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Diffusion-based network for unsupervised landmark detection Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-11 Tao Wu, Kai Wang, Chuanming Tang, Jianlin Zhang
Landmark detection is a fundamental task aiming at identifying specific landmarks that serve as representations of distinct object features within an image. However, the present landmark detection algorithms often adopt complex architectures and are trained in a supervised manner using large datasets to achieve satisfactory performance. When faced with limited data, these algorithms tend to experience
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Intention action anticipation model with guide-feedback loop mechanism Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-11 Zongnan Ma, Fuchun Zhang, Zhixiong Nan, Yao Ge
Anticipating human intention from videos has broad applications, such as automatic driving, robot assistive technology, and virtual reality. This study addresses the problem of intention action anticipation using egocentric video sequences to estimate actions that indicate human intention. We propose a Hierarchical Complete-Recent (HCR) information fusion model that makes full use of the features of
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April-GCN: Adjacency Position-velocity Relationship Interaction Learning GCN for Human motion prediction Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-11 Baoxuan Gu, Jin Tang, Rui Ding, Xiaoli Liu, Jianqin Yin, Zhicheng Zhang
The Human Motion Prediction (HMP) task attempts to model human kinematics, which requires considering both the physical connections between joints and the continuity of the joints’ trajectories. To handle this complex task, some recent works have employed Graph Convolutional Networks (GCN) in learning dynamic relations among joints. However, HMP task is essentially a time series task, to model the
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Superpixel-wise contrast exploration for salient object detection Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-09 Yu Qiu, Jie Mei, Jing Xu
Salient object detection (SOD) methods typically consider SOD as a pixel-wise binary classification problem and utilize the binary cross-entropy (BCE) loss for optimization. However, the BCE loss ignores the global dependencies between pixels of the entire image, which is important for ensuring the accuracy and integrity of objects. To address this limitation explicitly, contrastive learning is introduced
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A Novel Approach for Rumor Detection in Social Platforms: Memory-Augmented Transformer with Graph Convolutional Networks Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-08 Qian Chang, Xia Li, Zhao Duan
Rumor detection in social media platforms is of critical importance owing to the widespread dissemination and impact of false information. Conventional approaches to rumor detection frequently rely on labor-intensive manual fact-checking or handcrafted features that may not adequately account for the complex nature of rumor propagation. To overcome these limitations, recent studies in deep learning
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Fortune favors the invariant: Enhancing GNNs’ generalizability with Invariant Graph Learning Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-08 Guibin Zhang, Yiqiao Chen, Shiyu Wang, Kun Wang, Junfeng Fang
Generalizable and transferrable graph representation learning endows graph neural networks (GNN) with the ability to extrapolate potential test distributions. Nonetheless, current endeavors recklessly ascribe the demoralizing performance on a single entity (feature or edge) distribution shift and resort to uncontrollable augmentation. By inheriting the philosophy of Invariant graph learning (IGL),
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Node classification oriented Adaptive Multichannel Heterogeneous Graph Neural Network Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-08 Yuqi Li, Chuanfeng Jian, Guosheng Zang, Chunyao Song, Xiaojie Yuan
Heterogeneous graph neural networks (HGNNs) play an important role in accomplishing node classification on heterogeneous graphs (HGs). These models are built on the traditional spatial graph neural networks (GNNs) framework of neighborhood sampling, message passing, and aggregation. However, like GNNs, HGNNs face challenges in capturing high-order neighbor information without oversmoothing or classifying
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A novel heuristic Morlet wavelet neural network procedure to solve the delay differential perturbed singular model Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-07 Shahid Ahmad Bhat, Zulqurnain Sabir, Muhammad Asif Zahoor Raja, Tareq Saeed, Ahmed Mohammed Alshehri
This study designs the Morlet wavelet neural network (MWNN) for the numerical performance of the second-order delay differential perturbed singular model (DD-PSM). These stiff singular models are always challenging for the research community to numerically present their results. The DD-PSM is used as an objective function, and its boundary conditions are assembled and then optimised using the computing
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Deep Continuous Convolutional Networks for Fault Diagnosis Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-07 Xufeng Huang, Tingli Xie, Jinhong Wu, Qi Zhou, Jiexiang Hu
