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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Qing Liu Qian Gao Jun Fan Zhiqiang Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China China Telecom Digital Intelligence Techonology Co Ltd Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, research has illuminated the potency of implicit data processing in enhancing user preferences. Nevertheless, barriers remain in breaking through the constraints of implicit information. This study aims to bridge this gap by firstly constructing a triangular stable node network model, tailored to manage implicit information with precision. Recognizing the challenge of pinpointing novel structures within large-scale graphs, we propose SLTSNN— a triangular stable node network based on self-supervised learning for personalized prediction. SLTSNN innovates by maximizing mutual information between graph-level and patch-level representations, while augmenting graph representations through extended enhanced representations. Additionally, it incorporates triangular data associations and introduces a triangular allocation attention network, which emphasizes strongly correlated preference features among similar users. Furthermore, SLTSNN employs contrastive learning to maximize mutual information between graph vectors and hidden representations, distinguishing between high-order global and local representations. The model’s effectiveness in enhancing user preferences and capturing new graph structures is evidenced by its performance on hit rate and normalized discounted cumulative gain metrics across three datasets.

关键词： Measurement Bridges Contrastive learning Signal processing Data processing Vectors Space exploration Resource management Speech processing Mutual information

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FAST: a dual-tier few-shot learning paradigm for whole slide image classification 24

FAST: a dual-tier few-shot learning paradigm for whole slide...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Kexue Fu Xiaoyuan Luo Linhao Qu Shuo Wang Ying Xiong Ilias Maglogiannis Longxiang Gao Manning Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China and Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Digital Medical Research Center School of Basic Medical Sciences Fudan University and Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention Fudan University University of Piraeus

ISBN: (纸本)9798331314385

The expensive fine-grained annotation and data scarcity have become the primary obstacles for the widespread adoption of deep learning-based Whole Slide Images (WSI) classification algorithms in clinical practice. Unlike few-shot learning methods in natural images that can leverage the labels of each image, existing few-shot WSI classification methods only utilize a small number of fine-grained labels or weakly supervised slide labels for training in order to avoid expensive fine-grained annotation. They lack sufficient mining of available WSIs, severely limiting WSI classification performance. To address the above issues, we propose a novel and efficient dual-tier few-shot learning paradigm for WSI classification, named FAST. FAST consists of a dual-level annotation strategy and a dual-branch classification framework. Firstly, to avoid expensive fine-grained annotation, we collect a very small number of WSIs at the slide level, and annotate an extremely small number of patches. Then, to fully mining the available WSIs, we use all the patches and available patch labels to build a cache branch, which utilizes the labeled patches to learn the labels of unlabeled patches and through knowledge retrieval for patch classification. In addition to the cache branch, we also construct a prior branch that includes learnable prompt vectors, using the text encoder of visual-language models for patch classification. Finally, we integrate the results from both branches to achieve WSI classification. Extensive experiments on binary and multi-class datasets demonstrate that our proposed method significantly surpasses existing few-shot classification methods and approaches the accuracy of fully supervised methods with only 0.22% annotation costs. All codes and models will be publicly available on https://***/fukexue/FAST.

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Communication-Efficient Federated Learning for network Traffic Anomaly Detection

Communication-Efficient Federated Learning for Network Traff...

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International Conference on Mobile Ad-hoc and Sensor networks, MSN

作者： Xiao Cui Xiaohui Han Guangqi Liu Wenbo Zuo Zhiwen Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Quancheng Provincial Laboratory Jinan China

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

As an emerging machine learning method, Federated Learning is widely used in network anomaly detection scenarios. However, many current federated learning-based network anomaly detection works ignore the communication overhead problem during model training. The transmission of model parameters occupies a large amount of bandwidth, affecting the efficiency of network anomaly detection as well as the communication tasks of other programs. In this study, we propose eFedAD, an efficient federated learning framework for network traffic anomaly detection. Specifically, eFedAD uses singular value decomposition to compress the transmitted parameters and introduces a weighting mechanism to control the compression rate and aggregation proportion. In this way, eFedAD can significantly reduce the number of transmitted parameters and assign higher weights to more valuable clients, enhancing the generalization ability of the global model. Additionally, to address the slow convergence issue due to compressed parameters and minimize the total number of transmitted parameters, eFedAD uses a client selection method that is based on clustering clients’ data feature distributions. The experimental results demonstrate that eFedAD outperforms other compression methods and network anomaly detection approaches, achieving excellent performance.

关键词： Training Data privacy Federated learning Telecommunication traffic Data models Sensors Task analysis

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A SUPERVISED INFORMATION ENHANCED MULTI-GRANULARITY CONTRASTIVE LEARNING FRAMEWORK FOR EEG BASED EMOTION RECOGNITION

arXiv

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arXiv 2024年

作者： Li, Xiang Song, Jian Zhao, Zhigang Wang, Chunxiao Song, Dawei Hu, Bin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Institute of Engineering Medicine Beijing Institute of Technology Beijing China

This study introduces a novel Supervised Info-enhanced Contrastive Learning framework for EEG based Emotion Recognition (SI-CLEER). SI-CLEER employs multi-granularity contrastive learning to create robust EEG contextual representations, potentially improving emotion recognition effectiveness. Unlike existing methods solely guided by classification loss, we propose a joint learning model combining self-supervised contrastive learning loss and supervised classification loss. This model optimizes both loss functions, capturing subtle EEG signal differences specific to emotion detection. Extensive experiments demonstrate SI-CLEER's robustness and superior accuracy on the SEED dataset compared to state-of-the-art methods. Furthermore, we analyze electrode performance, highlighting the significance of central frontal and temporal brain region EEGs in emotion detection. This study offers an universally applicable approach with potential benefits for diverse EEG classification tasks. © 2024, CC0.

