Reviewerly/exHarmony huggingface dataset:
Hierarchical graph augmented stacked autoencoders for multi-view representation learning

Authors:
raw_author_name: Jianping Gou
raw_affiliation_string: College of Computer and Information Science, College of Software, Southwest University, Chongqing, 400715, China
raw_author_name: Nannan Xie
raw_affiliation_string: School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, 212013, China; School of Electronic and Information Engineering, Huaibei Institute of Technology, Huaibei, 235000, China
raw_author_name: Jinhua Liu
raw_affiliation_string: School of Mathematical and Computer Sciences, Shangrao Normal University, Shangrao, Jiangxi, 334001, China
raw_author_name: Baosheng Yu
raw_affiliation_string: School of Computer Science, The University of Sydney, NSW, 2008, Australia
raw_author_name: Weihua Ou
raw_affiliation_string: School of Big Data and Computer Science, Guizhou Normal University, Guiyang, Guizhou, 550025, China
raw_author_name: Zhang Yi
raw_affiliation_string: School of Computer Science, Sichuan University, Chengdu, 610065, China
raw_author_name: Wu Chen
raw_affiliation_string: College of Computer and Information Science, College of Software, Southwest University, Chongqing, 400715, China

Abstract: With recent success of deep neural networks, stacked autoencoder networks have received a lot of attention for robust unsupervised representation learning. However, recent autoencoder methods cannot make full use of multi-view information and thus fail to further improve many real-world applications by exploring the geometric structures of multi-view data. To address the above-mentioned issue, we introduce hierarchical graph augmented stacked autoencoders (HGSAE) for unsupervised multi-view representation learning. Specifically, a hierarchical graph structure is first adapted to stacked autoencoders to learn view-specific representations, aiming to preserve the geometric information of multi-view data through local and non-local graph regularizations. A general or common representation can then be learned by reconstructing each single view using fully connected neural networks. By doing this, the proposed method not only preserves the geometric information in multi-view data but also automatically balances the complementarity/consistency among different views. Extensive experiments on six popular unsupervised representation learning datasets demonstrate the effectiveness of our method when compared with recent state-of-the-art autoencoder methods.
SSL-Net: Sparse semantic learning for identifying reliable correspondences

Authors:
raw_author_name: Shunxing Chen
raw_affiliation_string: key Laboratory of Complex Systems Modeling and Simulation, School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China
raw_author_name: Guobao Xiao
raw_affiliation_string: key Laboratory of Complex Systems Modeling and Simulation, School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China
raw_author_name: Ziwei Shi
raw_affiliation_string: School of Informatics, Xiamen University, Xiamen, 361005, China
raw_author_name: Junwen Guo
raw_affiliation_string: College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China
raw_author_name: Jiayi Ma
raw_affiliation_string: Electronic Information School, Wuhan University, Wuhan, 430072, China

Abstract: Feature matching aims to identify reliable correspondences between two sets of given initial feature points, which is of considerable importance to photogrammetry and computer vision. In this study, we propose an innovative sparse semantic learning-based network, named SSL-Net, for feature matching. Specifically, SSL-Net includes a novel sparsity constraint (SC) block, which builds a sparse graph for sparse semantic learning. The SC block adopts a region-to-whole learning strategy to measure the confidence of nodes in the sparse graph. It helps the sparse graph preserve the semantic information of positive influence while rejecting unnecessary ones, thereby suppressing the negative influence of incorrect correspondences. In addition, SSL-Net also includes a channel-spatial attention feature gathering block, which gathers features along the spatial direction and channel dimension of correspondences. To mitigate the existence of label ambiguity, we incorporate the accommodation factor into the loss function of SSL-Net for feature matching. As a result, our network outperforms the state-of-the-art method by a considerable margin. Notably, SSL-Net achieves a 9.05% improvement under an error threshold of 5° over the state-of-the-art method for the relative pose estimation task on the YFCC100M dataset. Our code will be available at https://github.com/guobaoxiao/SSL-Net.
Beyond <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si135.svg" display="inline" id="d1e3713"><mml:mi>k</mml:mi></mml:math>-Means++: Towards better cluster exploration with geometrical information

Authors:
raw_author_name: Yuan Ping
raw_affiliation_string: School of Information Engineering, Xuchang University, Xuchang 461000, China
raw_author_name: Huina Li
raw_affiliation_string: School of Information Engineering, Xuchang University, Xuchang 461000, China
raw_author_name: Bin Hao
raw_affiliation_string: Here Data Technology Co., LTD, Shenzhen 518000, China
raw_author_name: Chun Guo
raw_affiliation_string: College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
raw_author_name: Baocang Wang
raw_affiliation_string: School of Telecommunication Engineering, Xidian University, Xi'an 710071, China

