Reviewerly/exHarmony huggingface dataset:
Exploring Multiple Hypergraphs for Heterogeneous Graph Neural Networks

Authors:
raw_author_name: Ying Wang
raw_affiliation_string: College of Computer Science and Technology, Jilin University, Changchun, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
raw_author_name: Yingji Li
raw_affiliation_string: College of Computer Science and Technology, Jilin University, Changchun, 130012, China
raw_author_name: Xin Wang
raw_affiliation_string: School of Artificial Intelligence, Jilin University, Changchun, 130012, China

Abstract: Graph neural networks have demonstrated significant power in learning graph representations for homogeneous networks. However, real-world network data can often be denoted by heterogeneous networks with different types of nodes and edges, such as social, traffic, and molecular networks. Network heterogeneity presents significant challenges for network analysis and mining. Motif-based hypergraphs preserve high-order proximity and capture composite semantic interactions. Because not all nodes and edges in the original network always exist in a specific hypergraph, it is essential that multiple motif-based hypergraphs are considered to enhance the network representation. Therefore, we propose a novel framework for exploring Multiple Motif-based Hypergraphs for Heterogeneous Graph Neural Networks to learn network representations, named MoH-HGNN, which leverages hypergraph convolution and attention operations to capture complex connectivity patterns. Specifically, we conducted two levels of attention networks with hierarchical structures, namely hyperedge-level attention to learn the importance among different types of nodes and comprehensive semantic-level attention to capture the importance of different types of motif structures. We extensively experimented on four real-world datasets to verify the effectiveness of our proposed framework.
Variational Bayesian and generalized maximum-likelihood based adaptive robust nonlinear filtering framework

Authors:
raw_author_name: Baojian Yang
raw_affiliation_string: Department of Vehicle and Electrical Engineering, Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang, China
raw_author_name: Huaiguang Wang
raw_affiliation_string: Department of Vehicle and Electrical Engineering, Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang, China
raw_author_name: Zhiyong Shi
raw_affiliation_string: Department of Vehicle and Electrical Engineering, Shijiazhuang Campus, Army Engineering University of PLA, Shijiazhuang, China

Abstract: An adaptive robust nonlinear filtering framework is proposed based on the variational Bayesian (VB) method and generalized maximum likelihood estimation (GM estimation) to simultaneously handle the outliers and uncertain measurement noise covariance matrix (MNCM) for state estimation. This framework utilizes the cubature criterion to solve nonlinear integration problems. By embedding GM estimation in the VB method, the uncertain MNCM is modified while robust estimation is performed. The modified MNCM also provides more accurate model parameters for GM estimation, achieving the unity of adaptability and robustness. The VBCEECKF algorithm was obtained by applying the strong robust centered error entropy criterion to the adaptive robust nonlinear filtering framework. The simulation results of target tracking under different noise conditions verify the superiority of the adaptive robust filter compared to existing methods.
Large-scale data classification based on the integrated fusion of fuzzy learning and graph neural network

Authors:
raw_author_name: Václav Snášel
raw_affiliation_string: Department of Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic
raw_author_name: Martin Štěpnička
raw_affiliation_string: CE IT4Innovations – IRAFM, University of Ostrava, Ostrava, Czech Republic
raw_author_name: Varun Ojha
raw_affiliation_string: School of Computing, Newcastle University, Newcastle, United Kingdom
raw_author_name: Ponnuthurai Nagaratnam Suganthan
raw_affiliation_string: KINDI Center for Computing Research, College of Engineering, Qatar University, Doha, Qatar
raw_author_name: Ruobin Gao
raw_affiliation_string: School of Civil and Environmental Engineering, Nanyang Technological University, Singapore, Singapore
raw_author_name: Lingping Kong
raw_affiliation_string: Department of Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic

Abstract: Deep learning and fuzzy models provide powerful and practical techniques for solving large-scale deep-learning tasks. The fusion technique on deep learning and fuzzy system are generally classified into ensemble and integrated modes and materializes in information fusion, model fusion, and feature fusion. In an ensemble-based fusion, the fuzzy model either acts as an activation function or is operated as a separate process aggregating/preprocessing the information. Some early attempts in the field have successfully fused deep neural networks and fuzzy modeling concepts in ensemble mode. However, no effective attempts were made to fuse fuzzy models as an integrated feature-level fusion learning with graph neural networks (GNNs). This is mainly due to two challenges related to this fusion: (1) the number of fuzzy rules grows exponentially with the number of features that causes computational inefficiency, and (2) the solution space created by this fusion of fuzzy rules becomes complex due to multiple regression relations between inputs and outputs. Additionally, a simple linear regression at the output space would not be sufficient to model deep learning tasks. Therefore, this paper addresses these challenges by proposing a feature-level fusion method to fuse deep learning and fuzzy modeling where the latter technique is for integrated feature learning, called fuzzy forest graph neural network (FuzzyGNN), which creates a fuzzy learning forest fusing the linear graph transformers for deep learning tasks. We conducted experiments on fourteen machine learning datasets to test and validate the efficiency of the proposed FuzzyGNN model. Compared to state-of-the-art methods, our algorithm achieves the best results on four out of five machine learning datasets. The source code will be available at https://github.com/lingping-fuzzy/ and https://github.com/P-N-Suganthan.
Coordinate Descent Optimized Trace Difference Model for Joint Clustering and Feature Extraction

Authors:
raw_author_name: Quan Wang
raw_affiliation_string: National Key Laboratory of Human–Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
raw_author_name: Fei Wang
raw_affiliation_string: National Key Laboratory of Human–Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
raw_author_name: Zhongheng Li
raw_affiliation_string: Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications, Beijing Institute of Spacecraft System Engineering, Beijing, 100094, China
raw_author_name: Zheng Wang
raw_affiliation_string: School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China
raw_author_name: Feiping Nie
raw_affiliation_string: School of Computer Science and School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China

Abstract: Joint clustering and dimensionality reduction methods are a promising solution to clustering due to its scalability to high-dimensional data. Some methods leverage trace ratio criterion and attain clusters by borrowing the K-means algorithm. However, trace ratio criterion has no close-formed solution for the discriminative projection matrix and the K-means algorithm has a limited capacity to handle the many-cluster problem. In this paper, Coordinate Descent Optimized Trace Difference model (CDOTD) is proposed for joint clustering and feature extraction. Formulating the objective function as a direct trace difference criterion containing a balance parameter, CDOTD harmonizes between-cluster scatter maximization and within-cluster scatter minimization by the balance parameter. Using the direct trace difference criterion, CDOTD can straightforward solve for the discriminative projection matrix and avoid obtaining a poor discriminative projection matrix in the iterative manner when a bad cluster start is given. CDOTD uses the coordinate descent method for clustering optimization, improving the ability to address the many-cluster problem. Extensive experiments show that CDOTD has achieved significant performance improvements compared to previous trace ratio criterion related joint clustering and feature extraction methods, and also outperformed other clustering methods in most cases.
Real-Time Local Greedy Search for Multiaxis Globally Time-Optimal Trajectory

Authors:
raw_author_name: Shize Lin
raw_affiliation_string: Department of Mechanical Engineering, Tsinghua University, Beijing, China
raw_author_name: Chuxiong Hu
raw_affiliation_string: Department of Mechanical Engineering, Tsinghua University, Beijing, China
raw_author_name: Suqin He
raw_affiliation_string: Department of Mechanical Engineering, Tsinghua University, Beijing, China
raw_author_name: Wenxiang Zhao
raw_affiliation_string: Department of Mechanical Engineering, Tsinghua University, Beijing, China
raw_author_name: Ze Wang
raw_affiliation_string: Department of Mechanical Engineering, Tsinghua University, Beijing, China
raw_author_name: Yu Zhu
raw_affiliation_string: Department of Mechanical Engineering, Tsinghua University, Beijing, China

