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International Conference on Information Fusion

作者： Yi Liu Xi Li Le Yang Lyudmila Mihaylova Yanbo Xue Department of Electrical and Computer Engineering University of Canterbury Christchurch NZ State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information Systems National University of Defense Technology Changsha China Department of Automatic Control and System Engineering University of Sheffield Sheffield UK Career Science Lab Beijing China

The quotient of two multivariate Gaussian densities can be written as an unnormalized Gaussian density, which has been applied in some recently developed multiple-model fixed-interval smoothing algorithms. However, this expression is invalid if instead of being positive definite, the covariance of the unnormalized Gaussian density is indefinite (i.e., it has both positive and negative eigenvalues) or undefined (i.e., computing it requires inverting a singular matrix). This paper considers approximating the quotient of two Gaussian densities in this case using two different approaches to mitigate the caused numerical problems. The first approach directly replaces the indefinite covariance of the unnormalized Gaussian density with a positive definite matrix nearest to it. The second approach computes the approximation through solving, using the natural gradient, an optimization problem with a Kullback-Leibler divergence-based cost function. This paper illustrates the application of the theoretical results by incorporating them into an existing smoothing method for jump Markov systems and utilizing the obtained smoothers to track a maneuvering target.

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Adversarial attacks and defenses in physiological computing:a systematic review

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National Science Open 2023年第1期2卷 62-90页

作者： Dongrui Wu Jiaxin Xu Weili Fang Yi Zhang Liuqing Yang Xiaodong Xu Hanbin Luo Xiang Yu Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China School of Civil and Hydraulic Engineering Huazhong University of Science and TechnologyWuhan 430074China College of Public Administration Huazhong University of Science and TechnologyWuhan 430074China Electrical Engineering and Computer Science Department University of MichiganAnn Arbor MI 48109USA School of Management and Sino-European Institute for Intellectual Property Huazhong University of Science and TechnologyWuhan 430074China

Physiological computing uses human physiological data as system inputs in real *** includes,or significantly overlaps with,brain-computer interfaces,affective computing,adaptive automation,health informatics,and physiological signal based *** computing increases the communication bandwidth from the user to the computer,but is also subject to various types of adversarial attacks,in which the attacker deliberately manipulates the training and/or test examples to hijack the machine learning algorithm output,leading to possible user confusion,frustration,injury,or even ***,the vulnerability of physiological computing systems has not been paid enough attention to,and there does not exist a comprehensive review on adversarial attacks to *** study fills this gap,by providing a systematic review on the main research areas of physiological computing,different types of adversarial attacks and their applications to physiological computing,and the corresponding defense *** hope this review will attract more research interests on the vulnerability of physiological computing systems,and more importantly,defense strategies to make them more secure.

关键词： physiological computing brain-computer interfaces health informatics biometrics machine learning adversarial attack

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3Deformer: A Common Framework for Image-Guided Mesh Deformation

arXiv

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arXiv 2023年

作者： Su, Hao Liu, Xuefeng Niu, Jianwei Wan, Ji Wu, Xinghao State Key Lab of VR Technology and System School of Computer Science and Engineering Beihang University Beijing100000 China Industrial Technology Research Institute School of Information Engineering Zhengzhou University Henan450000 China Hangzhou Innovation Institute Beihang University Zhejiang 310000 China

We propose 3Deformer, a general-purpose framework for interactive 3D shape editing. Given a source 3D mesh with semantic materials, and a user-specified semantic image, 3Deformer can accurately edit the source mesh following the shape guidance of the semantic image, while preserving the source topology as rigid as possible. Recent studies of 3D shape editing mostly focus on learning neural networks to predict 3D shapes, which requires high-cost 3D training datasets and is limited to handling objects involved in the datasets. Unlike these studies, our 3Deformer is a non-training and common framework, which only requires supervision of readily-available semantic images, and is compatible with editing various objects unlimited by datasets. In 3Deformer, the source mesh is deformed utilizing the differentiable renderer technique, according to the correspondences between semantic images and mesh materials. However, guiding complex 3D shapes with a simple 2D image incurs extra challenges, that is, the deform accuracy, surface smoothness, geometric rigidity, and global synchronization of the edited mesh should be guaranteed. To address these challenges, we propose a hierarchical optimization architecture to balance the global and local shape features, and propose further various strategies and losses to improve properties of accuracy, smoothness, rigidity, and so on. Extensive experiments show that our 3Deformer is able to produce impressive results and reaches the state-of-the-art level. Copyright © 2023, The Authors. All rights reserved.

