talk: Ballerina, a modern programming language focused on integration, 2pm Thr 9/6, ITE325

Ballerina, a modern programming language
focused on integration

Dr. Sanjiva Weerawarana
Founder, Chairman and Chief Architect, WSO2

2:00-3:00pm, Thursday, 6 September 2018, ITE325, UMBC

Ballerina is a concurrent, transactional, statically typed programming language. It provides all the functionality expected of a modern, general purpose programming language, but it is designed specifically for integration: it brings fundamental concepts, ideas and tools of distributed system integration into the language with direct support for providing and consuming network services, distributed transactions, reliable messaging, stream processing, security and workflows. It is intended to be a pragmatic language suitable for mass-market commercial adoption; it tries to feel familiar to programmers who are used to popular, modern C-family languages, notably Java, C# JavaScript.

Ballerina’s type system is much more flexible than traditional statically typed languages. The type system is structural, has union types and open records with optional/mandatory fields. This flexibility allows it also to be used as a schema for the data that is exchanged in distributed applications. Ballerina’s data types are designed to work particularly well with JSON; any JSON value has a direct, natural representation as a Ballerina value. Ballerina also provides support for XML and relational data.

Ballerina’s concurrency model is built on the sequence diagram metaphor and offers simple constructs for writing concurrent programs. Its type system is a modern type system designed with sufficient power to describe data that occurs in distributed applications. It also includes a distributed security architecture to make it easier to write applications that are secure by design.

Ballerina is designed for modern development practices with a modularity architecture based on packages that are easily shared widely. Version management, dependency management, testing, documentation, building and sharing are part of the language design architecture and not left for later add-on tools. The Ballerina standard library is in two parts: the usual standard library level functionality (akin to libc) and a standard library of network protocols, interface standards, data formats, authentication/authorization standards that make writing secure, resilient distributed applications significantly easier than with other languages.

Ballerina has been inspired by Java, Go, C, C++, Rust, Haskell, Kotlin, Dart, Typescript, Javascript, Swift and other languages. This talk will discuss the core principles behind Ballerina including the semantics of combining aspects of networking, security, transactions, concurrency and events into a single architecture.


Sanjiva Weerawarana founded WSO2 in 2005 with a vision to reinvent the way enterprise middleware is developed, sold, delivered, and supported through an open source model. Prior to starting WSO2, Sanjiva worked for nearly eight years in IBM Research, where he focused on innovations in middleware and emerging industry standards. At IBM, he was one of the founders of the Web services platform, and he co-authored many Web services specifications, including WSDL, BPEL4WS, WS-Addressing, WS-RF, and WS-Eventing. In recognition for his company-wide technical leadership, Sanjiva was elected to the IBM Academy of Technology in 2003.

Sanjiva also has been committed to open source development for many years. An elected member of the Apache Software Foundation, Sanjiva was the original creator of Apache SOAP, and he has contributed to Apache Axis, Apache Axis2 and most Apache Web services projects.

In 2003, Sanjiva founded the Lanka Software Foundation (LSF), a non-profit organization formed with the objective of promoting open source development, not usage, by Sri Lankan developers. He is currently its chief scientist and a director. LSF’s success stories include many Apache Web services projects and Sahana, the predominant disaster management system in the world. In recognition of his role in promoting open source participation from developing countries, Sanjiva was elected to the board of the Open Source Initiative (OSI) in April 2005, where he served for two years.

Sanjiva also teaches and guides student projects part-time in the Computer Science & Engineering department of the University of Moratuwa, and he is a member of the university’s Faculty of Engineering Industry consultative board. Prior to joining IBM, Sanjiva spent three years at Purdue University as visiting faculty, where he received his Ph.D. in Computer Science in 1994.

talk: Methods and Models: Data Science for Campus Parking, 11:15am Mon 8/13

Methods and Models: Data Science for Campus Parking

Professor John Hoag
Associate Professor, Ohio University
11:15-12:15pm Monday, 13 August 2018 in ITE 325B

How can data science improve the parking experience for students, faculty, and staff? Or are there other motives at work? This talk will define and approach this perennial campus problem from perspectives of telematics and modeling, starting with the “Smart Cities” life cycle of data collection and analysis – from best practices through optimization. Next, we will consider relevant probabilistic models and their implementations over a century of study. We will conclude by discussing unintended consequences such as LPRs and other outcomes.

