Semi-automatic knowledge extraction, representation and context-sensitive intelligent retrieval of video content using collateral context modelling with scalable ontological networks

Automatic indexing and retrieval of digital data poses major challenges. The main problem arises from the ever increasing mass of digital media and the lack of efficient methods for indexing and retrieval of such data based on the semantic content rather than keywords. To enable intelligent web interactions, or even web filtering, we need to be capable of interpreting the information base in an intelligent manner. For a number of years research has been ongoing in the field of ontological engineering with the aim of using ontologies to add such (meta) knowledge to information. In this paper, we describe the architecture of a system (Dynamic REtrieval Analysis and semantic metadata Management (DREAM)) designed to automatically and intelligently index huge repositories of special effects video clips, based on their semantic content, using a network of scalable ontologies to enable intelligent retrieval. The DREAM Demonstrator has been evaluated as deployed in the film post-production phase to support the process of storage, indexing and retrieval of large data sets of special effects video clips as an exemplar application domain. This paper provides its performance and usability results and highlights the scope for future enhancements of the DREAM architecture which has proven successful in its first and possibly most challenging proving ground, namely film production, where it is already in routine use within our test bed Partners' creative processes. (C) 2009 Published by Elsevier B.V.

A peer-to-peer approach to content dissemination and search in collaborative networks

There are three key driving forces behind the development of Internet Content Management Systems (CMS) - a desire to manage the explosion of content, a desire to provide structure and meaning to content in order to make it accessible, and a desire to work collaboratively to manipulate content in some meaningful way. Yet the traditional CMS has been unable to meet the latter of these requirements, often failing to provide sufficient tools for collaboration in a distributed context. Peer-to-Peer (P2P) systems are networks in which every node is an equal participant (whether transmitting data, exchanging content, or invoking services) and there is an absence of any centralised administrative or coordinating authorities. P2P systems are inherently more scalable than equivalent client-server implementations as they tend to use resources at the edge of the network much more effectively. This paper details the rationale and design of a P2P middleware for collaborative content management.

Propagation of errors from a null balance terahertz reflectometer to a sample's relative water content

The THz water content index of a sample is defined and advantages in using such metric in estimating a sample's relative water content are discussed. The errors from reflectance measurements performed at two different THz frequencies using a quasi-optical null-balance reflectometer are propagated to the errors in estimating the sample water content index.

Ontological modelling of content management and provision

Information provision to address the changing requirements can be best supported by content management. The Current information technology enables information to be stored and provided from various distributed sources. To identify and retrieve relevant information requires effective mechanisms for information discovery and assembly. This paper presents a method, which enables the design of such mechanisms, with a set of techniques for articulating and profiling users' requirements, formulating information provision specifications, realising management of information content in repositories, and facilitating response to the user's requirements dynamically during the process of knowledge construction. These functions are represented in an ontology which integrates the capability of the mechanisms. The ontological modelling in this paper has adopted semiotics principles with embedded norms to ensure coherent course of actions represented in these mechanisms. (C) 2008 Elsevier B.V. All rights reserved.

Semantic-associative visual content labelling and retrieval: A multimodal approach

A novel framework referred to as collaterally confirmed labelling (CCL) is proposed, aiming at localising the visual semantics to regions of interest in images with textual keywords. Both the primary image and collateral textual modalities are exploited in a mutually co-referencing 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. We introduce a novel high-level visual content descriptor that is devised for performing semantic-based image classification and retrieval. The proposed image feature vector model is fundamentally underpinned by the CCL framework. Two different high-level image feature vector models are developed based on the CCL labelling of results for the purposes of image data clustering and retrieval, respectively. A subset of the Corel image collection has been used for evaluating our proposed method. The experimental results to-date already indicate that the proposed semantic-based visual content descriptors outperform both traditional visual and textual image feature models. (C) 2007 Elsevier B.V. All rights reserved.

Collaterally cued labelling framework underpinning semantic-level visual content descriptor

In this paper, we introduce a novel high-level visual content descriptor devised for performing semantic-based image classification and retrieval. The work can be treated as an attempt for bridging the so called "semantic gap". The proposed image feature vector model is fundamentally underpinned by an automatic image labelling framework, called Collaterally Cued Labelling (CCL), which incorporates the collateral knowledge extracted from the collateral texts accompanying the images with the state-of-the-art low-level visual feature extraction techniques for automatically assigning textual keywords to image regions. A subset of the Corel image collection was used for evaluating the proposed method. The experimental results indicate that our semantic-level visual content descriptors outperform both conventional visual and textual image feature models.

