The Industrial Internet of Things (IIoT) enhances production efficiency through inter-factory resource sharing and intelligent human-machine interaction (HMI) in flexible manufacturing. This study addresses large-scale multiobjective resource scheduling optimization in the complex IIoT environments to improve production efficiency and resource scheduling under growing customization and order demands. We develop a multiobjective IIoT scheduling model under assumptions of task priority and resource constraints, optimizing makespan, logistics time, energy consumption, tardiness time, production cost, and carbon emission. To improve resource scheduling performance, a fuzzy decision-enhanced dual directed sampling-assisted large-scale multiobjective optimization algorithm (LMOEA-FDDS) is proposed. The algorithm employs a dual directed sampling method, uses two different types of search directions to guide the population evolution, and combines a complementary environmental selection strategy based on angle penalty distance (APD), effectively improving the algorithms search eiciency and performance. Experimental results show that LMOEA-FDDS achieves 30%-70% higher HV values than state-of-the-art algorithms, demonstrating its effectiveness in solving complex IIoT scheduling problems.

The increasing deployment of digital healthcare systems has led to the continuous transmission of highly sensitive patient data, raising urgent concerns about data leakage in high-noise, high-loss, and dynamically changing. Existing privacy-preservation techniques often struggle to provide robustness, low overhead, and real-time responsiveness under high jitter and packet loss, limiting their effectiveness in rapid detection and accurate tracing of leaks. To address these challenges, we propose a Reinforcement Learning-Driven Joint Watermarking Framework (RLDJ-W). First, it utilizes a reinforcement learning strategy to adaptively modulate the watermark embedding interval, ensuring both invisibility and enhancing the watermark’s survivability in harsh channels. Then, it leverages Bi-LSTM to capture and model multi-granularity time-series features of network flows, thereby dynamically evaluating the invisibility of the watermark flows. Finally, a high-performance decoding network based on MLP is designed to achieve efficient and accurate watermark information extraction. Experimental results demonstrate that the watermarking capacity of RLDJ-W achieves 2.25 bit/s, requiring only an average of 5.88 packets per bit of watermark. It also maintains over 85% detection accuracy even under 100ms delay jitter and 40% packet loss, consistently outperforming state-of-the-art baselines.

The exponential growth of sensitive patient information and diagnostic records in digital healthcare systems has increased the complexity of data protection, while frequent medical data breaches severely compromise system security and reliability. Existing privacy protection techniques often lack robustness and real-time capabilities in high-noise, high-packet-loss, and dynamic network environments, limiting their effectiveness in detecting healthcare data leaks. To address these challenges, we propose a Swarm Intelligence-Based Network Watermarking (SIBW) method for real-time privacy data leakage detection in digital healthcare systems. SIBW integrates fountain codes with outer error correction codes and employs a Multi-Phase Synergistic Swarm Optimization Algorithm (MPSSOA) to dynamically optimize encoding parameters, significantly enhancing the robustness and interference resistance of watermark detection. Additionally, a reliable synchronization sequence and lightweight embedding mechanism are designed to ensure adaptability to complex, dynamic networks. Experimental results demonstrate that SIBW achieves over 90% detection accuracy under high latency jitter and packet loss conditions, surpassing existing methods in both robustness and efficiency. With a compact design of only 3.7 MB, SIBW is particularly suited for rapid deployment in resource-constrained digital healthcare systems.

Deep learning has significantly advanced medical image processing, yet the inherent inclusion of personally identifiable information (PII) within medical images-such as facial features, distinctive anatomical structures, rare lesions, or specific textural patterns-poses a critical risk to patient privacy during data transmission. To mitigate this risk, we introduce the Medical Semantic Diffusion Model (MSDM), a novel framework designed to synthesize medical images guided by semantic information, synthesis images with the same distribution as the original data, which effectively removes the PPI of the original data to ensure robust privacy protection. Unlike conventional techniques that combine semantic and noisy images for denoising, MSDM integrates Adaptive Batch Normalization (AdaBN) to encode semantic information into high-dimensional latent space, embedding it directly within the denoising neural network. This approach enhances image quality and semantic accuracy while ensuring that the synthetic and original images belong to the same distribution. In addition, to further accelerate synthesis and reduce dependency on manually crafted semantic masks, we propose the Spread Algorithm, which automatically generates these masks. Extensive experiments conducted on the BraTS 2021, MSD Lung, DSB18, and FIVES datasets confirm the efficacy of MSDM, yielding state-of-the-art results across several performance metrics. Augmenting datasets with MSDM-generated images in nnUNet segmentation experiments led to Dice scores of 0.6243, 0.9531, 0.9406, and 0.9562 underscoring its potential for enhancing both image quality and privacy-preserving data augmentation.

The real-time processing of large-scale, heterogeneous tasks—including CPU-only, general-purpose GPU, and specialized GPU tasks—poses significant challenges in Internet of Things (IoT) systems, driven by severe GPU resource fragmentation, inefficient CPU and memory resource utilization on edge servers. These issues often compromise system processing performance and server stability. To address these issues, we formulate a multi-stage mixed-integer nonlinear programming (MINLP) model, to jointly optimize GPU fragmentation rate and system processing capability. We then introduce a novel deviation-based Lyapunov optimization framework that explicitly maintains memory utilization around a predefined optimal threshold, effectively balancing resource usage and system stability. Finally, to achieve real-time decision-making for massive tasks in dynamic systems with randomly arriving tasks, we propose the MA-LHTO algorithm, a multi-agent deep reinforcement learning approach that incorporates a multi-head architecture, entropy-based exploration, and a parameter reset mechanism. Experimental results confirm that our algorithm significantly improves resource utilization, and exhibits good performance under various working conditions.

Nowadays, massive amounts of facial images have been tampered with and then widely spread through social networks. Many studies have developed algorithms for frame-level DeepFake detection. However, they have low robustness due to their focus on tamper-independent features during training. To this end, we propose a framework, namely MIF-Net, based on multi-information fusion for robust frame-level DeepFake detection. Specifically, key landmarks and the facial area are first detected in the original frame. Then, the graph convolutional network constructs biometric information from these landmarks. Meanwhile, the facial region is processed into multi-view inputs by noise and edge enhancement algorithms. Finally, these products are encoded as high-level features and classified as real or fake. Five benchmark datasets are utilized for testing our model through within-dataset and cross-dataset validations. Extensive experiment results demonstrate that our proposed MIF-Net is robust and has advantages over peer algorithms.

Many multi-view stereo (MVS) networks with a cascaded structure can effectively estimate depth while saving memory. However, the accuracy of the depth map in the fine stage depends on the depth map estimated in the coarse stage. Additionally, the multi-stage depth maps generated by the cascaded structure are used to compute losses but are not reused, resulting in a loss of inter-stage differentiation information. To address these issues, we propose a dual-uncertainty estimation MVS method that learns an MVS network based on adjacent stage and pair-wise stage uncertainty estimation, named APMVS. The core of the proposed APMVS is to employ dual-uncertainty estimation to mitigate the adverse effects of the cascaded structure. Specifically, it involves two estimation modules: adjacent stage uncertainty (ASU) and pair-wise stage uncertainty (PSU). The ASU estimation module dynamically adjusts the depth-hypothesis range by leveraging uncertainty from the previous stage, thereby improving the accuracy of depth-map prediction in the current stage. The PSU estimation module estimates the uncertainty between each pair of stages. Thus, regions with high uncertainty have minimal impact. We evaluate the proposed APMVS on the DTU, Tanks and Temples, and BlendedMVS datasets. Experimental results show that our method achieves superior reconstruction quality compared with other state-of-the-art methods.

Abstract In the current realms of scientific research and commercial applications, the risk inference of regulatory violations by publicly listed enterprises has attracted considerable attention. However, there are some problems in the existing research on the deduction and prediction of violation risk of listed enterprises, such as the lack of analysis of the causal logic association between violation events, the low interpretability and effectiveness of the deduction and the lack of training data. To solve these problems, we propose a framework for enterprise violation risk deduction based on generative AI and event evolution graphs. First, the generative AI technology was used to generate a new text summary of the lengthy and complex enterprise violation announcement to realize a concise overview of the violation matters. Second, by fine‐tuning the generative AI model, an event entity and causality extraction framework based on automated data augmentation are proposed, and the UIE (Unified Structure Generation for Universal Information Extraction) event entity extraction model is used to create the event entity extraction for listed enterprises ‘violations. Then, a causality extraction model CDDP‐GAT (Event Causality Extraction Based on Chinese Dictionary and Dependency Parsing of GAT) is proposed. This model aims to identify and analyse the causal links between corporate breaches, thereby deepening the understanding of the event logic. Then, the merger of similar events was realized, and the causal correlation weights between enterprise violation‐related events were evaluated. Finally, the listed enterprise's violation risk event evolution graph was constructed, and the enterprise violation risk deduction was carried out to form an expert system of financial violations. The deduction results show that the method can effectively reveal signs of enterprise violations and adverse consequences.

Vehicle edge computing (VEC) can effectively meet the demand for computing resources in autonomous driving. However, complex resource constraints exist in the practical application of VEC, making offloading tasks a key challenge. Traditional scheduling algorithms are usually optimized only for latency and cost and can handle only a small number of tasks; however, they cannot handle real-world intensive vehicle-road cooperation scenarios involving many tasks. Thus, this article constructs a large-scale multiobjective computing offloading optimization model that comprehensively considers latency, energy consumption, load balancing, and resource utilization. To improve the offloading performance of VEC, we propose a large-scale multiobjective optimization algorithm with hybrid directed sampling and adaptive offspring generation (LMOEA-HDGS). The algorithm can generate adaptive offspring by sampling in two types of search directions in the decision space and can adapt to the complex shape of the Pareto front while balancing diversity and convergence. The experimental results show that the proposed algorithm can effectively optimize the task offloading problem of VEC in an intensive vehicle-road cooperation scenario.

Oil temperature is an important index to reflect the state of transformer and predict the fault and remaining useful life of transformer. However, the oil temperature of the transformer is affected by various factors, such as weather conditions, load changes, holidays, seasons, and so on. In order to predict oil temperature more accurately, this study proposes a VCEEMDAN data decomposition algorithm to extract the characteristics of oil temperature data. And we proposed a hybrid long-term series forecasting model of oil temperature based on VCEEMDAN, particle swarm optimization (PSO) and Informer. At the same time, the digital twins system of the manipulator is configured to assist personnel in maintenance when the oil temperature is abnormal. And the digital twins system of an oil-immersed power transformer to monitoring of transformer status. Finally, the above research is integrated into a digital twins system, including transformer condition monitoring, oil temperature prediction, and intelligent maintenance based on Internet of Things (IOT). The proposed prediction method was compared with the six existing models on three datasets. From the experimental results, the best index improvement is 77.97% higher than the second-best model. And the simulation system can provide a good reference for the practical application of the factory. This article presents a hybrid framework that achieves a accurate transformer oil temperature prediction.

Smoke detection is essential for fire prevention, yet it is significantly hampered by the visual similarities between smoke and fog. To address this challenge, a split top-k attention transformer framework (STKformer) is proposed. The STKformer incorporates split top-k attention (STKA), which partitions the attention map for top-k selection to retain informative self-attention values while capturing long-range dependencies. This approach effectively filters out irrelevant attention scores, preventing information loss. Furthermore, the adaptive dark-channel-prior guidance network (ADGN) is designed to enhance smoke recognition under foggy conditions. ADGN employs pooling operations instead of minimum value filtering, allowing for efficient dark channel extraction with learnable parameters and adaptively reducing the impact of fog. The extracted prior information subsequently guides feature extraction through a priorformer block, improving model robustness. Additionally, a cross-stage fusion module (CSFM) is introduced to aggregate features from different stages efficiently, enabling flexible adaptation to smoke features at various scales and enhancing detection accuracy. Comprehensive experiments demonstrate that the proposed method achieves state-of-the-art performance across multiple datasets, with an accuracy of 89.68% on dataset for smoke detection in fog, 99.76% on CCTV images of smoke, and 99.76% on UAV images of wildfire. The method maintains high speed and lightweight characteristics, validated with an inference speed of 211.46 FPS on an NVIDIA Jetson AGX Orin after TensorRT acceleration, confirming its effectiveness and efficiency for real-world applications. The source code is available athttps://github.com/Jiongze-Yu/STKformerhttps://github.com/Jiongze-Yu/STKformer.

In today’s increasingly complex and dynamic cyber threat environment, Zero Trust Architecture (ZTA) has emerged as a promising solution to address the limitations of traditional intrusion detection methods. While Intrusion Detection Systems (IDS) are essential for safeguarding organizational information assets, traditional methods have the risk of exposing security policies by collecting and utilizing alarm data, potentially revealing attack paths to adversaries. To overcome this challenge, we propose a novel intrusion detection strategy based on ZTA, emphasizing the protection of alarm data privacy. Additionally, we introduce an adaptive sparse connective evolutionary neural architecture search (ASCE-NAS) framework, which enables the automatic evolution of intrusion detection model structures to enhance adaptability and performance in dynamic environment. Notably, ASCE-NAS can also be beneficial for integrated sensing and computing chips and systems, contributing to a more secure and efficient cybersecurity framework to effectively combat evolving threats and attack methods.

Multi-modal emotion recognition plays a crucial role in human-computer interaction. Nowadays, many studies have developed fusion algorithms for this purpose. However, two challenges are still present, i.e., insufficient cross-modal information sharing and weak fusion feature representations. To this end, we develop a novel framework, namely CH-Net, for multi-modal emotion recognition with physiological signals. It is based on cross-modal attention and hypercomplex domain fusion. First, our learnable cross-modal attention mechanism adaptively aligns features across modalities, enhancing both complementarity and modality-specific discrepancies. Second, a hypercomplex fusion module encodes these features, yielding more robust representations while reducing parameter overhead. Two benchmark datasets, i.e., MAHNOB-HCI and DEAP, are utilized to train and test our model. Extensive experiments demonstrate that CH-Net is effective and outperforms state-of-the-art (SOTA) methods. Our code will be available athttps://github.com/xuxusky/CH-Net.

Deep learning has made remarkable achievements in many fields. However, although fuzzy neural networks with natural interpretability are widely used in prediction and control scenarios, there are very few studies on the deepening of fuzzy systems. By integrating rough set theory, fuzzy rough neural network has unique advantages benefiting from the complementarity of the fuzzy set theory and the rough set theory as well as the powerful learning ability of neural networks. Similarly, research on deep fuzzy rough neural networks is even rarer. In this article, in order to improve the performance of the fuzzy rough neural network and expand its application range, the deep fuzzy rough neural network model is constructed and optimized by stacking blocks of fuzzy rough neural network to imitate the deepening deep neural network based on multiobjective evolution. Each fuzzy rough neural network block is interpretable, and its stacked architecture also has high interpretability. To automatically generate deep fuzzy rough neural network models with high efficiency, a distributed parallel multiobjective neuroevolution framework is developed, thus blocks can be stacked flexibly and deep architecture can be optimized considering multiple optimization objectives of accuracy, interpretability, and generalization simultaneously. In addition, multiobjective evolution is combined with Wang–Mendel method, pseudoinverse, and backpropagation to effectively learn specific parameters. Finally, based on the time series prediction problems, the superiority of the multiobjective deep fuzzy rough neural network evolutionary framework is verified.

Motion artifacts compromise the quality of magnetic resonance imaging (MRI) and pose challenges to achieving diagnostic outcomes and image-guided therapies. In recent years, supervised deep learning approaches have emerged as successful solutions for motion artifact reduction (MAR). One disadvantage of these methods is their dependency on acquiring paired sets of motion artifact-corrupted (MA-corrupted) and motion artifact-free (MA-free) MR images for training purposes. Obtaining such image pairs is difficult and therefore limits the application of supervised training. In this paper, we propose a novel UNsupervised Abnormality Extraction Network (UNAEN) to alleviate this problem. Our network is capable of working with unpaired MA-corrupted and MA-free images. It converts the MA-corrupted images to MA-reduced images by extracting abnormalities from the MA-corrupted images using a proposed artifact extractor, which intercepts the residual artifact maps from the MA-corrupted MR images explicitly, and a reconstructor to restore the original input from the MA-reduced images. The performance of UNAEN was assessed by experimenting with various publicly available MRI datasets and comparing them with state-of-the-art methods. The quantitative evaluation demonstrates the superiority of UNAEN over alternative MAR methods and visually exhibits fewer residual artifacts. Our results substantiate the potential of UNAEN as a promising solution applicable in real-world clinical environments, with the capability to enhance diagnostic accuracy and facilitate image-guided therapies.

Embodied AI has notably advanced the autonomy of physical agents such as robots, vehicles, and AAVs, expanding their application scope. However, existing systems are predominantly data-driven, relying on static programming and pre-trained models. This limits their adaptability to dynamic and unforeseen scenarios. Additionally, the high computational cost of training large-scale models locally hinders their practical deployment. One promising solution lies in integrating Large Language Models (LLMs) into Embodied AI frameworks. Although LLMs excel in reasoning, coding, and perception, most existing frameworks adopt a single-LLM architecture, which restricts their effectiveness in addressing complex, multimodal tasks. The diverse strengths of individual LLMs, ranging from natural language understanding, visual processing to code generation, are seldom utilized in a collaborative and structured manner. To address these challenges, we propose a knowledge-driven framework, called EUF, that incorporates multi-LLMs into the Embodied AI architecture for AAV-as-a-Service in Intelligent Transportation Systems. Each LLM is dedicated to a specific stage of the AAV task, including user intent interpretation, adaptive path planning with code generation, and error-feedback mechanisms. Our research explores both One-shot and Segmented Code Generation approaches using various LLM-driven models to identify the optimal strategy. We conduct an in-depth analysis of different code errors to evaluate the strengths and limitations of each approach, including the feedback capabilities of the LLM-driven models. Experiments conducted in the AirSim environment demonstrate the framework’s robustness and accuracy in complex AAV path planning tasks, highlighting its practical potential for real-world ITS deployments.

The rapid expansion of intelligent vehicles in 6G networks has intensified the demand for real-time task processing. However, traditional cloud-edge collaboration models for large-scale vehicle task offloading are increasingly inadequate to address the growing complexity and demands. To address this challenge, we propose a unified cloud-edge-end collaborative vehicle task offloading multiobjective optimization model for large-scale vehicle task offloading, which simultaneously considers four optimization objectives: latency, energy consumption, load balancing and quality of service (QoS). To solve the large-scale multiobjective optimization problem, we propose a large-scale multiobjective evolutionary algorithm based on problem transformation and bidirectional vectors (LSMOEA-PTBV). Experiments in a simulated 6G vehicular network demonstrate that LSMOEA-PTBV outperforms state-of-the-art methods. Our work enhances the end-user experience, meets the increasingly complex demands of modern applications, and advances the development of integrated sensing and computing systems and intelligent transportation systems in the 6G era.

With higher peak data rates, enhanced reliability, improved energy efficiency, and reduced radio latency, 6G enables cooperative perception in autonomous vehicle clusters. Existing research mainly focuses on establishing communication-connected and structurally stable clusters, while overlooking how members collaborate in perception. To address this gap, we propose a collaborative perception-based autonomous vehicle cluster modeling method for expressways, leveraging the capabilities of 6G networks. This method facilitates collaborative perception within vehicle clusters through the exchange of sensory information among member vehicles. First, we introduce a perception interaction mechanism among vehicles as a foundation for constructing clusters. We then present a primary vehicle selection method and analyze the cluster’s perception gain, collaborative efficiency, and collaborative reliability. Based on this, we develop a vehicle cluster model and solve it using a genetic algorithm. We present a vehicle cluster formation method that brings together vehicles achieving Pareto optimal solutions through specific interaction rules, thereby forming a cluster. The simulation results demonstrate that the proposed method outperforms existing methods in terms of perception gain (PG), collaborative efficiency (CE), and collaborative reliability (CR).

Road ponding, a prevalent traffic hazard, poses a serious threat to road safety by causing vehicles to lose control and leading to accidents ranging from minor fender benders to severe collisions. Existing technologies struggle to accurately identify road ponding due to complex road textures and variable ponding coloration influenced by reflection characteristics. To address this challenge, we propose a novel approach called Self-Attention-based Global Saliency-Enhanced Network (AGSENet) for proactive road ponding detection and traffic safety improvement. AGSENet incorporates saliency detection techniques through the Channel Saliency Information Focus (CSIF) and Spatial Saliency Information Enhancement (SSIE) modules. The CSIF module, integrated into the encoder, employs self-attention to highlight similar features by fusing spatial and channel information. The SSIE module, embedded in the decoder, refines edge features and reduces noise by leveraging correlations across different feature levels. To ensure accurate and reliable evaluation, we corrected significant mislabeling and missing annotations in the Puddle-1000 dataset. Additionally, we constructed the Foggy-Puddle and Night-Puddle datasets for road ponding detection in low-light and foggy conditions, respectively. Experimental results demonstrate that AGSENet outperforms existing methods, achieving IoU improvements of 2.03%, 0.62%, and 1.06% on the Puddle-1000, Foggy-Puddle, and Night-Puddle datasets, respectively, setting a new state-of-the-art in this field. Finally, we verified the algorithm’s reliability on edge computing devices. This work provides a valuable reference for proactive warning research in road traffic safety. The source code and datasets are placed in thehttps://github.com/Lyu-Dakang/AGSENet.

Multi-modal neuroimaging analysis is crucial for a comprehensive understanding of brain function and pathology, as it allows for the integration of different imaging techniques, thus overcoming the limitations of individual modalities. However, the high costs and limited availability of certain modalities pose significant challenges. To address these issues, this paper proposes the Condition-Aligned Temporal Diffusion (CATD) framework for end-to-end cross-modal synthesis of neuroimaging, enabling the generation of functional magnetic resonance imaging (fMRI)-detected Blood Oxygen Level Dependent (BOLD) signals from more accessible Electroencephalography (EEG) signals. By constructing Conditionally Aligned Block (CAB), heterogeneous neuroimages are aligned into a latent space, achieving a unified representation that provides the foundation for cross-modal transformation in neuroimaging. The combination with the constructed Dynamic Time-Frequency Segmentation (DTFS) module also enables the use of EEG signals to improve the temporal resolution of BOLD signals, thus augmenting the capture of the dynamic details of the brain. Experimental validation demonstrates that the framework improves the accuracy of brain activity state prediction by 9.13% (reaching 69.8%), enhances the diagnostic accuracy of brain disorders by 4.10% (reaching 99.55%), effectively identifies abnormal brain regions, enhancing the temporal resolution of BOLD signals. The proposed framework establishes a new paradigm for cross-modal synthesis of neuroimaging by unifying heterogeneous neuroimaging data into a latent representation space, showing promise in medical applications such as improving Parkinson's disease prediction and identifying abnormal brain regions.

