Reviewerly/exHarmony huggingface dataset:
Reasoning for Local Graph Over Knowledge Graph With a Multi-Policy Agent

The reinforcement learning framework for multi-hop relational paths is one of the effective methods for solving knowledge graph incompletion. However, these models are associated with limited performances attributed to delayed rewards and far-fetched search trajectories. To overcome these challenges, we propose the searching window and multi-policy agent. The searching window provides a large action space, so that the agent can backtrack based on the newly obtained information and establish a local graph instead of a path chain. Based on the searching window, a double long short-term memory (DBL-LSTM) policy network is introduced to encode the local graph and relation sequence, after which the encoding information is used by the agent to select a correct entity to grow the local graph. Furthermore, multi-policy agent separately infers the local graph through three different policy networks, then, all local graphs are integrated into an information-rich local graph. Experiments using the WN18RR dataset revealed that local graph reasoning with searching window had greater rewards than path reasoning, the proposed DBL-LSTM policy network improved all HITS@N(N = 1,3,5,10) compared to prior works, and that the multi-policy agent achieved higher hit rates than single-policy agent.
Teaching-Free Intelligent Robotic Welding of Heterocyclic Medium and Thick Plates Based on Vision

Aiming at the complicated programming of traditional welding robot and the inability to adapt to the diversity of the geometric shapes and size of the lifting lug components of the port cranes, a composite sensing method using 2D macro-view panoramic photo coarse positioning and 3D laser vision precise measurement guidance was proposed, breaking through some difficulties in robotic autonomous sensing, decision-making and welding technical, such as contour recognition of randomly placed arc, full circle, ellipse and other heterocyclic medium and thick plates, groove angle and depth measurement, welding sequence arrangement, multilayer and multi-path and parameter planning, realized teaching-free intelligent welding of the lifting lug component of the port crane. This provides technical support for the “intelligent manufacturing” upgrade of China’s high-end marine engineering equipment and has important value in engineering promotion.
Prostate Cancer: Cancer Detection and Classification Using Deep Learning

Cancer has already taken the second leading position as the cause of death making the global burden world wide. There is an estimate of 9.6 million deaths in 2018 as per the survey of US cancer statistics. Prostate cancer is an abnormal, malignant growth of cells in the prostate gland uncontrollably. This disease mainly affects either middle-aged or older thousands of men each year. Cancer till now is a major issue that is continuously creating a burden to the individuals as well as health systems. Therefore, it is a big concern in front of health systems demanding a strong system which can detect early and help for treatment. Here, we have applied some deep learning models to study the classification for the detection of clinically significant prostate cancer from the large-scale cancer image data for each patient. This paper investigates the effect of deep learning architecture like VGG16, EfficientNet, Dense Net121, ResNext50 in the large-scale cancer image data classification setting. Our main contribution is to focus on the high-level accuracy because these deep learning algorithms have the capability of transfer learning with image instant segmentation.
Image restoration for irregular holes based on dual discrimination generation countermeasure network

In order to solve the problem that the global and local generated countermeasure network cannot inpaint the random irregular large holes, and to improve the standard convolution generator, which demonstrates the defects of color difference and blur, a network architecture of inpainting irregular large holes in an image based on double discrimination generation countermeasure network is proposed. Firstly, the image generator is a U-net architecture defined by partial convolution. The normalized partial convolution only completes the end-to-end mask update for the effective pixels. The skip link in U-net propagates the context information of the image to the higher resolution, and optimizes the training results of the model with the weighted loss function of reconstruction loss, perception loss and wind grid loss. Subsequently, the adversary loss function, the dual discrimination network including the synthetic discriminator and the global discriminator are trained separately to judge the consistency between the generated image and the real image. Finally, the weighted loss functions are trained together with generating network and double discrimination network to further enhance the detail and overall consistency of the inpainted area and make the inpainted results more natural. The simulation experiment is carried out on the Place 365 standard database. The subjective and objective experimental results show that the results of the proposed method has reasonable overall and detail semantic consistency than those of the existing methods when they are used to repair random, irregular and large-area holes. The proposed method effectively overcomes the defects of blurry details, color distortion and artifacts.
Uncertainty Weighted Actor-Critic for Offline Reinforcement Learning

Offline Reinforcement Learning promises to learn effective policies from previously-collected, static datasets without the need for exploration. However, existing Q-learning and actor-critic based off-policy RL algorithms fail when bootstrapping from out-of-distribution (OOD) actions or states. We hypothesize that a key missing ingredient from the existing methods is a proper treatment of uncertainty in the offline setting. We propose Uncertainty Weighted Actor-Critic (UWAC), an algorithm that detects OOD state-action pairs and down-weights their contribution in the training objectives accordingly. Implementation-wise, we adopt a practical and effective dropout-based uncertainty estimation method that introduces very little overhead over existing RL algorithms. Empirically, we observe that UWAC substantially improves model stability during training. In addition, UWAC out-performs existing offline RL methods on a variety of competitive tasks, and achieves significant performance gains over the state-of-the-art baseline on datasets with sparse demonstrations collected from human experts.
ScatterPlotAnalyzer: Digitizing Images of Charts Using Tensor-Based Computational Model

