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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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Recommendation Algorithm Based on Social Influence Diffusion 17

Recommendation Algorithm Based on Social Influence Diffusion

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Jin, Xin Chen, Shiyu Nanjing University of Science and Technology School of Mechanical Engineering Nanjing China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350351637

Social influence is everywhere in people's lives. It controls users' activities in social networks. Understanding the mechanism of interactions between users in social networks can benefit various applications, such as recommendations. Traditional recommendation systems based on collaborative filtering suppose that the behaviors of users are independent of each other and ignore the influence relationships among users. However, different historical behaviors and social relationships of users result in different influences among users. First, the complete user behavior sequences are fed into a recurrent neural network to capture long term patterns of user interest in items. Secondly, a self-attention network is used to obtain short lived interests of users. Then the outputs of the recurrent neural network as queries are fed into another self-attention network to obtain user representations that combine transient effect of and long term patterns of users' interests, thereby predicting the ratings of users to items. Finally, the proposed recommendation system is used to recommend products that need to be promoted to the maximum number of users. The proposed method is verified to outperform the state-of-the-art algorithms on three real-world datasets. © 2024 IEEE.

关键词： Chatbots

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An Embedded System I/O Isolation technology for Raspberry Pi 4

An Embedded System I/O Isolation Technology for Raspberry Pi

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4th International Conference on Intelligent technology and Embedded Systems, ICITES 2024

作者： Yaoyong, Wang Gongxuan, Zhang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798331506292

Nowadays user's data in devices is at high risk. To ensure security of program's runtime environment, Trusted Computing and Trusted Execution Environment(TEE) are proposed, which construct trusted base at the hardware level. Hardware vendors implement hardware trusted base for their new products, Intel's Software Guard eXtension(SGX) as an example. SGX lacks isolation for device I/O so there are many SGX-based mechanisms proposed that add protection for device I/O. However, all the above trusted mechanisms must be based on that the CPU has extensions for trusted computing, which means that lots of devices equipped with old chips and those embedded devices on other ISA like Arm and RISC-V have no possibility to support this trusted computing mechanism. Therefore, we proposes an I/O isolation technology for embedded devices and implements a trusted computing mechanism named XiRang(eXtended I/O Reliable Authentication Guard) for Raspberry Pi. Specifically, we do the following work. (1) Use an external trusted device as the physical trusted root for embedded devices, which made up for the lack of trusted computing extension of their chips. (2) On the premise of trusted hardware, XiRang realizes the trusted isolation of USB disk, uses trusted Linux container as the application runtime environment, and completes the trusted chain transfer from the external trusted device to the application. (3) Finally, we evaluate performance of XiRang. © 2024 IEEE.

关键词： Embedded System I/O Isolation Raspberry Pi Trusted Computing Trusted Execution Environment

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BiMoeFormer: A Novel Transformer-Based Framework for 3D Human Motion Prediction with Bi-Level Routing Attention and Mixture of Experts 5

BiMoeFormer: A Novel Transformer-Based Framework for 3D Huma...

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5th International Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2024

作者： Zhang, Wei Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350385557

We introduce a novel BiMoeFormer based on the Transformer architecture for 3D human motion prediction. Previous approaches primarily focus on the relationships between body joints in human poses, while neglecting their independencies. Our method addresses this by incorporating a Bi-Level Routing Attention (BRA) mechanism, which emphasizes balancing the correlation and independence among these joints. More specifically, BRA selects the top k most relevant joints for each joint, enhancing the overall modeling capability. Moreover, to accurately capture the diversity and complexity of human motion, we employ a Mixture of Experts (MoE) architecture to enhance the expressiveness of the Transformer. This allows us to control the model complexity by sharing the MoE across blocks. Comprehensive evaluations on the Human3.6M, AMASS, and 3DPW datasets demonstrate that our method consistently outperforms state-of-the-art baselines. © 2024 IEEE.

关键词： Motion estimation

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Multi-Dimensional Training Optimization for Efficient Federated Synergy Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6243-6258页

作者： Fu, Shucun Dong, Fang Chen, Runze Shen, Dian Zhang, Jinghui He, Qiang Southeast University School of Computer Science and Engineering Nanjing China Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan China

Edge learning (EL) is an end-to-edge collaborative learning paradigm enabling devices to participate in model training and data analysis, opening countless opportunities for edge intelligence. As a promising EL framework, federated synergy learning (FSyL) mitigates the computation and communication overhead on resource-constrained devices by offloading partial model layers to the edge server for synergistic training. Nevertheless, due to the system and statistical heterogeneity, naively using existing FSyL methods is significantly time-consuming and causes accuracy degradation. Motivated by this issue, this paper introduces a novel FSyL framework that integrates multi-dimensional training optimization and formulates the edge learning cost minimization (ELCM) problem. To tackle the ELCM efficiently, we design OL-MG, an OnLine Model Splitting and Resource Provisioning Game. Specifically, we first reformulate and decompose the original ELCM based on data quality evaluation. Then, given a model splitting decision, we determine the optimal resource provisioning in Sub-problem1, based on which optimal model splitting in Sub-problem2 is modeled as a potential game. Subsequently, we introduce a decentralized algorithm to find a Nash equilibrium (NE) solution. Furthermore, we further extend OL-MG to support a budget-aware multi-edge scenario. Extensive experiments demonstrate that the proposed mechanism significantly outperforms state-of-the-art methods in cost-saving and accuracy improvement. © 2025 IEEE.

