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MLRR-ATV: A Robust Manifold Nonnegative LowRank Representation with Adaptive Total-Variation Regularization for scRNA-seq data Clustering

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2024年 PP卷 1-12页

作者： Wang, Gao-Fei Wang, Juan Yuan, Shasha Zheng, Chun-Hou Liu, Jin-Xing School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing Haidian District China School of Computer Science Qufu Normal University Rizhao Shandong China

Since genomics was proposed, the exploration of genes has been the focus of research. The emergence of single-cell RNA sequencing (scRNA-seq) technology makes it possible to explore gene expression at the single-cell level. Due to the limitations of sequencing technology, the data contains a lot of noise. At the same time, it also has the characteristics of highdimensional and sparse. Clustering is a common method of analyzing scRNA-seq data. This paper proposes a novel singlecell clustering method called Robust Manifold Nonnegative LowRank Representation with Adaptive Total-Variation Regularization (MLRR-ATV). The Adaptive Total-Variation (ATV) regularization is introduced into Low-Rank Representation (LRR) model to reduce the influence of noise through gradient learning. Then, the linear and nonlinear manifold structures in the data are learned through Euclidean distance and cosine similarity, and more valuable information is retained. Because the model is non-convex, we use the Alternating Direction Method of Multipliers (ADMM) to optimize the model. We tested the performance of the MLRRATV model on eight real scRNA-seq datasets and selected nine state-of-the-art methods as comparison methods. The experimental results show that the performance of the MLRRATV model is better than the other nine methods. IEEE

关键词： RNA

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Maximum Power Point Tracking Control of Offshore Wind-Photovoltaic Hybrid Power Generation System with Crane-Assisted

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computer Modeling in Engineering & sciences 2025年第4期143卷 289-334页

作者： Xiangyang Cao Yaojie Zheng Hanbin Xiao Min Xiao School of Transportation and Logistics Engineering Wuhan University of TechnologyWuhan430063China Sanya Science and Education Innovation Park Wuhan University of TechnologySanya572024China School of Computer Science and Artificial Intelligence Wuhan University of TechnologyWuhan430063China

This study investigates the Maximum Power Point Tracking(MPPT)control method of offshore windphotovoltaic hybrid power generation system with offshore crane-assisted.A new algorithm of Global Fast Integral Sliding Mode Control(GFISMC)is proposed based on the tip speed ratio method and sliding mode *** algorithm uses fast integral sliding mode surface and fuzzy fast switching control items to ensure that the offshore wind power generation system can track the maximum power point quickly and with low *** offshore wind power generation system model is presented to verify the algorithm *** offshore off-grid wind-solar hybrid power generation systemis built in MATLAB/*** with other MPPT algorithms,this study has specific quantitative improvements in terms of convergence speed,tracking accuracy or computational ***,the improved algorithm is further analyzed and carried out by using Yuankuan Energy’s ModelingTech semi-physical simulation *** results verify the feasibility and effectiveness of the improved algorithm in the offshore wind-solar hybrid power generation system.

关键词： Offshore wind power generation efficiency maximum power point tracking(MPPT) integral sliding mode control grey wolf optimization algorithm offshore photovoltaic cells

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Towards High-Performance Graph Processing: From a Hardware/Software Co-Design Perspective

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Journal of computer science & technology 2024年第2期39卷 245-266页

作者：廖小飞赵文举金海姚鹏程黄禹王庆刚赵进郑龙张宇邵志远 National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China

Graph processing has been widely used in many scenarios,from scientific computing to artificial *** processing exhibits irregular computational parallelism and random memory accesses,unlike traditional ***,running graph processing workloads on conventional architectures(e.g.,CPUs and GPUs)often shows a significantly low compute-memory ratio with few performance benefits,which can be,in many cases,even slower than a specialized single-thread graph *** domain-specific hardware designs are essential for graph processing,it is still challenging to transform the hardware capability to performance boost without coupled software *** article presents a graph processing ecosystem from hardware to *** start by introducing a series of hardware accelerators as the foundation of this ***,the codesigned parallel graph systems and their distributed techniques are presented to support graph ***,we introduce our efforts on novel graph applications and hardware *** results show that various graph applications can be efficiently accelerated in this graph processing ecosystem.

关键词： graph processing hardware accelerator software system high performance ecosystem

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Short-term load forecasting based on improved optimization algorithms 9

Short-term load forecasting based on improved optimization a...

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9th International Symposium on Advances in Electrical, Electronics, and computer Engineering, ISAEECE 2024

作者： Zhang, Jiaan Ning, Ruanhao School of Artificial Intelligence and Data Science Hebei University of Technology TianJing China

ISBN: (纸本)9781510683303

Accurate predictions of electricity load are crucial for the safe and stable operation, as well as the economic performance, of power systems. To overcome the inadequacy of feature selection and the difficulty in tuning model hyperparameters in traditional load forecasting models — issues that may lead the predictive model into local optima — this paper proposes a short-term electricity load forecasting method based on ***, the eXtreme Gradient Boosting (XGBoost) tree model is employed for feature selection. Subsequently, the Improved artificial Rabbits Optimization (IARO) algorithm is enhanced using tent chaotic mapping, elite reverse learning strategy, and hybrid reverse learning strategy. The improved IARO algorithm is then utilized to tune the hyperparameters of Long Short-Term Memory (LSTM).Finally, the XGBoost-IARO-LSTM combined forecasting model is established to achieve accurate short-term load predictions. The proposed method is validated using real electricity load data from a specific region in northern China. The results indicate that the proposed approach exhibits faster parameter optimization speed and superior prediction performance. © 2024 SPIE.

