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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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Consensusless Blockchain: A Promising High-Performance Blockchain Without Consensus 5th

Consensusless Blockchain: A Promising High-Performance Block...

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5th International Conference on Blockchain and Trustworthy Systems, BlockSys 2023

作者： Zheng, Jian Wang, Qing Lin, Jianru Huang, Huawei Su, Chunhua Department of Computer Science and Engineering University of Aizu Aizu9650006 Japan School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Humi Technology Co. Ltd. Hainan570000 China School of Software Engineering Sun Yat-sen University Zhuhai519000 China

ISBN: (纸本)9789819981038

Consensus is unnecessary when the truth is available. In this paper, we present a new perspective of rebuilding the blockchain without consensus. When the consensus phase is eliminated from a blockchain, transactions could be canonized quickly using a well-defined universal rule without consuming hashing power. Thus, the transactions per second (TPS) metric of such the consensusless blockchain can be largely boosted. Although consensus blockchain is promising, several technical challenges are also crucial. For example, double-spending attacks and frequent forking events must be prevented, the credit of block’s minting must be carefully defined, and etc. To address those technical challenges, we propose several solutions for our consensusless blockchain (CB), including a naive monotonic scoring mechanism to calculate the ranking of each block in the chain, and a two-stage witness mechanism to add new blocks. The proposed CB chain is promising to offer a simplified and equipment-cheap infrastructure for rich real-world decentralized applications. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Blockchain

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Breast Microcalcification detection in digital mammograms using Deep Transfer learning approaches 23

Breast Microcalcification detection in digital mammograms us...

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9th International Conference on Computing and Data engineering, ICCDE 2023

作者： Rasool, Ehtsham Anwar, Muhammad Junaid Shaker, Bilawal Hashmi, Muhammad Harris Rehman, Khalil Ur Seed, Yousaf Department of Computer Science The University of Alabama Birmingham United States School of Software Engineering Beijing University of Technology Beijing China

ISBN: (纸本)9781450398022

Breast cancer is the most often diagnosed cancer in women affecting one in eight at the age of 80 in US. Breast is the most threatening cancer among women which leads to death. Early diagnosis of breast cancer can save their lives which decreases the mortality rate. Mammography is a standard screening method for breast cancer diagnosis that identifies occurrences of breast cancer in women's at early stages without symptoms. In this study, we employed transfer learning in deep learning to increase the neural network's performance and reduce the false positive rate. In addition, we proposed a pre-trained VGG-19 neural network to extract features of individual microcalcification to predict breast cancer. The proposed method was evaluated on two public databases the CBIS-DDSM and DDSM and achieved 0.98 sensitivities respectively. The proposed method obtained higher sensitivity than other residual neural networks and previous studies. © 2023 ACM.

关键词： Diseases

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Parallel Assembly Sequence Planning Based on Sparrow Search Algorithms 22

Parallel Assembly Sequence Planning Based on Sparrow Search ...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： He, Haichen Liu, Weibin Zhang, Yinan Song, Shasha Cheng, Yue Liu, Ziheng Xing, Weiwei Beijing Jiaotong University School of Computer Science and Technology China Crrc Information Technology Co. Ltd. China Beijing Jiaotong University School of Software Engineering China

ISBN: (纸本)9798331509712

Assembly sequence planning (ASP) is the most important process in the product lifecycle. This paper aims to propose a parallel assembly sequence planning method based on sparrow search algorithm (SSA) to improve the assembly quality and efficiency. The proposed method divides the problem into assembly unit division (AUD) part and ASP part. In AUD part, we use the markov clustering algorithm (MCA) to solve the AUD problem. In ASP part, the ASP evaluation system is designed for the assembly part sequence planning (APSP) problem and assembly unit sequence planning (AUSP) problem, and the discrete artificial sparrow search algorithm (DASSA) is proposed for solving the discrete APSP problem. The results of comparison experiment verify the effectiveness of the proposed method. Compared with genetic algorithm (GA) and particle swarm optimization (PSO), the proposed DASSA gets the optimal results and fastest convergence. The method proposed solves ASP problem to efficiently perform parallel assembly sequence planning and reduce production time, making it cost-effective in intelligent manufacturing. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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