Optical 3D information encryption and rapid decryption via interferenceless coded aperture correlation holography

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


Joint User Association and Bandwidth Allocation in Semantic Communication Networks

Abstract: Semantic communication (SemCom) has recently been considered a promising solution to guarantee high resource utilization and transmission reliability for future wireless networks.Nevertheless, the unique demand for background knowledge matching makes it challenging to achieve efficient wireless resource management for multiple users in SemComenabled networks (SC-Nets).To this end, this paper investigates SemCom from a networking perspective, where two fundamental problems of user association (UA) and bandwidth allocation (BA) are systematically addressed in the SC-Net.First, considering varying knowledge matching states between mobile users and associated base stations, we identify two general SC-Net scenarios, namely perfect knowledge matching-based SC-Net and imperfect knowledge matching-based SC-Net.Afterward, for each SC-Net scenario, we describe its distinctive semantic channel model from the semantic information theory perspective, whereby a concept of bit-rate-to-message-rate transformation is developed along with a new semantics-level metric, namely system throughput in message (STM), to measure the overall network performance.In this way, we then formulate a joint STM-maximization problem of UA and BA for each SC-Net scenario, followed by a corresponding optimal solution proposed.Numerical results in both scenarios demonstrate significant superiority and reliability of our solutions in the STM performance compared with two benchmarks.


Coherent chord computation and cross ratio for accurate ellipse detection

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


RFLPV: A robust federated learning scheme with privacy preservation and verifiable aggregation in IoMT

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


A guidable nonlocal low-rank approximation model for hyperspectral image denoising

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


RIC-CNN: Rotation-Invariant Coordinate Convolutional Neural Network

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


Multiple instance learning from similarity-confidence bags

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


Effective camera calibration by using phase-shifting fringe patterns

Abstract: The accuracy of conventional target-based camera calibration depends on the detection of feature points. To improve the calibration accuracy in the flat field, this paper proposes an innovative calibration method based on encoded phase-shifting fringe (PSF) patterns. By using arbitrary quadrilateral interpolation, the relationship between a point on the virtual phase plane and the corresponding four original phase points is determined, establishing a mapping between the simplified original phase field and the virtual phase field. This spatial relationship can be used for accurate camera calibration. The proposed research investigates the details of the PSF based camera calibration method and demonstrates its accuracy and effectiveness through experiments. The evaluation of this method includes the checkerboard edge length variance analysis and the linear distance analysis of the checkerboard fit, both of which serve as validation metrics. Camera distortion is described by the phase distribution, and the average phase error after correction is less than 0.001 pixels (px).


Learning disentangled representations for controllable human motion prediction

Abstract: Generative model-based motion prediction techniques have recently realized predicting controlled human motions, such as predicting multiple upper human body motions with similar lower-body motions. However, to achieve this, the state-of-the-art methods require either subsequently learning mapping functions to seek similar motions or training the model repetitively to enable control over the desired portion of body. In this paper, we propose a novel framework to learn disentangled representations for controllable human motion prediction. Our task is to predict multiple future human motions based on the past observed sequence, with the control of partial-body movements. Our network involves a conditional variational auto-encoder (CVAE) architecture to model full-body human motion, and an extra CVAE path to learn only the corresponding partial-body (e.g., lower-body) motion. Specifically, the inductive bias imposed by the extra CVAE path encourages two latent variables in two paths to respectively govern separate representations for each partial-body motion. With a single training, our model is able to provide two types of controls for the generated human motions: (i) strictly controlling one portion of human body and (ii) adaptively controlling the other portion, by sampling from a pair of latent spaces. Additionally, we extend and adapt a sampling strategy to our trained model to diversify the controllable predictions. Our framework also potentially allows new forms of control by flexibly customizing the input for the extra CVAE path. Extensive experimental results and ablation studies demonstrate that our approach is capable of predicting state-of-the-art controllable human motions both qualitatively and quantitatively.


Label-Weighted Graph-Based Learning for Semi-Supervised Classification Under Label Noise

Abstract: Graph-based semi-supervised learning (GSSL) is a quite important technology due to its effectiveness in practice. Existing GSSL works often treat the given labels equally and ignore the unbalance importance of labels. In some inaccurate systems, the collected labels usually contain noise (noisy labels) and the methods treating labels equally suffer from the label noise. In this paper, we propose a novel label-weighted learning method on graph for semi-supervised classification under label noise, which allows considering the contribution differences of labels. In particular, the label dependency of data is revealed by graph constraints. With the help of this label dependency, the proposed method develops the strategy of adaptive label weight, where label weights are assigned to labels adaptively. Accordingly, an efficient algorithm is developed to solve the proposed optimization objective, where each subproblem has a closed-form solution. Experimental results on a synthetic dataset and several real-world datasets show the advantage of the proposed method, compared to the state-of-the-art methods.


Multiclass autoencoder-based active learning for sensor-based human activity recognition

Abstract: Leveraging the enormous amounts of real-world data collected through Internet of Things (IoT) technologies, human activity recognition (HAR) has become a crucial component of numerous human-centric applications, with the aim of enhancing the quality of human life. While the recent advancements in deep learning have significantly improved HAR, the process of labeling data continues to remain a significant challenge due to the substantial costs associated with human annotation for supervised model training. Active learning (AL) addresses this issue by strategically selecting informative samples for labeling during model training, thereby enhancing model performance. Although numerous approaches have been proposed for sample selection, which consider aspects of uncertainty and representation, the difficulties in estimating uncertainty and exploiting distribution of high-dimensional data still pose a major issue. Our proposed deep learning-based active learning algorithm, called Multiclass Autoencoder-based Active Learning (MAAL), learns latent representation leveraging the capacity of Deep Support Vector Data Description (Deep SVDD). With the multiclass autoencoder which learns the normal characteristics of each activity class in the latent space, MAAL provides an informative sample selection for model training by establishing a link between the HAR model and the selection model. We evaluate our proposed MAAL using two publicly available datasets. The performance results demonstrate the improvements across the overall active learning rounds, achieving enhancements up to 3.23% accuracy and 3.67% in the F1 score. Furthermore, numerical results and analysis of sample selection are presented to validate the effectiveness of the proposed MAAL compared to the alternative comparison methods.


Multi-valued model checking IoT and intelligent systems with commitment protocols in multi-source data environments

Abstract: In today's world of connectivity, various domains use different multi-sensor Internet of Things (IoT) and Intelligent Systems (IS) applications. These applications involve extensive interactions between thousands or millions of components in open environments, which challenges verifying their reliability and efficiency. This paper introduces the first investigation in verifying IoT applications and intelligent systems in multi-source data settings with multi-agent commitment protocols in uncertain or inconsistent environments. Specifically, we present original and efficient solutions for modeling and verifying these systems with conditional and unconditional commitment protocols under uncertain or inconsistent settings. We introduce 4v-CTLc and 4v-CTLcc, multi-valued logics of commitment for reasoning about inconsistency over these systems that expand 3v-CTLc and 3v-CTLcc for reasoning about uncertainty. Moreover, we introduce new reduction algorithms for reducing our multi-valued model checking problems to the two-valued ones. To implement these algorithms, we develop two new automatic verification tools. The first tool translates the multi-valued logics to CTL and automatically interacts with the NuSMV model checker. The second tool translates these logics to the two-valued versions, CTLc and CTLcc, and automatically interacts with the MCMAS+ model checker. We apply our verification approaches to a Smart Home, a Smart Hospital and a Smart Mortgage system with multi-source data as case studies using sets of properties, including safety, liveness and reachability. The experimental results obtained by the proposed multi-valued model checking techniques proved these techniques' high efficiency and applicability to modeling and verifying intelligent and autonomous multi-source data systems.


