Catálogo de publicaciones - libros

This paper discusses a large number of logic circuit mapping methods for complex systems, focusing on network hardware system designs. This logic mapping technique enables significant logic simulation time savings by mapping identical logic processor modules. Under the logic mapping method which is called the time division multiplexing (TDM) logic mapping method, the speed of the required to simulate it is significantly reduced, compared with conventional mapping methods, when folding the identical modules into a single module copy is done at the hardware description language (HDL) level. In principle, this method can be applied to any type of a network design platform, e.g., communication data stream through physical channel (fiber optic line), video signal transfer logic display environment, etc. In this paper, we demonstrate this method using several configurations of the IBM Serial Link architecture.

Rapid advances in computer and database technologies have enabled organizations to accumulate vast amounts of data recently. These huge data make the data analysis task become more complicated. Feature selection is an effective dimensionality reduction technique by removing irrelevant, redundant, or noisy features. This research proposes a novel feature-selecting measure to evaluate feature importance for clustering process. The proposed measure aims at extracting useful information from the dissimilarity between two data objects since data dissimilarity is a common principle to determine whether data objects can be located within the same cluster or not. Therefore, the dissimilarity between a pair of data objects is used to develop the proposed feature-selecting measure. In the research, the probability distribution of the dissimilarity variable is considered as a mixture model consisting of the two “intra-cluster” and “inter-cluster” dissimilarity Gaussian distributions. The means of the two Gaussian distributions can be inferred by the EM algorithm. Accordingly, the difference between the two means is regarded as a meaningful measure to select important features for clustering. The effectiveness of the proposed feature-selecting measure for clustering is demonstrated using a set of experiments.

Web tutoring provides teachers with a variety of pedagogical options and is a convenient platform motivating learning materials for learners. This paper begins by retrieving relevant blog articles, and then integrating a learning map as a dynamic social learning model. Because these retrieved blog articles pertain to course materials, they can be used to promote learner engagement in their interactions with a learning map and hence, achieve their goals more easily. An experimental course has been launched and the results show that learners do make use of the blog-based learning and can eventually cross the specified test thresholds. Lecturers using the proposed approach can apply the principles of dynamic learning in ways which not only reduce teacher workload, but also enhance student learning through the active construction of knowledge supported by alternative perspectives within meaningful blog contexts.

Especially in the first decade of this century, learner adapted interaction and learner modeling are becoming more important in the area of web-based learning systems. The complicated nature of the problem is a serious challenge with vast amount of data available about the learners. Machine learning approaches have been used effectively in both user modeling, and learner modeling implementations. Recent studies on the challenges and solutions about learner modeling are explained in this paper with the proposal of a learner modeling framework to be used in a web-based learning system. The proposed system adopts a hybrid approach combining three machine learning techniques in three stages.

Even if the keyword-based search played an important role in finding the learning resources, it cannot satisfy users’ needs because of lack of the semantics of learning resources. This paper used two kinds of ontologies, core and domain, to search learning resources relevant to given users’ query. The general semantics of learning resources are represented by the core ontology, constructed from the metadata of learning resources. In our prototype, we built the domain ontology about middle school Mathematics with help of domain experts. And our system used the OWL-DL and SWRL to represent ontologies, and the reasoning engine, KAON2 and BOSSAM to handle these ontologies. This system will be used by the EDUNET - the largest nationwide service of sharing learning resources, that is operated by KERIS for students and instructors throughout Korea. Our performance results show that the proposed mechanism is better than the keyword-based mechanism.