Over the past several years, commercial use of biometric data has become increasingly prevalent. In response, several states have adopted biometric data privacy legislation. Consequently, companies that rely on biometric data face new regulatory risks, in addition to increased legal exposure to individual and class action lawsuits. In fact, the Ninth Circuit Court of Appeals recently affirmed certification of a class action alleging Facebook's face-scanning practices violate Illinois' biometric privacy law, finding that the class alleged sufficiently concrete injuries based on Facebook's alleged use of facial recognition technology without users' consent to establish standing. Insurance policies currently available on the market, including cyber insurance policies, may not adequately cover these risks.

The United States Geological Survey confirmed with the Center for Data Innovation over email on July 22, 2019, that "1-meter DEMs are available or in progress for 45% of the country."

--Regulatory compliance is an organization's adherence to laws, regulations, guidelines and specifications relevant to its business. Compliance officers responsible for maintaining adherence constantly struggle to keep up with the large amount of changes in regulatory requirements. Keeping up with the changes entail two main tasks: fetching the regulatory announcements that actually contain changes of interest, and incorporating those changes in the business process. In this paper we focus on the first task, and present a Compliance Change Tracking System, that gathers regulatory announcements from government sites, news sites, email subscriptions; classifies their importance i.e Actionability through a hierarchical classifier, and business process applicability through a multi-class classifier . Na ıve Bayes, logistic regression etc.), hierarchical classification method, rule based approach, hybrid approach with various preprocessing and feature selection methods; and show that despite the richness of other models, a simple hierarchical classification with bag-of-words features works the best for Actionability classifier and multi-class logistic regression works the best for Applicability classifier . The system has been deployed in global delivery centers, and has received positive feedback from payroll compliance officers. Organizations are faced with rapidly changing regulatory policies, and ever-growing number of regulations.

Most financial institutions know it's critical to manage the ever-increasing amounts of accessible data, but many miss the potential in using that data in innovative ways. Financial institutions have a plethora of data they can access, either through their own systems or through public sources. However, many can't -- or won't -- exploit the large volumes of data, particularly the "owned" data that an organization holds about customers. This kind of data is typically called customer relationship management data, such as the purchase history tied to app installs, email addresses and postal addresses. Though financial institutions maintain and collect massive volumes of data, many firms are restricted from fully using that data because they are required to comply with stringent regulations around what can and cannot be done with customer data.

The Hawaiian poʻo-uli, a small bird from the honeycreeper family, was first discovered in 1973. Less than half a century later, it disappeared from the planet. Declared extinct in 2018, it is one of almost 700 vertebrate species that have been driven to extinction in the last 500 years. According to a United Nations report issued earlier this year to policymakers, one million species are at risk of extinction: Human actions threaten more plants and animals than ever before. Although the precise number of species on the planet is difficult to calculate, recent estimates put it at around 8.7 million.

AI is hard at work delivering huge ROIs and efficiencies for businesses in every sector of commerce, but it's also something that can (quite spectacularly) fail, causing major financial losses, and harm to the brand your team has worked so hard to build. If not done carefully, AI deployments can quickly turn into disaster, as we see more and more of in the news each day. A business interested in building reliable AI, whose decisions can be trusted, is a business that puts appropriate guard rails around their model maintenance, long after it's been trained, tested, and deployed. But seldom do businesses truly take steps to ensure that, after those models are trained, they stay relevant, operational, and healthy. We will be updating this blog with deep explainers for all 3 of the above in the weeks to come, but to be quick about it, we'll explain why they're important… right now!

Mohsen Farhadloo, PhD John Molson Scool of Business, Concordia University mohsen.farhadloo@concordia.ca August 21, 2019 Abstract In this paper we introduce score embedding, a neural network based model to learn interpretable vector representations for words. Score embedding is a supervised method that takes advantage of the labeled training data and the neural network architecture to learn interpretable representations for words. Health care has been a controversial issue between political parties in the United States. In this paper we use the discussions on Twitter regarding different issues of affordable care act to identify the public opinion about the existing health care plans using the proposed score embedding. Our results indicate our approach effectively incorporates the sentiment information and outperforms or is at least comparable to the state-of-the-art methods and the negative sentiment towards "TrumpCare" was consistently greater than neutral and positive sentiment over time. 1 Introduction Sentiment analysis as a type of text categorization is the task of identifying the sentiment orientation of documents written in natural language which assigns one of the predefined sentiment categories into a whole document or pieces of the document such as phrases or sentences [23, 8]. Many studies used binary classification and reported high performance [18, 29, 24] and some studies have observed that the performance of the categorization reduces as the number of sentiment categories increases [2, 16, 3, 11]. Bag-Of-Words (BOW), a standard approach for text categorization, represents a document by a vector that indicates the words that appear in the document.

Patent data are often used to study the process of innovation and research, but patent databases lack unique identifiers for individual inventors, making it difficult to study innovation processes at the individual level. Here we introduce an algorithm that performs highly accurate disambiguation of inventors (named entities) in US patent data (F1: 99.09%, precision: 99.41%, recall: 98.76%). The algorithm involves a novel method for converting text-based record data into abstract image representations, in which text from a given pairwise comparison between two inventor name records is converted into a 2D RGB (stacked) image representation. We train an image classification neural network to discriminate between such pairwise comparison images, and then use the trained network to label each pair of records as either matched (same inventor) or non-matched (different inventors). The resulting disambiguation algorithm produces highly accurate results, out-performing other inventor name disambiguation studies on US patent data. Our new text-to-image representation method could potentially be used more broadly for other NLP comparison problems, as it allows image-based processing techniques (e.g. image classification networks) to be applied to text-based comparison problems (such as disambiguation of academic publications, or data linkage problems).

Last week, all of us who live in the UK, and all who visit us, discovered that our faces were being scanned secretly by private companies and have been for some time. We don't know what these companies are doing with our faces or how long they've been doing it because they refused to share this with the Financial Times, which reported on Monday that facial recognition technology is being used in King's Cross and may be deployed in Canary Wharf, two areas that cover more than 160 acres of London. We are just as ignorant about what has been happening to our faces when they're scanned by the property developers, shopping centres, museums, conference centres and casinos that have also been secretly using facial recognition technology on us, according to the civil liberties group Big Brother Watch. But we can take a good guess. They may be matching us against police watchlists, maintaining their own watchlists or sharing their watchlists with the police, other companies and other governments.

Their lifeless eyes peer from building facades, lampposts and streetlight poles. They never sleep, never even blink. And now, enabled by advances in computing power and artificial intelligence, surveillance cameras can do more than just watch. They can recognize, and they can remember. The district attorney for Pennsylvania's second-most-populous county has assembled a network of advanced surveillance cameras in and around Pittsburgh and has enlisted colleagues in four surrounding counties to extend its reach into their jurisdictions.