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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Chen, Jie Liao, Wenbin Wen, Du Li, Jianqiang Wang, Fangzhong Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China Tianjin University Laboratory of Synthetic Microbiology School of Chemical Engineering & Technology China

ISBN: (纸本)9798350337488

The annotation of Open Reading Frames (ORFs) is a crucial step in gene annotation, as it precisely delineates the specific regions of expressed genes. However, small Open Reading Frames (smORFs), in comparison to ORFs, are shorter in length, exhibit lower expression abundance, and are more challenging to predict. Particularly in the presence of noise in prokaryotic data and limited availability of positive sample data, the difficulty of prediction is amplified. Therefore, it is necessary to study smORF prediction methods. However, current machine learning models use limited data for modeling and overlook the existence of undiscovered positive samples within the negative samples. Additionally, they do not incorporate prior knowledge that can be calibrated to enhance the 3-nt periodicity. This work utilizes a multimodal VAE for data dimensionality reduction and employs a GAN to generate latent vectors for data augmentation. It incorporates PU learning to leverage unknown samples and combines Riboseq data from experiments with and without antibiotic treatment. Additionally, an adversarial training mechanism is employed to enhance the model's robustness. © 2023 IEEE.

关键词： Class Imbalance Data Generation Gene Annotation Positive-Unlabeled Learning

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A Surrogate-Assisted Evolutionary Algorithm for Expensive Dynamic Multimodal Optimzation 13

A Surrogate-Assisted Evolutionary Algorithm for Expensive Dy...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wu, Xunfeng Liu, Songbai Ji, Junkai Ma, Lijia Leung, Victor C. M. Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada

ISBN: (纸本)9798350308365

Surrogate-assisted evolutionary algorithms (SAEAs) have demonstrated promising optimization performance in addressing expensive dynamic optimization problems or expensive multimodal optimization problems. However, none of existing SAEAs are designed specifically for tackling expensive dynamic multimodal optimization problems (EDMMOPs). Therefore, in this paper, a first SAEA for tackling EDMMOPs is proposed. First, a nearest density clustering is designed to divide the population into a number of subpopulations, enhancing the diversity of the population. Then, a surrogate-assisted evolutionary optimizer is developed to construct surrogate models for each subpopulation and evolve all solutions in subpopulations by means of the built surrogate models, accelerating the population's converge towards several optimal solutions rapidly. Finally, a transfer learning-based prediction is devised to generate initial samples for next environment by leveraging the stored training samples in the previous environments. To assess the performance of our proposed algorithm, a set of complex benchmark problems is adopted, and the experimental results confirm its superior performance over several competitive algorithms on most test cases. © 2024 IEEE.

关键词： Digital elevation model

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High-Precision Sub-Seafloor Velocity Building Based on Joint Tomography and Deep Learning on OBS Data in the South China Sea

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Journal of Earth Science 2025年第2期36卷 830-834页

作者： Guoxin Chen Jun Li Jinxin Chen Rongsen Du Yutao Liu Yuli Qi Chun-Feng Li Xingguo Huang Department of Marine Sciences Zhejiang UniversityZhoushan 316021China Hainan Institute Zhejiang UniversitySanya 572025China Chinese Academy of Sciences Institute of SoftwareNational Key Laboratory of Space Integrated Information SystemBeijing 100190China College of Instrumentation and Electrical Engineering Jilin UniversityChangchun 130026China

*** velocity model is crucial for describing the subsurface composition and structure,and has significant implications in offshore oil and gas exploration and marine geophysical engineering(Xie et al.,2024).Currently,travel time tomography is the most commonly used method for velocity modeling based on ocean bottom seismometer(OBS)data(Zhang et al.,2023;Sambolian et al.,2021).This method usually assumes that the sub-seafloor structure is layered,and therefore faces challenges in high-precision modeling with strong lateral discontinuities.

关键词： velocity modeling time tomography marine geophysical engineering xie joint tomography describing subsurface composition structureand high precision velocity modeling offshore oil gas exploration ocean bottom seismometer obs data zhang

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TransRec++:Translation-based sequential recommendation with heterogeneous feedback

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Frontiers of Computer Science 2022年第2期16卷 190-192页

作者： Zhuo-Xin ZHAN Ming-Kai HE Wei-Ke PAN Zhong MING National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen 518060China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China

1 Introduction Nowadays,recommender systems are widely used because of the information overload problem on the *** are a variety of feedback in recommender systems,such as explicit feedback,implicit feedback,sequential feedback,*** them,modeling of heterogeneous sequential feedback,which contains not only different types of feedback such as examinations and purchases but also the sequential information,is an emerging and important problem receiving more and more *** sequential feedback is relatively easy to be collected in a deployed system and is also able to provide more information than the homogeneous feedback,which is thus expected to be helpful in improving the recommendation accuracy.

