Reviewerly/exHarmony huggingface dataset:
Characteristics of students’ learning behavior preferences — an analysis of self-commentary data based on the LDA model

Authors:
raw_author_name: Jincheng Zhou
raw_affiliation_string: Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province, Duyun, China; School of Computer and Information, Qiannan Normal University for Nationalities, Duyun, China
raw_author_name: Feng Wu
raw_affiliation_string: No. 2 High School of Duyun, Duyun, China
raw_author_name: Dan Wang
raw_affiliation_string: School of Mathematics and Statistics, Qiannan Normal University for Nationalities, Guizhou Duyun, China

Abstract: How to better grasp students’ learning preferences in the environment of rapid development of engineering and science and technology so as to guide them to high-quality learning is one of the important research topics in the field of educational technology research today. In order to achieve this goal, this paper utilizes the LDA (Latent Dirichlet Allocation) model for text mining of the survey results on the basis of a survey on students’ self-perception evaluation. The results show that the LDA model is capable of extracting terms from text, fuzzy identifying groups of students at different levels and presenting potential logical relationships between the groups, and further analyzing the learning preferences of students at different levels for IT courses. Based on the student’s learning needs, this paper proposes recommendations for developing students’ learning effectiveness. The LDA method proposed in this paper is a feasible and effective method for assessing students’ learning dynamics as it generates cognitive content about students’ learning and allows for the timely discovery of students’ learning expectations and cutting-edge dynamics.
Shadow-aware dynamic convolution for shadow removal

Authors:
raw_author_name: Yimin Xu
raw_affiliation_string: Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, 361005, Xiamen, Fujian, China
raw_author_name: Mingbao Lin
raw_affiliation_string: Tencent Youtu Lab, Shanghai, China
raw_author_name: Hong Yang
raw_affiliation_string: Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, 361005, Xiamen, Fujian, China
raw_author_name: Fei Chao
raw_affiliation_string: Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, 361005, Xiamen, Fujian, China
raw_author_name: Rongrong Ji
raw_affiliation_string: Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, 361005, Xiamen, Fujian, China

Abstract: With a wide range of shadows in many collected images, shadow removal has aroused increasing attention since uncontaminated images are of vital importance for many computer vision tasks. Current methods consider the same convolution operations for both shadow and non-shadow regions while ignoring the large gap between the color mappings for the shadow region and the non-shadow region, leading to poor quality of reconstructed images and a heavy computation burden. To solve this problem, this paper introduces a novel plug-and-play Shadow-Aware Dynamic Convolution (SADC) module to decouple the interdependence between the shadow region and the non-shadow region. Inspired by the fact that the color mapping of the non-shadow region is easier to learn, our SADC processes the non-shadow region with a lightweight convolution module in a computationally cheap manner and recovers the shadow region with a more complicated convolution module to ensure the quality of image reconstruction. Given that the non-shadow region often contains more background color information, we further develop a novel intra-convolution distillation loss to strengthen the information flow from the non-shadow region to the shadow region. Extensive experiments on the ISTD and SRD datasets show our method achieves better performance in shadow removal over many state-of-the-art methods. Codes have been made available at https://github.com/xuyimin0926/SADC.
Image inpainting exploiting global prior refined weighted low-rank representation

Authors:
raw_author_name: Xuya Liu
raw_affiliation_string: School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China
raw_author_name: Ruyi Han
raw_affiliation_string: School of Mathematics, Shandong University, Jinan, 250100, China
raw_author_name: Shujun Fu
raw_affiliation_string: School of Mathematics, Shandong University, Jinan, 250100, China
raw_author_name: Yuanfeng Zhou
raw_affiliation_string: School of Software, Shandong University, Jinan, 250101, China
raw_author_name: Yuliang Li
raw_affiliation_string: Department of Intervention Medicine, The Second Hospital of Shandong University, Jinan, 250033, China

