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Welcome to the May 20, 2020 edition of ACM TechNews, providing timely information for IT professionals three times a week.

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Molecular model of a coronavirus spike protein. Geometry Points to Coronavirus Drug Target Candidates
Scientific American
Michael Dhar
May 19, 2020

A study by Robert Penner at France’s Institute of Advanced Scientific Studies incorporates a mathematical model that predicts protein sites on viruses that might be particularly susceptible to disabling treatments, which has been used to identify potential drug and vaccine targets in the fight against the coronavirus that causes COVID-19. The technique exploits the fact that certain viral proteins change shape when viruses penetrate cells; Penner mathematically localized exotic sites that mediate this shift. John Yin, who studies viruses at the University of Wisconsin–Madison, said much work remains to verify the study’s predictions through experimentation. Said Arndt Benecke, a biological researcher at the French National Center for Scientific Research, “This could go far beyond the viruses.”

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Microsoft, Intel Project Converts Malware Into Images Before Analyzing It
Catalin Cimpanu
May 11, 2020

Microsoft and Intel collaborated on the STAMINA (STAtic Malware-as-Image Network Analysis) project to detect and classify malware by converting malware samples into grayscale images and scanning them for textural and structural patterns. The process involves taking an input file and converting its binary form into a stream of raw pixel data, then converting the one-dimensional pixel stream into a two-dimensional image for analysis by standard image analysis algorithms. STAMINA’s accuracy rate in identifying and classifying malware samples was 99.07%, with a false positives rate of 2.58%, according to the researchers. Microsoft said STAMINA is less effective with larger files "due to limitations in converting billions of pixels into JPEG images and then resizing them."

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Light, Fantastic: The Path Ahead for Faster, Smaller Computer Processors
University of Sydney
May 15, 2020

Researchers at the University of Sydney in Australia and the German Research Foundation have designed a nanoscale photonic processor with a hybrid architecture, in order to overcome engineering challenges to photonic data transmission. This approach closes the gap between industry-standard silicon photonic systems and metal-based waveguides. The modular architecture supports the rapid rotation of light polarization in the processor, and its fast nano-focusing down to about 100 times smaller than the wavelength of light carrying information. In addition to enabling light-based computer processing, Sydney's Stefano Palomba thinks this method also will be critical to developing quantum-optical information systems. Said Sydney's Martijn de Sterke, "The future of information processing is likely to involve photons using metals that allow us to compress light to the nanoscale and integrate these designs into conventional silicon photonics."

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This Robot Can Guess How You're Feeling by the Way You Walk
Matt Simon
May 18, 2020

University of Maryland (UMD) researchers have developed an algorithm to enable a small four-wheeled robot to perform real-time gait analysis in order to guess the walker's emotional state. The researchers collected information from humans who observed each other while walking, inquired about each other’s emotional state, and correlated that information with data about each subject's gait. The team then used videos of subjects walking, combined with connections between skeletal gaits and emotions associated with those walkers, to train the ProxEmo algorithm. UMD's Aniket Bera suggested robots equipped with the algorithm could intervene, or move out of the way, after determining a person's emotional state by reading their gait.

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Bluetooth May Not Work Well Enough to Trace Coronavirus Contacts
New Scientist
Adam Vaughan
May 12, 2020

Researchers at Trinity College Dublin in Ireland concluded it will be "challenging" to use Bluetooth to record contacts using contact tracing apps because Bluetooth signal strength can vary significantly depending on phones’ orientations, whether someone is between two phones, and the presence of materials that reflect and absorb the signals. The researchers tested four scenarios, and found that while proximity could generally be established while walking, when people met at a table the signal dropped by 38% if both phones were in pockets rather than placed on the table. In supermarkets, it was difficult to tell if two people were social distancing correctly by staying two meters (6.5 feet) apart, while on trains Bluetooth signal strength actually increased between phones more than 3.5 meters (11.5 feet) apart.

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Scientists trained an AI to spot lesions in CT scans of human brains. AI Gauges Head Injuries by Classifying Brain Lesions
New Atlas
Nick Lavars
May 14, 2020

Scientists at the U.K.’s University of Cambridge and Imperial College London have developed an artificial intelligence (AI) algorithm that can detect and classify different types of brain lesions, to gauge the impact of a head injury. The researchers trained the machine learning AI on more than 600 computed tomography (CT) brain scans featuring lesions of different sizes and types. When applied to another set of CT scans, the algorithm was able to classify the volume and progression of brain lesions. Said Cambridge's David Menon, "We hope it will help us identify which lesions get larger and progress, and understand why they progress so that we can develop more personalized treatment for patients in future."

