Virtual reality cinemas offer computer-generated screening environments that resemble physical-world movie theaters for avatar-based viewers. Reflecting on virtual spectatorship in the context of social isolation, the present study investigates whether VR cinemas could provide an alternative for collective movie watching and whether they could facilitate an engaging experience similar to other, physical-world co-viewing environments. To measure these effects, we designed a behavioral experiment in which participants watched a feature film sequence either in VR or a physical screening room in the presence or absence of viewing companions. After viewing, participants’ experiences—including emotional engagement, narrative empathy, presence, social experiences, and physical and mental well-being—were recorded using survey methods. We observed that VR viewing can produce an equally enjoyable film experience, as well as similar levels of emotional engagement and narrative empathy, while it leads to increased comprehension of characters’ feelings and sense of narrative engagement. In addition, social viewing may mean less engagement and more distractions depending on the screening environment. We also found that even though previous virtual reality exposure negatively correlates with comfort and well-being during viewing, early adopters of technology and VR supporters are more likely to have an enjoyable and engaging film experience.

Hybridity in immersive technologies has not been studied for factors that are likely to influence engagement. A noticeable factor is the spatial enclosure that defines where users meet. This involves a mutual object of interest, contents that the users may generate around the object, and the proximity between users. This study examines these factors, namely how object interactivity, user-generated contents (UGC) and avatar proximity influence engagement. We designed a Hybrid Virtual and Augmented Reality (HVAR) environment that supports paired users to experience cultural heritage in both Virtual Reality (VR) and Augmented Reality (AR) . A user study was conducted with 60 participants, providing assessments of engagement and presence via questionnaires, together with mobile electroencephalogram (mEEG) and user activity data that measures VR user engagement in real-time. Our findings provide insights into how engagement between users can occur in HVAR environments for the future hybrid reality with multi-device connectivity.

Cultural heritage presents both challenges and opportunities for the adoption and use of deep learning in 3D digitisation and digitalisation endeavours. While unique features in terms of the identity of artefacts are important factors that can contribute to training performance in deep learning algorithms, challenges remain with regards to the laborious efforts in our ability to obtain adequate datasets that would both provide for the diversity of imageries, and across the range of multi-facet images for each object in use. One solution, and perhaps an important step towards the broader applicability of deep learning in the field of digital heritage is the fusion of both real and virtual datasets via the automated creation of diverse datasets that covers multiple views of individual objects over a range of diversified objects in the training pipeline, all facilitated by closerange photogrammetry generated 3D objects. The question is the ratio of the combination of real and synthetic imageries in which an inflection point occurs whereby performance is reduced. In this research, we attempt to reduce the need for manual labour by leveraging the flexibility provided for in automated data generation via close-range photogrammetry models with a view for future deep learning facilitated cultural heritage activities, such as digital identification, sorting, asset management and categorisation.

Web sites and online services increasingly engage with users through live chats to provide support, advice, and offers. Such approaches require reliable methods to predict the user’s intent and make an informed decision when and how to intervene during an active session. Prior work on predicting purchase intent involved clickstream data mining and feature construction in an ad-hoc manner with a moderate success (AUC 0.70 range). We demonstrate the use of the consumer Purchase Decision Model (PDM) and a principled way of constructing features predictive of the purchase intent. We show that the Logistic Regression (LR) classifiers, trained with multi-action motifs, perform on par with the state-of-the-art LSTM sequence model achieving comparable AUC (0.95 vs 0.96) and performing better for the sparse purchase sessions, with higher recall (0.85 vs 0.61) and higher F1 score (0.73 vs 0.66). While LSTM performs better than LR in terms of weighted averages of F1, precision, and recall, it requires 7 times longer to train and offers no insights about the predictive model in terms of the user actions and the purchase decision stages. The LR predictors are robust and effective in simulating real-time interventions, achieving F1 of 0.84 and AUC of 0.85 after observing only 50% of an active session. For non-purchase sessions that leaves room for live intervention, on average within 8 actions before the session ends.

When does a machine-created work becomes art? What is art? Can machine artworks fit in to the historical and present discourse? Do machine artworks demonstrate creativity, or are they a type of new media from which artists extend their creativity with? Will solely machine-created artworks be acceptable by our artworlds? This article probes these questions by first identifying the frameworks for defining and explaining art and evaluating its suitability for explaining machine artworks. It then explores how artworks have a necessary relationship with their human artists and the wider context of history, institutions, styles, and approaches and with audiences and artworlds. The article then questions whether machines have such a relational context and whether machines will ever live up to our standard of what constitutes an artwork as defined by us or whether machines are good only for assisting creativity. The question of intellectual property, rights, and ownership are also discussed for human--machine artworks and purely machine-produced works of art. The article critically assesses the viability of machines as artists as the central question in the historical discourse, extended through art and the artworld and evaluates machine-produced work from such a basis.

