Article
Computer Science, Artificial Intelligence
Jianan Zhang, Yongfei Wu, Fang Hao, Xueyu Liu, Ming Li, Daoxiang Zhou, Wen Zheng
Summary: This paper proposes a double similarities weighted multi-instance learning (DSMIL) kernel framework, which utilizes the similarities of bag-to-bag (B2B) and instance-to-bag (I2B) to construct an effective kernel function. Experimental results show that the proposed method achieves competitive classification performance and is robust to parameters and noise.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Engineering, Biomedical
Zahra Nabizadeh-Shahre-Babak, Nader Karimi, Pejman Khadivi, Roshanak Roshandel, Ali Emami, Shadrokh Samavi
Summary: This paper proposes an approach using the bag of visual words and a neural network classifier to classify X-ray chest images into COVID-19 and non-COVID-19 with high performance. Experimental results show that extracting features with the bag of visual words leads to better classification accuracy compared to state-of-the-art techniques.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Computer Science, Hardware & Architecture
Shuai Liu, Xiyu Xu, Yang Zhang, Khan Muhammad, Weina Fu
Summary: This article introduces a reliable sample selection strategy for weakly supervised visual tracking and verifies its importance in improving model performance. Experiments demonstrate that a scientific sample quality assessment method is of great help to data-based weakly supervised learning systems.
IEEE TRANSACTIONS ON RELIABILITY
(2023)
Article
Computer Science, Artificial Intelligence
Xin Yang, Yong Song, Yufei Zhao, Zishuo Zhang, Chenyang Zhao
Summary: The paper proposes a similarity measurement method called Dynamic Cross-Attention (DCA), which utilizes Transformer encoders to explore feature interdependency and redesigns each part of the Siamese network, resulting in improved tracking performance.
Article
Computer Science, Artificial Intelligence
Kai Yang, Haijun Zhang, Dongliang Zhou, Li Dong, Jianghong Ma
Summary: Most existing trackers lack a meaningful exploration of defining positive and negative samples during training, which affects tracking performance. To address this, we propose an IoU-aware tracker with adaptive sample assignment (IASA), which achieves state-of-the-art performance on seven public datasets.
Article
Computer Science, Artificial Intelligence
Jinpu Zhang, Kaiheng Dai, Ziwen Li, Ruonan Wei, Yuehuan Wang
Summary: In this paper, a spatio-temporal matching process is proposed to thoroughly explore the capability of 4-D matching in space and time. The SI-Corr method is introduced in spatial matching to calibrate channel-wise responses for each matching position and distinguish the target and distractors. The ARM module is designed in temporal matching to restrict the abrupt alteration of interframe response maps and learn a temporal consistency of context structure distribution. Experimental results demonstrate the state-of-the-art performance of the proposed method in six benchmark tests.
Article
Computer Science, Information Systems
Rajkumari Bidyalakshmi Devi, Yambem Jina Chanu, Khumanthem Manglem Singh
Summary: The paper presents an efficient and robust visual object tracking method based on sparse discriminative classifier and principal component analysis. Through comparisons with existing tracking algorithms using both quantitative and qualitative analyses, the proposed method outperforms them.
MULTIMEDIA SYSTEMS
(2021)
Article
Computer Science, Software Engineering
Rajkumari Bidyalakshmi Devi, Yambem Jina Chanu, Khumanthem Manglem Singh
Summary: The importance of visual object tracking and the PCA-based tracking method were studied, and a new visual object tracking method incorporating sparse discriminative classifier feature selection was proposed, outperforming other existing tracking algorithms.
Article
Chemistry, Multidisciplinary
Weiwei Hu, Qinglei Lin, Lihuan Shao, Jiaxu Lin, Keke Zhang, Huibin Qin
Summary: In the monocular visual-inertia mode of ORB-SLAM3, insufficient excitation obtained by the inertial measurement unit (IMU) results in a long system initialization time, causing easy loss of trajectory and incomplete map creation. To address this problem, a fast map restoration method is proposed, improving accuracy by approximately 47.51% and time efficiency by approximately 55.96% through accelerated tracking and loop closure detection.
