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By extracting molecular interactions identified by experiments, gene regulatory networks or gene circuits have documented in a large number of knowledge-based repositories. They provide systematic information and guidance of the functional connections between regulators, e.g., transcription factor proteins and miRNAs, and target genes. Network activity is defined as the degree of consistency between a regulatory network architecture and a specific cellular context of gene expression and can also be measured as a score of statistical significance. The gene network activities are closely related to the dynamics of cell states. To evaluate the activity of regulatory events in the form of network, we propose a network activity evaluation (NAE) framework by measuring the consistency between network architecture and gene expression data across specific states based on mathematical programming. NAE firstly employs the dynamic Bayesian network model to formulate the network structure with time series profiling data. For the constraints of prior knowledge about gene regulatory network, NAE introduces an interpretable general loss function with regularization penalties to calculate the degree of consistency between gene network and gene expression data. Moreover, we design a fast and convergent alternating direction method of multipliers algorithm to optimize the regularized constraint programming. The efficiency and advantage of the NAE framework is deduced through numerous experiments and comparison studies. It reflects the possibility and potential of the match between network and data, thereby helping to reveal the network activity and to explain the dynamic responds underlying the network structure caused by changes in molecular environment of living cells. The code of NAE is freely available for academic use (https://github.com/zpliulab/NAE). [ABSTRACT FROM AUTHOR]

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Thanks to the powerful learning capability, deep neural networks (DNNs) have acquired broad applications in single image reflection removal. The DNN-based algorithms relax the constraints of specific priors and learn to generate visually pleasant background layers from massive training data. However, most of them employ a single network structure to recover both the semantic information and local details of the background, which may lead to obvious reflection residue or even failure. To mitigate this deficiency, in this work, we propose a Multi-stage Curvature-guided De-Reflection Network (MCDRNet), which combines multiple network architectures in a unified framework to progressively reconstruct the background layer and refine the fine-grained details. Our framework consists of three stages, where the encoder-decoders are exploited in the first two stages to recover the semantic components of background layers with lower scales and a variant ResNet is applied in the last stage to refine the background details with the original input resolution. In the first two stages, to introduce the structural guidance for the reflection removal, we cascade another decoder branch to restore the curvature map of the background. In addition, at the end of the first two stages, instead of directly passing the intermediate estimates to the next stage, we propose a Non-local Attention Module (NAM) to augment and transmit the features from decoders. Extensive experimental results on several public datasets demonstrate that the proposed MCDRNet outperforms the state-of-the-art methods quantitatively and generates visually better reflection removal results. The source code and pre-trained models are available at https://github.com/NamecantbeNULL/MCDRNet. [ABSTRACT FROM AUTHOR]

In next-generation network architecture, the Cybertwin drove the sixth generation of cellular networks sixth-generation (6G) to play an active role in many applications, such as healthcare and computer vision. Although the previous sixth-generation (5G) network provides the concept of edge cloud and core cloud, the internal communication mechanism has not been explained with a specific application. This article introduces a possible Cybertwin based multimodal network (beyond 5G) for electrocardiogram (ECG) patterns monitoring during daily activity. This network paradigm consists of a cloud-centric network and several Cybertwin communication ends. The Cybertwin nodes combine support locator/identifier identification, data caching, behavior logger, and communications assistant in the edge cloud. The application focuses on monitoring the ECG patterns during daily activity because few studies analyze them under different motions. We present a novel deep convolutional neural network based human activity recognition classifier to enhance identification accuracy. The healthcare monitoring values and potential clinical medicine are provided by the Cybertwin based network for ECG patterns observing. [ABSTRACT FROM AUTHOR]

Neural architecture search (NAS) provides an automatic solution in designing network architectures. Unfortunately, the direct search for complete task-dependent network architectures is laborious since training and evaluating complete neural architectures over a large search space are computationally prohibitive. Recently, one-shot NAS (OSNAS) has attracted great attention in the NAS community because it significantly speeds up the candidate architecture evaluation procedure through weight sharing. However, the full weight sharing training paradigm in OSNAS may result in strong interference across candidate architectures and mislead the architecture search. To alleviate the problem, we propose a partial weight sharing OSNAS framework that directly evolves complete neural network architectures. In particular, we suggest a novel node representation scheme that randomly activates a subset of nodes of the one-shot model in each generation to reduce the weight coupling in the one-shot model. During the evolutionary search, a tailored crossover operator randomly samples the nodes from two parent individuals or a single parent to construct new candidate architectures, thus effectively constraining the degree of weight sharing. Furthermore, we introduce a new mutation operator that replaces the chosen nodes of the one-shot model with randomly generated nodes to enhance the exploratory capability. Finally, we encode a set of pyramidal convolution operations in the search space, enabling the evolved neural networks to capture different levels of details in the images. The proposed method is examined and compared with 26 state-of-the-art algorithms on ten image classification tasks, including CIFAR series, CINIC10, ImageNet, and MedMNIST series. The experimental results demonstrate that the proposed method can computationally much more efficiently find neural architectures that achieve comparable classification accuracy to the state-of-the-art designs. [ABSTRACT FROM AUTHOR]

