Reviewerly/exHarmony huggingface dataset:
A Hybrid Path Planning Method for an Unmanned Cruise Ship in Water Quality Sampling

Cruise ships are widely used in water quality monitoring but suffer from path planning problems in a surface water environment.Solutions to path planning problems have higher requirements of path planning distance and path planning time and the ability of real-time obstacles avoidance.This paper introduces a hybrid path planning method to solve the path planning problem when using unmanned cruise ships.First, a model of the surface water environment with unknown districts is established by the grid method.Then, the global path is planned by the A * algorithm based on such a model, and in unknown areas of the model, the artificial potential filed (APF) is employed for local path planning.The problem of obtaining unreachable targets and falling into a local minimum introduced by the APF is improved by using the optimized repulsion potential field function and adding the directional random escape strategy.The A * algorithm with self-learning ability is proposed for secondary planning and cases where a local path cannot be generated.Finally, the optimal path combined with the global path and the local path is smoothed.The simulation results show that the proposed algorithm has better performance than other algorithms from the aspects of distance cost and time cost.
Teach Machine How to Read

Retrieval models aim to estimate the relevance of a document to a certain query. Although existing retrieval models have gained much success in both deepening our understanding of information seeking behavior and constructing practical retrieval systems (e.g. Web search engines), we have to admit that the models work in a rather different manner than how humans make relevance judgments. In this paper, we aim to reexamine the existing models as well as to propose new ones based on the findings in how human read documents during relevance judgment. First, we summarize a number of reading heuristics from practical user behavior patterns, which are categorized into implicit and explicit heuristics. By reviewing a variety of existing retrieval models, we find that most of them only satisfy a part of these reading heuristics. To evaluate the effectiveness of each heuristic, we conduct an ablation study and find that most heuristics have positive impacts on retrieval performance. We further integrate all the effective heuristics into a new retrieval model named Reading Inspired Model (RIM). Specifically, implicit reading heuristics are incorporated into the model framework and explicit reading heuristics are modeled as a Markov Decision Process and learned by reinforcement learning. Experimental results on a large-scale public available benchmark dataset and two test sets from NTCIR WWW tasks show that RIM outperforms most existing models, which illustrates the effectiveness of the reading heuristics. We believe that this work contributes to constructing retrieval models with both higher retrieval performance and better explainability.
Through-thickness strain field measurement using the mirror-assisted multi-view digital image correlation

A low-cost, easy-to-implement and effective mirror-assisted multi-view digital image correlation (multi-view DIC) technique, which uses only a regular two-camera stereo-DIC system and two planar reflective mirrors, is proposed for the full-field through-thickness strain (TTS) measurement of sheet samples. The front and rear surfaces of a plate-shaped sample are reflected by the mirrors and form two virtual surfaces behind the mirrors. The virtual surfaces are simultaneously captured and subsequently reconstructed by the stereo-DIC system. Furthermore, by utilizing the speckle patterns premade on the two mirrors, the 3D shapes of real surfaces can be estimated from the reconstructed virtual surfaces. By subtracting the 3D coordinates of the rear surface from those of the front surface, the full-field thickness distributions and TTS maps of the sample can be calculated. Compared with the regular multi-view DIC, which requires at least four cameras, the proposed technique has the prominent advantages of direct full-field TTS measurement by using only two cameras and simple implementation procedures. Two real experiments, including the full-field TTS measurement of a sheet sample subjected to in-plane translation and the thinning limit estimation of an advanced high-strength steel specimen subjected to uniaxial tensile loading, well validated the efficacy and accuracy of the developed technique.
Sentiment Analysis of Hotel Aspect Using Probabilistic Latent Semantic Analysis, Word Embedding and LSTM

In the industrial era 5.0, product reviews are necessary for the sustainability of a company.Product reviews are a User Generated Content (UGC) feature which describes customer satisfaction.The researcher used five hotel aspects including location, meal, service, comfort, and cleanliness to measure customer satisfaction.Each product review was preprocessed into a term list document.In this context, we proposed the Probabilistic Latent Semantic Analysis (PLSA) method to produce a hidden topic.Semantic Similarity was used to classify topics into five hotel aspects.The Term Frequency-Inverse Corpus Frequency (TF-ICF) method was used for weighting each term list, which had been expanded from each cluster in the document.The researcher used Word embedding to obtain vector values in the deep learning method from Long Short-Term Memory (LSTM) for sentiment classification.The result showed that the combination of the PLSA + TF ICF 100% + Semantic Similarity method was superior are 0.840 in the fifth categorization of the hotel aspects; the Word Embedding + LSTM method outperformed the sentiment classification at value 0.946; the service aspect received positive sentiment value higher are 45.545 than the other aspects; the comfort aspect received negative sentiment value higher are 12.871 than the other aspects.Other results also showed that sentiment was affected by the aspects.
Resource Reduction for Distributed Quantum Information Processing Using Quantum Multiplexed Photons