With the popularization of the metaverse, virtual reality mapping technology based on digital twins has generated a large amount of spatial data. These data are multidimensional, multi-scale, mobile, and distributed. In order to fully utilize these data, we propose a non mutation multidimensional hash geocoding that can organize and store data with geographic features, and achieve data mapping at different scales from macro to micro. The mapping between them can achieve joint utilization of data of various scales. On this basis, we propose a block network secure storage mapping model for spatial digital twins, which can securely and reliably organize and map spatial data. This article also looks forward to the possible emergence of digital twins of different dimensions and scales in the future metaverse, and proposes an adaptive 3D reconstruction method based on this to adapt to digital twins models of different scales in the metaverse. On the basis of our work, we will further promote the development of the spatial digital twin metaverse.

With the increasing popularity of the Metaverse concept, human beings have stepped to a new height in the intelligent technology progress. This work presents a literature review of the current research on Human-Computer Interaction in the Metaverse. Keywords like "Metaverse," "Human-Computer Interaction (HCI)," "virtual space," "virtual technology," "three-dimensional (3D) reconstruction," "parallel universe," "independent identity," "access to interests," and "blockchain" are used to search for relevant articles in the literature in databases like Scopus, Web of Science, and Google Academic. Nearly 100 advanced studies on the Metaverse are selected from over 20,000 documents covering 2018-2023. Finally, the current application state of the Metaverse's underlying technologies is explored and described using the PRISMA principles; these technologies include the fifth-generation (5 G) communication, HCI, virtual technology, blockchain, and 3D reconstruction. Moreover, the predictions for HCI's future growth in the Metaverse are made. The review concludes that the rapid advancement of the 5 G connection has made the concept of the Metaverse possible. The blockchain ensures the safety of currency transactions in the Metaverse virtual space. There will be a shift toward "invisibility" in the way that humans and computers interact in virtual worlds. In other words, HCI will be transparent to the user in the digital realm, and people will get along with the computer in a natural and equitable way. In the interaction, wearable devices allow for an immersive experience in the interaction, but they restrict the participants' freedom of movement and perception. The more humanized somatosensory connection will gain traction in the future, bringing people one step closer to the Metaverse.

Recent years have witnessed the increasing popularity and dramatic progress of contactless sensing technologies, which are able to conduct remote signal acquisition without body contact. Both the physical signs and the physiological parameters can be acquired with contactless sensing. This paper introduces popular contactless sensing technologies, explores their application scenarios, and delves into the underlying theoretical principles. It comprehensively reviews the open datasets released in this field, encompassing collection scenarios, sample counts, data formats, and volunteer information. The performance baseline, typical work, and accessible links are also furnished. In addition, it includes discussions on the primary challenges and potential solutions in the context of contactless sensing with open datasets. Finally, suggestions for establishing a high-quality dataset are also given to the community.

Abstract It aims to explore the efficient and reliable wireless transmission and cooperative communication mechanism of Internet of Vehicles (IoV) based on edge intelligence technology. It first proposes an intelligent network architecture for IoV services by combining network slicing and deep learning (DL) technology, and then began to study the key technologies needed to achieve the architecture. It designs the cooperative control mechanism of unmanned vehicle network based on the full study of wireless resource allocation algorithm from the micro level. Second, in order to improve the safety of vehicle driving, deep reinforcement learning is used to configure the wireless resources of IoV network to meet the needs of various IoV services. The research results show that the accuracy rate of the improved AlexNet algorithm model can reach 99.64%, the accuracy rate is more than 80%, the data transmission delay is less than 0.02 ms, and the data transmission packet loss rate is less than 0.05. The algorithm model has practical application value for solving the data transmission related problems of vehicular internet communication, providing an important reference value for the intelligent development of unmanned vehicle internet.

This work intends to enhance the standard of rehabilitative care provided to patients by optimizing the medical automation monitoring system enabled by wearable computing by edge cloud and Internet of Things technology. First, the recent research literature on edge cloud and wearable computing devices is analyzed. Recent studies in virtual reality and automated medical rehabilitation are used to analyze and contrast various data fusion techniques in wearable sensors. Subsequently, an edge cloud model is constructed to enable real-time tracking of patients’ vital signs, allowing for timely assessment of their health status and rehabilitation progress. Then, a wearable device information monitoring rehabilitation system is established to provide effective rehabilitation treatment for stroke patients. The monitoring module of the rehabilitation system incorporates an edge computing terminal device, which models the virtual circuit for enhanced functionality. Based on the findings, when the number of output codes from the network structure is set to 1000, the average conversion energy of the dynamic conversion system is 140, the first conversion energy of the proposed model is 160, and the second conversion energy is 220. Regarding the rehabilitation treatment effectiveness, the system developed here demonstrates superior operational efficiency and delivers improved outcomes in rehabilitation treatment. This work serves as a practical reference for advancing the intelligent transformation of the medical service system.Note to Practitioners—The goal of this work is to examine the viability of an automated virtual rehabilitation system for use in medical rehabilitation training for stroke, auto accident, and post-operative stroke rehabilitation. Combining the current research status of edge cloud and wearable devices reveals that the majority of existing rehabilitation training approaches are primarily reflected at the theoretical level, with only a few instances of practical applications highlighted. Therefore, this work presents a novel method for designing a wearable medical information monitoring system that operates on the edge cloud with the wearable device computing automation service as its foundation. It can track the patient’s vitals in real time to gauge the rehabilitation’s success. When compared to other cutting-edge rehabilitation options, the validated established edge cloud model clearly stands out as superior and can aid patients in achieving better outcomes from their rehabilitation treatment. The result demonstrates that the method is effective. In the future, it is planned to apply this rehabilitation system to more treatment cases and improve the overall design of the system.

While Digital Twins (DTs) bring convenience to city managers, they also generate new challenges to city network security. Currently, cyberspace security becomes increasingly complicated. Intrusion detection and Deep Learning (DL) are combined with shunning security threats in service computing systems and improving network defense capabilities. DTs can be applied to network security. People's understanding of cyberspace security can be improved using DTs to digitally define, model, and display the network environment and security status. The intrusion detection data are optimized based on DL technology, and a network intrusion detection algorithm integrated with Deep Neural Network (DNN) model is proposed. In the cloud service system, a trust model based on Keyed-Hashing-based Self-Synchronization (KHSS) is introduced. This model predicts the security state and detects attacks according to existing malicious attacks, ensuring the network security defense system's regular operation. Finally, simulation experiments verify the Deep Belief Networks (DBN) model's feasibility and the cloud trust model. The DBN algorithm proposed improves the correct detection rate of unknown samples by 4.05% compared with the Support Vector Machine (SVM) algorithm. From the 20,100 pieces of data in the test dataset, the number of correct attacks detected by the DBN algorithm exceeds those by the SVM algorithm by 818. DBN algorithm requires a short detection time while ensuring optimal detection accuracy. The KHSS+DBN model predicts cloud security states, and the results are the same as the actual states, with an error of only 1%∼2%.

With the rapid development of information technology, great changes have taken place in the way of managing, analyzing, and using data in all walks of life. Using deep learning algorithm for data analysis in the field of medicine can improve the accuracy of disease recognition. The purpose is to realize the intelligent medical service mode of sharing medical resources among many people under the dilemma of limited medical resources. Firstly, the Digital Twins module in the Deep Learning algorithm is used to establish the medical care and disease auxiliary diagnosis model. With the help of the digital visualization model of Internet of Things technology, data is collected at the client and server. Based on the improved Random Forest algorithm, the demand analysis and target function design of the medical and health care system are carried out. Based on data analysis, the medical and health care system is designed using the improved algorithm. The results show that the intelligent medical service platform can collect and analyze the clinical trial data of patients. The accuracy of improved ReliefF & Wrapper Random Forest (RW-RF) for sepsis disease recognition can reach about 98%, and the accuracy of algorithm for disease recognition is also more than 80%, which can provide better technical support for disease recognition and medical care services. It provides a solution and experimental reference for the practical problem of scarce medical resources.

In the trend of the accelerated progression of communication network technology, the emergence of virtual communities (VCs), virtual societies (VSs), metaverse, and other technologies not only makes data access and sharing easier but also leads to the proliferation of fake news (FN). To effectively monitor and identify FN in VC, VS, and metaverse, and to create a safer virtual space, this work takes FN in VC, VS, and metaverse as objects. First, the content and display methods of FN are reviewed and explained, and it is understood that FN is mainly displayed by single-modal and multimodal representations. Second, the application scenarios in many important fields such as transportation are reviewed and analyzed, so as to further understand the impact and detection effect of FN in different scenarios. Finally, an intelligent outlook and summary analysis are carried out on the detection and information security of FN, which provides theoretical reference and new opportunities for the detection and identification of FN in the virtual cyberspace.

With the proliferation of fake news, the spread of misleading information can easily cause social panic and group polarization. Many existing methods for detecting fake news rely on linguistic and semantic features extracted from the content of the news. Some existing approaches focus on sentiment analysis for fake news detection, but the sentiment changes and sentence-level emotional interactions in news classification are not fully analyzed. Fortunately, we observe that in long-form news, the change and mutual influence of sentiment between sentences are different. To extract the features of sentiment interaction between sentences in the article, we propose a graph attention network-based model that combines both sentiment and external knowledge comparison to meet the needs of fake news classification. We obtain the contextual sentiment representation and entity representation of the sentence through the heterogeneous network and the emotion interaction network and obtain the change of the sentiment vector through the emotion comparison network. We compare the entity vectors in the context with those corresponding knowledge base (KB)-based, combine them with the contextual semantic representation of the sentence, and finally input them into the classifier. In experiments, our model performs well in both single and multiclass classification, achieving the state-of-the-art accuracy on existing datasets.

Echocardiography is essential for evaluating cardiac anatomy and function during early recognition and screening for congenital heart disease (CHD), a widespread and complex congenital malformation. However, fetal CHD recognition still faces many difficulties due to instinctive fetal movements, artifacts in ultrasound images, and distinctive fetal cardiac structures. These factors hinder capturing robust and discriminative representations from ultrasound images, resulting in CHD's low prenatal detection rate. Hence, we propose a multi-scale gated axial-transformer network (MSGATNet) to capture fetal four-chamber semantic information. Then, we propose a SPReCHD: four-chamber semantic parsing network for recognizing fetal CHD in the clinical treatment of the medical metaverse, integrating MSGATNet to segment and locate four-chamber arbitrary contours, further capturing distinguished representations for the fetal heart. Comprehensive experiments indicate that our SPReCHD is sufficient in recognizing fetal CHD, achieving a precision of 95.92%, a recall of 94%, an accuracy of 95%, and a$F_{1}$score of 94.95% on the test set, dramatically improving the fetal CHD's prenatal detection rate.

There exists growing evidence that circRNAs are concerned with many complex diseases physiological processes and pathogenesis and may serve as critical therapeutic targets. Identifying disease-associated circRNAs through biological experiments is time-consuming, and designing an intelligent, precise calculation model is essential. Recently, many models based on graph technology have been proposed to predict circRNA-disease association. However, most existing methods only capture the neighborhood topology of the association network and ignore the complex semantic information. Therefore, we propose a Dual-view Edge and Topology Hybrid Attention model for predicting CircRNA-Disease Associations (DETHACDA), effectively capturing the neighborhood topology and various semantics of circRNA and disease nodes in a heterogeneous network. The 5-fold cross-validation experiments on circRNADisease indicate that the proposed DETHACDA achieves the area under receiver operating characteristic curve of 0.9882, better than four state-of-the-art calculation methods.

The aim of this research is to detect the post-disaster damage by drones as soon as possible so that decision makers can assign search and rescue teams effectively and efficiently. The main differences of this research from the others, which use drones in literature, are as: First, the regions are divided into grids and different importance values are assigned according to the number of buildings that are likely to be damaged and are vital for the response stage, such as hospitals, schools, and fire stations. Second, these importance levels are updated based on the day and time, which helps ordering the grids in a more realistic manner. Third, the depots are selected among the pre-determined candidate locations in accordance with the purpose of objective function. Fourth, detection times at grids are considered as uncertain. Fifth, two versions of Ant Colony Optimization (ACO) are developed as alternatives to exact solution tools. Last, sensitivity analyzes are performed by reducing the number of sorties, reducing the number of drones, and comparing day and night importance values for each instance. According to the results, only for very small-scale instances, exact solution tool was able to reach the optimal while both versions of ACO reached to similar results within a very less CPU times. Additionally, these ACO algorithms also found good results for the larger scaled problems. Then the performance of these ACO algorithms and the exact solution method are compared based on the CPU time and solution quality.

Lane detection is an important task in the field of automatic driving. Since lane lines usually have complex topologies and exist in various complex scenes (e.g., damaged lanes, severe occlusion, etc.), lane detection remains challenging. In this work, we propose a Polynomial Global Attention Network (PGA-Net) for lane detection, which is an end-to-end model for mining global road information and predicting lanes shape parameter formulas simultaneously. We model lane shape with cubic polynomial function and use the transformer-based DETR model to introduce the context information of lanes and roads to better regress the lane parameters. For polynomial curve modeling, we propose Mean Curvature Loss (MCL) to constrain the curvature of the predicted lanes, thereby enhancing the quality of curve lanes prediction. In addition, we design an improved supervision strategy to eliminate information bias between our parametric prediction methods and the labeling methods of lane datasets. Our method achieves state-of-the-art performance on two popular benchmarks (TuSimple and LLAMAS) and a most challenging benchmark (CULane), while exhibiting accelerated speed (>140fps on 3090 GPU, 28.9% improvement in average) and lightweight model size (https://github.com/qklee-lz/PGA-Net.

With the rapid development of autonomous driving and SLAM technology, the perception system of a vehicle heavily relies on laser and image sensors to capture the real-world scenario and avoid obstacles autonomously. To achieve accurate and robust multi-sensor fusion computation, high-precision extrinsic calibration of camera and laser scanner is a necessary requirement. Traditional multi-sensor calibration methods based on manual features rely on specific scenarios and may not provide feature information over long distances. In this paper, we present a novel approach for robustly calibrating the extrinsic parameters of a solid-state(SS) lidar-camera system in a natural environment. Our proposed method begins with obtaining robust line feature information. we first innovatively employ a super-voxel clustering method to extract global 3D line features from the complete point cloud and then back-project these 3D line features into 2D space. Afterward, a transformer-based edge detection network, EDTER, is used to detect the edge features and estimate the probability pixel-by-pixel. To consider the uncertainty of two-dimensional line features and the inconsistency of residuals at different distances, we construct a line feature weight model for line feature residual calculation. Finally, we minimize the residual errors using least squares optimization to recover the relative pose of the camera and the lidar sensor. We conducted a performance study to compare our proposed method against existing targetless calibration methods on various natural scenarios. The experimental results demonstrate that our proposed method achieves higher robustness, accuracy, and consistency, making it suitable for real-world applications.

Special Issue on Deep Learning for Intelligent Human Computer InteractionDeep Learning ( DL ) is growing at a fast pace with a plethora of related research being conducted and industrial applications being developed.Robotics, computer graphics, computer vision including areas such as feature extraction/matching, 3D reconstruction, manufacturing, medicine, knowledge acquisition, control theory, planning and scheduling, among others, have uncovered the potential of DL.The reason for such success mainly stems from two aspects.On the one hand, the theoretical advances in related disciplines such as optimization methods, pattern recognition, and hardware (GPUs) have undergone great breakthroughs.On the other hand, the technical applications in industry have addressed many actual problems that have in turn accelerated DL development.Leading technology companies like Microsoft, Apple, Google, Facebook, Nvidia, and Amazon all launched cutting-edge industrial products offering advanced functionalities that were only made possible thanks to the employment of DL algorithms.The effort in establishing new media and network technologies led to the identification of a niche for ML in the field of humancomputer interaction.Human-computer interaction ( HCI ) is a multidisciplinary field of study focusing on the design of computer technology through the analysis and understanding of how humans interact with computers.HCI addresses various areas, such as user interface design or computer-supported cooperative work for data modeling, system adaptation and optimization to fit user needs, complex systems, the development of smart equipment to ease utilization, and the design of smart environments to promote user comfort and safety.Nowadays, DL is largely employed in HCI to model human behavior as they have shown to outperform traditional modeling techniques.Although their potential is undoubtedly broad, there are several important research challenges that remain unaddressed.The explainability and interpretability of the decision-making process of DL approaches is key to assess models' behaviors, especially when unexpected or incorrect outputs occur.The complexity and the dimensionality of the underlying mathematical models are among the principal causes that hinder understanding.Privacy must be guaranteed as sensible private data is typically continually collected, processed, and stored over the Cloud in order to provide improved user-specific experiences.Such continual learning for DL approaches is itself a challenge as models may suffer from catastrophic forgetting, but it is a key ingredient that needs to be taken into consideration to enable scalability.Data annotation done at large scale is costly and hardly sustainable, therefore particular attention must be put on self-supervised or unsupervised learning mechanisms.DL-based user interfaces, such as gesture recognition, must be supported

Unsupervised person re-identification (Re-ID) has garnered significant attention because of its data-friendly nature, as it does not require labeled data. Existing approaches primarily address this challenge by employing feature-clustering techniques to generate pseudo-labels. In addition, camera-proxy-based methods have emerged because of their impressive ability to cluster sample identities. However, these methods often blur the distinctions between individuals within inter-camera views, which is crucial for effective person re-ID. To address this issue, this study introduces an inter-camera-identity-difference-based contrastive learning framework for unsupervised person Re-ID. The proposed framework comprises two key components: (1) a different sample cross-view close-range penalty module and (2) the same sample cross-view long-range constraint module. The former aims at penalizing excessive similarity among different subjects across inter-camera views, whereas the latter mitigates the challenge of excessive dissimilarity among the same subject across camera views. To validate the performance of our method, we conducted extensive experiments on three existing person Re-ID datasets (Market-1501, MSMT17, and PersonX). The results demonstrate the effectiveness of the proposed method, which shows a promising performance. The code is available at https://github.com/hooldylan/IIDCL .

Currently, people are actively seeking new and clean energy sources to replace traditional fossil fuels. As a widely distributed, abundant, and easily accessible renewable energy source, wind energy has multiple applications both domestically and internationally, and has a promising future. In wind power forecasting, this paper proposes a new algorithm LFTformer that combines the Transformer model with linear fuzzy information granulation (LFIG).The model uses the improved LFIG algorithm to extract the semantic information of time series, and divides the original time series into multiple information granules, which are then used as inputs to the Transformer model. Through comparative experiments for wind power prediction over 72 hours, 120 hours, and 168 hours, this research demonstrates that the LFTformer can improve the accuracy of wind power prediction. Finally, a Digital Twins system for wind power prediction is constructed using three-dimensional visualization techniques, displaying the prediction results and key data indicators, providing reliable data support for wind power systems.

In this paper, Digital Twins(DT) is combined with the sewage plant. Through Digital Twins, the actual needs are analyzed to solve the problems existing in the sewage plant. Combined with Augmented Reality(AR), Machine Learning(ML) and automatic control algorithms, various functions of sewage plant can be achieved. The system uses Long Short Term Memory(LSTM), Gate Recurrent Unit(GRU) and Fuzzy Neural Network(FNN) to predict the Chemical Oxygen Demand(COD) concentration in water quality. By using these algorithms, the Digital Twins Sewage Plant(DTSP) can be better interacted with workers. Through remote control, fault alarm, automatic regulation and prediction, Digital Twins can improve the efficiency of sewage treatment.

This work aims to analyze malicious communication behaviors that pose a threat to the security of digital twins (DTs) and safeguard user privacy. A unified and integrated multidimensional DTs Network (DTN) architecture is constructed. On this basis, the propagation process model of malware in the network is built to analyze the malicious propagation behavior that threatens network security. This model ensures the protection of mobile distributed machine learning system security. Blockchain technology is a distributed data protection mechanism with broad prospects. It is characterized by decentralization, transparency, and anonymity, which can help ensure secure network data sharing and privacy protection. Based on this, this work designs a secure distributed data sharing (DDS) architecture based on blockchain to improve the security and reliability of data protection with the support of the Internet of Things (IoT). Then, digital resource allocation based on semi-distributed learning is examined to propose a broad learning federated continuous learning (BL-FCL) algorithm combining blockchain and DTs. This algorithm significantly speeds up the model training process. Broad learning technology supports incremental learning. In this way, each client does not need to retrain when learning the newly generated data. In the experimental part, the prediction accuracy of BL-FCL on the mixed national institute of standards and technology data set is similar to that of the FedAvg-50 and FedAvg-80 schemes. As the number of devices increases from 1 to 6, the detection probability exhibits a rapid decrease. However, as the number of devices further increases from 6 to 10, the detection probability gradually decreases at a slower rate until it reaches 0. Comparatively, the prediction accuracy of the BL-FCL outperforms the federated averaging algorithm-based scheme by 20%–60%. The BL-FCL reported here can deal with the problem of inaccurate training while ensuring the privacy and security of users. This work is of great significance for ensuring the security of the DTN and promoting the development of the digital economy. The results can provide references for applying blockchain and distributed learning in the DT field.

In vehicular edge computing (VEC), vehicle users (VUs) can offload their computation-intensive tasks to edge server (ES) that provides additional computation resources. Due to the edge server being closer to VUs, the propagation delay between the ESs and the VUs is lower compared to cloud computing. Applying digital twin to VEC allows for low-cost trial in task offloading. In real-word, the mobility of VUs cannot be ignored and the downlink delay in receiving process results from ES is related to the mobility of VUs. Therefore, a five-objective optimization model including downlink delay, computation delay, energy consumption, load balancing, and user satisfaction of the VUs is constructed. To solve the above model, an improved CMA-ES algorithm based on the guiding point (GP-CMA-ES) is proposed. When the number of VUs increases, the dimension of variables also increases. Therefore, a convergence-related variable grouping strategy based on the relationship detection between variables and objectives is proposed. The performance of algorithm GP-CMA-ES is compared with five algorithms in the digital twin environment.

