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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Penghui X Debo Cheng Zhenyun Deng Guixian Zhang Shichao Zhang Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin China UniSA STEM University of South Australia School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

Anomaly detection is a crucial technique for comprehending the intricate structures of data, and it has garnered significant attention in various real-life applications, including finance, transportation, and network security. Presently, existing anomaly detection methods primarily concentrate on shallow techniques like residual analysis and community discovery, as well as deep learning methods that employ self-encoders as the underlying framework. However, these current methods overlook the local information during training, resulting in subpar and unexplained results. To recognize the local information, in this paper, we propose a novel Local information Recognition method for Attribute Graph Anomaly Detection (LRAGAD). Specifically, to use the contextual structural information, LRAGAD first constructs a contrastive learning representation by generating different substructures from the target nodes, while reconstructing the whole graph using self-encoders that can utilize the neighborhood information of target nodes. Moreover, to better understand the complex graph structure, LRAGAD uses anomaly score estimation for outlier prediction. Experimental results on five real-world datasets demonstrate that the proposed LRAGAD method obtains better performance on AUC scores.

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RDG-TE: Link reliability-aware DRL-GNN-based traffic engineering in SDN

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Expert Systems with Applications 2025年 265卷

作者： Farhan, Muhammad Shah, Nadir Wang, Lei Muntean, Gabriel-Miro Song, Houbing Herbert School of Software Dalian University of Technology Dalian China Department of Computer Science COMSATS University Islamabad Wah Campus Pakistan Key Lab of Ubiquitous Network and Service Software of Liaoning Province China School of Electronic Engineering Dublin City University Dublin Ireland Department of Information Systems University of Maryland Baltimore United States

The existing advanced machine learning approaches based on Graph Neural networks (GNN) for efficient traffic engineering (TE) in Software Defined networking (SDN) overlook consideration of link reliability values. Link reliability is a very important parameter, directly linked to end-to-end delay for data packet transmission, and can be used to improve the delivery performance. This research article proposes two versions of RDG-TE, a novel model that integrates Deep Reinforcement Learning (DRL) and GNN in order to solve efficiently network TE problems by considering link reliability in both model training and reward computation. The proposed model enables improved performance by more accurately predicting the SDN network behavior in case of link failure. Testing results show that, in comparison to the closest state-of-art approach, our proposed approach reduces the number of disturbed flows by 24.19% and the hop count by 35.38% while also increases the reliable route prediction accuracy by 40.17%. © 2024 Elsevier Ltd

关键词： Deep reinforcement learning

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Multilingual Knowledge Graph Completion via Multimodal Decouple and Relation-based Ensemble Inference

Multilingual Knowledge Graph Completion via Multimodal Decou...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Xiaojing Liu Xuesong Jiang Fengge Yi 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 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 Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

The objective of Multimodal Knowledge Graph Completion (MKGC) is to forecast absent entities within a knowledge graph by leveraging additional textual and visual modalities. Existing studies commonly utilize a singular relationship embedding to depict all modalities within an entity pair, thus connecting several relationships derived from diverse modalities. However, this coupling may introduce interference from conflicting information between modalities, as the relationships between modalities for a given entity pair can be contradictory. Moreover, existing Ensemble Inference methods fail to dynamically adjust modal weights based on their differences and importance, despite the varying contributions of different modalities. In this paper, we propose the Multimodal Decouple and Relation-based Ensemble inference (MDRE) model. For each modality, we construct corresponding relationship embeddings and build separate triple representations to avoid interferences among modalities. During the training phase, we employ confidence-constrained training with temperature scaling to alleviate conflicting information in textual and visual modalities. For inference, we utilize the Relation-based Ensemble Inference method to adjust modal weights at the relationship level, thus achieving improved prediction results. Experimental results on two datasets demonstrate that MDRE outperforms existing single-modal and multimodal knowledge graph completion methods in terms of performance.

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Efficient and large universe ciphertext-policy attribute-based encryption with black-box traceability for ehealth

Efficient and large universe ciphertext-policy attribute-bas...

