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2023 Ieee International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Tianyu, Guo Hongbin, Dong Jing, Zhou School of Computer Science and Technology Harbin Engineering University National Engineering Laboratory for E-government Modeling and Simulation Harbin China

ISBN: (纸本)9798350337020

Mayfly algorithm (MA) is a new type of intelligent optimization algorithm with excellent search ability and a broad application prospect in feature selection. However, as the dimension of the data increases, MA also has the problem of weak search ability and falling into the local optimal solution. To solve these problems, this paper combines the idea of collaborative evolution and proposes an improved Mayfly algorithm (CeIMA) suitable for feature selection problems. The CeIMA algorithm first divides the population into two sub-populations and initializes them in different ways to increase the diversity of the population. During evolution, information sharing is achieved through an information transmission mechanism between sub-populations, achieving collaborative evolution between sub-populations to enhance the global search ability of CeIMA. Finally, the mRMR-m strategy is proposed to mutate the global optimal individual based on feature importance, improving the ability of CeIMA to escape from local optimal solutions. Through a comparison with 7 feature selection algorithms on 12 UCI datasets, the effectiveness of CeIMA is proven. © 2023 Ieee.

关键词： co-evolution feature selection halton sequence max-relevance and min-redundancy mayfly algorithm

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Reparameterized Importance Sampling for Robust Variational Bayesian Neural Networks 41

Reparameterized Importance Sampling for Robust Variational B...

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41st International Conference on Machine Learning, ICML 2024

作者： Long, Yunfei Tian, Zilin Zhang, Liguo Xu, Huosheng College of Computer Science and Technology Harbin Engineering University Heilongjiang Harbin China Modeling and Emulation in E-Government National Engineering Laboratory Harbin Engineering University Heilongjiang Harbin China

Mean-field variational inference (MFVI) methods provide computationally cheap approximations to the posterior of Bayesian Neural Networks (BNNs) when compared to alternatives like MCMC. However, applying MFVI to BNNs encounters limitations due to the Monte Carlo sampling problem. This problem stems from two main issues. First, most samples do not accurately represent the most probable weights. Second, random sampling from variational distributions introduces high variance in gradient estimates, which can hinder the optimization process, leading to slow convergence or even failure. In this paper, we introduce a novel sampling method called Reparameterized Importance Sampling (RIS) to estimate the first moment in neural networks, reducing variance during feed-forward propagation. We begin by analyzing the generalized form of the optimal proposal distribution and presenting an inexpensive approximation. Next, we describe the sampling process from the proposal distribution as a transformation that combines exogenous randomness with the variational parameters. Our experimental results demonstrate the effectiveness of the proposed RIS method in three critical aspects: improved convergence, enhanced predictive performance, and successful uncertainty estimation for out-of-distribution data. Copyright 2024 by the author(s)

关键词： Importance sampling

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Profinder: Towards Professionals Recognition on Mobile Devices for Users with Cognitive Decline 18th

Profinder: Towards Professionals Recognition on Mobile Devi...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Wang, Yong Xiong, Yi Yang, Tianyu Shen, Yiran College of Computer Science and Technology Harbin Engineering University Harbin China Modeling and Emulation in E-Government National Engineering Laboratory Harbin China School of Software Shandong University Jinan China

ISBN: (纸本)9783031714696

As the global population continues to age, there is a concurrent rise in the number of individuals experiencing cognitive impairment and dementia, underscoring the critical necessity to address their hospice needs and quality of life concerns. Numerous studies have underscored the positive impact of exposure to outdoor environments on their overall condition and well-being. However, cognitive challenges often hinder their capacity to perform basic tasks independently, necessitating frequent assistance from onsite professionals. Complicating matters further, the complexity of outdoor settings often results in difficulties in identifying and recognizing personnel. This paper introduces Profinder, a system designed to assist individuals with cognitive impairment in identifying occupational personnel at specific outdoor locations and accessing essential assistance. Utilizing advanced deep learning techniques, Profinder seamlessly integrates foreground and background information, incorporates attention mechanisms, and harnesses siamese networks for efficient information decoupling, thereby achieving occupational recognition on smart mobile devices with an accuracy rate exceeding 90%. Additionally, a meticulously curated dataset dedicated to occupation recognition was devel0oped to facilitate and validate this research endeavor. By providing a robust auxiliary tool for individuals with cognitive impairment, this research endeavors to enhance their autonomy and overall quality of life, while also laying a solid foundation for further exploration and innovation in this burgeoning field, both within academia and industry. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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BCTM: A Topic modeling Method Based on external Information 13th

