Reviewerly/exHarmony huggingface dataset:
Explicit Sentence Compression for Neural Machine Translation

State-of-the-art Transformer-based neural machine translation (NMT) systems still follow a standard encoder-decoder framework, in which source sentence representation can be well done by an encoder with self-attention mechanism. Though Transformer-based encoder may effectively capture general information in its resulting source sentence representation, the backbone information, which stands for the gist of a sentence, is not specifically focused on. In this paper, we propose an explicit sentence compression method to enhance the source sentence representation for NMT. In practice, an explicit sentence compression goal used to learn the backbone information in a sentence. We propose three ways, including backbone source-side fusion, target-side fusion, and both-side fusion, to integrate the compressed sentence into NMT. Our empirical tests on the WMT English-to-French and English-to-German translation tasks show that the proposed sentence compression method significantly improves the translation performances over strong baselines.
Numerical investigation of the error caused by the aero-optical environment around an in-flight wing in optically measuring the wing deformation

A major concern for in-flight wing deformation measurement using optical methods is the influence of the aero-optical environment around the wing because the quality of the images degrades due to variations of the index of refraction and then the deformation ameasurement errors will be introduced. By taking a typical 2D OA309 airfoil as the research object, the error caused by the aero-optical environment is analyzed and numerically evaluated to investigate the effects of Mach number, angle of attack, and the placement of the camera on optical wing deformation measurement. At first, the error caused by the aero-optical environment in stereoscopic camera setup is analyzed, and virtual displacement is proposed as an evaluation parameter. Then, based on CFD computation, the fourth-order Runge-Kutta ray-tracing method in combination with triangular mesh interpolation is presented to determine the virtual displacement under different conditions. The results indicate that the virtual displacement in the streamwise direction has the largest level around the leading edge and the Root-Mean-Square value over the wing surface is dependent linearly on the square of Mach number. The virtual displacement reaches the minimum value at −2° angle of attack; within the range of stall angle of attack, the further away from −2° angle of attack, the larger virtual displacement the corresponding aero-optical environment will produce. The virtual displacement increases as the camera-wing distance increases, but the growth rate is slowed down. The overall virtual displacement reaches the minimum when the camera viewing angle is close to 90°. The proposed computational method and results can be helpful to appropriately position the camera in the actual in-flight wing measurement and minimize errors caused by the aero-optical environment.
Optimized Coefficient Vector and Sparse Representation-Based Classification Method for Face Recognition

Sparse representation based classification method has led to a wide variety of extensions of representation based methods for face recognition. All of these methods partially reveal that collaborative representation is a crucial factor to make sparse representation based classification powerful for face recognition. The collaborative representation based classification (CRC) methods and corresponding variations have achieved effective results in face recognition. For these methods, we found that the test sample has some relevance with the coefficient vector. For example, nonzero elements in the coefficient vector are associated with the classes which the test sample potentially belong to. Exploiting the relevance may obtain sparser coefficient vector in comparison with the traditional methods. Hence, we propose a novel method in which the test sample is closely involved in the solution procedure of optimal coefficient vector. The classification of the proposed method is performed by checking the minimal residual between the test sample and the collaborative representation with respect the test sample of the selected class, which is similar to that of CRC. The proposed method can intensify the corresponding coefficients in the coefficient vector by exploiting the test sample. Experimental results show that the proposed method does achieve more accurate recognition rate.
On the Learning Property of Logistic and Softmax Losses for Deep Neural Networks

Deep convolutional neural networks (CNNs) trained with logistic and softmax losses have made significant advancement in visual recognition tasks in computer vision. When training data exhibit class imbalances, the class-wise reweighted version of logistic and softmax losses are often used to boost performance of the unweighted version. In this paper, motivated to explain the reweighting mechanism, we explicate the learning property of those two loss functions by analyzing the necessary condition (e.g., gradient equals to zero) after training CNNs to converge to a local minimum. The analysis immediately provides us explanations for understanding (1) quantitative effects of the class-wise reweighting mechanism: deterministic effectiveness for binary classification using logistic loss yet indeterministic for multi-class classification using softmax loss; (2) disadvantage of logistic loss for single-label multi-class classification via one-vs.-all approach, which is due to the averaging effect on predicted probabilities for the negative class (e.g., non-target classes) in the learning process. With the disadvantage and advantage of logistic loss disentangled, we thereafter propose a novel reweighted logistic loss for multi-class classification. Our simple yet effective formulation improves ordinary logistic loss by focusing on learning hard non-target classes (target vs. non-target class in one-vs.-all) and turned out to be competitive with softmax loss. We evaluate our method on several benchmark datasets to demonstrate its effectiveness.
High Performance Depthwise and Pointwise Convolutions on Mobile Devices