Convolutional neural network (CNN) architectures have been extensively utilized in data-driven fault diagnosis and have demonstrated significant success. However, there remain certain constraints or restrictions of standard CNN for real-world applications, including discrete convolutions with a priori kernel size that failed to capture richer long-term features, performance deterioration when testing
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Improved Binary Meerkat Optimization Algorithm for efficient feature selection of supervised learning classification Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-07 Reda M. Hussien, Amr A. Abohany, Amr A. Abd El-Mageed, Khalid M. Hosny
Feature selection (FS) is a crucial step in machine learning and data mining projects. It aims to remove redundant and uncorrelated features, thus improving the accuracy of models. However, it can be challenging to select the optimal features, especially for datasets with many features. This paper proposes the Binary Meerkat Optimization Algorithm (BMOA) to address the issue of FS. The BMOA selects
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A reliable neural network framework for the Zika system based reservoirs and human movement Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-07 Zulqurnain Sabir, Sundas Naqeeb Khan, Muhammad Asif Zahoor Raja, M.M. Babatin, Atef F. Hashem, M A Abdelkawy
The motive of current investigations provides the numerical solutions of the neuro computing solver based on the Levenberg-Marquardt backpropagation neural network approach (LMB) to solve the Zika virus system of reservoirs and human movement. The mathematical form of the human movement model is based on ten different classes, which makes the model nonlinear. The solution of this nonlinear mathematical
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Link prediction by continuous spatiotemporal representation via neural differential equations Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-07 Liyi Huang, Bowen Pang, Qiming Yang, Xiangnan Feng, Wei Wei
With the continuous advancement of data science and machine learning, temporal link prediction has emerged as a crucial aspect of dynamic network analysis, providing significant research and application potential across various domains. While deep learning techniques have achieved remarkable results in temporal link prediction, most existing studies have focused on discrete model frameworks. These
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Bi-SGTAR: A simple yet efficient model for circRNA-disease association prediction based on known association pair only Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-07 Shiyuan Li, Qingfeng Chen, Zhixian Liu, Shirui Pan, Shichao Zhang
Identifying circRNA (circular RNA) associated with diseases holds promise as diagnostic and prognostic biomarkers, offering potential avenues for novel therapeutics. Several computational methods have been designed to predict circRNA-disease associations. Unfortunately, current computational models face issues stemming from the integration of data from multiple sources, leading to blind spots in data
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Consecutive knowledge meta-adaptation learning for unsupervised medical diagnosis Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-07 Yumin Zhang, Hongliu Li, Yawen Hou, Xiuyi Chen, Hongyuan Yu, Long Xia
Deep learning-based computer-aided diagnosis has garnered significant attention in both academic research and clinical applications. Due to the challenges in collecting well-labeled clinical data, unsupervised domain adaptation methods have become widely used in medical image analysis. However, existing unsupervised domain adaptation methods in the clinic field fail to adapt to scenarios where new
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A knowledge-assisted reinforcement learning optimization for road network design problems under uncertainty Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-06 S, u, h, -, W, e, n, , C, h, i, o, u
A knowledge-assisted reinforcement learning evolution optimization (KARLEO) is presented for a road network under uncertain demand and capacity. In order to hedge against stochastic link capacity and travel demand, a knowledge-assisted reinforcement learning optimization is proposed. Different from earlier studies, a stochastic link traffic model is first presented to capture time-varying cost incurred
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Three Stage Classification Framework with Ranking Scheme for Distracted Driver Detection using Heuristic-assisted Strategy Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-06 Dr. Prameeladevi Chillakuru, Dr. K. Ananthajothi, Dr. D. Divya
In Recent days, the number of road accidents are greatly increased in world wide. The people around the world died due to the rapid road crush. The driver behavior must be detected to neglect the occurrence of road accidents. In this work, the three stage deep learning-based techniques are developed to detect the distracted driver action. The developed model is implemented in three phase to detect
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Robust Gaussian process regression based on bias trimming Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-06 Jingkai Chi, Zhizhong Mao, Mingxing Jia
This paper presents a new robust Gaussian process regression (GPR) algorithm based on identifying and trimming outliers, and it can reduce the computation time and improve the accuracy compared with other robust algorithms. Currently, most existing robust GPR approaches model noise with heavy-tailed distributions that are less sensitive to outliers, such as the Laplace and Student-t distributions.
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RGB-Angle-Wheel: A new data augmentation method for deep learning models Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-05 Cuneyt Ozdemir, Yahya Dogan, Yılmaz Kaya
Deep learning models often rely on a diverse and well-augmented dataset for optimal performance. In this context, the methods of data augmentation are pivotal in boosting the models’ ability to generalize. In this paper, we introduce a novel data augmentation method, which we call RGB-Angle-Wheel, to improve the performance of deep learning models on RGB format images. This method involves rotating
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Optimizing vision transformers for CPU platforms via human-machine collaborative design Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-05 Dong Chen, Hao Shen, Ping Li
Over the past three years, various vision transformers (ViTs) have been proposed and applied to many vision tasks. However, for x86 CPU platforms, current ViTs fail to effectively balance inference efficiency and accuracy because they are specifically designed for high-end GPU platforms or smart mobile platforms. In this paper, we aim to explore high-performance transformer architectures for CPU platforms
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An edge-aware graph autoencoder trained on scale-imbalanced data for traveling salesman problems Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-05 Shiqing Liu, Xueming Yan, Yaochu Jin
In recent years, there has been a notable surge in research on machine learning techniques for combinatorial optimization. It has been shown that learning-based methods outperform traditional heuristics and mathematical solvers on the Traveling Salesman Problem (TSP) in terms of both performance and computational efficiency. However, most learning-based TSP solvers are primarily designed for fixed-scale
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Exploration of Polytomous-attribute Q-matrix Validation in Cognitive Diagnostic Assessment Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-03 Chunying Qin, Shenghong Dong, Xiaofeng Yu
Compared with typical binary attributes, polytomous attributes can take three or more values (corresponding to different levels of mastery of a respondent or measurement of an item). They can indicate whether a respondent possesses the attributes of interest and mastery levels. Therefore, the test with polytomous-attribute -matrix can become more informative and provide respondents with richer diagnostic
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Contrastive Learning-Based Knowledge Distillation for RGB-Thermal Urban Scene Semantic Segmentation Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-03 Xiaodong Guo, Wujie Zhou, Tong Liu
RGB thermal semantic segmentation facilitates unmanned platforms to perceive and characterize their surrounding environment, which is critical for autonomous driving tasks. Deep-learning-based algorithms have achieved dominance in terms of accuracy and robustness. However, their large parameter sizes and significant computational demands impede their application in terminal devices. To address this
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Content-aware Nakagami morphing for incremental brain MRI Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-02 O, r, c, a, n, , A, l, p, a, r
Within the carcinogenesis mechanism, from the initiation of the very first tumor cell to the preneoplastic and neoplastic cancer cell groups, cancer cells omnidirectionally and unpredictably proliferate in three-dimensional (3D) space during the promotion and progression steps. Whole tumors areas, consisting of edema, necrosis, enhancing and non-enhancing tumor subareas, could easily be identified
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Autonomous obstacle avoidance and target tracking of UAV: Transformer for observation sequence in reinforcement learning Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-02 Weilai Jiang, Tianqing Cai, Guoqiang Xu, Yaonan Wang
Reinforcement learning (RL) is an effective approach to solve autonomous obstacle avoidance and target tracking for Unmanned Aerial Vehicle (UAV). However, due to communication interruptions or delays, transmission information loss often occurs in practical environments, which greatly reduces the success rate of UAV tracking. Currently, most research methods focus on ideal environments where UAV can
