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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhou, Li Li, Jie Tan, Weilong Harbin Institute of Technology School of Computer Science and Technology Harbin China National Key Laboratory of Smart Farming Technology and Systems College of Computing Harbin Institute of Technology Harbin China Technology Center for Medicines Center for Medicines Nanjing China

ISBN: (纸本)9798350386226

Most pathway analysis methods based on simple nucleotide variations overlook mutations occurring in genes outside the pathway, fail to address mutation heterogeneity with targeted measures, and do not consider the functional relationships among gene components within biomolecular networks when modeling the contributions of biomarkers to pathways. So, we proposed a novel framework, named IntNet, for pathway analysis based on simple nucleotide variations. The core components of IntNet are sample reconstruction (SC) and a new biologically informed neural network model (BINN). To address the difficulty of extracting effective features caused by the mutation heterogeneity, IntNet reconstructs mutation samples based on the hypothesis that mutations in patients with the same phenotype may cluster in similar network regions. BINN uses the human gene network and expanded biological pathways to construct its neural network architecture. BINN implements mutation signaling propagation along the human gene network and integration within the pathways. IntNet's performance was validated using multiple cancer datasets. Experimental results demonstrated that IntNet could identify more accurate and reliable pathways. © 2024 IEEE.

关键词： Nucleotides

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Multiple adverse weather removal using adversarial and contrastive learning 9

Multiple adverse weather removal using adversarial and contr...

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9th IEEE Smart World Congress, SWC 2023

作者： Zhang, Yuanfan Wang, Jinghua Hu, Liang Harbin Institute of Technology Department of Automation Shenzhen China Harbin Institute of Technology School of Computer Science and Technology Shenzhen China

ISBN: (纸本)9798350319804

Adverse weather removal is significant and valuable for real-world computer vision systems. Most existing algorithms focus on removing one specific type of weather and rely on paired synthesized weather datasets for training. In this paper, to train a multiple weather removal model with unpaired datasets, we introduce multi-dimension contrastive learning mechanism into generative adversarial networks. Specifically, our method consists of two blocks: multi-channel cycle translation (MCCT) block and multi-dimension contrastive learning (MDCL) block. MCCT is used to preserve the color and structural information and gain informative supervisory signals. MDCL is proposed to discriminate feature distribution among different image domains. Results of extensive experiments show that our method is superior over existing unsupervised methods and maintains a decent performance degradation when adapted to multiple weather removal tasks. © 2023 IEEE.

关键词： adverse weather removal contrastive learning deep learning generative adversarial networks unsupervised learning

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AsyCo: an asymmetric dual-task co-training model for partial-label learning

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science China(Information sciences) 2025年第5期68卷 332-347页

作者： Beibei LI Yiyuan ZHENG Beihong JIN Tao XIANG Haobo WANG Lei FENG College of Computer Science Chongqing University State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences School of Software Technology Zhejiang University School of Computer Science and Engineering Nanyang Technological University

Partial-label learning(PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance but suffer from error accumulation problems caused by mistakenly disambiguated instances. Although co-training can alleviate this issue by training two networks simultaneously and allowing them to interact with each other, most existing co-training methods train two structurally identical networks with the same task, i.e., are symmetric, rendering it insufficient for them to correct each other due to their similar limitations. Therefore, in this paper, we propose an asymmetric dual-task co-training PLL model called AsyCo,which forces its two networks, i.e., a disambiguation network and an auxiliary network, to learn from different views explicitly by optimizing distinct tasks. Specifically, the disambiguation network is trained with a self-training PLL task to learn label confidence, while the auxiliary network is trained in a supervised learning paradigm to learn from the noisy pairwise similarity labels that are constructed according to the learned label confidence. Finally, the error accumulation problem is mitigated via information distillation and confidence refinement. Extensive experiments on both uniform and instance-dependent partially labeled datasets demonstrate the effectiveness of AsyCo.

关键词： machine learning weakly supervised learning partial-label learning co-training models candidate label sets

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Resource investment for DDoS attack resistant SDN: a practical assessment

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science China(Information sciences) 2023年第7期66卷 112-129页

作者： Bin YUAN Fan ZHANG Jun WAN Huan ZHAO Shui YU Deqing ZOU Qiangsheng HUA Hai JIN School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science University of Technology Sydney

Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of service(DDoS) attack, which aims to drain the resources of SDN switches and controllers,is one of the most common. Once the switch or controller is damaged, the network services can be *** defense schemes against DDoS attacks have been proposed from the perspective of attack detection;however, such defense schemes are known to suffer from a time consuming and unpromising accuracy, which could result in an unavailable network service before specific countermeasures are taken. To address this issue through a systematic investigation, we propose an elaborate resource-management mechanism against DDoS attacks in an SDN. Specifically, by considering the SDN topology, we leverage the M/M/c queuing model to measure the resistance of an SDN to DDoS attacks. Network administrators can therefore invest a reasonable number of resources into SDN switches and SDN controllers to defend against DDoS attacks while guaranteeing the quality of service(QoS). Comprehensive analyses and empirical data-based experiments demonstrate the effectiveness of the proposed approach.

