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IEEE information technology, Networking, Electronic and Automation Control Conference

作者： Haolin Xia Sirui Sun Xinyu Liu Ju Zhou Hanpu Wang Tong Chen School of Electronic and Information Engineering Southwest University Chongqing China Key Laboratory of Non-Linear Circuit and Intelligent Information Processing of Chongqing Chongqing China Institute of Legal Psychology and Intelligent Computing Chongqing China

Stress is an emotional state that affects our lives, so it is vital to detect it accurately. Numerous studies have shown that tissue oxygen saturation (StO2) is an effective physiological signal for identifying different stress states. However, previous StO2 feature extraction methods relied too heavily on baselines to be realized in practice. In this paper, a two-stream network combining shallow and deep features, called CRNet, is proposed for baseline-independent StO2 stress classification. In CRNet, shallow features are extracted using a CNN network and deep features are extracted using a ResNet network. For the extracted high-dimensional deep features, MLP module is employed for dimensionality reduction, which can effectively combine the two levels of features and improve the feature extraction capability of the network. Experimental results show that CRNet can significantly improve the performance of StO2 stress classification to the state-of-the-art (SOTA) even when using only raw imbalanced data.

关键词： Dimensionality reduction Automation Anxiety disorders Feature extraction Physiology Stress

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Multispectral and Hyperspectral Image Fusion Via Joint Low-Rank and Smooth Tensor Prior

Multispectral and Hyperspectral Image Fusion Via Joint Low-R...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Fan Li Yong Deng Shaoquan Zhang Yuyun Liang Hong Wang Chengzhi Deng Jun Li School of Computer Science Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan China School of Information Engineering Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Nanchang China Changjiang Institute of Survey Planning Design and Research Wuhan China TidBit Global Propell AI Norway

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Multispectral and hyperspectral image fusion has emerged as a highly effective technique for obtaining images with both high spatial and spectral resolution. This is an ill-posed problem that poses significant challenges to the optimization solution, which is often mitigated by incorporating low-rank and smooth priors to restrict the solution space. Traditionally, these priors are combined additively using nuclear norm and total variation (TV) regularization. However, the intricate interactions between these priors make it difficult to accurately characterize the prior structure using an additive approach. Moreover, their influence is heavily dependent on the trade-off parameter between the regularization terms. To address this issue, we propose a novel fusion method leveraging a joint low-rank and smooth tensor prior (LRST). The LRST method introduces a tensor nuclear norm on the gradient maps of various dimensions of the target image capitalizing on the analogous manifold structures shared between the original image and its gradient map, which seamlessly integrates the two priors into a unified regularization framework. This facilitates the precise and convenient exploitation of spatial and spectral correlations inherent in the desired hyperspectral image. Experimental findings demonstrate that compared to state-of-the-art fusion methods, the LRST approach yields finely fused images.

关键词： Manifolds Tensors TV Correlation Additives Geoscience and remote sensing Spatial resolution

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Scalable Graph Compressed Convolutions

arXiv

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arXiv 2024年

作者： Sun, Junshu Wang, Shuhui Yang, Chenxue Huang, Qingming The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The Agricultural Information Institute China Academy of Agricultural Sciences Beijing100081 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Peng Cheng Laboratory Shenzhen518066 China

Designing effective graph neural networks (GNNs) with message passing has two fundamental challenges, i.e., determining optimal message-passing pathways and designing local aggregators. Previous methods of designing optimal pathways are limited with information loss on the input features. On the other hand, existing local aggregators generally fail to extract multi-scale features and approximate diverse operators under limited parameter scales. In contrast to these methods, Euclidean convolution has been proven as an expressive aggregator, making it a perfect candidate for GNN construction. However, the challenges of generalizing Euclidean convolution to graphs arise from the irregular structure of graphs. To bridge the gap between Euclidean space and graph topology, we propose a differentiable method that applies permutations to calibrate input graphs for Euclidean convolution. The permutations constrain all nodes in a row regardless of their input order and therefore enable the flexible generalization of Euclidean convolution to graphs. Based on the graph calibration, we propose the Compressed Convolution Network (CoCN) for hierarchical graph representation learning. CoCN follows local feature-learning and global parameter-sharing mechanisms of convolution neural networks. The whole model can be trained end-to-end, with compressed convolution applied to learn individual node features and their corresponding structure features. CoCN can further borrow successful practices from Euclidean convolution, including residual connection and inception mechanism. We validate CoCN on both node-level and graph-level benchmarks. CoCN achieves superior performance over competitive GNN baselines. Codes are available at https://***/sunjss/CoCN. © 2024, CC BY-NC-SA.

关键词： Graph neural networks

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Optimized Statistical Model for Complex Chemical Processes Monitoring

Optimized Statistical Model for Complex Chemical Processes M...

