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24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023

作者： Chen, Wei-Kun Liu, Ya-Feng Zhang, Rui-Jin Dai, Yu-Hong Luo, Zhi-Quan Beijing Institute of Technology School of Mathematics and Statistics/Beijing Key Laboratory on Mcaaci Beijing China Chinese Academy of Sciences Lsec Icmsec Academy of Mathematics and Systems Science Beijing China Shenzhen Research Institute of Big Data Chinese University of HongKong Shenzhen China

ISBN: (纸本)9781665496261

In this paper, we consider the network slicing (NS) problem which attempts to map multiple customized virtual network requests to a common shared network infrastructure and allocate network resources to meet diverse service requirements. We propose an efficient decomposition algorithm for solving this NP-hard problem. The proposed algorithm decomposes the large-scale hard NS problem into two relatively easy function placement (FP) and traffic routing (TR) subproblems and iteratively solves them enabling information feedback between each other, which makes it particularly suitable to solve large-scale problems. Specifically, the FP subproblem is to place service functions into cloud nodes in the network, and solving it can return a function placement strategy based on which the TR subproblem is defined;and the TR subproblem is to find paths connecting two nodes hosting two adjacent functions in the network, and solving it can either verify that the solution of the FP subproblem is an optimal solution of the original problem, or return a valid inequality to the FP subproblem that cuts off the current infeasible solution. The proposed algorithm is guaranteed to find the global solution of the NS problem. We demonstrate the effectiveness and efficiency of the proposed algorithm via numerical experiments. © 2023 IEEE.

关键词： Iterative methods

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OUTLIER SYNTHESIS VIA HAMILTONIAN MONTE CARLO FOR OUT-OF-DISTRIBUTION DETECTION

arXiv

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arXiv 2025年

作者： Li, Hengzhuang Zhang, Teng National Engineering Research Center for Big Data Technology and System Service Computing Technology and Systems Laboratory Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Out-of-distribution (OOD) detection is crucial for developing trustworthy and reliable machine learning systems. Recent advances in training with auxiliary OOD data demonstrate efficacy in enhancing detection capabilities. Nonetheless, these methods heavily rely on acquiring a large pool of high-quality natural outliers. Some prior methods try to alleviate this problem by synthesizing virtual outliers but suffer from either poor quality or high cost due to the monotonous sampling strategy and the heavy-parameterized generative models. In this paper, we overcome all these problems by proposing the Hamiltonian Monte Carlo Outlier Synthesis (HamOS) framework, which views the synthesis process as sampling from Markov chains. Based solely on the in-distribution data, the Markov chains can extensively traverse the feature space and generate diverse and representative outliers, hence exposing the model to miscellaneous potential OOD scenarios. The Hamiltonian Monte Carlo with sampling acceptance rate almost close to 1 also makes our framework enjoy great efficiency. By empirically competing with SOTA baselines on both standard and large-scale benchmarks, we verify the efficacy and efficiency of our proposed HamOS. Our code is available at: https://***/Fir-lat/HamOS_OOD. © 2025, CC BY.

关键词： Markov chains

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A Hypergraph Framework Based on Neighborhood and Category Association Relationships for Session-Based Recommendation

A Hypergraph Framework Based on Neighborhood and Category As...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Dengfeng Liu Yitong Wang Fei Cai Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha China Beijing Big Data Center Beijing China

Session-based recommendation aims to generate personalized recommendations based on user behaviors within a browsing session. The traditional graph neural network (GNN) methods focus on modeling pair-wise relationships but ignore neighborhood and category relationships which is crucial for improving the accuracy of recommendations. This paper proposes a hypergraph framework based on neighborhood and category association relationships for session-based recommendation (HLNCA). In detail, we first construct a hypergraph that can acquire the neighborhood and category association relationships among items; then aggregate hyperedge representation to obtain the neighborhood and category relationships of nodes; and finally obtain the users' preference through the attention mechanism to calculate the predicted score of items in the candidate set according to the preference. The proposed HL-NCA model outperforms baseline models in terms of Recall@20 and MRR@20 evaluation metrics.

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Analysis of Aviation Equipment Maintenance Rules Based on Correlation

Analysis of Aviation Equipment Maintenance Rules Based on Co...

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International Conference on big data and Information Analytics (bigDIA)

作者： Zhanzhe Li Kai Li Lihua Liu Mao Wang Qingbao Liu Laboratory for Big Data and Decision National University of Defense Technology Changsha China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha China

The maintenance big data management of aviation equipment has always been a major issue in equipment maintenance reform. Especially, the analysis of maintenance errors based on aviation equipment maintenance database is particularly important. This paper proposes an association analysis method based on Apriori algorithm for date mining of a certain aviation equipment maintenance monthly report. Firstly, the BI-LSTM-CRF neural network is used for feature entity recognition. Then, the Apriori algorithm is used for association rule analysis to mine potential laws between multidimensional data, and radar charts are drawn to customize comprehensive evaluation indicators for overall visual analysis of different aviation equipment. Finally, a case demonstration is presented at the end. The proposed method aims to mine the hidden value information in unstructured text materials for key data such as equipment fault location, fault type, and impact degree.

