查看更多>>摘要：Deceptive reviews detection has attracted extensive attentions from the business and research communities in recent years. Existing work mainly uses traditional discrete models with rich features from the viewpoint of linguistics and psycholinguistics. The drawback is that these models fail to capture the global semantic information of a sentence or discourse. Recently, neural network models provide new solutions for this task, and can be used to learn global representation of a review text, achieving competitive performance. We observe that a review text usually contains two types of information. Some words or sentences describe the user's preferences, while others indicate the characteristics of the product. Based on this observation, this paper explores a hierarchical neural network model with attention mechanism, which can learn a global review representation from the viewpoint of user and product, to identify deceptive reviews. Experimental results show that the proposed neural model achieves 91.7% accuracy on the Yelp datasets, outperforming traditional discrete models and neural baseline systems by a large margin. (c) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：This paper is concerned with secure tracking control problems for cyber-physical systems subjected to a class of sensor and actuator attacks via the model-reference control techniques. The considered attack signals are the unknown bounded signals generated from any linear or nonlinear finite-L-2-gain exogenous dynamical system. The auxiliary outputs produced from a high-gain approximate differentiator are first used in co-design for the inputs of the systems and introduced filter, so that the matching condition on the system input and output matrices in the existing results is relaxed. Furthermore, with the aid of the auxiliary outputs, attack's property and small-gain control methods, a novel model-reference controller with a robust adaptive attack compensator is proposed, such that (a) the tracking error converges to a small set around zero where the set bound can be adjusted by the design parameters, and (b) the desired disturbance attenuation performance is obtained. Different from the existing results, the developed one not only compensates the effect of the attacks automatically by online adjusting the adaptive parameter, but also improves the robustness against the attacks efficiently. Finally, the developed method is validated by a simulation example. (C) 2022 Published by Elsevier Inc.
查看更多>>摘要：It is very difficult to improve the efficiency and accuracy of reducer lubrication due to the limitation of the traditional simulation method. In this paper, an optimization model with multiple parameters is first established to reflect the relationship between the churning loss and the optimized parameters of the zero-backlash high-precision roller developing reducer (ZHPRER). Then, an optimization spiking neural P system (OSNPS) is applied to optimize the multiparameter model. Finally, a simulation analysis (the semi-implicit moving particle method, MPS) is used to verify the correctness of the optimization results. The experimental results show that the multiparameter optimization model and OSNPS are effective and accurate for solving the multiparameter optimization problem of ZHPRER by MPS method.
查看更多>>摘要：Recently, significant progress has been made in graph-based hashing methods for the purpose of learning hash codes that can preserve semantic similarity. Many approaches have been formulated for supervised learning problems that require labels. However, large-scale labeled datasets are expensive to obtain, especially when the data are multimodal, thus imposing a restriction on the usage of such algorithms. In this study, a novel multi-view graph cross-modal hashing (MGCH) framework is proposed to generate hash codes in a semi-supervised manner using the outputs of multi-view graphs processed by a graph reasoning module. In contrast to conventional graph-based hashing methods, MGCH adopts multi-view graphs as the only learning assistance to connect labeled and unlabeled data in the process of pursuing binary embeddings. Multiview graphs that filter the features of multidirectional data in multiple anchor sets are beneficial in refining features. As the core component of our MGCH, an intuitive graph reasoning network consisting of two graph convolutional layers and one graph attention layer is employed to simultaneously convolve anchor graphs and asymmetric graphs with input data. Comprehensive cross-modal hashing evaluations on the Wiki, MIRFlickr-25K, NUS-WIDE, and MSCOCO datasets demonstrate the superiority of MGCH over the latest methods for limited labeled data. (c) 2022 Published by Elsevier Inc.
查看更多>>摘要：The operating state of insulators is directly related to the stability of power transmission line. The existing methods for insulator state recognition cannot achieve satisfactory performance. In this paper, the self-blast state recognition of glass insulators is investigated by using an adaptive learning representation. To increase the adaptability of the network to different scales, we propose a solution based on multi-scale information throughout the entire process, beginning from a low-scale to high-scale subnetworks. The multi-scale information is aggregated in parallel way to take advantage of rich information representation. Then, an imitation of the human thinking pattern is employed. Utilizing entropybased cost function, we update the parameters of the learner model in real-time. Based on the constraint of the evaluation index, adaptive depth representation for training glass insulators that are unsatisfied with the reliability evaluation is constructed to realize the self-optimizing regulation of feature space. Correspondingly, a stochastic configuration networks (SCNs) classifier is re-constructed to fit for the update multi-hierarchies knowledge space to carry out the re-recognition process. Finally, fuzzy integration is employed to ensemble multi-hierarchies network to improve the model's generalization. The recognition results on aerial dataset of insulators images demonstrate the effectiveness of our proposed approach.