Applying artificial intelligence to Chinese language translation in computational linguistics is of practical significance for economic boosts and cultural exchanges. In the present work, the bi-directional long short-term memory (BiLSTM) network is employed to extract Chinese text features regarding the overlapping semantic roles in Chinese language translation and hard-to-converge training of high-dimensional text word vectors in text classification during translation. In addition, AlexNet is optimized to extract the local features of the text and meanwhile update and learn network parameters in the deep network. Then, the attention mechanism is introduced to build a forecasting algorithm of Chinese language translation based on BiLSTM and improved AlexNet. Last, the forecasting algorithm is simulated to validate its performance. Some state-of-the-art algorithms are selected for a comparative experiment, including long short-term memory, regions with convolutional neural network features, AlexNet, and support vector machine. Results demonstrate that the forecasting algorithm proposed here can achieve a feature identification accuracy of 90.55%, at least an improvement of 4.24% over other algorithms. In addition, it provides an area under the curve of above 90%, a training duration of about 54.21 seconds, and a test duration of about 19.07 seconds. Regarding the performance of Chinese language translation, the algorithm proposed here provides a bilingual evaluation understudy (BLEU) value of 28.21 on the training set, with a performance gain ratio reaching 111.55%; on the test set, its BLEU reaches 40.45, with a performance gain ratio of 129.80%. Hence, this forecasting algorithm is notably superior to other algorithms, which can enhance the machine translation performance. Through experiments, the Chinese language translation algorithm constructed here improves translation performance while ensuring a high correct identification rate, providing experimental references for the later intelligent development of Chinese language translation in computational linguistics.

OBJECTIVE: it aims to adopt deep transfer learning combined with Digital Twins (DTs) in Magnetic Resonance Imaging (MRI) medical image enhancement. METHODS: MRI image enhancement method based on metamaterial composite technology is proposed by analyzing the application status of DTs in medical direction and the principle of MRI imaging. On the basis of deep transfer learning, MRI super-resolution deep neural network structure is established. To address the problem that different medical imaging methods have advantages and disadvantages, a multi-mode medical image fusion algorithm based on adaptive decomposition is proposed and verified by experiments. RESULTS: the optimal Peak Signal to Noise Ratio (PSNR) of 34.11dB can be obtained by introducing modified linear element and loss function of deep transfer learning neural network structure. The Structural Similarity Coefficient (SSIM) is 85.24%. It indicates that the MRI truthfulness and sharpness obtained by adding composite metasurface are improved greatly. The proposed medical image fusion algorithm has the highest overall score in the subjective evaluation of the six groups of fusion image results. Group III had the highest score in Magnetic Resonance Imaging- Positron Emission Computed Tomography (MRI-PET) image fusion, with a score of 4.67, close to the full score of 5. As for the objective evaluation in group I of Magnetic Resonance Imaging- Single Photon Emission Computed Tomography (MRI-SPECT) images, the Root Mean Square Error (RMSE), Relative Average Spectral Error (RASE) and Spectral Angle Mapper (SAM) are the highest, which are 39.2075, 116.688, and 0.594, respectively. Mutual Information (MI) is 5.8822. CONCLUSION: the proposed algorithm has better performance than other algorithms in preserving spatial details of MRI images and color information direction of SPECT images, and the other five groups have achieved similar results.

More data are generated through interaction between cyber space, physical space, and social space thanks to mobile network technology, giving birth to the so-called cyber–physical social intelligent ecosystem (C&P-SIE). This survey studies the development of physical social intelligence. First, it classifies and discusses the behavior modeling, learning, and adaptation applications of C&P-SIE from intelligent transportation, healthcare, public service, economy, and social networking. Then, it prospects the application of behavior modeling in the C&P-SIE from the perspectives of information security, data-driven techniques, and modeling learning under cooperative artificial intelligence technologies. The research provides a theoretical basis and new opportunities for the digital and intelligent development of smart cities and social systems.

The wide area measurement system (WAMS) based on synchronous phasor measurement technology plays an increasingly important role in dynamic monitoring and wide area protection of modern power systems. If the phasor measurement unit (PMU) is placed on all buses of the power system, the voltage and branch current of all buses can be directly observed. However, due to the high placement cost of PMU and its ability to measure the voltage phasor of the installed bus and the current of the associated branch, it is unrealistic and unnecessary to install PMU on all buses of the system. This article discusses the incomplete observability under single PMU loss (N-1) contingencies and its effect on PMUs placement. An improved two-archive algorithm is proposed to solve the five-objective placement optimization model. In addition, a fuzzy decision-making method combining subjective and objective is proposed to help power grid enterprises select the most appropriate solution. The proposed method is tested on several IEEE bus systems and Polish 2383-bus system, and the test results verify its effectiveness.

In this article, the proposed work aims to further optimize the fault diagnosis effect of manufacturing equipment, explore the application of digital twins technology in intelligent manufacturing. The equipment failures in Poka Yoke technology are adopted, and a fault identification and chopping algorithm is designed based on the active learning—deep neural network (AL-DNN) and domain adversarial neural networks (DANN). In addition, a digital twins workshop management and control system is designed for intelligent manufacturing management. The experimental exploration reveals that the accuracy of the AL-DNN algorithm is as high as 99.248%, which is more in line with practical applications. The DANN algorithm can realize fault identification and diagnosis under different working conditions. Compared with other deep learning algorithms, the accuracy of the DANN can be increased by up to 20.256%, showing higher accuracy in contrast to the traditional algorithm, so the effect is more stable. In addition, the digital twins manufacturing management system designed shows good performances, which can intuitively display the specific conditions of workshop and realize basic operating functions. The concept of digital twins is innovatively introduced into equipment fault diagnosis and trend prediction, which can provide scientific and effective reference data for subsequent research on intelligent manufacturing.

Current cyberspace is confronted with unprecedented security risks, whereas traditional passive protection techniques are ill-equipped for attacks or defects with unknown features. Dynamic heterogeneous redundancy (DHR), a built-in active defense approach, deploys uncertain, random, dynamic systems to change the asymmetry of attack and defense, where arbitration is one of the key mechanisms. In this article, an AI-assisted trustworthy architecture based on DHR and deep reinforcement learning-based intelligent arbitration (DRLIA) algorithm is presented to enhance security for industrial Internet of things (IIoT). A double deep Q network (DDQN) is introduced, which is capable to distinguish the reliable and credible IIoT message from executors through interaction with the DHR environment. Finally, the DRLIA is implemented to conduct arbitration tasks in an IIoT critical message transmission scenario, where several comparison experiments between DRLIA and other traditional algorithms are designed. The result on the testbed empirically demonstrates the effectiveness of the proposed architecture and the security enhancement.

Respiration rate is an important healthcare indicator, and it has become a popular research topic in remote healthcare applications with Internet of Things. Existing respiration monitoring systems have limitations in terms of convenience, comfort, and privacy, etc. This paper presents a contactless and real-time respiration monitoring system, the so-called Wi-Breath, based on off-the-shelf WiFi devices. The system monitors respiration with both the amplitude and phase difference of the WiFi channel state information (CSI), which is sensitive to human body micro movement. The phase information of the CSI signal is considered and both the amplitude and phase difference are used. For better respiration detection accuracy, a signal selection method is proposed to select an appropriate signal from the amplitude and phase difference based on a support vector machine (SVM) algorithm. Experimental results demonstrate that the Wi-Breath achieves an accuracy of 91.2% for respiration detection, and has a 17.0% reduction in average error in comparison with state-of-the-art counterparts.

With the dramatic progress of wearable devices, continuous collection of single lead ECG wave is able to be implemented in a comfortable fashion. Data mining on single lead ECG wave is therefore attracting increasing attention, where atrial fibrillation (AF) detection is a hot topic. In this paper, we propose a dual-channel neural network for AF detection from a single lead ECG wave. Two primary phases are included, the data preprocessing part followed by a dual-channel neural network. A two-stage denoising procedure is developed for data preprocessing, so as to tackle the high noise and disturbance which generally resides in the ECG wave collected by wearable devices. Then the time-frequency spectrum and Poincare plot of the denoised ECG signal are imported into the developed dual-channel neural network for feature extraction and AF detection. On the 2017 PhysioNet/CinC Challenge database, the F1 values were 0.83, 0.90, and 0.75 for AF rhythm and normal rhythm, and other rhythm, respectively. The results well validate the effectiveness of the proposed method for AF detection from a single lead ECG wave, and also indicate its performance advantages over some state-of-the-art counterparts.

Four-chamber (FC) views are the primary ultrasound(US) images that cardiologists diagnose whether the fetus has congenital heart disease (CHD) in prenatal diagnosis and screening. FC views intuitively depict the developmental morphology of the fetal heart. Early diagnosis of fetal CHD has always been the focus and difficulty of prenatal screening. Furthermore, deep learning technology has achieved great success in medical image analysis. Hence, applying deep learning technology in the early screening of fetal CHD helps improve diagnostic accuracy. However, the lack of large-scale and high-quality fetal FC views brings incredible difficulties to deep learning models or cardiologists. Hence, we propose a Pseudo-Siamese Feature Fusion Generative Adversarial Network (PSFFGAN), synthesizing high-quality fetal FC views using FC sketch images. In addition, we propose a novel Triplet Generative Adversarial Loss Function (TGALF), which optimizes PSFFGAN to fully extract the cardiac anatomical structure information provided by FC sketch images to synthesize the corresponding fetal FC views with speckle noises, artifacts, and other ultrasonic characteristics. The experimental results show that the fetal FC views synthesized by our proposed PSFFGAN have the best objective evaluation values: SSIM of 0.4627, MS-SSIM of 0.6224, and FID of 83.92, respectively. More importantly, two professional cardiologists evaluate healthy FC views and CHD FC views synthesized by our PSFFGAN, giving a subjective score that the average qualified rate is 82% and 79%, respectively, which further proves the effectiveness of the PSFFGAN.

MicroRNAs (miRNAs) influence several biological processes involved in human disease. Biological experiments for verifying the association between miRNA and disease are always costly in terms of both money and time. Although numerous biological experiments have identified multi-types of associations between miRNAs and diseases, existing computational methods are unable to sufficiently mine the knowledge in these associations to predict unknown associations. In this study, we innovatively propose a heterogeneous graph attention network model based on meta-subgraphs (MSHGANMDA) to predict the potential miRNA-disease associations. Firstly, we define five types of meta-subgraph from the known miRNA-disease associations. Then, we use meta-subgraph attention and meta-subgraph semantic attention to extract features of miRNA-disease pairs within and between these five meta-subgraphs, respectively. Finally, we apply a fully-connected layer (FCL) to predict the scores of unknown miRNA-disease associations and cross-entropy loss to train our model end-to-end. To evaluate the effectiveness of MSHGANMDA, we apply five-fold cross-validation to calculate the mean values of evaluation metrics Accuracy, Precision, Recall, and F1-score as 0.8595, 0.8601, 0.8596, and 0.8595, respectively. Experiments show that our model, which primarily utilizes multi-types of miRNA-disease association data, gets the greatest ROC-AUC value of 0.934 when compared to other state-of-the-art approaches. Furthermore, through case studies, we further confirm the effectiveness of MSHGANMDA in predicting unknown diseases.

Image segmentation is a challenging problem in imaging informatics, which stems from the intersection of imaging techniques, computer science and biomedicine. In particular, accurate segmentation of cardiac structures in late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) is of great clinical importance for cardiac function assessment and myocardial disease diagnosis. However, it is a well-known challenge due to its special imaging modality and the lack of labeled LGE samples. In this paper, we propose an unsupervised ventricular segmentation algorithm that can perform biventricular segmentation of LGE images in the absence of labeled LGE data. There are two primary modules, the data augmentation procedure and the segmentation network. The easily available annotated balanced-Steady State Free Precession (bSSFP) images are employed for cross-modal data augmentation by image translation, where a single bSSFP image is converted into multiple synthetic LGE images while preserving the original morphological structure. Then, the proposed segmentation network is trained with the synthetic LGE images and used for segmenting real LGE images. Validation experiments demonstrated the effectiveness and advantages of the proposed algorithm.

Advances in IoT and IoV technology have made connected autonomous vehicles (CAVs) data sources. Using CAVs as data sources and in incident management algorithms can create faster, more reliable, and more effective algorithms. This paper proposes a modified standard normal deviation (MSND) incident detection algorithm that uses CAVs as data sources and considers multiple traffic parameters. MSND is utilized in conjunction with two other incident detection algorithms, Standard Normal Deviation (SNS) and California (CAL), in a method of incident management known as Variable Speed Limits (VSL). SUMO Traffic Simulation Software is used to evaluate the effectiveness of the proposed method. A 10.4-kilometer road network is developed. Numerous scenarios are simulated on this road network, with variables including traffic demand, autonomous vehicle penetration rate, incident location, incident length, and incident lane. On the effectiveness metrics of detection rate, false alarm rate, and mean time to detect, simulation results demonstrate that the proposed method outperforms the SND and California methods. In terms of detection rate, the MSND algorithm performs the best, with a 12.27% improvement over the SND algorithm and a 21.99% improvement over the California method. After integrating all incident detection algorithms with the VSL traffic management method and simulating each combination, it was determined that the MSND-VSL integration reduced average density in the critical region by 19.73 percent, followed by SND-VSL with a 13.94 percent reduction and CAL-VSL with a 9.9 percent reduction.

The purposes are to explore the safety performance of the Maritime Transportation System (MTS) based on Digital Twins (DTs) Internet of Things (IoT) and develop maritime transportation towards intelligence and digitalization. Because the comprehensive operational security of modern MTS is not yet mature, historical transportation data of the Maritime Silk Road are acquired and preprocessed. Afterward, DTs are introduced, and relay nodes are added to data transmission paths to construct a maritime transportation DTs model based on relay cooperation IoT. Eventually, this model's security performance is validated through simulation experiments. Relay security analysis suggests that interference information is a vital guarantee to assist in information non-disclosure, from which the constructed model can harvest energy to increase the data transmission power, thereby improving communication performance and secrecy rate. Outage probability analysis reveals that the simulated and the theoretical results are almost the same; moreover, given the system's multi-hop paths in the same environment, the more the relays and the greater the fading index, the better the system performance and the lower the outage probability. Once the iterations reach a particular number, the node secrecy rate becomes optimal and cannot cause excessive burden to the system; besides, the power distribution can establish a new equilibrium when the nodes are in different locations, so that system security performance gets improved. The simulated value is closest to the actual result under 100% successful transmission probability and 0.01~0.05 λ value. To sum up, the constructed maritime transportation DTs model presents extraordinary transmission and security performance, providing an experimental basis for intelligent and secure maritime transportation in the future.

The Intelligent Transportation System under Digital Twins can provide accurate data sources for traffic control. The present work focuses on the real-time information processing and task scheduling problems of the Internet of Vehicles (IoV) system based on Virtual Reality. They are the Quality/Distance Algorithm (QDA), Task Density Algorithm, Distance Balance Algorithm (DBA), and Bionic-DBA (B-DBA). The simulation experiment analysis suggests that the DBA algorithm takes the balance of travel distance into account and effectively improves task quality. The Utility Function in B-DBA and the Biological Heuristic Search Algorithm in Pareto Ant Colony Optimization play a critically important role in enhancing the overall task quality. In addition, a Transmission based on Privacy Protection (TPP) algorithm is designed to protect the attribute-based privacy information in the traffic information transmission system. This algorithm ensures that the real-time traffic information processing system resists various attacks from malicious nodes. It has been verified that when the number of selfish nodes accounts for 30%, the transmission efficiency of the TPP algorithm reaches 0.77. The research content has a practical reference value for providing users with continuous and high-quality IoV network services.

The intelligent transportation systems improve the transportation system’s operational efficiency and enhance its safety and reliability by high-tech means such as information technology, control technology, and computer technology. In recent years, sustainable development has become an important topic in intelligent transportation’s development, including new infrastructure and energy distribution, new energy vehicles and new transportation systems, and the development of low-carbon and intelligent transportation equipment. New energy vehicles’ development is a significant part of green transportation, and its automation performance improvement is vital for smart transportation.

The satellite-terrestrial networks (STN) utilize the spacious coverage and low transmission latency of the Low Earth Orbit (LEO) constellation to transfer requested content for subscribers especially in remote areas. With the development of storage and computing capacity of satellite onboard equipment, it is considered promising to leverage in-network caching technology on STN to improve content distribution efficiency. However, traditional caching and distribution schemes are not suitable in STN, considering dynamic satellite propagation links and time-varying topology. More specifically, the unevenness of user distribution heightens difficulties for assurance of user quality of experience. To address these problems, we first propose a density-based network division algorithm. The STN is divided into a series of blocks with different sizes to amortize the data delivery costs. To deploy the caching satellites, we analyze the link connectivity and propose an approximate minimum coverage vertex set algorithm. Then, a novel cache node selection algorithm is designed for optimal subscriber matching. On the basis of time-varying network model, the STN cache content updating mechanism is derived to enable a stable and sustainable quality of user experience. The simulation results demonstrate that the proposed user-oriented STN content distribution scheme can obviously reduce the average propagation delay and network load under different network conditions and has better stability and self-adaptability under continuous time variation.

The work aims to study the application of Deoxyribonucleic Acid (DNA) multi-source data storage in Digital Twins (DT) . Through the investigation of the research status of DT and DNA computing, the work puts forward the concept of DNA multi-source data storage for DT. Raptor code is improved from the design direction of degree distribution function, and six degree function distribution schemes are proposed in turn in the process of describing the research method. Additionally, a quaternary dynamic Huffman coding method is applied in DNA data storage, combined with the improved concatenated code as the error correction code. Considering the content of cytosine deoxynucleotide (C) and guanine deoxynucleotide Guanine (G) and the distribution of homopolymer in DNA storage, the work proposes and verifies an improved concatenated code algorithm Deoxyribonucleic Acid-Improved Concatenated code (DNA-ICC) . The results show that while the Signal-to-Noise Ratio (SNR) increases, the Bit Error Rate (BER) decreases gradually and the trend is similar. But the anti-interference ability of the degree distribution function optimized by the probability transfer method is better. The BER of DNA-ICC scheme decreases with the decrease of error probability, which is stronger than other error correction codes. Compared with the original concatenated code, it saves at least 1.65 s, and has a good control effect on homopolymer. When the size of homopolymer exceeds 4 nt, the probability of homopolymer is only 0.44%. The proposed Quaternary dynamic Huffman code and concatenated error correction code have excellent performance.

This work emphasizes the current research status of the urban Digital Twins to establish an intelligent spatiotemporal framework. A Geospatial Artificial Intelligent (GeoAI) system is developed based on the Geographic Information System and Artificial Intelligence. It integrates multi-video technology and Virtual City in urban Digital Twins. Besides, an improved small object detection model is proposed: YOLOv5-Pyramid, and Siamese network video tracking models, namely MPSiam and FSSiamese, are established. Finally, an experimental platform is built to verify the georeferencing correction scheme of video images. The experimental results show that the Multiply-Accumulate value of MPSiam is 0.5B, and that of ResNet50-Siam is 4.5B. Besides, the model is compressed by 4.8 times. The inference speed has increased by 3.3 times, reaching 83 Frames Per Second. 3% of the Average Expectation Overlap is lost. Therefore, the urban Digital Twins-oriented GeoAI framework established here has excellent performance for video georeferencing and target detection problems.

This work aims to build a comprehensive and effective fire emergency management system based on the Internet of Things (IoT) and achieve an actual intelligent fire rescue. A smart fire protection information system was designed based on the IoT. A detailed analysis was conducted on the problem of rescue vehicle scheduling and the evacuation of trapped persons in the process of fire rescue. The intelligent fire visualization platform based on the three-dimensional (3D) Geographic Information Science (GIS) covers project overview, equipment status, equipment classification, equipment alarm information, alarm classification, alarm statistics, equipment account information, and other modules. The live video accessed through the visual interface can clearly identify the stage of the fire, which facilitates the arrangement of rescue equipment and personnel. The vehicle scheduling model in the system primarily used two objective functions to solve the Pareto Non-Dominated Solution Set Optimization: emergency rescue time and the number of vehicles. In addition, an evacuation path optimization method based on the Improved Ant Colony (IAC) algorithm was designed to realize the dynamic optimization of building fire evacuation paths. The experimental results indicate that all the values of detection signals were significantly larger in the smoldering fire scene at t = 17s than the initial value. In addition, the probability of smoldering fire and the probability of open fire were relatively large according to the probability function of the corresponding fire situation, demonstrating that this model could detect fire. The IAC algorithm reported here avoided the passages near the fire and spreading areas as much as possible and took the safety of the trapped persons as the premise when planning the evacuation route. Therefore, the IoT-based fire information system has important value for ensuring fire safety and carrying out emergency rescue and is worthy of popularization and application.

Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art deep learning model for representation learning on graphs. However, it remains notoriously challenging to train and inference GCNs over large graph datasets, limiting their application to large real-world graphs and hindering the exploration of deeper and more sophisticated GCN graphs. This is because as the graph size grows, the sheer number of node features and the large adjacency matrix can easily explode the required memory and data movements. To tackle the aforementioned challenges, we explore the possibility of drawing lottery tickets when sparsifying GCN graphs, i.e., subgraphs that largely shrink the adjacency matrix yet are capable of achieving accuracy comparable to or even better than their full graphs. Specifically, we for the first time discover the existence of graph early-bird (GEB) tickets that emerge at the very early stage when sparsifying GCN graphs, and propose a simple yet effective detector to automatically identify the emergence of such GEB tickets. Furthermore, we advocate graph-model co-optimization and develop a generic efficient GCN early-bird training framework dubbed GEBT that can significantly boost the efficiency of GCN training by (1) drawing joint early-bird tickets between the GCN graphs and models and (2) enabling simultaneously sparsification of both the GCN graphs and models. Experiments on various GCN models and datasets consistently validate our GEB finding and the effectiveness of our GEBT, e.g., our GEBT achieves up to 80.2% ~ 85.6% and 84.6% ~ 87.5% savings of GCN training and inference costs while offering a comparable or even better accuracy as compared to state-of-the-art methods. Our source code and supplementary appendix are available at https://github.com/RICE-EIC/Early-Bird-GCN.

This study aims to explore the security issues and computational intelligence of drone information system based on deep learning. Targeting at the security issues of the drone system when it is attacked, this study adopts the improved long short-term memory (LSTM) network to analyze the cyber physical system (CPS) data for prediction from the perspective of predicting the control signal data of the system before the attack occurs. At the same time, the differential privacy frequent subgraph (DPFS) is introduced to keep data privacy confidential, and the digital twins technology is used to map the operating environment of the drone in the physical space, and an attack prediction model for drone digital twins CPS is constructed based on differential privacy-improved LSTM. Finally, the tennessee eastman (TE) process is undertaken as a simulation platform to simulate the constructed model so as to verify its performance. In addition, the proposed model is compared with the Bidirectional LSTM (BiLSTM) and Attention-BiLSTM models proposed by other scholars. It was found that the root mean square error (RMSE) of the proposed model is the smallest (0.20) when the number of hidden layer nodes is 26. Comparison with the actual flow value shows that the proposed algorithm is more accurate with better fitting. Therefore, the constructed drone attack prediction model can achieve higher prediction accuracy and obvious better robustness under the premise of ensuring errors, which can provide experimental basis for the later security and intelligent development of drone system.

