Reviewerly/exHarmony huggingface dataset:
Joint method using Akamatsu and discrete wavelet transform for image restoration

Current technology makes it easy for humans to take an image and convert it to digital content, but sometimes there is additional noise in the image so it looks damaged. The damage that often occurs, like blurring and excessive noise in digital images, can certainly affect the meaning and quality of the image. Image restoration is a process used to restore the image to its original state before the image damage occurs. In this research, we proposed an image restoration method by combining Wavelet transformation and Akamatsu transformation. Based on previous research Akamatsu's transformation only works well on blurred images. In order not to focus solely on blurry images, Akamatsu's transformation will be applied based on Wavelet transformations on high-low (HL), low-high (LH), and high-high (HH) subunits. The result of the proposed method will be comparable with the previous methods. PSNR is used as a measure of image quality restoration. Based on the results the proposed method can improve the quality of the restoration on image noise, such as Gaussian, salt and pepper, and also works well on blurred images. The average increase is around 2 dB based on the PSNR calculation.
Information Stability in Heat-Assisted Magnetic Recording

The thermal stability factor KumVm /kT and the anisotropy constant ratio Ku /Kbulk necessary for 10 years of archiving in heat-assisted magnetic recording of 2 Tbpsi are evaluated by employing a bit error rate calculation using a grain error probability P. Although the attempt frequency f0 in P is a function of the Gilbert damping constant, the Curie temperature, Ku /Kbulk , the grain volume, and temperature, f0 can be treated as a constant. The Gilbert damping constant and the Curie temperature variation are parameters with little impact. On the other hand, the grain size variation, the grain number per bit n, the mean Curie temperature Tcm , and the storage temperature T are parameters with a strong impact on bit error rate. Although KumVm /kT decreases as n increases due to a statistical problem, a larger Ku /Kbulk is necessary as n increases due to a smaller grain size. A larger Ku /Kbulk is also necessary as Tcm decreases. The bit error rate increases rapidly as T increases.
The CURE for Class Imbalance

Addressing the class imbalance problem is critical for several real world applications. The application of pre-processing methods is a popular way of dealing with this problem. These solutions increase the rare class examples and/or decrease the normal class cases. However, these procedures typically only take into account the characteristics of each individual class. This segmented view of the data can have a negative impact. We propose a new method that uses an integrated view of the data classes to generate new examples and remove cases. ClUstered REsampling (CURE) is a method based on a holistic view of the data that uses hierarchical clustering and a new distance measure to guide the sampling procedure. Clusters generated in this way take into account the structure of the data. This enables CURE to avoid common mistakes made by other resampling methods. In particular, CURE prevents the generation of synthetic examples in dangerous regions and undersamples safe, non-borderline, regions of the majority class. We show the effectiveness of CURE in an extensive set of experiments with benchmark domains. We also show that CURE is a user-friendly method that does not require extensive fine-tuning of hyper-parameters.
Can AR Technologies Have an Impact on the Definition of Art?

AR seems to be one of the most advanced and near-future technologies that produce new experiences and values that have never been before. However, both AR and art have a common means of engaging the senses. Thus, the problem of where the borderline between AR and art exists should come into question. In order to consider how AR will have an influence on the definition and the significance of art, this study analyses real and fictional elements in AR and art. AR requires the physical field where sensory information mediated by computer is projected. Consequently, viewers perceive the mixed image of real things and those not existing before eyes, that is fiction. Art also needs a real environment where the fictional world is opened. Though art and AR have something in common, there are crucial differences between them. AR technologies include the firm aim of erasing fictional elements that remain as ever in spite of their accurate representation. On the other hand, art attempts to preserve a fictional area within the real world. From the comparison of AR and art, it will come to light that whether there is the frame or not plays an important role in deciding what is art or what is reality. While AR reduces fictionality from a multi-layered scene to enrich a real experience, art cuts fiction from a present scene to idealize the real world. In this way, they constitute a dialectical circle and mediate new reality through fictional images from the reverse direction. Article received: April 5, 2019; Article accepted: July 6, 2019; Published online: October 15, 2019; Review article
Robust Importance Weighting for Covariate Shift