关键词： Rigidity

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基于边缘计算的软件定义云制造和柔性资源调度研究

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engineering 2023年第3期22卷 60-70页

作者：杨晨廖方茵兰舒琳王力翚沈卫明黄国全 School of Cyberspace Science and Technology Beijing Institute of TechnologyBeijing 100081China School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China School of Mathematics and Computer Science Yan'an UniversityYan'an 716000China School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Department of Production Engineering KTH Royal Institute of TechnologyStockholm 10044Sweden State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Department of Industrial and Manufacturing Systems Engineering The University of Hong KongHong Kong 999077China

本研究的重点是在云制造环境中实现快速重构、实现灵活的资源调度、开发资源潜力以应对各种变化。因此,本文首先提出了一种新的基于云和软件定义网络(SDN)的制造模型——软件定义云制造(SDCM),该模型将控制逻辑从自动化硬件转移到软件... 详细信息

本研究的重点是在云制造环境中实现快速重构、实现灵活的资源调度、开发资源潜力以应对各种变化。因此,本文首先提出了一种新的基于云和软件定义网络(SDN)的制造模型——软件定义云制造(SDCM),该模型将控制逻辑从自动化硬件转移到软件上。这种转变意义重大,因为软件可以充当制造系统的“大脑”,并且可以轻松更改或更新以支持快速系统重新配置、运营和演进。随后,边缘计算被引入,以接近终端的计算和存储能力来补充云。另一个关键问题是管理由不同服务质量(QoS)要求的大量物联网(IoT)数据传输而导致的严重网络拥塞。基于SDCM的虚拟化和灵活的网络能力,本研究形式化了面向复杂制造任务集的时间敏感性数据流量控制问题,并考虑了子任务分配和数据路由路径选择。为了解决这一优化问题,提出了一种将遗传算法(GA)、Dijkstra最短路径算法和排队算法相结合的方法。实验结果表明,该方法能有效地防止网络拥塞,减少SDCM中的总通信延迟。

关键词： Cloud manufacturing Edge computing Software-defined networks industrial Internet of Things Industry 4.0

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Dbfilm: A Novel Dual-Branch Frequency Improved Legendre Memory Forecasting Model for Coagulant Dosage Determination

SSRN

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SSRN 2023年

作者： Xia, Sibo Zhu, Hongqiu Zhang, Ning Cheng, Fei Li, Yonggang Zhou, Can School of Automation Central South University Changsha410083 China Key Laboratory of Industrial Intelligence and Systems Central South University Ministry of Education Changsha410083 China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha410083 China

Abstract: In the process of drinking water preparation, the amount of coagulant added directly affects the quality of water and drinking safety. However, the coagulation process is influenced by many environmental factors, and the process of determining the dosage is complex and variable. The sedimentation reaction is severely delayed, making it difficult to establish a real-time coagulant dosage determination model. To address this issue, this study proposes a frequency-improved Legendre memory model based on deep learning. It has a dual-branch structure and accurately predicts the dosage of coagulant by learning the characteristic information in the historical time series. In the data instantiation module of the model, a local response mechanism is introduced to accurately describe the original distribution of the data by activating local information within the channel. In addition, the model adopts a dual-branch structure for parallel computation, capturing both local and global information of the sequence, and extracting richer information features from the historical data. With a lower computational cost, the model compensates for the loss of information in low-rank approximation operations, establishing longer time series mapping relationships. Experimental results verify that our proposed model has excellent performance, achieving a maximum improvement of 20.3% over the baseline model in a real coagulant dosage dataset, and achieving optimal performance in multiple multi-step prediction tasks on a public dataset. © 2023, The Authors. All rights reserved.

