Archives 8 December 2019

ProPublica reports that dozens of state and local agencies have purchased “SCAN” training from a company called LSI for reviewing a suspect’s written statements — even though there’s no scientific evidence that it works.

Local, state and federal agencies from the Louisville Metro Police Department to the Michigan State Police to the U.S. State Department have paid for SCAN training. The LSI website lists 417 agencies nationwide, from small-town police departments to the military, that have been trained in SCAN — and that list isn’t comprehensive, because additional ones show up in procurement databases and in public records obtained by ProPublica. Other training recipients include law enforcement agencies in Australia, Belgium, Canada, Israel, Mexico, the Netherlands, Singapore, South Africa and the United Kingdom, among others…

For Avinoam Sapir, the creator of SCAN, sifting truth from deception is as simple as one, two, three.

1. Give the subject a pen and paper.
2. Ask the subject to write down his/her version of what happened.
3. Analyze the statement and solve the case.

Those steps appear on the website for Sapir’s company, based in Phoenix. “SCAN Unlocks the Mystery!” the homepage says, alongside a logo of a question mark stamped on someone’s brain. The site includes dozens of testimonials with no names attached. “Since January when I first attended your course, everybody I meet just walks up to me and confesses!” one says. Another testimonial says “The Army finally got its money’s worth…” SCAN saves time, the site says. It saves money. Police can fax a questionnaire to a hundred people at once, the site says. Those hundred people can fax it back “and then, in less than an hour, the investigator will be able to review the questionnaires and solve the case.”

In 2009 the U.S. government created a special interagency task force to review scientific studies and independently investigate which interrogation techniques worked, assessed by the FBI, CIA and the U.S. Department of Defense. “When all 12 SCAN criteria were used in a laboratory study, SCAN did not distinguish truth-tellers from liars above the level of chance,” the review said, also challenging two of the method’s 12 criteria. “Both gaps in memory and spontaneous corrections have been shown to be indicators of truth, contrary to what is claimed by SCAN.”
In a footnote, the review identified three specific agencies that use SCAN: the FBI, CIA and U.S. Army military intelligence, which falls under the Department of Defense…

In 2016, the same year the federal task force released its review of interrogation techniques, four scholars published a study on SCAN in the journal Frontiers in Psychology. The authors — three from the Netherlands, one from England — noted that there had been only four prior studies in peer-reviewed journals on SCAN’s effectiveness. Each of those studies (in 1996, 2012, 2014 and 2015) concluded that SCAN failed to help discriminate between truthful and fabricated statements. The 2016 study found the same. Raters trained in SCAN evaluated 234 statements — 117 true, 117 false. Their results in trying to separate fact from fiction were about the same as chance….

Steven Drizin, a Northwestern University law professor who specializes in wrongful convictions, said SCAN and assorted other lie-detection tools suffer from “over-claim syndrome” — big claims made without scientific grounding. Asked why police would trust such tools, Drizin said: “A lot has to do with hubris — a belief on the part of police officers that they can tell when someone is lying to them with a high degree of accuracy. These tools play in to that belief and confirm that belief.”

SCAN’s creator “declined to be interviewed for this story,” but they spoke to some users of the technique. Travis Marsh, the head of an Indiana sheriff’s department, has been using the tool for nearly two decades, while acknowledging that he can’t explain how it works. “It really is, for lack of a better term, a faith-based system because you can’t see behind the curtain.”

Pro Publica also reports that “Years ago his wife left a note saying she and the kids were off doing one thing, whereas Marsh, analyzing her writing, could tell they had actually gone shopping. His wife has not left him another note in at least 15 years…”

One of the big challenges of making 100 percent renewable energy [sic] a reality is long-term storage,” says Julian Hunt, an engineering scientist at the International Institute for Applied Systems Analysis in Austria. Hunt and his collaborators have devised a novel system to complement lithium-ion battery use for energy storage over the long run: Mountain Gravity Energy Storage, or MGES for short. Similar to hydroelectric power, MGES involves storing material at elevation to produce gravitational energy. The energy is recovered when the stored material falls and turns turbines to generate electricity. The group describes its system in a paper published November 6 in Energy.

“Instead of building a dam, we propose building a big sand or gravel reservoir,” explains Hunt. The key to MGES lies in finding two mountaintop sites that have a suitable difference in elevation — 1,000 meters is ideal. “The greater the height difference, the cheaper the technology,” he says. The sites will look similar, with each comprised of a mine-like station to store the sand or gravel, and a filling station directly below it. Valves release the material into waiting vessels, which are then transported via cranes and motor-run cables to the upper site. There, the sand or gravel is stored — for weeks, months, or even years — until it’s ready to be used. When the material is moved back down the mountain, that stored gravitational energy is released and converted into electrical energy.

Not only is the system more environmentally friendly [sic] than pumped-storage hydropower and dams, but it’s more flexible to meet varying energy demands.

“Hunt estimates that the annual cost of storing energy via this system will vary between $50 to $100 per megawatt hour (MWh),” the report adds. “And he says that the energy expended to transport materials to the upper sits will be offset by the amount of gravitational energy the system produces.”

A lineup of female celebrities stand in front of you. Their faces move, smile, and blink as you move around them. They’re fully nude, hairless, waiting for you to decide what you’ll do to them as you peruse a menu of sex positions. This isn’t just another deepfake porn video, or the kind of interactive, 3D-generated porn Motherboard reported on last month, but a hybrid of both which gives people even more control of women’s virtual bodies. This new type of nonconsensual porn uses custom 3D models that can be articulated and animated, which are then made to look exactly like specific celebrities with deepfaked faces. Until recently, deepfake porn consisted of taking the face of a person — usually a celebrity, almost always a woman — and swapping it on to the face of an adult performer in an existing porn video. With this method, a user can make a 3D avatar with a generic face, capture footage of it performing any kind of sexual act, then run that video through an algorithm that swaps the generic face with a real person’s.

The FBI says owners of IoT (Internet of Things) devices should isolate this equipment on a separate WiFi network, different from the one they’re using for their primary devices, such as laptops, desktops, or smartphones. “Your fridge and your laptop should not be on the same network,” the FBI’s Portland office said in a weekly tech advice column. “Keep your most private, sensitive data on a separate system from your other IoT devices,” it added. The same advice — to keep devices on a separate WiFi network or LAN — has been shared in the past by multiple IT and security experts. The reasoning behind it is simple. By keeping all the IoT equipment on a separate network, any compromise of a “smart” device will not grant an attacker a direct route to a user’s primary devices — where most of their data is stored. Jumping across the two networks would require considerable effort from the attacker. However, placing primary devices and IoT devices on separate networks might not sound that easy for non-technical users. The simplest way is to use two routers.