MT for Minority Languages Using Elicitation-Based Learning of Syntactic Transfer Rules

The AVENUE project contains a run-time machine translationprogram that is surrounded by pre- and post-run-time modules. Thepost-run-time module selects among translation alternatives. Thepre-run-time modules are concerned with elicitation of data andautomatic learning of transfer rules in order to facilitate thedevelopment of machine translation between a language with extensiveresources for natural language processing and a language with fewresources for natural language processing. This paper describes therun-time transfer-based machine translation system as well as two ofthe pre-run-time modules: elicitation of data from the minoritylanguage and automated learning of transfer rules from theelicited data.     

Facebook single and cross domain data for recommendation systems

The emergence of social networks and the vast amount of data that they contain about their users make them a valuable source for personal information about users for recommender systems. In this paper we investigate the feasibility and effectiveness of utilizing existing available data from social networks for the recommendation process, specifically from Facebook. The data may replace or enrich explicit user ratings. We extract from Facebook content published by users on their personal pages about their favorite items and preferences in the domain of recommendation, and data about preferences related to other domains to allow cross-domain recommendation. We study several methods for integrating Facebook data with the recommendation process and compare the performance of these methods with that of traditional collaborative filtering that utilizes user ratings. In a field study that we conducted, recommendations obtained using Facebook data were tested and compared for 95 subjects and their crawled Facebook friends. Encouraging results show that when data is sparse or not available for a new user, recommendation results relying solely on Facebook data are at least equally as accurate as results obtained from user ratings. The experimental study also indicates that enriching sparse rating data by adding Facebook data can significantly improve results. Moreover, our findings highlight the benefits of utilizing cross domain Facebook data to achieve improvement in recommendation performance.     

Solving MAX-<Emphasis Type="Italic">r</Emphasis>-SAT Above a Tight Lower Bound

We present an exact algorithm that decides, for every fixed r≥2 in time O(m)+2^O(k2)O(m)+2^{O(k^{2})} whether a given multiset of m clauses of size r admits a truth assignment that satisfies at least ((2 ^r −1)m+k)/2 ^r clauses. Thus Max-r-Sat is fixed-parameter tractable when parameterized by the number of satisfied clauses above the tight lower bound (1−2^−r )m. This solves an open problem of Mahajan et al. (J. Comput. Syst. Sci. 75(2):137–153, 2009).     

BoostMap: an embedding method for efficient nearest neighbor retrieval

This paper describes BoostMap, a method for efficient nearest neighbor retrieval under computationally expensive distance measures. Database and query objects are embedded into a vector space, in which distances can be measured efficiently. Each embedding is treated as a classifier that predicts for any three objects X, A, B whether X is closer to A or to B. It is shown that a linear combination of such embeddingbased classifiers naturally corresponds to an embedding and a distance measure. Based on this property, the BoostMap method reduces the problem of embedding construction to the classical boosting problem of combining many weak classifiers into an optimized strong classifier. The classification accuracy of the resulting strong classifier is a direct measure of the amount of nearest neighbor structure preserved by the embedding. An important property of BoostMap is that the embedding optimization criterion is equally valid in both metric and non-metric spaces. Performance is evaluated in databases of hand images, handwritten digits, and time series. In all cases, BoostMap significantly improves retrieval efficiency with small losses in accuracy compared to brute-force search. Moreover, BoostMap significantly outperforms existing nearest neighbor retrieval methods, such as Lipschitz embeddings, FastMap, and VP-trees.     

A Unified Framework for Gesture Recognition and Spatiotemporal Gesture Segmentation

Within the context of hand gesture recognition, spatiotemporal gesture segmentation is the task of determining, in a video sequence, where the gesturing hand is located and when the gesture starts and ends. Existing gesture recognition methods typically assume either known spatial segmentation or known temporal segmentation, or both. This paper introduces a unified framework for simultaneously performing spatial segmentation, temporal segmentation, and recognition. In the proposed framework, information flows both bottom-up and top-down. A gesture can be recognized even when the hand location is highly ambiguous and when information about when the gesture begins and ends is unavailable. Thus, the method can be applied to continuous image streams where gestures are performed in front of moving, cluttered backgrounds. The proposed method consists of three novel contributions: a spatiotemporal matching algorithm that can accommodate multiple candidate hand detections in every frame, a classifier-based pruning framework that enables accurate and early rejection of poor matches to gesture models, and a subgesture reasoning algorithm that learns which gesture models can falsely match parts of other longer gestures. The performance of the approach is evaluated on two challenging applications: recognition of hand-signed digits gestured by users wearing short-sleeved shirts, in front of a cluttered background, and retrieval of occurrences of signs of interest in a video database containing continuous, unsegmented signing in American Sign Language (ASL).     

