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MAFormer: A transformer network with multi-scale attention fusion for visual recognition Neurocomputing (IF 6.0) Pub Date : 2024-05-10 Huixin Sun, Yunhao Wang, Xiaodi Wang, Bin Zhang, Ying Xin, Baochang Zhang, Xianbin Cao, Errui Ding, Shumin Han
Vision Transformer and its variants have demonstrated great potential in various computer vision tasks. However conventional vision transformers often focus on global dependency at a coarse level, which results in a learning challenge on global relationships and fine-grained representation at a token level. In this paper, we introduce Multi-scale Attention Fusion into transformer (), which explores
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Two-stage fine-tuning with ChatGPT data augmentation for learning class-imbalanced data Neurocomputing (IF 6.0) Pub Date : 2024-05-09 Taha ValizadehAslani, Yiwen Shi, Jing Wang, Ping Ren, Yi Zhang, Meng Hu, Liang Zhao, Hualou Liang
Classification of long-tailed distributed data is a challenging problem, which suffers from serious class imbalance and hence poor performance on tail classes, which have only a few samples. Owing to this paucity of samples, learning on the tail classes is especially challenging for fine-tuning when transferring a pretrained model to a downstream task. In this work, we present a simple modification
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Private-preserving language model inference based on secure multi-party computation Neurocomputing (IF 6.0) Pub Date : 2024-05-06 Chen Song, Ruwei Huang, Sai Hu
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Unsupervised detecting anomalies in multivariate time series by Robust Convolutional LSTM Encoder–Decoder (RCLED) Neurocomputing (IF 6.0) Pub Date : 2024-05-06 Tuan Le, Hai Canh Vu, Amélie Ponchet-Durupt, Nassim Boudaoud, Zohra Cherfi-Boulanger, Thao Nguyen-Trang
Monitoring modern industrial systems generates a large amount of multivariate time series data. One of the critical tasks of monitoring these systems is anomaly detection. The auto-encoder has emerged as a promising solution for detecting anomalies in systems lacking explicit anomaly data in their historical records; however, its performance can be sensitive to noisy data. This work aims at developing
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Observer-based adaptive neutral network inverse optimal containment control for nonlinear multiagent systems with input quantization Neurocomputing (IF 6.0) Pub Date : 2024-05-04 Shiqi Wen, Shaocheng Tong
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CompleteDT: Point cloud completion with information-perception transformers Neurocomputing (IF 6.0) Pub Date : 2024-05-04 Jun Li, Shangwei Guo, Luhan Wang, Shaokun Han
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Bidirectional neural network for trajectory planning: An application to medical emergency vehicle Neurocomputing (IF 6.0) Pub Date : 2024-05-03 Liqun Huang, Runqi Chai, Kaiyuan Chen, Senchun Chai, Yuanqing Xia, Guo-Ping Liu
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A hybrid optimization algorithm for multi-agent dynamic planning with guaranteed convergence in probability Neurocomputing (IF 6.0) Pub Date : 2024-04-27 Ye Zhang, Yutong Zhu, Haoyu Li, Jingyu Wang
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Lag synchronization for coupled neural networks with multistate or multiderivative couplings Neurocomputing (IF 6.0) Pub Date : 2024-04-27 Yan-Ran Zhu, Jin-Liang Wang, Xiao Han
In this paper, two kinds of lag synchronization for coupled neural networks (CNNs) are addressed, that is, the cases with multiple state or derivative couplings. Firstly, several lag synchronization criteria for these two CNNs are derived by selecting proper state feedback controllers. Furthermore, since external disturbances in neural network implementation should not be ignored, the impact of external
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Optimal bipartite graph matching-based goal selection for policy-based hindsight learning Neurocomputing (IF 6.0) Pub Date : 2024-04-27 Shiguang Sun, Hanbo Zhang, Zeyang Liu, Xingyu Chen, Xuguang Lan
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Resting-potential-adjustable soft-reset integrate-and-fire neuron model for highly reliable and energy-efficient hardware-based spiking neural networks Neurocomputing (IF 6.0) Pub Date : 2024-04-27 Kyungchul Park, Sungjoon Kim, Min-Hye Oh, Woo Young Choi
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On the effects of recursive convolutional layers in convolutional neural networks Neurocomputing (IF 6.0) Pub Date : 2024-04-26 Johan Chagnon, Markus Hagenbuchner, Ah Chung Tsoi, Franco Scarselli
