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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zeng, Xiaoting Liang, Yu Yang, Peng Chen, Weilin Lei, Baiying Shenzhen University School of Computer Science and Software Engineering Shenzhen China Shenzhen University School of Biomedical Engineering Shenzhen China Shenzhen University Medical School Shenzhen University Institute of Biological Therapy Shenzhen China Shenzhen University School of Biomedical Engineering Health Science Center Shenzhen China

ISBN: (纸本)9798350386226

Drug-target interaction (DTI) prediction is vital for drug discovery and repurposing. Hypergraph is utilized in DTI prediction for modeling higher-order relationships in biomedical networks. Although the strategies of modeling hypergraph-based drug-related interactions with multi-channel and utilizing self-supervised learning task to improve DTI prediction performance have been proven promising, current researches fail to effectively model feature interaction across different channels and fully exploit cross-channel information for self-supervised task. In this study, we propose a hypergraph-based self-supervised multi-channel interaction framework HSMI-DTI for DTI prediction. HSMI-DTI aims to extract hypergraph features effectively and model cross-channel correlations, leveraging hierarchical self-supervised learning to uncover the discover correlations between different channels. We compare HSMI-DTI with advanced baselines, and experiment results show our model outperforms existing methods, thereby optimizing DTI prediction performance. © 2024 IEEE.

关键词： Self-supervised learning

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DDPG Based Dynamic Scheduling Optimization Mechanism in Real Switch 15

DDPG Based Dynamic Scheduling Optimization Mechanism in Real...

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15th International Conference on Communication software and Networks, ICCSN 2023

作者： Wang, Xue Wang, Xingwei Jia, Jie Lu, Xijia Huang, Min School of Computer Science and Engineering Northeastern University China Software College Northeastern University China College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9798350329360

Nowadays, more and more traffic with different service requirements is transmitted simultaneously in the network, resulting in network congestion. In order to improve network Quality of Service (QoS), traffic will be classified and processed according to its requirements. The switch uses fair queuing and its extension schemes to schedule it to reduce the packet loss rate. However, statistical data such as packet arrival rate cannot be accurately obtained in the switches, and discrete reinforcement learning cannot model dynamically changing traffic, making existing dynamic bandwidth allocation solutions challenging to implement. Meanwhile, the same weight configuration will obviously cause the packet loss rate to fluctuate, so it is challenging to design an effective reward function. This paper proposes a dynamic scheduling optimization mechanism based on Deep Deterministic Policy Gradient (DDPG), called DDSO, in an actual switch to schedule flows with different service requirements. Firstly, the flow scheduling problem is modeled as a Markov Decision Process (MDP), and DDPG is used to find the optimal queue weight configuration. Queue weight adjustment is a continuous control problem, and DDPG can solve this problem very well. Secondly, we introduce two one-dimensional arrays to design a new reward function, to reduce the influence of packet loss rate fluctuation on reward determination. At last, this mechanism is evaluated through a hardware testbed in two scenarios. The results show that DDSO can effectively schedule the flows with different service requirements and alleviate network congestion. © 2023 IEEE.

关键词： Reinforcement learning

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A Two-Stream Word-level Information Fusion BERT Model for Event Element Entity Recognition 12

A Two-Stream Word-level Information Fusion BERT Model for Ev...

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12th International Conference on Advanced Cloud and Big Data, CBD 2024

作者： Zhang, Yan Li, Kaiwen Zhang, Jinghui Zhou, Jingya School of Information Engineering Shanghai Donghai Vocational& Technical Colledge Shanghai China School of Software Engineering Southeast University Nanjing China School of Computer Science and Technology Soochow University Suzhou China

