Welcome to the October 6, 2021 edition of ACM TechNews, providing timely information for IT professionals three times a week.
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Nobel in Physics: Climate Science Breakthroughs Earn Prize
BBC News
Paul Rincon
October 5, 2021
The 2021 Nobel Prize in Physics was awarded to Princeton University's Syukuro Manabe, Klaus Hasselmann of Germany's Max Planck Institute for Meteorology, and Giorgio Parisi of Italy's Sapienza University of Rome for their work in understanding the Earth's climate. Research by Manabe and Hasselmann resulted in computer models of the Earth's climate that can predict the impact of global warming. Manabe showed that increased levels of carbon dioxide in the atmosphere could result in higher temperatures at the Earth's surface. Hasselmann's research demonstrated that despite the changing weather, climate models can be reliable. Parisi is known for his work with spin glass, which the Nobel Committee viewed as a microcosm for the complex behavior of the Earth's climate. Climate models based on the winners' research will serve as the basis for decisions made at the upcoming United Nations Climate Change Conference.
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China Leaps Ahead in Effort to Rein In Algorithms
The Wall Street Journal
Stephanie Yang
October 5, 2021
A campaign launched by the Cyberspace Administration of China calls for the establishment of a comprehensive system to regulate the use of algorithms within three years, and for algorithms to be fair and transparent while complying with the ruling Communist Party's ideology. The move comes as an EU proposal aims to restrict certain uses of artificial intelligence to prevent possible harm, and as U.S. lawmakers consider the impact of algorithm-driven content on Facebook users. James Gong of the law firm Bird & Bird in Beijing said increased regulatory oversight of algorithms are likely to impact China's Internet industry, as nearly all companies "use algorithms and automatic decision-making and profiling to make sure their marketing is more precise and to improve their efficiency of the business and increase profit."
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Venice, Overwhelmed by Tourists, Tries Tracking Them
The New York Times
Emma Bubola
October 4, 2021
The leaders of Venice, Italy, are gathering the cellphone data of tourists, and using surveillance cameras to monitor them. The city’s surveillance system combines cameras, software, and phone data to collect people's age, sex, country of origin, and prior location. Officials say they are using this system to identify crowds they want to disperse, especially to make the city's famous bridges easier to cross; another aim is to distinguish day-trippers from longer-term visitors to inform the potential use of gates at the city’s entry points, and of a booking system to reserve the right to enter the city (for a fee). Mayor Luigi Brugnaro and colleagues say they want to make Venice more livable for residents, but many citizens see dystopian associations in the use of technology to regulate visitors.
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Robot Finds Lost Items
MIT News
Adam Zewe
October 5, 2021
A robot created by Massachusetts Institute of Technology (MIT) researchers can track down lost items using a robotic arm with a camera and radio frequency (RF) antenna attached to its gripper. The RFusion prototype combines antenna signals and camera images to locate misplaced items, even when buried under a pile and completely hidden. The robot relies on RF identification tags that can be attached to items and reflect antenna-emitted signals, which can travel through most surfaces. The robotic arm employs machine learning to automatically home in on the object's location, moves the items covering it, grips the object, and confirms that it grasped the correct item. MIT's Fadel Adib said, "Right now, you can think of this as a Roomba on steroids, but in the near term, this could have a lot of applications in manufacturing and warehouse environments."
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Quantum Computers Can Fix Their Own Mistakes Without Making More
New Scientist
Matthew Sparkes
October 4, 2021
A quantum computer has demonstrated an error-correction strategy that remedies more errors than it generates. Christopher Monroe and colleagues at Maryland's Joint Quantum Institute (JQI) set the state of a logical quantum bit (qubit)—specifically a 13-qubit cluster—and were able to measure it again 99.4% of the time, even with six individual operations that have only 98.9% reliability; that rate would be expected to decrease to 93.6% after all six operations without error correction. JQI's work uses trapped-ion qubits, and its computer employs up to 32 charged, laser-manipulated atoms. The trapped ions' higher stability enabled use of Bacon-Shor code error correction, which is beyond superconducting qubits' current capacity. Monroe envisions error correction as the key to developing practical quantum computers.
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AI Makes It Faster, Easier to Analyze Hockey Video
University of Waterloo News (Canada)
October 4, 2021
Engineers at Canada's University of Waterloo have advanced the use of artificial intelligence (AI) to automatically analyze video of hockey games by combining two deep learning methods. Waterloo's Kanav Vats explained, "Using different representations to teach the same thing can improve performance. We combined a wholistic representation and a digit-wise representation with great results." The researchers compiled a dataset of over 54,000 images from National Hockey League games to train algorithms to recognize sweater numbers in images. The multi-task learning technique can identify players by sweater numbers with 90% accuracy.
