Reviewerly/exHarmony huggingface dataset:
Temporal Attention Mechanism with Conditional Inference for Large-Scale Multi-label Video Classification

Here we show neural network based methods, which combine multimodal sequential inputs effectively and classify the inputs into multiple categories. Two key ideas are (1) to select informative frames among a sequence using attention mechanism and (2) to utilize correlation information between labels to solve multi-label classification problems. The attention mechanism is used in both modality (spatio) and sequential (temporal) dimensions to ignore noisy and meaningless frames. Furthermore, to tackle fundamental problems induced by independently predicting each label in conventional multi-label classification methods, the proposed method considers the dependencies among the labels by decomposing joint probability of labels into conditional terms. From the experimental results (5th in the Kaggle competition), we discuss how the suggested methods operate in the YouTube-8M Classification Task, what insights they have, and why they succeed or fail.
Personalized Mobile Learning System to Enhance Language Learning Outcomes

Objectives: This study describes the development and evaluation of a personalized mobile language learning system to teach and improve spelling. Methods/Statistical Analysis: The design process consisted of two stages: the first was directed by needs analysis sessions with a focus group of 5 language teachers. Next was analyzing the data collected during the previous phase and applied it to system design. It consists of: a practical component in which a learner can practice a specific spelling rule, and a game component where a learner plays a spelling game and scores her progress. Findings: The system adopts a personalized design approach allowing the instructor, or the parent, to add new spelling rules or new spelling words, which will be automatically embedded into both the practical and game components. The evaluation process involved testing the system on young students in classroom and home settings. Data were collected through observation and interview. Results indicated an overall positive attitude towards using the system. This study hopes to open a channel of communication that will facilitate development of systems that enhance learning outcomes among young learners using mobile gaming technology. Applications: Mobile application, Personalized android app. Keywords: Gasification, Language Teaching, Mobile Assisted Language Learning, Personalized Language Learning, Spelling
Out-domain Chinese new word detection with statistics-based character embedding

Abstract Unlike English and other Western languages, many Asian languages such as Chinese and Japanese do not delimit words by space. Word segmentation and new word detection are therefore key steps in processing these languages. Chinese word segmentation can be considered as a part-of-speech (POS)-tagging problem. We can segment corpus by assigning a label for each character which indicates the position of the character in a word (e.g., “B” for word beginning, and “E” for the end of the word, etc.). Chinese word segmentation seems to be well studied. Machine learning models such as conditional random field (CRF) and bi-directional long short-term memory (LSTM) have shown outstanding performances on this task. However, the segmentation accuracies drop significantly when applying the same approaches to out-domain cases, in which high-quality in-domain training data are not available. An example of out-domain applications is the new word detection in Chinese microblogs for which the availability of high-quality corpus is limited. In this paper, we focus on out-domain Chinese new word detection. We first design a new method Edge Likelihood (EL) for Chinese word boundary detection. Then we propose a domain-independent Chinese new word detector (DICND); each Chinese character is represented as a low-dimensional vector in the proposed framework, and segmentation-related features of the character are used as the values in the vector.
Epithermal Deposits of Turkey

In Europe and Middle East, Turkey is currently positioned as the largest producer of gold with a 2015 production of 27.5 t from predominantly porphyry and epithermal deposits. Although exploration for gold by advanced geochemical methods started in 1980s, Turkey entered into hard-rock mining based on cyanide process for extracting gold from low-grade ore in 2001. Before that, apart from placer mining, gold was sold in concentrate ore as by product in some deposits chiefly mined for base metals. Epithermal deposits are high-grade deposits dominated by open-space textures generally formed at depths of ~2 km with temperatures of < 400 °C. Due to their shallow-depth location, it is regarded that they represent easily mineable source of gold. This chapter is designed to focus attention on the geology of important epithermal gold mineralisation in Turkey with the aim to understand the basic regional and local geological setting and mineralogical and geochemical properties setting for the mineralisation. In the chapter the brief summary of selected epithermal deposits is emphasized the principal characteristics of important epithermal mineralisation all over Turkey including low, intermediate and high sulphidation types without taken into account their major economic element content on the base of reports from government and private companies and published or unpublished studies. Up to date, although literature on epithermal mineralisation in Turkey has been increasing with significant exploration and published data, still numerous questions remain unanswered and provide a source for future research.
Effective and Efficient Batch Normalization Using a Few Uncorrelated Data for Statistics Estimation

