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Vibration Control of an Experimental Flexible Manipulator Against Input Saturation

IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1340-1342页

作者： Zhijia Zhao Sentao Cai Ge Ma F.Richard Yu the School of Mechanical and Electrical Engineering Guangzhou UniversityGuangzhou 510006 the Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518060China the Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) College of Computer Science&Software EngineeringShenzhen UniversityShenzhen 518060China

Dear Editor, This letter considers the control problem of an experimental flexible manipulator in position tracking, vibration suppression, and saturation compensation. Based on the backstepping technology and a Nussbaum function, we develop an anti-windup control to restrain the manipulator’s vibration, realize the desire trajectory tracking, and eliminate the saturation.

关键词： restrain eliminate Vibration

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Linear Time Approximation Algorithm for Column Subset Selection with Local Search 38

Linear Time Approximation Algorithm for Column Subset Select...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zou, Yuanbin Huang, Ziyun Xu, Jinhui Wang, Jianxin Feng, Qilong School of Computer Science and Engineering Central South University Changsha410083 China Xiangjiang Laboratory Changsha410205 China Department of Computer Science and Software Engineering Penn State Erie The Behrend College United States Department of Computer Science and Engineering State University of New York BuffaloNY United States The Hunan Provincial Key Lab of Bioinformatics Central South University Changsha410083 China

The Column Subset Selection (CSS) problem has been widely studied in dimensionality reduction and feature selection. The goal of the CSS problem is to output a submatrix S, consisting of k columns from an n × d input matrix A that minimizes the residual error A − SS†A2F, where S† is the Moore-Penrose inverse matrix of S. Many previous approximation algorithms have non-linear running times in both n and d, while the existing linear-time algorithms have a relatively larger approximation ratios. Additionally, the local search algorithms in existing results for solving the CSS problem are heuristic. To achieve linear running time while maintaining better approximation using a local search strategy, we propose a local search-based approximation algorithm for the CSS problem with exactly k columns selected. A key challenge in achieving linear running time with the local search strategy is how to avoid exhaustive enumerations of candidate columns for constructing swap pairs in each local search step. To address this issue, we propose a two-step mixed sampling method that reduces the number of enumerations for swap pair construction from O(dk) to k in linear time. Although the two-step mixed sampling method reduces the search space of local search strategy, bounding the residual error after swaps is a non-trivial task. To estimate the changes in residual error after swaps, we propose a matched swap pair construction method to bound the approximation loss, ensuring a constant probability of loss reduction in each local search step. In expectation, these techniques enable us to obtain the local search algorithm for the CSS problem with theoretical guarantees, where a 53(k + 1)-approximate solution can be obtained in linear running time O(ndk4 log k). Empirical experiments show that our proposed algorithm achieves better quality and time compared to previous algorithms on both small and large datasets. Moreover, it is at least 10 times faster than state-of-the-art algorithms a

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Classical approaches and new deep learning trends to assist in accurately and efficiently diagnosing ear disease from otoscopic images 11

Classical approaches and new deep learning trends to assist ...

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11th European Workshop on Visual Information Processing, EUVIP 2023

作者： Jobanputra, Dhruv Chetan Bennamoun, Mohammed Boussaid, Farid Xu, Lian Kuthubutheen, Jafri The University of Western Australia Computer Science and Software Engineering Perth Australia The University of Western Australia Electrical Electronic and Computer Engineering Perth Australia The University of Western Australia Division of Surgery Medical School Perth Australia

ISBN: (纸本)9798350342185

Rural communities in Australia have limited access to Ear, Nose and Throat (ENT) specialists, resulting in a lack of expertise to provide a diagnosis of complex and chronic ear diseases. This literature review examines previous attempts at creating a computer-aided tool to accurately diagnose ear disease and gaps in the literature. A systematic search was conducted to identify relevant papers and the latest best trends in technology. Four papers showed significant results in ear disease detection with deep learning models providing the best performance. Some studies using larger datasets consisting of endoscopic images obtained accuracies of over 90%. No adequate model was found that used otoscopic images with a sensitivity of over 90%. Endoscopic images provide better quality images, making it unclear how the models would perform on otoscopic images. Advanced techniques such as Transformers have not yet been tested in ear disease detection and could help improve model accuracy. © 2023 IEEE.

