Ensuring the security of transactions is currently one of the major challenges facing banking systems. The usage of face for biometric authentication of users is becoming adopted worldwide due its convenience and acceptability by people, and also given that, nowadays, almost all computers and mobile devices have built-in cameras. Such user authentication approach is attracting large investments from banking and financial institutions, especially in cross-domain scenarios, in which facial images from ID documents are compared with digital self-portraits (selfies) taken with the cameras of mobile devices, for the automated opening of new checking accounts or financial transactions authorization. In this work, besides of collecting a large cross-domain face database, with 27,002 real facial images of selfies and ID documents (13,501 subjects) captured from the systems of the major public Brazilian bank, we propose a novel approach for such cross-domain face matching based on deep features extracted by two well-referenced Convolutional Neural Networks (CNN). Results obtained on the large dataset collected, which we called FaceBank, with accuracy rates higher than 93%, demonstrate the robustness of the proposed approach to the cross-domain problem (comparing faces in IDs and selfies) and its feasible application in real banking security systems.

Knowledge bases are employed in a variety of applications from natural language processing to semantic web search; alas, in practice their usefulness is hurt by their incompleteness. Embedding models attain state-of-the-art accuracy in knowledge base completion, but their predictions are notoriously hard to interpret. In this paper, we adapt "pedagogical approaches" (from the literature on neural networks) so as to interpret embedding models by extracting weighted Horn rules from them. We show how pedagogical approaches have to be adapted to take upon the large-scale relational aspects of knowledge bases and show experimentally their strengths and weaknesses.

This year, we have seen an acceleration of Silicon Valley tech giants opening AI research labs around the world as they seek to gain traction among researchers and fulfill their global ambitions. In the past six months or so, Google brought labs to China and France, Facebook opened labs in Pittsburgh and Seattle, and Microsoft announced plans to open labs near universities in Berkeley, California and Melbourne, Australia. This trend shows no signs of slowing down. Last month, Samsung announced labs in Cambridge, Moscow, and Toronto. This week, Nvidia announced plans to open a new lab in Toronto, while Google shared plans to open a lab in Accra, Ghana, Google's first in Africa and perhaps the first of any tech giant in Africa.

It may sound like a stalkers dream come true, but dating app Happn is adding a new'creepy' feature that will let potential love matches revisit your past movements. Starting next month, if you remember crossing paths with someone that took your fancy, you will be able to retrace your steps to try and find them again. If they are also a user of the popular dating app, which matches people through their device's geolocation, their profile will appear on the new map tool at that spot. Budding romantics may find the feature appealing, giving them the chance to tap locations they've visited over the past week to track down lost connections. However, some may find the idea of strangers tracing their movements more than a little creepy.

Billionaire Elon Musk has said cash handouts'will be necessary' if robots take human jobs, in his latest flurry of tweets. Musk made the comment in response to a question from a Twitter user about whether he supported universal basic income (UBI) - a cash handout that could be given to people irrespective of their employment. Musk believes UBI could be a possible solution for unemployment caused by machines taking over the workforce. Billionaire Elon Musk has said cash handouts'will be necessary' if robots take human jobs, in his latest flurry of tweets A universal basic income would give a standard amount of money to every citizen to cover basic expenses like food and living costs each month. Musk first joined the growing list of tech executives supporting the payment system in 2016 when he spoke about the concept in an interview.

By means of the CTBC systems, the exact position of a train is known more precisely than with the regular signaling systems. This results in a more efficient and safe way to manage the railway traffic. Metros and other railway systems are able to improve headways while maintaining or even improving safety. The main objective of the CTBC is to increase capacity by reducing the time interval (headway) between trains. Traditional signaling systems detect trains in discrete sections of the track called'blocks', each protected by signals that prevent a train from entering an occupied block.

Biometrics emerged as a robust solution for security systems. However, given the widespread of biometric applications, criminals are developing techniques to circumvent them by simulating physical or behavioral traits of legal users (spoofing attacks). Despite face being a promising characteristic due to its universality, acceptability and presence of cameras almost everywhere, face recognition systems are extremely vulnerable to such frauds since they can be easily fooled with common printed facial photographs. State-of-the-art approaches, based on Convolutional Neural Networks (CNNs), present good results in face spoofing detection. However, these methods do not exploit the importance of learning deep local features from each facial region, even though it is known from face recognition that different face regions have much different visual aspects, that can also be exploited for face spoofing detection. In this work we propose a novel CNN architecture trained in two steps for such task. Initially, each part of the neural network learns features from a given facial region. After, the whole model is fine-tuned on the whole facial images. Results show that such pretraining step allows the CNN to learn different local spoofing cues, improving the performance and convergence speed of the final model, outperforming the state-of-the-art approaches.

A group of Amazon shareholders is asking CEO Jeff Bezos to stop selling and marketing facial recognition technology to governments after civil liberties groups warned of the potential for abuse. Earlier this year, a group of advocacy organisations led by the American Civil Liberties Union (ACLU) published a report detailing how Amazon was marketing its Rekognition tool to American law enforcement agencies. In addition to touting the technology as helping to find suspects, Amazon has said it could be used to preemptively identify "persons of interest" and prevent crimes. A letter signed by 19 shareholders - and provided to The Independent by the ACLU - urges Mr Bezos to halt the tool's expansion until those concerns can be addressed. Amazon supplier investigated over'mistreatment' of workers in China How Alexa recorded a family's conversation then sent it to someone Amazon told to stop selling facial recognition tools to police Amazon supplier investigated over'mistreatment' of workers in China How Alexa recorded a family's conversation then sent it to someone Furnishing police and sheriff's departments with the tool would bolster "government surveillance infrastructure technology" and could drive down Amazon's value, the letter warned. It also echoed concerns about the potential for misuse. "While Rekognition may be intended to enhance some law enforcement activities, we are deeply concerned it may ultimately violate civil and human rights", the letter said.

The 1985 deal has less than 8,000 words and contains just 22 articles, mostly dedicated to tariffs, agricultural restrictions, import licensing and rules of origin – what Harvard economist Dani Rodrik calls conventional trade topics . While these issues are also covered in the US-Singapore deal, most of its 20 chapters and 70,000 or so words deal with other topics such as anti-competitive business conduct, e-commerce, intellectual property, investment rules, labour rights and the environment. AI has already proved its worth in the comparable field of law. A two-month test pitting 20 lawyers against LawGeek's AI showed that humans were no match for a robot in spotting risks within the legal documentation for non-disclosure agreements – deals meant to protect confidential information such as new manufacturing processes and marketing schemes. In terms of accuracy, the lawyers scored an average of 85%, compared to the robot's 94%.

Visa Canada, a unit of Visa Inc. V, recently announced a strategic collaboration with Finn Al, to add new stimulus to its conversational banking chatbots and artificial intelligence (AI), powered by the Visa Developer Platform. Stocks to Consider Investors interested in the Financial Transaction Services industry might take a look at some better-ranked stocks, namely Cardtronics plc CATM and WEX Inc. WEX, both sporting a Zacks Rank #1 (Strong Buy). You can see the complete list of today's Zacks #1 Rank stocks here . Cardtronics offers automated consumer financial services through its network of automated teller machines (ATMs) and multi-function financial services kiosks. The company managed to come up with an average four-quarter positive surprise of 27.17%.