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Motivated by applications such as viral marketing, the problem of influence maximization (IM) has been extensively studied in the literature. The goal is to select a small number of users to adopt an item such that it results in a large cascade of adoptions by others.Existing works have three key limitations. (1) They do not account for economic considerations of a user in buying/adopting items.(2) Most studies on multiple items focus on competition, with complementary items receiving limited attention.(3) For the network owner, maximizing social welfare is important to ensure customer loyalty, which is not addressed in prior work in the IM literature. In this paper, we address all three limitations and propose a novel model called UIC that combines utility-driven item adoption with influence propagation over networks. Focusing on the mutually complementary setting, we formulate the problem of social welfare maximization in this novel setting. We show that while the objective function is neither submodular nor supermodular, surprisingly a simple greedy allocation algorithm achieves a factor of (1 − 1/e − ϵ ) of the optimum expected social welfare. We develop bundleGRD, a scalable version of this approximation algorithm, and demonstrate, with comprehensive experiments on real and synthetic datasets, that it significantly outperforms all baselines.

Gesture recognition acts as a key enabler for user-friendly human-computer interfaces (HCI). To bridge the human-computer barrier, numerous efforts have been devoted to designing accurate fine-grained gesture recognition systems. Recent advances in wireless sensing hold promise for a ubiquitous, non-invasive and low-cost system with existing Wi-Fi infrastructures. In this paper, we propose DeepNum, which enables fine-grained finger gesture recognition with only a pair of commercial Wi-Fi devices. The key insight of DeepNum is to incorporate the quintessence of deep learning-based image processing so as to better depict the influence induced by subtle finger movements. In particular, we make multiple efforts to transfer sensitive Channel State Information (CSI) into depth radio images, including antenna selection, gesture segmentation and image construction, followed by noisy image purification using high-dimensional relations. To fulfill the restrictive size requirements of deep learning model, we propose a novel region-selection method to constrain the image size and select qualified regions with dominant color and texture features. Finally, a 7-layer Convolutional Neural Network (CNN) and SoftMax function are adopted to achieve automatic feature extraction and accurate gesture classification. Experimental results demonstrate the excellent performance of DeepNum, which recognizes 10 finger gestures with overall accuracy of 98% in three typical indoor scenarios.

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In this paper, we propose a novel max-weight secure link selection (MWSLS) scheme to enhance physical layer security of decode-and-forward (DF) buffer-aided relay networks. The MWSLS scheme can select the link with the largest weight among all secure and available source-to-relay and relayto-destination links. By modeling the dynamic buffer state transitions as a Markov chain, we derive the closed-form expressions of the secrecy outage probability, the secrecy diversity gain, the average secrecy throughput and the end to end delay, which provide a comprehensive and effective way to evaluate the impacts of different parameters on the secrecy performance. The results of this paper reveal that: 1) For the case with small buffer sizes, a significant enhancement on the secrecy outage performance can be observed compared with the popular max-link secure link selection (MLSLS) scheme. 2) When L ≥ 3, the secrecy diversity gain of the system can achieve 2M , while the MLSLS scheme achieves the same secrecy diversity gain only when L → ∞ (where M denotes the number of relays and L is the buffer size). 3) The MWSLS scheme outperforms the MLSLS scheme in terms of the average secrecy throughput and the end to end delay in the low signal-to-noise ratio (SNR) regime, and obtain the same performance as the latter in the high SNR regime. INDEX TERMS Buffer-aided relay, physical layer security, max-weight secure link selection, secrecy performance.