Reviewerly/exHarmony huggingface dataset:
Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation

Question Generation (QG) is fundamentally a simple syntactic transformation; however, many aspects of semantics influence what questions are good to form.We implement this observation by developing Syn-QG, a set of transparent syntactic rules leveraging universal dependencies, shallow semantic parsing, lexical resources, and custom rules which transform declarative sentences into questionanswer pairs.We utilize PropBank argument descriptions and VerbNet state predicates to incorporate shallow semantic content, which helps generate questions of a descriptive nature and produce inferential and semantically richer questions than existing systems.In order to improve syntactic fluency and eliminate grammatically incorrect questions, we employ back-translation over the output of these syntactic rules.A set of crowd-sourced evaluations shows that our system can generate a larger number of highly grammatical and relevant questions than previous QG systems and that back-translation drastically improves grammaticality at a slight cost of generating irrelevant questions.
A Top-down Neural Architecture towards Text-level Parsing of Discourse Rhetorical Structure

Due to its great importance in deep natural language understanding and various down-stream applications, text-level parsing of discourse rhetorical structure (DRS) has been drawing more and more attention in recent years.However, all the previous studies on text-level discourse parsing adopt bottom-up approaches, which much limit the DRS determination on local information and fail to well benefit from global information of the overall discourse.In this paper, we justify from both computational and perceptive points-of-view that the top-down architecture is more suitable for textlevel DRS parsing.On the basis, we propose a top-down neural architecture toward text-level DRS parsing.In particular, we cast discourse parsing as a recursive split point ranking task, where a split point is classified to different levels according to its rank and the elementary discourse units (EDUs) associated with it are arranged accordingly.In this way, we can determine the complete DRS as a hierarchical tree structure via an encoder-decoder with an internal stack.Experimentation on both the English RST-DT corpus and the Chinese CDTB corpus shows the great effectiveness of our proposed top-down approach towards textlevel DRS parsing.
SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing

Dialogue state tracker is responsible for inferring user intentions through dialogue history.Previous methods have difficulties in handling dialogues with long interaction context, due to the excessive information.We propose a Dialogue State Tracker with Slot Attention and Slot Information Sharing (SAS) to reduce redundant information's interference and improve long dialogue context tracking.Specially, we first apply a Slot Attention to learn a set of slot-specific features from the original dialogue and then integrate them using a Slot Information Sharing.The sharing improve the models ability to deduce value from related slots.Our model yields a significantly improved performance compared to previous state-of-the-art models on the Multi-WOZ dataset.
Generalized Entropy Regularization or: There’s Nothing Special about Label Smoothing

Prior work has explored directly regularizing the output distributions of probabilistic models to alleviate peaky (i.e.over-confident) predictions, a common sign of overfitting.This class of techniques, of which label smoothing is one, has a connection to entropy regularization.Despite the consistent success of label smoothing across architectures and datasets in language generation tasks, two problems remain open:(1) there is little understanding of the underlying effects entropy regularizers have on models, and (2) the full space of entropy regularization techniques is largely unexplored.We introduce a parametric family of entropy regularizers, which includes label smoothing as a special case, and use it to gain a better understanding of the relationship between the entropy of a trained model and its performance on language generation tasks.We also find that variance in model performance can be explained largely by the resulting entropy of the model.Lastly, we find that label smoothing provably does not allow for sparse distributions, an undesirable property for language generation models, and therefore advise the use of other entropy regularization methods in its place.Our code is available online at https://github.com/rycolab/entropyRegularization. 2 H(p, q) := -z∈Z p(z) log q(z) is cross-entropy and H(p) := H(p, p) = -z∈Z p(z) log p(z) is the Shannon entropy, for which log = log 2 and Z = supp(p).3 The notation used by Pereyra et al. ( 2017) is imprecise.
Set-Constrained Viterbi for Set-Supervised Action Segmentation

This paper is about weakly supervised action segmentation, where the ground truth specifies only a set of actions present in a training video, but not their true temporal ordering. Prior work typically uses a classifier that independently labels video frames for generating the pseudo ground truth, and multiple instance learning for training the classifier. We extend this framework by specifying an HMM, which accounts for co-occurrences of action classes and their temporal lengths, and by explicitly training the HMM on a Viterbi-based loss. Our first contribution is the formulation of a new set-constrained Viterbi algorithm (SCV). Given a video, the SCV generates the MAP action segmentation that satisfies the ground truth. This prediction is used as a framewise pseudo ground truth in our HMM training. Our second contribution in training is a new regularization of feature affinities between training videos that share the same action classes. Evaluation on action segmentation and alignment on the Breakfast, MPII Cooking2, Hollywood Extended datasets demonstrates our significant performance improvement for the two tasks over prior work.
CALPA-NET: Channel-Pruning-Assisted Deep Residual Network for Steganalysis of Digital Images

