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arXiv 2023年

作者： Yao, Qingsong He, Zecheng Li, Yuexiang Lin, Yi Ma, Kai Zheng, Yefeng Zhou, S. Kevin School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Institute of Computing Technology Chinese Academy of Sciences China Institute of Computing Technology Chinese Academy of Science China Jarvis Research Center Ten-cent YouTu Lab Shenzhen China Group Guangxi Key Laboratory for Genomic and Personalized Medicine Guangxi Medical University Nanning China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Meta Reality Labs United States

Deep learning based methods for medical images can be easily compromised by adversarial examples (AEs), posing a great security flaw in clinical decision-making. It has been discovered that conventional adversarial attacks like PGD which optimize the classification logits, are easy to distinguish in the feature space, resulting in accurate reactive defenses. To better understand this phenomenon and reassess the reliability of the reactive defenses for medical AEs, we thoroughly investigate the characteristic of conventional medical AEs. Specifically, we first theoretically prove that conventional adversarial attacks change the outputs by continuously optimizing vulnerable features in a fixed direction, thereby leading to outlier representations in the feature space. Then, a stress test is conducted to reveal the vulnerability of medical images, by comparing with natural images. Interestingly, this vulnerability is a double-edged sword, which can be exploited to hide AEs. We then propose a simple-yet-effective hierarchical feature constraint (HFC), a novel add-on to conventional white-box attacks, which assists to hide the adversarial feature in the target feature distribution. The proposed method is evaluated on three medical datasets, both 2D and 3D, with different modalities. The experimental results demonstrate the superiority of HFC, i.e., it bypasses an array of state-of-the-art adversarial medical AE detectors more efficiently than competing adaptive attacks1, which reveals the deficiencies of medical reactive defense and allows to develop more robust defenses in future. © 2023, CC BY.

关键词： Deep learning

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Sum Rate Maximization under AoI Constraints for RIS-Assisted mmWave Communications

arXiv

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arXiv 2023年

作者： Guo, Ziqi Niu, Yong Mao, Shiwen Zhang, Changming Wang, Ning Zhong, Zhangdui Ai, Bo The State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing100044 China The Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The Research Institute of Intelligent Networks Zhejiang Lab Hangzhou311121 China The School of Information Engineering Zhengzhou University Zhengzhou450001 China The Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China

The concept of age of information (AoI) has been proposed to quantify information freshness, which is crucial for time-sensitive applications. However, in millimeter wave (mmWave) communication systems, the link blockage caused by obstacles and the severe path loss greatly impair the freshness of information received by the user equipments (UEs). In this paper, we focus on reconfigurable intelligent surface (RIS)-assisted mmWave communications, where beamforming is performed at transceivers to provide directional beam gain and a RIS is deployed to combat link blockage. We aim to maximize the system sum rate while satisfying the information freshness requirements of UEs by jointly optimizing the beamforming at transceivers, the discrete RIS reflection coefficients, and the UE scheduling strategy. To facilitate a practical solution, we decompose the problem into two subproblems. For the first per-UE data rate maximization problem, we further decompose it into a beamforming optimization subproblem and a RIS reflection coefficient optimization subproblem. Considering the difficulty of channel estimation, we utilize the hierarchical search method for the former and the local search method for the latter, and then adopt the block coordinate descent (BCD) method to alternately solve them. For the second scheduling strategy design problem, a low-complexity heuristic scheduling algorithm is designed. Simulation results show that the proposed algorithm can effectively improve the system sum rate while satisfying the information freshness requirements of all UEs. Copyright © 2023, The Authors. All rights reserved.

关键词： Scheduling algorithms

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Automatic Classification of Strain in the Singing Voice Using Machine Learning

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Journal of Voice 2025年

作者： Liu, Yuanyuan Reddy, Mittapalle Kiran Yagnavajjula, Madhu Keerthana Räsänen, Okko Alku, Paavo Ikävalko, Tero Hakanpää, Tua Öyry, Aleksi Laukkanen, Anne-Maria Speech and Voice Research Laboratory Tampere University Tampere 33100 Finland Department of Computer Science and Engineering Indian Institute of Information Technology Raichur Karanataka Raichur 584135 India Advanced Technology Development Centre Indian Institute of Technology Kharagpur West Bengal Kharagpur 721302 India Unit of Computing Sciences Tampere University Tampere 33720 Finland Department of Information and Communications Engineering Aalto University Espoo 02150 Finland Faculty of Education University of Turku Turku 20014 Finland Aalto Acoustics Lab Aalto University Espoo 02150 Finland

