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2022 CCF International Conference on Service science, CCF ICSS 2022

作者： Cheng, Hangyu Kang, Guosheng Liu, Jianxun Wen, Yiping Cao, Buqing Liang, Bowen Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China

ISBN: (数字)9781665498616

ISBN: (纸本)9781665498616

Business process monitoring aims at identifying how well running processes are performing with respect to performance measures and objectives. The existing business process monitoring techniques focus on collecting and analyzing information on the way business processes themselves are executed. They neglect the evaluation of task-roles which play a key role in the performance of the whole business process execution. Different from the traditional perspective, this paper focuses on monitoring the behavior of task-roles and evaluating their performance with respect to timeliness. Specifically, this paper proposes to promote the performance of task-roles by time reminder via business process monitoring, which is implemented by semantic extension of BPMN elements. Further, we extend the information of process execution event log data, with which the performance of task-roles can be evaluated by analyzing the extended event log data. An empirical study of the proposed approach with real-world business processes reveals the effectiveness with respect to performance evaluation of task-roles. © 2022 IEEE.

关键词： Semantics

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A Network Security Situation Prediction Method Based On Attention-CNN-BiGRU 25

A Network Security Situation Prediction Method Based On Atte...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Hu, Chuhang Liu, Guikai Li, Ming Hunan University of Science and Technology Department of Computer Science and Engineering Xiangtan China

ISBN: (纸本)9781665405270

To improve the accuracy and efficiency of network security situation prediction, a network security situation prediction method based on Attention-CNN-BiGRU is proposed. It combined Convolutional Neural Network(CNN) and bidirectional Gated Recurrent Unit(BiGRU), The structure of the proposed model is more accurate for extraction and understanding of situation time series;besides, to improve the performance of proposed model, the Attention mechanism is utilized to optimize the model;particle swarm optimization(PSO) is applied to optimize varieties of hyperparameters of the model. The optimal combination of hyperparameters selected by PSO is used for training of the model. Finally, the method proposed in this paper was verified in experiment and compared with other models. The results shows that the method can acomplish the task of situation prediction with better capability. © 2022 IEEE.

关键词： Network security

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DATDM: Dynamic attention transformer diffusion model for underwater image enhancement

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Alexandria engineering Journal 2025年 126卷 591-604页

作者： Hu, Wang Chen, Shitu Luo, Tuyan Zhang, Lijun Zhang, Hongjin Liu, Zhixiang Zhang, Shiwen Xu, Jingxiang College of Engineering Science and Technology Shanghai Ocean University Shanghai201306 China Institute of Agricultural Quality Standards and Testing Technology Fujian Academy of Agricultural Sciences Fuzhou350003 China Shanghai Engineering Research Center of Marine Renewable Energy Shanghai201306 China College of Information Technology Shanghai Ocean University Shanghai201306 China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China

Underwater image degradation presents complex challenges, significantly impairing the efficiency of underwater tasks. The mainstream underwater image enhancement (UIE) methods are roughly divided into traditional physics-based and data-driven deep-learning methods. However, physical model-based methods have poor generalization capabilities. Data-driven methods require high-quality training data and suffer from limited model stability. We propose a novel UIE framework, DATDM, to address the above limitations and improve degraded underwater images. We first introduce a color correction module (CCM) to strengthen the model's image restoration capabilities. On the other hand, we propose a novel dynamic attention transformer (DAT) denoising network with excellent performance. The proposed DAT denoising network utilizes a dynamic attention transformer mechanism that adaptively extracts feature information based on the complexity of the feature map, effectively capturing rich features while optimizing computational efficiency. The proposed DATDM method significantly outperforms the existing state-of-the-art methods, with peak signal-to-noise ratios of 24.05 and 27.39 and structural similarity index measures of 0.9233 and 0.9504 on the UIEB and LSUI datasets, respectively. The final experiments demonstrate that our DATDM achieves better performance and visual effects on UIE tasks. © 2025 The Authors

关键词： Image enhancement

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Multimodal Data Trajectory Prediction: A Review 10

Multimodal Data Trajectory Prediction: A Review

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Wang, Xiaoliang Yue, Hao Yang, Qing Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Guangzhou Maritime University School of Information and Communication Engineering Guangzhou China

ISBN: (纸本)9798350312461

Trajectory prediction refers to predicting the future movement of an object, person, or vehicle based on past motion trajectory information and other relevant environmental information. Traditional trajectory prediction solutions are suitable for simple driving scenarios and only applicable for short-term predictions. With the improvement of computing power and data processing speed, people can analyze and utilize large-scale datasets faster, use more data and more complex algorithms to build models, and therefore better predict future trends and behaviors. In addition, different solutions focus on how to efficiently extract features from different types of information and how to more accurately predict the future trajectory of the target object. Our goal is to analyze the impact of different information on prediction, and to classify and compare deep learning-based trajectory prediction methods for vehicles or surrounding pedestrians based on the same data. According to the type and quantity of inputs, they are divided into five categories, and the use and prediction effect of different information solutions are elaborated in detail. © 2023 IEEE.

