Time series are found widely in engineering and science.  We study multiagent forecasting in time series, drawing from literature on time series, graphical models, and multiagent systems.  Knowledge representation of our agents is based on dynamic multiply sectioned Bayesian networks (DMSBNs), a class of cooperative multiagent graphical models.  We propose a method through which agents can perform one-step forecast with exact probabilistic inference.  Superior performance of our agents over agents based on dynamic Bayesian networks (DBNs) are demonstrated through experiment.

Possibilistic networks are useful tools for reasoning under uncertainty. Uncertain pieces of information can be described by different measures: possibility measures, necessity measures and more recently, guaranteed possibility measures, denoted by Delta. This paper first proposes the use of guaranteed possibility measures to define a so-called Delta-based possibilistic network. This graphical representation tries to express and to deal with the minimal (lower-bound) possibility degree guaranteed for each variable. We then establish relationships between graphical and logical-based representations of uncertain information encoded by guaranteed possibility measures. We show that possibilistic networks based on guaranteed possibility measures can be easily transformed, in a polynomial time, in Delta-based knowledge bases. Then we analyze propagation algorithms in Delta-based possibilistic networks. In fact, standard possibilistic propagation algorithms can be re-used since we show that a simple rewriting of the chain rule allows the transformation of the initial Delta-based possibilistic networks into standard min-based possibilistic networks.

Ontology mapping plays an important role in interoperability over ontologies. Many researchers have proposed algorithms and tools for (semi-)automatically mapping one concept to another concept. Among them, WordNet is widely used as the domain knowledge support in the mapping process. To our knowledge, however, most of them only use synonym, hypernym and hyponym relations in WordNet and the actual meanings provided in natural English(as gloss) are often ignored. In this paper, we treat the concepts(c) as English words (w) and propose an ontology mapping technique where we use the meanings of the words as given in Wordnet (in English) for semantic mapping by constructing their parse trees first and simplifying them for computing similarity measures. Our experimental results show that our method performs better in Recall and F1-Measure than many techniques reported in the literature.

Providing the first few letters of a missing word in a sentence gives information about this word. This paper attempts to measure the information transmitted in that case. In order to do so, we analyzed response accuracy for open cloze questions, that is fill-in-the-blank questions without multiple choice answers. In this study, native and non-native speakers of English answered a series of open cloze questions that were semi-automatically generated. Hints were provided that consisted of the first few letters of the missing word. Results showed that question difficulty, hence the quantity of information transmitted, is related to the number of letters that are provided, to physical properties of these letters and to syllables formed by these letters. Performances did not appear to depend on letter or syllable frequency. Controlling hint level in a word completion task is critical in order to provide practice exercises adapted to student levels.

We studied the effect of post-practice reflection on learning, using programming tutors, and multiple-choice format for reflection. We conducted in-vivo controlled studies with introductory programming students from multiple schools over 3 semesters, and used mixed-factor ANOVA to analyze the collected data. We found that reflecting on the concept underlying each problem neither promotes greater learning, measured as pre-post increase in the average score per problem, nor promotes faster learning, measured as the problems solved per concept learned. We conjecture that the benefits of reflecting on the concept underlying each problem may be limited if a tutor already promotes deep understanding of the domain.

Generally, learning systems suffer from a lack of an explicit and adaptable didactic design. A previously introduced modeling approach called storyboarding is setting the stage to apply Knowledge Engineering Technologies to verify and validate the didactics behind a learning process. Moreover, didactics can be refined according to revealed weaknesses and proven excellence. Successful didactic patterns can be explored by applying mining techniques to the various ways students went through the storyboard and their associated level of success.

In this paper certain knowledge and software engineering methods integration issues are discussed. The principal idea is to consider an effective design and implementation framework for rule design with UML, and implementation with Java. The solution proposed in the paper consists of using a custom knowledge engineering design method for rules in the design stage. The rule base is then transformed to UML behavioral diagrams, which can be considered a visual encoding. The rule implementation involves the serialization to Java language using classes representing the decision tables grouping rules sharing the same attributes.

Semi-supervised learning is an active research field. Previous results shown that unite background information into the original unsupervised clustering problem could archive higher accuracy. In this paper, we explore the cooperation between the pairwise constrains given by the user and the sematic information in natural language. In addition, we reduce the time complexity to make the algorithm feasible for large quantities of data. Experiments on different scales of corpus show the robustness and effectiveness of the proposed algorithm, which the F-measure archives 20% higher than previous algorithms.

The education community is moving towards constructed or free-text responses and computer-based assessment. At the same time, progress in natural language processing and knowledge representation has made it possible to consider free-text or constructed responses without having to fully understand the text. c-rater is a technology at Educational Testing Service (ETS) used for automatic content scoring for short, free-text responses. This paper describes some of the major developments made in c-rater recently.

Presentations are one of the most common and effective ways of communicating the overview of a work to the audience. Given a technical paper, automatic generation of presentation slides reduces the effort of the presenter and helps in creating a structured summary of the paper. In this paper, we propose the framework of a novel system that does this task. Any paper that has an abstract and whose sections can be categorized under introduction, related work, model, experiments and conclusions can be given as input. As documents in LaTeX are rich in structural and semantic information we used them as input to our system. These documents are initially converted to XML format. This XML file is parsed and information in it is extracted. A query specific extractive summarizer has been used to generate slides. All graphical elements from the paper are made well use of by placing them at appropriate locations in the slides. These slides are presented in the document order.