Dissertation Defense

Deep Representation of Lyrical Style and Semantics for Music Recommendation

Abhay L. Kashyap

11:00-1:00 Thursday, 20 July 2017, ITE 346

In the age of music streaming, the need for effective recommendations is important for music discovery and a personalized user experience. Collaborative filtering based recommenders suffer from popularity bias and cold-start which is commonly mitigated by content features. For music, research in content based methods have mainly been focused in the acoustic domain while lyrical content has received little attention. Lyrics contain information about a song’s topic and sentiment that cannot be easily extracted from the audio. This is especially important for lyrics-centric genres like Rap, which was the most streamed genre in 2016. The goal of this dissertation is to explore and evaluate different lyrical content features that could be useful for content, context and emotion based models for music recommendation systems.

With Rap as the primary use case, this dissertation focuses on featurizing two main aspects of lyrics; its artistic style of composition and its semantic content. For lyrical style, a suite of high level rhyme density features are extracted in addition to literary features like the use of figurative language, profanity and vocabulary strength. In contrast to these engineered features, Convolutional Neural Networks (CNN) are used to automatically learn rhyme patterns and other relevant features. For semantics, lyrics are represented using both traditional IR techniques and the more recent neural embedding methods.

These lyrical features are evaluated for artist identification and compared with artist and song similarity measures from a real-world collaborative filtering based recommendation system from Last.fm. It is shown that both rhyme and literary features serve as strong indicators to characterize artists with feature learning methods like CNNs achieving comparable results. For artist and song similarity, a strong relationship was observed between these features and the way users consume music while neural embedding methods significantly outperformed LSA. Finally, this work is accompanied by a web-application, Rapalytics.com, that is dedicated to visualizing all these lyrical features and has been featured on a number of media outlets, most notably, Vox, attn: and Metro.

Committee: Drs. Tim Finin (chair), Anupam Joshi, Tim Oates, Cynthia Matuszek and Pranam Kolari (Walmart Labs)

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Analysis of Irregular Event Sequences using Deep Learning, Reinforcement Learning, and Visualization

Filip Dabek

11:00-1:00 Thursday 13 July 2017, ITE 346, UMBC

History is nothing but a catalogued series of events organized into data. Amazon, the largest online retailer in the world, processes over 2,000 orders per minute. Orders come from customers on a recurring basis through subscriptions or as one-off spontaneous purchases, resulting in each customer exhibiting their own behavioral pattern when it comes to the way in which they place orders throughout the year. For a company such as Amazon, that generates over $130 billion of revenue each year, understanding and uncovering the hidden patterns and trends within this data is paramount in improving the efficiency of their infrastructure ranging from the management of the inventory within their warehouses, distribution of their labor force, and preparation of their online systems for the load of users. With the ever increasingly availability of big data, problems such as these are no longer limited to large corporations but are experienced across a wide range of domains and faced by analysts and researchers each and every day.

While many event analysis and time series tools have been developed for the purpose of analyzing such datasets, most approaches tend to target clean and evenly spaced data. When faced with noisy or irregular data, it has been recommended to undergo a pre-processing step of converting and transforming the data into being regular. This transformation technique arguably interferes on a fundamental level as to how the data is represented, and may irrevocably bias the way in which results are obtained. Therefore, operating on raw data, in its noisy natural form, is necessary to ensure that the insights gathered through analysis are accurate and valid.

In this dissertation novel approaches are presented for analyzing irregular event sequences using a variety of techniques ranging from deep learning, reinforcement learning, and visualization. We show how common tasks in event analysis can be performed directly on an irregular event dataset without requiring a transformation that alters the natural representation of the process that the data was captured from. The three tasks that we showcase include: (i) summarization of large event datasets, (ii) modeling the processes that create events, and (iii) predicting future events that will occur.

Committee: Drs. Tim Oates (Chair), Jesus Caban, Penny Rheingans, Jian Chen, Tim Finin

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Name: Alex Hart

Educational Background: Bachelor’s degree in Accounting from the University of Maryland, College Park

Hometown: Baltimore, MD (Go O’s and Ravens!)

Current role: As an Accountant I, Alex provides business services support to the CSEE department in the areas of contracts and grants/projects, which includes account monitoring, financial reporting, projections, reconciliations, etc. She also provides backup support for payroll, and she is the property custodian of inventory for CSEE.

Favorite thing about UMBC: “Without a doubt, my favorite thing about UMBC is the people here. I have met a lot of different people who have provided me with a wealth of knowledge since I started working here just a year ago. Everyone has been very inclusive and helpful!”

Students should ask me about: “Students can ask me anything, but maybe about the college experience, since I’m still a recent graduate.”

Alex is originally from Baltimore, MD. She joined CSEE’s Department in February of 2016. She attended UMBC for her first two years of college, then transferred to the University of Maryland College Park’s Robert H. Smith School of Business. She has a BS in Accounting from UMCP.

