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Multidimensional data fusion detection method based on time series data model 24

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Multidimensional data fusion detection method based on time ...

8th International Conference on High Performance Compilation, Computing and Communications, HP3C 2024

作者： Zhao, Lina Li, Ming Ni, Jinchao Liu, Xue Xu, Lijuan Yao, Zhiang Information & Telecommunications Company State Grid Shandong Electric Power Company Shandong Jinan China Shandong Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Shandong Jinan China

ISBN: (纸本)9798400716904

Network security is currently a topic of significant concern in today's society, as it directly impacts national security, social stability, and public welfare. Consequently, the issue of network security cannot be overlooked. In current research, log anomaly detection faces various challenges, including the neglect of the correlation and complementarity between traffic data and log data, as well as inadequate response capabilities to novel and complex attack behaviors. To address these challenges, this paper presents an intrusion detection method based on traffic-log data fusion. By merging the decision results of traffic and log data, this method maximizes the utilization of their correlation and complementarity, thereby enhancing the intrusion detection system's capability to identify complex attack behaviors. Experimental results demonstrate that the proposed intrusion detection method based on traffic-log data fusion exhibits significant advantages in key performance metrics such as accuracy, recall, and F-measure. Compared to traditional methods such as PCA, N-gram, and IM, this method achieves more accurate identification of normal and abnormal log data, as well as a balanced trade-off between recall and precision, indicating higher practical utility. © 2024 ACM.

关键词： Network intrusion

SEED: Accelerating Reasoning Tree Construction via Scheduled Speculative Decoding

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arXiv 2024年

作者： Wang, Zhenglin Wu, Jialong Lai, Yilong Zhang, Congzhi Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

Large Language Models (LLMs) demonstrate remarkable emergent abilities across various tasks, yet fall short of complex reasoning and planning tasks. The tree-search-based reasoning methods address this by encouraging the exploration of intermediate steps, surpassing the capabilities of chain-of-thought prompting. However, significant inference latency is introduced due to the systematic exploration and evaluation of multiple thought paths. This paper introduces SEED, a novel and efficient inference framework to improve both runtime speed and GPU memory management concurrently. Based on a scheduled speculative execution, SEED efficiently handles multiple iterations for thought generation and state evaluation, leveraging a rounds-scheduled strategy to manage draft model dispatching. Extensive experimental evaluations on three reasoning datasets demonstrate the superior speedup performance of SEED1 Copyright © 2024, The Authors. All rights reserved.

关键词： Memory management

Causal Walk: Debiasing Multi-Hop Fact Verification with Front-Door Adjustment

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arXiv 2024年

作者： Zhang, Congzhi Zhang, Linhai Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

Multi-hop fact verification aims to detect the veracity of the given claim by integrating and reasoning over multiple pieces of evidence. Conventional multi-hop fact verification models are prone to rely on spurious correlations from the annotation artifacts, leading to an obvious performance decline on unbiased datasets. Among the various debiasing works, the causal inference-based methods become popular by performing theoretically guaranteed debiasing such as casual intervention or counterfactual reasoning. However, existing causal inference-based debiasing methods, which mainly formulate fact verification as a single-hop reasoning task to tackle shallow bias patterns, cannot deal with the complicated bias patterns hidden in multiple hops of evidence. To address the challenge, we propose Causal Walk, a novel method for debiasing multi-hop fact verification from a causal perspective with front-door adjustment. Specifically, in the structural causal model, the reasoning path between the treatment (the input claim-evidence graph) and the outcome (the veracity label) is introduced as the mediator to block the confounder. With the front-door adjustment, the causal effect between the treatment and the outcome is decomposed into the causal effect between the treatment and the mediator, which is estimated by applying the idea of random walk, and the causal effect between the mediator and the outcome, which is estimated with normalized weighted geometric mean approximation. To investigate the effectiveness of the proposed method, an adversarial multi-hop fact verification dataset and a symmetric multi-hop fact verification dataset are proposed with the help of the large language model. Experimental results show that Causal Walk outperforms some previous debiasing methods on both existing datasets and the newly constructed datasets. Code and data will be released at https://***/zcccccz/CausalWalk. Copyright © 2024, The Authors. All rights reserved.

