Reviewerly/exHarmony huggingface dataset:
Deep learning-based fusion networks with high-order attention mechanism for 3D object detection in autonomous driving scenarios

Authors:
raw_author_name: Haiyang Jiang
raw_affiliation_string: School of Electrical and Control Engineering, North China University of Technology, Beijing, PR China
raw_author_name: Yuanyao Lu
raw_affiliation_string: School of Information Science and Technology, North China University of Technology, Beijing, PR China
raw_author_name: Duona Zhang
raw_affiliation_string: School of Information Science and Technology, North China University of Technology, Beijing, PR China
raw_author_name: Yuntao Shi
raw_affiliation_string: School of Electrical and Control Engineering, North China University of Technology, Beijing, PR China
raw_author_name: Jingxuan Wang
raw_affiliation_string: School of Information Science and Technology, North China University of Technology, Beijing, PR China

Abstract: In the realm of autonomous driving, accurately detecting 3D objects is both vital and challenging. Recently, deep convolutional networks have been successfully implemented in the fusion of LiDAR and camera data, delivering impressive results. However, prevailing approaches tend to concentrate on basic architectural designs and the use of fixed 3D bounding boxes, overlooking the exploration of feature interrelations and the varying scales of 3D objects. In this paper, we propose High-order Attention Mechanism Fusion Networks (HAMFNs) for image expression and multi-scale learning, based on a novel high-order attention mechanism with multi-scale detection and scale linear regression. High-order convolution layers are built for tenser filtering with discriminative representations of the holistic image. Multi-scale query module further characterizes the saliency properties of the 3D objects. Our tests on the nuScenes dataset show that HAMFNs outperform the latest top-performing methods, achieving a 0.7% increase in mean Average Precision (mAP). We further integrated high-order convolutional layers into ResNet-50, ResNet-101, and ResNet-152 architectures, enhancing their performance with minimal parameter increase. The Top-1 error rates were reduced by 1.65%, 1.63%, and 1.60% for each network, respectively.
An effective deep learning adversarial defense method based on spatial structural constraints in embedding space

Authors:
raw_author_name: Junzhong Miao
raw_affiliation_string: School of Cyberspace Science, Harbin Institute of Technology, Harbin, 150000, China
raw_author_name: Xiangzhan Yu
raw_affiliation_string: School of Cyberspace Science, Harbin Institute of Technology, Harbin, 150000, China
raw_author_name: Yanru Song
raw_affiliation_string: University of Göttingen, Institute of Computer Science, Göttingen, 37075, Germany
raw_author_name: Likun Liu
raw_affiliation_string: School of Cyberspace Science, Harbin Institute of Technology, Harbin, 150000, China
raw_author_name: Zhigang Zhou
raw_affiliation_string: School of information, Shanxi University of Finance and Economics, Taiyuan, 030006, China

Abstract: Deep neural networks are highly vulnerable to adversarial samples. Most existing adversarial defense methods do not consider the distribution of adversarial samples. We argue that very few adversarial samples in the natural sample set prevent the deep neural networks from learning a complete and effective representation of the adversarial samples. This causes the spatial structures between the natural and the adversarial samples to be vastly different from that of the input space, thus making the models vulnerable to adversarial attacks. Based on this viewpoint, this paper proposes an effective deep-learning adversarial defense method, which incorporates information about the spatial structures of the natural and the adversarial samples in the embedding space during the training process. This proposed approach improves the deep learning model's generalization to new adversarial samples and achieves the purpose of defending against adversarial attacks. Four deep neural networks with different scales are used and experimentally verified on four typical publicly available image data. The experimental results show that our method effectively improves the defense ability of deep learning models against adversarial attacks.
Stereo vision-based health monitoring method for wind turbine towers