Abstract: Although k-means and its variants are known for their remarkable efficiency, they suffer from a strong dependence on the prior knowledge of K and the assumption of a circle-like pattern, which can result in the algorithms dividing the input space instead of discovering non-predetermined data patterns. Thus, we propose beyond k-means++ that infers and utilizes explicit clusters by emphasizing local geometrical information for better cluster exploration. To avoid the K dependence, a novel framework of iterative division and aggregation (IDA) over k-means++ is presented. It begins with any K≥1, then increases and reduces K along with the procedure of clusters' division and aggregation, respectively. To break through the circle-like pattern limitation, we introduce a reasonability checking strategy (RCS) for cluster division. Given local geometrical information, RCS achieves arbitrary cluster shape support by rejecting edge patterns with distinguished convergence direction and merging adjacent clusters with pseudo-edge patterns. Furthermore, we design an edge shrinkage strategy (ESS). Taking edge patterns as the cluster prototype, it benefits accuracy by effectively avoiding representability reduction due to irregular distribution. To compensate for the loss of efficiency, a near maximin and random sampling algorithm is suggested for large-scale data with high dimensionality. Experimental results confirm that beyond k-means++ is featured by handling arbitrary cluster shapes with remarkable accuracy.
Adaptive local Principal Component Analysis improves the clustering of high-dimensional data

Authors:
raw_author_name: Nico Migenda
raw_affiliation_string: Center for Applied Data Science Gütersloh, Bielefeld University of Applied Sciences, Germany
raw_author_name: Ralf Möller
raw_affiliation_string: Computer Engineering Group, Faculty of Technology, Bielefeld University, Germany
raw_author_name: Wolfram Schenck
raw_affiliation_string: Center for Applied Data Science Gütersloh, Bielefeld University of Applied Sciences, Germany

Abstract: In local Principal Component Analysis (PCA), a distribution is approximated by multiple units, each representing a local region by a hyper-ellipsoid obtained through PCA. We present an extension for local PCA which adaptively adjusts both the learning rate of each unit and the potential function which guides the competition between the local units. Our local PCA method is an online neural network method where unit centers and shapes are modified after the presentation of each data point. For several benchmark distributions, we demonstrate that our method improves the overall quality of clustering, especially for high-dimensional distributions where many conventional methods do not perform satisfactorily. Our online method is also well suited for the processing of streaming data: The two adaptive mechanisms lead to a quick reorganization of the clustering when the underlying distribution changes.
Holistic Dynamic Frequency Transformer for image fusion and exposure correction

Authors:
raw_author_name: Xiaoke Shang
raw_affiliation_string: School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China
raw_author_name: Gehui Li
raw_affiliation_string: DUT-RU International School of Information Science & Engineering, Dalian University of Technology, Dalian 116620, China
raw_author_name: Zhiying Jiang
raw_affiliation_string: Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian 116620, China; School of Software Technology, Dalian University of Technology, Dalian 116024, China
raw_author_name: Shaomin Zhang
raw_affiliation_string: School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China
raw_author_name: Nai Ding
raw_affiliation_string: School of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China
raw_author_name: Jinyuan Liu
raw_affiliation_string: School of Mechanical Engineering, Dalian University of Technology, Dalian 116086, China

Abstract: The correction of exposure-related issues is a pivotal component in enhancing the quality of images, offering substantial implications for various computer vision tasks. Historically, most methodologies have predominantly utilized spatial domain recovery, offering limited consideration to the potentialities of the frequency domain. Additionally, there has been a lack of a unified perspective towards low-light enhancement, exposure correction, and multi-exposure fusion, complicating and impeding the optimization of image processing. In response to these challenges, this paper proposes a novel methodology that leverages the frequency domain to improve and unify the handling of exposure correction tasks. Our method introduces Holistic Frequency Attention and Dynamic Frequency Feed-Forward Network, which replace conventional correlation computation in the spatial-domain. They form a foundational building block that facilitates a U-shaped Holistic Dynamic Frequency Transformer as a filter to extract global information and dynamically select important frequency bands for image restoration. Complementing this, we employ a Laplacian pyramid to decompose images into distinct frequency bands, followed by multiple restorers, each tuned to recover specific frequency-band information. The pyramid fusion allows a more detailed and nuanced image restoration process. Ultimately, our structure unifies the three tasks of low-light enhancement, exposure correction, and multi-exposure fusion, enabling comprehensive treatment of all classical exposure errors. Benchmarking on mainstream datasets for these tasks, our proposed method achieves state-of-the-art results, paving the way for more sophisticated and unified solutions in exposure correction.
Learning Foreground Information Bottleneck for few-shot semantic segmentation

