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Recently the influence maximization problem has received much attention for its applications on viral marketing and product promotions. However, such influence maximization problems have not taken into account the monetary effect on the purchasing decision of individuals. To fulfill this gap, in this paper, we aim for maximizing the revenue by considering the quantity constraint on the promoted commodity. For this problem, we not only identify a proper small group of individuals as seeds for promotion but also determine the pricing of the commodity. To tackle the revenue maximization problem, we first introduce a strategic searching algorithm, referred to as Algorithm PRUB, which is able to derive the optimal solutions. After that, we further modify PRUB to propose a heuristic, Algorithm PRUB+IF, for obtaining feasible solutions more efficiently on larger instances. Experiments on real social networks with different valuation distributions demonstrate the effectiveness of PRUB and PRUB+IF.

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Mood state assessment (MSA) is increasingly important for diagnosis and treatment of depression. Recent years, many approaches have been proposed for the process of MSA. When using a single approach for MSA, the user often has to deal with possible noisy data and unacceptable error rates. Novelty: In order to improve the accuracy of MSA, in this paper, we propose a novel high-level information fusion method for determining the MS of users by fusing physiological data, such as heart rate and brainwave information collected through a wearable device, and psychological data collected through a monthly mood chart. The multifaceted information must be collected and analyzed simultaneously. Contribution: In the inference process of proposed framework, we adopted a Bayesian Network (BN) to perform high-level information fusion. We exploited various evaluation approaches to evaluate the performance of the proposed approach. Result: We have conducted experiments using two datasets and evaluated the performance using various factors. The results show that the proposed method (7-M Bayesian Fusion) is superior to other methods averagely 9.48 % improvement in most evaluation factors. It reveals that the proposed approach is efficient in fusing the MS information required for accurate diagnosis of depression compared with those approaches without fusion approach or with few information fusing.