Distributed quantum information processing is based on the transmission of quantum data over lossy channels between quantum processing nodes. These nodes may be separated by a few microns or on planetary scale distances, but transmission losses due to absorption and/or scattering in the channel are the major source of error for most distributed quantum information tasks. Of course, quantum error correction (QEC) and detection techniques can be used to mitigate such effects, but error detection approaches have severe performance limitations due to the signaling constraints between nodes, and so error correction approaches are preferable---assuming one has sufficient high quality local operations. Typically, performance comparisons between loss-mitigating codes assume one encoded qubit per photon. However, single photons can carry more than one qubit of information and so our focus in this Letter is to explore whether loss-based QEC codes utilizing quantum multiplexed photons are viable and advantageous, especially as photon loss results in more than one qubit of information being lost. We show that quantum multiplexing enables significant resource reduction, in terms of the number of single-photon sources, while at the same time maintaining (or even lowering) the number of 2-qubit gates required. Further, our multiplexing approach requires only conventional optical gates already necessary for the implementation of these codes.
Adaptive landscape analysis

Black-box optimization of a previously unknown problem can often prove to be a demanding task. In order for the optimization process to be as efficient as possible, one must first recognize the nature of the problem at hand and then proceed to choose the algorithm exhibiting the best performance for that type of problem. The problem characterization is done via underlying fitness landscape features, which allow to identify similarities and differences between various problems.
Comprehensive Analysis of the Literature for Age Estimation From Facial Images

Recently, vast attention has grown in the field of computer vision, especially in face recognition, detection, and facial landmarks localization. Many significant features can be directly derived from the human face, such as age, gender, and race. Estimating the age can be defined as the automatic process of classifying the facial image into the exact age or to a specific age range. Practically, age estimation from the face is still a challenging problem due to the effects from many internal factors, such as gender and race, and external factors, such as environments and lifestyle. Huge efforts have been addressed to reach an accepted and satisfied accuracy of age estimation task. In this paper, we try to analyze the main aspects that can increase the performance of the age estimation system, present the handcrafted-based models and deep learning-based models, and show how the evaluations are being conducted, discuss the proposed algorithms and models in the age estimation, and show the main limitations and challenges facing the age estimation process. Also, different aging databases that contain age annotations are discussed. Finally, few guidelines and the future prospect related to the age estimation are investigated.
Strand-Accurate Multi-View Hair Capture

Hair is one of the most challenging objects to reconstruct due to its micro-scale structure and a large number of repeated strands with heavy occlusions. In this paper, we present the first method to capture high-fidelity hair geometry with strand-level accuracy. Our method takes three stages to achieve this. In the first stage, a new multi-view stereo method with a slanted support line is proposed to solve the hair correspondences between different views. In detail, we contribute a novel cost function consisting of both photo-consistency term and geometric term that reconstructs each hair pixel as a 3D line. By merging all the depth maps, a point cloud, as well as local line directions for each point, is obtained. Thus, in the second stage, we feature a novel strand reconstruction method with the mean-shift to convert the noisy point data to a set of strands. Lastly, we grow the hair strands with multi-view geometric constraints to elongate the short strands and recover the missing strands, thus significantly increasing the reconstruction completeness. We evaluate our method on both synthetic data and real captured data, showing that our method can reconstruct hair strands with sub-millimeter accuracy.
A Differential Privacy Support Vector Machine Classifier Based on Dual Variable Perturbation

Data mining technology can be used to dig out potential and valuable information from massive data, and support vector machine (SVM) is one of the most widely used and most efficient methods in the field of data mining classification.However, the training set data often contains sensitive attributes, and the traditional training method of SVM reveals the individual privacy information.In view of the low prediction accuracy and poor versatility of the existing SVM classifiers with privacy protection, this paper proposed a new SVM training method for differential privacy protection.The algorithm first solved the dual problem of SVM by using SMO method and the difference E i between the estimated value and the real value for each support vector was recorded.Then the ratio of the E i of each support vector to the sum of the E i of all the support vectors was calculated.Next, different levels of Laplace random noise were added to the corresponding dual variables α i of each support vector to be released, according to the ratio of each support vector.According to the principle of differential privacy protection, the algorithm meets -differential privacy which can be used to effectively protect individual privacy.Experimental results on real datasets showed that the algorithm proposed in this paper could be used for classification prediction under a reasonable privacy budget.
Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter at SemEval-2019 Task 5: Frequency Analysis Interpolation for Hate in Speech Detection