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Video anomaly detection: A systematic review of issues and prospects Neurocomputing (IF 6.0) Pub Date : 2024-04-26 Yau Alhaji Samaila, Patrick Sebastian, Narinderjit Singh Sawaran Singh, Aliyu Nuhu Shuaibu, Syed Saad Azhar Ali, Temitope Ibrahim Amosa, Ghulam E. Mustafa Abro, Isiaka Shuaibu
The increase in the deployment of surveillance camera in outdoor and indoor settings have resulted in a growing demand for intelligent systems that can accurately detect and recognize human actions as well as other entities of interest within the captured video data. Although, human action recognition is a well-established topic in computer vision, abnormal behaviour detection has recently received
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Global exponential synchronization of complex networks with reaction diffusions and finite distributed delays coupling Neurocomputing (IF 6.0) Pub Date : 2024-04-26 Yun Xing, Chaoyang Zheng, Yin Sheng, Zhigang Zeng
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An overview on deep clustering Neurocomputing (IF 6.0) Pub Date : 2024-04-26 Xiuxi Wei, Zhihui Zhang, Huajuan Huang, Yongquan Zhou
In recent years, with the great success of deep learning and especially deep unsupervised learning, many deep architectural clustering methods, collectively known as deep clustering, have emerged. Deep clustering shows the potential to outperform traditional methods, especially in handling complex high-dimensional data, taking full advantage of deep learning. To achieve a comprehensive overview of
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Multivariate time series clustering based on fuzzy cognitive maps and community detection Neurocomputing (IF 6.0) Pub Date : 2024-04-26 Yingzhi Teng, Jing Liu, Kai Wu, Yang Liu, Baihao Qiao
Most time series clustering methods mainly focus on univariate time series (UTS). Compared with UTS, multivariate time series (MTS) consists of multiple components. Although interest in MTS clustering is increasing, its performance is far from satisfactory. Most traditional MTS clustering methods may have two limitations. First, they do not consider both the temporal features of each component and
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Blind image deblurring using both L0 and L1 regularization of Max-min prior Neurocomputing (IF 6.0) Pub Date : 2024-04-25 Amir Eqtedaei, Alireza Ahmadyfard
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TPN:Triple network algorithm for deep reinforcement learning Neurocomputing (IF 6.0) Pub Date : 2024-04-25 Chen Han, Xuanyin Wang
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Evolutionary Computation in bioinformatics: A survey Neurocomputing (IF 6.0) Pub Date : 2024-04-25 Yanyun Zhang, Li Cheng, Guanyu Chen, Daniyal Alghazzawi
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A comprehensive overview of core modules in visual SLAM framework Neurocomputing (IF 6.0) Pub Date : 2024-04-25 Dupeng Cai, Ruoqing Li, Zhuhua Hu, Junlin Lu, Shijiang Li, Yaochi Zhao
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Advanced insights through systematic analysis: Mapping future research directions and opportunities for xAI in deep learning and artificial intelligence used in cybersecurity Neurocomputing (IF 6.0) Pub Date : 2024-04-25 Marek Pawlicki, Aleksandra Pawlicka, Rafał Kozik, Michał Choraś
This paper engages in a comprehensive investigation concerning the application of Explainable Artificial Intelligence (xAI) within the context of deep learning and Artificial Intelligence, with a specific focus on its implications for cybersecurity. Firstly, the paper gives an overview of xAI techniques and their significance and benefits when applied in cybersecurity. Subsequently, the authors methodically
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Center-bridged Interaction Fusion for hyperspectral and LiDAR classification Neurocomputing (IF 6.0) Pub Date : 2024-04-25 Lu Huo, Jiahao Xia, Leijie Zhang, Haimin Zhang, Min Xu
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A novel spectral super-resolution network with dominant information between spatial and spectral domains Neurocomputing (IF 6.0) Pub Date : 2024-04-25 Weixiao Zhao, Minggang Dong, Yan Wang, Ruoqi Tan, Tianhao Wu
Existing spectral super-resolution (SSR) methods have achieved satisfactory performance by designing complicated deep convolution neural networks (DCNNs) to extract spectral and spatial features. However, these methods ignore the fact that the significance of spatial and spectral information in each hyperspectral image (HSI) is different, and most of them directly fuse two kinds of information with
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A novel two-stage wrapper feature selection approach based on greedy search for text sentiment classification Neurocomputing (IF 6.0) Pub Date : 2024-04-25 Ensar Arif Sağbaş
