Welcome to the March 16, 2022, edition of ACM TechNews, providing timely information for IT professionals three times a week.
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'No-Code' Brings the Power of AI to the Masses
The New York Times
Craig S. Smith
March 15, 2022
"Citizen developers" are tapping products that allow anyone to use artificial intelligence (AI) without writing any computer code, as part of the "no-code" movement envisioned by advocates as revolutionary. "We are trying to take AI and make it ridiculously easy," said Craig Wisneski at the startup Akkio, which lets anyone make predictions using data. No-code platforms replace coding languages with simple and familiar Web interfaces, and new startups are making the power of AI available to nontechnical people in visual, textual, and audio spheres. The Juji tool, for example, is engineered to simplify chatbot building, by using machine learning to automatically manage complex conversation flows and deduce user characteristics to personalize each engagement. The power of no-code platforms is also growing through AI innovations such as OpenAI's GPT-3 system, which can write code when prompted with simple English.
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Russia Faces IT Crisis with Just Two Months of Data Storage Left
BleepingComputer
Bill Toulas
March 15, 2022
The withdrawal of Western cloud computing companies from Russia has left the country with roughly two months of information technology (IT) data storage. Russian news outlet Kommersant says the situation is compounded by exponential growth of public Russian agencies' storage needs due to Smart City projects entailing extensive video-surveillance and facial-recognition systems. Options proposed at a meeting of the Ministry of Digital Transformation Solutions include leasing all available domestic data storage or mandating that Internet service providers ditch media streaming services and other online entertainment platforms. Russia also could seize IT servers and storage left behind by exiting businesses and incorporate them into public infrastructure. The last option would be to use Chinese cloud service providers and IT system sellers, although China has not yet decided how much aid it is willing to provide.
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COVID-19 Variants Can't Hide from Variabel
Rice University News
Mike Williams
March 14, 2022
Rice University and Baylor College of Medicine researchers have developed an algorithm that accurately identifies low-frequency variants of COVID-19's underlying virus. "Variabel directly enables the use of affordable nanopore sequencing technology for the identification of within-host variation after viral infection," said Rice's Todd Treangen. The research makes low-frequency variant mining available for approximately 500,000 SARS-CoV-2 genomes compiled by Oxford Nanopore Technologies (ONT). Researchers said the open source Variabel can differentiate true variants from sequencing errors in the ONT process. They validated Variabel by comparing data gathered over time from single positive patients as well as sequences from cross-patient datasets, generated by ONT and the Illumina method. The algorithm successfully corrected most sequencing errors, and Treangen said Variabel "ultimately could aid in the discovery of future mutations specific to variants of concern."
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Top Programming Languages: Java Takes Unexpected Leap Forward
ZDNet
Owen Hughes
March 11, 2022
Python, Java, SQL, and JavaScript are the most in-demand programming languages this year, with each appearing in more than 50,000 active job listings on the careers Website Indeed, according to a review of job ads by developer training platform CodingDojo. Java topped the list with a presence in more than 80,000 listings, signaling a resurgence in popularity after a slight decline over the past two years due to Python's growth. Although Python was the language that appeared most often on Indeed, CodingDojo found Java's sharp growth over the past year had thrust Python into second place. Still, demand for Python continues to grow, with CodingDojo noting its decline was only because demand for Java-savvy coders rose significantly. "The drop in demand for programmers caused by the pandemic is now gone," the platform said.
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To Catch a Falling Satellite
IEEE Spectrum
Ned Potter
March 14, 2022
The U.S.-German TumbleDock/ROAM project is exploring ways to contain and stabilize out-of-control satellites so they can be taken out of orbit, or possibly refueled or repaired. The project team has completed tests at the International Space Station (ISS), using the National Aeronautics and Space Administration's Astrobee robots. Researchers had the robots substitute for tumbling satellites and "chaser" spacecraft were sent to retrieve them to develop algorithms that chasers can use to locate and safely catch the debris in real time. Each cubical Astrobee is equipped with navigation cameras, compressed-air thrusters, and Snapdragon processors similar to those found in smartphones. ISS tests showed that such maneuvers are possible, in principle.
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Researcher Uses 379-Year-Old Algorithm to Crack Crypto Keys in the Wild
Ars Technica
Dan Goodin
March 14, 2022
Researcher Hanno Böck said he used a 379-year-old algorithm described by French mathematician Pierre de Fermat to break a handful of weak cryptographic keys found in the wild. The keys were generated with older software owned by technology company Rambus, derived from a basic version of the SafeZone Crypto Libraries. Böck said the SafeZone library insufficiently randomized the two prime numbers it used to generate RSA keys, and Fermat's factorization method can crack such keys easily. The algorithm was based on the fact that any odd number can be expressed as the difference between two squares, and factors near that number's root are easily and quickly calculable. Böck thinks all the keys he found in the wild were generated using software or methods unaffiliated with the SafeZone library, which if true means the Fermat algorithm might easily break keys crafted by other software.
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Hoverfly Brains Mapped to Detect Sound of Distant Drones
University of South Australia
March 15, 2022
An Australian research team from the University of South Australia (UniSA), Flinders University, and defense firm Midspar Systems have reverse-engineered the visual system of the hoverfly to improve distant drone detection. The bio-inspired signal-processing techniques detected the acoustic signatures of drones from nearly four kilometers away, improving the detection rate by up to 50% over existing methods. The process involves converting acoustic signals into two-dimensional images called spectrograms and using the hoverfly brain's neural pathway to suppress unrelated signals and noise. "Unauthorized drones pose distinctive threats to airports, individuals, and military bases,” said UniSA's Anthony Finn. “It is therefore becoming ever-more critical for us to be able to detect specific locations of drones at long distances, using techniques that can pick up even the weakest signals. Our trials using the hoverfly-based algorithms show we can now do this."