E-commerce platforms facilitate sales of products while product vendors engage in Social Media Activities (SMA) to drive E-commerce Platform Activities (EPA) of consumers, enticing them to search, browse and buy products. The frequency and timing of SMA are expected to affect levels of EPA, increasing the number of brand related queries, clickthrough, and purchase orders. This paper applies cross-sectional data analysis to explore such beliefs and demonstrates weak-to-moderate correlations between daily SMA and EPA volumes. Further correlation analysis, using 30-day rolling windows, shows a high variability in correlation of SMA-EPA pairs and calls into question the predictive potential of SMA in relation to EPA. Considering the moderate correlation of selected SMA and EPA pairs (e.g., Post-Orders), we investigate whether SMA features can predict changes in the EPA levels, instead of precise EPA daily volumes. We define such levels in terms of EPA distribution quantiles (2, 3, and 5 levels) over training data. We formulate the EPA quantile predictions as a multi-class categorization problem. The experiments with Random Forest and Logistic Regression show a varied success, performing better than random for the top quantiles of purchase orders and for the lowest quantile of search and clickthrough activities. Similar results are obtained when predicting multi-day cumulative EPA levels (1, 3, and 7 days). Our results have considerable practical implications but, most importantly, urge the common beliefs to be re-examined, seeking a stronger evidence of SMA effects on EPA.

Data visualization is an important aspect of data analytics in an age where decisions are all based on information. Approaches in data visualization, particularly those that have the capability of processing large-scale textual datasets and visualize them as structured information in real-time can be useful for monitoring trends in social media. In this article, we present our GPU accelerated project, which uses CUDA to distribute and parallelize the processing and analysis of textual data in order to visualize information in real-time, or close to real-time as a foundational system for the future of real-time applications which monitors trends in social media, applicable to political elections, social media analytics, and other needs in computational social sciences which are time-critical.

This paper proposes a novel framework for detecting redundancy in supervised sentence categorisation. Unlike traditional singleton neural network, our model incorporates character- aware convolutional neural network (Char-CNN) with character-aware recurrent neural network (Char-RNN) to form a convolutional recurrent neural network (CRNN). Our model benefits from Char-CNN in that only salient features are selected and fed into the integrated Char-RNN. Char-RNN effectively learns long sequence semantics via sophisticated update mechanism. We compare our framework against the state-of-the- art text classification algorithms on four popular benchmarking corpus. For instance, our model achieves competing precision rate, recall ratio, and F1 score on the Google-news data-set. For twenty- news-groups data stream, our algorithm obtains the optimum on precision rate, recall ratio, and F1 score. For Brown Corpus, our framework obtains the best F1 score and almost equivalent precision rate and recall ratio over the top competitor. For the question classification collection, CRNN produces the optimal recall rate and F1 score and comparable precision rate. We also analyse three different RNN hidden recurrent cells' impact on performance and their runtime efficiency. We observe that MGU achieves the optimal runtime and comparable performance against GRU and LSTM. For TFIDF based algorithms, we experiment with word2vec, GloVe, and sent2vec embeddings and report their performance differences.

Multitouch-Multiuser Table Computing is the new paradigm for accessing rich information in public spaces. As revolutionary natural user interfaces provide new ways of interacting with virtual information spaces, visual information developers must rethink their methodologies in the design of new applications that augments multiple user interaction and collaboration. This paper addresses a specific aspect of the multitouch-multiuser paradigm for virtual artefacts. Particularly, the paper addresses pilot developments of 3D multitouch-multiuser table applications in the presentation of heritage information in order to begin to qualitatively understand group behavior and user interactions on 3D information spaces using touch. The qualitative observations here pave the way for more structured quantitative usability studies.

The artist and principal author has been creating art works in multimedia through the semantic association of ideas into concepts, as a means of exploring the efficacy of hypermedia for a conceptual art practice. This is particularly evident in the developments apparent in the ongoing 'Deconstructing Duchamp' project, where 'flocking' behaviours have been applied to Duchampian digitised items to observe the familial relations within, and key to his work, at play. Following this project, a second work 'Shift-Life' has proceeded to further develop the idea of allotting animal-like behaviours to electronic data items giving them the appearance of possessing a basic intelligence. By then observing their response to our physical interactions we can glean a clearer understanding, from their inter-relationships, of a complex conceptual framework. While Marcel Duchamp offered the art world one of the most complex and formative pieces of art ever, initiating the shift of values from aesthetics to idea, Charles Darwin developed the theory of evolution, the 'big' idea of survivability through adaptation. Shift-Life was created as part of the national Darwin 200 project for the international bicentenary in 2009. It is a complex system of virtual life-forms struggling to survive in an environment made volatile through human interaction. Central to this installation work is the artificial life ecosystem as a self-sustaining, self-reproducing equilibrium of creatures and plants living in it. The general behaviour of each organism was more sophisticated than for the Duchampian creatures in that they were equipped for survival strategies and the reproduction of progenies. An exposition of the Shift-Life program is therefore presented followed by reflection on both projects and future directions for this collaborative research where potential emergent behaviours are concerned.