A High-Performance Intelligent Integrated Data Services System in Data Grid
It is very complex and of low efficiency for grid users to access heterogeneous diverse data resources distributed over the whole wide area network. The integrated data services can implement more wide range of transparent access to data resources in Data Grid. One of the important building blocks for any Data Grid is the server which implements data services. In this paper, we describe HPIIDS which implements integrated data services and achieves more wide range of transparencies. In order to improve its performances, we emphasis on the server called DSB which provides integrated data services. The DSB bases on cluster and agent technologies. Agent-based DSB can cleverly prefetch required data, replicate them among DSBs. Multiple DSBs are formed cluster HPIIDS. Cluster-based HPIIDS implements a single entry, provides a virtual integrated storage system for users, hides distributed heterogeneous low-level data resources, and insures load balance of each server. HPIIDS architecture supporting these various scenarios, are also described.

title: A Distributed Data Server in Grid Environment
abstract: This paper introduces a high-performance distributed data server called DRB (Data Request Broker). DRB is the core of GridDaEn system which is a general Data Grid middleware that can provide uniform access and management of distributed and heterogeneous storage resources. DRB provides most of the core functions of GridDaEn including its uniform access to all kinds of geographically distributed, heterogeneous storage resources from a uniform and virtual view, and its supporting simple and complicated coordination across multiple administrative domains to form a federation, etc. DRB has the capability of providing high-performance and federated data service for data-intensive applications and researches over wide area networks.

title: Repairing Inconsistency and Uncertainty in DL-based Ontologies
abstract: A method of detecting malignant or pre-malignant conditions of the cervix, and test kits therefor, involves selecting a fraction of a cervical cell sample consisting predominantly of epithelial cells and determining characteristics indicative of the malignant or pre-malignant conditions therein.

title: Flocking of Multi-Vehicle Systems With A Leader
abstract: We study the coordinated motion of a group of vehicles with a leader based on nearest neighbor rules. The leader may be a special vehicle in the group or an external signal (virtual leader) to steer the vehicle group. The control law consists of two parts: a potential field force that makes the vehicles attract to each other and at the same time avoid collision in the group, and an alignment force that makes all vehicles' headings and speeds to converge respectively to common values. We assume that the interaction patterns are either fixed or switching based on the information topologies of the leader and member vehicles. Our approach is based on the Lyapunov theory and basic graph theory. We also consider the effect of noise on the collective dynamics of the group. Numerical simulations are worked out to illustrate the analytical results

title: Towards the SocioScope: an information system for the study of social dynamics through digital traces
abstract: Over the past decade there has been an explosion in the deployment of pervasive systems like cell phone networks and content aggregators on the Internet that produce massive amounts of data as by-products of their interaction with users. This data is related to the actions and opinions of people and thereby to the overall dynamics of cities, how they function and evolve over time.
 Here we propose the implementation of a new information system, the SocioScope, that streamlines the operations of data gathering and data storage and that supports the diverse needs of the research community, local authorities, enterprises, and citizens. Our SocioScope allows for the collection, integration, and study of different types of spatiotemporal data.
 We present two use cases and an analysis of the requirements for the system. We introduce the multi-tier architecture that supports data gathering, storage, integration, analysis, and visualization. We describe the technical implementation of each module of the system and of the security mechanism for data access control. We conclude with a discussion on the opportunities and challenges of the study of digital traces and with an brief illustration of future directions of work.

title: Mining data records in Web pages
abstract: A large amount of information on the Web is contained in regularly structured objects, which we call data records. Such data records are important because they often present the essential information of their host pages, e.g., lists of products or services. It is useful to mine such data records in order to extract information from them to provide value-added services. Existing automatic techniques are not satisfactory because of their poor accuracies. In this paper, we propose a more effective technique to perform the task. The technique is based on two observations about data records on the Web and a string matching algorithm. The proposed technique is able to mine both contiguous and non-contiguous data records. Our experimental results show that the proposed technique outperforms existing techniques substantially.

title: Learning to Classify Documents with Only a Small Positive Training Set
abstract: Many real-world classification applications fall into the class of positive and unlabeled (PU) learning problems. In many such applications, not only could the negative training examples be missing, the number of positive examples available for learning may also be fairly limited due to the impracticality of hand-labeling a large number of training examples. Current PU learning techniques have focused mostly on identifying reliable negative instances from the unlabeled set U. In this paper, we address the oft-overlooked PU learning problem when the number of training examples in the positive set Pis small. We propose a novel technique LPLP (Learning from Probabilistically Labeled Positive examples) and apply the approach to classify product pages from commercial websites. The experimental results demonstrate that our approach outperforms existing methods significantly, even in the challenging cases where the positive examples in Pand the hidden positive examples in Uwere not drawn from the same distribution.