关键词： Potable water

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BotScan: An Unsupervised Bot Detection Based on Adversarial Learning and Social Perception

BotScan: An Unsupervised Bot Detection Based on Adversarial ...

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Asian control Conference

作者： Hong Lin Nuo Chen Yang Chen Xiang Li Cong Li Department of Electronic Engineering Adaptive Networks and Control Lab School of Information Science and Technology Fudan University China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

Within online social networks, the rapid expansion of automated accounts, known as social bots, poses a significant challenge. This dynamic has sparked a strategic contest between bots and detectors. Both sides continually learn and adjust their strategies to gain an edge in this silent war. To effectively manage risks from bot evolution, proactive detection methods have garnered widespread attention. In this work, we propose a graph-based and structure-aware adversarial learning framework for multi-category social bot detection. In detail, we adopt a categorical generative adversarial network to create synthetic evolved bot samples for adversarial learning, aiming to boost the detection performance. Besides, we design a user representation learning module, including local and remote feature extractors to mine neighborhood information and achieve multiple network structure representations. Results demonstrate that our model outperforms state-of-the-art approaches and exhibits strong robustness.

关键词： Representation learning Training Technological innovation Social networking (online) Image edge detection Detectors Feature extraction

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A Tensor Solution for Health Indicator Construction of Metro Wheelset Degradation with Irregular Noise

A Tensor Solution for Health Indicator Construction of Metro...

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Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Yu Wang Wentao Mao Linlin Kou keying Liu School of Computer and Information Engineering Henan Normal University Xinxiang China State Key Lab of Rail Traffic Control & Safety Beijing Jiaotong University Beijing China

During the operation of metro vehicles, wheel flange and wheel diameter continue to be abrased, so wheel degradation is inevitable. Influenced by the states of welded rail, subgrade and turnout, applied load, speed of running and other components, the degradation process of wheelset is vulnerable to irregular noise interference, which leads to abnormal fluctuation of vibration signal. As a result, the degradation process is hard to be accurately described. To address this problem, this paper proposes a new health indicator construction method for metro wheelset based on tensor reconstruction. First, tensor Tucker decomposition is utilized to obtain the core tensor of original signal, and then tensor reconstruction is applied to transform the signal into a new degradation sequence with noise reduction. Second, the Savitzky-Golay filter is employed to remove the irregular trend from the obtained degradation sequence. Finally, deep autoencoder network is used to extract deep degradation features, and after dimension reduction, a health indicator of wheelset degradation process can be obtained. The effectiveness of the proposed method is verified with Beijing subway wheelset degradation dataset in 2020. The results show that the constructed health indicator can accurately describe the whole degradation process with good trendability and monotonicity. More importantly, the proposed method possess good practicability since the key changing parts in the health indicator can correspond well to the actual maintenance records.

关键词： Degradation Vibrations Tensors Welding Wheels Interference Transforms

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On the Gaussian Filtering for Nonlinear Dynamic Systems Using Variational Inference

On the Gaussian Filtering for Nonlinear Dynamic Systems Usin...

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International Conference on Information Fusion

作者： Yi Liu Xi Li Le Yang Lyudmila Mihaylova Ji Li Department of Electrical and Computer Engineering University of Canterbury Christchurch NZ State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information Systems National University of Defense Technology Changsha China Department of Automatic Control and System Engineering University of Sheffield Sheffield UK Locaris Electronic Technology Co. Ltd Zhengzhou China

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

This paper introduces a new variational Gaussian filtering approach for estimating the state of a nonlinear dynamic system. We first assume that the predictive distribution of the state is Gaussian and derive an iterative method for updating the state posterior in the natural parameter space through KullbackLeibler divergence minimization. The obtained update rule is the same as that of the conjugate-computation variational inference technique in Bayesian learning. The derivation here is simpler and more insightful. We then impose a Wishart prior on the inverse of the state prediction covariance to take into account the impact of approximating the state predictive distribution using a Gaussian density on the state posterior estimation. The prediction covariance is identified jointly with the state using variational inference and the established state posterior update rule to achieve the desired Gaussian filtering. Simulation study examines the performance of the proposed filtering framework in target tracking based on bearing and range measurements.