关键词： Deep neural networks

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Station-level Demand Prediction for Bike-sharing Based on Multi-spatio-temporal Scale Features 19

Station-level Demand Prediction for Bike-sharing Based on Mu...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Jiang, Liming Li, Yang Chen, Shaomiao Jiang, Youfu Tang, Peng Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China

ISBN: (纸本)9798350346558

Nowadays, the bike-sharing system has advanced to new levels, solving the issue of people riding short distances. The most critical challenge for the successful implementation of the bike-sharing system is resolving the rent/return imbalance. By improving the predictability of bicycle demand, this issue can be solved. Accurate predictions can be made by taking temporal and spatial into account. However, these features obtained from different temporal and spatial scales are distinct. And the disadvantage of sequential structure is that the extracted spatial and temporal features may affect each other due to the stacked structure. As a result, we propose a parallel architecture with multi-spatio-temporal scale (MST-RLGCN). The parallel structure consists of a RLSTM model construct around historical data at multi-temporal scale (hour-level, day-level, and week-level) and an MGCN model construct around multi-spatial (first-order, second-order, and third-order graph convolution operators). Our framework not only efficiently extracts the temporal and spatial dependencies between station, but also allows the temporal and spatial features to be effectively combined, which improve the accuracy of demand predicting. We compare MST-RLGCN with other baseline models using New York bike-sharing system data. Experimental results show that our method outperforms baseline prediction models. © 2022 IEEE.

关键词： Parallel architectures

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Modeling and Analysis of Risk Propagation and Loss Causing Capacity for Key Nodes in Cyber-Physical Coupled Power Network

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Complex System Modeling and Simulation 2024年第2期4卷 124-136页

作者： Dongqi Liu Qiong Zhang Haolan Liang Tao Zhan Rui Wang National Key Laboratory of Disaster Prevention and Reduction for Power Grid Changsha University of Science and TechnologyChangsha 410114China the School of Electrical and Information Engineering Changsha University of Science and TechnologyChangsha 410114China the College of Electrical and Information Engineering Hunan Institute of EngineeringXiangtan 411104China the School of Systems Engineering National University of Defense TechnologyChangsha 410003China

The modern power system has evolved into a cyber-physical system with deep coupling of physical and information domains,which brings new security *** at the problem that the“information-physical”cross-domain attacks with key nodes as springboards seriously threaten the safe and stable operation of power grids,a risk propagation model considering key nodes of power communication coupling networks is proposed to study the risk propagation characteristics of malicious attacks on key nodes and the impact on the ***,combined with the complex network theory,a topological model of the power communication coupling network is established,and the key nodes of the coupling network are screened out by Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)method under the comprehensive evaluation index based on topological characteristics and physical ***,a risk propagation model is established for malicious attacks on key nodes to study its propagation characteristics and analyze the state changes of each node in the coupled ***,two loss-causing factors:the minimum load loss ratio and transmission delay factor are constructed to quantify the impact of risk propagation on the coupled ***,simulation analysis based on the IEEE 39-node system shows that the probability of node being breached(α)and the security tolerance of the system(β)are the key factors affecting the risk propagation characteristics of the coupled network,as well as the criticality of the node is positively correlated with the damage-causing *** proposed methodological model can provide an effective exploration of the diffusion of security risks in control systems on a macro level.

关键词： cyber-physical system risk propagation complex network loss causative factor

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An Investigation of Blockchain-Based Sharding 7th

An Investigation of Blockchain-Based Sharding

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Xiao, Jiahong Liang, Wei Cai, Jiahong Zhu, Hangyu Li, Xiong Xie, Songyou School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China Guangdong Financial High-Tech Zone "Blockchain +" Fintech Research Institute Foshan528253 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

ISBN: (纸本)9783031281235

Nowadays, blockchain distributed ledger technology is becoming more and more prominent, and its decentralization, anonymization, and tampering obvious features have been widely recognized. These excellent technical features of blockchain have also made it a hot issue for global research. With the wide application of blockchain technology in various industries, some defects are gradually exposed, and more prominently, the blockchain system is unable to meet the current demand of explosive growth of data volume and frequent data interaction. As one of the key technologies to solve this problem, sharding technology is gaining attention. This article introduces common blockchain scaling schemes and focuses on an overview of blockchain sharding. Sharding technology is introduced from two perspectives of intra-slice consensus and inter-slice consensus. The current mainstream slicing technology is summarized according to three different slicing methods: network sharding, transaction sharding, and state sharding. Finally, the challenges faced by current blockchain sharding technology are analyzed and the full text is summarized. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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Parameter determination and analysis of first-order gaussian-markov error model based on smartphone barometer 5

Parameter determination and analysis of first-order gaussian...