Authors:
raw_author_name: Hong Zhao
raw_affiliation_string: College of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, China
raw_author_name: Zibin Liu
raw_affiliation_string: College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China; Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation, Changsha 410073, China
raw_author_name: Zhilong Su
raw_affiliation_string: Shanghai Institute of Applied Mathematics and Mechanics, Shanghai Key Laboratory of Mechanics in Energy Engineering, School of Mechanics and Engineering Science, Shanghai University, Shanghai, 200444, China; Shaoxing Institute of Technology, Shanghai University, Shaoxing, 312074, China
raw_author_name: Banglei Guan
raw_affiliation_string: College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China; Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation, Changsha 410073, China
raw_author_name: Dongsheng Zhang
raw_affiliation_string: Shanghai Institute of Applied Mathematics and Mechanics, Shanghai Key Laboratory of Mechanics in Energy Engineering, School of Mechanics and Engineering Science, Shanghai University, Shanghai, 200444, China; Shaoxing Institute of Technology, Shanghai University, Shaoxing, 312074, China
raw_author_name: Qifeng Yu
raw_affiliation_string: College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China; Hunan Provincial Key Laboratory of Image Measurement and Vision Navigation, Changsha 410073, China

Abstract: Wind turbines are subjected to complex aerodynamic loads, which results in potential sway and tilt, even causing the collision between blades and the tower. Therefore, it is imperative to monitor the condition of the tower to ensure safety. In this paper, we propose a method to monitor the health of wind turbine towers based on stereo vision. Our approach extracts the tower central axis from the image based on the structural information. Then the tower's sway magnitude is measured by binocular intersection, and the tilt angle is computed by exploiting the angle information provided by an inclinometer. Moreover, we detect blade tips using their geometric shape, which can be used to estimate the clearance distance between blades and the tower. The proposed method can simultaneously monitor the tower tilt and sway, while also identifying instances of blade collision with the tower. Experiments demonstrate the accuracy and feasibility of the designed system.
Debiased Visual Question Answering via the perspective of question types

Authors:
raw_author_name: Tianyu Huai
raw_affiliation_string: School of Computer Science and Technology, East China Normal University, Shanghai, China
raw_author_name: Shuwen Yang
raw_affiliation_string: School of Computer Science and Technology, East China Normal University, Shanghai, China
raw_author_name: Junhang Zhang
raw_affiliation_string: School of Computer Science and Technology, East China Normal University, Shanghai, China
raw_author_name: Jiabao Zhao
raw_affiliation_string: Institute of AI Education, East China Normal University, Shanghai, China
raw_author_name: Liang He
raw_affiliation_string: School of Computer Science and Technology, East China Normal University, Shanghai, China

Abstract: Visual Question Answering (VQA) aims to answer questions according to the given image. However, current VQA models tend to rely solely on textual information from the questions and ignore the visual information in the images to get answers, which is caused by bias that is generated during the training phase. Previous studies have shown that bias in VQA is mainly caused by the text modality, and our analysis suggests that question type is a crucial factor in bias formation. To address this bias, we proposed a self-supervised method including the Against Biased Samples(ABS) module that performs targeted debiasing by selecting samples that are prone to bias, and the Shuffle Question types(SQT) module that constructs negative samples by randomly replacing the question types of the samples selected by the ABS, to interrupting the shortcuts from question type to answer. Our approach mitigates the question-to-answer bias without using external annotations, overcoming the prior language problem. Additionally, we designed a new objective function for negative samples. Experimental results indicate that our method outperforms both self-supervised-based and supervised-based state-of-the-art approaches, achieving 70.36% accuracy on the VQA-CP v2 dataset.
Blockchain-based multi-institution collaborative credit evaluation modeling method

Authors:
raw_author_name: Yuncheng Qiao
raw_affiliation_string: Hunan Key Laboratory of Data Science & Blockchain, Business School, Hunan University, Changsha 410082, China
raw_author_name: Qiujun Lan
raw_affiliation_string: Hunan Key Laboratory of Data Science & Blockchain, Business School, Hunan University, Changsha 410082, China
raw_author_name: Shiyu Jia
raw_affiliation_string: Hunan Key Laboratory of Data Science & Blockchain, Business School, Hunan University, Changsha 410082, China
raw_author_name: Xianhua Kuang
raw_affiliation_string: Hunan Key Laboratory of Data Science & Blockchain, Business School, Hunan University, Changsha 410082, China
raw_author_name: Chaoqun Ma
raw_affiliation_string: Hunan Key Laboratory of Data Science & Blockchain, Business School, Hunan University, Changsha 410082, China

