Reviewerly/exHarmony huggingface dataset:
Bridging the Realism Gap for CAD-Based Visual Recognition

Computer vision aims at developing algorithms to extract high-level information from images and videos. In the industry, for instance, such algorithms are applied to guide manufacturing robots, to visually monitor plants, or to assist human operators in recognizing specific components. Recent progress in computer vision has been dominated by deep artificial neural network, i.e., machine learning methods simulating the way that information flows in our biological brains, and the way that our neural networks adapt and learn from experience. For these methods to learn how to accurately perform complex visual tasks, large amounts of annotated images are needed. Collecting and labeling such domain-relevant training datasets is, however, a tedious—sometimes impossible—task. Therefore, it has become common practice to leverage pre-available three-dimensional (3D) models instead, to generate synthetic images for the recognition algorithms to be trained on. However, methods optimized over synthetic data usually suffer a significant performance drop when applied to real target images. This is due to the realism gap, i.e., the discrepancies between synthetic and real images (in terms of noise, clutter, etc.). In my work, three main directions were explored to bridge this gap. First, an innovative end-to-end framework is proposed to render realistic depth images from 3D models, as a growing number of solutions (especially in the industry) are utilizing low-cost depth cameras (e.g., Microsoft Kinect and Intel RealSense) for recognition tasks. Based on a thorough study of these devices and the different types of noise impairing them, the proposed framework simulates their inner mechanisms, comprehensively modeling vital factors such as sensor noise, material reflectance, surface geometry, etc. Able to simulate a wide panel of depth sensors and to quickly generate large datasets, this framework is used to train algorithms for various recognition tasks, consistently and significantly enhancing their performance compared to other state-of-the-art simulation tools.In some cases, however, relevant 2D or 3D object representations to generate synthetic samples are not available. Considering this different case of data scarcity, a solution is then proposed to incrementally build a representation of visual scenes from partial observations. Provided observations are localized from one to another based on their content and registered in a global memory with spatial properties. Simultaneously, this memory can be queried to render novel views of the scene. Furthermore, unobserved regions can be hallucinated in memory, in consistence with previous observations, hallucinations, and global priors. The efficacy of the proposed mnemonic and generative system, trainable end-to-end, is demonstrated on various 2D and 3D use-cases.Finally, an advanced convolutional neural network pipeline is introduced, tackling the realism gap from a novel angle. While most methods addressing this problem focus on bringing synthetic samples—or the…
FLIPPED CLASSROOM TO FOSTER INTERCULTURAL COMPETENCE IN ENGLISH LEARNERS

This article introduces the results of an experimental intercultural English class (IEC) using the flipped classroom methodology. Thematic units were developed with the 5 E's instructional model (Engage, Explore, Explain, Elaborate, and Evaluate) based on the constructivist approach to learning. This qualitative study collected data from 20 students at Atlántico University. The questionnaires used in the analysis of the 2 survey rounds rate Students’ responses in relation to learning of contents, methodology of the class and autonomy. Results based on the second round led to four major conclusions: (i). There is satisfaction with the use of the Flipped Classroom strategy, in terms of time management and freedom to prepare classes. (ii). Practical activities are more engaging to work collaboratively. (iii) Teachers have a subtle presence which provides more opportunities for students to negotiate control and participation. (iv) Although autonomous learning is possible, teacher support is pivotal to increasing it. These findings are discussed within the framework of the didactical component of teaching and its implications for this public university.&#x0D;
Relasi Tanda pada Karakter Tokoh dalam Novel Rara Mendut Karya Y.B Mangunwijaya dan Kontribusinya Terhadap Pembelajaran Sastra di Sekolah Menengah Atas

This study aims to describe the shape of the sign relations on character characters in the form of icons, indexes, and symbols as well as the contribution of novel Rara Mendut analysis to literature learning in high school. This research is a qualitative research using a literary semiotic approach with C.S. Peirce’s sign theory. The data source in this study is the novel Rara Mendut by Y.B. Mangunwijaya. The data of this research is written data in the form of words, clauses and sentences contained in the text of the novel. Data collection techniques used in this study is documentation, reading, and note-taking techniques. Data analysis techniques in this study is data reduction, data presentation, conclusions and data verification. The results of this study found that the figure of Rara Mendut as an icon of a brave and steadfast woman, the Pranacitra figure as a clever human icon, and the figure of Tumenggung Wiraguna as an icon of arbitrary ruler. The index found in this study includes the rejection of the of Rara Mendut character againts the proposal of the Tumenggung Wiraguna character and the true love story of the character Rara Mendut and Pranacitra. The symbols in this study is a symbols of Javanese culture that is thickly used by the characters in the novel. The results of this study can contribute as an alternative teaching material for learning literature in high school.
Generating Answer Candidates for Quizzes and Answer-Aware Question Generators