Image restoration via joint low-rank and external nonlocal self-similarity prior

Abstract: Recent studies have revealed that joint priors, such as joint sparsity and external nonlocal self-similarity (ENSS) prior and joint low-rank and sparsity prior, are extremely effective in various image inverse problems. Few works, however, make use of both low-rank and ENSS priors. With this in mind, in this paper we propose a new joint prior, namely LRENSS prior, which utilizes low-rank and ENSS priors jointly in a unified framework, and successfully adapt the proposed LRENSS prior to image restoration problems. Specifically, low-rank and ENSS priors are bridged by treating ENSS prior as dictionaries for structural sparse representation. Further, an elegant block coordinate descent method is developed to solve the corresponding optimization problem. The proposed LRENSS prior is validated on image denoising and image deblurring tasks. Experimental results illustrate that the proposed LRENSS prior has better performance than other state-of-the-art algorithms in both qualitative and quantitative assessments.


BSELA: A Blockchain Simulator with Event-Layered Architecture

Abstract: Blockchain technology has found widespread use in various fields, including finance, healthcare, the Internet of Things, and supply chain management, due to its ability to meet technical and non-technical requirements. Therefore, it is crucial for system designers to evaluate the performance and characteristics of a given blockchain platform before deploying it. One way to achieve this is through the use of blockchain simulators. However, existing simulators have limitations in areas such as model scaling, event scheduling, time control, object model evaluation, and simulation magnitude. To address these limitations, we propose a Blockchain Simulator based on Event-Layered Architecture (BSELA) in this paper. BSELA comprises multiple functional modules with effective model scalability, with the core being our proposed Event-Layered Architecture. This architecture introduces a new event-driven scheduling mechanism for discrete-event simulation, which improves the efficiency, stability, and maintainability of event scheduling during blockchain simulation. Furthermore, we propose a time advancement mechanism for subsequent events based on event rounds, which improves the accuracy of time advancement, simulation complexity, and real-time demand handling capability during the simulation. We begin by validating the efficiency of our simulator by comparing experiments with those of existing blockchain simulators. We then validate the accuracy of BSELA through simulation experiments on blockchain network connectivity, block propagation latency, and the number of INV messages propagated per hour. We validated the accuracy of the experimental results by conducting a comparison with Bitcoin data. Finally, in order to address the research gap in the performance of blockchain systems at the underlying peer-to-peer network level and in other fields, we designed and experimentally validated the node-trust network construction mechanism by adjusting the simulator's blockchain object model. Our experiments show that this tuning mechanism improves data transfer efficiency by 30%–40%, and also improves security. The design and experiments of the BSELA tuning mechanism validate the scalability and flexibility of our simulator for blockchain research.


Hybrid unsupervised paradigm based deformable image fusion for 4D CT lung image modality

Abstract: Deformable image registration plays a critical role in various clinical applications (e.g., image fusion, atlas creation, and tumors targeting). In radiation therapy, especially in the context of fast registration of computed tomography (CT) lung image modalities, it is used to determine the geometric transformation by relating the anatomic points in two images. The main challenge lies in effectively addressing the nonlinear large and small deformation between the inspiration and expiration phases. In this work, we propose an unsupervised hybrid paradigm-based registration network (HPRN) for the registration of 4D CT lung images without relying on ground truth data. The proposed HPRN exhibits effective learning of multi-scale and multi-resolution features, leading to the computation of a more accurate Deformation Vector Field (DVF). Furthermore, we incorporate the regularization, image similarity and Jacobian determinant loss functions, which results in improving capability in dealing with complex large and small deformations. We evaluate the effectiveness of the proposed model on the publicly accessible DIRLab 4DCT lung image dataset, which shows the effectiveness of the proposed framework by achieving better Target Registration Error (2.04±1.42mm) compared to other state-of-the-art unsupervised image registration algorithms.


A versatile device for implementing the optical quantum gates in multiple degrees of freedom

Abstract: Optical quantum logic gates are the basic elements for building optical quantum computer. Usually, to implement different logic gates, different optical elements and frameworks are needed. Here, we propose a versatile device that a modified Sagnac interferometer composed of a spatial light modulator (SLM) for implementing multiple optical gates within a classical light framework. We demonstrate that the device can manipulate the polarization and full-field spatial modes of photons to perform multi-qubit logic operations. And versatile conversions between different logic gates are achieved by simply altering the hologram imposed on SLM. Our device shows a good performance in exploring the photon's full degrees of freedom, which makes it an appropriate candidate for integrated optical computing.


Variational Quantum Linear Solver-based Combination Rules in Dempster–Shafer Theory

Abstract: Dempster–Shafer Theory (DST), as a method of handling uncertain information, is widely used in decision-making and information fusion. But the issue of exponential computational complexity limits its real-time application. Since quantum computers with its natural parallel computing capability can theoretically achieve speedup, an HHL-based algorithm for implementing DST operations on quantum circuits has been proposed. However, the algorithm is impossible to be implemented with high accuracy due to the limited performance of quantum computers in the current noisy intermediate-scale quantum (NISQ) era. In response to that, we utilize an up-to-date scheme Variational Quantum Linear Solver (VQLS) to conduct DST operations by solving the linear system. Besides, VQLS further reveals the structural consistency of DST with quantum computation, which enables faster circuit configuration and computation. In this paper, we first realize the transformation in belief functions by VQLS and then extends it to a methods of implementing combination rules. Simulated under a realistic classification task, the feasibility and accuracy of the proposed VQLS-based method are verified. The new VQLS-based method proposed is able to achieve combination rules at a lower error level with fewer quantum resources, which is more suitable for the NISQ era.


Privacy-Preserving Neural Networks for Smart Manufacturing

Abstract: Abstract The rapid advance in sensing technology has expedited data-driven innovation in manufacturing by enabling the collection of large amounts of data from factories. Big data provides an unprecedented opportunity for smart decision-making in the manufacturing process. However, big data also attracts cyberattacks and makes manufacturing systems vulnerable due to the inherent value of sensitive information. The increasing integration of artificial intelligence (AI) within smart factories also exposes manufacturing equipment susceptible to cyber threats, posing a critical risk to the integrity of smart manufacturing systems. Cyberattacks targeting manufacturing data can result in considerable financial losses and severe business disruption. Therefore, there is an urgent need to develop AI models that incorporate privacy-preserving methods to protect sensitive information implicit in the models against model inversion attacks. Hence, this paper presents the development of a new approach called mosaic neuron perturbation (MNP) to preserve latent information in the framework of the AI model, ensuring differential privacy requirements while mitigating the risk of model inversion attacks. MNP is flexible to implement into AI models, balancing the trade-off between model performance and robustness against cyberattacks while being highly scalable for large-scale computing. Experimental results, based on real-world manufacturing data collected from the computer numerical control (CNC) turning process, demonstrate that the proposed method significantly improves the ability to prevent inversion attacks while maintaining high prediction performance. The MNP method shows strong potential for making manufacturing systems both smart and secure by addressing the risk of data breaches while preserving the quality of AI models.


