paper: Knowledge Graph Fact Prediction via Knowledge-Enriched Tensor Factorization

February 7th, 2019

Knowledge Graph Fact Prediction via
Knowledge-Enriched Tensor Factorization

Ankur Padia, Kostantinos Kalpakis, Francis Ferraro and Tim Finin, Knowledge Graph Fact Prediction via Knowledge-Enriched Tensor Factorization, Journal of Web Semantics, to appear, 2019

We present a family of novel methods for embedding knowledge graphs into real-valued tensors. These tensor-based embeddings capture the ordered relations that are typical in the knowledge graphs represented by semantic web languages like RDF. Unlike many previous models, our methods can easily use prior background knowledge provided by users or extracted automatically from existing knowledge graphs. In addition to providing more robust methods for knowledge graph embedding, we provide a provably-convergent, linear tensor factorization algorithm. We demonstrate the efficacy of our models for the task of predicting new facts across eight different knowledge graphs, achieving between 5% and 50% relative improvement over existing state-of-the-art knowledge graph embedding techniques. Our empirical evaluation shows that all of the tensor decomposition models perform well when the average degree of an entity in a graph is high, with constraint-based models doing better on graphs with a small number of highly similar relations and regularization-based models dominating for graphs with relations of varying degrees of similarity.

paper: Quantum Annealing Based Binary Compressive Sensing with Matrix Uncertainty

January 13th, 2019

Quantum Annealing Based Binary Compressive Sensing with Matrix Uncertainty

Ramin Ayanzadeh, Seyedahmad Mousavi, Milton Halem and Tim Finin, Quantum Annealing Based Binary Compressive Sensing with Matrix Uncertainty, arXiv:1901.00088 [cs.IT], 1 January 2019.

Compressive sensing is a novel approach that linearly samples sparse or compressible signals at a rate much below the Nyquist-Shannon sampling rate and outperforms traditional signal processing techniques in acquiring and reconstructing such signals. Compressive sensing with matrix uncertainty is an extension of the standard compressive sensing problem that appears in various applications including but not limited to cognitive radio sensing, calibration of the antenna, and deconvolution. The original problem of compressive sensing is NP-hard so the traditional techniques, such as convex and nonconvex relaxations and greedy algorithms, apply stringent constraints on the measurement matrix to indirectly handle this problem in the realm of classical computing.

We propose well-posed approaches for both binary compressive sensing and binary compressive sensing with matrix uncertainty problems that are tractable by quantum annealers. Our approach formulates an Ising model whose ground state represents a sparse solution for the binary compressive sensing problem and then employs an alternating minimization scheme to tackle the binary compressive sensing with matrix uncertainty problem. This setting only requires the solution uniqueness of the considered problem to have a successful recovery process, and therefore the required conditions on the measurement matrix are notably looser. As a proof of concept, we can demonstrate the applicability of the proposed approach on the D-Wave quantum annealers; however, we can adapt our method to employ other modern computing phenomena–like adiabatic quantum computers (in general), CMOS annealers, optical parametric oscillators, and neuromorphic computing.

Sherman receives 5.4m in funding for cybersecurity research and scholarships

July 26th, 2018

UMBC receives $5.4m in funding for new cybersecurity projects

NSF and NSA Fund Three Cybersecurity Projects by Prof. Alan Sherman 

Professor Alan Sherman and colleagues were recently awarded more than $5.4 million dollars in three new grants to support cybersecurity research and education at UMBC, including two from the National Science Foundation (NSF) and one from the National Security Agency (NSA).  Dr. Sherman leads UMBC’s Center for Information Security and Assurance which was responsible for UMBC’s designation as a National Center of Academic Excellence in Cybersecurity Research and Education.

This summer, NSF funded Sherman’s second CyberCorps Scholarship for Service (SFS) grant (Richard Forno, CoPI) that will fund 34 cybersecurity scholars over five years and support research at UMBC and in the Cyber Defense Lab (CDL). The $5 million award supports scholarships for BS, MS, MPS, and PhD students to study cybersecurity through UMBC degree programs in computer science, computer engineering, cyber, or information systems. SFS scholars receive tuition, books, health benefits, professional expenses, and an annual stipend ($22,500 for undergraduates, $34,000 for graduate students). In return, each scholar must engage in a summer internship and work for government (federal, state, local, or tribal) for one year for each year of support. The program is highly competitive and many of the graduates now work for the NSA.

