In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[Q]: In this paper we present methods for attacking and defending k-gram statistical analysis techniques that are used, for example, in network traffic analysis and covert channel detection. The main new result is our demonstration of how to use a behavior’s or process’ k-order statistics to build a stochastic process that has those same k-order stationary statistics but possesses different, deliberately designed, (k + 1)order statistics if desired. Such a model realizes a “complexification” of the process or behavior which a defender can use to monitor whether an attacker is shaping the behavior. By deliberately introducing designed (k + 1)-order behaviors, the defender can check to see if those behaviors are present in the data. We also develop constructs for source codes that respect the k-order statistics of a process while encoding covert information. One fundamental consequence of these results is that certain types of behavior analyses techniques come down to an arms race in the sense that the advantage goes to the party that has more computing resources applied to the problem. Points of view in this document are those of the authors and do not necessarily represent the official position of the sponsoring agencies or the U.S. Government. V. Crespi is with the Department of Computer Science, California State University at Los Angeles, Los Angeles CA, 90032 USA. email: vcrespi@calstatela.edu. Crespi’s work was partially supported by AFOSR Grant FA9550-07-1-0421 and by NSF Grant HRD-0932421. G. Cybenko is with the Thayer School of Engineering, Dartmouth College, Hanover NH 03755. email: gvc@dartmouth.edu. Cybenko’s work was partially supported by Air Force Research Laboratory contracts FA8750-10-1-0045, FA8750-09-1-0174, AFOSR contract FA9550-07-1-0421, U.S. Department of Homeland Security Grant 2006-CS-001-000001 and DARPA Contract HR001-06-1-0033 A. Giani is with the Department of EECS, University of California at Berkeley, Berkeley CA 94720. email: agiani@eecs.berkeley.edu. Giani’s’s work was partially supported by U.S. Department of Homeland Security Grant 2006-CS001-000001 and DARPA Contract HR001-06-1-0033 when she was a Ph.D. student at Dartmouth April 28, 2011 DRAFT ar X iv :1 10 4. 50 71 v1 [ cs .L G ] 2 7 A pr 2 01 1 CRESPI, CYBENKO, GIANI 2
[A]: Attacking and Defending Covert Channels and Behavioral Models


[Q]: In this paper, we briefly introduce MiniNLP, a natural language processing library for clinical narratives. MiniNLP is an experiment of our ideas on efficient and effective medical language processing. We introduce the overall design of MiniNLP and its major components, and show the performance of it in real projects.
[A]: A Short Introduction to MiniNLP


[Q]: Independent Component Analysis (ICA) is the problem of learning a square matrix A, given samples of X = AS, where S is a random vector with independent coordinates. Most existing algorithms are provably efficient only when each Si has finite and moderately valued fourth moment. However, there are practical applications where this assumption need not be true, such as speech and finance. Algorithms have been proposed for heavy-tailed ICA, but they are not practical, using random walks and the full power of the ellipsoid algorithm multiple times. The main contributions of this paper are: (1) A practical algorithm for heavy-tailed ICA that we call HTICA. We provide theoretical guarantees and show that it outperforms other algorithms in some heavy-tailed regimes, both on real and synthetic data. Like the current state-of-the-art, the new algorithm is based on the centroid body (a first moment analogue of the covariance matrix). Unlike the state-of-the-art, our algorithm is practically efficient. To achieve this, we use explicit analytic representations of the centroid body, which bypasses the use of the ellipsoid method and random walks. (2) We study how heavy tails affect different ICA algorithms, including HTICA. Somewhat surprisingly, we show that some algorithms that use the covariance matrix or higher moments can successfully solve a range of ICA instances with infinite second moment. We study this theoretically and experimentally, with both synthetic and real-world heavy-tailed data.
[A]:
Heavy-Tailed Analogues of the Covariance Matrix for ICA