Hierarchical graph augmented stacked autoencoders for multi-view representation learning

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

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

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

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

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

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

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

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

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

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.


High-precision phase shift method for heavy-load reference mirrors based on nano-precision grating sensor monitoring

Abstract: In large-aperture interferometry, achieving high-precision phase shifting for heavy-load reference mirrors is challenging. This study introduces a method based on nano-precision grating sensor monitoring. The technique utilizes spatial three-point synchronous driving by piezoelectric ceramics, combined with flexible hinges, to enable nanoscale phase shifting of heavy-load reference mirrors. Additionally, it integrates in-situ PID closed-loop drive monitoring at these three points, using nano-precision grating sensors and piezoelectric ceramics. To ensure nanoscale resolved spatial translation, the mirrors are supported by air-bearings that counteract gravity, coupled with a phase shift error calibration model. The final realization of high-precision and high-stability mechanical phase shifting in large-aperture interferometry circumvents the principle defects of existing large-aperture wavelength-tuned phase shifting. The experiments show that the established mechanical phase shifting system with heavy-load reference mirror has a resolution of 1.5 nm, a frequency response of 116 Hz, and a synchronization accuracy of 2° for the three phase shifting quantities, and the method provides a new method and means of phase shifting for the construction of large-aperture phase shifting interferometer.


Temporal Action Segmentation: An Analysis of Modern Techniques

Abstract: Temporal action segmentation (TAS) in videos aims at densely identifying video frames in minutes-long videos with multiple action classes. As a long-range video understanding task, researchers have developed an extended collection of methods and examined their performance using various benchmarks. Despite the rapid growth of TAS techniques in recent years, no systematic survey has been conducted in these sectors. This survey analyzes and summarizes the most significant contributions and trends. In particular, we first examine the task definition, common benchmarks, types of supervision, and prevalent evaluation measures. In addition, we systematically investigate two essential techniques of this topic, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , frame representation and temporal modeling, which have been studied extensively in the literature. We then conduct a thorough review of existing TAS works categorized by their levels of supervision and conclude our survey by identifying and emphasizing several research gaps. In addition, we have curated a list of TAS resources, which is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/nus-cvml/awesome-temporal-action-segmentation</uri> .


FPGA design of FFT based intelligent accelerator with optimized Wallace tree multiplier for image super resolution and quality enhancement

Abstract: The super-resolution (SR) method has been widely used to improve the resolution and quality of natural images, and it is currently being employed in medical imaging. Recently, many techniques have been introduced by researchers to improve the resolution and quality of the image. However, these approaches generate blurring and jagged edges in images. Hence, this article introduces novel deep learning (DL) with a fast Fourier transform (FFT) based intelligent approach (IA) to improve the quality and resolution of the image. A convolutional neural network (CNN) is emphasized for quality enhancement and resolution to recover lost information during image acquisition. This work undergoes three significant stages: de-noising, segmentation, and resolution improvement of the noisy input image. The double density discrete wavelet transform (DDDWT) technique is introduced to remove the noise and improve the visual image’s quality. Finally, the image resolution and quality enhancement are done using CNN with overlap and add (OA)-FFT based Wallace tree multiplier-red deer optimization (WT-RDO) technique. In the experimental scenario, the quality enhancement and image resolution outcome achieved accuracy of 97 % and 98 %, precision of 97 % and 97 %, sensitivity of 96 % and 98 %, specificity of 96 % and 98 %, PSNR of 30 dB and 26 dB, structural similarity index (SSIM) of 0.86 and 0.85, mean square error (MSE) of 0.01, mean absolute error (MAE) of 0.8, the time complexity of 61, power of 0.08 W, delay of 0.6 ns and throughput of 3.2 MB/s.


Taxonomy and definition of audio augmented reality (AAR): A grounded theory study

Abstract: AR applications have mostly considered visual augmentations while excluding other modalities. Recent developments in audio augmented reality (AAR) applications have been based on the definitions of visual AR or mixed reality (MR), and thus, AAR technology development has lacked systematic efforts. We investigated the concept of augmented reality through audio to provide a systematic understanding and generate a taxonomy and a definition for AAR. A conference workshop (N = 28), focus groups (N = 18), and expert interviews (N = 6) generated qualitative data regarding the concept of AAR. Grounded Theory (GT) was used to analyze the data and produce a new taxonomy and a definition. The AAR taxonomy consists of three categories – Environment Connected, Goal Directed, and Context Adapted, with three subcategories respectively. The need for a separate taxonomy for AAR is highlighted to aid in the development of AAR applications in a systematic manner. The taxonomy is expected to be used as a heuristic tool that can guide developers to build AAR applications and can be used in evaluating user experience with AAR applications.


Condition Monitoring of Underground Power Cables Via Power-Line Modems and Anomaly Detection

Abstract: Underground power cables are a widely-deployed class of assets in electric power distribution systems. They are robust in adverse weather conditions and are an aesthetically pleasing alternative to overhead power lines. Unfortunately, the physical observability of underground cables is poor. Current methods for detecting and localizing defects known to progress to failure are cumbersome and expensive, both financially and operationally. In-service cable failures can lead to lengthy power outages and financial losses and can endanger utility staff and the general public. There is thus a need for non-invasive condition monitoring techniques that will allow for early detection of defects likely to progress to a fault, so cost-effective repair or replacement interventions can be planned. In this study, insulation overheating is specifically considered. Thermal damage of XLPE insulation is a prominent damage mechanism for extruded dielectric cable and is thus well studied and understood. A defect detection solution combining powerline modems with semi-supervised anomaly detection for underground cables is proposed. The performance of five well-regarded machine-learning-based anomaly detection algorithms is analyzed through simulation of a real-world 15kV distribution network. The best-performing algorithm studied had a false alarm rate of under 0.1% with no missed alarms across a range of damage scenarios.


High-accuracy vanishing-constraints-based calibration of fringe projection vision sensor

Abstract: In fringe projection profilometry (FPP), calibration of the vision sensor is crucial to ensure the accuracy of three-dimensional (3D) shape measurement. However, due to correspondence errors of planar calibration target (PCT) 3D points and its image points, and low mapping accuracy between projector pixels and camera pixels, traditional calibration methods based on the PCT sometimes cannot provide reliable and accurate results. Aiming at these problems, a high-accuracy calibration method based on vanishing constraints for fringe projection vision sensor (FPVS) is proposed in this paper. The intrinsic parameters are calibrated using the constraints of vanishing points, the rotation matrix is calibrated using the constraints of vanishing lines, and the optimal calibration parameters are estimated by the objective function established based on the 2D and 3D constraints of feature points. Experimental results demonstrated the performance of our calibration method.


