LogBERT: Log Anomaly Detection via BERT

When systems break down, administrators usually check the produced logs to diagnose the failures. Nowadays, systems grow larger and more complicated. It is labor-intensive to manually detect abnormal behaviors in logs. Therefore, it is necessary to develop an automated anomaly detection on system logs. Automated anomaly detection not only identifies malicious patterns promptly but also requires no prior domain knowledge. Many existing log anomaly detection approaches apply natural language models such as Recurrent Neural Network (RNN) to log analysis since both are based on sequential data. The proposed model, LogBERT, a BERT-based neural network, can capture the contextual information in log sequences. LogBERT is trained on normal log data considering the scarcity of labeled abnormal data in reality. Intuitively, LogBERT learns normal patterns in training data and flags test data that are deviated from prediction as anomalies. We compare LogBERT with four traditional machine learning models and two deep learning models in terms of precision, recall, and F1 score on three public datasets, HDFS, BGL, and Thunderbird. Overall, LogBERT outperforms the state-of-art models for log anomaly detection

Effect of lactic acid bacteria on the ensiling characteristics and in vitro ruminal fermentation parameters of alfalfa silage

The objective of this study was to evaluate the effects of lactic acid bacteria (LAB) inoculants on fermentation quality and subsequent in vitro gas production, ruminal fermentation parameters, cellulolytic bacteria and their activities of alfalfa silage. Primary growth of alfalfa (Medicago sativa L.) was harvested at 50% flowering stage, inoculated without (control) or with Lactobacillus plantarum, Enterococcus mundtii and Enterococcus faecalis at 1.0 × 106 cfu/g of fresh weight (FW) in quadruplicate laboratory silos for 45 d. The silage inoculated with LAB were well preserved, indicated by the lower (p < .05) pH and ammonia-N content and the higher (p < .05) dry matter (DM), organic matter (OM), crude protein and lactic acid contents than the control silage. In vitro asymptotic gas and total volatile fatty acids production were higher in all LAB-treated silages (p < .05). All inoculants increased carboxymethyl-cellulase and β-glycosidase activities, and obtained higher DM and neutral detergent fibre degradability (p < .05) except E. mundtii. Similarly, L. plantarum and E. faecalis inoculants had higher (p < .05) Ruminococcus albus and Fibrobacter succinogenes relative proportions than the control. However, L. plantarum inoculants had lower (p < .05) percentage of methane (CH4) in 72 h gas production than the control and E. faecalis inoculants. These results suggested that L. plantarum were more effective in enhancing alfalfa silage utilisation by promoting forage digestibility and reducing ruminal CH4 emission than E. mundtii and E. faecalis.HIGHLIGHTS Lactic acid bacteria (LAB) inoculants improved alfalfa silage quality. Silage treated with Lactobacillus plantarum or Enterococcus mundtii increased gas production but reduced the percentage of methane in vitro. L. plantarum and Enterococcus faecalis promoted neutral detergent fibre digestibility by increased rumen cellulolytic bacteria proportion and cellulase activity

A large-scale evaluation of computational protein function prediction.

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools