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DMF: A Deep Multimodal Fusion-Based Network Traffic Classification Model

computers, Materials & Continua 2025年第5期83卷 2267-2285页

作者： Xiangbin Wang Qingjun Yuan Weina Niu Qianwei Meng Yongjuan Wang Chunxiang Gu Henan Key Laboratory of Network Cryptography Technology Information Engineering UniversityZhengzhou450001China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China

With the rise of encrypted traffic,traditional network analysis methods have become less effective,leading to a shift towards deep learning-based *** these,multimodal learning-based classification methods have gained attention due to their ability to leverage diverse feature sets from encrypted traffic,improving classification ***,existing research predominantly relies on late fusion techniques,which hinder the full utilization of deep features within the *** address this limitation,we propose a novel multimodal encrypted traffic classification model that synchronizes modality fusion with multiscale feature ***,our approach performs real-time fusion of modalities at each stage of feature extraction,enhancing feature representation at each level and preserving inter-level correlations for more effective *** continuous fusion strategy improves the model’s ability to detect subtle variations in encrypted traffic,while boosting its robustness and adaptability to evolving network *** results on two real-world encrypted traffic datasets demonstrate that our method achieves a classification accuracy of 98.23% and 97.63%,outperforming existing multimodal learning-based methods.

关键词： Deep fusion intrusion detection multimodal learning network traffic classification

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PBCounter:weighted model counting on pseudo-boolean formulas

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Frontiers of computer science 2025年第3期19卷 55-63页

作者： Yong LAI Zhenghang XU Minghao YIN College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China School of Computer Science and Information Technology Northeast Normal UniversityChangchun 130017China

In Weighted Model Counting(WMC),we assign weights to literals and compute the sum of the weights of the models of a given propositional formula where the weight of an assignment is the product of the weights of its *** current WMC solvers work on Conjunctive Normal Form(CNF)***,CNF is not a natural representation for human-being in many *** by the stronger expressive power of Pseudo-Boolean(PB)formulas than CNF,we propose to perform WMC on PB *** on a recent dynamic programming algorithm framework called ADDMC for WMC,we implement a weighted PB counting tool *** compare PBCounter with the state-of-the-art weighted model counters SharpSAT-TD,ExactMC,D4,and ADDMC,where the latter tools work on CNF with encoding methods that convert PB constraints into a CNF *** experiments on three domains of benchmarks show that PBCounter is superior to the model counters on CNF formulas.

关键词： weighted model counting pseudo-boolean constraint algebraic decision diagrams preprocessing techniques

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ABL-SMOTE: A Novel Resampling Method by Handling noisy and borderline challenge for Imbalanced Dataset for Software Defect Prediction

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IEEE Access 2025年 13卷 78781-78794页

作者： Bashir, Kamal Ghorashi, Sara Abdelwahab Ahmed, Ali Khader, Abdolraheem Karary University College of Computer Science and Information Technology Department of Information Technology Omdurman12304 Sudan Princess Nourah bint Abdulrahman University P.O. Box 84428 Collage of Computer and Information Sciences Department of Computer Sciences Riyadh11671 Saudi Arabia King Abdulaziz University Faculty of Computing and Information Technology Jeddah21589 Saudi Arabia Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

Machine learning algorithms face important implementation difficulties due to imbalanced learning since the Synthetic Minority Oversampling Technique (SMOTE) helps improve performance through the creation of new minority class examples in feature space before preprocessing. The underlying problem leading to performance deterioration emerges from noise and boundary instances found in the minority class rather than an excessive imbalance ratio. Noise and marginal samples become problematic during oversampling operations since they reduce the performance of classification models. This paper proposes an advanced version of Borderline-SMOTE (BL-SMOTE), called ABL-SMOT approach that utilizes INFFC for noise filtering to optimize data quality in the dataset. This improvement method for minority class sample placement and noise filtering ensures higher classifier performance through ABL-SMOTE. The proposed technique receives assessment through the usage of software defect datasets along with multiple testing classifiers against conventional data sampling procedures. The experimental outcomes show that ABL-SMOTE performs better than alternative approaches in most tested datasets because it is an effective preprocessing tool for imbalanced classification problems. © 2013 IEEE.

