Cycle-Consistent Deep Generative Hashing for Cross-Modal Retrieval

In this paper, we propose a novel deep generative approach to cross-modal retrieval to learn hash functions in the absence of paired training samples through the cycle consistency loss. Our proposed approach employs adversarial training scheme to lean a couple of hash functions enabling translation between modalities while assuming the underlying semantic relationship. To induce the hash codes with semantics to the input-output pair, cycle consistency loss is further proposed upon the adversarial training to strengthen the correlations between inputs and corresponding outputs. Our approach is generative to learn hash functions such that the learned hash codes can maximally correlate each input-output correspondence, meanwhile can also regenerate the inputs so as to minimize the information loss. The learning to hash embedding is thus performed to jointly optimize the parameters of the hash functions across modalities as well as the associated generative models. Extensive experiments on a variety of large-scale cross-modal data sets demonstrate that our proposed method achieves better retrieval results than the state-of-the-arts.Comment: To appeared on IEEE Trans. Image Processing. arXiv admin note: text overlap with arXiv:1703.10593 by other author

An empirical two-group study into electronic note-taking

The relationship between students learning traditionally or through virtual worlds using computer-oriented tools is of keen interest. Although, the extent to electronic learning varies in great degrees from entire online environments to partial complimentary tools which differ according to their functions. The focus of this research paper is to discuss the paradigm shift from traditional means of study to computerisation, in particular relating to the area of note-taking. Research into cognitive factors associated with learning and performance including memory have put forward suggestions, which could assist the cumulative learning process. Comparative analysis between a number of note-taking techniques refined the study with the electronic adaptation of the popular Cornell method with the proposed En-AISR platform. Emphasis has been placed on the influence and significance towards the amalgamation of multi-modal features to enhance and stimulate students learning experience. A two-group study measured students learning, performance, and experience between both systems using usability criteria. Outcomes from this experiment suggest a positive influence of a multi-modal note-taking tool as a complimentary learning aid

An Exponential Lower Bound for the Runtime of the cGA on Jump Functions

In the first runtime analysis of an estimation-of-distribution algorithm (EDA) on the multi-modal jump function class, Hasen\"ohrl and Sutton (GECCO 2018) proved that the runtime of the compact genetic algorithm with suitable parameter choice on jump functions with high probability is at most polynomial (in the dimension) if the jump size is at most logarithmic (in the dimension), and is at most exponential in the jump size if the jump size is super-logarithmic. The exponential runtime guarantee was achieved with a hypothetical population size that is also exponential in the jump size. Consequently, this setting cannot lead to a better runtime. In this work, we show that any choice of the hypothetical population size leads to a runtime that, with high probability, is at least exponential in the jump size. This result might be the first non-trivial exponential lower bound for EDAs that holds for arbitrary parameter settings.Comment: To appear in the Proceedings of FOGA 2019. arXiv admin note: text overlap with arXiv:1903.1098

On Modal Logics of Partial Recursive Functions

The classical propositional logic is known to be sound and complete with respect to the set semantics that interprets connectives as set operations. The paper extends propositional language by a new binary modality that corresponds to partial recursive function type constructor under the above interpretation. The cases of deterministic and non-deterministic functions are considered and for both of them semantically complete modal logics are described and decidability of these logics is established