关键词： Large datasets

Inaccurate Label Distribution Learning with Dependency Noise

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arXiv 2024年

作者： Kou, Zhiqiang Wang, Jing Jia, Yuheng Geng, Xin MOE Key Laboratory of Computer Network and Information Integration School of Computer Science and Engineering Southeast University Nanjing China

In this paper, we introduce the Dependent Noise-based Inaccurate Label Distribution Learning (DN-ILDL) framework to tackle the challenges posed by noise in label distribution learning, which arise from dependencies on instances and labels. We start by modeling the inaccurate label distribution matrix as a combination of the true label distribution and a noise matrix influenced by specific instances and labels. To address this, we develop a linear mapping from instances to their true label distributions, incorporating label correlations, and decompose the noise matrix using feature and label representations, applying group sparsity constraints to accurately capture the noise. Furthermore, we employ graph regularization to align the topological structures of the input and output spaces, ensuring accurate reconstruction of the true label distribution matrix. Utilizing the Alternating Direction Method of Multipliers (ADMM) for efficient optimization, we validate our method's capability to recover true labels accurately and establish a generalization error bound. Extensive experiments demonstrate that DN-ILDL effectively addresses the ILDL problem and outperforms existing LDL methods. © 2024, CC BY.

关键词： Topology

Integrated Sensing,Computing and Communications Technologies in IoV and V2X

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China Communications 2023年第3期20卷 I0002-I0004页

作者： Shanzhi Chen FRichard Yu Weisong Shi Changle Li China Information and Communication Technology Group Co. Ltd.(CICT) State Key Laboratory of Wireless Mobile Communications SMIC IEEE VTS Mobile World newsletter IEEE Canadian Academy of Engineering(CAE) Engineering Institute of Canada(EIC) IET Department of Computer and Information Sciences at the University of Delaware(UD) State Key Laboratory of Integrated Services Networks of Xidian University

With the development of new generation of information and communication technology,the Internet of Vehicles(IoV)/Vehicle-to-Everything(V2X),which realizes the connection between vehicle and X(i.e.,vehicles,pedestrians,infrastructures,clouds,etc.),is playing an increasingly important role in improving traffic operation efficiency and driving safety as well as enhancing the intelligence level of social traffic services.

关键词： driving traffic operation

LGDB-Net: Dual-Branch Path for Building Extraction from Remote Sensing Image 30

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LGDB-Net: Dual-Branch Path for Building Extraction from Remo...

30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhang, Ronghuan Zhao, Jing Li, Ming Zou, Qingzhi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong University of Traditional Chinese Medicine School of Intelligence and Information Engineering Jinan China

ISBN: (纸本)9798331515966

Extracting buildings from remote sensing images using deep learning techniques is a widely applied and crucial task. Convolutional Neural networks (CNNs) adopt hierarchical feature representation, showcasing powerful capabilities in extracting local information but facing challenges in capturing global features. Transformers can address this limitation, but they perform poorly in extracting local features and significantly increase memory requirements and computational complexity. To overcome these challenges, we propose a method for building extraction from remote sensing images called LGDB-Net (Local-Global Dual-Branch Network), employing a dual-branch approach. Firstly, inspired by Swin Transformer, we designed GB-Former(Global Branch-Former) as the backbone network to model global information. We use a linear multi-head self-attention mechanism to reduce time and memory complexity while maintaining a large global receptive field. Additionally, we replace the traditional multi-layer perceptron with a convolution-enhanced multi-layer perceptron to improve channel feature representation, reduce model parameters, and enhance segmentation performance. Secondly, we use multiple Depth-wise Conv3×3 + LN (Layer Normalization) + GeLU (Gaussian Error Linear Unit) modules as the auxiliary branch for local detailed feature extraction. Finally, we adopt a multi-scale feature fusion strategy to integrate feature information from both branches. We conduct a series of experiments on three datasets: WHU, Massachusetts, and Inria. The experimental results demonstrate that the proposed method not only effectively improves segmentation accuracy with lower building omission and commission rates but also significantly reduces model parameters and computational complexity. © 2024 IEEE.