APPLIED SCIENCES-BASEL
(2022)
Article
Education, Special
Xiang Lian, Wilson Cheong Hin Hong, Fei Gao, Kimberly Kolletar-Zhu, Jiayin Wang, Chi Cai, Fuxing Yang, Xiangrong Chen, Zhi Wang, Hongzhi Gao
Summary: This study suggests that removing background and less important elements in children's storybook pictures can enhance attention and focus of children with Autism Spectrum Disorder (ASD). Eye-tracking experiments showed that ASD+ID children had longer fixations, fewer fixations, and higher fixation/saccade duration ratio when viewing the modified pictures compared to the original pictures. Additionally, fMRI scans revealed increased brain activation in the bilateral fusiform gyri of ASD+ID participants when viewing the modified pictures, indicating enhanced visual attention.
RESEARCH IN DEVELOPMENTAL DISABILITIES
(2023)
Article
Computer Science, Information Systems
Young-Min Song, Young-Chul Yoon, Kwangjin Yoon, Hyunsung Jang, Namkoo Ha, Moongu Jeon
Summary: In this paper, we propose a highly feasible fully online multi-object tracking and segmentation (MOTS) method that uses instance segmentation results as an input. The proposed method achieves high-performance online tracking by using the GMPHD filter, HDA, and MAF model. Our tracker achieves state-of-the-art MOTS performance in the experiments on two popular MOTS datasets.
Article
Ophthalmology
Weiju Wu, Noemi Lois, Alan R. Prescott, Adrian P. Brown, Veerle Van Gerwen, Marie-Jose Tassignon, Shane A. Richards, Christopher D. Saunter, Miguel Jarrin, Roy A. Quinlan
Summary: The success of human lens regeneration and cataract treatment with Bag-in-the-Lens surgery relies on lens capsule closure. Research shows that the first three days after surgery are critical, with cell proliferation, reorganization and marker expression playing important roles in the long-term outcomes. The spatial and cellular cues are essential for lens cells to produce a functional tissue in addition to maintaining capsule integrity and the epithelial-fibre cell interface.
EXPERIMENTAL EYE RESEARCH
(2021)
Article
Automation & Control Systems
Mingming Bai, Yulong Huang, Yonggang Zhang, Jonathon Chambers
Summary: This article presents an adaptive outlier-robust state estimator (AORSE) under the statistical similarity measures (SSMs) framework. The AORSE is developed by maximizing a hybrid SSMs based cost function, which improves the accuracy of the algorithm. Simulation and experimental examples show the effectiveness of the proposed algorithm.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Sociology
Alexander Patzina, Carina Toussaint
Summary: Ochsenfeld (2016) found that differences in vocational interests are a substantial factor in sex segregation in higher education, rather than constraints. Our replication study, using panel data and adjusting for potential biases, further confirms the validity of prior research. Additionally, our analysis demonstrates that the explanatory power of the overall model and the role of constraints vary depending on the sample composition, highlighting the importance of sample selection in testing sociological theories.
ZEITSCHRIFT FUR SOZIOLOGIE
(2023)
Proceedings Paper
Engineering, Electrical & Electronic
Fang Liu, Yu Xie, Biao Xu, Yu Lei, Honghai Wang
Summary: This paper proposes a method combining singular value decomposition (SVD) and sequential similarity detection algorithm (SSDA), with the support of ultra-weak fiber Bragg grating (UWFBG) arrays, to track trains in real-time from large-scale stream data. Experimental results indicate that the proposed method can effectively satisfy the requirements for real-time train tracking.
