We attempted to build models of affect of students using SQL-Tutor. Most exhibited states are engaged concentration, confusion and boredom. Though none correlated with achievement, boredom and frustration persisted. U...
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A buyer coalition is a group of buyers who join together to negotiate with sellers to purchase items for a larger discount. In this article, a novel buyer coalition scheme, called the "GroupSimilarBuyer Scheme, &...
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In this paper, we consider a two-dimensional (2-D) formation problem for multi-agent systems subject to switching topologies that dynamically change along both a finite time axis and an infinite iteration axis. We pre...
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ISBN:
(纸本)9781479901777
In this paper, we consider a two-dimensional (2-D) formation problem for multi-agent systems subject to switching topologies that dynamically change along both a finite time axis and an infinite iteration axis. We present a distributed iterative learning control (ILC) algorithm via the nearest neighbor rules. By employing the 2-D approach, we develop both the asymptotic and exponentially fast convergence of our formation ILC, which can be guaranteed by conditions in terms of the spectral radius and the matrix norms, respectively.
In recent years, there have been increasing interests in using Component-Based System Development (CBSD) approach to develop large and complex applications. It's believed that satisfying quality attributes is an e...
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In this paper, we study the problem of learning from weakly labeled data, where labels of the training examples are incomplete. This includes, for example, (i) semi-supervised learning where labels are partially known...
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In this paper, we study the problem of learning from weakly labeled data, where labels of the training examples are incomplete. This includes, for example, (i) semi-supervised learning where labels are partially known; (ii) multi-instance learning where labels are implicitly known; and (iii) clustering where labels are completely unknown. Unlike supervised learning, learning with weak labels involves a difficult Mixed-Integer Programming (MIP) problem. Therefore, it can suffer from poor scalability and may also get stuck in local minimum. In this paper, we focus on SVMs and propose the WELLSVM via a novel label generation strategy. This leads to a convex relaxation of the original MIP, which is at least as tight as existing convex Semi-Definite Programming (SDP) relaxations. Moreover, the WELLSVM can be solved via a sequence of SVM subproblems that are much more scalable than previous convex SDP relaxations. Experiments on three weakly labeled learning tasks, namely, (i) semi-supervised learning; (ii) multi-instance learning for locating regions of interest in content-based information retrieval; and (iii) clustering, clearly demonstrate improved performance, and WELLSVM is also readily applicable on large data sets.
Virtual machine (VM) based state machine approaches, i.e. VM replication, provide high availability without source code modifications, unfortunately, existing VM replication approaches suffer from excessive replicatio...
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This paper proposes a new efficient algorithm for mining share-frequent itemsets from BitTable knowledge - extracted once from a transaction database. The knowledge contains sufficient information for such a mining ta...
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To enhance classification performance by making use of easily available unlabelled data to overcome the scarcity of labelled data, this paper proposes an Embedded Co-Adaboost algorithm that integrates multi-view learn...
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The growth of multilingual web content and increasing internationalization portends the need for cross-language information retrieval. As a solution to this problem for narrow-domain, data-rich web content, we offer M...
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This paper presents an approach of semantic web technologies applied in data integration for gram-positive bacteria organism, Lactococcus lactis subsp. cremoris strain MG1363 (L. lactis). L. lactis data sources are he...
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This paper presents an approach of semantic web technologies applied in data integration for gram-positive bacteria organism, Lactococcus lactis subsp. cremoris strain MG1363 (L. lactis). L. lactis data sources are heterogeneous and not semantically connected among cross-references databases. Researchers continuously study and perform scientific experiments related to modeling and simulation to produce result in order to improve protein and vitamin production using this organism. The goal of this work is to construct an integration approach for bacteria organism L. lactis that correctly combines biological databases using semantic web and ontology, thus allows biological question to be answered using queries (e.g. SPARQL). In this paper, we demonstrate how semantic web components such as Ontology Web language (OWL) and Resource Description Framework (RDF) can be used to represent and integrate these resources. Gene, protein, pathway and ontology are main sources to make this organism semantically integrated. The sources are acquired from Entrez Gene (EG), Universal Protein Resource (UniProt), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO). We illustrate the result of our approach by presenting semantically connected knowledge instances from gene, protein, pathway and ontology data sources by using identifier. In addition, this integrated organism is vital which can lead for further analysis and hypothesis formulation in biological research.
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