检索结果-内蒙古大学图书馆

IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart data, Smartdata 2024

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

ISBN: (纸本)9798350351637

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. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Dynamic Graph Recommendation via Sparse Augmentation and Singular Adaptation

Dynamic Graph Recommendation via Sparse Augmentation and Sin...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Tao, Zhen Cao, Yuehang Fang, Yang Liu, Yunhui Zhao, Xiang He, Tieke State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Laboratory for Big Data and Decision National University of Defense Technology Changsha China National University of Defense Technology Changsha China

ISBN: (纸本)9798350368741

Dynamic recommendation, focusing on modeling user preference from historical interactions and providing recommendations on current time, plays a key role in many personalized services. Recent works show that pre-trained dynamic graph neural networks (GNNs) can achieve excellent performance. However, existing methods by fine-tuning node representations at large scales demand significant computational resources. Additionally, the long-tail distribution of degrees leads to insufficient representations for nodes with sparse interactions, posing challenges for efficient fine-tuning. To address these issues, we introduce GraphSASA, a novel method for efficient fine-tuning in dynamic recommendation systems. GraphSASA employs test-time augmentation by leveraging the similarity of node representation distributions during hierarchical graph aggregation, which enhances node representations. Then it applies singular value decomposition, freezing the original vector matrix while focusing fine-tuning on the derived singular value matrices, which reduces the parameter burden of fine-tuning and improves the fine-tuning adaptability. Experimental results demonstrate that our method achieves state-of-the-art performance on three large-scale datasets. © 2025 IEEE.

关键词： graph neural networks low-rank adaptation pre-training recommendation

来源：评论

学校读者我要写书评

暂无评论

Evaluating LLM's Code Reading Abilities in big data Contexts using Metamorphic Testing 9

Evaluating LLM's Code Reading Abilities in Big Data Contexts...

引用

9th International Conference on big data and Information Analytics, bigDIA 2023

作者： Li, Ziyu Li, Zhendu Xiao, Kaiming Li, Xuan University of Sheffield United Kingdom National University of Defense Technology College of Systems Engineering China National University of Defense Technology Laboratory for Big Data and Decision China

ISBN: (纸本)9798350330076

With the explosive growth of big data, understanding complex data-centering algorithms and software has become essential. Large Language Models (LLMs) especially ChatGPT models have been increasingly deployed in big data environments to improve workflow in various tasks, including code reading. The current testing method on LLMs' code reading ability focuses more on code structural understanding and sentiment understanding, all tests are conducted on different prompts with different assumptions. This paper analyzes current LLM code-reading testing methods and presents an innovative evaluation of Metamorphic Testing to evaluate LLMs' code-reading abilities. We proposed two new metamorphic relations specified f or code reading challenges and evaluated the ChatGPT-3.5 on its code understanding capabilities. Our study offers insights into LLMs' capabilities to correctly understand diverse code bases and maintain validity. © 2023 IEEE.

关键词： big data

来源：评论

学校读者我要写书评

暂无评论

Optimization of PISA Architecture Chip Resource Ranking Based on Genetic Algorithm

Optimization of PISA Architecture Chip Resource Ranking Base...

引用

International Conference on Communications, Circuits and Systems (ICCCAS)

作者： Changen Zhang Qing Cheng Yuehang Si Jincai Huang Laboratory for Big Data and Decision National University of Defense Technology Changsha Hunan China

ISBN: (数字)9798350386271

ISBN: (纸本)9798350386288

At present, high and new technology has become an important direction of scientific and technological development, and chips have become an important direction for the scientific and technological game of great powers. This article focuses on the PISA architecture of one of the mainstream architectures of the programmable chip. By discussing the multi -faceted constraints of the architecture in the execution process, the ranking method of the best source sequence is to reduce the number of levels required for the chip assembly line as much as possible. Save chip resources. This article first analyzes the multi -faceted dependencies of the basic block of the constraint procedures. For data dependence, after reading, post -reading dependencies in data dependence, designing joint judgment algorithms, and improving the judgment efficiency of two similar dependencies. After writing dependencies, the design corresponding algorithm is designed to judge and solve. The judgment of control dependence uses the relevant knowledge of the set. This article designs the True set control dependency judgment method. By analyzing whether the two basic blocks can be reached whether there is a real relationship, combined with the comprehensive judgment of the collection of the basic blocks of the difference, you can judge this two Whether the basic block is controlled. The seven resources constraints are used for modeling, and the maximum value of the streamliner level is set up to find the minimum target function, and the traditional genetic algorithm is used to solve the model.

