Weakly-Supervised Neural Text Classification

Deep neural networks are gaining increasing popularity for the classic text classification task, due to their strong expressive power and less requirement for feature engineering. Despite such attractiveness, neural text classification models suffer from the lack of training data in many real-world applications. Although many semi-supervised and weakly-supervised text classification models exist, they cannot be easily applied to deep neural models and meanwhile support limited supervision types. In this paper, we propose a weakly-supervised method that addresses the lack of training data in neural text classification. Our method consists of two modules: (1) a pseudo-document generator that leverages seed information to generate pseudo-labeled documents for model pre-training, and (2) a self-training module that bootstraps on real unlabeled data for model refinement. Our method has the flexibility to handle different types of weak supervision and can be easily integrated into existing deep neural models for text classification. We have performed extensive experiments on three real-world datasets from different domains. The results demonstrate that our proposed method achieves inspiring performance without requiring excessive training data and outperforms baseline methods significantly.Comment: CIKM 2018 Full Pape

Direction Detector on an Excitable Field: Field Computation with Coincidence Detection

Living organisms process information without any central control unit and without any ruling clock. We have been studying a novel computational strategy that uses a geometrically arranged excitable field, i.e., "field computation." As an extension of this research, in the present article we report the construction of a "direction detector" on an excitable field. Using a numerical simulation, we show that the direction of a input source signal can be detected by applying the characteristic as a "coincidence detector" embedded on an excitable field. In addition, we show that this direction detection actually works in an experiment using an excitable chemical system. These results are discussed in relation to the future development of "field computation."Comment: 6 pages, 3 figure

Autoencoders for strategic decision support

In the majority of executive domains, a notion of normality is involved in most strategic decisions. However, few data-driven tools that support strategic decision-making are available. We introduce and extend the use of autoencoders to provide strategically relevant granular feedback. A first experiment indicates that experts are inconsistent in their decision making, highlighting the need for strategic decision support. Furthermore, using two large industry-provided human resources datasets, the proposed solution is evaluated in terms of ranking accuracy, synergy with human experts, and dimension-level feedback. This three-point scheme is validated using (a) synthetic data, (b) the perspective of data quality, (c) blind expert validation, and (d) transparent expert evaluation. Our study confirms several principal weaknesses of human decision-making and stresses the importance of synergy between a model and humans. Moreover, unsupervised learning and in particular the autoencoder are shown to be valuable tools for strategic decision-making

From centrality to intermediacy in the global transport network? Ukraine’s trials and tribulations as a potential transit country

Ukraine currently is in a very complex economic and political situation, which in itself represents a pivotal point for its further recovery and evolution. Nevertheless, the rise of economic centres in Eastern and Central Europe creates opportunities for Ukraine to develop short sea shipping services (via the Black Sea) and water and land-based hub-feeder networks to and from these areas. This paper provides an academic study of the potential of Ukraine in taking up a role in emerging distribution systems in East and Central Europe facilitating the cargo transportation from regions such as Central Asia, Caucasus and even more distant overseas areas. Based on the concepts of intermediacy and centrality as introduced by Fleming and Hayuth (1994) the role of Ukraine in the global and regional transport networks will be analysed in order to assess to what extent particular regions in Ukraine can serve as important gateways to Europe. An extensive review and synthesis of the published studies during the last 20 years on Ukraine’s transit flows and transit function will be presented. The obtained results will be contraposed to the results obtained from about 20 interviews conducted with transport business representatives in Ukraine and abroad. Based on the outcome of bottlenecks and deficiencies in Ukraine’s transport system, the optimal road map for Ukraine’s integration into the European transport network will be defined