ISBN: (纸本)9798331511074

This paper primarily explores methods for enhancing the performance of event element entity recognition tasks by enriching the positional information of vocabulary and focusing on document context. It proposes and implements an event element entity recognition algorithm based on vocabulary-enhanced dual-stream encoders named Two-Stream Word-level Information Fusion BERT, TSWLIF-BERT. A word-level information fusion layer that fully integrates the vocabulary and its positional information was designed and implemented.: (1) Designed and implemented a word-level information fusion layer that fully integrates both the vocabulary and their positional information, deeply integrating the vocabulary and positional information in the pre-trained model, thereby obtaining an enhanced vocabulary model (Word-level Information Fusion BERT, WLIF-BERT).(2) Based on the model WLIF-BERT, designed a dual-stream encoder that considers cross-sentence information with a global encoder and focuses on sentence-internal information with a local encoder, fully modeling the contextual information.(3) Used masked conditional random fields to solve the problem of illegal sequence label transitions in models based on conditional random fields. Experiments show that this method significantly improves performance in event element entity recognition tasks. © 2024 IEEE.

关键词： Information fusion

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A Static Bi-dimensional Sample Selection for Federated Learning with Label Noise 28th

A Static Bi-dimensional Sample Selection for Federated Lear...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Rong, Qian Yuan, Ling Li, Guohui Li, Jianjun Zhang, Lu Ding, Xuanang School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Software Engineering Huazhong University of Science and Technology Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031306365

In real-world Federated learning(FL), client training data may contain label noise, which can harm the generalization performance of the global model. Most existing noisy label learning methods rely on sample selection strategies that treat small-loss samples as correctly labeled ones, and large-loss samples as mislabeled ones. However, large-loss samples may also be valuable hard samples with correct labels. Also, these sample selection strategies have an issue in FL setting: Moreover sample selection strategies require training two models simultaneously and are executed in every mini-batch, which increases the communication cost and client computation. In this paper, we propose an efficient multi-stage federated learning framework to tackle label noise. Firstly, we use self-supervision pre-training to extract category-independent features without using any labels. Secondly, we propose a federated static two-dimensional sample selection(FedSTSS) method, which uses category probability entropy and loss as separation metrics to identify samples. Notably, to improve the accuracy of recognition samples, we add a weight entropy minimization term to the cross-entropy loss function. Finally, we use a semi-supervised method to finetune the global model on identified clean samples and mislabeled samples. Extensive experiments on multiple synthetic and real-world noisy datasets demonstrate that our method outperforms the state-of-the-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Supervised learning

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CTNet:A Convolutional Transformer Network for Color Image Steganalysis

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Journal of computer science & Technology 2025年第2期40卷 413-427页

作者： Kang-Kang Wei Wei-Qi Luo Shun-Quan Tan Ji-Wu Huang School of Computer Science and Engineering Sun Yat-sen UniversityGuangzhou 510006China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen UniversityGuangzhou 510006China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen UniversityShenzhen 518060China Shenzhen Key Laboratory of Media Security Shenzhen UniversityShenzhen 518060China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518129China

Compared with convolutional neural network(CNN),Transformer can obtain global receptive field features more effectively and has recently achieved great success in natural language processing and computer *** to the particularity of steganography,however,almost all existing steganalytic networks just employ CNN with local receptive fields to detect embedding *** this paper,we propose a novel convolutional Transformer network for color image ***,we firstly obtain various image residuals for each color channel of an input image in the pre-processing *** capture more comprehensive steganalytic features,the truncated residuals after channel concatenation will pass through a feature extraction module composed of a CNN group and a Transformer *** CNN group aims to extract local receptive fields features,while the Transformer group with multi-head self-attention as the key tries to extract global steganalytic ***,we employ a global covariance pooling(GCP)and two fully-connected(FC)layers with dropout for *** comparative experiments demonstrate that the proposed method can significantly improve the detection performances in color image steganalysis and achieve state-of-the-art *** the proposed method is originally designed for color images,it can also obtain competitive results for grayscale images compared with the current best *** addition,we provide numerous ablation studies to verify the rationality of the proposed network architecture.