The space–air–ground-integrated network (SAGIN) can enhance the performance of the Internet of Vehicles (IoV). However, the basic hardware differences among communication systems are large, which leads to communication difficulties between different communication systems. To effectively manage multiple communication networks (satellite networks, air networks, and terrestrial networks) and computing resources in IoV, this article proposes a SAGIN-IoV edge–cloud architecture based on software-defined networking (SDN) and network function virtualization (NFV). In addition, we construct an optimization model based on SAGIN-IoV’s service requirements, and propose an improved algorithm. Experimental results show that the improved algorithm can effectively optimize the resource scheduling problem of SAGIN-IoV.

Recommender systems are of great significance for mining the data generated by the Internet of Things (IoT) and are important for the intelligent IoT systems. The traditional recommendation algorithms only consider the accuracy as the optimization objective. In this article, a many-objective optimization model consisting of the F1 measure, recommendation novelty, recommendation coverage, customer satisfaction, landmark similarity, and overfitting is constructed for recommendation. Then, to improve the recommendation performance, we propose to use a large-scale many-objective optimization algorithm based on problem transformation (LSMaOA) to optimize the matrix factorization model for the recommender system in the intelligent IoT systems. The experimental results show that LSMaOA is robust and can effectively optimize the model’s six objectives. Compared with the knee point-driven evolutionary algorithm (KnEA), the grid-based evolutionary algorithm (GrEA), the large-scale multiobjective optimization framework (LSMOF), and the reference vector guided evolutionary algorithm (RVEA), the proposed algorithm can promote the F1 measure by 7.78%, 13.63%, 21.85%, and 28.63%, respectively.

Internet-of-Things (IoT) technology is increasingly prominent in the current stage of social development. All walks of life have begun to implement the IoT integration technology, so as to strive to promote industrial modernization, intelligence, and digitalization. In this case, how to link high-risk network activities with entities has become the primary issue for promoting industrial development. However, at this stage, the security issues in the development of the IoT technology have contradictions that are difficult to resolve. According to this situation, how to make system defense intelligent and replace manual monitoring has become the future of the development of security architecture. This article combines existing security research to explore the possibility of deep learning (DL) in upgrading the IoT security architecture, discusses how the IoT can identify and respond to cyber attacks, and how to encrypt edge data transmission. Moreover, this article discusses security research in application fields, such as Industrial IoT, Internet of Vehicles, smart grid, smart home, and smart medical. Then, we summarized the areas that can be improved in future technological development, including sharing computing power through the edge network processing unit (NPU) central device and closely combining the environmental simulation model with the actual environment, as well as malicious code detection, intrusion detection, production safety, vulnerability detection, fault diagnosis, and blockchain technology.

The development of the Industrial Internet of Things (IIoT) and digital twins (DTs) technology brings new opportunities and challenges to all walks of life. The work aims to study the cross-layer optimization of DTs in IIoT. The specific application scenarios of hazardous gas leakage boundary tracking in the industry is explored. The work proposes an industrial hazardous gas tracking algorithm based on a parallel optimization framework, establishes a three-layer network of distributed edge computing based on IIoT, and develops a two-stage industrial hazardous gas tracking algorithm based on a state transition model. The performance of different algorithms is analyzed. The results indicate that the tracking state transition and target wake-up module can effectively track the gas boundary and reduce the network energy consumption. The task success rate of the parallel optimization algorithm exceeds 0.9 in 5 s. When the number of network nodes in the state transition algorithm is N = 600, the energy consumption is only 2.11 J. The minimum tracking error is 0.31, which is at least 1.33 lower than that of the exact conditional tracking algorithm. Therefore, the three-layer network edge computing architecture proposed here has an excellent performance in industrial gas diffusion boundary tracking.

Abstract In order to effectively extract the feature points of mobile network animation images and accurately reflect the main content of the video, an optimization method to extract the feature points of key frame images of mobile network animation is proposed. Firstly, the key frames are selected according to the content change degree of the animation video. The scale invariant feature transformation algorithm is used to describe the feature points of the key frame image of the animation video. The local feature points of the image are estimated by the constraint optimization method to realize the optimization extraction of the feature points of the key frame image of the mobile network animation. The efficiency of feature points extraction is analyzed from the number and effectiveness of feature points extraction, time-consuming and similarity invariance of feature points. The experimental results show that the proposed method has excellent adaptability, and can effectively extract feature points of mobile network animation image.

With the development of science and technology, the high-tech industry is developing rapidly, and various new-age technologies continue to appear, and Digital Twins (DT) is one of them. As a brand-new interactive technology, DT technology can handle the interaction between the real world and the virtual world well. It has become a hot spot in the academic circles of all countries in the world. DT have developed rapidly in recent years result from centrality, integrity and dynamics. It is integrated with other technologies and has been applied in many fields, such as smart factory in industrial production, digital model of life in medical field, construction of smart city, security guarantee in aerospace field, immersive shopping in commercial field and so on. The introduction of DT is mostly a summary of concepts, and few practical applications of Digital Twins are introduced. The purpose of this paper is to enable people to understand the application status of DT technology. At the same time, the introduction of core technologies related to DT is interspersed in the application introduction. Finally, combined with the current development status of DT, predict the future development trend of DT and make a summary.

With the continuous increase of the amount of information, people urgently need to identify the information in the image in more detail in order to obtain richer information from the image. This work explores the dynamic complex image segmentation of self-driving vehicle under Digital Twins (DTs) based on Memory-augmented Neural Networks (MANNs), so as to further improve the performance of self-driving in intelligent transportation. In view of the complexity of the environment and the dynamic changes of the scene in intelligent transportation, this work constructs a segmentation model for dynamic complex image of self-driving vehicle under DTs based on MANNs by optimizing the Deep Learning algorithm and further combining with the DTs technology, so as to recognize the information in the environment image during the self-driving. Finally, the performance of the constructed model is analyzed by experimenting with different image datasets (PASCALVOC 2012, NYUDv2, PASCAL CONTEXT, and real self-driving complex traffic image data). The results show that compared with other classical algorithms, the established MANN-based model has an accuracy of about 85.80%, the training time is shortened to 107.00 s, the test time is 0.70 s, and the speedup ratio is high. In addition, the average algorithm parameter of the given energy function α=0.06 reaches the maximum value. Therefore, it is found that the proposed model shows high accuracy and short training time, which can provide experimental reference for future image visual computing and intelligent information processing.

With Cloud/Fog Computing being a paradigm combination of IoT context and Edge Computing extended with Cloud/Fog, business process in it involves dataflows among multi-layers and multi-nodes, possibly provided by multi-organizations. Achieving end-to-end trustworthiness over the whole dataflow in such a Cloud/Fog Computing context is a challenging issue, nonetheless a necessary pre-condition for a successful business process on intra-/inter- organizational level. This paper investigates technical conundrums related to this target and proposes a policy-based approach for trustworthiness governance. An architectural layout is proposed with according modules, by carrying out two methodologies. One resides in tracing data derivation and maintaining security-level over the whole dataflow, handling data aggregation with several protocols. The other is to express data owner trustworthiness requirements with an enhanced attribute-based access control policy model and to evaluate data accessing nodes’ trustworthiness-related properties. Experiments show that processing time per attribute pair drops as the scales of policies increase, suggesting good scaling property of the system.

To accurately and effectively analyze electroencephalogram (EEG) with high complexity, large amount of data, and strong uncertainty, brain–computer interface (BCI) cognitive computing and its signal analysis algorithms are studied based on the digital twins (DTs) cognitive computing platform. To avoid the influence of noise on EEG analysis results, it is necessary to use filtering and defalsification methods to process EEG. Four methods, including Butterworth filter, finite impulse response (FIR) filter, elliptic filter, and wavelet decomposition, are summarized. Based on the Riemann manifold theory, a feature extraction algorithm under transfer learning based on tangent space selection (TL-TSS) is proposed. In the process of decoding EEG, an EEG decoding method combining entropy measure and singular spectrum analysis (SSA) is proposed. An algorithm performance is tested on the motor imagery dataset of the two International BCI Competitions. It is found that when the training sample size accounts for 5%, the TL-TSS algorithm proposed in this work is superior to other algorithms in classification accuracy. In particular, compared with common spatial pattern (CSP) algorithm, it has great advantages. The classification accuracy of A2, A4, A8, and A9 users is the best, and especially for A8 users, the classification accuracy reaches 97.88%. In summary, in the EEG interface technology of DT cognitive computing platform, the combination of cognitive computing and deep learning can improve the recognition and analysis effect of EEG, which is of great value for further optimization of DT cognitive computing system.

The fuzzy logic-based neural network usually forms fuzzy rules via multiplying the input membership degrees, which lacks expressiveness and flexibility. In this article, a novel neural network model is designed by integrating the gene expression programming into the interval type-2 fuzzy rough neural network, aiming to generate fuzzy rules with more expressiveness utilizing various logical operators. The network training is regarded as a multiobjective optimization problem through simultaneously considering network precision, explainability, and generalization. Specifically, the network complexity can be minimized to generate concise and few fuzzy rules for improving the network explainability. Inspired by the extreme learning machine and the broad learning system, an enhanced distributed parallel multiobjective evolutionary algorithm is proposed. This evolutionary algorithm can flexibly explore the forms of fuzzy rules, and the weight refinement of the final layer can significantly improve precision and convergence by solving the pseudoinverse. Experimental results show that the proposed multiobjective evolutionary network framework is superior in both effectiveness and explainability.

The sensor nodes in wireless sensor networks have the deficiency of limited energy, and the multihop transmission of information will lead to a premature paralysis of nodes near the sink. The use of the mobile sink can balance the energy consumption and greatly prolong the lifetime. Therefore, this article studies the scheduling strategy of multiple mobile sinks and proposes a heuristic strategy based on interval type-2 fuzzy rough neural network. The energy and lifetime of sensor nodes, as well as location information of the mobile sink and special nodes are taken as input features. Through neural network learning, the outputs determine whether to move, moving direction, moving distance, and residence time, which can complete the scheduling task. The scheduling problem is regarded as a multiobjective optimization problem, and the network lifetime, the moving path length, and the network interpretability are optimized at the same time, so as to obtain a lightweight network with good interpretability and performance. Based on the parallel multiobjective evolutionary algorithm, a multiobjective neural evolutionary framework is constructed. This framework can balance multiple objectives and complete complex scheduling tasks. Compared with static sinks, random-moving sinks, sinks with manually designed strategy, gene expression programming-based sinks, as well as the other state-of-the-art multiobjective evolutionary algorithms, the proposed framework can achieve superior results.

Medical data widely exist in the hospital and personal life, usually across institutions and regions. They have essential diagnostic value and therapeutic significance. The disclosure of patient information causes people’s panic, therefore, medical data security solution is very crucial for intelligent health care. The emergence of federated learning (FL) provides an effective solution, which only transmits model parameters, breaking through the bottleneck of medical data sharing, protecting data security, and avoiding economic losses. Meanwhile, the neural architecture search (NAS) has become a popular method to automatically search the optimal neural architecture for solving complex practical problems. However, few papers have combined the FL and NAS for simultaneous privacy protection and model architecture selection. Convolutional neural network (CNN) has outstanding performance in the image recognition field. Combining CNN and fuzzy rough sets can effectively improve the interpretability of deep neural networks. This article aims to develop a multiobjective convolutional interval type-2 fuzzy rough FL model based on NAS (CIT2FR-FL-NAS) for medical data security with an improved multiobjective evolutionary algorithm. We test the proposed framework on the LC25000 lung and colon histopathological image dataset. Experimental verification demonstrates that the designed multiobjective CIT2FR-FL-NAS framework can achieve high accuracy superior to state-of-the-art models and reduce network complexity under the condition of protecting medical data security.

The new Industry 4.0 standard has offered many advantages to the industries improving their production rate since it evaluates novel cutting-edge technologies like artificial intelligence, machine learning, cyber-physical systems, and Internet of Things (IoTs) to automate manufacturing processes so as to minimize time and economical costs while improving the quality of products. However, this rapid industrial transition carries risks in terms of security and privacy issues that arise. In this article, we propose a novel secure network model to enhance network security and employees’ privacy in the edge-enabled industrial IoTs. Experimental results demonstrate encouraging performance rates in terms of accuracy, precision, recall, fall-out, F-measure, and Matthews correlation coefficient against known and unknown bot attacks.

Studying the deep learning-based molecular representation has great significance on predicting molecular property, promoted the development of drug screening and new drug discovery, and improving human well-being for avoiding illnesses. It is essential to learn the characterization of drug for various downstream tasks, such as molecular property prediction. In particular, the 3D structure features of molecules play an important role in biochemical function and activity prediction. The 3D characteristics of molecules largely determine the properties of the drug and the binding characteristics of the target. However, most current methods merely rely on 1D or 2D properties while ignoring the 3D topological structure, thereby degrading the performance of molecular inferring. In this paper, we propose 3DMol-Net to enhance the molecular representation, considering both the topology and rotation invariance (RI) of the 3D molecular structure. Specifically, we construct a molecular graph with soft relations related to the spatial arrangement of the 3D coordinates to learn 3D topology of arbitrary graph structure and employ an adaptive graph convolutional network to predict molecular properties and biochemical activities. Comparing with current graph-based methods, 3DMol-Net demonstrates superior performance in terms of both regression and classification tasks. Further verification of RI and visualization also show better robustness and representation capacity of our model.

Augmetned reality is a key technology that will facilitate a major paradigm shift in the way users interact with data and has only just recently been recognized as a viable solution for solving many critical needs. Enter augmented reality (AR) technology, which can be used to visualize data from hundreds of sensors simultaneously, overlaying relevant and actionable information over your environment through a headset. With the rapid development of 5G, which makes AR technology much faster, with much more data flow. With easier and more accessible use, for a variety of different functions (besides video gaming), widespread adoption seems likely.

Fetal congenital heart disease (CHD) is the most common type of fatal congenital malformation. Fetal four-chamber (FC) view is a significant and easily accessible ultrasound (US) image among fetal echocardiography images. Automatic detection of four fetal heart chambers considerably contributes to the early diagnosis of fetal CHD. Furthermore, robust and discriminative features are essential for detecting crucial visualizing medical images, especially fetal FC views. However, it is an incredibly challenging task due to several key factors, such as numerous speckles in US images, the fetal four chambers with small size and unfixed positions, and category confusion caused by the similarity of cardiac chambers. These factors hinder the process of capturing robust and discriminative features, hence destroying the fetal four chambers’ precise detection. Therefore, we propose an intelligent feature learning detection system (FLDS) for FC views to detect the four chambers. A multistage residual hybrid attention module (MRHAM) presented in this paper is incorporated in the FLDS for learning powerful and robust features, helping FLDS accurately locate the four chambers in the fetal FC views. Extensive experiments demonstrate that our proposed FLDS outperforms the current state-of-the-art, including the precision of 0.919, the recall of 0.971, the$F_{1}$score of 0.944, the mAP of 0.953, and the frames per second (FPS) of 43. In addition, our proposed FLDS is also validated on other visualizing nature images such as the PASCAL VOC dataset, achieving a higher mAP of 0.878 while input size is 608 × 608.

With the popularity of the wireless body sensor network, real-time and continuous collection of single-lead electrocardiogram (ECG) data becomes possible in a convenient way. Data mining from the collected single-lead ECG waves has therefore aroused extensive attention worldwide, where early detection of atrial fibrillation (AF) is a hot research topic. In this paper, a two-channel convolutional neural network combined with a data augmentation method is proposed to detect AF from single-lead short ECG recordings. It consists of three modules, the first module denoises the raw ECG signals and produces 9-s ECG signals and heart rate (HR) values. Then, the ECG signals and HR rate values are fed into the convolutional layers for feature extraction, followed by three fully connected layers to perform the classification. The data augmentation method is used to generate synthetic signals to enlarge the training set and increase the diversity of the single-lead ECG signals. Validation experiments and the comparison with state-of-the-art studies demonstrate the effectiveness and advantages of the proposed method.

Recent years have witnessed an increasing popularity of wireless body area network (WBAN), with which continuous collection of physiological signals can be conveniently performed for healthcare monitoring. Energy consumption is a critical issue because it directly affects the duration of the equipped sensors. In this article, we propose a low-cost and confidential electrocardiogram (ECG) acquisition approach for WBAN. The compressed sensing (CS) is employed for low-cost signal acquisition, and its cryptographic features are exploited for promoting the framework's confidentiality. In particular, the RIPless measurement matrix is used to give CS the resistance against plaintext attack, while the first-order Σ∆ quantizer is employed to embed the cryptographic diffusion feature into the whole system. Two chaotic systems are employed for generating the required secret elements for the acquisition and encryption. Experiment results well demonstrate the signal reconstruction and security performance of the proposed framework.

The problems of traditional traffic flow detection and calculation methods include limited traffic scenes, high system costs, and lower efficiency over detecting and calculating. Therefore, in this paper, we presented the updating Connected and Automated Vehicles (CAVs) model as the scanner of heterogeneous traffic flow, which uses various sensors to detect the characteristics of traffic flow in several traffic scenes on the roads. The model contains the hardware platform, software algorithm of CAV, and the analysis of traffic flow detection and simulation by Flow Project, where the driving of vehicles is mainly controlled by Reinforcement Learning (RL). Finally, the effectiveness of the proposed model and the corresponding swarm intelligence strategy is evaluated through simulation experiments. The results showed that the traffic flow scanning, tracking, and data recording performed continuously by CAVs are effective. The increase in the penetration rate of CAVs in the overall traffic flow has a significant effect on vehicle detection and identification. In addition, the vehicle occlusion rate is independent of the CAV lane position in all cases. The complete street scanner is a new technology that realizes the perception of the human settlement environment with the help of the Internet of Vehicles based on 5G communications and sensors. Although there are some shortcomings in the experiment, it still provides an experimental reference for the development of smart vehicles.

The purpose of this exploration of blockchain in vehicle management based on Digital Twins in Vehicular Adhoc Networks (VANETs) is to further improve intelligent transportation in smart cities. In view of the complexity of pedestrians in the real road network, the Digital Twins (DTs) technology is used to map the traffic situation in the real road network to the virtual space. Furthermore, the concrete interaction of vehicle data information is stored and transmitted by using blockchain technology. Finally, the DTs model of vehicle-mounted Ad Hoc network based on blockchain is constructed, and its performance is analyzed by simulation. The results suggest that the model algorithm adopted in this work shows a lower average delay time, its data message delivery rate is basically stable at 80%, the data message leakage rate is basically stable at approximately 10%, and the communication overhead does not exceed 700 bytes. Therefore, the in-vehicle self-organizing network model constructed in this work shows high network security performance while ensuring low latency performance, enabling information to interact more efficiently. Therefore, it can provide an experimental basis for the intelligent development and safety performance improvement of the transportation field of smart cities.

The purposes are to analyze the safety and real-time performance of intelligent transportation under big data and solve urban transportation safety problems. The existing problems of complex traffic data collection and single factor analysis are analyzed. Guangzhou target road section and its historical traffic flow are taken as the data source, the big data analytics (BDA) platform is constructed after preprocessing, and then the deep belief network (DBN) is introduced to build the intelligent transportation model based on BDA. Finally, the model is simulated to analyze its performance. The results reveal that the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) of the proposed algorithm are 4.42%, 6.21%, and 8.03%, respectively compared to Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Neuro fuzzy c-means model (FCM), Deep Neural Network (DNN), and neural network (NN). The mean absolute error (MAE) and mean absolute percentage error (MAPE) are 4.42%, 6.21% and 8.03% respectively. The prediction accuracy is significantly better than that of other algorithms. The analysis of Accuracy, Precision, Recall and F1 suggests that the safety prediction accuracy of the proposed algorithm can reach up to 88.57%, which is at least 3.19% higher than that of other algorithms. Meantime, the proposed algorithm can effectively inhibit the spread of congestion, and achieve the effect of timely evacuation for traffic congestion. Regarding the data transmission performance, the delay is less than 40ms and the packet loss rate is less than 0.1, which is significantly better than other model algorithms in real-time performance. Hence, the constructed intelligent transportation algorithm based on BDA has high precision and excellent congestion evacuation performance, which can provide an experimental basis for constructing intelligent and safe transportation in the future.

The present work aims to improve the communication security of Internet of Vehicles (IoV) nodes in intelligent transportation through studying the safety of IoV in smart transportation based on Blockchain (BC). An IoV DTs model is built by combining big data with Digital Twins (DTs). Then, regarding the current IoV communication security issues, a secure communication architecture for the IoV system is proposed based on the immutable and trackable BC data. Besides, Wasserstein Distance Based Generative Adversarial Network (WaGAN) model constructs the IoV node risk forecast model. Because the WaGAN model calculates the loss function through Wasserstein distance, the learning rate of the model accelerates remarkably. After ten iterations, the loss rate of the WaGAN model is close to zero. Massive in-vehicle devices in IoV are connected simultaneously to the base station, causing network channel congestion. Therefore, a Group Authentication and Privacy-preserving (GAP) scheme is put forward. As users increase during authentication, the GAP scheme performs better than other authentication access schemes. In summary, the Intelligent Transportation System driven by DTs can promote intelligent transportation management. Besides, introducing BC into IoV can improve access control’s accuracy and response efficiency. The research reported here has significant value for improving the security of the information sharing of the IoV.

Ships are usually disturbed by waves when they are traveling at sea. When the waves are large, it is not conducive to driving safety, comfort and economy. Therefore, this paper proposed a new type of automatic driving scheme, which links the wave height prediction with ship driving. By studying the accurate prediction of wave height, ships can adjust their course in real time to ensure that they always travel in the area with the lowest wave height. According to the different driving conditions of ships in the open sea and the offshore sea, we designed two wave height prediction models based on LSTM, which are suitable for the above two types of sea areas. In particular, when we created the open sea model, we selected the data of the location points other than the predicted point as the training data. After comparative testing, the two types of models have reached satisfactory accuracy, which provided support for the ship automatic driving scheme proposed in this paper.

The purposes are to explore the effect of Digital Twins (DTs) in Unmanned Aerial Vehicles (UAVs) on providing medical resources quickly and accurately during COVID-19 prevention and control. The feasibility of UAV DTs during COVID-19 prevention and control is analyzed. Deep Learning (DL) algorithms are introduced. A UAV DTs information forecasting model is constructed based on improved AlexNet, whose performance is analyzed through simulation experiments. As end-users and task proportion increase, the proposed model can provide smaller transmission delays, lesser energy consumption in throughput demand, shorter task completion time, and higher resource utilization rate under reduced transmission power than other state-of-art models. Regarding forecasting accuracy, the proposed model can provide smaller errors and better accuracy in Signal-to-Noise Ratio (SNR), bit quantizer, number of pilots, pilot pollution coefficient, and number of different antennas. Specifically, its forecasting accuracy reaches 95.58% and forecasting velocity stabilizes at about 35 Frames-Per-Second (FPS). Hence, the proposed model has stronger robustness, making more accurate forecasts while minimizing the data transmission errors. The research results can reference the precise input of medical resources for COVID-19 prevention and control.