NOVAction23: Addressing the data diversity gap by uniquely generated synthetic sequences for real-world human action recognition

Abstract: Recognition of human actions using machine learning requires extensive datasets to develop robust models. Nevertheless, obtaining real-world data presents challenges due to the costly and time-consuming process involved. Additionally, existing datasets mostly contain indoor videos due to the challenges of capturing pose data outdoors. Synthetic data have been used to overcome these difficulties, yet the currently available synthetic datasets for human action recognition lack photorealism and diversity in their features. Addressing these shortcomings, we develop the NOVAction engine to generate highly diversified and photorealistic synthetic human action sequences. We use NOVAction to create the NOVAction23 dataset comprising 25,415 human action sequences with corresponding poses and labels (available at https://github.com/celikcan-cglab/NOVAction23). In NOVAction23, the performed motions and viewpoints are varied on the fly through procedural generation, to ensure that, for a given action class, each generated sequence features a distinct motion performed by one of the 1,105 synthetic humans captured from a unique viewpoint. Moreover, each synthetic human is unique in terms of body shape (height and weight), skin tone, gender, hair, facial hair, clothing, shoes and accessories. To further increase data diversity, the motion sequences are rendered under various weather conditions and at different times of day, across three outdoor and two indoor settings. We evaluate NOVAction23 by training three state-of-the-art recognizers on it, in addition to the NTU 120 dataset, and corroborating using real-world videos from YouTube. Our results confirm that the NOVAction23 dataset can improve the performance of state-of-the-art human action recognition.


HASP: Hierarchical Asynchronous Parallelism for Multi-NN Tasks

Abstract: The rapid development of deep learning has propelled many real-world artificial intelligence applications. Many of these applications integrate multiple neural networks (multi- NN) to cater to various functionalities. There are two challenges of multi-NN acceleration: (1) competition for shared resources becomes a bottleneck, and (2) heterogeneous workloads exhibit remarkably different computing-memory characteristics and various synchronization requirements. Therefore, resource isolation and fine-grained resource allocation for each task are two fundamental requirements for multi-NN computing systems. Although a number of multi-NN acceleration technologies have been explored, few can completely fulfill both of these requirements, especially for mobile scenarios. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">This paper reports a Hierarchical Asynchronous Parallel Model (HASP) to enhance multi-NN performance to meet both requirements. HASP can be implemented on a multicore processor that adopts Multiple Instruction Multiple Data (MIMD) or Single Instruction Multiple Thread (SIMT) architectures, with minor adaptive modification needed. Further, a prototype chip is developed to validate the hardware effectiveness of this design. A corresponding mapping strategy is also developed, allowing the proposed architecture to simultaneously promote resource utilization and throughput. With the same workload, the prototype chip demonstrates 3.62×, and 3.51× higher throughput over Planaria and 8.68×, 2.61× over Jetson AGX Orin for MobileNet-V1 and ResNet50, respectively.


Entropy-Oriented Domain Adaptation for Intelligent Diagnosis of Rotating Machinery

Abstract: To cater to fault diagnosis of rotating machinery under complex working conditions, unsupervised domain adaptation technology has been widely explored and applied. Existing methods mainly reduce domain bias in two ways, including metric learning and discriminator-based adversarial learning. Different from these technologies, in this work, we only resort to entropy optimization strategies and develop a novel entropy-oriented domain adaptation (EODA) model for intelligent diagnosis of rotating machinery. Specifically, a convolutional network with a cosine-distance classifier is introduced to construct the model framework, which can reduce intraclass variation and make the output more confident. In addition, negentropy-guided prediction diversity optimization and minimax entropy game-guided prototype-feature alignment are co-designed to realize domain adaptation. Extensive experiments based on two different mechanical systems are used to validate our method. Comprehensive results and discussions demonstrate that our EODA can achieve compelling performance.


Artificial Intelligence-Driven Mammography-Based Future Breast Cancer Risk Prediction: A Systematic Review

Abstract: To summarize the literature regarding the performance of mammography-image based artificial intelligence (AI) algorithms, with and without additional clinical data, for future breast cancer risk prediction.A systematic literature review was performed using six databases (medRixiv, bioRxiv, Embase, Engineer Village, IEEE Xplore, and PubMed) from 2012 through September 30, 2022. Studies were included if they used real-world screening mammography examinations to validate AI algorithms for future risk prediction based on images alone or in combination with clinical risk factors. The quality of studies was assessed, and predictive accuracy was recorded as the area under the receiver operating characteristic curve (AUC).Sixteen studies met inclusion and exclusion criteria, of which 14 studies provided AUC values. The median AUC performance of AI image-only models was 0.72 (range 0.62-0.90) compared with 0.61 for breast density or clinical risk factor-based tools (range 0.54-0.69). Of the seven studies that compared AI image-only performance directly to combined image + clinical risk factor performance, six demonstrated no significant improvement, and one study demonstrated increased improvement.Early efforts for predicting future breast cancer risk based on mammography images alone demonstrate comparable or better accuracy to traditional risk tools with little or no improvement when adding clinical risk factor data. Transitioning from clinical risk factor-based to AI image-based risk models may lead to more accurate, personalized risk-based screening approaches.


Convergence rates for shallow neural networks learned by gradient descent

Abstract: In this paper we analyze the L2 error of neural network regression estimates with one hidden layer. Under the assumption that the Fourier transform of the regression function decays suitably fast, we show that an estimate, where all initial weights are chosen according to proper uniform distributions and where the weights are learned by gradient descent, achieves a rate of convergence of 1∕n (up to a logarithmic factor). Our statistical analysis implies that the key aspect behind this result is the proper choice of the initial inner weights and the adjustment of the outer weights via gradient descent. This indicates that we can also simply use linear least squares to choose the outer weights. We prove a corresponding theoretical result and compare our new linear least squares neural network estimate with standard neural network estimates via simulated data. Our simulations show that our theoretical considerations lead to an estimate with an improved performance in many cases.


Risk-Averse Trajectory Optimization via Sample Average Approximation

Abstract: Trajectory optimization under uncertainty underpins a wide range of applications in robotics.However, existing methods are limited in terms of reasoning about sources of epistemic and aleatoric uncertainty, space and time correlations, nonlinear dynamics, and non-convex constraints.In this work, we first introduce a continuous-time planning formulation with an average-value-at-risk constraint over the entire planning horizon.Then, we propose a sample-based approximation that unlocks an efficient and general-purpose algorithm for risk-averse trajectory optimization.We prove that the method is asymptotically optimal and derive finite-sample error bounds.Simulations demonstrate the high speed and reliability of the approach on problems with stochasticity in nonlinear dynamics, obstacle fields, interactions, and terrain parameters.


Structural asymmetric convolution for wireframe parsing

Abstract: Simultaneously extracting junctions and their corresponding line segments from images presents a promising approach to structural environment cognition. However, conventional methods employ square convolution for line feature extraction, resulting in the exclusion of long-range dependencies and the generation of suboptimal wireframe predictions. In this paper, we introduce an efficient and concise parsing method named Structural Asymmetric Convolution-based Wireframe Parser (SACWP). Taking advantage of the inherent similarities between structural asymmetric convolution and the predominant distribution of line segments in man-made environments, we propose a Structural Asymmetric Convolution module (SAC) that captures long-range contextual features while efficiently filtering out irrelevant information from neighboring pixels. Additionally, we introduce a feature aggregation module based on dilated convolution (DCFA) to seamlessly integrate contextual information from multiple receptive fields. We thoroughly evaluate our approach on the Wireframe and YorkUrban datasets, achieving preferable results of 69.3% and 29.7% msAP respectively. On the other hand, the promising results adequately demonstrate the effectiveness of SACWP to Wireframe Parsing task.


