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Optimal pricing in black box producer-consumer Stackelberg games using revealed preference feedback Neurocomputing (IF 4.438) Pub Date : 2021-01-19 Anup Aprem; Stephen J. Roberts
This paper considers an optimal pricing problem for the black box producer-consumer Stackelberg game. A producer sets price over a set of goods to maximize profit (the difference in revenue and cost function). The consumer buys a quantity to maximize the difference between the value of the quantity consumed and the cost. The value function of the consumer and the cost function of the producer are ‘black
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A Nonparametric-Learning Visual Servoing Framework for Robot Manipulator in Unstructured Environments Neurocomputing (IF 4.438) Pub Date : 2021-01-19 Xungao Zhong; Xunyu Zhong; Huosheng Hu; Xiafu Peng
Current visual servoing methods used in robot manipulation require system modeling and parameters, only working in structured environments. This paper presents a nonparametric visual servoing for a robot manipulator operated in unstructured environments. A Gaussian-mapping likelihood process is used in Bayesian stochastic state estimation (SSE) for Robotic coordination control, in which the Monte Carlo
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Deep Convolutional Neural Network-based Bernoulli Heatmap for Head Pose Estimation Neurocomputing (IF 4.438) Pub Date : 2021-01-19 Zhongxu Hu; Yang Xing; Chen Lv; Peng Hang; Jie Liu
Head pose estimation is a crucial problem for many tasks, such as driver attention, fatigue detection, and human behaviour analysis. It is well known that neural networks are better at handling classification problems than regression problems. It is an extremely nonlinear process to let the network output the angle value directly for optimization learning, and the weight constraint of the loss function
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Multi-task Adversarial Autoencoder Network for Face Alignment in the Wild Neurocomputing (IF 4.438) Pub Date : 2021-01-19 Xiaoqian Yue; Jing Li; Jia Wu; Jun Chang; Jun Wan; Jinyan Ma
Face alignment has been applied widely in the field of computer vision, which is still a very challenging task for the existence of large pose, partial occlusion, and illumination, etc. The method based on deep regression neural network has achieved the most advanced performance in the field of face alignment in recent years, and how to learn more representative facial appearance is the key to face
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Non-reduced order strategies for global dissipativity of memristive neutral-type inertial neural networks with mixed time-varying delays Neurocomputing (IF 4.438) Pub Date : 2021-01-19 Kai Wu; Jigui Jian
The issue of the global dissipativity of memristive neutral-type inertial neural networks with distributed and discrete time-varying delays is discussed without converting the original system to first-order equations. By taking some new Lyapunov-Krasovskii functionals and adopting inequality techniques, several effective criteria formulated by testable algebraic inequalities are derived to assure the
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Calibrating Feature Maps for Deep CNNs Neurocomputing (IF 4.438) Pub Date : 2021-01-19 Pravendra Singh; Pratik Mazumder; Mohammed Asad Karim; Vinay P. Namboodiri
Many performance improvement techniques calibrate the outputs of convolutional layers to improve the performance of convolutional neural networks, e.g., Squeeze-and-Excitation Networks (SENets). These techniques train the network to extract calibration weights from the input itself. However, these methods increase the complexity of the model in order to perform calibration. We propose an approach to
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Soft-sensing of wastewater treatment process via deep belief network with event-triggered learning Neurocomputing (IF 4.438) Pub Date : 2021-01-19 Gongming Wang; Qing-Shan Jia; MengChu Zhou; Jing Bi; Junfei Qiao
Due to the complex dynamic behavior of a wastewater treatment process (WWTP), the existing soft-sensing models usually fail to efficiently and accurately predict its effluent water quality. Especially when a lot of practical data is provided and we do not know which data-pair is more valuable, WWTP modeling becomes a time-consuming process. The main reason is that the existing soft-sensing models update
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Incorporating Sentimental Trend into Gated Mechanism Based Transformer Network for Story Ending Generation Neurocomputing (IF 4.438) Pub Date : 2021-01-19 Linzhang Mo; Jielong Wei; Qingbao Huang; Yi Cai; Qingguang Liu; Xingmao Zhang; Qing Li
