This paper describes an adaptation of the Local Interpretable Model-Agnostic Explanations (LIME) AI method to operate under a biometric verification setting. LIME was initially proposed for networks with the same output classes used for training, and it employs the softmax probability to determine which regions of the image contribute the most to classification. However, in a verification setting, the classes to be recognized have not been seen during training. In addition, instead of using the softmax output, face descriptors are usually obtained from a layer before the classification layer. The model is adapted to achieve explainability via cosine similarity between feature vectors of perturbated versions of the input image. The method is showcased for face biometrics with two CNN models based on MobileNetv2 and ResNet50. © 2023 IEEE.

This study discusses the new stochastic maximum power point tracking (MPPT) control approach towards the photovoltaic cells (PCs). PC generator is isolated from the grid, resulting in a direct current (DC) microgrid that can provide changing loads. In the course of the nonlinear systems through the time-varying delays, we proposed a Networked Control Systems (NCSs) beneath an event-triggered approach basically in the fuzzy system. In this scenario, we look at how random, variable loads impact the PC generator's stability and efficiency. The basic premise of this article is to load changes and the value matching to a Markov chain. PC generators are complicated nonlinear systems that pose a modeling problem. Transforming this nonlinear PC generator model into the Takagi–Sugeno (T–S) fuzzy model is another option. Takagi–Sugeno (T–S) fuzzy model is presented in a unified framework, for which 1) the fuzzy observer–based on this premise variables can be used for approximately in the infinite states to the present system, 2) the fuzzy observer–based controller can be created using this same premises be the observer, and 3) to reduce the impact of transmission burden, an event-triggered method can be investigated. Simulating in the PC generator model for the realtime climate data obtained in China demonstrates the importance of our method. In addition, by using a new Lyapunov–Krasovskii functional (LKF) for combining to the allowed weighting matrices incorporating mode-dependent integral terms, the developed model can be stochastically stable and achieves the required performances. Based on the T-P transformation, a new depiction of the nonlinear system is derived in two separate steps in which an adequate controller input is guaranteed in the first step and an adequate vertex polytope is ensured in the second step. To present the potential of our proposed method, we simulate it for PC generators. © 2022 IEEE.

Recently, the rapid development of Unmanned Aerial Vehicles (UAVs) enables ecological conservation, such as low-carbon and “green” transport, which helps environmental sustainability. In order to address control issues in a given region, UAV charging infrastructure is urgently needed. To better achieve this task, an investigation into the T–S fuzzy modeling for Sustainable Hypersonic Vehicles (SHVs) with Markovian jump parameters and H∞ attitude control in three channels was conducted. Initially, the reentry dynamics were transformed into a control–oriented affine nonlinear model. Then, the original T–S local modeling method for SHV was projected by primarily referring to Taylor's expansion and fuzzy linearization methodologies. After the estimation of precision and controller complexity was assumed, the fuzzy model for jump nonlinear systems mainly consisted of two levels: a crisp level and a fuzzy level. The former illustrates the jumps, and the latter a fuzzy level that represents the nonlinearities of the system. Then, a systematic method built in a new coupled Lyapunov function for a stochastic fuzzy controller was used to guarantee the closed–loop system for H∞ gain in the presence of a predefined performance index. Ultimately, numerical simulations were conducted to show how the suggested controller can be successfully applied and functioned in controlling the original attitude dynamics. © 2023 Elsevier Inc.

As the high mobility nature of the vehicles results in frequent leaving and joining the transportation network, real-time data must be collected and shared in a timely manner. In such a transportation network, malicious vehicles can disrupt services and create serious issues, such as deadlocks and accidents. The blockchain is a technology that ensures traceability, consistency, and security in transportation networks. In this study, we integrated edge computing and blockchain technology to improve the optimal utilization of resources, especially in terms of computing, communication, security, and storage. We propose a novel, edge-integrated, blockchain-based vehicle platoon security scheme. For the vehicle platoon, we developed the security architecture, implemented smart contracts for practical network scenarios in NS-3, and integrated them with the SUMO TraCI API. We exhaustively simulated all the scenarios and analyzed the communication performance metrics, such as throughput, delay, and jitter, and the security performance metrics, such as mean squared error, communication, and computational cost. The performance results demonstrate that the developed scheme can solve security-related issues more effectively and efficiently in smart cities. © IEEE

Due to the strong adaptability and high flexibility, unmanned aerial vehicles (UAVs) have been extensively studied and widely applied in both civil and military applications. Although UAVs can achieve significant cost reduction and performance enhancement in large-scale systems by taking full advantage of their cooperation and coordination, they result in a serious cooperative behaviour control problem. Especially in dynamic environments, the cooperative behaviour control problem which has to quickly produce a safe and effective behaviour decision for each UAV to achieve group missions, is NP-hard and difficult to settle. In this work, we design a global-and-local attention-based reinforcement learning algorithm for the cooperative behaviour control problem of UAVs. First, with the motion and coordination models, we analyze the collision avoidance, motion state update, and task execution constraints of multiple UAVs, and abstract the cooperative behaviour control problem as a multi-constraint decision-making one. Then, inspired from the human-learning process where more attention is devoted to the important parts of data, we design a multi-agent reinforcement learning algorithm with a global-and-local attention mechanism to cooperatively control the behaviours of UAVs and achieve the coordination. Simulation experiments in a multi-agent particle environment provided by OpenAI are conducted to verify the effectiveness and efficiency of the proposed approach. Compared with baselines, our approach shows significant advantages in mean reward, training time, and coordination effect. © 2023 IEEE.

Pseudouridine is an RNA modification that is widely distributed in both prokaryotes and eukaryotes, and plays a critical role in numerous biological activities. Despite its importance, the precise identification of pseudouridine sites through experimental approaches poses significant challenges, requiring substantial time and resources.Therefore, there is a growing need for computational techniques that can reliably and quickly identify pseudouridine sites from vast amounts of RNA sequencing data. In this study, we propose fuzzy kernel evidence Random Forest (FKeERF) to identify pseudouridine sites. This method is called PseU-FKeERF, which demonstrates high accuracy in identifying pseudouridine sites from RNA sequencing data. The PseU-FKeERF model selected four RNA feature coding schemes with relatively good performance for feature combination, and then input them into the newly proposed FKeERF method for category prediction. FKeERF not only uses fuzzy logic to expand the original feature space, but also combines kernel methods that are easy to interpret in general for category prediction. Both cross-validation tests and independent tests on benchmark datasets have shown that PseU-FKeERF has better predictive performance than several state-of-the-art methods. This new method not only improves the accuracy of pseudouridine site identification, but also provides a certain reference for disease control and related drug development in the future. © The Author(s) 2024. Published by Oxford University Press.

