Researchers have been developing AI-based mod-eling assistants to help software modelers efficiently construct models. However, there are a number of issues with the current modeling assistants, including poor recommendation accuracy, limited support for diverse model types, and scalability issues. These problems stem from their attempt to utilize a single learning module to comprehensively extract multi-modal features from software models, such as semantic meanings of terms and model structures. Our key insight is that the utilization of modular deep learning architecture, allowing these features to be learned separately, by specifically tailored neural modules, and then be fused into one single vector. The fused vectors produced by these two learning stages significantly enhance recommendation accuracy. To adapt model formats for modular learning's input, we introduce a novel model representation, labeled graph, which offers two advantages: 1) able to segregate diverse features types, enabling modular learning; 2) adaptable to various types of software models. Building on these insights, we developed ReCo, a learning-based recommendation system for suggesting connections in models. ReCo employs several neural modules for extracting both the semantics of elements and topology of models, then computing the scores of potential connections. Our experimental result shows that for model types that are already supported, ReCo achieves more than 2X improvement in success rate and FRanks, compared to the state-of-the-art modeling assistants. Furthermore, ReCo also extends its support to previously unsupported models like UML usecase and activity models.

This article introduces a novel paradigm for hyperspectral image (HSI) denoising, which is termed pan-denoising. In a given scene, panchromatic (PAN) images capture similar structures and textures to HSIs but with less noise. This enables the utilization of PAN images to guide the HSI denoising process. Consequently, pan-denoising, which incorporates an additional prior, has the potential to uncover underlying structures and details beyond the internal information modeling of traditional HSI denoising methods. However, the proper modeling of this additional prior poses a significant challenge. To alleviate this issue, the article proposes a novel regularization term, panchromatic weighted representation coefficient total variation (PWRCTV). It employs the gradient maps of PAN images to automatically assign different weights of total variation (TV) regularization for each pixel, resulting in larger weights for smooth areas and smaller weights for edges. This regularization forms the basis of a pan-denoising model, which is solved using the alternating direction method of multipliers (ADMM). Extensive experiments on synthetic and real-world datasets demonstrate that PWRCTV outperforms several state-of-the-art methods in terms of metrics and visual quality. Furthermore, an HSI classification experiment confirms that PWRCTV, as a preprocessing method, can enhance the performance of downstream classification tasks. The code and data are available athttps://github.com/shuangxu96/PWRCTV.

Digital security in modern systems very often uses biometric, and increasingly, new implementations appear. Such applications can be found everywhere, even when picking up the package from courier, we certify its receipt through our signature on the tablet. However, verification of this form is not one of the simplest elements in information processing systems. Given the different sizes, angles, or writing conditions that may affect its stability, new methods to evaluate signatures are constantly needed. In this article, we propose the use of spline interpolation and two types of artificial neural networks to verify the identity of a person based on selected local and global features extracted from the image of a signature. Global features are extracted concerning interpolation and graphic processing methods, while local features are verified using convolutional neural networks. Both sets of features are used in the identity verification process. The article presents the model of the operation together with experiments, taking into account various parameters of the proposed extraction. We have reached an accuracy of 87.7% on the SVC2004 database.

Digital twin (DT) constructs virtual counterparts of physical devices to monitor and optimize their life cycle processes. With the emergence of industry 4.0, Industrial Internet of Things (IIoT) has became the backbone of the DT by providing a fundamental way to transform physical devices to their virtual counterparts. With the deployment of IIoT, built-in sensors enable real-time collection of critical DT data involving various physical parameters associated with devices during their life cycle. However, traditional data sharing services rely on a centralized infrastructure, which inevitably brings severe security threats to share large volume of sensitive DT data derived from numerous sensors. To address the above issue, this paper presents a blockchain based Multi-users Oblivious Data Sharing scheme (MODS) for the digital twin system in the context of IIoT. MODS supports a broad range of security properties including confidentiality, obliviousness, and access control for the DT data stored on the blockchain. MODS adopts a hybrid design approach by combing trusted hardware and cryptography to achieve well balances between security and efficiency. To demonstrate the design advantages of MODS, we explore the design space of a multi-users oblivious data sharing scheme by using pure cryptographic approach, which incurs several design tradeoffs that must be addressed. We show that MODS performs well in these tradeoffs. A comprehensive evaluation has been conducted to demonstrate that MODS is practical to support secure data sharing via blockchain for IIoT.

