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Reconfigurable reflectarray antennas (RRAs) have rapidly developed with various prototypes proposed in recent literature. However, designing wideband, multiband, or high-frequency RRAs faces great challenges, especially the lengthy simulation time due to the lack of systematic design guidance. The current scattering viewpoint of the RRA element, which couples antenna structures and switches during the design process, fails to address these issues. Here, we propose a novel radiation viewpoint to model, evaluate, and design RRA elements. Using this viewpoint, the design goal is to match the element impedance to a characteristic impedance pre-calculated by switch parameters, allowing various impedance matching techniques developed in classical antennas to be applied in RRA element design. Furthermore, the theoretical performance limit can be predetermined at given switch parameters before designing specific structures, and the constant loss curve is suggested as an intuitive tool to evaluate element performance in the Smith chart. The proposed method is validated by a practical 1-bit RRA element with degraded switch parameters. Then, a 1-bit RRA element with wideband performance is successfully designed using the proposed design process. The proposed method provides a novel perspective of RRA elements and offers systematic and effective guidance for designing wideband, multiband, and high-frequency RRAs.

The data sparsityis a well-known issue in the context of collaborative filtering, and it puts particular difficulties in making accurate recommendations. In this paper, we focus on the data sparsity problem in the context of neighborhood-basedcollaborative filtering, and propose a maximum imputation framework to tackle this. The basic idea is to identify an imputation area that can maximize the imputation benefit for recommendation purposes, while minimizing the imputation error brought in. To achieve the maximum imputation benefit, the imputation area is determined from both the user and the item perspectives; to minimize the imputation error, there is at least one real rating preserved for each item in the identified imputation area. A theoretical analysis is provided to prove that the proposed imputation method outperforms the conventional neighborhood-based CF methods through more accurate neighbor identification. We evaluate the proposed framework on two benchmark datasets by comparing it with seven relevant methods. The experimental results indicate that the proposed method significantly outperforms other relevant methods.

Privacy preserving is an essential aspect of modern recommender systems. However, the traditional approaches can hardly provide a rigid and provable privacy guarantee for recommender systems, especially for those systems based on collaborative filtering (CF) methods. Recent research revealed that by observing the public output of the CF, the adversary could infer the historical ratings of the particular user, which is known as the KNNattack and is considered a serious privacy violation for recommender systems. This paper addresses the privacy issue in CF by proposing a Private Neighbor Collaborative Filtering(PriCF) algorithm, which is constructed on the basis of the notion of differential privacy. PriCFcontains an essential privacy operation, Private Neighbor Selection, in which the Laplacenoise is added to hide the identity of neighbors and the ratings of each neighbor. To retain the utility, the Recommendation-Aware Sensitivityand a re-designed truncated similarityare introduced to enhance the performance of recommendations. A theoretical analysis shows that the proposed algorithm can resist the KNN attackwhile retaining the accuracy of recommendations. The experimental results on two real datasets show that the proposed PriCFalgorithm retains most of the utility with a fixed privacy budget.

In this paper, we tackle the incompleteness of user rating history in the context of collaborative filtering for Top-Nrecommendations. Previous research ignore a fact that two rating patterns exist in the user× itemrating matrix and influence each other. More importantly, their interactive influence characterizes the development of each other, which can consequently be exploited to improve the modelling of rating patterns, especially when the user× itemrating matrix is highly incomplete due to the well-known data sparsityissue. This paper proposes a Rating Pattern Subspaceto iteratively re-optimize the missing values in each user’s rating history by modelling both the globaland the personalrating patterns simultaneously. The basic idea is to project the user× itemrating matrix on a low-rank subspace to capture the global rating patterns. Then, the projection of each individual user on the subspace is further optimized according to his/her own rating history and the captured global rating patterns. Finally, the optimized user projections are used to improve the modelling of the global rating patterns. Based on this subspace, we propose a RapSVD-Lalgorithm for Top-Nrecommendations. In the experiments, the performance of the proposed method is compared with the state-of-the-art Top-Nrecommendation methods on two real datasets under various data sparsity levels. The experimental results show that RapSVD-Loutperforms the compared algorithms not only on the all itemsrecommendations but also on the long tailitem recommendations in terms of accuracy.

Internetware is envisioned as a new software paradigm for software development in platforms such as the Internet. The reliability of the developed software becomes a key challenge due to the open, dynamic and uncertain nature of such environment. To make the development more reliable, it is necessary to evaluate the trustworthiness of the resource providers or potential working partners. To this end, we propose a novel trust inference approach to evaluating the trustworthiness of potential partners to guide the software development in Internetware. The main insight of our approach is to employ the self-assessment information in order to improve the trust inference accuracy. Especially, we first extend the balance theory and the status theory from social science to incorporate self-assessment, and then propose a machine learning framework to extract several features from the extended theories and infer trustworthiness scores based on these features. Experimental results on a real software developer network show that the self-assessment information truly helps to improve the accuracy of trust inference, and the proposed SelfTrust model is more accurate than other state-of-the-art methods.