10767971
The duality between information embedding and source coding with side information and some applications
Aspects of the duality between the information-embedding problem and the Wyner-Ziv (1976) problem of source coding with side information at the decoder are developed and used to establish a spectrum new results on these and related problems, with implications for a number of important applications. The single-letter characterization of the information-embedding problem is developed and related to the corresponding characterization of the Wyner-Ziv problem, both of which correspond to optimization of a common mutual information difference. Dual variables and dual Markov conditions are identified, along with the dual role of noise and distortion in the two problems. For a Gaussian context with quadratic distortion metric, a geometric interpretation of the duality is developed. From such insights, we develop a capacity-achieving information-embedding system based on nested lattices. We show the resulting encoder-decoder has precisely the same decoder-encoder structure as the corresponding Wyner-Ziv system based on nested lattices that achieves the rate-distortion limit. For a binary context with Hamming distortion metric, the information-embedding capacity is developed, along with its relationship to the corresponding Wyner-Ziv rate-distortion function. In turn, an information-embedding system for this case based on nested linear codes is constructed having an encoder-decoder that is identical to the decoder-encoder structure for the corresponding system that achieves the Wyner-Ziv rate-distortion limit. Finally, based on these results, a simple layered joint source-channel coding system is developed with a perfectly symmetric encoder-decoder structure. Its application and performance is discussed in a broadcast setting in which there is a need to control the fidelity experienced by different receivers. Among other results, we show that such systems and their multilayer extensions retain attractive optimality properties in the Gaussian-quadratic case, but not in the binary-Hamming case.
1524685
Achievable performance of digital watermarking systems
A variety of digital watermarking applications have emerged recently that require the design of systems for embedding one signal (the "embedded signal" or "watermark") within another signal (the "host signal"). We develop a framework for analyzing achievable performance trade-offs of these systems among robustness, distortion, and embedding rate. We also describe a recently introduced class of embedding methods, quantization index modulation (QIM), in which an ensemble of quantizers is constructed and information is embedded by quantizing the host signal with a quantizer associated with the watermark. We introduce an implementation of such a method called spread-transform dither modulation where the embedded information modulates the dither signal of a dithered quantizer, which quantizes projections of the host signal onto a spreading vector. We show that QIM systems have considerable performance advantages over previously proposed spread-spectrum and low-bit modulation systems.
9470216
Quantization index modulation: A class of provably good methods for digital watermarking and information embedding
We consider the problem of embedding one signal (e.g., a digital watermark), within another "host" signal to form a third, "composite" signal. The embedding is designed to achieve efficient tradeoffs among the three conflicting goals of maximizing the information-embedding rate, minimizing the distortion between the host signal and composite signal, and maximizing the robustness of the embedding. We introduce new classes of embedding methods, termed quantization index modulation (QIM) and distortion-compensated QIM (DC-QIM), and develop convenient realizations in the form of what we refer to as dither modulation. Using deterministic models to evaluate digital watermarking methods, we show that QIM is "provably good" against arbitrary bounded and fully informed attacks, which arise in several copyright applications, and in particular it achieves provably better rate distortion-robustness tradeoffs than currently popular spread-spectrum and low-bit(s) modulation methods. Furthermore, we show that for some important classes of probabilistic models, DC-QIM is optimal (capacity-achieving) and regular QIM is near-optimal. These include both additive white Gaussian noise (AWGN) channels, which may be good models for hybrid transmission applications such as digital audio broadcasting, and mean-square-error-constrained attack channels that model private-key watermarking applications.
7632686
A web-based three-dimensional protein retrieval system by matching visual similarity
SUMMARY
A web-based three-dimensional (3D) protein retrieval system is available for protein structure data including all PDB and FSSP dataset. In this system, we use a visual-based matching method to compare the protein structure from multiple viewpoints. It takes less than three seconds for each query with 90% accuracy on an average.
15709443
Voice retrieval of Mandarin broadcast news speech
This paper presents an improved framework for voice retrieval of Mandarin broadcast news speech. First, several unsupervised and data-driven approaches for broadcast news transcription were proposed to improve the speech recognition accuracy and efficiency. Then, a multiscale indexing paradigm for broadcast news retrieval was exploited to alleviate the problems caused by the speech recognition errors and the flexible wording structure of the Chinese language. Finally, we used the PDA as the platform and broadcast radio programs collected in Taiwan as the document collection to establish a speech-based multimedia information retrieval prototype system. Very encouraging results were obtained.
4000197
Lot-sizing scheduling with batch setup times
This paper is concerned with scheduling independent jobs on m parallel machines in such a way that the makespan is minimized. Each job j is allowed to split arbitrarily into several parts, which can be individually processed on any machine at any time. However, a setup for uninterrupted sj time units is required before any split part of job j can be processed on any machine. The problem is strongly NP-hard if the number m of machines is variable and weakly NP-hard otherwise. We give a polynomial-time $$\frac{5}{3}$$-approximation algorithm for the former case and a fully polynomial-time approximation scheme for the latter.
11577696
A maximum-entropy chinese parser augmented by transformation-based learning
Parsing, the task of identifying syntactic components, e.g., noun and verb phrases, in a sentence, is one of the fundamental tasks in natural language processing. Many natural language applications such as spoken-language understanding, machine translation, and information extraction, would benefit from, or even require, high accuracy parsing as a preprocessing step.
 Even though most state-of-the-art statistical parsers were initially constructed for parsing in English, most of them are not language-specific, in that they do not rely on properties of the language that are specific to English. Therefore, construction of a parser in a given language becomes a matter of retraining the statistical parameters with a Treebank in the corresponding language.
 The development of the Chinese treebank [Xia et al. 2000] spurred the construction of parsers for Chinese. However, Chinese as a language poses some unique problems for the development of a statistical parser, the most apparent being word segmentation. Since words in written Chinese are not delimited in the same way as in Western languages, the first problem that needs to be solved before an existing statistical method can be applied to Chinese is to identify the word boundaries. This is a step that is neglected by most pre-existing Chinese parsers, which assume that the input data has already been pre-segmented.
 This article describes a character-based statistical parser, which gives the best performance to-date on the Chinese treebank data. We augment an existing maximum entropy parser with transformation-based learning, creating a parser that can operate at the character level. We present experiments that show that our parser achieves results that are close to those achievable under perfect word segmentation conditions.
607529
Toward a Query Language for Network Attack Data
The growing sophistication and diversity of malicious activity in the Internet presents a serious challenge for network security analysts. In this paper, we describe our efforts to develop a database and query language for network attack data from firewalls, intrusion detection systems and honeynets. Our first step toward this objective is to develop a prototype database and query interface to identify coordinated scanning activity in network attack data. We have created a set of aggregate views and templatized SQL queries that consider timing, persistence, targeted services, spatial dispersion and temporal dispersion, thereby enabling us to evaluate coordinated scanning along these dimensions. We demonstrate the utility of the interface by conducting a case study on a set of firewall and intrusion detection system logs from Dshield.org. We show that the interface is able to identify general characteristics of coordinated activity as well as instances of unusual activity that would otherwise be difficult to mine from the data. These results highlight the potential for developing a more generalized query language for a broad class of network intrusion data. The case study also exposes some of the challenges we face in extending our system to more generalized queries over potentially vast quantities of data.