The requirements and the fl exible and reconfi gurable network architecture for the space-integrated-ground information network was proposed based on analyzed the 6G network vision and challenges faced by the space-integrated-ground information network.Based on the proposed network architecture, the intelligent network management and control architecture and the virtualization technology of the space-integrated-ground information network were proposed to realized the dynamic confi gurability of the network.

Aiming at the shortcomings of traditional power credit investigation platform such as insufficient stability and low test accuracy, a big data power credit investigation platform was designed.The online analytical method was used to analyze power big data, which was divided into four categories: user behavior, expense rules, user value and personal credit.Based on the modular structure, the data acquisition module, data analysis module and user interaction module were optimized respectively.The KNN and cross validation method were used to classify and process the power consumption data, and the regional power consumption law was obtained, so as to design and adjust the distribution scheme.Finally, the platform was compared with the traditional power credit investigation platform.The experimental results show that the stability and accuracy of the platform are improved, and the accuracy is as high as 98.9% during the test.

IEEE 802.11ah, or Wi-Fi HaLow, is a long-range Internet of Things (IoT) communication technology with promising performance claims. Being IP-based makes it an attractive prospect when interfacing with existing IP networks. Through real-world performance experiments, this study evaluates the network performance of Wi-Fi HaLow in terms of throughput, latency, and reliability against IEEE 802.11n (Wi-Fi n) and a competing IoT technology LoRa. These experiments are enabled through three proposed network evaluation architectures that facilitate remote control of the devices in a secure manner. The performance of Wi-Fi HaLow is then assessed against the network requirements of various smart grid applications. Wi-Fi HaLow offers promising performance when compared to rival technology LoRa. This study is the first to evaluate Wi-Fi HaLow in an authentic experimental way, providing performance data and insights that are not possible through simulation and modelling alone. This work provides the basis for further evaluation and implementation of this emerging technology.

This research contributes to the security design of an advanced smart drone swarm network based on a variant of the Blockchain Governance Game (BGG), which is the theoretical game model to predict the moments of security actions before attacks, and the Strategic Alliance for Blockchain Governance Game (SABGG), which is one of the BGG variants which has been adapted to construct the best strategies to take preliminary actions based on strategic alliance for protecting smart drones in a blockchain-based swarm network. Smart drones are artificial intelligence (AI)-enabled drones which are capable of being operated autonomously without having any command center. Analytically tractable solutions from the SABGG allow us to estimate the moments of taking preliminary actions by delivering the optimal accountability of drones for preventing attacks. This advanced secured swarm network within AI-enabled drones is designed by adapting the SABGG model. This research helps users to develop a new network-architecture-level security of a smart drone swarm which is based on a decentralized network. [ABSTRACT FROM AUTHOR]

Aerial base stations (ABSs) are expected to be important supplementary components for the 5G-and-beyond communication systems to achieve global Internet of Everything. To fully exploit the advantages of the ABSs seamless connection, in this paper, we propose a high altitude platform (HAP) and low altitude platforms (LAPs) cooperated network architecture. Specifically, the HAP can extend the network coverage and LAPs can act as relays to improve the transmission performance for hot spots. In the considered network, we investigate the sum rate maximization problem by optimizing the downlink and the uplink transmissions, respectively. In the downlink, we adopt the orthogonal frequency division multiple access technique and take the basic rate requirement into account. In addition, we optimize the height of LAPs and the spectrum allocation between the HAP and LAPs, and between the LAP and users. Then, we decouple the downlink sum rate maximation problem as three subproblems. An alternating optimization framework is designed to deal with these non-convex optimization problems, where the height and spectrum allocation are tackled in turn. In the uplink, we adopt the non-orthogonal multiple access technique, and consider the decoding threshold of the successive interference cancellation technique. Afterward, we optimize the power allocation of each user, and the spectrum allocation between users and the LAP, and between LAPs and the HAP. Finally, simulation results show the effects of spectrum allocation, height optimization and power allocation on network performance, which verify that the proposed scheme can achieve higher sum rate on both downlink and uplink in comparison with the current works. [ABSTRACT FROM AUTHOR]

