The Library of Congress’s Coolidge Auditorium, one of the nation’s leading performing arts venues, is typically busy year-round, bustling with performers playing to capacity audiences. Now, with the venue silenced by the coronavirus pandemic, the Library has launched a new kind of program, The Boccaccio Project.

During the darkest period of the plague in 14th-century Italy, author Giovanni Boccaccio (1313–1375) wrote the Decameron, structured as a collection of stories told by ten characters, all of whom were in self-isolation. This artistic response during a time of quarantine inspired the Library of Congress’s contemporary Boccaccio Project, in which the Library has commissioned ten composers and ten performers to create and record new works of music that will reach audiences online.

UMBC’s Daniel Pesca, a pianist and assistant professor of music, is one of these performers. He was chosen by David Plylar, music specialist and concert producer at the Library of Congress. 

“I’ve known Daniel’s phenomenal work as a pianist and composer for many years,” says Plylar. “He has performed at the Library of Congress in the past, so our concert team knows him as well. We were thrilled to work with him again.” 

To bring Pesca into The Boccaccio Project, the Library of Congress collaborated with the University of Chicago’s Grossman Ensemble, a nationally-recognized new music group of which Pesca is a member.

Selecting a composer

Each commissioned performer was asked to select a composer with whom to partner. In Pesca’s case, he immediately turned to Aaron Travers, an assistant professor at Indiana University Bloomington. “I’ve long admired Aaron’s exquisite writing, and particularly love his piano solo piece Songs of Loss,” says Pesca.

“It’s a special thing as a performer when you encounter a composer who you feel understands not just what is technically possible on your instrument, but your instrument’s soul, its voice,” says Pesca. “Aaron has that affinity for the piano—I knew this from his earlier work, and I see that again in his new piece, Olcott Park. What I love here in particular is the layers of sound that he draws out of a solo instrument. I find this kind of layered writing particularly rewarding to play.”

Virtual performance

Pesca’s performance of Travers’s new composition, Olcott Park, will be broadcast on Tuesday, June 23 at 8 p.m. on the Library of Congress YouTube channel, and simultaneously on Facebook and Twitter. The performance will remain on the Library of Congress YouTube channel indefinitely. Other performances in The Boccaccio Project are already underway and will continue through Friday, June 26.

“We are thrilled that Daniel Pesca will be one of the select few performers chosen to premiere new works as part of the Library of Congress’s Boccaccio Project,” says Linda Dusman, chair of music. “This marks him not only as a pianist of note, but also as a socially conscious musician who responds dynamically to important contemporary issues.”

Banner image: Daniel Pesca by Rosen-Jones Photography.

By Zhiyuan Chen, associate professor, Information Systems, UMBC and Aryya Gangopadhyay, professor, Information Systems, UMBC

The stunning successes of artificial intelligence would not have happened without the availability of massive amounts of data, whether its smart speakers in the home or personalized book recommendations. And the spread of AI into new areas of the economy, such as AI-driven marketing and self driving vehicles, has been driving the collection of ever more data. These large databases are amassing a wide variety of information, some of it sensitive and personally identifiable. All that data in one place makes such databases tempting targets, ratcheting up the risk of privacy breaches.

The general public is largely wary of AI’s data-hungry ways. According to a survey by Brookings, 49% of people think AI will reduce privacy. Only 12% think it will have no effect, and a mere 5% think it may make it better.

As cybersecurity and privacy researchers, we believe that the relationship between AI and data privacy is more nuanced. The spread of AI raises a number of privacy concerns, most of which people may not even be aware. But in a twist, AI can also help mitigate many of these privacy problems.

Revealing models

Privacy risks from AI stem not just from the mass collection of personal data, but from the deep neural network models that power most of today’s artificial intelligence. Data isn’t vulnerable just from database breaches, but from “leaks” in the models that reveal the data on which they were trained.