Story ending generation is a challenging and under-explored task, which aims at generating a coherent, reasonable, and logical story ending given a context. Previous studies mainly focus on utilizing the contextual information and commonsense knowledge to generate story endings. However, there are still some issues must be addressed in the story endings generation processing, such as sentimental consistency
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Recurrent Convolutional Neural Network for Session-based Recommendation Neurocomputing (IF 4.438) Pub Date : 2021-01-19 Jinjin Zhang; Chenhui Ma; Xiaodong Mu; Peng Zhao; Chengliang Zhong; A. Ruhan
The task of session-based recommendation is predicting the next recommendation item when available information only includes the anonymous behavior sequence. Previous methods of session-based recommendation usually integrate the general interest, dynamic interest, and current interest to promote recommendation performance. However, most existing methods ignore the non-monotone feature interactions
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A NOVEL HYPERSPECTRAL UNMIXING MODEL BASED ON MULTILAYER NMF WITH HOYER’S PROJECTION Neurocomputing (IF 4.438) Pub Date : 2021-01-19 Yuan Yuan; Zihan Zhang; Ganchao Liu
Hyperspectral remote sensing is an important earth observation method with wide application. But the low spatial resolution of hyperspectral images makes it difficult to distinguish the ground objects. The hyperspectral image unmixing is a task to estimate the spectral signatures and corresponding fractional abundances. However, the unmixing speed and efficiency are still limited by traditional structures
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An Adaptive and Opposite K-means Operation based Memetic Algorithm for Data Clustering Neurocomputing (IF 4.438) Pub Date : 2021-01-19 Xi Wang; Zidong Wang; Mengmeng Sheng; Qi Li; Weiguo Sheng
Evolutionary algorithm (EA) incorporating with k-means local search operator represents an important approach for cluster analysis. In the existing EA approach, however, the k-means operators are usually directly employed on the individuals and generally applied with fixed intensity as well as frequency during evolution, which could significantly limit their performance. In this paper, we first introduce
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Robust License Plate Signatures Matching Based on Multi-Task Learning Approach Neurocomputing (IF 4.438) Pub Date : 2021-01-19 Abul Hasnat; Amir Nakib
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Leveraging Neighborhood Session Information with Dual Attentive Neural Network for Session-Based Recommendation Neurocomputing (IF 4.438) Pub Date : 2021-01-18 Yuan Wu; Jin Gou
Predicting users’ preference in a context of the uncertainty of user and the limited information is a challenging work in many online services, e.g., e-commerce and media streaming. Recent advances in session-based recommendation mostly focus on mining more available information within the current session. However, those methods ignored the sessions with similar context for the current session, which
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Generative Adversarial Learning for Detail-Preserving Face Sketch Synthesis Neurocomputing (IF 4.438) Pub Date : 2021-01-18 Weiguo Wan; Yong Yang; Hyo Jong Lee
Face sketch synthesis aims to generate a face sketch image from a corresponding photo image and has wide applications in law enforcement and digital entertainment. Despite the remarkable achievements that have been made in face sketch synthesis, most existing works pay main attention to the facial content transfer, at the expense of facial detail information. In this paper, we present a new generative
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Generative Adversarial Networks for Single Channel Separation of Convolutive mixed Speech Signals Neurocomputing (IF 4.438) Pub Date : 2021-01-18 Yang Li; Wei-Tao Zhang; Shun-Tian Lou
The suppression of interference for speech recognition is of great significance in noisy situation, especially in single channel receiving mode, the suppression of interference is much more difficult. In this paper, we propose a generative adversarial network (GAN) based method for single channel dereverberation and speech separation. Different from the existing methods, our method considers the influence
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Zero-Shot Learning with Self-Supervision by Shuffling Semantic Embeddings Neurocomputing (IF 4.438) Pub Date : 2021-01-18 Hoseong Kim; Jewook Lee; Hyeran Byun
Zero-shot learning and self-supervised learning have been widely studied due to the advantage of performing representation learning in a data shortage situation efficiently. However, few studies consider zero-shot learning using semantic embeddings (e.g., CNN features or attributes) and self-supervision simultaneously. The reason is that most zero-shot learning works employ vector-level semantic embeddings
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Anti-interference analysis of bio-inspired musculoskeletal robotic system Neurocomputing (IF 4.438) Pub Date : 2021-01-18 Yaxiong Wu; Jiahao Chen; Hong Qiao
Compared with general joint-link robotic systems, bio-inspired musculoskeletal robotic systems offer the advantages of higher robustness, flexibility, and redundancy. Hence, they are a promising option for the development of next-generation robots. However, theoretical analysis regarding the superiorities of musculoskeletal systems is scarce. This study analyzes and proves the anti-interference of
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Haze Concentration Adaptive Network for Image Dehazing Neurocomputing (IF 4.438) Pub Date : 2021-01-18 Tao Wang, Li Zhao; Pengcheng Huang; Xiaoqin Zhang; Jiawei Xu
Learning-based methods have attracted considerable interest in image dehazing. However, most existing methods are not well adapted to different hazy conditions, especially when dealing with the heavily hazy scene. There is often a significant amount of haze that remains in the images recovered by most methods. To address this issue, we propose an end-to-end Haze Concentration Adaptive Network (HCAN)
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Effects of burst-timing-dependent plasticity on synchronous behaviour in neuronal network Neurocomputing (IF 4.438) Pub Date : 2021-01-18 João Antonio Paludo Silveira; Paulo Ricardo Protachevicz; Ricardo Luiz Viana; Antonio Marcos Batista
Brain plasticity or neuroplasticity refers to the ability of the nervous system to reorganise itself in response to stimuli. For instance, sensory and motor stimulation, memory formation, and learning depend on brain plasticity. Neuronal synchronisation can be enhanced or suppressed by the plasticity. Synchronisation is related to many functions in the brain, as well as to some brain disorders. One
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Multi-Scale Stacking Attention Pooling for Remote Sensing Scene Classification Neurocomputing (IF 4.438) Pub Date : 2021-01-18 Qi Bi; Han Zhang; Kun Qin
Remote sensing image scene classification is challenging due to the complicated spatial arrangement and varied object sizes inside a large-scale aerial image. Among the bottlenecks for current deep learning methods to depict and discriminate the complexity of remote sensing scenes, strengthening the local semantic representation and multi-scale feature representation is necessary. In this paper, we
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Stochastic stability of fractional-order Markovian jumping complex-valued neural networks with time-varying delays Neurocomputing (IF 4.438) Pub Date : 2021-01-18 R. Vijay Aravind; P. Balasubramaniam
This paper is concerned with the problem of stochastic stability analysis for fractional-order Markovian jumping complex-valued neural networks (MJCVNNs) with time-varying delays. The novelty of this study is emphasized in two phases. In first phase, MJCVNNs is considered in the form of fractional-order systems. Secondly, complex-valued Wirtinger based integral inequality is newly constructed. The
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BCNet: Bidirectional Collaboration Network for Edge-Guided Salient Object Detection Neurocomputing (IF 4.438) Pub Date : 2021-01-18 Bo Dong; Yan Zhou; Chuanfei Hu; Keren Fu; Geng Chen
The boundary quality is a key factor determining the success of accurate salient object detection (SOD). A number of edge-guided SOD methods have been proposed to improve the boundary quality, but shown unsatisfactory performance due to the lack of a comprehensive consideration of multi-level feature fusion and multi-type feature aggregation. To resolve this issue, we propose a novel Bidirectional
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Multistability of state-dependent switching neural networks with discontinuous nonmonotonic piecewise linear activation functions Neurocomputing (IF 4.438) Pub Date : 2021-01-18 Jiahui Zhang; Song Zhu; Nannan Lu; Shiping Wen