The combination of Non-Orthogonal Multiplex Access and Unmanned Aerial Vehicles (UAV) can improve theenergy efficiency (EE) for Internet-of-Things (IoT). On the condition of interference constraint and minimum achievable rate of the secondary users, we propose an iterative optimization algorithm on EE. Firstly, with given UAV trajectory, the Dinkelbach method based fractional programming is adopted to obtain theoptimal transmission power factors. By using the previous power allocation scheme, the successive convex optimization algorithmis adopted in the second stage to update the system parameters. Finally, reinforcement learning based optimization is introducedto obtain the best UAV trajectory. © 2022 IEEE

Medical experts are often skeptical of data-driven models due to the lack of their explainability. Several experimental studies commence with wide-ranging unsupervised learning and precisely with clustering to obtain existing patterns without prior knowledge of newly acquired data. Explainable Artificial Intelligence (XAI) increases the trust between virtual assistance by Machine Learning models and medical experts. Awareness about how data is analyzed and what factors are considered during the decision-making process can be confidently answered with the help of XAI. In this paper, we introduce an improved hybrid classical-quantum clustering (improved qk-means algorithm) approach with the additional explainable method. The proposed model uses learning strategies such as the Local Interpretable Model-agnostic Explanations (LIME) method and improved quantum k-means (qk-means) algorithm to diagnose abnormal activities based on breast cancer images and Knee Magnetic Resonance Imaging (MRI) datasets to generate an explanation of the predictions. Compared with existing algorithms, the clustering accuracy of the generated clusters increases trust in the model-generated explanations. In practice, the experiment uses 600 breast cancer (BC) patient records with seven features and 510 knee MRI records with five features. The result shows that the improved hybrid approach outperforms the classical one in the BC and Knee MRI datasets. © 2023 Elsevier B.V.

The association between drugs and side effects encompasses information about approved medications and their documented adverse drug reactions. Traditional experimental approaches for studying this association tend to be time-consuming and expensive. To represent all drug-side effect associations, a bipartite network framework is employed. Consequently, numerous computational methods have been devised to tackle this problem, focusing on predicting new potential associations. However, a significant gap lies in the neglect of the Multi-View Learning (MVL) algorithm, which has the ability to integrate diverse information sources and enhance prediction accuracy. In our study, we have developed a novel predictor named Multi-View Semi-Supervised Sparse Model (Mv3SM) to address the drug side effect prediction problem. Our approach aims to explore the distinctive characteristics of various view features obtained from fully paired multi-view data and mitigate the influence of noisy data. To test the performance of Mv3SM and other computational approaches, we conducted experiments using three benchmark datasets. The obtained results clearly demonstrate that our proposed method achieves superior predictive performance compared to alternative approaches. © IEEE

The identification of DNA N4-methylcytosine (4mC) sites is an important field of bioinformatics. Statistical learning methods and deep learning have been applied in this direction. The previous methods focused on feature representation and feature selection, and did not take into account the deviation of noise samples for recognition. Moreover, these models were not established from the perspective of prediction error distribution. To solve the problem of complex error distribution, we propose a maximum multi-correntropy criterion based kernelized higher-order fuzzy inference system (MMC-KHFIS), which is constructed with multi-correntropy fusion. There are 6 4mC and 8 UCI data sets are employed to evaluate our model. The MMC-KHFIS achieves better performance in the experiment. © 2023

Non-coding RNAs (ncRNAs) play an important role in revealing the mechanism of human disease for anti-tumor and anti-virus substances. Detecting subcellular locations of ncRNAs is a necessary way to study ncRNA. Traditional biochemical methods are time-consuming and labor-intensive, and computational-based methods can help detect the location of ncRNAs on a large scale. However, many models did not consider the correlation information among multiple subcellular localizations of ncRNAs. This study proposes a radial basis function neural network based on shared subspace learning (RBFNN-SSL), which extract shared structures in multi-labels. To evaluate performance, our classifier is tested on three ncRNA datasets. Our model achieves better performance in experimental results. © 2022 The Author(s)

DNA N4-methylcytosine (4mC) site identification is a crucial field in bioinformatics, where machine learning methods have been effectively utilized. Due to the presence of noise, the existing deep learning methods for detecting 4mC have consistently low recognition rates in positive samples. With fuzzy rules and membership functions, fuzzy systems can achieve good results in processing noisy signals. In contrast to traditional fuzzy systems that lack deep feature representation and sample measurement, we introduce novel techniques to enhance generalization and feature representation. By incorporating the neural tangent kernel (NTK) and kernel learning algorithm into the fuzzy system, we propose the fuzzy neural tangent kernel (FNTK) model and the radius-based FNTK (R-FNTK) model to predict DNA 4mC sites. To achieve better generalization performance than traditional kernel functions, we first train the NTK for feature representation learning and sample measurement. Based on the membership function and NTK matrix, different fuzzy kernel matrices are constructed for each fuzzy subset of the fuzzy system. Finally, we utilize two types of iterative kernel optimization algorithms to effectively fuse multiple NTK-based fuzzy kernels and obtain the final prediction model. Rigorous testing using 6 benchmark datasets demonstrates the superiority of our approach, yielding significant improvements in the experiment's performance. © IEEE

Drug-drug interaction (DDI) is an important part of drug development and pharmacovigilance. At the same time, DDI is an important factor in treatment planning, monitoring effects of medicine and patient safety, and has a significant impact on public health. Therefore, using deep learning technology to extract DDI from scientific literature has become a valuable research direction to researchers. In existing DDI datasets, the number of positive instances is relatively small. This makes it difficult for existing deep learning models to obtain sufficient feature information directly from text data. Therefore, existing deep learning models mainly rely on multiple feature supplementation methods to collect sufficient feature information from different types of data. In this study, the general process of DDI relation extraction based on deep learning is introduced first for comprehensive analysis. Next, we summarize the various feature supplement methods and analyze their merits and demerits. We then review the state-of-the-art literature related to DDI extraction from the deep neural network perspective. Finally, all the feature supplement methods are compared, and some suggestions are given to approach the current problems and future research directions. The purpose of this article is to give researchers a more complete understanding of the feature complementation methods used in DDI extraction to be able to rapidly design and implement custom DDI relation extraction methods. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