Abstract: Low-rank and nonlocal self-similarity are important priors for image restoration. Based on nonlocal self-similarity assumption, similar patches are grouped into a matrix that is of low-rank or approximately low-rank. Traditional algorithms model low-rank prior of local group matrices (intra-group) and ignore the global inter-group relationship, which will result in suboptimal performance without considering global consistency. Due to overlapping of patches, groups are dependent, however, conventional methods process each group independently. To make full use of the intrinsic relationship among groups, we consider all groups simultaneously and propose a global prior refined weighted low-rank representation model for image inpainting. The local-groups and the aggregation of all groups are taken into account in the proposed model. By introducing auxiliary variable, we decouple the local–global model and optimize iteratively using the alternating direction method of multipliers. The proposed global refinement model significantly improves the performance of local low-rank model. A new strategy for grouping similar patches is also proposed by dividing the neighborhood of a target patch into multiple subregions and searching for a given number of similar patches within each subregion. The proposed method is applied to various tasks (line inpainting, destriping, and depth map completion) and obtains promising performance.
Underwater image enhancement utilizing adaptive color correction and model conversion for dehazing

Authors:
raw_author_name: Yiming Li
raw_affiliation_string: School of Information and Electronics & Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing, 100081, China
raw_author_name: Shuai Wang
raw_affiliation_string: School of Computer Science and Engineering, Beihang University, Beijing, 100083, China
raw_author_name: Qiang Jiao
raw_affiliation_string: Ministry of Public Security Information and Communication Center, Beijing, 100741, China
raw_author_name: Liheng bian
raw_affiliation_string: School of Information and Electronics & Advanced Research Institute of Multidisciplinary Science, Beijing Institute of Technology, Beijing, 100081, China

Abstract: Underwater imaging is typically affected by absorption and scattering, which can lead to color distortion and image blurring. However, no universal color constancy algorithm for correcting underwater images exists due to the wide variation in underwater scenes. Furthermore, methods based on learning to improve underwater image quality often suffer from a lack of ground truth, which can compromise the validity and authenticity of restored images. In this study, we propose an adaptive underwater image enhancement technique that corrects image color and reduces haze using a valid dataset with ground truth. Specifically, we first use hue channel statistics from underwater images to build a dataset of color-corrected images from different underwater scenes. We use this dataset to train an Unet-like network for adaptive color correction. After color correction, underwater images often exhibit characteristics similar to hazy terrestrial images. We refer to this phenomenon as “model conversion”. The hazy terrestrial images have corresponding ground truth. Therefore, we train a Transformer-like network with hazy terrestrial image datasets to remove haze in underwater images. Our method is more robust for different underwater scenes and elegantly solves the problem of lacking ground truth. A series of experiments demonstrate that our method achieves superior performance compared to state-of-the-art methods in terms of both visual quality and quantitative metrics.
AdaptiveStereo: Depth estimation from adaptive structured light

Authors:
raw_author_name: Tong Jia
raw_affiliation_string: Northeastern University, No. 11, Lane 3, Wenhua Road, Heping District, Shenyang, 110819, Liaoning Province, China
raw_author_name: Xiao Yang
raw_affiliation_string: Northeastern University, No. 11, Lane 3, Wenhua Road, Heping District, Shenyang, 110819, Liaoning Province, China
raw_author_name: Shuyang Lin
raw_affiliation_string: Northeastern University, No. 11, Lane 3, Wenhua Road, Heping District, Shenyang, 110819, Liaoning Province, China
raw_author_name: Yizhe Liu
raw_affiliation_string: Northeastern University, No. 11, Lane 3, Wenhua Road, Heping District, Shenyang, 110819, Liaoning Province, China
raw_author_name: Dongyue Chen
raw_affiliation_string: Northeastern University, No. 11, Lane 3, Wenhua Road, Heping District, Shenyang, 110819, Liaoning Province, China