Wireless connectivity is regarded as a fundamental need for our society. Between 2020 and 2030, it is forecast that the data traffic of the global internet protocol will increase by 55% each year, eventually reaching 5016 EB, with data rates scaling up to 1 Tb/s. Besides supporting very high data rates, future wireless networks are expected to offer several other heterogeneous services, which include sensing, localization, low-latency, and ultrareliable communications. Fifth-generation (5G) networks are, however, not designed to meet these requirements. As the demands and needs become more stringent, in fact, fundamental limitations arise, which are ultimately imposed by the inherent nature of the wireless operation. [ABSTRACT FROM AUTHOR]

For taking out the adjustment process of sparse auto-encoder for broad learning system, Feng et al. proposed fuzzy broad learning system by replacing the feature nodes of broad learning system with Takagi–Sugeno fuzzy systems. In fuzzy broad learning system, artificial parameters selection of ridge regression might result in the decrease in testing accuracy. To overcome this shortcoming of fuzzy broad learning system, this article builds a novel model of fuzzy broad learning system based on accelerating amount by introducing the accelerating amount into fuzzy broad learning system. The theoretical result on the universal approximation property of fuzzy broad learning system based on accelerating amount is presented. Three experiment studies on the regression problems of UCI, fashion MNIST, and medical MNIST datasets are performed to show the improvement in testing accuracy. [ABSTRACT FROM AUTHOR]

Spontaneous bursts in neuronal networks with propagation involving a large number of synchronously firing neurons are considered to be a crucial feature of these networks both in vivo and in vitro. Recently, learning has been shown to improve the association and synchronization of spontaneous events in neuronal networks by promoting the firing of spontaneous bursts. However, little is known about the relationship between the learning phase and spontaneous bursts. By combining highresolution measurement with a 4,096-channel complementary metaloxide-semiconductor (CMOS) microelectrode array (MEA) and graph theory, we studied how the learning phase influenced the initiation of spontaneous bursts in cultured networks of rat cortical neurons in vitro. We found that a small number of selected populations carried most of the stimulus information and contributed to learning. Moreover, several new burst propagation patterns appeared in spontaneous firing after learning. Importantly, these "learning populations" had more hubs in the functional network that governed the initiation of spontaneous burst activity. These results suggest that changes in the functional structure of learning populations may be the key mechanism underlying increased bursts after learning. Our findings could increase understanding of the important role that synaptic plasticity plays in the regulation of spontaneous activity. [ABSTRACT FROM AUTHOR]

In DNN processors, main memory consumes much more energy than arithmetic operations. Therefore, many memory-oriented network scheduling (MONS) techniques are introduced to exploit on-chip data reuse opportunities and reduce accesses to memory. However, to derive the theoretical lower bound of memory overhead for DNNs is still a significant challenge, which also sheds light on how to reach memory-level optimality by means of network scheduling. Prior work on MONS mainly focused on disparate optimization techniques or missed some of the data reusing opportunities in diverse network models, thus their results are likely to deviate from the true memory-optimality that can be achieved in processors. This paper introduces Olympus, which comprehensively considers the entire memory-level DNN scheduling space, formally analyzes the true memory-optimality and also how to reach the memory-optimal schedules for an arbitrary DNN running on a DNN processor. The key idea behind Olympus is to derive a true memory lower-bound regarding both the intra-layer and inter-layer reuse opportunities, which has not been simultaneously explored by prior works. Evaluation on SOTA DNN processors of different architectures shows that Olympus can guarantee the minimum off-chip memory access, and it reduces 12.3-85.6% DRAM access and saves 7.4-70.3% energy on the latest network models. [ABSTRACT FROM AUTHOR]