关键词： Emotion Recognition

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MGRR-SQL: Multi-Granularity Relevant Retrieval-Based in-Context Learning Approach for Text-to-SQL Generation

MGRR-SQL: Multi-Granularity Relevant Retrieval-Based in-Cont...

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Advanced Algorithms and Control Engineering (ICAACE), International Conference on

作者： Yiming Zhan Zhao Li Tong Chen Shiwei Wu Yang Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Evay Info Technology Co. Ltd. Jinan China School of Cyberspace Security (School of Cryptology) Hainan University Haikou China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

In-Context learning serves as a vital component for enhancing Text-to-SQL task performance. However, current in-context learning methodologies for Text-to-SQL applications fail to sufficiently bridge the significant cross-domain correlation gap that exists between few-shot examples and the intended query target. Our approach overcomes these challenges through a novel relevant retrieval-based in-context learning method, which operates across three distinct granularity levels: (1) semantic similarity retrieval for analogous questions, (2) query kernel entity matching for retrieving strongly relevant questions and (3) character-level similarity analysis for fine-grained question retrieval. Additionally, we introduce a novel Object-Relational Mapping (ORM)-based database prompt and a post cell value linking method that effectively describes the schema while avoiding error propagation common in traditional schema linking approaches. Our experimental evaluations conducted on the benchmark datasets BIRD and Spider reveal that our approach delivers strong performance, comparable to state-of-the-art methods.

关键词： Learning systems Structured Query Language Databases Large language models Computational modeling Semantics Benchmark testing Birds Business intelligence Standards

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Filter-Enhanced Hypergraph Transformer for Multi-Behavior Sequential Recommendation

Filter-Enhanced Hypergraph Transformer for Multi-Behavior Se...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhufeng Shao Shoujin Wang Wenpeng Lu Weiyu Zhang Hongjiao Guan Long Zhao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Data Science Institute University of Technology Sydney Sydney Australia

Sequential recommendation has been developed to predict the next item in which users are most interested by capturing user behavior patterns embedded in their historical interaction sequences. However, most existing methods appear to exhibit limitations in modeling fine-grained dependencies embedded in users’ various periodic behavior patterns and heterogeneous dependencies across multi-behaviors. Towards this end, we propose a Filter-enhanced Hypergraph Transformer framework for Multi-Behavior Sequential Recommendation (FHT-MB) to address the above challenges. Specifically, a multi-scale filter layer equipped with multi-learnable filters is devised to encode behavior-aware sequential patterns emerging from different periodic trends (e.g., daily or weekly routines), and then a hypergraph structure is devised to extract heterogeneous dependencies across users’ multiple types of behaviors. Extensive experiments on two real-world e-commerce datasets show the superiority of our proposed FHT-MB over various state-of-the-art methods. 1

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FusionNet: An Effective network Phishing Website Detection Framework Based on Multi-Modal Fusion

FusionNet: An Effective Network Phishing Website Detection F...

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Yu Sun Guangqi Liu Xiaohui Han Wenbo Zuo Weihua Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) (Shandong Academy of Sciences) Qilu University of Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Quancheng Provincial Laboratory Jinan China

Recently, the rapid advancement of communication technology has undoubtedly improved people's lives. Unfortunately, this progress has also given rise to a significant issue: the proliferation of phishing websites. Regrettably, phishing web sites have now emerged as one of the primary threats to cybersecurity. While there have been extensive efforts to develop anti-phishing techniques, many of these approaches focus solely on a single modality of information, such as the website's URL, HTML source code, or visual features. Given the growing sophistication of phishing attacks, relying on just one modality of information is no longer sufficient to accurately identify phishing websites. To tackle this challenge, our research aims to leverage the complementarity of various modalities to enhance phishing website detection. In this paper, we introduce a multi-modal framework called FusionNet to identify phishing web pages. This framework utilizes three distinct information modalities: URLs, HTML source codes, and visual features. Specifically, our approach first designs specific representation learning architectures tailored to each modality's unique properties. Subsequently, we incorpo-rate an attention mechanism to merge these representations, thereby exploiting the synergies between the multi-modal data. The learned feature representations are more discriminative, contributing to significantly improved phishing website detection accuracy. To evaluate the performance of FusionNet, we collect a dataset comprising phishing webpages, each sample contains all three modalities-URLs, HTML source codes, and visual features. Our extensive experiments conducted on this dataset demonstrate that FusionNet outperforms state-of-the-art methods in terms of both robustness and accuracy.