This paper presents the theoretical results on the multistability of state-dependent switching neural networks with discontinuous nonmonotonic piecewise linear activation functions. For n-neurons switching model, this paper shows that neural networks have 7n equilibrium points, 6n of which are located at the continuous points of activation functions and others are located at the discontinuous points
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Spatial-aware Stacked Regression Network for Real-time 3D Hand Pose Estimation Neurocomputing (IF 4.438) Pub Date : 2021-01-18 Pengfei Ren; Haifeng Sun; Weiting Huang; Jiachang hao; Daixuan Cheng; Qi Qi; Jingyu Wang; Jianxin Liao
Making full use of the spatial information of the depth data is crucial for 3D hand pose estimation from a single depth image. In this paper, we propose a Spatial-aware Stacked Regression Network (SSRN) for fast, robust and accurate 3D hand pose estimation from a single depth image. By adopting a differentiable pose re-parameterization process, our method efficiently encodes the pose-dependent 3D spatial
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Palmprint Orientation Field Recovery via Attention-based Generative Adversarial Network Neurocomputing (IF 4.438) Pub Date : 2021-01-18 Bing Liu; Jufu Feng
Orientation field is the key foundation of palmprint feature extraction and recognition. However, due to the presence of numerous wide creases, the palmprint orientation field can hardly be accurately estimated by previous methods, especially in the thenar region, which still faces huge challenges. To solve this problem, we formulate palmprint orientation field recovery as an inpainting task and propose
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Learning to Detect Anomaly Events in Crowd Scenes from Synthetic Data Neurocomputing (IF 4.438) Pub Date : 2021-01-18 Wei Lin; Junyu Gao; Qi Wang; Xuelong Li
Recently, due to its widespread applications in public safety, anomaly detection in crowd scenes has become a hot topic. Some deep-learning-based methods attain significant achievements in this field. Nevertheless, most of them suffer from over-fitting to some extent because of scarce data, which are usually abrupt and low-frequency in the real world. To remedy the above problem, this paper firstly
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Self-representation and Class-Specificity Distribution Based Multi-View Clustering Neurocomputing (IF 4.438) Pub Date : 2021-01-18 Yu Yun; Wei Xia; Yongqing Zhang; Quanxue Gao; Xinbo Gao
Despite the promising performance for clustering, weighted tensor nuclear norm based multi-view subspace clustering needs to artificially predefine a weighted vector when shrinking all the singular values of tensor. It is very difficult to select a suitable weighted vector due to the complex and unknown distribution of data in real applications. Another limitation is that, the learned affinity matrix
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Progressive Principle Component Analysis for Compressing Deep Convolutional Neural Networks Neurocomputing (IF 4.438) Pub Date : 2021-01-18 Jing Zhou; Haobo Qi; Yu Chen; Hansheng Wang
In this work, we propose a progressive principal component analysis (PPCA) method for compressing deep convolutional neural networks. The proposed method starts with a prespecified layer and progressively moves on to the final output layer. For each target layer, PPCA conducts kernel principal component analysis for the estimated kernel weights. This leads to a significant reduction in the number of
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Relevant information undersampling to support imbalanced data classification Neurocomputing (IF 4.438) Pub Date : 2021-01-18 J. Hoyos-Osorio; A. Alvarez-Meza; G. Daza-Santacoloma; A. Orozco-Gutierrez; G. Castellanos-Dominguez
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Micro-expression Action Unit Detection with Spatial and Channel Attention Neurocomputing (IF 4.438) Pub Date : 2021-01-18 Yante Li; Xiaohua Huang; Guoying Zhao
Action Unit (AU) detection plays an important role in facial behaviour analysis. In the literature, AU detection has extensive researches in macro-expressions. However, to the best of our knowledge, there is limited research about AU analysis for micro-expressions. In this paper, we focus on AU detection in micro-expressions. Due to the small quantity and low intensity of micro-expression databases
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GSA-GAN: Global Spatial Attention Generative Adversarial Networks Neurocomputing (IF 4.438) Pub Date : 2021-01-18 Lei An; Jiajia Zhao; Bo Ma
