Reviewerly/exHarmony huggingface dataset:
Beyond geofencing: Behavior detection using AIS

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

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

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

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

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

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

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

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

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

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.
Optimizing solar power plant efficiency through advanced analytical framework and comparative analysis

As the world embraces the transition towards renewable energy, the optimization of solar power plants becomes paramount. In this research, we present a comprehensive framework that leverages advanced analytical methodologies to address critical operational challenges and elevate the efficiency of solar power generation. Our framework encompasses data preprocessing, time series analysis, anomaly detection, and equipment performance assessment, synergistically combining their strengths to offer a holistic solution. The heart of our proposed approach lies in the precision and efficacy of anomaly detection. We introduce two powerful techniques—LSTM Autoencoder and Isolation Forest—to identify anomalies and equipment underperformance. Through meticulous evaluation, we showcase their comparative performance, revealing the nuanced strengths of each. Visualizations depict the model’s proficiency in pinpointing anomalies, with LSTM Autoencoder emerging as a standout performer, adept at capturing even subtle deviations from expected patterns. Our research extends beyond detection to equip stakeholders with real-time insights. The visualization of daily yield trends uncovers potential data anomalies, enabling timely intervention and rectification. Additionally, we address equipment failures by harnessing random forest modeling to establish a robust relationship between irradiance, temperature, and DC power. This approach provides a powerful tool for real-time condition monitoring and fault detection, enabling proactive maintenance and enhancing operational resilience.
A hybrid multi-criteria meta-learner based classifier for imbalanced data

Numerous imbalanced datasets exist in modern machine learning dilemmas. Challenges of generalization and fairness stem from the existence of underrepresented classes with sensitive characteristics. Additionally, defining an optimal criterion is crucial when dealing with imbalanced data. To address this, it is common in practice to employ composite metrics and loss functions that evenly distribute the cost across each class. However, relying solely on a single metric or loss function, frequently yields sub-par outcomes due to the tendency of the optimization criteria to be overly responsive to imbalanced data, resulting in excessive adaptation and flawed generalization. In order to overcome these limitations, various heuristics can be applied to optimize the criterion. To this end, we aim at addressing the problem of imbalanced data classification by leveraging a multicriteria ensemble procedure, the Hybrid Multi-criteria Meta-learner (HML). The suggested approach focuses on optimizing precision, recall together with balanced accuracy in the Multi-Objective optimization phase and brings forth optimal ensembles, from which the concluding Meta-learner can be chosen after a clustering step, based on a multicriteria decision-making aspect contingent on the selected metrics. Comprehensive experiments portrayed the effectiveness of HML, offering both quality assurance and addresses a vast array of imbalanced data problems.
Appearance flow estimation for online virtual clothing warping via optimal feature linear assignment

Clothing warping to spatially align source garments with the corresponding body parts is crucial in clothing media tasks such as virtual try-on and pose-guided person generation. Recent pioneering work has utilized flow fields with additional dimensions of freedom to simulate clothing warping flexibly. However, current methods for estimating appearance flow typically rely on calculating just the local cost volume which contains multiple noisy matching points, potentially leading to a mismatch between clothing and body parts. To address this issue, we propose a novel appearance flow estimation network(Warping-Flow) for clothing warping based on optimal features linear assignment. Specially, two remarkable contributions are made to improve feature matching precision. First, a local context feature aggregation module is proposed to enhance the semantic feature distinction of the source cloth and target pose. Second, Warping-Flow estimates a hard attention mask through the cost volume to filter irrelevant features, followed by the optimal linear assignment algorithm to normalize the cost volume to a discrete permutation matrix that explicitly models the most contributing bipartite matches. Experiments conducted on the VITON and VITON-HD datasets demonstrate that Warping-Flow outperforms existing state-of-the-art algorithms, particularly in cases involving complex clothing deformation. Furthermore, Warping-Flow can serve as a plug-in to improve existing garment media technologies.
Delta robot control by learning systems: Harnessing the power of deep reinforcement learning algorithms

