False recognition of abstract versus common objects in older and younger adults: testing the semantic categorization account

Older adults often demonstrate higher levels of false recognition than do younger adults. However, in experiments using novel shapes without preexisting semantic representations, this age-related elevation in false recognition was found to be greatly attenuated. Two experiments tested a semantic categorization account of these findings, examining whether older adults show especially heightened false recognition if the stimuli have preexisting semantic representations, such that semantic category information attenuates or truncates the encoding or retrieval of item-specific perceptual information. In Experiment 1, ambiguous shapes were presented with or without disambiguating semantic labels. Older adults showed higher false recognition when labels were present but not when labels were never presented. In Experiment 2, older adults showed higher false recognition for concrete but not abstract objects. The semantic categorization account was supported.

Semantic organization to enhance active learning based on distributed multimedia educational resources

In order to organize distributed educational resources efficiently, to provide active learners an integrated, extendible and cohesive interface to share the dynamically growing multimedia learning materials on the Internet, this paper proposes a generic resource organization model with semantic structures to improve expressiveness, scalability and cohesiveness. We developed an active learning system with semantic support for learners to access and navigate through efficient and flexible manner. We learning resources in an efficient and flexible manner. We provide facilities for instructors to manipulate the structured educational resources via a convenient visual interface. We also developed a resource discovering and gathering engine based on complex semantic associations for several specific topics.

Multimodal semantic-associative collateral labelling and indexing of still images

A novel framework for multimodal semantic-associative collateral image labelling, aiming at associating image regions with textual keywords, is described. Both the primary image and collateral textual modalities are exploited in a cooperative and complementary fashion. The collateral content and context based knowledge is used to bias the mapping from the low-level region-based visual primitives to the high-level visual concepts defined in a visual vocabulary. We introduce the notion of collateral context, which is represented as a co-occurrence matrix, of the visual keywords, A collaborative mapping scheme is devised using statistical methods like Gaussian distribution or Euclidean distance together with collateral content and context-driven inference mechanism. Finally, we use Self Organising Maps to examine the classification and retrieval effectiveness of the proposed high-level image feature vector model which is constructed based on the image labelling results.

Semantic-driven context-aware visual information indexing and retrieval: applied in the film post-production domain

A large volume of visual content is inaccessible until effective and efficient indexing and retrieval of such data is achieved. In this paper, we introduce the DREAM system, which is a knowledge-assisted semantic-driven context-aware visual information retrieval system applied in the film post production domain. We mainly focus on the automatic labelling and topic map related aspects of the framework. The use of the context- related collateral knowledge, represented by a novel probabilistic based visual keyword co-occurrence matrix, had been proven effective via the experiments conducted during system evaluation. The automatically generated semantic labels were fed into the Topic Map Engine which can automatically construct ontological networks using Topic Maps technology, which dramatically enhances the indexing and retrieval performance of the system towards an even higher semantic level.

Towards semantic resolution of security in ambient environments

Driven by new network and middleware technologies such as mobile broadband, near-field communication, and context awareness the so-called ambient lifestyle will foster innovative use cases in different domains. In the EU project Hydra high-level security, trust and privacy concerns such as loss of control, profiling and surveillance are considered at the outset. At the end of this project the. Hydra middleware development platform will have been designed so as to enable developers to realise secure ambient scenarios. This paper gives a short introduction to the Hydra project and its approach to ensure security by design. Based on the results of a focus group analysis of the user domain "building automation" typical threats are evaluated and their risks are assessed. Then, specific security requirements with respect to security, privacy, and trust are derived in order to incorporate them into the Hydra Security Meta-Model. How concepts such as context, semantic resolution of security, and virtualisation support the overall Hydra approach will be introduced and illustrated on the basis of it technical building automation scenario.

Slogger: a profiling and analysis system based on semantic web technologies

Increasingly, distributed systems are being used to host all manner of applications. While these platforms provide a relatively cheap and effective means of executing applications, so far there has been little work in developing tools and utilities that can help application developers understand problems with the supporting software, or the executing applications. To fully understand why an application executing on a distributed system is not behaving as would be expected it is important that not only the application, but also the underlying middleware, and the operating system are analysed too, otherwise issues could be missed and certainly overall performance profiling and fault diagnoses would be harder to understand. We believe that one approach to profiling and the analysis of distributed systems and the associated applications is via the plethora of log files generated at runtime. In this paper we report on a system (Slogger), that utilises various emerging Semantic Web technologies to gather the heterogeneous log files generated by the various layers in a distributed system and unify them in common data store. Once unified, the log data can be queried and visualised in order to highlight potential problems or issues that may be occurring in the supporting software or the application itself.

The effects of web logs and the semantic web on autonomous web agents

Search engines exploit the Web's hyperlink structure to help infer information content. The new phenomenon of personal Web logs, or 'blogs', encourage more extensive annotation of Web content. If their resulting link structures bias the Web crawling applications that search engines depend upon, there are implications for another form of annotation rapidly on the rise, the Semantic Web. We conducted a Web crawl of 160 000 pages in which the link structure of the Web is compared with that of several thousand blogs. Results show that the two link structures are significantly different. We analyse the differences and infer the likely effect upon the performance of existing and future Web agents. The Semantic Web offers new opportunities to navigate the Web, but Web agents should be designed to take advantage of the emerging link structures, or their effectiveness will diminish.