关键词： feedback recommendation sequential

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EagerLog: Active Learning Enhanced Retrieval Augmented Generation for Log-based Anomaly Detection

EagerLog: Active Learning Enhanced Retrieval Augmented Gener...

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

作者： Duan, Chiming Jia, Tong Yang, Yong Liu, Guiyang Liu, Jinbu Zhang, Huxing Zhou, Qi Li, Ying Huang, Gang School of Software and Microelectronics Peking University Beijing China Institute for Artificial Intelligence Peking University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China National Key Laboratory of Data Space Technology and System Beijing China Alibaba Cloud Computing Company Hangzhou China

ISBN: (纸本)9798350368741

Logs record essential information about system operations and serve as a critical source for anomaly detection, which has generated growing research interest. Utilizing large language models (LLMs) within a retrieval-augmented generation (RAG) framework for log-based anomaly detection is an effective approach due to its strong generalization capabilities and efficient few-shot performance. However, the effectiveness of this method hinges on the quality of the knowledge source, which can be impacted by noise and changes within the software systems. Facing these problems, in this paper, we propose a novel log-based anomaly detection method named EagerLog, employing active learning to choose the logs for humans to label, thereby adding them to the knowledge source, thus enhancing the knowledge source and maintaining its quality. Our experiments on three open datasets (BGL, Thunderbird, Zookeeper) and one industrial dataset demonstrate that EagerLog can achieve 93.65% F1 score with approximately 10 labeled log sequences, surpassing existing methods by 15.32%. © 2025 IEEE.

关键词： Active Learning Anomaly Detection Log Analysis Retrieval Augmented Generation

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Adaptive Evolutionary Reinforcement Learning Algorithm with Early Termination Strategy 23

Adaptive Evolutionary Reinforcement Learning Algorithm with ...

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23rd International Conference on Autonomous Agents and Multiagent systems, AAMAS 2024

作者： Wu, Xiaoqiang Zhu, Qingling Lin, Qiuzhen Chen, Weineng Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China

Evolutionary reinforcement learning algorithms (ERLs), which combine evolutionary algorithms (EAs) with reinforcement learning (RL), have demonstrated significant success in enhancing RL performance. However, most ERLs rely heavily on Gaussian mutation operators to generate new individuals. When the standard deviation is too large or small, this approach will result in the production of poor or highly similar offspring. Such outcomes can be detrimental to the learning process of the RL agent, as too many poor or similar experiences are generated by these individuals. In order to alleviate these issues, this paper proposes an Adaptive Evolutionary Reinforcement Learning (AERL) method that adaptively adjusts both the standard deviation and the evaluation process. By tracking the performance of new individuals, AERL maintains the mutation strength within a suitable range without the need for additional gradient computations. Moreover, the proposed AERL approach early terminates unnecessary evaluations and discards experiences arising from poor individuals, thereby resulting in enhanced learning efficiency. Empirical assessments conducted on a variety of continuous control problems demonstrate the effectiveness of the AERL method. © 2024 International Foundation for Autonomous Agents and Multiagent systems.

关键词： Reinforcement learning

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DD-UNet: Densely Dilated U-Net for Curvilinear Structure Segmentation in Fundus Image

DD-UNet: Densely Dilated U-Net for Curvilinear Structure Seg...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhang, Yindong Chen, Jie Wang, Li Chen, Miaohong Zhang, Guoming Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China Shenzhen Eye Hospital China

ISBN: (纸本)9798350337488

Retinopathy of Prematurity (ROP) is a retina disorder that affects premature infants with lower weights. If the patient cannot get the treatment in time when the illness reaches the last stage, irreversible vision loss will be caused. Nevertheless, there has been relatively little consideration given to the segmentation of the ridge, the key clinical characteristic of the illness. Additionally, existing research has not adequately addressed several segmentation issues, such as fragmentary topology, class imbalance, and false positives. This paper proposes a Densely Dilated U-Net (DD-UNet) improved from U-Net to tackle these challenges. Furthermore, the post-processing techniques based on the spatial relationship between vessels and ridges, along with the relative pixel counts of ridges and false positive results is integrated to mitigate false positive results in the predicted ridge. To enhance the precision of thin vessel, a sliding window sampling method is introduced for refined training. Compared with the state-of-the-art models in medical image segmentation, DD-UNet performs well in curvilinear structure segmentation of fundus image. For instance, our DD-UNet outperforms the Attention U-Net by 6.26% in terms of sensitivity and exhibits a 1.85% higher dice score in ridge segmentation. © 2023 IEEE.