Abstract: Recovering depth with active stereo methods has become an important approach to three-dimensional perception. However, when the properties of the measurement scene change, projecting predesigned structured light can cause improper exposure and defocus blur on some object surfaces. To address these problems, we propose an active stereo depth perception method called AdaptiveStereo, based on adaptive structured light (ASL). The core idea is to use prior depth information to generate optimal structured light for different measurement scenes, ensuring even illumination of the object's surface and thereby improving depth perception results. To this end, firstly, a novel ASL encoding scheme based on speckle pattern is designed. Then, we construct an adaptive structured light generation model (ASL-GM) to automatically generate the optimal structured light. Finally, depth perception is realized by using the active stereo matching network with adaptive guidance (AGS-Net). Compared with mainstream depth perception methods, the evaluation results show that our method has higher accuracy, and can obtain better depth perception results.
Optical 3D information encryption and rapid decryption via interferenceless coded aperture correlation holography

Authors:
raw_author_name: Le Yang
raw_affiliation_string: Key Laboratory of Photonic Technology for Integrated Sensing and Communication of Ministry of Education, Guangdong University of Technology, Guangzhou 510006, China
raw_author_name: Junpeng Yang
raw_affiliation_string: Key Laboratory of Photonic Technology for Integrated Sensing and Communication of Ministry of Education, Guangdong University of Technology, Guangzhou 510006, China
raw_author_name: Tao Huang
raw_affiliation_string: Key Laboratory of Photonic Technology for Integrated Sensing and Communication of Ministry of Education, Guangdong University of Technology, Guangzhou 510006, China
raw_author_name: Jiaosheng Li
raw_affiliation_string: School of Electronics and Information, Guangdong Polytechnic Normal University, Guangzhou 510665, China
raw_author_name: Qinnan Zhang
raw_affiliation_string: School of Electronics and Information, Guangdong Polytechnic Normal University, Guangzhou 510665, China
raw_author_name: Jianglei Di
raw_affiliation_string: Key Laboratory of Photonic Technology for Integrated Sensing and Communication of Ministry of Education, Guangdong University of Technology, Guangzhou 510006, China
raw_author_name: Liyun Zhong
raw_affiliation_string: Key Laboratory of Photonic Technology for Integrated Sensing and Communication of Ministry of Education, Guangdong University of Technology, Guangzhou 510006, China

Abstract: Holographic encryption has obvious advantages in large key space and anti-attack ability, but the corresponding coherent noise and optical alignment requirement greatly affect the encryption efficiency and decryption quality. Here, we present an optical 3D information encryption method based on the interferenceless coded aperture correlation holography (I-COACH), which consists of a coded phase mask and a lens. The speckle pattern, which is the convolution of the point spread hologram (PSH) with the object intensity distribution recorded in the image sensor plane, is used as the ciphertext, and these pre-recorded PSH are utilized as the decryption key. Due to the spatial decorrelation characteristics of the PSH at different axial positions, the plaintext can be decrypted only when the ciphertext is cross-correlated with the PSH of its corresponding axial position. Therefore, a 3D object composed of multiple 2D planes can only be decrypted by the PSH of its corresponding depth. For rapid and efficient decryption, we introduce the space division multiplexing technology. By multiplexing coded phase masks at different positions, the PSH at two positions can be simultaneously recorded in single-shot. In addition to improving the encryption efficiency and decryption quality, both numerical simulation and experimental results demonstrate the excellent security and robustness of the proposed I-COACH based 3D information encryption method.
Coherent chord computation and cross ratio for accurate ellipse detection

Authors:
raw_author_name: Mingyang Zhao
raw_affiliation_string: Beijing Academy of Artificial Intelligence, Beijing, China; MAIS & NLPR, Institute of Automation, Chinese Academy of Sciences, China
raw_author_name: Xiaohong Jia
raw_affiliation_string: KLMM, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China
raw_author_name: Lei Ma
raw_affiliation_string: Beijing Academy of Artificial Intelligence, Beijing, China; National Biomedical Imaging Center, College of Future Technology, Peking University, Beijing, China
raw_author_name: Li-Ming Hu
raw_affiliation_string: State Key Laboratory of Hydro-Science and Engineering, Tsinghua University, Beijing, China
raw_author_name: Dong-Ming Yan
raw_affiliation_string: MAIS & NLPR, Institute of Automation, Chinese Academy of Sciences, China; State Key Laboratory of Hydro-Science and Engineering, Tsinghua University, Beijing, China