Over the past few years, detection performance improvements of deep-learning based steganalyzers have been usually achieved through structure expansion. However, excessive expanded structure results in huge computational cost, storage overheads, and consequently difficulty in training and deployment. In this paper we propose CALPA-NET, a ChAnneL-Pruning-Assisted deep residual network architecture search approach to shrink the network structure of existing vast, over-parameterized deep-learning based steganalyzers. We observe that the broad inverted-pyramid structure of existing deep-learning based steganalyzers might contradict the well-established model diversity oriented philosophy, and therefore is not suitable for steganalysis. Then a hybrid criterion combined with two network pruning schemes is introduced to adaptively shrink every involved convolutional layer in a data-driven manner. The resulting network architecture presents a slender bottleneck-like structure. We have conducted extensive experiments on BOSSBase + BOWS2 dataset, more diverse ALASKA dataset and even a large-scale subset extracted from ImageNet CLS-LOC dataset. The experimental results show that the model structure generated by our proposed CALPA-NET can achieve comparative performance with less than two percent of parameters and about one third FLOPs compared to the original steganalytic model. The new model possesses even better adaptivity, transferability, and scalability.
Employing a Chatbot for News Dissemination during Crisis: Design, Implementation and Evaluation

The use of chatbots in news media platforms, although relatively recent, offers many advantages to journalists and media professionals and, at the same time, facilitates users’ interaction with useful and timely information. This study shows the usability of a news chatbot during a crisis situation, employing the 2020 COVID-19 pandemic as a case study. The basic targets of the research are to design and implement a chatbot in a news media platform with a two-fold aim in regard to evaluation: first, the technical effort of creating a functional and robust news chatbot in a crisis situation both from the AI perspective and interoperability with other platforms, which constitutes the novelty of the approach; and second, users’ perception regarding the appropriation of this news chatbot as an alternative means of accessing existing information during a crisis situation. The chatbot designed was evaluated in terms of effectively fulfilling the social responsibility function of crisis reporting, to deliver timely and accurate information on the COVID-19 pandemic to a wide audience. In this light, this study shows the advantages of implementing chatbots in news platforms during a crisis situation, when the audience’s needs for timely and accurate information rapidly increase.
Membership-Function-Dependent Fault Detection Filtering Design for Interval Type-2 T–S Fuzzy Systems in Finite Frequency Domain

This article studies the problem of finite frequency fault detection filtering design for uncertain nonlinear systems based on interval type-2 Takagi-Sugeno fuzzy models. It is assumed that the frequencies of disturbances and faults are in finite frequency sets, respectively. The objective is to design an admissible filter such that the fault detection system is asymptotically stable with prescribed finite frequency \mathscr H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> and \mathscr H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-</sub> performances. Based on Fourier transform and Projection lemma, finite frequency filtering synthesis results are obtained. Then, a novel membership-function-dependent finite frequency fault detection filtering design approach is proposed by using the information of the lower and upper membership functions together with the footprint of uncertainties. Two algorithms with linear matrix inequality constraints are developed to optimize the finite frequency \mathscr H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> performance and the finite frequency \mathscr H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-</sub> performance, respectively. Finally, simulation studies are provided to show the effectiveness of the proposed method.
Reformulation of the No-Free-Lunch Theorem for Entangled Datasets

The no-free-lunch (NFL) theorem is a celebrated result in learning theory that limits one's ability to learn a function with a training dataset. With the recent rise of quantum machine learning, it is natural to ask whether there is a quantum analog of the NFL theorem, which would restrict a quantum computer's ability to learn a unitary process with quantum training data. However, in the quantum setting, the training data can possess entanglement, a strong correlation with no classical analog. In this Letter, we show that entangled datasets lead to an apparent violation of the (classical) NFL theorem. This motivates a reformulation that accounts for the degree of entanglement in the training set. As our main result, we prove a quantum NFL theorem whereby the fundamental limit on the learnability of a unitary is reduced by entanglement. We employ Rigetti's quantum computer to test both the classical and quantum NFL theorems. Our Letter establishes that entanglement is a commodity in quantum machine learning.
Dance Emotion Recognition Based on Laban Motion Analysis Using Convolutional Neural Network and Long Short-Term Memory

