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The emergence of 360-video streaming systems has brought about new possibilities for immersive video experiences while requiring significantly higher bandwidth than traditional 2D video streaming. Viewport prediction is used to address this problem, but interesting storylines outside the viewport are ignored. To address this limitation, we present SAVG360, a novel viewport guidance system that utilizes global content information available on the server side to enhance streaming with the best saliency-captured storyline of 360-videos. The saliency analysis is performed offline on the media server with powerful GPU, and the saliency-aware guidance information is encoded and shared with clients through the Saliency-aware Guidance Descriptor. This enables the system to proactively guide users to switch between storylines of the video and allow users to follow or break guided storylines through a novel user interface. Additionally, we present a viewing mode prediction algorithms to enhance video delivery in SAVG360. Evaluation of user viewport traces in 360-videos demonstrate that SAVG360 outperforms existing tiled streaming solutions in terms of overall viewport prediction accuracy and the ability to stream high-quality 360 videos under bandwidth constraints. Furthermore, a user study highlights the advantages of our proactive guidance approach over predicting and streaming of where users look. 

Subteam Replacement: given a team of people em- bedded in a social network to complete a certain task, and a subset of members (i.e., subteam) in this team which have become unavailable, find another set of people who can perform the subteam’s role in the larger team. We conjecture that a good candidate subteam should have high skill and structural similarity with the replaced subteam while sharing a similar connection with the larger team as a whole. Based on this conjecture, we propose a novel graph kernel which evaluates the goodness of candidate subteams in this holistic way freely adjustable to the need of the situation. To tackle the significant computational difficulties, we equip our kernel with a fast approximation algorithm which (a) employs effective pruning strategies, (b) exploits the similarity between candidate team structures to reduce kernel computations, and (c) features a solid theoretical bound on the quality of the obtained solution. We extensively test our solution on both synthetic and real datasets to demonstrate its effectiveness and efficiency. Our proposed graph kernel outputs more human-agreeable recommendations compared to metrics used in previous work, and our algorithm consistently outperforms alternative choices by finding near- optimal solutions while scaling linearly with the size of the replaced subteam. 

Coverage-guided fuzzing has become mainstream in fuzzing to automatically expose program vulnerabilities. Recently, a group of fuzzers are proposed to adopt a random search mechanism namely Havoc, explicitly or implicitly, to augment their edge exploration. However, they only tend to adopt the default setup of Havoc as an implementation option while none of them attempts to explore its power under diverse setups or inspect its rationale for potential improvement. In this paper, to address such issues, we conduct the first empirical study on Havoc to enhance the understanding of its characteristics. Specifically, we first find that applying the default setup of Havoc to fuzzers can significantly improve their edge coverage performance. Interestingly, we further observe that even simply executing Havoc itself without appending it to any fuzzer can lead to strong edge coverage performance and outperform most of our studied fuzzers. Moreover, we also extend the execution time of Havoc and find that most fuzzers can not only achieve significantly higher edge coverage, but also tend to perform similarly (i.e., their performance gaps get largely bridged). Inspired by the findings, we further propose Havoc𝑀𝐴𝐵, which models the Havoc mutation strategy as a multi-armed bandit problem to be solved by dynamically adjusting the mutation strategy. The evaluation result presents that Havoc𝑀𝐴𝐵 can significantly increase the edge coverage by 11.1% on average for all the benchmark projects compared with Havoc and even slightly outperform state-of-the-art QSYM which augments its computing resource by adopting three parallel threads. We further execute Havoc𝑀𝐴𝐵 with three parallel threads and result in 9% higher average edge coverage over QSYM upon all the benchmark projects