This paper proposes a solution to translating the visible images into infrared images, which is challenging in computer vision. Our solution belongs to unsupervised learning, which has recently become popular in image-to-image translation. However, existing methods do not produce satisfactory results because (1) most existing methods are mainly used in entertainment scenarios with single scenes and
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Event-triggered sliding mode control with adaptive neural networks for uncertain nonlinear systems Neurocomputing (IF 4.438) Pub Date : 2021-01-18 Nana Wang; Fei Hao
In this paper, a robust non-singular fast terminal sliding mode control scheme with adaptive neural networks is presented for a class of nonlinear systems with unknown bounds of uncertainties. To reduce transmission and computation burden in resource-constrained networked systems, two kinds of event-triggering mechanisms are taken into consideration in the proposed adaptive sliding mode control scheme
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Change detection with various combinations of fluid pyramid integration networks Neurocomputing (IF 4.438) Pub Date : 2021-01-18 Rui Huan; Mo Zhou; Yan Xing; Yaobin Zou; Wei Fan
An increasing number of change detection models are designed with different convolutional neural network (CNNs). However, the mechanism for designing network layers that can effectively extract robust features for different scenes remains unclear. Thus, novel networks with fluid pyramid integration network (FPIN) to detect changes are proposed in this study. Specifically, we first extract multi-scale
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Efficient similarity search on multidimensional space of biometric databases Neurocomputing (IF 4.438) Pub Date : 2021-01-17 Umarani Jayaraman; Phalguni Gupta
The problem of pursuing the data items of a large database whose distances to a query item are the least is known as Similarity Search (Nearest Neighbor Search) problem. There exist various algorithms to address this problem. Some of the well known algorithms are i) exact algorithms ii) approximation algorithms and iii) randomized algorithms. This paper has made study only on exact and approximation
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Fast vertex-based graph convolutional neural network and its application to brain images Neurocomputing (IF 4.438) Pub Date : 2021-01-07 Chaoqiang Liu; Hui Ji; Anqi Qiu
This paper proposes a vertex-based graph convolutional neural network (vertex-CNN) for analyzing structured data on graphs. We represent graphs using semi-regular triangulated meshes in which each vertex has 6 connected neighbors. We generalize classical CNN defined on equi-spaced grids to that defined on semi-regular triangulated meshes by introducing main building blocks of the CNN, including convolution
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Attentive U-Recurrent Encoder-Decoder Network for Image Dehazing Neurocomputing (IF 4.438) Pub Date : 2021-01-14 Shibai Yin; Yibin Wang; Yee-Hong Yang
Haze removal is an important pre-processing step in many computer vision tasks. Convolutional neural networks, especially the U-shaped networks, have shown to be effective in image dehazing. Nevertheless, these networks have three main limitations. First, the relevant haze information, e.g. concentration of haze, is totally ignored. Second, spatial inconsistency and information dilution usually occur
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Performing multi-target regression via gene expression programming-based ensemble models Neurocomputing (IF 4.438) Pub Date : 2020-12-28 Jose M. Moyano; Oscar Reyes; Habib M. Fardoun; Sebastián Ventura
Multi-Target Regression problem comprises the prediction of multiple continuous variables given a common set of input features, unlike traditional regression tasks, where just one output target is available. There are two major challenges when addressing this problem, namely the exploration of the inter-target dependencies and the modeling of complex input–output relationships. This work proposes a
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One-shot cross-dataset palmprint recognition via adversarial domain adaptation Neurocomputing (IF 4.438) Pub Date : 2021-01-05 Huikai Shao; Dexing Zhong
Deep learning-based palmprint recognition algorithms have obtained promising performance. However, the previous methods require a large amount of labeled samples, which are difficult to obtain. In this paper, a novel cross-dataset palmprint recognition method is proposed using as low as one labelled sample per subject in the target palmprint dataset based on adversarial domain adaptation. Two different
