Double-network tough hydrogels have raised increasing interest in stretchable electronic applications as well as electronic skin (e-skin) owing to their excellent mechanical properties and functionalities. While hydrogels have been extensively explored as solid-state electrolytes, stretchable energy storage devices based on tough hydrogel electrolytes are still limited despite their high stretchability and strength. A key challenge remains in the robust electrode/electrolyte interface under large mechanical strains. Inspired by the skin structure that involves the microstructured interface for the tight connection between the dermis and epidermis, we demonstrated that a surface-microstructured tough hydrogel electrolyte composed of agar/polyacrylamide/LiCl (AG/PAAm/LiCl) could be exploited to allow stretchable supercapacitors with enhanced mechanical and electrochemical performance. The prestretched tough hydrogel electrolyte was treated to generate surface microstructures with a roughness of tens of micrometers simply via mechanical rubbing followed by the attachment of activated carbon electrodes on both sides to realize the fabrication of the stretchable supercapacitor. Through investigating the properties of the tough hydrogel electrolyte and the electrochemical performance of the as-fabricated supercapacitors under varied strains, the surface-microstructured hydrogel electrolyte was shown to enable robust adhesion to electrodes, improving electrochemical behavior and capacitance, as well as having better performance retention under repeated stretching cycles, which surpassed the pristine hydrogel with smooth surfaces. Our approach could provide an alternative and general strategy to improve the interfacial properties between the electrode and the hydrogel electrolyte, driving new directions for functional stretchable devices based on tough hydrogels.

Collaborative filtering recommender systems (CFRSs) are the key components of successful e-commerce systems. Actually, CFRSs are highly vulnerable to attacks since its openness. However, since attack size is far smaller than that of genuine users, conventional supervised learning based detection methods could be too "dull" to handle such imbalanced classification. In this paper, we improve detection performance from following two aspects. First, we extract well-designed features from user profiles based on the statistical properties of the diverse attack models, making hard classification task becomes easier to perform. Then, refer to the general idea of re-scale Boosting (RBoosting) and AdaBoost, we apply a variant of AdaBoost, called the rescale AdaBoost (RAdaBoost) as our detection method based on extracted features. RAdaBoost is comparable to the optimal Boosting-type algorithm and can effectively improve the performance in some hard scenarios. Finally, a series of experiments on the MovieLens-100K data set are conducted to demonstrate the outperformance of RAdaBoost comparing with some classical techniques such as SVM, kNN and AdaBoost. 2[29] and AdaBoost [9, 10], we apply a variant of Boosting algorithm, called the re-scale AdaBoost (RAdaBoost) as our detection method based on extracted features. RBoosting is theoretically and experimentally proved to be better than the classical Boosting algorithm [17]. Furthermore, the theoretical near optimality of the numerical convergence of RBoosting among all the variants of the Boosting-type algorithms was also specified. This means that if the parameter is appropriately selected, RBoosting is comparable to the optimal Boosting-type algorithm. And AdaBoost [9, 10] is one of the most popular ensemble techniques paradigm and has been shown to be very effective in practice in some hard scenarios [13]. Typically, AdaBoost employs re-weighted loss function for gradually increasing emphasis (or weights) on misclassifications (i.e., concerned attackers) and can distinctly improve the predictive performance on a difficult data set. Thus, with the help of the re-scale operator, RAdaBoost can be used in conjunction with many other types of learning algorithms (or weak learners) to improve the performance in "shilling" attacks detection. Finally, a series of experiments on the MovieLens-100K dataset are conducted to demonstrate the outperformance (i.e., classification error, detection rate and false alarm rate) of RAdaBoost comparing with conventional classification techniques such as SVM, kNN and the original non-rescale AdaBoost version. The experimental results show that RAdaBoost can effectively improve the performance.

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Abstract-Personalization collaborative filtering recommender systems (CFRSs) are the crucial components of popular e-commerce services. In practice, CFRSs are also particularly vulnerable to "shilling" attacks or "profile injection" attacks due to their openness. The attackers can carefully inject chosen attack profiles into CFRSs in order to bias the recommendation results to their benefits. To reduce this risk, various detection techniques have been proposed to detect such attacks, which use diverse features extracted from user profiles. However, relying on limited features to improve the detection performance is difficult seemingly, since the existing features can not fully characterize the attack profiles and genuine profiles. In this paper, we propose a novel detection method to make recommender systems resistant to the "shilling" attacks or "profile injection" attacks. The existing features can be briefly summarized as two aspects including rating behavior based and item distribution based. We firstly formulate the problem as finding a mapping model between rating behavior and item distribution by exploiting the least-squares approximate solution. Based on the trained model, we design a detector by employing a regressor to detect such attacks. Extensive experiments on both the MovieLens-100K and MovieLens-ml-latest-small datasets examine the effectiveness of our proposed detection method. Experimental results were included to validate the outperformance of our approach in comparison with benchmarked method including KNN.

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