Archive for the 'UMBC' Category
November 6th, 2017, by Tim Finin, posted in GENERAL, UMBC
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
October 22nd, 2017, by Tim Finin, posted in AI, cybersecurity, Data Science, Ebiquity, Machine Learning, meetings, talks
Multi-observable Session Reputation Scoring System
11:00-12:00 Monday, 23 October 2017, ITE 346
With increasing adoption of Cloud Computing, cyber attacks have become one of the most effective means for adversaries to inflict damage. To overcome limitations of existing blacklists and whitelists, our research focuses to develop a dynamic reputation scoring model for sessions based on a variety of observable and derived attributes of network traffic. Here we propose a technique to greylist sessions using observables like IP, Domain, URL and File Hash by scoring them numerically based on the events in the session. This enables automatic labeling of possible malicious hosts or users that can help in enriching the existing whitelists or blacklists.
June 7th, 2017, by Tim Finin, posted in Data Science, Semantic Web, UMBC
The University of Maryland, Baltimore County is looking to hire a Professor of the Practice to head a new graduate program in Data Science. See the job announcement for more information and apply online at Interfolio.
In addition to developing and teaching graduate data science courses, the new faculty member will serve as the Graduate Program Director of UMBC’s program leading to a master’s degree in Data Science. This cross-disciplinary program is offered to professional students through a partnership between the College of Engineering and Information Technology; the College of Arts, Humanities and Social Sciences; the College of Natural and Mathematical Sciences; the Department of Computer Science and Electrical Engineering; and UMBC’s Division of Professional Studies.
November 8th, 2016, by Tim Finin, posted in cybersecurity, Ebiquity, Mobile Computing, Policy, Privacy
In this week’s ebiquity meeting (11:30 8 Nov. 2016) Prajit Das will present his work on capturing policies for fine-grained access control on mobile devices.
As of 2016, there are more mobile devices than humans on earth. Today, mobile devices are a critical part of our lives and often hold sensitive corporate and personal data. As a result, they are a lucrative target for attackers, and managing data privacy and security on mobile devices has become a vital issue. Existing access control mechanisms in most devices are restrictive and inadequate. They do not take into account the context of a device and its user when making decisions. In many cases, the access granted to a subject should change based on context of a device. Such fine-grained, context-sensitive access control policies have to be personalized too. In this paper, we present the Mithril system, that uses policies represented in Semantic Web technologies and captured using user feedback, to handle access control on mobile devices. We present an iterative feedback process to capture user specific policy. We also present a policy violation metric that allows us to decide when the capture process is complete.
January 20th, 2015, by Tim Finin, posted in High performance computing, Multicore Computation Center
UMBC CSEE alumni Don Miner and Brandon Wilson have started a Meetup group for Hadoop users in and around the Baltimore area to discuss Hadoop technology and use cases.
Apache Hadoop is one of the most popular open-source tools used to harness clusters of computers to process, analyze or learn from massive amounts of data. Whether you are new to Hadoop or an experienced user, this is a great opportunity to improve your knowledge and network with others in the Baltimore computing technology community.
The first meeting will be held from 7:00pm to 9:30pm on Thursday, 19 February 2015 at AOL/Advertising.com at 1020 Hull St #100, Baltimore, MD (map). Join the group here.
June 15th, 2014, by Tim Finin, posted in alumni, Ebiquity, Privacy, Security, Semantic Web
Congratulations to ebiquity alumna Lalana Kagal (Ph.D. 2004) for being featured on MIT’s home page recently for recent work with Ph.D. student Oshani Seneviratne on enabling people to track how their private data is used online. You can read more about their work via this MIT news item and in their paper Enabling Privacy Through Transparency which will be presented next month in the 2014 IEEE Privacy Security and Trust conference.
March 27th, 2014, by Prajit Kumar Das, posted in Ebiquity, Google, Mobile Computing, Policy, Semantic Web, Social, Wearable Computing
If you are a Google Glass user, you might have been greeted with concerned looks or raised eyebrows at public places. There has been a lot of chatter in the “interweb” regarding the loss of privacy that results from people taking your pictures with Glass without notice. Google Glass has simplified photography but as what happens with revolutionary technology people are worried about the potential misuse.
FaceBlock helps to protect the privacy of people around you by allowing them to specify whether or not to be included in your pictures. This new application developed by the joint collaboration between researchers from the Ebiquity Research Group at University of Maryland, Baltimore County and Distributed Information Systems (DIS) at University of Zaragoza (Spain), selectively obscures the face of the people in pictures taken by Google Glass.
Comfort at the cost of Privacy?
As the saying goes, “The best camera is the one that’s with you”. Google Glass suits this description as it is always available and can take a picture with a simple voice command (“Okay Glass, take a picture”). This allows users to capture spontaneous life moments effortlessly. On the flip side, this raises significant privacy concerns as pictures can taken without one’s consent. If one does not use this device responsibly, one risks being labelled a “Glasshole”. Quite recently, a Google Glass user was assaulted by the patrons who objected against her wearing the device inside the bar. The list of establishments which has banned Google Glass within their premises is growing day by day. The dos and donts for Glass users released by Google is a good first step but it doesn’t solve the problem of privacy violation.
Privacy-Aware pictures to the rescue
FaceBlock takes regular pictures taken by your smartphone or Google Glass as input and converts it into privacy-aware pictures. This output is generated by using a combination of Face Detection and Face Recognition algorithms. By using FaceBlock, a user can take a picture of herself and specify her policy/rule regarding pictures taken by others (in this case ‘obscure my face in pictures from strangers’). The application would automatically generate a face identifier for this picture. The identifier is a mathematical representation of the image. To learn more about the working on FaceBlock, you should watch the following video.