Abstract: Time-optimal trajectory planning aims to minimize the traversal time of arbitrary geometric paths. The demand for real-time planning widely exists in robotics, numerical control manufacturing, and autonomous vehicle applications. Existing trajectory planning methods either compromise on time optimality to improve computational efficiency or suffer from at least linear time complexity, preventing the planning of long trajectories in real time. Motivated by these challenges, this article proposes a novel set invariant trajectory planning (SITP) method to address the problem of real-time time-optimal planning for continuous multiaxis trajectories under complete second-order kinodynamic constraints. First, a backup control strategy is synthesized to construct an implicit control invariant set (CIS). This set is designed so that a feasible control input always exists to keep the system within the defined kinodynamic bounds. Then, utilizing the principles of bang–bang control theory, the optimal control is sought within the CIS. A local greedy linear programming method is proposed to calculate the time-optimal trajectory at each control cycle. The proposed method is computationally efficient for even 1-kHz real-time applications and the planned results maintain strict global time optimality, which makes it promising in real-time planning scenarios of various automatic applications.
MSHGN: Multi-scenario adaptive hierarchical spatial graph convolution network for GPU utilization prediction in heterogeneous GPU clusters

Authors:
raw_author_name: Sheng Wang
raw_affiliation_string: Business School, University of Shanghai for Science and Technology, Shanghai 200082, PR China
raw_author_name: Shiping Chen
raw_affiliation_string: Business School, University of Shanghai for Science and Technology, Shanghai 200082, PR China

Abstract: Accurately predicting GPU utilization is crucial for effectively managing heterogeneous GPU clusters, yet existing prediction methods are tailored to homogeneous clusters or ignore the unique characteristics of heterogeneous ones. To address this problem, we propose the Multi-Scenarios Adaptive Hierarchical Spatial Graph Convolution Network (MSHGN) model. This model leverages the hierarchical relationships among users, tasks, and machines to construct multiple scenarios' undirected graphs and uses Graph Convolution Neural (GCN) to capture the spatial dependency relationships between the computational resources consumed during execution. The MSHGN model accurately predicts the future GPU utilization rates of each machine in a heterogeneous GPU cluster, achieving an RMSE of only 0.0101, an MAE of 0.0072, and an MAPE of 0.0641. We evaluate the MSHGN model on a real-world Alibaba dataset collected from a heterogeneous GPU cluster and find that it achieves superior accuracy and robustness in predicting resource utilization, outperforming other baseline models.
A novel dual-stage progressive enhancement network for single image deraining

Authors:
raw_author_name: Tao Gao
raw_affiliation_string: School of Information Engineering, Chang’an University, Xi’an 710064, China
raw_author_name: Yuanbo Wen
raw_affiliation_string: School of Information Engineering, Chang’an University, Xi’an 710064, China
raw_author_name: Jing Zhang
raw_affiliation_string: School of Computing, Australian National University, Canberra, ACT 2600, Australia
raw_author_name: Ting Chen
raw_affiliation_string: School of Information Engineering, Chang’an University, Xi’an 710064, China

Abstract: The dense rain accumulation in heavy rain can significantly wash out images and thus destroy the background details of images. Although existing deep rain removal models lead to improved performance for heavy rain removal, we find that most of them ignore the detail reconstruction accuracy of rain-free images. In this paper, we propose a dual-stage progressive enhancement network (DPENet-v2) to achieve effective deraining with structure-accurate rain-free images. Three main modules are included in our framework, namely a rain streaks removal network (R2Net), a details reconstruction network (DRNet) and a cross-stage feature interaction module (CFIM). The former aims to achieve accurate rain removal, and the latter is designed to recover the details of rain-free images. We introduce two main strategies within our networks to achieve trade-off between the effectiveness of deraining and the detail restoration of rain-free images. Firstly, a dilated dense residual block (DDRB) within the rain streaks removal network is presented to aggregate high/low level features of heavy rain. Secondly, an enhanced residual pixel-wise attention block (ERPAB) within the details reconstruction network is designed for context information aggregation. Meanwhile, CFIM learns the long-range dependencies and achieves cross-stage information communication. We also propose a comprehensive loss function to highlight the marginal and regional accuracy of rain-free images. Extensive experiments on benchmark public datasets show both efficiency and effectiveness of the proposed method in achieving structure-preserving rain-free images for heavy rain removal.
Neural-Based Compression Scheme for Solar Image Data