关键词： Target tracking Smoothing methods Filtering Estimation Filtering algorithms Prediction algorithms Minimization Nonlinear dynamical systems Bayes methods Iterative methods

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SCVCNet: Sliding cross-vector convolution network for cross-task and inter-individual-set EEG-based cognitive workload recognition

arXiv

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arXiv 2023年

作者： Wang, Qi Chen, Li Zhan, Zhiyuan Zhang, Jianhua Yin, Zhong The Department of Control Science and Engineering the School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China The OsloMet Artificial Intelligence Lab Department of Computer Science Oslo Metropolitan University OsloN-0130 Norway The Engineering Research Center of Optical Instrument and System Ministry of Education Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology Shanghai200093 China

This paper presents a generic approach for applying the cognitive workload recognizer by exploiting common electroencephalogram (EEG) patterns across different human-machine tasks and individual sets. We propose a neural network called SCVCNet, which eliminates task- and individual-set-related interferences in EEGs by analyzing finer-grained frequency structures in the power spectral densities. The SCVCNet utilizes a sliding cross-vector convolution (SCVC) operation, where paired input layers representing the theta and alpha power are employed. By extracting the weights from a kernel matrix's central row and column, we compute the weighted sum of the two vectors around a specified scalp location. Next, we introduce an inter-frequency-point feature integration module to fuse the SCVC feature maps. Finally, we combined the two modules with the output-channel pooling and classification layers to construct the model. To train the SCVCNet, we employ the regularized least-square method with ridge regression and the extreme learning machine theory. We validate its performance using three databases, each consisting of distinct tasks performed by independent participant groups. The average accuracy (0.6813 and 0.6229) and F1 score (0.6743 and 0.6076) achieved in two different validation paradigms show partially higher performance than the previous works. All features and algorithms are available on website: https://***/7ohnKeats/SCVCNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Electroencephalography

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Language-Queried Target Sound Extraction Without Parallel Training Data

Language-Queried Target Sound Extraction Without Parallel Tr...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hao Ma Zhiyuan Peng Xu Li Yukai Li Mingjie Shao Qiuqiang Kong Ju Liu School of Information Science and Engineering Shandong University Qingdao China Department of Computer Science North Carolina State University North Carolina USA ARC Lab Tencent PCG Key Laboratory of System and Control AMSS Chinese Academy of Sciences Beijing China The Chinese University of Hong Kong China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Language-queried target sound extraction (TSE) aims to extract specific sounds from mixtures based on language queries. Traditional fully-supervised training schemes require extensively annotated parallel audio-text data, which are labor-intensive. We introduce a parallel-data-free training scheme, requiring only unlabelled audio clips for TSE model training by utilizing the contrastive language-audio pre-trained model (CLAP). In a vanilla parallel-data-free training stage, target audio is encoded using the pre-trained CLAP audio encoder to form a condition embedding, while during testing, user language queries are encoded by CLAP text encoder as the condition embedding. This vanilla approach assumes perfect alignment between text and audio embeddings, which is unrealistic. Two major challenges arise from training-testing mismatch: the persistent modality gap between text and audio and the risk of overfitting due to the exposure of rich acoustic details in target audio embedding during training. To address this, we propose a retrieval-augmented strategy. Specifically, we create an embedding cache using audio captions generated by a large language model (LLM). During training, target audio embeddings retrieve text embeddings from this cache to use as condition embeddings, ensuring consistent modalities between training and testing and eliminating information leakage. Extensive experiment results show that our retrieval-augmented approach achieves consistent and notable performance improvements over existing state-of-the-art with better generalizability.

关键词： Training Large language models Training data Signal processing Information leakage Acoustics Data models Data mining Speech processing Overfitting

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