Dance emotion recognition technology is of great significance for the digitalization, virtual performance, inheritance and protection of folk dance. Based on the mechanism that emotion expression in dance performance can be fully expressed through the strength and rhythm of dance movements, a novel dance emotion expression method is proposed to train hybrid deep learning neural network, to effectively identify the seven basic dance emotions of fear, anger, boredom, excitement, joy, relaxation and sadness. First, in order to fully express the emotions contained in the dance movements, this paper defines a dance emotion expression method through Laban Movement Analysis (LMA) method, which includes the characteristic parameters of the three aspects of body structure, spatial orientation and force effect, and converts the original dance movement data into three characteristic expression parameters to obtain dance emotion data. Then, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) hybrid neural network models are used to test and train dance emotion data. Finally, in order to verify the applicability of the CNN-LSTM model, decision tree, random forest, CNN and LSTM are established and compared for accuracy. The results show that it is feasible to identify dance emotion from the perspective of dance movement, and the CNN-LSTM model is of high accuracy.
Sparse multi-output Gaussian processes for online medical time series prediction

Abstract Background For real-time monitoring of hospital patients, high-quality inference of patients’ health status using all information available from clinical covariates and lab test results is essential to enable successful medical interventions and improve patient outcomes. Developing a computational framework that can learn from observational large-scale electronic health records (EHRs) and make accurate real-time predictions is a critical step. In this work, we develop and explore a Bayesian nonparametric model based on multi-output Gaussian process (GP) regression for hospital patient monitoring. Methods We propose MedGP, a statistical framework that incorporates 24 clinical covariates and supports a rich reference data set from which relationships between observed covariates may be inferred and exploited for high-quality inference of patient state over time. To do this, we develop a highly structured sparse GP kernel to enable tractable computation over tens of thousands of time points while estimating correlations among clinical covariates, patients, and periodicity in patient observations. MedGP has a number of benefits over current methods, including (i) not requiring an alignment of the time series data, (ii) quantifying confidence regions in the predictions, (iii) exploiting a vast and rich database of patients, and (iv) inferring interpretable relationships among clinical covariates. Results We evaluate and compare results from MedGP on the task of online prediction for three patient subgroups from two medical data sets across 8,043 patients. We find MedGP improves online prediction over baseline and state-of-the-art methods for nearly all covariates across different disease subgroups and hospitals. Conclusions The MedGP framework is robust and efficient in estimating the temporal dependencies from sparse and irregularly sampled medical time series data for online prediction. The publicly available code is at https://github.com/bee-hive/MedGP .
Real-time high dynamic range 3D measurement using fringe projection

Fringe projection profilometry (FPP) is a widely used technique for real-time three-dimensional (3D) shape measurement. However, it tends to compromise when measuring objects that have a large variation range of surface reflectivity. In this paper, we present a FPP method that can increase the dynamic range for real-time 3D measurements. First, binary fringe patterns are projected to generate grayscale sinusoidal patterns with the defocusing technique. Each pattern is then captured twice with different exposure values in one projection period. With image fusion, surfaces under appropriate exposure are retained. To improve the real-time performance of high dynamic range (HDR) 3D shape measurements, we build a binocular fringe projection profilometry system that saves the number of patterns by geometry constraint. Further, to ensure the accuracy and robustness of HDR 3D measurements, we propose a mixed phase unwrapping method that can reduce phase unwrapping errors for dense fringe patterns. Experiment results show that the proposed method can realize accurate and real-time 3D measurement for HDR scenes at 28 frames per second.
Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island

Smart islands have focused on renewable energy sources, such as solar and wind, to achieve energy self-sufficiency. Because solar photovoltaic (PV) power has the advantage of less noise and easier installation than wind power, it is more flexible in selecting a location for installation. A PV power system can be operated more efficiently by predicting the amount of global solar radiation for solar power generation. Thus far, most studies have addressed day-ahead probabilistic forecasting to predict global solar radiation. However, day-ahead probabilistic forecasting has limitations in responding quickly to sudden changes in the external environment. Although multistep-ahead (MSA) forecasting can be used for this purpose, traditional machine learning models are unsuitable because of the substantial training time. In this paper, we propose an accurate MSA global solar radiation forecasting model based on the light gradient boosting machine (LightGBM), which can handle the training-time problem and provide higher prediction performance compared to other boosting methods. To demonstrate the validity of the proposed model, we conducted a global solar radiation prediction for two regions on Jeju Island, the largest island in South Korea. The experiment results demonstrated that the proposed model can achieve better predictive performance than the tree-based ensemble and deep learning methods.
A multi-task Faster R-CNN method for 3D vehicle detection based on a single image