The present research develops a framework to refine the classification of an individual's activities and recognize wellness associated with their routine. The framework improves the accuracy of the classification of routine activities of a person, the activation time data of sensors fixed on objects linked with the routine activities of the person, and the aptness of an incessant activity pattern with the routine activities. The existing techniques need continuous monitoring and are non-adaptive to a person's persistent habitual variations or individualities. The research involves applying Internet of Medical Things (IoMT)-based sensor information fusion to the novel multimodel data analytics to develop Activities of Daily Living (ADL) pattern, behavioral pattern generation and anomaly recognition. The novel multimodel data analytics approach is named AiCareLiving. AicareLiving is an IoMT and artificial intelligence (AI) enabled approach. The research work describes activity data using an individual's activities within a specified area before evaluating the activity data to detect the existence of an anomaly by identifying the deviation of the activity data from the activity profile, which indicates the anticipated behavior and activity of the person. This wellness information would be shared to the caregivers, related healthcare professionals, care providers and municipalities through the secured healthcare information exchange protocol and IoMT. AiCareLiving framework aims to least false positive in terms of anomaly detection and forecasting; the high precision is close to the confidence level of 95%.

© 2024 The Authors

The detection of therapeutic peptides is usually a biochemical experimental method, which is time-consuming and labor-intensive. Lots of computational biology methods had been proposed to solve the problem of therapeutic peptide prediction. However, the existing methods did not consider the processing of noisy samples. We propose a kernel risk-sensitive mean p-power error-based echo state network with sparse learning (KRP-ESN-SL). An efficient iterative optimization algorithm is used to train the model. The KRP-ESN-SL has better performance than other methods. © 2022 IEEE.

DNA-binding proteins (DBPs) protect DNA from nuclease hydrolysis, inhibit the action of RNA polymerase,prevents replication and transcription from occurring simultaneously on a piece of DNA. Most of theconventional methods for detecting DBPs are biochemical methods, but the time cost is high. In recent years,a variety of machine learning-based methods that have been used on a large scale for large-scale screeningof DBPs. To improve the prediction performance of DBPs, we propose a random Fourier features-based sparserepresentation classifier (RFF-SRC), which randomly map the features into a high-dimensional space to solvenonlinear classification problems. And 𝐿2,1-matrix norm is introduced to get sparse solution of model. Toevaluate performance, our model is tested on several benchmark data sets of DBPs and 8 UCI data sets. RFF-SRCachieves better performance in experimental results. © 2022 Elsevier Ltd. 

Detection of therapeutic peptide is a major research direction in the current biopharmaceutical field. However, traditional biochemical experimental detection methods take a lot of time. As supplementary methods for biochemical experiments, the computational methods can improve the efficiency of therapeutic peptide detection. Currently, most machine learning-based therapeutic peptide identification algorithms do not consider the processing of noisy samples. We propose a therapeutic peptide classifier, called weighted echo state networks based on subspace projection (WESN-SP), which reduces the bias caused by high-dimensional noisy features and noisy samples. WESN-SP is trained by sparse Bayesian learning algorithm (SBL) and introduces a weight coefficient for each sample by kernel dependence maximization-based subspace projection. The experimental results show that WESN-SP has better performance than other existing methods. © 2023 The Author(s). Published by Elsevier B.V.

The detection of therapeutic peptides is a topic of immense interest in the biomedical field. Conventional biochemical experiment-based detection techniques are tedious and time-consuming. Computational biology has become a useful tool for improving the detection efficiency of therapeutic peptides. Most computational methods do not consider the deviation caused by noise. To improve the generalization performance of therapeutic peptide prediction methods, this work presents a sequence homology score-based deep fuzzy echo-state network with maximizing mixture correntropy (SHS-DFESN-MMC) model. Our method is compared with the existing methods on eight types of therapeutic peptide datasets. The model parameters are determined by 10 fold cross-validation on their training sets and verified by independent test sets. Across the 8 datasets, the average area under the receiver operating characteristic curve (AUC) values of SHS-DFESN-MMC are the highest on both the training (0.926) and independent sets (0.923). © 2024 The Authors

Document-level relation extraction faces two often overlooked challenges: long-tail problem and multi-label problem. Previous work focuses mainly on obtaining better contextual representations for entity pairs, hardly address the above challenges. In this paper, we analyze the co-occurrence correlation of relations, and introduce it into the document-level relation extraction task for the first time. We argue that the correlations can not only transfer knowledge between data-rich relations and data-scarce ones to assist in the training of long-tailed relations, but also reflect semantic distance guiding the classifier to identify semantically close relations for multi-label entity pairs. Specifically, we use relation embedding as a medium, and propose two co-occurrence prediction sub-tasks from both coarse- and fine-grained perspectives to capture relation correlations. Finally, the learned correlation-aware embeddings are used to guide the extraction of relational facts. Substantial experiments on two popular datasets (i.e., DocRED and DWIE) are conducted, and our method achieves superior results compared to baselines. Insightful analysis also demonstrates the potential of relation correlations to address the above challenges. The data and code are released at https://github.com/RidongHan/DocRE-Co-Occur. © 2023 Elsevier Ltd

Enhancing original LiDAR point cloud features with virtual points has gained widespread attention in multimodal information fusion. However, existing methods struggle to leverage image depth information due to the sparse nature of point clouds, hindering proper alignment with camera-derived features. We propose a novel 3D object detection method that refines virtual point clouds using a coarse-to-fine approach, incorporating a dynamic 2D Gaussian distribution for better matching and a dynamic posterior density-aware RoI network for refined feature extraction. Our method achieves an average precision (AP) of 90.02% for moderate car detection on the KITTI validation set, outperforming state-of-the-art methods. Additionally, our approach yields AP scores of 86.58% and 82.16% for moderate and hard car detection categories on the KITTI test set, respectively. These results underscore the effectiveness of our method in addressing point cloud sparsity and enhancing 3D object detection performance. The code is available at https://github.com/ZhongkangZ/LidarIG. © 2024 Elsevier B.V.