关键词： Photons

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Federated Learning Based on data Divergence and Differential Privacy in Financial Risk Control Research

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computers, Materials & Continua 2023年第4期75卷 863-878页

作者： Mao Yuxin Wang Honglin School of Artificial Intelligence/School of Future Technology Nanjing University of Information Science and TechnologyNanjing210044China

In the financial sector, data are highly confidential and sensitive,and ensuring data privacy is critical. Sample fusion is the basis of horizontalfederation learning, but it is suitable only for scenarios where customershave the same format but different targets, namely for scenarios with strongfeature overlapping and weak user overlapping. To solve this limitation, thispaper proposes a federated learning-based model with local data sharing anddifferential privacy. The indexing mechanism of differential privacy is used toobtain different degrees of privacy budgets, which are applied to the gradientaccording to the contribution degree to ensure privacy without affectingaccuracy. In addition, data sharing is performed to improve the utility ofthe global model. Further, the distributed prediction model is used to predictcustomers’ loan propensity on the premise of protecting user privacy. Usingan aggregation mechanism based on federated learning can help to train themodel on distributed data without exposing local data. The proposed methodis verified by experiments, and experimental results show that for non-iiddata, the proposed method can effectively improve data accuracy and reducethe impact of sample tilt. The proposed method can be extended to edgecomputing, blockchain, and the Industrial Internet of Things (IIoT) *** theoretical analysis and experimental results show that the proposedmethod can ensure the privacy and accuracy of the federated learning processand can also improve the model utility for non-iid data by 7% compared tothe federated averaging method (FedAvg).

关键词： data privacy federated learning machine learning data difference

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Reasoning-Oriented and Analogy-Based Methods for Locating and Editing in Zero-Shot Event-Relational Reasoning 31

Reasoning-Oriented and Analogy-Based Methods for Locating an...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Tang, Jingyao Li, Lishuang Mi, Liteng Wu, Haiming Lu, Hongbin School of Computer Science and Technology Dalian University of Technology China School of Computer Science and Technology Beijing Institute of Technology China School of Computer Science and Artificial Intelligence Liaoning Normal University China

ISBN: (纸本)9798891761964

Zero-shot event-relational reasoning is an important task in natural language processing, and existing methods jointly learn a variety of event-relational prefixes and inference-form prefixes to achieve such tasks. However, training prefixes consumes large computational resources and lacks interpretability. Additionally, learning various relational and inferential knowledge inefficiently exploits the connections between tasks. Therefore, we first propose a method for Reasoning-Oriented Locating and Editing (ROLE), which locates and edits the key modules of the language model for reasoning about event relations, enhancing interpretability and also resource-efficiently optimizing the reasoning ability. Subsequently, we propose a method for Analogy-Based Locating and Editing (ABLE), which efficiently exploits the similarities and differences between tasks to optimize the zero-shot reasoning capability. Experimental results show that ROLE improves interpretability and reasoning performance with reduced computational cost. ABLE achieves SOTA results in zero-shot reasoning. © 2025 Association for Computational Linguistics.

关键词： Zero-shot learning

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Unstructured Big data Threat intelligence Parallel Mining Algorithm

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Big data Mining and Analytics 2024年第2期7卷 531-546页

作者： Zhihua Li Xinye Yu Tao Wei Junhao Qian School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi 214122China School of IoT Engineering Jiangnan UniversityWuxi 214122China

To efficiently mine threat intelligence from the vast array of open-source cybersecurity analysis reports on the web,we have developed the Parallel Deep Forest-based Multi-Label Classification(PDFMLC)***,open-source cybersecurity analysis reports are collected and converted into a standardized text ***,five tactics category labels are annotated,creating a multi-label dataset for tactics *** the limitations of low execution efficiency and scalability in the sequential deep forest algorithm,our PDFMLC algorithm employs broadcast variables and the Lempel-Ziv-Welch(LZW)algorithm,significantly enhancing its acceleration ***,our proposed PDFMLC algorithm incorporates label mutual information from the established dataset as input *** captures latent label associations,significantly improving classification ***,we present the PDFMLC-based Threat intelligence Mining(PDFMLC-TIM)*** results demonstrate that the PDFMLC algorithm exhibits exceptional node scalability and execution ***,the PDFMLC-TIM method proficiently conducts text classification on cybersecurity analysis reports,extracting tactics entities to construct comprehensive threat *** a result,successfully formatted STIX2.1 threat intelligence is established.