In education, quiz questions have become an important tool for assessing the knowledge of students.Yet, manually preparing such questions is a tedious task, and thus automatic question generation has been proposed as a possible alternative.So far, the vast majority of research has focused on generating the question text, relying on question answering datasets with readily picked answers, and the problem of how to come up with answer candidates in the first place has been largely ignored.Here, we aim to bridge this gap.In particular, we propose a model that can generate a specified number of answer candidates for a given passage of text, which can then be used by instructors to write questions manually or can be passed as an input to automatic answer-aware question generators.Our experiments show that our proposed answer candidate generation model outperforms several baselines.
An Improved Estimation Algorithm of Space Targets Pose Based on Multi-Modal Feature Fusion

The traditional estimation methods of space targets pose are based on artificial features to match the transformation relationship between the image and the object model. With the explosion of deep learning technology, the approach based on deep neural networks (DNN) has significantly improved the performance of pose estimation. However, the current methods still have problems such as complex calculation, low accuracy, and poor real-time performance. Therefore, a new pose estimation algorithm is proposed in this paper. Firstly, the mask image of the target is obtained by the instance segmentation algorithm, and its point cloud image is obtained based on a depth map combined with camera parameters. Finally, the correlation among points is established to realize the prediction of pose based on multi-modal feature fusion. Experimental results in the YCB-Video dataset show that the proposed algorithm can recognize complex images at a speed of about 24 images per second with an accuracy of more than 80%. In conclusion, the proposed algorithm can realize fast pose estimation for complex stacked objects and has strong stability for different objects.
Real Time Neural Network Path Planning Algorithm for Robot

The emergence of robots not only changed the traditional industrial production mode, but also greatly promoted the progress of social civilization.Whether in daily life or in industrial production practice, the technical level of robots is improving every day, which emphasizes the high level of national science and technology.Robot path planning technology is an important part of robot research.The purpose of this paper is to focus on the research of robot path planning algorithm, learning and in-depth development.This paper introduces the discovery and communication of robot real-time neural network planning algorithm in machine learning, and analyzes and studies it.In order to study the robot real-time neural network path planning algorithm, through the experimental comparison of different algorithms, focus on exploring the effect of different algorithms on path planning.The research results show that the algorithm speed of robot on real-time network path is 24% higher than that of normal network path algorithm, and can be increased to 30% after deep mining, and the efficiency of machine algorithm can be increased to 35% under more complete algorithm.Therefore, the robot real-time neural network path planning algorithm can complete the task more efficiently.
Research on End-to-end Voiceprint Recognition Model Based on Convolutional Neural Network

Speech signal is a time-varying signal, which is greatly affected by individual and environment. In order to improve the end-to-end voice print recognition rate, it is necessary to preprocess the original speech signal to some extent. An end-to-end voiceprint recognition algorithm based on convolutional neural network is proposed. In this algorithm, the convolution and down-sampling of convolutional neural network are used to preprocess the speech signals in end-to-end voiceprint recognition. The one-dimensional and two-dimensional convolution operations were established to extract the characteristic parameters of Meier frequency cepstrum coefficient from the preprocessed signals, and the classical universal background model was used to model the recognition model of voice print. In this study, the principle of end-to-end voiceprint recognition was firstly analyzed, and the process of end-to-end voice print recognition, end-to-end voice print recognition features and Res-FD-CNN network structure were studied. Then the convolutional neural network recognition model was constructed, and the data were preprocessed to form the convolutional layer in frequency domain and the algorithm was tested.
Pathologist pupil dilation reflects difficulty in diagnosing digital breast tissue biopsies