When not working, Alex loves cheering on the Terps in football and basketball. She also enjoys traveling to new places, cooking, practicing yoga, and reading.

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Every day, people interact with large amounts of text online, including legal documents they might quickly skim and sign without full, careful review. In an article recently published in The Conversation, Karuna Joshi, research associate professor of computer science and electrical engineering, and Tim Finin, professor of computer science and electrical engineering, explain how artificial intelligence (AI) is helping to summarize lengthy and complex legalese so people can more easily understand terms of service and similar agreements before they click “accept” to access a new app or online service.

The legal documents that Joshi and Finin are working to summarize—terms of service, privacy policies, and user agreements—often accompany new online services, contests, apps, and subscriptions. “As computer science researchers, we are working on ways artificial intelligence algorithms could digest these massive texts and extract their meaning, presenting it in terms regular people can understand,” they explain.

Through their research, Joshi and Finin ask computers to break down the terms and conditions that regular users “agree” to or “accept.” To process the text, Joshi and Finin employ a range of AI technologies, including machine learning, knowledge representation, speech recognition, and human language comprehension.

Joshi and Finin have found that in many of the privacy policies people are prompted to review and accept online, there are sections that do not actually apply to the consumer or service provider. These sections of the agreements might, for example, “include rules for third parties…that people might not even know are involved in data storage or retrieval,” they note.

After examining these documents, the software Joshi and Finin have developed pinpoints specific items that people should be aware of when they are granting their consent or agreement—what they describe as “key information specifying the legal rights, obligations and prohibitions identified in the document.” In other words, the software takes in all that complex legal language, and then then presents just the most essential information in “clear, direct, human-readable statements,” making it much more feasible for users to understand what they are consenting to before they click “agree.”

Read “Teaching machines to understand — and summarize — text” in The Conversation to learn more about Joshi and Finin’s approach to making online legal documents more accessible through AI.

Adapted from a UMBC News article by Megan Hanks Banner image: Karuna Joshi. Photo by Marlayna Demond ’11 for UMBC.

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Workshop on Solvers for Large, Sparse Linear Systems

Monday and Tuesday, 17-18 July 2017
Engineering Room 022, UMBC

UMBC will host a free, two-day workshop for faculty and students on solvers for large, sparse linear systems on Monday and Tuesday, July 17-18 in Engineering 022 at UMBC. Thanks to UMBC Prof. Matthias Gobbert for organizing and to University of Kassel Prof. Andreas Meister for presenting. If you plan on attending, please RSVP online.

The simulation of real life applications possesses a crucial importance in a wide variety of scientific as well as industrial areas. Thereby, the performance of the whole numerical method is often decisively depend on the properties of the incorporated solver for linear systems of equations.

The course provides a comprehensive introduction to both classical and modern iterative solvers for a stable, efficient and reliable solution of linear systems and is design for students from many disciplines, including Mathematics, Engineering, Physics, Computer Science, Computer Engineering and Electrical Engineering.

The course content covers

• Introduction to basics from numerical linear algebra
• Splitting methods
• Multi-grid schemes
• Krylov subspace methods like CG, GMRES, BiCG, CGS, BiCGSTAB
• Preconditioning

The lectures will be accompanied by practical exercises in MATLAB.

Monday, July 17, 2017

Tuesday, July 18, 2017:

The workshop will be presented by Prof. Dr. Andreas Meister from the Institute for Mathematics, University of Kassel, Germany.  He is an internationally renowned researcher in Numerical Analysis with a specialization including iterative solvers for linear system of equations. These methods are modern and form the basis of all numerical kernels in modern software, such as COMSOL, Matlab, PETSc, and many others. Prof. Dr. Meister has taught classes at UMBC during Fall 2013 when he spent a sabbatical at UMBC as part of the partnership between UMBC and the University of Kassel in Germany.

This workshop is hosted by the UMBC High Performance Computing Facility. Light refreshments are graciously sponsored by the UMBC Division of Information Technology.

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Applications sought for major UMBC cybersecurity scholarships

NSF CyberCorps: Scholarship For Service (SFS)

Scholarships for careers in cybersecurity. Earn full tuition, fees, stipends ($22,500 – $34,000), and more ($2000 books, up to $3000 health benefits, $4000 professional expenses).  For BS, MS, MPS, or PhD in CS, CE, IS, Cyber or related fields. USA citizenship or permanent residency required. Contact Dr. Alan Sherman, *protected email* who will send you an application.

In academic year 2017-2018, UMBC will support a total of about six additional SFS Scholars at the BS, MS, MPS, and PhD levels in CS and related fields. Each scholarship is potentially for up to the final two years (three years for PhD and combined BS/MS). Interested full-time degree students should contact *protected email* and visit the CISA scholarship page.