Background and Objective:: By 2030, depression is projected to become the predominant mental disorder. With the rising prominence of depression, a great number of affective computing studies has been observed, with the majority emphasizing the use of audiovisual methods for estimating depression scales. Present studies often overlook the potential patterns of sequential data and not adopt the fine-grained features of Transformer to model the behavior features for video-based depression recognition (VDR). Methods: To address above-mentioned gaps, we present an end-to-end sequential framework called Depressformer for VDR. This innovative structure is delineated into the three structures: the Video Swin Transformer (VST) for deep feature extraction, a module dedicated to depression-specific fine-grained local feature extraction (DFLFE), and the depression channel attention fusion (DCAF) module to fuse the latent local and global features. By utilizing the VST as a backbone network, it is possible to discern pivotal features more effectively. The DFLFE enriches this process by focusing on the nuanced local features indicative of depression. To enhance the modeling of combined features pertinent to VDR, DCAF module is also presented. Results: Our methodology underwent extensive validations using the AVEC2013/2014 depression databases. The empirical results underscore its efficacy, yielding a root mean square error (RMSE) of 7.47 and a mean absolute error (MAE) of 5.49 for the first dataset. For the second database, the corresponding values were 7.22 and 5.56, respectively. And the F1-score is 0.59 on the D-vlog dataset. Conclusions: In summary, the experimental evaluations suggest that Depressformer architecture demonstrates superior performances with stability and adaptability across various tasks, making it capable of effectively identifying the severity of depression. Code will released at the link: https://github.com/helang818/Depressformer/. © 2024 Elsevier Ltd

Depression will be the first prevalent mental disorder to result in the negative impact on individuals and society globally by 2030. Artificial intelligence (AI) algorithms have the potentials to significantly advance depression treatment. Existing deep learning-based architectures for the automatic diagnosis of a patient depression severity have the two primary challenges: (1) How to effectively learn both long-term and short-term patterns of depression? (2) How to efficiently merge long-term and short-term depressive features to achieve extended predictions from facial videos? To mitigate these challenges, a novel long short-term cross-attention-aware Transformer (LSCAformer) that is engineered for video-based depression recognition. Within LSCAformer, two architectures are introduced, i.e., a long short-term feature extraction (LSTFE) and a cross-attention-aware Transformer. Initially, LSTFE employs two separate branches to capture depression behaviors across long and short-term intervals. Subsequently, cross-attention-aware Transformer is implemented to identify complementary patterns within both long-term and short-term features, employing temporal-directed attention (TDA) to discern complementary temporal patterns across the long and short duration branches. On the AVEC2013/AVEC2014, the LSCAformer demonstrated superior performances with a root mean square error (RMSE), a mean absolute error (MAE) and a concordance correlation coefficient (CCC) of 7.69/5.89/0.868 and 7.55/5.91/0.845, respectively. Additionally, cross dataset experiments are performed to valid the generalization of the LSCAformer with a RMSE of 7.21, a MAE of 5.63, and a CCC of 0.874 (AVEC2013 for training, and the Northwind task of AVEC2014 for testing). Moreover, the proposed method can model the complementary behavioral patterns between long-term and short-term sequences for depression recognition. Code will be available at: https://github.com/helang818/LSCAformer/. © 2024

In the context of smart aquaculture, real-time Multi-fish Tracking (MFT) poses a significant challenge. Existing Multi-Object Tracking (MOT) methods are often designed for objects with specific shapes or regular motion patterns, such as pedestrians or cars. The unique characteristics of fish, including their uniform appearance and deformation during motion, have been largely overlooked in research. To address this gap, we introduce the Uniform and Deformable Multi-fish Tracking (UD-MFT) benchmark. This dataset not only incorporates challenges related to uniform appearance and diverse deformable shapes of fish during motion in daily activities but also encompasses common MOT challenges like occlusion and disappearance. All sequences are sourced from industrialized aquaculture environments, providing a practical and relevant setting. To understand the distinctiveness of UD-MFT, we quantify the degrees of deformation, appearance, and occlusion levels within the dataset and compare them with tracking targets in existing datasets. Furthermore, to facilitate practical applications, we conduct a comprehensive evaluation of state-of-the-art real-time MOT models on UD-MFT, establishing a comparative baseline for accuracy and computational requirements. Additionally, we perform an in-depth analysis of the impact of deformation and appearance similarity on tracking accuracy. Finally, we provide reflections and recommendations concerning potential avenues for future research in this field. The proposed UD-MFT aims to serve as a robust platform for developing algorithms capable of handling fish with multiple motion patterns, thereby contributing to the advancement of intelligent fish farming. © 2024 Elsevier Ltd

This paper focuses on affective emotion recognition, aiming to perform in the subject-agnostic paradigm based on EEG signals. However, EEG signals manifest subject instability in subject-agnostic affective Brain-computer interfaces (aBCIs), which led to the problem of distributional shift. Furthermore, this problem is alleviated by approaches such as domain generalisation and domain adaptation. Typically, methods based on domain adaptation confer comparatively better results than the domain generalisation methods but demand more computational resources given new subjects. We propose a novel framework, meta-learning based augmented domain adaptation for subject-agnostic aBCIs. Our domain adaptation approach is augmented through meta-learning, which consists of a recurrent neural network, a classifier, and a distributional shift controller based on a sum-decomposable function. Also, we present that a neural network explicating a sum-decomposable function can effectively estimate the divergence between varied domains. The network setting for augmented domain adaptation follows meta-learning and adversarial learning, where the controller promptly adapts to new domains employing the target data via a few self-adaptation steps in the test phase. Our proposed approach is shown to be effective in experiments on a public aBICs dataset and achieves similar performance to state-of-the-art domain adaptation methods while avoiding the use of additional computational resources. © 2023 Copyright for this paper by its authors.

One of the most prominent research areas in information technology is the Internet of things (IoT) as its applications are widely used such as structural monitoring, health care management systems, agriculture and battlefield management, and so on. Due to its self-organizing network and simple installation of the network, the researchers have been attracted to pursue research in the various fields of IoTs. However, a huge amount of work has been addressed on various problems confronted by IoT. The nodes densely deploy over critical environments and those are operated on tiny batteries. Moreover, the replacement of dead batteries in the nodes is almost impractical. Therefore, the problem of energy preservation and maximization of IoT networks has become the most prominent research area. However, numerous state-of-the-art algorithms have addressed this issue. Thus, it has become necessary to gather the information and send it to the base station in an optimized method to maximize the network. Therefore, we propose a novel quantum-informed ant colony optimization (ACO) routing algorithm with the efficient encoding scheme of cluster head selection and derivation of information heuristic factors. The algorithm has been tested by simulation for various network scenarios. The simulation results of the proposed algorithm show its efficacy over a few existing evolutionary algorithms using various performance metrics such as residual energy of the network, network lifetime, and the number of live IoT nodes. © 2022 IEEE