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Wearables-based multi-task gait and activity segmentation using recurrent neural networks Neurocomputing (IF 4.438) Pub Date : 2020-11-24 Chrsitine F. Martindale; Vincent Christlein; Philipp Klumpp; Bjoern M. Eskofier
Human activity recognition (HAR) and cycle analysis, such as gait analysis, have become an integral part of daily lives from gesture recognition to step counting. As the available data and the possible application areas grow, an efficient solution without the need of handcrafted feature extraction is needed. We propose a multi-task recurrent neural network architecture that uses inertial sensor data
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Synchronization of neural networks with memristor-resistor bridge synapses and Lévy noise Neurocomputing (IF 4.438) Pub Date : 2021-01-13 Liangchen Li; Rui Xu; Qintao Gan; Jiazhe Lin
In this paper, a kind of memristor-resistor bridge synapses are applied to neural networks, which makes the connection weights of networks continuously adjustable. A novel model for this new kind of neural networks is established, in which the memory characteristic of memristors is retained. The state synchronization of the model with the influence of Lévy noise is investigated. By making use of the
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Bi-directional skip connection feature pyramid network and Sub-pixel convolution for high-quality object detection Neurocomputing (IF 4.438) Pub Date : 2021-01-13 Shuqi Xiong; Xiaohong Wu; Honggang Chen; Linbo Qing; Tong Chen; Xiaohai He
In existing state-of-the-art object detectors, feature pyramid networks (FPN) and multiscale feature fusion are still typically used. The traditional FPN fusion strategy is based on the top-down fusion of high-level semantic information. The top-down fusion method generally uses upsampling based on interpolation, which often results in jagged edges, mosaic distortion, and edge blurring. Moreover, in
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Efficient Attention Based Deep Fusion CNN for Smoke Detection in Fog Environment Neurocomputing (IF 4.438) Pub Date : 2021-01-13 Lijun He; Xiaoli Gong; Sirou Zhang; Liejun Wang; Fan Li
Smoke detection based on video monitoring is of great importance for early fire warning. However, most of the smoke detection methods based on neural network only consider the normal weather. The harsh weather such as the fog environment is ignored. In this paper, we propose a smoke detection in normal and fog weather, which combines attention mechanism and feature-level and decision-level fusion module
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A Multilevel Fusion Network for 3D Object Detection Neurocomputing (IF 4.438) Pub Date : 2021-01-13 Chunlong Xia; Ping Wei; Wenwen Wei; Nanning Zheng
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Observer-based H∞ Sliding Mode Control for Networked Systems subject to Communication Channel Fading and Randomly Varying Nonlinearities Neurocomputing (IF 4.438) Pub Date : 2021-01-13 Xinyu Guan; Jun Hu; Jun Qi; Dongyan Chen; Fanyueyang Zhang; Guang Yang
In this paper, the H∞ sliding mode control (SMC) problem is discussed for a class of uncertain discrete networked systems with channel fading and randomly varying nonlinearities (RVNs) by employing the observer-based approach. Here, the Jth-order Rice fading model is adopted to describe the phenomenon of channel fading, where the channel coefficients are mutually independent random variables that take
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Virtual Guide Automatic Berthing Control of Marine Ships Based on Heuristic Dynamic Programming Iteration Method Neurocomputing (IF 4.438) Pub Date : 2021-01-13 Qi Liu; Tieshan Li; Qihe Shan; Renhai Yu; Xiaoyang Gao
This paper addresses the berthing control problem for automatic ships by using a virtual guide system based on heuristic dynamic programming (HDP) method. Firstly, by introducing an automatic virtual guide system, the berthing control problem can be transformed into a tracking control problem, and then can be further transformed into an optimal regulation problem. Secondly, the HDP method is used to
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A consensus-based decentralized training algorithm for deep neural networks with communication compression Neurocomputing (IF 4.438) Pub Date : 2021-01-13 Bo Liu; Zhengtao Ding
Facing the challenge of distributed computing on processing large-scale data, this paper proposes a consensus-based decentralized training method with communication compression. First, the decentralized training method is designed based on the decentralized topology to reduce the communication burden on the busiest agent and avoid any agent revealing its locally stored data. The convergence of the