The present study focuses on the implementation of Deep Reinforcement Learning (Deep-RL) techniques for a parallel manipulator robot, specifically the Delta Robot, within a simulated setting. We introduced a simulation framework designed to guide the Delta Robot’s end-effector to a designated spatial point accurately. Within this environment, the robotic agent undergoes a learning process grounded in trial and error. It garners positive rewards for successful predictions regarding the next action and faces negative repercussions for inaccuracies. Through this iterative learning mechanism, the robot refines its strategies, thereby establishing improved decision-making rules based on the ever-evolving environment states. Our investigation delved into three distinct Deep-RL algorithms: the Deep Q-Network Algorithm (DQN), the Double Deep Q-Network (DDQN), and the Trust Region Policy Optimization Algorithm (TRPO). All three methodologies were adept at addressing the challenge presented, and a comprehensive discussion of the findings is encapsulated in the subsequent sections of the paper.
Pyramid hybrid pooling quantization for efficient fine-grained image retrieval

Deep hashing approaches, including deep quantization and deep binary hashing, have become a common solution to large-scale image retrieval due to their high computation and storage efficiency. Most existing hashing methods cannot produce satisfactory results for fine-grained retrieval, because they usually adopt the outputs of the last CNN layer to generate binary codes. Since deeper layers tend to summarize visual clues, e.g., texture, into abstract semantics, e.g., dogs and cats, the feature produced by the last CNN layer is less effective in capturing subtle but discriminative visual details that mostly exist in shallow layers. To improve fine-grained image hashing, we propose Pyramid Hybrid Pooling Quantization (PHPQ). Specifically, we propose a Pyramid Hybrid Pooling (PHP) module to capture and preserve fine-grained semantic information from multi-level features, which emphasizes the subtle discrimination of different sub-categories. Besides, we propose a learnable quantization module with a partial codebook attention mechanism, which helps to optimize the most relevant codewords and improves the quantization. Comprehensive experiments on two widely-used public benchmarks, i.e., CUB-200-2011 and Stanford Dogs, demonstrate that PHPQ outperforms state-of-the-art methods.
Music curriculum integration and reconstruction model based on advanced music waveform iterative reconstruction algorithm

To improve the effect of intelligent teaching in music classrooms, this paper combines the advanced music waveform iterative reconstruction algorithm to analyze the integration and reconstruction of the music curriculum. Aiming at the problem that the projection matrix occupies a large space and takes a long time to calculate in iterative reconstruction, a fast and real-time incremental method for generating a music wave matrix is proposed. The improved method avoids the judgment and comparison calculations performed by the incremental method when calculating the length and number of each voxel that the ray passes through. The research results show that the music curriculum integration and reconstruction model based on the advanced music waveform iterative reconstruction algorithm can effectively improve the teaching effect of modern music classrooms.
Bayesian network structure learning based on HC-PSO algorithm

Structure learning is the core of graph model Bayesian Network learning, and the current mainstream single search algorithm has problems such as poor learning effect, fuzzy initial network, and easy falling into local optimum. In this paper, we propose a heuristic learning algorithm HC-PSO combining the HC (Hill Climbing) algorithm and PSO (Particle Swarm Optimization) algorithm, which firstly uses HC algorithm to search for locally optimal network structures, takes these networks as the initial networks, then introduces mutation operator and crossover operator, and uses PSO algorithm for global search. Meanwhile, we use the DE (Differential Evolution) strategy to select the mutation operator and crossover operator. Finally, experiments are conducted in four different datasets to calculate BIC (Bayesian Information Criterion) and HD (Hamming Distance), and comparative analysis is made with other algorithms, the structure shows that the HC-PSO algorithm is superior in feasibility and accuracy.
GLOCAL: A self-supervised learning framework for global and local motion estimation

Motions in videos are typically a mixture of local dynamic object motions and global camera motion, which are inconsistent in some cases, and even interfere with each other, causing difficulties in various downstream applications, such as video stabilization that requires the global motion, and action recognition that consumes local motions. Therefore, it is crucial to estimate them separately. Existing methods separate two motions from the mixed motion fields, such as optical flow. However, the quality of mixed motion determines the higher bounds of the performance. In this work, we propose a framework, GLOCAL, to directly estimate global and local motions simultaneously from adjacent frames in a self-supervised manner. Our GLOCAL consists of a Global Motion Estimation (GME) module and a Local Motion Estimation (LME) module. The GME module involves a mixed motion estimation backbone, an implicit bottleneck structure for feature dimension reduction, and an explicit bottleneck for global motion recovery based on the global motion bases with foreground mask under the training guidance of proposed global reconstruction loss. An attention U-Net is adopted for LME which produces local motions while excluding motion of irrelevant regions under the guidance of proposed local reconstruction loss. Our method can achieve better performance than the existing methods on the homography estimation dataset DHE and the action recognition dataset NCAA and UCF-101.