关键词： unstructured big data mining parallel deep forest multi-label classification algorithm threat intelligence

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Robust multi-frequency band joint dictionary learning with low-rank representation

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8075-8088页

作者： Ding, Huafeng Shang, Junyan Zhou, Guohua School of Intelligent Control Changzhou Vocational Institute of Industry Technology Changzhou China School of Computer Science and Artificial Intelligence Changzhou University Changzhou China

Emotional state recognition is an important part of emotional research. Compared to non-physiological signals, the electroencephalogram (EEG) signals can truly and objectively reflect a person’s emotional state. To explore the multi-frequency band emotional information and address the noise problem of EEG signals, this paper proposes a robust multi-frequency band joint dictionary learning with low-rank representation (RMBDLL). Based on the dictionary learning, the technologies of sparse and low-rank representation are jointly integrated to reveal the intrinsic connections and discriminative information of EEG multi-frequency band. RMBDLL consists of robust dictionary learning and intra-class/inter-class local constraint learning. In robust dictionary learning part, RMBDLL separates complex noise in EEG signals and establishes clean sub-dictionaries on each frequency band to improve the robustness of the model. In this case, different frequency data obtains the same encoding coefficients according to the consistency of emotional state recognition. In intra-class/interclass local constraint learning part, RMBDLL introduces a regularization term composed of intra-class and inter-class local constraints, which are constructed from the local structural information of dictionary atoms, resulting in intra-class similarity and inter-class difference of EEG multi-frequency bands. The effectiveness of RMBDLL is verified on the SEED dataset with different noises. The experimental results show that the RMBDLL algorithm can maintain the discriminative local structure in the training samples and achieve good recognition performance on noisy EEG emotion datasets. © 2024 – IOS Press.

关键词： Electroencephalography

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SAR Images Change Detection Based on Maximum Classifier Difference with Multiscale Multilevel Features

SAR Images Change Detection Based on Maximum Classifier Diff...

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2024 China Automation Congress, CAC 2024

作者： Ling, Zhiquan Yang, Dedong The School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China

ISBN: (纸本)9798350368604

Synthetic aperture radar (SAR) images can still be effective under harsh weather and lighting conditions, but the presence of scattering noise and the lack of labeled sufficient datasets have severely impacted the development of change detection (CD) methods and limited the performance of CD models. In order to solve the above problems and make full use of the existing labeled data, we propose a CD model based on the maximum classifier difference of multi-scale features. Firstly, we extract multiscale features directly from the original images and fuse them, after which the multiscale features are fed into a Siamese network for multilevel feature extraction, followed by fusion of the multilevel features for use in subsequent CD. The extracted features are then fed into the classification module and subjected to maximum classifier variance processing. While ensuring the performance of the model on the source domain dataset, the parameters of the two classifiers are updated so that the difference between their results on the target domain dataset increases, and then the parameters of the feature extractor are updated so that the difference between the results of the two classifiers on the target domain dataset decreases. Repeating the above process can achieve a reduction in the difference between the source and target domain datasets, so that the model can make full use of the labeled data and can perform well on the unlabeled data as well. We conducted comparative experiments on four publicly available SAR datasets, and the experimental results demonstrate the effectiveness and sophistication of the method. © 2024 IEEE.

关键词： Change detection

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Facial Depression Estimation via Multi-Cue Contrastive Learning

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 6007-6020页

作者： Wang, Xinke Xu, Jingyuan Sun, Xiao Li, Mingzheng Hu, Bin Qian, Wei Guo, Dan Wang, Meng Anhui University School of Artificial Intelligence Hefei230601 China Hefei Comprehensive National Science Center Institute of Artificial Intelligence Hefei230031 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China University of Science and Technology of China Institute of Advanced Technology Hefei230094 China Beijing Institute of Technology School of Medical Technology Beijing100081 China

Vision-based depression estimation is an emerging yet impactful task, whose challenge lies in predicting the severity of depression from facial videos lasting at least several minutes. Existing methods primarily focus on fusing frame-level features to create comprehensive representations. However, they often overlook two crucial aspects: 1) inter- and intra-cue correlations, and 2) variations among samples. Hence, simply characterizing sample embeddings while ignoring to mine the relation among multiple cues leads to limitations. To address this problem, we propose a novel Multi-Cue Contrastive Learning (MCCL) framework to mine the relation among multiple cues for discriminative representation. Specifically, we first introduce a novel cross-characteristic attentive interaction module to model the relationship among multiple cues from four facial features (e.g., 3D landmarks, head poses, gazes, FAUs). Then, we propose a temporal segment attentive interaction module to capture the temporal relationships within each facial feature over time intervals. Moreover, we integrate contrastive learning to leverage the variations among samples by regarding the embeddings of inter-cue and intra-cue as positive pairs while considering embeddings from other samples as negative. In this way, the proposed MCCL framework leverages the relationships among the facial features and the variations among samples to enhance the process of multi-cue mining, thereby achieving more accurate facial depression estimation. Extensive experiments on public datasets, DAIC-WOZ, CMDC, and E-DAIC, demonstrate that our model not only outperforms the advanced depression methods but that the discriminative representations of facial behaviors provide potential insights about depression. Our code is available at:https://***/xkwangcn/***. © 1991-2012 IEEE.

关键词： Face recognition

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