Inference Rules and their Application to Recognizing Textual Entailment
In this paper, we explore ways of improving an inference rule collection and its application to the task of recognizing textual entailment. For this purpose, we start with an automatically acquired collection and we propose methods to refine it and obtain more rules using a hand-crafted lexical resource. Following this, we derive a dependency-based structure representation from texts, which aims to provide a proper base for the inference rule application. The evaluation of our approach on the recognizing textual entailment data shows promising results on precision and the error analysis suggests possible improvements.

title: Hybrid Constituent and Dependency Parsing with Tsinghua Chinese Treebank
abstract: In this paper, we describe our hybrid parsing model on Mandarin Chinese processing. The model combines the mainstream constitute and dependency parsing and the dataset we use it the Tsinghua Chinese Treebank, whose annotation has both constitutes and head information. We show the adaption of this annotation scheme to the normal constitute structure, dependency structure, and the integration of both. We achieve a f1-score of 85.23% for the constitute parsing, 82.35% for the partial head information, and 74.27% for the complete head information.

title: Recognizing Textual Entailment Using a Subsequence Kernel Method
abstract: We present a novel approach to recognizing Textual Entailment. Structural features are constructed from abstract tree descriptions, which are automatically extracted from syntactic dependency trees. These features are then applied in a subsequence-kernel-based classifier to learn whether an entailment relation holds between two texts. Our method makes use of machine learning techniques using a limited data set, no external knowledge bases (e.g. WordNet), and no handcrafted inference rules. We achieve an accuracy of 74.5% for text pairs in the Information Extraction and Question Answering task, 63.6% for the RTE-2 test data, and 66.9% for the RET-3 test data.

title: Recognizing Textual Relatedness with Predicate-Argument Structures
abstract: In this paper, we first compare several strategies to handle the newly proposed three-way Recognizing Textual Entailment (RTE) task. Then we define a new measurement for a pair of texts, called Textual Relatedness, which is a weaker concept than semantic similarity or paraphrase. We show that an alignment model based on the predicate-argument structures using this measurement can help an RTE system to recognize the Unknown cases at the first stage, and contribute to the improvement of the overall performance in the RTE task. In addition, several heterogeneous lexical resources are tested, and different contributions from them are observed.

title: SketchML a representation language for novel sketch recognition approach
abstract: Multimodal interfaces can be profitably used to support increasingly complex services in assistive environments. In particular, sketch-based interfaces offer users an effortless and powerful communication way to represent concepts and commands on different devices. Unlike other modalities, sketch-based interaction can be easily fitted according to heterogeneous services. Moreover it can be quickly personalized according to the user needs.
 Developing a sketch-based interface for a specific service is a time-consuming operation that requires the re-engineering and/or the re-designing of the whole recognizer framework. This paper describes a definitive framework by which the user, simply by using freehand drawing, can define every kind of sketch-based interface. The definition of the interface and its recognition process are performed by using our developed Sketch Modeling Language (SketchML).

title: Ontology-based Query Construction for GeoCLEF
abstract: This paper describes experiments with geographical information retrieval (GIR). Being different from the traditional information IR, we focus more on the query expansion instead of document ranking. We parse each topic into the event part and the geographic part and use different ontologies to expand both parts respectively. The results show promising results of our strategy for this task.

title: Context-Centric Needs Anticipation Using Information Needs Graphs
abstract: Effective agent teamwork requires information exchange to be conducted in a proactive, selective, and intelligent way. In the field of distributed artificial intelligence, there has been increasing number of research focusing on need-driven proactive communication, both theoretically and practically. Among these works, CAST has realized a team-oriented agent architecture where agents, based on a computational shared mental model, are able to anticipate teammates' information needs and proactively deliver relevant information to the needers in a timely manner. However, the first implementation of CAST takes little consideration of the dynamics of the anticipated information needs, which can change in various ways as the context develops. In this paper we describe a novel mechanism for organizing and managing the “context” of information needs. This allows agents to dynamically activate and deactivate information needs progressively. It has been shown that the two-level context-centric approach can enhance team performance considerably.

title: Closed-loop cross-layer SDMA designs with outdated CSIT
abstract: In this paper, we propose a novel closed-loop approach for robust downlink multi-antenna cross-layer design with outdated channel state information at the transmitter (CSIT). Based on the ACK/NAK feedbacks from the mobiles, the proposed cross-layer design does not require the knowledge of CSIT error statistics. We formulate the cross-layer design as a mixed combinatorial search and Markov decision process (MDP). While it is well-known that general solutions for MDP are very complex, one important contribution of this paper is that we obtain simple closed-form power, rate and user allocation policies which are asymptotically optimal for small target frame error rate (FER). Simulation results illustrate that the performance of the proposed closed-loop cross-layer design is very robust with respect to the outdated CSIT, CSIT error model mismatch as well as channel variations due to Doppler.

title: Bayesian Face Recognition Based on Markov Random Field Modeling
abstract: In this paper, a Bayesian method for face recognition is proposed based on Markov Random Fields (MRF) modeling. Constraints on image features as well as contextual relationships between them are explored and encoded into a cost function derived based on a statistical model of MRF. Gabor wavelet coefficients are used as the base features, and relationships between Gabor features at different pixel locations are used to provide higher order contextual constraints. The posterior probability of matching configuration is derived based on MRF modeling. Local search and discriminate analysis are used to evaluate local matches, and a contextual constraint is applied to evaluate mutual matches between local matches. The proposed MRF method provides a new perspective for modeling the face recognition problem. Experiments demonstrate promising results.

title: Web mapping service on second life compatible virtual world system
abstract: Online virtual world such as second life becomes more and more popular nowadays. And way-finding in large-scale virtual worlds becomes important problem for both novices and experts. Similar to the physical world, a well-designed mapping system would play an important role in way-finding process. However, Second Life only supports thumbnail map system right now. The thumbnail map system could only provide an overview of the area and with no query support. There are several challenges to develop a better virtual mapping system. First, identify the virtual world geo-features and to develop proper criteria and algorithms to generate features from primitive which is the basic unit in virtual world. Second, provide web service for data access through the Internet from systems with different architectures. In this paper, we implemented a prototype map service system called VWMS conformed to OWS specification from OpenGIS consortium. The prototype includes an AJAX style map site and a feature generalization server-module extracting geo-feature from Open Simulator primitive database. Besides, the system also provides a Web Map Service interface for

title: On the hardness of minimizing space for all-shortest-path interval routing schemes
abstract: k-Interval Routing Scheme (k-IRS) is a compact routing method that allows up to k interval labels to be assigned to an arc; and global k-IRS allows not more than a total of k interval labels in the whole network. A fundamental problem is to characterize the networks that admit k-IRS (or global k-IRS). Many of the problems related to single-shortest-path k-IRS have already been shown to be NP-complete. For all-shortest-path k-IRS, the characterization problem remains open for k>=1. In this paper, we study the time complexity of devising minimal-space all-shortest-path k-IRSs and show that it is NP-complete to decide whether a graph admits an all-shortest-path k-IRS, for every integer k>=3, and so is that of deciding whether a graph admits an all-shortest-path k-strict IRS, for every integer k>=4. These are the first NP-completeness results for all-shortest-path k-IRS where k is a constant and the graph is unweighted. The NP-completeness holds also for the linear case. We also prove that it is NP-complete to decide whether an unweighted graph admits an all-shortest-path IRS with global compactness of at most k, which also holds for the linear and strict cases.