Abstract: Multi-institution credit data sharing-aggregation can improve the accuracy of credit evaluation, however, it also encounters the problems of data fraud, and the privacy, compliance of collaborative modeling. Faced with increasingly stringent and comprehensive privacy protection regulations, the potential security risk call into question the current data sharing-aggregation mode. Blockchain has shown that trusted and auditable computing is possible using a decentralized network of peers, and the immutable distributed ledger. Moreover, oblivious transfer (OT) guarantees the confidentiality of cross-institution transmission and calculation. The integration of blockchain with OT is rapidly increasing in computing environment. In this paper, we design and implement a blockchain-OT-based credit evaluation system dubbed as MEChain. Blockchain acts as a distributed ledger which facilitates efficient and credible data sharing-aggregation. Combined the 1-out-of-N OT, data transformation is used for raw data encryption achieving private, secure, and compliant sharing-aggregation operations. The calculation case and security analysis, prototype system implementation, performance and accuracy rate analysis are presented to validate the proposed solution.
Sequential visual and semantic consistency for semi-supervised text recognition

Authors:
raw_author_name: Mingkun Yang
raw_affiliation_string: School of Electronic Information and Communications, Huazhong University of Science and Technology, Luoyu Road, No. 1037, Wuhan, 430074, Hubei, China
raw_author_name: Biao Yang
raw_affiliation_string: School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Luoyu Road, No. 1037, Wuhan, 430074, Hubei, China
raw_author_name: Minghui Liao
raw_affiliation_string: Huawei Inc., Shenzhen, 518129, Guangdong, China
raw_author_name: Xiang Bai
raw_affiliation_string: School of Software Engineering, Huazhong University of Science and Technology, Luoyu Road, No. 1037, Wuhan, 430074, Hubei, China

Abstract: Scene text recognition (STR) is a challenging task that requires large-scale annotated data for training. However, collecting and labeling real text images is expensive and time-consuming, which limits the availability of real data. Therefore, most existing STR methods resort to synthetic data, which may introduce domain discrepancy and degrade the performance of STR models. To alleviate this problem, recent semi-supervised STR methods exploit unlabeled real data by enforcing character-level consistency regularization between weakly and strongly augmented views of the same image. However, these methods neglect word-level consistency, which is crucial for sequence recognition tasks. This paper proposes a novel semi-supervised learning method for STR that incorporates word-level consistency regularization from both visual and semantic aspects. Specifically, we devise a shortest path alignment module to align the sequential visual features of different views and minimize their distance. Moreover, we adopt a reinforcement learning framework to optimize the semantic similarity of the predicted strings in the embedding space. We conduct extensive experiments on several standard and challenging STR benchmarks and demonstrate the superiority of our proposed method over existing semi-supervised STR methods.
Hybrid approach of type-2 fuzzy inference system and PSO in asthma disease

Authors:
raw_author_name: Tarun Kumar
raw_affiliation_string: Department of Mathematics, Ch. Charan Singh University, Meerut, Uttar Pradesh, India
raw_author_name: M.K. Sharma
raw_affiliation_string: Department of Mathematics, Ch. Charan Singh University, Meerut, Uttar Pradesh, India
raw_author_name: Nitesh Dhiman
raw_affiliation_string: Zakir Hussain Delhi College, Delhi University, New Delhi, India
raw_author_name: Neha Nain
raw_affiliation_string: Department of Mathematics, Ch. Charan Singh University, Meerut, Uttar Pradesh, India