Deep neural networks – which are a collection of algorithms designed to spot patterns in data – consist of many layers. In those layers are a large number of nodes called neurons, and neurons from adjacent layers are interconnected. Each node, as well as the links between them, encode certain bits of information. These bits of information are created when a special process scans large amounts of data to train the model.

For example, a facial recognition algorithm may be trained on a series of selfies so it can more accurately predict a person’s gender. Such models are very accurate, but they also may store too much information – actually remembering certain faces from the training data. In fact, that’s exactly what researchers at Cornell University discovered. Attackers could identify people in training data by probing the deep neural networks that classified the gender of facial images.

They also found that even if the original neural network model is not available to attackers, attackers may still be able to tell whether a person is in the training data. They do this by using a set of models that are trained on data similar, but not identical, to the training data. So if a man with a beard was present in the original training data, then a model trained on photos of different bearded men may be able to reveal his identity.

AI to the rescue?

On the other hand, AI can be used to mitigate many privacy problems. According to Verizon’s 2019 Data Breach Investigations Report, about 52% of data breaches involve hacking. Most existing techniques to detect cyberattacks rely on patterns. By studying previous attacks, and identifying how the attacker’s behavior deviates from the norm, these techniques can flag suspicious activity. It’s the sort of thing at which AI excels: studying existing information to recognize similar patterns in new data.

Still, AI is no panacea. Attackers can often modify their behavior to evade detection. Take the following two examples. For one, suppose anti-malware software uses AI techniques to detect a certain malicious program by scanning for a certain sequence of software code. In that case, an attacker can simply shuffle the order the code. In another example, the anti-malware software might first run the suspicious program in a safe environment, called a sandbox, where it can look for any malicious behavior. Here, an attacker can instruct the malware to detect if it’s being run in a sandbox. If it is, it can behave normally until it’s released from the sandbox – like a possum playing dead until the threat has passed.

Making AI more privacy friendly

A recent branch of AI research called adversarial learning seeks to improve AI technologies so they’re less susceptible to such evasion attacks. For example, we have done some initial research on how to make it harder for malware, which could be used to violate a person’s privacy, to evade detection. One method we came up with was to add uncertainty to the AI models so the attackers cannot accurately predict what the model will do. Will it scan for a certain data sequence? Or will it run the sandbox? Ideally, a malicious piece of software won’t know and will unwittingly expose its motives.

Another way we can use AI to improve privacy is by probing the vulnerabilities of deep neural networks. No algorithm is perfect, and these models are vulnerable because they are often very sensitive to small changes in the data they are reading. For example, researchers have shown that a Post-it note added to a stop sign can trick an AI model into thinking it is seeing a speed limit sign instead. Subtle alterations like that take advantage of the way models are trained to reduce error. Those error-reduction techniques open a vulnerability that allows attackers to find the smallest changes that will fool the model.

These vulnerabilities can be used to improve privacy by adding noise to personal data. For example, researchers from Max Planck Institute for Informatics in Germany have designed clever ways to alter Flickr images to foil facial recognition software. The alterations are incredibly subtle, so much so that they’re undetectable by the human eye.

The third way that AI can help mitigate privacy issues is by preserving data privacy when the models are being built. One promising development is called federated learning, which Google uses in its Gboard smart keyboard to predict which word to type next. Federated learning builds a final deep neural network from data stored on many different devices, such as cellphones, rather than one central data repository. The key benefit of federated learning is that the original data never leaves the local devices. Thus privacy is protected to some degree. It’s not a perfect solution, though, because while the local devices complete some of the computations, they do not finish them. The intermediate results could reveal some data about the device and its user.

Federated learning offers a glimpse of a future where AI is more respectful of privacy. We are hopeful that continued research into AI will find more ways it can be part of the solution rather than a source of problems.

*****

Header image from The Conversation.

[You’re smart and curious about the world. So are The Conversation’s authors and editors. You can read us daily by subscribing to our newsletter.]