This document describes a text change of representation approach to the task of Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter, as part of SemEval-2019 1 .The task is divided in two sub-tasks.Sub-task A consists in classifying tweets as being hateful or not hateful, whereas sub-task B requires fine tuning the classification by classifying the hateful tweets as being directed to single individuals or generic, if the tweet is aggressive or not.Our approach consists of a change of the space of representation of text into statistical descriptors which characterize the text.In addition, dimensional reduction is performed to 6 characteristics per class in order to make the method suitable for a Big Data environment.Frequency Analysis Interpolation (FAI) is the approach we use to achieve rank 5th in Spanish language and 9th in English language in sub-task B in both cases.
Dependency Grammar Induction with a Neural Variational Transition-Based Parser

Dependency grammar induction is the task of learning dependency syntax without annotated training data. Traditional graph-based models with global inference achieve state-ofthe-art results on this task but they require O(n3) run time. Transition-based models enable faster inference with O(n) time complexity, but their performance still lags behind. In this work, we propose a neural transition-based parser for dependency grammar induction, whose inference procedure utilizes rich neural features with O(n) time complexity. We train the parser with an integration of variational inference, posterior regularization and variance reduction techniques. The resulting framework outperforms previous unsupervised transition-based dependency parsers and achieves performance comparable to graph-based models, both on the English Penn Treebank and on the Universal Dependency Treebank. In an empirical comparison, we show that our approach substantially increases parsing speed over graphbased models.
Strong and Simple Baselines for Multimodal Utterance Embeddings

Paul Pu Liang, Yao Chong Lim, Yao-Hung Hubert Tsai, Ruslan Salakhutdinov, Louis-Philippe Morency. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Color-Sensitive Person Re-Identification

Recent deep Re-ID models mainly focus on learning high-level semantic features, while failing to explicitly explore color information which is one of the most important cues for person Re-ID. In this paper, we propose a novel Color-Sensitive Re-ID to take full advantage of color information. On one hand, we train our model with real and fake images. By using the extra fake images, more color information can be exploited and it can avoid overfitting during training. On the other hand, we also train our model with images of the same person with different colors. By doing so, features can be forced to focus on the color difference in regions. To generate fake images with specified colors, we propose a novel Color Translation GAN (CTGAN) to learn mappings between different clothing colors and preserve identity consistency among the same clothing color. Extensive evaluations on two benchmark datasets show that our approach significantly outperforms state-of-the-art Re-ID models.
DBDNet: Learning Bi-directional Dynamics for Early Action Prediction

Predicting future actions from observed partial videos is very challenging as the missing future is uncertain and sometimes has multiple possibilities. To obtain a reliable future estimation, a novel encoder-decoder architecture is proposed for integrating the tasks of synthesizing future motions from observed videos and reconstructing observed motions from synthesized future motions in an unified framework, which can capture the bi-directional dynamics depicted in partial videos along the temporal (past-to-future) direction and reverse chronological (future-back-to-past) direction. We then employ a bi-directional long short-term memory (Bi-LSTM) architecture to exploit the learned bi-directional dynamics for predicting early actions. Our experiments on two benchmark action datasets show that learning bi-directional dynamics benefits the early action prediction and our system clearly outperforms the state-of-the-art methods.
Hierarchical Multi-Task Word Embedding Learning for Synonym Prediction

Automatic synonym recognition is of great importance for entity-centric text mining and interpretation. Due to the high language use variability in real-life, manual construction of semantic resources to cover all synonyms is prohibitively expensive and may also result in limited coverage. Although there are public knowledge bases, they only have limited coverage for languages other than English. In this paper, we focus on medical domain and propose an automatic way to accelerate the process of medical synonymy resource development for Chinese, including both formal entities from healthcare professionals and noisy descriptions from end-users. Motivated by the success of distributed word representations, we design a multi-task model with hierarchical task relationship to learn more representative entity/term embeddings and apply them to synonym prediction. In our model, we extend the classical skip-gram word embedding model by introducing an auxiliary task "neighboring word semantic type prediction'' and hierarchically organize them based on the task complexity. Meanwhile, we incorporate existing medical term-term synonymous knowledge into our word embedding learning framework. We demonstrate that the embeddings trained from our proposed multi-task model yield significant improvement for entity semantic relatedness evaluation, neighboring word semantic type prediction and synonym prediction compared with baselines. Furthermore, we create a large medical text corpus in Chinese that includes annotations for entities, descriptions and synonymous pairs for future research in this direction.
No-Go Theorems for Distributive Laws