Abstract: This research work presents a hybrid approach combining a type-2 fuzzy inference system with particle swarm optimization (PSO) to develop a type-2 fuzzy optimized inference system, specifically tailored for asthma patient data. Addressing the inherent uncertainty in medical diagnostics, this model enhances traditional type-1 fuzzy logic by incorporating ambiguity into linguistic variables and utilizing type-2 fuzzy if-then rules. The system is trained to minimize diagnostic error in asthma disease identification. Applied to a dataset comprising eight medical entities from asthma patients, the model demonstrates substantial accuracy improvements. Numerical computations validate the system, showing a decrease in error rate from 1.445 to 0.03, indicating a significant enhancement in diagnostic precision. These results underscore the potential of our model in medical diagnostic problems, providing a novel and effective tool for tackling the complexities of asthma diagnosis.
GA-RCNN:Graph self-attention feature extraction for 3D object detection

Authors:
raw_author_name: Long Yu
raw_affiliation_string: Collage of Software, Xinjiang University, Xinjiang Uyghur Autonomous Region, China
raw_author_name: Shengwei Tian
raw_affiliation_string: Collage of Software, Xinjiang University, Xinjiang Uyghur Autonomous Region, China
raw_author_name: Xuezhuang Gao
raw_affiliation_string: Collage of Software, Xinjiang University, Xinjiang Uyghur Autonomous Region, China
raw_author_name: Jie Li
raw_affiliation_string: Collage of Software, Xinjiang University, Xinjiang Uyghur Autonomous Region, China
raw_author_name: Xingang Zhao
raw_affiliation_string: Land and Property Department, China Railway Urumqi Bureau Group Co., Xinjiang Uyghur Autonomous Region, China

Abstract: In recent years, 3D object detection based on LiDAR point clouds is a key component of autonomous driving. In pursuit of enhancing the accuracy of 3D point cloud feature extraction and point cloud detection, this paper introduces a novel 3D object detection model, termed as Graph Self-Attention-RCNN (GA-RCNN). This model is designed to integrate voxel information and point location information, enhancing the quality of 3D object proposals while maintaining contextual accuracy. The first stage rectifies the previous approach that relied on local features for preselected boxes, overlooking crucial global contextual information. An improved method is suggested in this work, utilizing BEV to capture long-range dependencies via a cross-attention mechanism. The second stage addresses the overreliance on local neighborhood point feature extraction. The Graph Self-Attention Pooling method is proposed, characterized by its dynamic computation of contribution weights for inputs. This enhances the model’s flexibility and generalization performance. Extensive evaluations on KITTI and Waymo datasets demonstrate GA-RCNN’s superior accuracy compared to other methods, affirming its efficacy in 3D object detection.
LIKFCM: Linear interpolation-based kernelized fuzzy C-means clustering imputation method for handling incomplete data

Authors:
raw_author_name: None Jyoti
raw_affiliation_string: University School of Information, Communication & Technology, Guru Gobind Singh Indraprastha University, New Delhi, India
raw_author_name: Jaspreeti Singh
raw_affiliation_string: University School of Information, Communication & Technology, Guru Gobind Singh Indraprastha University, New Delhi, India
raw_author_name: Anjana Gosain
raw_affiliation_string: University School of Information, Communication & Technology, Guru Gobind Singh Indraprastha University, New Delhi, India

Abstract: Addressing missing values is a persistent challenge in the field of data mining. The presence of incomplete data can significantly compromise the overall data quality. Consequently, it is crucial to handle incomplete data efficiently. This paper presents a novel approach for imputing missing values that incorporates Kernelized Fuzzy C-Means (KFCM) clustering and proposes a method termed LIKFCM, which combines its benefits with Linear Interpolation (LI). The proposed LIKFCM’s performance is assessed through a comparison against nine state-of-the-art imputation techniques (mean, median, LI, EMI, KNNI, KMI, FKMI, LIFCM, and LIPFCM) across ten widely used real-world datasets from the UCI repository with six combinations of missing ratios to assess the efficacy of the proposed imputation method. From the experimental results, it is evident that our proposed method outperforms the existing imputation methods with significant improvements in terms of RMSE &amp; MAE for these datasets. Additionally, experiments examining the effect of missing values validate the robustness of the proposed approach by handling different missing ratios. The performance validation of the proposed approach against other state-of-the-art imputation methods has been conducted utilizing a Kendall’s W statistical test, involving a comparison of their mean ranks across different missing ratios. The outcomes indicate that LIKFCM has outperformed other imputation methods, attaining the highest rank in terms of different evaluation criteria.
Hierarchical alignment network for domain adaptive object detection in aerial images