Dr. John Hoag is Associate Professor of Information and Telecommunication Systems at Ohio University in Athens, OH. He earned Ph.D. and M.S. Degrees in Operations Research from Ohio State University and holds a Bachelor’s degree in Computer Science. His current portfolio can be termed Smart Cities, which subsumes transportation, energy, finance, public health, and more, for which he is forming interdisciplinary public-private teams whose scope encompasses data collection, telemetry, storage, and analysis. The Smart Cities displaced work he started in bioinformatics and translational biomedical science, where his efforts focused on computational complexity and system performance. He maintains an adjunct appointment in EECS at Case Western Reserve University.

Host: Dr. Richard Forno ()

talk: Robot Governance – Institutions and Issues, 10:30 Tue 7/24, ITR346

 

Robot Governance – Institutions and Issues

 

Dr. Aaron Mannes, ISHPI Information Technologies

10:30-11:30 Tuesday, 24 July 2018, ITE 346

 

Inexpensive sensors and information storage and processing have enabled the large-scale production of robots: autonomous systems capable of acting on the world. These systems represent an enormous technological and economic opportunity that will change society in countless and unpredictable ways. They will also bring new policy challenges. This presentation examines the missions the government will need to undertake to address the challenges raised by this new technology, identifies critical gaps the government faces in carrying out these missions, and discusses institutional options to address these gaps.

 


 

Dr. Aaron Mannes is the Senior Policy Advisor at ISHPI Information Technologies, where he supports the Apex Data Analytics Engine (DA-E) at the Department of Homeland Security Science and Technology Directorate. In supporting DA-E, Dr. Mannes collaborates on big data projects that support the Homeland Security Enterprise and researches technology policy. He started at DHS as an American Association for the Advancement of Science Policy Fellow in September 2015. From 2004 to 2015, Dr. Mannes was a researcher at the University of Maryland Institute for Advanced Computer Studies (UMIACS) where he was the subject matter expert on terrorism and international affairs collaborating with a team of inter-disciplinary scientists to build computational tools to support decision-makers facing 21st century security and development problems. Dr. Mannes earned his Ph.D. at the University of Maryland’s School of Public Policy in 2014. His dissertation topic was the evolving national security role of the vice president.

Dr. Mannes is the author or co-author of four books on terrorism and has written scores of articles, papers, and book chapters on an array of topics including Middle East affairs, terrorism, technology, and other international security issues for popular and scholarly publications including Politico, Policy Review, The Wall Street Journal, Foreign Policy, The Journal of International Security Affairs, The Huffington Post, The National Interest, The Jerusalem Post, and The Guardian.

This research was conducted with the support of the Apex Data Analytics Engine in the Department of Homeland Security (DHS) Science and Technology Directorate (S&T). In no way should anything stated in this seminar be construed as representing the official position of DHS S&T or any other component of DHS. Opinions and findings expressed in this seminar, as well as any errors and omissions, are the responsibility of the presenter alone.

talk: Big Data, Security and Privacy, 11am Wed 5/16

Big Data, Security and Privacy

Prof. Bhavani Thuraisingham, University of Texas at Dallas
11:00-12:00 Wednesday, 16 May 2018, ITE 459, UMBC

The collection, storage, manipulation and retention of massive amounts of data have resulted in serious security and privacy considerations. Various regulations are being proposed to handle big data so that the privacy of the individuals is not violated. For example, even if personally identifiable information is removed from the data, when data is combined with other data, an individual can be identified. This is essentially the inference and aggregation problem that data security researchers have been exploring for the past four decades. This problem is exacerbated with the management of big data as different sources of data now exist that are related to various individuals.