关键词： Costs Circuits and systems Games Writing Robustness Artificial intelligence Optimization

来源：评论

学校读者我要写书评

暂无评论

Dynamical relaxation of a long-range Kitaev chain

引用

Physical Review B 2024年第9期109卷 094309-094309页

作者： Yu-Huan Huang Yin-Tao Zou Chengxiang Ding School of Microelectronics and Data Science Anhui University of Technology Maanshan 243002 China Anhui Provincial Joint Key Laboratory of Disciplines for Industrial Big Data Analysis and Intelligent Decision Maanshan 243002 China

We study the universal real-time relaxation behaviors of a long-range Kitaev chain following a quench, and the results are compared to a short-range quantum XY chain. Our research includes both noncritical and critical quenches. In the case of noncritical quench, i.e., neither the initial state nor the postquench Hamiltonian is at a critical point of the equilibrium phase transition, a quench to the commensurate phase or incommensurate phase gives a scaling of t−3/2 or t−1/2, respectively, which is the same as the counterpart of the short-range quantum XY model. However, for a quench to the boundary line between the commensurate and incommensurate phases, the scaling law t−μ may be different from the t−3/4 law of the counterpart of the short-range quantum XY model. More interestingly, the decaying exponent μ may depend on the choice of the parameters of the postquench Hamiltonian because of the different asymptotic behaviors of the energy spectrum. Furthermore, in certain cases, the scaling behavior may be outside the range of predictions made by the stationary phase approximation, because an inflection point emerges in the energy spectrum. For the critical quench, i.e., the initial state or the postquench Hamiltonian is at a critical point of equilibrium phase transition, the aforementioned scaling law t−μ may be changed because of the gap-closing property of the energy spectrum of the critical point.

关键词： Quantum quench Spin relaxation Exact solutions for many-body systems Integrable systems

来源：评论

学校读者我要写书评

暂无评论

Discovering Frequency Bursting Patterns in Temporal Graphs 39

Discovering Frequency Bursting Patterns in Temporal Graphs

引用

39th IEEE International Conference on data Engineering, ICDE 2023

作者： Zhang, Qianzhen Guo, Deke Zhao, Xiang Yuan, Long Luo, Lailong National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory China National University of Defense Technology Laboratory for Big Data and Decision China Nanjing University of Science and Technology China

ISBN: (纸本)9798350322279

A frequency bursting pattern (FBP) in temporal graphs represents some interaction behavior that accumulates its frequency at the fastest rate. Mining FBPs is essential to early warning of emergencies. However, existing studies on frequency-based pattern mining in graphs do not consider the temporal information and bursting features of a subgraph pattern. As a result, they may not provide effective and efficient mining algorithms for FBP discovery. In this paper, we study the problem of discovering top-k FBPs in temporal graphs. We present a novel model, referred to as maximal (m, θ)-bursting pattern, to describe FBPs in a temporal graph, which is a subgraph with a size larger than m that accumulates its frequency at the fastest rate during a time interval of length no less than θ. A naive solution for top-k FBPs discovery is to use the best-first search algorithm, where the burstiness threshold changes as more patterns are mined. However, this method will result in huge search space since we need to check every possible time interval for a candidate pattern in the temporal graph. To tackle this problem, we devise an online top-k framework in which k candidate results are maintained from the initial timestamp to the end in the temporal graph. Under the new framework, we further conceive two optimization strategies by exploiting incremental subgraph matching and Evolutionary Game Theory to boost the performance. Extensive experiment results on five real temporal graphs show that our algorithm has higher efficiency, effectiveness and scalability. © 2023 IEEE.

关键词： Game theory

来源：评论

学校读者我要写书评

暂无评论

Gac: A Dual-Perspective Approach for Identifying Influential Nodes in Complex Networks

SSRN

引用

SSRN 2024年

作者： Ruan, Yirun Liu, Sizheng Tang, Jun Guo, Yanming Yu, Tianyuan Department of Laboratory for Big Data and Decision National University of Defense Technology Changsha410073 China

Identifying influential spreaders is crucial for understanding the dynamics of information diffusion within complex networks. To address this, we introduce a dual-perspective approach termed GAC, which considers both local and global perspectives for identifying influential nodes in complex networks to solve the shortcoming of the existing influential node identification methods that consider either local or global information. From a global perspective, if a node has the capability to efficiently transmit information to various clusters within a network, then the information initiate from the node will quickly spread to a large scope. From a local perspective, when a node has a greater number of neighbors, especially when these neighboring nodes are important within the network, the information originating from that node is less likely to be confined to a localized region. Based on this understanding, we firstly design a novel clustering method for detecting groups wherein connections between nodes among them are denser than connections with the rest of the network. The most influential nodes in each group are identified as global critical nodes. Then the local influnce of a node is difined by the number and significance of its neighboring nodes. Ultimately, nodes are ranked according to their local influence, their proximity to the global critical nodes using the shortest paths, and the importance of these global critical nodes. To evaluate the performance of the proposed method, the susceptible-infected-removed (SIR) diffusion model is used. Results of the investigation on real networks and realistic synthetic benchmarks show that GAC can identify nodes with high influence better than other centrality methods. © 2024, The Authors. All rights reserved.