关键词： Image classification

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Adaptive Threshold for Unknown Traffic Identification 8

Adaptive Threshold for Unknown Traffic Identification

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8th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2022

作者： Wang, Pengcheng Guan, Jianfeng Han, Zhuang Beijing University of Posts and Telecommunications School of Computer Science National Pilot Software Engineering School Beijing100876 China

ISBN: (纸本)9781665477352

Network traffic classification has become an important foundation of network security. However, as the types of protocols and applications of the network continue to increase, unknown network traffic is also emerging. In the face of unknown network threats, how to identify unknown network threats in a complex network environment to make corresponding preparations in advance has become extremely important. Aiming at the influence of unknown traffic on classification accuracy in the prediction process, this paper proposes an Adaptive Threshold for Unknown Traffic Identification (AT-UTI) algorithm using particle swarm optimization algorithm to optimize the search of the set threshold, to reduce the impact of unknown traffic on the accuracy of the model. We evaluated our model achieving an accuracy of 93.27%. Our results demonstrate the effectiveness of AT-UTI in unknown traffic identification. © 2022 IEEE.

关键词： Network security

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The Art of Deception: Black-box Attack Against Text-to-Image Diffusion Model 29

The Art of Deception: Black-box Attack Against Text-to-Image...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Lu, Yuetong Xu, Jingyao Li, Yandong Lu, Siyang Wei, Xiang Lu, Wei Beijing Jiaotong University School of Software Engineering Beijing China Beijing Jiaotong University School of Computer and Information Technology Beijing China University of Central Florida Department of Computer Science OrlandoFL United States

ISBN: (纸本)9798350330717

With the rise of Foundation models, Text-to-Image models, as one of its important branches, have been increasingly applied. While focusing on the impressive generation capabilities of these models, it is also crucial to pay attention to the robustness of the models against attacks. In this paper, we shift our focus towards studying the vulnerability of Text-to-Image (T2I) models. To this end, we propose a black-box attack method and demonstrate that T2I models are susceptible to adversarial text attacks. Specifically, this method can disrupt T2I models by making subtle modifications to the model's input (i.e., prompt) without accessing the model parameters, resulting in the generation of incorrect images. It is worth mentioning that we discovered the ability to switch different types of tokenizers within this black-box framework to handle text, furthermore, this attack framework can be applied to target different versions of T2I models. The experiments indicate that the images generated through adversarial text exhibit noticeable errors. We also employ CLIP score, a metric used to evaluate the similarity between images and image descriptions, to assess the results. The findings demonstrate a significant decrease in the visual-textual similarity after the model is subjected to attacks. Additionally, we have identified a specific type of error that T2I models tend to make when facing attacks - when confronted with unrecognizable text, the model often interprets it as human-related content. This paper not only highlights the vulnerability of T2I models to adversarial text attacks but also further discusses potential methods that could enhance the robustness of these attack techniques. This provides a valuable reference for future research directions in this field. © 2023 IEEE.

关键词： Diffusion ModelAdversarial Attack Foundation Model Text-to-Image

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LaVIDE: A Language-Vision Discriminator for Detecting Changes in Satellite Image with Map References

arXiv

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arXiv 2024年

作者： Jiang, Shuguo Xu, Fang Jia, Sen Xia, Gui-Song School of Computer Science Wuhan University China College of Computer Science and Software Engineering Shenzhen University China

Change detection, which typically relies on the comparison of bi-temporal images, is significantly hindered when only a single image is available. Comparing a single image with an existing map, such as OpenStreetMap, which is continuously updated through crowd-sourcing, offers a viable solution to this challenge. Unlike images that carry low-level visual details of ground objects, maps convey high-level categorical information. This discrepancy in abstraction levels complicates the alignment and comparison of the two data types. In this paper, we propose a Language-VIsion Discriminator for dEtecting changes in satellite image with map references, namely LaVIDE, which leverages language to bridge the information gap between maps and images. Specifically, LaVIDE formulates change detection as the problem of "Does the pixel belong to [class]?", aligning maps and images within the feature space of the language-vision model to associate high-level map categories with low-level image details. Moreover, we build a mixture-of-experts discriminative module, which compares linguistic features from maps with visual features from images across various semantic perspectives, achieving comprehensive semantic comparison for change detection. Extensive evaluation on four benchmark datasets demonstrates that LaVIDE can effectively detect changes in satellite image with map references, outperforming state-of-the-art change detection algorithms, e.g., with gains of about 13.8% on the DynamicEarthNet dataset and 4.3% on the SECOND dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Satellite imagery

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A Simplified Method for Automatic Verification of Java Programs 34

A Simplified Method for Automatic Verification of Java Progr...

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34th International Conference on software engineering and Knowledge engineering, SEKE 2022

作者： Li, Zhi Xie, Ling Yang, Yilong School of Computer Science and Engineering Guangxi Normal University Guilin China School of Software Beihang University Beijing China

ISBN: (纸本)1891706543

Current KeY verification tool for Java programs provides limited capability for verifying Java programs. In order to solve this problem, we provide a method for simplifying complex Java programs into a format that is compatible with the KeY. A set of simplification rules based on abstract syntax tree (AST) are proposed. These rules can keep the logic and semantics of the original Java programs mostly unchanged, while meeting the requirements of KeY verification tool. The paper concludes with a bank ATM example to demonstrate the feasibility of our work. © 2022 Knowledge Systems Institute Graduate school. All rights reserved.