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Residual Attention and Other Aspects module for Aspect-Based Sentiment Analysis Neurocomputing (IF 4.438) Pub Date : 2021-01-13 Chao Wu; Qingyu Xiong; Zhengyi Yang; Min Gao; Qiude Li; Yang Yu; Kaige Wang; Qiwu Zhu
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task designed to predict the sentiment polarity of each aspect term in a text. Recent research mainly uses neural networks to model text and utilizes attention mechanisms to interact for associate aspect terms and context to obtain more effective feature representation. However, the general attention mechanism is easy to lose
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Extended variational inference for gamma mixture model in positive vectors modeling Neurocomputing (IF 4.438) Pub Date : 2021-01-11 Yuping Lai; Huirui Cao; Lijuan Luo; Yongmei Zhang; Fukun Bi; Xiaolin Gui; Yuan Ping
Bayesian estimation of finite Gamma mixture model (GaMM) has attracted considerable attention recently due to its capability of modeling positive data. With conventional variational inference (VI) frameworks, we cannot derive an analytically tractable solution for the variational posterior, since the expectation of the joint distribution of all the random variables cannot be estimated in a closed form
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Semi-global bipartite consensus tracking of singular multi-agent systems with input saturation Neurocomputing (IF 4.438) Pub Date : 2021-01-11 Zhen-Hua Zhu; Zhi-Hong Guan; Bin Hu; Ding-Xue Zhang; Xin-Ming Cheng; Tao Li
This paper investigates the problem of bipartite consensus tracking for a class of linear singular multi-agent systems under a signed graph topology, where the control input of each agent is subject to saturation. By exploiting a parametric algebraic Riccati equation (ARE)-based low-gain feedback approach, a static state feedback control protocol and a dynamic observer-based output feedback control
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Fully integer-based quantization for mobile convolutional neural network inference Neurocomputing (IF 4.438) Pub Date : 2020-12-23 Peng Peng; Mingyu You; Weisheng Xu; Jiaxin Li
Deploying deep convolutional neural networks on mobile devices is challenging because of the conflict between their heavy computational overhead and the hardware’s restricted computing capacity. Network quantization is typically used to alleviate this problem. However, we found that a “datatype mismatch” issue in existing low bitwidth quantization approaches can generate severe instruction redundancy
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Learning multi-granularity features from multi-granularity regions for person re-identification Neurocomputing (IF 4.438) Pub Date : 2020-12-16 Kaiwen Yang; Jiwei Yang; Xinmei Tian
Part-based methods for person re-identification have been widely studied. In existing part-based methods, although multiple parts are explored, only coarse-grained features of these parts are utilized. Thus, too much fine-grained information is discarded, which limits their ability to extract detailed discriminative features. To tackle this problem, we propose a novel person re-identification network
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Deep convolutional neural networks for data delivery in vehicular networks Neurocomputing (IF 4.438) Pub Date : 2021-01-11 Hejun Jiang; Xiaolan Tang; Kai Jin; Wenlong Chen; Juhua Pu
In vehicular networks, most content delivery schemes only utilize vehicle cooperation or powerful infrastructure to satisfy data requests. How to fully utilize vehicle-to-vehicle and vehicle-to-infrastructure communications to improve data acquisition still requires further analysis. In this paper, the content delivery problem is formulated as a maximum flow of a directed network, which implies the
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Binary classification of floor vibrations for human activity detection based on dynamic mode decomposition Neurocomputing (IF 4.438) Pub Date : 2020-12-29 Shichao Zhou; Guang Lin; Qinfang Qian; Chao Xu
Analyzing small amplitude of floor vibrations is a new promising means for identifying the types of human activities, e.g., walking around and accidental falls. In this paper, we consider the binary classification problem of floor vibrations for the applications like fall detection. For practical use, there are two main issues of the problem. First, the prediction of the classifier should be fast.
