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High-quality experimental data are important when developing predictive models for studying nanomaterial environmental impact (NEI). Given that raw data from experimental laboratories and manufacturing workplaces are usually proprietary and small-scaled, extracting information from publications is an attractive alternative for collecting data. We developed an information extraction system that can...
Frequent pattern mining plays an essential role in association rule mining, which has been a focused theme in data mining research for over the past 15 years. Since the pioneering work of Agrawal, abundant literature has been dedicated to this research. In this article, we provide a brief overview of the current status of frequent pattern mining and discuss a few promising research directions.
Query suggestion algorithms, which aim to suggest a set of similar but independent queries to users, have been widely studied to simplify user searches. However, in many cases, the users will accomplish their search tasks through a sequence of search behaviors instead of by one single query, which may make the classical query suggestion algorithms fail to satisfy end users in terms of task completion...
In the conventional regularized learning, training time increases as the training set expands. Recent work on L2 linear SVM challenges this common sense by proposing the inverse time dependency on the training set size. In this paper, we first put forward a Primal Gradient Solver (PGS) to effectively solve the convex regularized learning problem. This solver is based on the stochastic gradient descent...
The paper focuses on the field of research on next generational e-Learning facility, in which knowledge-enhanced systems are the most important candidates. In the paper, a reference architecture based on the technologies of knowledge engineering is proposed, which has following three intrinsic characteristics, first, education ontologies are used to facilitate the integration of static learning resource...
In recent years, BitTorrent file distribution network has been more and more widely used for media file distribution. Its build-in resource scheduling policies (local rare first, tit-for-tat, etc.) work well in file distribution in single swarm environments. However, the resource scheduling policy is missing in multiple swarms environments and the resource utilization has not been optimized. In this...
Speculative Multithreading (SpMT) is an effective mechanism for parallelizing irregular programs which are hard by conventional approaches. SpMT technology can be applied to exploit Thread-Level Parallelism effectively through allowing multiple threads executed in the presence of ambiguous data and control dependences while the correctness of the programs maintained by hardware support. This paper...
After years of research on ubiquitous learning, there is a trend of incorporating ubiquitous learning into mainstream of education. This demands new generation e-Learning system for learning anywhere, at any time, with any device. The paper introduces our on-going research efforts in the field. In the work, the concept called ubiquitous learning object (ULO) is proposed, and the functions on a ULO...
This paper considers the problem of finding a common linear copositive Lyapunov function for a set of second order systems. Based on mathematical programming, we give a new proof of the result in (O. Mason, 2004). Then we extend this result to a finite number of systems, and a common linear copositive Lyapunov function is obtained.
Subspace learning techniques for text analysis, such as latent semantic indexing (LSI), have been widely studied in the past decade. However, to our best knowledge, no previous study has leveraged the rank information for subspace learning in ranking tasks. In this paper, we propose a novel algorithm, called learning latent semantics for ranking (LLSR), to seek the optimal latent semantic space tailored...
Dimension reduction for large-scale text data is attracting much attention lately due to the rapid growth of World Wide Web. We can consider dimension reduction algorithms in two categories: feature extraction and feature selection. An important problem remains: it has been difficult to integrate these two algorithm categories into a single framework, making it difficult to reap the benefit of both...
DubinCore, LOM resource description norm and Sharable Content Object Reference Model (SCORM) standard based on the idea of metadata have solved the sharing problem of static Web resources, by bringing forward description norms of Web resources sharing. However, the modular sharing research of dynamic teaching assistant resources, such as questions and answers database and case database, is almost...
The rapid growth of blog (also known as "weblog") data provides a rich resource for social community mining. In this paper, we put forward a novel research problem of mining the latent friends of bloggers based on the contents of their blog entries. Latent friends are defined in this paper as people who share the similar topic distribution in their blogs. These people may not actually know...
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