Objectives: Classifying strain in the singing voice can help protect professional singers from vocal overuse and support singing training. This study investigates whether machine learning can automatically classify singing voices into two levels of perceived strain. The singing samples represent two genres: classical and contemporary commercial music (CCM). Methods: A total of 324 singing voice samples from 15 professional normophonic singers (nine female, six male) were analyzed. Nine singers were classical, and six were CCM singers. The samples consisted of syllable strings produced at three to six pitches and three loudness levels. Based on expert auditory-perceptual ratings, the samples were categorized into two strain levels: normal-mild and moderate-severe. Three acoustic feature sets (mel-frequency cepstral coefficients (MFCCs), the extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS), and wavelet scattering features) were compared using two classifier models [support vector machine (SVM) and multilayer perceptron (MLP)]. Feature selection was performed using recursive feature elimination, and the Mann-Whitney U test was used to assess the discriminative power of the selected features. Results: The highest classification accuracy of 86.1% was achieved using a subset of wavelet scattering features with the MLP classifier. A comparison between individual features showed that the first MFCC coefficient, representing spectral tilt, exhibited the greatest between-class separation. Conclusion: This study demonstrates that machine learning models utilizing selected acoustic features can classify perceptual strain of singing voices automatically with high accuracy. These preliminary findings highlight the potential for larger studies involving more diverse singer groups across different genres. © 2025 The Authors

关键词： Auditive-perceptual evaluation—Support vector machine—Multiple layer perceptron—Fisher vector—Wavelet scattering coefficients—Mel-frequency cepstral coefficients

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Distributed formation trajectory planning for multi-vehicle systems

Distributed formation trajectory planning for multi-vehicle ...

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American Control Conference (ACC)

作者： Binh Nguyen Truong Nghiem Linh Nguyen Tung Nguyen Hung La Mehdi Sookhak Thang Nguyen Department of Engineering Texas A&M University–Corpus Christi Corpus Christi TX USA School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA School of Engineering Information Technology and Physical Sciences Federation University Australia Churchill VIC Australia Department of Information Technology Uppsala University Uppsala Sweden Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA Department of Computing Sciences Texas A&M University–Corpus Christi Corpus Christi TX USA

This paper addresses the problem of distributed formation trajectory planning for multi-vehicle systems with collision avoidance among vehicles. Unlike some previous distributed formation trajectory planning methods, our proposed approach offers great flexibility in handling computational tasks for each vehicle when the global formation of all the vehicles changes. It affords the system the ability to adapt to the computational capabilities of the vehicles. Furthermore, global formation constraints can be handled at any selected vehicles. Thus, any formation change can be effectively updated without recomputing all local formations at all the vehicles. To guarantee the above features, we first formulate a dynamic consensus-based optimization problem to achieve desired formations while guaranteeing collision avoidance among vehicles. Then, the optimization problem is effectively solved by ADMM-based or alternating projection-based algorithms, which are also presented. Theoretical analysis is provided not only to ensure the convergence of our method but also to show that the proposed algorithm can surely be implemented in a fully distributed manner. The effectiveness of the proposed method is illustrated by a numerical example of a 9-vehicle system.

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Filter Pruning for Efficient CNNs via Knowledge-driven Differential Filter Sampler

arXiv

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arXiv 2023年

作者： Lin, Shaohui Huang, Wenxuan Xie, Jiao Zhang, Baochang Shen, Yunhang Yu, Zhou Han, Jungong Doermann, David The School of Computer Science and Technology East China Normal University Shanghai China The Key Laboratory of Advanced Theory and Application in Statistics and Data Science Ministry of Education China The Department of Automation School of Aerospace Engineering Xiamen University Xiamen China The Institute of Artificial Intelligence Beihang University Beijing China Zhongguancun Laboratory Beijing China The Youtu Lab Tencent Shanghai China The School of Statistics East China Normal University Shanghai China The Department of Computer Science University of Sheffield United Kingdom The University at Buffalo BuffaloNY United States