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From Data Integrity to Global ModeI Integrity for Decentralized Federated Learning: A Blockchain-based Approach

From Data Integrity to Global ModeI Integrity for Decentrali...

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International Joint Conference on Neural networks (IJCNN)

作者： Na Wang Yao Zhao Youyang Qu Lei Cui Bai Li Longxiang Gao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of IT Deakin University VIC Australia Zhejiang Tianheng Information Technology Company China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Decentralized Federated Learning (DFL) is extensively applied in various areas, e.g., healthcare, finance, and Internet of Things (loT), offering practical solutions for distributed intelligent applications and data collaboration. In DFL systems, participants, e.g., edge devices, organizations, or nodes, collaborate in the training of a shared global model by aggregating local models from various participants. During this process, participants need to communicate frequently with a central authority/node/server to share model parameters. Such communication is vulnerable to malicious attacks or tampering, posing a significant threat to the integrity of model training. The integrity verification method can provide an integrity guarantee for the global model of DFL. However, most of the existing integrity verification schemes are centralized and not suitable for resource-constrained DFL scenarios. Therefore, how to verify the integrity of the global model becomes an important issue in DFL. To address it, we devise a global model integrity verification method for DFL. Specifically, we generate a digital signature for each global model parameter as proof of integrity, while improving the efficiency of integrity verification by electing delegates to conduct the verification process. A series of experiments is conducted to validate the performance of the proposed method. The experimental results demonstrate that our approach not only effectively ensures the integrity of the global model but also functions well under limited resources.

关键词： Training Federated learning Data integrity Neural networks Finance Distributed databases Organizations

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Grouped Federated Meta-Learning for Privacy-Preserving Rare Disease Diagnosis

Grouped Federated Meta-Learning for Privacy-Preserving Rare ...

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International Joint Conference on Neural networks (IJCNN)

作者： Xinru Song Feng Wu Zongchao Xie Longxiang Gao Lei Cui Youyang Qu Shujun Gu Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Computer Science University of Technology Sydney Australia Faculty of Science University of Melbourne Australia

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Federated learning (FL) has been widely applied in medical field, which allows clients to collaboratively train global models without sharing local data. Nevertheless, the diversity and scarcity of samples from rare diseases may result in a decline in the performance of local models on client-side due to using a singular global model. Moreover, direct transmission of local models or parameters will likely lead to user privacy violations. To solve these problems, we propose a Grouped Federated Meta-Learning (GrFML) method to improve the performance of local personalization models while protecting data privacy. Specifically, we first utilize a self-attention mechanism to extract partial features from the client’s local data, which are uploaded to the server (medical data is susceptible to perturbation and data integrity, thus this process does not expose the private data). The server groups clients with similar features based on these extracted features. Then, multiple meta-models are trained on these groups and distributed back to the clients to enhance the performance of the client’s local models. Furthermore, during the FL process, we introduce dynamic perturbation to the uploaded gradients based on the model’s test accuracy to protect their privacy. Typically, the perturbation magnitude is directly proportional to the model’s test accuracy. Extensive experiments shown that the GrFML model significantly improves client personalization model accuracy and achieves a good privacy-utility trade-off.

关键词： Metalearning Privacy Data privacy Accuracy Perturbation methods Feature extraction Data models

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Risk Modeling and Traceability of Boundary Attacks on Internet of Power Systems Based on Complex networks 9

Risk Modeling and Traceability of Boundary Attacks on Intern...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Li, Xin Qu, Zhaoyang Yu, Tong Xie, Ming Yu, Fu Ding, Wei Northeast Electric Power University School of Electrical Engineering Jilin132012 China People's Bank of China Qinghai Provincial Branch Xining810000 China China Southern Power Grid Guangxi Power Grid Co. Ltd. Research Institute Nanning530023 China China Guizhou Power Grid Co. Ltd. Bijie Power Supply Bureau Bijie551700 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan250014 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China

ISBN: (纸本)9798350391367

With the increasing intelligence of power IoT terminal devices, the massive volume of data transmission, and the widespread adoption of shared services, traditional terminal boundary security mechanisms are unable to keep up with the rapid development of network technologies and applications. Additionally, they fail to trace and source network attacks, leading to frequent organized and purposeful malicious attack incidents. To address these issues, this paper proposes a research method for risk modeling and traceability of boundary attacks on power IoT terminals based on complex networks. First, based on the node information of power IoT terminal devices and complex network theory, a terminal boundary attack network (TBAN) is established. Next, using indicators related to complex network theory, such as degree, in-degree, and out-degree, the terminal boundary attack network is analyzed. Furthermore, a staged TBAN is proposed to analyze the attack risks at different times. Then, combining causal theory, a method for tracing and sourcing terminal boundary attacks based on a causal Bayesian network is proposed to identify and trace malicious attacks. Finally, validation and testing analysis are conducted using the actual deployment of power IoT terminal devices. The proposed analysis method transforms the spatial characteristics of the power IoT terminal boundary into a temporally sequential TBAN, which not only visually reflects attack risks but also reveals the temporal relationships of terminal boundary attacks between lines. © 2024 IEEE.

关键词： Internet of things

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