In many learning problems, the training and testing data follow different distributions and a particularly common situation is the \textit{covariate shift}. To correct for sampling biases, most approaches, including the popular kernel mean matching (KMM), focus on estimating the importance weights between the two distributions. Reweighting-based methods, however, are exposed to high variance when the distributional discrepancy is large and the weights are poorly estimated. On the other hand, the alternate approach of using nonparametric regression (NR) incurs high bias when the training size is limited. In this paper, we propose and analyze a new estimator that systematically integrates the residuals of NR with KMM reweighting, based on a control-variate perspective. The proposed estimator can be shown to either strictly outperform or match the best-known existing rates for both KMM and NR, and thus is a robust combination of both estimators. The experiments shows the estimator works well in practice.
In-Plane Shear Response of GFRP Laminates by ±45º and 10º Off-Axis Tensile Testing Using Digital Image Correlation1

For the determination of the shear strength, strain and modulus of reinforced composites, nearly a dozen measurement techniques exist, several of which are supported by ASTM or ISO standards. One reason for so many options is the difficulty in obtaining a reasonably pure and uniform shear stress state in the test specimen. Typically, linear strain gauges and rosette strain gauges have been used to measure deformations for the determination of the elastic modulus and the strain to failure in many of these methods. The advent of optical strain measurement techniques such as digital image correlation (DIC), provide new opportunities to generate high resolution maps of the shear strain field as a function of the globally applied strain. DIC allows for the full-field mapping of the deformations (in-plane and out-of-plane for stereo DIC) and strains (normal and shear), revealing the pattern of deformation and damage throughout the specimen. In this work, it is shown that non-contact measurements of the strain field are an important enhancement to measuring the shear properties of FRP composites. This contribution describes the use of DIC for the shear strain response while performing standardized bias extension tests on ± 45° layups and non-standardized 10° off-axis tensile tests on glass fiber reinforced (GFRP) coupon specimens. Shear moduli and shear strengths obtained will be compared to results using the V-notched method.
A Simple Method of Solution For Multi-label Feature Selection

Multi-label learning has been a topic of research interest in multimedia, text & speech recognitions, music, image processing, information retrieval etc. In Multi-label classification (MLC) each instance is associated with a set of multiple class labels. Like other machine learning algorithms, data preprocessing plays an key role in MLC. Feature selection is an important preprocessing step in MLC, due to high dimensionality of datasets and associated computational costs.Extracting the most informative features considerably reduces the computational loads of MLC. Most of the Multi-label feature selection algorithms available in literature involve conversions to multiple single labeled feature selection problems. We proposed an efficient modification of a recent multi-label feature selection algorithm [1] available in literature. Our algorithm consists of two steps: in the first step we decompose the output label space into lower dimensions using simple matrix factorization method; subsequently we employ feature selection process in the decoupled reduced space. Our simulations with real world datasets reveal the efficiency of proposed framework.
Towards a KPI-based Ontology for Condition Monitoring of Automation Systems

Condition monitoring enables companies from all industrial branches to maintain high availability of their automation systems. Thus, condition monitoring is a fundamental technology to maximize operational productivity by predicting potential fault occurrences that lead to unavailability of the automation systems. Today, performing condition monitoring is successfully achieved using custom-developed software solutions. However, the development of these software solutions is a challenging task as it requires extensive knowledge from multiple engineering disciplines. In this context, there is a high need for a systematic way to formalize this knowledge. Ontologies have had significant success in fulfilling this need by providing a formal model of domain concepts and their relations.In this paper, we present our condition monitoring ontology for automation systems and contribute with a conceptual model. This ontology incorporates ISO standards for condition monitoring and key performance indicators (KPIs). KPIs aggregate numerous sensor values in a few understandable numbers and, therefore, provide more efficient insight in condition of automation systems. To evaluate our work, we developed a condition monitoring knowledge-based system for a centrifugal separator based on the proposed ontology in an industrial project and report about lessons learned.
Il corpus parallelo italiano-russo del NKRJa. Progetto di ampliamento, applicazioni e sviluppi

With this article, we introduce our project for enlarging the Italian-Russian parallel corpus of the NKRJa (Nacional’nyj korpus russkogo jazyka) and outline several of its current and future applications. The newly expanded corpus has now reached roughly four million words, and we expect to reach 20 million, the size required for a corpus that is balanced and reliable. In order to illustrate possible applications for the Italian-Russian parallel corpus, we offer two examples: the first is its experimental use in the teaching of translation at the University of Bologna; the second its use in the context of research in Russian-Italian Contrastive Linguistics on the topic of the non-temporal qualities of the Russian future tense.
Approximate Inference in Discrete Distributions with Monte Carlo Tree Search and Value Functions

