Digital Violence Exposes NSO’s Tools Targetting Activists

computer screen

NSO made the news again due to their tools being used to spy on Bahraini and Hungarian activists, which obviously isn’t good. NSO is a cyber security organization that focuses on offensive rather than defence; they sell hacking tools and exploits to target individuals. Anyone with enough money can buy their attack tools, including rich individuals or companies. In Mexico their spying tool was used to intimidate campaigners asking the government to regulate sugar content in sofas.

We know spying on human rights activists is not good for anyone, and three organizations teamed up to expose how NSO supports such spying (and thus abuse). Forensic Architecture, Amnesty International, and Citizen Lab all worked together to create a neat website called Digital Violence which explores the complexity and reach of NSO’s tools.

First detected in 2015, the NSO Group’s Pegasus malware has reportedly been used in at least 45 countries worldwide to infect the phones of activists, journalists and human rights defenders. Having learnt that our former collaborators and close associates were hacked by Pegasus, Forensic Architecture undertook 15 months of extensive open-source research, interviews assisted by Laura Poitras, and developed bespoke software to present this data as an interactive 3D platform, along with video investigations narrated by Edward Snowden to tell the stories of the individuals targeted and the web of corporate affiliations within which NSO is nested. Supported by Amnesty International and the Citizen Lab, our analysis reveals relations and patterns between separate incidents in the physical and digital sphere, demonstrating how infections are entangled with real world violence, and extend within the professional and personal networks of civil society actors worldwide.

Check out digital violence.

One way to defend yourself from NSO group and other malicious agents is to keep your software up to date. Apple released a patch this week, so update your Apple devices.

A Real Smart City Lacks Smart Technology

Intersection

A few years ago Silicon Valley mega corps thought all cities should be made “smart” by tracking all citizen data. There was a concentrated effort by Google to violate privacy rights in Toronto and bullying the city into a finance deal which only benefit the advertising giant. Torontonians protested and the company backed out.

In Columbus, they ran a well funded research project into the smart city only to discover that the “smart” aspects showed mediocre results. We already have solutions to most problems cities face like mass transit and better funded health services. It’s time to fund the boring, old, not “smart” solutions in our cities.

Now it’s clear that private firms can’t predict the future of cities and may not have their best interests in mind. Davis says Columbus’ selection led to a flood of proposals from companies that ultimately proved difficult to manage, and “at times distracting.” Meanwhile, Uber (and Lyft) have pulled out of autonomous vehicles, notably after an Uber testing vehicle struck and killed a pedestrian in Arizona. Google sibling Sidewalk Labs promised in 2017 to construct a sensored-up neighborhood of the future in Toronto. But it killed the project last year amid the pandemic and a bitter political battle with privacy advocates and local groups and developers.

Read more.

A Startup Wants to Help Predict Floods

Water

For years engineers tried to prevent flooding, then they realized they can’t stop nature. Now instead of trying to stop it, we try to mitigate flooding by creating spaces that can absorb a lot of water (parks along rivers are an example of this). Still, these attempts don’t always work and with increasing instability in our climate it’s getting harder to deal with more extreme flooding instances. This is where a new startup, Floodmapp, fits in. They are using machine learning and AI to improve how we understand flooding instead of the traditional physics-driven modelling.

The company’s premise is simple: We have the tools to build real-time flooding models today, but we just have chosen not to take advantage of them. Water follows gravity, which means that if you know the topology of a place, you can predict where the water will flow to. The challenge has been that calculating second-order differential equations at high resolution remains computationally expensive.

Murphy and Prosser decided to eschew the traditional physics-based approach that has been popular in hydrology for decades for a completely data-based approach that takes advantage of widely available techniques in machine learning to make those calculations much more palatable. “We do top down what used to be bottoms up,” Murphy said. “We have really sort of broken the speed barrier.” That work led to the creation of DASH, the startup’s real-time flood model.

Real time.

A Cheap Material for Producing Power from Waste Heat

Solar panels on grass

Machines produce a lot of waste heat, and if we can capture that heat we can convert it into electric energy. Capturing thermal energy is currently inefficient because of thermal dynamics and the lack of super capacitors. Not to be deterred by these obstacles, researchers have found ways to efficiently capture thermal energy. By using an inexpensive material a small thermoelectric can be placed to convert heat to power.

“This looks like a very smart way to realize high performance,” says Li-Dong Zhao, a materials scientist at Beihang University who was not involved with the work. He notes there are still a few more steps to take before these materials can become high-performing thermoelectric generators. However, he says, “I think this will be used in the not too far future.”

The result, which they report today in Nature Materials, was not only a thermal conductivity below that of single-crystal tin selenide but also a ZT of 3.1. “This opens the door for new devices to be built from polycrystalline tin selenide pellets and their applications to be explored,” Kanatzidis says.

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Experimenting on Digital Twins Helps us Understand Reality

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It’s hard to predict the future, but with enough data we can at least get better at it. That’s exactly what digital twins are all about. By using as much real world data as possible to model out anything from a building, to a person, to a city in a digital space we can run simulations on what can work best for the real world counterpart. Of course, since simulations are only as good as the data source (and how we process the data) there are limits to effectiveness of digital twins; still, the idea that we can effectively model solutions and their potential outcomes in higher fidelity is appealing.

With the rise of the internet of things, sensor technology is increasingly being installed in our homes and workplaces, as well as the physical infrastructure that surrounds us. Meanwhile, cloud computing makes it easier than ever for data to be shared across different devices and networks.

As a result, businesses and other organisations have been able to build up huge volumes of data. Not all of this is private either – online sources such as the London Datastore are making live data readily available to anyone who wants to use it.

“We see digital twins as a way of improving decision making,” Hayes told Dezeen.

“A city is effectively a system of systems – water, electricity, housing, schools, hospitals, prisons, natural environment – it all fits together,” she said. “When you start to connect the datasets from these digital twins, you can build a bird’s eye view of a city, which gives you better information about the consequences of your decisions.”

Read more.

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