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3rd International Conference on Cloud Computing, Big Data Application and Software Engineering, CBASE 2024

作者： Zhang, Tingting Wang, Yongli Liu, Dongmei School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798331506582

Multimodel knowledge graph completion (MKGC) aims to predict missing entities in MKGs. Differing from prior Single-model knowledge graph completion efforts, MKGC leverages rich textual descriptions, images, and other multi-model information associated with entities. Traditional MKGC methods often use simple concatennation, weighted fusion, or tensor-based approaches for multi-modal information fusion, but they fail to capture the in-tra-modality and inter-modality dynamics. Moreover, traditional models like TransE and TransH, based on translational operations, struggle with complex relationships such as 1-N, N-N. To address these issue, this paper proposes a multi-modal information fusion enhanced by multi-level attention mechanism and relation-specific graph attention network method for MKGC. Firstly, obtaining text, attribute, and image embeddings of entities by leveraging pre-trained modals such as BERT and Vision Transformer. Secondly, using low-rank tensors for multimodal fusion and introducing self-attention and cross-attention blocks to capture complementary information within and across modalities respectively. Finally, constructing a multi-source node graph and a relation graph attention network is employed to capture the structural information of the graph. The ConvE modal is used as a decoder, and the performance is evaluated through link prediction tasks on real datasets. Experimental results show that new method out-performs other baselines, such as TransE, R-GCN, RSME, KG-BERT. © 2024 IEEE.

关键词： Knowledge graph

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A Few-Shot and Anti-Forgetting Network Intrusion Detection System based on Online Meta Learning

A Few-Shot and Anti-Forgetting Network Intrusion Detection S...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Wang, Zhen Lu, Yifei Wu, Wenxin Lu, Yun Wang, Hongxiang Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350351255

In the actual Internet of Things (IoT) environment, the proportion of abnormal behavior is much lower than that of normal behavior, and abnormal samples are often scarce, so it is a significant challenge to train efficient network intrusion detection systems using limited labeled samples. Meanwhile, intrusion detection systems based on online learning are prone to catastrophic forgetting, which greatly reduces the performance of online models. Previous research has not comprehensively addressed these two issues. Therefore, this paper proposes a few-shot and anti-forgetting network intrusion detection system based on online meta-learning. The system uses meta-learning as the basic algorithm to efficiently utilize data to train well-performing models with very few samples, thereby addressing the problem of insufficient samples;meanwhile, by saving previous models and invoking them when necessary to combat catastrophic forgetting. The experiments on the CIC-IDS2017 and CIC-IDS2018 datasets show that our proposed detection system can maintain good performance over the long term without forgetting. © 2024 IEEE.

关键词： Network intrusion

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U-HRNet+: a real-time precipitation estimation method based on the fusion of temporal and spatial domain features

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International Journal of Remote Sensing 2024年第13期45卷 4300-4323页

作者： Tao, Runzhe Zhang, Yonghong Wang, Jiangeng Zheng, Peibei Liu, Liyang School of Computing and Software Nanjing Vocational University of Industry Technology Nanjing China School of Automation Nanjing University of Information Science and Technology Nanjing China

The precipitation estimation with high spatial and temporal resolution over Tibet is of great significance for monitoring the stability of the plateau ecosystem and early warning of natural disasters. In response to the problem of the unsatisfactory accuracy in traditional real-time precipitation inversion methods and quantitative accuracy estimation products in this region. This paper adopts deep-learning methods to explore the corresponding relationship between precipitation and multi-band observation data from geostationary satellite imagery. The U-High Resolution Network (U-HRNet) as backbone is fully exploited to generate multi-scale features and fused using attention gates to extract high-resolution spatial representations representing precipitation clouds, thereby achieving precipitation estimation of target clouds at different scales. In addition, in response to the characteristics of high precipitation intensity and drastic cloud cluster changes in summer convective weather within a short period of time, a multi-head attention mechanism is introduced to extract temporal information from time series inputs and fuse it with spatial features to improve the accuracy of real-time precipitation estimation. Compared with the operational precipitation products and baseline deep-learning models, the classification metrics such as Probability of detection (POD), False Alarm Ratio (FAR) and Critical success index (CSI) show that the proposed model (U-HRNet+) has better performance in test set and has high generalization ability for estimating short-term heavy rainfall over Tibet and its surrounding areas in summer. Therefore, the proposed model has the potential to serve as a more accurate and reliable satellite-based precipitation estimation product. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Geostationary satellites

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When Software Security Meets Large Language Models:A Survey

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IEEE/CAA Journal of Automatica Sinica 2025年第2期12卷 317-334页

