Network-wide heavy hitter detection is usually performed by sampling on several network measurement points (NMPs) and merging the measurement results in the centralized controller to get a network-wide view. However, a packet may pass several NMPs and be counted multiple times when measurement results are merged, which causes the double-counting problem and leads to incorrect detection. Existing studies either overlook this problem or require significant memory usage. This paper proposes SAROS, a self-adaptive routing oblivious sampling method for accurate network-wide heavy hitter detection. Specifically, SAROS exploits a sampling mechanism in the data plane, where the sampling threshold on each measurement point is predicted and adaptively set by the control plane. Such guidance from the control plane greatly reduces the memory usage in the data plane, while mitigating the double-counting problem. Experimental results show that, compared with existing solutions, SAROS improves the F1-Score of heavy hitter detection by 10 ∼ 40%.

Video action segmentation under timestamp supervision has recently received much attention due to lower annotation costs. Most existing methods generate pseudo-labels for all frames in each video to train the segmentation model. However, these methods suffer from incorrect pseudo-labels, especially for the semantically unclear frames in the transition region between two consecutive actions, which we call ambiguous intervals. To address this issue, we propose a novel framework from the perspective of clustering, which includes the following two parts. First, pseudo-label ensembling generates incomplete but high-quality pseudo-label sequences, where the frames in ambiguous intervals have no pseudo-labels. Second, iterative clustering iteratively propagates the pseudo-labels to the ambiguous intervals by clustering, and thus updates the pseudo-label sequences to train the model. We further introduce a clustering loss, which encourages the features of frames within the same action segment more compact. Extensive experiments show the effectiveness of our method.