pwc-archive/papers-with-abstracts huggingface dataset:
Learning to Assign: Towards Fair Task Assignment in Large-Scale Ride Hailing
Authors: Dingyuan Shi, Yongxin Tong, Zimu Zhou, Bingchen Song, Weifeng Lv, Qiang Yang
Date: 2021-08-14 00:00:00
Proceeding: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining 2021 8
Tasks: Fairness, reinforcement-learning, Reinforcement Learning (RL)

Ride hailing is a widespread shared mobility application where the central issue is to assign taxi requests to drivers with various objectives. Despite extensive research on task assignment in ride hailing, the fairness of earnings among drivers is largely neglected. Pioneer studies on fair task assignment in ride hailing are ineffective and inefficient due to their myopic optimization perspective and time-consuming assignment techniques. In this work, we propose LAF, an effective and efficient task assignment scheme that optimizes both utility and fairness. We adopt reinforcement learning to make assignments in a holistic manner and propose a set of acceleration techniques to enable fast fair assignment on large-scale data. Experiments show that LAF outperforms the state-of-the-arts by up to 86.7%, 29.1%, 797% on fairness, utility and efficiency, respectively.
Learn from Concepts: Towards the Purified Memory for Few-shot Learning
Authors: Xuncheng Liu, Xudong Tian, Shaohui Lin, Yanyun Qu, Lizhuang Ma, Wang Yuan, Zhizhong Zhang, Yuan Xie
Date: 2021-01-23 00:00:00
Proceeding: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence 2021 1
Tasks: Few-Shot Learning, Graph Neural Network

Human beings have a great generalization ability to recognize a novel category by only seeing a few number of samples. This is because humans possess the ability to learn from the concepts that already exist in our minds. However, many existing few-shot approaches fail in addressing such a fundamental problem, {\it i.e.,} how to utilize the knowledge learned in the past to improve the prediction for the new task. In this paper, we present a novel purified memory mechanism that simulates the recognition process of human beings. This new memory updating scheme enables the model to purify the information from semantic labels and progressively learn consistent, stable, and expressive concepts when episodes are trained one by one. On its basis, a Graph Augmentation Module (GAM) is introduced to aggregate these concepts and knowledge learned from new tasks via a graph neural network, making the prediction more accurate. Generally, our approach is model-agnostic and computing efficient with negligible memory cost. Extensive experiments performed on several benchmarks demonstrate the proposed method can consistently outperform a vast number of state-of-the-art few-shot learning methods.
Modified Newton Integration Algorithm With Noise Tolerance Applied to Robotics
Authors: Dongyang Fu, Haoen Huang, Lin Wei, Xiuchun Xiao, Long Jin, Shan Liao, Jialiang Fan, Zhengtai Xie
Date: 2021-01-21 00:00:00
Proceeding: IEEE Transactions on Systems, Man, and Cybernetics: Systems 2021 1

Currently, the Newton-Raphson iterative algorithm has been extensively employed in the fields of basic research and engineering. However, when noise components exist in a system, its performance is largely affected. To remedy shortcomings that the conventional computing methods have encountered in a noisy workspace, a novel modified Newton integration (MNI) algorithm is proposed in this article. In addition, the steady-state error of the proposed MNI algorithm is smaller than that of the Newton-Raphson algorithm under a noise-free or noisy workspace. To lay the foundations for the corresponding theoretical analyses, the proposed MNI algorithm is first converted into a homogeneous linear equation with a residual term. Then, the related theoretical analyses are carried out, which indicate that the MNI algorithm possesses noise-tolerance ability under various noisy environments. Finally, multiple computer simulations and physical experiments on robot control applications are performed to verify the feasibility and advantage of the proposed MNI algorithm.
Bag of Tricks for Long-Tailed Visual Recognition with Deep Convolutional Neural Networks
Authors: Yongshun Zhang, Xiu-Shen Wei, Boyan Zhou, Jianxin Wu
Date: 2021-05-19 00:00:00
Proceeding: Association for the Advancement of Artificial Intelligence 2021 5
Tasks: Data Augmentation, Meta-Learning