Effect of feed restriction and supplemental dietary fat on gut peptide and hypothalamic neuropeptide mRNA concentrations in growing wethers

The objectives of the present study were 1) to evaluate the effects of supplemental fat and ME intake on plasma concentrations of glucagon-like peptide-1 (GLP-1), cholecystokinin (CCK), glucose-dependent insulinotropic polypeptide, ghrelin, and oxyntomodulin; and 2) to determine the association of these peptides with DMI and the hypothalamic concentration of mRNA for the following neuropeptides: neuropeptide Y (NPY), agouti-related peptide (AgRP), and proopiomelanocortin (POMC). In a completely randomized block design with a 2 x 2 factorial arrangement of treatments, 32 pens with 2 wethers each were restricted-fed (2.45 Mcal/lamb per day) or offered diets ad libitum (n = 16) with or without 6% supplemental fat (n = 16) for a period of 30 d. Dry matter intake was measured daily. On d 8, 15, 22, and 29, BW was measured before feeding, and 6 h after feeding, blood samples were collected for plasma measurement of insulin, GLP-1, CCK, ghrelin, glucose-dependent insulinotropic polypeptide, oxyntomodulin, glucose, and NEFA concentrations. On d 29, blood was collected 30 min before feeding for the same hormone and metabolite analyses. At the end of the experiment, wethers were slaughtered and the hypothalami were collected to measure concentrations of NPY, AgRP, and POMC mRNA. Offering feed ad libitum (resulting in greater ME intake) increased plasma insulin and NEFA concentrations (P = 0.02 and 0.02, respectively) and decreased hypothalamic mRNA expression of NPY and AgRP (P = 0.07 and 0.02, respectively) compared with the restricted-fed wethers. There was a trend for the addition of dietary fat to decrease DMI (P = 0.12). Addition of dietary fat decreased insulin and glucose concentrations (P < 0.05 and 0.01, respectively) and tended to increase hypothalamic mRNA concentrations for NPY and AgRP (P = 0.07 and 0.11, respectively). Plasma GLP-1 and CCK concentrations increased in wethers offered feed ad libitum compared with restricted-fed wethers, but the response was greater when wethers were offered feed ad libitum and had supplemental fat in the diet (fat x intake interaction, P = 0.04). The prefeeding plasma ghrelin concentration was greater in restricted-fed wethers compared with those offered feed ad libitum, but the concentrations were similar 6 h after feeding (intake x time interaction, P < 0.01). Supplemental dietary fat did not affect (P = 0.22) plasma ghrelin concentration. We conclude that insulin, ghrelin, CCK, and GLP-1 may regulate DMI in sheep by regulating the hypothalamic gene expression of NPY, AgRP, and POMC.

Effect of abomasal glucose infusion on plasma concentrations of gut peptides in periparturient dairy cows

Six Holstein cows fitted with ruminal cannulas and permanent indwelling catheters in the portal vein, hepatic vein, mesenteric vein, and an artery were used to study the effects of abomasal glucose infusion on splanchnic plasma concentrations of gut peptides. The experimental design was a randomized block design with repeated measurements. Cows were assigned to one of 2 treatments: control or infusion of 1,500 g of glucose/d into the abomasum from the day of parturition to 29 d in milk. Cows were sampled 12 ± 6 d prepartum and at 4, 15, and 29 d in milk. Concentrations of glucose-dependent insulinotropic polypeptide, glucagon-like peptide 1(7–36) amide, and oxyntomodulin were measured in pooled samples within cow and sampling day, whereas active ghrelin was measured in samples obtained 30 min before and after feeding at 0800 h. Postpartum, dry matter intake increased at a lower rate with infusion compared with the control. Arterial, portal venous, and hepatic venous plasma concentrations of the measured gut peptides were unaffected by abomasal glucose infusion. The arterial, portal venous, and hepatic venous plasma concentrations of glucose-dependent insulinotropic polypeptide and glucagon-like peptide 1(7–36) amide increased linearly from 12 d prepartum to 29 d postpartum. Plasma concentrations of oxyntomodulin were unaffected by day relative to parturition. Arterial and portal venous plasma concentrations of ghrelin were lower postfeeding compared with prefeeding concentrations. Arterial plasma concentrations of ghrelin were greatest prepartum and lowest at 4 d postpartum, giving a quadratic pattern of change over the transition period. Positive portal venous-arterial and hepatic venous–arterial concentration differences were observed for glucagon-like peptide 1(7–36) amide. A negative portal venous–arterial concentration difference was observed for ghrelin pre-feeding. The remaining portal venous–arterial and hepatic venous–arterial concentration differences of gut peptides did not differ from zero. In conclusion, increased postruminal glucose supply to postpartum transition dairy cows reduced feed intake relative to control cows, but did not affect arterial, portal venous, or hepatic venous plasma concentrations of gut peptide hormones. Instead, gut peptide plasma concentrations increased as lactation progressed. Thus, the lower feed intake of postpartum dairy cows receiving abomasal glucose infusion was not attributable to changes in gut peptide concentrations.

The relation between content and structure in language production: an analysis of speech errors in semantic dementia

In order to explore the impact of a degraded semantic system on the structure of language production, we analysed transcripts from autobiographical memory interviews to identify naturally-occurring speech errors by eight patients with semantic dementia (SD) and eight age-matched normal speakers. Relative to controls, patients were significantly more likely to (a) substitute and omit open class words, (b) substitute (but not omit) closed class words, (c) substitute incorrect complex morphological forms and (d) produce semantically and/or syntactically anomalous sentences. Phonological errors were scarce in both groups. The study confirms previous evidence of SD patients’ problems with open class content words which are replaced by higher frequency, less specific terms. It presents the first evidence that SD patients have problems with closed class items and make syntactic as well as semantic speech errors, although these grammatical abnormalities are mostly subtle rather than gross. The results can be explained by the semantic deficit which disrupts the representation of a pre-verbal message, lexical retrieval and the early stages of grammatical encoding.