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GCNs-Based Context-Aware Short Text Similarity Model

GCNs-Based Context-Aware Short Text Similarity Model

引用

international conference on pattern recognition

作者： Xiaoqi Sun Shaochun Wu Yue Liu School of Computer Engineering and Science Shanghai University School of Computer Engineering and Science Shanghai University Shanghai Institute for Advanced Communication and Data Science Shanghai Engineering Research Center of Intelligent Computing System Shanghai China

ISBN: (纸本)9781728188089;9781728188096

Semantic textual similarity is a fundamental task in text mining and natural language processing (NLP), which has profound research value. The essential step for text similarity is text representation learning. Recently, researches have explored the graph convolutional network (GCN) techniques on text representation, since GCN does well in handling complex structures and preserving syntactic information. However, current GCN models are usually limited to very shallow layers due to the vanishing gradient problem, which can not capture non-local dependency information of sentences. In this paper, we propose a GCNs-based context-aware (GCSTS) model that applies iterated GCN blocks to train deeper GCNs. Recurrently employing the same GCN block prevents over-fitting and provides broad effective input width. Combined with dense connections, GCSTS can be trained more deeply. Besides, we use dynamic graph structures in the block, which further extends the receptive field of each vertex in graphs, learning better sentence representations. Experiments show that our model outperforms existing models on several text similarity datasets, while also verify that GCNs-based text representation models can be trained in a deeper manner, rather than being trained in two or three layers.

关键词： Text mining Semantics Syntactics Natural language processing pattern recognition Task analysis Context modeling

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Causal Relationship recognition Based on Lexcial-Syntactic pattern for Emergency Events

Causal Relationship Recognition Based on Lexcial-Syntactic P...

引用

international conference on Applications and Techniques in Cyber Intelligence, ATCI 2019

作者： Liao, Tao Zhou, Xu Liu, Zongtian Zhang, Shunxiang School of Computer Science and Engineering Anhui University of Science and Technology Huainan232001 China School of Computer Engineering and Science Shanghai University Shanghai200072 China

ISBN: (纸本)9783030251277

The pattern matching method based on the causal recognition of existing events is mainly aimed at the display of causal relationship with the display of related words and the method is not universal. The statistical-based machine learning method has certain dependence on the features. Moreover, the larger the corpus size, the better the recognition effect. Aiming at the above problems, this paper proposes a method based on Lexcial-Syntactic pattern event causality recognition. This method combines event denoters, event elements, event types and related words, starting from text semantics and syntactic structure, extracting Lexcial-Syntactic pattern in training texts. The statistical method calculates the correlation strength to construct the Lexcial-Syntactic pattern candidate set, and finally identifies the causal relationship of the emergency by using the candidate set and constraining the pattern in the test corpus for pattern matching. Experiments show that this method has a good effect on causality recognition and it does not need to deliberately distinguish between explicit causality and implicit causality, which reduces the dependence on text corpus size to a certain extent. © 2020, Springer Nature Switzerland AG.

关键词： pattern matching

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Randomized Transferable Machine

Randomized Transferable Machine

引用

international conference on pattern recognition

作者： Pengfei Wei Tze Yun Leong School of Computing National University of Singapore Singapore

Feature-based transfer is one of the most effective methodologies for transfer learning. Existing studies usually assume that the learned new feature representation is truly domain-invariant, and thus directly train a transfer model M on source domain. In this paper, we consider a more realistic scenario where the new feature representation is suboptimal and small divergence still exists across domains. We propose a new learning strategy with a transfer model called Randomized Transferable Machine (RTM). More specifically, we work on source data with the new feature representation learned from existing feature-based transfer methods. The key idea is to enlarge source training data populations by randomly corrupting source data using some noises, and then train a transfer model ~M that performs well on all the corrupted source data populations. In principle, the more corruptions are made, the higher the probability of the target data can be covered by the constructed source populations. and thus better transfer performance can be achieved by ~M An ideal case is with infinite corruptions, which however is infeasible in reality. We develop a marginalized solution with linear regression model and dropout noise. With a marginalization trick, we can train an RTM that is equivalently to training using infinite source noisy populations without truly conducting any corruption. More importantly, such an RTM has a closed-form solution, which enables very fast and efficient training. Extensive experiments on various real-world transfer tasks show that RTM is a promising transfer model.

关键词： Training Sociology Transfer learning Training data Data models pattern recognition Noise measurement

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computing stable resultant-based minimal solvers by hiding a variable

Computing stable resultant-based minimal solvers by hiding a...

