Modern software systems’ increasing complexity and scale makes it challenging to accurately detect system issues and outages, which have been tackled as an anomaly detection task. Conventionally, such anomalous events barely happen, and annotating them is time-consuming and impractical in big data streams. Even with automated anomaly detection, resolving issues promptly is a remaining challenge that can only be done by providing specific contexts such as root causes, target/affected services, and more. To address these fundamentally important problems, we present Grid Transformer (GT), a framework designed to detect and explain $\log$ anomalies in an unsupervised setting. We first train an Auto-Encoder model to generate pseudo labels. Then, we train the proposed grid transformer that not only predicts anomalies but also generates why a particular instance is an anomaly. Through extensive experiments, we demonstrate the effectiveness of our approach where it is shown to outperform the other $\log$ anomaly detection models by 20% while also able to generate time-wise and message-wise explanations of the anomalies.

State-of-the-art video database management systems (VDBMSs) often use lightweight proxy models to accelerate object retrieval and aggregate queries. The key assumption underlying these systems is that the proxy model is an order of magnitude faster than the heavyweight oracle model. However, recent advances in computer vision have invalidated this assumption. Inference time of recently proposed oracle models is on par with or even lower than the proxy models used in state-of-the-art (SoTA) VDBMSs. This paper presents Seiden, a VDBMS that leverages this radical shift in the runtime gap between the oracle and proxy models. Instead of relying on a proxy model, Seiden directly applies the oracle model over a subset of frames to build a query-agnostic index, and samples additional frames to answer the query using an exploration-exploitation scheme during query processing. By leveraging the temporal continuity of the video and the output of the oracle model on the sampled frames, Seiden delivers faster query processing and better query accuracy than SoTA VDBMSs. Our empirical evaluation shows that Seiden is on average 6.6 x faster than SoTA VDBMSs across diverse queries and datasets.

Detection and localization of actions in videos is an important problem in practice. State-of-the-art video analytics systems are unable to efficiently and effectively answer such action queries because actions often involve a complex interaction between objects and are spread across a sequence of frames; detecting and localizing them requires computationally expensive deep neural networks. It is also important to consider the entire sequence of frames to answer the query effectively.

The interaction between swarm robots and human operators is significantly different from the traditional humanrobot interaction due to unique characteristics of the system, such as high cognitive complexity and difficulties in state estimation. In this paper, we concentrated on the method of conveying input from the operator to the swarm. Previous research has shown that control through switching between behaviors offers the greatest flexibility but is particularly difficult for human operators. A recently developed method for finding optimal sequences for composing behaviors offered a potential tool for aiding human operators controlling swarms through behavior switching. This paper compared participants performing a navigation task with and without the availability of the optimal sequencing aid. Results showed that the task of preplanning a sequence of behaviors and durations appeared more difficult for participants than switching between executing behaviors to navigate. Users who used the aid frequently was found to create shorter paths than infrequent users and the control group. In the trails that the aid was used, participants tended to generate more complicated sequences and achieve the first attempt more rapidly, compared to the trails that the aid was not used.