ADVANCED SENSOR SYSTEMS AND APPLICATIONS XII
(2022)
Article
Automation & Control Systems
Ran Duan, Danda Pani Paudel, Changhong Fu, Peng Lu
Summary: This article presents a novel outlier rejection approach for feature-based visual odometry, which takes advantage of the empirical observation that certain 2D-3D correspondences with very low reprojection error can lead to significant pose estimation error. By explicitly measuring plausible pose error using known orientation of stereo cameras, the proposed method is able to classify correspondences into inliers and outliers, improving long-term odometry estimation.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
Robotics
Junjie Ye, Changhong Fu, Ziang Cao, Shan An, Guangze Zheng, Bowen Li
Summary: Most progress in object tracking has been focused on daytime scenes, but the new SCT low-light enhancer specifically targets nighttime UAV tracking, with significant performance gains shown in evaluations compared to other top-ranked enhancers. The proposed approach utilizes a special attention module and denoising techniques to simultaneously enhance low-light images, allowing for more reliable UAV tracking in challenging nighttime conditions.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2022)
Article
Engineering, Civil
Fuling Lin, Changhong Fu, Yujie He, Weijiang Xiong, Fan Li
Summary: An adaptive response reasoning approach is proposed for CF learning in UAV object tracking, which leverages temporal information in filter training and significantly enhances the robustness of the tracker. The method goes beyond standard response consistency requirements, creating an auxiliary label for the current sample and learning a generic relationship between previous and current filters to achieve self-regulated filter updating and improved discriminability.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Biochemical Research Methods
Zhongxu Zhuang, Fengxia Wang, Xuan Yang, Li Zhang, Chang-Hong Fu, Jing Xu, Changzhi Li, Hong Hong
Summary: The detection of sleep apnea is crucial for evaluating sleep quality and diagnosing diseases. This paper proposes a framework for sleep apnea detection based on FMCW radar, which utilizes signal processing methods and machine learning techniques to improve detection accuracy. Experimental results demonstrate that the proposed system achieves good classification performance.
Article
Robotics
Haobo Zuo, Changhong Fu, Sihang Li, Kunhan Lu, Yiming Li, Chen Feng
Summary: This work proposes an adversarial blur-deblur network (ABDNet) for UAV tracking, which includes a deblurrer to recover the visual appearance of the tracked object and a blur generator to produce realistic blurry images for adversarial training. ABDNet is trained with blurring-deblurring loss and tracking loss, and during inference, the blur generator is removed while the deblurrer and the tracker work together for UAV tracking.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Robotics
Sihang Li, Changhong Fu, Kunhan Lu, Haobo Zuo, Yiming Li, Chen Feng
Summary: This research presents TRTrack, a comprehensive framework that fully utilizes stereoscopic representation for UAV tracking. Through trajectory-aware reconstruction training (TRT) and spatial correlation refinement (SCR), the framework improves the performance of UAV tracking.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Robotics
Changhong Fu, Teng Li, Junjie Ye, Guangze Zheng, Sihang Li, Peng Lu
Summary: This study proposes a novel scale-aware domain adaptation framework, ScaleAwareDA, to tailor general Siamese trackers for UAV tracking. By constructing the target domain and using training datasets with UAV-specific attributes, this approach can represent objects in UAV scenarios more effectively and maintain robustness. Extensive experiments and real-world tests have demonstrated the superior tracking performance and practicality of ScaleAwareDA.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Article
Robotics
Kunhan Lu, Changhong Fu, Yucheng Wang, Haobo Zuo, Guangze Zheng, Jia Pan
Summary: This paper proposes an efficient plug-and-play cascaded denoising Transformer (CDT) to suppress cluttered and complex real noise in UAV visual tracking, thereby improving tracking performance.
IEEE ROBOTICS AND AUTOMATION LETTERS
(2023)
Review
Computer Science, Artificial Intelligence
Changhong Fu, Kunhan Lu, Guangze Zheng, Junjie Ye, Ziang Cao, Bowen Li, Geng Lu
Summary: UAV-based visual object tracking using Siamese networks is versatile and effective, but it faces obstacles due to limited computational resources and complex real-world circumstances. This study provides a comprehensive review and analysis of leading-edge Siamese trackers, evaluates their feasibility and efficacy through onboard tests, identifies limitations, and discusses the prospects for the development of Siamese tracking in UAV-based AI systems.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Automation & Control Systems
Guangze Zheng, Changhong Fu, Junjie Ye, Bowen Li, Geng Lu, Jia Pan
Summary: In many industrial applications, visual approaching the object is crucial to subsequent manipulating in unmanned aerial manipulator (UAM). The key to efficient vision-based UAM object tracking is still limited. To address this problem, a novel model-free scale-aware Siamese tracker (SiamSA) is proposed. Furthermore, two novel UAM tracking benchmarks are first recorded and comprehensive experiments validate the effectiveness of SiamSA. Real-world tests also confirm practicality for industrial UAM approaching tasks with high efficiency and robustness.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2023)
Article
Computer Science, Information Systems
Bowen Li, Changhong Fu, Fangqiang Ding, Junjie Ye, Fuling Lin
Summary: This paper proposes a novel discriminative correlation filter-based tracker ADTrack with illumination adaptive and anti-dark capability, and establishes a UAV nighttime tracking benchmark UAVDark135. Extensive experiments validate the superiority and robustness of ADTrack in all-day conditions.
