July 31st, 2018
PAL: Privacy-Enhancing AI and Language Technologies
AAAI Spring Symposium
25-27 March 2019, Stanford University
This symposium will bring together researchers in privacy and researchers in either artificial intelligence (AI) or human language technologies (HLTs), so that we may collectively assess the state of the art in this growing intersection of interests. Privacy remains an evolving and nuanced concern of computer users, as new technologies that use the web, smartphones, and the internet of things (IoT) collect a myriad of personal information. Rather than viewing AI and HLT as problems for privacy, the goal of this symposium is to “flip the script” and explore how AI and HLT can help meet users’ desires for privacy when interacting with computers.
It will focus on two loosely-defined research questions:
- How can AI and HLT preserve or protect privacy in challenging situations?
- How can AI and HLT help interested parties (e.g., computer users, companies, regulatory agencies) understand privacy in the status quo and what people want?
The symposium will consist of invited speakers, oral presentations of submitted papers, a poster session, and panel discussions. This event is a successor to Privacy and Language Technologies (“PLT”), a 2016 AAAI Fall Symposium. Submissions are due 2 November 2018. For more information, see the symposium site.
July 24th, 2018
Ontology-Grounded Topic Modeling for Climate Science Research
Jennifer Sleeman, Milton Halem and Tim Finin, Ontology-Grounded Topic Modeling for Climate Science Research
, Semantic Web for Social Good Workshop, Int. Semantic Web Conf., Monterey, Oct. 2018. (Selected as best paper), to appear, Emerging Topics in Semantic Technologies, E. Demidova, A.J. Zaveri, E. Simperl (Eds.), AKA Verlag Berlin, 2018.
In scientific disciplines where research findings have a strong impact on society, reducing the amount of time it takes to understand, synthesize and exploit the research is invaluable. Topic modeling is an effective technique for summarizing a collection of documents to find the main themes among them and to classify other documents that have a similar mixture of co-occurring words. We show how grounding a topic model with an ontology, extracted from a glossary of important domain phrases, improves the topics generated and makes them easier to understand. We apply and evaluate this method to the climate science domain. The result improves the topics generated and supports faster research understanding, discovery of social networks among researchers, and automatic ontology generation.
July 23rd, 2018
Understanding and representing the semantics of large structured documents
Understanding large, structured documents like scholarly articles, requests for proposals or business reports is a complex and difficult task. It involves discovering a document’s overall purpose and subject(s), understanding the function and meaning of its sections and subsections, and extracting low level entities and facts about them. In this research, we present a deep learning based document ontology to capture the general purpose semantic structure and domain specific semantic concepts from a large number of academic articles and business documents. The ontology is able to describe different functional parts of a document, which can be used to enhance semantic indexing for a better understanding by human beings and machines. We evaluate our models through extensive experiments on datasets of scholarly articles from arXiv and Request for Proposal documents.
July 1st, 2018
MS Thesis Defense
Open Information Extraction for Code-Mixed Hindi-English Social Media Data
1:00pm Monday, 2 July 2018, ITE 325b, UMBC
Open domain relation extraction (Angeli, Premkumar, & Manning 2015) is a process of finding relation triples. While there are a number of available systems for open information extraction (Open IE) for a single language, traditional Open IE systems are not well suited to content that contains multiple languages in a single utterance. In this thesis, we have extended a existing code mix corpus (Das, Jamatia, & Gambck 2015) by finding and annotating relation triples in Open IE fashion. Using this newly annotated corpus, we have experimented with seq2seq neural network (Zhang, Duh, & Durme 2017) for finding the relationship triples. As prerequisite for relationship extraction pipeline, we have developed part-of-speech tagger and named entity and predicate recognizer for code-mix content. We have experimented with various approaches such as Conditional Random Fields (CRF), Average Perceptron and deep neural networks. According to our knowledge, this relationship extraction system is first ever contribution for any codemix natural language. We have achieved promising results for all of the components and it could be improved in future with more codemix data.
Committee: Drs. Frank Ferraro (Chair), Tim Finin, Hamed Pirsiavash, Bryan Wilkinson
June 4th, 2018
Attribute Based Encryption for Secure Access to Cloud Based EHR Systems
Medical organizations find it challenging to adopt cloud-based electronic medical records services, due to the risk of data breaches and the resulting compromise of patient data. Existing authorization models follow a patient centric approach for EHR management where the responsibility of authorizing data access is handled at the patients’ end. This however creates a significant overhead for the patient who has to authorize every access of their health record. This is not practical given the multiple personnel involved in providing care and that at times the patient may not be in a state to provide this authorization. Hence there is a need of developing a proper authorization delegation mechanism for safe, secure and easy cloud-based EHR management. We have developed a novel, centralized, attribute based authorization mechanism that uses Attribute Based Encryption (ABE) and allows for delegated secure access of patient records. This mechanism transfers the service management overhead from the patient to the medical organization and allows easy delegation of cloud-based EHR’s access authority to the medical providers. In this paper, we describe this novel ABE approach as well as the prototype system that we have created to illustrate it.
May 29th, 2018
Understanding the Logical and Semantic Structure of Large Documents
Muhammad Mahbubur Rahman
11:00am Wednesday, 30 May 2018, ITE 325b
Understanding and extracting of information from large documents, such as business opportunities, academic articles, medical documents and technical reports poses challenges not present in short documents. The reasons behind this challenge are that large documents may be multi-themed, complex, noisy and cover diverse topics. This dissertation describes a framework that can analyze large documents, and help people and computer systems locate desired information in them. It aims to automatically identify and classify different sections of documents and understand their purpose within the document. A key contribution of this research is modeling and extracting the logical and semantic structure of electronic documents using deep learning techniques. The effectiveness and robustness of ?the framework is evaluated through extensive experiments on arXiv and requests for proposals datasets.
Committee Members: Drs. Tim Finin (Chair), Anupam Joshi, Tim Oates, Cynthia Matuszek, James Mayfield (JHU)
April 22nd, 2018
Preventing Poisoning Attacks on Threat Intelligence Systems
Nitika Khurana, Graduate Student, UMBC
11:00-12:00 Monday, 23 April 2018, ITE346, UMBC
As AI systems become more ubiquitous, securing them becomes an emerging challenge. Over the years, with the surge in online social media use and the data available for analysis, AI systems have been built to extract, represent and use this information. The credibility of this information extracted from open sources, however, can often be questionable. Malicious or incorrect information can cause a loss of money, reputation, and resources; and in certain situations, pose a threat to human life. In this paper, we determine the credibility of Reddit posts by estimating their reputation score to ensure the validity of information ingested by AI systems. We also maintain the provenance of the output generated to ensure information and source reliability and identify the background data that caused an attack. We demonstrate our approach in the cybersecurity domain, where security analysts utilize these systems to determine possible threats by analyzing the data scattered on social media websites, forums, blogs, etc.
April 21st, 2018
UMBC at SemEval-2018 Task 8: Understanding Text about Malware
Ankur Padia, Arpita Roy, Taneeya Satyapanich, Francis Ferraro, Shimei Pan, Anupam Joshi and Tim Finin, UMBC at SemEval-2018 Task 8: Understanding Text about Malware
, Int. Workshop on Semantic Evaluation (collocated with NAACL-HLT), New Orleans, LA, June 2018.
We describe the systems developed by the UMBC team for 2018 SemEval Task 8, SecureNLP (Semantic Extraction from CybersecUrity REports using Natural Language Processing). We participated in three of the sub-tasks: (1) classifying sentences as being relevant or irrelevant to malware, (2) predicting token labels for sentences, and (4) predicting attribute labels from the Malware Attribute Enumeration and Characterization vocabulary for defining malware characteristics. We achieved F1 scores of 50.34/18.0 (dev/test), 22.23 (test-data), and 31.98 (test-data) for Task1, Task2 and Task2 respectively. We also make our cybersecurity embeddings publicly available at https://bit.ly/cybr2vec.