Monads are commonplace in computer science, and can be composed using Beck's distributive laws. Unfortunately, finding distributive laws can be extremely difficult and error-prone. The literature contains some principles for constructing distributive laws. However, until now there have been no general techniques for establishing when no such law exists. We present two families of theorems for showing when there can be no distributive law for two monads. The first widely generalizes a counterexample attributed to Plotkin. It covers all the previous known no-go results for specific pairs of monads, and includes many new results. The second family is entirely novel, encompassing various new practical situations. For example, it negatively resolves the open question of whether the list monad distributes over itself, and also reveals a previously unobserved error in the literature.
Hierarchical Inter-Attention Network for Document Classification with Multi-Task Learning

Document classification is an essential task in many real world applications. Existing approaches adopt both text semantics and document structure to obtain the document representation. However, these models usually require a large collection of annotated training instances, which are not always feasible, especially in low-resource settings. In this paper, we propose a multi-task learning framework to jointly train multiple related document classification tasks. We devise a hierarchical architecture to make use of the shared knowledge from all tasks to enhance the document representation of each task. We further propose an inter-attention approach to improve the task-specific modeling of documents with global information. Experimental results on 15 public datasets demonstrate the benefits of our proposed model.
Computed Tomography (CT) Image Quality Enhancement via a Uniform Framework Integrating Noise Estimation and Super-Resolution Networks

Computed tomography (CT) imaging technology has been widely used to assist medical diagnosis in recent years. However, noise during the process of imaging, and data compression during the process of storage and transmission always interrupt the image quality, resulting in unreliable performance of the post-processing steps in the computer assisted diagnosis system (CADs), such as medical image segmentation, feature extraction, and medical image classification. Since the degradation of medical images typically appears as noise and low-resolution blurring, in this paper, we propose a uniform deep convolutional neural network (DCNN) framework to handle the de-noising and super-resolution of the CT image at the same time. The framework consists of two steps: Firstly, a dense-inception network integrating an inception structure and dense skip connection is proposed to estimate the noise level. The inception structure is used to extract the noise and blurring features with respect to multiple receptive fields, while the dense skip connection can reuse those extracted features and transfer them across the network. Secondly, a modified residual-dense network combined with joint loss is proposed to reconstruct the high-resolution image with low noise. The inception block is applied on each skip connection of the dense-residual network so that the structure features of the image are transferred through the network more than the noise and blurring features. Moreover, both the perceptual loss and the mean square error (MSE) loss are used to restrain the network, leading to better performance in the reconstruction of image edges and details. Our proposed network integrates the degradation estimation, noise removal, and image super-resolution in one uniform framework to enhance medical image quality. We apply our method to the Cancer Imaging Archive (TCIA) public dataset to evaluate its ability in medical image quality enhancement. The experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on de-noising and super-resolution by providing higher peak signal to noise ratio (PSNR) and structure similarity index (SSIM) values.
Counterexample-Guided Strategy Improvement for POMDPs Using Recurrent Neural Networks

We study strategy synthesis for partially observable Markov decision processes (POMDPs). The particular problem is to determine strategies that provably adhere to (probabilistic) temporal logic constraints. This problem is computationally intractable and theoretically hard. We propose a novel method that combines techniques from machine learning and formal verification. First, we train a recurrent neural network (RNN) to encode POMDP strategies. The RNN accounts for memory-based decisions without the need to expand the full belief space of a POMDP. Secondly, we restrict the RNN-based strategy to represent a finite-memory strategy and implement it on a specific POMDP. For the resulting finite Markov chain, efficient formal verification techniques provide provable guarantees against temporal logic specifications. If the specification is not satisfied, counterexamples supply diagnostic information. We use this information to improve the strategy by iteratively training the RNN. Numerical experiments show that the proposed method elevates the state of the art in POMDP solving by up to three orders of magnitude in terms of solving times and model sizes.