Filter pruning simultaneously accelerates the computation and reduces the memory overhead of CNNs, which can be effectively applied to edge devices and cloud services. In this paper, we propose a novel Knowledge-driven Differential Filter Sampler (KDFS) with Masked Filter Modeling (MFM) framework for filter pruning, which globally prunes the redundant filters based on the prior knowledge of a pre-trained model in a differential and non-alternative optimization. Specifically, we design a differential sampler with learnable sampling parameters to build a binary mask vector for each layer, determining whether the corresponding filters are redundant. To learn the mask, we introduce masked filter modeling to construct PCA-like knowledge by aligning the intermediate features from the pre-trained teacher model and the outputs of the student decoder taking sampling features as the input. The mask and sampler are directly optimized by the Gumbel-Softmax Straight-Through Gradient Estimator in an end-to-end manner in combination with global pruning constraint, MFM reconstruction error, and dark knowledge. Extensive experiments demonstrate the proposed KDFS’s effectiveness in compressing the base models on various datasets. For instance, the pruned ResNet-50 on ImageNet achieves 55.36% computation reduction, and 42.86% parameter reduction, while only dropping 0.35% Top-1 accuracy, significantly outperforming the state-of-the-art methods. The code is available at https://***/Osilly/KDFS. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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YOLOStereo3D: A step back to 2D for efficient stereo 3D detection

arXiv

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arXiv 2021年

作者： Liu, Yuxuan Wang, Lujia Liu, Ming Department of Electronic and Computer Engineering Robotics and Multi-Perception Laborotary Hong Kong University of Science and Technology Hong Kong Cloud Computing Lab of Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

Object detection in 3D with stereo cameras is an important problem in computer vision, and is particularly crucial in low-cost autonomous mobile robots without LiDARs. Nowadays, most of the best-performing frameworks for stereo 3D object detection are based on dense depth reconstruction from disparity estimation, making them extremely computationally expensive. To enable real-world deployments of vision detection with binocular images, we take a step back to gain insights from 2D imagebased detection frameworks and enhance them with stereo features. We incorporate knowledge and the inference structure from real-time one-stage 2D/3D object detector and introduce a light-weight stereo matching module. Our proposed framework, YOLOStereo3D, is trained on one single GPU and runs at more than ten fps. It demonstrates performance comparable to state-of-the-art stereo 3D detection frameworks without usage of LiDAR data. The code will be published in https://***/Owen-Liuyuxuan/visualDet3D. Copyright © 2021, The Authors. All rights reserved.

关键词： Object detection

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MIST: Multiple instance self-training framework for video anomaly detection

arXiv

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arXiv 2021年

作者： Feng, Jia-Chang Hong, Fa-Ting Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-Sen University Peng Cheng Laboratory Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Pazhou Lab Guangzhou China

Weakly supervised video anomaly detection (WS-VAD) is to distinguish anomalies from normal events based on discriminative representations. Most existing works are limited in insufficient video representations. In this work, we develop a multiple instance self-training framework (MIST) to efficiently refine task-specific discriminative representations with only video-level annotations. In particular, MIST is composed of 1) a multiple instance pseudo label generator, which adapts a sparse continuous sampling strategy to produce more reliable clip-level pseudo labels, and 2) a self-guided attention boosted feature encoder that aims to automatically focus on anomalous regions in frames while extracting task-specific representations. Moreover, we adopt a self-training scheme to optimize both components and finally obtain a task-specific feature encoder. Extensive experiments on two public datasets demonstrate the efficacy of our method, and our method performs comparably to or even better than existing supervised and weakly supervised methods, specifically obtaining a frame-level AUC 94.83% on ShanghaiTech. © 2021, CC BY.

关键词： Anomaly detection

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Multi-directional bicycle robot for steel structure inspection

arXiv

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arXiv 2021年

作者： Nguyen, Son Thanh Nguyen, Hai Bui, Son Tien Ho, Van Anh La, Hung Manh Lab Department of Computer Science and Engineering University of Nevada RenoNV89557 United States Northeastern University BostonMA02115 United States Japan Advanced Institute of Science and Tech. Japan

This paper presents a novel design of a multi-directional bicycle robot, which targets inspecting general ferromagnetic structures including complex-shaped structures. The locomotion concept is based on arranging two magnetic wheels in a bicycle-like configuration with two independent steering actuators. This configuration allows the robot to possess multi-directional mobility. An additional free joint helps the robot naturally adapt to non-flat and complex surfaces of steel structures. The robot has the biggest advantage to be mechanically simple with high mobility. Besides, the robot is equipped with sensing tools for structure health monitoring. We demonstrate the deployment of our robot to perform steel rust detection on steel bridges. The final inspection results are visualized as 3D models of the bridges together with marked locations of detected rusty areas. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine design

