Outcome aware ranking in value creation networks

In this paper, we consider a natural ranking problem that arises in settings in which a community of people (or agents) are engaged in regular interactions with an end goal of creating value. Examples of such scenarios are academic collaboration networks, creative collaborations, and interactions between agents of a service delivery organization. For instance, consider a service delivery organization which essentially resolves a sequence of service requests from its customers by deploying its agents to resolve the requests. Typically, resolving a request requires interaction between multiple agents and results in an outcome (or value). The outcome could be success or failure of problem resolution or an index of customer satisfaction. For this scenario, the ranking of the agents of the network should take into account two aspects: importance of the agents in the network structure that arises as a result of interactions and the value generated by the interactions involving the respective agents. Such a ranking can be used for several purposes such as identifying influential agents of the interaction network, effective and efficient spreading of messages in the network. In this paper, we formally model the above ranking problem and develop a novel algorithm for computing the ranking. The key aspect of our approach is creating special nodes in the interaction network corresponding to the outcomes and endowing them independent, external status. The algorithm then iteratively spreads the external status of the outcomes to the agents based on their interactions and the outcome of those interactions. This results in an eigenvector like formulation, which results in a method requiring computing the inverse of a matrix rather than the eigenvector. We present several theoretical characterizations of our algorithmic approach. We present experimental results on the public domain real-life datasets from the Internet Movie Database and a dataset constructed by retrieving impact and citation ratings for papers listed in the DBLP database.     

Adaptive Channel Estimation Aided Log-Map Turbo Decoder Implementation for DS-CDMA Based Mobile Systems

Practical implementation of convolutional turbo codec is impeded by the difficulty of real-time execution in high transmission rate communication systems due to high computational complexity, iterative block decoding structure, as well as the requirement of accurate on-line channel reliability estimation for maximum-likelihood decoding. Relying on innovative channel estimation techniques involving DS-CDMA specific noise/interference variance estimation and fading channel variation tracking, this paper provides a low-complexity all-digital design of an iterative SISO log-MAP turbo decoder for DS-CDMA based mobile communication systems. The issues of quantization and data flow in both pre-decoder processing module and iterative trellis decoding module are prudently addressed to ensure highly efficient hardware implementation. The efficient design strategies applied confine the decoding complexity while leading to an excellent performance within 0.2 dB of the software decoder.     

Leveraging semantic resources in diversified query expansion

A search query, being a very concise grounding of user intent, could potentially have many possible interpretations. Search engines hedge their bets by diversifying top results to cover multiple such possibilities so that the user is likely to be satisfied, whatever be her intended interpretation. Diversified Query Expansion is the problem of diversifying query expansion suggestions, so that the user can specialize the query to better suit her intent, even before perusing search results. In this paper, we consider the usage of semantic resources and tools to arrive at improved methods for diversified query expansion. In particular, we develop two methods, those that leverage Wikipedia and pre-learnt distributional word embeddings respectively. Both the approaches operate on a common three-phase framework; that of first taking a set of informative terms from the search results of the initial query, then building a graph, following by using a diversity-conscious node ranking to prioritize candidate terms for diversified query expansion. Our methods differ in the second phase, with the first method Select-Link-Rank (SLR) linking terms with Wikipedia entities to accomplish graph construction; on the other hand, our second method, Select-Embed-Rank (SER), constructs the graph using similarities between distributional word embeddings. Through an empirical analysis and user study, we show that SLR ourperforms state-of-the-art diversified query expansion methods, thus establishing that Wikipedia is an effective resource to aid diversified query expansion. Our empirical analysis also illustrates that SER outperforms the baselines convincingly, asserting that it is the best available method for those cases where SLR is not applicable; these include narrow-focus search systems where a relevant knowledge base is unavailable. Our SLR method is also seen to outperform a state-of-the-art method in the task of diversified entity ranking.