Welcome to the April 17, 2020 edition of ACM TechNews, providing timely information for IT professionals three times a week.

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The streets of Seoul, Korea. How Coronavirus Is Eroding Privacy
The Wall Street Journal
Liza Lin; Timothy W. Martin; Dasl Yoon
April 15, 2020; et al.

Governments worldwide are using digital surveillance technologies to track the spread of the coronavirus pandemic, raising concerns about the erosion of privacy. Many Asian governments are tracking people through their cellphones to identify those suspected of being infected with COVID-19, without prior consent. European countries are tracking citizens' movements via telecommunications data that they claim conceals individuals' identities; American officials are drawing cellphone location data from mobile advertising firms to monitor crowds, but not individuals. The biggest privacy debate concerns involuntary use of smartphones and other digital data to identify everyone with whom the infected had recent contact, then testing and quarantining at-risk individuals to halt the further spread of the disease. Public health officials say surveillance will be necessary in the months ahead, as quarantines are relaxed and the virus remains a threat while a vaccine is developed.

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Sarit Kraus Named ACM Athena Lecturer for Wide-Ranging Contributions to AI
April 15, 2020

ACM has named Sarit Kraus of Israel's Bar-Ilan University the 2020-2021 ACM Athena Lecturer for foundational contributions to artificial intelligence (AI), including multi-agent systems (MAS) and other fields. Kraus, an ACM fellow, is recognized as one of the world's preeminent researchers of MAS, in which a distributed group of agents collaborates with each other to solve problems; Kraus combined machine learning techniques for human modeling, formal decision-making, and game theory to enable agents to interact well with humans. She also gained recognition for advancing automated negotiation, in which the goal is to build computers that can reach agreements with other computers, negotiate for humans, or even outperform human negotiators. ACM president Cherri M. Pancake said, "With seminal work in AI stretching back to the early 1990s ... Sarit Kraus has introduced new ways of thinking in multi-agent systems research, while also shepherding research ideas into practical applications."

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An illustration of qubits. Quantum Chips Demonstrated at Highest Temperatures Ever
New Scientist
Leah Crane
April 15, 2020

Scientists at QuTech, an advanced research center created by Delft University of Technology (TU Delft) and the Netherlands Organization for Applied Scientific Research (TNO), working with colleagues at Australia's University of New South Wales, have demonstrated quantum computer chips that operate at temperatures exceeding -272 degrees Celsius (1 kelvin). Quantum systems typically operate at temperatures below -273.05 degrees Celsius (100 millikelvin), requiring storage of quantum bits (qubits) in refrigerators. The Australian researchers controlled the state of two qubits on a chip at temperatures up to 1.5 kelvin, and the QuTech group used two qubits at 1.1 kelvin in a logic gate.

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AI Is Evolving All by Itself
Edd Gent
April 13, 2020

Google's Quoc Le and colleagues have designed a program that borrows concepts from Darwinian evolution, including survival of the fittest, to assemble artificial intelligence (AI) that generationally improves with effectively no human input. The AutoML-Zero program generates 100 candidate algorithms by randomly combining mathematical operations, then tests them on a simple task, like an image-recognition problem. AutoML-Zero compares the algorithms' performance to that of hand-designed algorithms, with copies of top-performing algorithms mutated by randomly replacing, editing, or deleting some of their code to create variations; these ‘offspring’ are added to the population while older algorithms are removed. AutoML-Zero was able to reproduce decades of AI research in days. Le said, "Our ultimate goal is to actually develop novel machine learning concepts that even researchers could not find."

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Field Programmable Gate Arrays. Critical 'Starbleed' Vulnerability in FPGA Chips Identified
Ruhr-University Bochum
April 16, 2020

Scientists at Ruhr-Universitat Bochum's Horst Gortz Institute for Information Technology Security and the Max Planck Institute for Security and Privacy in Germany have discovered a vulnerability in field-programmable gate array (FPGA) chips. The "Starbleed" bug allows hackers to completely commandeer the chips and their functionalities; replacing the chips is the only remedy, because the vulnerability becomes integrated with the hardware. The researchers analyzed FPGAs from Xilinx, one of the two leading FPGA manufacturers. They exploited an update and fallback feature in the FPGAs to successfully decrypt the encrypted bitstream file used to program the chips, and to access and modify file content. The Max Planck Institute's Christof Paar said, "Although detailed knowledge is required, an attack can eventually be carried out remotely, [and] the attacker does not even have to have physical access to the FPGA."

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Low-Cost Imaging System Poised to Provide Automatic Mosquito Tracking
Optical Society of America
April 15, 2020

Johns Hopkins University (JHU) researchers have developed a low-cost imaging system to automatically track mosquitoes that carry disease, enabling more timely and targeted responses to outbreaks. JHU's Adam Goodwin called the system a classic application of an Internet of Things device, which eventually could be coupled with computer-vision algorithms to automatically ascertain species and supply that data to public health systems. The system can be installed within a traditional mosquito trap to remotely survey the population, diversity, and distribution of species, and consistently high image quality would enable the identification of several mosquitoes at once. Said Goodwin, "Our new system would be particularly useful in monitoring Aedes aegypti in hard to reach areas and at commercial ports of entry where invasive species can be brought from other countries. It could also expand current surveillance operations for regions already monitoring local populations of Aedes aegypti.”

