The following publication was presented at the 2014 IEEE International Workshop on Cyber Crime and received the Best Paper Award on 5/18/2014. The original IEEE LaTeX formatted PDF publication can also be downloaded from here: IWCC Combined Clustering. To be successful, cybercriminals must figure out how to scale their scams. They duplicate content on new websites, often staying one step ahead of defenders that shut down past schemes. For some scams, such as phishing and counterfeitgoods shops, the duplicated content remains nearly identical. In others, such as advanced-fee fraud and online Ponzi schemes, the criminal must alter content so that it appears different in order to evade detection by victims and law enforcement. Nevertheless, similarities often remain, in terms of the website structure or content, since making truly unique copies does not scale well. In this paper, we present a novel combined clustering method that links together replicated scam websites, even when the criminal has taken steps to hide connections. We evaluate its performance against two collected datasets of scam websites: fake-escrow services and high-yield investment programs (HYIPs). We find that our method more accurately groups similar websites together than does existing general-purpose consensus clustering methods.

A number of people talk about metadata. Some actually practice what they preach. When it comes to resources needed to apply/associate/embed metadata to digital assets (audio, video, text, graphics, photos) in a consistent manner, too many fall short. We consistently hear about issues with budgets and headcount, while what is really lacking is a clear business case with a total cost of ownership per year. This must include continual executive support (year over year) to operate a successful and sustainable Digital Asset Management (DAM) solution with the necessary people, processes, technology and information (metadata).

It's a weird feeling, cruising around Silicon Valley in a car driven by no one. I am in the back seat of one of Google's self-driving cars – a converted Lexus SUV with lasers, radar and low-res cameras strapped to the roof and fenders – as it manoeuvres the streets of Mountain View, California, not far from Google's headquarters. I grew up about eight kilometres from here and remember riding around on these same streets on a Schwinn Sting-Ray. Now, I am riding an algorithm, you might say – a mathematical equation, which, written as computer code, controls the Lexus. The car does not feel dangerous, nor does it feel like it is being driven by a human. It rolls to a full stop at stop signs, veers too far away from a delivery van, taps the brakes for no apparent reason as we pass a line of parked cars. I wonder if the flaw is in me, not the car: Is it reacting to something I can't see? The car is capable of detecting the motion of a cat, or a car crossing the street hundreds of metres away in any direction, day or night (snow and fog can be another matter). "It sees much better than a human being," Dmitri Dolgov, the lead software engineer for Google's self-driving-car project, says proudly. He is sitting behind the wheel, his hands on his lap. As we stop at the intersection, waiting for a left turn, I glance over at a laptop in the passenger seat that provides a real-time look at how the car interprets its surroundings. On it, I see a gridlike world of colourful objects – cars, trucks, bicyclists, pedestrians – drifting by in a video-game-like tableau. Each sensor offers a different view – the lasers provide three-dimensional depth, the cameras identify road signs, turn signals, colours and lights. The computer in the back processes all this information in real time, gauging the speed of oncoming traffic, making a judgment about when it is OK to make a left turn. Waiting for the car to make that decision is a spooky moment. I am betting my life that one of the coders who worked on the algorithm for when it's safe to make a left-hand turn in traffic had not had a fight with his girlfriend (or boyfriend) the night before and screwed up the code.

R. Lee Coulter, one of the organizers of the 2016 World BPO/ITO Forum, is senior vice president of St. Louis-based Ascension Health, the nation's largest nonprofit health system, and CEO of its shared services subsidiary, the Ascension Ministry Service Center. His focus on shared services, business process outsourcing, or BPO, and technology spans a 30-year career in leadership positions at companies, including General Electric, AON and Kraft Foods. Here, he talks to SearchCIO senior executive editor Linda Tucci about the current state of artificial intelligence (AI) applications in outsourcing and what's on the horizon. This interview has been condensed and edited. Lee Coulter: AI is not being applied to outsourcing today, in so far as we're talking about the shared services and outsourcing industry.