Sentiment analysis is a crucial step in obtaining subjective data from online text sources. Nevertheless, the substantial challenge of high dimensionality prevails within text classification. Addressing this, dimension reduction emerges as a valuable approach to enhance the efficacy of classification in the domain of machine learning. The discerning removal of redundant features not only expedites
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Semantic-aware normalizing flow with feature fusion for image anomaly detection Neurocomputing (IF 6.0) Pub Date : 2024-04-25 Wei Ma, Yao Li, Shiyong Lan, Wenwu Wang, Weikang Huang, Wujiang Zhu
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MD-GCCF: Multi-view deep graph contrastive learning for collaborative filtering Neurocomputing (IF 6.0) Pub Date : 2024-04-25 Xinlu Li, Yujie Tian, Bingbing Dong, Shengwei Ji
Collaborative Filtering (CF), a classical recommender system approach, learns users’ interests and behavioral preferences for items through a user–item interaction graph. CF based on graph neural network (GNN) and CF based on graph contrastive learning (GCL) show strong advantages in both modeling multi-layer signals and solving label sparsity, respectively. However, there are still two key problems
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Dynamic loss yielding more transferable targeted adversarial examples Neurocomputing (IF 6.0) Pub Date : 2024-04-25 Ming Zhang, Yongkang Chen, Hu Li, Cheng Qian, Xiaohui Kuang
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Class-driven nonnegative matrix factorization with manifold regularization for data clustering Neurocomputing (IF 6.0) Pub Date : 2024-04-24 Huirong Li, Yani Zhou, Pengjun Zhao, Lei Wang, Chengxiang Yu
Nonnegative matrix factorization (NMF) is an effective technique to extract the underlying low-dimensional structure of data by utilizing its parts-based representation, which has been widely used in feature extraction and machine learning. However, NMF is an unsupervised learning algorithm without utilizing the discriminative prior information. In this paper, we put forward a new class-driven NMF
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Consensus Multi-view subspace clustering based on Graph Filtering Neurocomputing (IF 6.0) Pub Date : 2024-04-24 Mei Chen, Yiying Yao, Yuanyuxiu You, Boya Liu, Yu Wang, Song Wang
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OV-VG: A benchmark for open-vocabulary visual grounding Neurocomputing (IF 6.0) Pub Date : 2024-04-24 Chunlei Wang, Wenquan Feng, Xiangtai Li, Guangliang Cheng, Shuchang Lyu, Binghao Liu, Lijiang Chen, Qi Zhao
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Quantifying uncertainty of uplift: Trees and T-learners Neurocomputing (IF 6.0) Pub Date : 2024-04-24 Otto Nyberg, Arto Klami
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One-step incremental multi-view spectral clustering based on graph linkage learning Neurocomputing (IF 6.0) Pub Date : 2024-04-24 Weijun Wang, Ling Jing
Most traditional multi-view spectral clustering algorithms involve two separate steps: solving the spectral embedding matrix and clustering, which may introduce errors during the clustering process. Moreover, in practical applications, the number of available views may increase over time, and the approach of re-fusing all views in each computation would result in elevated computational costs. In this
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Graph learning with label attention and hyperbolic embedding for temporal event prediction in healthcare Neurocomputing (IF 6.0) Pub Date : 2024-04-24 Usman Naseem, Surendrabikram Thapa, Qi Zhang, Shoujin Wang, Junaid Rashid, Liang Hu, Amir Hussain
The digitization of healthcare systems has led to the proliferation of electronic health records (EHRs), serving as comprehensive repositories of patient information. However, the vast volume and complexity of EHR data present challenges in extracting meaningful insights. This paper addresses the need for automated analysis of EHRs by proposing a novel graph learning model with label attention (GLLA)
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Improving texture integrity through second-order constraints on warping maps Neurocomputing (IF 6.0) Pub Date : 2024-04-23 Mohsen Tabejamaat, Farhood Negin, François Bremond
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OF-DFN: Optical flow prediction network for different perspective image fusion Neurocomputing (IF 6.0) Pub Date : 2024-04-23 Tianshun You, Ming Liu, Yongming Zhao, Liquan Dong
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Model-free anti-disturbance tracking control for high-order discrete-time nonlinear system based on concurrent learning extended state observer Neurocomputing (IF 6.0) Pub Date : 2024-04-23 Huijuan Li, Nan Gu, Dan Wang, Zhouhua Peng
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Element-conditioned GAN for graphic layout generation Neurocomputing (IF 6.0) Pub Date : 2024-04-23 Liuqing Chen, Qianzhi Jing, Yunzhan Zhou, Zhaoxing Li, Lei Shi, Lingyun Sun