The purpose is to solve the security problems of the Cooperative Intelligent Transportation System (CITS) Digital Twins (DTs) in the Deep Learning (DL) environment. The DL algorithm is improved; the Convolutional Neural Network (CNN) is combined with Support Vector Regression (SVR); the DTs technology is introduced. Eventually, a CITS DTs model is constructed based on CNN-SVR, whose security performance and effect are analyzed through simulation experiments. Compared with other algorithms, the security prediction accuracy of the proposed algorithm reaches 90.43%. Besides, the proposed algorithm outperforms other algorithms regarding Precision, Recall, and F1. The data transmission performances of the proposed algorithm and other algorithms are compared. The proposed algorithm can ensure that emergency messages can be responded to in time, with a delay of less than 1.8s. Meanwhile, it can better adapt to the road environment, maintain high data transmission speed, and provide reasonable path planning for vehicles so that vehicles can reach their destinations faster. The impacts of different factors on the transportation network are analyzed further. Results suggest that under path guidance, as the Market Penetration Rate (MPR), Following Rate (FR), and Congestion Level (CL) increase, the guidance strategy’s effects become more apparent. When MPR ranges between 40% ~ 80% and the congestion is level III, the ATT decreases the fastest, and the improvement effect of the guidance strategy is more apparent. The proposed DL algorithm model can lower the data transmission delay of the system, increase the prediction accuracy, and reasonably changes the paths to suppress the sprawl of traffic congestions, providing an experimental reference for developing and improving urban transportation.

To realize the great potential of the intelligent transportation infrastructure, the investment in the transportation infrastructure in the intelligent transportation system should be rationally planned. Firstly, the application status of cutting-edge Data Envelopment Analysis (DEA) model in transportation infrastructure efficiency evaluation is analyzed, and based on this, a DEA model of transportation infrastructure efficiency evaluation under Digital Twins technology is established. Secondly, with the transportation infrastructure of 12 prefecture-level cities in Jiangsu Province from 2005 to 2020 as the research object, the Digital Twins DEA model and the traditional Stochastic Frontier Approach (SFA) model are used to estimate the efficiency of transportation infrastructure in 12 cities. Finally, the traffic flow data of a certain road section in Zhenjiang City (J11 City) is simulated and predicted by using the Long Short-term Memory (LSTM) traffic flow prediction model. The results show that the average efficiency of the 12 cities estimated by the DEA model based on the Digital Twins is 0.7083, the average efficiency of the 12 cities estimated by the SFA model is 0.6445, and there are significant differences in the efficiency rankings of the cities. Compared with the actual efficiency, the established Digital Twins DEA model is more reasonable for the calculation of transportation infrastructure efficiency. The results of the LSTM traffic flow prediction model show that the Mean Absolute Error (MAE) of the LSTM model is 24.29, the Root Mean Square Error (RSME) is 0.1186, and the Mean Absolute Perce (MAPE) is 17.78, which are all lower than other models. Compared with other models, the proposed LSTM-based traffic flow prediction model is more accurate in traffic flow prediction. Hence, the research content provides a reference for the investment planning of intelligent transportation system infrastructure.

To explore the airspace structure and safety performance of unmanned aerial vehicle (UAV) system based on spatial digital twins (DTs), the study introduces DTs technology, and combines convolutional neural network (CNN) algorithm with UAV autonomous network. The DTs system of UAV is constructed by using wireless communication technology, and its security performance is simulated. The results show that in the analysis of the system packet loss rate, it is found that with the increase of the acquisition points, the amount of transmitted data only increases slightly, but the packet loss rate does not change significantly. In the analysis of the network performance of the unmanned aircraft system, it is found that the node energy-based weighted clustering algorithm (EWCA) can be used to increase the life of the overall network and enhance its availability by rationally controlling the number of nodes and the number of switching between clusters. As the number of nodes increases, the minimum survival time of each clustering algorithm decreases linearly. When the number of nodes is less than 600, the growth rate of cluster head is higher; When the number of nodes is more than 600, the curve growth is relatively smooth. In the analysis of the probability of network safety interruption, it is found that using the model constructed, when the energy acquisition coefficient is close to 0.5, the energy conversion efficiency is higher, the signal-to-noise ratio is larger. Also, when the number of intermediate nodes is increased to 10, the UAV has the best network safety performance. Therefore, through the research, it is found that the UAV DTs system constructed can significantly improve the safety performance of the UAV during its airspace flight. It can provide experimental references for the widespread application of the UAV in the later period.

The purposes are to investigate how blockchain can solve the security problems in Intelligent Autonomous Transport System (IATS) and intelligentize the logistics transportation development. Regarding the scarcity of trust and concentration of rights caused by the centralized structure of traditional logistics information systems, a blockchain-based IATS is proposed. The system employs Ethereum as the underlying blockchain to record sensitive information, such as system orders, cargos, and personnel information on the blockchain, ensuring the non-tampering and credibility of data. Simultaneously, an order management module, a warehouse management module, a transportation management module, a transaction management module, and a system management module are established. In the meantime, the Light Gradient Boosting Machine (LightGBM) algorithm is utilized to recommend vehicle and cargo matching during transportation. Finally, the constructed algorithm model is simulated to analyze its performance. Results demonstrate that the security prediction accuracy of the proposed algorithm reaches 88.72%; moreover, the security prediction precision, recall, and F1 of the proposed algorithm are considerably better than those of other algorithms. Furthermore, the actual effect of each algorithm is analyzed. The LightGBM algorithm outperforms other algorithms and unused algorithms in click rate, conversion rate, turnover rate, and average response time. Therefore, the constructed blockchain-based IATS has excellent security performance and prediction accuracy, which provides an experimental basis for the later intelligent logistics transportation development.

Objective: Green computing meets the needs of a low-carbon society and it is an important aspect of promoting social sustainable development and technological progress. In the investigation, green computing for resource management and allocation issues is only discussed. Therefore, in the context of the 5G communication network, the investigation of the data classification and resource optimization of the Internet of Things are conducted. Method: The virtualization architecture of the heterogeneous wireless network resource based on 5G technology is designed. The related investigation is conducted based on 5G network and Internet of Things technology. Under the traditional method, the transfer learning is introduced to improve the AdaBoost (Adaptive Boosting) algorithm to classify the data. The investigated complete resource reuse method is used to optimize resources. A method that a sub-channel can be reused by a cellular link and any number of D2D links at the same time is proposed to conduct resource optimization investigation. Results: The investigation indicates that the classification accuracy of the algorithm is excellent for the data classification of the Internet of Things and has different advantages in various aspects compared with other algorithms. The designed algorithm can find a larger set of resource reuse and have a significant increase in spectrum utilization efficiency. Conclusion: The investigation can contribute to the boom in the Internet of Things in terms of data classification and resource optimization based on 5G.

With the rapid development of information technology and the spread of Corona Virus Disease 2019 (COVID-19), the government and urban managers are looking for ways to use technology to make the city smarter and safer. Intelligent transportation can play a very important role in the joint prevention. This work expects to explore thebuilding information modeling (BIM) big data (BD)processing method ofdigital twins (DTs)of Smart City, thus speeding up the construction of Smart City and improve the accuracy of data processing. During construction, DTs build the same digital copy of the smart city. On this basis, BIM designs the building's keel and structure, optimizing various resources and configurations of the building. Regarding the fast data growth in smart cities, a complex data fusion and efficient learning algorithm, namely Multi-Graphics Processing Unit (GPU), is proposed to process the multi-dimensional and complex BD based on the compositive rough set model. The Bayesian network solves the multi-label classification. Each label is regarded as a Bayesian network node. Then, the structural learning approach is adopted to learn the label Bayesian network's structure from data. On the P53-old and the P53-new datasets, the running time of Multi-GPU decreases as the number of GPUs increases, approaching the ideal linear speedup ratio. With the continuous increase of K value, the deterministic information input into the tag BN will be reduced, thus reducing the classification accuracy. When K = 3, MLBN can provide the best data analysis performance. On genbase dataset, the accuracy of MLBN is 0.982 ± 0.013. Through experiments, the BIM BD processing algorithm based onBayesian Network Structural Learning (BNSL)helps decision-makers use complex data in smart cities efficiently.

Two-dimensional 1 arrays of bi-component structures made of cobalt and permalloy elliptical dots with thickness of 25 nm, length 1 mm and width of 225 nm, have been prepared by a self-aligned shadow deposition technique. Brillouin light scattering has been exploited to study the frequency dependence of thermally excited magnetic eigenmodes on the intensity of the external magnetic field, applied along the easy axis of the elements. This study aims to enhance the security for people's health, improve the medical level further, and increase the confidentiality of people's privacy information. Under the trend of wide application of deep learning algorithms, the convolutional neural network (CNN) is modified to build an interactive smart healthcare prediction and evaluation model (SHPE model) based on the deep learning model. The model is optimized and standardized for data processing. Then, the constructed model is simulated to analyze its performance. The results show that accuracy of the constructed system reaches 82.4%, which is at least 2.4% higher than other advanced CNN algorithms and 3.3% higher than other classical machine algorithms. It is proved based on comparison that the accuracy, precision, recall, and F1 of the constructed model are the highest. Further analysis on error shows that the constructed model shows the smallest error of 23.34 pixels. Therefore, it is proved that the built SHPE model shows higher prediction accuracy and smaller error while ensuring the safety performance, which provides an experimental reference for the prediction and evaluation of smart healthcare treatment in the later stage.

The particularity of the marine underwater environment has brought many challenges to the development of underwater sensor networks (UWSNs) . This research realized the effective monitoring of targets by UWSNs and achieved higher quality of service in various applications such as communication, monitoring, and data transmission in the marine environment. After analysis of the architecture, the marine integrated communication network system (MICN system) is constructed based on the maritime wireless Mesh network (MWMN) by combining with the UWSNs. A distributed hybrid fish swarm optimization algorithm (FSOA) based on mobility of underwater environment and artificial fish swarm (AFS) theory is proposed in response to the actual needs of UWSNs. The proposed FSOA algorithm makes full use of the perceptual communication of sensor nodes and lets the sensor nodes share the information covered by each other as much as possible, enhancing the global search ability. In addition, a reliable transmission protocol NC-HARQ is put forward based on the combination of network coding (NC) and hybrid automatic repeat request (HARQ) . In this work, three sets of experiments are performed in an area of 200 × 200 × 200 m. The simulation results show that the FSOA algorithm can fully cover the events, effectively avoid the blind movement of nodes, and ensure consistent distribution density of nodes and events. The NC-HARQ protocol proposed uses relay nodes for retransmission, and the probability of successful retransmission is much higher than that of the source node. At a distance of more than 2,000 m, the successful delivery rate of data packets is as high as 99.6%. Based on the MICN system, the intelligent ship constructed with the digital twins framework can provide effective ship operating state prediction information. In summary, this study is of great value for improving the overall performance of UWSNs and advancing the monitoring of marine data information.

Several new models and formats for the digital transformation of the manufacturing industry appear because of the rapid integration of information technology and the real economy, as well as the increasingly obvious evolution trend of industrial digitalization, networking, and intelligence.Among them, digital twins have increasingly become a research hotspot in all sectors of the industry and have broad prospects.It maps physical objects in virtual space in a digital way and simulates their behavioral characteristics in real environments.It makes the gap between virtuality and reality disappear based on their closed-loop interaction.Digital twins are undoubtedly an important and strategic technology in response to familiar products, production, and services.It can also speculate some indicators that cannot be directly measured by machine learning through collecting the direct data of limited physical sensor indicators.This can realize an assessment of the current state, a diagnosis of past problems, and a prediction of future trends, and simulate possibilities to provide more comprehensive decision support.Driven by "Industry 4.0/5.0", the concept of "Digital Twins" is setting off a new wave of industrial simulation boom.Entity industrial objects or systems are fully digitized, and models including virtual objects, virtual processes, and virtual plant areas are established.These digital twins can be placed in virtual environments to analyze, simulate, verify, test, and adjust various situations such as product or process optimization, optimizing the operation of real objects or systems, and increasing their added value.Also, the expression of "virtual reality" for scene visualization and the new scene interaction mode will be more conducive to better visual effects and interactive operations in the digital world.The combination of digital twins and virtual reality is currently widely used as guidance instructions for operation and maintenance, early warning of error prevention, and information tips to improve the efficiency and accuracy of operators.Virtual reality brings users better vision and senses through smart hardware like glasses and helmets.However, there is still room for improvement in the technical level of smart hardware.And rational hardware configuration and cost-reduction can be realized gradually with the improvement of R & D. Now the focus in this field should be how to enhance user experience and enrich products.In "Integrating digital twins and deep learning for medical image analysis in the era of COVID-19", the authors introduce a digital-twin-based smart healthcare system integrated with medical devices to collect information regarding the current health condition, configuration, and maintenance history of the device/ machine/system.Furthermore, medical images, that is, X-rays, are analyzed by using a deep-learning model to detect the infection of COVID-19.The designed system is based on the cascade recurrent convolution neural network (RCNN) architecture.In this architecture, the detector stages are deeper and more sequentially selective against small and close false positives.This architecture is a multi-stage extension of the RCNN model and sequentially trained using the output of one stage for training the other.At each stage, the bounding

The new-generation information technology development enables Digital Twins to reshape the physical world into the virtual digital space and provide technical support for Metaverse construction. The Metaverse objects can be mesoscale or macro-micro-scales. Metaverse is a complex collection of both solid substances and liquid, gaseous, plasma, and other uncertain states. Additionally, Metaverse integrates the tangibles with social relations, such as interpersonal relations (friendship, love, and blood relations) and the overall social relations (ethics, morality, and law). This work also introduces some principles or laws to construct the Digital Twins model for social relations, such as broken windows theory, small-world phenomenon, survivor bias, and herd behavior. Thus, from multiple angles, it reviews mapping the tangible and intangible real-world objects to the Metaverse using the Digital Twins model.

There has been a booming demand for integrating Convolutional Neural Networks (CNNs) powered functionalities into Internet-of-Thing (IoT) devices to enable ubiquitous intelligent "IoT cameras". However, more extensive applications of such IoT systems are still limited by two challenges. First, some applications, especially medicine-and wearable-related ones, impose stringent requirements on the camera form factor. Second, powerful CNNs often require considerable storage and energy cost, whereas IoT devices often suffer from limited resources. PhlatCam, with its form factor potentially reduced by orders of magnitude, has emerged as a promising solution to the first aforementioned challenge, while the second one remains a bottleneck. Existing compression techniques, which can potentially tackle the second challenge, are far from realizing the full potential in storage and energy reduction, because they mostly focus on the CNN algorithm itself. To this end, this work proposes SACoD, a Sensor Algorithm Co-Design framework to develop more efficient CNN-powered PhlatCam. In particular, the mask coded in the Phlat-Cam sensor and the backend CNN model are jointly optimized in terms of both model parameters and architectures via differential neural architecture search. Extensive experiments including both simulation and physical measurement on manufactured masks show that the proposed SACoD framework achieves aggressive model compression and energy savings while maintaining or even boosting the task accuracy, when benchmarking over two state-of-the-art (SOTA) designs with six datasets across four different vision tasks including classification, segmentation, image translation, and face recognition. Our codes are available at: https://github.com/RICE-EIC/SACoD.

The LEO satellite network (LSN) is envisioned to be highly advanced and ubiquitous, as a function complement and enhancement of ground networks. The satellite networking enables low-latency and high-speed data transmission over long distances for global users, especially in remote areas. Since the nature of time-variability, it is not easy to arrange the satellite networking scheme for tasks at each time slot. Specifically, the main problem is how to ensure that the networking scheme always follows the maximum network transmission capacity during the task duration. To address the problem, this paper first constructs a time-varying LSN model to describe the network characteristics. The networking problem is formulated as the maximum network transmission capacity (NTC) problem at each time slot. Next, a two-stage contact optimization scheme is given. The transmission-based depth first search (TDFS) algorithm is first proposed to calculate the optimal networking for each specified time slot. Then a network performance graph (NPG) is constructed to show the NTC performances of different time slot combinations. The dynamic programming is utilized on NPG to find the optimal time slot sequence. Simulation results show that the proposed contact management with dynamic programming (CMDP) scheme achieves better network throughput and service continuity for LSN.

The mobile satellite communication networks (MSCN) enable network expansion and supplement in remote areas. Users in these regions can obtain specific network services with low latency and high transmission rates utilizing the low-earth-orbit (LEO) satellite constellation. However, due to the frequent switching of MSCN topology, the challenge is how to ensure the quality and continuity of data transmission paths in a certain time period. In this paper, we build a user satisfaction (US) indicator to measure the performance of pathfinding. The MSCN pathfinding optimization problem for the maximum US is first formulated. To simplify the complex calculation, we utilize the special-temporal division to solve the problem in two stages. In each time slot, the modified heuristic algorithm is utilized to find paths for the maximum US. Then, an active time slot division scheme is proposed. The divided time slot sequences are disconnected and reorganized to seek the time-extended optimal solution. Simulation results show that the proposed scheme achieves superior performance in improving the total US and guarantees reliable service continuity for MSCN.

Determination of image authenticity usually requires the identification and localization of the manipulated regions of images. Hence, image manipulation detection has become one of the most important tasks in the field of multimedia forensics. Recently, Convolutional Neural Networks (CNNs) have achieved promising performance in image manipulation detection. However, it is hard for the existing CNN-based manipulation detection approaches to accurately identify and localize the manipulated regions that have undergone geometric transformations, since CNNs are limited by their inability to be geometrically invariant. To address this issue, we propose a geometric rectification-based neural network architecture for image manipulation detection. In this type of network architecture, following the detection of a set of potential manipulated regions (PMRs) using Region Proposal Network, the Spatial Transformer Network is employed to geometrically rectify the convolutional feature maps (CFMs) of these regions to obtain the geometrically rectified CFMs (GR-CFMs). Subsequently, the residual feature maps (RFMs) are computed to capture the characteristic inconsistency between the CFMs and GR-CFMs of each PMR. Finally, the computed RFMs are automatically integrated with the GR-CFMs by a designed attention module to determine whether each PMR is a manipulated region and to localize the manipulated part at the pixel-level. Extensive experiments on the public data set as well as on our challenging data set demonstrate that the proposed network architecture achieves desirable performance in identifying and localizing regions with common tampering artifacts, which involve geometric transformations.

The deployment of a very large number of readers in a limited space may increase the probability of collision among radio-frequency identification (RFID) readers and reduce the dependability and controllability of Internet-of-Things (IoT) systems. Intelligent computing technologies can be used to realize intelligent management by scheduling resources to circumvent collision issues. In this article, an improved RFID reader anticollision model is constructed by modifying the measure index, introducing a constraint function, and simultaneously considering collisions among readers and between readers and tags. The dense deployment of large numbers of readers increases the number of variables to be encoded, resulting in a high-dimensional problem that cannot be effectively and efficiently solved by traditional algorithms. Accordingly, distributed parallel cooperative co-evolution particle swarm optimization (DPCCPSO) is proposed. The inertia weight and learning factors are adjusted during evolution, and an improved grouping strategy is presented. Moreover, various combinations of random number generation functions are tested. For improved efficiency, DPCCPSO is implemented with distributed parallelism. Experimental verification shows that the proposed novel algorithm exhibits superior performance to existing state-of-the-art algorithms, particularly when numerous RFID readers are deployed.

In order to protect industrial safety, improve the operation stability of the industrial control system, conduct the response measures for network environment attacked by the external world, and realize simulation in virtual reality environment, in this study, class and sample weighted C-support vector machine (CSWC-SVM) algorithm is first proposed using SVM. Then, the intrusion detection model of industrial control network is built based on the CSWC-SVM algorithm. Finally, KDD CUP 1999 data are introduced to carry out simulation experiments on the algorithm model constructed in this study in the virtual reality simulation environment. The results show when the penalty factor of the polynomial kernel function, radial basis kernel function, and sigmoid kernel function is 104, the average number of support vectors is 45, 46, and 37, respectively; the average training time are about 0.43, 0.45, and 0.47 s, and the average test time is about 9.7, 9.9, and 10.2 s, respectively; the average recognition accuracy is about 85.7%, 86.2%, and 86.7%, and the false positive rate is 3.8%, 2.8%, and 2.3%, respectively; the accuracy of the CSWC-SVM algorithm in different sample sizes (1000-6000) can be kept above 90%. The operation error rate of the CSWC-SVM algorithm is lower than that of C-SVM, C-SVM, and RS-SVM algorithms under different validation data sets. After dimension reduction, the classification accuracy of the CSWC-SVM algorithm is higher than that of C-SVM and WC-SVM algorithms. The weight value increases from 0 to 200, and the number of model errors on 1000, 2000, and 3000 pieces of data decreases significantly. When the weight value is 200, the number of errors drops to 0, and the classification accuracy reaches 100%. In a word, the CSWC-SVM algorithm constructed in this study performs well in response to the attack of the industrial control system in the virtual reality simulation environment, which provides practical significance for the application of virtual reality in industrial monitoring.

The purposes are to enable large-scale Internet of Things (IoT) devices to analyze data more effectively and provide high-efficiency, low-energy, and wide-coverage technical services for terminals. The channel model and energy loss model analyze the devices' access performance, data transmission path delay, energy consumption in the IoT, and large-scale devices' access in the cellular narrowband IoT (NB-IoT) based on big data analysis technology are also discussed. The results show that in the access success rate analysis, the access success rate is the highest with an access time ( T) of 5 s and a preamble resource number ( K) of 25. The restriction factor is inversely proportional to the access success rate. In the node utilization analysis, different transmission node priorities result in different node utilization, and priority 2's node utilization is better than that of priority 1. Moreover, local data makes data analysis and transmission faster. The search time is prolonged, and the corresponding energy consumption is also higher without local data. In the energy consumption analysis, with the 6-generation (6G) technology, different interference thresholds lead to the different energy efficiency of data transmission. The larger the interference threshold, the higher the energy efficiency. Therefore, the 6G-based big data analysis technology can significantly improve large-scale IoT devices' access success rate and enable the system to meet the requirements of low energy consumption and high access success rate, significant for research on more devices' access data analysis.

