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Most recommender systems recommend a list of items. The user examines the list, from the first item to the last, and often chooses the first attractive item and does not examine the rest. This type of user behavior can be modeled by the cascade model. In this work, we study cascading bandits, an online learning variant of the cascade model where the goal is(More)
—Online handwritten Chinese text recognition (OHCTR) is a challenging problem as it involves a large-scale character set, ambiguous segmentation, and variable-length input sequences. In this paper, we exploit the outstanding capability of path signature to translate online pen-tip trajectories into informative signature feature maps using a sliding(More)
We propose a set of features to study the effects of data streams on complex systems. This feature set is called the the signature representation of a stream. It has its origin in pure mathematics and relies on a relationship between non-commutative polynomials and paths. This representation had already signifcant impact on algebraic topology, control(More)
Nonlinear distortion of fingerprint images still degrades system performance especially the fingerprint images generated by 1-line swipe sensors. In this paper, a robust fingerprint-matching algorithm based on multiple minutiae partitions (MMP) is presented to match general distorted fingerprints. Minutiae in an input and a template fingerprint are globally(More)
Assessment based on electronic portfolio is the main method to assess the performance of students' learning in web-based instructional system. In this paper, we create a quantitative assessment base on electronic portfolio to assess the quantity and quality of students' online learning including system assessment, self assessment, peer assessment and(More)
In order to solve the problems of face image super-resolution, a robust online dictionary learning method based on sparse representation is proposed in this paper. The online dictionary learning algorithms which can be used to train big sample datasets is introduced in the dictionary learning phase to generate better overcomplete dictionaries. Additionally,(More)
This paper proposes an approach to perform accent adaptation by using accent dependent bottleneck (BN) features to improve the performance of multi-accent Mandarin speech recognition system. The architecture of the adaptation uses two neural networks. First, deep neural network (DNN) acoustic model acts as a feature extractor which is used to extract accent(More)