April 15th, 2018
Cognitively Rich Framework to Automate Extraction and Representation of Legal Knowledge
Srishty Saha, UMBC
11-12 Monday, 16 April 2018, ITE 346
With the explosive growth in cloud-based services, businesses are increasingly maintaining large datasets containing information about their consumers to provide a seamless user experience. To ensure privacy and security of these datasets, regulatory bodies have specified rules and compliance policies that must be adhered to by organizations. These regulatory policies are currently available as text documents that are not machine processable and so require extensive manual effort to monitor them continuously to ensure data compliance. We have developed a cognitive framework to automatically parse and extract knowledge from legal documents and represent it using an Ontology. The legal ontology captures key-entities and their relations, the provenance of legal-policy and cross-referenced semantically similar legal facts and rules. We have applied this framework to the United States government’s Code of Federal Regulations (CFR) which includes facts and rules for individuals and organizations seeking to do business with the US Federal government.
April 11th, 2018
2018 Mid-Atlantic Student Colloquium on Speech, Language and Learning
The 2018 Mid-Atlantic Student Colloquium on Speech, Language and Learning (MASC-SLL) is a student-run, one-day event on speech, language & machine learning research to be held at the University of Maryland, Baltimore County (UMBC) from 10:00am to 6:00pm on Saturday May 12. There is no registration charge and lunch and refreshments will be provided. Students, postdocs, faculty and researchers from universities & industry are invited to participate and network with other researchers working in related fields.
Students and postdocs are encouraged to submit abstracts describing ongoing, planned, or completed research projects, including previously published results and negative results. Research in any field applying computational methods to any aspect of human language, including speech and learning, from all areas of computer science, linguistics, engineering, neuroscience, information science, and related fields is welcome. Submissions and presentations must be made by students or postdocs. Accepted submissions will be presented as either posters or talks.
Important Dates are:
- Submission deadline (abstracts): April 16
- Decisions announced: April 21
- Registration opens: April 10
- Registration closes: May 6
- Colloquium: May 12
March 25th, 2018
AI for Cybersecurity: Intrusion Detection Using Neural Networks
Sowmya Ramapatruni, UMBC
11:00-12:00 Monday 26 March, 2018, ITE346, UMBC
The constant growth in the use of computer networks raised concerns about security and privacy. Intrusion attacks on computer networks is a very common attack on internet today. Intrusion detection systems have been considered essential in keeping network security and therefore have been commonly adopted by network administrators. A possible disadvantage is the fact that such systems are usually based on signature systems, which make them strongly dependent on updated database and consequently inefficient against novel attacks (unknown attacks). In this study we analyze the use of machine learning in the development of intrusion detection system.
The focus of this presentation is to analyze the various machine learning algorithms that can be used to perform classification of network attacks. We will also analyze the common techniques used to build and fine tune artificial neural networks for network attack classification and address the drawbacks in these systems. We will also analyze the data sets and the information that is critical for the classification. The understanding of network packet data is essential for the feature engineering, which is an essential precursor activity for any machine learning systems. Finally, we study the drawbacks of existing machine learning systems and walk through the further study possible in this area.
January 27th, 2018
Cleaning Noisy Knowledge Graphs
My dissertation research is developing an approach to identify and explain errors in a knowledge graph constructed by extracting entities and relations from text. Information extraction systems can automatically construct knowledge graphs from a large collection of documents, which might be drawn from news articles, Web pages, social media posts or discussion forums. The language understanding task is challenging and current extraction systems introduce many kinds of errors. Previous work on improving the quality of knowledge graphs uses additional evidence from background knowledge bases or Web searches. Such approaches are diffuclt to apply when emerging entities are present and/or only one knowledge graph is available. In order to address the problem I am using multiple complementary techniques including entitylinking, common sense reasoning, and linguistic analysis.