Subspace distance is an invaluable tool exploited in a wide range of feature selection methods. The power of subspace distance is that it can identify a representative subspace, including a group of features that can efficiently approximate the space of original features. On the other hand, employing intrinsic statistical information of data can play a significant role in a feature selection process. Nevertheless, most of the existing feature selection methods founded on the subspace distance are limited in properly fulfilling this objective. To pursue this void, we propose a framework that takes a subspace distance into account which is called “Variance–Covariance subspace distance”. The approach gains advantages from the correlation of information included in the features of data, thus determines all the feature subsets whose corresponding Variance–Covariance matrix has the minimum norm property. Consequently, a novel, yet efficient unsupervised feature selection framework is introduced based on the Variance–Covariance distance to handle both the dimensionality reduction and subspace learning tasks. The proposed framework has the ability to exclude those features that have the least variance from the original feature set. Moreover, an efficient update algorithm is provided along with its associated convergence analysis to solve the optimization side of the proposed approach. An extensive number of experiments on nine benchmark datasets are also conducted to assess the performance of our method from which the results demonstrate its superiority over a variety of state-of-the-art unsupervised feature selection methods. The source code is available at https://github.com/SaeedKarami/VCSDFS. © 2023 The Author(s)

Breast cancer is one of the most common cancer types among women, and it is a deadly disease caused by the uncontrolled proliferation of cells. Pathologists face a challenging issue of mitotic cell identification and counting during manual detection and identification of cancer. Artificial intelligence can help the medical professional with early, quick, and accurate diagnosis of breast cancer. Consequently, the survival rate will be improved and mortality rate will be decreased. Different deep learning techniques are used in computational pathology for cancer diagnosis. In this study, the SMDetector is proposed which is a deep learning model for detecting small objects such as mitotic and non-mitotic nuclei. This model employs dilated layers in the backbone to prevent small objects from disappearing in the deep layers. The purpose of the dilated layers in this model is to reduce the size gap between the image and the objects it contains. Region proposal network is optimized to accurately identify small objects. The proposed model yielded overall average precision (AP) of 50.31% and average recall (AR) of 55.90% that outperforms the existing standard object detection models on ICPR 2014 (Mitos-Atypia-14) dataset. To best of our knowledge the proposed model is state-of-the-art model for precision and recall of mitotic object detection on ICPR 2014 (Mitos-Atypia-14) dataset. The proposed model has achieved average precision for mitotic nuclei 68.49%, average recall for mitotic nuclei 59.86% and f-measure 63.88%. © 2022 The Authors

Our system, VISU, participated in the WASSA 2023 Shared Task (3) of Emotion Classification from essays written in reaction to news articles. Emotion detection from complex dialogues is challenging and often requires context/domain understanding. Therefore in this research, we have focused on developing deep learning (DL) models using the combination of word embedding representations with tailored prepossessing strategies to capture the nuances of emotions expressed. Our experiments used static and contextual embeddings (individual and stacked) with Bidirectional Long short-term memory (BiLSTM) and Transformer based models. We occupied rank tenth in the emotion detection task by scoring a Macro F1-Score of 0.2717, validating the efficacy of our implemented approaches for small and imbalanced datasets with mixed categories of target emotions. © 2023 Association for Computational Linguistics.

Cybersecurity issues such as malware, denial of service attacks, and unauthorized access to data for different applications are growing daily. The Industrial Internet of Healthcare Things (IIoHT) has recently been a new healthcare mechanism where many healthcare applications can run on hospital servers for remote medical services. For instance, cloud medical applications offer different services remotely from home. However, the existing IIoHT mechanisms can not handle critical cybersecurity issues and incur many medical care application processing and data security costs. The processing costs associated with security and deadline are the main findings of this proposed work. This work devises a cost-efficient blockchain task scheduling (CBTS) cyber-physical system (CPS) with different heuristics. All tasks are sorted, scheduled, and stored in a secure form in the IIoHT network. The performance evaluation proves that the CBTS framework outperforms the simulation results for the IIoHT application and reduces the cost by 50% of security execution and 33% of cybersecurity data validation blockchain costs compared to existing scheduling and blockchain schemes. © Copyright 2022 IEEE

Industrial Internet of Things (IIoT) is the new paradigm to perform different healthcare applications with different services in daily life. Healthcare applications based on IIoT paradigm are widely used to track patients health status using remote healthcare technologies. Complex biomedical sensors exploit wireless technologies, and remote services in terms of industrial workflow applications to perform different healthcare tasks, such as like heartbeat, blood pressure and others. However, existing industrial healthcare technoloiges still has to deal with many problems, such as security, task scheduling, and the cost of processing tasks in IIoT based healthcare paradigms. This paper proposes a new solution to the above-mentioned issues and presents the deep reinforcement learning-aware blockchain-based task scheduling (DRLBTS) algorithm framework with different goals. DRLBTS provides security and makespan efficient scheduling for the healthcare applications. Then, it shares secure and valid data between connected network nodes after the initial assignment and data validation. Statistical results show that DRLBTS is adaptive and meets the security, privacy, and makespan requirements of healthcare applications in the distributed network. © 2023, The Author(s).

Vision-dependent underwater vehicles are widely used in seabed resource exploration. The visual perception system of underwater vehicles relies heavily on high-quality images for its regular operation. However, underwater images taken underwater often have color distortion, blurriness, and poor contrast. To address these degradation issues, we develop an adaptive weighted multiscale retinex (AWMR) method for enhancing underwater images. To utilize the local detail features, we first divide the image into multiple sub-blocks and calculate the detail sparsity index for each one. Then, we combine the global detail sparsity index with the local detail sparsity indices to determine the optimal scale parameter and corresponding weights for each sub-block. We apply retinex processing to each sub-block using these parameters and then subject the processed sub-blocks to detail enhancement, color correction, and saturation correction. Finally, we use a gradient domain fusion method based on structure tensors to fuse the corrected and enhanced sub-blocks and obtain the final output image. Our approach improves underwater images through comparisons with current state-of-the-art (SOTA) techniques on several open-source datasets, both quality, and performance. © 2023 Elsevier Ltd

Language is the principal tool for human communication, in which humor is one of the most attractive parts. Producing natural language like humans using computers, a.k.a, Natural Language Generation (NLG), has been widely used for dialogue systems, chatbots, text summarization, as well as AI-Generated Content (AIGC), e.g., idea generation, and scriptwriting. However, the humor aspect of natural language is relatively under-investigated, especially in the age of pre-trained language models. In this work, we aim to preliminarily test whether NLG can generate humor as humans do. We build the largest dataset consisting of numerous Chinese Comical Crosstalk scripts (called C3 in short), which is for a popular Chinese performing art called 'Xiangsheng' or '相声' since 1800s. We benchmark various generation approaches including training-from-scratch Seq2seq, fine-tuned middle-scale PLMs, and large-scale PLMs with and without fine-tuning. Moreover, we also conduct a human assessment, showing that 1) large-scale pretraining largely improves crosstalk generation quality; and 2) even the scripts generated from the best PLM is far from what we expect. We conclude humor generation could be largely improved using large-scale PLMs, but it is still in its infancy. The data and benchmarking code are publicly available in https://github.com/anonNo2/crosstalk-generation. © 2023 Association for Computational Linguistics.