This article studies the decentralized event-triggered control problem for a class of constrained nonlinear interconnected systems. By assigning a specific cost function for each constrained auxiliary subsystem, the original control problem is equivalently transformed into finding a series of optimal control policies updating in an aperiodic manner, and these optimal event-triggered control laws together constitute the desired decentralized controller. It is strictly proven that the system under consideration is stable in the sense of uniformly ultimate boundedness provided by the solutions of event-triggered Hamilton–Jacobi–Bellman equations. Different from the traditional adaptive critic design methods, we present an identifier-critic network architecture to relax the restrictions posed on the system dynamics, and the actor network commonly used to approximate the optimal control law is circumvented. The weights in the critic network are tuned on the basis of the gradient descent approach as well as the historical data, such that the persistence of excitation condition is no longer needed. The validity of our control scheme is demonstrated through a simulation example. [ABSTRACT FROM AUTHOR]

Self-organizing vehicular social networks underpin many location-based services (LBS) such as those that collect and share environmental information (e.g., traffic and weather conditions) among vehicular users and the infrastructure. There are, however, security and privacy considerations in the sharing of such information, and one popular approach is to design lightweight authentication solutions for LBS. Existing approaches may suffer from limitations such as significant computational and/or storage overheads, latency and time delays, and consequently impractical for resource-constrained on-board units. In this paper, we propose an efficient privacy-preserving LBS bundle authentication scheme (hereafter referred to as SAVE) through secure redundancy filtering in self-organizing vehicular social networks. Firstly, an enhanced self-healing key distribution protocol with distributed revocation is proposed to reduce communication cost for retransmitting lost key material and resist free-riding attacks to enhance the authentication efficiency. Then, based on it, a generalized version of online/offline aggregate signature is proposed to achieve batch LBS bundle verification based on arbitrary one-way function holding the property of multiplicative homomorphism. Finally, an efficient zero-knowledge range proof based on lightweight one-way hash chain is designed to decide the redundancy of LBS bundles without disclosing vehicular users’ location privacy. Formal security proof and extensive simulation results demonstrate that our proposed SAVE achieves identity privacy, two levels of location privacy and the practicability in reality. [ABSTRACT FROM AUTHOR]

Lighting is a determining factor in photography that affects the style, expression of emotion, and even quality of images. Creating or finding satisfying lighting conditions, in reality, is laborious and time-consuming, so it is of great value to develop a technology to manipulate illumination in an image as post-processing. Although previous works have explored techniques based on the physical viewpoint for relighting images, extensive supervisions and prior knowledge are necessary to generate reasonable images, restricting the generalization ability of these works. In contrast, we take the viewpoint of image-to-image translation and implicitly merge ideas of the conventional physical viewpoint. In this paper, we present an Illumination-Aware Network (IAN) which follows the guidance from hierarchical sampling to progressively relight a scene from a single image with high efficiency. In addition, an Illumination-Aware Residual Block (IARB) is designed to approximate the physical rendering process and to extract precise descriptors of light sources for further manipulations. We also introduce a depth-guided geometry encoder for acquiring valuable geometry- and structure-related representations once the depth information is available. Experimental results show that our proposed method produces better quantitative and qualitative relighting results than previous state-of-the-art methods. The code and models are publicly available on https://github.com/NK-CS-ZZL/IAN. [ABSTRACT FROM AUTHOR]

Temporal action localization is currently an active research topic in computer vision and machine learning due to its usage in smart surveillance. It is a challenging problem since the categories of the actions must be classified in untrimmed videos and the start and end of the actions need to be accurately found. Although many temporal action localization methods have been proposed, they require substantial amounts of computational resources for the training and inference processes. To solve these issues, in this work, a novel temporal-aware relation and attention network (abbreviated as TRA) is proposed for the temporal action localization task. TRA has an anchor-free and end-to-end architecture that fully uses temporal-aware information. Specifically, a temporal self-attention module is first designed to determine the relationship between different temporal positions, and more weight is given to features within the actions. Then, a multiple temporal aggregation module is constructed to aggregate the temporal domain information. Finally, a graph relation module is designed to obtain the aggregated graph features, which are used to refine the boundaries and classification results. Most importantly, these three modules are jointly explored in a unified framework, and temporal awareness is always fully used. Extensive experiments demonstrate that the proposed method can outperform all state-of-the-art methods on the THUMOS14 dataset with an average mAP that reaches 67.6% and obtain a comparable result on the ActivityNet1.3 dataset with an average mAP that reaches 34.4%. Compared with A2Net (TIP20), PCG-TAL (TIP21), and AFSD (CVPR21) TRA can achieve improvements of 11.7%, 4.4%, and 1.8%, respectively on the THUMOS14 dataset. [ABSTRACT FROM AUTHOR]