In recent years, visual recognition on challenging long-tailed distributions, where classes often exhibit extremely imbalanced frequencies, has made great progress mostly based on various complex paradigms (e.g., meta learning). Apart from these complex methods, simple refinements on training procedure also make contributions. These refinements, also called tricks, are minor but effective, such as adjustments in the data distribution or loss functions. However, different tricks might conflict with each other. If users apply these long-tail related tricks inappropriately, it could cause worse recognition accuracy than expected. Unfortunately, there has not been a scientific guideline of these tricks in the literature. In this paper, we first collect existing tricks in long-tailed visual recognition and then perform extensive and systematic experiments, in order to give a detailed experimental guideline
and obtain an effective combination of these tricks. Furthermore, we also propose a novel data augmentation approach based on class activation maps for long-tailed recognition, which can be friendly combined with re-sampling methods and shows excellent results. By assembling these tricks scientifically, we can outperform state-of-the-art methods on four long-tailed benchmark datasets, including ImageNet-LT and iNaturalist 2018.
Learning Graph Convolutional Networks for Multi-Label Recognition and Applications
Authors: ZhaoMin Chen, Xiu-Shen Wei, Peng Wang, Yanwen Guo
Date: 2021-03-03 00:00:00
Proceeding: IEEE Transactions on Pattern Analysis and Machine Intelligence 2021 3
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Label Image Recognition, Multi-label Image Recognition with Partial Labels

The task of multi-label image recognition is to predict a set of object labels that present in an image. As objects normally co-occur in an image, it is desirable to model label dependencies to improve recognition performance. To capture and explore such important information, we propose Graph Convolutional Networks based models for multi-label recognition, where directed graphs are constructed over classes and information is propagated between classes to learn inter-dependent class-level representations. Following this idea, we design two particular models that approach multi-label classification from different views. In our first model, the prior knowledge about the class dependencies is integrated into classifier learning. Specifically, we propose Classifier-Learning-GCN to map class-level semantic representations (\eg, word embedding) into classifiers that maintain the inter-class topology. In our second model, we decompose the visual representation of an image into a set of label-aware features and propose Prediction-Learning-GCN to encode such features into inter-dependent image-level prediction scores. Furthermore, we also present an effective correlation matrix construction approach to capture inter-class relationships and consequently guide information propagation among classes. Empirical results on generic multi-label recognition demonstrate that the effectiveness of both two proposed models. Moreover, the proposed methods also show advantages in other multi-label related applications.
New Generation Deep Learning For Video Object Dection:A Survery
Authors: Licheng Jiao, Ruohan Zhang, Fang Liu, Shuyuan Yang, Biao Hou, Lingling Li, Xu Tang
Date: 2021-02-03 00:00:00
Proceeding: IEEE Transactions on Neural Networks and Learning Systems 2021 2
Tasks: Deep Learning, Object, object-detection, Object Detection, Video Object Detection

Video object detection, a basic task in the computer vision field, is rapidly evolving and widely used. In recent years, deep learning methods have rapidly become widespread in the field of video object detection, achieving excellent results compared with those of traditional methods. However, the presence of duplicate information and abundant spatiotemporal information in video data poses a serious challenge to video object detection. Therefore, in recent years, many scholars have investigated deep learning detection algorithms in the context of video data and have achieved remarkable results. Considering the wide range of applications, a comprehensive review of the research related to video object detection is both a necessary and challenging task. This survey attempts to link and systematize the latest cutting-edge research on video object detection with the goal of classifying and analyzing video detection algorithms based on specific representative models. The differences and connections between video object detection and similar tasks are systematically demonstrated, and the evaluation metrics and video detection performance of nearly 40 models on two data sets are presented. Finally, the various applications and challenges facing video object detection are discussed.
GAFnet: Group Attention Fusion Network for PAN and MS Image High-Resolution Classification
Authors: Xu Liu, Lingling Li, Fang Liu, Biao Hou, Shuyuan Yang, Licheng Jiao
Date: 2021-03-30 00:00:00
Proceeding: IEEE Transactions on Cybernetics 2021 3