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Researchers Improve Algorithm that Detects Alzheimer's Disease from MRI Images
News-Medical Life Sciences
Emily Henderson
March 14, 2022
Researchers at Lithuania's Kaunas University of Technology (KTU) have augmented an algorithm that detects Alzheimer's disease (AD) from magnetic resonance imaging (MRI) with more than 98% accuracy. The team modified the algorithm and used a broader convolutional neural network to improve previous research, and KTU's Rytis Maskeliuna said the DensNet201 network variant tapped for the latest study offers better parameter optimization than the modified ResNet18 network used for the earlier analysis. The algorithm reviewed images of brain MRIs from 125 subjects in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset in terms of AD, mild cognitive impairment, and dementia. "Using the ever-increasing ADNI dataset, the algorithm is getting ready to recognize the symptoms of the disease in various images and becomes less sensitive to a specific data source,” said Maskeliuna. “It's not a revolution, but certainly an evolution."
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A Quantum Computer Could Design Your Next Smartphone
University of British Columbia (Canada)
March 14, 2022
Canadian scientists are using quantum computing models to accurately forecast the color of light discharged by molecules that generate the hues seen in modern smartphones, tablets, and TV screens. Researchers at the University of British Columbia (UBC) and Toronto-based electronics manufacturer OTI Lumionics have demonstrated that quantum computers are superior to standard classical methods in simulating organic light-emitting diode display-emitter materials. UBC's Zac Hudson said accurate quantum models can be employed to simulate such colors in software, saving time and money and producing less waste. "This work establishes a clear practical use for quantum computing, which isn't to be taken lightly," Hudson said.
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WARNING: Objects in Driverless Car Sensors May Be Closer Than They Appear
Duke University Pratt School of Engineering
Ken Kingery
March 14, 2022
Duke University researchers have identified an attack strategy that can trick industry-standard autonomous vehicle sensors into believing nearby objects are closer or further than they appear. This involves using a laser gun to strategically place 3D LiDAR data points within a certain area of the vehicle camera's 2D field of view. The researchers determined the vulnerable area extends out in front of the camera's lens in a frustum shape. "This so-called frustum attack can fool adaptive cruise control into thinking a vehicle is slowing down or speeding up," said Duke's Miroslav Pajic. Pajic suggested adding redundancy in the form of "stereo cameras" with overlapping fields of view to better estimate distances and detect LiDAR data that does not match their perception, or the developing systems that allow cars in close proximity to share some of their data.
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From Museum to Laptop: Visual Leaf Library Tool for Identifying Plants
Penn State News
Matthew Carroll
March 15, 2022
An international team of researchers led by The Pennsylvania State University (Penn State) has developed an open-access visual leaf library as a resource for identifying and classifying fossil plants. Researchers blended images of modern and fossil leaves from major collections, and formatted the data into the library with standardized, searchable filenames and high-resolution images. The dataset, available from the Figshare Plus repository, compiles 30,252 images, including 4,076 images of fossil leaves. The library also could be tapped to train machine-learning programs to better identify leaves and to find visual patterns that humans may have missed. "This database makes the information in these collections available to people around the world in a form that is easier to search than the original and more amenable to digital analyses,” said the Smithsonian's Scott Wing.
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A Simulation Success Story
Brookhaven National Laboratory
Charity Plata
March 15, 2022
A multidisciplinary research team including researchers from the Computational Science Initiative at the U.S. Department of Energy's (DOE) Brookhaven National Laboratory created a multiscale SARS-CoV-2 model comprising 305 million atoms, one of the largest biological systems ever simulated. The team's goal was to produce simulations that would provide a better understanding of the virus's structure and dynamics to observe the way it moves, responds, and infects a host. Researchers used all-atom molecular dynamics simulations to model the spike protein. The simulations offered accurate views of the spike protein's fully glycan shield, demonstrating how the carbohydrate-based polymers regulate its infectivity. They also demonstrated interactions between the spike and ACE2 receptor, the enzyme that enables the virus to enter the body. The simulations, which relied on the Summit supercomputer at DOE's Oak Ridge National Laboratory, could lead to more efficient drug discovery and therapeutics research.
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Drones, AI Help Find Pebble-sized Meteorite that Landed in 2021
New Scientist
Chen Ly
March 11, 2022
Researchers at Australia's Curtin University have, for the first time, recovered a meteorite using drones and a machine-learning, artificial intelligence-powered algorithm. Researchers used a drone to shoot aerial images of a plain in Western Australia, where a fireball meteor was spotted in the sky last December, and then an algorithm trained to identify actual meteorites in desert environments inspected the images. The algorithm selected potential meteorites from the photos, which the team manually vetted to eliminate false positives, before dispatching another drone to capture more images of the most promising candidates for further inspection. Researchers surveyed the four remaining candidates and collected a pebble-sized meteorite weighing 70 grams (0.03 ounces). The entire process took just four days. The research could help simplify meteorite hunting, as well as locate meteorites in more far-flung locations.
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