关键词： curvilinear structure segmentation fundus image Retinopathy of Prematurity (ROP) ridge segmentation vessel segmentation

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FedWNS: Data Distribution-Wise Node Selection in Federated Learning via Reinforcement Learning 26

FedWNS: Data Distribution-Wise Node Selection in Federated L...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Tu, Chengwu Zhao, Shengjie Deng, Hao Tongji University School of Software Engineering Shanghai201800 China Key Laboratory of Embedded System and Service Computing Shanghai201800 China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai201800 China

ISBN: (纸本)9798350331684

To deal with the discrepancy between global and local objectives in the federated learning invoked by the non-independent, identically distributed (non-IID) data and mitigate the impact of catastrophic forgetting in the training phase, we propose a federated learning framework with data distribution-wise reinforcement learning to perform node selection to accelerate the convergence process and alleviate the accuracy degradation. In this framework, the agent on the central server observes the number of samples every node owns, the derived distribution information of every dataset, and the current local and global accuracy. Then infer the selected node-set to participate in the current federated learning round through policy network in reinforcement learning. Finally, we conduct simulations with publicly data sets. Simulation results indicate that our FedWNS outperforms the existing FedAvg and CSFedAvg on the testing accuracy and the communication rounds to reach target accuracy under different settings. © 2023 IEEE.

关键词： Reinforcement learning

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A Transformer-based Time Series Prediction Model with Contrastive Learning for Nuclear Power system State Monitoring 15

A Transformer-based Time Series Prediction Model with Contra...

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15th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Beijing 2024

作者： Gou, Pengfei Zheng, Daiwei Tang, Lei Liu, Mengjuan School of Information and Software Engineering University of Electronic Science and Technology of China UESTC Chengdu China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China NPIC Chengdu China

ISBN: (纸本)9798350354010

Prognostics and Health Management (PHM) is currently the mainstream solution for the condition monitoring and maintenance of industrial systems. In PHM, the system condition is monitored by monitoring the operational status parameters of the system to diagnose equipment faults, predict the remaining useful life, etc. Utilizing sequential operational status parameters to complete prediction and classification tasks is usually modeled as a time series processing task. Therefore, a high-performance time series prediction model is crucial for PHM. This paper focuses on the use of time series prediction models to achieve the prediction of key state parameters of the nuclear power system. However, there are still some challenges when directly applying existing time series prediction models to industrial systems. First, obtaining operational data on the nuclear power system is relatively difficult, resulting in a small amount of training data, which causes many models to face the risk of insufficient training or overfitting. Second, most of the existing time series prediction models are composed of high- and low-frequency decoupling modules, while the operational data of nuclear power systems often have fewer high-frequency components, making it difficult for many existing SOTA models to achieve good results. In order to address these challenges, we have developed a Transformer-based time series prediction model with contrastive learning for monitoring the status of nuclear power systems. We evaluated the new model on three nuclear power simulation datasets and four power transformer datasets. The experimental results show that the new model's performance is superior to baseline models. We also proved the effectiveness of our improvement through ablation experiments. © 2024 IEEE.

关键词： Prediction models

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MSE-CapsPPISP: Spatial Hierarchical Protein-Protein Interaction Sites Prediction Using Squeeze-and-Excitation Capsule Networks

MSE-CapsPPISP: Spatial Hierarchical Protein-Protein Interact...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Lv, Weipeng Jiang, Changkun Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (纸本)9781665468190

The discovery of protein-protein interaction sites (PPIs) is vital for exploring the principle of PPI and understanding the nature of life activities. Developing computational approaches to predict PPIs can effectively compensate for the shortcomings of biological experiments, which are mostly time-consuming and vulnerable to noise. In recent years, deep learning has been used to develop PPIs prediction models. Most of them consider the contextual information of the target amino acid residues and use a local protein sequence to represent the targets. However, the traditional deep-learning techniques, e.g., deep neural networks (DNNs) and convolutional neural networks (CNNs), disregard the important spatial hierarchies contained in the features of protein sequences, leading to their failure to effectively distinguish the interaction sites from different residue regions. In this work, we design MSE-CapsPPISP, a new deep-learning model to address the PPIs prediction with spatial hierarchies. The key idea of MSE-CapsPPISP is to take into account the hierarchical relationships between the features of protein sequences. We characterize the hierarchical relationships by designing a tailored Capsule Network (CapsNet) model, which is a novel type of neural network with vector neurons. Moreover, to make the network representation more robust, MSE-CapsPPISP uses multi-scale CNNs to extract multi-scale features of protein sequences and Squeeze-and-Excitation blocks to recalibrate the features. The validation results show that our MSE-CapsPPISP outperforms the baseline CNNs-based architecture DeepPPISP and other competing schemes in the PPIs prediction task. © 2022 IEEE.

关键词： Forecasting

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