5597681
An Ensemble Approach to Learning to Rank
In recent years, 'learning to rank' is a focused approach for information retrieval, which can learn the ranking order given by experts and construct a uniform model to rank for new query. But in practice user queries vary in large diversity, it makes a single learned ranker not representative. Therefore, we propose an ensemble approach to 'learning to rank,' in which a lower generalization error can be gotten by generating a set of rankers and leveraging these rankers for the final prediction. Moreover, two strategies of creating multiple base rankers are proposed to make the ensemble more effective for information retrieval. The experiment results on two real world datasets indicate that the proposed approach can outperform the original 'learning to rank' methods significantly.
43411240
Semi-Supervised Graph-Ranking for Text Retrieval
Much work has been done on supervised ranking for information retrieval, where the goal is to rank all searched documents in a known repository with many labeled query-document pairs. Unfortunately, the labeled pairs are lack because human labeling is often expensive, difficult and time consuming. To address this issue, we employ graph to represent pairwise relationships among the labeled and unlabeled documents, in order that the ranking score can be propagated to their neighbors. Our main contribution in this paper is to propose a semi-supervised ranking method based on graph-ranking and different weighting schemas. Experimental results show that our method called SSG-Rank on 20-newsgroups dataset outperforms supervised ranking (Ranking SVM and PRank) and unsupervised graph ranking significantly.
40027798
Multiple Ranker Method in Document Retrieval
In this paper, we propose a multiple-ranker approach to make learning to rank methods more effective for document retrieval application. In traditional learning to rank methods, a ranker is learned from a set of queries together with their corresponding document rankings labeled by experts, and it is then used to predict the document rankings for new queries. But in practice, user queries vary in large diversity, which makes the single ranker learned from a close set of data not representative. The single ranker cannot be guaranteed with the best ranking result for every single query, and this becomes the bottleneck of traditional learning to rank approaches. To address this problem, we propose a multi-ranker approach. We train multiple diverse rankers which can cover diverse categories of queries, instead of an isolated one, and take an ensemble of these rankers for final prediction. We verify the proposed multipleranker approach over real-world datasets. The experimental results indicate that the proposed approach can outperform existing ‘learning to rank’ methods significantly.
714502
Face Re-Lighting from a Single Image under Harsh Lighting Conditions
In this paper, we present a new method to change the illumination condition of a face image, with unknown face geometry and albedo information. This problem is particularly difficult when there is only one single image of the subject available and it was taken under a harsh lighting condition. Recent research demonstrates that the set of images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated accurately by a low-dimensional linear subspace using spherical harmonic representation. However, the approximation error can be large under harsh lighting conditions thus making it difficult to recover albedo information. In order to address this problem, we propose a subregion based framework that uses a Markov Random Field to model the statistical distribution and spatial coherence of face texture, which makes our approach not only robust to harsh lighting conditions, but insensitive to partial occlusions as well. The performance of our framework is demonstrated through various experimental results, including the improvement to the face recognition rate under harsh lighting conditions.
44448884
Products of Mealy-type fuzzy finite state machines
We introduce seven ways of constructing products for Mealy-type fuzzy finite state machines as well as the concept of covering. The properties of transition functions and output functions are discussed. Furthermore, we investigate the covering properties and mutual relationship with regard to these products. We also prove that the covering relationship holds in the product of factor machines.
8581173
Recent database challenges in China on data consolidation and integration
During the first phase of IT exploitation in China, a lot of isolated systems were developed and deployed over all the sites where the business took place. Each system typically dealt with one simple local business application. Since the mid 90's, data consolidation and data integration became critical to address the business need for cheaper cost, shorter time to the market as well as better business decision and control. Without consolidated and integrated data, there is no way to support new and complex applications for rapid and large scale business growth in China across lots of industries such as financial, telecom, government, manufacture and distribution.