While collecting massive amounts of data causes security and privacy concerns, big data analytics applications in cyber security is exploding. For example, an organization can outsource activities such as identity management, email filtering and intrusion detection to the cloud. This is because massive amounts of data are being collected for such applications and this data has to be analyzed. The question is, how can the developments in big data management and analytics techniques be used to solve security problems? These problems include malware detection, insider threat detection, and intrusion detection.

To address the challenges of big data security and privacy as well as big data analytics for cyber security applications, we organized a workshop sponsored by the National Science Foundation in September 2014 and presented the results in 2015 at an inter-agency workshop in Washington DC. Since then several developments have been reported on big data security and privacy as well as on big data analytics of cyber security. This presenting will summarize the findings of the workshop and discuss the developments and directions.


Dr. Bhavani Thuraisingham is the Louis A. Beecherl, Jr. Distinguished Professor in the Erik Jonsson School of Engineering and Computer Science at The University of Texas at Dallas (UTD) and the Executive Director of UTD’s Cyber Security Research and Education Institute since October 2004. She is also a Senior Research Fellow at Kings College, University of London (2015-2018) and a New America Cyber Security Policy Fellow (2017-2018). Her current research is on integrating cyber security and data science. Prior to joining UTD she worked at the MITRE Corporation for 16 years including a three-year stint as a Program Director at the NSF. She initiated the Data and Applications Security program at NSF and was a member of the Cyber Trust theme. While at MITRE she was a department head and was also a technical advisor to the DoD, the NSA, the CIA, and the IRS. Prior to that, she worked for the commercial industry for six years including at Honeywell, Inc. She is the recipient of numerous awards including the IEEE CS 1997 Technical Achievement Award, the IEEE ISI 2010 Research Leadership Award, ACM SIGSAC 2010 Outstanding Contributions Award, SDPS 2012 Transformative Achievement Gold Medal, 2013 IBM Faculty Award, ACM CODASPY 2017 Innovative and Lasting Research Contributions Award, IEEE CS Services Computing 2017 Research Innovation Award, and Dallas Business Journal 2017 Women in Technology Award. She is a 2003 Fellow of the IEEE and the AAAS and a 2005 Fellow of the British Computer Society. She has published over 120 journal articles, 250 conference papers, 15 books, has delivered over 130 keynote and featured addresses, and is the inventor of six US patents. She has chaired/co-chaired top tier conferences including the Women in Cyber Security (WiCyS) 2016, ACM CCS 2017, and is serving as the Program co-Chair for IEEE ICDM 2018. She also delivered a featured address at the Women in Data Science (WiDS) conference in 2018. She received her PhD at the University of Wales, Swansea, UK, and the earned higher doctorate (D. Eng) from the University of Bristol, England, UK for her published research in secure data management.

Meet Your Professor Series: Marie desJardins, 12-1 Wed. May 2, ITE239

Meet Your Professor Series: Marie desJardins

Join the CS Education Club for its fourth and final installment of the Meet Your Professor series this semester featuring Dr. Marie desJardins. The series provides students with the opportunity to learn more about their professors, including how they achieved their position, what they believe makes an effective teacher, what research they conduct, and more!

Dr. Marie desJardins is Associate Dean of Academic Affairs in the College of Engineering and Information Technology, and Professor in the Department of Computer Science and Electrical Engineering, at the University of Maryland, Baltimore County.  Prior to joining the faculty in 2001, Dr. desJardins was a senior computer scientist at SRI International in Menlo Park, California.  Her research is in artificial intelligence, focusing on the areas of machine learning, multi-agent systems, planning, interactive AI techniques, information management, reasoning with uncertainty, and decision theory.  She has mentored 13 Ph.D. students, 27 M.S. students, and nearly 100 undergraduate researchers.   She is also active in the CS education community, chairs the Maryland Steering Committee for Computer Science Education, and frequently serves as a mentor and invited speaker at CS education and outreach events.