A novel privacy-preserving graph convolutional network via secure matrix multiplication

Abstract: Graph convolutional network (GCN) is one of the most representative methods in the realm of graph neural networks (GNNs). In the convolution process, GCN combines the structural information of the networks with the features of nodes. In practice, the structure information of the networks and the features of nodes may be controlled by different parties, which can render GCN ineffective when information sharing is constrained by privacy concerns or licensing issues. Therefore, it is of significant importance to design an effective and secure scheme for GCN that can collaboratively merge information from both parties while safeguarding their sensitive data. In this paper, we introduce S-GCN (i.e., Secure GCN Scheme), which employs secure matrix multiplication (SMM) to compute the product of two matrices in a privacy-preserving manner. The S-GCN scheme requires frequent utilization of SMM to merge information from both parties, resulting in high time and space complexity. To address this issue, we introduce SF-GCN (i.e., Secure and Fast GCN Scheme), which minimizes the use of SMM. Additionally, both S-GCN and SF-GCN may be susceptible to privacy breaches when dealing with dense networks. Hence, we further enhance security by introducing differential privacy through the Laplacian mechanism. Experimental results demonstrate that the proposed schemes do not significantly reduce accuracy in downstream tasks, and, more importantly, effectively protect the privacy information of both parties.


Comparison of current quantum devices for quantum computing of Heisenberg spin chain dynamics

Abstract: We simulate the time-dependent dynamics of a chain of three interacting spins described by the Heisenberg XXX Hamiltonian using two types of different quantum computers: Quantinuum H1-1, a trapped-ion quantum computer, and ibm_prague, a superconducting qubit type quantum computer. The time evolution of the wave function is accomplished by the Suzuki–Trotter approximation. We find that very accurate results are obtained using H1-1 after applying error mitigation by renormalization of the time-dependent spin-state populations as well as using ibm_prague after renormalization, dynamical decoupling, Pauli twirling, and measurement error mitigation of the populations.


Multiscale variational autoencoder regressor for production prediction and energy saving of industrial processes

Abstract: As modern industries increase in scale and integration, the industrial process data show more complex dynamic time variability. To solve the dynamic time-varying problem of industrial process data, a novel multiscale variational autoencoder (MSVAE) based Regressor (REG) (MSVAE-REG) is proposed for production prediction and energy saving. The encoder of the MSVAE is constructed by multiscale convolutional neural network (MSCNN) to extract the multiscale temporal dynamic features, including the overall and local trend features in the Gaussian latent space. Then, the decoder of the MSVAE recovers the input data to enhance the dynamic adaptability of multiscale features. Moreover, the REG utilizes the gated recurrent unit (GRU) to build a dynamic relationship between multiscale temporal dynamic features and the predicted output. Finally, the proposed model is verified on the propylene and ethylene production datasets. Compared with the back propagation (BP) network, the extreme learning machine (ELM), the radial basis function (RBF), the convolutional neural network (CNN) and the variational autoencoder (VAE), the MSVAE-REG achieves state-of-the-art results, with the prediction accuracy respectively reaching about 95% and 96%, providing a new judgment basis for optimizing the production process and saving energy.


Monocular depth estimation using self-supervised learning with more effective geometric constraints

Abstract: Self-supervised learning-based depth estimation from monocular videos is a challenging yet promising way for 3D environment perception. Existing methods that use photometric consistency as supervision are often fragile in the case of textureless environments or illumination variations. In this paper, we adopt geometric constraints to improve the reliability and accuracy of monocular depth estimation. We introduce an equation derived from the two-view imaging geometry, from which we also develop a novel geometric loss function that is shown to be more effective than the epipolar constraint in self-supervised learning. In addition, we design a depth reconstruction loss by explicitly aligning the depth scale of the consecutive estimated depth maps to keep the depth scale consistent. Finally, a new self-supervised learning framework is formulated by incorporating the proposed geometric constraints into depth, pose, and optical flow estimation models. Extensive experiments on KITTI, Make3D, and NYU Depth v2 datasets show that, by adding our geometric constraints, the depth estimation model compares favorably with state-of-the-art self-supervised learning methods, and each sub-task has a significant improvement.


Dual attention and channel transformer based generative adversarial network for restoration of the damaged artwork

Abstract: Artworks are treasures of valuable cultural and historical heritage. Artworks get damaged due to environmental and other factors. The artificial intelligence-based restoration of digitized artwork images can guide the artists in physically restoring the damaged artworks. Previous methods have not been able to restore artwork images well. This paper proposes a dual (spatial and channel) attention and channel transformer-based generative adversarial network to restore damaged artwork images digitally. The proposed generative adversarial network has spatial and channel attention layers in the encoder part of the generator and a channel transformer between skip connections from the encoder to the decoder part of the generator. Spatial and channel attention helps learn inter-spatial and inter-channel global relationships among image features. Channel transformer ensures multiscale feature fusion and reduces the semantic gap between encoder and decoder layer features. Moreover, the proposed network has been trained using a linear combination of perceptual, adversarial, and structured similarity index measure loss, which helps better train the network. Further, the proposed network has been validated on two different datasets, and the results indicate that the proposed method outperforms state-of-the-art artwork restoration methods.


TransPV: Refining photovoltaic panel detection accuracy through a vision transformer-based deep learning model

Abstract: The increasing need to develop renewable energy sources to combat climate change has led to a significant rise in demand for photovoltaic (PV) installations. Consequently, accurately detecting and estimating the capacity and potential for electricity generation of these installed PV systems has become crucial for effective energy management. However, due to the limitation of localized mechanism in convolution operations, traditional FCN-based methods face challenges in capturing global dependencies among remote sensing image patches. As a result, they struggle to model long-range interactions between different PV panels with diverse structures, including varying size, shape, and texture, particularly in distributed residential areas. To tackle the challenge of modeling PV panels with diverse structures, we propose a coupled U-Net and Vision Transformer model named TransPV for refining PV semantic segmentation. Specifically, Mix Transformer block is incorporated in the encoder to enhance the modeling of global context, while the U-Shaped structure enables the combination of multi-level features, resulting in enriched feature representation, which helps the model better understand objects in the images, regardless of their shape, size, and texture. In addition, we implement the PointRend module in the decoder to obtain finer segmentation boundary details, and utilize a novel refiner loss function during the training process to alleviate the problem of extremely unbalanced samples. The experimental results from the Heilbronn datasets demonstrate the remarkable performance of our proposed TransPV model in addressing intra-class diversity of PV structures, surpassing previous state-of-the-art methods with an impressive IoU and accuracy of 0.802 and 0.876, respectively. Furthermore, our model exhibits high generalization capability on the BDPV dataset with IoU of 0.745. The obtained results highlight the superiority of TransPV in improving accuracy and addressing diverse structure issues in PV segmentation, which provides valuable insights for optimizing the efficiency and sustainability of renewable energy.


