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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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From Data Integrity to Global Model Integrity for Federated Learning: An MHT-based Approach

From Data Integrity to Global Model Integrity for Federated ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Nannan Zhang Yao Zhao Y. Neil Qu Bruce Gu Keshav Sood Longxiang Gao Shui Yu 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 Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China School of IT Deakin University VIC Australia School of Computer Science University of Technology Sydney NSW Australia

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Federated Learning (FL) is a distributed machine learning (ML) approach that enables multiple edge nodes to collaboratively train ML models by sharing model parameters, thus addressing privacy concerns. However, in highly distributed, dynamic, and volatile FL environments, the global model is vulnerable to various corruptions. For instance, edge nodes might falsely claim that the received global model is incomplete, or the channel that transmits the global model is untrustworthy. Effectively verifying the integrity of the global model poses a critical challenge. To tackle this issue, we introduce a method for verifying model integrity called Federated learning global Model Integrity Verification (FMIV). It leverages Merkle Hash Tree (MHT) to generate integrity proofs of the global model during verification. To improve security, we integrate random security codes during proof generation. FMIV is capable of verifying the global model updated by the central server and shared with untrusted edge nodes, while efficiently identifying the edge node caching the incomplete global model. Furthermore, we conduct theoretical analysis and extensive experiments to validate the performance of FMIV. Compared to the two state-of-the-art approaches, FMIV consistently exhibits a notable improvement in verification efficiency and effectiveness in detecting model corruption.

关键词： Analytical models Privacy Codes Federated learning Computational modeling Image edge detection Data integrity Data models Security Servers

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Reinforced Label Denoising for Weakly-Supervised Audio-Visual Video Parsing

arXiv

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arXiv 2024年

作者： Gao, Yongbiao Sun, Xiangcheng Lv, Guohua Yu, Deng Niu, Sijiu Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application [Southeast University Ministry of Education China School of Artificial Intelligence Shandong University China Shandong Key Laboratory of Ubiquitous Intelligent Computing Jinan China

Audio-visual video parsing (AVVP) aims to recognize audio and visual event labels with precise temporal boundaries, which is quite challenging since audio or visual modality might include only one event label with only the overall video labels available. Existing label denoising models often treat the denoising process as a separate preprocessing step, leading to a disconnect between label denoising and AVVP tasks. To bridge this gap, we present a novel joint reinforcement learning-based label denoising approach (RLLD). This approach enables simultaneous training of both label denoising and video parsing models through a joint optimization strategy. We introduce a novel AVVP-validation and soft inter-reward feedback mechanism that directly guides the learning of label denoising policy. Extensive experiments on AVVP tasks demonstrate the superior performance of our proposed method compared to label denoising techniques. Furthermore, by incorporating our label denoising method into other AVVP models, we find that it can further enhance parsing results. © 2024, CC BY.

关键词： Self-supervised learning

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FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classification

arXiv

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arXiv 2024年

作者： Fu, Kexue Luo, Xiaoyuan Qu, Linhao Wang, Shuo Xiong, Ying Maglogiannis, Ilias Gao, Longxiang Wang, Manning Jinan China Digital Medical Research Center School of Basic Medical Sciences Fudan University China Fudan University China University of Piraeus Greece Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

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. © 2024, CC BY-NC-SA.

关键词： Visual languages

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Construction and Application of the SMART Model for Adaptive Industrial Data Collection Based on Knowledge Graphs

Construction and Application of the SMART Model for Adaptive...

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IEEE International Conference on Big Data

作者： Wendan Cheng Zhen Zhang Heng Qian Qiuyue Wang Guanqun Su Lingge Meng 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 Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shu Ju (Shandong) Intelligent Technology Co. Ltd. Jinan China Shandong Standard Institute of Emerging Technologies and Innovations Co. Ltd. Jinan China Shandong Qingniao IIoT Co. Ltd. Jinan China

