Reviewerly/exHarmony huggingface dataset:
UniTTS: Residual Learning of Unified Embedding Space for Speech Style Control

We propose a novel high-fidelity expressive speech synthesis model, UniTTS, that learns and controls overlapping style attributes avoiding interference. UniTTS represents multiple style attributes in a single unified embedding space by the residuals between the phoneme embeddings before and after applying the attributes. The proposed method is especially effective in controlling multiple attributes that are difficult to separate cleanly, such as speaker ID and emotion, because it minimizes redundancy when adding variance in speaker ID and emotion, and additionally, predicts duration, pitch, and energy based on the speaker ID and emotion. In experiments, the visualization results exhibit that the proposed methods learned multiple attributes harmoniously in a manner that can be easily separated again. As well, UniTTS synthesized high-fidelity speech signals controlling multiple style attributes. The synthesized speech samples are presented at https://anonymous-authors2022.github.io/paper_works/UniTTS/demos/.
Stochastic Parametrization using Compressed Sensing: Application to the Lorenz-96 Atmospheric Model

Growing set of optimization and regression techniques, based upon sparse representations of signals, to build models from data sets has received widespread attention recently with the advent of compressed sensing. In this paper, sparse approximations in high-dimensional spaces are used to build models (vector fields) to emulate the behavior of the fine-scale process, so that explicit simulations become an online benchmark for parameterization. Observations are assimilated during the integration of low-dimensional built model to provide predictions. We outline how the parameterization schemes developed here and the low-dimensional filtering algorithm can be applied to the Lorenz-96 atmospheric model that mimics mid-latitude atmospheric dynamics with microscopic convective processes.
A Novel Approach for the Characterization of Triangular Modulated Frequency Modulated Continuous Wave Low Probability of Intercept Radar Signals via Application of the Wigner-Ville Distribution

Digital intercept receivers are changing from Fourier-based analysis to classical time-frequency analysis techniques for analyzing low probability of intercept radar signals. This paper presents a novel approach of characterizing low probability of intercept triangular modulated frequency modulated continuous wave radar signals through utilization and direct comparison of the signal processing techniques Wigner-Ville Distribution versus the Reassigned Smooth Pseudo Wigner-Ville Distribution. The following metrics were used for evaluation: percent error of: carrier frequency, modulation bandwidth, modulation period, chirp rate, and time-frequency localization (x and y direction). Also used were: percent detection, lowest signal-to-noise ratio for signal detection, and plot (processing) time. Experimental results demonstrate that overall, the Reassigned Smooth Pseudo Wigner-Ville Distribution signal processing technique produced more accurate characterization metrics than the Wigner-Ville Distribution signal processing technique.
Democratizar y descolonizar la universidad. Dos desafíos para la universidad pospandémica

The pandemic raises deep debates about the transformations that it can generate in all areas, and the university is called to participate in the characterization of the crisis and the transition that it implies; Faced with this panorama, there are four challenges for her, of which two will be addressed in this essay: Democratize and Decolonize. The objective is to analyze the implications of these challenges for the post-pandemic university from the conceptual categories of the Epistemologies of the South, starting from the analysis of the qualitative content of the documentary source of the work of Santos Boaventura de Souza and other authors of this current.
대학 교양에서 미술작품을 활용한 창의적 글쓰기 연구 - 근대 조선 시기 미술작품을 중심으로 -

The purpose of this study is to find a proper method of developing creativity in university writing education using the modern Joseon art works. The importance of “writing,” the basic expression ability of communication, has been gradually emphasized. Writing education in universities has also been recognizing this importance. Therefore, interpretation of creative competence became diversifying for writing methods to promote creative thinking rather than formalized writing.In this study, creative writing tasks using artworks were carried out in communication skills classes for freshmen. It is a task to create front and back stories focusing on the characters in the artworks with the visual images to help writing. In particular, it was aimed to allow them to understand the situation of the times modern Korean art, and develop their creativity at the same time. In this process, students organized creative contents through “open questions” and were able to submit writing tasks combined with the times as the results.Creativity, regardless of genre, has become an important ability to show individual competence. Various writing classes using art are currently being studied to promote students'' creativity. I hope that the process of asking and responding to questions and creating new stories centered on our art will help us develop creative capabilities.
Role Fluidity in Companies

