Debugging in production cloud systems (or live debugging) is a critical yet challenging task for on-call developers due to the financial impact of cloud service downtime and the inherent complexity of cloud systems. Unfortunately, how debugging is performed, and the unique challenges faced in the production cloud environment have not been investigated in detail.

Weakly-supervised temporal action localization (WTAL) is a challenging task in understanding untrimmed videos, in which no frame-wise annotation is provided during training, only the video-level category label is available. Current methods mainly adopt temporal attention branches to conduct foreground-background separation with RGB and optical flow features simply concatenated, regardless of the discriminative spacial features and the complementarity between different modalities. In this work, we propose a Multi-Dimensional Attention (MDA) method to explore attention mechanism across three dimensions in weakly supervised action localization,i.e., 1) temporal attention that focuses on segments containing action instances, 2) channel attention that discovers the most relevant cues for action description, and 3) modal attention that fuses RGB and flow information adaptively based on feature magnitudes during background modeling. In addition, we introduce a similarity constraint loss to refine the action segment representation in feature space, which helps the network to detect less discriminative frames of an action to capture the full action boundaries. The proposed MDA with similarity constraints can be easily applied to existing action detection frameworks with few parameters. Extensive experiments on THUMOS’14 and ActivityNet v1.2 datasets show that the proposed method outperforms the current state-of-the-art WTAL approaches, and achieves comparable results with some advanced fully-supervised methods.

Audio-visual speech recognition (AVSR) integrates both audio and visual information to perform automatic speech recognition (ASR), which improves the robustness of human-robot interaction systems especially in noise environments. However, few methods and applications have paid attention to AVSR in tonal languages, in which the linguistic feature can play an important role as well as visual information. In this work, we propose a method for AVSR in Mandarin based on adaptive decision fusion as well as making full use of tone features. Firstly, we introduce tone features calculated by Constant Q trasform (CQT) and put them into a CNN-based audio network together with Mel-Frequency Cepstral Coefficient (MFCC) audio features. Then, the visual features are extracted by Discrete Cosine Transform (DCT) from mouth regions in video frames and modeled by an LSTM-based visual network. Finally, an adaptive decision fusion network combines the outputs from both streams to make final predictions. Experimental results on the PKU-AV2 dataset show that the tone features can significantly improve the robustness of Mandarin speech recognition systems, and the adaptability of the proposed method to various noise environments.