New spring course: Principles of Human-Robot Interaction

Principles of Human-Robot Interaction

CSEE professor Cynthia Matuszek will teach a new special topics course this spring on Principles of Human-Robot Interaction. The graduate level course (CMSC 691-08) will meet on Tuesday and Thursdays from 4:00 to 5:30pm in 013 Sherman Hall.


Principles of Human-Robot Interaction

An introduction to robots in our daily lives

CMSC691-08, 4:00-5:15pm Tue/Thr, starting 26 January 2016, UMBC

Robots are becoming ubiquitous. From Roombas in our homes, to surgical robots in hospitals, to giant manipulators that assemble cars, robots are everywhere. In the past, robots have only ever interacted with highly trained experts. Now, as they are being deployed more widely, we must address new questions about how our robots can interact day-to-day with end users — non-experts — safely, usefully, and pleasantly. This new area of research is called Human-Robot Interaction, or HRI.

This 3-credit special topics course aims to introduce students to current research in HRI and provide hands-on experience with HRI research. Students will explore the diverse range of research topics in this area, learn to identify HRI problems in their own research, and carry out a collaborative project involving human-robot interactions. Topics to be covered include:

  • Social robots: how can robots be social beings? When do we want them to?
  • Human-robot collaboration: humans and robots working together on tasks
  • Natural-language interactions with robots and human-robot dialog
  • Telerobotics: the uses of remote presence and teleoperation
  • Expressive robots: how can robots express emotion – and should they?

Students may benefit from having some previous coursework or experience in AI, machine learning, or robotics, but none are necessary. Undergraduate students can enroll with the instructor’s permission. For more information, contact Dr. Matuszek at cmat at

Apply to CRA-W 2016 Grad Cohort Workshop by Nov. 30


CRA-W is now accepting applications for Grad Cohort 2016, a two-day workshop during which participants will learn graduate school survival skills, receive mentoring, and develop networks with senior female computing researchers. This is a great opportunity for female graduate students to build mentoring relationships and develop peer networks to form the foundation of their graduate career and beyond.p

Female graduate students in their first three years are eligible to apply. Reasonable travel expenses, meals, and lodging will be provided for students chosen to participate in this program.

The Grad Cohort 2016 workshop will be held at the Hilton San Diego Bayfront in San Diego, California, on April 15-16, 2016. The application deadline is 30 November 2015. Apply online here and get more information at the Grad Cohort 2016 Workshop site.

PhD defense: Yungsu Lee

Ph.D. Dissertation Defense

Automatic Service Search and Composability
Analysis in Large Scale Service Networks

Yunsu Lee

10:00am Wednesday 25 November 2015, ITE 346, UMBC

Currently, software and hardware system components are trending toward modularized and virtualized as atomic services on the cloud. A number of cloud platforms or marketplaces are available where everybody can provide their system components as services. In this situation, service composition is essential, because the functionalities offered by a single atomic service might not satisfy users’ complex requirements. Since there are already a number of available services and significant increase in the number of new services over time, manual service composition is impractical.

In our research, we propose computer-aided methods to help find and compose appropriate services to fulfill users’ requirement in large scale service network. For this purpose, we explore the following methods. First, we develop a method for formally representing a service in term of composability by considering various functional and non-functional characteristics of services. Second, we develop a method for aiding the development of the reference ontologies that are crucial for representing a service. We explore a bottom-up-based statistical method for the ontology development. Third, we architect a framework that encompasses the reference models, effective strategy, and necessary procedures for the services search and composition. Finally, we develop a graph-based algorithm that is highly specialized for services search and composition. Experimental comparative performance analysis against existing automatic services composition methods is also provided.

Commitee: Drs. Yun Peng (chair), Tim Finin, Yelena Yesha, Milton Halem, Nenad Ivezic (NIST) and Boonserm Kulvatunyou (NIST)

MS defense: Distance Adaptation of Diffuse Reflectance and Subsurface Scattering


MS Defense
UMBC Computer Science and Electrical Engineering

Distance Adaptation of Diffuse Reflectance
and Subsurface Scattering

Elizabeth Baumel

1:30pm Friday, November 20, ITE 352, UMBC

Objects in the world around us are made of a myriad of materials, both metallic and non-metallic. Most non-metallic materials scatter light in varying amounts within their surfaces, giving softer, more saturated diffuse colors and softer-edged shadows. This effect, subsurface scattering, is important to make translucent objects look realistic. Non-metallic objects that are opaque also scatter light, just at a very small distance. These non-metallic materials may look somewhat translucent at very close viewing distances, but from farther away they exhibit a more opaque, but still soft diffuse appearance. To shade these objects realistically from all distances, a method is needed to model subsurface scattering effects at close ranges and to smoothly transition to a soft diffuse reflection at larger viewing distances. We present a method that takes advantage of graphics processor texture filtering hardware to linearly filter maps that encode diffuse reflection and translucency information and to interpolate between a close-range subsurface scattering effect and a long-range reflectance function.

