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

Correlation Aware Optimizations for Analytic Databases

Hideaki Kimura, Brown University

1:00pm Friday 9 March 2012, ITE 325b, UMBC

Recent years have seen that the analysis of large data-sets is crucially important in a wide range of business, governmental, and scientific applications. For example, research projects in astronomy need to analyze petabytes of image data taken from telescopes. Providing a fast and scalable analytical data management system for such users has become increasingly important.

The major bottlenecks for analytics on such big data are disk- and network-I/O. Because the data is too large to fit in RAM, each query causes substantial disk I/O. Traditional database systems provide indexes to speed up disk reads, but many analytic queries do not benefit from indexes because data is scattered over a large number of disk blocks and disk seeks are prohibitively expensive. Furthermore, such huge data sets need to be partitioned and distributed over hundreds or many thousands of nodes. When a query requires more than one data at once, such as a query involving a JOIN operation, the data management system must transmit a large amount of data over the network. For example, the Shuffle phase in Map-Reduce systems copies file blocks over the network and causes a significant bottleneck in many cases.

Our approach to tackling these challenges in big data analytics is to exploit correlations. I will describe our correlation-aware indexing, replication, and data placement which make big data analytics faster and more scalable.

Finally, if time allows, I will also introduce another on-going project to develop a scalable transactional processing system on modern hardware in collaboration with Hewlett-Packard Laboratories.

Hideaki Kimura is a doctoral candidate in the Computer Science Department at Brown University. His main research interests are in data management systems. His dissertation research with Prof. Stan Zdonik is on correlation-based optimizations for large analytic databases. He also worked on transaction processing systems exploiting modern hardware at HP Labs.

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

Are you thinking about running for SGA Office but would like a little bit more information before you decide to make the plunge?  Do you have a super pressing question that you really want to ask your handy dandy Election Board?

Lucky for you, we’ve got three info sessions coming up in the next three weeks, just for that reason!

Wednesday, Feb. 29, 7:00 pm, Commons 331
Tuesday, March 6, 2:30 pm, Commons 331
Monday, March 12, 12:00 pm, Commons 327

Hope to see you there!

It’s here. Finally Oscar-mania moves center stage as the Academy awards are announced in Hollywood.

Here’s your chance to pick your slate of winners and compare/compete against USDemocrazy’s own Movie Maven’s choices. Check out our choice for winners (not necessarily our favorites) from some of the key categories listed below. Then submit your own list… let’s see who gets the highest score by the end of the night!

You can also follow our Movie Maven tonight as she will be tweeting the Oscars live on twitter @usdemocrazy.

USDemocrazy Oscar Choices:

Best Picture : The Artist
Director : Michel Hazanavicius, The Artist
Best Leading Actor : George Clooney, The Descendants
Best Leading Actress : Viola Davis, The Help
Best Supporting Actor : Christopher Plummer, Beginners
Best Supporting Actress : Octavia Spencer, The Help
Best Original Screenplay : Midnight in Paris
Best Adapted Screenplay: The Descendants
Best Animated feature: Rango
Best Score : The Artist
Best Song: Man or Muppet, The Muppets

Image credit: VoicesofNY.org

The proverbial cat exploded out of the bag this week when the Associated Press published the formerly-confidential papers detailing NYPD’s further surveillance on suspected or potential terrorist plots.

A wave of criticism and disappointment towards the NYPS’s actions quickly ensued.

We are confronted with the time-old conundrum: Is it right to sacrifice the individual liberties of one for the sake of a greater collective?

But this issue bears another debate in itself.  How far should a department like the NYPD be able to operate? In other words, can they work outside of their jurisdiction for the sake of their operation?

After further examination, it had beendiscoveredthat:

“…the NYPD had been operating well outside its jurisdiction, cataloging Muslim communities on Long Island and New Jersey, and monitoring Muslim college students across the region.”

Quick to come to the NYPD’s defense, New York City Mayor Bloomberg announced:

“Everything the New York City Police Department has done is legal. It is appropriate. It is constitutional…They are permitted to travel beyond the borders of New York City to investigate cases … We don’t target individuals based on race or religion.”

As this dilemma continued to unravel, many began to wonder if the NYPD was working with Newark police to carry out the operation in Muslim communities in Newark. As the finger-pointing and the he-said, she-said nonsense continued, Newark Mayor Cory Booker had the following to say about this ordeal:

“If anyone in my police department had known this was a blanket investigation of individuals based on nothing but their religion, that strikes at the core of our beliefs and my beliefs very personally, and it would have merited a far sterner response.”

However, as the police force to the city that bore witness to the worst terrorist attack on American soil, it should be stated that:

“…[terrorists] have used Internet cafes, worked out at gyms, visited travel agencies, attended student groups and prayed at mosques. So the NYPD monitored those areas. In doing so, they monitored many innocent people as they went about their daily lives.”

While we may never know the truth about how and why the NYPD’s investigation wriggled past its jurisdiction. But it happened. Now how do we handle this, if at all?

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