Charts or scientific plots are widely used visualizations for efficient knowledge dissemination from datasets. Nowadays, these charts are predominantly available in image format in print media, the internet, and research publications. There are various scenarios where these images are to be interpreted in the absence of datasets that were originally used to generate the charts. This leads to a pertinent need for automating data extraction from an available chart image. We narrow down our scope to scatter plots and propose a semi-automated algorithm, ScatterPlotAnalyzer, for data extraction from chart images. Our algorithm is designed around the use of second-order tensor fields to model the chart image. ScatterPlotAnalyzer integrates the following tasks in sequence: chart type classification, image annotation, object detection, text detection and recognition, data transformation, text summarization, and optionally, chart redesign. The novelty of our algorithm is in analyzing both simple and multi-class scatter plots. Our results show that our algorithm can effectively extract data from images of different resolutions. We also discuss specific test cases where ScatterPlotAnalyzer fails.
MCA-Unet: Multi-class Attention-aware U-net for Seismic Phase Picking

&amp;lt;p&amp;gt;This study presents a deep learning based algorithm for seismic event detection and simultaneous phase picking in seismic waveforms. U-net structure-based solutions which consists of a contracting path (encoder) to capture feature information and a symmetric expanding path (decoder) that enables precise localization, have proven to be effective in phase picking. The network architecture of these U-net models mainly comprise of 1D CNN, Bi- &amp;amp; Uni-directional LSTM, transformers and self-attentive layers. Althought, these networks have proven to be a good solution, they may not fully harness the information extracted from multi-scales.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;In this study, we propose a simple yet powerful deep learning architecture by combining multi-class with attention mechanism, named MCA-Unet, for phase picking. &amp;amp;#160;Specially, we treat the phase picking as an image segmentation problem, and incorporate the attention mechanism into the U-net structure to efficiently deal with the features extracted at different levels with the goal to improve the performance on the seismic phase picking. Our neural network is based on an encoder-decoder architecture composed of 1D convolutions, pooling layers, deconvolutions and multi-attention layers. This architecture is applied and tested to a field seismic dataset (e.g. Wenchuan Earthquake Aftershocks Classification Dataset) to check its performance.&amp;lt;/p&amp;gt;
Super Resolution of Magnetic Resonance Images

In this work, novel denoising and super resolution (SR) approaches for magnetic resonance (MR) images are addressed, and are integrated in a unified framework, which do not require example low resolution (LR)/high resolution (HR)/cross-modality/noise-free images and prior information of noise–noise variance. The proposed method categorizes the patches as either smooth or textured and then denoises them by deploying different denoising strategies for efficient denoising. The denoising algorithm is integrated into the SR approach, which uses a gradient profile-based constraint in a sparse representation-based framework to improve the resolution of MR images with reduced smearing of image details. This constraint regularizes the estimation of HR images such that the estimated HR image has gradient profiles similar to the gradient profiles of the original HR image. For this, the gradient profile sharpness (GPS) values of an unknown HR image are estimated using an approximated piece-wise linear relation among GPS values of LR and upsampled LR images. The experiments are performed on three different publicly available datasets. The proposed SR approach outperforms the existing unsupervised SR approach addressed for real MR images that exploits low rank and total variation (LRTV) regularization, by an average peak signal to noise ratio (PSNR) of 0.73 dB and 0.38 dB for upsampling factors 2 and 3, respectively. For the super resolution of noisy real MR images (degraded with 2% noise), the proposed approach outperforms the LRTV approach by an average PSNR of 0.54 dB and 0.46 dB for upsampling factors 2 and 3, respectively. The qualitative analysis is shown for real MR images from healthy subjects and subjects with Alzheimer’s disease and structural deformity, i.e., cavernoma.
MADE: Exploration via Maximizing Deviation from Explored Regions

