Automatic parsing of sports videos with grammars

Motivated by the analogies between languages and sports videos, we introduce a novel approach for video parsing with grammars. It utilizes compiler techniques for integrating both semantic annotation and syntactic analysis to generate a semantic index of events and a table of content for a given sports video. The video sequence is first segmented and annotated by event detection with domain knowledge. A grammar-based parser is then used to identify the structure of the video content. Meanwhile, facilities for error handling are introduced which are particularly useful when the results of automatic parsing need to be adjusted. As a case study, we have developed a system for video parsing in the particular domain of TV diving programs. Experimental results indicate the proposed approach is effectiv

Tourism in contemporary cities. Proceedings of the International Tourism Studies Association Conference: University of Greenwich, London, UK 17–19 August 2016 Conference Proceedings

The 6th International Tourism Studies Association (ITSA) Biennial conference was held at the University of Greenwich, London, England from 17‐19 August 2016. This was the first time that the conference had been held in Europe and it provided a unique opportunity to meet, hear from and network with tourism scholars and professionals from across Europe, Asia, Australasia, and North and South America. ITSA has a mission to encourage interaction and cooperation between developing and developed countries and the conference was successful in attracting 130 delegates from 29 countries. The main theme of the conference was 'Tourism in Contemporary Cities' with four conference sub‐themes of ‘Tourism Cities and Urban Tourism’, ‘The Chinese Market for European Tourism’, ‘River, Cruise and Maritime Tourism’, and ‘Heritage Tourism in Cities’, The subthemes were chosen to reflect the unique location of the conference on the UNESCO Maritime Greenwich World Heritage Site, and London which is Europe’s most visited tourist destination. The conference also presented ‘Dark Tourism and Cities’ and ‘Tourism and Communist Heritage’ as special sessions

Personalization by Partial Evaluation.

The central contribution of this paper is to model personalization by the programmatic notion of partial evaluation.Partial evaluation is a technique used to automatically specialize programs, given incomplete information about their input.The methodology presented here models a collection of information resources as a program (which abstracts the underlying schema of organization and flow of information),partially evaluates the program with respect to user input,and recreates a personalized site from the specialized program.This enables a customizable methodology called PIPE that supports the automatic specialization of resources,without enumerating the interaction sequences beforehand .Issues relating to the scalability of PIPE,information integration,sessioniz-ling scenarios,and case studies are presented

Learning space-time structures for action recognition and localization

In this thesis the problem of automatic human action recognition and localization in videos is studied. In this problem, our goal is to recognize the category of the human action that is happening in the video, and also to localize the action in space and/or time. This problem is challenging due to the complexity of the human actions, the large intra-class variations and the distraction of backgrounds. Human actions are inherently structured patterns of body movements. However, past works are inadequate in learning the space-time structures in human actions and exploring them for better recognition and localization. In this thesis new methods are proposed that exploit such space-time structures for effective human action recognition and localization in videos, including sports videos, YouTube videos, TV programs and movies. A new local space-time video representation, the hierarchical Space-Time Segments, is first proposed. Using this new video representation, ensembles of hierarchical spatio-temporal trees, discovered directly from the training videos, are constructed to model the hierarchical, spatial and temporal structures of human actions. This proposed approach achieves promising performances in action recognition and localization on challenging benchmark datasets. Moreover, the discovered trees show good cross-dataset generalizability: trees learned on one dataset can be used to recognize and localize similar actions in another dataset. To handle large scale data, a deep model is explored that learns temporal progression of the actions using Long Short Term Memory (LSTM), which is a type of Recurrent Neural Network (RNN). Two novel ranking losses are proposed to train the model to better capture the temporal structures of actions for accurate action recognition and temporal localization. This model achieves state-of-art performance on a large scale video dataset. A deep model usually employs a Convolutional Neural Network (CNN) to learn visual features from video frames. The problem of utilizing web action images for training a Convolutional Neural Network (CNN) is also studied: training CNN typically requires a large number of training videos, but the findings of this study show that web action images can be utilized as additional training data to significantly reduce the burden of video training data collection

The Vectorial λ\lambda-Calculus

We describe a type system for the linear-algebraic $\lambda$-calculus. The type system accounts for the linear-algebraic aspects of this extension of $\lambda$-calculus: it is able to statically describe the linear combinations of terms that will be obtained when reducing the programs. This gives rise to an original type theory where types, in the same way as terms, can be superposed into linear combinations. We prove that the resulting typed $\lambda$-calculus is strongly normalising and features weak subject reduction. Finally, we show how to naturally encode matrices and vectors in this typed calculus.Comment: Long and corrected version of arXiv:1012.4032 (EPTCS 88:1-15), to appear in Information and Computatio

Form Explanation in Modification of Listening Input in L2 Vocabulary Learning

The effectiveness of vocabulary explanation as modifications of listening input - explicit (EE) and implicit (IE) - were investigated in contrast to unmodified (baseline, BL) condition. One hundred and nine university students from Japan listened to two texts, which included different vocabulary elaborations for 12 items. Students listened three times to each text. After each listening, they indicatec the meanings of the items. Four weeks later, a delayed posttest was administered. Positive effects of multiple listenings were found in vocabulary learning from listening input. As hypothesized, the EE condition resulted in significant superiority over the other two on the immediate posttests. However, IE was not significantly better than the BL. The findings suggested that the IE mostly remained unnoticed during the listening. On the delayed posttest, the score of EE dropped and there was no significant difference among the three conditions, though all conditions resulted in a significant increase from the pretest

Space Station Human Factors Research Review. Volume 4: Inhouse Advanced Development and Research

A variety of human factors studies related to space station design are presented. Subjects include proximity operations and window design, spatial perceptual issues regarding displays, image management, workload research, spatial cognition, virtual interface, fault diagnosis in orbital refueling, and error tolerance and procedure aids