Welcome to the February 21, 2018 edition of ACM TechNews, providing timely information for IT professionals three times a week.

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Are Bots a Danger for Political Election Campaigns?
Friedrich-Alexander University Erlangen-Nurnberg (Germany)
February 19, 2018

Researchers at Friedrich-Alexander University (FAU) in Germany have probed the extent to which autonomous social bots were used on Twitter during Japan's general elections in 2014. The team analyzed more than 540,000 tweets using a corpus linguistics strategy so large volumes of text could be examined, and found nearly 80 percent of the investigated tweets were duplicates traced back to a total of 3,722 original tweets. Five proliferation patterns were uncovered, four of which were used by right-wing activists, and one by users who acted similarly to bots. FAU professor Fabian Schafer says it seems as if social bots were widely used by right-wing users, to give indirect online backing to Shinzo Abe's nationalistic agenda. "As a result, Abe's position was not only supported by the conservative organizations of a group of users with close links to the [Liberal Democratic Party] but also by the large...group of right-wing Internet activists," Schafer notes.

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Screenshot from the Fake News Game, created by Drog media collective and the University of Cambridge Fake News 'Vaccine': Online Game May 'Inoculate' by Simulating Propaganda Tactics
University of Cambridge
February 19, 2018

Researchers at the University of Cambridge in the U.K. say they have developed a new online game designed to provide "general immunity" against fake news and propaganda. Players are encouraged to sow anger, mistrust, and fear in the public by manipulating digital news and social media within a simulation. Participants build audiences by publishing polarizing lies, deploying Twitter bots, visually doctoring evidence, and inciting conspiracy theories following public tragedies. Players also must maintain a "credibility score" to remain as convincing as possible. "If you know what it is like to walk in the shoes of someone who is actively trying to deceive you, it should increase your ability to spot and resist the techniques of deceit," notes Cambridge professor Sander van der Linden. "We want to help grow 'mental antibodies' that can provide some immunity against the rapid spread of misinformation."

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A young girl at a desk completing an engineering project Building a Passion for STEM Studies Among Women and Girls
Troy Media
Art Eggleton; Raymonde Saint-Germain
February 19, 2018

Twenty percent of Canada's post-secondary science, technology, engineering, and math (STEM) students are currently female, even though jobs in STEM-related fields are growing three times faster than other parts of the economy and paying 12 percent higher, note Canadian Sens. Art Eggleton and Raymonde Saint-Germaine. They cite several ways to address the STEM gender deficit, such as by providing more active female STEM role models for girls at a much younger age. "We need to invest in our elementary teachers' abilities and love of math to energize girls at a very young age," says University of Toronto professor Ismael Mourifie. A second initiative would involve public elementary schools doing more to kindle student interest and passion in STEM, while a third strategy is to reexamine and rethink the teaching of STEM subjects. Key to this is emphasizing social aspects of STEM education, such as the human factors and societal benefits of good design.

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The Telegraph (India)
G.S. Mudur
February 19, 2018

Indraprastha Institute of Information in India professor Mayank Vatsa and colleagues have created a deep-learning algorithm to help people identify doctored images by focusing on specific areas of the human face. "We look at areas of the face that are most likely to be altered--the skin around the eyes, the mouth, or the forehead," Vatsa says. "In an untouched image, the face shows up as a blend of finely changing textures. Our algorithm looks for unusual smoothness, a sign of retouching." When randomly presented with 100 facial images, the software correctly identified 87 images as original or retouched, and the incorporation of gender and ethnicity features into the method has improved performance. Vatsa is one of many researchers worldwide seeking to apply deep learning to a broad spectrum of areas that include computer security, industrial fault detection, medical diagnosis, and financial analysis.

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Alexa, How Do Word Senses Evolve?
Lehigh University
February 20, 2018

Researchers at Lehigh University recently conducted a study examining 1,000 years of English language development, and found the kinds of algorithms that human minds have used to extend existing words to new senses of meaning. They say this reverse engineering of how human language evolved could be applied to neural language processing by machines. The researchers identified an algorithm called "nearest-neighbor chaining" as the mechanism that best describes how word senses accumulate over time. In the nearest-neighbor chaining algorithm, points of input are analyzed as a hierarchy of clusters. The Lehigh model captured the chaining process that occurs as emerging ideas were expressed using the word with the most closely related existing sense. After developing the algorithms that predicted the historical order in which the sense of a word have emerged, the team tested these predictions against actual historical records; the findings suggest word senses emerge in ways that minimize cognitive costs.

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A Small Emperor Moth on a branch Why Even a Moth's Brain Is Smarter Than an AI
Technology Review
February 19, 2018

Researchers at the University of Washington in Seattle say they have developed an artificial neural network that mimics the structure and behavior of a moth's olfactory learning system. The system is comprised of five networks that relay information forward from one to the next, which includes the engagement of the antenna lobe and mushroom body under octopamine stimulation. The model specifically simulates the noisy signals generated by odor receptors and the change in dimension as information flows from the antenna lobe to the mushroom body, and it has an analog of the role octopamine plays. "Our model is able to robustly learn new odors, and our simulations of integrate-and-fire neurons match the statistical features of in vivo firing rate data," the team notes. The researchers also think the model supports bioinspired machine-learning mechanisms that have potential utility in constructing neural nets for rapid learning from a small number of samples.

