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4th International Conference on Electronic Information engineering and computer science, EIECS 2024

作者： Wang, Lingchao Ai, Qiang Yan, Hailong Hao, Ming Li, Xiaoxin School of Information Engineering Liaoning Institute of Science and Engineering Jinzhou China College of Computer Qinghai Normal University Xining China Software College Liaoning Technical University Huludao China

ISBN: (纸本)9798331531409

This study proposes a bearing fault diagnosis method that combines the Cuckoo Optimization Algorithm (COA) with the KAN algorithm. COA, as an intelligent optimization algorithm, is primarily used to find the optimal hyperparameters of the model, thereby enhancing the efficiency and robustness of the diagnosis model. The KAN algorithm is employed to build an intelligent classification model, achieving accurate recognition of multiple fault modes. Experimental results show that the proposed method maintains high diagnostic accuracy even with limited data, providing an effective solution for bearing fault diagnosis. © 2024 IEEE.

关键词： Fault detection

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software Defect Prediction via Code Grayscale Pixel Visualization with Fusion Attention 36

Software Defect Prediction via Code Grayscale Pixel Visualiz...

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36th International Conference on software engineering and Knowledge engineering, SEKE 2024

作者： Qiu, Shaojian Wang, Shaosheng Rong, Wei Liao, Lili Lin, Yishen College of Software Engineering South China Agricultural University China School of Computer Science and Engineering South China University of Technology China Guangdong Tobacco Meizhou Limited Company China

ISBN: (纸本)1891706594

software defect prediction helps quality assurance teams find defects in software, thereby enhancing the reliability of the systems. In existing code-visualization-based defect prediction methods, challenges arise from mixing code information and the potential omission of critical defect features. To enhance the completeness of code features, this paper proposes a defect prediction model based on code grayscale pixel visualization with a fusion attention mechanism (Gpv2DP). Gpv2DP converts code into grayscale images and reshapes the images to a standard size, effectively alleviating the information loss problem caused by element mixing and image cropping. Furthermore, it constructs a code feature extracting network that simultaneously integrates the channel, spatial and 3D attention. We conduct empirical experiments on ten open-source Java projects from the PROMISE repository. The results show that the F-measure and AUC metrics of Gpv2DP outperform related defect prediction methods. © 2024 Knowledge Systems Institute Graduate school. All rights reserved.

关键词： software reliability

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Look Deep into the Microservice System Anomaly through Very Sparse Logs 23

Look Deep into the Microservice System Anomaly through Very ...

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2023 World Wide Web Conference, WWW 2023

作者： Jiang, Xinrui Pan, Yicheng Ma, Meng Wang, Ping School of Software and Microelectronics Peking University Beijing China School of Computer Science Peking University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China Ministry of Education China

ISBN: (纸本)9781450394161

Intensive monitoring and anomaly diagnosis have become a knotty problem for modern microservice architecture due to the dynamics of service dependency. While most previous studies rely heavily on ample monitoring metrics, we raise a fundamental but always neglected issue - the diagnostic metric integrity problem. This paper solves the problem by proposing MicroCU - a novel approach to diagnose microservice systems using very sparse API logs. We design a structure named dynamic causal curves to portray time-varying service dependencies and a temporal dynamics discovery algorithm based on Granger causal intervals. Our algorithm generates a smoother space of causal curves and designs the concept of causal unimodalization to calibrate the causality infidelities brought by missing metrics. Finally, a path search algorithm on dynamic causality graphs is proposed to pinpoint the root cause. Experiments on commercial system cases show that MicroCU outperforms many state-of-the-art approaches and reflects the superiorities of causal unimodalization to raw metric imputation. © 2023 ACM.

关键词： Anomaly detection

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Radiology Report Generation via Structured Knowledge-Enhanced Multi-modal Attention and Contrastive Learning

Radiology Report Generation via Structured Knowledge-Enhance...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Xu, Dexuan Chen, Yanyuan Zhang, Jiayu Lou, Yiwei Wang, Hanpin He, Jing Huang, Yu Peking University National Engineering Research Center for S.E. School of Software & Micro. China Peking University School of Computer Science China School of Computer Science China Peking University People's Hospital Department of Rheumatology and Immunology China Peking University National Engineering Research Center for Software Engineering China

ISBN: (纸本)9798350337488

The automated generation of radiology reports has attracted significant attention in the field of bioinformatics. Currently, the main limitations of this task include insufficient utilization of prior medical knowledge, lack of efficient knowledge fusion algorithms, and less distinctiveness between different generated reports. To address these issues, we propose a novel algorithm for radiology report generation, which includes Structured Knowledge-Enhanced Multi-modal Attention (SKEMA) and Dual-Branch Contrastive Learning (DBCL) for the first time. SKEMA aims to effectively bridge the gap between visual and prior knowledge by leveraging the high-order adjacency matrix of the knowledge graph to weightedly fuse image features and knowledge features. We enhance both features through masking, and use the original features and augmented features as positive and negative samples in the dual-branch contrastive learning (DBCL). DBCL increases the differences between positive and negative samples to avoid generating templated results, and enhances the robustness of the model. Finally, we conducted experiments to demonstrate the effectiveness of our model on two public radiology datasets, IU-Xray and MIMIC-CXR. Our model outperformed previous baseline methods on both datasets and achieved excellent evaluation scores. © 2023 IEEE.

