R. Chandramouli

Twitter analytics: Architecture, tools and analysis

We study the temporal behavior of messages arriving in a social network. We specifically study the tweets and re-tweets sent to president Barack Obama on Twitter. We characterize the inter-arrival times between the tweets, the number of re-tweets and the spatial coordinates (latitude, longitude) of the users who sent the tweets. The modeling of the arrival process of tweets in Twitter can be applied to predict co-ordinated user behavior in social networks. While there is sufficient literature on social networks that present large volumes of collected data, the modeling and characterization of the data have been rarely discussed. The available data are usually very expensive and not comprehensive. Here, we develop a software architecture that uses a Twitter application program interface (API) to collect the tweets sent to specific users. We then extract the user ids and the exact time-stamps of the tweets. We use the collected data to characterize the inter-arrival times between tweets and the number of re-tweets. Our studies indicate that the arrival process of new tweets to a user can be modeled as a Poisson Process while the number of re-tweets follow a geometric distribution. Our data collection architecture is operating system (OS) independent. The results obtained in this research can be applied to study correlations between patterns of user behavior and their locations.

Active steganalysis of spread spectrum image steganography

We propose two active steganalysis schemes for spread spectrum image steganography. One is a simple estimate and subtract type algorithm that does not exploit higher order statistics while the other one is more sophisticated. We present conditions for successful steganalysis along with experimental results. Experiments show that the second method can extract up to 70% of message bits for large message sizes while the simple scheme saturates at 45%.

Stochastic channel-adaptive rate control for wireless video transmission

In this paper, an empirically optimized channel-matched quantizer, and a joint stochastic-control based rate controller and channel estimator for H.261 based video transmission over a noisy channel is proposed. The rate controller adaptively learns to choose the correct channel matched quantizer using a stochastic learning algorithm. The stochastic automaton based learning algorithm aids in estimating the channel bit error rate based on a one bit feedback from the decoder. The algorithm is observed to converge to the optimal choice of the quantizer very quickly for various channel bit error probabilities and for different video sequences. When compared to traditional channel estimation schemes the proposed technique has several advantages. First, the proposed method results in a significant reduction in the delay and bandwidth requirement for channel estimation when compared to pilot symbol aided channel estimation schemes. Next, the stochastic learning algorithm used to estimate the channel bit error rate has simple computations. This makes it attractive for low power applications such as wireless video communications. This is in contrast to traditional blind channel estimation schemes that are computationally expensive, in general.

Secondary Spectrum Access with LT Codes for Delay-Constrained Applications

With the advent of cognitive radios, secondary usage of spectrum on opportunistic basis, in the licensed band, is becoming practical. In this type of opportunistic spectrum access, secondary users may loose access due to the sudden arrival of a primary user who owns the spectrum. In order to ensure the link reliability and to compensate for the loss, some spectrally efficient mechanisms are required. In this paper we consider the spectrum pooling model for secondary spectrum access for delay-constrained applications. Along with this we study the application of Luby Transform (LT) codes to compensate the secondary user for the loss incurred due to primary user interference. We study the trade-offs between spectral efficiency, link reliability and overhead due to LT coding. Primary user traffic model is used to parametrize the trade- offs. Simulation results are presented for the secondary spectrum access model.

Nonparametric Steganalysis of QIM Steganography Using Approximate Entropy

This paper proposes an active steganalysis method for quantization index modulation (QIM)-based steganography. The proposed nonparametric steganalysis method uses irregularity (or randomness) in the test image to distinguish between the cover image and the stego image. We have shown that plain quantization (quantization without message embedding) induces regularity in the resulting quantized object, whereas message embedding using QIM increases irregularity in the resulting QIM-stego. Approximate entropy, an algorithmic entropy measure, is used to quantify irregularity in the test image. The QIM-stego image is then analyzed to estimate secret message length. To this end, the QIM codebook is estimated from the QIM-stego image using first-order statistics of the image coefficients in the embedding domain. The estimated codebook is then used to estimate secret message. Simulation results show that the proposed scheme can successfully estimate the hidden message from the QIM-stego with very low decoding error probability. For a given cover object the decoding error probability depends on embedding rate and decreases monotonically, approaching zero as the embedding rate approaches one.

Codes and Games for Dynamic Spectrum Access

ly, a learning automaton [2] can be considered to be an object that can choose from a finite number of actions. For every action that it chooses, the random environment in which it operates evaluates that action. A corresponding feedback is sent to the automaton based on which the next action is chosen. As this process progresses the automaton learns to choose the optimal action for that unknown environment asymptotically. The stochastic iterative algorithm used by the automaton to select its successive actions based on the environment’s response defines the stochastic learning algorithm. An important property of the learning automaton is its ability to improve its performance with time while operation in an unknown environment. In this chapter, for the sake of consistency our notations follow or parallels that from standard books on game theory (e.g., [3]) and stochastic learning [4]. In multiple automata games, instead of one automaton (player) playing against the environment, N automata, say A1, A2, ..., AN take part in a game. Consider a typical automaton Ai described by a 4-tuple {Si, ri, Ti,pi}. Each player i has a finite set of actions or pure strategies, Si, 1 ≤ i ≤ N . Let the cardinality of Si be mi, 1 ≤ i ≤ N . The result of each play is a random payoff to each player. Let ri denote the random payoff to player i, 1 ≤ i ≤ N . It is assumed here that ri ∈ [0, 1]. Define functions d : Π j=1Sj → [0, 1], 1 ≤ i ≤ N, by d(a1, ..., aN ) = E[ri|player j chose action aj , aj ∈ Sj , 1 ≤ j ≤ N ]. (0.1) The function d is called the expected payoff function or utility function of player i, 1 ≤ i ≤ N . The objective of each player is to maximize its expected payoff. Players choose their strategies based on a time-varying probability distribution. Let pi(k) = [pi1(k)...pimi(k)] t denote the action choice probability distribution of the i automaton at time instance k. Then pil(k) denotes the probability with which i automaton player chooses the l pure strategy at instant k. Thus pi(k) is the strategy probability vector employed by the i player at instant k. Ti denotes the stochastic learning algorithm according to which the elements of the set pi are updated at each time k, i.e.,

Perceptually based waterfilling for watermarking

Presents a common ground between information theoretic and perceptually based approaches to watermarking. A perceptually based iterative waterfilling algorithm that achieves information theoretic watermarking capacity within perceptual distortion constraints is proposed. Image watermarking is considered for experimental analysis. Experimental results are presented to explain how the proposed algorithm achieves a trade-off between information theoretic capacity, robustness and perceptual distortion. Some general observations about the visual quality of an image and its information theoretic capacity are also made.

Reciprocity and Fairness in Medium Access Control Games

In wireless communication systems users compete for communication opportunities through a medium access control protocol. Previous research has shown that selfish behavior in medium access games could lead to inefficient and unfair resource allocation. We introduce a new notion of reciprocity in a medium access game and derive the corresponding Fairness Nash equilibrium. Further, using mechanism design we show that this type of reciprocity can remove unfair/inefficient equilibrium solutions.