1107258
Improving image clustering: An unsupervised feature weight learning framework
We address the problem of feature weight learning for image clustering. In practice, before clustering data, we generally normalize all data features between 0 and 1, because we cannot determine which features are more important. In this paper, we provide a feature weight learning framework for clustering which can obtain the feature weights and cluster labels simultaneously. An alternative optimization algorithm is adopted to solve this problem. Empirical studies on the toy data and real image data demonstrate our algorithm's effectiveness in improving the clustering performance.
14012459
MSFSS: A Storage System for Mass Small Files
We designed and implemented MSFSS, a scalable and flexible distributed file system for storage and retrieval of mass small files. MSFSS is a platform built upon the existing commodity file systems. It automatically stores files onto the best-fit commodity file systems according their access patterns. To avoid central bottleneck, it optimizes metadata size, separates metadata operations from file data transfer, and implements batch metadata operations. The system provides data migration, hot file caching, and replication, which are essential for large scale, reliable storage systems. It has successfully been deployed as storage system for our Web application which has about 50 TB of small files. Experimental results show that MSFSS provides high scalability and throughput in file operation services.
8051159
A Web-based Framework For On-line Collaborative Learning
This paper presents a framework for design of online courses and collaborative learning based on Web technologies. It provides the following features: construction of structured learning materials in a distributed environment; personalization of course contents in the run-time that adapts to the progress of individual students; it can supply relevant Web resources to the students by analyzing the learning history dynamically; students in a group could exchange their learning experiences and discover relevant knowledge via the communication channels provided by the framework. The proposed techniques could be deployed to a variety of online computer-assisted modules to improve the effectiveness of Web-based e-learning
8340253
UGE4B: An Universal Grid Environment for Bioinformatics Research
Recent years, grid technology has been used widely in the fields of scientific research. In this paper, we present a universal grid environment for bioinformatics research (UGE4B). Although there have been several similar projects successfully constructed in the world, we need to meet some unique requirements from bioinformatics research in China. UGE4B consists of 6 components, including portal, task management, data management, information server, security and computing node. As a national-wide bioinformatics research platform, UGE4B is built through interconnecting multiple sub grids provided by each bioinformatics institutes. Besides provides a universal portal to end users for bioinformatics computing and data accessing, UGE4B also supported some special bioinformatics applications
44697622
MPX: A Multiversion Concurrency Control Protocol for XML Documents
With the rapid popularity of XML, especially in web-base applications, concurrency control of XML documents becomes an important issue. In this paper, we present MPX, a multiversion concurrency control protocol for XML documents, which is designed for synchronizing concurrent queries and modifications of XML documents stored in a native XML databases or XML document repository. Distinguished from other existing protocols, MPX ensures serializability, recoverability and cascadelessness. Furthermore, MPX also guarantees read-only transactions and update transactions will never block each other, which is especially beneficial for XML for queries on XML are much more frequent than updates. Plenty of experiments have also been carried out, confirming that MPX can ensure a better degree of concurrency and less rollbacks.
30047573
Self-adaptive Statistics Management for Efficient Query Processing
Consistently good performance required by mission-critical information systems has made it a pressing demand for self-tuning technologies in DBMSs. Automated Statistics management is an important step towards a self-tuning DBMS and plays a key role in improving the quality of execution plans generated by the optimizer, and hence leads to shorter query processing times. In this paper, we present SASM, a framework for Self-Adaptive Statistics Management where, using query feedback information, an appropriate set of histograms is recommended and refined, and through histogram refining and reconstruction, fixed amount of memory is dynamically distributed to histograms which are most useful to the current workload. Extensive experiments showed the effectiveness of our techniques.
