Royal Holloway University of London

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    9793 research outputs found

    Holmes, David

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    The Early Modern Legacy of the Stoics

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    Training and Search On the Job

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    Maqbool, Mohammad

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    The Rise of Human-Machine Collaboration: Managers’ Perceptions of Leveraging Artificial Intelligence for Enhanced B2B Service Recovery

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    This research analyzes managers’ perceptions of the multiple types of artificial intelligence (AI) required at each stage of the business-to-business (B2B) service recovery journey for successful human-AI collaboration in this context. Study 1 is an exploratory study that identifies managers’ perceptions of the main stages of a B2B service recovery journey based on human-AI collaboration and the corresponding roles of the human-AI collaboration at each stage. Study 2 provides an empirical examination of the proposed theoretical framework to identify the specific types of intelligence required by AI to enhance performance in each stage of B2B service recovery, based on managers’ perceptions. Our findings show that the prediction stage benefits from collaborations involving processing-speed and visual-spatial AI. The detection stage requires logic-mathematical, social, and processing-speed AI. The recovery stage requires logic-mathematical, social, verbal-linguistic, and processing-speed AI. The post-recovery stage calls for logic-mathematical, social, verbal-linguistic, and processing-speed AI

    Human Action Recognition Using Multi-Stream Fusion and Hybrid Deep Neural Networks

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    Action Recognition in videos is a topic of interest in the area of computer vision, due to potential applications such as multimedia indexing and surveillance in public areas. In this research, we first propose spatial and temporal Convolutional Neural Network (CNNs), based on transfer learning using ResNet101, GoogleNet and VGG16, for undertaking human action recognition. Besides that, hybrid networks such as CNNRecurrent Neural Network (RNN) models are also exploited as encoder-decoder architectures for video action classification. In particular, different types of RNNs such as Long Short-Term Memory (LSTM), Bidirectional-LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Bidirectional-GRU (BiGRU), are exploited as the decoders for action recognition. To further enhance performance, diverse aggregation networks of CNN and CNN-RNN models are implemented. Specifically, an Average Fusion method is used to integrate spatial and temporal CNNs trained on images, as well as CNN-RNN trained on videos, where the final classification is formed by combining Softmax scores of these models via a late fusion. A total of 22 models (1 motion CNN, 3 spatial CNNs, 12 CNN-RNNs and 6 fusion networks) are implemented which are evaluated using UCF11, UCF50, and UCF101 datasets for performance comparison. The empirical results indicate the significant efficiency of Average Fusion of multiple Spatial-CNNs with one Motion-CNN, and ResNet101-BiGRU, among all the networks for undertaking realistic video action recognition

    Tersmette, Keye

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    Wrigley, Stuart

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    The Children and Young People’s Books Lexicon (CYP-LEX):A large-scale lexical database of books read by children and young people in the United Kingdom

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    This article introduces CYP-LEX, a large-scale lexical database derived from books popular with children and young people in the United Kingdom. CYP-LEX includes 1,200 books evenly distributed across three age bands (7–9, 10–12, 13+) and comprises over 70 million tokens and over 105,000 types. For each word in each age band, we provide its raw and Zipf-transformed frequencies, all parts-of-speech in which it occurs with raw frequency and lemma for each occurrence, and measures of count-based contextual diversity. Together and individually, the three CYP-LEX age bands contain substantially more words than any other publicly available database of books for primary and secondary school children. Most of these words are very low in frequency, and a substantial proportion of the words in each age band do not occur on British television. Although the three age bands share some very frequent words, they differ substantially regarding words that occur less frequently, and this pattern also holds at the level of individual books. Initial analyses of CYP-LEX illustrate why independent reading constitutes a challenge for children and young people, and they also underscore the importance of reading widely for the development of reading expertise. Overall, CYP-LEX provides unprecedented information into the nature of vocabulary in books that British children aged 7+ read, and is a highly valuable resource for those studying reading and language development


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