Andreas Wichert

From symbolic to sub-symbolic information in question classification

Question Answering (QA) is undoubtedly a growing field of current research in Artificial Intelligence. Question classification, a QA subtask, aims to associate a category to each question, typically representing the semantic class of its answer. This step is of major importance in the QA process, since it is the basis of several key decisions. For instance, classification helps reducing the number of possible answer candidates, as only answers matching the question category should be taken into account. This paper presents and evaluates a rule-based question classifier that partially founds its performance in the detection of the question headword and in its mapping into the target category through the use of WordNet. Moreover, we use the rule-based classifier as a features’ provider of a machine learning-based question classifier. A detailed analysis of the rule-base contribution is presented. Despite using a very compact feature space, state of the art results are obtained.

Respiratory motion analysis: Towards gated augmentation of the liver

Abstract Laparoscopic surgery had a rapid development over the past decade. However, the view of the surgeon is limited to the image of the laparoscope. Augmented reality can provide further information to the surgeon by enabling a view inside the patient, and thus supporting a more precise and less invasive procedure. The limiting factors for realistic augmentation during liver surgery are movement and deformation of the organ due to respiratory motion. In our experiments we analyzed respiratory motion patterns of the liver caused by the respirator. Throughout our experiments we validated our assumption that repositioning after one breathing cycle is within a range of 1 mm. For an optimal augmentation of the liver in the laparoscopic image we suggest to adjust the respirator thus that we have a static exhalation phase of 2 to 3 s on which our augmentation is performed.

Finding Academic Experts on a MultiSensor Approach using Shannon's Entropy

Expert finding is an information retrieval task concerned with the search for the most knowledgeable people, in some topic, with basis on documents describing peoples activities. The task involves taking a user query as input and returning a list of people sorted by their level of expertise regarding the user query. This paper introduces a novel approach for combining multiple estimators of expertise based on a multisensor data fusion framework together with the Dempster–Shafer theory of evidence and Shannon’s entropy. More specifically, we defined three sensors which detect heterogeneous information derived from the textual contents, from the graph structure of the citation patterns for the community of experts, and from profile information about the academic experts. Given the evidences collected, each sensor may define different candidates as experts and consequently do not agree in a final ranking decision. To deal with these conflicts, we applied the Dempster–Shafer theory of evidence combined with Shannon’s Entropy formula to fuse this information and come up with a more accurate and reliable final ranking list. Experiments made over two datasets of academic publications from the Computer Science domain attest for the adequacy of the proposed approach over the traditional state of the art approaches. We also made experiments against representative supervised state of the art algorithms. Results revealed that the proposed method achieved a similar performance when compared to these supervised techniques, confirming the capabilities of the proposed framework.

Iterative random projections for high-dimensional data clustering

In this text we propose a method which efficiently performs clustering of high-dimensional data. The method builds on random projection and the K-means algorithm. The idea is to apply K-means several times, increasing the dimensionality of the data after each convergence of K-means. We compare the proposed algorithm on four high-dimensional datasets, image, text and two synthetic, with K-means clustering using a single random projection and K-means clustering of the original high-dimensional data. Regarding time we observe that the algorithm reduces drastically the time when compared to K-means on the original high-dimensional data. Regarding mean squared error the proposed method reaches a better solution than clustering using a single random projection. More notably in the experiments performed it also reaches a better solution than clustering on the original high-dimensional data.

Flexible kernels for RBF networks

Abstract In this paper we propose a novel approach for modeling kernels in Radial Basis Function networks. The method provides an extra degree of flexibility to the kernel structure. This flexibility comes through the use of modifier functions applied to the distance computation procedure, essential for all kernel evaluations. Initially the classifier uses an unsupervised method to construct the network topology, where most parameters of the network are defined without any customization from the user. During the second phase only one parameter per kernel is estimated. Experimental evidence on four datasets shows that the algorithm is robust and competitive.

Letters: Handwritten digit recognition using biologically inspired features

Image recognition problems are usually difficult to solve using raw pixel data. To improve the recognition it is often needed some form of feature extraction to represent the data in a feature space. We use the output of a biologically inspired model for visual recognition as a feature space. The output of the model is a binary code which is used to train a linear classifier for recognizing handwritten digits using the MNIST and USPS datasets. We evaluate the robustness of the approach to a variable number of training samples and compare its performance on these popular datasets to other published results. We achieve competitive error rates on both datasets while greatly improving relatively to related networks using a linear classifier.