关键词： Contrastive Learning Knowledge Graph Multi-modal Fusion Radiology Report Generation

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Revisiting Kernel Attention with Correlated Gaussian Process Representation 40

Revisiting Kernel Attention with Correlated Gaussian Process...

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40th Conference on Uncertainty in Artificial Intelligence, UAI 2024

作者： Bui, Long Minh Huu, Tho Tran Dinh, Duy Nguyen, Tan Minh Hoang, Trong Nghia FPT Software AI Center Department of Mathematics National University of Singapore Singapore School of Electrical Engineering and Computer Science Washington State University United States

Transformers have increasingly become the de facto method to model sequential data with state-of-the-art performance. Due to its widespread use, being able to estimate and calibrate its modeling uncertainty is important to understand and design robust transformer models. To achieve this, previous works have used Gaussian processes (GPs) to perform uncertainty calibration for the attention units of transformers and attained notable successes. However, such approaches have to confine the transformers to the space of symmetric attention to ensure the necessary symmetric requirement of their GP’s kernel specification, which reduces the representation capacity of the model. To mitigate this restriction, we propose the Correlated Gaussian Process Transformer (CGPT), a new class of transformers whose self-attention units are modeled as cross-covariance between two correlated GPs (CGPs). This allows asymmetries in attention and can enhance the representation capacity of GP-based transformers. We also derive a sparse approximation for CGP to make it scale better. Our empirical studies show that both CGP-based and sparse CGP-based transformers achieve better performance than state-of-the-art GP-based transformers on a variety of benchmark tasks. © 2024 Proceedings of Machine Learning Research. All rights reserved.

关键词： Distribution transformers

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A Theoretical Analysis of Backdoor Poisoning Attacks in Convolutional Neural Networks 41

A Theoretical Analysis of Backdoor Poisoning Attacks in Conv...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Boqi Liu, Weiwei School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China

The rising threat of backdoor poisoning attacks (BPAs) on Deep Neural Networks (DNNs) has become a significant concern in recent years. In such attacks, the adversaries strategically target a specific class and generate a poisoned training set. The neural network (NN), well-trained on the poisoned training set, is able to predict any input with the trigger pattern as the targeted label, while maintaining accurate outputs for clean inputs. However, why the BPAs work remains less explored. To fill this gap, we employ a dirty-label attack and conduct a detailed analysis of BPAs in a two-layer convolutional neural network. We provide theoretical insights and results on the effectiveness of BPAs. Our experimental results on two real-world datasets validate our theoretical findings. Copyright 2024 by the author(s)

关键词： Convolutional neural networks

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HGTRDA: Enhancing Prediction of ncRNA-Mediated Drug Resistance with Hypergraph Transformer

HGTRDA: Enhancing Prediction of ncRNA-Mediated Drug Resistan...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Che He, Ruohui Fan, Yanhao Hu, Xiaowen Qian, Yurong Deng, Lei People's Hospital of Ningyuan Clinical Pharmacy Yongzhou China Xinjiang University School of Software Urumqi China Central South University School of Computer Science and Engineering Changsha China

ISBN: (纸本)9798350386226

Exploring the intricate connections between non-coding RNAs (ncRNAs) and drug resistance is crucial for understanding the molecular mechanisms behind drug resistance, identifying novel drug development targets, and uncovering key biomarkers to optimize therapeutic strategies. Traditional biological assays face significant challenges, including high costs and lengthy timelines, prompting the need for advanced computational methods to predict ncRNA-drug resistance associations. In this study, we introduce HGTRDA, a novel computational framework designed to predict potential associations between ncRNAs and drug resistance. HGTRDA leverages LightGCN to generate node representations that capture topological information from the surrounding node neighborhood. These representations are then dynamically optimized using a global hypergraph transformer to model the relationships between ncRNAs and drug resistance. To enhance the quality of the learned embeddings, HGTRDA employs self-supervised learning to fine-tune topology-aware embeddings, reducing the impact of noise and improving representation quality. The final association scores between ncRNAs and drugs are computed using an inner product method. Empirical evaluations on the ncRNADrug database demonstrate that HGTRDA outperforms six contemporary state-of-the-art methods in predicting ncRNA-drug resistance associations. Furthermore, case studies illustrate the practical utility of HGTRDA as a predictive tool in real-world scenarios. The code and dataset for HGTRDA are freely available at https://***/one-melon/HGTRDA. © 2024 IEEE.