关键词： Convolutional neural networks

ADACQR: Enhancing Query Reformulation for Conversational Search via Sparse and Dense Retrieval Alignment

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arXiv 2024年

作者： Lai, Yilong Wu, Jialong Zhang, Congzhi Sun, Haowen Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

Conversational Query Reformulation (CQR) has significantly advanced in addressing the challenges of conversational search, particularly those stemming from the latent user intent and the need for historical context. Recent works aimed to boost the performance of CQR through alignment. However, they are designed for one specific retrieval system, which potentially results in sub-optimal generalization. To overcome this limitation, we present a novel framework ADACQR. By aligning reformulation models with both term-based and semantic-based retrieval systems, ADACQR enhances the generalizability of information-seeking queries among diverse retrieval environments through a two-stage training strategy. Moreover, two effective approaches are proposed to obtain superior labels and diverse input candidates, boosting the efficiency and robustness of the framework. Experimental results on the TopiOCQA, QReCC and TREC CAsT datasets demonstrate that ADACQR outperforms the existing methods in a more efficient framework, offering both quantitative and qualitative improvements in conversational query reformulation.1 Copyright © 2024, The Authors. All rights reserved.

关键词： Structured Query Language

PE: A Poincare Explanation Method for Fast Text Hierarchy Generation

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PE: A Poincare Explanation Method for Fast Text Hierarchy Ge...

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Chen, Qian Li, Dongyang He, Xiaofeng Li, Hongzhao Yi, Hongyu School of Computer Science and Technology East China Normal University Shanghai China NPPA Key Laboratory of Publishing Integration Development ECNUP Shanghai China Sichuan Caizi Software Information Network Co. Ltd. China

ISBN: (纸本)9798891761681

The black-box nature of deep learning models in NLP hinders their widespread *** research focus has shifted to Hierarchical Attribution (HA) for its ability to model feature *** works model non-contiguous combinations with a time-costly greedy search in Euclidean spaces, neglecting underlying linguistic information in feature *** this work, we introduce a novel method, namely Poincare Explanation (PE), for modeling feature interactions with hyperbolic spaces in a time efficient ***, we take building text hierarchies as finding spanning trees in hyperbolic *** we project the embeddings into hyperbolic spaces to elicit inherit semantic and syntax hierarchical *** we propose a simple yet effective strategy to calculate Shapley *** we build the the hierarchy with proving the constructing process in the projected space could be viewed as building a minimum spanning tree and introduce a time efficient building *** results demonstrate the effectiveness of our approach. © 2024 Association for Computational Linguistics.

关键词： Semantics

ID-based Signcryption KEM to Multiple Recipients

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Chinese Journal of Electronics 2023年第2期20卷 317-322页

作者： Yinxia Sun Hui Li Key Laboratory of Computer Networks and Information Security Ministry of Education Xidian University Xi'an China

Identity based signcryption key encapsulation mechanism (KEM) is used to encapsulate a symmetric key during the construction of hybrid signcryption in the identity based setting. We introduce the notion of Identity based signcryption KEM to multiple recipients (mIDSC-KEM), and define security models for this new primitive. We illustrate by proposing an efficient mIDSC-KEM scheme. Our scheme is proved secure in the random oracle model under the Gap bilinear Diffie-Hellman (Gap-BDH) and the Computational Diffie-Hellman (CDH) assumption.

关键词： Encapsulation Costs Computational modeling Security Research and development Standards