关键词： SDN security DDoS attacks resource management queueing theory QoS

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Formation control and path planning of multi-robot systems via large language models

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science China(Information sciences) 2025年第5期68卷 72-88页

作者： Dong XUE Xuanjie ZHOU Ming WANG Fangzhou LIU Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of EducationEast China University of Science and Technology Research Institute of Intelligent Control and Systems School of Astronautics Harbin Institute of Technology

Existing path planning and coordination control methods for multi-robot systems(MRS) typically rely on predefined rules and rudimentary algorithms. However, these methods often struggle to adapt flexibly to complex environments and to adjust motion targets appropriately. To address this challenge, this study presents a large language model(LLM)-assisted framework. By integrating textual descriptions of complex motion constraints, robot information, and local environmental data as inputs, LLMs generate motion objectives and translate them into executable control commands for the robots, thereby achieving coordinated control and path planning. This framework facilitates the generation, maintenance, and reshaping of formations in MRSs during path planning, applicable to both obstacle-free and obstacle-avoidance environments. Simulation results demonstrate that LLM-based control strategies enhance the autonomy, adaptability, flexibility, and robustness of MRS by processing complex information, making intelligent decisions, adapting to environmental changes, and handling disturbances and uncertainties.

关键词： multi-robot systems formation control path planning obstacle avoidance large language models

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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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Improving flexural strength of Ti_(2)C-Ti cermet by hot compressive deformation:Microstructure evolution and fracture behaviors

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science China(Technological sciences) 2023年第11期66卷 3298-3308页

作者： SUN FengBo WANG Shuai CHEN Xin ZHANG Rui AN Qi LIU WenQi HUANG LuJun GENG Lin School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001China Waikato Centre for Advanced Materials and Manufacturing School of EngineeringUniversity of WaikatoHamilton 3240New Zealand State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001China

In order to improve the microstructure and mechanical properties,the hot compressive deformation with 50%height reduction at 1100℃was conducted on a Ti_(2)C-Ti *** results showed that the lamellar Ti precipitates in Ti_(2)C grains were transformed to bimodal size distribution,which was approximately 290 nm and 5.8μm in diameter,respectively,after the hot *** bimodal Ti precipitates suppressed{011}cleavage surfaces of Ti_(2)C during flexural fracture,which resulted in an 18.5%increment of *** phenomenon can be attributed to the bimodal Ti precipitates that decreased the average crack driving force due to their gentle variation in elastic modulus compared with the monolithic lamellar Ti *** present work can guide further deformation and mechanical property improvement of Ti_(2)C cermets.

关键词： cermet sub-stoichiometric titanium carbide hot deformation precipitate fracture behavior

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Application of a microalga, Tetradesmus obliquus PF3, for NO and CO2 removal from actual flue gas via cultivating in wastewater

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Environmental science and Pollution Research 2024年第42期31卷 54938-54949页

作者： Ma, Shanshan Yu, Yanling Li, Dianlin Cui, Hao Feng, Yujie School of Ecological Environment Technology Guangdong Industry Polytechnic University Guangzhou510300 China State Key Laboratory of Urban Water Resource and Environment School of Environment Harbin Institute of Technology Harbin150090 China School of Chemical and Engineering & amp Technology Harbin Institute of Technology Harbin150001 China Guangzhou Institute of Building Science Group Co. Ltd. Guangzhou510440 China