Current human biomedical research shows that human diseases are closely related to non-coding RNAs, so it is of great significance for human medicine to study the relationship between diseases and non-coding RNAs. Current research has found associations between non-coding RNAs and human diseases through a variety of effective methods, but most of the methods are complex and targeted at a single RNA or disease. Therefore, we urgently need an effective and simple method to discover the associations between non-coding RNAs and human diseases. In this paper, we propose a sparse regularized joint projection model (SRJP) to identify the associations between non-coding RNAs and diseases. First, we extract information through a series of ncRNA similarity matrices and disease similarity matrices and assign average weights to the similarity matrices of the two sides. Then we decompose the similarity matrices of the two spaces into low-rank matrices and put them into SRJP. In SRJP, we innovatively use the projection matrix to combine the ncRNA side and the disease side to identify the associations between ncRNAs and diseases. Finally, the regularization term in SRJP effectively improves the robustness and generalization ability of the model. We test our model on different datasets involving three types of ncRNAs: circRNA, microRNA and long non-coding RNA. The experimental results show that SRJP has superior ability to identify and predict the associations between ncRNAs and diseases. © 2022 The Author(s)

Traffic flow forecasting is a challenging task due to its spatio-temporal nature and the stochastic features underlying complex traffic situations. Currently, Graph Convolutional Network (GCN) methods are among the most successful and promising approaches. However, most GCNs methods rely on a static graph structure, which is generally unable to extract the dynamic spatio-temporal relationships of traffic data and to interpret trip patterns or motivation behind traffic flows. In this paper, we propose a novel Semantics-aware Dynamic Graph Convolutional Network (SDGCN) for traffic flow forecasting. A sparse, state-sharing, hidden Markov model is applied to capture the patterns of traffic flows from sparse trajectory data; this way, latent states, as well as transition matrices that govern the observed trajectory, can be learned. Consequently, we can build dynamic Laplacian matrices adaptively by jointly considering the trip pattern and motivation of traffic flows. Moreover, high-order Laplacian matrices can be obtained by a newly designed forward algorithm of low time complexity. GCN is then employed to exploit spatial features, and Gated Recurrent Unit (GRU) is applied to exploit temporal features. We conduct extensive experiments on three real-world traffic datasets. Experimental results demonstrate that the prediction accuracy of SDGCN outperforms existing traffic flow forecasting methods. In addition, it provides better explanations of the generative Laplace matrices, making it suitable for traffic flow forecasting in large cities and providing insight into the causes of various phenomena such as traffic congestion. The code is publicly available at https://github.com/gorgen2020/SDGCN. © 2023 IEEE.

Identification of new indications for existing drugs is crucial through the various stages of drug discovery. Computational methods are valuable in establishing meaningful associations between drugs and diseases. However, most methods predict the drug–disease associations based solely on similarity data, neglecting valuable biological and chemical information. These methods often use basic concatenation to integrate information from different modalities, limiting their ability to capture features from a comprehensive and in-depth perspective. Therefore, a novel multimodal framework called AMDGT was proposed to predict new drug associations based on dual-graph transformer modules. By combining similarity data and complex biochemical information, AMDGT understands the multimodal feature fusion of drugs and diseases effectively and comprehensively with an attention-aware modality interaction architecture. Extensive experimental results indicate that AMDGT surpasses state-of-the-art methods in real-world datasets. Moreover, case and molecular docking studies demonstrated that AMDGT is an effective tool for drug repositioning. Our code is available at GitHub: https://github.com/JK-Liu7/AMDGT. © 2023 The Author(s)

Drug repositioning (DR) is a promising approach for identifying novel indications of existing drugs. Computational methods for drug repositioning have been recognised as effective ways to discover the associations between drugs and diseases. However, most computational DR methods ignore the significance of heterogeneous graph augmentation when conducting contrastive learning, which plays a critical role in improving the generalisation and robustness. The high-order similarity information from multiple data sources is still under-explored. Furthermore, only a limited number of computational DR methods can effectively screen for the most informative negative samples for model training. To address these limitations, we propose a novel DR method called DRMAHGC that employs multi-aspect graph contrastive learning to predict drug-disease associations (DDAs). First, high-order features were generated from the similarity network using a graph-masked autoencoder. Then, heterogeneous graph contrastive learning with structure- and metapath-level augmentation was employed to enhance semantic comprehension and learn expressive representations. Subsequently, the positive-fusion negative sampling strategy was exploited to synthesise informative negative sample embeddings to train the classifier for predicting novel DDAs. Extensive results on three benchmark datasets indicate that DRMAHGC significantly and consistently outperformed the state-of-the-art methods in the DR task. Moreover, the case study of two common diseases further demonstrates its effectiveness and provides novel insights into DRMAHGC in identifying novel DDAs. © 2024 The Author(s)

With the emergence of the resource and equipment sharing concept, many enterprises and organizations begin to implement cross-domain sharing of devices, especially in the field of the Internet of Things (IoT). However, there are many problems in the cross-domain usage process of devices, such as access control, authentication, and privacy protection. In this paper, we make the following contributions. First, we propose a blockchain-based cross-domain authentication management system for IoT devices. The sensitive device information is stored in a Merkle tree structure where only the Merkle root is uploaded to the smart contract. Second, a detailed security and performance analysis is given. We prove that our system is secure against several potential security threats and satisfies validity and liveness. Compared to existing schemes, our schemes realize decentralization, privacy, scalability, fast off-chain authentication, and low on-chain storage. Third, we implement the system on Ethereum with varying parameters known as domain number, concurrent authentication request number, and Merkle tree leaf number. Experimental results show that our solution supports the management of millions of devices in a domain and can process more than 10,000 concurrent cross-domain authentication requests, consuming only 5531 ms. Meanwhile, the gas costs are shown to be acceptable. © IEEE

Understanding the aspects of progression for atherosclerotic cardiovascular disease and treatment is key to building reliable clinical decision-support systems. To promote system trust, one step is to make the machine learning models (used by the decision support systems) explainable for clinicians, developers, and researchers. Recently, working with longitudinal clinical trajectories using Graph Neural Networks (GNNs) has attracted attention among machine learning researchers. Although GNNs are seen as black-box methods, promising explainable AI (XAI) methods for GNNs have lately been proposed. In this paper, which describes initial project stages, we aim at utilizing GNNs for modeling, predicting, and exploring the model explainability of the low-density lipoprotein cholesterol level in long-term atherosclerotic cardiovascular disease progression and treatment.

