Anupam Joshi and Yelena Yesha working with several UMBC students. Photo by Mitro Hood/Feature Photo Service.
UMBC and IBM Research have announced a collaboration to create the Accelerated Cognitive Cybersecurity Lab (ACCL), opening at UMBC in fall 2016. Housed in the College of Engineering and Information Technology and supported with a multi-year commitment from IBM, it will advance scientific frontiers in cybersecurity and machine learning. Anupam Joshi, director of the UMBC Center for Cybersecurity and chair of the Computer Science and Electrical Engineering Department, will lead the ACCL.
The lab will build on UMBC’s prior research on AI, high performance computing, data visualization and cybersecurity and work IBM researchers to apply IBM’s cognitive computing systems and tools, including the Watson computer system.
You can read more about the new partnership from IBM’s press releases (here and here), UMBC’s announcement and other new media (e.g., here).
IT & Engineering Alumni Happy Hour
6:00–8:00pm Tuesday, 14 June 2016
Union Jack’s, 10400 Little Patuxent Parkway, Columbia MD 21044
Join fellow graduates of the College of Engineering and Information Technology and those who now work in those fields for a networking happy hour. Enjoy fabulous food and drinks on us while connecting with other IT&E Retrievers. Sponsored by the UMBC Alumni Association. RSVP by June 6 here. Questions? Contact Amy Dalrymple at
UMBC’s Julia Ross, Dean of the College of Engineering and Information Technology, and IBM Watson General Manager David Kenny discussed Everyday AI — new and developing technologies in Artificial Intelligence that are leaving the lab and entering the consumer market at the Washington Post’s Transformers live journalism event on 18 May 2016.
Cognitive Computing and Visualization at IBM Research/RPI CISL
Dr. Hui Su, IBM Research
10:00-11:00am, Thursday, 19 May 2016, ITE 325b
Dr. Hui Su will talk about Cognitive and Immersive Systems Lab, a research initiative to develop the new frontier of immersive cognitive systems that explore and advance natural, collaborative problem-solving among groups of humans and machines. This lab is a collaboration between IBM Research and Rensselaer Polytechnic Institute. Dr. Su will talk about why the research for human computer interaction is extended to build a symbiotic relationship between human beings and smart machines, what research is going to be important to build immersive cognitive systems in order to transform the way professionals work in the future.
Dr. Hui Su is the Director of Cognitive and Immersive Systems Lab, a collaboration between IBM Research and Rensselaer Polytechnic Institute. He has been a technical leader and an executive at IBM Research. Most recently, he was the Director of IBM Cambridge Research Lab in Cambridge, MA and was responsible for a broad scope of global missions in IBM Research, including Cognitive User Experience, Center for Innovation in Visual Analytics and Center for Social Business. As a technical leader and a researcher for 19 years at IBM Research, Dr. Su has been an expert in multiple areas ranging from Human Computer Interaction, Cloud Computing, Visual Analytics, and Neural Network Algorithms for Image Recognition etc. As an executive, he has been leading research labs and research teams in the US and China. He is passionate about game-changing ideas and fundamental research, passionate in speeding up the impact generation process for technical innovations, discovering and developing new linkages between innovative research work and business needs.
America is ‘dropping cyberbombs’ – but how do they work?
Richard Forno and Anupam Joshi
Recently, United States Deputy Defense Secretary Robert Work publicly confirmed that the Pentagon’s Cyber Command was “dropping cyberbombs,” taking its ongoing battle against the Islamic State group into the online world. Other American officials, including President Barack Obama, have discussed offensive cyber activities, too.
The American public has only glimpsed the country’s alleged cyberattack abilities. In 2012 The New York Times revealed the first digital weapon, the Stuxnet attack against Iran’s nuclear program. In 2013, former NSA contractor Edward Snowden released a classified presidential directive outlining America’s approach to conducting Internet-based warfare.
The terms “cyberbomb” and “cyberweapon” create a simplistic, if not also sensational, frame of reference for the public. Real military or intelligence cyber activities are less exaggerated but much more complex. The most basic types are off-the-shelf commercial products used by companies and security consultants to test system and network security. The most advanced are specialized proprietary systems made for exclusive – and often classified – use by the defense, intelligence and law enforcement communities.
So what exactly are these “cyberbombs” America is “dropping” in the Middle East? The country’s actual cyber capabilities are classified; we, as researchers, are limited by what has been made public. Monitoring books, reports, news events and congressional testimony is not enough to separate fact from fiction. However, we can analyze the underlying technologies and look at the global strategic considerations of those seeking to wage cyber warfare. That work allows us to offer ideas about cyber weapons and how they might be used.
Social media predictive analytics bring unique opportunities to study people and their behaviors in real time, at an unprecedented scale: who they are, what they like and what they think and feel. Such large-scale real-time social media predictive analytics provide a novel set of conditions for the construction of predictive models. This talk focuses on various approaches to handling this dynamic data for predicting latent user demographics, from constrained-resource batch classification, to incremental bootstrapping, and then iterative learning via interactive rationale (feature) crowdsourcing. In addition, we present the relationships between a variety of perceived user properties e.g., income, education etc. and opinions, emotions and interests in a social network.
Svitlana Volkova received her PhD in Computer Science from Johns Hopkins University. She was affiliated with the Center for Language and Speech Processing and the Human Language Technology Center of Excellence. Her PhD research focused on building predictive models for sociolinguistic content analysis in social media. She built online models for streaming social media analytics, fine-grained emotion detection and multilingual sentiment analysis, and effective annotation techniques via crowdsourcing incorporated into the active learning framework. She interned at Microsoft Research in 2011, 2012 and 2014 at the Natural Language Processing and Machine Learning and Perception teams. She was awarded the Google Anita Borg Memorial Scholarship in 2010 and the Fulbright Scholarship in 2008.