Panchromatic (PAN) and multispectral (MS) images have coordinated and paired spatial spectral information, which can complement each other and make up for their shortcomings for image interpretation. In this article, a novel classification method called the deep group spatial-spectral attention fusion network is proposed for PAN and MS images. First, the MS image is processed by unpooling to obtain the same resolution as that of the PAN image. Second, the group spatial attention and group spectral attention modules are proposed to extract image features. The PAN and the processed MS images are regarded as the input of the two modules, respectively. Third, the features from the previous step are fused by the attention fusion module, which aims to fully fuse multilevel features, take into account both the low-level features and the high-level features, and maintain the global abstract and local detailed information of the pixels. Finally, the fusion feature is fed into the classifier and the resulting map is obtained by pixel level. Extensive experiments and analysis on four datasets show that the proposed method achieves comparable results.
SKFAC:Training Neural Networks with Faster Kronecker-Factored Approximate Curvature
Authors: Zedong Tang, Fenlong Jiang, Maoguo Gong, Hao Li, Yue Wu, Fan Yu, Zidong Wang, Min Wang
Date: 2021-06-01 00:00:00
Proceeding: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2021 6
Tasks: Dimensionality Reduction

The bottleneck of computation burden limits the widespread use of the 2nd order optimization algorithms for training deep neural networks. In this paper, we present a computationally efficient approximation for natural gradient descent, named Swift Kronecker-Factored Approximate Curvature (SKFAC), which combines Kronecker factorization and a fast low-rank matrix inversion technique. Our research aims at both fully connected and convolutional layers. For the fully connected layers, by utilizing the low-rank property of Kronecker factors of Fisher information matrix, our method only requires inverting a small matrix to approximate the curvature with desirable accuracy. For convolutional layers, we propose a way with two strategies to save computational efforts without affecting the empirical performance by reducing across the spatial dimension or receptive fields of feature maps. Specifically, we propose two effective dimension reduction methods for this purpose:
Spatial Subsampling and Reduce Sum. Experimental results of training several deep neural networks on Cifar-10 and ImageNet-1k datasets demonstrate that SKFAC can capture the main curvature and yield comparative performance to K-FAC. The proposed method bridges the wall-clock time gap between the 1st and 2nd order algorithms.
Spatially Self-Paced Convolutional Networks for Change Detection in Heterogeneous Images
Authors: Hao Li, Maoguo Gong, Mingyang Zhang, Yue Wu
Date: 2021-05-10 00:00:00
Proceeding: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021 5
Tasks: Change Detection

Change detection in heterogeneous remote sensing images is a challenging problem because it is hard to make a direct comparison in the original observation spaces, and most methods rely on a set of manually labeled samples. In this article, a spatially self-paced convolutional network (SSPCN) is constructed for change detection in an unsupervised way. Self-paced learning (SPL) is incorporated into convolutional networks to dynamically select reliable samples and learn the representation of the relations between the two heterogeneous images. In the proposed method, the pseudo labels are initialized by a classification-based method, and each sample is assigned to a weight to reflect the easiness of the sample. Then, SPL is used to learn the easy samples at first and then gradually take more complex samples into account. In the training process, the sample weights are dynamically updated based on the network parameters. Finally, a binary change map is acquired based on the trained convolutional network. The proposed SSPCN has three main advantages compared to the traditional methods. First, the proposed method is robust to noisy samples because the SSPCN involves the reliable samples into training. Second, the samples have different learning rates for converging to better values, and the learning rates are dynamically changed based on the current sample weights during iterations. Finally, we take the spatial information among the samples into account for further enhancing the robustness of the proposed method. Experimental results on four pairs of heterogeneous remote sensing images confirm the effectiveness of the proposed technique.
Graph-Based Stock Recommendation by Time-Aware Relational Attention Network
Authors: Jianliang Gao, Xiaoting Ying, Cong Xu, Jianxin Wang, Shichao Zhang, Zhao Li
Date: 2022-02-01 00:00:00
Proceeding: ACM Transactions on Knowledge Discovery from Data 2022 2
Tasks: Relation, Stock Prediction