Deep neural networks (DNNs) thrive in recent years, wherein batch normalization (BN) plays an indispensable role. However, it has been observed that BN is costly due to the huge reduction and elementwise operations that are hard to be executed in parallel, which heavily reduces the training speed. To address this issue, in this article, we propose a methodology to alleviate the BN's cost by using only a few sampled or generated data for mean and variance estimation at each iteration. The key challenge to reach this goal is how to achieve a satisfactory balance between normalization effectiveness and execution efficiency. We identify that the effectiveness expects less data correlation in sampling while the efficiency expects more regular execution patterns. To this end, we design two categories of approach: sampling or creating a few uncorrelated data for statistics' estimation with certain strategy constraints. The former includes “batch sampling (BS)” that randomly selects a few samples from each batch and “feature sampling (FS)” that randomly selects a small patch from each feature map of all samples, and the latter is “virtual data set normalization (VDN)” that generates a few synthetic random samples to directly create uncorrelated data for statistics' estimation. Accordingly, multiway strategies are designed to reduce the data correlation for accurate estimation and optimize the execution pattern for running acceleration in the meantime. The proposed methods are comprehensively evaluated on various DNN models, where the loss of model accuracy and the convergence rate are negligible. Without the support of any specialized libraries, 1.98× BN layer acceleration and 23.2% overall training speedup can be practically achieved on modern GPUs. Furthermore, our methods demonstrate powerful performance when solving the well-known “micro-BN” problem in the case of a tiny batch size. This article provides a promising solution for the efficient training of high-performance DNNs.
DarkSky Halos: Use-Based Exploration of Dark Matter Formation Data in a Hybrid Immersive Virtual Environment

Hybrid virtual reality environments allow analysts to choose how much of the screen real estate they want to use for Virtual Reality (VR) immersion, and how much they want to use for displaying different types of 2D data. We present the use-based design and evaluation of an immersive visual analytics application for cosmological data that uses such a 2D/3D hybrid environment. The applications is a first-in-kind immersive instantiation of the Activity-Centered-Design theoretical paradigm, as well as a first documented immersive instantiation of a details-first paradigm based on scientific workflow theory. Based on a rigorous analysis of the user activities and on a details-first paradigm, the application was designed to allow multiple domain experts to interactively analyze visual representations of spatial (3D) and nonspatial (2D) cosmology data pertaining to dark matter formation. These hybrid data are represented at multiple spatiotemporal scales as time-aligned merger trees, pixel-based heatmaps, GPU-accelerated point clouds and geometric primitives, which can further be animated according to simulation data and played back for analysis. We have demonstrated this multi-scale application to several groups of lay users and domain experts, as well as to two senior domain experts from the Adler Planetarium, who have significant experience in immersive environments. Their collective feedback shows that this hybrid, immersive application can assist researchers in the interactive visual analysis of large-scale cosmological simulation data while overcoming navigation limitations of desktop visualizations.
Jointly discriminative projection and dictionary learning for domain adaptive collaborative representation-based classification

In recent years, collaborative representation-based classification (CRC) methods have shown impressive performance in many recognition tasks. However, when the training data have different distributions with the testing data, the performance of CRC will be degraded significantly. On the other hand, concatenating training data from different sources as a single data set will affect the performance of CRC, as the shift exists between the different source domains. To address these problems, in this paper, we propose a Jointly Discriminative projection and Dictionary learning for domain adaptive Collaborative Representation-based Classification method (JD2-CRC). As the distributions of different source domains may be dissimilar, the data from all domains are projected into a common feature subspace where the latent shared structures can be found. Then a compact dictionary is learned to represent the projected data well. To find the most suitable projection matrices and dictionary for CRC, we design the objective function of JD2-CRC,according to the classification rule of CRC in feature subspace, which minimizes the ratio of within-class reconstruction errors over between-class reconstruction errors. Different to traditional optimization methods, an effective optimization procedure is presented based on gradient descent. Thus, the obtained collaborative representations have a better discriminability and suit the classification rule of CRC well. The experimental results demonstrate that the proposed method can achieve superior performance against other state-of-the-art methods.
Every Shred Helps: Assembling Evidence From Orphaned JPEG Fragments

In this paper, we address the problem of forensic photo carving, which serves as one of the key sources of digital evidence in modern law enforcement. We propose efficient algorithms for assembling meaningful photographs from orphaned photofragments, carved without access to file headers, meta-data, or compression settings. The addressed problem raises a novel variant of a jigsaw puzzle with an unknown number of mixed images, missing pieces, and severe brightness and colorization artifacts. We construct an efficient compatibility metric for matching puzzle pieces and a corresponding image stitching procedure which allows us to mitigate these artifacts. To facilitate photo assembly, we perform a forensic analysis of the fragments to provide clues about their location within the frame of the imaging sensor. The proposed algorithm formulates the assembly problem as finding non-overlapping sets in an interval graph spanned over the input fragments. The algorithm exhibits lower computational complexity compared with a popular puzzle-solving approach based on minimal spanning trees.
Continuous‐discrete filters for bearings‐only underwater target tracking problems