Authors:
raw_author_name: Ali Zafari
raw_affiliation_string: West Virginia University, Morgantown, WV, USA
raw_author_name: Atefeh Khoshkhahtinat
raw_affiliation_string: West Virginia University, Morgantown, WV, USA
raw_author_name: Jeremy A. Grajeda
raw_affiliation_string: New Mexico State University, Las Cruces, NM, USA
raw_author_name: Piyush M. Mehta
raw_affiliation_string: West Virginia University, Morgantown, WV, USA
raw_author_name: Nasser M. Nasrabadi
raw_affiliation_string: West Virginia University, Morgantown, WV, USA
raw_author_name: Laura E. Boucheron
raw_affiliation_string: New Mexico State University, Las Cruces, NM, USA
raw_author_name: Barbara J. Thompson
raw_affiliation_string: NASA Goddard Space Flight Center, Greenbelt, MD, USA
raw_author_name: Michael S. F. Kirk
raw_affiliation_string: NASA Goddard Space Flight Center, Greenbelt, MD, USA
raw_author_name: Daniel E. da Silva
raw_affiliation_string: NASA Goddard Space Flight Center, Greenbelt, MD, USA

Abstract: Studying the solar system and especially the Sun relies on the data gathered daily from space missions.These missions are data-intensive and compressing this data to make them efficiently transferable to the ground station is a twofold decision to make.Stronger compression methods, by distorting the data, can increase data throughput at the cost of accuracy which could affect scientific analysis of the data.On the other hand, preserving subtle details in the compressed data requires a high amount of data to be transferred, reducing the desired gains from compression.In this work, we propose a neural network-based lossy compression method to be used in NASA's data-intensive imagery missions.We chose NASA's Solar Dynamics Observatory (SDO) mission which transmits 1.4 terabytes of data each day as a proof of concept for the proposed algorithm.In this work, we propose an adversarially trained neural network, equipped with local and non-local attention modules to capture both the local and global structure of the image resulting in a better trade-off in rate-distortion (RD) compared to conventional hand-engineered codecs.The RD variational autoencoder used in this work is jointly trained with a channel-dependent entropy model as a shared prior between the analysis and synthesis transforms to make the entropy coding of the latent code more effective.We also studied how optimizing perceptual losses could help our neural compressor to preserve high-frequency details of the data in the reconstructed compressed image.Our neural image compression algorithm outperforms currently-in-use and state-of-the-art codecs such as JPEG and JPEG-2000 in terms of the RD performance when compressing extreme-ultraviolet (EUV) data.As a proof of concept for use of this algorithm in SDO data analysis, we have performed coronal hole (CH) detection using our compressed images, and generated consistent segmentations, even at a compression rate of ∼ 0.1 bits per pixel (compared to 8 bits per pixel on the original data) using EUV data from SDO.
Detecting anomalies in security cameras with 3D-convolutional neural network and convolutional long short-term memory

Authors:
raw_author_name: Esraa A. Mahareek
raw_affiliation_string: Al-Azhar University (Girls branch)
raw_author_name: Eman K. ElSayed
raw_affiliation_string: Al-Azhar University (Girls branch)
raw_author_name: Kamal A. ElDahshan
raw_affiliation_string: Al-Azhar University (Girls branch)