引用

international conference on pattern recognition

作者： Snehal Bhayani Zuzana Kukelova Janne Heikkilä Center for Machine Vision and Signal Analysis University of Oulu Finland Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague

Many computer vision applications require robust and efficient estimation of camera geometry. The robust estimation is usually based on solving camera geometry problems from a minimal number of input data measurements, i.e., solving minimal problems, in a RANSAC-style framework. Minimal problems often result in complex systems of polynomial equations. The existing state-of-the-art methods for solving such systems are either based on Gröbner bases and the action matrix method, which have been extensively studied and optimized in the recent years or recently proposed approach based on a resultant computation using an extra variable. In this paper, we study an interesting alternative resultant-based method for solving sparse systems of polynomial equations by hiding one variable. This approach results in a larger eigenvalue problem than the action matrix and extra variable resultant-based methods; however, it does not need to compute an inverse or elimination of large matrices that may be numerically unstable. The proposed approach includes several improvements to the standard sparse resultant algorithms, which significantly improves the efficiency and stability of the hidden variable resultant-based solvers as we demonstrate on several interesting computer vision problems. We show that for the studied problems, our sparse resultant based approach leads to more stable solvers than the state-of-the-art Gröbner basis as well as existing resultant-based solvers, especially in close to critical configurations. Our new method can be fully automated and incorporated into existing tools for the automatic generation of efficient minimal solvers.

关键词： Geometry Computer vision Pose estimation Tools Cameras Eigenvalues and eigenfunctions pattern recognition

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Performance Evaluation between RGB and YCrCb in TC-SF-CNNLS for Vehicle Type recognition System

Performance Evaluation between RGB and YCrCb in TC-SF-CNNLS ...

引用

international conference on Industrial Engineering and Applications (ICIEA)

作者： Suryanti Awang Nik Mohamad Aizuddin Nik Azmi Faculty of Computing Soft Computing & Intelligent Systems (SPINT) Universiti Malaysia Pahang Kuantan Malaysia

ISBN: (纸本)9781665428958;9781665428965

In this paper, the performance impact of vehicle type recognition (VTR) using Three-Channel of Sparse-Filtered Convolutional Neural Network with Layer-Skipping strategy (TC-SF-CNNLS) technique is observed. Unlike other techniques that lacking in extracting unique features, TC-SF-CNNLS able to extract the unique features from colour image. However, colour image can be in many schemes. Thus, we implemented the technique with two colour schemes which is RGB and YCrCb to analyze which one is able to give better performance in the VTR. We tested the implementation with a benchmark dataset known as BIT and self-obtained dataset known as SPINT. The results are observed based on the accuracy performance that represented in a confusion table. Based on the results, YCrCb outperformed RGB with the highest average accuracy 90.5% and 90.4% for both datasets, respectively. We can conclude that TC-SF-CNNLS is best performed when YCrCb is used as the colour scheme in extracting the vehicle features from the colour image.

关键词： Performance evaluation Image segmentation Image recognition Image color analysis conferences Color Industrial engineering

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Interpretable Weighted soft Decision Forest for Credit Scoring 1

引用

2nd international conference on Decision Science and Management, ICDSM 2019

作者： Zhang, Zeyu Liu, Xinyang Gao, Zijian Qu, Yanwen Jiangxi Normal University Nanchang330022 China

ISBN: (数字)9789811535888

ISBN: (纸本)9789811535871

Deep neural network (DNN) has been widely applied in image recognition, sentiment analysis, natural language processing, and other fields. Because of its excellent classification capacity, many researchers have used it for credit scoring problems. However, compared to the traditional machine learning methods, DNN is often referred to as ‘black box’ because of the lack of interpretability. In this paper, a weighted soft decision forest (WSDF) model is proposed for credit scoring problem. WSDF tries to simulate the credit scoring mechanism under multiple expert decisions. In the new model, multiple soft decision trees are ensembled by an interpretable weighting mechanism to form a forest. The new weighting mechanism also uses a soft decision tree structure function to calculate the weight for each tree’s output. We test the performance of the new model on German Credit dataset. Compared with baseline algorithms, WSDF has good classification performance and interpretability. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Decision trees

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HUMA'20: 1st international Workshop on Human-Centric Multimedia Analysis 20

HUMA'20: 1st International Workshop on Human-Centric Multime...

