UMBC Hour of Code, 11-2 Dec 7-8, Main Street


The CS Education student org, with support from the CS Matters in Maryland CS education project, is planning UMBC’s first-ever Hour of Code event. Hour of Code is an initiative that organizes hands-on learning experiences for students of all ages during CS Education Week (December 5-11, 2016, coinciding with Admiral Grace Hopper’s birthday).

UMBC’s Hour of Code will offer a hands-on experience for anybody who wants to try their hand at coding, December 7 and 8 from 11am-2pm on Main Street. We will have several special guests on Thursday — some students from Lakeland Elementary School will learn to code along with President Freeman Hrabowski from 11am-noon that day.

We need many volunteers to help make this event a success! Although coding experience is useful, it is NOT necessary! We will have training events in advance of the event for everyone who volunteers, and we also have some no-coding-required jobs as well, including running a Makey Makey activity and helping out with the elementary school students.

Please sign up to volunteer. Stephanie Milani (Psychology major / CS minor) is organizing the volunteer effort — please feel free to contact her if you have any questions at

PhD defense: Deep Neural Networks in Real-Time Embedded Systems


PhD Dissertation Defense

Deploying Deep Neural Networks in Real-Time Embedded Systems

Adam Page

10:00am Monday, 21 November 2016, ITE 325b

Deep neural networks have been shown to outperform prior state-of-the-art solutions that rely heavily on hand-engineered features coupled with simple classification techniques. In addition to achieving several orders of magnitude improvement, they offer a number of additional benefits such as the ability to perform end-to-end learning by performing both hierarchical feature abstraction and inference. Furthermore, their success continues to be demonstrated in a growing number of fields for a wide-range of applications, including computer vision, speech recognition, biomedical, and model forecasting. As this area of machine learning matures, a major challenge that remains is the ability to efficiently deploy such deep networks in embedded, resource-bound settings that have strict power and area budgets. While GPUs have been shown to improve throughput and energy efficiency over traditional computing paradigms, they still impose significant power burden for such low-power embedded settings. In order to further reduce power while still achieving desired throughput and accuracy, classification-efficient networks are required in addition to optimal deployment onto embedded hardware.

In this dissertation, we target both of these enterprises. For the first objective, we analyze simple, biologically-inspired reduction strategies that are applied both before and after training. The central theme of the techniques is the introduction of sparsification to help dissolve away the dense connectivity that is often found at different levels in neural networks. The sparsification techniques developed include feature compression partition, structured filter pruning and dynamic feature pruning.

In the second contribution, we propose scalable, hardware-based accelerators that enable deploying networks in such resource-bound settings by both exploiting efficient forms of parallelism inherent in convolutional layers and by exploiting the sparsification and approximation techniques proposed. In particular, we developed SPARCNet, an efficient and scalable hardware convolutional neural network accelerator, along with a corresponding resource-aware API to reduce, translate, and deploy a pre-trained network. The SPARCNet accelerator has been fully implemented in FPGA hardware and successfully employed for a number of case studies and evaluated against several existing state-of-the-art embedded platforms including NVIDIA Jetson TK1/TX1 in real-time. A full hardware demonstration with the developed API will be showcased that enables selecting between hardware platforms and state-of-the-art vision datasets while performing real-time power, throughput, and classification analysis.

Committee: Drs. Tinoosh Mohsenin (chair), Anupam Joshi, Tim Oates, Mohamed Younis, Farinaz Koushanfar

talk: Mobile Security Architectures, Threats & Mitigation, 6-8pm Tue 11/15

Mobile Security Architectures, Threats and Mitigation

Joshua Franklin, NIST

6:00-8:00pm Tuesday, 15 November 2016
Building III Room 2226, UMBC@Universities at Shady Grove

Cellular technology plays an increasingly large role in society as it has become the primary portal to the internet for a large segment of the population. One of the main drivers making this change possible is the deployment of modern 4G LTE cellular technologies. This talk will cover the fundamentals of cellular network operation and explores the evolution of 2G GSM, 3G UMTS and 4G cellular security architectures. Then, the talk will turn to an analysis and discussion of the threats posed to cellular networks and supporting mitigation techniques. Although the talk will include older GSM and UMTS technologies, it will be focused heavily on LTE as the current-state of industry.