"Think about just how interconnected the world is now," said Matt Gould, the co-founder of Arria NLG, a prominent enterprise in the development and deployment of Natural Language Generation (NLG) technologies worldwide. "Think about it from just a personal context. How much data are you generating personally every day?" Modern, connected humans interact constantly online with computers, mobile phones, and many other devices. They pay bills, watch movies, purchase products, interact with medical professionals, use fitness apps, listen to music, and work online. "That whole drifting miasma of invisible data is spilling off you constantly and consistently, and it's happening for at least half the world's population now," said Gould during a recent DATAVERSITY phone interview.

TAIPEI, TAIWAN--(Marketwired - May 31, 2016) - Computex - NVIDIA (NASDAQ: NVDA) won big at the Computex Best Choice Awards, with the NVIDIA Tesla M40 GPU and NVIDIA Jetson TX1 module hauling in Gold Awards and the NVIDIA SHIELD Android TV clinching a Category Award. Garnering these three prestigious awards extends the company's winning streak -- the longest of any international Computex exhibitor -- to eight consecutive years. Taiwan's President Tsai Ing-wen will hand out the awards. The Best Choice Awards, established in 2002, honor innovation, functionality and market potential. The Gold Award-winning NVIDIA Tesla M40 GPU is the world's fastest deep learning training accelerator.

IBM Watson burst onto the scene in 2011 as a Jeopardy-playing computer and quickly became synonymous with artificial intelligence. Since then, AI has become a lot more commonplace thanks to the rise of digital assistants like Siri, Amazon's Alexa and Google Assistant, but Watson is still chugging away in more ways than you might think, powering everything from cancer treatment software to hotel concierge robots (pictured above). I had a chance to talk to Maya Weinstein, Senior Interaction Design and Creative Director at IBM Watson, about everything from the current crop of AI assistants to how the technology can help save lives. Check out the full interview (with some light edits for clarity) below. Jacob Kleinman: What do you think of the rise of consumer AI products like chatbots or Amazon Echo?

Physicists are putting themselves out of a job, using artificial intelligence to run a complex experiment. The experiment, developed by physicists from The Australian National University (ANU) and UNSW ADFA, created an extremely cold gas trapped in a laser beam, known as a Bose-Einstein condensate, replicating the experiment that won the 2001 Nobel Prize. "I didn't expect the machine could learn to do the experiment itself, from scratch, in under an hour," said co-lead researcher Paul Wigley from the ANU Research School of Physics and Engineering. "A simple computer program would have taken longer than the age of the Universe to run through all the combinations and work this out." Bose-Einstein condensates are some of the coldest places in the Universe, far colder than outer space, typically less than a billionth of a degree above absolute zero.

He was a key Apple employee, known as the'father of the iPod' - and now Tony Fadell, who also invested the Nest smart thermostat, has beaten his old employer to launch an electric vehicle. The 600 Arrow'smart-kart' includes includes GPS and WiFi to keep drivers safe, and is aimed at 5-9 year olds. Parents using a mobile app can geofence the kart's driving area, limit the top speed or even hit a stop button in an emergency. The 600 Arrow'smart-kart' includes includes GPS and WiFi to keep drivers safe, and is aimed at 5-9 year olds. There's also a proximity sensor to automatically prevent accidents.

Almost 200 years after Mary Shelley made AI an object of fear and awe in Frankenstein, humanity is looking at a proliferation of super-smart creations: robots. Automated technology of all sorts – from industrial to humanoid to seemingly harmless conveyors of "artificial smartness" – will soon transform our lives. But need we fear it? Will AI take our jobs away? Or will robots, by handling all the mechanics and rote work necessary for economic productivity, liberate us to be more creative, caring and "humanly intelligent"? What, in the end, can humans do that machines cannot? I sat down with Kevin Kelly to get some answers, as he lays them out in his new book, The Inevitable: Understanding the Twelve Technological Forces That Will Shape Our Future," out June 6th. Take a listen over at Ricochet, and read edited excerpts below. PETHOKOUKIS: The book obviously is about technology but it's not a book about gadgets. Thank you, that's a synopsis of the general drift of the book, which is to ...