Vehicle detection is an important part of robot environmental perception. In this paper, a 3D vehicle detection method using a single image is proposed to generate the 3D space coordinate information of the object using monocular vision for autonomous driving. The proposed method works under the multi-task framework and integrates 2D object detection, 3D object detection, orientation estimation and key point detection into one unified deep convolution neural network (DCNN) which could be trained by end-to-end learning. Besides, our proposed method is built by modifying Fast R-CNN using multi-task learning, and thus our proposed method is named multi-task Faster R-CNN (MT-Faster R-CNN). The experiments on KITTI dataset are conducted to evaluate our proposed method and the other 3D vehicle detection methods. The experimental results demonstrate that our proposed method is competitive and could significantly assist autonomous driving.
Temperature Estimation of PMSM Using a Difference-Estimating Feedforward Neural Network

Keeping the temperature of a permanent magnet synchronous machine (PMSM) in a safe range is essential for maintaining machine performance.In this paper, a feedforward neural network (FNN) for the temperature estimation of a PMSM is proposed.An FNN obtains the past temperature and operating condition values and gives the temperatures information at the next moment.Similar to a thermalcircuit-based model, the proposed model estimates the temperatures of multiple parts of interest with a heat point of view.The proposed FNN estimates the temperature differences, rather than the temperatures, and can enhance the learning and estimation performance of the FNN.Various FNN structures are compared, and the model generation steps are presented.For the experiments performed, where the temperature of the PMSM changes from 20 to 130 • C, the best FNN-based model showed closed-loop estimation errors less than 4.5 • C for the side of the permanent magnet and four parts on the winding, even in 2 hours of closed-loop estimation.
Light Field Image Quality Assessment via the Light Field Coherence

In this paper, a novel full-reference <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">image quality assessment</i> (IQA) method for evaluating the quality of the distorted <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">light field</i> (LF) image against its reference LF image is proposed, called the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">log-Gabor feature-based</i> light field coherence (LGF-LFC). Based on the fact that to compare two LF images, it essentially boils down to measure how <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coherent</i> of these two LF images, we attempt to measure the degree of their LF <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coherence</i> (LFC). To pursue this goal, the salient features from the reference and distorted LF images under comparison need to be extracted. By considering that the Gabor feature has the ability to well characterize the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">human visual system</i> (HVS) perception, and the special characteristics of the LF images, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi</i> -scale and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">single</i> -scale Gabor feature extraction schemes are developed to extract the multi-scale log-Gabor features from the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sub-aperture images</i> (SAIs) and the single-scale log-Gabor feature from the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">epi-polar images</i> (EPIs), respectively. Note that the former can reflect the image details (via the SAIs), while the latter indicates the viewing consistency (via the EPI’s depth information). The similarity measurements are subsequently conducted on the comparison of their SAIs and that of their EPIs separately, followed by combining them together for arriving at the final score. Extensive simulation results have clearly demonstrated that the proposed LGF-LFC is more consistent with the perception of the HVS on the quality evaluation of the LF images than multiple classical and state-of-the-art IQA methods.
Learning Coarse-to-Fine Graph Neural Networks for Video-Text Retrieval

We address the problem of video-text retrieval that searches videos via natural language description or vice versa. Most state-of-the-art methods only consider cross-modal learning for two or three data points in isolation, ignoring to get benefit from the structural information of other data points from a global view. In this paper, we propose to exploit the comprehensive relationships among cross-modal samples via Graph Neural Networks (GNN). To improve the discriminative ability for accurately finding the positive sample, a Coarse-to-Fine GNN is constructed, which can progressively optimize the retrieval results via multi-step reasoning. Specifically, we first adopt heuristic edge features to represent relationships. Then we design a scoring module in each layer to rank the edges connected to the query node and drop the edges with lower scores. Finally, to alleviate the class imbalance issue, we propose a random-drop focal loss to optimize the whole framework. Extensive experimental results show that our method consistently outperforms the state-of-the-arts on four benchmarks.