Authors:
raw_author_name: You Ma
raw_affiliation_string: Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education and School of Automation, Southeast University, Nanjing 210096, China
raw_author_name: Lin Chai
raw_affiliation_string: Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education and School of Automation, Southeast University, Nanjing 210096, China
raw_author_name: Lizuo Jin
raw_affiliation_string: Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education and School of Automation, Southeast University, Nanjing 210096, China
raw_author_name: Jun Yan
raw_affiliation_string: Department of Geriatric Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing 210024, China

Abstract: Domain Adaptive Object Detection (DAOD) alleviates the reliance on labeled data by transferring knowledge learned from labeled source domain to unlabeled target domain. Recent DAOD methods is modeled mainly based on ground-level images. Compared to ground-level images, aerial images suffer from scale variation and viewpoint diversity. This means that domain adaptive object detection in aerial images is a more challenging task. In this work, we address the domain shift in aerial images from two levels: 1) image-level shifts, such as weather, lighting, viewpoint, etc., 2) instance-level shifts, such as object appearance, scale, etc. Specifically, multiple domain-confusion classifiers are designed to learn image-level common knowledge of the source and target domains. Different levels of domain classifiers are further assigned adaptive weights to coordinate the transferability and discriminability of the adaptive detectors. Meanwhile, instance-level alignment is realized by forcing the intrinsic relationship between classes in both domains to be consistent. In addition, we perform instance-level alignment in different semantic-level feature layers to improve the scale awareness of the adaptation model. Extensive experimental results on VisDrone, UAVDT, DIOR and DOTA datasets demonstrate that our method achieves optimal detection performance in four domain adaption scenarios compared to other state-of-the-art methods, e.g., in Daytime → Night (VisDrone), the mAP50 is 23.5 %; in VisDrone → UAVDT, DIOR → UAVDT and DOTA → VisDrone, the AP50 of the car is 63.1 %, 46.6 % and 44.8 % respectively. Code will be available online (https://github.com/MaYou1997/HANet).
A learning and evolution-based intelligence algorithm for multi-objective heterogeneous cloud scheduling optimization

Authors:
raw_author_name: Bingfeng Si
raw_affiliation_string: School of Systems Science, Beijing Jiaotong University, No. 3, Shangyuan Village, Beijing, 100028, China
raw_author_name: Herwig Unger
raw_affiliation_string: Faculty of Mathematics and Computer Science, Fern University in Hagen, No. 47, Hagen, 58097, Germany

Abstract: The multi-objective directed acyclic graph scheduling problem (MDAGSP) is prevalent in cloud scheduling systems, involving the selection, assignment, and execution of multiple tasks/jobs with complex coupling interdependencies. High-quality solutions can yield substantial economic benefits. However, prevailing methods face challenges in obtaining a set of superior solutions for MDAGSP, due to the multifaceted nature of its variables, objectives, constraints, and heterogeneity. Firstly, this paper formulates a three-objective MDAGSP that includes makespan, energy costs and revenue, to model cloud scheduling systems. Subsequently, we propose a composite algorithm consisting of a selection phase and an assignment phase to automatically generate an efficient scheduling policy for this model. During the selection phase, a graph convolutional neural network learns high-level features to extract complex dependencies between tasks. During the assignment phase, an adaptive evolutionary algorithm assigns tasks to the appropriate executors. Finally, a series of experiments are conducted to validate the model's accuracy and assess the algorithm's efficiency. Compared to heuristic approaches, the algorithm achieves at least a 20.1% makespan reduction and 3.17% revenue increase. Remarkably, the algorithm obtains at least an 8.86% reduction in energy costs over the eleven baselines. In conclusion, the proposed algorithm provides decision-makers with a global view of scheduling plan.