关键词： Software testing

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Hierarchical adaptive cuckoo search algorithm for global optimization

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Cluster Computing 2025年第5期28卷 1-36页

作者： Ouyang, Chengtian Liu, Xin Zhu, Donglin Li, Yehong Mao, Jihong Zhou, Changjun Xue, Jiankai School of Information Engineering Jiangxi University of Science and Technology Ganzhou341000 China School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China School of Computer Science and Technology Anhui University of Technology Anhui Ma’anshan243032 China School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu611130 China

The Cuckoo Search algorithm (CS) still has some shortcomings, such as insufficient global search capability, challenges in exploring values beyond local optima, and underutilization of effective information within populations. In order to address these existing shortcomings and enhance the search performance of the Cuckoo Search algorithm, a proposed solution is the introduction of the Hierarchical Adaptive Cuckoo Search algorithm for Global Optimization (HAGCS). This algorithm integrates the concept of distribution entropy and a weighted fitness-value-distance-balanced ranking selection method. These additions aim to enhance information exchange within populations and strengthen the local search abilities of CS algorithms. Furthermore, the algorithm includes an adaptive global search strategy to improve global search capabilities, enhance population diversity, and aid in the discovery of global optimal solutions. Additionally, a hierarchical nest replacement strategy is introduced to refine position updates, reducing the risk of getting trapped in local extremes. The incorporation of a sawtooth linear function population adaptive strategy accelerates algorithm convergence, while a shifting population enhances the algorithm’s capacity to navigate suboptimal scenarios. The experiment assesses the impact of the proposed strategy on step size, compares it with variant algorithms and the TOP algorithm on the CEC 2022 test set, and applies the algorithm to three practical engineering optimization problems. The results demonstrate that HAGCS exhibits good practicality and feasibility. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Learning algorithms

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Event-Driven Attention Network:A Cross-Modal Framework for Efficient Image-Text Retrieval in Mass Gathering Events

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computers, Materials & Continua 2025年第5期83卷 3277-3301页

作者： Kamil Yasen Heyan Jin Sijie Yang Li Zhan Xuyang Zhang Ke Qin Ye Li School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Information and Software Engineering University of Electronic Science and Technology ofChinaChengdu611731China Kashi Institute of Electronics and Information Industry Kashi844508China

Research on mass gathering events is critical for ensuring public security and maintaining social ***,most of the existing works focus on crowd behavior analysis areas such as anomaly detection and crowd counting,and there is a relative lack of research on mass gathering *** believe real-time detection and monitoring of mass gathering behaviors are essential formigrating potential security risks and ***,it is imperative to develop a method capable of accurately identifying and localizing mass gatherings before disasters occur,enabling prompt and effective *** address this problem,we propose an innovative Event-Driven Attention Network(EDAN),which achieves image-text matching in the scenario of mass gathering events with good results for the first *** image-text retrieval methods based on global alignment are difficult to capture the local details within complex scenes,limiting retrieval *** local alignment-based methods aremore effective at extracting detailed features,they frequently process raw textual features directly,which often contain ambiguities and redundant information that can diminish retrieval efficiency and degrade model *** overcome these challenges,EDAN introduces an Event-Driven AttentionModule that adaptively focuses attention on image regions or textual words relevant to the event *** calculating the semantic distance between event labels and textual content,this module effectively significantly reduces computational complexity and enhances retrieval *** validate the effectiveness of EDAN,we construct a dedicated multimodal dataset tailored for the analysis of mass gathering events,providing a reliable foundation for subsequent *** conduct comparative experiments with other methods on our dataset,the experimental results demonstrate the effectiveness of *** the image-to-text retrieval task,EDAN achieved the best performance on the R@5 metric,w

关键词： Mass gathering events image-text retrieval attention mechanism

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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Robust Trajectory Design and Task Scheduling With Data Compression in Industrial Internet of Things Assisted by UAV

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IEEE Internet of Things Journal 2025年第9期12卷 12501-12511页

作者： Li, Bin Zhu, Xiao Wang, Junyi Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Guilin University of Electronic Technology Key Laboratory of Cognitive Radio and Information Processing Ministry of Education Guilin541004 China Guilin University of Electronic Technology School of Information and Communication Guilin541004 China

Data compression technology is able to reduce data size, which can be applied to lower the cost of task offloading in mobile edge computing (MEC). This article addresses the practical challenges for robust trajectory and scheduling optimization based on data compression in the uncrewed aerial vehicle (UAV)-assisted MEC, aiming to minimize the sum energy cost of terminal users while maintaining robust performance during UAV flight. Considering the nonconvexity of the problem and the dynamic nature of the scenario, the optimization problem is reformulated as a Markov decision process (MDP). Then, a randomized ensembled double Q-learning (REDQ) algorithm is adopted to solve the issue. The algorithm allows for higher feasible update-to-data ratio, enabling more effective learning from observed data. The simulation results show that the proposed scheme effectively reduces the energy consumption while ensuring flight robustness. Compared to the PPO and A2C algorithms, energy consumption is reduced by approximately 21.9% and 35.4%, respectively. This method demonstrates significant advantages in complex environments and holds great potential for practical applications. © 2014 IEEE.