The stock market investors aim at maximizing their investment returns. Stock recommendation task is to recommend stocks with higher return ratios for the investors. Most stock prediction methods study the historical sequence patterns to predict stock trend or price in the near future. In fact, the future price of a stock is correlated not only with its historical price, but also with other stocks. In this article, we take into account the relationships between stocks (corporations) by stock relation graph. Furthermore, we propose a Time-aware Relational Attention Network (TRAN) for graph-based stock recommendation according to return ratio ranking. In TRAN, the time-aware relational attention mechanism is designed to capture time-varying correlation strengths between stocks by the interaction of historical sequences and stock description documents. With the dynamic strengths, the nodes of the stock relation graph aggregate the features of neighbor stock nodes by graph convolution operation. For a given group of stocks, the proposed TRAN model can output the ranking results of stocks according to their return ratios. The experimental results on several real-world datasets demonstrate the effectiveness of our TRAN for stock recommendation.
Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture
Authors: C. L. Philip Chen, Zhulin Liu
Date: 2017-07-21 00:00:00
Proceeding: IEEE Transactions on Neural Networks and Learning Systems 2017 7
Tasks: Incremental Learning, Object Recognition

Broad Learning System (BLS) that aims to offer an alternative way of learning in deep structure is proposed in this paper. Deep structure and learning suffer from a time-consuming training process because of a large number of connecting parameters in filters and layers. Moreover, it encounters a complete retraining process if the structure is not sufficient to model the system. The BLS is established in the form of a flat network, where the original inputs are transferred and placed as “mapped features” in feature nodes and the structure is expanded in wide sense in the “enhancement nodes.” The incremental learning algorithms are developed for fast remodeling in broad expansion without a retraining process if the network deems to be expanded. Two incremental learning algorithms are given for both the increment of the feature nodes (or filters in deep structure) and the increment of the enhancement nodes. The designed model and algorithms are very versatile for selecting a model rapidly. In addition, another incremental learning is developed for a system that has been modeled encounters a new incoming input. Specifically, the system can be remodeled in an incremental way without the entire retraining from the beginning. Satisfactory result for model reduction using singular value decomposition is conducted to simplify the final structure. Compared with existing deep neural networks, experimental results on the Modified National Institute of Standards and Technology database and NYU NORB object recognition dataset benchmark data demonstrate the effectiveness of the proposed BLS.
EPRNet: Efficient Pyramid Representation Network for Real-Time Street Scene Segmentation
Authors: Quan Tang, Fagui Liu, Jun Jiang, Yu Zhang
Date: 2021-03-23 00:00:00
Proceeding: IEEE Transactions on Intelligent Transportation Systems 2021 3
Tasks: image-classification, Image Classification, Scene Segmentation, Transfer Learning

Current scene segmentation methods suffer from cumbersome model structures and high computational complexity, impeding their applications to real-world scenarios that require real-time processing. This paper proposes a novel Efficient Pyramid Representation Network (EPRNet), which strikes an innovative record on segmentation accuracy, model lightness and inference efficiency. Unlike existing methods delivering transfer learning based on pixel features of limited receptive fields encoded by shallow image classification backbones, EPRNet distributes multi-scale representations throughout the feature encoding flow to quickly enlarge and enrich receptive fields. Specifically, we introduce an extremely lightweight and efficient Multi-scale Processing Unit (MPU) that encodes multi-scale features through parallel convolutions of different kernels. By combining MPU and residual learning, we propose a core Pyramid Representation Module (PRM) to correctly acquire and aggregate region-based contexts in both shallow and deep layers. In this way, EPRNet can encode discriminative and comprehensive representations of multi-scale objects with a compact structure. We conduct extensive experiments on Cityscapes and CamVid datasets, demonstrating the superiority. Without any extra and coarse labeled data, EPRNet obtains mIoU 73.9% on the Cityscapes test set with only 0.9 million parameters at a speed of 42 FPS.
Image denoising using deep CNN with batch renormalization
Authors: Chunwei Tian, Yong Xu, WangMeng Zuo
Date: 2019-09-05 00:00:00
Proceeding: Neural Networks 2019 9
Tasks: Denoising, Image Denoising