Authors:
raw_author_name: Yutao Hu
raw_affiliation_string: School of Electronics and Information Engineering, Beihang University, Beijing, 100191, China
raw_author_name: Xin Huang
raw_affiliation_string: School of Electronics and Information Engineering, Beihang University, Beijing, 100191, China
raw_author_name: Xiaoyan Luo
raw_affiliation_string: School of Astronautics, Beihang University, Beijing, 100191, China
raw_author_name: Jungong Han
raw_affiliation_string: Computer Science Department, Aberystwyth University, SY23 3FL, UK
raw_author_name: Xianbin Cao
raw_affiliation_string: School of Electronics and Information Engineering, Beihang University, Beijing, 100191, China
raw_author_name: Jun Zhang
raw_affiliation_string: School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China

Abstract: Few-shot semantic segmentation aims to segment unseen classes with only a few annotated samples, which has great values for the real-world application in the wild. However, since the target class is treated as the background in the training, the network tends to extract much irrelevant nuisance factors, which results in the feature undermining problem for the target class. Consequently, it is difficult to produce an accurate segmentation map. To address this problem, in this paper, we apply the information bottleneck theory to few-shot semantic segmentation and propose the Foreground Information Bottleneck (FIB) module. Based on the support information, FIB module filters out the irrelevant information and promotes the foreground-related feature paradigms. Meanwhile, to solve the intractable mutual information and enable the end-to-end optimization of FIB module, we derive the Foreground Information Bottleneck Loss (FIBLoss) according to the inherent attribute of few-shot segmentation. Moreover, since there exists severe noise interference in the wild, we design a Target Information Refinement (TIR) block to further exploit discriminative cues of foreground. TIR block calculates the pairwise interaction and exploits the detailed information of the foreground object, which is beneficial to the feature refinement. Extensive experiments on two challenging datasets reflect the proposed FIB module significantly improves the performance of few-shot segmentation and delivers the state-of-the-art results.
EHR-KnowGen: Knowledge-enhanced multimodal learning for disease diagnosis generation

Authors:
raw_author_name: Jing Ma
raw_affiliation_string: The Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China
raw_author_name: Liang Bai
raw_affiliation_string: The Computer and Information Technology School, Shanxi University, Shanxi Road, Tai Yuan, Shan Xi, 030006, China
raw_author_name: Zhihua Wang
raw_affiliation_string: Shanghai Institute for Advanced Study of Zhejiang University, Dangui Road, Shanghai, China
raw_author_name: Li Guo
raw_affiliation_string: Department of Computing and Mathematics, Manchester Metropolitan University, Oxford Road, Manchester, M15 6BH, UK
raw_author_name: Xian Yang
raw_affiliation_string: Alliance Manchester Business School, University of Manchester, Oxford Road, Manchester, M13 9PL, UK; Data Science Institute, Imperial College London, Exhibition Road, London, SW7 2BX, UK

Abstract: Electronic health records (EHRs) contain diverse patient information, including medical notes, clinical events, and laboratory test results. Integrating this multimodal data can improve disease diagnoses using deep learning models. However, effectively combining different modalities for diagnosis remains challenging. Previous approaches, such as attention mechanisms and contrastive learning, have attempted to address this but do not fully integrate the modalities into a unified feature space. This paper presents EHR-KnowGen, a multimodal learning model enhanced with external domain knowledge, for improved disease diagnosis generation from diverse patient information in EHRs. Unlike previous approaches, our model integrates different modalities into a unified feature space with soft prompts learning and leverages large language models (LLMs) to generate disease diagnoses. By incorporating external domain knowledge from different levels of granularity, we enhance the extraction and fusion of multimodal information, resulting in more accurate diagnosis generation. Experimental results on real-world EHR datasets demonstrate the superiority of our generative model over comparative methods, providing explainable evidence to enhance the understanding of diagnosis results.
A Swarm Intelligence Assisted IoT-Based Activity Recognition System for Basketball Rookies

Authors:
raw_author_name: Yu Zhou
raw_affiliation_string: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
raw_author_name: Shilong Sun
raw_affiliation_string: School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen, China
raw_author_name: Xiao Zhang
raw_affiliation_string: Department of Computer Science, South-Central Minzu University, Wuhan, China

