Many information systems use tags and keywords to describe and annotate content. These allow for efficient organization and categorization of items, as well as facilitate relevant search queries. As such, the selected set of tags for an item can have a considerable effect on the volume of traffic that eventually reaches an item. In tagging systems where tags are exclusively chosen by an item's owner, who in turn is interested in maximizing traffic, a principled approach for assigning tags can prove valuable. In this paper we introduce the problem of optimal tagging, where the task is to choose a subset of tags for a new item such that the probability of browsing users reaching that item is maximized.

The app reads your email inbox and your meeting calendar, then gives you a short audio summary. It can help you spend less time scrolling, but of course, there are privacy drawbacks to consider.

Deep Learning (DL) have significantly enhanced Network Intrusion Detection Systems (NIDS), improving the effectiveness of cybersecurity operations.

On User-Generated Content (UGC) platforms, recommendation algorithms significantly impact creators' motivation to produce content as they compete for

From small publishers to US federal agencies, websites are reporting unusual spikes in automated traffic linked to IP addresses in Lanzhou, China. For a brief moment in October, Alejandro Quintero thought he had made it big in China . The Bogotá-based data analyst owns and manages a website that publishes articles about paranormal activities, like ghosts and aliens. The content is written in "Spanglish," he says, and was never intended for an Asian audience. But last fall, Quintero's site suddenly began receiving a large volume of visits from China and Singapore.

A.1 Dataset Description We describe the additional details of each dataset in the followings. For electricity, we take 500k training windows between 2014-01-01 to 2014-09-01 by reference [14, 24]. And we use the first 90% for the training set and the last 10% as the validation set. Testing set is the next 7 days after the training set. We apply the z-score normalization to the real-valued inputs of each time series.

Network intrusion detection system Continual learning with shallow methods Detailed illustration of configuration changes Datasets details Data preprocessing and feature selection Task formulation Task similarity via optimal transport dataset distance Training time comparison of the proposed ECBRS with the baselines Additional experiments with anomaly detection datasets Ablation studies Implementation, hardware details, and hyperparameter selection Occurrence of task dissimilarity between two different tasks is rare Limitations and broader impact A.1 Network intrusion detection system NID comprises two parts: the training module and the anomaly detection engine. The training can be periodic or triggered by an event like decay in intrusion detection accuracy. These features are fed to the anomaly detection engine to identify anomaly pattern(s). In our work, shallow methods are the non-neural network-based approaches. BWT is the influence that learning a task ' t ' has on the performance of BWT occurs when learning a task diminishes proficiency in prior tasks.