Lightweight convolutional neural networks (e.g., MobileNets) are specifically designed to carry out inference directly on mobile devices. Among the various lightweight models, depthwise convolution (DWConv) and pointwise convolution (PWConv) are their key operations. In this paper, we observe that the existing implementations of DWConv and PWConv are not well utilizing the ARM processors in the mobile devices, and exhibit lots of cache misses under multi-core and poor data reuse at register level. We propose techniques to re-optimize the implementations of DWConv and PWConv based on ARM architecture. Experimental results show that our implementation can respectively achieve a speedup of up to 5.5× and 2.1× against TVM (Chen et al. 2018) on DWConv and PWConv.
Neuron Interaction Based Representation Composition for Neural Machine Translation

Recent NLP studies reveal that substantial linguistic information can be attributed to single neurons, i.e., individual dimensions of the representation vectors. We hypothesize that modeling strong interactions among neurons helps to better capture complex information by composing the linguistic properties embedded in individual neurons. Starting from this intuition, we propose a novel approach to compose representations learned by different components in neural machine translation (e.g., multi-layer networks or multi-head attention), based on modeling strong interactions among neurons in the representation vectors. Specifically, we leverage bilinear pooling to model pairwise multiplicative interactions among individual neurons, and a low-rank approximation to make the model computationally feasible. We further propose extended bilinear pooling to incorporate first-order representations. Experiments on WMT14 English⇒German and English⇒French translation tasks show that our model consistently improves performances over the SOTA Transformer baseline. Further analyses demonstrate that our approach indeed captures more syntactic and semantic information as expected.
Fourth order indirect diffusion coupled with shock filter and source for text binarization

In this paper, we present a binarization method for degraded document images with contrast variation or low contrast, which couples a selective shock filter and a time-dependent source to a fourth order indirect diffusion process. The role of the selective shock filter is to enhance the edges as far as possible, while the time-dependent source plays an increasingly predominant role in the process of binarization. The fourth order indirect diffusion not only keeps the merit of noise immunity of the direct diffusion counterpart, but also preferably preserves the low-frequency components to avoid blur and dislocation of weak edges. We develop a novel numerical scheme for solving our model effectively and efficiently, by incorporating finite differencing with the parallel splitting up method in the literature. The proposed algorithm is tested on seven publicly available datasets (Document Image Binarization COmpetition (DIBCO) 2009 to 2014 and 2016). The experimental results indicate that our model is very effective for binarization of document images with contrast variation or low contrast, and has mostly the best effect for document images from the DIBCO datasets, compared with four partial differential equation (PDE) models and four benchmark and recent non-PDE methods.
Performance Enhancement for Conventional Tightly Coupled INS/DVL Navigation System Using Regeneration of Partial DVL Measurements

Inertial navigation systems/Doppler velocity log (INS/DVL) integrated navigation systems are widely used in underwater environments where GPS is unavailable. An INS/DVL integrated navigation system is generally loosely coupled; however, this does not work if any of the DVL transducers do not work. If a system is tightly coupled, velocity error can be estimated with fair accuracy even if some of the transducers fail. However, despite the robustness of a tightly coupled system compared to a loosely coupled one, velocity error estimation accuracy of the former decreases as the number of faulty transducers increases. Therefore, this paper proposes an INS/DVL/revolutions per minute (RPM) integrated navigation filter designed to improve the performance of conventional tightly coupled integrated systems by estimating data from faulty transducers using RPM data. Two salient features of the proposed filter are (1) estimating RPM data accounting for error from the effect of tidal currents and (2) continuous estimation of error in RPM data by selectively converting only the measurements of faulty transducers. The performance of the proposed filter was first verified using Monte Carlo numerical simulations with the analysis range set to 1 standard deviation (1 σ , 68%) and then with real sea test measurement data.
Transferring entangled states of photonic cat-state qubits in circuit QED