In order to explore the application value of deep learning denoising autoencoder (DAE) in Internet-of-Things (IoT) fusion security, in this study, a hierarchical intrusion security detection model stacked DAE supporting vector machine (SDAE-SVM) is constructed based on the three-layer neural network of self-encoder. The sample data after dimension reduction are obtained by layer by layer pretraining and fine-tuning. The traditional deep learning algorithms [stacked noise autoencoder (SNAE), stacked autoencoder (SAE), stacked contractive autoencoder (SCAE), stacked sparse autoencoder (SSAE), deep belief network (DBN)] are introduced to carry out the comparative simulation with the model in this study. The results show that when the encoder in the model is a 4-layer network structure, the accuracy rate (Ac) of the model is the highest (97.83%), the false-negative rate (Fn) (1.27%) and the false-positive rate (Fp) (3.21%) are the lowest. When the number of nodes in the first hidden layer is about 110, the model accuracy is about 98%. When comparing the model designed in this study with the common feature dimension reduction methods, the Ac, Fn, and Fp of this model are the best, which are 98.12%, 3.21%, and 1.27%, respectively. When compared with other deep learning algorithms of the same type, the recognition rate, Ac, error rate, and rejection rate show good results. In multiple data sets, the recognition rate, Ac, error rate, and rejection rate of the model in this study are always better than the traditional deep learning algorithms. In conclusion, when deep learning SDAE is applied to IoT convergence-based intrusion security detection, the detection load can be reduced, the detection effect can be improved, and the operation is more secure and stable.

Summary The purpose of the study is to meet the more needs of the future social development for the Internet of things (IoT) and make IoT better provide more convenience for people and reduce the risk of data leakage in the IoT system. Starting with the IoT edge devices, the centralized and distributed hit rate and average corresponding speed of the edge nodes are studied. Meanwhile, the gateway security of the edge devices designed is discussed to obtain a more reliable gateway. The research results show that the Max‐PSN cache algorithm proposed in this article is superior to the other two algorithms in terms of hit rate and average response speed of centralized and distributed fog systems, which shows the effectiveness of the cache algorithm. The gateway designed shows good randomness through frequency detection and poker detection, which can effectively resist attacks, thus ensuring the security of the gateway. Therefore, the research on the security of the IoT edge devices plays a positive role in reducing the risk of data leakage and the wide application of the IoT system.

Natural language processing technologies, such as topic models, have been proven to be effective for scholarly recommendation tasks with the ability to deal with content information. Recently, venue recommendation is becoming an increasingly important research task due to the unprecedented number of publication venues. However, traditional methods focus on either the author’s local network or author-venue similarity, where the multiple relationships between scholars and venues are overlooked, especially the venue–venue interaction. To solve this problem, we propose an author topic model–enhanced joint graph modeling approach that consists of venue topic modeling, venue-specific topic influence modeling, and scholar preference modeling. We first model the venue topic with Latent Dirichlet Allocation. Then, we model the venue-specific topic influence in an asymmetric and low-dimensional way by considering the topic similarity between venues, the top-influence of venues, and the top-susceptibility of venues. The top-influence characterizes venues’ capacity of exerting topic influence on other venues. The top-susceptibility captures venues’ propensity of being topically influenced by other venues. Extensive experiments on two real-world datasets show that our proposed joint graph modeling approach outperforms the state-of-the-art methods.

The brain-computer interface (BCI) connects the brain and the external world through an information transmission channel by interpreting the physiological information of the brain during thinking activities. The effective classification of electroencephalogram (EEG) signals is the key to improving the performance of the system. To improve the classification accuracy of EEG signals in the BCI system, the transfer learning algorithm and the improved Common Spatial Pattern (CSP) algorithm are combined to construct a data classification model. Finally, the effectiveness of the proposed algorithm is verified. The results show that in actual and imagined movements, the accuracy of the left- and right-hand movements at different speeds is higher than when the speeds are the same. The proposed Adaptive Composite Common Spatial Pattern (ACCSP) and Self Adaptive Common Spatial Pattern (SACSP) algorithms have good classification effects on 5 subjects, with an average classification accuracy rate of 83.58 percent, which is an increase of 6.96 percent compared with traditional algorithms. When the training sample size is 10, the classification accuracy of the ACCSP algorithm is higher than that of the traditional CSP algorithm. The improved CSP algorithm combined with transfer learning embodies a good classification effect in both ACCSP and SACSP. Especially, the performance of SACSP mode is better. Combining the improved CSP algorithm proposed with the CSP-based transfer learning algorithm can improve the classification accuracy of the BCI classifier.

With the increasing size of data, massive amounts of data are being generated continuously. It is hoped to find a cognitive computing technology that can effectively learn and process large-scale data. The decision tree algorithm is optimized from the perspective of machine learning. A cognitive computing model based on context-aware data flow is constructed. Classification and regression tree (CART) algorithm is used in the data computing layer of the cognitive model. In addition, the clustering effectiveness index based on frequent patterns optimizes the K-means clustering method. The performance of the algorithm is analyzed through simulation experiments. The results show that the CART algorithm requires fewer training data sets while guaranteeing classification accuracy. Also, the algorithm has obvious advantages under large-scale data. In the application of actual data set, on Over, Over+Noise, and Bridge, only the clustering validity index based on frequent pattern (FPCVI) index proposed finds the correct number of clusters. The application of DPCK-K-means clustering algorithm can ensure the accuracy and stability of behavior classification, which is greatly significant for operators to analyze user behavior and develop personalized services.

Today, a new generation of artificial intelligence has brought several new research domains such as computer vision (CV). Thus, target tracking, the base of CV, has been a hotspot research domain. Correlation filter (CF)-based algorithm has been the basis of real-time tracking algorithms because of the high tracking efficiency. However, CF-based algorithms usually failed to track objects in complex environments. Therefore, this article proposes a fuzzy detection strategy to prejudge the tracking result. If the prejudge process determines that the tracking result is not good enough in the current frame, the stored target template is used for following tracking to avoid the template pollution. During testing on the OTB100 dataset, the experimental results show that the proposed auxiliary detection strategy improves the tracking robustness under complex environment by ensuring the tracking speed.

The intelligent industrial environment developed with the support of the new generation network cyber-physical system (CPS) can realize the high concentration of information resources. In order to carry out the analysis and quantification for the reliability of CPS, an automatic online assessment method for the reliability of CPS is proposed in this article. It builds an evaluation framework based on the knowledge of machine learning, designs an online rank algorithm, and realizes the online analysis and assessment in real time. The preventive measures can be taken timely, and the system can operate normally and continuously. Its reliability has been greatly improved. Based on the credibility of the Internet and the Internet of Things, a typical CPS control model based on the spatiotemporal correlation detection model is analyzed to determine the comprehensive reliability model analysis strategy. Based on this, in this article, we propose a CPS trusted robust intelligent control strategy and a trusted intelligent prediction model. Through the simulation analysis, the influential factors of attack defense resources and the dynamic process of distributed cooperative control are obtained. CPS defenders in the distributed cooperative control mode can be guided and select the appropriate defense resource input according to the CPS attack and defense environment.

With the advent of the Internet of Things, especially the Internet of Vehicles, abundant environmental and mobile data can be generated continuously. A personalized recommender system is one of the important methods for solving the problem of big data overload. However, to make use of these mobile data from vehicles, traditional recommender services are confronted by severe challenges. Therefore, we study the diversified recommendation problem based on a real-world dataset, represented as a tensor with three dimensions of user, location and activity. As the tensor is rather sparse, we employ tensor decomposition to predict missing values. Additionally, we directly regard recommendation precision as an objective. In addition to precision, we also consider the recommendation novelty and coverage, providing a more comprehensive view of the recommender system. Thus, visitors can discover attractive spots that are less visited in a personalized manner, relieving traffic pressure at famous scenic spots and balancing overall transportation. By integrating all these objectives, we construct a many-objective recommendation model. To optimize this model, we propose a distributed parallel evolutionary algorithm employing the nondominated ranking and crowding distance. Compared with the state-of-the-art algorithms, the proposed algorithm performs well and is very efficient.

The development of 5G technology has brought the information revolution of the Internet of Things (IoT). With the emergence of intelligent products, such as the Internet of connected vehicles (IoCV) and wireless sensor nodes, the classification and disposal of municipal waste is now more intelligent and efficient. How to arrange waste collection vehicles reasonably and optimize their service routes in real time based on signals sent by intelligent waste bins to meet the daily needs of residents has become an urgent problem. An improved multiobjective model for the split-delivery vehicle routing problem (SDVRP) is proposed based on the multiple cleaning services for the same collection point in a certain period of time according to the real-time waste volume. The process of classified waste disposal in the Tianjin Wudadao area is analyzed as an example. The experimental results show that the Pareto-optimal solutions obtained by the algorithm can effectively arrange the vehicle service routes, meet the needs of classified waste collection in the area, and provide a scientific theoretical basis for the further development of waste classification.

Electric vehicles networks have become hot topics in research and industry and played an important role in smart future cities. However, high energy consumption is a significantly challenge for these applications. This article proposes a electric vehicles cloud computing framework to perform energy-efficient heterogeneous networking for electric vehicles network in smart future cities. The software-defined networking ideas is used to enable different devices including electric vehicles to access the cloud computing network for electric vehicles connected. The edge computing is exploited to run quick computing and communication for these application. Then an energy-efficient heterogeneous networking method is presented to overcome high energy consumption. The mixed integer linear programming optimization model and two heuristic models are proposed to perform energy-efficient networking. An networking algorithm is proposed to achieve highly energy-efficient networking for electric vehicles network. The detailed simulation experiments are conducted to validate our approach. Simulation results illustrate that the proposed method is efficient and feasible.

To investigate the diversified technologies in Internet of Vehicles (IoV) under intelligent edge computing, artificial intelligence, intelligent edge computing, and IoV are combined. Also, it proposes an IoV model for intelligent edge computing task offloading and migration under the SDVN (Software Defined Vehicular Networks) architecture, that is, the JDE-VCO (Joint Delay and Energy-Vehicle Computational task Offloading) optimization. And the simulation is performed. The results show that in the analysis of the impact of different offloading strategies on the IoV, it is found that the JDE-VCO algorithm is superior to other schemes in terms of transmission delay and total offloading energy consumption. In the analysis of the impact of the task unloading of the IoV, the JDE-VCO algorithm is less than RTO (Random Tasks Offloading) and UTO (Uniform Tasks Offloading) algorithm schemes in terms of the number of tasks per unit time, and the average task completion time for the same amount of uploaded data. In the analysis of the packet loss ratio and transmission delay, it can be found that the packet loss ratio and transmission delay of the JDE-VCO algorithm are less than the RTO and UTO algorithms. Moreover, the packet loss ratio of the JDE-VCO algorithm is about 0.1, and the transmission delay is stable at 0.2s, which has obvious advantages. Therefore, through research, the IoV model of task offloading and migration built by intelligent edge computing can significantly improve the load sharing rate, offloading efficiency, packet loss ratio, and transmission delay when the IoV is processing tasks and uploading data. It provides experimental basis for the improvement of the IoV system.

Internet of Vehicles (IoV) is a large interactive network composed of information such as vehicle location, speed and route. Vehicles can collect their own environment and state information through GPS, RFID, sensors, camera image processing, and other devices. They can transmit their various information to the central processing unit through Internet technology. These large amounts of vehicle information can be analyzed and processed through computer technology to calculate the optimal route for different vehicles, and report road conditions in time and schedule signal light cycles. Internet of Vehicles (IoV) is a large-scale system network for wireless communication and information exchange between vehicles and people, vehicles and roads, vehicles and the Internet, which is based on intra-vehicle network, inter-vehicle network and vehicle mobile Internet, in accordance with the agreed communication protocols and data interaction standards. The system realizes the integrated network of intelligent traffic management, intelligent dynamic information service, and intelligent vehicle control by “filtering and cleaning” massive data and processing data on the platform. Internet of Vehicles (IoV) system utilizes advanced IoT technology, cloud computing, and big data to make the system fully aware of roads and traffic. It enables all vehicles to collect information through their own environment and state, and upload all kinds of information to the Internet big data platform. The central processing unit collects, analyzes, and processes a large amount of uploaded information. The system will control every vehicle involved in the traffic and control every road in real time to provide users with traffic efficiency and safety.

Intelligent control of traffic has significant influence on the scheduling efficiency of urban traffic flow. Therefore, in order to improve the efficiency of vehicles at intersections, first, the Back Propagation (BP) neural network is used to propose a vehicle passing model at the intersection, and based on the intelligent traffic control system model, the Earliest Deadline First (EDF) dynamic scheduling algorithm is used to improve the Controller Area Network (CAN) communication network. Finally, the simulation test is used to evaluate the effectiveness of the proposed model and the improved CAN bus communication network. The results show that the neural network model can be used to predict the passage time of vehicles queuing at intersections with an error of less than 10%. The improved CAN bus communication can improve the data transmission rate, and the success rate of data transmission under different load rates is above 95%. In conclusion, the application of artificial intelligence technology in intelligent traffic system can improve the efficiency of vehicle scheduling and the efficiency of communication system. This research is of great significance to improve the communication performance of the transportation system and scheduling efficiency.

To explore the electric vehicle networks in smart cities through big data analysis technology, this study utilizes K-means and fuzzy theory in big data analysis technology to construct an objective function-based fuzzy mean clustering algorithm theory (FCM). Then, the FCM algorithm is improved, and the electric vehicle network is simulated. The results show that in the analysis of network data transmission performance, when the probability of successful propagation is 100% and the λ value is between 0.01-0.05, it is closest to the actual result, and the data delay is the smallest. In the analysis of the route guidance effects, when facing congested road sections, the route guidance strategy of this study can restrain the spread of congestion effectively and achieve timely evacuation of traffic congestion. In the further analysis of the impact of different factors on traffic conditions, under route guidance, with the increase in market penetration rate (MPR) of devices, following rate (FR) of vehicles, and congestion level (CL), the improvement of the induction strategy becomes clearer, and greater economic benefits are achieved. This study has found that utilizing big data analysis technology to improve the electric vehicle transportation networks can reduce the network data transmission performance delay significantly and change the path to suppress the spread of congestion effectively, which has provided experimental references for the development of electric vehicle transportation networks.

Objective: To realize the full coverage, full spectrum, and full application, of 6G networks, the channel measurement, channel characteristics, and channel research of the 6G-oriented full-spectrum full-scene wireless network are explored. Methods: At present, constructing an information network covering three dimensions of sea, land and air is one of the research directions of 6G technology development. Meanwhile, the full coverage of three dimensions of communication requires multiple channels including multiple frequency bands and multiple scenarios, mainly including terahertz, light band, satellite, unmanned aerial vehicle, ocean, high-speed rail, and vehicle-to-vehicle. For different channels, different parameters are analyzed and targeted discussed according to channel characteristics. Also, there are many other problems of wireless communication channel that need to be solved. Here, only the above problems are analyzed in detail. Because of the current development of the Internet of Vehicles, the interconnection of connected vehicles is analyzed, and this scenario is also the most widely common scenario in daily life. Results: For the above-mentioned communication channels, the relevant measurement and modeling results of the 6G channel are shown; besides, the characteristics, time, and space correlation functions are obtained for each channel. Conclusion: The existing research results of 6G-oriented wireless channel measurement and modeling are analyzed; the research methods are summarized; new ideas are proposed to provide researchers with important references.

Objective: the objective of this study is to study deep learning to solve the safety problems of intelligent transportation system. Method: the intelligent transportation system is improved by using the deep learning algorithm, and the improved system is simulated, and the data transmission performance, accuracy prediction performance and path change strategy of the system are statistically analyzed. Results: in the analysis of the data transmission performance of the system, the probability of successful propagation is found to be 100%. When the value of λ is 0.01~0.05, it is the closest to the actual result and the data delay is the smallest. In the analysis of the accuracy prediction of the system, it is found that the system of this study has the best accuracy prediction performance with the increase of the number of iterations compared with other models in different categories. After further analyzing the path induction strategy of the system, it is found that the route guidance strategy of this study can effectively restrain the spread of congestion and achieve the effect of timely evacuation of traffic congestion in the face of congested road sections. Conclusion: it is found that the improvement of the intelligent transportation system by using deep learning can significantly reduce the data transmission delay of the system, improve the prediction accuracy, and effectively change the path in the face of congestion to suppress the congestion spread. Although there are some shortcomings in the experiment, it still provides experimental reference for the development of the transportation industry in the later stage.

In order to calculate the node big data contained in complex networks and realize the efficient calculation of complex networks, based on voluntary computing, taking ICE middleware as the communication medium, the loose coupling distributed framework DCBV based on voluntary computing is proposed. Then, the Master, Worker, and MiddleWare layers in the framework, and the development structure of a DCBV framework are designed. The task allocation and recovery strategy, message passing and communication mode, and fault tolerance processing are discussed. Finally, to calculate and verify parameters such as the average shortest path of the framework and shorten calculation time, an improved accurate shortest path algorithm, the N-SPFA algorithm, is proposed. Under different datasets, the node calculation and performance of the N-SPFA algorithm are explored. The algorithm is compared with four approximate shortest-path algorithms: Combined Link and Attribute (CLA), Lexicographic Breadth First Search (LBFS), Approximate algorithm of shortest path length based on center distance of area division (CDZ), and Hub Vertex of area and Core Expressway (HEA-CE). The results show that when the number of CPU threads is 4, the computation time of the DCBV framework is the shortest (514.63 ms). As the number of CPU cores increases, the overall computation time of the framework decreases gradually. For every 2 additional CPU cores, the number of tasks increases by 1. When the number of Worker nodes is 8 and the number of nodes is 1, the computation time of the framework is the shortest (210,979 ms), and the IO statistics data increase with the increase of Worker nodes. When the datasets are Undirected01 and Undirected02, the computation time of the N-SPFA algorithm is the shortest, which is 4520 ms and 7324 ms, respectively. However, the calculation time in the ca-condmat_undirected dataset is 175,292 ms, and the performance is slightly worse. Overall, however, the performance of the N-SPFA and SPFA algorithms is good. Therefore, the two algorithms are combined. For networks with less complexity, the computational scale coefficient of the SPFA algorithm can be set to 0.06, and for general networks, 0.2. When compared with other algorithms in different datasets, the pretreatment time, average query time, and overall query time of N-SPFA algorithm are the shortest, being 49.67 ms, 5.12 ms, and 94,720 ms, respectively. The accuracy (1.0087) and error rate (0.024) are also the best. In conclusion, voluntary computing can be applied to the processing of big data, which has a good reference significance for the distributed analysis of large-scale complex networks.

The study aims at discussing the security of medical data in the Internet era. By using k-anonymity (K-A) and differential privacy (DP), an algorithm model combining K-A and DP was proposed, which was simulated through the experiments. In the Magic and EIA datasets, the algorithm constructed was compared with K-A and the L-diversity model to verify the performance of the model. The model constructed based on DP had the lowest privacy-leakage risks, which increased with the number of identifiers in the Magic and EIA datasets, and the information disclosure was the least. In addition, in its usability analysis, it was found that its value was the most obviously improved and its operation efficiency was the highest. The K-A-DP algorithm can effectively reduce the risk of privacy leakage and information loss, and has achieved excellent results. Despite the deficiencies in the process of the experiment, the study still provides a reference for solving the problem of medical data security.

Smart cities fully utilize the new generation of Internet of Things (IoT) technology in the process of urban informatization to optimize the urban management and service. However, in the IoT system, while information exchange and communication, wireless sensor network devices may not be able to resist all forms of attacks, which may lead to security issues such as user data disclosure. Aiming at the information security risks in smart city, the typical technologies in IoT is analyzed from the perspective of IoT perception layer and provides corresponding security solutions for the existing security threats. Regarding the communication security, the emerging wireless technology, long range (LoRa), is discussed, and the performance of wireless communication protocol is analyzed through simulation experiments, to verify that the IoT technology based on LoRa communication technology can improve the security of the system in the construction of smart city. The results show that REBEB, a new backoff algorithm, is similar to the binary exponential backoff algorithm in terms of throughput performance. REBEB focuses more on fairness, which is up to 0.985, and to a certain extent, its security is significantly improved. The fairness of REBEB algorithm is more than 0.4 in different nodes and competing windows, and the fairness of the system is better when the number of nodes is small. To sum up, the IoT system based on LoRa communication can effectively improve the security performance of the system in the construction of smart city and avoid the security threats in the IoT signal transmission.

As deep learning, virtual reality, and other technologies become mature, real-time data processing applications running on intelligent terminals are emerging endlessly; meanwhile, edge computing has developed rapidly and has become a popular research direction in the field of distributed computing. Edge computing network is a network computing environment composed of multi-edge computing nodes and data centers. First, the edge computing framework and key technologies are analyzed to improve the performance of real-time data processing applications. In the system scenario where the collaborative deployment tasks of multi-edge nodes and data centers are considered, the stream processing task deployment process is formally described, and an efficient multi-edge node-computing center collaborative task deployment algorithm is proposed, which solves the problem of copy-free task deployment in the task deployment problem. Furthermore, a heterogeneous edge collaborative storage mechanism with tight coupling of computing and data is proposed, which solves the contradiction between the limited computing and storage capabilities of data and intelligent terminals, thereby improving the performance of data processing applications. Here, a Feasible Solution (FS) algorithm is designed to solve the problem of placing copy-free data processing tasks in the system. The FS algorithm has excellent results once considering the overall coordination. Under light load, the V value is reduced by 73% compared to the Only Data Center-available (ODC) algorithm and 41% compared to the Hash algorithm. Under heavy load, the V value is reduced by 66% compared to the ODC algorithm and 35% compared to the Hash algorithm. The algorithm has achieved good results after considering the overall coordination and cooperation and can more effectively use the bandwidth of edge nodes to transmit and process data stream, so that more tasks can be deployed in edge computing nodes, thereby saving time for data transmission to the data centers. The end-to-end collaborative real-time data processing task scheduling mechanism proposed here can effectively avoid the disadvantages of long waiting times and unable to obtain the required data, which significantly improves the success rate of the task and thus ensures the performance of real-time data processing.

Emotional cognitive ability is a key technical indicator to measure the friendliness of interaction. Therefore, this research aims to explore robots with human emotion cognitively. By discussing the prospects of 5G technology and cognitive robots, the main direction of the study is cognitive robots. For the emotional cognitive robots, the analysis logic similar to humans is difficult to imitate; the information processing levels of robots are divided into three levels in this study: cognitive algorithm, feature extraction, and information collection by comparing human information processing levels. In addition, a multi-scale rectangular direction gradient histogram is used for facial expression recognition, and robust principal component analysis algorithm is used for facial expression recognition. In the pictures where humans intuitively feel smiles in sad emotions, the proportion of emotions obtained by the method in this study are as follows: calmness accounted for 0%, sadness accounted for 15.78%, fear accounted for 0%, happiness accounted for 76.53%, disgust accounted for 7.69%, anger accounted for 0%, and astonishment accounted for 0%. In the recognition of micro-expressions, humans intuitively feel negative emotions such as surprise and fear, and the proportion of emotions obtained by the method adopted in this study are as follows: calmness accounted for 32.34%, sadness accounted for 34.07%, fear accounted for 6.79%, happiness accounted for 0%, disgust accounted for 0%, anger accounted for 13.91%, and astonishment accounted for 15.89%. Therefore, the algorithm explored in this study can realize accuracy in cognition of emotions. From the preceding research results, it can be seen that the research method in this study can intuitively reflect the proportion of human expressions, and the recognition methods based on facial expressions and micro-expressions have good recognition effects, which is in line with human intuitive experience.