IEEE TRANSACTIONS ON MOBILE COMPUTING
(2023)
Proceedings Paper
Automation & Control Systems
Changhong Fu, Sihang Li, Xinnan Yuan, Junjie Ye, Ziang Cao, Fangqiang Ding
Summary: This paper proposes a novel adaptive adversarial attack approach to address the potential risk and robustness issues in UAV object tracking. By generating online adversarial examples, the tracker can be fooled and lose track of the target. Experimental results demonstrate the effectiveness of this approach in significantly reducing the performance of state-of-the-art trackers.
2022 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2022)
(2022)
Proceedings Paper
Automation & Control Systems
Guangze Zheng, Changhong Fu, Junjie Ye, Bowen Li, Geng Lu, Jia Pan
Summary: This paper proposes a novel Siamese network for vision-based UAM approaching, addressing the issue of object tracking in the presence of scale variation. It introduces pairwise scale-channel attention and scale-aware anchor proposal to effectively deal with the challenges. A new UAM tracking benchmark, UAMT100, is also provided for evaluation.
2022 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
(2022)
Article
Automation & Control Systems
Ran Duan, Danda Pani Paudel, Changhong Fu, Peng Lu
Summary: This article introduces a novel outlier rejection approach for feature-based visual odometry, which distinguishes inliers and outliers by testing the boundaries of 2D-3D correspondences, resulting in more accurate odometry estimation compared to traditional methods.
IEEE-ASME TRANSACTIONS ON MECHATRONICS
(2022)
Article
Computer Science, Information Systems
Xia Liang, Jie Guo, Peide Liu
Summary: This paper investigates a novel consensus model based on social networks to manage manipulative and overconfident behaviors in large-scale group decision-making. By proposing a novel clustering model and improved methods, the consensus reaching is effectively facilitated. The feedback mechanism and management approach are employed to handle decision makers' behaviors. Simulation experiments and comparative analysis demonstrate the effectiveness of the model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiang Li, Haiwang Guo, Xinyang Deng, Wen Jiang
Summary: This paper proposes a method based on class gradient networks for generating high-quality adversarial samples. By introducing a high-level class gradient matrix and combining classification loss and perturbation loss, the method demonstrates superiority in the transferability of adversarial samples on targeted attacks.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Lingyun Lu, Bang Wang, Zizhuo Zhang, Shenghao Liu
Summary: Many recommendation algorithms only rely on implicit feedbacks due to privacy concerns. However, the encoding of interaction types is often ignored. This paper proposes a relation-aware neural model that classifies implicit feedbacks by encoding edges, thereby enhancing recommendation performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jaehong Yu, Hyungrok Do
Summary: This study discusses unsupervised anomaly detection using one-class classification, which determines whether a new instance belongs to the target class by constructing a decision boundary. The proposed method uses a proximity-based density description and a regularized reconstruction algorithm to overcome the limitations of existing one-class classification methods. Experimental results demonstrate the superior performance of the proposed algorithm.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Hui Tu, Shifei Ding, Xiao Xu, Haiwei Hou, Chao Li, Ling Ding
Summary: Border-Peeling algorithm is a density-based clustering algorithm, but its complexity and issues on unbalanced datasets restrict its application. This paper proposes a non-iterative border-peeling clustering algorithm, which improves the clustering performance by distinguishing and associating core points and border points.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Long Tang, Pan Zhao, Zhigeng Pan, Xingxing Duan, Panos M. Pardalos
Summary: In this work, a two-stage denoising framework (TSDF) is proposed for zero-shot learning (ZSL) to address the issue of noisy labels. The framework includes a tailored loss function to remove suspected noisy-label instances and a ramp-style loss function to reduce the negative impact of remaining noisy labels. In addition, a dynamic screening strategy (DSS) is developed to efficiently handle the nonconvexity of the ramp-style loss.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Raghunathan Krishankumar, Sundararajan Dhruva, Kattur S. Ravichandran, Samarjit Kar