Heterogeneous network and device-to-device communication are two possible solutions for improving wireless network spectral efficiency. Techniques based on the Internet of Things (IoT) can interact between a large number of smart devices as well as heterogeneous networks. The goal of this study is to investigate proposed quality of service-aware resource selection in an IoT network for healthcare data using a deep auto encoder neural network with spectrum reuse utilizing mixed integer nonlinear programming (MINLP). The suggested MINLP spectrum reuse was used to address the optimization problem, and the spectrum allocation was done using fast Fourier transform based reinforcement Q-learning. Increased transmission repetitions have been identified as a promising strategy for improving IoT coverage by up to 164 dB in terms of maximum coupling loss for uplink transmissions, which is a significant improvement over traditional LTE technology, particularly for serving customers in deep coverage. Based on a comparison of existing methodologies, the experimental study is performed using parameters such as bit error rate of 40%, signal-to-interference plus noise ratio of 72%, sum rate of 88%, and spectral efficiency of 98% © 2022. Human-centric Computing and Information Sciences. All Rights Reserved.

Over the last decade, with the increasing popularity and usage of the internet of things worldwide, Internet of Medical Things (IoMT) has emerged as a key technology of the modern era. IoMT uses Artificial Intelligence, 5G, big data, edge computing, and blockchain to provide users with electronic medical services. However, it may face several security threats and attacks over an insecure public network. Therefore, to protect sensitive medical data in IoMT, it is necessary to design a secure and efficient authentication protocol. In this study, we propose a privacy-preserving authentication management protocol based on blockchain. The protocol uses a blockchain to store identities and related parameters to assist communication entities in authentication. In addition, the protocol adopts a three-factor authentication method and introduces Chebyshev chaotic map to ensure the security of user login and authentication. Formal security proof and analysis using the random oracle model and Burrows-Abadi-Needham logic show that the proposed protocol is secure. Moreover, we use informal security analysis to demonstrate that the protocol can resist various security attacks. The functional comparison shows that the protocol has high security. Through performance analysis and comparison with other protocols, the proposed protocol can increase computation overhead, communication overhead, and storage overhead by up to 39.8%, 93.6%, and 86.7%,respectively. © 2023 Elsevier Ltd

Malware software now encrypts the data of Internet of Things (IoT) enabled fog nodes, preventing the victim from accessing it unless they pay a ransom to the attacker. The ransom injunction is constantly accompanied by a deadline. These days, ransomware attacks are too common on IoT healthcare devices. On the other hand, IoT-based heartbeat digital healthcare applications have been steadily increasing in popularity. These applications make a lot of data, which they send to the fog cloud to be processed further. In healthcare networks, it is critical to examine healthcare data for malicious intent. The malware is a peace code with polymorphic and metamorphic attack forms. Existing malware analysis techniques did not find malware in the content-aware heartbeat data. The Adaptive Malware Analysis Dynamic Machine Learning (AMDML) algorithm for content-aware heartbeat data in fog cloud computing is described in this article. Based on heartbeat data from health records, an adaptive method can train both pre- and post-train malware models. AMDML is based on a rule called ‘federated learning,’ which says that malware analysis models are made at both the local fog node and the remote cloud to meet the performance workload safely. The simulation results show that AMDML outperforms machine learning malware analysis models in terms of accuracy by 60%, delay by 50%, and detection of original heartbeat data by 66% compared to existing malware analysis schemes. © 2023 The Authors. CAAI Transactions on Intelligence Technology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology and Chongqing University of Technology.

The Big Video Data generated in today’s smart cities has raised concerns from its purposeful usage perspective, where surveillance cameras, among many others are the most prominent resources to contribute to the huge volumes of data, making its automated analysis adifcult task in terms of computation and preciseness. Violence detection (VD), broadly plunging under action and activity recognition domain, is used to analyze Big Video data for anomalous actions incurred due to humans. The VD literature is traditionally basedon manually engineered features, though advancements to deep learning based standalone models are developed for real-time VD analysis. This paper focuses on overview of deepsequence learning approaches along with localization strategies of the detected violence.This overview also dives into the initial image processing and machine learning-based VD literature and their possible advantages such as efciency against the current complex models. Furthermore,the datasets are discussed, to provide an analysis of the current models, explaining their pros and cons with future directions in VD domain derived from anin-depth analysis of the previous methods. © The Author(s), under exclusive licence to Springer Nature B.V. 2022.

Person re-identification (Re-ID) technology plays an increasingly crucial role in intelligent surveillance systems. Widespread occlusion significantly impacts the performance of person Re-ID. Occluded person Re-ID refers to a pedestrian matching method that deals with challenges such as pedestrian information loss, noise interference, and perspective misalignment. It has garnered extensive attention from researchers. Over the past few years, several occlusion-solving person Re-ID methods have been proposed, tackling various sub-problems arising from occlusion. However, there is a lack of comprehensive studies that compare, summarize, and evaluate the potential of occluded person Re-ID methods in detail. In this review, we commence by offering a meticulous overview of the datasets and evaluation criteria utilized in the realm of occluded person Re-ID. Subsequently, we undertake a rigorous scientific classification and analysis of existing deep learning-based occluded person Re-ID methodologies, examining them from diverse perspectives and presenting concise summaries for each approach. Furthermore, we execute a systematic comparative analysis among these methods, pinpointing the state-of-the-art solutions, and provide insights into the future trajectory of occluded person Re-ID research. © 2023 Elsevier Ltd

Face-attribute synthesis is a typical application of neural network technology. However, most current methods suffer from the problem of uncontrollable attribute intensity. In this study, we proposed a novel intensity-controllable generation network (ICGNet) based on covering learning for face attribute synthesis. Specifically, it includes an encoder module based on the principle of homology continuity between homologous samples to map different facial images onto the face feature space, which constructs sufficient and effective representation vectors by extracting the input information from different condition spaces. It then models the relationships between attribute instances and representational vectors in space to ensure accurate synthesis of the target attribute and complete preservation of the irrelevant region. Finally, the progressive changes in the facial attributes by applying different intensity constraints to the representation vectors. ICGNet achieves intensity-controllable face editing compared to other methods by extracting sufficient and effective representation features, exploring and transferring attribute relationships, and maintaining identity information. The source code is available at https://github.com/kllaodong/-ICGNet.