A novel aspect of UMBC’s SFS program is that it builds connections with two nearby community colleges—Montgomery College (MC) and Prince Georges Community College (PGCC). Each year, one student from each of these schools is selected for a scholarship. Upon graduation from community college, the student transfers to UMBC to complete their four-year degree. In doing so, UMBC taps into a significant pool of talent and increases the number of cybersecurity professionals who will enter government service. Each January, all SFS scholars from UMBC, MC, and PGCC engage in a one-week research study. Working collaboratively, they analyze a targeted aspect of the security of the UMBC computer system. The students enjoy the hands-on experience while helping to improve UMBC’s computer security. Students interested in applying for an SFS scholarship should consult the CISA SFS page and contact Professor Sherman. The next application deadline is November 15.

With $310,000 of support from NSF, Sherman and his CoPIs, Drs. Dhananjay Phatak and Linda Oliva, are developing educational Cybersecurity Assessment Tools (CATS) to measure student understanding of cybersecurity concepts. In particular, they are developing and validating two concept inventories: one for any first course in cybersecurity, and one for college graduates beginning a career in cybersecurity. These inventories will provide science-based criteria by which different approaches to cybersecurity education can be assessed (e.g., competition, gaming, hands-on exercises, and traditional classroom). This project is collaborative with the University of Illinois at Urbana-Champaign.

With $97,000 of support from NSA, Sherman is developing a virtual Protocol Analysis Lab that uses state-of-the-art tools to analyze cryptographic protocols for structural weaknesses. Protocols are the structured communications that take place when computers interact with each other, as for example happens when a browser visits a web page. Experience has shown that protocols are so complicated to analyze that there is tremendous value in studying them using formal methods. Sherman and his graduate students are making it easier to use existing tools including CPSA, Maude NPA, and Tamerin, applying them to analyze particular protocols, and developing associated educational materials.

UMBC to upgrade High Performance Computing Facility with new NSF MRI grant

November 6th, 2017


UMBC upgrades High Performance Computing Facility with new NSF grant


The National Science Foundation recently awarded UMBC a Major Research Instrumentation (MRI) award totaling more than $550,000 to expand the university’s High Performance Computing Facility (HPCF). The funding will go toward upgraded hardware and increased computing speeds for the interdisciplinary core facility, which supports scientific computing and other complex, data-intensive research across disciplines, university-wide. As part of the NSF grant, UMBC is required to contribute 30 percent of the amount that NSF is providing to further support the project, meaning a total new investment of more than $780,000 in UMBC’s High Performance Community Facility.

Meilin Yu, assistant professor of mechanical engineering, is the principal investigator on the grant. He replaced Matthias Gobbert, professor of mathematics, who served as principal investigator on previous grants for the core facility in 2008, 2012 and 2017 on behalf of the 51 faculty investigators from academic departments and research centers across all three colleges. Co-Principal investigators on the grant are Professors Marc Olano, Jianwu Wang and Daniel Lobo.

Adapted for a UMBC news article by Megan Hanks

New paper: Question and Answering System for Management of Cloud Service Level Agreements

May 21st, 2017

Sudip Mittal, Aditi Gupta, Karuna Pande Joshi, Claudia Pearce and Anupam Joshi, A Question and Answering System for Management of Cloud Service Level Agreements, Proceedings of the IEEE International Conference on Cloud Computing, June 2017.

One of the key challenges faced by consumers is to efficiently manage and monitor the quality of cloud services. To manage service performance, consumers have to validate rules embedded in cloud legal contracts, such as Service Level Agreements (SLA) and Privacy Policies, that are available as text documents. Currently this analysis requires significant time and manual labor and is thus inefficient. We propose a cognitive assistant that can be used to manage cloud legal documents by automatically extracting knowledge (terms, rules, constraints) from them and reasoning over it to validate service performance. In this paper, we present this Question and Answering (Q&A) system that can be used to analyze and obtain information from the SLA documents. We have created a knowledgebase of Cloud SLAs from various providers which forms the underlying repository of our Q&A system. We utilized techniques from natural language processing and semantic web (RDF, SPARQL and Fuseki server) to build our framework. We also present sample queries on how a consumer can compute metrics such as service credit.

