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Welcome to the April 15, 2022, edition of ACM TechNews, providing timely information for IT professionals three times a week.

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Northeastern University professor Carla E. Brodley will receive the first ACM Frances E. Allen Award for Outstanding Mentoring. Carla E. Brodley to Receive Inaugural ACM Frances E. Allen Award for Outstanding Mentoring
April 13, 2022

ACM announced that Northeastern University's Carla E. Brodley has earned the inaugural ACM Frances E. Allen Award for Outstanding Mentoring for personal mentorship and leadership. Brodley has strived to develop and disseminate data-driven mentoring practices to boost computer science's diversity, inclusivity, and equality sustainably and systematically. She also founded Northeastern's Center for Inclusive Computing to facilitate systemic changes that faculty and administrators can effect to increase women's representation in undergraduate computing. ACM President Gabriele Kotsis said, "Brodley not only put effective strategies into practice at Northeastern University, but she has developed a program to help dozens of computer science departments around the U.S. effectively diagnose their diversity challenges and systemically address them."

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Web Surfing that Feels Instantaneous, Even Though It's Not
Duke Today
Robin A. Smith
April 13, 2022

Researchers at Duke University, the University of Illinois, Yale University, and Switzerland's ETH Zurich have developed a design for a speed-of-light Internet network across 120 U.S. cities. Rather than relying on buried fiber optic cables that zigzag across the landscape, the network would carry data wirelessly via microwave radio transmissions, since signals travel through air 50% faster than light traveling through fiber. The approach is based on a custom-built network from the early 2010s that reduced the time needed to transmit data between the Chicago Mercantile Exchange and stock exchanges in New Jersey by a few thousandths of a second. The researchers estimate data transmission over such a network would cost 81 cents per gigabyte, and reduce lag to within 5% of what is possible at light speed.

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This LNG plant in Barcelona, Spain is the largest export terminal in Europe. U.S. Warns Newly Discovered Malware Could Sabotage Energy Plants
The Washington Post
Joseph Menn
April 13, 2022

U.S. officials warn of newly discovered malware that could infiltrate industrial facilities and cause explosions at energy plants. Investigators said the Pipedream malware can target virtually any power plant by manipulating common equipment found in nearly all complex industrial plants, such as the programmable logic controllers (PLCs) that link industrial operations. Private security experts who analyzed Pipedream in tandem with government agencies suspect it is Russian and targets liquefied natural gas plants; they said building effective countermeasures would take months or years. Federal agencies are advising the energy sector and others to deploy monitoring programs, and to impose multifactor authentication for remote logins.

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MIT’s computational network segregates recognition of faces and objects. An Optimized Solution for Face Recognition
MIT News
Jennifer Michalowski
April 6, 2022

Neuroscientists at the Massachusetts Institute of Technology (MIT) found that a deep neural network taught to identify faces and other objects assigns specific components to perform these tasks. Former MIT researcher Katharina Dobs said any system trained to recognize faces and sort objects would hypothetically mimic the brain's approach of segregating facial and object processing. Dobs compiled scores of images of faces and objects, without telling the network which was which. In learning to recognize them, the network structured itself into an information-processing network that committed specialized units to face recognition, relying on general vision processing machinery in the early stages, and on face-dedicated components in the final steps. MIT's Nancy Kanwisher said the research addresses why the brain has a separate face recognition system, "because that is what an optimized solution looks like."

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Data-Driven Mechanistic Approach Can Help Reduce Drug Costs, Treat Disease
News-Medical Life Sciences
Emily Henderson
April 14, 2022

Researchers at West Virginia University have developed a data-driven mechanistic strategy for predicting cell types within tissue. David Klinke and colleagues analyzed immunocompetent mouse models of cancer via digital cytometry and Bayesian network inference, and were able to anticipate how a protein secreted by cancerous cells affects the heterocellular network in terms of melanoma and breast cancer. "We can change the expression of a gene and then see whether the prevalence and functional orientation of different cell types in the tumor changes similarly as predicted by the Bayesian network model," explained Klinke. He said mechanistic modeling and simulation can cluster the myriad aspects of drug biology together in the same context, which will cumulatively "help reduce drug costs and help treat diseases that were difficult to develop drugs for."

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AI Strips Out City Noise to Improve Earthquake Monitoring Systems
New Scientist
Chris Stokel-Walker
April 13, 2022

Stanford University's Gregory Baroza and colleagues used a deep learning algorithm to eliminate city noise from earthquake monitors, in an attempt to fine-tune the ability to locate where tremors originate. The researchers trained the artificial intelligence on 80,000 samples of urban noise and 33,751 samples of earthquake signals to distinguish between the two. Running audio through the neural network enhanced the signal-to-noise ratio by an average of 15 decibels, triple the average of previous denoising methods. Rice University's Maarten de Hoop said one shortcoming of the approach was the network's training via supervised learning using human-labeled data sampled from one area; he said this makes the technique less likely to be effective when presented with noise from somewhere else.

