The ND-Tree: A Dynamic Indexing Technique for Multidimensional Non-ordered Discrete Data Spaces
Similarity searches in multidimensional Nonordered Discrete Data Spaces (NDDS) are becoming increasingly important for application areas such as genome sequence databases. Existing indexing methods developed for multidimensional (ordered) Continuous Data Spaces (CDS) such as R-tree cannot be directly applied to an NDDS. This is because some essential geometric concepts/properties such as the minimum bounding region and the area of a region in a CDS are no longer valid in an NDDS. On the other hand, indexing methods based on metric spaces such as M-tree are too general to effectively utilize the data distribution characteristics in an NDDS. Therefore, their retrieval performance is not optimized. To support efficient similarity searches in an NDDS, we propose a new dynamic indexing technique, called the ND-tree. The key idea is to extend the relevant geometric concepts as well as some indexing strategies used in CDSs to NDDSs. Efficient algorithms for ND-tree construction are presented. Our experimental results on synthetic and genomic sequence data demonstrate that the performance of the ND-tree is significantly better than that of the linear scan and M-tree in high dimensional NDDSs.

title: OBJ-NET: An Object Oriented System Design Tool
abstract: Computer-based Systems are used in a wide range of complex systems like automobile assembly lines, power plants, etc. These systems consist of heterogeneous subsystems having a multitude of interactions with other subsystems and environments. Designing software for such systems is a difficult task due to the inherent complexity both at conceptual and implementation levels. When developing a large system, the constituent subsystems may be developed separately. Those subsystems may work well on their own, but may fail to work together with other subsystems and fail to respond to interfacing events. This paper presents OBJ-NET, an object oriented system design tool, which provides a suitable framework for the specification and simulation of complex dynamic systems. The theoretical basis of OBJ-NET is from Category Theory and Petri Nets. OBJ-NET helps users to integrate complex systems by modeling a system as a hierarchy of components. It accepts the flow of control from one set of objects to another and simulates the flow of control among the various components of the system. This will allow a user to validate the correctedness of design and interactively change the design to refine the working of the system. This paper describes the various components of OBJ-NET in detail and illustrates the application of OBJ-NET using, as a working example, the specification and simulation of an Automotive Dashboard System.

title: Query processing with quality control in the World Wide Web
abstract: Recent research on integrating database and World Wide Web (WWW) technologies has changed the navigation approach to searching information in the Web. People now can issue queries via a simple query interface or a database‐like query language to retrieve information from semistructured WWW data sources. However, the quality of query processing in the WWW is still low due to many factors such as unpredictable response time, irrelevant results, and out‐of‐date data. Such low‐quality query processing is intolerable to either users or service providers. In this paper, we present a quality‐controlled query processing method in the WWW. Quality parameters that users can specify with their queries are introduced. Distance functions that are used to evaluate the goodness of query quality parameters are defined. A query processing model with quality control is introduced. A quality control protocol in query processing is presented. Quality‐controlled query scheduling algorithms including admission scheduling, promotion/demotion scheduling and execution scheduling are proposed. Other relevant issues such as query classification, system parameter estimation, and query queue management are also discussed. Query processing with quality control is a promising way to solve the uncertain and low‐quality query processing problems in the WWW.

title: Global Query Optimization Based on Multistate Cost Models for a Dynamic Multidatabase System
abstract: Global query optimization in a multidatabase system (MDBS) is a challenging issue since some local optimization information such as local cost models may not be available at the global level due to local autonomy. It becomes even more difficult when dynamic environmental factors are taken into consideration. In our previous work, a qualitative approach was suggested to build so-called multistate cost models to capture the performance behavior of a dynamic multidatabase environment. It has been shown that a multistate cost model can give a good cost estimate for a query run in any contention state in the dynamic environment. In this paper, we present a technique to perform query optimization based on multistate cost models for a dynamic MDBS. Two relevant algorithms are proposed. The first one selects a set of representative system environmental states for generating an execution plan with multiple versions for a given query at compile time, while the second one efficiently determines the best version to invoke for the query at run time. Experiments demonstrate that the proposed technique is quite promising for performing global query optimization in a dynamic MDBS. Compared with related work on dynamic query optimization, our approach has an advantage of avoiding the high overhead for modifying or re-generating an execution plan for a query based on dynamic run-time information.

title: Query optimization using fuzzy set theory for multidatabase systems
abstract: One of the challenges for global query optimization in a multidatabase system (MDBS) is that some local optimization information may not be accurately known at the global level because of local autonomy. In this paper, we introduce a fuzzy query optimization approach that is based on fuzzy set theory, introduced by Zadeh in the 1960s, to tackle the challenge. We describe the problem of fuzzy query optimization and compare the fuzzy optimization approach with the traditional (crisp) optimization approach. We show that the fuzzy approach has a better chance to find a good execution strategy for a query than the crisp approach in an MDBS environment, but the complexity of the former may grow exponentially as the complexity of the later. To reduce the complexity, we use a weighted k-approximate fuzzy value to approximate every fuzzy value during fuzzy query optimization. It is proven that the improved fuzzy approach has the same order of complexity as the crisp approach.