关键词： Complex networks

来源：评论

学校读者我要写书评

暂无评论

A Two-stage Framework for Online Unmanned Aerial Vehicles Search Planning 22

A Two-stage Framework for Online Unmanned Aerial Vehicles Se...

引用

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence

作者： Hongbin Huang Haopeng Duan Lihua Liu Kaiming Xiao Laboratory for Big Data and Decision National University of Defense Technology China

ISBN: (纸本)9781450398336

Unmanned Aerial Vehicles (UAV), also known as drones, have been widely used in regional data collection and information search, but there are also many practical challenges. In real-world operations of UAV search, the payoff and cost at each search point are unknown for the planner in advance which poses a great challenge to decision making. To this end, we first propose the problem of online decision making in UAV search planning where the drone has limited energy supply as a constraints and has to make an irrevocable decision to search this area or route to the next in an online manner. Then the online UAV search planning problem is decoupled into a traveling salesman problem (TSP) and an online resource planning problem such that it can be solved in a two-stage procedure. Specifically, the routing of search is obtained by solving TSP based on ant colony optimization, and the online decision is made through an online linear programming which is proven to be near-optimal. The effectiveness of the proposed two-stage approach is validated in wide-applied dataset, and experimental results show the superior performance of online search decision making.

关键词： information search

来源：评论

学校读者我要写书评

暂无评论

Is Mamba Compatible with Trajectory Optimization in Offline Reinforcement Learning? 38

Is Mamba Compatible with Trajectory Optimization in Offline ...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Dai, Yang Ma, Oubo Zhang, Longfei Liang, Xingxing Hu, Shengchao Wang, Mengzhu Ji, Shouling Huang, Jincai Shen, Li Laboratory for Big Data and Decision National University of Defense Technology China Zhejiang University China Shanghai Jiao Tong University China Hebei University of Technology China Shenzhen Campus of Sun Yat-sen University China

Transformer-based trajectory optimization methods have demonstrated exceptional performance in offline Reinforcement Learning (offline RL). Yet, it poses challenges due to substantial parameter size and limited scalability, which is particularly critical in sequential decision-making scenarios where resources are constrained such as in robots and drones with limited computational power. Mamba, a promising new linear-time sequence model, offers performance on par with transformers while delivering substantially fewer parameters on long sequences. As it remains unclear whether Mamba is compatible with trajectory optimization, this work aims to conduct comprehensive experiments to explore the potential of decision Mamba (dubbed DeMa) in offline RL from the aspect of data structures and essential components with the following insights: (1) Long sequences impose a significant computational burden without contributing to performance improvements since DeMa's focus on sequences diminishes approximately exponentially. Consequently, we introduce a Transformer-like DeMa as opposed to an RNN-like DeMa. (2) For the components of DeMa, we identify the hidden attention mechanism as a critical factor in its success, which can also work well with other residual structures and does not require position embedding. Extensive evaluations demonstrate that our specially designed DeMa is compatible with trajectory optimization and surpasses previous methods, outperforming decision Transformer (DT) with higher performance while using 30% fewer parameters in Atari, and exceeding DT with only a quarter of the parameters in MuJoCo. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Deep Event-Based Object Detection in Autonomous Driving: A Survey

Deep Event-Based Object Detection in Autonomous Driving: A S...

引用

International Conference on big data and Information Analytics (bigDIA)

作者： Jie Jiang Bingquan Zhou Tianjian Zhou Yi Zhong Laboratory for Big Data and Decision School of System Engineering National University of Defense Technology Changsha China

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638

Reliable Object detection plays a pivotal role in the realm of autonomous driving, requiring the precise and swift identification of objects within rapidly changing environments. Conventional frame-based cameras encounter difficulties in managing latency and bandwidth, highlighting the urgency for novel approaches. Event cameras have risen to prominence for their potential in autonomous vehicles, offering advantages such as minimal latency, extensive dynamic range, and reduced power usage. Nonetheless, harnessing the asynchronous and sparse nature of event data poses a challenge, especially when it comes to sustaining low-latency operations and creating efficient, lightweight detection frameworks. This paper offers a comprehensive review of object detection with event data for autonomous driving, highlighting the superior benefits that event cameras can provide.

关键词： Surveys Power demand Event detection Reviews Object detection Cameras Reliability Object recognition Low latency communication Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：