The efficient deployment of Big Data processing tasks in cloud environments is the basic core function of Big Data processing, which refers to the effective deployment of tasks to the computing resources of cloud platforms, achieving high-performance and high-throughput data processing. In this process, task deployment needs to consider load balancing on the cloud platform to ensure that tasks can be evenly deployed to each computing node. However, currently in the process of providing services on cloud platforms, the available resources of all hosts will be automatically and dynamically readjusted, and it cannot be guaranteed that each task will be deployed to the host with the most remaining resources. This load imbalance in the platform will result in computational results that cannot be returned to users in a timely and effective manner. So, a heuristic multi-task efficient deployment approach for Big Data processing based on QoS awareness and Bayesian classification in cloud environments called QBC is proposed. The QBC first performs long-run QoS awareness on hosts in the cloud; Then, based on user task requirements, selects host nodes that meet QoS constraints to form a candidate set, and performs Bayesian classification to find the host node which has highest a posteriori probability to serve as the clustering center; thirdly, designs an objective function based on Euclidean spatial distance to acquire the optimum host clustering set in the candidate set; Finally, deploys the user's tasks to this optimal host cluster set. The experimental results show that this approach implements optimization of long-run load balancing in Big Data cloud platforms with minimal resource consumption, enhances the ability of the cloud platform to provide external support, and thus promotes efficient deployment of multitasking in Big Data processing under cloud computing. The proposed QBC based framework reduces the overall Energy Consumption by an average of 47.98%, MakeSpan by an average of 24.42%, Total Cost by an average of 30.17%, Average Waiting Time by an average of 36.92%, and the Throughput is increased by an average of 41.93% as compared to the existing algorithms.

This article examines the mechanisms by which aperiodic denial-of-service (DoS) attacks can exploit vulnerabilities in the TCP/IP transport protocol and its three-way handshake during communication data transmission to hack and cause data loss in networked control systems (NCSs). Such data loss caused by DoS attacks can eventually lead to system performance degradation and impose network resource constraints on the system. Therefore, estimating system performance degradation is of practical importance. By formulating the problem as an ellipsoid-constrained performance error estimation (PEE) problem, we can estimate the system performance degradation caused by DoS attacks. We propose a new Lyapunov-Krasovskii function (LKF) using the fractional weight segmentation method (FWSM) to examine the sampling interval and introduce a relaxed, positive definite constraint to optimize the control algorithm. We also propose a relaxed, positive definite constraint that reduces the initial constraints to optimize the control algorithm. Next, we introduce an alternate direction algorithm (ADA) to solve the optimal trigger threshold and design an integral-based event-triggered controller (IETC) to estimate the error performance of NCSs with limited network resources. Finally, we verify the effectiveness and feasibility of the proposed method using the Simulink joint platform autonomous ground vehicle (AGV) model.

Emotion recognition is a topic of significant interest in assistive robotics due to the need to equip robots with the ability to comprehend human behavior, facilitating their effective interaction in our society. Consequently, efficient and dependable emotion recognition systems supporting optimal human-machine communication are required. Multi-modality (including speech, audio, text, images, and videos) is typically exploited in emotion recognition tasks. Much relevant research is based on merging multiple data modalities and training deep learning models utilizing low-level data representations. However, most existing emotion databases are not large (or complex) enough to allow machine learning approaches to learn detailed representations. This paper explores modality-specific pre-trained transformer frameworks for self-supervised learning of speech and text representations for data-efficient emotion recognition while achieving state-of-the-art performance in recognizing emotions. This model applies feature-level fusion using nonverbal cue data points from motion capture to provide multimodal speech emotion recognition. The model was trained using the publicly available IEMOCAP dataset, achieving an overall accuracy of 77.58% for four emotions, outperforming state-of-the-art approaches

This paper studies the secure aperiodic sampling control issue for Micro-Grids(MGs) under abnormal deception cyber attacks (ADCAs). Firstly, a novel relaxed condition that depends on the time delay (TD) is constructed, further reducing the existing constraint condition. Secondly, a new bilateral delay-dependent looped-functional (BDDLF)$V_{c}(x_{t})$is developed. Thirdly, based on the characteristics of the aperiodic sampling control, another improved BDDLF$V_{d}(x_{t})$is developed for acquiring more state information. Furthermore, an optimized control algorithm is designed using proper integral inequalities and the convex combination method. Then, a new secure aperiodic sampled data (SASD) controller under ADCAs is achieved to guarantee real power-sharing between distributed generators (DGs) and energy storage systems (ESSs) in MG. Finally, simulation experiments are carried out on MG to verify the effectiveness and feasibility of the designed control algorithm.

