Reviewerly/exHarmony huggingface dataset:
Partial multi-label learning via robust feature selection and relevance fusion optimization

Partial Multi-Label Learning (PML) is a more practical learning paradigm, in which the labeling information is ambiguated. Most existing PML algorithms rely on assumptions to resolve ambiguity. However, these assumptions do not account for the origin of the noise labeling and therefore fail to address the impact of noise on the learner's performance at the root. In this paper, we will propose a PML method jointly granular ball-based robust feature selection and relevance fusion optimization (PML-GR). Specifically, in the first stage, we construct a granular ball to compute the core-set with weights and then design a feature importance evaluation function to assign weights to each feature in the core-set, resulting in a ranking of feature importance for the PML learner; in the second stage, based on the selected features, a fusion-based objective function is constructed to compute the label confidence by taking into account the joint effect of the global sample similarity and local label relevance. Finally, a multi-label prediction model is learned by fitting the multi-output regressor to the label confidence. The experimental results demonstrate that the proposed method achieves competitive generalization performance by effective feature selection and relevance fusion optimization, which can focus more on discriminative features and minimize the effect of noisy labels during training.
Multi-Semantic Decoding of Visual Perception with Graph Neural Networks

Constructing computational decoding models to account for the cortical representation of semantic information plays a crucial role in understanding visual perception. The human visual system processes interactive relationships among different objects when perceiving the semantic contents of natural visions. However, the existing semantic decoding models commonly regard categories as completely separate and independent visually and semantically and rarely consider the relationships from prior information. In this work, a novel semantic graph learning model was proposed to decode multiple semantic categories of perceived natural images from brain activity. The proposed model was validated on the functional magnetic resonance imaging data collected from five normal subjects while viewing 2750 natural images comprising 52 semantic categories. The results showed that the Graph Neural Network-based decoding model achieved higher accuracies than other deep neural network models. Moreover, the co-occurrence probability among semantic categories showed a significant correlation with the decoding accuracy. Additionally, the results suggested that semantic content organized in a hierarchical way with higher visual areas was more closely related to the internal visual experience. Together, this study provides a superior computational framework for multi-semantic decoding that supports the visual integration mechanism of semantic processing.
Identifiability and Consistent Estimation for Gaussian Chain Graph Models

The chain graph model admits both undirected and directed edges in one graph, where symmetric conditional dependencies are encoded via undirected edges and asymmetric causal relations are encoded via directed edges. Though frequently encountered in practice, the chain graph model has been largely under investigated in the literature, possibly due to the lack of identifiability conditions between undirected and directed edges. In this article, we first establish a set of novel identifiability conditions for the Gaussian chain graph model, exploiting a low rank plus sparse decomposition of the precision matrix. Further, an efficient learning algorithm is built upon the identifiability conditions to fully recover the chain graph structure. Theoretical analysis on the proposed method is conducted, assuring its asymptotic consistency in recovering the exact chain graph structure. The advantage of the proposed method is also supported by numerical experiments on both simulated examples and a real application on the Standard & Poor 500 index data. Supplementary materials for this article are available online.
Application of Bayesian method for mining-induced tremors: A case study of the Xinjulong coal mine in China

Mining-induced tremor is a dynamic phenomenon occurring during the fracturing or slipping of rock and coal mass in coal mines, and the tremor mechanism is one of the key problems in the field of rockburst. The Bayesian inversion method can more accurately analyse the source rupture type under the premise of considering waveform data and model errors. First, we added different noise levels to the waveform and determined filtering parameters based on the spectrum. The rationality of the Bayesian inversion was verified by observing the focal mechanism solution. Finally, a total of fourteen mining-induced tremors were selected, and the parameters of the moment tensor (MT), type of rupture, and fault plane solutions were obtained. The results demonstrate that (1) the tremor events in the roof are mainly tensile/shear failure, and compression failure mainly occurs in the coal seam. (2) Events on the floor are closely related to gob and mining activities. (3) The rupture form of the events near the tectonic region is more complex, and the fault plane solutions (strike, dip, and slip) were obtained by MT conversion. (4) The relation between rockbursts and mining-induced tremors is discussed by analysing events 1# and 14# that occurred in the Xinjulong coal mine. These research findings can provide a reference for the analysis of source parameters and rupture forms of mining-induced tremors.
Versatile correlation learning for size-robust generalized counting: A new perspective