关键词： Energy utilization

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Unsupervised Outlier Detection with Reinforced Noise Discriminator

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ACM Transactions on Intelligent Systems and technology 2025年第2期16卷 1-26页

作者： Zhang, Zhongping Liu, Daoheng Zhu, Jinwei Wu, Youxi Hebei Key Laboratory of Computer Virtual Technology and System Integration School of Information Science and Engineering Yanshan University Qinhuangdao China School of Information Science and Engineering Yanshan University Qinhuangdao China School of Artificial Intelligence Hebei University of Technology Tianjin China

Outlier detection is one of the hot topics in the field of machine learning and data mining. At present, there are many kinds of outlier detection algorithms. The accuracies of traditional outlier detection algorithms are often affected by unique parameters, and an increase in the amount of data and the dimensions of the data can seriously affect their efficiency and effectiveness. Methods based on generative adversarial networks (GANs) can solve the above problems, but they are unacceptable since the model often collapses during the training period. In this article, to solve the problems of curse of dimensionality and model collapse, we propose a novel reinforced noise discriminator (RND) method for unsupervised outlier detection in tabular data. We consider outlier detection as a binary classification problem. Thus, we apply a learnable reinforced discriminator and generate a large number of potential outliers with a uniform distribution and potential outliers that are close to the original data that are used as a negative sample to train the discriminator, which learns the distribution of the original data to detect outliers. We empirically compare the proposed approach with ten state-of-the-art outlier detection methods on both synthetic and real-world tabular datasets. The experimental results show that RND outperforms its competitors in the majority of cases. The codes used to perform the experiments described in this article are available at https://***/urlhearts/r-n-d. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Generative adversarial networks

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Towards Multiple-in-One Image Deraining Via Scale-Aware Trident Transformer Network

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IEEE Signal Processing Letters 2025年 32卷 1780-1784页

作者： Jiao, Shuang Zhao, Jianping Li, Hua Sun, Junxi Changchun University of Science and Technology School of Computer Science and Technology Changchun130022 China Changchun Institute of Education Changchun130033 China Northeast Normal University School of Information Science and Technology Changchun130117 China

Despite significant progress has been made in image deraining, most methods only independently handle a single type of rain degradation (e.g., rain streak, raindrop or nighttime rain), which limits the model's capacity to adapt to real-world dynamic degradations, especially on all-time autonomous driving. To advance this field, we introduce a new task: multiple-in-one image deraining, which aims to simultaneously address multiple types of rain degradation using a universal mix-trained model. To benchmark this task, we first construct a high-quality dataset based on driving scenarios, called MIO-Rain, which contains four patterns: daytime rain streak, daytime raindrop, nighttime rain streak and nighttime raindrop. Furthermore, we develop an effective scale-aware trident Transformer network (STTformer) for multiple-in-one image deraining. The proposed framework integrates coarse-to-fine and multi-patch parallel multi-branch architectures, with each branch employing multi-scale learning for its robustness to diverse rain appearances. Extensive experiments demonstrate the effectiveness of our model, and show that it achieves favorable performance against state-of-the-art ones. © 1994-2012 IEEE.

关键词： Benchmarking

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DRL-Based UAV Collaborative Task Offloading for Post-disaster Scenarios 24th

DRL-Based UAV Collaborative Task Offloading for Post-disast...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Zhao, Long Chen, Xin Jiao, Libo Xu, Mingyang Wei, Jiyuan School of Computer Science Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9789819615476

The complexity and unpredictability of disaster areas pose significant challenges to post-disaster rescue operations. UAVs, with their flexibility and rapid response capability, are able to perform various tasks and provide critical field data. However, these tasks are often computationally intensive and have strict latency requirements, and UAVs have limited computational resources. Therefore, effectively handling a large number of real-time tasks becomes a significant challenge. To address this, we propose a solution combining UAVs and mobile edge computing to optimize task processing. We focus on UAV-assisted task computation in post-disaster scenarios, considering indivisible and latency-sensitive tasks, and construct an edge computing framework for task offloading. We propose a deep reinforcement learning-based task offloading algorithm (TODRL), which predicts UAV loads using LSTM and adjusts offloading strategies with Dueling DQN to minimize latency. Experimental results show significant latency reduction compared to traditional methods, validating the approach’s potential and effectiveness in post-disaster rescue. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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