Reviewerly/exHarmony huggingface dataset:
Influence of the operating regime of the magnonic active ring oscillator on the performance of the magnonic reservoir computer based on it

Authors:
raw_author_name: Alexandr V. Kondrashov
raw_affiliation_string: Department of Physical Electronics and Technology, Saint Petersburg Electrotechnical University "LETI", St. Petersburg 197022, Russia
raw_author_name: Mikhail Kostylev
raw_affiliation_string: Department of Physics, University of Western Australia, Crawley, Western Australia 6009, Australia
raw_author_name: Alexey B. Ustinov
raw_affiliation_string: Department of Physical Electronics and Technology, Saint Petersburg Electrotechnical University "LETI", St. Petersburg 197022, Russia

Abstract: A magnonic active ring oscillator based on an yttrium iron garnet (YIG) is a promising hardware platform for physical reservoir computing. It meets the required conditions for the availability of fading memory and nonlinearity. However, its performance still needs further improvement. Two methods can be employed to improve the performance. One is increasing the time of establishing steady self-oscillations in the auto-oscillator. The second one is increasing the amplitude of spin waves propagating in the YIG film. In this work, we achieve an increase of the amplitude of the reservoir response by broadening the available range for the ring gain coefficient. We use a metallized YIG film as a waveguide in which spin waves propagate. This shifts up the amplitude threshold for the development of spin wave modulation instability in the ring. The threshold defines the upper limit of the gain range for the ring. Binary pulse sequence is entered into the ring by switching its gain between upper and lower levels. The modulation instability threshold then sets the upper limit for the higher gain level. In addition, in this work, we set the lower level of the gain to negative values. This is one more improvement with respect to earlier works. Switching the gain between a negative and a positive value increases the amplitude of the reservoir response and the depth of its fading memory. We employ the standard short-term memory and parity-check tests to quantify the performance of the reservoir computer. Both tests demonstrate an increase in the computational efficiency of the magnonic reservoir.
Mission-Oriented Gaussian Process Motion Planning for UUVs Over Complex Seafloor Terrain and Current Flows

Authors:
raw_author_name: Yewei Huang
raw_affiliation_string: Stevens Institute of Technology, Hoboken, NJ, USA
raw_author_name: Xi Lin
raw_affiliation_string: Stevens Institute of Technology, Hoboken, NJ, USA
raw_author_name: Kishore Pochiraju
raw_affiliation_string: Stevens Institute of Technology, Hoboken, NJ, USA
raw_author_name: Brendan Englot
raw_affiliation_string: Stevens Institute of Technology, Hoboken, NJ, USA

Abstract: We present a novel motion planning framework for unmanned underwater vehicles (UUVs) - the first framework that applies Gaussian process motion planning to solve a 3D path planning problem for a 6-DoF robot in underwater environments. We address missions requiring UUVs to remain in close proximity to seafloor terrain, which must be achieved alongside collision avoidance. Our framework also considers the influence of current flows as part of the cost function, allowing for more accurate planning. To evaluate the performance of our proposed framework, we compare it with the widely used RRT* and STOMP algorithms over a range of underwater environments. Our experimental results demonstrate the stability and time efficiency of our framework.
Hermitian self-dual 2-quasi-abelian codes

Authors:
raw_author_name: Guanghui Zhang
raw_affiliation_string: Department of Mathematics, Suqian University, Suqian, Jiangsu province, 223800, China
raw_author_name: Ruibo Li
raw_affiliation_string: School of Mathematics, South China University of Technology, Guangzhou, Guangdong province, 510641, China

Abstract: In this paper, we construct a class of Hermitian self-dual 2-quasi-abelian codes over a finite field. Based on counting the number of such codes and estimating the number of the codes in this class whose relative minimum weights are small, we prove that the class of Hermitian self-dual 2-quasi-abelian codes over any finite field is asymptotically good. The existence of such codes is unconditional, which is different from the case of Euclidean self-dual 2-quasi-abelian codes over a special finite field.
Beyond geofencing: Behavior detection using AIS

Authors:
raw_author_name: Valentin Emiya
raw_affiliation_string: Aix Marseille Univ, CNRS, LIS, Marseille, France

Abstract: This research paper proposes two different methods for removing biases from geo-spatial trajectories obtained from AIS, enabling the creation of machine learning models that generalize well to areas with no training data. We focus here on the task of behavior detection such as moored, underway or drifting from AIS. The first method utilizes data augmentation techniques specifically designed for augmenting geo-spatial trajectories that include angled information such as direction, while the second method is based on engineering features that eliminate these geographical biases. These two methods are compared using different types of machine learning models, including random forests, hidden Markov models, LSTMs, and transformer networks.
Learning Technologies