6370123
Full integration of subtyping and if-expression
The combination of full subsumption and conditional expression is a challenging problem, because in such a system, a term might not have a type which is a representative of all types of the term. Therefore, the traditional type checking technique fails. Due to such a difficulty, Java typing rule for if-expression uses only a restricted form of subtyping. In this paper, we propose a type system which includes both of the above mentioned features and enjoys decidable type checking. We also show that the system has the subject reduction property. It is expected that this technique could be used to improve the type system of Java with full subsumption.
1767089
Fast and Practical Algorithms for Computing All the Runs in a String
A repetition in a string x is a substring w = ue of x, maximum e ≥ 2, where u is not itself a repetition in w. A run in x is a substring w = ueu* of "maximal periodicity", where ue is a repetition and u* a maximum-length possibly empty proper prefix of u. A run may encode as many as |u| repetitions. The maximum number of repetitions in any string x = x[1..n] iswell known to be Θ(n log n). In 2000 Kolpakov & Kucherov showed that the maximum number of runs in x is O(n); they also described a Θ(n)-time algorithm, based on Farach's Θ(n)-time suffix tree construction algorithm (STCA), Θ(n)-time Lempel-Ziv factorization, and Main's Θ(n)-time leftmost runs algorithm, to compute all the runs in x. Recently Abouelhoda et al. proposed a Θ(n)-time Lempel-Ziv factorization algorithm based on an "enhanced" suffix array -- a suffix array together with other supporting data structures. In this paper we introduce a collection of fast space-efficient algorithms for computing all the runs in a string that appear in many circumstances to be superior to those previously proposed.
8846257
A Noise Insensitive Cluster Validity Measure for Pattern Classification
When trying to discover knowledge on a collection of data, one of the first arising tasks is to identify groups of similar objects, that is, to carry out cluster analysis for obtaining data partitions. Thus, a decision must be taken for choosing the clustering result that produces the best data partition for a given data collection. In order to support such a decision, indexes for measuring the quality of a data partitioning must be constructed. So far, several cluster validity indexes have been formulated in the literatures. Each of those indexes has strengths and drawbacks when compared with the others. In the present study, an alternative cluster validity index is formulated. The proposed validity index not only takes the contribution of each pattern into consideration, but also relies on information of intra-cluster and inter-cluster distance. The main advantage of the presented index is that is insensitive to noise by introducing the Gaussian kernel into the proposed validity index. An experimental design was devised in order to determine the comparative performance of the proposed cluster validity index against DB index previously formulated in the literature. Experimental results show that the proposed index is insensitive to noise and adaptive to produce good clustering solution.
2501005
Coordinating Multiple Agents via Reinforcement Learning
In this paper, we attempt to use reinforcement learning techniques to solve agent coordination problems in task-oriented environments. The Fuzzy Subjective Task Structure model (FSTS) is presented to model the general agent coordination. We show that an agent coordination problem modeled in FSTS is a Decision-Theoretic Planning (DTP) problem, to which reinforcement learning can be applied. Two learning algorithms, ‘‘coarse-grained’’ and ‘‘fine-grained’’, are proposed to address agents coordination behavior at two different levels. The ‘‘coarse-grained’’ algorithm operates at one level and tackle hard system constraints, and the ‘‘fine-grained’’ at another level and for soft constraints. We argue that it is important to explicitly model and explore coordination-specific (particularly system constraints) information, which underpins the two algorithms and attributes to the effectiveness of the algorithms. The algorithms are formally proved to converge and experimentally shown to be effective.
30040964
Agent-Mediated Genetic Super-Scheduling in Grid Environments
Super-scheduling in a dynamic grid environment is a very challenging issue that remains to be solved before a grid can be deployed and effectively utilized. In this paper we investigate a paradigm based on genetic algorithms (GA) to efficiently solve the scheduling problem. This GA paradigm is architecturally combined with the multiagent system (MAS) paradigm to form a flexible super-scheduling system. A three-layered scheduling architecture is presented and the corresponding realization of a multiagent-based system is described. The experiment shows that the better scheduling results are obtained for the adopted metrics of flow time and job stretch.