In online reinforcement learning (RL), efficient exploration remains particularly challenging in high-dimensional environments with sparse rewards. In low-dimensional environments, where tabular parameterization is possible, count-based upper confidence bound (UCB) exploration methods achieve minimax near-optimal rates. However, it remains unclear how to efficiently implement UCB in realistic RL tasks that involve non-linear function approximation. To address this, we propose a new exploration approach via \textit{maximizing} the deviation of the occupancy of the next policy from the explored regions. We add this term as an adaptive regularizer to the standard RL objective to balance exploration vs. exploitation. We pair the new objective with a provably convergent algorithm, giving rise to a new intrinsic reward that adjusts existing bonuses. The proposed intrinsic reward is easy to implement and combine with other existing RL algorithms to conduct exploration. As a proof of concept, we evaluate the new intrinsic reward on tabular examples across a variety of model-based and model-free algorithms, showing improvements over count-only exploration strategies. When tested on navigation and locomotion tasks from MiniGrid and DeepMind Control Suite benchmarks, our approach significantly improves sample efficiency over state-of-the-art methods. Our code is available at https://github.com/tianjunz/MADE.
A Dialogue-based Information Extraction System for Medical Insurance Assessment

In the Chinese medical insurance industry, the assessor's role is essential and requires significant efforts to converse with the claimant.This is a highly professional job that involves many parts, such as identifying personal information, collecting related evidence, and making a final insurance report.Due to the coronavirus (COVID-19) pandemic, the previous offline insurance assessment has to be conducted online.However, for the junior assessor often lacking practical experience, it is not easy to quickly handle such a complex online procedure, yet this is important as the insurance company needs to decide how much compensation the claimant should receive based on the assessor's feedback.In order to promote assessors' work efficiency and speed up the overall procedure, in this paper, we propose a dialogue-based information extraction system that integrates advanced NLP technologies for medical insurance assessment.With the assistance of our system, the average time cost of the procedure is reduced from 55 minutes to 35 minutes, and the total human resources cost is saved 30% compared with the previous offline procedure.Until now, the system has already served thousands of online claim cases.
Bigram and Unigram Based Text Attack via Adaptive Monotonic Heuristic Search

Deep neural networks (DNNs) are known to be vulnerable to adversarial images, while their robustness in text classification are rarely studied. Several lines of text attack methods have been proposed in the literature, such as character-level, word-level, and sentence-level attacks. However, it is still a challenge to minimize the number of word distortions necessary to induce misclassification, while simultaneously ensuring the lexical correctness, syntactic correctness, and semantic similarity. In this paper, we propose the Bigram and Unigram based Monotonic Heuristic Search (BU-MHS) method to examine the vulnerability of deep models. Our method has three major merits. Firstly, we propose to attack text documents not only at the unigram word level but also at the bigram level to avoid producing meaningless outputs. Secondly, we propose a hybrid method to replace the input words with both their synonyms and sememe candidates, which greatly enriches potential substitutions compared to only using synonyms. Lastly, we design a search algorithm, i.e., Monotonic Heuristic Search (MHS), to determine the priority of word replacements, aiming to reduce the modification cost in an adversarial attack. We evaluate the effectiveness of BU-MHS on IMDB, AG's News, and Yahoo! Answers text datasets by attacking four state-of-the-art DNNs models. Experimental results show that our BU-MHS achieves the highest attack success rate by changing the smallest number of words compared with other existing models.
Zoetrope genetic programming for regression

The Zoetrope Genetic Programming (ZGP) algorithm is based on an original representation for mathematical expressions, targeting evolutionary symbolic regression.The zoetropic representation uses repeated fusion operations between partial expressions, starting from the terminal set. Repeated fusions within an individual gradually generate more complex expressions, ending up in what can be viewed as new features. These features are then linearly combined to best fit the training data. ZGP individuals then undergo specific crossover and mutation operators, and selection takes place between parents and offspring. ZGP is validated using a large number of public domain regression datasets, and compared to other symbolic regression algorithms, as well as to traditional machine learning algorithms. ZGP reaches state-of-the-art performance with respect to both types of algorithms, and demonstrates a low computational time compared to other symbolic regression approaches.
Comparing Pocketsphinx and Vosk Recognition in human speech decoding

Proceedings of IV Brazilian Humanoid Robot Workshop (BRAHUR) and V Brazilian Workshop on Service Robotics (BRASERO) (978-65-5941-261-7) - Comparing Pocketsphinx And Vosk Recognition In Human Speech Decoding
Destriping Hyperspectral Imagery By Adaptive Anisotropic Total Variation And Truncated Nuclear Norm

Destriping is an important hyperspectral image processing procedure. Many variation-based methods show adequate destriping performance. However, global variation constraints cause loss of texture information in unstriped image regions. To alleviate this effect, we propose an adaptive anisotropy total variation method to adaptively smoothen the striped regions. Furthermore, considering the highly linear relationship among stripes, we introduce, for the first time, the truncated nuclear norm to constrain the rank of the stripes to 1. When combining the adaptive anisotropy total variation and the truncated nuclear norm, a hyperspectral image destriping model is established, which can easily be solved by the alternating direction method of multipliers (ADMM). Experiments demonstrate the effectiveness and superiority of the proposed destriping method.
Personalized news recommendation based on an improved conditional restricted Boltzmann machine

