of participants.Hence the overall objective of this study is to assess the trading practices and challenges of the newly established commodity exchange in Ethiopian.Design/Methodology/Approach: The study adopted a descriptive cross-sectional research design.Using a structured questionnaire survey by interviewing a sample of 80 randomly selected participants an analyzed using both descriptive and inferential data analysis techniques.Findings: The study found that practicing an open outcry and spot contract trading system.Results of the econometric-model analysis revealed that time of participation, limited membership seat, membership seat fee and occupation were found to be significant predictors that influence the choice of membership category.Result of the study also indicated that lack of adequate warehouses, grading and sampling system, higher penalty cost related to warehouse service, availability of in-store credit, higher membership seat fee and transaction cost, were found to be some of constraints that hinder participants from the smooth functioning of transactions in the exchange.Originality/value: This study is important especially to policymakers and development practitioners to make informed decisions in an effort to transform the economy through strengthening transparent marketing system in the country.In this regard, this study provides valuable information and evidence on the performance and challenges of ECX.
Interethnic tolerance development ways among pedagogical students at University

The presented article is devoted to interethnic tolerance development among pedagogical students at a university.Based on the works by B. Barber, J. Berry, A.G. Asmolov, M.S. Mirimanov, V.A. Lektorsky, G.U. Soldatov and other researchers, the authors clarified the goal and objectives of interethnic tolerance development among future teachers, and also systematized its main criteria and indicators. Also, the article presents the author's complex of pedagogical conditions that ensure the development of interethnic tolerance among pedagogical students at a university. The complex includes the following components: the use of active didactic forms and interactive teaching methods in the educational process that contribute to the formation of a tolerant worldview of students; organization of interpersonal interaction of pedagogical students by their inclusion in research activities; actualization of tolerant interethnic communication experience among future teachers through the introduction of tolerance episodes into the educational process; the development of competence among pedagogical students in the field of interethnic tolerance development among schoolchildren during the educational process.
if you keep your important files on ...

if you keep your important files on a specific type of medium, let's see an SD-Card, make sure to also keep some hardware nearby so
AriEL: Volume Coding for Sentence Generation Comparisons

Mapping sequences of discrete data to a point in a continuous space makes it difficult to retrieve those sequences via random sampling. Mapping the input to a volume would make it easier to retrieve at test time, and that is the strategy followed by the family of approaches based on Variational Autoencoder. However the fact that they are at the same time optimizing for prediction and for smoothness of representation, forces them to trade-off between the two. We benchmark the performance of some of the standard methods in deep learning to generate sentences by uniformly sampling a continuous space. We do it by proposing AriEL, that constructs volumes in a continuous space, without the need of encouraging the creation of volumes through the loss function. We first benchmark on a toy grammar, that allows to automatically evaluate the language learned and generated by the models. Then, we benchmark on a real dataset of human dialogues. Our results indicate that the random access to the stored information can be significantly improved, since our method AriEL is able to generate a wider variety of correct language by randomly sampling the latent space. VAE follows in performance for the toy dataset while, AE and Transformer follow for the real dataset. This partially supports the hypothesis that encoding information into volumes instead of into points, leads to improved retrieval of learned information with random sampling. We hope this analysis can clarify directions to lead to better generators.