Using Bluetooth, FaceBlock can automatically detect and share this policy with Glass users near by. After receiving this face identifier from a nearby user, the following post processing steps happen on Glass as shown in the images.
What promises does it hold?
FaceBlock is a proof of concept implementation of a system that can create privacy-aware pictures using smart devices. The pervasiveness of privacy-aware pictures could be a right step towards balancing privacy needs and comfort afforded by technology. Thus, we can get the best out of Wearable Technology without being oblivious about the privacy of those around you.
FaceBlock is part of the efforts of Ebiquity and SID in building systems for preserving user privacy on mobile devices. For more details, visit http://face-block.me
June 10th, 2013, by Tim Finin, posted in Multicore Computation Center
UMBC's Center for Hybrid Multicore Productivity Research, an NSF Industry & University Cooperative Research Center is holding its Industry Advisory Board meeting at UMBC 12-14 June. Students from UMBC and UCSD will present tutorials on a number of the technologies underlying ongoing CHMPR projects in a session from 1:00-5:00 on Wednesday June 12 in ITE 456. The tutorial session is free and open to the public.
- 3-D Printing – Timothy Blattner (UMBC)
- Semantic Table Information – Varish Mulwad (UMBC)
- Social Media Elastic Search – Oleg Aulov (UMBC)
- Machine Learning for Social Media – Han Dong (UMBC)
- Virtual World Interactions – Erik Hill (UCSD)
Directions and parking information is available here.
October 5th, 2012, by Tim Finin, posted in Ebiquity, GENERAL
Three Ph.D. students from the ebiquity lab have posters at the ACM Student Research Competition and General Poster Session of the 2012 Grace Hopper Celebration of Women in Computing conference. The GHC conference is the largest technical conference for women in computing and results in collaborative proposals, networking and mentoring for junior women and increased visibility for the contributions of women in computing. Conference presenters are leaders in their respective fields, representing industry, academia and government. Top researchers present their work while special sessions focus on the role of women in today’s technology fields.
The three ebiquity lab students with posters this year are:
Automation of Cloud Services lifecycle by using Semantic technologies,
Karuna Panda Joshi
We have developed a new framework for automating the configuration, negotiation and procurement of services in a cloud computing environment using semantic web technologies.We have developed detailed Ontologies for the framework. We have designed a prototype, called Smart Cloud Services, which is based on this framework and also incorporates NIST’s policies on cloud computing. This prototype is integrated with different cloud platforms like Eucalyptus and VCL.
A Knowledge-Based Approach To Intrusion Detection Modeling,
M. Lisa Mathews
Current state of the art intrusion detection and prevention systems (IDPS) are signature-based systems that detect threats and vulnerabilities by cross-referencing the threat/vulnerability signatures in their databases. These systems are incapable of taking advantage of heterogeneous data sources for analysis of system activities for threat detection. This work presents a situation-aware intrusion detection model that integrates these heterogeneous data sources and builds a semantically rich knowledge-base to detect cyber threats/vulnerabilities.
Unsupervised Coreference Resolution for FOAF Instances,
Jennifer Alexander Sleeman
Coreference Resolution determines when two entity descriptions represent the same real world entity. Friend of a Friend (FOAF) is an ontology about people and their social networks. Currently there is not a way to easily recognize when two FOAF instances represent the same entity. Existing techniques that use supervised learning typically do not support incremental processing. I present an unsupervised approach that supports both heterogeneous data and incremental online processing.
December 24th, 2011, by Tim Finin, posted in Ebiquity, UMBC
It’s very nice to see ebiquity alumna Akshaya Iyengar (MS, 2011) helping Wikipedia during its fund raising campaign. If you visit Wikipedia you might see her gracing a page you get, as I did just a minutes ago. See this screenshot and read her statement on why she has been donating to Wikipedia here. Her generosity has inspired me to contribute also.
September 16th, 2011, by Tim Finin, posted in Ebiquity, NLP, Semantic Web
Here’s a word cloud that visualizes the 200 most significant words extracted from over 400 papers from our research group over the past ten years. Significance was estimated by tf-idf where the idf data is from a collection of newswire articles (thanks Paul!). The word cloud was created with Wordle.
April 5th, 2011, by Tim Finin, posted in AI, GAIM, Machine Learning
DARPA is developing a new component to track “quiet submarines” to be part of the Navy’s Anti Submarine Warfare toolkit and is using a software game to collect effective strategies for its use.
“Before autonomous software is developed for ACTUV’s computers, DARPA needs to determine what approaches and methods are most effective. To gather information from a broad spectrum of users, ACTUV has been integrated into the Dangerous Waters game. DARPA is offering this new ACTUV Tactics Simulator for free public download.
This software has been written to simulate actual evasion techniques used by submarines, challenging each player to track them successfully. Your tracking vessel is not the only ship at sea, so you’ll need to safely navigate among commercial shipping traffic as you attempt to track the submarine, whose driver has some tricks up his sleeve. You will earn points as you complete mission objectives, and will have the opportunity to see how you rank against the competition on DARPA’s leaderboard page. You can also share your experiences and insights from playing the simulator with others.”
This is a kind of crowdsourcing — leveraging the experiences of a large number of people playing a game. Applying various kinds of machine learning algorithms to the simulator data could be an effective way to train an autonomous tool for this task.