People sending final goodbye messages to their loved ones. Mother clinging to children, while the father hurriedly makes for a safe getaway for the family to the basement. Imagine the state of confusion and chaos every Hawaiian would have faced on Jan 13, 2018 when they received an approaching ballistic missile alert. Well given the current twitter threats by'Rocket Man' as President Trump calls the North Korean dictator, many Hawaiian's had all the logical reasons to believe the alert. Thankfully'Rocket Man' had not pressed the tiny nuclear button probably because Trump had a bigger button!! It all thankfully turned out to be a'False Alarm'.

People sending final goodbye messages to their loved ones. Mother clinging to children, while the father hurriedly makes for a safe getaway for the family to the basement. Imagine the state of confusion and chaos every Hawaiian would have faced on Jan 13, 2018 when they received an approaching ballistic missile alert. Well given the current twitter threats by'Rocket Man' as President Trump calls the North Korean dictator, many Hawaiian's had all the logical reasons to believe the alert. Thankfully'Rocket Man' had not pressed the tiny nuclear button probably because Trump had a bigger button!! It all thankfully turned out to be a'False Alarm'.

The first WWE pay-per-view is also among the year's biggest. Royal Rumble 2018 kicks off the road to WrestleMania Sunday night in Philadelphia to determine two of the wrestlers that will challenge for titles in New Orleans on April 8. As one of the "Big Four" pay-per-views, the Royal Rumble begins earlier than usual with a 7 p.m. EST start time. The kickoff show starts at 5 p.m. EST on WWE Network, which costs subscribers $9.99 per month, followed by the Royal Rumble itself. Fans can watch the Royal Rumble on PPV for $54.99.

While we commonly associate recommendation systems with e-commerce, their application extends to any decision-making problem which requires pairing two types of things together. To understand why recommenders don't always work as well as we'd like them to, we set out to build some basic recommendation systems using publicly available data. The first ingredient for building a recommendation system is user interaction data. We experimented with two different datasets, one from Flickr and one from Amazon. The Flickr dataset contains interactions between users and photos that they liked; the Amazon dataset contains user ratings on books.

Internet of things (IoT) applications have become increasingly popular in recent years, with applications ranging from building energy monitoring to personal health tracking and activity recognition. In order to leverage these data, automatic knowledge extraction - whereby we map from observations to interpretable states and transitions - must be done at scale. As such, we have seen many recent IoT data sets include annotations with a human expert specifying states, recorded as a set of boundaries and associated labels in a data sequence. These data can be used to build automatic labeling algorithms that produce labels as an expert would. Here, we refer to human-specified boundaries as breakpoints. Traditional changepoint detection methods only look for statistically-detectable boundaries that are defined as abrupt variations in the generative parameters of a data sequence. However, we observe that breakpoints occur on more subtle boundaries that are non-trivial to detect with these statistical methods. In this work, we propose a new unsupervised approach, based on deep learning, that outperforms existing techniques and learns the more subtle, breakpoint boundaries with a high accuracy. Through extensive experiments on various real-world data sets - including human-activity sensing data, speech signals, and electroencephalogram (EEG) activity traces - we demonstrate the effectiveness of our algorithm for practical applications. Furthermore, we show that our approach achieves significantly better performance than previous methods.

Nowadays, there are numerous risks related to bank loans both for the banks and the borrowers getting the loans. The risk analysis about bank loans needs understanding about the risk and the risk level. Banks need to analyze their customers for loan eligibility so that they can specifically target those customers. Banks wanted to automate the loan eligibility process (real time) based on customer details such as Gender, Marital Status, Age, Occupation, Income, debts, and others provided in their online application form. As the number of transactions in banking sector is rapidly growing and huge data volumes are available, the customers' behavior can be easily analyzed and the risks around loan can be reduced.

Decision trees algorithms use a gain function to select the best split during the tree's induction. This function is crucial to obtain trees with high predictive accuracy. Some gain functions can suffer from a bias when it compares splits of different arities. Quinlan proposed a gain ratio in C4.5's information gain function to fix this bias. In this paper, we present an updated version of the gain ratio that performs better as it tries to fix the gain ratio's bias for unbalanced trees and some splits with low predictive interest.

Kangaroo Kapital is the largest credit card company in Australia. Animals across the continent use Kangaroo Kapital credit cards to make all of their daily purchases, racking up points in the company's reward system. Since Australian animals have traditionally not worn much clothing, the challenges of carrying around cash are substantial. Only having to keep track of a single credit card is a big help for your average working wallaby. But since Australian animals have typically not worn much clothing, they still have a problem keeping track of even a single credit card.

Training set bugs are flaws in the data that adversely affect machine learning. The training set is usually too large for man- ual inspection, but one may have the resources to verify a few trusted items. The set of trusted items may not by itself be adequate for learning, so we propose an algorithm that uses these items to identify bugs in the training set and thus im- proves learning. Specifically, our approach seeks the smallest set of changes to the training set labels such that the model learned from this corrected training set predicts labels of the trusted items correctly. We flag the items whose labels are changed as potential bugs, whose labels can be checked for veracity by human experts. To find the bugs in this way is a challenging combinatorial bilevel optimization problem, but it can be relaxed into a continuous optimization problem. Ex- periments on toy and real data demonstrate that our approach can identify training set bugs effectively and suggest appro- priate changes to the labels. Our algorithm is a step toward trustworthy machine learning.

Finding Tiny Faces (by Hu and Ramanan) proposes a novel approach to find small objects in an image. Our contribution consists in deeply understanding the choices of the paper together with applying and extending a similar method to a real world subject which is the counting of people in a public demonstration.