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Industrial data collection is the foundation for implementing enterprise digitization, which is of great significance to the development of intelligent manufacturing. However, Industrial Data Collection Standards (IDCS) are primarily published in paper or PDF formats, which makes it challenging to associate and reuse knowledge. This brings difficulties to data collection and management of heterogeneous devices, thus affecting the adaptive collection of industrial data. For this reason, this paper introduces SMART into industrial data collection and proposes the construction and application of a knowledge graph-based SMART Model. First, ontology semantic reasoning and natural language processing techniques are utilized to develop an ontology model for industrial data collection and extract fine-grained IDCS knowledge. Then, the knowledge is integrated according to the standard primitive structure and conceptual reasoning rules by constructing a standard association model to form the Industrial Data Collection Standards Knowledge Graph (IDCS-KG). Finally, the SMART competence level is quantitatively assessed based on the completion degree of each operation within the SMART Model. An experimental case study demonstrates that the SMART Model can collect intelligent adaptive industrial data through the reasoning and analysis of equipment adaptation protocols and data quality management strategies.

关键词： Adaptation models Analytical models Protocols Knowledge graphs Data collection Ontologies Data models Cognition Natural language processing Standards

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Generalization Boosted Adapter for Open-Vocabulary Segmentation

arXiv

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arXiv 2024年

作者： Xu, Wenhao Wang, Changwei Feng, Xuxiang Xu, Rongtao Huang, Longzhao Zhang, Zherui Guo, Li Xu, Shibiao School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The 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 Jinan250013 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China The Aerospace Information Research Institute Chinese Academy of Sciences China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China

Vision-language models (VLMs) have demonstrated remarkable open-vocabulary object recognition capabilities, motivating their adaptation for dense prediction tasks like segmentation. However, directly applying VLMs to such tasks remains challenging due to their lack of pixel-level granularity and the limited data available for fine-tuning, leading to overfitting and poor generalization. To address these limitations, we propose Generalization Boosted Adapter (GBA), a novel adapter strategy that enhances the generalization and robustness of VLMs for open-vocabulary segmentation. GBA comprises two core components: (1) a Style Diversification Adapter (SDA) that decouples features into amplitude and phase components, operating solely on the amplitude to enrich the feature space representation while preserving semantic consistency;and (2) a Correlation Constraint Adapter (CCA) that employs cross-attention to establish tighter semantic associations between text categories and target regions, suppressing irrelevant low-frequency "noise" information and avoiding erroneous associations. Through the synergistic effect of the shallow SDA and the deep CCA, GBA effectively alleviates overfitting issues and enhances the semantic relevance of feature representations. As a simple, efficient, and plug-and-play component, GBA can be flexibly integrated into various CLIP-based methods, demonstrating broad applicability and achieving state-of-the-art performance on multiple open-vocabulary segmentation benchmarks. © 2024, CC BY.

关键词： Semantics

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Dual Prototypes Contrastive Learning for Semi-Supervised Medical Image Segmentation

SSRN

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SSRN 2024年

作者： Yue, Tianai Xu, Rongtao Wu, Jingqian Yang, Wenjie Du, Shide Wang, Changwei Johns Hopkins University Baltimore21218 United States State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The University of Hong Kong Hong Kong999077 Hong Kong College of Computer and Big Data Fuzhou University Fuzhou350108 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan250014 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan250014 China

Semi-supervised techniques for medical image segmentation have demonstrated potential, effectively training models using scarce labeled data alongside a wealth of unlabeled data. Therefore, semi-supervised medical image segmentation is one of the key issues in engineering applications of medical artificial intelligence. Consistency constraints based on prototype alignment provide an intuitively sensible way to discover valuable insights from unlabeled data that can motivate segmentation performance. In this work, we propose a dual prototypes contrastive network (DPC-Net) to motivate semi-supervised medical segmentation accuracy by imposing image-level global prototype constraints and pixel-level local prototype constraints. First, we introduce a Background-Separation Global Prototype Contrastive Learning technique that utilizes the natural mutual exclusivity of foreground and background to separate the inter-class distances and encourage the segmentation network to obtain segmentation results that are more complete and do not contain background regions. Second, we design a Cross-Consistent Local Prototype Contrastive Learning techniques to extend the perturbation consistency of the two networks to the prototype's localized response to the feature map, thereby shaping a more stable intra-class prototype space and producing accurate and robust pixel-level predictions. Finally, we comprehensively evaluate our method on mainstream semi-supervised medical image segmentation benchmarks and settings, and experimental results show that our DPC-Net outperforms current state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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A Patent Fast Recommendation Algorithm Based on User Requirements Combined with an Inverted Table

A Patent Fast Recommendation Algorithm Based on User Require...