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International Conference on Cyber security Intelligence and Analytics, CSIA 2020

作者： Zhao, Jing Zeng, Peng Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Key Lab of Information Network Security of Ministry of Public Security The Third Research Institute of Ministry of Public Security Shanghai China

In the modern medical environment, massive application of electronic health systems has brought us great convenience. At the same time, the large and cumbersome data storage has become easier with the emergence of cloud computing. However, the emergence of shared platforms has also brought many security issues such as data loss and privacy exposures, especially the exposures of sensitive data by illegal users. In this paper we propose an efficient and large universe ciphertext-policy attribute-based encryption (CP-ABE) scheme with black-box traceability for the collaborative eHealth system. It is of a higher efficiency by reducing greatly the cost of encryption and decryption operations. We prove the security of our new scheme under the q-parallel BDHE assumption in the standard model. © Springer Nature Switzerland AG 2020.

关键词： Sensitive data

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EMO: An Ontology for Supporting the Management of Epidemic

EMO: An Ontology for Supporting the Management of Epidemic

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Applied Physics and Computing (ICAPC), International Conference on

作者： Yongchao Gao Qiuyue Wang Mark S. Fox Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Qilu University of Technology (Shandong Academy of Sciences) Jinan China Standard Institute of Emerging Technologies and Innovations Jinan China Urban Data Centre University of Toronto Toronto Canada

In order to improve the coverage of concepts in epidemic management, this paper constructs an epidemic management ontology EMO to help manage and analyse epidemic data. First, based on the ISO/lEC 5087 city data model series of standards and TOVE method, EMO is constructed using Protégé. Secondly, the data types and their relationships required in the epidemic management use cases are defined. Finally, the instance data help to verify the ability of EMO to represent the city epidemic management data. Results show that EMO provides a clear, accurate and unified definition of epidemic management data, improves the efficiency of data sharing and integration, and reduces the difficulty of developing epidemic management tools.

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A Novel Text Matching Model Based on Multilayer Coding and Feature Enhancement

A Novel Text Matching Model Based on Multilayer Coding and F...

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International Conference on computer Supported Cooperative Work in Design

作者： Yu Zhang Jianqiang Zhang Gongpeng Song Qin Lu 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 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 Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

With the rapid development of Natural Language Processing (NLP), text matching has become the basis of many downstream tasks in NLP, and the study of text matching is of great research significance for solving tasks such as question and answer and information retrieval in NLP. Most of the current text matching methods tend to have problems such as mismatch of grammatical structures and insufficient interaction information in sentences. In order to solve the problems of insufficient interaction information and lack of features and ability to capture keyword and sequence information in text matching, this paper proposes a text matching method based on multi-layer coding and soft attention mechanism. The method first embeds the text and sends it to a gating module for processing, then sends the processed result to a module containing a combination of multilayer coding and soft attention mechanism for further operations such as multiple alignment, and finally sends it to a classifier containing a three-layer fully-connected network for predicting whether the input text pairs match or not. The two modules proposed in this paper are practically feasible, and comparison and ablation experiments have been conducted on the publicly available datasets LCQMC dataset and BQ dataset, and the experimental results show that the two modules improve the accuracy of text matching by 2.44% and 1.02%, respectively.

关键词： Attention mechanisms Accuracy Computational modeling Impedance matching Nonhomogeneous media information retrieval Encoding

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Semantic information Reasoning and Multi-Step Cross-Modal Interaction network for Image-Text Retrieval

Semantic Information Reasoning and Multi-Step Cross-Modal In...

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International Joint Conference on Neural networks (IJCNN)

作者： Xishan Ma Yushui Geng Jing Zhao Huanxiao Zhou 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 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 Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Graduate School Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Image-text retrieval is a basic cross-modal task, and the basic idea is to learn image-text matching. The key to this task is to accurately measure the similarity between these two modalities. However, most existing methods tend to measure image-text similarity based on highly correlated region-word pairs, which lacks the ability to capture complex cross-modal semantic associations, thereby resulting in failure to distinguish hard negative examples that are similar in semantic content. Furthermore, existing methods focus more on alignment of different modal features, thus neglecting deep reasoning about the semantic concepts within the modality. In this paper, we propose a new semantic information reasoning and multi-step cross-modal interaction (SRMCI) network to simultaneously explore intra-modal and inter-modal relationships. Specifically, we first propose a semantic information reasoning module to fully explore potential semantic relationships within modalities and focus on fragments with important semantic information. Then, we design a multi-step cross-modal interaction module that utilizes two different inter-modal interactive attentions and an effective gated fusion strategy to capture fragment-level and fragment-instance-level cross-modal semantic associations. In particular, this gated fusion strategy is used to learn better fine-grained feature representations to aid in understanding and establishing semantic associations between different modalities. Experiments on the benchmark datasets MS-COCO and Flickr30K demonstrate the effectiveness of our method.

关键词： Correlation Semantics Neural networks Logic gates Benchmark testing Cognition

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Fast Noise Removal in Hyperspectral Images via Representative Coefficient Total Variation

arXiv

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arXiv 2022年

作者： Peng, Jiangjun Wang, Hailin Cao, Xiangyong Liu, Xinlin Rui, Xiangyu Meng, Deyu School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University Shaan’xi Xi'An710049 China School of Computer Science and Technology Ministry of Education Key Lab For Intelligent Networks and Network Security Xi’an Jiaotong University Xi'An710049 China The School of Mathematics and Statistics Southwest University Chongqing400715 China School of Mathematics and Information China West Normal University Nanchong637002 China

Mining structural priors in data is a widely recognized technique for hyperspectral image (HSI) denoising tasks, whose typical ways include model-based methods and data-based methods. The model-based methods have good generalization ability, while the runtime cannot meet the fast processing requirements of the practical situations due to the large size of an HSI data X ∈ RMN×B. For the data-based methods, they perform very fast on new test data once they have been trained. However, their generalization ability is always insufficient. In this paper, we propose a fast model-based HSI denoising approach. Specifically, we propose a novel regularizer named Representative Coefficient Total Variation (RCTV) to simultaneously characterize the low rank and local smooth properties. The RCTV regularizer is proposed based on the observation that the representative coefficient matrix U ∈ RMN×R(R Copyright © 2022, The Authors. All rights reserved.

关键词： Image denoising

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Map2Schedule: An End-to-End Link Scheduling Method for Urban V2V Communications

arXiv

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arXiv 2023年

作者： Zhang, Lihao Sun, Haijian Sun, Jin Parasuraman, Ramviyas Ye, Yinghui Hu, Rose Qingyang The School of Electrical and Computer Engineering University of Georgia AthensGA United States The School of Computing University of Georgia AthensGA United States Shanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts & Telecommunications China The Electrical and Computer Engineering Department Utah State University LoganUT United States

Urban vehicle-to-vehicle (V2V) link scheduling with shared spectrum is a challenging problem. Its main goal is to find the scheduling policy that can maximize system performance (usually the sum capacity of each link or their energy efficiency). Given that each link can experience interference from all other active links, the scheduling becomes a combinatorial integer programming problem and generally does not scale well with the number of V2V pairs. Moreover, link scheduling requires accurate channel state information (CSI), which is very difficult to estimate with good accuracy under high vehicle mobility. In this paper, we propose an end-to-end urban V2V link scheduling method called Map2Schedule, which can directly generate V2V scheduling policy from the city map and vehicle locations. Map2Schedule delivers comparable performance to the physical-model-based methods in urban settings while maintaining low computation complexity. This enhanced performance is achieved by machine learning (ML) technologies. Specifically, we first deploy the convolutional neural network (CNN) model to estimate the CSI from street layout and vehicle locations and then apply the graph embedding model for optimal scheduling policy. The results show that the proposed method can achieve high accuracy with much lower overhead and latency. © 2023, CC BY.

关键词： Channel state information

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Detecting Flexible Partial Subcarrier Jamming for Mission-Critical Internet of Things Services

Detecting Flexible Partial Subcarrier Jamming for Mission-Cr...

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Ubiquitous Communication (Ucom), International Conference on

作者： Shao-Di Wang Hui-Ming Wang School of Information and Communications Engineering Xi'an Jiaotong University Xi'an Shaanxi P. R. China Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an Shaanxi P. R. China

Due to the weak security protection capabilities of the low-cost and power limited IoT devices, active jamming attacks pose a serious threat to the Internet of Things (IoT) services by blocking the data transmission and forcing the IoT devices to repeatedly retry to transmit, especially in mission-critical IoT (MC-IoT) services which demands a higher standard of high reliability and low latency. In this paper, we consider a MC-IoT health monitoring system using multiple orthogonal subcarriers in the presence of an intelligent adversary with a flexible jamming strategy, who can selectively attack partial subcarrier in order to effectively use the jamming energy and reduce the risk of being detected. Inspired by the excellent identification capability of the gaussian mixture model, we propose a mixture component weights (MCW) based method to distinguish the jamming attack from the normal state for each subcarrier effectively, which does not rely on any attacker's information thanks to the match degree of the MCW-based feature with different beliefs. Numeric results evaluate and confirm the effectiveness of our detection method.

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