Committee: Drs. Marc Olano (Advisor, Chair), Penny Rheingans, Jian Chen

talk: Matuszek on Giving Successful Technical Presentations, 2pm 11/18

UMBC Professor CYnthia Matuszek

UMBC ACM Tech Talk

Giving Successful Technical Presentations
Prof. Cynthia Matuszek, UMBC

2:00pm Wednesday 18 November 2015, ITE325

Giving talks is one of the core tasks of a researcher. Technical presentations are how we accomplish some of our most important tasks: talks are the first step in getting other people excited about our work, getting suggestions and feedback, teaching, and applying for jobs and grants. Nonetheless, the art and science of giving a really good technical talk is one we are more likely to leave to chance than to deliberately train in. Not only does this mean we aren’t accomplishing everything we could with our presentations; we’re missing a chance to distinguish ourselves by improving a comparatively rare — but learnable — skill.

In this talk, I will describe the idea of the “culture of conveying information,” and give a number of specific suggestions for improving technical talks — including tools, rules of thumb, social conventions, and suggestions for making your talks engaging, informative, and memorable.

Cynthia Matuszek is an Assistant Professor at the University of Maryland, Baltimore County’s Computer Science and Electrical Engineering department where she heads the Interactive Robotics and Language lab. She completed her Ph.D. at the University of Washington in 2014, where she was a member of both the Robotics and State Estimation lab and the Language, Interaction, and Learning group. She is published in the areas of artificial intelligence, robotics, ubiquitous computing, and human-robot interaction. Her research interests include human-robot interaction, natural language processing, and machine learning.

MS Defense: Blind source separation for detection of abandoned objects

ENEE MS Thesis Defense

Blind source separation for detection of abandoned objects:
Exploiting different types of diversity

Suchita Bhinge

2:30pm Friday, 13 November 2015, ITE 325B

Due to the increase in security concerns, automated detection of abandoned objects has become an important application in video surveillance. Because of its increasing importance, a number of techniques have been proposed recently to automatically detect abandoned objects. The general procedure implemented for detection of abandoned objects includes background subtraction or foreground object extraction followed by post-processing steps in order to classify the foreground object as an abandoned or non-abandoned object. However, these techniques make use of a number of user-defined parameters such as track time, co-ordinates of the object/owner, the vicinity of the object, and properties of the object such as its shape, color, among others.

In this thesis, we present a new technique based on blind source separation (BSS) for detection of abandoned objects that does not keep track of the extracted objects or owners and does not require a dual background scheme for stationary object extraction. Order selection is an important step for our implementation of blind source separation based scheme since this step captures the signals with high energy and disregards signals that are not relevant to the detection of abandoned objects. In this thesis, we show that the performance of ICA improves when an algorithm that assumes a flexible source distribution along with multiple types of diversity, such as higher-order statistics and sample dependence is used for the estimation of the source components. ICA, however, can only model one dataset at a time, thus limiting its usage to monochrome frames. In order to address this issue, we also present another implementation of blind source separation called independent vector analysis (IVA), a recent extension of ICA to multiple data that takes the dependence across multiple datasets into account while retaining the model of independent components within each dataset. We show that the proposed blind source separation techniques performs successfully in complicated scenarios such as crowd, occlusion, and illumination changes.

Committee: Drs. Tulay Adali (chair), Joel Morris and Mohamed Younis

Free workshop on using the Arduino microcontroller, Sat. 11/14 and 11/21


The UMBC IEEE Branch will hold an Arduino workshop on Saturday November 14th and next Saturday November 21st from 2:00-6:00pm in SHER 003 (Lecture Hall 4). It’s a great opportunity for people to learn about microcontrollers and circuit basics and how to use Arduino for building cyber-physical systems for home automation, robotics, games and more.

The Arduino microcontroller is a great device for anyone who wants to learn more about technology. It is used in a variety of fields in research and academia and may even help you get an internship. Our instructors have used the Arduino for researching self-replicating robots and remote-controlled helicopters, hacking into a vehicle’s control system, and using radars to detect human activity in a room. Some of the hackathon projects by our IEEE members include developing a drink mixer that wirelessly connects with a Tesla Model S and a full-body haptic feedback suit for the Oculus Rift. The Arduino is a wonderful tool and is fairly easy to use. Everyone should learn how to use it!

UMBC’s Institute of Electrical and Electronics Engineers is hosting two Level 1 workshops this semester. They are hosted this Saturday (Nov. 14th) and next Saturday (Nov. 21st). The workshop will be SHER 003 (Lecture Hall 4) from 2pm to 6pm. Please register online to sign up for either workshop. Contact Sekar Kulandaivel () if you have any questions.

The workshop is open to all majors (minimum coding experience recommended). You only need to bring your laptop and charger and download and install the Arduino IDE. We hope to see many of you this weekend! You REALLY don’t want to miss out on this opportunity.

