Abstract-For the purpose of propagating information and ideas through a social network, a seeding strategy aims to find a small set of seed users that are able to maximize the spread of the influence, which is termed as influence maximization problem. Despite a large number of works have studied this problem, the existing seeding strategies are limited to the static social networks. In fact, due to the high speed data transmission and the large population of participants, the diffusion processes in real-world social networks have many aspects of uncertainness. Unfortunately, as shown in the experiments, in such cases the state-of-art seeding strategies are pessimistic as they fails to trace the dynamic changes in a social network. In this paper, we study the strategies selecting seed users in an adaptive manner. We first formally model the Dynamic Independent Cascade model and introduce the concept of adaptive seeding strategy. Then based on the proposed model, we show that a simple greedy adaptive seeding strategy finds an effective solution with a provable performance guarantee. Besides the greedy algorithm an efficient heuristic algorithm is provided in order to meet practical requirements. Extensive experiments have been performed on both the real-world networks and synthetic power-law networks. The results herein demonstrate the superiority of the adaptive seeding strategies to other standard methods.Index Terms-Social network influence, adaptive seeding strategy, stochastic submodular maximization.

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Graphics processing units (GPUs) are being widely used as co-processors in many application domains to accelerate general-purpose workloads that are computationally intensive, known as GPGPU computing. Real-time multi-tasking support is a critical requirement for many emerging GPGPU computing domains. However, due to the asynchronous and non-preemptive nature of GPU processing, in multi-tasking environments, tasks with higher priority may be blocked by lower priority tasks for a lengthy duration. This severely harms the system's timing predictability and is a serious impediment limiting the applicability of GPGPU in many real-time and embedded systems. In this paper, we present an efficient GPGPU preemptive execution system (GPES), which combines user-level and driverlevel runtime engines to reduce the pending time of high-priority GPGPU tasks that may be blocked by long-freezing low-priority competing workloads. GPES automatically slices a long-running kernel execution into multiple subkernel launches and splits data transaction into multiple chunks at user-level, then inserts preemption points between subkernel launches and memorycopy operations at driver-level. We implement a prototype of GPES, and use real-world benchmarks and case studies for evaluation. Experimental results demonstrate that GPES is able to reduce the pending time of high-priority tasks in a multitasking environment by up to 90% over the existing GPU driver solutions, while introducing small overheads.

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Online misinformation has been considered as one of the top global risks as it may cause serious consequences such as economic damages and public panic. The misinformation prevention problem aims at generating a positive cascade with appropriate seed nodes in order to compete against the misinformation. In this paper, we study the misinformation prevention problem under the prominent independent cascade model. Due to the #P-hardness in computing influence, the core problem is to design effective sampling methods to estimate the function value. The main contribution of this paper is a novel sampling method. Different from the classic reverse sampling technique which treats all nodes equally and samples the node uniformly, the proposed method proceeds with a hybrid sampling process which is able to attach high weights to the users who are prone to be affected by the misinformation. Consequently, the new sampling method is more powerful in generating effective samples used for computing seed nodes for the positive cascade. Based on the new hybrid sample technique, we design an algorithm offering a (1−1/e− )approximation. We experimentally evaluate the proposed method on extensive datasets and show that it outperforms the state-ofthe-art solutions.Index Terms-misinformation prevention, sampling, social network 2 We say S covers a sample Sv if S ∩ Sv = ∅