关键词： Federated learning

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Prediction of Social Influence in Social Networks 17

Prediction of Social Influence in Social Networks

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作者： Chen, Shiyu Li, Qianmu Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350351637

Social influence is an individual's ability to change the thoughts or behaviors of others due to factors such as social status, social connections, and social wealth. Studying social influence, especially modeling and measuring the influence between people on large-scale social network data, is very important for product promotion, friend recommendation, expert discovery and user behavior prediction in social applications. In addition, it plays an important role in promoting the virtual business market and national public opinion monitoring. From a computational point of view, the research on social influence includes detection, modeling, measurement, and application of social influence. First, a training dataset is generated through independent cascade and linear threshold models. The influence of each node in the network is then calculated through graph convolutional network and reinforcement learning techniques. Finally, the proposed model is tested on three real-world large-scale datasets. Experimental results show that the proposed method can help predict the influence of nodes in social networks. © 2024 IEEE.

关键词： deep learning graph convolutional network graph embedding reinforcement learning social influence social networks

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Q2Cypher: Converting Natural Language Questions to Cypher with Fine-Tuned Large Language Models 5

Q2Cypher: Converting Natural Language Questions to Cypher wi...

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

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

ISBN: (纸本)9798331528911

Knowledge Base Question Answering (KBQA) intends to obtain credible answers to natural language questions based on knowledge bases. Knowledge graphs, also known as graph databases, explicitly present a large amount of factual knowledge and the associations among them. Neo4j, one of the most popular graph databases, uses the Cypher Query Language (CQL) for efficient querying. Due to its complexity, converting natural language questions into CQL, i.e., Text-to-CQL, is in demand, but the performance is undesirable for existing models. To address the issue, we propose Q2Cypher, a Text-to-CQL framework that is able to generate corresponding CQL based on natural language questions by fine-tuning open-source large language models (LLMs). Q2Cypher employs an unsupervised joint retrieval method to further align entity and relation names in the CQL, thus improving generation from the models more directly. We conduct experiments on open-source LLMs such as ChatGLM2, Baichuan2 and Llama-2, using different fine-tuning methods such as LoRA, Freeze and QLoRA for comparison. The experimental results demonstrate that Q2Cypher is effective and achieves better performance than existing methods on the SpCQL dataset. © 2024 IEEE.

关键词： Knowledge graph

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MTPNet: Learning Multiple Twin-support Prototypes for Few-shot Medical Image Segmentation

MTPNet: Learning Multiple Twin-support Prototypes for Few-sh...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Song, Feifan Cheng, Ziming Li, Lunbo Zhang, Haofeng Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350386226

Given the high annotation costs and ethical considerations associated with medical images, leveraging a limited number of annotated samples for Few-Shot Medical Image Segmentation (FSMIS) has become increasingly prevalent. However, existing models tend to focus on visible foreground support information, often overlooking extreme foreground-background imbalances. In addition, query images sometimes have slight different appearance compared to support images of the same category due to the differences in size as well as slicing angle, thus employing only support images to generate prototypes inevitably leads to matching bias. To address these challenges, we present an innovative approach through learning a Multiple Twin-support Prototypes Network (MTPNet). Our approach includes the design of the Scale Consistent Sampling (SCS) module, which adaptively adjusts the foreground and background points within the support set, thereby balancing the influence of various structural elements in the image. Additionally, the Twin-support Prototypes Extraction (TPE) module facilitates the critical interaction between query and support features to extract twin-support prototypes. This module incorporates a Backtrace Interaction Filter (BIF) to eliminate erroneous interaction prototypes. Extensive experimental validation on three widely used medical image datasets demonstrates that our method surpasses current State-of-the-arts, showcasing its potential to address key limitations in FSMIS. The code is available at https://***/FeifanSong/MTPNet. © 2024 IEEE.

关键词： Image segmentation

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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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An Enhanced Vision-Language Pre-Training Approach for Scene Text Detection 5

An Enhanced Vision-Language Pre-Training Approach for Scene ...

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5th International Symposium on computer engineering and Intelligent Communications, ISCEIC 2024

作者： Gu, Tao Zhang, Chongyang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798331518677

Recent advancements in Vision-Language Pre-training (VLP) techniques have greatly improved performance in Scene Text Detection tasks by leveraging the rich visual and textual content in scene text images. We propose an innovative integration of contrastive learning with Masked Language Modeling (MLM) and Masked Image Modeling (MIM), inspired by the Masked Autoencoder (MAE) approach. This synthesis enhances self-supervised learning by combining discriminative and generative learning. Notably, we introduce masked image modeling for text detection, enabling effective representation learning in text image regions. To distinguish our approach from traditional BERT's MLM, we develop a custom tokenizer tailored for scene text detection. Our pre-trained model aligns visual and textual information, resulting in improved performance of existing scene text detectors. Extensive experiments on datasets such as ICDAR2015 show that our method significantly outperforms previous pre-training approaches. © 2024 IEEE.

关键词： Contrastive Learning

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