Dealing with Dubious Facts in Knowledge Graphs

November 22nd, 2016

In this week’s meeting, Ankur Padia will about about his work on the problem of identifying and managing ‘dubious facts’ extracted from text and added to a knowledge graph.

Dealing with Dubious Facts in Knowledge Graphs

Ankur Padia

Knowledge graphs are used to represent real-world facts and events with entities as nodes and relations as labeled edges. Generally, a knowledge graph is automatically constructed by extracting facts from text corpus using information extraction (IE) techniques. Such IE techniques are scalable but often extract low quality (or dubious) facts due to errors caused by NLP libraries, internal components of an extraction system, choice of learning techniques, heuristics and syntactic complexity of underlying text. We wish to explore techniques to process such dubious facts and improve the quality of a knowledge graph.

Dynamic Topic Modeling to Infer the Influence of Research Citations on IPCC Assessment Reports

November 19th, 2016

IPCC Assessment Reports

A temporal analysis of the 200,000 documents cited in thirty years worth of Intergovernmental Panel on Climate Change (IPCC) assessment reports sheds light on how climate change research is evolving.

Jenifer Sleeman, Milton Halem, Tim Finin and Mark Cane, Dynamic Topic Modeling to Infer the Influence of Research Citations on IPCC Assessment Reports, Big Data Challenges, Research, and Technologies in the Earth and Planetary Sciences Workshop, IEEE Int. Conf. on Big Data, December 2016.

A common Big Data problem is the need to integrate large temporal data sets from various data sources into one comprehensive structure. Having the ability to correlate evolving facts between data sources can be especially useful in supporting a number of desired application functions such as inference and influence identification. As a real world application we use climate change publications based on the Intergovernmental Panel on Climate Change, which publishes climate change assessment reports every five years, with currently over 25 years of published content. Often these reports reference thousands of research papers. We use dynamic topic modeling as a basis for combining report and citation domains into one structure. We are able to correlate documents between the two domains to understand how the research has influenced the reports and how this influence has changed over time. In this use case, the topic report model used a total number of 410 documents and 5911 terms in the vocabulary while in the topic citations the vocabulary consisted of 25,154 terms and the number of documents was closer to 200,000 research papers.

Inferring Relations in Knowledge Graphs with Tensor Decomposition

November 6th, 2016


Ankur Padia, Kostantinos Kalpakis, and Tim Finin, Inferring Relations in Multi-relational Knowledge Graphs with Tensor Decomposition, IEEE BigData, Dec. 2016.

Multi-relational data, like knowledge graphs, are generated from multiple data sources by extracting entities and their relationships. We often want to include inferred, implicit or likely relationships that are not explicitly stated, which can be viewed as link-prediction in a graph. Tensor decomposition models have been shown to produce state-of-the-art results in link-prediction tasks. We describe a simple but novel extension to an existing tensor decomposition model to predict missing links using similarity among tensor slices, as opposed to an existing tensor decomposition models which assumes each slice to contribute equally in predicting links. Our extended model performs better than the original tensor decomposition and the non-negative tensor decomposition variant of it in an evaluation on several datasets.

Forum on Cybersecurity Concerns in Local Governments, Baltimore 4/15

April 3rd, 2016

The UMBC School of Public Policy, bwtech@UMBC Cyber Incubator, and UMBC Center for Cybersecurity are sponsoring a form on Cybersecurity Concerns in Local Governments from 8:30-11:00am on Friday, April 15, 2016 at the Columbus Center in Baltimore.

“Like their counterparts in the private sector, it is important for local government officials and managers to understand cybersecurity threats to their websites and information systems and to take actions to prevent cyber attacks. The purpose of this forum is to present research on cybersecurity initiatives in local governments in Maryland, and highlight the public policy implications of these initiatives.”