BCTM: A Topic Modeling Method Based on External Information

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Proceedings of the 13th eAI International Conference on Broadband Communications, Networks, and Systems, BROADNeTS 2022

作者： Liu, Gang Wan, Taiying Yu, Jinfeng Zhan, Kai Wang, Wei College of Computer Science and Technology Harbin Engineering University Harbin150001 China PwC Enterprise Digital PricewaterhouseCoopers SydneyNSW2070 Australia National Engineering Laboratory of E-Government Modeling Simulation Harbin Engineering University Harbin150001 China

ISBN: (纸本)9783031404665

Topic models are often used as intermediate algorithms for text mining and semantic analysis in natural language processing, and have a wide range of functions. However, most of the existing improvements to the topic model use word embedding to improve the accuracy of text modeling, but ignore the external information in the text. This paper proposes a topic model BCTM (Bi-Concept Topic Model) using the word feature information and concept information. Based on the BTM topic model, BCTM introduces word feature information through word vector technology and concept information based on ConceptNet to optimize topic modeling. The construction method of Bi-Concept pair is proposed. Based on ConceptNet semantic network, and the content of text is enriched with concept information. A more accurate topic distribution is obtained through the improved topic model, at the same time, due to the rich feature information, the model is also superior to the baseline model in short text modeling. The experiments prove that the bilingual topic model proposed in this paper has a good performance in modeling accuracy. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： embeddings

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Improved Mayfly Algorithm Based on Co-evolution for Feature Selection

Improved Mayfly Algorithm Based on Co-Evolution for Feature ...

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Ieee International Conference on Systems, Man and Cybernetics

作者： Guo Tianyu Dong Hongbin Zhou Jing National Engineering Laboratory for E-government Modeling and Simulation School of Computer Science and Technology Harbin Engineering University Harbin China

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Dynamic Graph Convolutional Networks with Spatiotemporal Missing Pattern Awareness

Dynamic Graph Convolutional Networks with Spatiotemporal Mis...

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

作者： Pang, Bingheng Liang, Zhuoxuan Li, Wei Zheng, Xiangping Abdein, Rokia College of Computer Science and Technology Harbin Engineering University Harbin China Modeling and Emulation in E-Government National Engineering Laboratory Harbin Engineering University Harbin China Qingdao Innovation and Development Base of Harbin Engineering University Qingdao China

ISBN: (纸本)9798350368741

Missing data is ubiquitous phenomenon in the time series community, significantly challenging forecasting due to incomplete ground truth and sparse data. Most previous Multivariate Time Series Forecasting with Missing Values (MTSFMV) approaches usually assume static missing patterns, neglecting the dynamic changes over time and space, leading to suboptimal forecasting results. To tackle these challenges, we propose novel STMPANets, which are capable of perceiving time-varying spatiotemporal missing patterns to refine the forecasting sequences. Specifically, we decompose the series into seasonal trend components, allowing STMPANets to highlight inherent sequence properties and adapt to missing patterns. We then propose a Multi-granularity Conditional Partial TCN (MGCPT) to regulate the imputation rate of missing values over time, modeling temporal correlation. Additionally, we design an Adaptive Dynamic GCN (ADGCN) to capture spatial dependencies by perceiving dynamic missing patterns. extensive experiments demonstrate that STMPANets outperform state-of-the-art models. © 2025 Ieee.

关键词： Dynamic graphs Missing patterns Multivariate time series forecasting Time series

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MFG-R: Chinese Text Matching with Multi-Information Fusion Graph embedding and Residual Connections 28

MFG-R: Chinese Text Matching with Multi-Information Fusion G...

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28th Ieee Symposium on Computers and Communications, ISCC 2023

作者： Liu, Gang Wang, Tongli Dong, Yichao Zhan, Kai Yang, Wenli Harbin Engineering University College of Computer Science and Technology Harbin China Harbin Engineering University Modeling and Emulation in E-Government National Engineering Laboratory Harbin China PwC Enterprise Digital Pricewaterhouse Coopers Sydney Australia

ISBN: (纸本)9798350300482

Chinese text matching is an important task in natural language processing research, but the current techniques have problems in text feature extraction, such as insufficient word information extraction and lack of deep information in graph convolution networks. In this paper, we propose a model MFG-R for Chinese text matching with multi-information fusion graph embedding and residual connection. The model fuses the word embedding representation of the text obtained by graph convolution network with character-level information and word weight information to extract text features. At the same time, in order to perform deep interaction matching, we construct a word-level similarity interaction matrix between text pairs, and build a text interaction and feature extraction model based on residual network on this basis. experiments show that MFG-R has excellent performance on two common Chinese datasets, Ant Financial Question Matching Corpus(AFQMC) and Large-scale Chinese Question Matching Corpus(LCQMC). © 2023 Ieee.

