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9th IEEE International Conference on data Science in Cyberspace, DSC 2024

作者： Shi, Jinchen Fang, Yang Tan, Zhen Yang, Hui Zhao, Xiang National University of Defense Technology National Key Laboratory of Information Systems Engineering Changsha China National University of Defense Technology Changsha China The 30th Research Institute of China Electronics Technology Group Corporation Chengdu China National University of Defense Technology Laboratory for Big Data and Decision Changsha China

ISBN: (纸本)9798350391367

Since the widespread transmission of viruses, especially the outbreak of COVID-19, the identification of the source has become a crucial research direction. Due to the inability to acquire the states of all nodes and the significant cost of obtaining the state of each node in large networks, utilizing a limited number of sensors to monitor the entire network has become an important research focus. Previous methods simply fused observation results in various ways, while a recent model ESL based on expert support scores was proposed to enhance the model flexibility and improve the computational process. However, this method only improved the calculation process as it deploys the sensors randomly without effective information acquisition process. Therefore, we chose a sensor deployment method suitable for evidence theory and combined it with evidence theory to further enhance the accuracy and transferability of source localization using evidence theory. The results have been confirmed with real network data, demonstrating the effectiveness of our approach. © 2024 IEEE.

关键词： Sensor nodes

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Fine-Grained Anomaly Detection on Dynamic Graphs via Attention Alignment

Fine-Grained Anomaly Detection on Dynamic Graphs via Attenti...

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International Conference on data Engineering

作者： Dong Chen Xiang Zhao Weidong Xiao 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

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Dynamic graphs are ubiquitous in our lives, yet they are also susceptible to the risks imposed by malicious activities. However, identifying anomalies in these dynamic graphs presents a challenging task due to the complex graph structures. Existing methods for dynamic anomaly detection primarily focus on learning representations for each timestamp and using sequence modeling techniques to capture and model the temporal information. Despite extensive research, dynamic anomaly detection still faces two key challenges. First, existing methods are limited in effectively using fine-grained temporal information. Second, they have limited generalization capabilities under unsupervised settings. Overcoming these challenges is crucial for advances in dynamic anomaly detection. In this paper, we propose a novel unsupervised anomaly detection method for dynamic graphs. Our approach leverages complex temporal information through fine-grained sampling and embedding modules. Additionally, we introduce an attention alignment strategy to minimize discrepancies in contextual attention between source and target nodes. Through a comprehensive evaluation, we demonstrate that our strategy effectively mitigates overfitting and improves generalization. Experiments on ten dynamic graph datasets validate the effectiveness of our proposed method in detecting anomalies.

关键词： Image edge detection Contrastive learning Benchmark testing Transformers data engineering Task analysis Anomaly detection

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LearnSC: An Efficient and Unified Learning-Based Framework for Subgraph Counting Problem

LearnSC: An Efficient and Unified Learning-Based Framework f...

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International Conference on data Engineering

作者： Wenzhe Hou Xiang Zhao Bo Tang Laboratory for Big Data and Decision National University of Defense Technology China Department of Computer Science and Engineering Southern University of Science and Technology China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Graphs are valuable data structures used to represent complex relationships between entities in a wide range of applications, such as social networks and chemical reactions. Subgraph counting problem is a well-known hard problem, as its core subroutine, the subgraph matching, is NP-complete. In this work, we propose an efficient and unified deep learning-based solution framework LearnSC, which solves the subgraph counting problem approximately. This framework offers two key advantages: (i) it is a generic solution that is orthogonal to the existing techniques of learning-based solutions; and (ii) it is equipped with a suite of optimizations to significantly improve the accuracy of the estimated results. Our experimental results on 7 datasets demonstrate that our proposal is highly accurate, robust, and scalable, making it an excellent solution for subgraph counting problem among all statistics-based and learning-based competitors.

关键词： Accuracy Social networking (online) Algorithms Estimation data structures data engineering Chemical reactions

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RPID: Boosting Transferability of Adversarial Attacks on Vision Transformers

RPID: Boosting Transferability of Adversarial Attacks on Vis...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Shujuan Wang Panpan Wang Yunpeng Sun Zhichao Lian Shuohao Li School of Cyberspace Security Nanjing University of Science and Technology Wuxi China Laboratory for Big Data and Decision National University of Defense Technology China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Vision Transformers (ViTs) have achieved excellent performance on many computer vision tasks, which has attracted attention of many researchers for their adversarial robustness. As a kind of black-box attack, transfer-based at-tacks usually use adversarial examples generated by a surrogate model to attack structurally different models. It is practical and poses a certain threat to the application of ViTs in critical security areas. Existing transfer-based attacks against ViTs suffer from weak adversarial transferability and noticeable perceptibility. In this work, we propose a method called Reduce Regional Perturbation Interaction and Differentiated (RPID) attack, which employs two strategies of reducing correlation between regional perturbations and adding differentiated perturbations to produce adversarial examples. Extensive experiments demonstrate that our proposed method improves the transferability of the baseline methods for adversarial attacks against ViTs while maintaining stealthiness.