作者： Xiaogang Zhu Wei Zhou Qing-Long Han Wanlun Ma Sheng Wen Yang Xiang IEEE the School of Computer and Mathematical Sciences the University of Adelaide the School of Science Computing and Engineering Technologies Swinburne University of Technology

Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, software testing and analysis are two of the critical methods, which significantly benefit from the advancements in deep learning technologies. Due to the successful use of deep learning in software security, recently,researchers have explored the potential of using large language models(LLMs) in this area. In this paper, we systematically review the results focusing on LLMs in software security. We analyze the topics of fuzzing, unit test, program repair, bug reproduction, data-driven bug detection, and bug triage. We deconstruct these techniques into several stages and analyze how LLMs can be used in the stages. We also discuss the future directions of using LLMs in software security, including the future directions for the existing use of LLMs and extensions from conventional deep learning research.

关键词： Large language models (LLMs) software analysis software security software testing

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Self-Supervised Learning Recommendation with Enhanced Long-Tail Nodes 5

Self-Supervised Learning Recommendation with Enhanced Long-T...

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5th International Conference on Artificial Intelligence and computer Engineering, ICAICE 2024

作者： Zhao, Zhiyu Wang, Yongli Liu, Dongmei School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798331528911

Achieving a balance between recommending popular items and long-tail items has consistently posed a challenge in the field of recommendation systems. Traditional recommendation algorithms often exhibit a bias towards recommending popular items, resulting in limited exposure for long-tail items. To address this issue, our study introduces an innovative recommendation algorithm based on graph contrastive learning. This algorithm incorporates a dual-view learning mechanism with the objective of increasing the exposure of long-tail items while ensuring the accuracy of recommendations. Two distinct views are generated: one captures in-depth information of long-tail nodes through random walk strategies, while the other utilizes Graph Attention Networks (GAT) to acquire node importance and contextual information. By means of contrastive learning with these two views, our model attains comprehensive node representations. The study strives to achieve a balanced learning process between long-tail and high-frequency nodes. Experimental results conducted on multiple recommendation system datasets substantiate that our approach significantly enhances the exposure of long-tail items compared to existing recommendation algorithms, while maintaining high accuracy and recall in recommendations. This research attests that the contrastive learning framework provides an effective strategy for addressing the long-tail recommendation problem. © 2024 IEEE.

关键词： Contrastive Learning

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A Multi-Resolution Feature Fusion Method for Pedestrian Re-identification

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第9期51卷 1393-1403页

作者： Qin, Haitian Xu, Yang Chen, Xupeng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China

Pedestrian re-identification technology enables accurate identification of individuals and is widely used in modern intelligent video surveillance systems to aid law enforcement, including criminal apprehension and locating missing persons. However, variations in lighting, background, resolution, and other imaging conditions captured by different cameras create significant challenges in pedestrian feature extraction, often leading to poor recognition accuracy. To overcome these challenges, this paper presents a Multi-resolution Feature Fusion (MRFF) method for pedestrian re-identification, based on the Pedestrian Re-identification Relational Network (RNFPR). This approach incorporates the Coordinate Attention (CA) module into the DenseNet161 network to enhance feature extraction capabilities. Improving the discriminative and recognition accuracy of features requires learning and fusing pedestrian features from multiple low-resolution images. This process enhances the expressive power of feature maps, ultimately improving pedestrian recognition performance. Additionally, this method introduces a multi-resolution feature fusion module that segments and integrates multi-resolution features from image data. This enables the model to effectively combine feature information from various resolution levels, resulting in a more comprehensive feature representation. Experimental results show that the MRFF method achieves a 1.3% increase in mean Average Precision (mAP) and a 1.0% improvement in Rank-1 accuracy on the Market1501 dataset. For the DukeMTMC-reID dataset, it provides a 0.2% increase in mAP and a 0.7% enhancement in Rank-1 accuracy. Consequently, the MRFF approach results in an overall mAP increase of 0.7% on the DukeMTMC-ReID dataset, significantly improving pedestrian gender re-identification accuracy. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Security systems

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Infrared Image Human Detection Based on Improved RT-DETR Algorithm 4

Infrared Image Human Detection Based on Improved RT-DETR Alg...

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4th International Conference on Electronic Information Engineering and computer technology, EIECT 2024

作者： Xie, Long Ren, Mingwu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798331528850

This study presents a novel approach for human detection in infrared images by enhancing the RT-DETR algorithm. The aim is to overcome challenges related to low detection accuracy and real-time performance issues in infrared target detection. The proposed method involves replacing the original RT-DETR algorithm's backbone network with ConvNeXt to improve feature extraction capabilities for low-resolution infrared images. Additionally, a Feature Pyramid Network (FPN) is incorporated to facilitate multi-scale feature fusion, thereby enhancing small target detection accuracy. Furthermore, optimization of the detection head and the replacement of GIoU loss with CIoU loss are carried out to enhance bounding box localization precision. Experimental results demonstrate that the proposed method achieves higher mean Average Precision (mAP) and precision on the Teledyne FLIR ADAS infrared dataset. This improvement effectively balances inference time and real-time performance, offering an efficient solution for human detection in infrared images suitable for practical applications. © 2024 IEEE.