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Data Driven Control and Learning Systems (DDCLS)

作者： Jian Zhang Xiaohe Qu Zhiwei Lei Haifeng Zhang Kai Zhong Datang East China Electric Power Test and Research Institute Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei Anhui China

The traditional kernel principal components analysis (KPCA) and linear discriminant analysis (LDA) have been verified to be two effective approaches for fault detection and diagnosis in recent years. Nevertheless, the conventional method and corresponding improved ones still exposed their deficiencies in some ways. Facing this dilemma, this paper presents a combination of optimized KPCA and modified LDA (OKPCA-MLDA), in which the OKPCA avoids the loss of original features after centralizing data in the eigenspace by adjusting covariance matrix's eigenvalue and transforming the distribution of variables thus providing representative and abundant principal components for the classifier. In addition, the MLDA maximizes a brand new objective function in feature space which achieves better classification performance than the conventional LDA, then utilizing diagnostic thresholds and similarity coefficients to identify the fault types. Based on the combined model, not only the fault detection and diagnosis can be realized simultaneously but also the accuracy of detection and diagnosis can be guaranteed. Furthermore, the simulation experiments on Tennessee Eastman (TE) benchmark process clearly illustrated the superiority of our proposed strategy.

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Learned Fingerprint Embedding for Large-Scale Peptide Mass Spectra Retrieval

Learned Fingerprint Embedding for Large-Scale Peptide Mass S...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yongshuai Wang Xiaojun Cai Defeng Li Shiwei Sun Cheng Chen Xuefeng Cui School of Computer Science and Technology Shandong University Qingdao China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Tandem mass spectrometry (MS/MS) is a widely used technique for protein identification, post-translational modifications, immunotherapy, and other applications. As the amount of MS/MS spectra data increases, new computational methods are needed to efficiently search through these databases. This study introduces MS2VEC, a novel fingerprint embedding model designed to facilitate large-scale retrieval of peptide mass spectra. MS2VEC captures the relationships between distant peaks and incorporates position-aware fingerprint features from all peaks. To do this, dilated convolutions are used to capture remote relationships, and a novel position-aware multi-head attention pooling mechanism is used to abstract fingerprint features. The results demonstrate that MS2VEC achieves a top-1 retrieval accuracy of 0.810, outperforming existing methods by 5.1%. Interestingly, the precursor charge is not essential for the retrieval task, as the spectra itself contains enough information to accurately predict the charge. Additionally, the results suggest that weight-balanced fragment ions and water losses are important contributors to fingerprint features.

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Beacon wavelength selection for wavefront detection in horizontal path under strong turbulence 13

Beacon wavelength selection for wavefront detection in horiz...

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Holography, Diffractive Optics, and Applications XIII 2023

作者： Wu, Fengyang Zhong, Sheng Wang, Xun Dong, Lizhi Liu, Ziyu Jing, Jianhang Zhang, Ying School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China National Key Laboratory of Multi-Spectral Information Intelligent Processing Huazhong University of Science and Technology Wuhan430074 China Aerospace Science & Industry Microelectronics System Institute Co. Ltd Chengdu610213 China

ISBN: (纸本)9781510667853

Atmospheric turbulence has severe influence on laser beam propagations. The well accepted method of compensating for the aberrations in the turbulence is generating a beacon on the target or at the point-ahead location. In most scenarios, the wavelength of the beacon is similar to or shorter than the launched beam. However, when the strength of the turbulence gets stronger, intensity scintillations and wavefront branch points also arise, making it difficult to accurately measure the wavefront. As scintillations and branch points weakens with wavelength increases, it is reasonable to consider using beacons with longer wavelengths to improve wavefront detection accuracy. To further investigate the feasibility of beacons with longer wavelengths, we established a simulation model of wavefront detection and correction in horizontal path under strong turbulence. A split-step multi-phase screen method is employed to simulate the propagations of laser beams. Then a series of simulations with different refractive index structure parameter Cn2 and beacon wavelengths are studied. We show that under weak turbulence, the optimal wavelength of beacon is similar to the launched beam, as is widely investigated and concluded. However, beacons with longer wavelength are preferred under strong turbulence, especially with a general wavefront reconstruction algorithm without considering branch points. This research would be helpful to for designing adaptive optics systems to improve beam qualities in strong turbulence with adaptive optics systems. © 2023 SPIE.

关键词： Atmospheric turbulence

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OpenViewer: Openness-Aware Multi-View Learning

arXiv

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arXiv 2024年

作者： Du, Shide Fang, Zihan Tan, Yanchao Wang, Changwei Wang, Shiping Guo, Wenzhong College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Jinan China

Multi-view learning methods leverage multiple data sources to enhance perception by mining correlations across views, typically relying on predefined categories. However, deploying these models in real-world scenarios presents two primary openness challenges. 1) Lack of Interpretability: The integration mechanisms of multi-view data in existing black-box models remain poorly explained;2) Insufficient Generalization: Most models are not adapted to multi-view scenarios involving unknown categories. To address these challenges, we propose OpenViewer, an openness-aware multi-view learning framework with theoretical support. This framework begins with a Pseudo-Unknown Sample Generation Mechanism to efficiently simulate open multi-view environments and previously adapt to potential unknown samples. Subsequently, we introduce an Expression-Enhanced Deep Unfolding Network to intuitively promote interpretability by systematically constructing functional prior-mapping modules and effectively providing a more transparent integration mechanism for multi-view data. Additionally, we establish a Perception-Augmented Open-Set Training Regime to significantly enhance generalization by precisely boosting confidences for known categories and carefully suppressing inappropriate confidences for unknown ones. Experimental results demonstrate that OpenViewer effectively addresses openness challenges while ensuring recognition performance for both known and unknown samples. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Who is in Charge here? Understanding How Runtime Configuration Affects Software Along with Variables&Constants

Who is in Charge here? Understanding How Runtime Configurati...