Department of Computer Science, University of Miami
11:00am Monday, 16 May 2016, ITE 325b, UMBC
Topic modeling (in particular, Latent Dirichlet Analysis) is a technique for analyzing a large collection of documents. In topic modeling we view each document as a frequency vector over a vocabulary and each topic as a static distribution over the vocabulary. Given a desired number, K, of document classes, a topic modeling algorithm attempts to estimate concurrently K static distributions and for each document how much each K class contributes. Mathematically, this is the problem of approximating the matrix generated by stacking the frequency vectors into the product of two non-negative matrices, where both the column dimension of the first matrix and the row dimension of the second matrix are equal to K. Topic modeling is gaining popularity recently, for analyzing large collections of documents.
In this talk I will present some examples of applying topic modeling: (1) a small sentiment analysis of a small collection of short patient surveys, (2) exploratory content analysis of a large collection of letters, (3) document classification based upon topics and other linguistic features, and (4) exploratory analysis of a large collection of literally works. I will speak not only the exact topic modeling steps but also all the preprocessing steps for preparing the documents for topic modeling.
Mitsunori Ogihara is a Professor of Computer Science at the University of Miami, Coral Gables, Florida. There he directs the Data Mining Group in the Center for Computational Science, a university-wide organization for providing resources and consultation for large-scale computation. He has published three books and approximately 190 papers in conferences and journals. He is on the editorial board for Theory of Computing Systems and International Journal of Foundations of Computer Science. Ogihara received a Ph.D. in Information Sciences from Tokyo Institute of Technology in 1993 and was a tenure-track/tenured faculty member in the Department of Computer Science at the University of Rochester from 1994 to 2007.
Human mental models and robots:
Grasping and tele-presence
Dr. Cindy Grimm, Oregon State University
11:00-12:00 Monday 9 May 2016, ITE 325b
In this talk I will cover two separate research efforts in robotics, both of which use human mental models to improve robotic functionality. Robots struggle to pick up and manipulate physical objects, yet humans do this with ease – but can’t tell you how they do it. In this research we focus on how to capture human data in such a way as to gain insight into how people structure the grasping task. Specifically, we look at the role of perceptual cues in evaluating grasps and mental classification models of grasps (i.e., all these grasps are the “same”). In the second half of the talk I will switch to discussing how human mental models of privacy, trust, and presence come in to play in remote tele-presence applications (“Skype-on-a-movable-stick”).
Dr. Cindy Grimm is currently an associate professor at Oregon State University (since 2013) in the School of Mechanical, Industrial, and Manufacturing Engineering (application area robotics). Prior to that she was tenured faculty at Washington University in St. Louis in Computer Science (12 years). Her research areas range from 3D sketching to biological modeling to human-robot interaction. She approaches these problems with a combination of mathematical models and empirically-verified human-centered design (HCD). Mathematical models provide a sound, quantitative, rigorous, elegant basis for representing shape and function, and are a core part of the “language” of computation. Including a human in the loop is a key component of the application areas she works in; HCD provides the mechanism for addressing the fundamental problem of how to make mathematical computation “useful” for humans. She has worked with collaborators in fields ranging from psychology, mechanical and biological engineering, statistics, to art.
We tested bit sequences generated from the MD5 hash function using multinomial distribution and close-point spatial statistical tests for randomness. We found that bit sequences generated from truncated-round MD5 hash fail these tests for high- and low-density input choices.
In 2000, the National Institute of Standards and Technology concluded a competition to select the Advanced Encryption Standard. One of the requirements for candidates was randomness of output bits. The techniques used to evaluate symmetric block cipher randomness have not been extensively applied to hash functions.
In this study, we adapt a subset of the techniques used to analyze the randomness of AES candidate algorithms to study the randomness of the well-known MD5 hash function. Our approach uses high-density, lo- density, and chained-input methods to generate MD5 hashes. We concatenate these hash outputs and subjected them to multinomial distribution and close-point spatial tests. We iterated this approach over reduced-round versions of MD5. Our presentation includes specifications for the input methods, details on the statistical tests, and analysis of the statistical results.
Through statistical testing of concatenated MD5 hashes, we derive results that demonstrate a link between the performance of the concatenated hash bit sequences in our statistical testing and the number of hash rounds applied to the high-density and low-density input methods. Randomness is a desirable property for cryptographic hash functions. We present a new approach that facilitates the analysis and interpretation of hash functions for statistical randomness.
About the Speaker. Enis Golaszewski is a prospective PhD student in CS at UMBC, working with Dr. Alan T. Sherman. His research interests include the security of software-defined networks. He graduated from UMBC in CS in December 2015 and was a student in the fall 2015 INSuRE class. Email: <>
The two faculty were awarded a grant from the fall 2015 round of the Hrabowski Fund for Innovation competition to develop and evaluate the collaboration between the two courses. The classes held regular joint sessions and each project group comprised students from both Engineering and Visual Arts.
In ART 434 Prof. Cordova concentrated on the visual experience of the interface in mobile and desktop applications, while in CMSC 628 Prof. Banerjee provided the tools necessary to design and implement mobile applications. Specific mobile development topics such as user interface design and implementation, accessing and displaying sensor and location data, and mobile visual design were co-‐taught by both instructors. Teams comprising Engineering and Visual Arts students designed and built mobile applications for local clients in Baltimore and Washington DC area.
A poster describing the event has brief descriptions of the twelve class projects.