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Composite Community-Aware Diversified Influence Maximization with Efficient Approximation

arXiv

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arXiv 2022年

作者： Guo, Jianxiong Ni, Qiufen Wu, Weili Du, Ding-Zhu The Advanced Institute of Natural Sciences Beijing Normal University Zhuhai519087 China The Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China The School of Computers Guangdong University of Technology Guangzhou510006 China The Department of Computer Science Erik Jonsson School of Engineering and Computer Science The University of Texas at Dallas RichardsonTX75080 United States

Influence Maximization (IM) is a famous topic in mobile networks and social computing, which aims at finding a small subset of users to maximize the influence spread through online information cascade. Recently, some careful researchers paid attention to diversity of information dissemination, especially community-aware diversity, and formulated the diversified IM problem. The diversity is ubiquitous in a lot of real-world applications, but they are all based on a given community structure. In social networks, we can form heterogeneous community structures for the same group of users according to different metrics. Therefore, how to quantify the diversity based on multiple community structures is an interesting question. In this paper, we propose the Composite Community-Aware Diversified IM (CC-DIM) problem, which aims at selecting a seed set to maximize the influence spread and the composite diversity over all possible community structures under consideration. To address the NP-hardness of CC-DIM problem, we adopt the technique of reverse influence sampling and design a random Generalized Reverse Reachable (G-RR) set to estimate the objective function. The composition of a random G-RR set is much more complex than the RR set used for the IM problem, which will lead to inefficiency of traditional sampling-based approximation algorithms. Because of this, we further propose a two-stage algorithm, Generalized HIST (G-HIST). It can not only return a (1 − 1/e − Ε) approximate solution with at least (1 − δ) probability, but also improve the efficiency of sampling and ease the difficulty of searching by significantly reducing the average size of G-RR sets. Finally, we evaluate our G-HIST on real datasets against existing algorithms. The experimental results show the effectiveness of our proposed algorithm and its superiority over other baseline algorithms. Copyright © 2022, The Authors. All rights reserved.

关键词： Approximation algorithms

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NTIRE 2023 Quality Assessment of Video Enhancement Challenge

NTIRE 2023 Quality Assessment of Video Enhancement Challenge

引用

2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Liu, Xiaohong Min, Xiongkuo Sun, Wei Zhang, Yulun Zhang, Kai Timofte, Radu Zhai, Guangtao Gao, Yixuan Cao, Yuqin Kou, Tengchuan Dong, Yunlong Jia, Ziheng Li, Yilin Wu, Wei Hu, Shuming Deng, Sibin Xiao, Pengxiang Chen, Ying Li, Kai Zhao, Kai Yuan, Kun Sun, Ming Cong, Heng Wang, Hao Fu, Lingzhi Zhang, Yusheng Zhang, Rongyu Shi, Hang Xu, Qihang Xiao, Longan Ma, Zhiliang Agarla, Mirko Celona, Luigi Rota, Claudio Schettini, Raimondo Huang, Zhiwei Li, Ya'nan Wang, Xiaotao Lei, Lei Liu, Hongye Hong, Wei Chuang, Ironhead Lin, Allen Guan, Drake Chen, Iris Lou, Kae Huang, Willy Tasi, Yachun Kao, Yvonne Fan, Haotian Kong, Fangyuan Zhou, Shiqi Liu, Hao Lai, Yu Chen, Shanshan Wang, Wenqi Wu, Haoning Chen, Chaofeng Zhu, Chunzheng Guo, Zekun Zhao, Shiling Yin, Haibing Wang, Hongkui Meftah, Hanene Brachemi Fezza, Sid Ahmed Hamidouche, Wassim Déforges, Olivier Shi, Tengfei Mansouri, Azadeh Motamednia, Hossein Bakhtiari, Amir Hossein Aznaveh, Ahmad Mahmoudi Shanghai Jiao Tong University China Eth Zürich Switzerland University of Würzburg Germany Department of Tao Technology Alibaba Group China Kuaishou Technology China Interactive Entertainment Group of Netease Inc Guangzhou China Transsion China Hefei University of Technology China Department of Informatics Systems and Communication University of Milano - Bicocca Italy Xiaomi Inc. China China Ji Liang University China FreeTech Advanced Research Center KKCompany Taiwan ByteDance China Mgtv China Fuzhou University China Shopee Information Technology Co. Ltd. S-Lab Nanyang Technological University Singapore Hunan University China Hangzhou Dianzi University China Insa Rennes Cnrs Ietr - Umr 6164 Rennes France National Higher School of Telecommunications and Ict Oran Algeria Buaa Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Cyberspace Research Institute Shahid Beheshti University Tehran Iran

ISBN: (纸本)9798350302493

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance. © 2023 IEEE.

关键词： Image enhancement

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