Recording runtime status via logs is common for almost every computer system, and detecting anomalies in logs is crucial for timely identifying malfunctions of systems. However, manually detecting anomalies for logs is time-consuming, error-prone, and infeasible. Existing automatic log anomaly detection approaches, using indexes rather than semantics of log templates, tend to cause false alarms. In this work, we propose LogAnomaly, a framework to model unstructured a log stream as a natural language sequence. Empowered by template2vec, a novel, simple yet effective method to extract the semantic information hidden in log templates, LogAnomaly can detect both sequential and quantitive log anomalies simultaneously, which were not done by any previous work. Moreover, LogAnomaly can avoid the false alarms caused by the newly appearing log templates between periodic model retrainings. Our evaluation on two public production log datasets show that LogAnomaly outperforms existing log-based anomaly detection methods.

Abiotic stresses such as drought, extreme temperature, and salinity can negatively impact seed germination and plant growth and have become major limitations to crop production. Most crops are vulnerable to abiotic stress factors during their early growth phase, especially during seed germination and seedling emergence. Rapid crop seed germination and seedling establishment is known to provide competitive advantages over weeds and improve yields. Seed osmopriming is defined as a pre-sowing treatment in which seeds are soaked in osmotic solutions to undergo the first stage of germination, but radicle protrusion has not occurred. The process of osmopriming involves prior exposure of seeds in low-water-potential solutions. Osmopriming can generate a series of pre-germination metabolic activities, increase the antioxidant system activities, and prepare the seed for radicle protrusion. Polyethylene glycol (PEG) is a popular osmopriming agent that can alleviate the negative impacts of abiotic stresses. This review summarizes research findings on crop responses to seed priming with PEG under abiotic stresses. The challenges, limitations, and opportunities of using PEG for crop seed priming are discussed with the goal of providing insights into future research towards effective application of seed priming in crop production.

BACKGROUND In‐field weed detection in wheat (Triticum aestivum L.) is challenging due to the occurrence of weeds in close proximity with the crop. The objective of this research was to evaluate the feasibility of using deep convolutional neural networks for detecting broadleaf weed seedlings growing in wheat. RESULTS The object detection neural networks, including CenterNet, Faster R‐CNN, TridenNet, VFNet, and You Only Look Once Version 3 (YOLOv3) were insufficient for weed detection in wheat because the recall never exceeded 0.58 in the testing dataset. The image classification neural networks including AlexNet, DenseNet, ResNet, and VGGNet were trained with small (5500 negative and 5500 positive images) or large training datasets (11 000 negative and 11 000 positive images) and three training image sizes (200 × 200, 300 × 300, and 400 × 400 pixels). For the small training dataset, increasing image sizes decreased the F1 scores of AlexNet and VGGNet but generally increased the F1 scores of DenseNet and ResNet. For the large training dataset, no obvious difference was detected between the training image sizes since all neural networks exhibited remarkable classification accuracies with high F1 scores (≥0.96). All image classification neural networks exhibited high F1 scores (≥0.99) when trained with the large training dataset and the training images of 200 × 200 pixels. CONCLUSION CenterNet, Faster R‐CNN, TridentNet, VFNet, and YOLOv3 were insufficient, while AlexNet, DenseNet, ResNet, and VGGNet trained with a large training dataset were highly effective for detection of broadleaf weed seedlings in wheat. © 2021 Society of Chemical Industry.

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