Reviewerly/exHarmony huggingface dataset:
Ready Species One: Exploring the Use of Augmented Reality to Enhance Systematic Biology with a Revision of Fijian Strumigenys (Hymenoptera: Formicidae)

Abstract Advances in technology are rapidly changing the way people transmit, view, and interact with information. These advances offer new opportunities for researchers to share scientific discoveries with each other and the general public as never before. The field of revisionary biology has audiences confined to small groups of specialists, but the core task of systematic biology—documenting the endless forms of nature—is particularly well suited to capitalize on innovations in the realm of virtual, mixed and augmented reality. Interactive three-dimensional (3D) digital models of biological specimens can help bridge barriers across scientific disciplines by circumventing technical jargon, and also promise to open exciting new vistas for public engagement. Here, we explore the potential of augmented reality for communicating the discovery of new species. As a test case, we revise a radiation of Strumigenys Smith (Hymenoptera: Formicidae) miniature trap-jaw ants in Fiji. In addition to traditional revisionary elements, we present the augmented reality application ‘Insects3D’ built specifically for this study. The application runs on mobile devices and allows users to interact with X-ray microtomography-derived 3D specimen models and visualize 3D geographic distribution maps. We recognize 23 species in Fiji, including 6 new species: S. anorak n. sp., S. artemis n. sp., S. avatar n. sp., S. gunter n. sp., S. oasis n. sp., and S. parzival n. sp. This study demonstrates the potential of leveraging 3D data and technology for a more interactive systematic biology, and the need for research programs to develop robust and generalized tools to realize this potential.
Improving Language Generation from Feature-Rich Tree-Structured Data with Relational Graph Convolutional Encoders

The Multilingual Surface Realization Shared Task 2019 focuses on generating sentences from lemmatized sets of universal dependency parses with rich features. This paper describes the results of our participation in the deep track. The core innovation in our approach is to use a graph convolutional network to encode the dependency trees given as input. Upon adding morphological features, our system achieves the third rank without using data augmentation techniques or additional components (such as a re-ranker).
Snow Removal From Light Field Images

In this paper, we propose a novel deep convolutional neural network (DCNN) for removing snowflakes from light field (LF) images. We observe that snowflakes in LF images always interrupt slopes in background scenes in epipolar plane images (EPIs), which means that snowflakes may be easily detected in EPIs. Our method takes 3D EPI volumes (i.e., stacked subaperture views along the same row or column of an LF image) as input. In this way, our snowflake detector based on a 3D residual network with a convolutional long short-term memory (ResNet-ConvLSTM) can utilize both contextual information and 3D scene structural information to effectively detect snowflakes of different sizes in LF images. Then, an encoder-decoder-based LF image restoration network is proposed to restore the background image. Finally, extensive experiments for comparison with the state-of-the-art methods demonstrate the effectiveness of our method for challenging scenes.
Dual-sensitive spatial phase-shifting shearography based on a common-path configuration

A common-path spatial phase shifting digital speckle pattern shearing interferometry setup is introduced for simultaneous measurement of in-plane and out-plane strain components under dynamic loading using two laser beams with different wavelengths that symmetrically illuminate the test object and a single detector. The simplicity, stability, and efficiency of the arrangement are provided by using a glass plate as a shearing device, which is capable of tuning the sensitivity continuously. The phase is recovered from a single frame by the Fourier method. In this setup, the spatial carrier frequencies can be adjusted independent of the amount of the lateral shear. Ultimately, the desired field of view can easily be achieved by simple imaging optics. To validate the feasibility and the flexibility of our technique, the proposed setup is used to evaluate the in-plane and out-plane strain maps of an aluminum plate, which is deformed under dynamic stress in its plane. The temporal phase stability of the proposed system is also investigated.
Towards a Unified End-to-End Approach for Fully Unsupervised Cross-Lingual Sentiment Analysis

Sentiment analysis in low-resource languages suffers from the lack of training data.Crosslingual sentiment analysis (CLSA) aims to improve the performance on these languages by leveraging annotated data from other languages.Recent studies have shown that CLSA can be performed in a fully unsupervised manner, without exploiting either target language supervision or cross-lingual supervision.However, these methods rely heavily on unsupervised cross-lingual word embeddings (CLWE), which has been shown to have serious drawbacks on distant language pairs (e.g.English -Japanese).In this paper, we propose an end-to-end CLSA model by leveraging unlabeled data in multiple languages and multiple domains and eliminate the need for unsupervised CLWE.Our model applies to two CLSA settings: the traditional cross-lingual in-domain setting and the more challenging cross-lingual cross-domain setting.We empirically evaluate our approach on the multilingual multi-domain Amazon review dataset.Experimental results show that our model outperforms the baselines by a large margin despite its minimal resource requirement. 1
Cuckoo Counter: A Novel Framework for Accurate Per-Flow Frequency Estimation in Network Measurement

Per-flow frequency estimation plays a fundamental role in network measurement. As a probabilistic data structure, sketch has been extensively investigated and used for per-flow frequency estimation, but most sketch-based proposals in previous literatures cannot achieve high accuracy and high speed simultaneously. Moreover, because each insertion to a sketch causes increment in multiple entries, the over-estimation error will accumulate quickly over time. In this paper, we propose Cuckoo Counter, a compact and accurate framework for per-flow frequency estimation, which employs three novel ideas: (1)kicking out conflicting flows instead of using multiple entries counts to improve accuracy; (2)using different sizes of entries to insulate mice flows from elephant flows, which can handle the skewed data streams efficiently and improve memory utilization; (3) a Cuckoo-like replacement strategy for mice flows, so as to maintain accurate records for elephant flows. To verify the effectiveness and efficiency of our framework, we compared it with two well-known sketches as well as the recent proposed Augmented sketch and Pyramid sketch. Extensive experimental results on three different types of test datasets show that Cuckoo Counter outperforms these sketches considerably.
Towards Annotating and Creating Summary Highlights at Sub-sentence Level

Highlighting is a powerful tool to pick out important content and emphasize.Creating summary highlights at the sub-sentence level is particularly desirable, because sub-sentences are more concise than whole sentences.They are also better suited than individual words and phrases that can potentially lead to disfluent, fragmented summaries.In this paper we seek to generate summary highlights by annotating summary-worthy sub-sentences and teaching classifiers to do the same.We frame the task as jointly selecting important sentences and identifying a single most informative textual unit from each sentence.This formulation dramatically reduces the task complexity involved in sentence compression.Our study provides new benchmarks and baselines for generating highlights at the sub-sentence level.
Statistical tests for latent class in censored data due to detection limit

Measures of substance concentration in urine, serum or other biological matrices often have an assay limit of detection. When concentration levels fall below the limit, the exact measures cannot be obtained. Instead, the measures are censored as only partial information that the levels are under the limit is known. Assuming the concentration levels are from a single population with a normal distribution or follow a normal distribution after some transformation, Tobit regression models, or censored normal regression models, are the standard approach for analyzing such data. However, in practice, it is often the case that the data can exhibit more censored observations than what would be expected under the Tobit regression models. One common cause is the heterogeneity of the study population, caused by the existence of a latent group of subjects who lack the substance measured. For such subjects, the measurements will always be under the limit. If a censored normal regression model is appropriate for modeling the subjects with the substance, the whole population follows a mixture of a censored normal regression model and a degenerate distribution of the latent class. While there are some studies on such mixture models, a fundamental question about testing whether such mixture modeling is necessary, i.e. whether such a latent class exists, has not been studied yet. In this paper, three tests including Wald test, likelihood ratio test and score test are developed for testing the existence of such latent class. Simulation studies are conducted to evaluate the performance of the tests, and two real data examples are employed to illustrate the tests.
Mathematically optimized, recursive prepartitioning strategies for k-anonymous microaggregation of large-scale datasets

The technical contents of this work fall within the statistical disclosure control (SDC) field, which concerns the postprocessing of the demographic portion of the statistical results of surveys containing sensitive personal information, in order to effectively safeguard the anonymity of the participating respondents. A widely known technique to solve the problem of protecting the privacy of the respondents involved beyond the mere suppression of their identifiers is the k-anonymous microaggregation. Unfortunately, most microaggregation algorithms that produce competitively low levels of distortions exhibit a superlinear running time, typically scaling with the square of the number of records in the dataset. This work proposes and analyzes an optimized prepartitioning strategy to reduce significantly the running time for the k-anonymous microaggregation algorithm operating on large datasets, with mild loss in data utility with respect to that of MDAV, the underlying method. The optimization strategy is based on prepartitioning a dataset recursively until the desired k-anonymity parameter is achieved. Traditional microaggregation algorithms have quadratic computational complexity in the form Θ(n2). By using the proposed method and fixing the number of recurrent prepartitions we obtain subquadratic complexity in the form Θ(n3/2), Θ(n4/3), ..., depending on the number of prepartitions. Alternatively, fixing the ratio between the size of the microcell and the macrocell on each prepartition, quasilinear complexity in the form Θ(nlog n) is achieved. Our method is readily applicable to large-scale datasets with numerical demographic attributes.
Philosophical and societal elements of human upbringing

The study is focused on analysis of topical issues of creating human personality.The author's base is on the theory of philosophy of education and in particular on its importance.The text gradually resolves the philosophy of man in relation to the nature and the society, the philosophy of education and the philosophy of pedagogy as educational science.The summary of meanings of the philosophy of education links the human being with nature and society and with socialization of man and his decent life.Formation of man's personality is connected with the education and training as part of culture, analysis of the conditions of existence of the contemporary world with the need of natural activities and responsibility of educators and those who are brought up.From the aspect of the educational philosophy, educational context is bound to global issues, which we can deduce the content and orientation of education and training within a national scale from.The study also addresses the need to analyze the position and importance of man in human life and in the world in meaning of his naturalness, offers the solution for the development of man and his place in the society based on his natural growth influenced by heredity, environment and education.
Linguistic Indicators in the Identification of Fake News

&lt;p&gt;The issue of fake news identification was approached from the corpus linguistics and discursive studies perspective. The texts of both actual and fake news have been analysed in search of dependences that would permit the increase of the ability to determine the probability of the given news being real or fake, taking into account the discursive characteristics of the particular texts.&lt;/p&gt;
Augmented Reality User Interface Evaluation – Performance Measurement of Hololens, Moverio and Mouse Input

&lt;div class="page" title="Page 1"&gt;&lt;div class="section"&gt;&lt;div class="layoutArea"&gt;&lt;div class="column"&gt;&lt;p&gt;&lt;span&gt;Recent innovation in the field of Augmented Reality (AR) and Virtual Reality (VR) has brought new devices on the market. Many industries see a big future in AR business and applications. The present research focuses on the user input performance of these AR-devices. This paper proposes an evaluation procedure using a server based input interface with a built-in assessment control. The evaluation is performed by test persons exposed to two AR devices: Microsoft Hololens and Epson Moverio BT-200. A conventional mouse input is used as a benchmark. The assessment reveals a trend of strength and weaknesses of each device and can orient developers to create more optimized AR experiences and improve the user experience.&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;
Empirical Evaluation of Active Learning Techniques for Neural MT

Active learning (AL) for machine translation (MT) has been well-studied for the phrasebased MT paradigm.Several AL algorithms for data sampling have been proposed over the years.However, given the rapid advancement in neural methods, these algorithms have not been thoroughly investigated in the context of neural MT (NMT).In this work, we address this missing aspect by conducting a systematic comparison of different AL methods in a simulated AL framework.Our experimental setup to compare different AL methods uses: i) State-of-the-art NMT architecture to achieve realistic results; and ii) the same dataset (WMT'13 English-Spanish) to have fair comparison across different methods.We then demonstrate how recent advancements in unsupervised pre-training and paraphrastic embedding can be used to improve existing AL methods.Finally, we propose a neural extension for an AL sampling method used in the context of phrase-based MT -Round Trip Translation Likelihood (RTTL).RTTL uses a bidirectional translation model to estimate the loss of information during translation and outperforms previous methods.
AUTHENTICATION OF MP4 FILE BY PERCEPTUAL HASH AND DATA HIDING

With advances in multimedia signal processing and abundance of data, multimedia authentication becomes more and more challenging and demanding. Although many prior techniques were put forward to authenticate individual video or audio stream, the cross-layer authentication of audio-visual content as a whole remains unexplored. In this work, an authentication system of the dominant audio-visual content file format (MP4) is presented. Specifically, the perceptual hashes of I-frames from video stream are encrypted and embedded into the audio stream, and the audio hash vector is encrypted and embedded in the synchronization information, i.e., stts box, in the MP4 file. The proposed system is implemented and tested on various videos downloaded from YouTube. Results show that the proposed system can authenticate MP4 files correctly without degrading the video quality while being able to provide indication of tampering.
Reliable interoperation of demand response entities considering reputation based on blockchain

Demand response (DR) in the smart grid will promote the stable operation of the power system and reduce economic costs effectively. However, uncertainty of users' behavior becomes a barrier of DR, in which the credibility between the DR users and aggregators will have a crucial impact on the DR. Blockchain is a new technology to solve the trust problem without third‐party endorsement, which has the advantages of decentralization and irreversibility. Considering the difference of computing capability between users and aggregators, this article proposes a blockchain‐based strategy to support the bidirectional selection between DR users and aggregators based on reputation values, which improves the success rate of the DR and mitigates the impact of user behavior on the DR. At the same time, this strategy can also increase the enthusiasm of users with a high reputation value to participate in DR. Using the network topology and DR incentive rules of Suzhou, Jiangsu Province, the rationality of blockchain‐based reliable interoperation for DR entities proposed in this paper is verified. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley &amp; Sons, Inc.
Local Tilt Optimization of Photovoltaic Solar Panels for Maximum Radiation Absorption