Layout guides the position and scale of design elements for desirable aesthetics and effective demonstration. Recently, Generative Adversarial Networks (GANs) have proved their capability in generating effective layouts. However, current GANs ignore the situation where the amounts and types of the input design elements are given and determined. In this paper, we propose EcGAN, an element-conditioned
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ESIE-BERT: Enriching sub-words information explicitly with BERT for intent classification and slot filling Neurocomputing (IF 6.0) Pub Date : 2024-04-23 Yu Guo, Zhilong Xie, Xingyan Chen, Huangen Chen, Leilei Wang, Huaming Du, Shaopeng Wei, Yu Zhao, Qing Li, Gang Wu
Natural language understanding (NLU) has two core tasks: intent classification and slot filling. The success of pre-training language models resulted in a significant breakthrough in the two tasks. The architecture based on autoencoding (BERT-based model) can optimize the two tasks jointly. However, we note that BERT-based models convert each complex token into multiple sub-tokens by the Wordpiece
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Mitigating biases in long-tailed recognition via semantic-guided feature transfer Neurocomputing (IF 6.0) Pub Date : 2024-04-23 Sheng Shi, Peng Wang, Xinfeng Zhang, Jianping Fan
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Multi-modal fusion network guided by prior knowledge for 3D CAD model recognition Neurocomputing (IF 6.0) Pub Date : 2024-04-23 Qiang Li, Zibo Xu, Shaojin Bai, Weizhi Nie, Anan Liu
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Iterative image rain removal network using consecutive residual long short-term memory Neurocomputing (IF 6.0) Pub Date : 2024-04-23 Su Yeon Park, Tae Hee Park, Il Kyu Eom
Image rain removal is designed to effectively separate rain streaks from the background image layer. However, rain streaks in real-world scenarios vary in density, shape, and direction, making it difficult to decompose rainy images into clean backgrounds and rain layers. In this study, we introduce an iterative framework for image deraining to progressively enhance rainy images using a residual long
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Weakly supervised classification through manifold learning and rank-based contextual measures Neurocomputing (IF 6.0) Pub Date : 2024-04-19 João Gabriel Camacho Presotto, Lucas Pascotti Valem, Nikolas Gomes de Sá, Daniel Carlos Guimarães Pedronette, João Paulo Papa
Over the last decade, significant advances have been achieved by machine learning approaches, notably in supervised learning scenarios. Supported by the advent of deep learning and comprehensive training sets, the accuracy achieved on classification tasks has improved significantly. Simultaneously, we have experienced massive growth in multimedia data and applications, which have become ubiquitous
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The neural network models with delays for solving absolute value equations Neurocomputing (IF 6.0) Pub Date : 2024-04-18 Dongmei Yu, Gehao Zhang, Cairong Chen, Deren Han
An inverse-free neural network model with mixed delays is proposed for solving the absolute value equation (AVE) , which includes an inverse-free neural network model with discrete delay as a special case. By using the Lyapunov–Krasovskii theory and the linear matrix inequality (LMI) method, the developed neural network models are proved to be exponentially convergent to the solution of the AVE. Compared
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Enhancing class imbalance solutions: A projection-based fuzzy LS-TSVM approach Neurocomputing (IF 6.0) Pub Date : 2024-04-16 M. Tanveer, Ritik Mishra, Bharat Richhariya
Class imbalance and noise present significant challenges in numerous real-world classification tasks. The prevalence of an uneven distribution of samples typically results in a bias towards the majority class in Support Vector Machine (SVM) classifiers, compounded by the often inherent noise within these samples. Addressing both class imbalance and noise, we introduce two fuzzy-based methodologies
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RadarNet: A parallel spatiotemporal encoder network for radar extrapolation Neurocomputing (IF 6.0) Pub Date : 2024-04-16 Wei Tian, Lei Yi, Xianghua Niu, Rong Fang, Lixia Zhang, Huanhuan Liu, Zhuo Xu, Shengqin Jiang, Yonghong Zhang
Radar extrapolation has been one of the most important means for nowcasting. Most current models achieve good performance in high-frequency sequences (e.g., video, more than 24 fps), while the temporal resolution of radar echo sequences is much lower (1 frame every 6 min) and the transforms are much more complex. The spatiotemporal characters with some similarities would not change a lot in video sequences;