Based on sensor networks within a dynamic and real-time environment, a novel parking-lot navigation method is proposed based on diffusion equation and Poisson equation to achieve convenient and efficient navigation process with edge computing mind to aid information query and navigation. From the perspective of theoretical proof, is presented parallel method mainly for ordinary differential equations (ODEs) by partitioning the time domain. In this article, our new method is combined by parallelization for linear heat equations. The model problem is decoupled into several sub-problems in space-time sub-domains. We prove the super-linear convergence when the time interval is bounded. Numerical experiments testify our theoretical analysis. Simultaneously, the proposed method holds the lower constraint condition and some improper navigation routes can be updated. Mathematical analysis and simulations show that the method is accurately and efficiently enabled to solve typical sensor network configuration information navigation problem.

In general, hyperspectral images (HSIs) are degraded by a mixture of complicated noise (i.e., mixture of Gaussian and sparse noise), and how to precisely model HSI noise plays a vital role in the task of HSI denoising. The most popular choices for encoding the noise distribution are Gaussian, Laplacian, and the mixture of Gaussians, but they are always incompatible with real-world HSI noise. By investigating histograms of the error map, we first explore that asymmetry is a typical and general feature of HSI noise. Inspired by this discovery, we find that a bandwise asymmetric Laplacian (AL) distribution can be finely used to model this type of noise. Equipped with the low-rank matrix factorization (LRMF) framework, we formulate a novel model by the maximum likelihood estimation (MLE) principle, which can be efficiently solved using the iterative optimization algorithm. Extensive experimental results on synthetic and real datasets demonstrate that the proposed model outperforms other counterparts. It is also found that scale and asymmetry parameters in the AL distribution can well interpret the pattern of real-world HSI noise.

In this article, we propose malfunction classifications for trucks, a novel idea for smart fleet management systems. In the proposed cooperative cooperative intelligent transportation (C-ITS), the developed neural network work with information from truck fleets to select the trucks that need a service. From the results returned from the deep neural network classifier, the applied heuristic algorithm uses the classification outputs to select the most important results. The proposed process is multithreaded; thus, the composed system gains additional efficiency. The implemented deep learning model achieved an accuracy above 98%, and an above 95% recall. The developed solution was tested on the Scania Truck data collection. The research results show the importance of the advances and validate our concept for potential further development.

The fusion of Internet of Things (IoTs) and computational intelligence makes it possible to increase energetic efficiency of our homes. Connected devices can be optimally adjusted to the needs of a family. In this article, we present our developed IoT convection installation for a small house with the developed remote platform control system. The control module is gartering readings from sensors and information from users about conditions in the house and, by the use of computational intelligence, optimizes parameters to adjust the developed IoT convection system for better comfort of a family. We have done a full convection installation, both in practical and theoretical models, together with remote control system and the proposed security model. Optimization results show increased comfort of use with lower changes in the temperature inside. The system after optimization shows significant improvement in lower changes of the temperature and lower consumption.

A new speckle suppression algorithm is proposed for high-resolution synthetic aperture radar (SAR) images. It is based on the nonlocal means (NLM) filter and the modified Aubert and Aujol (AA) model. This method takes the nonlocal Dirichlet function as a linear regularization item, which constructs the weight by measuring the similarity of images. Then, a new despeckling model is introduced by combining the regularization item and the data item of the AA model, and an iterative algorithm is proposed to solve the new model. The experiments show that, compared with the AA model, the proposed model has more effective performance in suppressing speckle; namely, ENL and DCV measures are 21.75% and 4.5% higher, respectively, than for NLM. Moreover, it also has better performance in keeping the edge information.