Authors:
raw_author_name: Daina Gudoniene
raw_affiliation_string: Kaunas University of Technology, Lithuania
raw_author_name: Tomas Blazauskas
raw_affiliation_string: Kaunas University of Technology, Lithuania
raw_author_name: Rita Butkiene
raw_affiliation_string: Kaunas University of Technology, Lithuania
raw_author_name: Lina Čeponienė
raw_affiliation_string: Kaunas University of Technology, Lithuania

Abstract: The aim of the chapter is to present the learning technologies helping students to get high-quality personalised assistance in the learning process. Digital transformation in education makes a huge impact on the quality of education; however, authors are focusing on the chatbots that can be used for providing personalised support and guidance to students in education. Moreover, chatbots can help to improve academic performance and reduce the number of drop out students. The authors will discuss the chatbot aspects based on the 6 main chatbot parameters that determine design and development presented by Kumar: (1) knowledge, (2) services, (3) goals, (4) input processing, (5) human aid, and (6) building open-source or closed platforms. These parameters convey that a chatbot can fulfil numerous communication and interaction functionalities based on needs, platforms, and technologies.
A Compact and Robust 6-DoF Pose Tracking System Using Magnetic-Inertial Sensors and a Single Uniaxial Electromagnetic Coil

Authors:
raw_author_name: Shuda Dong
raw_affiliation_string: Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China

Abstract: Magnetic pose tracking, which is non-contacting and occlusion-free, is increasingly used in virtual/augmented reality, rehabilitation, and computer-assisted medical interventions to track the motion of human body and various tools and devices. Most existing magnetic tracking systems employ multiple magnetic sources and multiple magnetic sensors, making the system bulky and energy consuming. Complex signal processing circuits and algorithms are needed to distinguish the magnetic signal from each magnetic source. In this paper, a compact 6-DoF magnetic-inertial tracking system is developed using only a single stationary uniaxial electromagnetic coil and a single magnetic-inertial sensor board attached to the moving object. Magnetic measurement of the electromagnetic source, geomagnetic measurement, and inertial measurements are fused using an extended Kalman filter framework to provide accurate, drift-free, and robust pose estimates. An on-off switching strategy for electromagnet control and a difference magnetic measurement technique are used to cancel out potential ambient magnetic disturbances. Magnetic measurement of the electromagnetic source is mainly responsible for 3-DoF position estimation while geomagnetic field measurement is used to correct the yaw angle drift in inertial orientation estimation. Since geomagnetic measurement is susceptible to magnetic disturbances, it is switched off adaptively once detecting disturbances to maintain the robustness of the pose tracking system. Experimental results show that the RMS Euclidean position error is 2.84 mm and the average RMS orientation angle error is 1.22° even in presence of magnetic disturbance of a permanent magnet.
Firearm-related action recognition and object detection dataset for video surveillance systems

Authors:
raw_author_name: Jesús Ruiz-Santaquiteria Alegre
raw_affiliation_string: VISILAB, E.T.S. Ingeniería Industrial, University of Castilla-La Mancha, Avda. Camilo José Cela, Ciudad Real, Spain
raw_author_name: Óscar Déniz Suárez
raw_affiliation_string: VISILAB, E.T.S. Ingeniería Industrial, University of Castilla-La Mancha, Avda. Camilo José Cela, Ciudad Real, Spain

Abstract: The proposed dataset is comprised of 398 videos, each featuring an individual engaged in specific video surveillance actions. The ground truth for this dataset was expertly curated and is presented in JSON format (standard COCO), offering vital information about the dataset, video frames, and annotations, including precise bounding boxes outlining detected objects. The dataset encompasses three distinct categories for object detection: "Handgun", "Machine_Gun", and "No_Gun", dependent on the video's content. This dataset serves as a resource for research in firearm-related action recognition, firearm detection, security, and surveillance applications, enabling researchers and practitioners to develop and evaluate machine learning models for the detection of handguns and rifles across various scenarios. The meticulous ground truth annotations facilitate precise model evaluation and performance analysis, making this dataset an asset in the field of computer vision and public safety.
Two-stream Multi-level Dynamic Point Transformer for Two-person Interaction Recognition

Authors:
raw_author_name: Yao Liu
raw_affiliation_string: School of Computer Science and Engineering, University of New South Wales, Australia
raw_author_name: Jiahui Luo
raw_affiliation_string: School of Computing and Information Systems, University of Melbourne, Australia
raw_author_name: Xiaojun Chang
raw_affiliation_string: Faculty of Engineering and Information Technology, University of Technology Sydney, Australia
raw_author_name: Lina Yao
raw_affiliation_string: Data 61, CSIRO and School of Computer Science and Engineering, University of New South Wales, Australia