Incident solar radiation on photovoltaic (PV) solar panels is not constant throughout the year. Besides dependence on the season, solar radiation is reliant on the location and weather conditions. For a given location on Earth, the best-fixed orientation of a PV panel can be determined by achieving the maximum incident solar irradiance throughout the year or for a predetermined period. In this paper, we use a sophisticated atmospheric radiative transfer model to calculate the direct and diffuse solar irradiation (radiant exposure) for the solar spectrum incident on PV solar panels to determine the best tilt angle of the panel in order to maximize absorption of solar radiation for selected periods. We used the Regula-Falsi numerical method to obtain the tilt angle at which the derivative of solar irradiation (concerning the tilt angle) approaches zero. Moreover, the spectral response of typical silicon cells is taken into account. These calculations were carried out in São Carlos (SP), a town in the southeast of Brazil. The best tilt angle was obtained for three selected periods. Additionally, we provide results for Southern latitudes ranging from 0° to −55° in steps of −5° for the meteorological seasons. We have shown that for each period, there is an increase in solar radiation absorption compared to the traditional installation angle based exclusively on the local latitude. These calculations can be extended to any location.
Semantic Segmentation of High Resolution Remote Sensing Image Based on Batch-Attention Mechanism

Deep convolution neural network has been widely used in recent works for semantic segmentation of High Resolution Remote Sensing(HRRS) images. Because of the limitation of GPU memory, HRRS images are usually split into several sub-images for training convolutional neural networks. For each sub-image, the segmentation model may not have enough information to predict the segmentation map very well. In order to alleviate this problem, we propose to apply a batch-attention module to capture the discriminative information from similar objects, which come from other sub-images in a mini-batch. We also utilize global attention upsample module as the decoder to provide global context and fuse high and low level information better. We evaluate our model on the Potsdam dataset and achieve 88.30% pixAcc and 73.78% mIoU.
Neural Token Representations and Negation and Speculation Scope Detection in Biomedical and General Domain Text

Since the introduction of context-aware token representation techniques such as Embeddings from Language Models (ELMo) and Bidirectional Encoder Representations from Transformers (BERT), there have been numerous reports on improved performance on a variety of natural language tasks.Nevertheless, the degree to which the resulting context-aware representations can encode information about morpho-syntactic properties of the tokens in a sentence remains unclear.In this paper, we investigate the application and impact of state-of-the-art neural token representations for automatic cueconditional speculation and negation scope detection coupled with the independently computed morpho-syntactic information.Through this work, We establish a new state-of-the-art for the BioScope and NegPar corpora.Furthermore, we provide a thorough analysis of neural representations and additional features interactions, cue-representation for conditioning, discussing model behavior on different datasets and, finally, address the annotation-induced biases in the learned representations.
Combining Lexical Substitutes in NeuralWord Sense Induction

Word Sense Induction (WSI) is the task of grouping of occurrences of an ambiguous word according to their meaning.In this work, we improve the approach to WSI proposed by Amrami and Goldberg (2018) based on clustering of lexical substitutes for an ambiguous word in a particular context obtained from neural language models.Namely, we propose methods for combining information from left and right context and similarity to the ambiguous word, which result in generating more accurate substitutes than the original approach.Our simple yet efficient improvement establishes a new state-of-theart on WSI datasets for two languages.Besides, we show improvements to the original approach on a lexical substitution dataset.
Accurate Real-Time Estimation of Power System Transients Using Constrained Symmetric Strong Tracking Square-Root Cubature Kalman Filter

The paper presents a fast and accurate algorithm for estimating four significant parameters (i.e., amplitude, frequency, phase angle, and damping factor) of a typical transient signal. The method can be connoted as the constrained symmetric strong tracking square-root cubature Kalman filter (CSSTSCKF). The important aspects of the proposed algorithm are: 1) constraints are imposed on the state vectors by way of a logarithmic barrier function that is either ignored or handled heuristically; 2) symmetric sub-optimal multiple fading factors (FFs) are augmented into the predicted covariance matrix to capture sudden changes and to tune the gain matrix in real-time; moreover, symmetry of the covariance matrix is guaranteed by the influence of Cholesky triangular decomposition; 3) effect of noise can be adjusted by tuning the soften factor. Several case studies have been simulated to evaluate the proposed algorithm with respect to some of the well-known state-of-the-art methods. The real-time performance has been evaluated by flashing the filter codes into an ARM Cortex-M7 processor board and tracking the real-time signal from the experimental test bench. The results, presented herein, indicate that the CSSTSCKF remarkably outperforms all other considered techniques. Furthermore, the stability analysis of the nonlinear filter has been proved based on the constructor expression considering the boundedness of the estimation errors and other sub-items.