关键词： steganalysis steganography convolutional neural network Transformer color image

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Sketching Very Large-scale Dynamic Attributed Networks More Practically 25

Sketching Very Large-scale Dynamic Attributed Networks More ...

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34th ACM Web Conference, WWW 2025

作者： Wu, Wei Li, Shiqi Chen, Ling Li, Fangfang Luo, Chuan School of Computer Science and Engineering Central South University Changsha China Australian Artificial Intelligence Institute University of Technology Sydney Sydney Australia School of Software Beihang University Beijing China

ISBN: (纸本)9798400712746

Real-world networks, particularly those in web and social media, are dynamic with evolving node attributes and structures, often involving billions of nodes and edges. Dynamic attributed network embedding is a powerful tool for capturing these changes, enabling data owners and problem owners to better understand interactions and trends for more effective engagement and decision-making. While some existing algorithms are capable of handling very large-scale dynamic attributed networks with billions of nodes and edges, they often suffer from accuracy loss or high computational overhead. In this paper, we propose a practical and sustainable framework of sketching very large-scale dynamic attributed networks called VLS2ketch, which incorporates incremental embedding updates alongside storage-efficient, binarized representation of both node attributes and topological variations. By the sparse random projection technique in an incremental update manner, VLS2ketch significantly reduces the energy-intensive computational workload while maintaining accuracy. Also, we introduce an information decay mechanism, which adapts to temporally varying topologies and node attributes. This mechanism ensures that outdated information gradually diminishes over time. Extensive experiments on real-world very large-scale datasets demonstrate that our proposed VLS2ketch method delivers comparable embedding quality against the state-of-the-art learning-based competitors with dramatically reduced runtime. © 2025 Copyright held by the owner/author(s).

关键词： network embedding

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Improving Generalization for Hyperspectral Image Classification:The Impact of Disjoint Sampling on Deep Models

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computers, Materials & Continua 2024年第10期81卷 503-532页

作者： Muhammad Ahmad Manuel Mazzara Salvatore Distefano Adil Mehmood Khan Hamad Ahmed Altuwaijri Department of Computer Science National University of Computer and Emerging SciencesChiniot35400Pakistan Institute of Software Development and Engineering Innopolis UniversityInnopolis420500Russia Dipartimento di Matematica e Informatica—MIFT University of MessinaMessina98121Italy School of Computer Science University of HullHullHU67RXUK Department of Geography College of Humanities and Social SciencesKing Saud UniversityRiyadh11451Saudi Arabia

Disjoint sampling is critical for rigorous and unbiased evaluation of state-of-the-art(SOTA)models e.g.,Attention Graph and Vision *** training,validation,and test sets overlap or share data,it introduces a bias that inflates performance metrics and prevents accurate assessment of a model’s true ability to generalize to new *** paper presents an innovative disjoint sampling approach for training SOTA models for the Hyperspectral Image Classification(HSIC).By separating training,validation,and test data without overlap,the proposed method facilitates a fairer evaluation of how well a model can classify pixels it was not exposed to during training or *** demonstrate the approach significantly improves a model’s generalization compared to alternatives that include training and validation data in test data(A trivial approach involves testing the model on the entire Hyperspectral dataset to generate the ground truth *** approach produces higher accuracy but ultimately results in low generalization performance).Disjoint sampling eliminates data leakage between sets and provides reliable metrics for benchmarking progress in *** sampling is critical for advancing SOTA models and their real-world application to large-scale land mapping with Hyperspectral ***,with the disjoint test set,the performance of the deep models achieves 96.36%accuracy on Indian Pines data,99.73%on Pavia university data,98.29%on university of Houston data,99.43%on Botswana data,and 99.88%on Salinas data.