A plethora of problems in AI, engineering and the sciences are naturally formalized as inference in discrete probabilistic models. Exact inference is often prohibitively expensive, as it may require evaluating the (unnormalized) target density on its entire domain. Here we consider the setting where only a limited budget of calls to the unnormalized density oracle is available, raising the challenge of where in the domain to allocate these function calls in order to construct a good approximate solution. We formulate this problem as an instance of sequential decision-making under uncertainty and leverage methods from reinforcement learning for probabilistic inference with budget constraints. In particular, we propose the TreeSample algorithm, an adaptation of Monte Carlo Tree Search to approximate inference. This algorithm caches all previous queries to the density oracle in an explicit search tree, and dynamically allocates new queries based on a "best-first" heuristic for exploration, using existing upper confidence bound methods. Our non-parametric inference method can be effectively combined with neural networks that compile approximate conditionals of the target, which are then used to guide the inference search and enable generalization across multiple target distributions. We show empirically that TreeSample outperforms standard approximate inference methods on synthetic factor graphs.
Proxemics in the Context of the Digital School

It is impossible to live in society, without communicating with other people.Communication gives participants of school life, and thus the interpersonal relationships, the opportunity to exchange ideas, interact, follow in the same direction. Integrated planning of school places, may be described and evaluated in the light of the objectives of proxemics in the context of the "Digital School".
Stellar Speckle and Correlation Derived From Classical Wave Expansions for Spherical Antennas

Michelson phase and Hanbury Brown-Twiss intensity stellar interferometry require expressions for the first-and second-order correlation functions, respectively, of the fields radiated by stars in terms of their diameters and measured quasi-monochromatic wavelengths. Although our sun and most other stars are spherical in shape at optical wavelengths, previous determinations of speckle and correlation functions have modeled stars as circular disks rather than spheres because of the mathematical tools available for partially coherent fields on planar surfaces. However, with the incentive that most stars are indeed shaped like spheres and not disks, this article models a star as a spherical antenna composed of a random distribution of uncorrelated volume sources within a thin surface layer (photosphere). Working directly with the time-domain fields, a self-contained, straightforward, detailed derivation of speckle patterns and correlation functions is given based on a novel, angularly symmetric, spherical mode expansion with coefficients determined by the assumed Lambertian nature of the star's radiation and the uniform asymptotic behavior of the spherical Hankel functions. First-order spatially averaged and temporally averaged correlation functions are proven to be identical, and the normalized second-order correlation function is shown to equal one plus the square of the first-order correlation function. The direct time-domain approach reveals explicit expressions for the quasi-monochromatic wave-packet fields of stellar radiation as well as new criteria for the validity of the far-field approximation for the fields of incoherent sources that are much less restrictive than the Rayleigh-distance criterion for coherent sources.
Development of Quick Three-Dimensional Shape Measurement Projection Mapping System Using a Whole-Space Tabulation Method

A grating projection method is often used as a highly accurate 3D shape measurement method. A real-time 3D shape measurement system can also be applied to measure a wide and smooth curved surface, such as in sheet metal processing. In this case, operators take much effort to recognize the positions of some problem areas on an object from a measured result displayed on a monitor. This study develops a projection mapping system projecting an evaluation image, such as height, displacement, gradient, curvature factor, and area of defect, onto an object. These evaluation results are obtained from the measured 3D shape. The evaluation image should be deformed according to the 3D shape of the object because the camera and projector positions are different. Therefore, this study proposes a method to quickly produce a deformed evaluation image using a whole-space tabulation method. A coordinate transform table allowed the conversion of a camera pixel coordinate into a projector pixel coordinate by using reference planes to apply deformation to the evaluation image according to the measured 3D shape. The quick coordinate transformation from a camera pixel coordinate into a projector pixel coordinate was realized using the coordinate transform table. This is a key idea of this study. It was confirmed that the coordinate transformation from the camera pixel coordinate to the projector coordinate could be performed in 4.5 ms using the coordinate transform table. In addition, 3D shape measurement projection mapping was applied to a curved sheet metal with small deformation, and the deformation part was clearly shown by projecting the height distribution. The architecture and the experimental results are shown herein.
Gradient Boosted Decision Tree Neural Network

In this paper we propose a method to build a neural network that is similar to an ensemble of decision trees. We first illustrate how to convert a learned ensemble of decision trees to a single neural network with one hidden layer and an input transformation. We then relax some properties of this network such as thresholds and activation functions to train an approximately equivalent decision tree ensemble. The final model, Hammock, is surprisingly simple: a fully connected two layers neural network where the input is quantized and one-hot encoded. Experiments on large and small datasets show this simple method can achieve performance similar to that of Gradient Boosted Decision Trees.
Facebook AI's WAT19 Myanmar-English Translation Task Submission

