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7th International Conference on computer Information science and Application technology, CISAT 2024

作者： Chen, Siqi Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350375107

Human motion prediction aims to predict future human motion based on past observations, playing important roles in several fields. However, previous works have often focused on the temporal sequential nature of human motion while neglecting the coordinated linkage of multiple components of the human body in space. Therefore, we introduce SCMotion, focused on predicting human motion using spatial dependencies. We divide the human body joints into four joint groups and predict each module individually. Additionally, SCMotion uses the more stable center of gravity of the human body as an anchor point for prediction, greatly enhancing accuracy. Our experiments on the H3.6M dataset demonstrate the excellent prediction capabilities of SCMotion. © 2024 IEEE.

关键词： Graph Convolutional Networks Human Motion Prediction Humanrobot Interaction

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Slender loris optimization algorithm for solving optimization problems

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8799-8810页

作者： Lakshmi, A. Vijaya Vaitheki, K. Joseph, K. Suresh Department of Computer Science School of Engineering and Technology Pondicherry University India

Over the years, numerous optimization problems have been addressed utilizing meta-heuristic algorithms. Continuing initiatives have always been to create and develop new, practical algorithms. This work proposes a novel meta-heuristic approach employing the slender Loris optimization algorithm (SLOA), miming slender Loris behavior. The behavior includes foraging, hunting, migration and communication with each other. The ultimate goal of the devised algorithm is to replicate the food-foraging behaviour of Slender Loris (SL) and the quick movement of SL when threatened (i.e.) their escape from predators and also mathematically modelled the special communication techniques of SL using their urine scent smell. SLOA modelled SL’s slow food foraging behaviour as the exploitation phase, and moving between the tree and escaping from a predator is modelled as the exploration phase. The Eyesight of slender Loris plays a vital role in food foraging during nighttime in dim light. The operator’s Eyesight is modelled based on the angle of inclination of SL. The urine scent intensity is used here to be instrumental in preventing already exploited territory activities, which improves algorithm performance. The suggested algorithm is assessed and tested against nineteen benchmark test operations and evaluated for effectiveness with standard widely recognized meta-heuristics algorithms. The result shows SLOA performing better and achieving near-optimal solutions and dominance in exploration–exploitation balance in most cases than the existing state-of-the-art algorithms. © 2024 – IOS Press. All rights reserved.

关键词： Benchmarking

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Deep Contrastive Clustering for Protocol Analysis in 5G Network engineering 4

Deep Contrastive Clustering for Protocol Analysis in 5G Netw...

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4th International Conference on Communication technology and Information technology, ICCTIT 2024

作者： Lu, Jianhong Zhang, Kun Nanjing University of Science and Technology School of Cyber Science and Engineering Nanjing China Nanjing University of Science and Technology School of Computer and Engineering Nanjing China

ISBN: (纸本)9798331528973

With the widespread adoption of 5G networks in satellite communication and vehicular communication systems, the complexity of communication and signaling interactions has significantly increased. Traditional protocol analysis methods primarily focus on high-level structured data, often neglecting the low-level binary data critical for understanding 5G-specific protocols. This paper proposes a novel approach that employs a deep contrastive clustering framework to analyze and classify unknown protocols in 5G communication networks. The proposed method leverages depthwise separable convolutions for feature extraction, transforming binary protocol data into structured feature representations while significantly reducing computational overhead. Additionally, by integrating contrastive learning into the clustering process, the framework enhances the discrimination of protocol embeddings through a positive-negative sample feedback mechanism, ensuring more accurate clustering results. Additionally, this paper collected and analyzed a comprehensive dataset of 5 G protocols, which includes wireless access layer protocols and core network communication protocols. © 2024 IEEE.

关键词： Satellite communication systems

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Dynamic Path-Sensitive Memory Dependence Prediction 9

Dynamic Path-Sensitive Memory Dependence Prediction

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9th International Conference on Integrated Circuits and Microsystems, ICICM 2024

作者： Liu, Weiding Li, Lunbo College of Computer Science & Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798331509453

Memory dependence prediction is essential in superscalar out-of-order processors as it improves instruction-level parallelism efficiency by predicting dependencies between memory access instructions. However, existing state-of-the-art memory dependence predictors lack the ability to effectively utilize the dynamic execution information of conflicting instructions, leading to an excessive number of false positives and subsequently hindering overall performance. This work proposes Dynamic Path-Sensitive Memory Dependence Prediction (DPMDP), a novel dynamic path-sensitive memory dependence prediction method that dynamically selects the history length for each conflicting load instruction when multiple paths exist. Our key observation is that for load-store pairs with an initial occurrence of a violation if the conflicting store is waiting for an address translation that causes an execution delay, it does not create a dependency in the predictor initially and only creates it upon subsequent occurrences effectively. This observation is proven by running the SPEC CPU 2006 suite on the clock-accurate simulator GEM5. Through cycle-accurate simulation of the SPEC CPU 2006 suite, we show that DPMDP achieved an average performance improvement of 1.08% compared to Store Set and 13.06% compared to PHAST. © 2024 IEEE.

关键词： Parallel processing systems

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Multi-Modal Knowledge Graph Completion Enhanced by Muti-Level Attention Mechanisms 3

Multi-Modal Knowledge Graph Completion Enhanced by Muti-Leve...

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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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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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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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AGG: attention-based gated convolutional GAN with prior guidance for image inpainting

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Neural Computing and Applications 2024年第20期36卷 12589-12604页

作者： Yu, Xiankang Dai, Lei Chen, Zhihua Sheng, Bin Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China

Image inpainting has made great achievements recently, but it is often tough to generate a semantically consistent image when faced with large missing areas in complex scenes. To address semantic and structural alignment in existing methods for image inpainting, this paper proposes an end-to-end attention-based gated convolution GAN with prior guidance named AGG, which designs the spatial and channel attention mechanisms for full extraction of semantic and structural features. Moreover, AGG constructs the attention-based upsampling module based on the channel attention to refine the feature map and capture more details from features of up-level low sizes. AGG uses the image contour as prior, allowing the gated convolution and attention mechanism may fill the image efficiently by focusing on the contour information. The attention-based gated convolution can effectively capture the global features and compensate for the limitations of the restricted receptive field of the naive convolution. Compared to other models, AGG generates images with finer outline features and no common problems such as the watermark and blur, which shows the best overall performance on the Paris StreetView, CelebA-HQ, and Places2 datasets. The best FID, LPIPS, PSNR, and SSIM values achieved by AGG are 2.18, 0.046, 30.82, and 0.951 on the CelebA-HQ dataset, with at least 3.21% and 6.52% performance improvement on FID and LPIPS compared to state-of-the-art methods, respectively. The source code will be available at https://***/Shawn-Yu-1/AGGNet. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. corrected publication 2024.

关键词： Convolution

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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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