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NYU, IBM Research Take Electrons for a Spin Toward More Efficient, Higher Density Data Storage
New York University
James Devitt
May 18, 2020

Researchers at New York University (NYU) and IBM Research have demonstrated a system involving electron motion in magnetic materials that could lead to new ways to enhance data storage. The process sets the direction of the magnetic spin of electrons in magnetic materials based on an electrical current; spin direction is where information can be stored. The team used the planar-Hall effect in a ferromagnetic conductor to control the orientation of the spin-polarization axis; current flow in the conductor produces spin polarization in a direction determined by its magnetic motion. Said NYU’s Andrew Kent, “This research shows a new and fundamental mechanism for setting the electron spin direction in a conducting material.”

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UCLA Physicists Develop World's Best Quantum Bits
University of California, Los Angeles College
May 15, 2020

University of California, Los Angeles (UCLA) physicists say they have achieved a new world record for error-free preparation and measurement of quantum bits (qubits) within a quantum computer. The most powerful existing quantum computers are noisy intermediate-scale quantum (NISQ) devices, which are especially susceptible to error. The UCLA researchers engineered a qubit in a laser-cooled, radioactive barium ion, which exhibits near-perfect properties for building ultra-low error rate quantum systems. UCLA's Eric Hudson said this type of qubit should impact virtually all fields of quantum information science, and shows promise for quantum networking, sensing, timing, modeling, and computation.

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DeepMind Researchers Develop Method to Efficiently Teach Robots Tasks Like Grasping
Kyle Wiggers
May 18, 2020

DeepMind researchers said simple sensor intentions (SSIs) can streamline the knowledge required to define rewards in reinforcement learning systems, which can help address complex robotic tasks using only raw sensor data. SSIs ostensibly deliver a generic means of encouraging artificial intelligence agents to probe their environments, as well as providing guidance for collecting data to solve a main task. SSIs are sets of auxiliary tasks defined by acquiring a sensor response and calculating a commensurate reward, either rewarding an agent for reaching a specific target response or for incurring a specific change in response. The researchers converted raw images from a camera-equipped robot into a set of SSIs, then applied them to a controller to train the robot to lift colored blocks. The researchers said the approach “requires less prior knowledge than the broadly used shaping reward formulation, that typically rely on task insight for their definition and state estimation for their computation."

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Bee on sunflower App State Creates Pollinator-Tracking App
Appalachian State University News
Jan Todd
May 18, 2020

Researchers at Appalachian State University's Center for Analytics Research and Education (CARE) developed a smartphone application for tracking bees and other pollinators, as part of a project to monitor the world's pollinator population. Users of the World Bee Count app can capture and upload photos of pollinators in their natural habitats, with location coordinates and other data automatically recorded. CARE has partnered with analytics leader SAS to analyze data captured and learn more about different pollinator types, based on species-identifying machine learning algorithms; the pollinators' habitat and dates of activity, based on location and time stamps; and the types of flowers or plants that draw pollinators, based on what is visible in each image’s background. Said Appalachian’s James Wilkes, “The project is designed to be as simple as possible. We want to build awareness of the critical role various insects and other pollinators play in our food systems.”

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Android malware. Sneakier, More Sophisticated Malware On the Loose
IEEE Spectrum
Michelle Hampson
May 18, 2020

Researchers at Boston University and King's College London in the U.K., after analyzing more than 1 million samples of Android malware, have found that malware coding is being hidden more cleverly. The researchers used differential analysis to isolate software components irrelevant to the malware campaign, allowing them to study the behavior of just the malicious parts. The technique revealed several trends, including a major shift away from malware that supports premium rate fraud. Said King’s College London’s Guillermo Suarez-Tangil, “We observed that cryptography is present in 90% of the recent families [of malware]. To the best of our knowledge, there are only few malware-detection systems capable of dealing with these forms of obfuscation, and they all have limitations.”

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Individual wearing a Tempo wristband. Elderly Home Turns to Wearables for Contact Tracing, Sidestepping Apple-Google Limits
Paresh Dave; Stephen Nellis; Joseph White
May 19, 2020

The Legacy at Town Square senior living facility in Amarillo, TX, recently accelerated contact tracing by having staffers and residents wear high-tech wristbands from CarePredict. The technology demonstrates a way to conduct contract tracing using wearable computing products, while bypassing the limitations of Google and Apple's smartphone-based solution. Although the Google-Apple system promises to improve reliability in contact tracing, devices that use it would need Internet access and a government-sanctioned application. CarePredict's Tempo wristbands use infrared light to connect with beacons on a wall in each room of The Legacy facility, documenting who has been near whom, where, and for how long. Such technology "makes you ready to make the best operational decisions at a moment’s notice,” said Joseph Walter, executive director of the Legacy facility.

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ACM Books Collection 1
2020 ACM Transactions on Internet of Things (TIOT)

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