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Asia-Pacific Conference on Software Engineering

作者： Chaopeng Luo Yuanliang Zhang Haochen He Zhouyang Jia Teng Wang Shulin Zhou Si Zheng Shanshan Li National University of Defense Technology Changsha China Key Laboratory of Satellite Information Intelligent Processing and Application Research Beijing China National Innovation Institute of Defense Technology Beijing China

ISBN: (数字)9798331534011

ISBN: (纸本)9798331534028

Runtime misconfiguration can lead to software performance degradation and even cause failure. It is usually caused by invalid parameter values set by users. Developers typically perform sanity checks during the configuration parsing stage to prevent invalid parameter values. However, we discovered that even valid values that pass these checks can also lead to unexpected severe consequences. Our study reveals the underlying reason: the value of runtime configuration parameters may interact with other constants and variables when propagated and used, altering its original effect on software behavior. Consequently, parameter values may no longer be valid when encountering complex runtime environments and workloads. Therefore, it is extremely challenging for users to properly configure the software before it starts running. This paper presents the first comprehensive and in-depth study (to the best of our knowledge) on how configuration affects software at runtime through the interaction with constants, and variables (PCV Interaction). Parameter values represent user intentions, constants embody developer knowledge, and variables are typically defined by the runtime environment and workload. This interaction essentially illustrates how different roles jointly determine software behavior. In this regard, we studied 705 configuration parameters from 10 large-scale Software systems. We reveal that a large portion of configuration parameters interact with constants/variables after parsing. We analyzed the interaction patterns and their effects on software runtime behavior. Furthermore, we highlighted the risks of PCV interaction and identified potential issues behind specific interaction patterns. Our findings expose the “double edge” of PCV interaction, providing new insights and motivating the development of new automated techniques to help users configure software appropriately and assist developers in designing better configurations.

关键词： Degradation Runtime environment Focusing Software performance Software systems Software Software reliability

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DISC: Learning from Noisy Labels via Dynamic Instance-Specific Selection and Correction

DISC: Learning from Noisy Labels via Dynamic Instance-Specif...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yifan Li Hu Han Shiguang Shan Xilin Chen Key Laboratory of Intelligent Information Processing Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China University of the Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen China

Existing studies indicate that deep neural networks (DNNs) can eventually memorize the label noise. We observe that the memorization strength of DNNs towards each instance is different and can be represented by the confidence value, which becomes larger and larger during the training process. Based on this, we propose a Dynamic Instance-specific Selection and Correction method (DISC) for learning from noisy labels (LNL). We first use a two- view-based backbone for image classification, obtaining confidence for each image from two views. Then we propose a dynamic threshold strategy for each instance, based on the momentum of each instance's memorization strength in previous epochs to select and correct noisy labeled data. Benefiting from the dynamic threshold strategy and two-view learning, we can effectively group each instance into one of the three subsets (i.e., clean, hard, and purified) based on the prediction consistency and discrepancy by two views at each epoch. Finally, we employ different regularization strategies to conquer subsets with different degrees of label noise, improving the whole network's robustness. Comprehensive evaluations on three controllable and four real-world LNL benchmarks show that our method outperforms the state-of-the-art (SOTA) methods to leverage useful information in noisy data while alleviating the pollution of label noise. Code is available at https://***/JackYFL/DISC.

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Fine-Grained Annotation for Face Anti-Spoofing

arXiv

引用

arXiv 2023年

作者： Chen, Xu Jia, Yunde Wu, Yuwei Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China

Face anti-spoofing plays a critical role in safeguarding facial recognition systems against presentation attacks. While existing deep learning methods show promising results, they still suffer from the lack of fine-grained annotations, which lead models to learn task-irrelevant or unfaithful features. In this paper, we propose a fine-grained annotation method for face anti-spoofing. Specifically, we first leverage the Segment Anything Model (SAM) to obtain pixel-wise segmentation masks by utilizing face landmarks as point prompts. The face landmarks provide segmentation semantics, which segments the face into regions. We then adopt these regions as masks and assemble them into three separate annotation maps: spoof, living, and background maps. Finally, we combine three separate maps into a three-channel map as annotations for model training. Furthermore, we introduce the Multi-Channel Region Exchange Augmentation (MCREA) to diversify training data and reduce overfitting. Experimental results demonstrate that our method outperforms existing state-of-the-art approaches in both intra-dataset and cross-dataset evaluations. Copyright © 2023, The Authors. All rights reserved.

关键词： Face recognition

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