Summary: Health 4.0 is gaining global attention for better healthcare through digital technologies. This study proposes a new decision-making framework for selecting viable blockchain service providers in the Internet of Medical Things (IoMT). The framework addresses the limitations in previous studies and demonstrates its applicability in the Indian healthcare sector. The results show the top ranking BSPs, the importance of various criteria, and the effectiveness of the developed model.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Tao Tan, Hong Xie, Liang Feng
Summary: This paper proposes a heterogeneous update idea and designs HetUp Q-learning algorithm to enlarge the normalized gap by overestimating the Q-value corresponding to the optimal action and underestimating the Q-value corresponding to the other actions. To address the limitation, a softmax strategy is applied to estimate the optimal action, resulting in HetUpSoft Q-learning and HetUpSoft DQN. Extensive experimental results show significant improvements over SOTA baselines.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Chao Yang, Xianzhi Wang, Lina Yao, Guodong Long, Guandong Xu
Summary: This paper proposes a dynamic transformer-based architecture called Dyformer for multivariate time series classification. Dyformer captures multi-scale features through hierarchical pooling and adaptive learning strategies, and improves model performance by introducing feature-map-wise attention mechanisms and a joint loss function.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Xiguang Li, Baolu Feng, Yunhe Sun, Ammar Hawbani, Saeed Hammod Alsamhi, Liang Zhao
Summary: This paper proposes an enhanced scatter search strategy, using opposition-based learning, to solve the problem of automated test case generation based on path coverage (ATCG-PC). The proposed ESSENT algorithm selects the path with the lowest path entropy among the uncovered paths as the target path and generates new test cases to cover the target path by modifying the dimensions of existing test cases. Experimental results show that the ESSENT algorithm outperforms other state-of-the-art algorithms, achieving maximum path coverage with fewer test cases.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Shirin Dabbaghi Varnosfaderani, Piotr Kasprzak, Aytaj Badirova, Ralph Krimmel, Christof Pohl, Ramin Yahyapour
Summary: Linking digital accounts belonging to the same user is crucial for security, user satisfaction, and next-generation service development. However, research on account linkage is mainly focused on social networks, and there is a lack of studies in other domains. To address this, we propose SmartSSO, a framework that automates the account linkage process by analyzing user routines and behavior during login processes. Our experiments on a large dataset show that SmartSSO achieves over 98% accuracy in hit-precision.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Renchao Wu, Jianjun He, Xin Li, Zuguo Chen
Summary: This paper proposes a memetic algorithm with fuzzy-based population control (MA-FPC) to solve the joint order batching and picker routing problem (JOBPRP). The algorithm incorporates batch exchange crossover and a two-level local improvement procedure. Experimental results show that MA-FPC outperforms existing algorithms in terms of solution quality.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Guoxiang Zhong, Fagui Liu, Jun Jiang, Bin Wang, C. L. Philip Chen
Summary: In this study, we propose the AMFormer framework to address the problem of mixed normal and anomaly samples in deep unsupervised time-series anomaly detection. By refining the one-class representation and introducing the masked operation mechanism and cost sensitive learning theory, our approach significantly improves anomaly detection performance.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Jin Zhou, Kang Zhou, Gexiang Zhang, Ferrante Neri, Wangyang Shen, Weiping Jin
Summary: In this paper, the authors focus on the issue of multi-objective optimisation problems with redundant variables and indefinite objective functions (MOPRVIF) in practical problem-solving. They propose a dual data-driven method for solving this problem, which consists of eliminating redundant variables, constructing objective functions, selecting evolution operators, and using a multi-objective evolutionary algorithm. The experiments conducted on two different problem domains demonstrate the effectiveness, practicality, and scalability of the proposed method.
INFORMATION SCIENCES
(2024)
Article
Computer Science, Information Systems
Georgios Charizanos, Haydar Demirhan, Duygu Icen
Summary: This article proposes a new fuzzy logistic regression framework that addresses the problems of separation and imbalance while maintaining the interpretability of classical logistic regression. By fuzzifying binary variables and classifying subjects based on a fuzzy threshold, the framework demonstrates superior performance on imbalanced datasets.
INFORMATION SCIENCES
(2024)
