It is now accepted that the most eective video shot retrieval is based on indexing and retrieving clips using multiple, parallel modalities such as text-matching, image-matching and feature matching and then combining or fusing these parallel retrieval streams in some way. In this paper we investigate a range of fusion methods for combining based on multiple visual features (colour, edge and texture), for combining based on multiple visual examples in the query and for combining multiple modalities (text and visual). Using three TRECVid collections and the TRECVid search task, we specically compare fusion methods based on normalised score and rank that use either the average, weighted average or maximum of retrieval results from a discrete Jelinek-Mercer smoothed language model. We also compare these results with a simple probability-based combination of the language model results that assumes all features and visual examples are fully independent.
Abstract. As Digital TV subscribers are offered more and more channels, it is becoming increasingly difficult for them to locate the right programme information at the right time. The personalized Electronic Programme Guide (pEPG) is one solution to this problem; it leverages artificial intelligence and user profiling techniques to learn about the viewing preferences of individual users in order to compile personalized viewing guides that fit their individual preferences. Very often the limited availability of profiling information is a key limiting factor in such personalized recommender systems. For example, it is well known that collaborative filtering approaches suffer significantly from the sparsity problem, which exists because the expected item-overlap between profiles is usually very low. In this article we address the sparsity problem in the Digital TV domain. We propose the use of data mining techniques as a way of supplementing meagre ratings-based profile knowledge with additional item-similarity knowledge that can be automatically discovered by mining user profiles. We argue that this new similarity knowledge can significantly enhance the performance of a recommender system in even the sparsest of profile spaces. Moreover, we provide an extensive evaluation of our approach using two large-scale, state-of-the-art online systems-PTVPlus, a personalized TV listings portal and Físchlár, an online digital video library system.
Intelligent personal assistants on mobile devices such as Apple’s Siri and Microsoft Cortana are increasingly important. Instead of passively reacting to queries, they provide users with brand new proactive experiences that aim to offer the right information at the right time. It is, therefore, crucial for personal assistants to understand users’ intent, that is, what information users need now. Intent is closely related to context. Various contextual signals, including spatio-temporal information and users’ activities, can signify users’ intent. It is, however, challenging to model the correlation between intent and context. Intent and context are highly dynamic and often sequentially correlated. Contextual signals are usually sparse, heterogeneous, and not simultaneously available. We propose an innovative
collaborative nowcasting
model to jointly address all these issues. The model effectively addresses the complex sequential and concurring correlation between context and intent and recognizes users’ real-time intent with continuously arrived contextual signals. We extensively evaluate the proposed model with real-world data sets from a commercial personal assistant. The results validate the effectiveness the proposed model, and demonstrate its capability of handling the real-time flow of contextual signals. The studied problem and model also provide inspiring implications for new paradigms of recommendation on mobile intelligent devices.
The growing ubiquity of smartphones and tablet devices integrated into personal, social and professional life, facilitated by expansive communication networks globally, has the potential to disrupt higher education. Academics and students are considering the future possibilities of exploiting these tools and utilising networks to consolidate and expand knowledge, enhancing learning gain. Bluetooth beacon technology has been developed by both Apple and Google as a way to situate digital information within physical spaces, and this paper reflects on a beacon intervention in a contemporary art school in higher education conducted by the authors intended to develop a situated community of practice in Art & Design. The paper describes the project, including relevant theoretical foundations and background to the beacon technology, with regards to the potential of using these devices to create a connected learning community by enhancing learning and facilitating knowledge creation in a borderless learning space.
In this paper we present and discuss the system we developed for the search task of the TRECVID 2002, and its evaluation in an interactive search task. To do this we will look at the strategy we used in designing the system, and we discuss and evaluate the experiments used to determine the value and effectiveness of one system incorporating both feature evidence and transcript retrieval compared to a transcript-only retrieval system. Both systems tested are built on the foundation of the Físchlár System developed and running for a number of years at the CDVP. The system is fully MPEG-7 compliant and uses XML for exchange of information within the overall architecture.
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