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Advanced Information, Mechanical Engineering, Robotics and Automation (AIMERA), IEEE International Conference on

作者： Wan Zheng Qingyan Ding Na Li Yu Pan Xuecheng Dong 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

ISBN: (数字)9798350343335

ISBN: (纸本)9798350343342

Patent recommendation has always been an essential service for the innovation and development of all walks of life. The matching of demand and patent information can enable enterprises to quickly obtain the innovation and development trend of the industry. The current patent recommendation method has the problem of slow recommendation speed and insufficient comprehensive recommendation results. According to the existing problems, a patent fast recommendation algorithm based on user needs combined with inverted table is proposed. The algorithm using dual track recommended way for accurate recommendation and extensive recommendation, in the process of recommendation built by patent data inversion table and document list auxiliary recommendation to improve recommendation speed and accuracy, when accurate recommendation when using improved requirements based on GPT-2 compression model recommended after demand text compression, when widely recommended using TF-IDF mechanism for demand text of each paragraph after keyword extraction, finally generate widely recommendation list and accurate recommendation list. This recommendation method can not only realize the fast and accurate recommendation of patents, but also provide users with more innovative ideas for extensive recommendation. The experiments show that the proposed recommendation algorithm has higher speed and accuracy than the original recommendation algorithm.

关键词： Patents Vocabulary Technological innovation Accuracy Semantics Predictive models Writing Prediction algorithms Data mining Robotics and automation

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A Novel Collaborative Edge Caching via User Preference Awareness and Adaptive Clustering in MENs

A Novel Collaborative Edge Caching via User Preference Aware...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Pengfei Gu Huiling Shi Hao Hao Mingfa Li Wei 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 Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

In this paper, we propose a collaborative caching placement scheme leveraging user preference awareness and adaptive clustering (UPAAC), which jointly considers the heterogeneity of user preferences and user activity to reduce user access latency and improve the quality of service. Specifically, we first utilize Bidirectional Long Short-Term Memory (Bi-LSTM) to capture dynamic user behavior to predict user preferences, and then density-based spatial clustering of applications with noise (DBSCAN) is used to cluster users based on the predicted user preferences for more efficient cache placement. Furthermore, the cache placement optimal problem is defined as the latency reduction maximization problem under the constraint of the cache capacity and latency threshold, and a genetic algorithm (GA) is used to optimize the problem and design the cache placement strategy. The simulation outcomes indicate that the suggested approach outperforms the conventional caching methods by significantly decreasing content access latency and enhancing the cache hit ratio.

关键词： Noise Collaboration Quality of service Prediction algorithms Smart systems Probability distribution Timing History Long short term memory Genetic algorithms

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enCBS: Delay Guarantee Mechanism based on Credit-based Shaping for Programmable Switch

enCBS: Delay Guarantee Mechanism based on Credit-based Shapi...

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Asia-Pacific Network Operations and Management Symposium (APNOMS)

作者： Xinhang Wang Lizhuang Tan Wei 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 Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

The Time-Sensitive Networking (TSN) aims to achieve delay guarantee through traffic shaping, and the programmable switch provides a data plane implementation solution for the flexible and deployable TSN network. However, the built-in quality of service guarantee mechanism of the current programmable switch cannot satisfy the deterministic guarantee of queue control and end-to-end delay of TSN network. Credit-based Shaping (CBS) is a common traffic shaping scheme for static TSN network, and it is helpless in the face of topology changes and dynamic entry/exit of flows. In this paper, we design the enhance CBS (enCBS), a CBS-based delay guarantee mechanism on the programmable switch, using the advantages of Software-Defined Network (SDN) architecture to optimize flow management and parameter configuration. In addition, we improved the traditional CBS and strengthened the packet interval constraints. The experimental results prove that, compared with the existing quality of service control mechanism of programmable switch, the delay guarantee mechanism based on CBS shaping has better delay guarantee performance.

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