Generalized counting has recently emerged to count novel-class objects within a query image, leveraging limited exemplars. Although methods based on exemplar-query pairs matching have made impressive progress, they typically rely on a single correlation representation, regardless of the varying sizes of objects, which limits more accurate counting. In this paper, we introduce a novel and conceptually straightforward perspective to guide the design of our correlation mechanism that enhances the effectiveness of counting size-diversity objects. Our new perspective encompasses three key aspects: (1) Small objects typically exhibit features concentrated within limited spatial regions, underscoring the importance of an effective channel-wise correlation mechanism for small object counting. (2) Large objects tend to possess rich spatial semantics, making an effective spatial-wise correlation mechanism crucial for large object counting. (3) Integrating both channel-wise and spatial-wise correlation mechanisms holds the potential to enhance counting accuracy across different object sizes. Building upon the above perspective, firstly, we propose a simple yet effective Dual-level Channel-wise Correlation (DCC) module that utilizes kernel-wise correlation and distinct correlation to encode global-to-local channel-wise relationships, enhancing small objects counting accuracy. Secondly, we develop a 4D-convolution-based Spatial-aware Correlation (4DSC) module to extract local-to-local spatial correlation in 4D space, promoting large objects counting accuracy. Finally, we combine the proposed DCC and 4DSC to realize our Versatile Correlation Module (VCM) to simultaneously process both small and large objects, providing adaptability to object size diversity. Extensive experiments on the FSC-147 dataset and CARPK dataset demonstrate the effectiveness of the proposed methods and the superior performance of our counting model.
Optimal algorithm for color medical encryption and compression images based on DNA coding and a hyperchaotic system in the moments

Currently, visual data security plays a significant role in various fields, especially in medical imaging. Addressing the challenges associated with limited key space and vulnerability to different types of attacks within current encryption schemes, this work proposes an optimal compression-encryption scheme for large medical images that incorporates elements of Archimedes' optimization algorithm, discrete orthogonal Hahn moments, chaotic systems, and DNA coding. The primary aim of this study is to develop an optimal and exceptionally resilient compression-encryption scheme capable of countering various attack types effectively. This approach is structured into three principal phases: a compression phase harnessing the efficiencies of Hahn's discrete orthogonal moments (HMs) in signal and image representation, coupled with the Archimedes optimization algorithm (AO) to ensure optimal tuning of polynomial parameters (a, b) for superior image reconstruction quality. The encryption phase is performed on the compressed image, using hyperchaotic memristive 4-D (HCM-4D), adapted logistics map (ALM) and DNA coding. Initially, the adapted logistics map is responsible for generating random sequences linked to the compressed image. Subsequently, chaotic sequences originating from the hyperchaotic 4-D memristive system govern both random sequences and DNA processes. The optimization phase, facilitated by the AO algorithm, focuses on minimizing the value of the objective function (correlation) on the compressed and encrypted images. Ultimately, the image with the lowest correlation value is designated as the optimal compressed-encrypted representation. The simulation results clearly illustrate the resilience of the AO algorithm when juxtaposed with other optimization algorithms, especially with respect to convergence speed and computational efficiency. On the other hand, the proposed compression approach demonstrated exceptional efficiency in compressing medical images, offering us the possibility to achieve impressive compression ratio (CR) as well as exceptional quality in decompressed images, evident thanks to high PSNR values. In addition, security analyze demonstrate that the proposed compression-encryption benefits from a larger key space and superior resistance against different types of attacks. Furthermore, our approach was subjected to a comparative analysis alongside various encryption method. These comparisons demonstrate that our encryption algorithm surpasses others in terms of both security and effectiveness.
Practical application of interactive AI technology based on visual analysis in professional system of physical education in universities

Currently, physical education teaching in universities tends to adopt the traditional model of one teacher for multiple students, which has high requirements for teachers and is difficult to consider students' strengths. On this basis, an interactive system has been established, including three modules: behavior information, user data collection, and behavior evaluation. Taking the 400 m running physical education teaching as an example, Kinectv2 was used to collect students' movements and contours while running, and ORB (Oriented FAST and Rotated BRIEF) feature extraction algorithm was used to extract students' movement features. After importing the data into the interactive system, students and teachers could view it in the system and provide guidance based on the students’ actions. This article took 10 students as examples to test their performance changes in the 400 m running before and after systematic training. The results showed that the evaluation score after receiving systematic instruction increased by 6–7 s compared to the score without receiving instruction, with a significant change. This indicated that the interactive AI (artificial intelligence) system constructed in this article can play a significant role in sports teaching of 400 m running.
Texture-aware and color-consistent learning for underwater image enhancement