This article analyzes the mechanism of denial-of-service (DoS) attacks initiated by hackers from the perspective of computer networks. In order to effectively estimate the performance error generated by the T–S fuzzy networked control systems under DoS attacks, we transform the performance error estimation problem into the one of finding ellipsoid constraints$\mathfrak {J}(P_{i})$. First, improved Lyapunov–Krasovskii functions (LKFs) based on fuzzy membership functions are constructed, which combine the characteristics of nonlinear problems in the system to reduce the initial constraints. Then, a second-order weight method (SOWM) is introduced to divide the time interval of sampling meticulously. Besides, the information stored in the LKFs is enhanced. Furthermore, we construct the novel looped functions by relying on the SOWM. Next, a suitable integral elastic event trigger mechanism is established to ensure that the performance errors caused by the attacks are estimated. Finally, the feasibility of the proposed method is verified by a two-degree-of-freedom helicopter system.

This article proposes an improved security-based event-triggered fuzzy control (ETFC) method for studying the asymptotic stability problem of nonlinear networked control systems (NCSs) under denial-of-service (DoS) attacks. The nonlinear NCSs are linearized using the central mean fuzzy method to obtain Takagi-Sugeno (T-S) fuzzy NCSs. Lyapunov–Krasovskii functionals (LKFs) with nonlinear problems are constructed based on fuzzy membership functions. A relaxed condition including nonlinear parameters is given, which considers the nonlinear optimization problem and reduces the positive-definiteness constraint problem of LKFs. An improved double-closed delay correlation function is constructed to obtain more sampling information. Furthermore, a quadratic scaling method is used to obtain a tighter upper bound, making the criterion less conservative. A DoS attack causes the channel's throughput to drop and collapse. Thus, in this article, we design ETFC based on the improved descent gradient algorithm to ensure the communication security of the helicopter system (HS). Finally, the communication security and stability of HS are verified using the Simulink platform.

This article investigates the stability problem of Takagi–Sugeno fuzzy networked control systems (TSNCSs) under deception cyberattacks via a reliable sampling mechanism, which has important research value for applications in network security. First, a fuzzy weight functional method is introduced, and a new Lyapunov–Krasovskii functional is developed, which better incorporates nonlinear problems in the model. Then, in order to reduce the initial constraints, improved time delay closed-loop functions are constructed that consider the delay information and the characteristics of the sampling time points. Furthermore, considering the reliability issues of controllers in real industry, we establish some sufficient conditions and implement a novel reliable sampling controller with deception attacks (DAs) to control the asymptotic stability of TSNCS. Finally, the correctness and feasibility of the proposed method are verified experimentally using an inverted pendulum system.

Abstract An effective feature representation can boost recognition tasks in the sketch domain. Due to an abstract and diverse structure of the sketch relatively with a natural image, it is complex to generate a discriminative features representation for sketch recognition. Accordingly, this article presents a novel scheme for sketch recognition. It generates a discriminative features representation as a result of integrating asymmetry essential information from deep features. This information is kept as an original feature‐vector space for making a final decision. Specifically, five different well‐known pre‐trained deep convolutional neural networks (DCNNs), namely, AlexNet, VGGNet‐19, Inception V3, Xception, and InceptionResNetV2 are fine‐tuned and utilised for feature extraction. First, the high‐level deep layers of the networks were used to get multi‐features hierarchy from sketch images. Second, an entropy‐based neighbourhood component analysis was employed to optimise the fusion of features in order of rank from multiple different layers of various deep networks. Finally, the ranked features vector space was fed into the support vector machine (SVM) classifier for sketch classification outcomes. The performance of the proposed scheme is evaluated on two different sketch datasets such as TU‐Berlin and Sketchy for classification and retrieval tasks. Experimental outcomes demonstrate that the proposed scheme brings substantial improvement over human recognition accuracy and other state‐of‐the‐art algorithms.

Multi‐access edge computing (MEC) has attracted much more attention to revolutionising smart communication technologies and Internet of Everything. Nowadays, smart end‐user devices are designed to execute sophisticated applications that demand more resources and explosively connected to the global ecosystem. As a result, the backhaul network traffic congestion grows enormously and user quality of experience is compromised as well. To address these challenges, the authors proposed congestion‐aware adaptive decentralised computing, caching, and communication framework which can orchestrate the dynamic network environment based on deep reinforcement learning for MEC networks. MEC is a paradigm shift that transforms cloud services and capabilities platform at the edge of ubiquitous radio access networks in close proximity to mobile subscribers. The framework can evolve to perform augmented decision‐making capabilities for the upcoming network generation. Hence, the problem is formulated using non‐cooperative game theory which is nondeterministic polynomial (NP)hard to solve and the authors show that the game admits a Nash equilibrium. In addition, they have constructed a decentralised adaptive scheduling algorithm to leverage the utility of each smart end‐user device. Therefore, their methodical observations using theoretical analysis and simulation results substantiate that the proposed algorithm can achieve ultra‐low latency, enhanced storage capability, low energy consumption, and scalable than the baseline scheme.