The event is Wednesday 5/2 from 12:00-12:50 in ITE 239. Light refreshments will be provided. Bring questions!

talk: SPARCLE: Practical Homomorphic Encryption, 12pm Fri 4/27

UMBC Cyber Defense Lab

SPARCLE: Practical Homomorphic Encryption

Russ Fink

Senior Scientist
Johns Hopkins University / Applied Physics Laboratory

12:00–1:00pm Friday, April 27, 2018, ITE 237, UMBC

In the newly coined Privacy Age, researchers are building systems with homomorphic algorithms that enable “never decrypt” operations on sensitive data in applications such as computational private information retrieval (cPIR). The trouble is, the leading algorithms incur significant computational and space challenges, relegating them mainly to large cloud computing platforms. We have invented a special-purpose, ring-homomorphic (aka, “fully homomorphic”) algorithm that, owing to some specializing assumptions, trades general-purpose cryptographic utility for linear performance in speed and space.

We will present the cryptosystem and discuss several current challenges. We will also throw in a fun, simple, tactile concept demonstration of PIR for those just generally curious about what all this is, hopefully demystifying how you can enable a server to search for something without knowing what it’s looking for, and without knowing what (if any) results it found.

Russ Fink (UMBC ’10) is a senior scientist at the Johns Hopkins University / Applied Physics Laboratory. His current research interests include private information retrieval, applied cryptography, and cyber security.

Host: Alan T. Sherman,

🗣 talk: Classifying Malware using Data Compression, 12-1 Fri 4/20, ITE229

The UMBC Cyber Defense Lab presents

Classifying Malware using Data Compression

Charles Nicholas, UMBC

12:00–1:00pm Friday, 20 April 2018, ITE 229

Comparing large binary objects can be tricky and expensive. We describe a method for comparing such strings, using ideas form data compression, that is both fast and effective. We present results from experiments applying this method, which we refer to as LZJD, to the areas of malware classification and digital forensics.

Charles Nicholas () earned his B.S. in Computer Science from the University of Michigan – Flint in 1979, and the M.S. and Ph.D. degrees in Computer Science from Ohio State University in 1982 and 1988, respectively. He joined the Computer Science Department at UMBC in 1988. His research interests include electronic document processing, intelligent information systems, and software engineering. In recent years he has focused on the problems of storing and retrieving information from large collections of documents. Intelligent software agents are an important aspect of this work. Host: Alan T. Sherman,

The UMBC Cyber Defense Lab meets biweekly Fridays. All meetings are open to the public.

🤖 talk: Where’s my Robot Butler? 1-2pm Friday 4/13, ITE 231

UMBC ACM Student Chapter Talk

Where’s my Robot Butler?
Robotics, NLP and Robots in Human Environments

Professor Cynthia Matuszek, UMBC

1:00-2:00pm Friday, 13 April 2018, ITE 231, UMBC

As robots become more powerful, capable, and autonomous, they are moving from controlled industrial settings to human-centric spaces such as medical environments, workplaces, and homes. As physical agents, they will soon be able help with entirely new categories of tasks that require intelligence. Before that can happen, though, robots must be able to interact gracefully with people and the noisy, unpredictable world they occupy, an undertaking that requires insight from multiple areas of AI. Useful robots will need to be flexible in dynamic environments with evolving tasks, meaning they must learn from and communicate effectively with people. In this talk, I will describe current research in our lab on combining natural language learning and robotics to build robots people can use in the home.


Dr. Cynthia Matuszek is an assistant professor of computer science and electrical engineering at the University of Maryland, Baltimore County. Her research occurs at in the intersection of robotics, natural language processing, and machine learning, and their application to human-robot interaction. She works on building robotic systems that non-specialists can instruct, control, and interact with intuitively and naturally. She has published on AI, robotics, machine learning, and human-robot interaction. Matuszek received her Ph.D. in computer science and engineering from the University of Washington.