•We designed a new encoder module to map face images of different condition spaces into face feature space to obtain sufficient and effective face feature representation.

•Based on feature extraction, we proposed a novel Intensity-Controllable Generation Network (ICGNet), which can realize face attribute synthesis with continuous intensity control while maintaining identity and semantic information.

•The quantitative and qualitative results showed that the performance of ICGNet is superior to current advanced models.

© 2024 Elsevier Inc.

Recent developments in Swin Transformer have shown its great potential in various computer vision tasks, including image classification, semantic segmentation, and object detection. However, it is challenging to achieve desired performance by directly employing the Swin Transformer in multi-view 3D object recognition since the Swin Transformer independently extracts the characteristics of each view and relies heavily on a subsequent fusion strategy to unify the multi-view information. This leads to the problem of the insufficient extraction of interdependencies between the multi-view images. To this end, we propose an aggregation strategy integrated into the Swin Transformer to reinforce the connections between internal features across multiple views, thus leading to a complete interpretation of isolated features extracted by the Swin Transformer. Specifically, we utilize Swin Transformer to learn view-level feature representations from multi-view images and then calculate their view discrimination scores. The scores are employed to assign the view-level features to different groups. Finally, a grouping and fusion network is proposed to aggregate the features from view and group levels. The experimental results indicate that our method attains state-of-the-art performance compared to prior approaches in multi-view 3D object recognition tasks. The source code is available at https://github.com/Qishaohua94/DEST. ©2020 IEEE.

Zero-shot 3D shape understanding aims to recognize “unseen” 3D categories that are not present in training data. Recently, Contrastive Language–Image Pre-training (CLIP) has shown promising open-world performance in zero-shot 3D shape understanding tasks by information fusion among language and 3D modality. It first renders 3D objects into multiple 2D image views and then learns to understand the semantic relationships between the textual descriptions and images, enabling the model to generalize to new and unseen categories. However, existing studies in zero-shot 3D shape understanding rely on predefined rendering parameters, resulting in repetitive, redundant, and low-quality views. This limitation hinders the model's ability to fully comprehend 3D shapes and adversely impacts the text–image fusion in a shared latent space. To this end, we propose a novel approach called Differentiable rendering-based multi-view Image–Language Fusion (DILF) for zero-shot 3D shape understanding. Specifically, DILF leverages large-scale language models (LLMs) to generate textual prompts enriched with 3D semantics and designs a differentiable renderer with learnable rendering parameters to produce representative multi-view images. These rendering parameters can be iteratively updated using a text–image fusion loss, which aids in parameters’ regression, allowing the model to determine the optimal viewpoint positions for each 3D object. Then a group-view mechanism is introduced to model interdependencies across views, enabling efficient information fusion to achieve a more comprehensive 3D shape understanding. Experimental results can demonstrate that DILF outperforms state-of-the-art methods for zero-shot 3D classification while maintaining competitive performance for standard 3D classification. The code is available at https://github.com/yuzaiyang123/DILP. © 2023 The Author(s)

The class imbalance of electrocardiogram (ECG) data is a serious impediment to the development of diagnostic systems for heart disease. To address this issue, this paper proposes HQ-DCGAN, a hybrid quantum deep convolutional generative adversarial network, specifically designed for the generation of ECGs. The proposed algorithm employs different quantum convolutional layers for the generator and discriminator as feature extractors and utilizes parameterized quantum circuits (PQCs) to enhance computational capabilities, along with the model-feature mapping process. Moreover, this algorithm preserves the nonlinearity and scalability inherent to classical convolutional neural networks (CNNs), thereby optimizing the utilization of quantum resources, and ensuring compatibility with contemporary quantum devices. In addition, this paper proposes a novel evaluation metric, 1D Fréchet Inception Distance (1DFID), to assess the quality of the generated ECG signals. Simulation experiments show that HQ-DCGAN exhibits strong performance in ECG signal generation. Furthermore, the generated signals achieve an average classification accuracy of 82.2%, outperforming the baseline algorithms. It has been experimentally proven that HQ-DCGAN is friendly to currently noisy intermediate-scale quantum (NISQ) computers, in terms of both number of qubits and circuit depths, while improving the stability. © 2024 The Author(s)

The technology of speech emotion recognition (SER) has been widely applied in the field of human-computer interaction within the Internet of Vehicles (IoV). The incorporation of emerging technologies such as artificial intelligence and big data has accelerated the advancement of SER technology. However, this reveals challenges such as limited computational resources, data processing inefficiency, and security and privacy concerns. In recent years, quantum machine learning has been applied to the field of intelligent transportation, which has demonstrated its various advantages, including high prediction accuracy, robust noise resistance, and strong security. This study first integrates quantum federated learning (QFL) into 5G IoV using a quantum minimal gated unit (QMGU) recurrent neural network for local training. Then, it proposes a novel quantum federated learning algorithm, QFSM, to further enhance computational efficiency and privacy protection. Experimental results demonstrate that compared to existing algorithms using quantum long short-term memory network or quantum gated recurrent unit models, the QFSM algorithm has a higher recognition accuracy and faster training convergence rate. It also performs better in terms of privacy protection and noise robustness, enhancing its applicability and practicality. © IEEE

The increasing popularity of 6G communication within the Internet of Vehicles (IoV) ecosystem is expected to induce a surge in both user numbers and data volumes. This expansion will cause substantial challenges in ensuring network security and privacy protection, as well as in addressing the associated issue of inadequate cloud computing resources. In this article, we propose a Quantum Efficient Privacy Protection (QEPP) protocol that leverages reversible information hiding in quantum point clouds. This protocol utilizes quantum communication technology in edge-to-cloud communication of the IoV to transmit sensitive information embedded in quantum state data, thereby ensuring privacy protection. It employs quantum error-correction coding and efficient coding techniques to extract information and recover the carriers. In addition, the protocol utilizes an improved quantum Grover algorithm in the cloud to accelerate the processing speed of quantum data. By addressing security vulnerabilities and improving cloud-computing capabilities, the QEPP can effectively accommodate critical requirements, including precision, timeliness, and robust privacy protection. © IEEE