The crucial hurdle faced by the watermarking technique is to maintain the steadiness corresponding to several attacks while assisting a sufficient level of security. In this study, a robust lossless sparse domain‐based watermarking approach combined with discrete cosine transform (DCT) is introduced to hide the secret message in the selected significant sparse elements of the host image. The proposed method takes advantage of a sparse representation‐based dictionary learning process. To enhance the security of the original image, the authors first apply the DCT on a secret message. These DCT coefficients with some regularised parameters will be inserted into the selected significant sparse coefficients. At the extraction stage, the secret message is extracted from those significant sparse coefficients by employing the sparse domain orthogonal matching pursuit algorithm. Finally, the inverse DCT is applied to extract the secret message without any information loss. To show the effectiveness of the proposed method, different commonly used attacks are simulated. Simulation results in terms of peak signal‐to‐noise ratio, structural similarity, normal correlation, and feature similarity indicate that the proposed method can recover the hidden secret message accurately against seven different types of attacks including speckle, Gaussian, salt and pepper, rotate, crop, fold, and blur attack.

It is very interesting to reconstruct high‐resolution computed tomography (CT) medical images that are very useful for clinicians to analyse the diseases. This study proposes an improved super‐resolution method for CT medical images in the sparse representation domain with dictionary learning. The sparse coupled K‐singular value decomposition (KSVD) algorithm is employed for dictionary learning purposes. Images are divided into two sets of low resolution (LR) and high resolution (HR), to improve the quality of low‐resolution images, the authors prepare dictionaries over LR and HR image patches using the KSVD algorithm. The main idea behind the proposed method is that sparse coupled dictionaries learn about each patch and establish the relationship between sparse coefficients of LR and HR image patches to recover the HR image patch for LR image. The proposed method is compared to conventional algorithms in terms of mean peak signal‐to‐noise ratio and structural similarity index measurements by using three different data set images, including CT chest, CT dental and CT brain images. The authors also analysed the proposed improved method for different dictionary sizes and patch size to obtain a similar high‐resolution image. These parameters play an essential role in the reconstruction of the HR images.

This paper studies the problems of stability and stabilization of a class of chaotic systems (CSs) with Takagi-Sugeno fuzzy model and nonuniform sampling. It designs a fuzzy sampled-data protocol based on a switched idea to tackle the stability issue of such systems. Novel criteria on stabilization of CSs are established by employing a fuzzy membership function (FMF)-dependent Lyapunov functional and the information of the time derivative of FMFs, which significantly utilize the available characteristics of the actual sampling pattern and FMFs simultaneously. Unlike the existing works, a larger sampling interval is obtained by this new approach. A simulation example on chaotic Rossler's system is employed to demonstrate the superiority and reduced conservatism of the proposed method.

This paper studies the problem of synchronization for a class of stochastic complex dynamical networks. It designs for the first time a distributed impulsive protocol based on pinning control that involves a constant signal transmission delay to tackle synchronization issues of such networks. Novel criteria on network synchronization are established by employing a time-dependent Lyapunov functional and a mathematical induction approach, where information on the state variables themselves and their neighbors is sufficiently utilized. Moreover, it is shown that the frequency of impulsive occurrence, impulsive input delays, stochastic perturbations, and the feedback control strength can significantly affect the synchronization performance. Numerical simulations are given to illustrate the effectiveness of the derived theoretical results.

Abstract Neighbourhood rough set theory has proven already, as an efficient tool for knowledge discovering from heterogeneous data. However, some types of the data are incomplete and noisy in practical environments, such as signal analysis, fault diagnosis etc. To solve this problem, a universal neighbourhood rough sets model (variable precision tolerance neighbourhood rough sets [VPTNRS] model) is proposed based on a tolerance neighbourhood relation and the probabilistic theory. The proposed model can be inducing a family of much more comprehensive information granules to characterize arbitrary concepts in complex universe. In this paper, we discussed the properties of the model as well as some important relevant theorems are also introduced and proved. Furthermore, a heuristic heterogeneous feature selection algorithm is given based on the model. The experimental results with 10 choices University of California Irvine (UCI) standard data sets showed that the universal model performed well both in feature selection and classification, especially in incomplete environment.

With the deep penetration of the Internet, uncontrolled flood of information has become one of the most serious problems to Internet users. Harmful contents about pornography, violence and other illegal messages, etc have posed serious influence to the whole society, especially to the young people. In this paper, a novel Web text filter based on rough set and Bayesian theory is proposed to analysis text content of Web pages to filter harmful pages. Some of current feature selection methods such as inverse document frequency (IDF) does not take the classification information into account. To avoid this shortcoming rough set is used to reduce original feature terms. Meanwhile, a novel coefficient weighted method based on rough set is proposed and introduced into Bayesian formula, which will greatly improve filtering performance. In the final experiment, this paper compared the novel method with other weighted methods applied in Bayesian formula, such as Tf, IDF and TFIDF. The results demonstrate that this novel filter works efficiently.