Joshua Franklin is a Security Engineer at the National Institute of Standards and Technology (NIST) focusing on cellular security, electronic voting, and public safety. Prior to NIST, Joshua worked at the U.S. Election Assistance Commission gathering extensive experience with voting technologies. After graduating from Kennesaw State University with a Bachelors of Science in Information Systems, he received a Masters of Science in Information Security and Assurance from George Mason University.

Host: Dr. Ben Shariati ()

Dissertation defense: Cross-Layer Techniques for Boosting Base-Station Anonymity in Wireless Sensor Networks

Dissertation Defense Announcement

Cross-Layer Techniques for Boosting Base-Station Anonymity in Wireless Sensor Networks

Sami Alsemairi

9:30 Wednesday, 9 November 2016, ITE 346

Wireless Sensor Networks (WSNs) provide an effective solution for surveillance and data gathering applications in hostile environments where human presence is infeasible, risky or very costly. Examples of these applications include military reconnaissance, guarding boarders against human trafficking, security surveillance, etc. A WSN is typically composed of a large number of sensor nodes that probe their surrounding and transmit measurements over multi-hop paths to an in-situ Base-Station (BS). The BS not only acts as a sink of all collected sensor data but also provides network management and serves as a gateway to remote commend centers. Such an important role makes the BS a target of adversary attacks that opt to achieve Denial-of-Service (DoS) and nullify the WSN utility to the application. Even if the WSN applies conventional security mechanisms such as authentication and data encryption, the adversary may apply traffic analysis techniques to locate the BS and target it with attacks. This motivates a significant need for boosting BS anonymity to conceal its location.

In this dissertation, we address the challenges of BS anonymity and develop a library of techniques to counter the threat of traffic analysis. The focus of our work is on the link and network layers. We first exploit packet combining as a means to vary the traffic density throughout the network. We call this technique combining the data payload of multiple packets (CoDa), where a node groups the payload of multiple incoming data packets into a single packet that is forwarded toward the BS. CoDa cuts on the number of transmissions that constitute evidences for implicating the BS as a destination of all traffic and thus degrades the adversary’s ability in conducting effective traffic analysis.

Next we develop a novel technique for increasing BS anonymity by establishing a sleep/active schedule among the nodes that are far away from the BS, and increasing the traffic density in selected parts of the network in order to give the impression that the BS is located in the vicinity of the sleeping nodes. We call this technique Adaptive Sampling Rate for increased Anonymity (ASRA). Moreover, we develop three novel techniques based on a hierarchical routing topology. The first, which we call Hierarchical Anonymity-aware Routing Topology (HART), forms clusters and an inter-cluster-head routing topology so that a high traffic volume can be observed in areas away from the BS. The second is a novel cross-layer technique that forms a mesh topology. We call this technique cluster mesh topology to boost BS’s anonymity (CMBA). CMBA opts to establish a routing topology such that the traffic pattern does not implicate any particular node as a sink.

The third technique creates multiple mesh-based routing topologies among the cluster-heads (CHs). By applying the closed space-filling curves such as the Moore curve, for forming a mesh, the CHs are offered a number of choices for disseminating aggregated data to the BS through inter-CH paths. Then, the BS forwards the aggregated data as well so that it appears as one of the CH. We call this technique boosting the BS anonymity through multiple mesh-based routing topologies (BAMT). We validate the effectiveness of all anonymity-boosting techniques through simulation and highlight the trade-off between anonymity and overhead.

Committee: Drs. Mohamed Younis (Chair), Charles Nicholas, Chintan Patel, Richard Forno and Waleed Youssef

talk: Engineering Plaintext Private Information Retrieval Systems, 1pm Fri 4/11, UMBC

The UMBC CSEE Seminar Series Presents

Practical Engineering of Plaintext
Private Information Retrieval Systems

Dr. Russell Fink

Chief Engineer, Cyber Operations Branch,
Johns Hopkins University / Applied Physics Laboratory

1-2pm Friday, 4 November 2016, ITE 229

Cloud computing has come a long way in the last decade, with many advances in supported platforms, security, and cost effectiveness. As organizations are increasingly turning to the cloud to outsource their big data storage and processing needs, both problems and opportunities arise for understanding and analyzing large repositories of data.

