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Machine understanding of questions is tightly related to recognition of articulation in the context of the computational capabilities of an underlying processing algorithm. In this paper a mathematical model to capture and distinguish the latent structure in the articulation of questions is presented. We propose an objective-driven approach to represent this latent structure and show that such an approach is beneficial when examples of complementary objectives are not available. We show that the latent structure can be represented as a system that maximizes a cost function related to the underlying objective. Further, we show that the optimization formulation can be approximated to building a memory of patterns represented as a trained neural auto-encoder. Experimental evaluation using many clusters of questions, each related to an objective, shows 80% recognition accuracy and negligible false positive across these clusters of questions. We then extend the same memory to a related task where the goal is to iteratively refine a dataset of questions based on the latent articulation. We also demonstrate a refinement scheme called K-fingerprints, that achieves nearly 100% recognition with negligible false positive across the different clusters of questions., Comment: Pre-Print, ACCEPTED FOR PRESENTATION AT the 17th IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA2018)

We recorded high-density EEG in a flanker task experiment (31 subjects) and an online BCI control paradigm (4 subjects). On these datasets, we evaluated the use of transfer learning for error decoding with deep convolutional neural networks (deep ConvNets). In comparison with a regularized linear discriminant analysis (rLDA) classifier, ConvNets were significantly better in both intra- and inter-subject decoding, achieving an average accuracy of 84.1 % within subject and 81.7 % on unknown subjects (flanker task). Neither method was, however, able to generalize reliably between paradigms. Visualization of features the ConvNets learned from the data showed plausible patterns of brain activity, revealing both similarities and differences between the different kinds of errors. Our findings indicate that deep learning techniques are useful to infer information about the correctness of action in BCI applications, particularly for the transfer of pre-trained classifiers to new recording sessions or subjects., 6 pages, 9 figures, The 6th International Winter Conference on Brain-Computer Interface 2018

There is a wide gap between symbolic reasoning and deep learning. In this research, we explore the possibility of using deep learning to improve symbolic reasoning. Briefly, in a reasoning system, a deep feedforward neural network is used to guide rewriting processes after learning from algebraic reasoning examples produced by humans. To enable the neural network to recognise patterns of algebraic expressions with non-deterministic sizes, reduced partial trees are used to represent the expressions. Also, to represent both top-down and bottom-up information of the expressions, a centralisation technique is used to improve the reduced partial trees. Besides, symbolic association vectors and rule application records are used to improve the rewriting processes. Experimental results reveal that the algebraic reasoning examples can be accurately learnt only if the feedforward neural network has enough hidden layers. Also, the centralisation technique, the symbolic association vectors and the rule application records can reduce error rates of reasoning. In particular, the above approaches have led to 4.6% error rate of reasoning on a dataset of linear equations, differentials and integrals., Comment: 8 pages, 7 figures

We consider the problem of pattern matching with $k$ mismatches, where there can be don't care or wild card characters in the pattern. Specifically, given a pattern $P$ of length $m$ and a text $T$ of length $n$, we want to find all occurrences of $P$ in $T$ that have no more than $k$ mismatches. The pattern can have don't care characters, which match any character. Without don't cares, the best known algorithm for pattern matching with $k$ mismatches has a runtime of $O(n\sqrt{k \log k})$. With don't cares in the pattern, the best deterministic algorithm has a runtime of $O(nk polylog m)$. Therefore, there is an important gap between the versions with and without don't cares. In this paper we give an algorithm whose runtime increases with the number of don't cares. We define an {\em island} to be a maximal length substring of $P$ that does not contain don't cares. Let $q$ be the number of islands in $P$. We present an algorithm that runs in $O(n\sqrt{k\log m}+n\min\{\sqrt[3]{qk\log^2 m},\sqrt{q\log m}\})$ time. If the number of islands $q$ is $O(k)$ this runtime becomes $O(n\sqrt{k\log m})$, which essentially matches the best known runtime for pattern matching with $k$ mismatches without don't cares. If the number of islands $q$ is $O(k^2)$, this algorithm is asymptotically faster than the previous best algorithm for pattern matching with $k$ mismatches with don't cares in the pattern., Comment: Information Processing Letters, Available online 27 October 2016, ISSN 0020-0190

We present a new approach for recognition of complex graphic symbols in technical documents. Graphic symbol recognition is a well known challenge in the field of document image analysis and is at heart of most graphic recognition systems. Our method uses structural approach for symbol representation and statistical classifier for symbol recognition. In our system we represent symbols by their graph based signatures: a graphic symbol is vectorized and is converted to an attributed relational graph, which is used for computing a feature vector for the symbol. This signature corresponds to geometry and topology of the symbol. We learn a Bayesian network to encode joint probability distribution of symbol signatures and use it in a supervised learning scenario for graphic symbol recognition. We have evaluated our method on synthetically deformed and degraded images of pre-segmented 2D architectural and electronic symbols from GREC databases and have obtained encouraging recognition rates., 5 pages, 8 figures, Tenth International Conference on Document Analysis and Recognition (ICDAR), IEEE Computer Society, 2009, volume 10, 1325-1329

We consider the design of a pattern recognition that matches templates to images, both of which are spatially sampled and encoded as temporal sequences. The image is subject to a combination of various perturbations. These include ones that can be modeled as parameterized uncertainties such as image blur, luminance, translation, and rotation as well as unmodeled ones. Biological and neural systems require that these perturbations be processed through a minimal number of channels by simple adaptation mechanisms. We found that the most suitable mathematical framework to meet this requirement is that of weakly attracting sets. This framework provides us with a normative and unifying solution to the pattern recognition problem. We analyze the consequences of its explicit implementation in neural systems. Several properties inherent to the systems designed in accordance with our normative mathematical argument coincide with known empirical facts. This is illustrated in mental rotation, visual search and blur/intensity adaptation. We demonstrate how our results can be applied to a range of practical problems in template matching and pattern recognition., 52 pages, 12 figures

Associating distinct groups of objects (clusters) with contiguous regions of high probability density (high-density clusters), is central to many statistical and machine learning approaches to the classification of unlabelled data. We propose a novel hyperplane classifier for clustering and semi-supervised classification which is motivated by this objective. The proposed minimum density hyperplane minimises the integral of the empirical probability density function along it, thereby avoiding intersection with high density clusters. We show that the minimum density and the maximum margin hyperplanes are asymptotically equivalent, thus linking this approach to maximum margin clustering and semi-supervised support vector classifiers. We propose a projection pursuit formulation of the associated optimisation problem which allows us to find minimum density hyperplanes efficiently in practice, and evaluate its performance on a range of benchmark data sets. The proposed approach is found to be very competitive with state of the art methods for clustering and semi-supervised classification.