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Complex expressional characterizations learning based on block decomposition for temporal knowledge graph completion Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-02 Lupeng Yue, Yongjian Ren, Yan Zeng, Jilin Zhang, Kaisheng Zeng, Jian Wan, Mingyao Zhou
A temporal knowledge graph (TKG) is a set of facts associated with different timestamps. TKG completion (TKGC) is the task of inferring unknown facts based on known facts, where mining and understanding the expressional characterization of timestamps from the fact sets are key. Expressional characterizations are complex because of two aspects: type-diversity and dependence-variability. Existing approaches
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A self-supervised learning model for graph clustering optimization problems Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-02 Qingqiong Cai, Xingyue Guo, Shenwei Huang
The graph clustering optimization problems are ubiquitous in both scientific and engineering fields, for example the graph partitioning problem and facility location problem. Since most of these problems are NP-hard, it is challenging to design effective algorithms for them. Recently, deep learning has achieved promising performances in solving combinatorial optimization problems. However, existing
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PATNet: Patch-to-pixel attention-aware transformer network for RGB-D and RGB-T salient object detection Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-02 Mingfeng Jiang, Jianhua Ma, Jiatong Chen, Yaming Wang, Xian Fang
Multimodal salient object detection (SOD) combines different modal images to generate the most visually appealing saliency map. When fusing multimodal and multiscale features, maintaining the integrity and fine granularity of the target is critical for improving the performance of multimodal SOD. The fine-grained information differences between the modalities and the size of the features in the transformer
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Entropy-based concept drift detection in information systems Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-02 Yingying Sun, Jusheng Mi, Chenxia Jin
As time passes, the data within information systems may continuously evolve, causing the target concept to drift. To ensure the effectiveness of data-driven decision making, it is crucial to detect drift in a timely manner and gather relevant information. In this paper, we introduce two methods that can directly detect concept drift in the provided information system, by considering a new perspective
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Identifying the reaction centers of molecule based on dual-view representation Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-02 Hui Yu, Jing Wang, Chao Song, Jian-Yu Shi
In the process of drug retrosynthesis, identifying the reaction centers where the chemical reactions occurring is an important fundamental issue in semi-templated models. However, few publications pay their attention on this research point currently and most of them only simply employ GNN-based or Transformer-based approaches to detect the reaction centers in a drug, overlooking the significance of
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A general explicable forecasting framework for weather events based on ordinal classification and inductive rules combined with fuzzy logic Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-01 C. Peláez-Rodríguez, J. Pérez-Aracil, C.M. Marina, L. Prieto-Godino, C. Casanova-Mateo, P.A. Gutiérrez, S. Salcedo-Sanz
This paper presents a method for providing explainability in the integration of artificial intelligence (AI) and data mining techniques when dealing with meteorological prediction. Explainable artificial intelligence (XAI) refers to the transparency of AI systems in providing explanations for their predictions and decision-making processes, and contribute to improve prediction accuracy and enhance
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Data privacy protection: A novel federated transfer learning scheme for bearing fault diagnosis Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-01 Lilan Liu, Zhenhao Yan, Tingting Zhang, Zenggui Gao, Hongxia Cai, Jinrui Wang
Research on the health diagnosis of mechanical equipment has developed unprecedentedly in recent years, and a large number of diagnostic solutions have considerably improved the stability of mechanical equipment in industrial production. However, such satisfactory diagnostic performance relies on a large number of data samples, which are frequently difficult to obtain in real industrial scenarios.