One problem in particular is querying large data in a safe and secure way – querying a large data set can compromise search privacy, revealing the interests, motivations, and true identity of the data querier to the data owner, hindering legitimate uses including data analytics, security, and law enforcement. Alice, wishing to search Bob’s queue of plaintext data, may turn to Private Information Retrieval (PIR) techniques to maintain her privacy without sacrificing bandwidth or deploying a trusted device in Bob’s spaces.

We have prototyped a PIR system based on the homomorphic Paillier cryptosystem and Bethencourt/Song search method, and discovered important engineering techniques along the way that are useful for deploying a scalable system. In this talk, I will introduce and motivate the PIR problem and describe the Paillier homomorphic retrieval system and Bethencourt’s technique. I will give an overview of our specific advances, notably, a novel technique for private regular expression pattern searching over plaintext, including an algorithm for resisting a privacy attack against the resulting search automaton.


Russell A. (“Russ”) Fink is the Chief Engineer of the Cyber Operations Branch, Asymmetric Operations Sector, of the Johns Hopkins University Applied Physics Laboratory. He holds a Bachelor’s degree in computer science from the University of Maryland, College Park; a Master’s degree in computer systems management from the University of Maryland, University College; and a Ph.D. from the University of Maryland, Baltimore County for his work on electronic voting and trustworthy computing. His research interests include systems security engineering, trusted computing, machine learning, and privacy preserving cryptographic applications.

Organizers: Professors Tulay Adali () and Alan T. Sherman ()

About the CSEE Seminar Series: The UMBC Department of Computer Science and Electrical Engineering presents technical talks on current significant research projects of broad interest to the Department and the research community. Each talk is free and open to the public. We welcome your feedback and suggestions for future talks.

talk: Statistics and Big Data at Google, 5-6pm Thr 11/3, UC310, UMBC

Statistics and Big Data at Google

Dr. Tim Hesterberg, Google

5:00-6:00pm Thursday, 3 November 2016, UC 310, UMBC

Google lives on data. Search, Ads, YouTube, Maps…they all live on data. Join Senior Quantitative Analyst (and Lady Statistician) Tim Hesterberg, as he shares stories about how we use data, how we’re experimenting to make improvements (yes, this includes your searches), and how we adapt statistical ideas to do things that have never been done before. This will be a general-audience, non-technical talk. No statistics background is needed!

Dr. Hesterberg previously worked at Insightful (S-PLUS), Franklin & Marshall College, and Pacific Gas & Electric Co. He received his Ph.D. in Statistics from Stanford University, under Brad Efron. Hesterberg is author of the “Resample” package for R and primary author of the “S+Resample” package for bootstrapping, permutation tests, jackknife, and other resampling procedures, is co-author of Chihara and Hesterberg “Mathematical Statistics with Resampling and R” (2011), and is lead author of “Bootstrap Methods and Permutation Tests” (2010), W. H. Freeman, ISBN 0-7167-5726-5, and numerous technical articles on resampling.


Six new SFS cybersecurity scholars to join UMBC in 2017


Six new cybersecurity scholars were inducted into UMBC’s NSF-sponsored Scholarship for Service program in an event held in Germantown on October 20. Three are currently students at Montgomery College and three are from Prince Georges Community College. After they complete their associates degree in spring 2017, they will transfer to UMBC to complete their undergraduate degrees.

This pioneering cooperation between UMBC, Montgomery College, and Prince Georges Community College in cybersecurity is made possible by a grant from the National Science Foundation (Dr. Alan Sherman (UMBC), Joe Roundy (MC), and Casey O’Brien (PGCC), CoPIs). As part of their education, the SFS scholars will solve IT security problems for their county government.

As SFS Scholars, the students receive tuition, fees, annual reimbursement of professional development expenses, a nine-month stipend and assistance with federal cybersecurity internships and career placement.

talk: Understanding Ambiguity in Privacy and Security Requirements, 11:15 Fri 11/4 ITE229

The UMBC Cyber Defense Lab presents

Regulatory Compliance Software Engineering:
Understanding Ambiguity in Privacy and Security Requirements

