To support more precise query translation for English-Chinese Bi-Directional Cross-Language Information Retrieval (CLIR), we have developed a novel framework by integrating a semantic network to characterize the correlations between multiple inter-related text terms of interest and learn their inter-related statistical query translation models. First, a semantic network is automatically generated from large-scale English-Chinese bilingual parallel corpora to characterize the correlations between a large number of text terms of interest. Second, the semantic network is exploited to learn the statistical query translation models for such text terms of interest. Finally, these inter-related query translation models are used to translate the queries more precisely and achieve more effective CLIR. Our experiments on a large number of official public data have obtained very positive results.

In this paper we propose a mechanism that encourages agents, participating in an open MAS, to follow a desirable behaviour, by introducing modifications in the environment. This mechanism is deployed by using an infrastructure based on institutional agents called incentivators. Each external agent is assigned to an incentivator that is able to discover its preferences, and to learn the suitable modifications in the environment, in order to improve the global utility of a system in response to inadequate design or changes in the population of participating agents. The mechanism is evaluated in a p2p scenario.

Lifted message passing algorithms exploit repeated structure within a given graphical model to answer queries efficiently. Given evidence, they construct a lifted network of supernodes and superpotentials corresponding to sets of nodes and potentials that are indistinguishable given the evidence. Recently, efficient algorithms were presented for updating the structure of an existing lifted network with incremental changes to the evidence. In the inference stage, however, current algorithms need to construct a separate lifted network for each evidence case and run a modified message passing algorithm on each lifted network separately. Consequently, symmetries across the inference tasks are not exploited. In this paper, we present a novel lifted message passing technique that exploits symmetries across multiple evidence cases. The benefits of this multi-evidence lifted inference are shown for several important AI tasks such as computing personalized PageRanks and Kalman filters via multi-evidence lifted Gaussian belief propagation.

It is well known that the three water categories science. Methods in biophysics only provide qualitative have different functions. Individually bound water has multiple description of the structure and thus clarifying contacts that stabilize the protein structure. Hydration the collective phenomena of a huge number water has heterogeneous dynamical behavior, contributing to of water molecules is still beyond intuition protein folding, stability and dynamics, and interacting with in biophysics. We introduce a simulation-based the bulk water. Bulk water is free to move and continuously data mining approach that quantitatively model the exchanges with hydration water, and indirectly influences on structure of water surrounding a protein as clusters the protein [Bizzarri and Cannistraro, 2002], [Halle, 2004]. of water molecules having similar moving behavior. Much effort has been devoted to quantitatively model the The paper presents and explains how the advances relative motion (orientation, rotation and velocity) and dynamical of AI technique can potentially solve this properties of individual water molecules in protein challenging data-intensive problem.

We propose a nonmonotonic extension of low complexity Description Logics EL⊥ and DL-Litecore for reasoning about typicality and defeasible properties. The resulting logics are called EL⊥ T min and DL-Litec T min . Concerning DL-Litec T min , we prove that entailment is in \Pi^p_2. With regard to EL⊥ T min , we first show that entailment remains EXPTIME-hard. Next we consider the known fragment of Left Local EL⊥ T min and we prove that the complexity of entailment drops to \Pi^p_2.

In today's services driven economic environment, it is imperative for organizations to provide better quality service experience to differentiate and grow their business. Customer satisfaction (C-SAT) is the key driver for retention and growth in Retail Banking. Wait time, the time spent by a customer at the branch before getting serviced, contributes significantly to C-SAT. Due to high footfall, it is improbable to improve the wait time of every customer walking in the branch. Therefore, banks in developing countries are strategically looking to segment its customers and services and offer differentiated QoS based service delivery. In this work, we present a system for customer segmentation, and scheduling based on historic value of the customer and characteristics of current service request. We describe the system and give mathematical formulation of the scheduling problem and the associated heuristics. We present results and experience of deployment of this solution in multiple branches of a leading bank in India.

An online double auction mechanism for dynamic environments, especially dynamic has to match sellers and buyers dynamically and calculate double auctions. After a brief review of related a payment for each matched trader without knowing work, we specify the problem we are tackling, and about future orders. Such uncertainty is more challenging for then briefly outline our research plan, the results we double auction mechanism design because modelling traders' have achieved to date, and the ongoing directions.

One of main difficulties of multi-dimensional packing problems is the fragmentation of free space into several unusable small parts after a few items are packed. This study proposes a defragmentation technique to combine the fragmented space into a continuous usable space, which potentially allows the packing of additional items. We illustrate the effectiveness of this technique on the two- and three-dimensional Bin Packing Problems. In conjunction with a bin shuffling strategy for incremental improvement, our resultant algorithm outperforms all leading meta-heuristic approaches.

In many of the possible applications as well as the theoretical models of computational social choice,the agents’ preferences are represented as partialorders. In this paper, we extend the maximum likelihood approach for defining “optimal” voting rules to this setting. We consider distributions in which the pairwise comparisons / incomparabilities between alternatives are drawn i.i.d. We call suchmodels pairwise-independentmodels and show that they correspond to a class of voting rules that we call pairwise scoring rules. This generalizes rulessuch as Kemeny and Borda. Moreover, we show that Borda is the only pairwise scoring rule that satisfies neutrality, when the outcome space is the set of all alternatives. We then study which voting rules defined for linear orders can be extended to partial orders via our MLE model. We show that any weakly neutral outcome scoring rule (includingany ranking/candidate scoring rule) based onthe weighted majority graph can be represented as the MLE of a weakly neutral pairwise-independent model. Therefore, all such rules admit natural extensionsto profiles of partial orders. Finally, we propose a specific MLE model π k for generating a set of k winning alternatives, and study the computational complexity of winner determination for the MLE of π k .

Machine learning is traditionally formalized and researched as the study of learning concepts and decision functions from labeled examples, requiring a representation that encodes information about the domain of the decision function to be learned. We are interested in providing a way for a human teacher to interact with an automated learner using natural instructions, thus allowing the teacher to communicate the relevant domain expertise to the learner without necessarily knowing anything about the internal representations used in the learning process. In this paper we suggest to view the process of learning a decision function as a natural language lesson interpretation problem instead of learning from labeled examples. This interpretation of machine learning is motivated by human learning processes, in which the learner is given a lesson describing the target concept directly, and a few instances exemplifying it. We introduce a learning algorithm for the lesson interpretation problem that gets feedback from its performance on the final task, while learning jointly (1) how to interpret the lesson and (2) how to use this interpretation to do well on the final task. his approach alleviates the supervision burden of traditional machine learning by focusing on supplying the learner with only human-level task expertise for learning. We evaluate our approach by applying it to the rules of the Freecell solitaire card game. We show that our learning approach can eventually use natural language instructions to learn the target concept and play the game legally. Furthermore, we show that the learned semantic interpreter also generalizes to previously unseen instructions.