Purpose Because of the extensive user coverage of news sites and apps, greater social and commercial value can be realized if users can access their favourite news as easily as possible. However, news has a timeliness factor; there are serious cold start and data sparsity in news recommendation, and news users are more susceptible to recent topical news. Therefore, this study aims to propose a personalized news recommendation approach based on topic model and restricted Boltzmann machine (RBM). Design/methodology/approach Firstly, the model extracts the news topic information based on the LDA2vec topic model. Then, the implicit behaviour data are analysed and converted into explicit rating data according to the rules. The highest weight is assigned to recent hot news stories. Finally, the topic information and the rating data are regarded as the conditional layer and visual layer of the conditional RBM (CRBM) model, respectively, to implement news recommendations. Findings The experimental results show that using LDA2vec-based news topic as a conditional layer in the CRBM model provides a higher prediction rating and improves the effectiveness of news recommendations. Originality/value This study proposes a personalized news recommendation approach based on an improved CRBM. Topic model is applied to news topic extraction and used as the conditional layer of the CRBM. It not only alleviates the sparseness of rating data to improve the efficient in CRBM but also considers that readers are more susceptible to popular or trending news.
Solving Bisymmetric Solution of a Class of Matrix Equations Based on Linear Saturated System Model Neural Network

In order to solve the complicated process and low efficiency and low accuracy of solving a class of matrix equations, this paper introduces the linear saturated system model neural network architecture to solve the bisymmetric solution of a class of matrix equations. Firstly, a class of matrix equations is constructed to determine the key problems of solving the equations. Secondly, the linear saturated system model neural network structure is constructed to determine the characteristic parameters in the process of bisymmetric solution. Then, the matrix equations is solved by using backpropagation neural network topology. Finally, the class normalization is realized by using the objective function of bisymmetric solution, and the bisymmetric solution of a class of matrix equations is realized. In order to verify the solving effect of the method in this paper, three indexes (accuracy, correction accuracy, and solving time) are designed in the experiment. The experimental results show that the proposed method can effectively reduce the solving time, can improve the accuracy and correction effect of the bisymmetric solution, and has high practicability.
Commonsense-Focused Dialogues for Response Generation: An Empirical Study

Pei Zhou, Karthik Gopalakrishnan, Behnam Hedayatnia, Seokhwan Kim, Jay Pujara, Xiang Ren, Yang Liu, Dilek Hakkani-Tur. Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue. 2021.
A Color/Illuminance Aware Data Augmentation and Style Adaptation Approach to Person Re-Identification

Person re-identification problems usually suffer from large subject appearance variations and limited training data. This paper proposes a novel physically motivated Color/Illuminance-Aware data-augmentation (CIADA) scheme and a style-adaptive fusion approach to address these issues. The CIADA scheme estimates the color/illuminance distribution from the training data via manifold learning and generates new samples under different color/illuminance perturbations to better capture objects’ appearance for mitigating the small-sample-size and color variation problems. A Color/Illuminance Aware Feature Augmentation (CIAFA) approach, which is applicable to state-of-the-art features and metric learning algorithms, is then proposed to integrate the features generated by the augmented samples for metric learning. A new Color/Illuminance-Aware Style Fusion (CIASF) scheme, which allows the learning and matching process to be performed independently on each pair of datasets generated for estimating a set of ‘local’ distance functions, is also proposed. A canonical correlation analysis-based weighting scheme is developed to fuse these local distances to an overall distance for recognition. This reduces the memory requirement and complexity over the original CIAFA. Experiments on common datasets show that the proposed methodologies substantially improve the performance of state-of-the-art subspace learning algorithms. It is applicable to both small and large datasets with hand-craft and deep features.
Progressive Open-Domain Response Generation with Multiple Controllable Attributes

It is desirable to include more controllable attributes to enhance the diversity of generated responses in open-domain dialogue systems. However, existing methods can generate responses with only one controllable attribute or lack a flexible way to generate them with multiple controllable attributes. In this paper, we propose a Progressively trained Hierarchical Encoder-Decoder (PHED) to tackle this task. More specifically, PHED deploys Conditional Variational AutoEncoder (CVAE) on Transformer to include one aspect of attributes at one stage. A vital characteristic of the CVAE is to separate the latent variables at each stage into two types: a global variable capturing the common semantic features and a specific variable absorbing the attribute information at that stage. PHED then couples the CVAE latent variables with the Transformer encoder and is trained by minimizing a newly derived ELBO and controlled losses to produce the next stage's input and produce responses as required. Finally, we conduct extensive evaluations to show that PHED significantly outperforms the state-of-the-art neural generation models and produces more diverse responses as expected.