查看更多>>摘要：The wireless body area network (WBAN) provides users with real-time medical services. Meanwhile, the cloud technology provides greater storage space and computing power for medical data. Both of them have contribute to the development of telemedicine. In a cloud-assisted WBAN, the open network environment and the semi-trust cloud service providers expose the user's private medical data to backdoor adversaries who can make exfiltration attacks, such as the algorithm substitution attack (ASA) through the process of data sharing. Therefore, it is necessary to find a secure and efficient medical data sharing scheme for the huge amount of medical data. In this paper, we first design an identity based proxy re-encryption scheme with cryptographic reverse firewall (IBPRE-CRF), then show the application in a multiple-access telemedicine data sharing scenario. Security analysis shows that the IBPRE-CRF scheme provides chosen plaintext attack security and resists exfiltration attacks. Performance analysis shows that the IBPRE-CRF scheme has a significant communication and computational cost advantage while being resistant to exfiltration attacks in clouds. Therefore, our IBPRE-CRF scheme is suitable for telemedicine data sharing in a cloud-assisted WBAN. (c) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Zero-watermarking is a key technique for achieving lossless and flexible copyright protection of depth image-based rendering (DIBR) videos. Existing approaches extract features of both 2D frames and depth maps via a single mechanism to protect them simultaneously. However, it is difficult for these schemes to fully satisfy the copyright protection requirements of the two components, including the remarkable discriminative capability of 3D videos and robustness against various attacks. Hence, in this paper, we propose a novel multiple-feature-based zero-watermarking scheme to protect the copyright of DIBR 3D videos. To the best of our knowledge, this is the first scheme that integrates multiple features to improve both the discriminative capability and robustness against various attacks. Specifically, dual-tree complex wavelet transform and discrete cosine transform features enhance the robustness against DIBR conversion and noise addition, respectively, while ring-partition statistical residual features ensure robustness against geometric attacks and provide sufficient discriminative capacity. In addition, we use a logistic-logistic chaotic system to encrypt these multiple features for enhanced security and design an attention based fusion approach to offer an optimal copyright protection solution. Extensive experimental results demonstrate that our proposed scheme has stronger robustness and discriminative capacity compared to state-of-the-art zero-watermarking methods. (C) 2022 The Author(s). Published by Elsevier Inc.
查看更多>>摘要：Owing to the rapid development of cloud services and personal privacy requirements, content-based encrypted image retrieval in the cloud has been increasing. Outsourced images are encrypted into noiselike ones to protect privacy, however, the obtained unrecognized appearance limits their availability. Besides, users have to decrypt all search results to browse, while some of them may not be needed, which undoubtedly wastes bandwidth and computing resources. To cope with this problem, a compromise strategy is proposed that considers the tradeoff between privacy and usability of cipher images. Wherein, a thumbnail preserving encryption (TPE) based on genetic algorithm is proposed. The pixels in the sub-blocks of the plain image are scrambled and diffused at the bit-level through crossover and mutation operators of the genetic algorithm. Moreover, two new operators of Mutation Compensation and Mutation Failure are defined and incorporated into the traditional genetic algorithm to achieve an ideal TPE, that cipher image has the same thumbnail as the original image. Additionally, a color histogram-based retrieval algorithm is introduced to retrieve cipher images using the color information preserved by thumbnails; and to improve retrieval accuracy by using the Bhattacharyya distance. A series of simulations verify the security and effectiveness of our scheme. (c) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Clonal selection algorithms have provided significant insights into numerical optimization problems. However, most mutation operators in conventional clonal selection algorithms have semi-blindness and lack an effective guidance mechanism, which has thus become one of the important factors restricting the performance of algorithms. To address these problems, this study develops an improved clonal selection algorithm called an adaptive clonal selection algorithm with multiple differential evolution strategies (ADECSA) with three features: (1) an adaptive mutation strategy pool based on its historical records of success is introduced to guide the immune response process effectively; (2) an adaptive population resizing method is adopted to speed up convergence; and (3) a premature convergence detection method and a stagnation detection method are proposed to alleviate premature convergence and stagnation problems in the evolution by enhancing the diversity of the population. Experimental results on a wide variety of benchmark functions demonstrate that our proposed method achieves better performance than both state-ofthe-art clonal selection algorithms and differential evolution algorithms. Especially in the comparisons with other clonal selection algorithms, our proposed method outperforms at least 23 out of 30 benchmark functions from the CEC2014 test suite. (c) 2022 Elsevier Inc. All rights reserved.
查看更多>>摘要：Recent advancements in mobile technology have facilitated location-based social networks. The location-based influence maximization problem, which aims to find top influential seed users for promoting a target location to attract the most individuals, has drawn increasing attention. However, the existing studies largely neglect the importance of user preference, which considerably hinders their practicability. In addition, time efficiency is a critical issue for handling large-scale datasets. To address the above problems, we propose a new framework named IM2Vec, which incorporates representation learning into location-based influence maximization problem. Specifically, we first propose a representation learning model, All2Vec, to capture user preferences for the target location from check-in records, which takes both user preference and geographical location influence into consideration. Then, based on the learned user preferences, we extend the reverse influence sampling (RIS) model and propose a highly efficient preference maximization algorithm, which ensures a (1 - 1/e - epsilon)-approximate solution with a substantially lower sample size. The experimental results of the two tasks (future visitor prediction and influence maximization) on two real geo-social networks show that the All2Vec model achieves considerably higher accuracy in future visitor prediction, and IM2Vec exhibits a higher influence spread and a lower running time than the state-of-the-art baselines. (C) 2022 Elsevier Inc. All rights reserved.
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