Image Appearance Exploration by Model-Based Navigation

Changing the appearance of an image can be a complex and non-intuitive task. Many times the target image colors and look are only known vaguely and many trials are needed to reach the desired results. Moreover, the effect of a specific change on an image is difficult to envision, since one must take into account spatial image considerations along with the color constraints. Tools provided today by image processing applications can become highly technical and non-intuitive including various gauges and knobs.
In this paper we introduce a method for changing image appearance by navigation, focusing on recoloring images. The user visually navigates a high dimensional space of possible color manipulations of an image. He can either explore in it for inspiration or refine his choices by navigating into sub regions of this space to a specific goal. This navigation is enabled by modeling the chroma channels of an image's colors using a Gaussian Mixture Model (GMM). The Gaussians model both color and spatial image coordinates, and provide a high dimensional parameterization space of a rich variety of color manipulations. The user's actions are translated into transformations of the parameters of the model, which recolor the image. This approach provides both inspiration and intuitive navigation in the complex space of image color manipulations.     

Information Filtering: Overview of Issues,Research and Systems

An abundant amount of information is created and delivered over electronic media. Users risk becoming overwhelmed by the flow of information, and they lack adequate tools to help them manage the situation. Information filtering (IF) is one of the methods that is rapidly evolving to manage large information flows. The aim of IF is to expose users to only information that is relevant to them. Many IF systems have been developed in recent years for various application domains. Some examples of filtering applications are: filters for search results on the internet that are employed in the Internet software, personal e-mail filters based on personal profiles, listservers or newsgroups filters for groups or individuals, browser filters that block non-valuable information, filters designed to give children access them only to suitable pages, filters for e-commerce applications that address products and promotions to potential customers only, and many more. The different systems use various methods, concepts, and techniques from diverse research areas like: Information Retrieval, Artificial Intelligence, or Behavioral Science. Various systems cover different scope, have divergent functionality, and various platforms. There are many systems of widely varying philosophies, but all share the goal of automatically directing the most valuable information to users in accordance with their User Model, and of helping them use their limited reading time most optimally. This paper clarifies the difference between IF systems and related systems, such as information retrieval (IR) systems, or Extraction systems. The paper defines a framework to classify IF systems according to several parameters, and illustrates the approach with commercial and academic systems. The paper describes the underlying concepts of IF systems and the techniques that are used to implement them. It discusses methods and measurements that are used for evaluation of IF systems and limitations of the current systems. In the conclusion we present research issues in the Information Filtering research arena, such as user modeling, evaluation standardization and integration with digital libraries and Web repositories.     

Properties of hydroxypropyl methylcellulose–polyvinyl alcohol water systems,dispersants in vinyl chloride suspension polymerization

Aqueous solutions of partly hydrolyzed polyvinyl acetate (polyvinyl alcohol, PVA) and hydroxypropyl methylcellulose (HPMC) are used together as dispersants in vinyl chloride suspension polymerization. Surface tension, viscosity, and cloud point (CP) of diluted PVA and HPMC solutions at room temperature, viscosity of concentrated solutions as a function of temperature, and incipient gelation temperatures (IGT) were determined. Viscosity measurements show synergism of binary polymer mixtures. The synergetic effect diminishes with rise of temperature. IGT of PVA–HPMC solutions do not differ remarkably from those of solutions containing only HPMC and are much higher than the corresponding CP. At definite relative concentrations of HPMC and PVA, separation of the mixtures into two immiscible phases occurs. Each phase contains both polymers, but in the denser and more concentrated phase (lower layer), PVA is the prevailing component. The mutual influence of the two polymers causes changes in solute–solvent interaction, that is, dehydration of the polymers' molecules. It is assumed that in the processes occurring in the system, preferential molecules of definite structure take part. The merits of a system using both polymers as dispersants in the vinyl chloride polymerization are discussed in the light of ascertained data. © 1993 John Wiley & Sons, Inc.     

Glycosynthase activity of Geobacillus stearothermophilus GH52 beta-xylosidase: efficient synthesis of xylooligosaccharides from alpha-D-xylopyranosyl fluoride through a conjugated reaction

Glycosynthases are mutant glycosidases in which the acidic nucleophile is replaced by a small inert residue. In the presence of glycosyl fluorides of the opposite anomeric configuration (to that of their natural substrates), these enzymes can catalyze glycosidic bond formation with various acceptors. In this study we demonstrate that XynB2E335G, a nucleophile-deficient mutant of a glycoside hydrolase family 52 beta-xylosidase from G. stearothermophilus, can function as an efficient glycosynthase, using alpha-D-xylopyranosyl fluoride as a donor and various aryl sugars as acceptors. The mutant enzyme can also catalyze the self-condensation reaction of alpha-D-xylopyranosyl fluoride, providing mainly alpha-D-xylobiosyl fluoride. The self-condensation kinetics exhibited apparent classical Michaelis-Menten behavior, with kinetic constants of 1.3 s(-1) and 2.2 mM for k(cat) and K(M(acceptor)), respectively, and a k(cat)/K(M(acceptor)) value of 0.59 s(-1) mM(-1). When the beta-xylosidase E335G mutant was combined with a glycoside hydrolase family 10 glycosynthase, high-molecular-weight xylooligomers were readily obtained from the affordable alpha-D-xylopyranosyl fluoride as the sole substrate.