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Creating Wireless Signals with Ethernet Cable to Steal Data From Air-Gapped Systems
The Hacker News
Ravie Lakshmanan
October 4, 2021
The LANtenna attack demonstrated by researchers at Israel's Ben Gurion University of the Negev can steal sensitive data from air-gapped systems by using Ethernet cables as a "transmitting antenna." The exploit enables malware in air-gapped computers to collect and encode data over radio waves emitted from the cables for wireless interception and decoding by a nearby software-defined radio, which sends the information to a hacker in an adjacent room. According to the researchers, "The malicious code can run in an ordinary user-mode process and successfully operate from within a virtual machine." Recommended countermeasures include banning the use of radio receivers in and around air-gapped networks; monitoring network interface card link layer activity for covert channels; jamming the signals, and using metal shielding to block electromagnetic interference with (or emanations from) the shielded wires.
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DeFi Platform Mistakenly Sends $89 Million; CEO Begs Return
Bloomberg
Joe Light
October 1, 2021
The decentralized finance (DeFi) platform Compound mistakenly sent users almost $90 million in cryptocurrency due to a bug in a recent update, and CEO Robert Leshner is calling on users to return it voluntarily. DeFi platforms rely on "smart contracts" between users that are governed entirely by computer code. Compound also distributes COMP tokens that give users a say in how the protocol works. Leshner noted that the error involved 280,000 COMP tokens worth about $89.3 million that were distributed on Oct. 1. Said University of Pennsylvania Wharton School's Kevin Werbach, "The vast majority of people in the world are not going to trust their money to something if they are told a bug will cause you immutably to lose everything."
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Google to Pay Developers to Make Open Source Projects More Secure
ZDNet
Liam Tung
October 4, 2021
Google is investing $1 million in the Linux Foundation's Secure Open Source (SOS) pilot program to make open source projects more secure. The program will reward developers financially for fortifying software against attacks and correcting potential bugs before they emerge. Google said the incentives range from $505 for "small improvements" to $10,000 or more for hardening software to prevent major vulnerabilities. SOS targets initiatives that proactively strengthen critical open source projects and defend infrastructure against application and supply-chain attacks, and intends to close the funding gap for largely voluntary software projects. According to Google, SOS is "the starting point for future efforts that will hopefully bring together other large organizations and turn it into a sustainable, long-term initiative under the OpenSSF (Open Source Security Foundation)," a cross-industry forum that collaborates on the improvement of open source software security.
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Deep Learning-Based Image Analysis Now a Click Away
EPFL (Switzerland)
October 1, 2021
Engineers at Switzerland's École polytechnique fédérale de Lausanne (EPFL) and Spain's Universidad Carlos III de Madrid have developed deepImageJ, an open source plugin that facilitates deep learning-based image analysis for life science research. With deepImageJ, life scientists can ask a computer engineer to design and train a machine learning algorithm to execute a specific task; the scientists then can run the resulting algorithm through a user interface without the need to review any code. The plugin was developed as a resource to enable engineers, computer scientists, mathematicians, and biologists to collaborate more efficiently.
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Making Self-Driving Cars Human-Friendly
University of Leeds (U.K.)
October 5, 2021
Researchers at the U.K.'s University of Leeds developed a drift diffusion model that could make automated vehicles safer for pedestrians by helping predict when people will cross the road. The model was tested under different scenarios using the university's HIKER (Highly Immersive Kinematic Experimental Research) pedestrian simulator. Researchers found that participants used sensory data from vehicle distance, speed, and acceleration and communicative cues to determine when to cross. Leeds' Gustav Markkula said, "Pedestrians will often feel quite uncertain about whether the car is actually yielding, and will often end up waiting until the car has almost come to a full stop before starting to cross. Our model clearly shows this state of uncertainty borne out, meaning it can be used to help design how automated vehicles behave around pedestrians in order to limit uncertainty, which in turn can improve both traffic safety and traffic flow."
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Microsoft Predicts Weather for Individual Farms
IEEE Spectrum
Rebecca Sohn
October 4, 2021
Microsoft’s DeepMC framework uses machine learning and artificial intelligence (AI) to predict local weather accurately. DeepMC combines data from on-site sensors with standard local weather forecast data accessed through application programming interfaces from sources including the U.S. National Oceanic and Atmospheric Administration and the National Weather Service. Microsoft Research's Peeyush Kumar said a "fusion mechanism" integrates data from on-site sensors with standard forecast data, which the framework then provides to an AI trained to find errors between local forecasts and micro-climate weather conditions. The system predicts each weather parameter based on historical forecast and local sensor data, and applies decomposition to determine short- and long-term trends and patterns in weather data.
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