title: Multiple-granularity interleaving for piggyback query processing
abstract: Piggyback query processing is a new technique, described in [24], intended to perform additional useful computation (e.g., database statistics collection) during normal query processing, taking full advantage of data resident in main memory. Different types of benecial piggybacking have been identifed and studied, but how to efficiently integrate piggyback operations with a given user query is still an open issue. In this paper, we propose a technique of multiple-granularity interleaving to efficiently integrate multiple piggyback operations with a given query at different levels of data granularity. We introduce an algebraic notation to capture the main characteristics of data flows in a database management system (DBMS), facilitating the study of piggybacking and enabling the automated integration of piggyback operations and user queries in a DBMS supporting the piggyback method. Various integration techniques are introduced to facilitate multiple-granularity interleaving including merging shared work, augmenting user queries, and downgrading piggyback operations. A set of transformations and heuristics are suggested that preserve the semantics of a user query, while efficiently interleaving the operations. Our preliminary experiments indicate that interleaving at proper levels of data granularity is key to the efficient implementation of the piggyback method.

title: Tandem Connectionist Feature Extraction for Conversational Speech Recognition
abstract: Multi-Layer Perceptrons (MLPs) can be used in automatic speech recognition in many ways. A particular application of this tool over the last few years has been the Tandem approach, as described in [7] and other more recent publications. Here we discuss the characteristics of the MLP-based features used for the Tandem approach, and conclude with a report on their application to conversational speech recognition. The paper shows that MLP transformations yield variables that have regular distributions, which can be further modified by using logarithm to make the distribution easier to model by a Gaussian-HMM. Two or more vectors of these features can easily be combined without increasing the feature dimension. We also report recognition results that show that MLP features can significantly improve recognition performance for the NIST 2001 Hub-5 evaluation set with models trained on the Switchboard Corpus, even for complex systems incorporating MMIE training and other enhancements.

title: Multimodal fusion using learned text concepts for image categorization
abstract: Conventional image categorization techniques primarily rely on low-level visual cues. In this paper, we describe a multimodal fusion scheme which improves the image classification accuracy by incorporating the information derived from the embedded texts detected in the image under classification. Specific to each image category, a text concept is first learned from a set of labeled texts in images of the target category using Multiple Instance Learning . For an image under classification which contains multiple detected text lines, we calculate a weighted Euclidian distance between each text line and the learned text concept of the target category. Subsequently, the minimum distance, along with low-level visual cues, are jointly used as the features for SVM-based classification. Experiments on a challenging image database demonstrate that the proposed fusion framework achieves a higher accuracy than the state-of-art methods for image classification.

title: JPEG encoding on the Intel MXP5800: a platform-based design case study
abstract: Multimedia systems are becoming increasingly complex and concurrent. The platform-based design (PBD) methodology (Keutzer et al., 2000) tackles these issues by recommending the use of formal models, carefully defined abstraction layers and the separation of concerns. Models of computation (Lee and Sangiovanni-Vincentelli, 1998) (MoCs) can be used within this methodology to enable specialized synthesis and verification techniques. In this paper, these concepts are leveraged in an industrial case study: the JPEG encoder application deployed on the Intel MXP5800 imaging processor. The modeling is carried out in the Metropolis (Balarin et al., 2003) design framework. We show that the system-level model using our chosen model of computation allows performance estimation within 5% of the actual implementation. Moreover, the chosen MoC is amenable to automation, which enables future synthesis techniques.

title: Adaptive learning of an accurate skin-color model
abstract: Due to variations of lighting conditions, camera hardware settings, and the range of skin coloration among human beings, a pre-defined skin-color model cannot accurately capture the wide distribution of skin colors in individual images. In this paper, we propose an adaptive skin-detection method, which allows modeling true skin-color distribution with significantly higher accuracy and flexibility than other methods attain. In principle, the proposed method follows a two-step process. For a given image, we first perform a rough skin classification using a generic skin model, which defines the skin-similar space. In the second step, a Gaussian mixture model (GMM), specific to the image under consideration and refined from the skin-similar space, is derived using the standard expectation-maximization (EM) algorithm. Then, we use an SVM (support vector machine) classifier to identify the skin Gaussian from the trained GMM (which contains two Gaussian components) by incorporating spatial and shape information of the skin pixels. This adaptive method can be applied to both still images and video applications. Results of extensive experiments performed on live video sequences and large image databases have demonstrated the effectiveness and benefits of the proposed model.

title: Using visual features for anti-spam filtering
abstract: Unsolicited commercial email (UCE), also known as spam, has been a major problem on the Internet. In the past, researchers have addressed this problem as a text classification or categorization problem. However, as spammers' techniques continue to evolve and the genre of email content becomes more and more diverse, text-based anti-spam approaches alone are no longer sufficient. In this paper, we propose a novel anti-spam system which utilizes visual clues, in addition to text information in the email body, to determine whether a message is spam. We analyze a large collection of spam emails containing images and identify a number of useful visual features for this application. We then propose using one-class support vector machines (SVM) as the underlying base classifier for anti-spam filtering. The experimental results demonstrate that the proposed system can add significant filtering power to the existing text-based anti-spam filters.