This paper proposes a novel non-intrusive system failure prediction technique
using available information from developers and minimal information from raw
logs (rather than mining entire logs) but keeping the data entirely private
with the data owners. A neural network based multi-class classifier is
developed for failure prediction, using artificially generated anonymous data
set, applying a combination of techniques, viz., genetic algorithm (steps),
pattern repetition, etc., to train and test the network. The proposed mechanism
completely decouples the data set used for training process from the actual
data which is kept private. Moreover, multi-criteria decision making (MCDM)
schemes are used to prioritize failures meeting business requirements. Results
show high accuracy in failure prediction under different parameter
configurations. On a broader context, any classification problem, beyond
failure prediction, can be performed using the proposed mechanism with
artificially generated data set without looking into the actual data as long as
the input features can be translated to binary values (e.g. output from private
binary classifiers) and can provide classification-as-a-service.

本文提出了一种新颖的非侵入式系统故障预测技术，该技术利用开发人员提供的信息和原始日志中的最小信息（而不是挖掘整个日志），并将数 据完全保留在数据所有者的管辖范围内。

基于人工和匿名训练的多分类器故障预测模型，确保数据隐私

Multi-class Classifier based Failure Prediction with Artificial and Anonymous Training for Data Privacy

Nowadays, most of the objective speech quality assessment tools (e.g.,
perceptual evaluation of speech quality (PESQ)) are based on the comparison of
the degraded/processed speech with its clean counterpart. The need of a
"golden" reference considerably restricts the practicality of such assessment
tools in real-world scenarios since the clean reference usually cannot be
accessed. On the other hand, human beings can readily evaluate the speech
quality without any reference (e.g., mean opinion score (MOS) tests), implying
the existence of an objective and non-intrusive (no clean reference needed)
quality assessment mechanism. In this study, we propose a novel end-to-end,
non-intrusive speech quality evaluation model, termed Quality-Net, based on
bidirectional long short-term memory. The evaluation of utterance-level quality
in Quality-Net is based on the frame-level assessment. Frame constraints and
sensible initializations of forget gate biases are applied to learn meaningful
frame-level quality assessment from the utterance-level quality label.
Experimental results show that Quality-Net can yield high correlation to PESQ
(0.9 for the noisy speech and 0.84 for the speech processed by speech
enhancement). We believe that Quality-Net has potential to be used in a wide
variety of applications of speech signal processing.

本研究提出了一种基于双向长短时记忆的全新端到端的非侵入式语音质量评估模型 Quality-Net，该模型可在没有干净参考语音的情况下对语音质量进行评估，实验结果表明 Quality-Net 可以产生与 PESQ 高相关性（噪声语音为 0.9，经过语音增强处理的语音为 0.84），有潜力在语音信号处理的广泛应用中使用。