Abstract: This paper presents a new method for detecting ellipses in images, which has many applications in pattern recognition and robotic tasks. Previous approaches typically use sophisticated arc grouping strategies or calculate differential such as tangents, and thereby they are less efficient or more sensitive to noise. In this work, we present a novel ellipse detector, based on the simple yet effective chord computation, and on the projective invariant cross ratio, which achieves promising performance in both accuracy and efficiency. First, elliptical arcs are extracted by fast vector computations along with the removal of straight segments to speed up detection. Then, arcs from the same ellipse are grouped together according to the relative location and the intersecting chord constraints, both are on coherent chord computation without differential. Additionally, an efficient additive principle is applied to further accelerate the grouping process. Finally, a novel and robust verification by area-deduced cross ratio is introduced to pick out salient ellipses. Compared with predecessor methods, cross ratio is not only simple for computation, but also has invariant properties (used to discriminate ellipses). Extensive experiments on seven public datasets (including synthetic and real-world images) are implemented. The results highlight the salient advantages of the proposed method compared to state-of-the-art detectors: Easier to implementation, more robust against occlusion and noise, as well as attaining higher F-measure.
RFLPV: A robust federated learning scheme with privacy preservation and verifiable aggregation in IoMT

Authors:
raw_author_name: Ruyan Wang
raw_affiliation_string: Key Laboratory of Ubiquitous Sensing and Networking, Chongqing, 400065, China; School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China; The Advanced Network and Intelligent Connection Technology Key Laboratory, Chongqing, 400065, China
raw_author_name: Zhigang Yang
raw_affiliation_string: Key Laboratory of Ubiquitous Sensing and Networking, Chongqing, 400065, China; School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China; The Advanced Network and Intelligent Connection Technology Key Laboratory, Chongqing, 400065, China
raw_author_name: Man Luo
raw_affiliation_string: China Mobile IoT Company Limited, Chongqing, 401120, China
raw_author_name: Dapeng Wu
raw_affiliation_string: Key Laboratory of Ubiquitous Sensing and Networking, Chongqing, 400065, China; School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China; The Advanced Network and Intelligent Connection Technology Key Laboratory, Chongqing, 400065, China

Abstract: With the rapid development of the Internet of Medical Things (IoMT), medical institutions are accumulating vast amounts of medical data and aiming to utilize this data to train high-quality deep-learning models for assisting doctors in diagnosis. However, due to the highly sensitive nature of medical data, data fusion has always been problematic. Recently, Federated Learning (FL) has gained significant attention as it allows for model training without exposing raw data. Nevertheless, existing research demonstrates that FL still brings the risk of privacy breaches. Furthermore, within the FL framework, a malicious server could potentially provide forged aggregated results, leading to disastrous consequences for the medical system. In this paper, we propose the RFLPV, a robust FL scheme with privacy preservation and verifiable aggregation to enable collaboration among medical institutions and facilitate the fusion of medical data. Specifically, we preserve gradient privacy by adding pairwise masks. Additionally, we design an efficient verification mechanism to tackle the threat posed by a malicious server. Furthermore, we develop a customized local training strategy to accommodate the heterogeneity of medical data. Finally, we demonstrate the superior performance of RFLPV using a real medical dataset.
A guidable nonlocal low-rank approximation model for hyperspectral image denoising

Authors:
raw_author_name: Yong Chen
raw_affiliation_string: School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, China
raw_author_name: Juan Zhang
raw_affiliation_string: School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, China
raw_author_name: Jinshan Zeng
raw_affiliation_string: School of Computer and Information Engineering, Jiangxi Normal University, Nanchang, China
raw_author_name: Tai-Xiang Jiang
raw_affiliation_string: School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu, China