Weakly supervised action localization is a challenging task with extensive applications, which aims to identify actions and the corresponding temporal intervals with only video-level annotations available. This paper analyzes the order-sensitive and location-insensitive properties of actions, and embodies them into a self-augmented learning framework to improve the weakly supervised action localization performance. To be specific, we propose a novel two-branch network architecture with intra/inter-action shuffling, referred to as ActShufNet. The intra-action shuffling branch lays out a self-supervised order prediction task to augment the video representation with inner-video relevance, whereas the inter-action shuffling branch imposes a reorganizing strategy on the existing action contents to augment the training set without resorting to any external resources. Furthermore, the global-local adversarial training is presented to enhance the model’s robustness to irrelevant noises. Extensive experiments are conducted on three benchmark datasets, and the results clearly demonstrate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of single image de-raining, that is, the task of recovering clean and rain-free background scenes from a single image obscured by a rainy artifact. Although recent advances adopt real-world time-lapse data to overcome the need for paired rain-clean images, they are limited to fully exploit the time-lapse data. The main cause is that, in terms of network architectures, they could not capture long-term rain streak information in the time-lapse data during training owing to the lack of memory components. To address this problem, we propose a novel network architecture combining the time-lapse data and, the memory network that explicitly helps to capture long-term rain streak information. Our network comprises the encoder-decoder networks and a memory network. The features extracted from the encoder are read and updated in the memory network that contains several memory items to store rain streak-aware feature representations. With the read/update operation, the memory network retrieves relevant memory items in terms of the queries, enabling the memory items to represent the various rain streaks included in the time-lapse data. To boost the discriminative power of memory features, we also present a novel background selective whitening (BSW) loss for capturing only rain streak information in the memory network by erasing the background information. Experimental results on standard benchmarks demonstrate the effectiveness and superiority of our approach. [ABSTRACT FROM AUTHOR]

Sketch recognition relies on two types of information, namely, spatial contexts like the local structures in images and temporal contexts like the orders of strokes. Existing methods usually adopt convolutional neural networks (CNNs) to model spatial contexts, and recurrent neural networks (RNNs) for temporal contexts. However, most of them combine spatial and temporal features with late fusion or single-stage transformation, which is prone to losing the informative details in sketches. To tackle this problem, we propose a novel framework that aims at the multi-stage interactions and refinements of spatial and temporal features. Specifically, given a sketch represented by a stroke array, we first generate a temporal-enriched image (TEI), which is a pseudo-color image retaining the temporal order of strokes, to overcome the difficulty of CNNs in leveraging temporal information. We then construct a dual-branch network, in which a CNN branch and a RNN branch are adopted to process the stroke array and the TEI respectively. In the early stages of our network, considering the limited ability of RNNs in capturing spatial structures, we utilize multiple enhancement modules to enhance the stroke features with the TEI features. While in the last stage of our network, we propose a spatio-temporal enhancement module that refines stroke features and TEI features in a joint feature space. Furthermore, a bidirectional temporal-compatible unit that adaptively merges features in opposite temporal orders, is proposed to help RNNs tackle abrupt strokes. Comprehensive experimental results on QuickDraw and TU-Berlin demonstrate that the proposed method is a robust and efficient solution for sketch recognition. [ABSTRACT FROM AUTHOR]