Abstract: Recent years have witnessed many applications of wearable sensor technology and machine learning on smart sports training, e.g., basketball. For rookie players, the fundamental skills are very important and can be improved using a scientific auxiliary training system, the core of which is human activity recognition (HAR) technique. For basketball players, one complete action is usually performed in a moment with significant dynamics and high-frequency spikes or noise. So, extracting meaningful features from intricate sensor signals is one of the important prerequisites. Besides, to improve the recognition accuracy, it is challenging to empirically determine an ideal feature subset out of the extracted high-dimensional features for particular types of basketball activities, which is essentially an NP-hard optimization problem. To address the above issues, we propose a smart activity recognition system hybridizing an IoT-based edge-cloud system and a swarm intelligence-based feature selection algorithm, named Adaptive Binary Particle Swarm Optimization (ABPSO). The devices can automatically collect and process basketball players' action signals. A comprehensive feature set with 300 features that can cover and capture the characteristics of different activities is established. Then, ABPSO with global search ability is able to identify the optimal feature subset combined with classifiers for accurate activity recognition. Thanks to ABPSO, the size of machine learning model and the amount of transmitted data is largely reduced, which makes it well compatible for IoT-based applications. Experiments performed on nine human subjects demonstrate that ABPSO can achieve accuracy of 97.26% for five fundamental activities on average, which outperforms five state-of-the-art FS algorithms, two classic filter-based FS methods and one deep learning based method.
Sharing-Net: Lightweight feedforward network for skeleton-based action recognition based on information sharing mechanism

Authors:
raw_author_name: Yinan Zhao
raw_affiliation_string: School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, PR China; School of Mechanical Engineering, Tongji University, Shanghai, 201804, PR China
raw_author_name: Qing Gao
raw_affiliation_string: School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, PR China
raw_author_name: Zhaojie Ju
raw_affiliation_string: School of Computing, University of Portsmouth, Portsmouth, PO1 3HE, UK
raw_author_name: Jian Zhou
raw_affiliation_string: School of Mechanical Engineering, Tongji University, Shanghai, 201804, PR China
raw_author_name: Yulan Guo
raw_affiliation_string: School of Electronics and Communication Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, PR China

Abstract: With the development of metaverse, augmented reality and human–robot teleoperation, action recognition plays an increasingly important role. In this work, we propose a Lightweight Feedforward Cross-channel Information Sharing Network (Sharing-Net) for action recognition. A multi-feature input module is constructed, which includes Cartesian Motion features, Global Joint Distances (GJD), and Global Joint Angles (GJA). The three types of features can tackle the problems of velocity differentiation, viewpoint diversification and object-distance variation, respectively. In order to take full use of the restricted parameters caused by the lightweight structure to enhance the accuracy under the premise of guaranteeing high speed, a multi-feature cross-channel information sharing mechanism is proposed. Dynamic nonlinear composite mapping between feature channels uses cross-channel residual blocks to share data information and establish coupling relationships. Extensive experiments on 3 public datasets and a self-built dataset verify the effectiveness of proposed methods. Compared with the state-of-the-art (SOAT) methods, Sharing-Net achieves the best accuracy with high speed on JHMDB and SHREC and performs superior balance of accuracy and computational cost on NTU RGB+D.
An analysis of ensemble pruning methods under the explanation of Random Forest

Authors:
raw_author_name: Hatem M. Abdelkader
raw_affiliation_string: Information Systems department, Faculty of Computers and Information, Menoufia University, Menoufia, Egypt
raw_author_name: Asmaa H. Elsaid
raw_affiliation_string: Information Systems department, Faculty of Computers and Information, Menoufia University, Menoufia, Egypt

Abstract: “Black box” models created by modern machine learning techniques are typically hard to interpret. Thus, the necessity of explainable artificial intelligence (XAI) has grown for understanding the rationale behind those models and converting them into white boxes. Random Forest is a black box model essential in various domains due to its flexibility, ease of use, and remarkable predictive performance. One method for explaining a Random Forest is transforming it into a self-explainable Decision Tree using Forest-Based Tree (FBT) algorithm. It basically consists of three main phases, pruning, conjunction set generation, and Decision Tree construction. In this paper, we examine six state-of-the-art pruning approaches and analyze their effect on FBT performance through pruned FBT (PFBT) in order to minimize its computational complexity. This would make it appropriate for forests and datasets of any size. They are assessed on 30 datasets, and the results show that UMEP and Hybrid pruning methods can be effectively used in the pruning stage of the PFBT algorithm in terms of pruning time and predictive performance. However, the AUC-Greedy method achieves good performance with small-size datasets.