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28th ACM international conference on Multimedia (MM)

作者： Liu, Wu Gan, Chuang Song, Jingkuan Zhang, Dingwen Huang, Wenbing Smith, John AI Res JDcom Beijing Peoples R China MIT IBM Watson AI Lab Cambridge MA USA UESTC Chengdu Peoples R China Xidian Univ Xian Peoples R China Tsinghua Univ Beijing Peoples R China IBM TJ Watson Res Ctr Yorktown Hts NY USA

ISBN: (纸本)9781450379885

The First international Workshop on Human-Centric Multimedia Analysis is concentrated on the tasks of human-centric analysis with multimedia and multimodal information. It is one of the fundamental and challenging problems of multimedia understanding. The human-centric multimedia analysis involves multiple tasks such as face detection and recognition, human body pattern analysis, person re-identification, human action detection, person tracking, human-object interaction, and so on. Today, multiple multimedia sensing technologies and large-scale computing infrastructures are producing at a rapid velocity a wide variety of big multi-modality data for human-centric analysis, which provides rich knowledge to help tackle these challenges. Researchers have strived to push the limits of human-centric multimedia analysis in a wide variety of applications, such as intelligent surveillance, retailing, fashion design, and services. Therefore, this workshop aims to provide a platform to bridge the gap between the communities of human analysis and multimedia.

关键词： Human-centered computing Human Analysis Face Analysis Multimedia and Multimodal Analysis

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Impact of Variability Issues of Resistive Memory Synapses on pattern recognition Systems 17

Impact of Variability Issues of Resistive Memory Synapses on...

引用

17th international SoC Design conference (ISOCC)

作者： Woo, Jiyong Lee, Miyoung Kim, Jeong Hun Im, Jong-Pil Im, Solyee Kim, Yeriaron Moon, Seung Eon Lee, Joohyun Elect & Telecommun Res Inst ICT Creat Res Lab Gwangju South Korea Elect & Telecommun Res Inst Artificial Intelligence Res Lab Gwangju South Korea Kyungpook Natl Univ Sch Elect Engn Daegu South Korea

ISBN: (纸本)9781728183312

Neuromorphic systems heavily rely on multiply-accumulate (MAC) operations to recognize image patterns. The computation performed repeatedly can be speeded up by a cross-point array. In the architecture, a weight storage element should consist of resistive memory and additional selector. To evaluate the recognition accuracy of handwritten digits, we construct cross-point arrays in simulation with the 2-bit weights obtained from Ta2O5 based resistive memory experimentally connected in series with multi tunnel-barrier selector. Based on the high accuracy close to 95 % obtained from quantized convolutional neural networks, how the accuracy is related to non-ideal factors that can be generated from array and device is studied.

关键词： Resistive memory synapse neuromoprhic computing

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Real-time texture extraction based on the improved median robust extended local binary pattern 9

Real-time texture extraction based on the improved median ro...

引用

9th international conference on computing and pattern recognition, ICCPR 2020

作者： Wang, Mengnan Zhang, Yanjun School of Information and Electronics Beijing Institute of Technology Beijing100081 China

ISBN: (纸本)9781450387835

Local binary patterns (LBP) are considered as the most computation efficient and high-performance texture features. Among all variants of the LBPs, Median Robust Extended Local Binary pattern (MRELBP) [1] is considered as one of the highest-performance one. Based on the MRELBP, this paper proposes an improved real-time texture extraction architecture implemented on a field-programmable gate array (FPGA) device. The fixed-point fraction is used to replace the double float-point fraction, only results in less than 1% difference to the accuracy. The proposed system is implemented on Xilinx Zynq-7045, and the extraction time is linear with the size of the input image. The extraction time of the 128x128 image in the dataset Outex-TC is merely 85.64μs, which is more than 4800 times faster than Liu et al. implemented on MATLAB [2]. © 2020 ACM.

关键词： MATLAB

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A new geodesic-based feature for characterization of 3D shapes: application to soft tissue organ temporal deformations

A new geodesic-based feature for characterization of 3D shap...

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international conference on pattern recognition

作者： Karim Makki Amine Bohi Augustin C. Ogier Marc Emmanuel Bellemare Aix Marseille Univ Université de Toulon CNRS LIS Marseille France

Spatio-temporal feature descriptors are of great importance for characterizing the local changes of 3D deformable shapes. In this study, we propose a method for characterizing 3D shapes from point clouds and we show a direct application on a study of organ temporal deformations. As an example, we characterize the behavior of the bladder during forced respiratory motion with a reduced number of 3D surface points: first, a set of geodesically equidistant points representing the vertices of quadrilateral mesh for the organ surface are tracked throughout a long dynamic MRI sequence using a large deformation diffeomorphic metric mapping (LDDMM) framework. Second, a novel 3D shape descriptor invariant to translation, scale and rotation is proposed for characterizing the temporal organ deformations by employing an Eulerian Partial Differential Equations (PDEs) methodology. We demonstrate the robustness of our feature on both synthetic 3D shapes and realistic dynamic Magnetic Resonance Imaging (MRI) data sequences portraying the bladder deformation during a forced breathing exercise. Promising results are obtained, showing that the proposed feature may be useful for several computer vision applications such as medical imaging, aerodynamics and robotics.

关键词： Three-dimensional displays Shape Tracking Magnetic resonance imaging Biological systems Bladder Aerodynamics

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