To provide semantic image style transfer results which are consistent with human perception, transferring styles of semantic regions of the style image to their corresponding semantic regions of the content image is necessary. However, when the object categories between the content and style images are not the same, it is difficult to match semantic regions between two images for semantic image style transfer. To solve the semantic matching problem and guide the semantic image style transfer based on matched regions, we propose a novel semantic context-aware image style transfer method by performing semantic context matching followed by a hierarchical local-to-global network architecture. The semantic context matching aims to obtain the corresponding regions between the content and style images by using context correlations of different object categories. Based on the matching results, we retrieve semantic context pairs where each pair is composed of two semantically matched regions from the content and style images. To achieve semantic context-aware style transfer, a hierarchical local-to-global network architecture, which contains two sub-networks including the local context network and the global context network, is proposed. The former focuses on style transfer for each semantic context pair from the style image to the content image, and generates a local style transfer image storing the detailed style feature representations for corresponding semantic regions. The latter aims to derive the stylized image by considering the content, the style, and the intermediate local style transfer images, so that inconsistency between different corresponding semantic regions can be addressed and solved. The experimental results show that the stylized results using our method are more consistent with human perception compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Video deraining is an important issue for outdoor vision systems and has been investigated extensively. However, designing optimal architectures by the aggregating model formation and data distribution is a challenging task for video deraining. In this paper, we develop a model-guided triple-level optimization framework to deduce network architecture with cooperating optimization and auto-searching mechanism, named Triple-level Model Inferred Cooperating Searching (TMICS), for dealing with various video rain circumstances. In particular, to mitigate the problem that existing methods cannot cover various rain streaks distribution, we first design a hyper-parameter optimization model about task variable and hyper-parameter. Based on the proposed optimization model, we design a collaborative structure for video deraining. This structure includes Dominant Network Architecture (DNA) and Companionate Network Architecture (CNA) that is cooperated by introducing an Attention-based Averaging Scheme (AAS). To better explore inter-frame information from videos, we introduce a macroscopic structure searching scheme that searches from Optical Flow Module (OFM) and Temporal Grouping Module (TGM) to help restore latent frame. In addition, we apply the differentiable neural architecture searching from a compact candidate set of task-specific operations to discover desirable rain streaks removal architectures automatically. Extensive experiments on various datasets demonstrate that our model shows significant improvements in fidelity and temporal consistency over the state-of-the-art works. Source code is available at https://github.com/vis-opt-group/TMICS. [ABSTRACT FROM AUTHOR]

In the Internet of Medical Things (IoMT), the Internet of Things (IoT) is integrated with medical devices, enabling improved patient comfort, cost-effective medical solutions, quick hospital treatments, and even more personalized healthcare. The paper first provides the introduction of IoMTs and then introduces an architecture of IoMTs. Later, it provides the current operations of the healthcare system and discusses the mapping of these operations into the architectural diagram. Further, several emerging technologies such as Physically Unclonable Functions (PUF), Blockchain, Artificial Intelligence (AI), and Software-Defined Networking (SDN) are envisioned as important technologies to overcome several challenges in e-healthcare such as security, privacy, accuracy, and performance. Finally, we provide three case studies for IoMT based on – (1) PUF-based Authentication, (2) AI-enabled SDN Assisted e-healthcare, and (3) Blockchain Assisted Patient Centric System. The solutions presented in this paper may have a huge impact on the speed at which IoMT infrastructure can efficiently evolve with market evolution. [ABSTRACT FROM AUTHOR]

Data-driven network design suggests that substantial technology advances of 5G and 6G networks will be enabled with enhanced automation, intelligence, and user-experience-focused capabilities. Network operators need to upgrade the standard network models by applying machine learning (ML) to address the complexities of next-generation network deployments. This article explores the role of ML and its interplay with wireless communications networks to develop the next-generation network architecture. A use case scenario for self-configuration of radio-access-network-based notification areas (RNAs) for effective resource management is analyzed to exemplify the proposed architecture where a paging load reduction of 64 percent is observed in the resulting RNA clusters. A conceptual framework for RNA configuration and management enabling a broader perspective toward an ML-driven hybrid self-organizing network is discussed to improve the signaling load to attain reduced latency and improved network capacity. [ABSTRACT FROM AUTHOR]

The usage of the Software-Defined Sensor Networks (SD-WSNs) as support for applications of the Internet of Things (IoT) has been the focus of recent research to reduce the complexity of configuration and management. This simplification allows greater flexibility for the configuration of these networks in operating time. However, although more flexible than traditional approaches, the existing SD-WSN architectures found in the literature have limitations mainly with regard to the reprogramming of devices in operating time. In this article, we propose a novel approach called Extended Software-Defined Wireless Sensor Network (ESD-WSN), in which the concept of the SDNs is extended to the application layer allowing the reprogramming of low-end devices in operation time without the need for firmware update. The proposed architecture was validated by simulation and an experimental study using very constrained low-end devices. The simulation/experimental results showed the effectiveness, flexibility, and efficiency of the proposal. [ABSTRACT FROM AUTHOR]