This research study will focus on investigating role fluidity within the workplace, looking specifically at stakeholders such as employees and managers. The study will look at how managers cope with managing individuals, role changes in society and roles within their workplace. In fact, there appears to be a dearth of research within the field and the notion that fluid role enactment can impact how stakeholders can implement and develop flexible strategies, remains ignored. This study aims to conceptualise role fluidity of stakeholders within U.K. telecoms companies, particularly reflecting on market dynamics and the ways in which is communicated as a consequence of role changes in society. We suggest a concept of role fluidity that U.K. telecoms organisations can implement, in order to encourage their stakeholders to manage customer projects in a flexible way. We also provide suggestions as to how to communicate in such a way that these organisations develop innovative and adaptive capacity. The logic behind this research being that the U.K. telecoms industry is saturated, dynamic and adaptive with expected potential for role fluidity in customer projects. In order to answer the research question, we have identified three objectives that we will need to achieve in order to develop a concept and benchmark for role fluidity within the U.K. telecoms industry. The first objective breaks down and identifies the fluid roles that are operating within dynamic markets. The expected outcomes demonstrate patterns of communication. A pilot test identifies how stakeholders utilise emojis within customers projects as a means to reducing their emotional barriers. The second objective examines how stakeholders evaluate their actual versus their desired roles through the use of selfies and emojis. The expected outcomes show the evaluated potential for fluid roles. The third objective conceptualises role fluidity for U.K. telecom stakeholders to encourage innovativeness in their customer operations and management. The expected outcomes are that stakeholders evaluate the concept and industry experts identify benchmark patterns. The outcomes of this research will be extensive in that a new concept and benchmark will be developed. This conceptualisation will assist managing dynamic markets, by exploiting stakeholders’ fluidity at work. This concept will guide stakeholders' fluid approaches, with distinct suggestions as to how to introduce role fluidity. The concept will impact and change stakeholders’ operations, implying new advances in management to empower innovation and adaptiveness. The benchmark will guide telecoms companies and other innovative service providers to introduce role fluidity.
오픈퀄리티 품질성과지표(QSC)의 중요도 분석 연구

Purpose: The purpose of this study is to study the relative weights of Quality Scorecard (QSC) which was developed for a new quality concept, called Open Quality, in the era of Industry 4.0. Open Quality aims to ensure quality responsibility by accurately measuring the quality level and sharing the quality data with stakeholders.Methods: To analyze the importance of QSC, a survey study was conducted based on 10 types of industry. The relative weights of QSC measures are investigated by both the industry types and the organizational life cycle. Further, the relative importance of QSC was analyzed according to the organization’s lifecycle. By dividing the aspects with respect to the result data, application method for QSC is further provided.Results: The result indicates that there are significant differences among Korean companies in terms of the level of Industry revolution stages. Among ten industry types, manufacturing and finance sectors show the highest importance of QSC. QSC measures are also considered more importance in growth stage of the maturity cycle than generator and decline stages.Conclusion: When QSC is adopted for Open Quality, its relative weights must be considered to cope with the unique characteristics of industrial types. Besides, QSC''s contribution may vary according to the level of maturity of the target company.
THE USE OF MOBILE-ASSISTED LANGUAGE LEARNING APPLICATION IN PRONUNCIATION TRAINING

Pronunciation is one of the most important things to master when it comes to Second Language Acquisition (SLA). Nowadays, mobile-assisted language learning (MALL) applications have been emerging. Learners can practice at the reach of their fingertips through the use of their own mobile devices. This study aims to investigate whether the existing mobile-assisted language learning application (ELSA Speak) is effective to help EFL learners train and improve their pronunciations. This study also seeks to know the most difficult sounds to pronounce by Indonesian EFL learners and highlights learners’ thoughts on the application itself including the ease of use, the clarity of pronunciation models by native speakers, as well as the strength and weaknesses of the application. 15 EFL learner participants were involved in this study. They were asked to do a pretest before using the application. The participants then practiced their pronunciation on their own using this application for a week. A post-test was conducted to know their pronunciation progress. They were also given a questionnaire to examine their perceptions of the application. The results show that the mobile-assisted language learning application ELSA speak is quite effective to help learners train and improve their pronunciation. This application is also easily operated and has a lot of features that help learners train their pronunciation easily. Thus, it supports distance language education (DLE). The most difficult English sounds to pronounce according to the participants are Ɵ, t, f, v, tʃ, e, ae, ð, and d, with Ɵ and t as the majority. Lastly, despite being considered as a helpful and convenient pronunciation training application, some things need to be improved, such as the need for Indonesian translation on the words provided to enrich learners’ vocabulary at the same time. Keywords: Pronunciation, Pronunciation Training App,
What Taggers Fail to Learn, Parsers Need the Most.