关键词： Hyperspectral image classification disjoint sampling Graph CNN spatial-spectral transformer

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Accelerating the cryo-EM structure determination in RELION on GPU cluster

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Frontiers of computer science 2022年第3期16卷 21-39页

作者： Xin YOU Hailong YANG Zhongzhi LUAN Depei QIAN Sino-German Joint Software Institute School of Computer Science and EngineeringBeihang UniversityBeijing 100191China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125

The cryo-electron microscopy(cryo-EM)is one of the most powerful technologies available today for structural *** RELION(Regularized Likelihood Optimization)implements a Bayesian algorithm for cryo-EM structure determination,which is one of the most widely used software in this *** researchers have devoted effort to improve the performance of RELION to satisfy the analysis for the ever-increasing volume of *** this paper,we focus on performance analysis of the most time-consuming computation steps in RELION and identify their performance bottlenecks for specific *** propose several performance optimization strategies to improve the overall performance of RELION,including optimization of expectation step,parallelization of maximization step,accelerating the computation of symmetries,and memory affinity *** experiment results show that our proposed optimizations achieve significant speedups of RELION across representative *** addition,we perform roofline model analysis to understand the effectiveness of our optimizations.

关键词： cryo-EM structure determination performance optimization GPU acceleration RELION

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Unsigned Road Incidents Detection Using Improved RESNET From Driver-View Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1203-1216页

作者： Li, Changping Wang, Bingshu Zheng, Jiangbin Zhang, Yongjun Chen, C.L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Frequent road incidents cause significant physical harm and economic losses globally. The key to ensuring road safety lies in accurately perceiving surrounding road incidents. However, the highly dynamic nature of traffic introduces significant challenges, particularly in detecting sudden and temporary incidents. In this article, we propose a novel detection framework, multihead attention ResNet with dynamic bottleneck (DB_RESNET_MHA), to identify physical unsigned road incidents. Our approach introduces three key innovations. First, we develop a tailored data augmentation strategy to create images that closely mimic the complex variations found in real-world road environments. Second, we enhance model expressiveness by employing attention mechanisms to nonlinearly integrate convolutional kernels within the residual network. Furthermore, we refine the prediction head by applying spatially distinct attention weights, enabling the model to capture intricate correlations between different features more effectively. To demonstrate the effectiveness of our method, we create a dataset for unsigned road incidents (UnsignRI), comprising a total of 16 323 images that capture 12 distinct types of incidents. It stands out as the most comprehensive dataset in the field, encompassing a wide range of geographical features and incident categories. Experimental results show that DB_RESNET_MHA achieves an average accuracy of 96.2% and a f1-score of 0.955 across various categories of unsigned incidents, surpassing other models. © 2020 IEEE.

关键词： Highway accidents

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Weakly Supervised Myeloma Cells Segmentation based on Point Annotation

Weakly Supervised Myeloma Cells Segmentation based on Point ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Lei, Haijun Zhao, Jia Tong, Guanjie Qiu, Xinyun Su, Huaqiang Lei, Baiying Shenzhen University College of Computer Science and Software Engineering Shenzhen China Shenzhen University School of Biomedical Engineering Shenzhen University Medical School Shenzhen China

ISBN: (纸本)9798350337488

Multiple Myeloma (MM) is a growing global health concern, and early diagnosis is crucial for effective treatment. Efforts are underway to produce digital pathology tools with human-level intelligence that are efficient, scalable, accessible, and cost-effective. Microscopic images have high resolution, where cells are enormous and dense. Therefore, the annotation process is time-consuming and complex for tasks such as segmentation due to pixel-level marking. In this paper, we design an end-to-end weakly supervised myeloma cell segmentation framework based on point annotation. It can achieve accurate cell segmentation comparable to fully supervised methods while reducing the need for manual annotation, greatly shortening annotation time. Experimental results demonstrate that our method achieves 98% of its fully-supervised performance with only 10 annotated random points per instance, and outperforms the fully-supervised Mask RCNN. © 2023 IEEE.

关键词： Cell Segmentation Point Annotation Weakly-supervised

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