关键词： Computer vision Correlation Perturbation methods Computational modeling Closed box Transformers Robustness Security Cybernetics Glass box

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Learning-Based Dynamic Pricing Strategy with Pay-Per-Chapter Mode for Online Publisher with Case Study Ofcol

SSRN

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SSRN 2024年

作者： Fang, Lang Pan, Zhendong Tang, Jiafu School of Management Science & Engineering Liaoning Key Laboratory of Big Data Management and Optimization Decision Dongbei University of Finance and Economics Dalian China

We consider how to make dynamic pricing decision for Chinese Online (COL) at T time-points, an online publisher that allow authors to sell their ongoing book projects. Instead of paying for a book, readers pay for each chapter (pay-per-chapter mode) of the ongoing book project. This mode allows readers to pay for as many chapters as they want without taking the risk that the releasing of new chapters might be delayed or stopped. Despite of the dynamics of chapter-by-chapter released of COL products, the fixed pricing strategy (FPS) does not make fully use of the reading data generated by releasing chapters of the ongoing book. We propose a learning-based dynamic pricing strategy (LDPS) that exploits the newly information to maximize cumulative revenue for the publisher. The LDPS captures the ever changing features of readers. It employs the Thompson sampling method to balance the exploration of the readers’ features with the exploitation of publisher’s knowledge to determine optimal price. Taking COL as a case study and implementing our policies in the context of the aforementioned real-life data set, we show that LDPS outperform several classical strategies such as Greedy, Prior-Free TS and Prior-Given TS, and average revenue of LDPS is increased by 0.5% average per time-point compared to the publisher’s historical decisions. We also provide some management implications for the COL publisher by analyzing the pricing range of different genres of books and choice of the exploration threshold parameter. © 2024, The Authors. All rights reserved.

关键词： Costs

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Smart Assessment of Urban Old Residential Areas Based on Object Detection

Smart Assessment of Urban Old Residential Areas Based on Obj...

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2023 International Conference on Construction and Real Estate Management: The Human-Centered Construction Transformation, ICCREM 2023

作者： Wang, Shijia Gao, Ming Wang, Hao Gao, Ran School of Management Science and Engineering Central Univ. of Finance and Economics Beijing China School of Management Science and Engineering Key Laboratory of Big Data Management Optimization and Decision of Liaoning Province Dongbei Univ. of Finance and Economics Dalian China

ISBN: (数字)9780784485217

ISBN: (纸本)9780784485217

In the stage of rapid urban renewal, the renovation of old residential areas is moving along very slowly. Traditional methods of old residential areas assessment have the pain points of low efficiency and high consumption of manpower and material resources, while the application of object detection technology in the field of intelligent assessment is more and more extensive, and satisfactory results have been achieved. This paper attempts to explore how to use object detection technology to automatically identify and locate key objects in old communities, such as buildings, parking spaces, and trash bins, so as to realize smart assessment and management of the environment in old residential areas. Through the analysis of experimental results, the effectiveness and practicality of the proposed method are verified. © ASCE.

关键词： Object detection

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GLoM-PCNet: A Deep Network for Multi-scale Information Fusion for Point Cloud Completion

GLoM-PCNet: A Deep Network for Multi-scale Information Fusio...

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Artificial Intelligence and Intelligent Manufacturing (AIIM), International Symposium on

作者： Yi Zhong Xiaoxing Chen Jie Jiang Yingmei Wei The Laboratory for Big Data and Decision National University of Defense Technology Changsha China School of automation Chengdu University of Information Technology Chengdu China

ISBN: (数字)9798331541729

ISBN: (纸本)9798331541736

Point cloud completion is a pivotal task within point cloud analysis, with the objective of transforming incomplete and sparse point clouds into their dense and complete 3D shape. Conventional methods often falter in effectively harnessing and synthesizing multi-scale, global, and local information during the reconstruction process. To surmount these hurdles, we propose GLoM-PCNet, a transformer-based framework to efficiently extract and amalgamate global and local multi-scale information for point cloud completion. This framework processes the input point cloud by employing a trilayered self-attention mechanism across different scales, which fosters dynamic interactions with global information and ensures the proficient fusion of multi-scale data. Furthermore, we innovate a k-nearest neighbor knn-based local multi-scale perception technique that significantly enhances the representation of local detail features through multi-scale integration. The harmonious fusion of multi-scale features at both global and local levels empowers GLoM-PCNet to achieve exceptional performance across a spectrum of benchmark datasets.