Abstract In this work, we develop a continuous‐discrete shifted Rayleigh filter (CD‐SRF) and a continuous‐discrete sparse‐grid Gauss‐Hermite filter (CD‐SGHF) for a real‐life passive underwater bearings‐only target tracking problem. The stochastic difference equation describing the process model is derived from its continuous equivalent using Ito‐Taylor expansion of order 1.5. The performance of the proposed filters is compared in terms of root mean square error (RMSE), track divergence and computational time. For a fair comparison, popular filters like the unscented Kalman filter (UKF), cubature Kalman filter (CKF) and Gauss–Hermite filter (GHF) are implemented. The effect of initial uncertainty, measurement noise covariance and sampling time on filtering accuracy is also studied. Finally, RMSEs of all the filters are evaluated in comparison with the Cramer–Rao lower bound (CRLB). From simulation results, it was observed that CD filters performed with higher accuracy than their discrete equivalents, with CD‐SRF proving to be the most accurate among all the filters.
Mechanism of gold precipitation in the Gezigou gold deposit, Xinjiang, NW China: Evidence from fluid inclusions and thermodynamic modeling

The Gezigou gold deposit, which is located in west Junggar (Xinjiang, China), is mainly hosted in graphite-bearing siltstone. These ore bodies, which consist of gold-bearing hydrothermal veins and mineralized siltstone with disseminated sulfides, formed in three stages: quartz-sulfides-native gold (I), arsenopyrite-calcite-native gold (II), and quartz-calcite (III) stages. The microthermometric and laser Raman spectroscopic analysis of the fluid inclusions in stage I-quartz demonstrate that the ore-forming fluid was moderate temperature (353–392 °C), low-salinity (2.9–5.3 wt% NaCl equiv.), and H2O-CH4-dominant. The thermodynamic modeling of the Au-Cu-As-Fe-S system performed using the SUPCRT92 software package with the updated database of slop16.dat indicates that a decrease in H2S activity (aH2S) and oxygen fugacity (fO2) were responsible for reducing the solubilities of As and Au, which might be responsible for the coexistence of arsenopyrite and native gold. The decreases in aH2S and fO2 caused by phase separation and fluid-rock reactions induced the precipitation of native gold in the quartz-sulfides-native gold stage. Graphite-bearing siltstone, which may lower the fO2 of ore-forming fluids, is thus an important indicator of prospective gold deposits.
The effect of electronic body protector and gamification on the performance of taekwondo athletes

The aim of the study is to analyze changes in the performance of taekwondo athletes after training with an electronic body protector and the effects of gamification on this change. The study included 15 licensed taekwondo athletes between the ages of 10 and 13 training in a private taekwondo hall. The three techniques most frequently used in matches were selected according to the level of difficulty, and the athletes followed a three-month training program. In addition, five athletes were interviewed to determine the effects of using the electronic body protector system and the gamification components on their performance. The results showed significant difference between the mean scores of all three taekwondo techniques over time. The data obtained through interviews indicated that training with an electronic body protector improved the participants’ condition and technical skills, and also helped them learn the correct use of the techniques. The electronic body protector was observed to indirectly affect performance. The participants emphasized fair scoring for everyone as the electronic body protector was perceived as an objective system. Announcement of the scores of each measurement on the scoreboard was reported to increase competition, improvement and motivation.
On Stylometric Features of H. Beam Piper’s<i>Omnilingual</i>

The article will focus on H. Beam Piper's classical story Omnilingual (1957). This Piper-esque writing has entered the records of the science fiction prose for the 'Martian' periodic table of elements, being synonymous with a scientific 'Rosetta-like stone' in the decipherment area. The work, while having a search potential in text analysis and stylistics, may add in a parallel fashion some lustre to the validity of science as a communicative channel in non-conventional circumstances. In order to capture stylistic features of the novelette, a number of quantitative indicators are drawn in. The study will concentrate on vocabulary-richness indexes (TTR, entropy, RR, RRMc, G, ATL, HL, MATTR, and Lambda), a complex assessment of activity (Busemann's coefficient, the chi-square testing classification), and a sketch of the Belza chain analysis. The goal of the article is to find distinctive features of the piece in question, and point out ways for further research.
Digital Twin and Virtual Reality and Augmented Reality/Mixed Reality