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Transformer-based multivariate time series anomaly detection using inter-variable attention mechanism Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-01 Hyeongwon Kang, Pilsung Kang
The primary objective of multivariate time-series anomaly detection is to spot deviations from regular patterns in time-series data compiled concurrently from various sensors and systems. This method finds application across diverse industries, aiding in system maintenance tasks. Capturing temporal dependencies and correlations between variables simultaneously is challenging due to the interconnectedness
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SSER: Semi-Supervised Emotion Recognition Based on Triplet Loss and Pseudo Label Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-01 Lili Pan, Weizhi Shao, Siyu Xiong, Qianhui Lei, Shiqi Huang, Eric Beckman, Qinghua Hu
Recently, emotion recognition from facial expressions has achieved unprecedented accuracy with the development of deep learning. Despite this progress, most existing emotion recognition methods are supervised and thus require extensive annotation. This issue is particularly pronounced in continuous domain datasets where annotation costs are very high. Furthermore, discrete domain datasets containing
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GCNSLIM: Graph convolutional network with sparse linear methods for e-government service recommendation Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-01 Lingyuan Kong, Hao Ding, Guangwei Hu
E-government service recommendation aims to find target service items for users based on their preferences, behaviors and other information, which is one of the key technologies for information overload in e-government cloud platforms. However, it has not received adequate attention in comparison to other recommendation scenarios like news and music recommendation. Graph Convolutional Networks (GCNs)
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ASAFormer: Visual tracking with convolutional vision transformer and asymmetric selective attention Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-01 Xiaomei Gong, Yi Zhang, Shu Hu
Recently, Vision Transformer (ViT) has exhibited remarkable performances in many computer vision tasks (e.g. object detection, segmentation and tracking). However, the output feature map of ViT is only single scale with low resolution, which may lose rich detailed semantic information. Meanwhile, ViT implements feature embedding through the linear projection, which makes it unable to capture local
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Attribute imputation autoencoders for attribute-missing graphs Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-01 Riting Xia, Chunxu Zhang, Anchen Li, Xueyan Liu, Bo Yang
Analyzing attribute-missing graphs with a complete topology, but missing the attributes of some nodes, is an emerging and challenging research topic. Data imputation techniques based on graph autoencoders are commonly used for attribute-missing graphs. However, this method cannot effectively integrate existing attributes and structural information during the encoding stage and is prone to introducing
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Lorentz equivariant model for knowledge-enhanced hyperbolic collaborative filtering Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-01 Bosong Huang, Weihao Yu, Ruzhong Xie, Junming Luo, Jing Xiao, Jin Huang
Introducing prior auxiliary information from the knowledge graph (KG) to assist the user–item graph can improve the comprehensive performance of the recommender system. Many recent studies have shown that the ensemble properties of hyperbolic spaces fit the scale-free and hierarchical characteristics exhibited in the above two types of graphs well. Therefore, hyperbolic-based recommender systems have
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Vertical-horizontal latent space with iterative memory review network for multi-class anomaly detection Knowl. Based Syst. (IF 8.8) Pub Date : 2024-03-01 Chunlei Wu, Xu Liu, Jie Wu, Huan Zhang, Leiquan Wang
Anomaly detection has been recently proposed as a visual scene understanding task and widely applied in industrial detection. Traditional unsupervised methods aim to enhance anomaly detection performance by improving input reconstruction quality. However, these approaches rarely address the problem of poor quality reconstruction information. The inferior information affects the reconstruction effect
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Distributed representations of entities in open-world knowledge graphs Knowl. Based Syst. (IF 8.8) Pub Date : 2024-02-29 Lingbing Guo, Zhuo Chen, Jiaoyan Chen, Yichi Zhang, Zequn Sun, Zhongpu Bo, Yin Fang, Xiaoze Liu, Huajun Chen, Wen Zhang