Digital whole slide imaging allows pathologists to view biopsy slides on a computer screen instead of under a traditional microscope. Digital viewing facilitates real-time monitoring of search behavior and pupil diameter during the diagnostic process. In particular, eye-tracking could provide a basis for objective evaluation of clinical competence during training or automated tools that alert pathologists to conditions that may result in diagnostic errors. Prior research has shown that pupil diameter is sensitive to cognitive load, arousal, and switches between exploration and exploitation of the environment. Different categories of lesions in breast biopsies, from benign to cancerous, pose different levels of challenge to pathologists as indicated by elevated diagnostic disagreement for precancer and early stage cancer. If pupil diameter was sensitive to perceived difficulty in diagnosing biopsies, perhaps eye-tracking could be used to identify biopsies that may benefit from a second opinion in an effort to reduce diagnostic error. We measured baseline-corrected (phasic) and raw pupil diameter (tonic) in 89 pathologists who each viewed and diagnosed 14 breast biopsy cases that cover the diagnostic spectrum. Pupil data were extracted from the beginning of each trial. After removing 106 trials with poor eye-tracking quality, 1,140 trials remained. We used multiple linear regression with robust standard error estimates to account for dependent observations within pathologists. We discovered a positive association between phasic dilation and subject-centered difficulty, on the one hand, and between tonic dilation and untransformed difficulty ratings on the other. When controlling for biopsy ID, only the tonic-difficulty relationship persisted. Results suggest that tonic dilation may indicate overall arousal differences between pathologists and could signal a need for additional training and experience. Phasic dilation is sensitive to intrinsic characteristics of biopsies that tend to elicit higher difficulty ratings and could prompt second opinions for difficult cases.
Echelle gratings tiling method based on lateral shearing interferometry

A method for echelle gratings tiling based on lateral shearing interferometry is proposed. Fringe patterns of the grating assembly are generated by a wedge plate. Mosaic errors can be calculated and corrected by observing the working order and its adjacent order. An experimental mosaic of two 50mm×50mm,79grooves/mm echelles is aligned to verify the theory. The rms wavefront is better than 0.04 wavelength. Angular and translation sensitivities of less than 1 arcsec and 10 nm are achieved respectively.
Automation of Educational Institution Documentation

The article addresses issues related to the implementation of the competence approach in order to make the teaching process of an educational institution more efficient, analyzes the reasons for diminished efficiency of putting into practice the automated systems used for university management. The authors propose the structure of an automated system, which allows the university to create the required package of teaching documents taking into account the three-level system: administrator, supervisor of educational program, and lecturer. Designing object-oriented model in the area of a special subject requires identification of business-requirements to the system. On the basis of the model of subject area and specified business-requirements, the diagram of applying variants and the schemes of functioning is constructed. The suggested system of the automated execution of teaching documents enables to reduce error rate while carrying out and implementing it due to the integrated system of conceptions and feedback between its elements.
Advanced Attendance Management Systems: Technologies and Applications

The attendance management of the organization is an essential activity of their mundane life, has necessitated the need for revolutionary automatic attendance record-keeping and tracking systems. Paper surveys the emerging automated tools and applications which are popularly dominating this activity and fulfilling the requirement of accurate attendance marking systems. The technological era has gravitated more towards biometric attendance systems, but there are myriad other technologies that have been neglected thoroughly. The author identifies, extract, classify, and highlight all the evolving AMS (Attendance Management System) and have analyzed and compared their performances precisely and conscientiously. This survey identifies assorted AMS as biometric, NFC (Near Field Communication), RIFD (Radio Frequency Identification), Bluetooth, and cloud computing-based attendance systems. This article not only provides the literature review on the earlier work, but also provides an analytical report related to cloud-based AMS, discussions, and future recommendations.
CBench

A plethora of question answering (QA) systems that retrieve answers to natural language questions from knowledge graphs have been developed in recent years. However, choosing a benchmark to accurately assess the quality of a question answering system is a challenging task due to the high degree of variations among the available benchmarks with respect to their fine-grained properties. In this demonstration, we introduce CBench, an extensible, and more informative benchmarking suite for analyzing benchmarks and evaluating QA systems. CBench can be used to analyze existing benchmarks with respect to several fine-grained linguistic, syntactic, and structural properties of the questions and queries in the benchmarks. Moreover, CBench can be used to facilitate the evaluation of QA systems using a set of popular benchmarks that can be augmented with other user-provided benchmarks. CBench not only evaluates a QA system based on popular single-number metrics but also gives a detailed analysis of the linguistic, syntactic, and structural properties of answered and unanswered questions to help the developers of QA systems to better understand where their system excels and where it struggles.
Flexible Bayesian Dirichlet mixtures of generalized linear mixed models for count data