Virtual reality (VR) and augmented reality (AR)/mixed reality (MR) are becoming more mature with the development of related technologies, such as the advanced sensors, computer graphics, real-time registration and tracking, etc. The VR and AR/MR technologies play important roles in the seamless integration and fusion of the physical and virtual worlds, which is crucial for digital twin (DT) implementation. In this chapter, the DT is combined with these technologies. Based on the existing and potential applications of VR and AR/MR in the DT, a general framework is proposed to integrate these technologies with the DT to provide more immersive and interactive services to users. According to the proposed framework, an assembly process is then discussed.
Sparse and Decoupling Control Strategies Based on Takagi–Sugeno Fuzzy Models

In order to better handle the coupling effects when controlling multiple-input multiple-output (MIMO) systems, taking the decentralized control structure as the basis, this paper proposes a sparse control strategy and a decoupling control strategy. Type-1 and type-2 Takagi-Sugeno (T-S) fuzzy models are used to describe the MIMO system, and the relative normalized gain array-based criterion is employed to measure the coupling effects. The main contributions include: 1) compared to the previous studies, a manner with less computational cost to build fuzzy models for the MIMO systems is provided, and a more accurate method to construct the so-called effective T-S fuzzy model (ETSM) to express the coupling effects is developed; 2) for the sparse control strategy, four indexes are defined in order to extend a decentralized control structure to a sparse one. Afterward, an ETSM-based method is presented that a sparse control system can be realized by designing multiple independent single-input single-output (SISO) control loops; and 3) for the decoupling control strategy, a novel and simple ETSM-based decoupling compensator is developed that can effectively compensate for both steady and dynamic coupling effects. As a result, the MIMO controller design can be transformed to multiple noninteracting SISO controller designs. Both of the sparse and decoupling strategies allow to use linear SISO control algorithms to regulate a closely coupled nonlinear MIMO system without knowing its exact mathematical functions. Two examples are used to show the effectiveness of the proposed strategies.
Robust Segmentation of Nucleus in Histopathology Images via Mask R-CNN

Nuclei segmentation plays an import role in histopathology images analysis. Deep learning approaches have shown its strength for histopathology images processing in various studies. In this paper, we proposed a novel deep learning framework for automatic nuclei segmentation. The framework adopts the Mask R-CNN as backbone and employs structure-preserving color normalization (SPCN) and watershed for pre- and post-processing. The proposed framework achieved a Dice score of 90.46% on the validation set, which demonstrates its competing segmentation performance.
Empirical Dynamic Quantiles for Visualization of High-Dimensional Time Series

The empirical quantiles of independent data provide a good summary of the underlying distribution of the observations. For high-dimensional time series defined in two dimensions, such as in space and time, one can define empirical quantiles of all observations at a given time point, but such time-wise quantiles can only reflect properties of the data at that time point. They often fail to capture the dynamic dependence of the data. In this article, we propose a new definition of empirical dynamic quantiles (EDQ) for high-dimensional time series that mitigates this limitation by imposing that the quantile must be one of the observed time series. The word dynamic emphasizes the fact that these newly defined quantiles capture the time evolution of the data. We prove that the EDQ converge to the time-wise quantiles under some weak conditions as the dimension increases. A fast algorithm to compute the dynamic quantiles is presented and the resulting quantiles are used to produce summary plots for a collection of many time series. We illustrate with two real datasets that the time-wise and dynamic quantiles convey different and complementary information. We also briefly compare the visualization provided by EDQ with that obtained by functional depth. The R code and a vignette for computing and plotting EDQ are available at https://github.com/dpena157/HDts/.
High-Meets-Low: Construction of Strictly Almost Optimal Resilient Boolean Functions via Fragmentary Walsh Spectra

This paper considers the construction of resilient Boolean functions on an odd number of variables with strictly almost optimal (SAO) nonlinearity. Through introducing the fragmentary Walsh transform, a construction technique called “High-Meets-Low” is proposed. The detailed design procedures of a 39-variable 3-resilient Boolean function with SAO nonlinearity 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">38</sup> - 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">19</sup> + 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">16</sup> + 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">14</sup> are given. It is shown that the nonlinearity of an n-variable t-resilient Boolean function can reach 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n-1</sup> -2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(n-1)</sup> /2+5·2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(n-11)/2</sup> or 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n-1</sup> -2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(n-1)/2</sup> +2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(n-7)/2</sup> , which are the largest known values for the corresponding n and t values. Finally, by constructing a 29-variable balanced Boolean function with SAO nonlinearity 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">28</sup> -2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">14</sup> + 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sup> + 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">9</sup> , we show an alternative method to realize the High-Meets-Low construction technique.