关键词： Infrared imaging

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A Multimodal Named Entity Recognition Approach Based on Multi-Perspective Contrastive Learning 7

A Multimodal Named Entity Recognition Approach Based on Mult...

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7th International Conference on Machine Learning and Natural Language Processing, MLNLP 2024

作者： Liu, Huafu Wang, Yongli Liu, Dongmei School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798350354973

Multimodal Named Entity Recognition (MNER) is a vision-language task aimed at detecting the scope of entities and determining their types in given text-image pairs. Although existing MNER models have made some progress, they often encounter issues like mismatched feature dimensions and inconsistent feature representations when interacting with text sequences. This makes it difficult to bridge the semantic gap between images and text. Therefore, this paper proposes a multimodal named entity recognition method based on multiperspective contrastive learning (MPCL-NER). Initially, the ALBERT pretrained language model and the ViT visual model are used to extract features from text and images, respectively. Based on this, image feature representations are enhanced through multi-perspective contrastive learning, which also promotes the integration of image and text information. Finally, the two types of features are merged through a multi-head adaptive attention mechanism and a gated fusion mechanism. This is followed by entity tagging with a CRF (Conditional Random Field) decoder. Experimental results show that this method outperforms other baseline methods on the public dataset Twitter-2017, achieving an F1 score of 86.11%, demonstrating the effectiveness of this approach. © 2024 IEEE.

关键词： Contrastive Learning

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A Fractional-Order Ultra-Local Model-Based Adaptive Neural Network Sliding Mode Control of n-DOF Upper-Limb Exoskeleton With Input Deadzone

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 760-781页

作者： Dingxin He HaoPing Wang Yang Tian Yida Guo the School of Automation Nanjing University of Science and TechnologyNanjing 210094China IEEE Norinco Group Institute of Navigation and Control Technology Beijing 100089China

This paper proposes an adaptive neural network sliding mode control based on fractional-order ultra-local model for n-DOF upper-limb exoskeleton in presence of uncertainties,external disturbances and input *** the model complexity and input deadzone,a fractional-order ultra-local model is proposed to formulate the original dynamic system for simple controller ***,the control gain of ultra-local model is considered as a *** fractional-order sliding mode technique is designed to stabilize the closed-loop system,while fractional-order time-delay estimation is combined with neural network to estimate the lumped ***,a fractional-order ultra-local model-based neural network sliding mode controller(FO-NNSMC) is ***,to avoid disadvantageous effect of improper gain selection on the control performance,the control gain of ultra-local model is considered as an unknown ***,the Nussbaum technique is introduced into the FO-NNSMC to deal with the stability problem with unknown ***,a fractional-order ultra-local model-based adaptive neural network sliding mode controller(FO-ANNSMC) is ***,the stability analysis of the closed-loop system with the proposed method is presented by using the Lyapunov ***,with the co-simulations on virtual prototype of 7-DOF iReHave upper-limb exoskeleton and experiments on 2-DOF upper-limb exoskeleton,the obtained compared results illustrate the effectiveness and superiority of the proposed method.

关键词： Adaptive control input deadzone model-free control n-DOF upper-limb exoskeleton neural network

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Fault-tolerant control of the highly reliable PMSM system for industrial robots under winding open-circuit faults

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International Journal of Power Electronics 2024年第2期20卷 180-199页

作者： Li, Peng Xu, Xiaosu Liu, Jiazheng Wei, Zhijian Jiang, Xuefeng School of Instrument Science and Engineering Southeast University Nanjing210096 China School of Automation Nanjing University of Science and Technology Nanjing210094 China

The reliability and fault tolerance of the PMSM system for industrial robots are very important for the electric drive system of industrial robots. In this paper, we design a highly reliable PMSM system for industrial robots based on a four-switch two-phase (FSTP) fault-tolerant (FT) topology and conduct FT research for the single-phase open-circuit (OC) fault of PMSM systems. The FT control strategy based on the constant magnetomotive force (MMF) is derived, the mathematical model of the FSTP fault-tolerant electric drive system is established, and the space voltage vector of the OC fault state is reconstructed to achieve FT control. The proposed FT control strategy has the advantages of low cost, small weight, small size and high system utilisation after a fault. The simulation and experimental results verify the proposed FT control strategy to implement FT control for the single-phase OC fault and increase the safety of the electric drive system. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Industrial robots

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