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Innovating the Peer-Review Research Process
MSU Today
April 15, 2020

Michigan State University (MSU)'s Wolfgang Kerzendorf and colleagues at New York University and Germany's European Southern Observatory have developed a new process for assessing proposed scientific research projects. The typical process involves researchers submitting a project proposal, and the funding agency asking peers in that field to evaluate the proposal and make funding recommendations. The new distributed peer-review system spreads the workload of reviewing project proposals among the proposers, and uses machine learning to match reviewers with proposals while adding a feedback mechanism on the review. The team reviewed 172 proposals that each requested use of telescopes at the European Southern Observatory, in both the traditional manner and via the proposed distributed peer review. The results of the two review processes were not statistically different.

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Renewable energy sources. Twitter Data May Offer Policy Makers a Glimpse Into Demand for Renewable Energy
Penn State News
Matt Swayne
April 13, 2020

Researchers from Penn State University, Deakin University in Australia, Shahrekord University in Iran, and the non-profit Population Research Institute studied Twitter data from users in Alaska, and found they could track changing opinions about renewable energy over time. The researchers said they also could gauge which forms of renewable energy were more acceptable, and which generated the most conversations. Penn State’s Somayeh Asadi said the information could be used to educate people about certain types of renewable energy, which could prove useful for policy makers, energy companies, and governments. "Researchers have found that energy plays a significant role in the food-energy-water nexus, and, if people could just use more sustainable sources of energy, this would help make the whole system — this entire nexus — more sustainable," Asadi said.

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Scientists Design Novel Algorithmic Framework for Alzheimer's Disease Prediction
Chinese Academy of Sciences
Li Yuan
April 8, 2020

Researchers at the Chinese Academy of Sciences' Shenzhen Institutes of Advanced Technology (SIAT) designed a novel algorithmic framework for predicting Alzheimer's disease (AD) through a combined machine learning and deep learning regression framework (CTDE) to forecast clinical scores for neuroimaging data. The investigators utilized the CTDE in two situations—predictions with baseline dataset and with longitudinal dataset—to anticipate scores on the dataset from the Alzheimer's Disease Neuroimaging Initiative database. The outcomes demonstrated that combining correntropy regularized joint learning and feature encoding in a deep polynomial network promoted their respective benefits to boost prediction accuracy and discover AD biomarkers.

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An AMP Robotics system sorting recyclable material at the company’s lab. Robots Welcome to Take Over, as Pandemic Accelerates Automation
The New York Times
Michael Corkery; David Gelles
April 10, 2020

Automation had been gradually replacing human work in various industries to reduce labor costs and boost profits prior to the coronavirus pandemic, but labor and robotics experts say social-distancing directives could spur more industries to automate. As more cities suspend recycling services amid concerns that workers could contract the coronavirus from one another, robots increasingly are being called in to sort through recycled materials. Robots also are being used by grocery stores to free up employees so they can focus on disinfecting and sanitizing surfaces and keeping shelves stocked. Fully automated stores, like Amazon Go, could become a more viable option for retailers. While retailers says robots are augmenting the work of employees, not replacing them, there are concerns that the wave of automation will depress hiring when the recovery occurs. Said Mark Muro of Brookings Institution, "People become more expensive as companies' revenues decline."

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Automated 'Pipeline' Improves Access to Advanced Microscopy Data
University of Michigan News
April 14, 2020

Researchers at the University of Michigan Life Sciences Institute (LSI) have developed a data processing technique for more rapidly generating data from cryo-electron microscopy (Cryo-EM) instruments, by using an automated preprocessing pipeline. Cryo-EM allows scientists to determine the three-dimensional configuration of molecules flash-frozen in a layer of ice, but current preprocessing steps require human supervision, and have long thwarted the formulation of a general set of guidelines. LSI's Yilai Li and colleagues created the pipeline by linking deep learning and image-analysis tools with preexisting software data preprocessing algorithms to winnow down massive datasets to the information researchers need to start analysis. LSI's Michael Cianfrocco said, "It really streamlines the process stage so that researchers can jump in and focus on what's important: the scientific questions they want to ask and answer."

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Academics Steal Data From Air-Gapped Systems Using PC Fan Vibrations
Catalin Cimpanu
April 17, 2020

Academics at the Ben-Gurion University of the Negev (BGU) in Israel have developed the AiR-ViBeR technique for stealing data from air-gapped systems by manipulating the vibrations of fans inside computers. BGU's Mordechai Guri said malware planted on an air-gapped system can control fan speed, and attackers can rig the frequency of vibrations by moderating fan speed up and down, with the vibrational patterns spreading throughout the nearby environment. A nearby attacker can use accelerometer sensors in smartphones to record the vibrations, then decode the information concealed within the vibration pattern to reassemble the stolen data. Hackers can either record the vibrations by positioning a smartphone on a desk near an air-gapped system, or use malware to infect the smartphones of employees working for the targeted company operating an air-gapped system. However, AiR-ViBeR is extremely slow, enabling data exfiltration of only a half-bit per second.

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Making Big Data Processing More Energy Efficient Using Magnetic Circuits
UTA Electrical and Computer Engineering
April 10, 2020

Researchers at the University of Texas at Austin's Cockrell School of Engineering have developed a technique to boost the energy efficiency of next-generation smart computers, using magnetic circuits. A study by Cockrell's Jean Anne Incorvia and Can Cui found that spacing magnetic nanowires, functioning as artificial neurons, enhances the ability of the neurons to compete against each other, with those most activated prevailing. This lateral inhibition transpires when the neurons firing prevent slower neurons from firing, slashing energy use in data processing. Lateral inhibition typically demands extra circuitry and compounds cost, energy, and space consumption. Incorvia said their magnetic-circuit concept reduces the energy consumption of a standard back-propagation algorithm up to 30-fold when performing the same learning task.

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