Graph neural network (GNN)-based methods have demonstrated remarkable performance in various knowledge graph (KG) tasks. However, most existing approaches rely on observing all entities during training, posing a challenge in real-world knowledge graphs where new entities emerge frequently. To address this limitation, we introduce Decentralized Attention Network (DAN). DAN leverages neighbor context
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Intra- and inter-sector contextual information fusion with joint self-attention for file fragment classification Knowl. Based Syst. (IF 8.8) Pub Date : 2024-02-29 Yi Wang, Wenyang Liu, Kejun Wu, Kim-Hui Yap, Lap-Pui Chau
File fragment classification (FFC) aims to identify the file type of file fragments in memory sectors, which is of great importance in memory forensics and information security. Existing works focused on processing the bytes within sectors separately and ignoring contextual information between adjacent sectors. In this paper, we introduce a joint self-attention network (JSANet) for FFC to learn intra-sector
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Video generalized semantic segmentation via Non-Salient Feature Reasoning and Consistency Knowl. Based Syst. (IF 8.8) Pub Date : 2024-02-29 Yuhang Zhang, Zhengyu Zhang, Muxin Liao, Shishun Tian, Rong You, Wenbin Zou, Chen Xu
Video semantic segmentation is beneficial for dynamic scene processing in real-world environments, and achieves superior performance on independent and identically distributed data. However, it suffers from performance degradation in environments with various domain styles, which is known as the distribution shift problem. Although some previous studies on image generalized semantic segmentation considered
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Cooperative Markov Decision Process model for human–machine co-adaptation in robot-assisted rehabilitation Knowl. Based Syst. (IF 8.8) Pub Date : 2024-02-29 Kairui Guo, Adrian Cheng, Yaqi Li, Jun Li, Rob Duffield, Steven Weidong Su
Human–machine interaction is a critical component in robotic rehabilitation systems. A mutual learning strategy involving both machine- and human-oriented learning has shown improvements in learning efficiency and receptiveness. Despite these advancements, a theoretical framework that encompasses high-level human responses during robot-assisted rehabilitation is still needed. This paper introduces
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A multi-objectives framework for secure blockchain in fog–cloud network of vehicle-to-infrastructure applications Knowl. Based Syst. (IF 8.8) Pub Date : 2024-02-29 Abdullah Lakhan, Mazin Abed Mohammed, Karrar Hameed Abdulkareem, Muhammet Deveci, Haydar Abdulameer Marhoon, Jan Nedoma, Radek Martinek
The Intelligent Transport System (ITS) is an emerging paradigm that offers numerous services at the infrastructure level for vehicle applications. Vehicle-to-infrastructure (V2I) is an advanced form of ITS where diverse vehicle services are deployed on the roadside unit. V2I consists of distributed computing nodes where transport applications are parallel processed. Many research challenges exist in
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Time-varying polynomial grey prediction modeling with integral matching Knowl. Based Syst. (IF 8.8) Pub Date : 2024-02-28 Lili Ye, Naiming Xie, John E. Boylan
Owing to the variable characteristics of system structures, traditional grey prediction models with fixed parameters often struggle to accurately capture real-world dynamic changes. In this paper, we propose a novel time-varying polynomial grey model (TPGM) to address this issue. The TPGM leverages polynomial functions to approximate time-varying parameters, enabling it to capture trend variations
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SelectE: Multi-scale adaptive selection network for knowledge graph representation learning Knowl. Based Syst. (IF 8.8) Pub Date : 2024-02-28 Lizheng Zu, Lin Lin, Song Fu, Feng Guo, Jinlei Wu
Most knowledge graphs in the real world suffer from incompleteness which can be addressed through knowledge graph representation learning (KGRL) techniques that use known facts to infer missing links. In this paper, a novel multi-scale adaptive selection network for KGRL, namely SelectE, is developed to learn richer multi-scale interactive features and automatically select important features, thereby
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An extended proximity relation and quantified aggregation for designing robust fuzzy query engine Knowl. Based Syst. (IF 8.8) Pub Date : 2024-02-28 Miroslav Hudec, Miljan Vučetić, Nina Barčáková