Abstract: Hyperspectral image (HSI) denoising is an essential preprocessing step for improving HSI applications. Recently, subspace-based nonlocal low-rank approximation (SNLR) methods have shown their superiority. However, most of these methods ignore such a potentially important phenomenon, that is, real HSIs contain many high signal-to-noise ratio bands (HSNRBs), and thus result in the underutilization of information. In this paper, we propose a new method called Subspace-based Guided Nonlocal Low-Rank Approximation (SGNLR) for HSI denoising. Our method takes advantage of the abundant spatial information in the representation coefficients of HSNRBs to improve denoising performances. Specifically, we employ low-rank subspace representation to exploit the global spectral correlation of HSI and transform the HSI denoising task as the estimation of spectral basis and spatial representation coefficients (SRCs). Motivated by the consistency of coefficient features between the whole HSI and HSNRBs, we employ the SRCs of HSNRBs to guide the restoration of target coefficients. To restore the SRCs accurately, we design a powerful nonlocal low-rank approximation that takes into account the nonlocal self-similarity (NSS) of SRCs. An efficient algorithm based on alternating minimization is developed to optimize the proposed model. Extensive experiments on both simulated and real-world data demonstrate the outperformance of our method.
RIC-CNN: Rotation-Invariant Coordinate Convolutional Neural Network

Authors:
raw_author_name: Hanlin Mo
raw_affiliation_string: Center for Machine Vision and Signal Analysis, University of Oulu, Finland
raw_author_name: Guoying Zhao
raw_affiliation_string: Center for Machine Vision and Signal Analysis, University of Oulu, Finland

Abstract: Due to the lack of rotation invariance in traditional convolution operations, even acting a slight rotation on the input can severely degrade the performance of Convolutional Neural Networks (CNNs). To address this, we propose a Rotation-Invariant Coordinate Convolution (RIC-C), which achieves natural invariance to arbitrary rotations around the input center without additional trainable parameters or data augmentation. We first evaluate the rotational invariance of RIC-C using the MNIST dataset and compare its performance with most previous rotation-invariant CNN models. RIC-C achieves state-of-the-art classification on the MNIST-rot test set without data augmentation and with lower computational costs. Then, the interchangeability of RIC-C with traditional convolution operations is demonstrated by seamlessly integrating it into common CNN models like VGG, ResNet, and DenseNet. We conduct remote sensing image classification on the NWPU VHR-10, MTARSI and AID datasets and patch matching experiments on the UBC benchmark dataset, showing that RIC-C significantly enhances the performance of CNN models across different applications, especially when training data is limited. Our codes can be downloaded from https://github.com/HanlinMo/Rotation-Invariant-Coordinate-Convolutional-Neural-Network.git.
Multiple instance learning from similarity-confidence bags

Authors:
raw_author_name: Xuan Zhang
raw_affiliation_string: College of Science, China Agricultural University, Beijing 100083, China
raw_author_name: Yitian Xu
raw_affiliation_string: College of Science, China Agricultural University, Beijing 100083, China
raw_author_name: Xuhua Liu
raw_affiliation_string: College of Science, China Agricultural University, Beijing 100083, China

Abstract: Multiple instance learning (MIL) is a classic weakly supervised learning approach, in which samples are grouped into bags that may contain varying numbers of instances. A bag is designated as positive if it contains at least one positive instance; otherwise, it is considered negative. Previous studies have consistently assumed that the bag labels are completely known. In fact, labeling every bag can be extremely challenging or even unfeasible due to the exorbitant expenses in terms of time and labor. Fortunately, it is much easier to obtain the similarity confidence, which represents the probability of two bags sharing the same label. How to employ it in MIL is worthy of study. Inspired by the above study, we present the first attempt to investigate MIL from similarity-confidence bags. Therefore, this paper proposes a new framework for training bag-level classifiers that adheres to the principle of empirical risk minimization. Moreover, we theoretically derive a generalization error bound to guarantee model convergence. Finally, we implement risk correction to mitigate potential over-fitting problem and provide theoretical consistency. Numerical experiments on eight datasets further validate the effectiveness of the proposed bag-level classifier.