If you are interested in entrepreneurship and the local startup community, especially in information technology, you should take a look at the Greater Baltimore Technology Council. This is a local organization of people devoted to "expanding the region’s technology community and supports technology companies with innovative programming, resources, links to capital markets and connections."

One useful new resource is the GBTC's event calendar, which lists technology events in and around Baltimore. If you have will be of interest to the GBTC community you can submit them easily via a simple form.

photo credit: Justin Sullivan/Getty Images

Fresh tomatoes, melting cheese, crunchy onions, crisp lettuce, warm hamburger bun and a juicy patty of…pink slime?

While it may sound completely inedible, the product known as “pink slime” it is actually made by:

“grinding together beef scraps and connective tissue. [Beef Products, Inc.] then uses a mixture of ammonia and water (ammonium hydroxide) to prevent the risk for E. coli or salmonella contamination.”

British celeb chef Jamie Oliver publicly declared:

“Basically, we’re taking a product that would be sold at the cheapest form for gods and after this process, we can give it to humans.”

On the other hand, H. Russell Cross (head of the department of animal science at Texas A&M University) stated:

“Finely textured lean beef helps us meet consumer demand for safe, affordable and nutritious food.”

How “nutritious” can this product be if it’s treated with ammonium hydroxide, which is… also used in bombs and cleaning materials?

Major fast food chains such as McDonalds and Taco Bell have already stopped their use of this product due to public demand.

However, the USDA plans to buy 7 million pounds of “pink slime” (also known as Lean Beef Trimmings) from BPI in the coming months for the national school lunch program.In a public statement, they defended their actions by stating that all of its ground beef purchases “meet the highest standard for food safety.”

Feel strongly against it? Here’s the online petition that denounces the USDA’s use of “pink slime” for lunch programs.

Self-sustainable Cyber-physical System Design

Dr. Nilanjan Banerjee
University of Arkansas Fayetteville

1:00pm Tuesday 13 March 2012, ITE 325b UMBC

Renewable energy can enable diverse self-sustainable cyber-physical systems with applications ranging from healthcare to off-grid home energy management. However, there are several challenges that need to be addressed before such systems can be realized. For instance, how do we balance the small and often variable energy budgets imposed by renewables with system functionality? How can we design sensitive physical sensors and efficient harvesting circuits for mW energy sources such as sound and indoor light? For systems such as off-grid homes that interact with humans, how do we balance demand and supply while being cognizant to usability needs?

In this talk, I will present techniques that address these challenges. Specifically, I will propose a Hierarchical Power Management paradigm that combines platforms with varied energy needs to balance energy consumption and functionality, the design of an efficient harvester for sound scavenging, and sensitive ECG sensors. I will also present a measurement study that reveals the energy management challenges faced by off-grid home residents. Finally, I will conclude with the design of a solar replayer platform that allows immense flexibility in evaluating solar panel driven systems, and works for a wide range of panels.

Nilanjan Banerjee is an Assistant Professor in the department of Computer Science and Computer Engineering at University of Arkansas Fayetteville. He graduated with a M.S. and a Ph.D. from the University of Massachusetts at Amherst in 2009 and a BTech. (Hons.) from IIT Kharagpur in 2004. He has won the Yahoo! Outstanding dissertation award at UMass, a best undergraduate thesis award at IIT Kharagpur, and an Outstanding Researcher award at University of Arkansas. He is a 2011 NSF Career awardee and has won three other NSF awards (including the NSF I-Corp grant). His research interests span renewable energy driven systems, healthcare systems, and mobile systems.

Host: Anupam Joshi
See http://csee.umbc.edu/talks for more information

Analytics for Detecting Web and Social Media Abuse

Dr. Justin Ma, UC Berkeley

1:00pm Friday 16 March 2012, ITE 325, UMBC

The Web and online social media provide invaluable communication services to a global Internet user base. The tremendous success of these services, however, has also created valuable opportunities for criminals and other miscreants to abuse them for their own gain. As a result, it is both an important yet challenging problem to detect, monitor, and curtail this abuse. However, the large scale and diversity of these services, combined with the tactics used by attackers, make it difficult to discern one clear and robust signal for detecting abuse. One approach, relying on domain expertise, is to construct a small set of well-crafted heuristics, but such heuristics tend to rapidly become obsolete. In this talk, I will describe more robust approaches based on machine learning, statistical modeling, and large-scale analytics of large data sets.

First I will describe online learning approaches for detecting malicious Web sites (those involved in criminal scams) using lexical and host-based features of the associated URLs. This application is particularly appropriate for online algorithms as the size of the training data is larger than can be efficiently processed in batch and because the features that typify malicious URLs evolve continuously. Motivated by this application, we built a real-time system to gather URL features and analyze them against a source of labeled URLs from a large Web mail provider. Our system adapts in an online fashion to the evolving characteristics of malicious URLs, achieving daily classification accuracies up to 99% over a balanced data set.

Next I will describe our ongoing efforts for creating analytics for detecting social media abuse. Deciding on a universal definition of social media abuse is difficult, as abuse is often in the eye of the beholder. In light of this challenge, we explore a more formal definition based on information theory. In particular, we hypothesize that messages with low information content are likely to be abusive. From this, we develop a measure of content complexity to identify abusive users that shows promise in our early evaluations.

In addition to our own experiments in the lab, this work has found success in practice as well. Companies serving hundreds of millions of users have adopted these ideas to improve abuse detection within their own services.

Justin Ma is a postdoc in the UC Berkeley AMPLab. His primary research is in systems security, and his other interests include applications of machine learning to systems problems, systems for large-scale machine learning, and the impact of energy availability on computing. He received B.S. degrees in Computer Science and Mathematics from the University of Maryland in 2004, and he received his Ph.D. in Computer Science from UC San Diego in 2010.

Host: Anupam Joshi
See http://csee.umbc.edu/talks for more information