Texture and color are pivotal factors for evaluating the quality of underwater image enhancement. However, current methods for enhancing underwater images still exhibit deficiencies in the restoration of texture and color. Diverging from previous approaches, we conduct heuristic modeling specifically targeting color and texture, culminating in the proposal of a texture-aware and color-consistent network (TACC-Net). Specifically, it first obtains preliminary enhanced images of more global features to facilitate feature decoupling. Then, we designed a color feature decoupling (CFD) sub-module to decouple the preliminary enhanced images into color-consistent color histograms to generate textured gray images. Finally, we designed a color space adjustment embedding (CSAE) sub-module, which is used to evaluate the similarity between features when embedding color information, and adjust the embedding of color information on textures. The experimental results indicate that our method enhances images and exhibits strong competitiveness in improving visual quality.
MCFP: A multi-target 3D perception method with weak dependence on 2D detectors

3D perception for multi-target in complex traffic environments plays an important role in autonomous driving. Nowadays, some mainstream models use 2D detectors to provide auxiliary information for 3D perception. But it is a challenge that the accuracy of 2D detectors has a sensitive impact on the final results. In this paper, we propose MCFP, a multi-target 3D perception model with weak dependence on 2D detectors, to improve the 3D perception performance. We use the point cloud expansion as the main idea to provide more information about instance objects for model learning. Firstly, the centroid awareness strategy is integrated into the front-end part for downsampling and feature extraction to reduce the loss of object-related information. Secondly, the ball query operation with adaptive radius is designed to obtain more complete object information. Experiments show that our model outperforms baseline models in normal environments, and achieves detection performances close to those of SOTA models in extreme environments, which implies that our model has a strong ability to obtain semantic information and hence enhancing the perception ability. The code and data will be made available at https://github.com/MrSakasky/MCFP.
Guided learning strategy: A novel update mechanism for metaheuristic algorithms design and improvement

Meta-heuristic algorithms (MH) are naturally inspired global optimization algorithms. They are often relatively simple and can solve problems in a short period of time, with certain benefits. However, as the problem becomes more complex, the solution that the algorithm can obtain is often not the optimal solution to the problem, which limits its application scenarios. Therefore, improving the performance and solving accuracy of existing algorithms is crucial for expanding their application ability. In traditional optimization algorithms, there are often two concepts, namely exploration and exploitation. Exploration refers to a wide range of discrete search, used to avoid falling into local optima, and exploitation refers to a small range of focused exploration, used to improve algorithm accuracy. How to balance exploration and exploitation is the key to enhancing algorithm performance and problem adaptability. This paper proposes a brand new strategy named Guided Learning Strategy (GLS) to solve above problem. GLS obtains the dispersion degree of the population by calculating the standard deviation of the historical locations of individuals in recent generations, and infers what guidance the algorithm currently needs. When the algorithm is biased towards exploration, it will guide the algorithm to exploit. Otherwise, it will guide the algorithm to explore. It is precisely because this strategy can identify the current needs of the algorithm and provide assistance that it can improve the performance of most algorithms. This article improves three types of algorithms based on evolution (LSHADE_SPACMA), Stochastic Fractal Search (SFS), and Marine Predators Algorithm (MPA) with better performance, and tests their performance on 57 constrained engineering problems and CEC2020. The effectiveness of this strategy has been confirmed and proved for optimization problem. The source codes of the proposed GLS (GLS_MPA) can be accessed by https://github.com/luchenghao2022/Guided-Learning-Strategy
Steered coherence of a central two-qubit system coupled to an XY spin chain

We investigate the average steered coherence (ASC) of a central two-qubit system coupled to an XY spin-chain environment. It is shown that there exists subspace in which the initial states are immune to this environment. Outside of this subspace, the decay of the ASC can be significantly suppressed by tuning the interaction of the central qubits and its anisotropy in the z axis, as well as the anisotropy of the spin-spin interaction in the xy plane of the environment and the strength of the transverse magnetic field. For the initial Bell states and their unitary equivalent classes, one can also identify system and environmental parameters for which the ASC oscillates near its maximum even in the long-time interval.
Nonlocal correlations dynamics of two-qubit Heisenberg XYZ states with influence of intrinsic decoherence and y-component of Dzyaloshinskii-Moriya interaction