This paper describes Facebook AI's submission to the WAT 2019 Myanmar-English translation task. Our baseline systems are BPE-based transformer models. We explore methods to leverage monolingual data to improve generalization, including self-training, back-translation and their combination. We further improve results by using noisy channel re-ranking and ensembling. We demonstrate that these techniques can significantly improve not only a system trained with additional monolingual data, but even the baseline system trained exclusively on the provided small parallel dataset. Our system ranks first in both directions according to human evaluation and BLEU, with a gain of over 8 BLEU points above the second best system.
The Effect of Extensive Reading on Students’ Reading Achievement Of Senior High School

The aim of this study was to investigate the effects of extensive reading on students’ reading achievement for senior high school grade X. The extensive reading was an alternative method in the teaching reading to make the students fun and easy to get the meaning of texts from many sources. The subjects were 27 students at grade X in the academic year 2018/2019. The researcher applied by using in a quasi-experimental design using a quantitative approach. The instrument of this research used pre-questionnaires, test and observation. The technique of analyzing data was applied by using a paired sample t-test from the Statistical Package for the Social Sciences 22 (SPSS22). The result of the analysis showed that the mean of the pre-test was 79, 85 and the mean of post-test was 85, 56. The mean score of posttest was higher than pretest in experimental class who were taught by extensive reading. It concluded that the implementation of the extensive reading can give positive effects to the students. It was supported by the result of observation showed that the students were enthusiastically to answer the question, many vocabularies, fun, enjoyable, easy to understand the meaning of the texts and more active in the teaching learning.
Semantic Hyperlapse: a Sparse Coding-based and Multi-Importance Approach for First-Person Videos

The availability of low-cost, high-quality personal wearable cameras combined with the unlimited storage capacity of video-sharing websites has evoked a growing interest in First-Person Videos (FPVs). Such videos are usually composed of long-running unedited streams captured by a device attached to the user body, which makes them tedious and visually unpleasant to watch. Consequently, there is a rise in the need to provide quick access to the information therein. To address this need, efforts have been applied to the development of techniques such as Hyperlapse and Semantic Hyperlapse, which aims to create visually pleasant shorter videos and emphasize semantic portions of the video, respectively. The state-of-the-art Semantic Hyperlapse method SSFF, negligees the level of importance of the relevant information, by only evaluating if it is significant or not. Other limitations of SSFF are the number of input parameters, the scalability in the number of visual features to describe the frames, and the abrupt change in the speed-up rate of consecutive video segments. In this dissertation, we propose a parameter-free Sparse Coding based methodology to adaptively fast-forward First-Person Videos, that emphasize the semantic portions applying a multi-importance approach. Experimental evaluations show that the proposed method creates shorter version video retaining more semantic information, with fewer abrupt transitions of speed-up rates, and more stable final videos than the output of SSFF. Visual results and graphical explanation of the methodology can be visualized through the link: https://youtu.be/8uStih8P5-Y.
Designovel's system description for Fashion-IQ challenge 2019

This paper describes Designovel's systems which are submitted to the Fashion IQ Challenge 2019. Goal of the challenge is building an image retrieval system where input query is a candidate image plus two text phrases describe user's feedback about visual differences between the candidate image and the search target. We built the systems by combining methods from recent work on deep metric learning, multi-modal retrieval and natual language processing. First, we encode both candidate and target images with CNNs into high-level representations, and encode text descriptions to a single text vector using Transformer-based encoder. Then we compose candidate image vector and text representation into a single vector which is exptected to be biased toward target image vector. Finally, we compute cosine similarities between composed vector and encoded vectors of whole dataset, and rank them in desceding order to get ranked list. We experimented with Fashion IQ 2019 dataset in various settings of hyperparameters, achieved 39.12% average recall by a single model and 43.67% average recall by an ensemble of 16 models on test dataset.
A study of semi-supervised speaker diarization system using gan mixture model.

We propose a new speaker diarization system based on a recently introduced unsupervised clustering technique namely, generative adversarial network mixture model (GANMM). The proposed system uses x-vectors as front-end representation. Spectral embedding is used for dimensionality reduction followed by k-means initialization during GANMM pre-training. GANMM performs unsupervised speaker clustering by efficiently capturing complex data distributions. Experimental results on the AMI meeting corpus show that the proposed semi-supervised diarization system matches or exceeds the performance of competitive baselines. On an evaluation set containing fifty sessions with varying durations, the best achieved average diarization error rate (DER) is 17.11%, a relative improvement of 33% over the information bottleneck baseline and comparable to xvector baseline.