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A new single-photon source that operates at room temperature and fits in a compact tabletop system. Quantum Communication at Room Temperature
IEEE Spectrum
Edd Gent
April 7, 2022

Researchers at Australia's University of Technology Sydney have produced a single-photon source (SPS) that operates at room temperature. The device employs hexagonal boron nitride (hBN), whose structural impurities emit single photons when excited by a laser or electricity. The researchers made the SPS efficient enough to support practical applications by placing a solid-immersion lens atop the hBN, increasing the angles from which photons can be collected; the photons discharged by the hBN are focused by the lens and collected by a microscope feeding into an optical fiber. The SPS can generate over 10 million single photons per second, sufficient for quantum communication tasks that include quantum-key distribution. Alisa Javadi, a postdoctoral Researcher at the University of Basel in Switzerland, said we’re still a long way from a practical device.

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7-Foot-Tall Robots Watch for Unmasked Travelers, Curbside Loiterers
The Dallas Morning News
Kyle Arnold
April 8, 2022

Two seven-foot-tall robots have been deployed at Dallas Love Field Airport in Dallas, TX, to assist and monitor passengers. The Security Control Observation Tower (SCOT) robotic kiosks from Robotic Assistance Devices can help travelers navigate the airport, ensure passengers are wearing masks, and call airport security and operations if necessary. The goal of the deployment is to determine whether robotic assistants can supplement airport operations efficiently. Said Love Field's Lauren Rounds, "The units currently make scheduled and detection-based announcements directed toward our passengers and visitors. Some of these focus on reducing vehicular congestion at our curb using license plate recognition and increasing federal mask compliance using facial recognition technology, while others provide standard information."

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A 3D-printed Microsoft logo displayed in front of a LinkedIn logo. AI Is Explaining Itself to Humans. It's Paying Off.
Paresh Dave
April 6, 2022

Startups and major technology companies are investing heavily in explainable artificial intelligence (XAI), as U.S. and EU regulators campaign for fairness and transparency in automated decision-making. XAI advocates say it has helped make AI more effective in fields such as healthcare and sales. Microsoft saw its LinkedIn subscription revenue increase 8% after providing its sales team with CrystalCandle software, which identifies clients potentially at risk of cancellation, while explaining its reasoning. Skeptics say an AI’s explanations of why it made the predictions it did are still too unreliable. LinkedIn says an algorithm's integrity cannot be judged without understanding its reasoning, while tools like CrystalCandle, for example, could help physicians learn why AI predicts someone is at greater risk of disease.

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A simulation from a new global atmosphere model. Better Clouds Than Ever with Exascale Computing-Ready Atmosphere Model
U.S. Department of Energy
April 7, 2022

The U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) project has developed a new global atmosphere model featuring 30 times finer resolution than other global climate models. Scientists can use the model to analyze the atmosphere with greater detail than previously possible, and its assessment validates the E3SM approach by showing that simulating storms and topography at global-storm-level resolution corrects persistent biases. For example, it enhances precipitation modeling in terms of the diurnal cycle's timing and the diffusion of light versus heavy rainfall; it also models the structure of key weather events like tropical and extratropical cyclones with greater fidelity.

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A person sleeping due to fatigue from negative Twitter experiences. Bot Can Spot Depressed Twitter Users in 9 of 10 Cases
Brunel University London (U.K.)
Tim Pilgrim
April 7, 2022

An algorithm developed by researchers at Brunel University London and the University of Leicester in the U.K. can ascertain a person's mental state by extracting and analyzing 38 data points from their public Twitter profile. The researchers trained the bot on two databases containing thousands of users' Twitter histories, and additional data about their mental health. It excluded all users with fewer than five tweets, then corrected for misspellings and abbreviations in the remaining profiles using natural language software. The algorithm identified depression with 88.39% accuracy in one of the datasets, and 70.69% in the other. Said Brunel's Abdul Sadka, “It's not 100% accurate, but I don't think at this level any machine learning solution can achieve 100% reliability. However, the closer you get to the 90 percent figure, the better.”

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A chemist taking notes. Chemical Data Management: An Open Way Forward
EPFL (Switzerland)
April 4, 2022

Berend Smit, Luc Patiny, and Kevin Jablonka at the Swiss Federal Institute of Technology Lausanne (EPFL) have proposed an open platform for managing massive chemical research datasets from a project's inception to its publication. The researchers envisage the platform as "seamlessly" combining data collection, processing, and publication at minimal cost, based on a FAIR (findable, accessible, interoperable, and reusable) framework. "At the moment of data collection, the data will be automatically converted into a standard FAIR format, making it possible to automatically publish all 'failed' and partially successful experiments together with the most successful experiment," said Smit. The researchers suggested the data should be machine-actionable, said Jablonka, because while their group has made progress in predicting optimal reaction conditions using machine learning models, “Those models would be much more valuable if they could also learn reaction conditions that fail.”

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