🗣️ talk: Human Factors in Cyber Security, 12-1 Fri 4/13, ITE 229, UMBC

The UMBC Cyber Defense Lab presents

Human Factors in Cyber Security

Dr. Josiah Dykstra

Cyber Security Researcher, US Department of Defense

12:00–1:00pm Friday, 13 April 2018, ITE 229, UMBC

Humans play many roles in the effectiveness of cyber security. While users are often blamed for security compromises, human strengths and weaknesses also affect people who perform design, implementation, configuration, monitoring, analysis, and response. The fields of human computer interaction generally, and usable security specifically, have drawn attention and research to some aspects of human factors, but many opportunities remain for future work.

In this talk, I describe several of my research projects related to human factors in cyber security. The first was a study of how individual differences affect cyber security behavior, and active follow-on research to predict users who might become victimized. The second was a study of stress and fatigue in security operations centers, including a new survey instrument for collecting data in tactical environments. The third was a research prototype using augmented reality to assist humans in cyber security analysis, and an analysis of preliminary results.

Finally, I will present and invite discussion about a new idea for improving security by making it “disappear.” Despite decades of tools and techniques for secure development, and valiant work at adoption and usability, it is clear that many users cannot or will not avail themselves of appropriate cyber security options. It may be time to rethink the amount of interaction required for most users, and if hands-off, behind-the-scenes cyber defense should be the norm.


Josiah Dykstra serves as a Senior Executive Service government civilian and Subject Matter Expert for Computer Network Operations research in the Laboratory for Telecommunication Sciences within the Research Directorate of the National Security Agency. His research includes human augmentation, cyber risk assessment, and cyber effects. He is an active collaborator with academic, industry, and government researchers around the country. Dykstra earned the PhD degree in computer science at UMBC in 2013 studying under Alan T. Sherman. Dr. Dykstra is the author of the 2016 O’Reilly book, Essential Cybersecurity Science, Fellow of the American Academy of Forensic Sciences, and winner of the Presidential Early Career Award for Scientists and Engineers.


Host: Alan T. Sherman,

The UMBC Cyber Defense Lab meets biweekly Fridays. All meetings are open to the public.

🗣 talk: Mixed Membership Word Embeddings for Computational Social Science, 12pm Thr 4/5

ACM ​Faculty Talk

Mixed Membership Word Embeddings for Computational Social Science

​Dr. James Foulds, Information Systems, UMBC

12:00-1:00pm ​Thursday​,​ 5 ​April​ 2018, ITE​459,​ UMBC

Word embeddings improve the performance of natural language processing (NLP) systems by revealing the hidden structural relationships between words. Despite their success in many applications, word embeddings have seen very little use in computational social science NLP tasks, presumably due to their reliance on big data, and to a lack of interpretability. I propose a probabilistic model-based word embedding method which can recover interpretable embeddings, without big data. The key insight is to leverage mixed membership modeling, in which global representations are shared, but individual entities (i.e., dictionary words) are free to use these representations to uniquely differing degrees. I show how to train the model using a combination of state-of-the-art training techniques for word embeddings and topic models. The experimental results show an improvement in predictive language modeling of up to 63% in MRR over the skip-gram, and demonstrate that the representations are beneficial for supervised learning. I illustrate the interpretability of the models with computational social science case studies on State of the Union addresses and NIPS articles.

James (a.k.a. Jimmy) Foulds is an assistant professor in the Department of Information Systems at UMBC. His research interests are in both applied and foundational machine learning, focusing on probabilistic latent variable models and the inference algorithms to learn them from data. His work aims to promote the practice of latent variable modeling for multidisciplinary research in areas including computational social science and the digital humanities. He earned his Ph.D. in computer science at the University of California, Irvine, and was a postdoctoral scholar at the University of California, Santa Cruz, followed by the University of California, San Diego. His master’s and bachelor’s degrees were earned with first class honours at the University of Waikato, New Zealand, where he also contributed to the Weka data mining system.

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