Quantum-Like Bayesian Networks for Modeling Decision Making

In this work, we explore an alternative quantum structure to perform quantum probabilistic inferences to accommodate the paradoxical findings of the Sure Thing Principle. We propose a Quantum-Like Bayesian Network, which consists in replacing classical probabilities by quantum probability amplitudes. However, since this approach suffers from the problem of exponential growth of quantum parameters, we also propose a similarity heuristic that automatically fits quantum parameters through vector similarities. This makes the proposed model general and predictive in contrast to the current state of the art models, which cannot be generalized for more complex decision scenarios and that only provide an explanatory nature for the observed paradoxes. In the end, the model that we propose consists in a nonparametric method for estimating inference effects from a statistical point of view. It is a statistical model that is simpler than the previous quantum dynamic and quantum-like models proposed in the literature. We tested the proposed network with several empirical data from the literature, mainly from the Prisoners Dilemma game and the Two Stage Gambling game. The results obtained show that the proposed quantum Bayesian Network is a general method that can accommodate violations of the laws of classical probability theory and make accurate predictions regarding human decision-making in these scenarios.

Content-based image retrieval by hierarchical linear subspace method

We describe a hierarchical linear subspace method to query large on-line image databases using image similarity as the basis of the queries. The method is based on the generic multimedia indexing (GEMINI) approach which is used in the IBM query through the image content search system. Our approach is demonstrated on image indexing, in which the subspaces correspond to different resolutions of the images. During content-based image retrieval, the search starts in the subspace with the lowest resolution of the images. In this subspace, the set of all possible similar images is determined. In the next subspace, additional metric information corresponding to a higher resolution is used to reduce this set. This procedure is repeated until the similar images can be determined. For evaluation we used three image databases and two different subspace sequences.

Interference effects in quantum belief networks

Graphical abstractDisplay Omitted HighlightsQuantum probability can deal with violations to the laws of classical probability.We propose a Quantum Bayesian Network with interference effects.Mathematical derivation of quantum interference terms from complex numbers.Analysis of the impact of quantum parameters in probabilistic inferences.Analysis of the impact of uncertainty in the proposed quantum Bayesian Network. Probabilistic graphical models such as Bayesian Networks are one of the most powerful structures known by the Computer Science community for deriving probabilistic inferences. However, modern cognitive psychology has revealed that human decisions could not follow the rules of classical probability theory, because humans cannot process large amounts of data in order to make judgments. Consequently, the inferences performed are based on limited data coupled with several heuristics, leading to violations of the law of total probability. This means that probabilistic graphical models based on classical probability theory are too limited to fully simulate and explain various aspects of human decision making.Quantum probability theory was developed in order to accommodate the paradoxical findings that the classical theory could not explain. Recent findings in cognitive psychology revealed that quantum probability can fully describe human decisions in an elegant framework. Their findings suggest that, before taking a decision, human thoughts are seen as superposed waves that can interfere with each other, influencing the final decision.In this work, we propose a new Bayesian Network based on the psychological findings of cognitive scientists. In Computer Science, to the best of our knowledge, there is no quantum like probabilistic system proposed, despite their promising performances. We made experiments with two very well known Bayesian Networks from the literature. The results obtained revealed that the quantum like Bayesian Network can affect drastically the probabilistic inferences, specially when the levels of uncertainty of the network are very high (no pieces of evidence observed). When the levels of uncertainty are very low, then the proposed quantum like network collapses to its classical counterpart.

Tree search and quantum computation

Traditional tree search algorithms supply a blueprint for modeling problem solving behaviour. A diverse spectrum of problems can be formulated in terms of tree search. Quantum computation, namely Grover’s algorithm, has aroused a great deal of interest since it allows for a quadratic speedup to be obtained in search procedures. In this work we consider the impact of incorporating classical search concepts alongside Grover’s algorithm into a hybrid quantum search system. Some of the crucial points examined include: (1) the reverberations of contemplating the use of non-constant branching factors; (2) determining the consequences of incorporating an heuristic perspective into a quantum tree search model.