In this paper, we explore nonlocal correlation and Bell non-locality in a two-qubit Heisenberg XYZ model by using Bell-CHSH inequality and uncertainty induced nonlocality (UIN) as well as log-negativity. We consider the effects of spin-qubits XYZ-Heisenberg couplings, the y-component of DM interaction, and intrinsic decoherence. Our results show that spin-qubits Heisenberg couplings have a strong ability to create two-qubit nonlocal correlations, enhanced by combining with DM interactions. The generated Bell-CHSH nonlocality and entanglement conform to the hierarchy principle. The UIN correlation is more resistant to intrinsic decoherence compared to Bell-CHSH inequality and log-negativity. Intrinsic decoherence causes the disappearance of Bell-CHSH non-locality and log-negativity entanglement, while leading to the appearance of partial stationary spin-qubits UIN correlation.
Block-matching and 3D filtering-based denoising of acoustic images obtained through point contact excitation and detection method

A novel experimental method based on the Coulomb coupling technique is used for visualizing the interaction of ultrasonic waves in PZT (Lead Zirconate Titanate - (Pb[ZrxTi1-x]O3 (0 ≤ x ≤ 1)) ceramic materials. The method of excitation and detection through a point contact produces representations of propagating wave vectors in the time domain as a series of images. In the acoustic image, several types of noises from different types of sources corrupt the signal as well as the image. As a result, the resolution and quality of the images are reduced. Block matching and 3D filtering (BM3D) algorithm is implemented for the reduction of noises from ultrasonic signals that are in the form of 2D images. Here, we also implemented a custom-designed hybrid filter and conventional total variation (TV-L1) filter on the noisy images to compare the final denoised outputs from the BM3D filter. Comparison metrics like PSNR (peak signal-to-noise ratio) and SSIM (structural similarity index measure) are used for quantitative analysis of those denoised ultrasonic images with respect to their ground truth or almost noise-free counterparts. Based upon those parameter values, the BM3D filter demonstrated the best results for the denoising of the acoustic images acquired from the sample PZT material. This paper discusses and explains the results and proves the successful application of the BM3D algorithm on acoustic image data.
Neural radiance fields for multi-scale constraint-free 3D reconstruction and rendering in orchard scenes

Efficiently, accurately, and realistically reconstructing large-scale 3D orchard scenes in a virtual world is an immensely challenging task. This complexity stems from the intricate and expansive of real orchard scenes. Traditional 3D reconstruction and rendering methods have encountered limitations in terms of modeling efficiency and computational costs, hindering the ability to provide users with immersive experiences. In response to these challenges, this study introduces a strategy for 3D scene reconstruction and rendering grounded in implicit neural representation: the NeRF-Ag model. Building upon the baseline NeRF, this model integrates a multi-resolution latent feature encoding technique, notably heightening training efficiency and elevating modeling precision. Furthermore, by means of environmental factor embedding, the model's robustness and practical applicability are further enhanced. The experimental outcomes illustrate that NeRF-Ag attains photo-realistic rendering outcomes across small, medium, and large scales. Moreover, it surpasses NeRF concerning the evaluation metrics of PSNR, SSIM, and LPIPS. Notably, the training speed of NeRF-Ag is roughly 39 times faster than NeRF. In 3D reconstruction tasks, NeRF-Ag showcases enhanced texture detail representation and higher modeling accuracy compared to the COLMAP-based 3D reconstruction method. Additionally, this study accomplishes free-viewpoint rendering of 3D scenes employing NeRF-Ag and provides evidence substantiating the connection between the quantity of training images and the precision of 3D rendering. The conclusions of this study will contribute to supporting and referencing the implementation of immersive visual interactive features within agricultural digital twin systems.
Privacy-preserving recommendation system based on social relationships

In the era of internet-based data, recommendation systems are crucial for helping users access personalized content and facilitating business promotion and sales. Recommendation systems based on social relationships have gained popularity due to their ability to use social influence and enhance the impact and credibility of recommendations. However, building a successful recommendation system faces the challenge of protecting user data privacy, as it requires a significant amount of sensitive user data. For this challenge, we propose a privacy-preserving recommendation system based on social relationships in the dual-cloud model. To achieve the secure computation of this system, we also design and improve some underlying secure protocols such as secure equality protocol and secure comparison protocol. Our underlying protocol has the advantages of high efficiency and provable security. As a result, our recommendation system can provide excellent recommendations while ensuring privacy protection. Experiments show that our system can perform secure recommendation calculations on 128-bit data with semi-honest security in approximately 5.5 s.