关键词： Point cloud compression Three-dimensional displays Shape Benchmark testing Nearest neighbor methods Transformers Feature extraction Information retrieval Manufacturing data mining

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Mining Top-k Frequent Patterns over Streaming Graphs 28th

Mining Top-k Frequent Patterns over Streaming Graphs

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Wang, Xi Zhang, Qianzhen Guo, Deke Zhao, Xiang Institute for Quantum Information and State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha China Laboratory for Big Data and Decision National University of Defense Technology Changsha China

ISBN: (纸本)9783031306747

Mining top-k frequent patterns is an important operation on graphs, which is defined as finding k interesting subgraphs with the highest frequency. Most existing work assumes a static graph. However, graphs are dynamic in nature, which is described as streaming graphs. Mining top-k frequent patterns in streaming graphs is challenging due to the streaming nature of the input and the exponential time complexity of the problem. A naive solution is to calculate approximations of the frequent patterns in the streaming graph and then find the top-k answers, which is a memory- and time-consuming method. In this paper, we design a novel auxiliary data structure, FPC, to detect valid subgraph patterns and their frequency in real-time. We first convert each newly produced subgraph into a sequence and then map it into corresponding tracks in FPC based on hash functions. We theoretically prove that FPC can provide unbiased estimation and then give an error bound of our algorithm. In addition, we propose a vertical hashing and candidate buckets sampling technique to further improve FPC with higher space utilization and higher accuracy. Extensive experiments confirm that our approach generates high-quality results compared to the baseline method. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Hash functions

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Energy Relay-Enhanced UAV data Collection: A Case Study of Wildlife Monitoring Application

Energy Relay-Enhanced UAV Data Collection: A Case Study of W...

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International Conference on big data and Information Analytics (bigDIA)

作者： Haopeng Duan Kaiming Xiao Mao Wang Xin Lu Lihua Liu Hongbin Huang Laboratory for Big Data and Decision National University of Defense Technology Changsha China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638

Wildlife data collection is a critical means for the sustainable development and conservation of wildlife resources. In recent years, an increasing number of studies have utilized wireless sensor networks for wildlife data collection. However, communication within widely dispersed sensor networks poses challenges. In this regard, this paper first proposes the use of drone as a data collector in wireless sensor networks, with the limited energy supply of Unmanned Aerial Vehicle (UAV) as a constraint, and considers the deployment of energy supply points for replenishment, while the cost and revenue of each sensor monitoring point are a priori unknown. This paper proposes a two-stage solution for energy relay-enhanced UAV search planning based on wireless sensor networks. Initially, offline path planning for the UAV is conducted to traverse all sensor monitoring points. Subsequently, an online linear programming phase determines whether the UAV should collect sensor data or perform energy replenishment. Finally, the effectiveness of the proposed approach is validated through experiments conducted on a real-world monitoring dataset from the Qilian Mountains.

关键词： Wireless sensor networks Wildlife decision making Autonomous aerial vehicles Linear programming Path planning Planning Sustainable development Monitoring Drones

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Bibliometric Analysis of Chinese Research on City Brand and Brand Building Strategies for Beijing

Bibliometric Analysis of Chinese Research on City Brand and ...

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作者： Yingxiao Gu Zongshui Wang Beijing Information Science and Technology University Beijing Key Laboratory of Green Development and Decision Making Based on Big Data

With the gradual improvement of urbanization in China, it is important to research city branding to promote high-quality urban development. This paper uses CSSCI journals(including the expanded edition) in CNKI as the data source to collect the papers related to city branding from 2001-2022, analyzes the research content of city branding based on the bibliometric method, and accordingly proposes strategies for city branding in Beijing. The analysis result shows that the research on city branding involves a wide range of content, mainly including city branding, place marketing, and multilayer brand strategy. Based on this, we propose strategic suggestions for city branding for Beijing: exploring the characteristics of city resources and enhancing the cultural connotation;building a multilayer brand strategy to promote regional development;expanding multi-channel marketing to highlight the differentiation of city brand.

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