Aaron Massey

Department of Information Systems
University of Maryland, Baltimore County

11:15am-12:30pm Friday, 4 November  2016, ITE 229

Software engineers building software systems in regulated environments must ensure that software requirements accurately represent obligations described in laws and regulations. Ambiguities in legal texts can make the difference between compliance and non-compliance. Ensuring alignment and compatibility is challenging because policy analysts who write laws and regulations approach ambiguity differently than the software engineers who implement software in regulated environments. Although software regulation continues to increase in visibility, prevalence, and importance–particularly for security and privacy, few software processes address challenge of identifying, classifying, and understanding regulatory ambiguity. Herein, we develop an ambiguity taxonomy based on software engineering, legal, and linguistic approaches to ambiguity. We also present two case studies of policy analysts and technologists identifying and classifying ambiguities in a portion of the Health Insurance Portability and Accountability Act (HIPAA) using this taxonomy. Results of this work suggest that the taxonomy developed can serve as a guide for identifying and classifying ambiguity but participants were not able to consistently agree on a rationale defending their ambiguity classification. These results suggest a strategy for addressing ambiguities in regulatory text—software engineers are likely to be successful at identifying elements of a legal text that then require supplemental expertise to resolve. The contributions of this work include the ambiguity taxonomy developed as well as mechanism for reporting identified ambiguities in a legal text which we call Ambiguity Intensity Maps.

 Aaron Massey is an Assistant Professor of Software Engineering at UMBC and the Co-Director of  His research interests include computer security, privacy, software engineering, and regulatory compliance in software systems.  Aaron is a recipient of the Walter H. Wilkinson Graduate Research Ethics Fellowship and a recipient of a Google Policy Fellowship.  Before coming to UMBC, he was a Postdoctoral Fellow at Georgia Tech’s School of Interactive Computing.  Aaron earned a PhD and MS in Computer Science from North Carolina State University and a BS in Computer Engineering from Purdue University.  He is a member of the ACM, IEEE, IAPP, and the USACM Public Policy Council.

Host: Alan T. Sherman,


talk: Learning to Predict the Future from Unlabeled Data, 1pm Fri 10/28, ITE229, UMBC

The UMBC CSEE Seminar Series Presents

Learning to Predict the Future from Unlabeled Data

Hamed Pirsiavash, CSEE Department, UMBC

1-2pm Friday, 28 October 2016, ITE 229

Anticipating actions and objects before they start or appear is a difficult problem in computer vision with several real-world applications. This task is challenging partly because it requires leveraging extensive knowledge of the world that is difficult to write down. We believe that a promising resource for efficiently learning this knowledge is through readily available unlabeled video. I will talk about our framework that capitalizes on temporal structure in unlabeled video to learn to anticipate human actions and objects. The key idea behind our approach is that we can train deep networks to predict the visual representation of images in the future. I will also talk about our recent work on a Generative Adversarial learNing (GAN) architecture that generates a novel video given the first frame.

Hamed Pirsiavash is an assistant professor at the University of Maryland, Baltimore County (UMBC) since August 2015. Prior to that, he was a postdoctoral research associate at MIT working with Antonio Torralba. He earned his PhD at the University of California Irvine under the supervision of Deva Ramanan (now at CMU). He performs research in the intersection of computer vision and machine learning.

Organizers: Professors Tulay Adali () and Alan T. Sherman ()

About the CSEE Seminar Series: The UMBC Department of Computer Science and Electrical Engineering presents technical talks on current significant research projects of broad interest to the Department and the research community. Each talk is free and open to the public. We welcome your feedback and suggestions for future talks.

Online discussion with NGC: Building the Cyber Workforce, 10am Fri Oct 28

Today’s cybersecurity industry is expected to grow by almost $100 billion dollars by 2020. That means that there will be an unprecedented number of jobs to fill to meet the demand and keep to our economic and national security intact. Job postings for cybersecurity positions have increased 74%  in the past five years, with a global projection of 1.5 million unfilled jobs over the next five years. Many are calling the increasing shortage of workers a national crisis.

Northrop Grumman will host an online event to discuss Building the Cyber Workforce from 10:00-11:00am on Friday, 28 October 2016. The discussion features UMBC president Freeman Hrabowski and two UMBC alumni: Lauren Mazzoli (’15 CS, Math) who is currently a Northrop Grumman Cyber Software Engineer and Eric Conn (’85 CS) who is the founder and CEO of Leverege and a bwtech@UMBC Cync Incubator participant.  The discussion will be moderated by Tom Temin of Federal News Radio.

You can watch the discussion live this Friday on the Web, tweet questions to @NGCNews and follow @UMBC, which will be live tweeting the #NGcyber event. If you are on campus, join us in ITE325 to watch the event and discuss it afterward.

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