Deep convolutional neural networks (CNNs) have attracted great attention in the field of image denoising. However, there are two drawbacks: (1) it is very difficult to train a deeper CNN for denoising tasks, and (2) most of deeper CNNs suffer from performance saturation. In this paper, we report the design of a novel network called a batch-renormalization denoising network (BRDNet). Specifically, we combine two networks to increase the width of the network, and thus obtain more features. Because batch renormalization is fused into BRDNet, we can address the internal covariate shift and small mini-batch problems. Residual learning is also adopted in a holistic way to facilitate the network training. Dilated convolutions are exploited to extract more information for denoising tasks. Extensive experimental results show that BRDNet outperforms state-of-the-art image-denoising methods. The code of BRDNet is accessible at http://www.yongxu.org/lunwen.html.
Identification of multiple damage in beams based on robust curvature mode shapes
Authors: Maosen Cao, Maciej Radzieński, Wei Xu, Wiesław Ostachowicz
Date: 2014-02-07 00:00:00
Proceeding: Mechanical Systems and Signal Processing 2014 2
Tasks: Sensitivity

Multiple damage identification in beams using curvature mode shape has become a
research focus of increasing interest during the last few years. On this topic, most existing
studies address the sensitivity of curvature mode shape to multiple damage. A noticeable
deficiency of curvature mode shape, however, is its susceptibility to measurement
noise, easily impairing its advantage of sensitivity to multiple damage. To overcome this
drawback, the synergy between a wavelet transform (WT) and a Teager energy operator
(TEO) is explored, with the aim of ameliorating the curvature mode shape. The improved
curvature mode shape, termed the TEO-WT curvature mode shape, has inherent capabilities of immunity to noise and sensitivity to multiple damage. The efficacy of the
TEO-WT curvature mode shape is analytically verified by identifying multiple cracks
in cantilever beams, with particular emphasis on its ability to locate multiple damage in
noisy conditions; the applicability of the proposed curvature mode shape is experimentally validated by detecting multiple fairly thin slots in steel beams with mode shapes
acquired by a scanning laser vibrometer. The proposed curvature mode shape appears
sensitive to multiple damage and robust against noise, and therefore is well suited to
identification of multiple damage in beams in noisy environments.
HFL: Hybrid Fuzzing on the Linux Kernel
Authors: Kyungtae Kim† Dae R. Jeong‡ Chung Hwan Kim¶ Yeongjin Jang§ Insik Shin‡ Byoungyoung Lee∗†
Date: 2020-02-24 00:00:00
Proceeding: NDSS 2020 2
Tasks: CPU

Hybrid fuzzing, combining symbolic execution and
fuzzing, is a promising approach for vulnerability discovery
because each approach can complement the other. However,
we observe that applying hybrid fuzzing to kernel testing is
challenging because the following unique characteristics of the
kernel make a naive adoption of hybrid fuzzing inefficient:
1) having indirect control transfers determined by system call
arguments, 2) controlling and matching internal system state via
system calls, and 3) inferring nested argument type for invoking
system calls. Failure to handling such challenges will render both
fuzzing and symbolic execution inefficient, and thereby, will result
in an inefficient hybrid fuzzing. Although these challenges are
essential to both fuzzing and symbolic execution, to the best of
our knowledge, existing kernel testing approaches either naively
use each technique separately without handling such challenges
or imprecisely handle a part of challenges only by static analysis.
To this end, this paper proposes HFL, which not only
combines fuzzing with symbolic execution for hybrid fuzzing
but also addresses kernel-specific fuzzing challenges via three
distinct features: 1) converting indirect control transfers to direct
transfers, 2) inferring system call sequence to build a consistent
system state, and 3) identifying nested arguments types of system
calls. As a result, HFL found 24 previously unknown vulnerabilities
in recent Linux kernels. Additionally, HFL achieves 15%
and 26% higher code coverage than Moonshine and Syzkaller,
respectively, and over kAFL/S2E/TriforceAFL, achieving even
four times better coverage, using the same amount of resources
(CPU, time, etc.). Regarding vulnerability discovery performance,
HFL found 13 known vulnerabilities more than three times faster
than Syzkaller.