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Two flashlights creating one single beam of light Physicists Create New Form of Light
MIT News
Jennifer Chu
February 15, 2018

Researchers at the Massachusetts Institute of Technology, Harvard University, and elsewhere have demonstrated the successful interaction of photons, which could clear a path toward using light particles in quantum computing. Their controlled experiments showed that when shining a weak laser beam through a dense cloud of ultracold rubidium atoms, the photons bound together in pairs or triplets, suggesting an attraction occurring among them. The researchers also measured the photonic phase before and after traveling through the cloud, and they noted as the three-photon particles exited the atom cloud simultaneously, their phase was shifted compared to when the photons did not interact at all, and was three times larger than the phase shift of two-photon molecules. The team says photons that have interacted with each other, in this case via an attraction between them, can be considered strongly correlated, or entangled, which is essential for any quantum computing bit.

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Countries With Greater Gender Equality Have Lower Percentage of Female STEM Graduates, MU Study Finds
University of Missouri
Jeff Sossamon
February 14, 2018

Researchers at the University of Missouri (MU) and Leeds Beckett University in the U.K. found as societies become wealthier and more gender-equal, women are less likely to obtain degrees in science, technology, engineering, and math (STEM) fields. They also discovered a near-universal sex difference in academic strengths and weaknesses that contributes to the STEM gap. The researchers determined throughout the world, boys' academic strengths tend to be in science or math, while girls' strengths tend to be in reading. These differences, as well as a general interest in science could explain why the gender differences in STEM fields have been stable for decades and why previous strategies to address them have failed. MU professor David Gear says the researchers "analyzed data on 475,000 adolescents across 67 countries or regions and found that while boys' and girls' achievements in STEM subjects were broadly similar in all countries, science was more likely to be boys' best subject."

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Silicon Qubits Plus Light Add Up to New Quantum Computing Capability
Princeton University
Catherine Zandonella
February 14, 2018

Researchers at Princeton University, the Joint Quantum Institute, and the University of Konstanz in Germany have generated silicon-based quantum bits (qubits) from single electrons trapped in double quantum dots. They demonstrated that they could transfer quantum information, encoded in electron spin, by applying a magnetic field to a photon. To get the qubit to transmit its spin state to the photon, the researchers placed the electron spin in a large magnetic field gradient such that the electron spin had a different orientation depending on which side of the quantum dot it occupied. The magnetic field gradient plus a charge coupling technique paired the qubit's spin direction to the photon's electric field. "This work...takes you out of living in a two-dimensional landscape, where you can only do nearest-neighbor coupling, and into a world of all-to-all connectivity," says Princeton professor Jason Petta. "That creates flexibility in how we make our devices."

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Close up of a women wearing AR glasses with different icons on the lenses DARPA Awards $4.7 Million Grant to Transform Augmented-Reality Glasses
Military Embedded Systems
Lisa Daigle
February 13, 2018

Researchers at Columbia University, Stanford University, and the University of Massachusetts at Amherst have won a $4.7-million U.S. Defense Advanced Research Projects Agency grant to develop a lightweight glass that dynamically monitors the wearer's vision and displays vision-corrected contextual images. The goal is to create an ultra-high-resolution, see-through, head-mounted display with a large field of view and significantly reduced size, weight, and power consumption. The device also will be correct the user's vision deficiencies in real time and project aberration-corrected visible contextual information onto the retina. The augmented reality glass relies on the ultrafast generation of arbitrary wavefronts, both in visible and near-infrared wavelengths. The researchers will develop a scalable fabrication process based on standard complementary metal-oxide semiconductor techniques, and well-established procedures to integrate the new materials into the silicon nitride-integrated photonics platform. The team will develop analytical and computational tools for modeling large resonator arrays and dynamics of device performance.

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Brain Scan and Artificial Intelligence Could Help Predict Whether OCD Will Improve With Treatment
UCLA Newsroom
Leigh Hopper
February 13, 2018

Researchers at the University of California, Los Angeles (UCLA) have developed a way to use brain scans and machine learning to predict whether people with obsessive compulsive disorder (OCD) will benefit from cognitive behavior therapy. This technique could help improve the therapy's success rate, enabling therapists to tailor treatment to each patient. The researchers used a functional magnetic resonance imaging (fMRI) machine to scan the brains of 42 people with OCD, before and after four weeks of intensive, daily cognitive therapy. The team also assessed the severity of participants' symptoms before and after treatment using a scaled system. The researchers fed the participants' fMRI data and symptom scores into a computer and used machine learning to determine which patients would respond to cognitive behavioral therapy with 70-percent accuracy. "The algorithm performed far better than our own predictions based on their symptoms and other clinical information," says UCLA's Jamie Feusner.

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