3544821
11,001 New Features for Statistical Machine Translation
We use the Margin Infused Relaxed Algorithm of Crammer et al. to add a large number of new features to two machine translation systems: the Hiero hierarchical phrase-based translation system and our syntax-based translation system. On a large-scale Chinese-English translation task, we obtain statistically significant improvements of +1.5 Bleu and + 1.1 Bleu, respectively. We analyze the impact of the new features and the performance of the learning algorithm.
765547
Scalable Inference and Training of Context-Rich Syntactic Translation Models
Statistical MT has made great progress in the last few years, but current translation models are weak on re-ordering and target language fluency. Syntactic approaches seek to remedy these problems. In this paper, we take the framework for acquiring multi-level syntactic translation rules of (Galley et al., 2004) from aligned tree-string pairs, and present two main extensions of their approach: first, instead of merely computing a single derivation that minimally explains a sentence pair, we construct a large number of derivations that include contextually richer rules, and account for multiple interpretations of unaligned words. Second, we propose probability estimates and a training procedure for weighting these rules. We contrast different approaches on real examples, show that our estimates based on multiple derivations favor phrasal re-orderings that are linguistically better motivated, and establish that our larger rules provide a 3.63 BLEU point increase over minimal rules.
32581766
A Privacy-Preserving Classification Mining Algorithm
Privacy-preserving classification mining is one of the fast-growing sub-areas of data mining. How to perturb original data and then build a decision tree based on perturbed data is the key research challenge. By applying transition probability matrix this paper proposes a novel privacy-preserving classification mining algorithm which suits all data types, arbitrary probability distribution of original data, and perturbing all attributes (including label attribute). Experimental results demonstrate that decision tree built using this algorithm on perturbed data has comparable classifying accuracy to decision tree built using un-privacy-preserving algorithm on original data.
9921122
Probabilistic Skyline Operator over Sliding Windows
Skyline computation has many applications including multi-criteria decision making. In this paper, we study the problem of efficient processing of continuous skyline queries over sliding windows on uncertain data elements regarding given probability thresholds. We first characterize what kind of elements we need to keep in our query computation. Then we show the size of dynamically maintained candidate set and the size of skyline. We develop novel, efficient techniques to process a continuous, probabilistic skyline query. Finally, we extend our techniques to the applications where multiple probability thresholds are given or we want to retrieve "top-k" skyline data objects. Our extensive experiments demonstrate that the proposed techniques are very efficient and handle a high-speed data stream in real time.
1655622
Ed-Join: an efficient algorithm for similarity joins with edit distance constraints
There has been considerable interest in similarity join in the research community recently. Similarity join is a fundamental operation in many application areas, such as data integration and cleaning, bioinformatics, and pattern recognition. We focus on efficient algorithms for similarity join with edit distance constraints. Existing approaches are mainly based on converting the edit distance constraint to a weaker constraint on the number of matching q-grams between pair of strings. 
 
In this paper, we propose the novel perspective of investigating mismatching q-grams. Technically, we derive two new edit distance lower bounds by analyzing the locations and contents of mismatching q-grams. A new algorithm, Ed-Join, is proposed that exploits the new mismatch-based filtering methods; it achieves substantial reduction of the candidate sizes and hence saves computation time. We demonstrate experimentally that the new algorithm outperforms alternative methods on large-scale real datasets under a wide range of parameter settings.
5447146
Mining long sequential patterns in a noisy environment
Pattern discovery in long sequences is of great importance in many applications including computational biology study, consumer behavior analysis, system performance analysis, etc. In a noisy environment, an observed sequence may not accurately reflect the underlying behavior. For example, in a protein sequence, the amino acid N is likely to mutate to D with little impact to the biological function of the protein. It would be desirable if the occurrence of D in the observation can be related to a possible mutation from N in an appropriate manner. Unfortunately, the support measure (i.e., the number of occurrences) of a pattern does not serve this purpose. In this paper, we introduce the concept of compatibility matrix as the means to provide a probabilistic connection from the observation to the underlying true value. A new metric match is also proposed to capture the "real support" of a pattern which would be expected if a noise-free environment is assumed. In addition, in the context we address, a pattern could be very long. The standard pruning technique developed for the market basket problem may not work efficiently. As a result, a novel algorithm that combines statistical sampling and a new technique (namely border collapsing) is devised to discover long patterns in a minimal number of scans of the sequence database with sufficiently high confidence. Empirical results demonstrate the robustness of the match model (with respect to the noise) and the efficiency of the probabilistic algorithm.
19598296
Type I HARQ performance modeling and evaluation of mobile WiMAX for network simulators
While the development of Mobile WiMAX devices is still an ongoing process, complete and accurate simulations become more important in order to study the performance of Mobile WiMAX based broadband wireless access networks. To further improve network simulation models for Mobile WiMAX, we have theoretically modeled the Hybrid Automatic Retransmission reQuest (HARQ) mechanism and evaluated its performance and accuracy. In this paper, we present the design and implementation methodology of the Mobile WiMAX HARQ simulation model in system-level network simulators in order to gain the benefit of link quality improved by enabling HARQ. Our results show that HARQ achieves higher throughput even at low signal strength which can be considered as non-line-of-sight (NLOS) environments. We believe the implementation of HARQ in the network simulation model and its evaluation studies will provide profound understanding to Mobile WiMAX field test studies.