There is no charge to attend this forum, but registration is required. For questions or more information, contact

8:30 a.m. Coffee, light breakfast and networking

9:00 Welcome and Overview

Cybersecurity Challenges in American Local Government
Donald F. Norris, Professor and Director, UMBC School of Public Policy

Policy-driven Approaches to Security
Anupam Joshi, Professor and Director, UMBC Center for Cybersecurity

Perspectives from Maryland Local Governments
Rob O’Connor, Chief Technology Officer, Baltimore County
Jerome Mullen, Chief Technology Officer, City of Baltimore

10:15 Audience Q & A

11:00 Adjourn

1100-line Perl emulator for BBN-LISP runs original Doctor program

January 6th, 2015

Screen Shot

Jeff Shager’s Genealogy of Eliza project has added an 1100-line Perl emulator written by James Markevitch for the 1966 version of BBN-LISP for the PDP-1 computer that can run Bernie Cosell’s original LISP version of doctor.

Markevitch writes in the comments

This is a Perl hack to implement the 1966 version of BBN-LISP for the PDP-1 computer. This was written primarily to run the 1966 LISP version of the “doctor” program (aka Eliza) written by Bernie Cosell. The intent is to be compatible with the version of LISP described in The BBN-LISP System, Daniel G. Bobrow et al, February, 1966, AFCRL-66-180 [BBN66]. However, because many of the quirks of that version of LISP are not documented, The BBN-LISP System Reference Manual April 1969, D. G. Bobrow et al [BBN69] was used as a reference. Finally, LISP 1.5 Programmer’s Manual, John McCarthy et al [LISP1.5] was also used as a reference. N.B. The 1966 version of BBN-LISP has differences from later versions and this interpreter will not properly execute programs written for those later versions.

You can download the Perl Lisp emulator, the doctor lisp code and the script file from the elizagen github repository.

UMBC seeks nine new computing faculty

December 13th, 2014


UMBC has a total of nine open full-time positions for computing faculty including five tenure track professors, a professor of the practice and three lecturers.

UMBC’s Computer Science and Electrical Engineering department is seeking to fill five positions for the coming year. They include two tenure track positions in Computer Science, up to three full-time lecturers. See the CSEE jobs page for more information.

The College of Engineering and Information Technology has a position for a full-time lecturer or Professor of Practice to focus on the needs of incoming computing majors through teaching, advising, and helping develop programs in computing. This person will work closely with faculty in the Computer Science and Electrical Engineering Department and Information Systems Department.

UMBC’s Information Systems department is accepting applications for three tenure track faculty positions in data science, software engineering and human-centered computing.

TISA Topic Independence Scoring Algorithm

June 23rd, 2014

Justin Martineau, Doreen Cheng and Tim Finin, TISA: topic independence scoring algorithm. In Proc. 9th Int. Conf. on Machine Learning and Data Mining (MLDM’13), pp. 555-570, July 2013, Springer-Verlag.

Textual analysis using machine learning is in high demand for a wide range of applications including recommender systems, business intelligence tools, and electronic personal assistants. Some of these applications need to operate over a wide and unpredictable array of topic areas, but current in-domain, domain adaptation, and multi-domain approaches cannot adequately support this need, due to their low accuracy on topic areas that they are not trained for, slow adaptation speed, or high implementation and maintenance costs.

To create a true domain-independent solution, we introduce the Topic Independence Scoring Algorithm (TISA) and demonstrate how to build a domain-independent bag-of-words model for sentiment analysis. This model is the best preforming sentiment model published on the popular 25 category Amazon product reviews dataset. The model is on average 89.6% accurate as measured on 20 held-out test topic areas. This compares very favorably with the 82.28% average accuracy of the 20 baseline in-domain models. Moreover, the TISA model is highly uniformly accurate, with a variance of 5 percentage points, which provides strong assurance that the model will be just as accurate on new topic areas. Consequently, TISAs models are truly domain independent. In other words, they require no changes or human intervention to accurately classify documents in never before seen topic areas.