Deep learning-based object detection in maritime unmanned aerial vehicle imagery: Review and experimental comparisons

Abstract: With the advancement of maritime unmanned aerial vehicles (UAVs) and deep learning technologies, the application of UAV-based object detection has become increasingly significant in the fields of maritime industry and ocean engineering. Endowed with intelligent sensing capabilities, the maritime UAVs enable effective and efficient maritime surveillance. To further promote the development of maritime UAV-based object detection, this paper provides a comprehensive review of challenges, relative methods, and UAV aerial datasets. Specifically, in this work, we first briefly summarize four challenges for object detection on maritime UAVs, i.e., object feature diversity, device limitation, maritime environment variability, and dataset scarcity. We then focus on computational methods to improve maritime UAV-based object detection performance in terms of scale-aware, small object detection, view-aware, rotated object detection, lightweight methods, and others. Next, we review the UAV aerial image/video datasets and propose a maritime UAV aerial dataset named MS2ship for ship detection. Furthermore, we conduct a series of experiments to present the performance evaluation and robustness analysis of object detection methods on maritime datasets. Eventually, we give the discussion and outlook on future works for maritime UAV-based object detection. The MS2ship dataset is available at https://github.com/zcj234/MS2ship.


Novel diversified echo state network for improved accuracy and explainability of EEG-based stroke prediction

Abstract: Echo State Networks (ESNs) are a powerful machine learning technique that can be used for EEG-based stroke prediction. However, conventional ESNs suffer from two main limitations: they are not always accurate, and they are not always interpretable. This paper presents a novel multi-level framework that addresses these limitations. The framework consists of three main components: optimized feature extraction, ensemble learning, and output refinement for improved interpretability. The optimized feature extraction component uses a novel algorithm to extract features from EEG data that are more relevant to stroke prediction. The ensemble learning component uses a diversified Echo State Networks (D-ESN) to combine the predictions of multiple ESNs, which improves the accuracy of the predictions. The output improvement component uses two Explainability techniques, LIME and ELI5, to gain insight into the decision-making of the D-ESN model. These techniques allow users to see how each feature in the dataset contributed to the model's prediction. The framework was evaluated on a well-known EEG dataset from stroke patients. The experimental results showed that the framework significantly outperformed baseline approaches in terms of both accuracy with 95 % and interpretability. These results suggest that the proposed framework has the potential to advance the field of stroke prediction and enable informed decision-making in clinical settings.


Reinforcement learning to achieve real-time control of triple inverted pendulum

Abstract: This work uses reinforcement learning (RL) to achieve the first-ever data-driven real-time control of an actual, not simulated, triple inverted pendulum (TIP) in a model-free way. A swing-up control task for the TIP is formulated as a Markov decision process with a dense reward function, then conducted in real time by using a model-free RL approach. To increase the sample efficiency of learning, a structure-aware virtual experience replay (VER) method is proposed; it works together with an off-policy actor-critic algorithm. The VER exploits the geometrically-symmetric property of TIPs to create virtual sample trajectories from measured ones, then uses the resulting multifold augmented dataset to effectively train actor and critic networks during the learning process. These structure-infused training data serve to obtain additional information and hence increase the convergence speed of network learning. We combine the proposed VER with a state-of-the-art actor-critic algorithm, and then validate its effectiveness through numerical simulations. Notably, the inclusion of VER amplifies computational efficiency, slashing the requisite trials, training steps, and overall duration by approximately 66.67%. Finally experiments demonstrate the real-time control capability of the proposed approach on an actual TIP system.


Improved coverage path planning for indoor robots based on BIM and robotic configurations

Abstract: Robotics holds great potential to improve productivity in construction, and coverage path planning (CPP) is an essential capability crucial to various applications, including floor cleaning and environmental monitoring. However, there is still a lack of a comprehensive CPP system that can handle complex indoor conditions and various robotic properties. An improved CPP (ICCP) system that leverages building information modeling (BIM) and robotic configurations for indoor robots is proposed in this study. Firstly, BIM is semantically enriched to generate semantic trapezoidal grid maps (TGMs); Next, a novel concept called "coverage bonus" is incorporated into coverage pattern analysis to enable farsighted decision making; Finally, the coverage sequence is optimized by solving the cluster generalized traveling salesman problem, resulting in routes that minimize both coverage distances and disruptions in indoor activities. Experimental validation shows that the ICPP system can not only attain optimal coverage performance with the highest coverage ratio (97.6%) but also ensure the adherence to indoor coverage rules. Future research will focus on enhancing coverage ratio through tunable hyperparameters, optimizing computation time in ICCP, and expanding the study to multi-robot scenarios.


Selection of a viable blockchain service provider for data management within the internet of medical things: An MCDM approach to Indian healthcare

Abstract: Health 4.0 is gaining significant attention globally to support better treatment and care for people. Digital technologies such as the IoMT "("Internet of Medical Things"") and blockchain substantially promote quality health services. Literature shows that embedding blockchain in IoMT is an effective way to attain secure and quality healthcare. Selecting a viable blockchain service provider (BSP) becomes a complex task and can be considered an MCDM "("Multi-Criteria Decision-Making"") problem. Earlier studies on BSP selection indicate that complex expressions cannot be well-modelled and methodical estimation of decision parameters by capturing hesitation and interaction of entities is not adequately explored. Driven by the issues, authors put forward a novel MCDM framework with (i) a double hierarchy linguistic structure for data collection in natural expressions; (ii) regret measure for experts' reliability calculation; (iii) a weighted CRITIC approach for criteria weight determination, and (iv) CRADIS-Copeland algorithm for ranking BSPs. Finally, a case example from Recent Indian healthcare is exemplified to demonstrate the framework's applicability. Recently, the Indian healthcare sector launched initiatives/plans as part of Health 4.0 to promote data management for a better quality of treatment by seeking support from digital technologies such as IoMT and blockchains to ensure data privacy and improved customer experience. Sensitivity analysis and comparison with extant methods infer that (i) BC1, BC5, and BC3 are the top three BSPs for the considered problem; (ii) criteria such as privacy aspects, intercommunication capability, global accessibility, and total cost constitute 58% importance in the supposed decision problem; (iii) developed model reduces human intervention/biases; (iv) developed model is robust to weight alteration and yields unique rank orders with both personalised and cumulative ordering of BSPs; and (v) proposed model produces broader rank values that aid in effective backup planning.


FLAD: Adaptive Federated Learning for DDoS attack detection

Abstract: Federated Learning (FL) has been recently receiving increasing consideration from the cybersecurity community as a way to collaboratively train deep learning models with distributed profiles of cyber threats, with no disclosure of training data. Nevertheless, the adoption of FL in cybersecurity is still in its infancy, and a range of practical aspects have not been properly addressed yet. Indeed, the Federated Averaging algorithm at the core of the FL concept requires the availability of test data to control the FL process. Although this might be feasible in some domains, test network traffic of newly discovered attacks cannot be always shared without disclosing sensitive information. In this paper, we address the convergence of the FL process in dynamic cybersecurity scenarios, where the trained model must be frequently updated with new recent attack profiles to empower all members of the federation with the latest detection features. To this aim, we propose FLAD (adaptive Federated Learning Approach to DDoS attack detection), an FL solution for cybersecurity applications based on an adaptive mechanism that orchestrates the FL process by dynamically assigning more computation to those members whose attacks profiles are harder to learn, without the need of sharing any test data to monitor the performance of the trained model. Using a recent dataset of DDoS attacks, we demonstrate that FLAD outperforms state-of-the-art FL algorithms in terms of convergence time and accuracy across a range of unbalanced datasets of heterogeneous DDoS attacks. We also show the robustness of our approach in a realistic scenario, where we retrain the deep learning model multiple times to introduce the profiles of new attacks on a pre-trained model.