In this article, we propose a novel model of a robust fuzzy query engine that addresses vagueness in data and users’ requirements. It aims to assist users in recommending similar products or services by retrieving the most suitable entities when the limitations of queries and recommendation approaches are recognized. The proposed fuzzy engine model considers various complex aspects, including imprecise
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FL-OTCSEnc: Towards secure federated learning with deep compressed sensing Knowl. Based Syst. (IF 8.8) Pub Date : 2024-02-28 Leming Wu, Yaochu Jin, Yuping Yan, Kuangrong Hao
In recent years, federated learning has made significant progress in preserving data privacy. In this paradigm, clients train local models without sharing their raw data, thereby substantially mitigating the vulnerability to private data exposure. However, it is still possible to infer clients’ raw data by leveraging the gradient parameters exchanged between the clients and the server. To address this
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Heterogeneous network influence maximization algorithm based on multi-scale propagation strength and repulsive force of propagation field Knowl. Based Syst. (IF 8.8) Pub Date : 2024-02-27 Chang Guo, Weimin Li, Jingchao Wang, Xiao Yu, Xiao Liu, Alex Munyole Luvembe, Can Wang, Qun Jin
Heterogeneous networks, like social and academic networks are widespread in the real world, characterized by diverse nodes and complex relationships. Influence maximization is a crucial research topic, in these networks, as it can help in comprehending the mechanisms of information propagation and diffusion. Effectively utilizing complex structural information poses a challenge in current research
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Rolling mill fault diagnosis under limited datasets Knowl. Based Syst. (IF 8.8) Pub Date : 2024-02-27 Junjie He, Peiming Shi, Xuefang Xu, Dongying Han
Sensor technology and deep learning have gained a lot of attention in the field of mill fault detection, which provides new possibilities for the condition monitoring of mills. The study provides a novel dual impact feature enhancement framework for rolling mill condition monitoring to address the issue of variable condition diagnosis with limited data. This feature enhancement framework is jointly
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Search space division method for wrapper feature selection on high-dimensional data classification Knowl. Based Syst. (IF 8.8) Pub Date : 2024-02-27 A, b, h, i, l, a, s, h, a, , C, h, a, u, d, h, u, r, i
Feature selection (FS) is an essential pre-processing technique for high-dimensional data. Wrapper-based FS techniques are known for their superior performance over filter FS. However, when the dimensionality of data is very high the wrapper techniques become computationally very expensive. To solve this problem of scalability, this paper proposes the concept of search space division (SSD) which leads
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On Memory-Based Precise Calibration of Cost-Efficient NO2 Sensor Using Artificial Intelligence and Global Response Correction Knowl. Based Syst. (IF 8.8) Pub Date : 2024-02-24 Slawomir Koziel, Anna Pietrenko-Dabrowska, Marek Wojcikowski, Bogdan Pankiewicz
Nitrogen dioxide (NO) is a prevalent air pollutant, particularly abundant in densely populated urban regions. Given its harmful impact on health and the environment, precise real-time monitoring of NO concentration is crucial, particularly for devising and executing risk mitigation strategies. However, achieving precise measurements of NO is challenging due to the need for expensive and cumbersome
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Generalized linear models for symbolic polygonal data Knowl. Based Syst. (IF 8.8) Pub Date : 2024-02-24 Rafaella L.S. do Nascimento, Renata M.C.R. de Souza, Francisco José de A. Cysneiros
Symbolic data analysis data has provided several advances in regression models concerning the type of symbolic variable. Due to the advantages of using symbolic polygonal data, this paper introduces a linear regression approach for polygonal data based on the generalize linear model theory that provides a unified method to broad range of modeling problems for different types of response as asymmetric
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Learning bayesian network parameters from limited data by integrating entropy and monotonicity Knowl. Based Syst. (IF 8.8) Pub Date : 2024-02-24 Zhiping Fan, Liang Zhou, Temitope Emmanuel Komolafe, Zhengyun Ren, Yinghao Tong, Xue Feng