Each scholarship covers full tuition, fees, travel, books, and academic year stipend of $34,000 for MS/MPS/PhD, and $22,500 for BS. Applicants must be US citizens or permanent residents capable of obtaining a SECRET or TOP SECRET clearance. Each scholar must work for the federal, state, local, or tribal government (for pay) for one year for each year of award.

Awards made for 2017-2018 will be for one year only, with the potential of renewal if funding permits (we should know by August 31, 2017).  The number of awards to be made will be determined by available funds, since there are differences in costs depending on level and in-state status (we have approximately $352,000 to award in 2017-2018).

All applications must be submitted in paper form with official transcripts and signed original letters on letterhead—no staples, folders, or binders.

Application Deadline: 12noon, Friday, July 14, 2017.   If positions remain open after the deadline, we will continue to accept applications until classes start.

See https://www.sfs.opm.gov/  and http://www.cisa.umbc.edu for more details.

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bwtech@UMBC’s International Cybersecurity Center Launch

bwtech@UMBC will hold a launch event for its new International Cybersecurity Center from 9:00 to 10:30am on Thursday, 6 July 2017 at UMBC’s bwtech Cyber Incubator (5520 Research Park Drive, Suite 110, Catonsville, MD 21228).

The iCyberCenter@bwtech is a US market entry training and incubation program for overseas cybersecurity entrepreneurs that is offered in two parts. The first is a CEO-level Executive Training Program for overseas cyber entrepreneurs, which is an intensive, week-long course to help international cyber business leaders accelerate their understanding of the US cyber market and develop the most effective US market entry business strategies. The second is a mentored, year long US Market Entry Incubator Program that provides ongoing market entry and development support for qualifying overseas companies within the bwtech@UMBC Cyber Incubator.

Attend the iCyberCenter launch to learn more about its exciting new international programs and how you can get involved. It is looking for subject matter experts, professional service provider partners, sponsors, and supporters to help with the innovative and exciting program. Light breakfast will be served. RSVP for the event here.

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UMBC Data Science Graduate Programs

UMBC’s Data Science Master’s program prepares students from a wide range of disciplinary backgrounds for careers in data science. In the core courses, students will gain a thorough understanding of data science through classes that highlight machine learning, data analysis, data management, ethical and legal considerations, and more.

Students will develop an in-depth understanding of the basic computing principles behind data science, to include, but not limited to, data ingestion, curation and cleaning and the 4Vs of data science: Volume, Variety, Velocity, Veracity, as well as the implicit 5th V — Value. Through applying principles of data science to the analysis of problems within specific domains expressed through the program pathways, students will gain practical, real world industry relevant experience.

The MPS in Data Science is an industry-recognized credential and the program prepares students with the technical and management skills that they need to succeed in the workplace.

Why Data Science?

• Organizations have a growing need for employees who are experts in the management and interpretation of big data;
• Our classes are taught by industry experts who combine their professional experience with theory to provide a rigorous classroom experience; and
• Our small classes are taught with a mix of in-person and online instruction, providing students the best of an in-classroom experience while allowing for work-school life balance.

Why UMBC?

The Data Science graduate program at UMBC is designed to respond to the growing regional and national demand for professionals with data science knowledge, skills, and abilities. Bringing together faculty from a wide range of fields who have a deep understanding of the real-world applications of data analytics, UMBC’s Data Science program prepares students for the workplace through hands-on experience, rigorous academics, and access to a robust network of knowledgeable industry professionals. UMBC’s graduate programs in Data Science offers a wide variety of benefits:

• Exceptional faculty. The Data Science curriculum brings together UMBC’s Departments of Computer Science & Electrical Engineering, Information Systems, Mathematics and Statistics, and several departments from the social sciences to provide students with a rigorous and thorough base of knowledge. Faculty have particular strengths in addressing critical social questions through the application of data science.
• Rigorous research. UMBC is classified by the Carnegie Foundation as a Research University (High Research Activity).
• National recognition. For six years running (2009-2014), UMBC was ranked #1 in the U.S. News and World Report’s list of “national up-and-coming” universities
• Convenient classes. Classes are conveniently offered in the evening on UMBC’s main campus, located just ten minutes from BWI Airport, with easy access to I-95 and the 695 Beltway

For more information and to apply online, see the Data Science MPS site.

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Ahead of final exams this spring, nearly 200 students gathered at UMBC for “Innovate Good,” a hackathon co-hosted by HackUMBC and The Shriver Center, UMBC’s hub for service-learning, civic engagement, and community-based service delivery. During the 24-hour tech innovation marathon, students collaborated to build mobile, web, and hardware projects focused on solving problems and creating positive social outcomes. Four overarching categories inspired participants in their tech development: health and environment, housing, legal and justice, and education.