关键词： Syntactics

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Spatio-temporal Dual-Attention Network for View-invariant Human Action Recognition 14

Spatio-temporal Dual-Attention Network for View-invariant Hu...

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14th International Conference on Digital Image Processing, ICDIP 2022

作者： Gedamu, Kumie Yilma, Getinet Assefa, Maregu Ayalew, Melese School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and technology of China Chengdu China

ISBN: (纸本)9781510657564

Due to the action occlusion and information loss caused by the view changes, view-invariant human action recognition is challenging in plenty of real-world applications. One possible solution to this problem is minimizing representation discrepancy in different views while learning discriminative feature representation for view-invariant action recognition. To solve the problem, we propose a Spatio-temporal Dual-Attention Network (SDA-Net) for view-invariant human action recognition. The SDA-Net is composed of a spatial/temporal self-attention and spatial/temporal cross-attention modules. The spatial/temporal self-attention module captures global long-range dependencies of action features. The cross-attention module is designed to learn view-invariant co-occurrence attention maps and generates discriminative features for a semantic representation of actions in different views. We exhaustively evaluate our approach on the NTU-60, NTU-120, and UESTC datasets with multi-type evaluations, i.e., Cross-Subject, Cross-View, Cross-Set, and Arbitrary-view. Extensive experiment results demonstrate that our approach exceeds the state-of-the-art approaches with a significant margin in view-invariant human action recognition. © 2022 SPIE.

关键词： Semantics

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Multimodal News Recommendation Based on Deep Reinforcement Learning 7

Multimodal News Recommendation Based on Deep Reinforcement L...

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7th International Conference on Intelligent Computing and Signal Processing, ICSP 2022

作者： Guo, Nan Fu, Zhangpeng Zhao, Qihui Northeastern University School of Computer Science and Engineering Shenyang China Northeastern University School of Software College Shenyang China

ISBN: (纸本)9781665478571

Multimodal news recommendation is a challenging problem due to the rapid expansion of Internet information, bringing different levels of knowledge expression such as text, images, audio, and video, etc. In this paper, we propose a multimodal news recommendation method based on a deep reinforcement learning framework to represent user interests as multimodal information. The proposed method feeds the multimodal fusion feature into the rainbow agent of deep reinforcement learning to learn news representation. The experiment on the MIND and IM-MIND datasets give the result of AUC, MRR, nDCG@5, and nDCG@10 scores, which outperform LSTUR, FIM, and DKN, NRMS, NPA, and DeepFM models. It shows that reinforcement learning is an excellent choice to fulfill recommendation tasks, and the multimodal fusion feature is effective for learning accurate news representations. © 2022 IEEE.

关键词： Deep learning

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Positive Sample Augmentation with Graph Mixup for DDI Prediction 1

Positive Sample Augmentation with Graph Mixup for DDI Predic...

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1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Yu, Hailong Ma, Xinyu Chu, Xu Wang, Yasha Wu, Rengyu Zhou, Qiang School of Computer Science Peking University Beijing China Tsinghua University Department of Computer Science and Technology Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China Shenzhen Center for Prehospital Care Shenzhen China

ISBN: (纸本)9798350358780

Unexpected drug-drug interactions (DDIs) may occur when drugs are taken at the same time. Harmful DDIs can reduce drug efficacy and even increase unintended toxicity, putting patients treated with different drugs at risk. Previous works on DDI prediction mainly rely on hand-engineered domain knowledge, which is laborious to obtain and cannot handle the problem of sparse positive samples in DDI prediction. Even though some works attempt to handle data sparsity from the perspective of data distribution to improve the generalization ability of the model, they are limited to negative samples distribution. To address these limitations, we propose a novel method, Graph Mixup for Positive Sample Augmentation (GMPSA), to effectively combine positive drug molecule graphs and extend the training distribution to alleviate the problem of sparse positive samples. To model the graph signals and graph structures jointly, we map source graphs to the fused gromov-wasserstein metric space, then minimizes the weighted sum of transportation distances between the distributions of the source graphs and the objective graph in this metric space. By doing so, interpolated positive samples improve the the generalization error of the model. Experiments results on a real-word dataset show that our method effectively improve the perfomermance in DDI prediction on a state-of-the-art architecture. Further studies also verify the robustness and generalizability of GMPSA under different GNN backbones and parameter settings. © 2023 IEEE.

关键词： Forecasting

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