PhD defense: Connectivity Restoration in Damaged Wireless Sensor Networks

PhD Dissertation Defense

Distributed Protocols for Connectivity Restoration
in Damaged Wireless Sensor Networks

Yatish Joshi

9:30 Monday, 23 November 2015, ITE 325b

Decreasing costs and increasing functionality of hardware devices have made Wireless Sensor Networks (WSNs) attractive for applications that serve in inhospitable environments like battlefields, planetary exploration or environmental monitoring. WSNs employed in these environments are expected to work autonomously and extend network lifespan for as long as possible while carrying out their designated tasks. The harsh environment exposes individual nodes to a high risk of failure and their failure can partition the network into disjoint segments. Therefore, a network must be able to self-heal and restore lost connectivity using available resources. The ad-hoc nature of deployment, harsh operating environment means that proactive strategies based on redundancy cannot be applied as the scope of the damage could be so large that redundant nodes could be lost as well. The lack of external resources like satellite coverage preclude the application of centralized recovery approaches since they require the entire network state to be available for recovery. Hence distributed approaches that employ reactive strategies are the most viable solutions for these networks.

In this dissertation, we tackle the problem of distributed connectivity restoration in a WSN that has been partitioned into multiple disjoint segments due to multi-node failures. We consider multiple variants of the problem based on the available resources, and present a set of novel recovery schemes that suit the capabilities and requirements of the WSN being repaired. The correctness and time-complexity of all proposed approaches are analyzed and their performance is validated through extensive experiments.

Committee: Drs. Mohamed Younis (Chair), Charles Nicholas, Chintan Patel, Kemal Akkaya (FIU), Waleed Youssef (IBM)

NSF CyberCorps: Scholarship For Service, Nov 20 deadline

UMBC undergraduate and graduate students interested in cybersecurity can apply for an NSF CyberCorps: Scholarship For Service scholarship by 20 November 2015.

The NSF CyberCorps: Scholarship For Service program is designed to increase and strengthen the cadre of federal information assurance professionals that protect the government’s critical information infrastructure. This program provides scholarships that may fully fund the typical costs incurred by full-time students while attending a participating institution, including tuition and education and related fees. Participants also receive stipends of $22,500 for undergraduate students and $34,000 for graduate students.

Applicants must be be full-time UMBC students within two years of graduation with a BS or MS degree; a student within three years of graduation with both the BS/MS degree; a student participating in a combined BS/MS degree program; or a research-based doctoral student within three years of graduation in an academic program focused on cybersecurity or information assurance. Recipients must also be US citizens; meet criteria for Federal employment; and be able to obtain a security clearance, if required.

For more information and instructions on how to apply see the UMBC CISA site or the OPM SFS site. Contact Dr. Alan Sherman () for questions not answered on those sites.

PhD proposal: Lyrics Augmented Multi-modal Music Recommendation, 1pm 10/30

Lyrics Augmented Multi-modal
Music Recommendation

Abhay Kashyap

1:00pm Friday 30 October, ITE 325b

In an increasingly mobile and connected world, digital music consumption has rapidly increased. More recently, faster and cheaper mobile bandwidth has given the average mobile user the potential to access large troves of music through streaming services like Spotify and Google Music that boast catalogs with tens of millions of songs. At this scale, effective music recommendation is critical for music discovery and personalized user experience.

Recommenders that rely on collaborative information suffer from two major problems: the long tail problem, which is induced by popularity bias, and the cold start problem caused by new items with no data. In such cases, they fall back on content to compute similarity. For music, content based features can be divided into acoustic and textual domains. Acoustic features are extracted from the audio signal while textual features come from song metadata, lyrical content, collaborative tags and associated web text.

Research in content based music similarity has largely been focused in the acoustic domain while text based features have been limited to metadata, tags and shallow methods for web text and lyrics. Song lyrics house information about the sentiment and topic of a song that cannot be easily extracted from the audio. Past work has shown that even shallow lyrical features improved audio-only features and in some tasks like mood classification, outperformed audio-only features. In addition, lyrics are also easily available which make them a valuable resource and warrant a deeper analysis.

The goal of this research is to fill the lyrical gap in existing music recommender systems. The first step is to build algorithms to extract and represent the meaning and emotion contained in the song’s lyrics. The next step is to effectively combine lyrical features with acoustic and collaborative information to build a multi-modal recommendation engine.

For this work, the genre is restricted to Rap because it is a lyrics-centric genre and techniques built for Rap can be generalized to other genres. It was also the highest streamed genre in 2014, accounting for 28.5% of all music streamed. Rap lyrics are scraped from dedicated lyrics websites like and while the semantic knowledge base comprising artists, albums and song metadata come from the MusicBrainz project. Acoustic features are directly used from EchoNest while collaborative information like tags, plays, co-plays etc. come from

Preliminary work involved extraction of compositional style features like rhyme patterns and density, vocabulary size, simile and profanity usage from over 10,000 songs by over 150 artists. These features are available for users to browse and explore through interactive visualizations on Song semantics were represented using off-the-shelf neural language based vector models (doc2vec). Future work will involve building novel language models for lyrics and latent representations for attributes that is driven by collaborative information for multi-modal recommendation.

Committee: Drs. Tim Finin (Chair), Anupam Joshi, Pranam Kolari (WalmartLabs), Cynthia Matuszek and Tim Oates

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