Collaborating with The Shriver Center to host the hackathon was an exciting experience, says Celeste Wong ‘18, computer science, co-president of HackUMBC. “There were different aspects to the hackathon we had to think about and work through since we’ve never done a social good Hackathon,” she explained. “In the end it was very rewarding to see students who are passionate about different areas of social justice. Seeing the participants demo and explain their projects really showcased their passion and heart for others at our school and in our community.”

Rick Forno, assistant director for UMBC’s Center for Cybersecurity and director of the Cybersecurity Graduate Program, serves as faculty advisor of the HackUMBC student organization. “Hackathons like HackUMBC are diverse, high-energy events that bring together novices, experts, and mentors to share and apply their knowledge, skills, and personal enthusiasm toward exploring the world of technology and/or developing innovative solutions with technology,” he says. He was delighted at the success of this first themed HackUMBC event in bringing together participants with diverse interests, experiences, and perspectives.

Hannah Schmitz, program coordinator of public service scholars programs in The Shriver Center, agreed, sharing, “So often we see a divide between ‘techie’ and ‘non-techie’ students. The Shriver Center and HackUMBC designed ‘Innovate Good’ to bridge that divide, bringing students together to harness their skills and experiences to co-create interdisciplinary solutions to social challenges.” She explained, “Participants had a chance to collaborate with students with varying disciplinary perspectives, and had an opportunity to learn from, work with, and teach each other.”

An “expo” portion toward the end of the event gave participants an the opportunity to share the hacks that they developed with their peers and receive feedback from faculty judges.

“It’s our hope that each participant walked away with new skill sets and a deepened interest in using their talents to make an impact in their community,” says Schmitz.

For more information about upcoming hackathons at UMBC, visit the HackUMBC website.

Adapted from a UMBC News article by Megan Hanks; header image: Students presenting their hacks at the “expo” portion of the hackathon. All photos by Marlayna Demond ’11 for UMBC.

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CSEE Professor Anupam Joshi talking in front of UMBC’s π² Immersive Hybrid Reality display

Professor of the Practice
Graduate Program Director, Data Science

Department of Computer Science and Electrical Engineering
University of Maryland, Baltimore County (UMBC)

The Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC) invites applications for a full time, non-tenure track Professor of the Practice position at its Catonsville, MD main campus.

Responsibilities: Reporting to the Chair of the Department of Computer Science and Electrical Engineering, this position serves as the Graduate Program Director of UMBC’s program leading to a master’s degree in Data Science. This cross-disciplinary program is offered to professional students through a partnership between the College of Engineering and Information Technology; the College of Arts, Humanities, and Social Sciences; the College of Natural and Mathematical Sciences; the Department of Computer Science and Electrical Engineering; and UMBC’s Division of Professional Studies (DPS). The incumbent will teach at least two courses per semester in the program. In addition to teaching, among other faculty duties, the incumbent will oversee curriculum and instruction; recruit and supervise qualified part-time faculty; collaborate with DPS on program marketing, student recruitment and retention activities; advise students; pursue business development opportunities with industry; manage UMBC’s Data Science Advisory Board; and carry out the necessary administrative duties associated with academic program oversight.

Salary: Salary is competitive and commensurate with qualifications and experience, and includes regular university benefits.

About UMBC: UMBC’s campus is located on 500 acres just off I-95 between Baltimore and Washington DC, and less than 10 minutes from the BWI airport and Amtrak station. The campus includes the bwtech@UMBC research and technology park, which has special programs for startups focused on cybersecurity, clean energy, life sciences and training. We are surrounded by one of the greatest concentrations of commercial, cultural and scientific activity in the nation. Located at the head of the Chesapeake Bay, Baltimore has all the advantages of modern, urban living, including professional sports, major art galleries, theaters and a symphony orchestra. The city’s famous Inner Harbor is an exciting center for entertainment and commerce. The nation’s capital, Washington, DC, is a great tourist attraction with its historical monuments and museums. Just ten minutes from downtown Baltimore and 30 from the D.C. Beltway, UMBC offers easy access to the region’s attractions by car or public transportation.

Qualifications: Earned doctoral degree or anticipated completion of a doctoral program in a relevant field within one year of hire. Preference will be given to applicants with at least five years of relevant professional experience, preferably as a data scientist. Teaching experience in classroom and/or on-line environments for industry-oriented graduate programs is desired. Candidates should have strong organizational, management, and public communication skills.

Application Instructions: For best consideration, submit a cover letter, a brief statement of teaching, academic, industrial or government experience, complete CV, and three professional or academic references by 1 July 2017 via Interfolio. Document review and selection of candidates will start immediately. Position will remain open until filled. Emailed applications will not be accepted.

We are committed to inclusive excellence and innovation and welcome applications from women, minorities, veterans, and individuals with disabilities. UMBC is an Affirmative Action/Equal Opportunity Employer.

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