Dialogue games are a two-player semantics for a variety of logics, including intuitionistic and classical logic. Dialogues can be viewed as a kind of analytic calculus not unlike tableaux. Can dialogue games be an effective foundation for proof search in intuitionistic logic (both first-order and propositional)? We announce Kuno, an automated theorem prover for intuitionistic first-order logic based on dialogue games.

The increasing demand of World Wide Web raises the need of predicting the user's web page request. The most widely used approach to predict the web pages is the pattern discovery process of Web usage mining. This process involves inevitability of many techniques like Markov model, association rules and clustering. Fuzzy theory with different techniques has been introduced for the better results. Our focus is on Markov models. This paper is introducing the vague Rules with Markov models for more accuracy using the vague set theory.

Today most of the information in all areas is available over the web. It increases the web utilization as well as attracts the interest of researchers to improve the effectiveness of web access and web utilization. As the number of web clients gets increased, the bandwidth sharing is performed that decreases the web access efficiency. Web page prefetching improves the effectiveness of web access by availing the next required web page before the user demand. It is an intelligent predictive mining that analyze the user web access history and predict the next page. In this work, vague improved markov model is presented to perform the prediction. In this work, vague rules are suggested to perform the pruning at different levels of markov model. Once the prediction table is generated, the association mining will be implemented to identify the most effective next page. In this paper, an integrated model is suggested to improve the prediction accuracy and effectiveness.

A highly comparative, feature-based approach to time series classification is introduced that uses an extensive database of algorithms to extract thousands of interpretable features from time series. These features are derived from across the scientific time-series analysis literature, and include summaries of time series in terms of their correlation structure, distribution, entropy, stationarity, scaling properties, and fits to a range of time-series models. After computing thousands of features for each time series in a training set, those that are most informative of the class structure are selected using greedy forward feature selection with a linear classifier. The resulting feature-based classifiers automatically learn the differences between classes using a reduced number of time-series properties, and circumvent the need to calculate distances between time series. Representing time series in this way results in orders of magnitude of dimensionality reduction, allowing the method to perform well on very large datasets containing long time series or time series of different lengths. For many of the datasets studied, classification performance exceeded that of conventional instance-based classifiers, including one nearest neighbor classifiers using Euclidean distances and dynamic time warping and, most importantly, the features selected provide an understanding of the properties of the dataset, insight that can guide further scientific investigation.

We report the results of an empirical evaluation of structural simplification of Bayesian networks by removing weak arcs. We conduct a series of experiments on six networks built from real data sets selected from the UC Irvine Machine Learning Repository. We systematically remove arcs from the weakest to the strongest, relying on four measures of arc strength, and measure the classification accuracy of the resulting simplified models. Our results show that removing up to roughly 20 percent of the weakest arcs in a network has minimal effect on its classification accuracy. At the same time, structural simplification of networks leads to significant reduction of both the amount of memory taken by the clique tree and the amount of computation needed to perform inference.

High dimensionality (having a large number of independent attributes) is a major problem for bioinformatics datasets such as gene microarray datasets. Feature selection algorithms are necessary to remove the irrelevant (not useful) and redundant (contain duplicate information) features. One approach to handle this problem is wrapper-based subset evaluation, which builds classification models on different feature subsets to discover which performs best. Although the computational complexity of this technique has led to it being rarely used for bioinformatics, its ability to find the features which give the best model make it important in this domain. However, when using wrapper-based feature selection, it is not obvious whether the learner used within the wrapper should match the learner used for building the final classification model. Furthermore, this question may depend on other properties of the dataset, such as difficulty of learning (general performance without feature selection) and dataset balance (ratio of minority and majority instances). To study this, we use nine datasets with varying levels of difficulty and balance. We find that across all datasets, the best strategy is to use one learner (Na¨ıve Bayes) inside the wrapper regardless of the learner which will be used outside. However, when broken down by difficulty and balance levels, our results show that the more balanced and less difficult datasets work best when the learners inside and outside the wrapper match. Thus, the answer to this question will depend on properties of the dataset.

Class imbalance is a frequent problem found in bioinformatics datasets. Unfortunately, the minority class is usually also the class of interest. One of the methods to improve this situation is data sampling. There are a number of dierent data sampling methods, each with their own strengths and weaknesses, which makes choosing one a dicult prospect. In our work we compare three data sampling techniques (Random Undersampling, Random Oversampling, and SMOTE) on six bioinformatics datasets with varying levels of class imbalance. Additionally, we apply two dierent classiers to the problem (5-NN and SVM), and use feature selection to reduce our datasets to 25 features prior to applying sampling. Our results show that there is very little dierence between the data sampling techniques, although Random Undersampling is the most frequent top performing data sampling technique for both of our classiers. We also performed statistical analysis which conrms that there is no statistical dierence between the techniques. Therefore, our recommendation is to use Random Undersampling when choosing a data sampling technique, because it is less computationally expensive to implement than SMOTE and it also reduces the size of the dataset, which will improve subsequent computational costs without sacricing classication performance.

Current research suggests that using multiple can improve affect detection accuracy. Combining facial expression and physiological signals is one of the most common approaches in multimodal affect detection. Several methods and devices have been proposed for measuring physiological signals with simplicity and have been used widely in affective computing applications. Out of the various approaches, contact-less sensors which can measure physiological signals remotely are more desirable for everyday use and naturalistic applications. In this paper we proposed a novel fusion model for affect detection, which combines facial expression features and heart rate using a single video recording sensor. To our knowledge this is the first attempt to use physiological sensor remotely for affect detection. Results suggest that fusion of these features (facial expression and heart rate) can improve the accuracy of affect detection systems.

Past research on grammar induction has found promising results in predicting parts-of-speech from n-grams using a fixed vocabulary and a fixed context. In this study, we investigated grammar induction whereby we varied vocabulary size and context size. Results indicated that as context increased for a fixed vocabulary, overall accuracy initially increased but then leveled off. Importantly, this increase in accuracy did not occur at the same rate across all syntactic categories. We also address the dynamic relation between context and vocabulary in terms of grammar induction in an unsupervised methodology. We formulate a model that represents a relationship between vocabulary and context for grammar induction. Our results concur with what has been called the word spurt phenomenon in the child language acquisition literature.

Semantic networks are a popular way of simulating human memory in ACT-R-like cognitive architectures. However, existing implementations fall short in their ability to efficiently work with very large networks required for full-scale simulations of human memories. In this paper, we present SemMemDB, an in-database realization of semantic networks and spreading activation. We describe a relational representation for semantic networks and an efficient SQL-based spreading activation algorithm. We provide a simple interface for users to invoke retrieval queries. The key benefits of our approach are: (1) Databases have mature query engines and optimizers that generate efficient query plans for memory activation and retrieval; (2) Databases can provide massive storage capacity to potentially support human-scale memories; (3) Spreading activation is implemented in SQL, a widely-used query language for big data analytics. We evaluate SemMemDB in a comprehensive experimental study using DBPedia, a web-scale ontology constructed from the Wikipedia corpus. The results show that our system runs over 500 times faster than previous works.