The study aims at exploring the Internet of things (IoT) system from the perspective of data and further improving the performance of the IoT system. The IoT data energy collection and information transmission system model is constructed by combining IoT and wireless relay cooperative transmission technology. Moreover, the energy efficiency, outage probability (OP), and accuracy of the model are evaluated by simulation experiments. The results show that, in the energy efficiency analysis, with the increase of power split factor ρ, the information transmission ability of the system increases. Whereas, the energy collection ability decreases, so the energy efficiency is reduced. Thus, choosing a more suitable power split factor for the energy efficiency of IoT is important. By analyzing OP and bit error rate (BER), as the values of m (Nakagami, the fading index of the fading distribution) and multi-hop paths increase, the OP and BER are reduced while the system performance is increased. Therefore, this article uses wireless relay cooperative transmission technology to integrate big data analysis into the IoT system. Finally, by adding multi-hop path and other methods to reduce the OP and BER of system, the system performance is improved. It provides experimental basis for the development of IoT systems.

With the remarkable development of internet technologies, the popularity of smart healthcare has regularly come to the fore. Smart healthcare uses advanced technologies to transform the traditional medical system in an all-round way, making healthcare more efficient, more convenient, and more personalized. Unfortunately, medical data security is a serious issue in the smart healthcare systems. It becomes a fundamental challenge that requires the development of efficient innovative strategies towards fulfilling the healthcare needs and supporting secure healthcare transfer and delivery. This article provides a comprehensive survey on state-of-the-art techniques for health data security and their new trends for solving challenges in real-world applications. We survey the various notable cryptography, biometrics, watermarking, and blockchain-based security techniques for healthcare applications. A comparative analysis is also performed to identify the contribution of reviewed techniques in terms of their objective, methodology, type of medical data, important features, and limitations. At the end, we discuss the open issues and research directions to explore the promising areas for future research.

introduction Share on Introduction to the Special Issue on Recent Trends in Medical Data Security for e-Health Applications Editors: Amit Kumar Singh Search about this author , Zhihan Lv Search about this author , Hoon Ko Search about this author Authors Info & Claims ACM Transactions on Multimedia Computing, Communications, and ApplicationsVolume 17Issue 2sJune 2021 Article No.: 58pp 1–3https://doi.org/10.1145/3459601Published:18 May 2021Publication History 6citation187DownloadsMetricsTotal Citations6Total Downloads187Last 12 Months106Last 6 weeks24 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

With increasing amounts of information, the image information received by people also increases exponentially. To perform fine-grained categorization and recognition of images and visual calculations, this study combines the Visual Geometry Group Network 16 model of convolutional neural networks and the vision attention mechanism to build a multi-level fine-grained image feature categorization model. Finally, the TensorFlow platform is utilized to simulate the fine-grained image classification model based on the visual attention mechanism. The results show that in terms of accuracy and required training time, the fine-grained image categorization effect of the multi-level feature categorization model constructed by this study is optimal, with an accuracy rate of 85.3% and a minimum training time of 108 s. In the similarity effect analysis, it is found that the chi-square distance between Log Gabor features and the degree of image distortion show a strong positive correlation; in addition, the validity of this measure is verified. Therefore, through the research in this study, it is found that the constructed fine-grained image categorization model has higher accuracy in image recognition categorization, shorter training time, and significantly better performance in similar feature effects, which provides an experimental reference for the visual computing of fine-grained images in the future.

It is necessary to solve the inaccurate data arising from data reliability ignored by most data fusion algorithms drawing upon collaborative filtering and fuzzy network theory. Therefore, a model is constructed based on the collaborative filtering algorithm and fuzzy network theory to calculate the node trust value as the weight of weighted data fusion. First, aFTWDF (Feedback Trust Weighted for Data Fusion)is proposed. Second,EEFA (Efficiency unequal Fuzzy clustering Algorithm) is introduced into FTWDF considering the defects of the clustering structure caused by ignoring the randomness of node energy consumption and cluster head selection in the practical application of the existing data fusion algorithm. Besides, the fuzzy logic is applied to cluster head selection and node clustering. Finally, an FTWDF-EEFA clustering algorithm is constructed for generating candidate cluster head nodes, which is verified by simulation experiments. The comparative analysis reveals that the accuracy of the FTWDF-EEFA clustering algorithm is 4.1% higher than that of theTMDF (Trust Multiple attributes Decision-making-based data Fusion)algorithm, and 8.3% higher than that ofLDTS(Larger Data fusion based on node Trust evaluation in wireless Sensor networks)algorithm. It performs better in accuracy and recommendation results during the processing of ML100M dataset and NF5M dataset. Besides, the new clustering algorithm increases the survival time of nodes when analyzing the number of death nodes to prolong networks’ lifespan. It improves the survival period of nodes, balances the network load, and prolongs networks’ lifespan. Furthermore, the FTWDF-EEFA clustering algorithm can balance nodes’ energy consumption and effectively save nodes’ overall energy through analysis. Therefore, the optimized algorithm can increase the lifespan of network and improve the trust mechanism effectively. The performance of the algorithm has reached the expected effect, providing a reference for the practical application of the trust mechanism in networks.

Abstract Internet of things (IoT) is a global information infrastructure that supports access to thousands of monitoring devices and user terminals. A large amount of monitoring data generated by IoT is integrated to cloud computing through the network to improve the quality of life of citizens. Fine‐grained and accurate traffic information is important for IoT network management. Software‐defined networking (SDN) is a centralized control plane as a logical control center, making network management more flexible and efficient. Then, we collect fine‐grained traffic information in SDN‐based IoT networks to improve network management. To acquire the traffic information with low overhead and high accuracy, first, we collect the statistics of coarse‐grained traffic of flows and fine‐grained traffic of links, and then we utilize the intelligent optimization methods to estimate the network traffic. To improve the granularity and accuracy of the acquired traffic information, we construct an optimization function with constraints to decrease the estimation errors. As the optimization function of traffic information is a non‐deterministic polynomial‐hard problem, we present a heuristic algorithm to obtain the optimal solution of the fine‐grained measurement. Finally, we conduct some simulations to verify the proposed measurement scheme. Simulation results show that our approach can improve the granularity and accuracy of traffic information with intelligent optimization methods.

In smart cities, urban intelligent transportation systems (ITSs) are highly anticipated to improve transportation efficiency, decrease traffic congestion, and promote sustainable transportation development. However, the ITS-based transportation network may fail as a result of a traffic congestion which is considered as one of the challenging issues in large-scale smart cities. The possible traffic congestion on the link is with spatiotemporal features and may vary over time. Nevertheless, the continuous or delay-sensitive traffic flow is always requested in smart cities even under serious traffic congestion. In this article, we will prove such spatiotemporal features of traffic congestion can be forecasted, and the path for the delay-sensitive urban traffic can be accurately planed before the traffic is started. Our main contributions can be summarized as follows: 1) we employ the software-defined networking (SDN) technology to improve the scalability of ITS in smart cities and propose a grid-based model to quantify the traffic-congestion probability of the transportation network; 2) we propose a polynomial-time solvable algorithm to recognize the grids that affect the traffic-congestion probability of the network links or paths; and 3) we utilize the time-expanded network technology to expand the time slots in the spatial dimension and propose a polynomial-time path planning algorithm that can seek for a congestion-aware path to schedule the traffic within a given time threshold.

In order to investigate security and privacy issues in the mobile Internet of Things (IoT), the access control four states (AC4E) IoT model is constructed, data physical fusion technology is adopted, and simulation experiments are conducted to explore its performance in terms of optimal response path, trust mechanism, and privacy rate. The results show that AC4E has good reactivity, correctness, security, parallel processing, and other characteristics in the analysis of the optimal response path, trust mechanism, and security rate. The AC4E trust model can be used to isolate the influence of “malicious” nodes and reduce the energy loss of the system. Further analysis of its secrecy rate shows that when the number of iterations is 5, nodes at different positions can maximize the secrecy performance of the system according to different power distribution. Therefore, the application of data physical fusion technology in the mobile IoT can effectively improve the security and privacy performance of the network, and provide a certain experimental reference for the improvement of the performance of the mobile IoT in the later stage, which has important research significance.

In order to improve the stability of mobile network system for application of the next generation of Internet of Things (IoT), balance the network load and guarantee the quality of user service experience, this article first introduces the computing migration framework for the network of the next generation, and summarizes the concept and content of mobile edge computing (MEC) using software-defined network (SDN) and network function virtualization (NFV). And then, this article proceeds to introduce the MEC strategy based on SDN and NFV technology as well as multiattribute decision making, computing migration, multiattribute decision, the MEC decision model based on SDN and NFV technology and the solving process of the MEC decision model based on SDN and NFV. Finally, the three sets of simulation experiments based on MATLAB are designed to validate the multiattribute decision of MEC migration strategy based on SDN and NFV. The results show that the multiattribute decision making based on SDN and NFV can select the appropriate MEC center, further reduce the server response time and improve the quality of user service experience. This article is of great significance to the application of IoT terminal in the next generation of network environment.

We study the problem of the energy-efficient networking in cloud services with geographically distributed data centers for industrial Internet-of-Things (IIoT) networks, specially for multimedia IIoT networks. This is significantly challenged by dynamic end-to-end request demands and unbalanced link energy efficiency, unbalanced and time-varying link utilization, and bandwidth and delay constraints for service requirements. To solve these issues, we propose a multi-constraint optimization model for the energy efficiency optimization in cloud computing services where data centers are geographically distributed and are interconnected by cloud networks. Our model jointly optimizes energy efficiency in data centers and cloud networks. An intelligent heuristic algorithm is presented to solve this model for dynamic request demands between different data centers and between data centers and users. This is implemented by combining the niche genetic algorithm and the random depth-first search. Simulation results for energy-efficient networking show that better gains in network energy efficiency can be achieved by our joint optimization. Joint optimization between industrial data centers and industrial cloud networks can further improve energy savings and link utilization for time-varying requests.

The economics, reliability, and carbon efficiency of hybrid microgrid systems (HMSs) are often in conflict; hence, a reasonable design for the sizing of the initial microgrid is important. In this article, we propose an improved two-archive many-objective evolutionary algorithm (TA-MaEA) based on fuzzy decision to solve the sizing optimization problem for HMSs. For the HMS simulated in this article, costs, loss of power supply probability, pollutant emissions, and power balance are considered as objective functions. For the proposed algorithm, we employ two archives with different diversity selection strategies to balance convergence and diversity in the high-dimensional objective space. In addition, a fuzzy decision making method is proposed to further help decision makers obtain a solution from the Pareto front that optimally balances the objectives. The effectiveness of the proposed algorithm in solving the HMS sizing optimization problem is investigated for the case of Yanbu, Saudi Arabia. The experimental results show that, compared with the two-archive evolutionary algorithm for constrained many-objective optimization (C-TAEA), the clustering-based adaptive many-objective evolutionary algorithm (CA-MOEA), and the improved decomposition-based evolutionary algorithm (I-DBEA), the proposed algorithm can reduce the system costs by 7%, 13%, and 21%, respectively.

Fuzzy rough theory can describe real-world situations in a mathematically effective and interpretable way, while evolutionary neural networks can be utilized to solve complex problems. Combining them with these complementary capabilities may lead to evolutionary fuzzy rough neural network with the interpretability and prediction capability. In this article, we propose modifications to the existing models of fuzzy rough neural network and then develop a powerful evolutionary framework for fuzzy rough neural networks by inheriting the merits of both the aforementioned systems. We first introduce rough neurons and enhance the consequence nodes, and further integrate the interval type-2 fuzzy set into the existing fuzzy rough neural network model. Thus, several modified fuzzy rough neural network models are proposed. While simultaneously considering the objectives of prediction precision and network simplicity, each model is transformed into a multiobjective optimization problem by encoding the structure, membership functions, and the parameters of the network. To solve these optimization problems, distributed parallel multiobjective evolutionary algorithms are proposed. We enhance the optimization processes with several measures including optimizer replacement and parameter adaption. In the distributed parallel environment, the tedious and time-consuming neural network optimization can be alleviated by numerous computational resources, significantly reducing the computational time. Through experimental verification on complex stock time series prediction tasks, the proposed optimization algorithms and the modified fuzzy rough neural network models exhibit significant improvements the existing fuzzy rough neural network and the long short-term memory network.

In this article, we propose a big data based analysis framework to analyze and extract network behaviors in cellular networks for Industry 4.0 applications from a big data perspective, using Hadoop, Hive, HBase, and so on. The data prehandling and traffic flow extraction approaches are presented to construct effective traffic matrices. Accordingly, we can capture network behaviors in cellular networks from a networkwide perspective. Although there have been a number of prior studies on cellular network usage, to the best of our knowledge, this article is a first study that characterizes network behaviors using the big data analytics to analyze a network big data of call detail records over a longer duration (five months), with more users (five million), more records (several hundred million lines) and nationwide coverage. The call pattern analysis and network behavior extraction approaches are designed to perform big data analysis and feature extractions. Then, the corresponding algorithms are proposed to characterize network behaviors, i.e., cellular call patterns and network resource usage. The detailed evaluation is proposed to validate our method. For example, we find that some unpopular calls can last longer time and thus consume more network resources.

This paper designs a smart urban environment monitoring system based on the wireless network of ZigBee to complete the real-time collection of urban environment information. The system consists of the basic monitoring network and the remote receiving terminal. The basic monitoring network connects the streetlights as routes and the taxis as nodes. After dynamically organizing the network, each node is assigned with an address as the only identity in the network. Then, the system designed conducts the simulation experiment to prove that it could meet the needs and send the collected information to the designated terminal in the form of message according to the setting. The sensor organized through the wireless network of ZigBee could inspire the infrastructure construction of the smart city. With the network, a smarter and more comfortable society could be well offered to people.

The distributed energy system is used as the prototype of the energy Internet, including a variety of forms of energy networks, plenty of distributed equipment and energy storage equipment composed of energy flow, and real-time communication and data volume of information systems. As an important energy system that is closely related to people's lives, its security and stability is one of the cores of its development. With the access of a large number of distributed devices, the structure of the power system has changed greatly. The addition of various forms of energy network, distributed equipment, and energy storage equipment has made it more difficult for the energy Internet to achieve the coordination among and control over these devices. Regardless of the fluctuation of the power load and the sudden change of the thermal load, problems such as energy network failure and demand will affect the security and stability of the energy Internet. Traditional energy systems are independent of one another, while integrated energy systems include subsystems, such as the power system, thermal system, and natural gas system, which can complement one another in planning and operation. To improve the utilization rate of all kinds of energy, reduce the waste of energy, and cut the emission of pollutants, it is crucial to realize the economic utilization of energy as well as the safe and stable operation of the energy Internet.

In the context of big data and the Internet of Things, with the advancement of geospatial data acquisition and retrieval, the volume of available geospatial data is increasing every minute. Thus, new data-management architecture is needed. We proposed a building information model (BIM) big data-storage-management solution with hybrid storage architecture based on web virtual reality geographical information system (WebVRGIS). BIM is associated with the integration of spatial and semantic information on the various stages of urban building. In this paper, based on the spatial distribution characteristics of BIM geospatial big data, a data storage and management model is proposed for BIM geospatial big data management. The architecture primarily includes Not only Structured Query Language (NoSQL) database and distributed peer-to-peer storage. The evaluation of the proposed storage method is conducted on the same software platform as our previous research about WebVR. The experimental results show that the hybrid storage architecture proposed in this research has a lower response time compared to the traditional relational database in geospatial big data searches. The integration and fusion of BIM big data in WebVRGIS realizes a revolutionary transformation of city information management during a full lifecycle. The system also has great promise for the storage of other geospatial big data, such as traffic data.

To study the security performance of the Internet of multimedia things on the privacy protection of user identity, behavior trajectory, and preference under the new information technology industry wave, in this study, aiming at the problems of the sharing of Internet of things perception data and the exposure of users’ privacy information, the Anonymous Batch Authentication Scheme (ABAH) for privacy protection is designed. Hash-based Message Authentication Code is used to cancel the list-checking process and analyze its security performance. Compared with the methods of elliptic curve digital signature algorithm, Bayes least-square method, identity-based bulk verification, anonymous batch authentication and key protocol, conditional privacy authentication scheme, and expert message authentication protocol, the transmission delay, packet loss rate, and computation cost are studied without considering the undo list and during the undo check. The results show that with the increase of information size, the transmission delay and packet loss rate also increase, and the transmission delay of ABAH increases by about 15%, while the correlation between speed and transmission delay is small. In the case of the same amount of validation information, ABAH has the highest validation efficiency, and it still has an efficient validation effect in the case of invalid information. The message packet loss rate for ABAH is always 0 when the undo check validation overhead is considered. It can be found that ABAH can avoid the communication overhead and privacy leakage caused by the revocation list, ensure the integrity of batch verification information, meet the security performance of the vehicular ad hoc network under the Internet of Things, and protect the privacy of users from being disclosed.

Blind or visually impaired people face special difficulties in daily life. With the advances in vision sensors and computer vision, the design of wearable vision assistance system is promising. In order to improve the life quality of the visually impaired group, a wearable system is proposed in this paper. Typically the performance of visual sensors is affected by a variety of complex factors in practice, resulting in a large number of noise and distortion. In this paper, we will creatively leverage image quality evaluation to select the captured images through vision sensors, which can ensure the input quality of scenes for the final identification system. First, we use binocular vision sensors to capture images in a fixed frequency and choose the informative ones based on stereo image quality assessment. Then the captured images will be sent to cloud for further computing. Specially, the detection and automatic result will be done for all the received images. Convolutional neural network based on big data will be used in this step. According to image analysis, the cloud computing can return the requested information for users, which can help them make a more reasonable decision in further action. Simulations and experiments show that the proposed method can solve the problem effectively. In addition, statistical results also demonstrate that wearable vision system can make visually impaired group more satisfied in visual needed situations.

The traditional deployment research on wireless sensor networks (WSNs) has mainly focused on 2D plane and 3D full space; also, the sensors considered are almost always omni-directional sensors and are usually homogeneous. However, this type of research cannot fulfill the diverse requirements required for practical 3D environment. We study the deployment problem of heterogeneous directional sensor networks (HDSNs) on 3D terrain, which is more suitable for practical security monitoring requirements and has more practical significance. In this paper, we propose a novel uncertain comprehensive coverage model: a modified 3D directional sensing model is presented, and a non-probabilistic measure based fusion operator is utilized. We transform the deployment problem into a multiobjective optimization problem by comprehensively considering Coverage, Connectivity Uniformity and Deployment Cost, and we use various state-of-the-art multiobjective optimization algorithm based deployment approaches to address it. We conduct deployment experiments on three types of real-world 3D terrain data (plain, hill and mountain). Through the analysis of the deployment results, we obtain a deeper understanding and insight into the multiobjective deployment problem of HDSN. Meanwhile, we more clearly recognize the characteristics of the deployment approaches.

A public safety event is a danger and urgent event that need early detection, quick response, and accuracy recover. The efficient method for responding to a happening public safety event is to collect and describe the related data. Besides the surveillance cameras from the physical space, the social media data can also be used to collect and describe the related data of a public safety event. In this work, the proposed method focuses on the step for describing public safety events. Given a public safety event, videos from the surveillance cameras and social messages from social sensors are collected. The multi-modal information including texts, images, videos, and spatial-temporal data is mined to give a description precisely and concisely. First, the social sensors are associated to surveillance cameras by the spatial and temporal information. In the second stage, the social messages are associated to surveillance cameras by the semantic information. In the third stage, the social messages are associated to surveillance cameras by the visual feature. Besides the text, social sensors may upload images or videos. Finally, the multi-modal description step is given based on the three different associations. The experiments on the real data demonstrate the superiority of the proposed framework. Case studies on the real public safety event show the proposed model has good performance and high effectiveness.

“Smart” has gradually infiltrating all areas of people's daily life and the environments where we lead our life. The term of “Smart Industrial Environment” can be used to refer to each aspect of the industrial environments focused on the future, being smart vehicles, smart systems of transportation, smart devices (wearables and smartphones), smart services (such as just-in-time production pipelines adjusted to the requirements of the supply-chain), smart grids, smart factories and smart plants management utilizing information technology. It includes the inter-connection of all the smart technologies, involving every type of political and technological borders besides being a term that involves all the aspects.

As the rapid growth of social media technologies continues, Cyber-Physical-Social System (CPSS) has been a hot topic in many industrial applications. The use of “microblogging” services, such as Twitter, has rapidly become an influential way to share information. While recent studies have revealed that understanding and modelling microblog user behaviour with massive users’ data in social media are keen to success of many practical applications in CPSS, a key challenge in literatures is that diversity of geography and cultures in social media technologies strongly affect user behaviour and activity. The motivation of this article is to understand differences and similarities between microblogging users from different countries using social media technologies, and to attempt to design a Country-Level Micro-Blog User (CLMB) behaviour and activity model for supporting CPSS applications. We proposed a CLMB model for analysing microblogging user behaviour and their activity across different countries in the CPSS applications. The model has considered three important characteristics of user behaviour in microblogging data, including content of microblogging messages, user emotion index, and user relationship network. We evaluated CLBM model under the collected microblog dataset from 16 countries with the largest number of representative and active users in the world. Experimental results show that (1) for some countries with small population and strong cohesiveness, users pay more attention to social functionalities of microblogging service; (2) for some countries containing mostly large loose social groups, users use microblogging services as a news dissemination platform; (3) users in countries whose social network structure exhibits reciprocity rather than hierarchy will use more linguistic elements to express happiness in microblogging services.

Convolutional neural networks (CNNs) have yielded state-of-the-art performance in image classification and other computer vision tasks. Their application in fire detection systems will substantially improve detection accuracy, which will eventually minimize fire disasters and reduce the ecological and social ramifications. However, the major concern with CNN-based fire detection systems is their implementation in real-world surveillance networks, due to their high memory and computational requirements for inference. In this paper, we propose an original, energy-friendly, and computationally efficient CNN architecture, inspired by the SqueezeNet architecture for fire detection, localization, and semantic understanding of the scene of the fire. It uses smaller convolutional kernels and contains no dense, fully connected layers, which helps keep the computational requirements to a minimum. Despite its low computational needs, the experimental results demonstrate that our proposed solution achieves accuracies that are comparable to other, more complex models, mainly due to its increased depth. Moreover, this paper shows how a tradeoff can be reached between fire detection accuracy and efficiency, by considering the specific characteristics of the problem of interest and the variety of fire data.