Greenhouse gas (GHG) emissions, particularly anthropogenic emissions, are the primary drivers of climate change. The cultivation of microalgae represents a highly promising strategy for mitigating atmospheric GHG levels. The growth characteristics and GHG mitigation capabilities of Tetradesmus obliquus PF3 were investigated in domestic wastewater at a thermal power plant. The maximum cell density and productivity were 1.52 ± 0.01 g L−1 and 0.33 ± 0.01 g L−1 day−1, respectively. Utilizing a serial configuration of two reactors, the elimination efficiency of NO and CO2 attained values of 78 ± 4% and 14 ± 4%, respectively. NO concentration at the outlet was less than 24.6 ± 2.9 mg m−3, meeting the latest Chinese discharge limits. Besides, the recovery efficiency of NO and CO2 increased to 77 ± 8% and 2.24 ± 0.04%, respectively, compared to that of the single reactor (40 ± 3%, 0.9 ± 0.0%). A removal efficiency of over 90% was achieved for TN and TP in domestic wastewater. The concentrations of COD (76.5 mg L−1), NH4+-N (0.9 mg L−1), TN(6.31 mg L−1), and TP (0.35 mg L−1) in effluent were below the thresholds of 100 mg L−1, 25 mg L−1, none data, and 3 mg L−1, respectively, complying with the Chinese Discharge Standard (Class II criteria set forth) for Municipal Wastewater Treatment Plants Pollutants. The harvested biomass exhibited a high content of carbohydrates and proteins, making it a viable feedstock for biofuels and bio-fertilizers. Our results demonstrate that Tetradesmus obliquus PF3–based flue gas treatment technology can simultaneously realize GHG removal, wastewater bio-remediation, and biomass recovery. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Microalgae

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TSC-AutoML: Meta-learning for Automatic Time Series Classification Algorithm Selection 39

TSC-AutoML: Meta-learning for Automatic Time Series Classifi...

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Mu, Tianyu Wang, Hongzhi Zheng, Shenghe Liang, Zhiyu Wang, Chunnan Shao, Xinyue Liang, Zheng Harbin Institute of Technology School of Computer Science Harbin China Shenzhen China

ISBN: (纸本)9798350322279

With years of development, a significant number of Time Series Classification (TSC) algorithms have been proposed and applied to various fields such as scientific research and industry scenarios, including traditional statistical methods, machine learning methods, and recently deep learning models. However, choosing a suitable model along with good parameter values that perform well on a given task, which is also known as Combined Algorithm Selection and Hyperparameter optimization problem (CASH), is still challenging. How to automatically select the appropriate algorithm according to the task during analyzing is a topic worthy of further research. Nevertheless, for TSC, a field that has been developed for decades, there is no effective and efficient approach for automatic algorithm selection. To the best of our knowledge, the current approach is based on genetic search, which is very computationally intensive and time-consuming. Therefore, in this paper, we propose TSC-AutoML, a zero-configuration and meta-learning-based approach for the automatic Time Series Classification algorithm CASH (also known as TSC-CASH). TSC-AutoML extracts knowledge from historical tasks and performs automatic feature selection and knowledge filtering with a reinforcement learning policy. The experience extracted is filtered and transformed into metadata. The meta-learner trained on the metadata together with our proposed warm start strategy will select an optimal algorithm for tasks uploaded by users, and then our proposed Hyperparameter Optimization method based on the Fast Warm Start strategy searches for hyperparameter combinations of the selected algorithm and adjusts parameter configuration to achieve top performance. The entire process is pre-trained, automated for the new task, and parameter-free for the user to decide, making it easy for users with the little domain experience to get started easily. Experimental results illustrate that TSC-AutoML outperforms existing methods in

关键词： Metadata

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Iron species activating chlorite:Neglected selective oxidation for water treatment

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Environmental science and Ecotechnology 2023年第2期14卷 63-71页

作者： Qihui Xu Zhipeng Li Feng Liu Hong You Binghan Xie State Key Laboratory of Urban Water Resources and Environment Harbin Institute of TechnologyHarbin150090China School of Marine Science and Technology Harbin Institute of Technology at WeihaiWeihai264209China

Chlorite(ClO_(2)-)is the by-product of the water treatment process carried out using chlorine dioxide(ClO_(2))as an effective disinfectant and oxidant;however,the reactivation of ClO_(2)has commonly been ***,it was unprecedentedly found that ClO_(2)could be activated by iron species(Feb:Fe0,FeII,or FeIII),which contributed to the synchronous removal of ClO_(2)and selective oxidative treatment of organic ***,the above-mentioned activation process presented intensive Ht-dependent *** introduction of Feb significantly shortened the autocatalysis process via the accumulation of Clor ClOduring the protonation of ClO_(2)driven by ultrasonic ***,it was found that the interdependent high-valent-Fe-oxo and ClO_(2),after identification,were the dominant active species for accelerating the oxidation ***,the unified mechanisms based on coordination catalysis([FeN(H_(2)O)a(ClOxm)b]nt-P)were putative,and this process was thus used to account for the pollutant removal by the Feb-activated protonated ClO_(2).This study pioneers the activation of ClO_(2)for water treatment and provides a novel strategy for“waste treating waste”.Derivatively,this activation process further provides the preparation methods for sulfones and ClO_(2),including the oriented oxidation of sulfoxides to sulfones and the production of ClO_(2) for on-site use.

关键词： Iron species Chlorite High-valent-Fe-oxo Chlorine dioxide Coordination catalysis

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