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EvoPath: Evolutionary Meta-path Discovery with Large Language Models for Complex Heterogeneous Information Networks

arXiv

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arXiv 2025年

作者： Liu, Shixuan Cheng, Haoxiang Wang, Yunfei He, Yue Fan, Changjun Liu, Zhong Laboratory for Big Data and Decision College of Systems Engineering National University of Defense Technology Hunan Changsha China National Key Laboratory of Information Systems Engineering College of Systems Engineering National University of Defense Technology Hunan Changsha China Department of Computer Science and Technology Tsinghua University Beijing China

Heterogeneous Information Networks (HINs) encapsulate diverse entity and relation types, with meta-paths providing essential meta-level semantics for knowledge reasoning, although their utility is constrained by discovery challenges. While Large Language Models (LLMs) offer new prospects for meta-path discovery due to their extensive knowledge encoding and efficiency, their adaptation faces challenges such as corpora bias, lexical discrepancies, and hallucination. This paper pioneers the mitigation of these challenges by presenting EvoPath, an innovative framework that leverages LLMs to efficiently identify high-quality meta-paths. EvoPath is carefully designed, with each component aimed at addressing issues that could lead to potential knowledge conflicts. With a minimal subset of HIN facts, EvoPath iteratively generates and evolves meta-paths by dynamically replaying meta-paths in the buffer with prioritization based on their scores. Comprehensive experiments on three large, complex HINs with hundreds of relations demonstrate that our framework, EvoPath, enables LLMs to generate high-quality meta-paths through effective prompting, confirming its superior performance in HIN reasoning tasks. Further ablation studies validate the effectiveness of each module within the framework. © 2025, CC BY-NC-ND.

关键词： Semantics

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Logic Induced High-Order Reasoning Network for Event-Event Relation Extraction

arXiv

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arXiv 2024年

作者： Huang, Peixin Zhao, Xiang Hu, Minghao Tan, Zhen Xiao, Weidong National Key Laboratory of Information Systems Engineering National University of Defense Technology China Laboratory for Big Data and Decision National University of Defense Technology China Information Research Center of Military Science China

To understand a document with multiple events, event-event relation extraction (ERE) emerges as a crucial task, aiming to discern how natural events temporally or structurally associate with each other. To achieve this goal, our work addresses the problems of temporal event relation extraction (TRE) and subevent relation extraction (SRE). The latest methods for such problems have commonly built document-level event graphs for global reasoning across sentences. However, the edges between events are usually derived from external tools heuristically, which are not always reliable and may introduce noise. Moreover, they are not capable of preserving logical constraints among event relations, e.g., coreference constraint, symmetry constraint and conjunction constraint. These constraints guarantee coherence between different relation types, enabling the generation of a unified event evolution graph. In this work, we propose a novel method named LogicERE, which performs high-order event relation reasoning through modeling logic constraints. Specifically, different from conventional event graphs, we design a logic constraint induced graph (LCG) without any external tools. LCG involves event nodes where the interactions among them can model the coreference constraint, and event pairs nodes where the interactions among them can retain the symmetry constraint and conjunction constraint. Then we perform high-order reasoning on LCG with relational graph transformer to obtain enhanced event and event pair embeddings. Finally, we further incorporate logic constraint information via a joint logic learning module. Extensive experiments demonstrate the effectiveness of the proposed method with state-of-the-art performance on benchmark datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Constraint theory

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PentraFormer: Learning Agents for Automated Penetration Testing via Sequence Modeling

PentraFormer: Learning Agents for Automated Penetration Test...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Yunfei Wang Shixuan Liu Wenhao Wang Cheng Zhu Changjun Fan Kuihua Huang Chao Chen National Key Laboratory of Information Systems Engineering National University of Defense Technology Changsha China Laboratory for Big Data and Decision National University of Defense Technology Changsha China National University of Defense Technology Changsha China

ISBN: (数字)9798350351637

ISBN: (纸本)9798350351644

The exponential growth of computer networks has intensified the requirement for robust security measures, rendering penetration testing—an essential practice that assesses vulnerabilities by simulating attacks—critically important. Automated penetration testing (APT) has evolved from rule-based approaches to intelligent decision-making, such as reinforcement learning (RL), better emulating the adaptability and decision-making process of human penetration testers. However, using RL for APT is challenged by the high cost of real-network interactions, the scarcity of security datasets, and the unique complexities of APT scenarios, which include long decision sequences and delayed rewards, complicating RL efficacy and convergence. Inspired by the Decision Transformer’s proficiency in predicting action sequences for offline RL, coupled with its effectiveness in dealing with data efficiency and adaptability, we introduce a novel sequence-based methodology for designing APT agents. This approach is tailored to effectively manage the intricate aspects of data-scarce and complex APT scenarios. Our proposed model, PentraFormer, adeptly addresses these challenges prevalent in APT scenarios. The robustness of our model is attested to through extensive empirical evaluations conducted on APT-dedicated tasks.