Wireless sensor networks (WSNs) are applied more and more widely in real life. In actual scenarios, 3-D directional wireless sensor nodes are constantly employed, thus, research on the real-time deployment optimization issue of 3-D directional WSNs based on terrain big data has more practical significance. Based on this, we study the deployment optimization issue of directional WSNs in the 3-D terrain through comprehensive consideration of coverage, lifetime, connectivity of sensor nodes, connectivity of cluster headers, and reliability of directional WSNs. We present a modified differential evolution algorithm by adopting crossover rate sort and polynomial-based mutation on the basis of the cooperative coevolutionary framework, and apply it to address the deployment problem of 3-D directional WSNs. In addition, to reduce computation time, we realize implementation of message passing interface parallelism. As is revealed by the experimentation results, the modified algorithm proposed in this paper achieves better performance with respect to either optimization results or operation time.

Effectively monitoring maritime environments has become a vital problem in maritime applications. Traditional methods are not only expensive and time consuming but also restricted in both time and space. More recently, the concept of an industrial wireless sensor network (IWSN) has become a promising alternative for monitoring next-generation intelligent maritime grids, because IWSNs are cost-effective and easy to deploy. This paper focuses on solving the issue of 3-D IWSN deployment in a 3-D engine room space of a very large crude-oil carrier and also considers numerous power facilities. To address this 3-D IWSN deployment problem for maritime applications, a 3-D uncertain coverage model is proposed that uses a modified 3-D sensing model and an uncertain fusion operator. The deployment problem is converted into a multiobjective optimization problem that simultaneously addresses three objectives: coverage, lifetime, and reliability. Our goal is to achieve extensive coverage, long network lifetime, and high reliability. We also propose a distributed parallel cooperative coevolutionary multiobjective large-scale evolutionary algorithm for maritime applications. We verify the effectiveness of this algorithm through experiments by comparing it with five state-of-the-art algorithms. Numerical results demonstrate that the proposed method performs most effectively both in optimization performance and in minimizing the computation time.

The emerging technologies for connected vehicles have become hot topics. In addition, connected vehicle applications are generally found in heterogeneous wireless networks. In such a context, user terminals face the challenge of access network selection. The method of selecting the appropriate access network is quite important for connected vehicle applications. This paper jointly considers multiple decision factors to facilitate vehicle-to-infrastructure networking, where the energy efficiency of the networks is adopted as an important factor in the network selection process. To effectively characterize users' preference and network performance, we exploit energy efficiency, signal intensity, network cost, delay, and bandwidth to establish utility functions. Then, these utility functions and multi-criteria utility theory are used to construct an energy-efficient network selection approach. We propose design strategies to establish a joint multi-criteria utility function for network selection. Then, we model network selection in connected vehicle applications as a multi-constraint optimization problem. Finally, a multi-criteria access selection algorithm is presented to solve the built model. Simulation results show that the proposed access network selection approach is feasible and effective.

Nowadays, big data analysts in scientific, government, industrial, and commercial domains are confronted with the challenges associated with rapidly growing volumes of data that are collected in multiple applications, such as biochemical and genetics research, fundamental physical experiments and astronomical observations, social networks, and consumer behaviour studies. In these applications, large amounts of raw data can be applied for decision making and action planning, yet their volume and increasingly complex structure limit the applicability of multiple well-known approaches that are widely utilised with small datasets, including principal component analysis, singular value decomposition, and spectral analysis. Visual data in particular usually includes a large amount of image data, massive unstructured pixels and multispectral images. Visual data has always been 'Big Data', which includes surveillance, multimedia, YouTube/Flickr, and medical imaging by image analysis techniques – for example feature extraction, dimensionality reduction, parallel/GPU processing, and tracking. New paradigms, techniques, and algorithms are required to address the issue of big data. Several approaches have been put forward for representing and processing large datasets with complex structures. Multi-dimensional data, described by multiple parameters, can be expressed and analysed using multi-way arrays, which have been applied in image processing, biomedical signal processing, telecommunications and sensor array processing as well as other domains. The main goal of large image data processing is to extract the characteristics of the image itself, including semantics, quality, relevancy, and other physical senses. Animal biometrics based recognition systems are gradually gaining more proliferation due to their diversity of application and uses. The recognition system is applied for representation, recognition of generic visual features, and classification of different species based on their phenotype appearances, the morphological image pattern, and biometric characteristics. The muzzle point image pattern is a primary animal biometric characteristic for the recognition of individual cattle. It is similar to the identification of minutiae points in human fingerprints. This study presents an automatic recognition algorithm of muzzle point image pattern of cattle for the identification of individual cattle, verification of false insurance claims, registration, and traceability process. In 'Muzzle point pattern based techniques for individual cattle identification', the proposed recognition algorithm uses the texture feature descriptors, such as speeded up robust features and local binary pattern for the extraction of features from the muzzle point images at different smoothed levels of Gaussian pyramid. The feature descriptors acquired at each Gaussian smoothed level are combined using fusion weighted sum-rule method. With a muzzle point image pattern database of 500 cattle, the proposed algorithm yields the desired level of 93.87% identification accuracy. The comparative analysis of experimental results for proposed work and appearance based face recognition algorithms has been done at each level. With the rapid development of computer science, problems with digital products piracy and copyright disputes become more serious; therefore, it is an urgent task to find solutions for these problems. In 'Digital image watermarking method based on DCT and fractal encoding', the authors' develop a digital watermarking algorithm based on a fractal encoding method and the discrete cosine transform (DCT). The proposed method combines fractal encoding method and DCT method for double encryptions to improve traditional DCT method. The image is encoded by fractal encoding as the first encryption, and then encoded parameters are used in DCT method as the second encryption. First, the fractal encoding method is adopted to encode a private image with private scales. Encoding parameters are applied as digital watermarking. Then, digital watermarking is added to the original image to reversibly use DCT, which means the authors can extract the private image from the carrier image with private encoding scales. Finally, attacking experiments are carried out on the carrier image by using several attacking methods. Experimental results show that the presented method has higher performance characteristics such as robustness and peak signal to noise ratio than classical methods. A 3D model watermarking method robust to geometric attacks is proposed in '3D model watermarking algorithm robust to geometric attacks'. The vertices of the model are classified into three groups. The vertices of the low-resolution group are used to establish an invariant space in which to resist geometric attacks. In the medium-resolution group, appropriate vertices in which to embed the watermarking information are selected. The selection of vertices that contain information about the watermark is based on the area of the local set of the vertex, the curvature of which determines the embedding strength of the watermark. The vertices of the high-resolution group are reserved for resisting simplification and smoothing attacks. The choice of the embedding position and the embedding strength can provide a suitable trade-off between good transparency and maximum robustness of the proposed method. The simulation results show that, compared to existing state-of-the-art methods, the proposed method is robust against attacks such as noise, smoothing, simplification, cropping, rotation, translation, and scaling while ensuring high visual quality of the watermarked model. A 3D model watermarking method robust to geometric attacks is proposed. The vertices of the model are classified into three groups. The vertices of the low-resolution group are used to establish an invariant space in which to resist geometric attacks. In the medium-resolution group, appropriate vertices in which to embed the watermarking information are selected. The selection of vertices that contain information about the watermark is based on the area of the local set of the vertex, the curvature of which determines the embedding strength of the watermark. The vertices of the high-resolution group are reserved for resisting simplification and smoothing attacks. The choice of the embedding position and the embedding strength can provide a suitable trade-off between good transparency and maximum robustness of the proposed method. The simulation results show that, compared to existing state-of-the-art methods, the proposed method is robust against attacks such as noise, smoothing, simplification, cropping, rotation, translation, and scaling while ensuring high visual quality of the watermarked model. Pedestrian detection has become one of the hottest topics in intelligent traffic systems because of its potential applications in driver assistance and automatic driving. In 'Fast pedestrian detection and dynamic tracking for intelligent vehicles within V2V cooperative environment', a fast pedestrian detection and dynamic tracking method within vehicle-to-vehicle (V2V) cooperative environment is proposed. A dynamic tracking-by-detection framework for real-time pedestrian detection is developed. First, cascade classifiers, based on selected Haar-like features, are trained to detect pedestrians. Then, CamShift algorithm combined with extended Kalman filtering is used in pedestrian dynamic tracking. Finally, with the crowdsourcing detected information, a smartphone-based V2V cooperative warning system is developed to share useful detection results within blind spots. The experiment results show that the proposed method has a real-time and accurate performance, which can provide a reference for road traffic safety monitoring technology. Due to storage conditions and material's non-planar shape, geometric distortion of the two-dimensional content is widely present in scanned document images. Effective geometric restoration of these distorted document images considerably increases character recognition rate in large-scale digitisation. For large-scale digitisation of historical books, geometric restoration solutions expect to be accurate, generic, robust, unsupervised and reversible. However, most methods in the literature concentrate on improving restoration accuracy for specific distortion effect, but not their applicability in large-scale digitisation. 'Effective geometric restoration of distorted historical document for large-scale digitisation' proposes an effective mesh based geometric restoration system (GRLSD) for large-scale distorted historical document digitisation. In this system, an automatic mesh generation based dewarping tool is proposed to geometrically model and correct arbitrary warping historical documents. An XML-based mesh recorder is proposed to record the mesh of distortion information for reversible use. A graphic user interface (GUI) toolkit is designed to visually display and manually manipulate the mesh for improving geometric restoration accuracy. Experimental results show that the proposed automatic dewarping approach efficiently corrects arbitrarily warped historical documents, with an improved performance over several state-of-the-art geometric restoration methods. By using XML mesh recorder and GUI toolkit, the GRLSD system greatly aids users to flexibly monitor and correct ambiguous points of mesh for the prevention of damaging historical document images without distortions in large-scale digitalisation. The ultimate receiver of image and video is human visual system (HVS). An important problem in the domain of image and video processing is how to establish visual information representation model meeting the HVS perception property. In 'Structured entropy of primitive: big databased stereoscopic image quality assessment', authors give theory analysis and experiment results to prove that l_1 norm-based entropy of primitive (EoP) is superior to the l_0 norm-based EoP for the monocular cue in image quality assessment. By developing the concept of mutual information of primitive (MIP) as the binocular cue, an l_1 EoP-based stereoscopic image quality assessment metric is proposed. With EoP as monocular cue and MIP as binocular cue, the relative entropy between the original stereoscopic image and the distorted one is explored to predict the quality score with support vector regression. To avoid destroying images' structured information, the structured EoP (SEoP) is further explored to measure the stereoscopic image information. Extensive experimental results demonstrate that the stereoscopic image quality assessment algorithm with SEoP as monocular cue and MIP as binocular cue outperforms many state-of-the-art ones.

Currently the false data injection (FDI) attack bring direct challenges in synchronized phase measurement unit (PMU) based network state estimation in wide-area measurement system, resulting in degraded system reliability and power supply security. This paper assesses the performance of state estimation in electric cyber-physical system paradigm considering the presence of FDI attacks. The adverse impact on network state estimation is evaluated through simulations for a range of FDI attack scenarios using IEEE 14-bus network model. In addition, an algorithmic solution is proposed to address the issue of additional PMU installation and placement with cyber security consideration and evaluated for a set of standard electric transmission networks (IEEE 14-bus, 30-bus, and 57-bus network). The numerical result confirms that the FDI attack can significantly degrade the state estimation and the cyber security can be improved by an appropriate placement of a limited number of additional PMUs.

In Big Data era, applications are generating orders of magnitude more data in both volume and quantity. While many systems emerge to address such data explosion, the fact that these data's descriptors, i.e., metadata, are also “big” is often overlooked. The conventional approach to address the big metadata issue is to disperse metadata into multiple machines. However, it is extremely difficult to preserve both load-balance and data-locality in this approach. To this end, in this work we propose hierarchical indirection layers for indexing the underlying distributed metadata. By doing this, data locality is achieved efficiently by the indirection while load-balance is preserved. Three key challenges exist in this approach, however: first, how to achieve high resilience; second, how to ensure flexible granularity; third, how to restrain performance overhead. To address above challenges, we design Dindex, a distributed indexing service for metadata. Dindex incorporates a hierarchy of coarse-grained aggregation and horizontal key-coalition. Theoretical analysis shows that the overhead of building Dindex is compensated by only two or three queries. Dindex has been implemented by a lightweight distributed key-value store and integrated to a fully-fledged distributed filesystem. Experiments demonstrated that Dindex accelerated metadata queries by up to 60 percent with a negligible overhead.

A considerable amount of research has been devoted to multiobjective optimization problems. However, few studies have aimed at multiobjective large-scale optimization problems (MOLSOPs). To address MOLSOPs, which may involve big data, this paper proposes a message passing interface MPI -based distributed parallel cooperative coevolutionary multiobjective evolutionary algorithm (DPCCMOEA). DPCCMOEA tackles MOLSOPs based on decomposition. First, based on a modified variable analysis method, we separate decision variables into several groups, each of which is optimized by a subpopulation (species). Then, the individuals in each subpopulation are further separated to several sets. DPCCMOEA is implemented with MPI distributed parallelism and a two-layer parallel structure is constructed. We examine the proposed algorithm using the multiobjective test suites Deb-Thiele-Laumanns-Zitzler and Walking-Fish-Group. In comparison with cooperative coevolutionary generalized differential evolution 3 and multiobjective evolutionary algorithm based on decision variable analyses, which are state-of-the-art cooperative coevolutionary multiobjective evolutionary algorithms, experimental results show that the novel algorithm has better performance in both optimization results and time consumption.

Cycling is a typical green traffic mode, and takes a growing part of urban traffic volume. Yet limited cyclist behavior models shed light on cases at unsignalized intersections with heterogeneous traffic, where bicycle behavior is characterized by frequent confrontations with other road users (vehicles, bicycles, and pedestrians). This study developed a microscopic simulation model for cyclist behavior analysis at unsignalized intersection with heterogeneous traffic. The cyclist crossing model applied fuzzy logic and social force theory for this purpose. The parameters are either estimated directly based on empirical data or derived indirectly through maximum likelihood estimation. Finally model performance was confirmed through comparisons between estimations and observations on individual trajectory, minimum distances, and average riding speeds of collision avoidance behaviors with different conflicting road users. Simulation results indicated that the model can represent cyclist crossing behavior at unsignalized intersection with heterogeneous traffic as in the real world.

The term big data occurs more frequently now than ever before. A large number of fields and subjects, ranging from everyday life to traditional research fields (i.e., geography and transportation, biology and chemistry, medicine and rehabilitation), involve big data problems. The popularizing of various types of network has diversified types, issues, and solutions for big data more than ever before. In this paper, we review recent research in data types, storage models, privacy, data security, analysis methods, and applications related to network big data. Finally, we summarize the challenges and development of big data to predict current and future trends.

The papers in this special section examine recent advancements in the area of network big data analysis.

In the field of big data applications, image information is widely used. The value density of information utilization in big data is very low, and how to extract useful information quickly is very important. So we should transform the unstructured image data source into a form that can be analyzed. In this paper, we proposed a fast image retrieval method which designed for big data. First of all, the feature extraction method is necessary and the feature vectors can be obtained for every image. Then, it is the most important step for us to encode the image feature vectors and make them into database, which can optimize the feature structure. Finally, the corresponding similarity matching is used to determined the retrieval results. There are three main contributions for image retrieval in this paper. New feature extraction method, reasonable elements ranking, and appropriate distance metric can improve the algorithm performance. Experiments show that our method has a great improvement in the effective performance of feature extraction and can also get better search matching results.

Traditional particle swarm optimization algorithms (PSO) targeted to solve large scale problems are mostly serial, such as CCPSO2, and the computing time is very long in general. Therefore, this paper presents a novel parallel PSO, which explores the usage of new probability distribution functions for the replacement of traditional Gaussian and Cauchy distributions, and the combination of GPSO and LPSO to make use of space exploration and speed up the convergence. As to the implementation of algorithm parallelization, we adopt the Spark platform, which is one of the currently most popular big data processing tools. We make modification to dynamic grouping and multiple calculations, in order to increase the degree of parallelism, reduce the computation time and improve algorithm efficiency as far as possible. Multiple computing refers to that in each single distribution of tasks, one computing node processes the particle position information of multiple algorithms. In the control of space exploration and convergence rate, we present a more efficient method to explore the solution space, which controls the convergence rate to enhance the exploration to a greater extent and also ensures fast convergence rate at the later stage, thus, it not only guarantees the calculation speed, but also improves the optimization effect as more as possible. We used twenty LSGO benchmark functions in CEC'2010 to make experiments, showing that the proposed algorithm could obtain satisfactory results, and for some functions, it outperforms DECC and MLCC.

Many researches show that the power consumption of network devices of ICT is nearly 10% of total global consumption. While the redundant deployment of network equipment makes the network utilization is relatively low, which leads to a very low energy efficiency of networks. With the dynamic and high quality demands of users, how to improve network energy efficiency becomes a focus under the premise of ensuring network performance and customer service quality. For this reason, we propose an energy consumption model based on link loads, and use the network’s bit energy consumption parameter to measure the network energy efficiency. This paper is to minimize the network’s bit energy consumption parameter, and then we propose the energy-efficient minimum criticality routing algorithm, which includes energy efficiency routing and load balancing. To further improve network energy efficiency, this paper proposes an energy-efficient multi-constraint rerouting (E2MR2) algorithm. E2MR2 uses the energy consumption model to set up the link weight for maximum energy efficiency and exploits rerouting strategy to ensure network QoS and maximum delay constraints. The simulation uses synthetic traffic data in the real network topology to analyze the performance of our method. Simulation results that our approach is feasible and promising.

A network virtual reality (VR) engine for interactive analysis and smart city immersive visualization which integrates several latest information technologies, i.e., Web virtual reality (WebVR), Internet of Things (IoT), and three-dimensional (3-D) geographical information system (3-DGIS) with peer-to-peer (P2P) network, has been proposed in this study. It can be used in big spatial data (e.g., remote sensing data) organization and presentation while achieving the online sharing by hash-based P2P networks. In the P2P network, a map, which contains the real geographic space users and the virtual scene user avatar with prep the virtual network nodes, has been made. Moreover, it can also support the following integrated functions of VRGIS: 3-D spatial analysis function, 3-D visualization for spatial information process, and Web engine for 3-D globe and smart big city. To evaluate WebVRGIS system, a user study had been conducted by comparing it with virtual community system. The performance evaluation results had confirmed the higher usability and user satisfaction of the proposed WebVRGIS system.

This paper takes Shenzhen Fustian comprehensive transportation junction as the case, and makes use of continuous multiple real-time dynamic traffic information to carry out monitoring and analysis on spatial and temporal distribution of passenger flow under different means of transportation and service capacity of junction from multi-dimensional space-time perspectives such as different period and special period. Virtual reality geographic information system is employed to present the forecasting result.

Latent Dirichlet Allocation (LDA) has been increasingly applied in the area of computer vision. LDA is based on the 'bag of words' assumption that ignores the spatial structure of images. This problem poses a non-trivial impact on the performance of the model. There exist a number of methods that attempt to address the limit. One representative work can be Spatial Latent Topic Model (Spatial-LTM) for unsupervised joint object discovery and segmentation, which improves over LDA by assigning locally co-occurring visual words with the same topic. However, this model still ignores the spatial relations between visual words which are spatially distant from each other. In this paper, we add a spatial regularization term to the model's posterior distribution that regulates the difference of multinomial weight between each pair of visual words in a topic based on their spatial distance apart in an image set. We call the improved model Spatially Regularized Latent Topic Model (SR-LTM). Experiment result shows that SR-LTM outperforms Spatial-LTM in both unsupervised object discovery accuracy and segmentation accuracy.

A touch-less interaction technology on vision based wearable device is designed and evaluated. Users interact with the application with dynamic hands/feet gestures in front of the camera. Several proof-of-concept prototypes with eleven dynamic gestures are developed based on the touch-less interaction. At last, a comparing user study evaluation is proposed to demonstrate the usability of the touch-less approach, as well as the impact on user's emotion, running on a wearable framework or Google Glass.

We present a hand-and-foot-based multimodal interaction approach for handheld devices. Our method combines input modalities (i.e., hand and foot) and provides a coordinated output to both modalities along with audio and video. Human foot gesture is detected and tracked using contour-based template detection (CTD) and Tracking-Learning-Detection (TLD) algorithm. 3D foot pose is estimated from passive homography matrix of the camera. 3D stereoscopic and vibrotactile are used to enhance the immersive feeling. We developed a multimodal football game based on the multimodal approach as a proof-of-concept. We confirm our systems user satisfaction through a user study.

In this paper we present hand and foot based immersive multimodal interaction approach for handheld devices. A smart phone based immersive football game is designed as a proof of concept. Our proposed method combines input modalities (i.e. hand & foot) and provides a coordinated output to both modalities along with audio and video. In this work, human foot gesture is detected and tracked using template matching method and Tracking-Learning-Detection (TLD) framework. We evaluated our system's usability through a user study in which we asked participants to evaluate proposed interaction method. Our preliminary evaluation demonstrates the efficiency and ease of use of proposed multimodal interaction approach.

We propose a design of teleconference system which express nonverbal behavior (in our case head gesture) along with audio-video communication. Previous audio-video conferencing systems are abortive in presenting nonverbal behaviors which we, as human, usually use in face to face interaction. Recently, research in teleconferencing systems has expanded to include nonverbal cues of remote person in their distance communication. The accurate representation of non-verbal gestures for such systems is still challenging because they are dependent on hand-operated devices (like mouse or keyboard). Furthermore, they still lack in presenting accurate human gestures. We believe that incorporating embodied interaction in video teleconferencing, (i.e., using the physical world as a medium for interacting with digital technology) can result in nonverbal behavior representation. The experimental platform named Tele-Embodied Agent (TEA) is introduced which incorperates remote person's head gestures to study new paradigm of embodied interaction in video teleconferencing. Our preliminary test shows accuracy (with respect to pose angles) and efficiency (with respect to time) of our proposed design. TEA can be used in medical field, factories, offices, gaming industry, music industry and for training.

In this paper we present a vision based intuitive interaction method for smart mobile devices. It is based on markerless finger gesture detection which attempts to provide a 'natural user interface'. There is no additional hardware necessary for real-time finger gesture estimation. To evaluate the strengths and effectiveness of proposed method, we design two smart phone applications, namely circle menu application - provides user with graphics and smart phone's status information, and bouncing ball game- a finger gesture based bouncing ball application. The users interact with these applications using finger gestures through the smart phone's camera view, which trigger the interaction event and generate activity sequences for interactive buffers. Our preliminary user study evaluation demonstrates effectiveness and the social acceptability of proposed interaction approach.