We present an error analysis of neural UPOS taggers to evaluate why using gold standard tags has such a large positive contribution to parsing performance while using predicted UPOS tags either harms performance or offers a negligible improvement. We evaluate what neural dependency parsers implicitly learn about word types and how this relates to the errors taggers make to explain the minimal impact using predicted tags has on parsers. We also present a short analysis on what contexts result in reductions in tagging performance. We then mask UPOS tags based on errors made by taggers to tease away the contribution of UPOS tags which taggers succeed and fail to classify correctly and the impact of tagging errors.
Detecting tectonic tremor during slow slip events in Costa-Rica using templates of ordinary earthquakes

&amp;lt;p&amp;gt;Slow slip events (SSEs) have been observed beneath the Nicoya peninsula in Costa-Rica for more than 10 years, and are accompanied by tremor activity both updip and downdip of the seismogenic region. However, tremor detection in this region can be challenging and time-consuming, as many local earthquakes occur amidst the tremor, so envelope-based techniques do not perform as well as they do in other regions. Matched-filter techniques are more appropriate to detect many of the individual low-frequency earthquakes (LFEs) that constitute tremor, but these techniques can also be time-consuming and restricted to small areas because they require a set of template seismograms for each LFE family.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In this study, we attempt to take advantage of the many local earthquakes to use the ordinary earthquakes' waveforms as templates to detect tremor all along the subduction interface. We use an extension of matched-filter techniques, a phase coherence (or matched field) method which can identify signals from locations near the template event even if the template and target signals have different source time functions. Because of this specificity of the coherence method, we should be able to detect tremor co-located with an ordinary earthquake, as long as they share similar Green's functions.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We create template waveforms from a catalog created by the Nicoya Seismic Cycle Observatory, whose events are located using local 3-D velocity model (DeShon et al. 2006). We first apply the method during a SSE event in June 2009, and initial investigations suggest that the tremor and earthquakes are similar enough: high coherence values are found at time of known tremor. Bursts of activity with various duration close to the trench are successfully detected, and their location is consistent with slip distribution of the SSE. Our final goal is to identify potential migration of these bursts related to the propagation of the main front of the SSE, as well as investigate the relation between their released seismic energy and duration. These findings will be finally discussed in comparison with tremor characteristics in other subduction areas.&amp;lt;/p&amp;gt;
hSMAL: Detailed Horse Shape and Pose Reconstruction for Motion Pattern Recognition

In this paper we present our preliminary work on model-based behavioral analysis of horse motion. Our approach is based on the SMAL model, a 3D articulated statistical model of animal shape. We define a novel SMAL model for horses based on a new template, skeleton and shape space learned from $37$ horse toys. We test the accuracy of our hSMAL model in reconstructing a horse from 3D mocap data and images. We apply the hSMAL model to the problem of lameness detection from video, where we fit the model to images to recover 3D pose and train an ST-GCN network on pose data. A comparison with the same network trained on mocap points illustrates the benefit of our approach.
Memory Augmented Optimizers for Deep Learning

Popular approaches for minimizing loss in data-driven learning often involve an abstraction or an explicit retention of the history of gradients for efficient parameter updates. The aggregated history of gradients nudges the parameter updates in the right direction even when the gradients at any given step are not informative. Although the history of gradients summarized in meta-parameters or explicitly stored in memory has been shown effective in theory and practice, the question of whether $all$ or only a subset of the gradients in the history are sufficient in deciding the parameter updates remains unanswered. In this paper, we propose a framework of memory-augmented gradient descent optimizers that retain a limited view of their gradient history in their internal memory. Such optimizers scale well to large real-life datasets, and our experiments show that the memory augmented extensions of standard optimizers enjoy accelerated convergence and improved performance on a majority of computer vision and language tasks that we considered. Additionally, we prove that the proposed class of optimizers with fixed-size memory converge under assumptions of strong convexity, regardless of which gradients are selected or how they are linearly combined to form the update step.
SOCIOCULTURAL ENVIRONMENT AND ITS IMPORTANCE IN THE DEVELOPMENT OF A CHILD OF PRESCHOOL AGE

The sociocultural environment is considered as a set of purposefully created conditions that should ensure the process of development and self-development of a child. On the one hand, this is a space for the development and self-determination of the child, and on the other hand, these are pedagogical influences aimed at the development of the child's personality.
PAC-Bayes, MAC-Bayes and Conditional Mutual Information: Fast rate bounds that handle general VC classes