We propose a method for transferring quantum entangled states of two photonic cat-state qubits (cqubits) from two microwave cavities to the other two microwave cavities. This proposal is realized by using four microwave cavities coupled to a superconducting flux qutrit. Because of using four cavities with different frequencies, the inter-cavity crosstalk is significantly reduced. Since only one coupler qutrit is used, the circuit resource is minimized. The entanglement transfer is completed with a singlestep operation only, thus this proposal is quite simple. The third energy level of the coupler qutrit is not populated during the state transfer, therefore decoherence from the higher energy level is greatly suppressed. Our numerical simulations show that high-fidelity transfer of two-cqubit entangled states from two transmission line resonators to the other two transmission line resonators is feasible with current circuit QED technology. This proposal is universal and can be applied to accomplish the same task in a wide range of physical systems, such as four microwave or optical cavities, which are coupled to a natural or artificial three-level atom.
Local-Set Based-on Instance Selection Approach for Autonomous Object Modelling

With the increasing presence of robotic agents in our daily life, computationally efficient modelling of real-world objects by autonomous systems is of prime importance for enabling these artificial agents to automatically and effectively perform tasks such as visual object recognition.For this purpose, we introduce a novel, machine-learning approach for instance selection called Approach for Selection of Border Instances (ASBI).This method adopts the notion of local sets to select the most representative instances at the boundaries of the classes, in order to reduce the set of training instances and, consequently, to reduce the computational resources that are necessary to perform the learning process of real-world objects by the artificial agents.Our new algorithm was validated on 27 standard datasets and applied on 2 challenging object-modelling datasets to test the automated object recognition task.ASBI performances were compared to those of 6 state-of-art algorithms, considering three standard metrics, namely, accuracy, reduction, and effectiveness.All the obtained results show that the proposed method is promising for the autonomous recognition task, while presenting the best trade-off between the classification accuracy and the data size reduction.
Real-Time Image Stabilization Method Based on Optical Flow and Binary Point Feature Matching

The strap-down missile-borne image guidance system can be easily affected by the unwanted jitters of the motion of the camera, and the subsequent recognition and tracking functions are also influenced, thus severely affecting the navigation accuracy of the image guidance system. So, a real-time image stabilization technology is needed to help improve the image quality of the image guidance system. To satisfy the real-time and accuracy requirements of image stabilization in the strap-down missile-borne image guidance system, an image stabilization method based on optical flow and image matching with binary feature descriptors is proposed. The global motion of consecutive frames is estimated by the pyramid Lucas-Kanade (LK) optical flow algorithm, and the interval frames image matching based on fast retina keypoint (FREAK) algorithm is used to reduce the cumulative trajectory error. A Kalman filter is designed to smooth the trajectory, which is conducive to fitting to the main motion of the guidance system. Simulations have been carried out, and the results show that the proposed algorithm improves the accuracy and real-time performance simultaneously compared to the state-of-art algorithms.
Visualizing the Impacts of Renewable Energy Growth in the U.S. Midcontinent

This paper presents the visualization approach for enhancing analyses of the ongoing Renewable Integration Impact (RIIA) Assessment by the Midcontinent Independent System Operator (MISO), and brings the following contributions. First, this paper details the customization of state-of-the-art, open-source visualization code to complement existing power system visualization tools and data analytic processes used in MISO. Second, this paper describes how MISO integrates this visualization tool within a novel and holistic process for studying renewable integration issues. This visualization tool provides additional insights for RIIA from multiple aspects with fine spatial-temporal granularity, including the comparison of thermal generation performance at different renewable integration scenarios, improved understanding of complex interactions between regions within MISO, the effects of transmission upgrades on curtailment reduction using optimization techniques, and the improved verification of MISO's simulation results. Lastly, we make the updated visualization code package publicly available.
Multi-Scale Invertible Network for Image Super-Resolution

Deep convolutional neural networks (CNNs) based image super-resolution approaches have reached significant success in recent years. However, due to the information-discarded nature of CNN, they inevitably suffer from information loss during the feature embedding process, in which extracted intermediate features cannot effectively represent or reconstruct the input. As a result, the super-resolved image will have large deviations in image structure with its low-resolution version, leading to inaccurate representations in some local details. In this study, we address this problem by designing an end-to-end invertible architecture that can reversely represent low-resolution images in any feature embedding level. Specifically, we propose a novel image super-resolution method, named multi-scale invertible network (MSIN) to keep information lossless and introduce multi-scale learning in a unified framework. In MSIN, a novel multi-scale invertible stack is proposed, which adopts four parallel branches to respectively capture features with different scales and keeps balanced information-interaction by branch shifting. In addition, we employee global and hierarchical feature fusion to learn elaborate and comprehensive feature representations, in order to further benefit the quality of final image reconstruction. We show the reversibility of the proposed MSIN, and extensive experiments conducted on benchmark datasets demonstrate the state-of-the-art performance of our method.
Approaching Non-Disruptive Distributed Ledger Technologies via the Exchange Network Architecture