 When database is used for companies and government agencies in China, the large Chinese population makes the database volume huge. A nation-wide bank or an insurance company has hundreds of millions accounts easily. A province-level telecom service provider has tens of millions subscribers. In certain domains, such as banking and telecom, many applications are executed as on-line transactions which ask for large transaction throughput with good response time. A province-level telecom service provider expects to handle up to 100 billion calls in a month. In this case, most of the phone calls will be pre-paid type and these calls need to be handled in near-real time.
 In order to satisfy the performance requirement, some applications implement application cache on application servers. This approach improves the application performance in getting data but it also introduces the complexity and longer delay when a failure in application occurs. Database management systems can provide client cache for powerful application servers in these days. As more and more application servers are added to respond large number of concurrent users and high volume transaction throughput, database management system must be highly scalable. Both logical and physical contentions should be avoided or greatly reduced. Continuous availability and high reliability are more critical for a consolidated large database because many users and applications rely on it. Both planned outage and unplanned outage should be avoided or minimized through appropriate duplication and redundancy. Smooth transition through cooperative versions of data helps reduce planned outages.
 Swift switch over to hot stand-by database system helps minimize the unplanned outages. Database management systems are expected to be autonomic and remain healthy in resource utilization with minimal administrative involvement. As a summary, data consolidation in China asks for low-cost, high performance database systems with high scalability, availability and reliability on very large database volume.
 Data integration in China often deals with both a horizontal layer of multiple lines of business and a vertical organizational structure. For government agencies, there are at least four levels across nation, province, district and county. There are a few industry specific data models and solutions to address the requirements of data integration. However, a common data integration platform has been constantly requested by many IT projects of large scale. In many cases, business control processes need to integrate business operation processes to support near-real time decision and control. There is a high demand for a practical and well-proven methodology with tool sets which can support data integration life cycle of architect, design, development, deployment and change management.
 Rapid economic and business growth in combination of the join to WTO created big demand in IT evolution across China business, industry and government. There are many demands and opportunities of data consolidation and integration with large data volume and system complexity. The demand of low-cost solutions makes it even more challenging.
35134245
Single Table Access Using Multiple Indexes: Optimization, Execution, and Concurrency Control Techniques
Many data base management systems' query optimizers choose at most one index for accessing the records of a table in a given query, even though many indexes may exist on the table. In spite of the fact that there are some systems which use multiple indexes, very little has been published about the concurrency control or query optimization implications (e.g., deciding how many indexes to use) of using multiple indexes. This paper addresses these issues and presents solutions to the associated problems. Techniques are presented for the efficient handling of record ID lists, elimination of some locking, and determination of how many and which indexes to use. The techniques are adaptive in the sense that the execution strategies may be modified at run-time (e.g., not use some indexes which were to have been used), if the assumptions made at optimization-time (e.g., about selectivities) turn out to be wrong. Opportunities for exploiting parallelism are also identified. A subset of our ideas have been implemented in IBM's DB2 V2R2 relational data base management system.
19552464
Attribute reduction and optimal decision rules acquisition for continuous valued information systems
For continuous valued information systems, the attribute values of objects for the same attribute represent not only their ordinal relationship but also their relative distances. Therefore, the classical rough set model is not suitable for deducing attribute reductions and optimal decision rules for continuous valued information systems. Though some discretization methods are proposed to transform the continuous valued information systems into discrete ones, but those methods are too categorical and may lead to loss of information in some cases. To solve such information loss problem, we propose a tolerance rough set model in this paper. With a given level, the proposed model can divide a universe into some maximal tolerance classes. Also two types of lower and upper approximations are defined accordingly. Then the reductions of the maximal tolerance class and optimal decision rules based on the proposed attribute descriptors are defined, and the approximate discernibility function for the maximal tolerance class is constructed and used to compute all the corresponding optimal decision rules via using Boolean reasoning techniques. Finally, the general reductions and consistent reductions for continuous valued information systems are discussed.