IntentDial: An Intent Graph based Multi-Turn Dialogue System with Reasoning Path Visualization

Intent detection and identification from multi-turn dialogue has become a widely explored technique in conversational agents, for example, voice assistants and intelligent customer services. The conventional approaches typically cast the intent mining process as a classification task. Although neural classifiers have proven adept at such classification tasks, the issue of neural network models often impedes their practical deployment in real-world settings. We present a novel graph-based multi-turn dialogue system called , which identifies a user's intent by identifying intent elements and a standard query from a dynamically constructed and extensible intent graph using reinforcement learning. In addition, we provide visualization components to monitor the immediate reasoning path for each turn of a dialogue, which greatly facilitates further improvement of the system.Comment: 4pages, 5 figure

Multi-lead model-based ECG signal denoising by guided filter

The electrocardiogram (ECG) denoising is of paramount importance for accurate disease diagnosis, but individual differences bring great difficulties for ECG denoising, especially for Dynamic Electrocardiography (DCG). In this paper, a multi-lead model-based ECG signal denoising method is proposed, in which a guided filter is inherently adapted to denoise ECG signal. For each person, a patient-specific statistical model will be constructed by sparse autoencoder (SAE) which can effectively preserve the detailed signal features. Thus, the guided signal producing by the statistical model can perform well in the guided filter. Especially, even the sudden morphological changes, the denoised ECG signals can still be conserved. The results on the 12-lead Arrhythmia Database and the MIT-BIH Arrhythmia Database demonstrate that the signal-to-noise ratio (SNR) improvement of the proposed method can reach as high as 21.54 dB, and the mean squared error (MSE) is less than 0.0401. Besides achievement of minimum signal distortion in comparisons with the major of the current denoising algorithms for complex noise environment, the proposed method demonstrate robustness in the complex interferences, especially in tracing the sudden morphological changes of ECG signals. Due to the remarkable superiority in preserving diagnostic and detail features of ECG signals, the proposed method can handle ECG signals with abnormal heart beats, and then can improve the accuracy detection of the disease.This research is partially supported by the National Natural Science Foundation of China (61673158, 61703133, 61473112), and the Natural Science Foundation of Hebei Province, China (F2016201186, F2017201222, F2018201070)

A Surface Defect Inspection Model via Rich Feature Extraction and Residual-Based Progressive Integration CNN

Surface defect inspection is vital for the quality control of products and the fault diagnosis of equipment. Defect inspection remains challenging due to the low level of automation in some manufacturing plants and the difficulty in identifying defects. To improve the automation and intelligence levels of defect inspection, a CNN model is proposed for the high-precision defect inspection of USB components in the actual demands of factories. First, the defect inspection system was built, and a dataset named USB-SG, which contained five types of defects—dents, scratches, spots, stains, and normal—was established. The pixel-level defect ground-truth annotations were manually marked. This paper puts forward a CNN model for solving the problem of defect inspection tasks, and three strategies are proposed to improve the model’s performance. The proposed model is built based on the lightweight SqueezeNet network, and a rich feature extraction block is designed to capture semantic and detailed information. Residual-based progressive feature integration is proposed to fuse the extracted features, which can reduce the difficulty of model fine-tuning and improve the generalization ability. Finally, a multi-step deep supervision scheme is proposed to supervise the feature integration process. The experiments on the USB-SG dataset prove that the model proposed in this paper has better performance than that of other methods, and the running speed can meet the real-time demand, which has broad application prospects in the industrial inspection scene