Zhiyuan Chen, Associate Professor of Information Systems, University of Maryland, Baltimore County and Aryya Gangopadhyay, Professor, Information Systems, University of Maryland, Baltimore County

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Hello Retrievers! Just a friendly reminder of a great scholarship opportunity for students interested in entrepreneurship!

The Joseph and Frieda Faiman Eisenberg/VPC, Inc. Scholarship is now available to UMBC students who meet the application requirements.This need-based scholarship is awarded to students who are interested in becoming an entrepreneur and are associated with the Alex. Brown Center for Entrepreneurship. 

Please click on the link below for additional information and to apply.  The scholarship award is $600 and the deadline is June 26th, so don’t delay!

https://umbc.academicworks.com/opportunities/2087

Hello Retrievers! Just a friendly reminder of a great scholarship opportunity for students interested in entrepreneurship!

The Joseph and Frieda Faiman Eisenberg/VPC, Inc. Scholarship is now available to UMBC students who meet the application requirements.This need-based scholarship is awarded to students who are interested in becoming an entrepreneur and are associated with the Alex. Brown Center for Entrepreneurship. 

Please click on the link below for additional information and to apply.  The scholarship award is $600 and the deadline is June 26th, so don’t delay!

https://umbc.academicworks.com/opportunities/2087

NIH Summary Statement Available to the Signing Officials (SO) through the eRA Commons

Notice Number: NOT-OD-20-126

National Institutes of Health

https://grants.nih.gov/grants/guide/notice-files/NOT-OD-20-126.html

New Process: “To facilitate award management and communications with applicant organizations via Authorized Organization Representatives (AORs), NIH has modified the system of records by adding a new routine use to clarify that information may be shared with applicant organizations."

"Effective June 24, 2020, the overall impact score and the NIH Summary Statement will be made available in the eRA Commons to the Authorized Organization Representatives (AORs) of the applicant organization with the Signing Official (SO) user role in the eRA Commons.”

Please review full notice or contact your OSP representative for further assistance.

This story was written by Catalina Sofia Dansberger Duque and first appeared on news.umbc.edu

Governors across the United States have been working to determine what safe reopening might look during the COVID-19 pandemic. When the National Governors Association (NGA) needed experts to outline considerations, they reached out to UMBC’s Lucy Wilson. 

For more than a decade, Wilson has served as a public health expert on disease response and public health planning at the international, national, and state levels. When the NGA called, she joined an interdisciplinary team of experts in developing “The Roadmap to Recovery: A Public Health Guide for Governors,” published this April.

Keeping Maryland safe

Wilson is an infectious disease physician who is also a professor and graduate program director of emergency health services at UMBC. Prior to coming to UMBC, she served as a medical epidemiologist at the Maryland Department of Health, as chief for the Center for Surveillance, Infection Prevention and Outbreak Response for ten years. There she oversaw Maryland’s infectious disease outbreak responses and infection control guidance in all types of settings. 

She has also served on the State’s Physician on Call Team, where she was one of a small group of doctors responsible for assessing potential biological, chemical, and nuclear threats. And she currently is the co-principal investigator of the Healthcare-Associated Infections (HAI) branch of Maryland’s Emerging Infections Program (EIP). Through EIP, she conducts surveillance epidemiology and antibiotic resistance research focusing on healthcare-associated infections (including COVID-19) in hospitals and nursing homes. At UMBC, her research focuses on visualizing health outcomes across the continuum of healthcare.

“I have experience working with stakeholders throughout the state to examine different areas of response…that cut across the spectrum of emergency management and response,” explains Wilson. She notes that this could range from education to agriculture to transportation. “This gave me the perspective to help make recommendations to governors about how to create a team and what type of considerations to have for COVID-19 response.”

Learning from previous outbreaks

The NGA provides governors, cabinet members, Congress, private business, and the international community with guidance related to public policy and governance. To develop a COVID-19 recovery roadmap, Wilson helped assess the various settings impacted by COVID-19. She then applied the science of infection control and disease transmission to help develop criteria to maintain the safety of settings like schools, hospitals, prisons, and nursing homes. 