High-accuracy parameter learning in Bayesian Networks (BNs) is a key challenge in real-time decision support applications, particularly when the available data are limited. Prior/Expert knowledge was introduced to eliminate the drawbacks of insufficient information; however, this method is subjective. In this study, we explored the use of monotonicity constraints to control the causal relationships
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Correlation concept-cognitive learning model for multi-label classification Knowl. Based Syst. (IF 8.8) Pub Date : 2024-02-24 Jiaming Wu, Eric C.C. Tsang, Weihua Xu, Chengling Zhang, Lanzhen Yang
As a cognitive process, concept-cognitive learning (CCL) emphasizes the structured expression of data through systematic cognition and understanding, to obtain valuable information in the data. Although concept-cognitive learning has achieved good results in single-label classification tasks, it has not yet been applied to multi-label learning. The difficulty is that the existing concept-cognitive
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ASSL-HGAT: Active semi-supervised learning empowered heterogeneous graph attention network Knowl. Based Syst. (IF 8.8) Pub Date : 2024-02-24 Zhenyan Ji, Deyan Kong, Yanyan Yang, Jiqiang Liu, Zhao Li
Recently, heterogeneous graph attention network (HGAT) has been widely applied to various machine learning tasks and achieved remarkable results with sufficient labeled data. However, it is noteworthy that in many tasks, labeled data is scarce and the data labeling process is expensive. To that end, this paper presents a novel framework for learning from data with limited labels by organically integrating
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REB: Reducing biases in representation for industrial anomaly detection Knowl. Based Syst. (IF 8.8) Pub Date : 2024-02-24 Shuai Lyu, Dongmei Mo, Wai keung Wong
Existing representation-based methods usually conduct industrial anomaly detection in two stages: obtain feature representations with a pre-trained model and perform distance measures for anomaly detection. Among them, K-nearest neighbor (KNN) retrieval-based anomaly detection methods show promising results. However, the features are not fully exploited as these methods ignore domain bias of pre-trained
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MP-NeRF: More refined deblurred neural radiance field for 3D reconstruction of blurred images Knowl. Based Syst. (IF 8.8) Pub Date : 2024-02-24 Xiaohui Wang, Zhenyu Yin, Feiqing Zhang, Dan Feng, Zisong Wang
Neural Radiance Fields (NeRF) has gained prominence in the domain of 3D reconstruction. Despite its popularity, NeRF algorithm typically require clear, static images to function effectively, leading to reduced performance when dealing with real-world scenarios that present non-ideal conditions such as complex reflections, low dynamic range, dark scenes and blurriness resulting from camera motion or
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Complementary Labels Learning with Augmented Classes Knowl. Based Syst. (IF 8.8) Pub Date : 2024-02-24 Zhongnian Li, Mengting Xu, Xinzheng Xu, Daoqiang Zhang
Complementary Labels Learning (CLL) arises in many real-world tasks such as private questions classification and online learning, which aims to alleviate the annotation cost compared with standard supervised learning. Unfortunately, most previous CLL algorithms were in a stable environment rather than an open and dynamic scenarios, where data collected from unseen augmented classes in the training
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DSTEA: Improving Dialogue State Tracking via Entity Adaptive pre-training Knowl. Based Syst. (IF 8.8) Pub Date : 2024-02-23 Yukyung Lee, Takyoung Kim, Hoonsang Yoon, Pilsung Kang, Junseong Bang, Misuk Kim
Dialogue State Tracking (DST) is critical for comprehensively interpreting user and system utterances, thereby forming the cornerstone of efficient dialogue systems. Despite past research efforts focused on enhancing DST performance through alterations to the model structure or integrating additional features like graph relations, they often require additional pre-training with external dialogue corpora
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CNN-LSTM and transfer learning models for malware classification based on opcodes and API calls Knowl. Based Syst. (IF 8.8) Pub Date : 2024-02-23 Ahmed Bensaoud, Jugal Kalita
In this paper, we propose a novel model for a malware classification system based on Application Programming Interface (API) calls and opcodes, to improve classification accuracy. This system uses a novel design of combined Convolutional Neural Network and Long Short-Term Memory. We extract opcode sequences and API Calls from Windows malware samples for classification. We transform these features into