Abstract: As a fundamental aspect of human life, two-person interactions contain meaningful information about people’s activities, relationships, and social settings. Human action recognition serves as the foundation for many smart applications, with a strong focus on personal privacy. However, recognizing two-person interactions poses more challenges due to increased body occlusion and overlap compared to single-person actions. In this article, we propose a point cloud-based network named Two-stream Multi-level Dynamic Point Transformer for two-person interaction recognition. Our model addresses the challenge of recognizing two-person interactions by incorporating local-region spatial information, appearance information, and motion information. To achieve this, we introduce a designed frame selection method named Interval Frame Sampling (IFS), which efficiently samples frames from videos, capturing more discriminative information in a relatively short processing time. Subsequently, a frame features learning module and a two-stream multi-level feature aggregation module extract global and partial features from the sampled frames, effectively representing the local-region spatial information, appearance information, and motion information related to the interactions. Finally, we apply a transformer to perform self-attention on the learned features for the final classification. Extensive experiments are conducted on two large-scale datasets, the interaction subsets of NTU RGB+D 60 and NTU RGB+D 120. The results show that our network outperforms state-of-the-art approaches in most standard evaluation settings.
Unified Model Based on Reinforced Feature Reconstruction for Metro Track Anomaly Detection

Authors:
raw_author_name: Mengfei Duan
raw_affiliation_string: School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China
raw_author_name: Liang Mao
raw_affiliation_string: Guangdong Pearl River Delta Intercity Railway Company Ltd., Guangzhou, China
raw_author_name: Ruikang Liu
raw_affiliation_string: School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China
raw_author_name: Weiming Liu
raw_affiliation_string: School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China

Abstract: Metro track anomaly detection can prevent accidents, thus avoiding severe life safety and property losses. Unsupervised methods that rely on one model per category or scene are unsuitable for complex and diverse track environments and unified detection, exhibiting poor stability. For most feature-based methods, the multi-stage features extracted by the pre-trained model contain the redundant information and noise, which interferes the feature reconstruction and anomaly detection. Additionally, the presence of abnormal information in the reconstructed feature further degrades the performance of anomaly detection. To address the aforementioned issues, a unified anomaly detection model based on feature reconstruction, named Reinforced Feature Reconstruction-based Anomaly Detection Network (RFReconAD), is proposed. The proposed efficient channel feature reinforcement module cooperated with the designed loss function weakens the interference of redundant information and noise on feature reconstruction task. The layer-wise learnable queries embedded in the decoder alleviate the problem of anomaly reconstruction. Moreover, the proposed detection scheme achieves more accurate anomaly detection. Under unified training and inference, our method achieves 99.8% and 98.2% Image-level AUROC, as well as 99.2% and 97.2% Pixel-level AUROC, on the Track Foreign Object Detection dataset and MVTec-AD dataset, respectively; And its inference speed reaches 37 frames/s, outperforming the state-of-the-art methods.
GTEA: Guided Taylor Expansion Approximation Network for Optical Flow Estimation

Authors:
raw_author_name: Zifan Zhu
raw_affiliation_string: School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan, China
raw_author_name: Menghan Xia
raw_affiliation_string: School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan, China
raw_author_name: Biyun Xu
raw_affiliation_string: School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan, China
raw_author_name: Qian Li
raw_affiliation_string: School of Electronics and Information Engineering, Wuhan Donghu University, Wuhan, China
raw_author_name: Zhenghua Huang
raw_affiliation_string: School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan, China

Abstract: Deep neural network (DNN) has proven its powerful ability in feature representation and has been widely applied in computer vision tasks, especially optical flow estimation (OFE). However, these DNN-based OFE approaches have a black-box nature, resulting in lacking a clear interpretation for their success. More unfortunately, they usually suffer from the loss of objects’ shape, which is not beneficial for performance improvement. To address these issues, we develop a novel guided Taylor expansion approximation (GTEA) network, including the following key procedures: First, we present the detail analysis of the relationship between Taylor expansion and optical flow. Then, a convolutional guided filter (ConvGF) network is developed to refine the shape of objects. Finally, the final output is obtained by employing basic network, ConvGF, and detail network to approximate the Taylor expansion. Quantitatively and qualitatively experimental results on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sintel</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">KITTI</i> datasets demonstrate that the proposed GTEA is effective and even performs better than the state-of-the-art optical flow estimation methods on performance improvement and shape preservation.
RCS Information Aided Poisson Multi-Bernoulli Mixture Filter in Clutter Background

