We model information dissemination as a susceptible-infected epidemic process and formulate a problem to jointly optimize seeds for the epidemic and time varying resource allocation over the period of a fixed duration campaign running on a social network with a given adjacency matrix. Individuals in the network are grouped according to their centrality measure and each group is influenced by an external control function-implemented through advertisements-during the campaign duration. The aim is to maximize an objective function which is a linear combination of the reward due to the fraction of informed individuals at the deadline, and the aggregated cost of applying controls (advertising) over the campaign duration. We also study a problem variant with a fixed budget constraint. We set up the optimality system using Pontryagin's maximum principle from optimal control theory and solve it numerically using the forward-backward sweep technique. Our formulation allows us to compare the performance of various centrality measures (pagerank, degree, closeness, and betweenness) in maximizing the spread of a message in the optimal control framework. We find that degree-a simple and local measure-performs well on the three social networks used to demonstrate results: 1) scientific collaboration; 2) Slashdot; and 3) Facebook. The optimal strategy targets central nodes when the resource is scarce, but noncentral nodes are targeted when the resource is in abundance. Our framework is general and can be used in similar studies for other disease or information spread models-that can be modeled using a system of ordinary differential equations-for a network with a known adjacency matrix.

We study the optimal control problem of maximizing the spread of an information epidemic on a social network. Information propagation is modeled as a susceptible-infected (SI) process, and the campaign budget is fixed. Direct recruitment and word-of-mouth incentives are the two strategies to accelerate information spreading (controls). We allow for multiple controls depending on the degree of the nodes/individuals. The solution optimally allocates the scarce resource over the campaign duration and the degree class groups. We study the impact of the degree distribution of the network on the controls and present results for Erdős–Rényi and scale-free networks. Results show that more resource is allocated to high-degree nodes in the case of scale-free networks, but medium-degree nodes in the case of Erdős–Rényi networks. We study the effects of various model parameters on the optimal strategy and quantify the improvement offered by the optimal strategy over the static and bang-bang control strategies. The effect of the time-varying spreading rate on the controls is explored as the interest level of the population in the subject of the campaign may change over time. We show the existence of a solution to the formulated optimal control problem, which has nonlinear isoperimetric constraints, using novel techniques that is general and can be used in other similar optimal control problems. This work may be of interest to political, social awareness, or crowdfunding campaigners and product marketing managers, and with some modifications may be used for mitigating biological epidemics.

We study the optimal control problem of allocating campaigning resources over the campaign duration and degree classes in a social network. Information diffusion is modeled as a Susceptible-Infected epidemic and direct recruitment of susceptible nodes to the infected (informed) class is used as a strategy to accelerate the spread of information. We formulate an optimal control problem for optimizing a net reward function, a linear combination of the reward due to information spread and cost due to application of controls. The time varying resource allocation and seeds for the epidemic are jointly optimized. A problem variation includes a fixed budget constraint. We prove the existence of a solution for the optimal control problem, provide conditions for uniqueness of the solution, and prove some structural results for the controls (e.g., controls are non-increasing functions of time). The solution technique uses Pontryagin's Maximum Principle and the forward-backward sweep algorithm (and its modifications) for numerical computations. Our formulations lead to large optimality systems with up to about 200 differential equations and allow us to study the effect of network topology (Erdos-Rényi/scale-free) on the controls. Results reveal that the allocation of campaigning resources to various degree classes depends not only on the network topology but also on system parameters such as cost/abundance of resources. The optimal strategies lead to significant gains over heuristic strategies for various model parameters. Our modeling approach assumes uncorrelated network, however, we find the approach useful for real networks as well. This work is useful in product advertising, political and crowdfunding campaigns in social networks.

In this paper, we propose centralized and distributed power allocation algorithms for a multi-user, multi relay cellular network using decode-and-forward (DF) cooperation strategy taking channel uncertainty into account. The objective is to minimize the total uplink power of the network taking each user's target data rate as the quality of service (QoS) constraint under imperfect channel state information (CSI). We consider the worst-case optimization approach, in which QoS constraint is satisfied for all channels contained in some uncertainty region. First, a centralized power allocation scheme is developed to optimally allocate the power among the users and the relay nodes. Then, a suboptimal distributed algorithm is proposed based on a standard primal decomposition approach, where each relay can independently and separately minimize its own power. The proposed solutions are based on second order cone programming (SOCP), which is computationally efficient. Simulation results show that the performance of the suboptimal distributed solution is near-optimal and reveals the fact that DF cooperation strategy is more efficient in total power reduction and more robust under channel uncertainty over non-cooperative systems.

In this paper, we propose a novel multiuser cooperative communication scheme using random network coding for uplink wireless transmissions. The participating users transmit a linear combination of their new data packet and the packets successfully decoded due to preceding transmissions by other users, thus sending the new information as well as relaying the information for other users at the same time. The coefficients used for linear combination are selected at random from the finite field used to carry out the network coding. At the base station, original data packets are recovered from the received packets by solving the system of linear equations in the finite field. The proposed scheme is very robust against inter-user channel noise and can offer very high diversity gain in moderate inter-user channel conditions. Moreover, the transmission delay required by the proposed scheme is 2N as compared to N² in the traditional Time Division Multiple Access (TDMA) cooperative systems. The performance benefits of the proposed scheme are demonstrated through simulation analysis.

Detection of filled pauses is a challenging research problem which has several practical applications. It can be used to evaluate the spoken fluency skills of the speaker, to improve the performance of automatic speech recognition systems or to predict the mental state of the speaker. This paper presents an algorithm for filled pause detection that is based on the premise that the vocal tract characteristics, and hence the formants, are stable during the production of a filled pause. The performance of the proposed algorithm is evaluated on real-life recordings of call center agents where the locations of the filled pauses are hand labeled. The proposed algorithm outperforms a standard cepstral stability based filled pause detection algorithm and a standard pitch-based detection technique.

This paper presents a method to automatically quantify the spoken English fluency skills of speakers. The focus of this work is to automatically compute a numeric score of spoken fluency that is correlated with the numerical score the human assessors would assign. The proposed method combines several novel prosodic and lexical features to compute the fluency score. It is shown that the prosodic and the lexical features provide complementary information for fluency evaluation. Extensive evaluation on human-labeled utterances shows that the proposed technique exhibits similar trends in performance and confusions as shown by human assessors. The proposed technique leads to 84.2% classification accuracy when the two extreme classes of fluency are considered.