DHGAT: Hyperbolic representation learning on dynamic graphs via attention networks

Abstract: Hyperbolic graph embedding has garnered significant attention in a wide range of downstream applications. This is because hyperbolic geometry provides a valuable mapping tool, whereby the scale-free or hierarchical properties of complex networks can be naturally reflected as hyperbolic metric properties. Despite this, most advancements have focused on learning hyperbolic representations of static graphs, the potential advantages of hyperbolic metrics in dynamic graphs embedding have not been fully exploited. To fully harness the properties of hyperbolic space, we propose DHGAT1, a dynamic hyperbolic graph attention network with a novel architecture that designs a spatiotemporal self-attention mechanism based on hyperbolic distance. DHGAT maps dynamic graphs into the hyperbolic space, generates spatiotemporal self-attention of nodes, and directly aggregates weighted node representations without the tangent space. This is achieved by using the Einstein gyromidpoints, which reduces distortions and preserves manifold properties. The results of experiments on real-world datasets demonstrate that DHGAT performs exceptionally well in multi-step link prediction tasks, particularly in predicting new links compared to the baselines.


Research on a double image security transmission algorithm of image encryption and hiding

Abstract: Image encryption is an important research field. In some application scenarios, only encrypting can actually attract the attention of attackers. Therefore, this article provides dual protection for images that require confidentiality. The image is first encrypted and then hidden in the normally transmitted image to avoid attacks from attackers. The encryption adopts a combination of DNA encoding and chaos theory, while the hiding adopts LSB algorithm. The experimental results show that the algorithm has achieved good security effects, and the correlation coefficient analysis has also achieved ideal results.


Dynamics-aware navigation among moving obstacles with application to ground and flying robots

Abstract: We present a novel method for navigation of mobile robots in challenging dynamic environments. The method, which is based on Nonlinear Model Predictive Control (NMPC), hinges upon a specially devised constraint for dynamics-aware collision avoidance. In particular, the constraint builds on the notion of avoidable collision state, taking into account the robot actuation capabilities in addition to the robot-obstacle relative distance and velocity. The proposed approach is applied to both ground and flying robots and tested in a variety of static and dynamic environments. Comparative simulations with an NMPC using a purely distance-based collision avoidance constraint confirm the superiority of the dynamics-aware version, especially for high-speed navigation among moving obstacles. Moreover, the results indicate that the method can work with relatively short prediction horizons and is therefore amenable to real-time implementation.


Towards a generalized hybrid deep learning model with optimized hyperparameters for malicious traffic detection in the Industrial Internet of Things

Abstract: Detecting malicious attacks in Industrial Internet of Things (IIoT) is crucial to minimize downtime and financial losses. However, existing deep learning (DL) research faces limitations in reliability and generalization due to reliance on a single dataset. This single dataset approach hinders the effectiveness of the models when applied to new and unseen datasets. This paper addresses the issue using multiple-domain learning, creating a generalized DL framework for IIoT traffic classification. It combines three datasets: Edge-IIoTSet, WUSTL-IIoT-2021, and X-IIoTID. The proposed framework has two stages. The first stage involves the fusion of different datasets into a common feature space. To achieve this, an autoencoder architecture is proposed to match the dimensionality of the datasets into a common feature space. Subsequently, a modified locally linear embedding is used as a manifold alignment to ensure statistical matching of multiple datasets such that the maximum mean discrepancy between multiple datasets is minimized to compare and combine datasets effectively. The second stage presents a hybrid DL model with a convolutional neural network (CNN) and gated recurrent unit (GRU) for IIoT traffic classification. Bayesian optimization fine-tunes hyperparameters. Experiments on combined, single, and cross-datasets show the superiority of the framework. The CNN-GRU model achieved 97.68% accuracy, 97.70% recall, 97.67% precision, and 97.68% F1-score for binary classification on the combined dataset. Transfer learning improved accuracy from 50.95% to 97.80% and F1-score from 48.52% to 97.79% when trained on X-IIoTID and tested on Edge-IIoTSet. Multi-classification accuracy and weighted averages of recall, precision, and F1-score were approximately 98.85%.


A methodological and theoretical framework for implementing explainable artificial intelligence (XAI) in business applications

Abstract: Artificial Intelligence (AI) is becoming fundamental in almost all activity sectors in our society. However, most of the modern AI techniques (e.g., Machine Learning – ML) have a black box nature, which hinder their adoption by practitioners in many application fields. This issue raises a recent emergence of a new research area in AI called Explainable artificial intelligence (XAI), aiming at providing AI-based decision-making processes and outcomes to be easily understood, interpreted, and justified by humans. Since 2018, there has been an exponential growth of research studies on XAI, which has justified some review studies. However, these reviews currently focus on proposing taxonomies of XAI methods. Yet, XAI is by nature a highly applicative research field, and beyond XAI methods, it is also very important to investigate how XAI is concretely used in industries, and consequently derive the best practices to follow for better implementations and adoptions. There is a lack of studies on this latter point. To fill this research gap, we first propose a holistic review of business applications of XAI, by following the Theory, Context, Characteristics, and Methodology (TCCM) protocol. Based on the findings of this review, we secondly propose a methodological and theoretical framework in six steps that can be followed by all practitioners or stakeholders for improving the implementation and adoption of XAI in their business applications. We particularly highlight the need to rely on domain field and analytical theories to explain the whole analytical process, from the relevance of the business question to the robustness checking and the validation of explanations provided by XAI methods. Finally, we propose seven important future research avenues.


Age, gender and handedness prediction using handwritten text: A comprehensive survey

Abstract: Handwriting recognition is widely acknowledged as one of the most challenging tasks in pattern recognition and machine learning. This study explores the intriguing field of age, gender and handedness prediction through handwriting analysis by offering valuable insights into its potential applications in forensics, psychology and education. A comprehensive survey has been conducted encompassing an introduction, motivational aspects, background information, sources of data, analysis schemes, survey protocols, reported findings and methodologies used for predicting age, gender and handedness in both Indic and non-Indic scripts. The investigation highlights research gaps and concludes with an in-depth analysis of unresolved issues accompanied by a roadmap for future advancements in this domain. The survey focuses on a systematic examination of eight Indic scripts (Assamese, Bangla, Devanagari, Gurumukhi, Kannada, Malayalam, Tamil and Telugu) and six non-Indic scripts (Arabic, Chinese, Japanese, Persian, Roman and Thai) and it can be concluded that non-Indic scripts attain more accuracy in comparison with Indic scripts. Furthermore, the study mostly focuses on providing a catalog of publicly accessible online datasets featuring diverse handwriting samples from various scripting languages by giving a roadmap for further research in this area.


