While we often think of cyber threats to be conducted by remote external hackers, this research finds that insider threats often take advantage of internal organizational knowledge to exploit cyber- vulnerabilities and perpetrate identity theft and fraud. The ITAP research program provides insight into the habits and methods of identity thieves. The analytical repository of ITAP offers identity solutions relevant to people, organizations, and devices across multiple domains, including financial services, consumer services, healthcare, education, defense, energy, and government. 2018 UT CID ITAP Report: Healthcare, Government, Consumer Services, and Financial Services. In doing so, ITAP intends to offer behavioral and cognitive recommendations to thwart future identity theft crimes. Cyber vulnerabilities represent almost 75% of the cases, indicating the significant use of computers and the internet to execute these crimes. In particular, we show how to use the learning with counts technique to produce compact summaries of high dimensional categorical variables.

We use the Azure ML DRACuLa (learning with counts) modules for building count features on the categorical data, and a two-class boosted decision tree learner for the binary classification problem. Traditionally, categorical features are dealt with via one-hot encoding. Don’t let this change your vacation plans, though, if you find that no hurricanes are predicted! Here you will find all of our predictions for upcoming games across our various leagues and countries. This is a vast temperature range that few countries can equal, and it aptly demonstrates the great diversity of weather to be found within Sweden. Cold weather is always brutal and therefore you car needs special care. But it’s not just the cold that we need to be concerned about. You don’t need to go looking for a wifi connection for it. Before choosing seeds from a catalog, youll need to know which growing zone you live in. Ad click prediction is a multi-billion dollar industry, and one that is still growing rapidly.

Branch prediction can be thought of as a sophisticated form of prefetch or a limited form of data prediction that attempts to predict the result of branch instructions so that a processor can speculatively fetch across basic-block boundaries. Through predictions, one will be able to ascertain the possible result of the game thereby allowing a sports bettor to make an informed and logical decision in placing bets. For each game we select a set of variables from the simulator which we think will influence the outcome. 20) are stored in the directory raw/count; we use these 21 days of data for generating count features on the high dimensional categorical variables (explained in some detail in what follows). Understanding the number of unique values that the categorical variables take is of interest when building ML models, since high-dimensional categorical features can be challenging for some algorithms to handle. THOR GUARD has always detected lightning, but it is our prediction algorithms that have enabled our Lightning Prediction Systems to stand apart from any other company claiming to be our competitor.

In this post, we build ML models on the largest publicly available ad click prediction dataset, from Criteo. For a given example, will the user click or not? We model this as a binary classification problem, where a click gets the label “1” and lack of a click gets the label “0”. The outcome of this walkthrough is to obtain the downsampled train and test datasets that are used in our model building below. I’m sure you’ve seen on your local television during a storm situation about model A, B, and C. Well what is model A, B, and C? Listen to radio or television reports of travel advisories issued by the National Weather Service. Tornadoes have the potential to travel over 60 mph (96 kph), and unlike automobiles, don’t have to follow road patterns. The section “Create Hive database and tables” describes creating Hive tables over the count, train, and test datasets. We use an Azure HDInsight cluster to load the Criteo data into Hive tables, and use Azure ML to build ML models on the dataset and understand it better. For manipulating the data prior to building counts, we use Azure HDInsight clusters. With all of this in mind, we are building TrekWeather.

As mentioned in an earlier blog post, count dataset is used for building count tables, which are used for featurization of the train and test datasets using resulting count features. In Azure ML experiments that we illustrate below, we use the Build Counting Transform and Apply Transformation modules to build count features from categorical variables, and featurize the train and test datasets with them. ” shown below. We then apply the resulting featurization using the Apply Transformation module to the train and test datasets. Applying the transformation essentially transforms the categorical features into class-conditional counts (and optionally log-odds if so desired). An efficient way of dealing with high-cardinality categorical features is a method called DRACuLa based on label-conditional counts for the categorical features. While this approach works well when the categorical features have few values, it results in feature space explosion for high-cardinality categorical features and is thus unsuitable for them.