关键词： Adaptation models Social computing Computational modeling Decision making Transformers Robustness data models Complexity theory Security Penetration testing

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8th International Conference on big data and Information Analytics, bigDIA 2022

作者： Liu, Lihua Pei, Ziiian Meng, Zhuopeng Wang, Mao Li, Xuan Wu, Jibing School of Systems Engineering National University of Defense Technology Laboratory for Big Data and Decision Changsha China Undergraduate School National University of Defense Technology Changsha China Unit 92677 of the People Liberation Army Qingdao China

ISBN: (数字)9781665487962

ISBN: (纸本)9781665487962

In English-Chinese translation, there have always been problems such as multiple information, confusing and inconsistent translation names, etc. Faced with these problems, the phonetic similarity between translated entities is taken as the research point in this paper, and a similarity measurement algorithm based on improved phonetic code is proposed, by which the matching accuracy between strings is improved effectively. It could be applied to the cross-language entity matching scene with high accuracy and usability. © 2022 IEEE.

关键词： Linguistics

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Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale Image Super-Resolution 27

Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Xu, Tianyi Zhou, Yijie Hu, Xiaotao Zhang, Kai Zhang, Anran Qiu, Xingye Xu, Jun School of Statistics and Data Science Nankai University Tianjin China College of Computer Science Nankai University Tianjin China School of Intelligence Science and Technology Nanjing University Suzhou China Tencent Data Platform Beijing China Zhejiang University Hangzhou China Systems Engineering Research Institute China State Shipbuilding Corporation Limited Beijing China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781643685489

Arbitrary-scale super-resolution (ASSR) aims to learn a single model for image super-resolution at arbitrary magnifying scales. Existing ASSR networks typically comprise an off-the-shelf scale-agnostic feature extractor and an arbitrary scale upsampler. These feature extractors often use fixed network architectures to address different ASSR inference tasks, each of which is characterized by an input image and an upsampling scale. However, this overlooks the difficulty variance of super-resolution on different inference scenarios, where simple images or small SR scales could be resolved with less computational effort than difficult images or large SR scales. To tackle this difficulty variability, in this paper, we propose a Task-Aware Dynamic Transformer (TADT) as an input-adaptive feature extractor for efficient image ASSR. Our TADT consists of a multi-scale feature extraction backbone built upon groups of Multi-Scale Transformer Blocks (MSTBs) and a Task-Aware Routing Controller (TARC). The TARC predicts the inference paths within feature extraction backbone, specifically selecting MSTBs based on the input images and SR scales. The prediction of inference path is guided by a new loss function to trade-off the SR accuracy and efficiency. Experiments demonstrate that, when working with three popular arbitrary-scale upsamplers, our TADT achieves state-of-the-art ASSR performance when compared with mainstream feature extractors, but with relatively fewer computational costs. The code is available at https://***/Tillyhere/TADT. © 2024 The Authors.

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A Region Enhanced Discrete Multi-Objective Fireworks Algorithm for Low-Carbon Vehicle Routing Problem

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Complex System Modeling and Simulation 2022年第2期2卷 142-155页

作者： Xiaoning Shen Jiaqi Lu Xuan You Liyan Song Zhongpei Ge Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Jiangsu Key Laboratory of Big Data Analysis TechnologySchool of AutomationNanjing University of Information Science and TechnologyNanjing 210044China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Institute of Trustworthy Autonomous Systems and also with the Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent ComputationDepartment of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China

A constrained multi-objective optimization model for the low-carbon vehicle routing problem(VRP)is established.A carbon emission measurement method considering various practical factors is *** minimizes both the total carbon emissions and the longest time consumed by the sub-tours,subject to the limited number of available *** to the characteristics of the model,a region enhanced discrete multi-objective fireworks algorithm is proposed.A partial mapping explosion operator,a hybrid mutation for adjusting the sub-tours,and an objective-driven extending search are designed,which aim to improve the convergence,diversity,and spread of the non-dominated solutions produced by the algorithm,*** low-carbon VRP instances with different scales are used to verify the effectiveness of the new ***,comparison results with four state-of-the-art algorithms indicate that the proposed algorithm has better performance of convergence and distribution on the low-carbon *** provides a promising scalability to the problem size.

关键词： vehicle routing problem carbon emission multi-objective optimization fireworks algorithm region enhanced

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