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Asynchronous finite-time state estimation for semi-Markovian jump neural networks with randomly occurred sensor nonlinearities Neurocomputing (IF 4.438) Pub Date : 2021-01-12 Yao Wang; Shengyuan Xu; Yongmin Li; Yuming Chu; Zhengqiang Zhang
This paper addresses the finite-time state estimation problem for semi-Markovian jump neural networks with sensor nonlinearities under the consideration of leakage delay and time-varying delay. The modes of original system and desired estimator are supposed to be asynchronous. Some sufficient conditions are proposed to guarantee the finite-time boundedness as well as mixed H∞ and passive performance
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Comparison Detector for Cervical Cell/Clumps Detection in the Limited Data Scenario Neurocomputing (IF 4.438) Pub Date : 2021-01-12 Yixiong Liang; Zhihong Tang; Meng Yan; Jialin Chen; Qing Liu; Yao Xiang
Automated detection of cervical cancer cells/clumps has the potential to significantly reduce error rate and increase productivity in cervical cancer screening. However, most traditional methods rely on the success of accurate cell segmentation and discriminative hand-crafted features extraction. Recently there are emerging deep learning-based methods which train Convolutional Neural Networks (CNN)
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A novel improved trigonometric neural network algorithm for solving price-dividend functions of continuous time one-dimensional asset-pricing models Neurocomputing (IF 4.438) Pub Date : 2021-01-12 Mingjie Ma; Lunan Zheng; Jianhui Yang
Asset pricing model is the pillar of modern financial market price theory. It is of great practical and theoretical significance to solve the equilibrium price- dividend function of the asset pricing model. To solve the asset pricing model, this paper develops a novel neural network method called improved trigonometric neural network, which consists of three parts: the improved trigonometric function
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Learning to estimate smooth and accurate semantic correspondence Neurocomputing (IF 4.438) Pub Date : 2021-01-12 Huaiyuan Xu; Xiaodong Chen; Jiaqi Xi; Jing Liao
We tackle the problem of estimating dense semantic correspondence between two images depicting different instances of the same category. In this paper, we consider semantic context and correspondence information from the neighborhood in order to overcome the drawback of previous works that estimate the correspondence of each pixel or patch independently. To this end, a novel network, called SANet,
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Multi-level Cross-view Consistent Feature Learning for Person Re-identification Neurocomputing (IF 4.438) Pub Date : 2021-01-12 Yixiu Liu; Yunzhou Zhang; Bir Bhanu; Sonya Coleman; Dermot Kerr
Person re-identification plays an important role in searching for a specific person in a camera network with non-overlapping cameras. The most critical problem for re-identification is feature representation. In this paper, a multi-level cross-view consistent feature learning framework is proposed for person re-identification. First, local deep, LOMO and SIFT features are extracted to form multi-level
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Neural networks with finite-time convergence for solving time-varying linear complementarity problem Neurocomputing (IF 4.438) Pub Date : 2021-01-12 Haojin Li; Shuai Shao; Sitian Qin; Yunbo Yang
Time-varying linear complementarity problem (TLCP) has received a great deal of attention due to its broad variety of scientific and engineering applications. Several efficient Zhang neural networks are introduced for solving TLCP in this paper. Theoretical analysis shows that the related error function of the model proposed in this paper eventually tends to zero. The state convergence time periods
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Vibration fault diagnosis based on stochastic configuration neural networks Neurocomputing (IF 4.438) Pub Date : 2021-01-12 Jingna Liu; Rujiang Hao; Tianlun Zhang; XiZhao Wang
This work presents a study on fault diagnosis in vibration signal processing. Rather than building a fault model through frequently used approaches to handling the series data such as LSTM or hidden Markov field, this work processes the vibration signal by moving the time window to generate multiple samples and then transfers fault diagnosis into a traditional supervised learning problem. Stochastic
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