Authors:
raw_author_name: Mengdi Bai
raw_affiliation_string: College of Electronic Science and Technology, National University of Defense Technology (NUDT), Changsha, China
raw_author_name: Qilei Zhang
raw_affiliation_string: College of Electronic Science and Technology, National University of Defense Technology (NUDT), Changsha, China
raw_author_name: Yongshneg Zhang
raw_affiliation_string: College of Electronic Science and Technology, National University of Defense Technology (NUDT), Changsha, China
raw_author_name: Bin Sun
raw_affiliation_string: Beijing Institute of Tracking and Telecommunication Technology, Beijing, China

Abstract: For modern radar systems, the measurement of the target’s radar cross section (RCS) is a standard output besides kinematic measurements. It is straightforward to incorporate RCS information into tracking algorithms for performance improvements in more realistic and difficult scenarios. However, in practice, the proper integration of RCS recursion and Bayesian filter is promising but challenging. To address this issue, an RCS information aided Poisson multi-Bernoulli mixture (RCSI-PMBM) filter in clutter background is proposed in this paper. Firstly the Bayesian RCS estimation strategy is presented, and then the RCSI-PMBM filter is analytically developed. Moreover, based on the Gamma Gaussian mixture (GGM) form, an effective and efficient implementation of the proposed RCSI-PMBM filter is developed. Finally, the validity of the proposed algorithm is verified by simulation tests with challenging scenarios.
Privacy-preserving Multi-source Cross-domain Recommendation Based on Knowledge Graph

Authors:
raw_author_name: Jing Liu
raw_affiliation_string: Tianjin University, China
raw_author_name: Litao Shang
raw_affiliation_string: Tianjin University, China
raw_author_name: Yuting Su
raw_affiliation_string: Tianjin University, China
raw_author_name: Weizhi Nie
raw_affiliation_string: Tianjin University, China
raw_author_name: Xin Wen
raw_affiliation_string: Tianjin University, China
raw_author_name: Anan Liu
raw_affiliation_string: Tianjin University, China

Abstract: The cross-domain recommender systems aim to alleviate the data sparsity problem in the target domain by transferring knowledge from the auxiliary domain. However, existing works ignore the fact that the data sparsity problem may also exist in the single auxiliary domain, and sharing user behavior data is restricted by the privacy policy. In addition, their cross-domain models lack interpretability. To address these concerns, we propose a novel multi-source cross-domain model based on knowledge graph. Specifically, to avoid the insufficiency of single auxiliary domain, we construct a knowledge graph comprehensively leveraging items from multiple auxiliary domains. To avoid the leakage of user privacy when user information is transferred to multiple domains, we construct graph for information transfer between items to effectively avoid the propagation of users’ private information between different domains. We implicitly integrate the user–item interaction by transferring the learned item embeddings. To improve the interpretability of cross-domain knowledge transfer, we propose a knowledge graph-based retrieval and fusion method to transfer knowledge derived from multiple auxiliary domains. An attention-based fusion network is designed to enhance the representation of the targeted user and items with the transferred item embedding. We perform extensive experiments on three real-world datasets, demonstrating that our model outperforms the states of the art.
Neighbor2Global: Self-supervised image denoising for Poisson-Gaussian noise

Authors:
raw_author_name: Yuanxiu Xing
raw_affiliation_string: Hubei Province Key Laboratory of Systems Science in Metallurgical Process (Wuhan University of Science and Technology), Wuhan, 430081, Hubei, China; Wuhan University of Science and Technology, College of Science, Wuhan, 430065, Hubei, China
raw_author_name: Linlin Song
raw_affiliation_string: Hubei Province Key Laboratory of Systems Science in Metallurgical Process (Wuhan University of Science and Technology), Wuhan, 430081, Hubei, China; Wuhan University of Science and Technology, College of Science, Wuhan, 430065, Hubei, China

Abstract: Sub-sampled pairs generated from noisy images solve the lack of noisy-clean pairs in denoising model training. However, real sub-sampled image pairs with signal-dependent Poisson-Gaussian noise and the approximation of neighboring pixels lead to denoising performance degradation. For this reason, a novel self-supervised Neighbor2Global is proposed to train an efficient denoising model for real-world images denoising. Firstly, the problems faced by model training are analyzed theoretically, and a GAT-based image generation strategy is introduced to make the sub-sampled pair from a single noisy image approximately independent. Secondly, a complementary training strategy is presented to train the denoising network on sub-sampled GAT image pairs, GAT images and PD-transformed GAT images, with improved reconstruction loss and optimized regulation for better performance. Extensive experiments have been conducted on both synthetic and real-world datasets to validate the superior performance of the proposed method in balancing detailed texture and global information, as well as weakening over-smoothing.