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ZS-SRT: An efficient zero-shot super-resolution training method for Neural Radiance Fields Neurocomputing (IF 6.0) Pub Date : 2024-04-16 Xiang Feng, Yongbo He, Yubo Wang, Chengkai Wang, Zhenzhong Kuang, Jiajun Ding, Feiwei Qin, Jun Yu, Jianping Fan
Neural Radiance Fields (NeRF) have achieved great success in the task of synthesizing novel views that preserve the same resolution as the training views. However, it is challenging for NeRF to synthesize high-quality high-resolution novel views with low-resolution training data. To solve this problem, we propose a zero-shot super-resolution training framework (ZS-SRT) for NeRF. This framework aims
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TISE-LSTM: A LSTM model for precipitation nowcasting with temporal interactions and spatial extract blocks Neurocomputing (IF 6.0) Pub Date : 2024-04-16 Changyong Zheng, Yifan Tao, Jingjing Zhang, Lina Xun, Teng Li, Qing Yan
Precipitation nowcasting has a profound impact on humanity and society, especially in areas with heavy rainfall, playing a central role in alerting against rainstorm disasters. At present, numerous deep learning-based methods have been proposed and proven superior to traditional radar echo extrapolation techniques like the Recurrent Neural Networks (RNNs). Our study introduces a novel precipitation
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Trust region policy optimization via entropy regularization for Kullback–Leibler divergence constraint Neurocomputing (IF 6.0) Pub Date : 2024-04-16 Haotian Xu, Junyu Xuan, Guangquan Zhang, Jie Lu
Trust region policy optimization (TRPO) is one of the landmark policy optimization algorithms in deep reinforcement learning. Its purpose is to maximize a surrogate objective based on an advantage function, subject to the limited Kullback–Leibler (KL) divergence of two consecutive policies. Although there have been many successful applications of this algorithm in the literature, the approach has often
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PatchDetector: Pluggable and non-intrusive patch for small object detection Neurocomputing (IF 6.0) Pub Date : 2024-04-16 Linyun Zhou, Shengxuming Zhang, Tian Qiu, Wenxiang Xu, Zunlei Feng, Mingli Song
Object detection is one of the core tasks in computer vision that serves as a crucial underpinning for numerous applications. In recent years, deep learning-based methods have achieved remarkable performance in object detection. However, the performance of small objects still remains unsatisfactory. Therefore, some specific architectures have been proposed to address this issue in certain areas, such
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Sampled-data model-free adaptive integral sliding mode control for nonlinear continuous-time networked control systems with fading channels and packet dropouts Neurocomputing (IF 6.0) Pub Date : 2024-04-16 Lina Chang, Zhongsheng Hou
In this work, a sampled-data model-free adaptive integral sliding mode control (SMFAISMC) scheme is presented for nonlinear continuous-time networked control systems (NCSs) where the control input of the NCSs is assumed to be transmitted over the fading channel and the random packet dropouts is encountered at the output side simultaneously. Firstly, the original nonlinear continuous-time NCSs are converted
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Coexistence and locally exponential stability of multiple equilibrium points for fractional-order impulsive control Cohen–Grossberg neural networks Neurocomputing (IF 6.0) Pub Date : 2024-04-16 Jinsen Zhang, Xiaobing Nie
Different from the existing multiple Mittag-Leffler stability or multiple asymptotic stability, the multiple exponential stability, which has explicit and faster convergence rate, is investigated in this paper for fractional-order impulsive control Cohen–Grossberg neural networks. First, by using the definition of Dirac delta function, the fractional-order control Cohen–Grossberg neural networks are
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Finding robust and influential nodes from networks under cascading failures using a memetic algorithm Neurocomputing (IF 6.0) Pub Date : 2024-04-16 Shun Cai, Shuai Wang, Minghao Chen
In the research of complex networks, how to find a set of nodes in the network with the most extensive range in the propagation process, i.e., the Influence Maximization (IM) problem, is one of the focal topics. Existing studies mainly consider the information dissemination process on networks and how to select diffusive nodes efficiently, but little attention has been paid to changes related to the
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Synergizing triple attention with depth quality for RGB-D salient object detection Neurocomputing (IF 6.0) Pub Date : 2024-04-16 Peipei Song, Wenyu Li, Peiyan Zhong, Jing Zhang, Piotr Konuisz, Feng Duan, Nick Barnes