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2024 5th International Conference on Electrical, Electronic Information and Communication engineering, EEICE 2024

作者： Zhou, Peng Liu, Kai Sheng, Menggang Zhou, Chao College of Automation and Electronic Information Hunan Xiangtan411100 China College of Intelligent Science and Technology National University of Defense Technology Hunan Changsha410073 China

The measurement error of the barometer equipped in smartphones is commonly modeled as a first-order Gaussian-Markov (GM) model, with the time constant and noise variance being two important parameters for establishing the model. In this paper, the time constant and variance of the barometer sensor in smartphones were determined by the Discrete Wavelet Transform (DWT) and the autocorrelation function, and the time constant is applied to the barometer calibration to verify the rationality of the parameters. The barometric altimeter calibration utilizes GPS altitude information as an aid, and the fusion algorithm adopts Kalman filtering. A set of different time constants was established to validate the time constant obtained through autocorrelation analysis. The experimental results indicate that this parameter is reasonable and reliable, providing an experimental basis and research foundation for the parameter selection of the first-order GM model for smartphone barometric pressure sensors. © Published under licence by IOP Publishing Ltd.

关键词： Autocorrelation

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MOOC Dropout Prediction Using Learning Process Model and LightGBM Algorithm 18th

MOOC Dropout Prediction Using Learning Process Model and Lig...

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18th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Nie, Hejing Wen, Yiping Cao, Buqing Liang, Bowen School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China The 14Th Five-Year Plan" Research Base for Education Science Education Informatization Research Base-Technology Application Direction Hunan China

ISBN: (纸本)9789819996360

With the development and widespread application of Massive Open Online Courses (MOOC) platforms, the issue of reducing learners’ dropout has become a challenge. Therefore, it is necessary to accurately predict whether a learner will complete the course or not. Existing researches mainly use machine learning methods to predict MOOC dropout, which still face challenges such as inadequate explanation and low prediction accuracy. In view of these problems, this paper proposes a MOOC dropout prediction method using learning process model and LightGBM algorithm. This method utilizes the learning process model to analyze learning behavior and generate feature vectors to provide a clear interpretation. Based on these feature vectors, the LightGBM algorithm is adopted to predict dropout. Compared to the related methods, the dropout prediction method proposed in this paper demonstrates improvements of 1.58%, 1.2%, 1.1%, and 0.864% in Recall, F1, Precision, and AUC respectively. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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Reducing the Length Divergence Bias for Textual Matching Models via Alternating Adversarial Training 10

Reducing the Length Divergence Bias for Textual Matching Mod...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Zheng, Lantao Kuang, Wenxin Liang, Qizhuang Liang, Wei Hu, Qiao Fu, Wei Ding, Xiashu Xu, Bijiang Hu, Yupeng Hunan University College of Computer Science and Electronic Engineering Changsha China Hunan University of Science and Technology College of Computer Science and Engineering XiangTan China Naval University of Engineering Department of Information Security Wuhan China Hunan Provincial Traffic Cost Management Station Changsha China QI-ANXIN GROUP Changsha China

ISBN: (纸本)9798350312461

Although deep learning has made remarkable achievements in natural language processing tasks, many researchers have recently indicated that models achieve high performance by exploiting statistical bias in datasets. However, once such models obtained on statistically biased datasets are applied in scenarios where statistical bias does not exist, they show a significant decrease in accuracy. In this work, we focus on the length divergence bias, which makes language models tend to classify samples with high length divergence as negative and vice versa. We propose a solution to make the model pay more attention to semantics and not be affected by bias. First, we propose constructing an adversarial test set to magnify the effect of bias on models. Then, we introduce some novel techniques to demote length divergence bias. Finally, we conduct our experiments on two textual matching corpora, and the results show that our approach effectively improves the generalization and robustness of the model, although the degree of bias of the two corpora is not the same. © 2023 IEEE.

关键词： Semantics

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