The rise of distributed ledger technologies, such as R3 Corda, Hyperledger Fabric and Ethereum, has lead to a surge of interest in digitalizing different forms of contractual cooperation. By allowing for ledgers of collaboration-critical data to be reliably maintained between stakeholders without intermediaries, these solutions might enable unprecedented degrees of automation across organizational boundaries, which could have major implications for supply chain integration, medical journal sharing and many other use cases. However, these technologies tend to break with prevailing business practices by relying on code-as-contracts and distributed consensus algorithms, which can impose disruptive requirements on contract language, cooperation governance and interaction privacy. In this paper, we show how our Exchange Network architecture could be applied to avoid these disruptors. To be able to reason about the adequacy of our architecture, we present six requirements for effective contractual collaboration, which notably includes negotiable terms and effective adjudication. After outlining the architecture and our implementation of it, we describe how the latter meets our requirements by facilitating (1) negotiation, (2) user registries, (3) ownership ledgers and (4) definition sharing, as well as by only replicating ledgers between stakeholder pairs. To show how our approach compares to other solutions, we also consider how Corda, Fabric and Ethereum meet our requirements. We conclude that digital negotiation and ownership could replace many proposed uses of code-as-contracts for better compatibility with current contractual practices, as well as noting that distributed consensus algorithms are not mandatory for digital cooperation.
Residual Graph Convolutional Networks for Zero-Shot Learning

Most existing Zero-Shot Learning (ZSL) approaches adopt the semantic space as a bridge to classify unseen categories. However, it is difficult to transfer knowledge from seen categories to unseen categories through semantic space, since the correlations among categories are uncertain and ambiguous in the semantic space. In this paper, we formulated zero-shot learning as a classifier weight regression problem. Specifically, we propose a novel Residual Graph Convolution Network (ResGCN) which takes word embeddings and knowledge graph as inputs and outputs a visual classifier for each category. ResGCN can effectively alleviate the problem of over-smoothing and over-fitting. During the test, an unseen image can be classified by ranking the inner product of its visual feature and predictive visual classifiers. Moreover, we provide a new method to build a better knowledge graph. Our approach not only further enhances the correlations among categories, but also makes it easy to add new categories to the knowledge graph. Experiments conducted on the large-scale ImageNet 2011 21K dataset demonstrate that our method significantly outperforms existing state-of-the-art approaches.
NegPSpan: efficient extraction of negative sequential patterns with embedding constraints

Sequential pattern mining is concerned with the extraction of frequent or recurrent behaviors, modeled as subsequences, from a sequence dataset. Such patterns inform about which events are frequently observed in sequences, i.e. events that really happen. Sometimes, knowing that some specific event does not happen is more informative than extracting observed events. Negative sequential patterns (NSPs) capture recurrent behaviors by patterns having the form of sequences mentioning both observed events and absence of events. Few approaches have been proposed to mine such NSPs. In addition, the syntax and semantics of NSPs differ in the different methods which makes it difficult to compare them. This article provides a unified framework for the formulation of the syntax and the semantics of NSPs. Then, we introduce a new algorithm, NegPSpan, that extracts NSPs using a prefix-based depth-first scheme, enabling maxgap constraints that other approaches do not take into account. The formal framework highlights the differences between the proposed approach and methods from the literature, especially against the state of the art approach eNSP. Intensive experiments on synthetic and real datasets show that NegPSpan can extract meaningful NSPs and that it can process bigger datasets than eNSP thanks to significantly lower memory requirements and better computation times.
The effects of compassion experienced by SME employees on affective commitment: Double-mediation of authenticity and positive emotion

The purpose of this study is to empirically examine the effects of compassion experienced by smallto-medium enterprise (SME) employees on affective commitment through authenticity and positive emotion.This study was conducted with 200 employees working at SMEs located in South Korea.The results show that the relationships between compassion and authenticity, between authenticity and positive emotion, between positive emotion and affective commitment, and between compassion and affective commitment were significant.In addition, the relationship between compassion and affective commitment was significantly double-mediated by authenticity and positive emotion.The results provide a meaningful implication that SME employees would build authenticity and positive emotions by experiencing compassion and that those who build authenticity and positive emotions would enhance their affective commitment.