The sixth-generation (6G) communication networks being highly data-intensive and enabled by the Internet of Everything (IoE) are envisaged to find applications in various domains including smart healthcare, smart industry, and gaming. In this article, a novel hybrid 6G-based cybertwin cooperative architecture is studied and an analytical framework for the secrecy outage analysis is presented. The base station (BS) considered utilizes nonorthogonal-multiple-access for content request transmission to the cybertwin host and the server with a direct communication link present between the BS and the server. A wireless link experiencing Nakagami- $m$ fading is considered which is further assisted in the communication network by a wired link which is a power line communication link and experiences Rayleigh fading, for the communication between the cybertwin host and the server. Secrecy error probability expressions are derived for the cybertwin host and the server for the scenario when both the wireless and the wired links are available for communication and only the wireless link is available for communication. Furthermore, the presented analytical results are corroborated with simulation results which demonstrate the optimality of operating the wireless links at lower signal-to-noise ratio (SNR) values. It is observed that for the lower values of Nakagami fading parameter, better secrecy performance is observed with the usage of wired link along with the wireless link, and also the secrecy performance of the cybertwin host and the server is dependent on the system parameters like the fading parameter and the average SNR. [ABSTRACT FROM AUTHOR]

Security and privacy of data-in-transit are critical issues in Industry 4.0, which are further amplified by the use of faster communication technologies such as 6G. Along with security issues, computation and communication costs, as well as data confidentiality, must be also accommodated. In this article, we design a cybertwin-based cloud-centric network architecture to improve the flexibility and scalability of 6G industrial networks. Cybertwin not only enables the deployment of advanced security solutions but also provides an always-on connection. However, the security of data-in-transit over wireless communication between users/things and cybertwin remains a concern. Hence, a privacy-preserving authentication scheme based on digital signature and authenticated key exchange protocol is designed to address the security concerns of data exchanged. In addition, we conduct a security analysis that proves that the scheme resists several attacks in the Industry 4.0 environment. Moreover, the evaluation performed confirmed the superiority of the proposed work comparing to the existing works. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI)'s rapid development has produced a variety of state-of-the-art models and methods that rely on network architectures and features engineering. However, some AI approaches achieve high accurate results only at the expense of interpretability and reliability. These problems may easily lead to bad experiences, lower trust levels, and systematic or even catastrophic risks. This article introduces the theoretical framework of scenarios engineering for building trustworthy AI techniques. We propose six key dimensions, including intelligence and index, calibration and certification, and verification and validation to achieve more robust and trusting AI, and address issues for future research directions and applications along this direction. [ABSTRACT FROM AUTHOR]

Single image super-resolution (SISR) is the task of inferring a high-resolution image from a single low-resolution image. Recent research on super-resolution has achieved great progress because of the development of deep convolutional neural networks in the field of computer vision. Existing super-resolution methods have high performances in mean square error (MSE), but most methods fail to reconstruct an image with shape edges. To solve this problem, the mixed gradient error, which is composed by MSE and mean gradient error, is proposed and applied to a modified U-net network. The modified U-net removes all batch normalization layers and one of the convolution layers in each block. The operation reduces the parameter number and therefore accelerates the model. Compared with the existing SISR algorithms, the proposed method has better performance and time consumption. The experiments demonstrate that modified U-net with mixed gradient loss yields high-level results on three widely used datasets, SET14, BSD300 and Manga109, and outperforms other state-of-the-art methods on the text dataset, ICDAR2003. Code is available online [ABSTRACT FROM AUTHOR]

Combined with the characteristics of open-pit mines in China, the definition of intelligent open-pit mines is given. This paper introduces the overall network architecture, business architecture and collaborative process of the intelligent open-pit mine. The overall network architecture of cloud, edge and end is adopted to realize the collaborative correlation of various production links in the open-pit mine by accessing multi-source heterogeneous data and fully applying advanced technologies such as big data and cloud computing. The business architecture adopts the "5 layers + 3 system" model. The "5 layers" from bottom to top are the basic equipment layer, business data layer, analysis service layer, analysis business layer and analysis presentation layer. And the "3 systems" are data analysis standard system, safety specification standard system, metadata and code specification standard system. The bottom-up collaboration of intelligent open-pit mine should realize "basic + data" support, mine production system collaboration, disaster risk analysis collaboration and business decision-making collaboration. The key technologies such as unmanned technology, digital twin technology, big data collection and analysis technology, risk monitoring and early warning technology of intelligent open-pit mine are discussed. It is pointed out that technologies such as big data and artificial intelligence will become the core driver of open-pit mine intelligence. The platform-based architecture will become the common choice for future intelligent open-pit mine systems.