Emojifying chatbot interactions: An exploration of emoji utilization in human-chatbot communications

Abstract: The prevalence of chatbots in human–computer communication has significantly increased. Emojis, as a form of emotional disclosure, have gained significant attention for their potential to boost chatbot service satisfaction. However, how and when emoji usage can increase satisfaction toward chatbots is not fully examined. This paper aims to fill this gap and contribute to the rapidly evolving field of human-chatbot communication research. Through three experiments, this paper investigates and explores the role of emojis in enhancing chatbot interactions. The results reveal that emojis heighten chatbot's perceived warmth but do not necessarily augment their competence. This warmth promoting effect leads to boosted service satisfaction and is more apparent when chatbots serve hedonic purposes and are pre-programmed rather than highly autonomous. However, the warmth upshot of emojis is not as potent for chatbots as it is for humans. While this study unravels the intricate pathway of how emojis augment service satisfaction, it also extends the dialogue of the Stereotype Content Model (SCM) and propels the new wave of the Computers Are Social Actors (CASA) paradigm. Thus, this research lays down pathways for further studies in understanding the role of emotionally simulated interactions in automated technologies.


A partition-based problem transformation algorithm for classifying imbalanced multi-label data

Abstract: Multi-label learning has garnered much research interest due to its wide range of real-world applications. Many multi-label learning methods have been proposed; however, few have addressed the class imbalance problem existing in multi-label data. Even though some studies have taken this issue into account, most of them have ignored the label correlations or only considered random correlations between them. In this study, we propose a novel partition-based imbalanced multi-label learning algorithm, named Multi-label Learning based on Hierarchical Clustering (MLHC), to tackle this problem. MLHC first carries out hierarchical clustering on the original label space to divide it into several disconnected subspaces, each of which contains several labels that are strongly correlated with each other. Then, for each label subspace, we use the problem transformation strategy to convert it into a multi-class problem by binary coding. Any multi-class imbalance learning algorithm can be applied to the transformed multi-class data. Finally, the classification results will be decoded to retrieve the corresponding label subspace, and all label subspace results are combined to show the predicted label vector in the original label space. We conducted experiments not only on thirteen benchmark multi-label datasets but also carried out them on XJTU-SY which is a multi-label engineering application dataset, and the results indicated that our proposed MLHC learning algorithm outperforms several state-of-the-art class imbalance multi-label learning algorithms, demonstrating the effectiveness and necessity of discovering label correlations and transforming the original imbalanced multi-label learning problem into multiple strongly correlated multi-class imbalanced learning problems.


Arithmetic Average Density Fusion-Part III: Heterogeneous Unlabeled and Labeled RFS Filter Fusion

Abstract: This paper proposes a heterogenous density fusion approach to scalable multisensor multitarget tracking where the inter-connected sensors run different types of random finite set (RFS) filters according to their respective capacity and need.These diverse RFS filters result in heterogenous multitarget densities that are to be fused with each other in a proper means for more robust and accurate detection and localization of the targets.Our approach is based on Gaussian mixture implementations where the local Gaussian components (L-GCs) are revised for PHD consensus, i.e., the corresponding unlabeled probability hypothesis densities (PHDs) of each filter best fit their average regardless of the specific type of the local densities.To this end, a computationally efficient, coordinate descent approach is proposed which only revises the weights of the L-GCs, keeping the other parameters unchanged.In particular, the PHD filter, the unlabeled and labeled multi-Bernoulli (MB/LMB) filters are considered.Simulations have demonstrated the effectiveness of the proposed approach for both homogeneous and heterogenous fusion of the PHD-MB-LMB filters in different configurations.


Manipulating hidden-Markov-model inferences by corrupting batch data

Abstract: Time-series models typically assume untainted and legitimate streams of data. However, a self-interested adversary may have incentive to corrupt this data, thereby altering a decision maker's inference. Within the broader field of adversarial machine learning, this research provides a novel, probabilistic perspective toward the manipulation of hidden Markov model inferences via corrupted data. In particular, we provision a suite of corruption problems for filtering, smoothing, and decoding inferences leveraging an adversarial risk analysis approach. Multiple stochastic programming models are set forth that incorporate realistic uncertainties and varied attacker objectives. Three general solution methods are developed by alternatively viewing estimation from frequentist and Bayesian perspectives. The efficacy of each method is illustrated via extensive, empirical testing. The developed methods are characterized by their solution quality and computational effort, resulting in a stratification of techniques across varying problem-instance architectures. This research highlights the weaknesses of hidden Markov models under adversarial activity, thereby motivating the need for robustification techniques to ensure their security.


MixCam-attack: Boosting the transferability of adversarial examples with targeted data augmentation

Abstract: Many black-box adversarial attack algorithms perform attacks on machine learning models based on the transferability of adversarial examples, and the input transformation-based attack is one of the most effective methods. However, existing input transformation methods ignore that each pixel contributes differently to the output of the model, and the focus regions of different models on the same images are similar. Therefore, this paper proposes a targeted data augmentation-based adversarial attack algorithm named MixCam, which augments the input data based on the contribution of each pixel to the prediction result. This is done to enhance the transferability of the crafted adversarial example from the perspective of shifting the regions to which the model pays most of its attention. In addition, this paper proposes further boosting the transferability of the crafted adversarial examples by fusing the class activation maps of multiple models for the input image. Furthermore, the MixCam can integrate other input transformation methods to further boost the transferability of crafted adversarial examples. Extensive experiments on ImageNet demonstrate that MixCam outperforms other state-of-the-art methods in black-box attacks against considered adversarially trained models, with an average increase of 11.7% and 10.7% in attack success rates for single and ensemble attack settings, respectively.


Progressive network based on detail scaling and texture extraction: A more general framework for image deraining

Abstract: Many feature extraction components have been proposed for image deraining tasks, aiming to improve feature learning. However, few models have addressed the integration of multi-scale features from derain images. The fusion of multiple features at different scales in one model has the potential to significantly enhance the authenticity and detail of rainy images restoration. This study introduces a migratable multi-scale feature blending model, which is a progressive learning model based on detail dilation and texture extraction. First, the degraded image is sent to the detail dilation module, which is designed to increase the detailed outline and obtain the coarse image features. Second, the extracted feature maps are sent to the multi-scale feature extraction (MFE) module and the multi-scale hybrid strategy (MHS) module for improved texture restoration. Third, the simple convolution modules are replaced by an optimized transformer model to more efficiently extract contextual features and multi-scale information in images. Finally, a progressive learning strategy is employed to incrementally restore the degraded images. Empirical results show that our proposed module for progressive restoration achieves near state-of-the-art performance in several rain removal tasks. In particular, our model exhibits better rain removal realism compared to state-of-the-art models. The source code is available at https://github.com/JackAILab/DTPNet.


Collaborative Planning for Catching and Transporting Objects in Unstructured Environments

Abstract: Multi-robot teams have attracted attention from industry and academia for their ability to perform collaborative tasks in unstructured environments, such as wilderness rescue and collaborative transportation. In this paper, we propose a trajectory planning method for a non-holonomic robotic team with collaboration in unstructured environments. For the adaptive state collaboration of a robot team to catch and transport targets to be rescued using a net, we model the process of catching the falling target with a net in a continuous and differentiable form. This enables the robot team to fully exploit the kinematic potential, thereby adaptively catching the target in an appropriate state. Furthermore, the size safety and topological safety of the net, resulting from the collaborative support of the robots, are guaranteed through geometric constraints. We integrate our algorithm on a car-like robot team and test it in simulations and real-world experiments to validate our performance. Our method is compared to state-of-the-art multi-vehicle trajectory planning methods, demonstrating significant performance in efficiency and trajectory quality.