关键词： Self-supervised learning

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Semantic-Aware Pseudo-Labeling for Unsupervised Meta-Learning

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5475-5488页

作者： Ouyang, Tianran Dong, Xingping Ye, Mang Du, Bo Shao, Ling Shen, Jianbing Wuhan University School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan430072 China University of Chinese Academy of Sciences UCAS-Terminus AI Lab Beijing101408 China University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science 314100 China

In unsupervised meta-learning, the clustering-based pseudo-labeling approach is an attractive framework, since it is model-agnostic, allowing it to synergize with supervised algorithms to learn from unlabeled data. However, the pseudo-labels suffer from clustering noise and semantic chaos problems, further impacting the effectiveness of meta-learning. In this paper, we analyze and optimize the pseudo-labeling process, including encoding and clustering, aiming to generate semantic-like pseudo-labels to narrow the gap between unsupervised and supervised meta-learning. First, during the encoding, we observe that the embedding space of existing methods lacks clustering-friendly properties, which is the primary reason for clustering noise. To address this issue, we minimize the inter-to-intra-class similarity ratio to generate clustering-friendly embedding features and validate our approach through comprehensive experiments. Then, during the clustering, we find that the semantic quality of pseudo-labels is not adequately controlled, resulting in semantic chaos of pseudo-labels. We propose a semantic-stability index to measure the semantic quality of pseudo-labels quantitatively. Based on this index, we propose the Semantic-aware Pseudo-label Reassignment mechanism to generate semantic-like pseudo-labels for all samples. Our approach is model-agnostic and can easily be integrated into existing supervised methods. To demonstrate its generalization ability, we integrate it into two representative algorithms: MAML and EP. The results on three main few-shot benchmarks clearly show that the proposed method achieves significant improvement compared to state-of-the-art models. Notably, our approach also outperforms the corresponding supervised method in three tasks. © 1979-2012 IEEE.

关键词： Latent semantic analysis

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Small object detection based on WavesNET 10

Small object detection based on WavesNET

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10th International Conference on Information Systems and Computing Technology, ISCTech 2022

作者： Li, Zhengnan Zhao, Ji Lu, Xiaowei School of Computer and Software Engineering University of Science and Technology Liaoning Anshan China

ISBN: (纸本)9798350332933

In recent years, object detection technology has developed rapidly, especially the FPN has been widely adopted in production application and academic research. Compared with the traditional network, the accuracy of EfficientDet based on the FPN structure has reached 55.1% on the COCO dataset, and the number of parameters and computation cost is also greatly reduced by 9-25 times. However, the EfficientDet training process does not integrate the upper and lower level feature information quickly enough, especially depending on the multi-layer BiFPN overlap and increasing input size and channel number, therefore, we proposed WavesNet based on EfficientDet. First, we integrated seven channels on the basis of BiFPN to make all levels of the fusion more quickly and effectively, and then applied the ComC (Combine Convolution) extraction method in the initial phase of feature extraction. In addition, the EfficientDet fast normalization formula was improved to ensure the numerical stability of the features. Experimental results show the WavesNet has 30% faster convergence rate and 0.5% higher accuracy on COCO dataset than the EfficientDet. The detection effect on small targets is obvious. © 2022 IEEE.

关键词： Object recognition

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SegIt: Empowering Sensor Data Labeling with Enhanced Efficiency and Security 24

SegIt: Empowering Sensor Data Labeling with Enhanced Efficie...

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7th International Conference on Machine Learning and Machine Intelligence, MLMI 2024

作者： Zhang, Zhen Abraham, Samuel Lee, Alex Li, Yichen Morota, Gota Ha, Dong Sam Shin, Sook BlacksburgVA United States The Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA United States School of Computer Science and Software Engineering University of Science and Technology Liaoning Liaoning Anshan China School of Animal Sciences Virginia Tech BlacksburgVA United States

ISBN: (纸本)9798400717833

SegIt is a novel, user-friendly, and highly efficient sensor data labeling tool designed to tackle critical challenges such as data privacy, synchronization accuracy, and memory efficiency inherent in existing labeling tools. While many current sensor data labeling tools provide free online services, they typically necessitate users to upload unlabeled sensor data, alongside video or audio references, to cloud storage for labeling. Nevertheless, such third-party storage exposes user data to potential security risks. SegIt, an innovative open-source tool, provides a software solution for tagging unlabeled sensor data directly on a local computer, ensuring enhanced accuracy, convenience, and, most importantly, data security. © 2024 Copyright held by the owner/author(s).

关键词： Data accuracy

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