A refinement framework for cross language text categorization

Abstract. Cross language text categorization is the task of exploiting labelled documents in a source language (e.g. English) to classify documents in a target language (e.g. Chinese). In this paper, we focus on investigating the use of a bilingual lexicon for cross language text categorization. To this end, we propose a novel refinement framework for cross language text categorization. The framework consists of two stages. In the first stage, a cross language model transfer is proposed to generate initial labels of documents in target language. In the second stage, expectation maximization algorithm based on naive Bayes model is introduced to yield resulting labels of documents. Preliminary experimental results on collected corpora show that the proposed framework is effective

Autism research : An objective quantitative review of progress and focus between 1994 and 2015

The nosology and epidemiology of Autism has undergone transformation following consolidation of once disparate disorders under the umbrella diagnostic, autism spectrum disorders. Despite this re-conceptualization, research initiatives, including the NIMH's Research Domain Criteria and Precision Medicine, highlight the need to bridge psychiatric and psychological classification methodologies with biomedical techniques. Combining traditional bibliometric co-word techniques, with tenets of graph theory and network analysis, this article provides an objective thematic review of research between 1994 and 2015 to consider evolution and focus. Results illustrate growth in Autism research since 2006, with nascent focus on physiology. However, modularity and citation analytics demonstrate dominance of subjective psychological or psychiatric constructs, which may impede progress in the identification and stratification of biomarkers as endorsed by new research initiatives.Peer reviewedFinal Published versio

Efficient Action Detection in Untrimmed Videos via Multi-Task Learning

This paper studies the joint learning of action recognition and temporal localization in long, untrimmed videos. We employ a multi-task learning framework that performs the three highly related steps of action proposal, action recognition, and action localization refinement in parallel instead of the standard sequential pipeline that performs the steps in order. We develop a novel temporal actionness regression module that estimates what proportion of a clip contains action. We use it for temporal localization but it could have other applications like video retrieval, surveillance, summarization, etc. We also introduce random shear augmentation during training to simulate viewpoint change. We evaluate our framework on three popular video benchmarks. Results demonstrate that our joint model is efficient in terms of storage and computation in that we do not need to compute and cache dense trajectory features, and that it is several times faster than its sequential ConvNets counterpart. Yet, despite being more efficient, it outperforms state-of-the-art methods with respect to accuracy.Comment: WACV 2017 camera ready, minor updates about test time efficienc

Interactive Search and Exploration in Online Discussion Forums Using Multimodal Embeddings

In this paper we present a novel interactive multimodal learning system, which facilitates search and exploration in large networks of social multimedia users. It allows the analyst to identify and select users of interest, and to find similar users in an interactive learning setting. Our approach is based on novel multimodal representations of users, words and concepts, which we simultaneously learn by deploying a general-purpose neural embedding model. We show these representations to be useful not only for categorizing users, but also for automatically generating user and community profiles. Inspired by traditional summarization approaches, we create the profiles by selecting diverse and representative content from all available modalities, i.e. the text, image and user modality. The usefulness of the approach is evaluated using artificial actors, which simulate user behavior in a relevance feedback scenario. Multiple experiments were conducted in order to evaluate the quality of our multimodal representations, to compare different embedding strategies, and to determine the importance of different modalities. We demonstrate the capabilities of the proposed approach on two different multimedia collections originating from the violent online extremism forum Stormfront and the microblogging platform Twitter, which are particularly interesting due to the high semantic level of the discussions they feature

Microgenesis, immediate experience and visual processes in reading

The concept of microgenesis refers to the development on a brief present-time scale of a percept, a thought, an object of imagination, or an expression. It defines the occurrence of immediate experience as dynamic unfolding and differentiation in which the ‘germ’ of the final experience is already embodied in the early stages of its development. Immediate experience typically concerns the focal experience of an object that is thematized as a ‘figure’ in the global field of consciousness; this can involve a percept, thought, object of imagination, or expression (verbal and/or gestural). Yet, whatever its modality or content, focal experience is postulated to develop and stabilize through dynamic differentiation and unfolding. Such a microgenetic description of immediate experience substantiates a phenomenological and genetic theory of cognition where any process of perception, thought, expression or imagination is primarily a process of genetic differentiation and development, rather than one of detection (of a stimulus array or information), transformation, and integration (of multiple primitive components) as theories of cognitivist kind have contended. My purpose in this essay is to provide an overview of the main constructs of microgenetic theory, to outline its potential avenues of future development in the field of cognitive science, and to illustrate an application of the theory to research, using visual processes in reading as an example