Reviewerly/exHarmony huggingface dataset:
AdaptiveStereo: Depth estimation from adaptive structured light

Recovering depth with active stereo methods has become an important approach to three-dimensional perception. However, when the properties of the measurement scene change, projecting predesigned structured light can cause improper exposure and defocus blur on some object surfaces. To address these problems, we propose an active stereo depth perception method called AdaptiveStereo, based on adaptive structured light (ASL). The core idea is to use prior depth information to generate optimal structured light for different measurement scenes, ensuring even illumination of the object's surface and thereby improving depth perception results. To this end, firstly, a novel ASL encoding scheme based on speckle pattern is designed. Then, we construct an adaptive structured light generation model (ASL-GM) to automatically generate the optimal structured light. Finally, depth perception is realized by using the active stereo matching network with adaptive guidance (AGS-Net). Compared with mainstream depth perception methods, the evaluation results show that our method has higher accuracy, and can obtain better depth perception results.
Optical 3D information encryption and rapid decryption via interferenceless coded aperture correlation holography

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

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

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

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

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

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

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

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

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

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

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

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

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

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.