The need to model count data correctly calls for introducting a flexible yet robust model that can sufficiently handle various types of count data. Models such as Ordinary Least Squares (OLS) used in the past were considered unsuitable. The introduction of the Generalized Linear Model (GLM) and its various extensions was the first breakthrough recorded in modeling count data. Thishis article, propose the Bayesian Dirichlet process mixture prior of generalized linear mixed models (DPMglmm). Metropolis Hasting Monte Carlo Markov Chain (M-H MCMC) was used to draw parameters from target posterior distribution. The Iterated Weighted Least Square (IWLS) proposal was used to determine the acceptance probability in the M-H MCMC phase. Under and over-dispersed count data were simulated, 500 Burn-in was scanned to allow for stability in the chain. 100 thinning interval was allowed so as to nullify the possible effect of autocorrelation in the data due to the Monte Carlo procedure. The DPMglmm and other competing models were fitted to the simulated data, and real-life health insurance claims data sets. The results obtained showed that DPMglmm outperformed MCMCglmm, Bayesian Discrete Weibull, and four other frequentist models. This indicates that DPMglmm is flexible and can fit count data best, either under-dispersed or over-dispersed data.
Vigorous Replication Strategy With Balanced Quorum for Minimizing the Storage Consumption and Response Time in Cloud Environments

Digital data is establishing enormously across the globe, which is considered highly valuable due to its significant contributions to facilitate different individuals’ lives. Even though technology evolving rapidly, data availability remains a major challenge due to the heterogeneity of the data with different types, platforms, and technologies. Additionally, the acquired data must be always available and accessible for users regardless of time and location, which appears to be another crucial issue due to many inefficient system implementations. The most essential option to solve this issue is by providing the best replication strategies that are able to afford business continuity without interruption. Essentially, cloud replication must be able to secure huge data by enabling comprehensive replication strategies, optimal data availability, fast data retrieval, and cost-effective data management and maintenance. Therefore, this paper mainly introduces the Vigorous Replication Strategy with Balanced Quorum (VRS-BQ) replica placement technique for minimizing storage consumption, response time and replication process time in cloud environments. Comprehensive experiments have been conducted using the well-known CloudSim simulation tool, in which the results reveal that the proposed VRS-BQ algorithm attains 25% lower average response time, 22% lower replication time, and 20% lower storage consumption compared to the existing DPRS algorithm while maintaining a high level of data availability.
Deep EHR Spotlight: a Framework and Mechanism to Highlight Events in Electronic Health Records for Explainable Predictions.

The wide adoption of Electronic Health Records (EHR) has resulted in large amounts of clinical data becoming available, which promises to support service delivery and advance clinical and informatics research. Deep learning techniques have demonstrated performance in predictive analytic tasks using EHRs yet they typically lack model result transparency or explainability functionalities and require cumbersome pre-processing tasks. Moreover, EHRs contain heterogeneous and multi-modal data points such as text, numbers and time series which further hinder visualisation and interpretability. This paper proposes a deep learning framework to: 1) encode patient pathways from EHRs into images, 2) highlight important events within pathway images, and 3) enable more complex predictions with additional intelligibility. The proposed method relies on a deep attention mechanism for visualisation of the predictions and allows predicting multiple sequential outcomes.
Towards Micropayment for Intermediary Based Trading

Involving an intermediary between producer-consumer environments is a common practice that reduces the managerial overhead on both parties. However, this mediation has both pros and cons. For example, it can overtake the power of producers in pricing, which could cause unfair revenue distribution. We present a novel micropayment protocol called MARI, which enables verifiable pricing and facilitates payment delegation for the prod-con environment. The proposed protocol is a blockchain-based unidirectional off-chain payment mechanism using a novel Merkel tree-based smart contract. MARI allows producers to communicate the fee structure directly to their consumers. It grants total control over producers’ inbound payment flow, which an intermediary mediates and preserves the low overhead on the producers and consumers at the same time. Further, we explore how MARI meets the general requirements as a payment protocol and its scalability.
Relational Data Selection for Data Augmentation of Speaker-Dependent Multi-Band MelGAN Vocoder

Nowadays, neural vocoders can generate very high-fidelity speech when a bunch of training data is available. Although a speaker-dependent (SD) vocoder usually outperforms a speaker-independent (SI) vocoder, it is impractical to collect a large amount of data of a specific target speaker for most real-world applications. To tackle the problem of limited target data, a data augmentation method based on speaker representation and similarity measurement of speaker verification is proposed in this paper. The proposed method selects utterances that have similar speaker identity to the target speaker from an external corpus, and then combines the selected utterances with the limited target data for SD vocoder adaptation. The evaluation results show that, compared with the vocoder adapted using only limited target data, the vocoder adapted using augmented data improves both the quality and similarity of synthesized speech.