6845274
Fingerprinting Curves
This paper presents a new method for robust data hiding in curves and highlights potential applications including digital fingerprinting of map document for trace and track purposes. We parameterize a curve using the B-spline model and add a spread spectrum sequence to the coordinates of the B-spline control points. In order to achieve robust fingerprint detection, we propose an iterative alignment-minimization algorithm to perform curve registration and deal with the non-uniqueness of B-spline control points. We show through experiments the robustness of our method against various attacks such as collusion, geometric transformations and perturbation, printing-and-scanning, and some of their combinations. We demonstrate the feasibility of our method for fingerprinting topographic maps as well as writings and drawings. The extension from hiding data in 2D topographic maps to 3D elevation data sets is also discussed.
1320918
Data hiding in binary image for authentication and annotation
This paper proposes a new method to embed data in binary images, including scanned text, figures, and signatures. The method manipulates "flippable" pixels to enforce specific block-based relationship in order to embed a significant amount of data without causing noticeable artifacts. Shuffling is applied before embedding to equalize the uneven embedding capacity from region to region. The hidden data can be extracted without using the original image, and can also be accurately extracted after high quality printing and scanning with the help of a few registration marks. The proposed data embedding method can be used to detect unauthorized use of a digitized signature, and annotate or authenticate binary documents. The paper also presents analysis and discussions on robustness and security issues.
3261989
Robust error-resilient approach for MPEG video transmission over Internet
In network delivery of compressed video, packets may be lost if the channel is unreliable. Such losses tend to occur in burst. In this paper, we develop an error resilient video encoding approach to help error concealment at the decoder. We introduce a new block shuffling scheme to isolate erroneous blocks caused by packet losses. And we apply data hiding to add additional protection for motion vectors. The incorporation of these scheme adds little complexity to the standard encoder. Experimental results suggest that our approach can achieve a reasonable quality for packet loss up to 30% over a wide range of video materials.
1434507
Watermarking for image authentication
A data embedding method is proposed for image authentication based on table look-up in frequency domain. A visually meaningful watermark and a set of simple features are embedded invisibly in the marked image, which can be stored in the compressed form. The scheme can detect and localize alterations of the original image, such as the tampering of images exported from a digital camera.
1744642
A Bayesian Method for Guessing the Extreme Values in a Data Set
For a large number of data management problems, it would be very useful to be able to obtain a few samples from a data set, and to use the samples to guess the largest (or smallest) value in the entire data set. Min/max online aggregation, top-k query processing, outlier detection, and distance join are just a few possible applications. This paper details a statistically rigorous, Bayesian approach to attacking this problem. Just as importantly, we demonstrate the utility of our approach by showing how it can be applied to two specific problems that arise in the context of data management.
13867867
Outlier detection by sampling with accuracy guarantees
An effective approach to detecting anomalous points in a data setis distance-based outlier detection. This paper describes a simplesampling algorithm to effciently detect distance-based outliers indomains where each and every distance computation is veryexpensive. Unlike any existing algorithms, the sampling algorithmrequires a xed number of distance computations and can return goodresults with accuracy guarantees. The most computationallyexpensive aspect of estimating the accuracy of the result issorting all of the distances computed by the sampling algorithm.The experimental study on two expensive domains as well as tenadditional real-life datasets demonstrates both the effciency andeffectiveness of the sampling algorithm in comparison with thestate-of-the-art algorithm and there liability of the accuracyguarantees.
10609544
Balancing productivity and consumer satisfaction for profitability: Statistical and fuzzy regression analysis
This paper examines the relationships among productivity, consumer satisfaction and profitability using the conventional statistical regression and the new fuzzy regression approaches. For service firms in the context of Hong Kong, we verify the profit hypothesis that individually both productivity and consumer satisfaction are positively linked to profitability as well as the tradeoff hypothesis that aggregately there are negative interactions between productivity and consumer satisfaction for enhancing profitability. Hence service firms should balance their efforts in productivity and consumer satisfaction, possibly by employing appropriate information technologies to improve productivity while without hurting consumer satisfaction, to optimize their profitability. The study takes advantage of the Hong Kong Consumer Satisfaction Index and deliberately focuses on total rather than partial productivity. Several models are first estimated using the ordinary least squares (OLS) method and the results generally support the two hypotheses, but the OLS approach also leaves two puzzles that estimates of the regression coefficients are: (1) not significant before considering the interactions between consumer satisfaction and productivity but significant after introducing the interaction term, and (2) significant although sample data for productivity and the interaction term are highly correlated. These puzzles, together with the observed imprecision in productivity and profitability measurements and especially the subjectivity in measuring consumer satisfaction, lead us to adopt the fuzzy linear regression (FLR) techniques to further examine the two hypotheses. The popular FLR model continues to favor our research hypotheses but fail to offer any additional insights into the examined relationships over the OLS models. We then propose a revised FLR model which, in addition to reconfirming the hypotheses, does help to explain the encountered puzzles and fuzziness, and hence suggests an encouraging methodology for marketing.
31560341
The role of a judge in a user based retrieval experiment (poster session)
A tuning device for tympani that displays the pitch of each instrument's tympanum. The pitch is determined by measuring the strain in an adjustable tensioning mechanism employed in all tympani to keep proper tension on the tympanum. The pitch or tone of the tympani is determined by the vibration frequency of the tympanum when the tympani is played and the frequency is determined by the tension in the tympanum. A transducer measures the strain in the tensioning mechanism to give an accurate indication of what the tympanum tension, and therefore the pitch, of the tympani would be when played. The transducer includes a resistance-type strain gage mounted on the tympanum tensioning mechanism. The length of the conductor in the strain gage and therefore its resistance, changes with stress changes in the tensioning mechanism used to tune the tympani. A switch means selects a desired one of the strain gages mounted on each of the one or more tympani for inclusion as one section of a four-section resistance bridge circuit configuration. A signal conditioning module provides excitation to the bridge circuit and also conditions the output of the bridge circuit. The output of the bridge circuit varies as the resistance of the strain gage varies. The conditioned output of the bridge circuit is displayed on a voltmeter whose scale has been calibrated to indicate the pitch of the tympani being monitored.
524265
A Direct Method for Building Sparse Kernel Learning Algorithms
Many kernel learning algorithms, including support vector machines, result in a kernel machine, such as a kernel classifier, whose key component is a weight vector in a feature space implicitly introduced by a positive definite kernel function. This weight vector is usually obtained by solving a convex optimization problem. Based on this fact we present a direct method to build sparse kernel learning algorithms by adding one more constraint to the original convex optimization problem, such that the sparseness of the resulting kernel machine is explicitly controlled while at the same time performance is kept as high as possible. A gradient based approach is provided to solve this modified optimization problem. Applying this method to the support vectom machine results in a concrete algorithm for building sparse large margin classifiers. These classifiers essentially find a discriminating subspace that can be spanned by a small number of vectors, and in this subspace, the different classes of data are linearly well separated. Experimental results over several classification benchmarks demonstrate the effectiveness of our approach.