Abstract: &lt;p&gt;This paper presents a novel deep learning-based approach for anomaly detection in surveillance films. A deep network that has been trained to recognize objects and human activity in movies forms the foundation of the suggested approach. In order to detect anomalies in surveillance films, the proposed method combines the strengths of 3D-convolutional neural network (3DCNN) and convolutional long short-term memory (ConvLSTM). From the video frames, the 3DCNN is utilized to extract spatiotemporal features,while ConvLSTM is employed to record temporal relationships between frames. The technique was evaluated on five large-scale datasets from the actual world (UCFCrime, XDViolence, UBIFights, CCTVFights, UCF101) that had both indoor and outdoor video clips as well as synthetic datasets with a range of object shapes, sizes, and behaviors. The results further demonstrate that combining 3DCNN with ConvLSTM can increase precision and reduce false positives, achieving a high accuracy and area under the receiver operating characteristic-area under the curve (ROC-AUC) in both indoor and outdoor scenarios when compared to cuttingedge techniques mentioned in the comparison.&lt;/p&gt;
A start-stop points CenterNet for wideband signals detection and time-frequency localization in spectrum sensing

Authors:
raw_author_name: Teng Cheng
raw_affiliation_string: Engineering Research Center for Intelligent Transportation and Cooperative Vehicle-Infrastructure of Anhui Province, Hefei, 230000, China; School of Automotive and Transportation Engineering, Hefei University of Technology, China
raw_author_name: Lei Sun
raw_affiliation_string: Engineering Research Center for Intelligent Transportation and Cooperative Vehicle-Infrastructure of Anhui Province, Hefei, 230000, China; School of Automotive and Transportation Engineering, Hefei University of Technology, China
raw_author_name: Junning Zhang
raw_affiliation_string: College of electronic countermeasures, National University of Defense Science and technology, Hefei, 230000, China
raw_author_name: Jinling Wang
raw_affiliation_string: College of electronic countermeasures, National University of Defense Science and technology, Hefei, 230000, China

Abstract: Recently, deep learning (DL) based object detection methods have attracted significant attention for wideband multisignal detection, which has been viewed as an essential part in the field of cognitive radio spectrum sensing. However, the existing DL methods are difficult or very likely fail to detect discontinuous burst signals, not to mention the signals with wide, instantaneous, dynamic bandwidth, and multiple channels. To solve this problem, the present study proposes a scheme that combines the start-stop point signal features for wideband multi-signal detection, namely the Fast Spectrum-Size Self-Training network (FSSNet). Considering the horizontal rectangle form of a wideband signal in the time-frequency domain, we innovatively utilize the start-stop points of the two-dimensional (2D) Box to build the signal model. Specifically, We propose a fast Start-stop HeatMap where the proposed LPS-YXE simultaneously labels and divides the start-stop points positions in the X-Y axis of a single HeatMap. We attribute the method’s success in discontinuous signal detection to the multidimensional space transformation of HeatMap, which is used to locate the start-stop points and extract features separated from the signal regions of start-stop points. Furthermore, FSSNet can realize the 2D Box estimation of the wideband signal by regressing only a single variable, and thus with satisfactory detection speed. Simulation results verify the effectiveness and superiority of the proposed start-stop based wideband signal detection scheme with practical received signals. All our models and code are available athttps://github.com/jn-z/SSNet2.
Sensing complicated meanings from unstructured data: a novel hybrid approach

Authors:
raw_author_name: Shankarayya Shastri
raw_affiliation_string: Visvesvaraya Technological University

Abstract: The majority of data on computers nowadays is in the form of unstructured data and unstructured text. The inherent ambiguity of natural language makes it incredibly difficult but also highly profitable to find hidden information or comprehend complex semantics in unstructured text. In this paper, we present the combination of natural language processing (NLP) and convolution neural network (CNN) hybrid architecture called automated analysis of unstructured text using machine learning (AAUT-ML) for the detection of complex semantics from unstructured data that enables different users to make understand formal semantic knowledge to be extracted from an unstructured text corpus. The AAUT-ML has been evaluated using three datasets data mining (DM), operating system (OS), and data base (DB), and compared with the existing models, i.e., YAKE, term frequency-inverse document frequency (TF-IDF) and text-R. The results show better outcomes in terms of precision, recall, and macro-averaged F1-score. This work presents a novel method for identifying complex semantics using unstructured data.