Wilson’s recommendations were informed by her prior work in infection control, disease surveillance, and government response to outbreaks, diseases, and pandemics at a national and international level. These include responding to the novel influenza A (H1N1) virus pandemic of 2009, Ebola, Zika, statewide food-related outbreaks, and medication recalls.

When it comes to COVID-19, Wilson says, “This level of pandemic is unprecedented in modern times.” She explains, “We can look back to 1918 to see how social distancing worked. We can look at other countries who have had different COVID-19 strategies in terms of their response and reopening. But what we need to be prepared for is that the coronavirus is difficult to control.”

Using available tools

“Governors must respond with tools we know will work, as appropriate to their region, to try to minimize illness,” states Wilson. These tools focus on minimizing physical contact between people, from using touchless purchasing and curbside pickup at businesses to adjusting work schedules to spread out employees.

The Roadmap to Recovery report divides states’ COVID-19 responses into two stages. The first stage focused on reducing the spread of infection through mandated physical distancing in the absence of comprehensive testing, treatment, or vaccines. Closing gathering spaces like schools, businesses, places of worship, and recreation areas helped to “flatten the curve.” 

The second stage involves necessary measures and infrastructure needed to safely open up society. This includes continuous assessments to determine whether to keep moving forward or to return to previous restrictions. 

“This is a multifactorial situation where we will likely see more cases and potentially more waves of disease,” shares Wilson. “It will be difficult to eradicate the coronavirus until we have a vaccine or valid treatment. Hopefully, as we implement and determine best practices, we can minimize the number of infections and deaths.” 

Beyond the report, Wilson also provided support to policymakers as a panelist for a Johns Hopkins University Diagnostic Excellence Summit on COVID-19. The summit focused on “Diagnostic Strategy for the COVID-19 Pandemic — Bench to Bedside to Blueprint for Policymakers.” She is currently helping to develop and implement campus policy as part of UMBC’s Incident Management Team and Fall Planning Coordinating Committee.

Informing the public

Since the start of the pandemic, Wilson has also provided guidance to the general public on how to take precautions to reduce coronavirus transmission. “People are being asked to take very drastic measures, which have radically changed their lives,” she says. She takes the time to speak with the media because she knows it is essential for people to understand the reasoning behind public health guidelines. 

In March, Wilson explained the need for sufficient protective equipment for healthcare workers in the New York Times. When the virus began to impact cruises she explained the quarantine process in the Washington Post. 

When COVID-19 testing became a public concern and broader restrictions began to be implemented, Wilson helped Maryland and Washington D.C. residents understand these issues. She contributed to several additional articles in The Washington Post. This included coverage on increasing restrictions, testing issues, the rising rate of infection, contact tracing, and the closing of gathering spaces. 

“It is important to know what is science and what is reasonable. People want to know what actions people can take to take care of themselves,” says Wilson. Wilson was a guest on the Public Health On Call podcast, produced by Johns Hopkins. She spoke about how nursing homes and first responders can address the threat of COVID-19. 

Wilson has also offered guidance on what people can do on an individual level. She addressed issues with physical activities like golfing and running. In the Huffington Post she discussed how to properly remove disposable gloves and why they shouldn’t be reused, what to do about jewelry and beards to avoid disease transmission, and reusing face masks. And in The Conversation, Wilson wrote about the matters to consider after a loved one dies from coronavirus, to avoid further infection.

How to begin reopening

Wilson has now also begun speaking with news agencies, including CNN. She shares what the process of reopening the country might look like. Leaders are learning how to manage rapid changes on a daily basis in all aspects of life, beyond just healthcare. 

Wilson expects that, moving forward, some interventions will work better than others, and communities will learn from each others’ experiences. “This is an evolving process. There will be changes in recommendations and information. Our understanding of this virus is that it affects everyone and every sector of our society,” says Wilson. “We need to have patience and be flexible because we are all learning as we go along.”

Banner image: Lucy Wilson. Photo courtesy of Wilson.