Salient object refers to the conspicuous objects or regions within an image that stand out prominently from its surroundings. Depth maps are commonly utilized as supplementary inputs for salient object detection, referred to as RGB-D SOD. Due to the diverse acquisition sensors, such as infrared detectors and stereo cameras, the quality of acquired depth maps varies considerably. The low-quality depth
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Kernel support vector machine classifiers with [formula omitted]-norm hinge loss Neurocomputing (IF 6.0) Pub Date : 2024-04-16 Rongrong Lin, Yingjia Yao, Yulan Liu
Support vector machines (SVMs) are some of the most successful machine learning models for binary classification problems. Their key idea is maximizing the margin from the data to the hyperplane subject to correct classification on training samples. In the SVM training model, hinge loss is sensitive to label noise and unstable for resampling. Moreover, binary loss is the most natural choice for modeling
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Differential privacy in deep learning: A literature survey Neurocomputing (IF 6.0) Pub Date : 2024-04-16 Ke Pan, Yew-Soon Ong, Maoguo Gong, Hui Li, A.K. Qin, Yuan Gao
The widespread adoption of deep learning is facilitated in part by the availability of large-scale data for training desirable models. However, these data may involve sensitive personal information, which raises privacy concerns for data providers. Differential privacy has been thought of as a key technique in the privacy preservation field, which has drawn much attention owing to its capability of
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Supervised contrastive learning for graph representation enhancement Neurocomputing (IF 6.0) Pub Date : 2024-04-16 Mohadeseh Ghayekhloo, Ahmad Nickabadi
Graph Neural Networks (GNNs) have exhibited significant success in various applications, but they face challenges when labeled nodes are limited. A novel self-supervised learning paradigm has emerged, enabling GNN training without labeled nodes and even surpassing GNNs with limited labeled data. However, self-supervised methods lack class-discriminative node representations due to the absence of labeled
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Optimal control strategies and target selection in multi-pursuer multi-evader differential games Neurocomputing (IF 6.0) Pub Date : 2024-04-16 Yinglu Zhou, Yinya Li, Andong Sheng, Guoqing Qi, Jinliang Cong
This paper is concerned with a conflict that -pursuers versus -evaders. It is a multi-pursuer multi-evader game extended from classical differential game theory to simultaneously address target selection and multi-player pursuit-evasion. Every pursuer attempts to intercept the evader it has chosen as its target while every evader does the opposite. This is modeled as a multi-player nonzero-sum differential
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Multi-YOLOv8: An infrared moving small object detection model based on YOLOv8 for air vehicle Neurocomputing (IF 6.0) Pub Date : 2024-04-16 Shizun Sun, Bo Mo, Junwei Xu, Dawei Li, Jie Zhao, Shuo Han
The detection of infrared moving small objects faces significant challenges in the field of object detection for air vehicles. These types of objects usually occupy a small number of pixels in an infrared image, resulting in limited feature information, considerable feature loss, low recognition accuracy, and various challenges in single-frame detection. To address these challenges, this paper proposes
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Syntax-guided controllable sentence simplification Neurocomputing (IF 6.0) Pub Date : 2024-04-16 Lulu Wang, Aishan Wumaier, Tuergen Yibulayin, Maihemuti Maimaiti
Sentence simplification is to rephrase a sentence into a form that is easier to read and understand while preserving its essential meaning and information. Recently, monolingual neural machine translation methods have emerged as a popular approach for this task. However, these methods often overlook the syntactic tree information of sentences, which can be crucial for effective simplification. To address
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A multi-view graph learning model with dual strategies for solving math word problems Neurocomputing (IF 6.0) Pub Date : 2024-04-16 Zhiwei Wang, Qi Lang, Xiaodong Liu, Wenlin Jing
Recently, graph-based deep learning models have exhibited remarkable performance in generating solution expressions for the math word problem (MWP). However, most of these models have not taken into account the limitations and errors in constructing prior knowledge graphs, which may affect their accuracy and reliability in practical applications. In addition, during graph learning, they focus on extracting