关键词： Convolution

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Improving Robustness of GNN-based Anomaly Detection by Graph Adversarial Training 30

Improving Robustness of GNN-based Anomaly Detection by Graph...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and evaluation, LReC-COLING 2024

作者： Zheng, Xiangping Wu, Bo Zhang, Alex X. Li, Wei College of Computer Science and Technology Harbin Engineering University China R&D and External Relations Department Xiangjiang Laboratory China Modeling and Emulation in E-Government National Engineering Laboratory China Harvest Fund Management Co. Ltd. China Qingdao Innovation and Development Base of Harbin Engineering University China

ISBN: (纸本)9782493814104

Graph neural networks (GNNs) play a fundamental role in anomaly detection, excelling at the identification of node anomalies by aggregating information from neighboring nodes. Nonetheless, they exhibit vulnerability to attacks, with even minor alterations in the graph structure or node attributes resulting in substantial performance degradation. To address this critical challenge, we introduce an innovative mechanism for graph adversarial training, meticulously designed to bolster GNN-based anomaly detection systems against potential poisoning attacks. This novel approach follows a two-step framework. (1) In the initial phase, we employ a Multiple-Objective Generative Adversarial Attack (MO-GAA), which focuses on generating feature modifications and inducing structural disruptions within the graph. Its primary objective is to mimic the adversarial behavior of potential attackers on the anomaly detection graph, with the explicit intention of confounding the anomaly detector. (2) In the subsequent stage, we introduce Purification-Based Adversarial Attack Defense (PB-AAD), a method specifically designed to rectify any contamination and restore the integrity of the graph. The central aim of PB-AAD is to counteract the destructive actions carried out by potential attackers. Our empirical findings, derived from extensive experiments conducted on four real-world anomaly detection datasets, serve to demonstrate how MO-GAA systematically disrupts the graph, compromising the effectiveness of GNN-based detectors, while PB-AAD effectively mitigates these adversarial actions, thereby enhancing the overall robustness of GNN-based anomaly detectors. © 2024 eLRA Language Resource Association: CC BY-NC 4.0.

关键词： Anomaly detection

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Hypergraph-Based Session modeling: A Multi-Collaborative Self-Supervised Approach for enhanced Recommender Systems 30

Hypergraph-Based Session Modeling: A Multi-Collaborative Sel...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and evaluation, LReC-COLING 2024

ISBN: (纸本)9782493814104

Session-based recommendation (SBR) is a challenging task that involves predicting a user's next item click based on their recent session history. Presently, many state-of-the-art methodologies employ graph neural networks to model item transitions. Notwithstanding their impressive performance, graph-based models encounter significant challenges when confronted with intricate session dependencies and data sparsity in real-world scenarios, ultimately constraining their capacity to enhance recommendation accuracy. In recognition of these challenges, we introduce an innovative methodology known as'Mssen,' which stands for Multi-collaborative self-supervised learning in hypergraph neural networks. Mssen is meticulously crafted to adeptly discern user intent. Our approach initiates by representing session-based data as a hypergraph, adeptly capturing intricate, high-order relationships. Subsequently, we employ self-supervised learning on item-session hypergraphs to mitigate the challenges of data sparsity, all without necessitating manual fine-tuning, extensive search, or domain-specific expertise in augmentation selection. Comprehensive experimental analyses conducted across multiple datasets consistently underscore the superior performance of our approach when compared to existing methodologies. © 2024 eLRA Language Resource Association: CC BY-NC 4.0.

关键词： Graph neural networks

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Design and Implementation of a Lightweight Security-enhanced Scheme for Modbus TCP Protocol

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Security and Communication Networks 2023年第1期2023卷

作者： Liu, Zechao Liang, Tao Wang, Wenshan Sun, Ruochen Li, Sizhao Harbin Engineering University Harbin Heilongjiang150001 China Modeling and Emulation in E-Government National Engineering Laboratory Beijing100037 China

The advent of Industry 4.0 has made people pay more and more attention to the security of the industrial control system. As a common and typical communication protocol, Modbus does not consider the problem of data security at the beginning of its design, which provides opportunities for criminals. In this paper, we design a security scheme to protect the traditional Modbus TCP protocol, by using domestic encryption algorithms. As a result, the proposed scheme is able to identity authentication, data encryption, data integrity check, and anti-replay attacks. Security analysis and experimental results show that our proposed scheme solves the security problem of Modbus TCP protocol with minimal overhead increase. © 2023 Zechao Liu et al.

关键词： Transmission control protocol

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