We give a novel, unified derivation of conditional PAC-Bayesian and mutual information (MI) generalization bounds. We derive conditional MI bounds as an instance, with special choice of prior, of conditional MAC-Bayesian (Mean Approximately Correct) bounds, itself derived from conditional PAC-Bayesian bounds, where `conditional' means that one can use priors conditioned on a joint training and ghost sample. This allows us to get nontrivial PAC-Bayes and MI-style bounds for general VC classes, something recently shown to be impossible with standard PAC-Bayesian/MI bounds. Second, it allows us to get fast rates of order $O((\text{KL}/n)^{\gamma}$ for $\gamma > 1/2$ if a Bernstein condition holds and for exp-concave losses (with $\gamma=1$), which is impossible with both standard PAC-Bayes generalization and MI bounds. Our work extends the recent work by Steinke and Zakynthinou (2020) who handle MI with VC but neither PAC-Bayes nor fast rates and Mhammedi et al. (2019) who initiated fast rate PAC-Bayes generalization error bounds but handle neither MI nor general VC classes.
Multiple Object Tracking with Mixture Density Networks for Trajectory Estimation

Multiple object tracking faces several challenges that may be alleviated with trajectory information. Knowing the posterior locations of an object helps disambiguating and solving situations such as occlusions, re-identification, and identity switching. In this work, we show that trajectory estimation can become a key factor for tracking, and present TrajE, a trajectory estimator based on recurrent mixture density networks, as a generic module that can be added to existing object trackers. To provide several trajectory hypotheses, our method uses beam search. Also, relying on the same estimated trajectory, we propose to reconstruct a track after an occlusion occurs. We integrate TrajE into two state of the art tracking algorithms, CenterTrack [63] and Tracktor [3]. Their respective performances in the MOTChallenge 2017 test set are boosted 6.3 and 0.3 points in MOTA score, and 1.8 and 3.1 in IDF1, setting a new state of the art for the CenterTrack+TrajE configuration
An Overview of the Basic NLP Resources Towards Building the Assamese-English Machine Translation

Machine Translation (MT) is the process of automatically converting one natural language into another, preserving the exact meaning of the input text to the output text. It is one of the classical problems in the Natural Language Processing (NLP) domain and there is a wide application in our daily life. Though the research in MT in English and some other language is relatively in an advanced stage, but for most of the languages, it is far from the human-level performance in the translation task. From the computational point of view, for MT a lot of preprocessing and basic NLP tools and resources are needed. This study gives an overview of the available basic NLP resources in the context of Assamese-English machine translation.
Predictive Modeling for Differential Diagnosis and Mortality Risk Assessment

The prevalence of electronic health record (EHR) systems has brought prodigious biomedical informatics opportunity. Automated machine learning methods can effectively utilize such data and have become common tools for healthcare predictive modeling. Researches in medical informatics have explored the potential of deep learning and classical models in emergent care scenarios. In particular, predicting differential diagnoses for admissions have proven useful in decreasing unnecessary lab tests and improving inpatient triage decision-making. Moreover, identification of high-risk patients for in-hospital mortality is vitally important to maximize allocation of medical resources.The Medical Information Mart for Intensive Care (MIMIC-III) database, containing de-identified critical care inpatient was used in our study. This data set captures hospital patient laboratory measurements, pharmacologic prescriptions, diagnostic data and procedure event recordings. When considering adult patients and discounting admissions with ICU length of stay less than 24 hours, there were 37,787 unique admissions and 30,414 total patients. We examined the top 25 most prevalent ICD-9 group-level disease specificities in MIMIC-III using a multi-label classification model. In-hospital mortality was modeled as binary classification with 4,155 (13%) adult patients that expired, of which 3,138 (75.5%) were in the ICU setting. The metrics AUC, F1 score, sensitivity and specificity values calculated for each disease label measured prediction performance.The usage of ICD-9 group codes reduced feature dimension from 14,567 to 942 and greatly improved distribution of patient diagnostic categories. Disease temporal patterns were captured by considering the most frequently sampled 6 vital signs and 13 laboratory values. Missing data were imputed at each time-stamp. Time-series raw hourly average values were converted into 5 summary features (mean, standard deviation, number of observations, min & max values). Patient demographic variables such as age, gender, marital status and ethnicity were also factored into the modeling. Choi et al showed that contextual embedding of medical data, diagnostic and procedural codes alone can predict future diagnoses with sensitivity as high as 0.79. We utilized an embedding technique called word2vec which allowed sparse representations of medical history to be transformed into dense word vectors. The mappings captured contextual information by treating each admission as a sentence and learning the most likely neighboring words in a sliding window fashion. Binary and multi-label classification was achieved via collapse models, which do not consider temporal information, as well as recurrent neural networks with regularization, Softmax output layer activation together with categorical cross-entropy as the loss function.