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.

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.

The Role of Collateral in Entrepreneurial Finance

Previous research has suggested collateral has the role of sorting entrepreneurs either by observed risk or by private information. In order to test these roles, this paper develops a model which incorporates a signalling process (sorting by observed risk) into the design of an incentivecompatible menu of loan contracts which works as a self-selection mechanism (sorting by private information). It then tests this Sorting by Signalling and Self-Selection Model, using the 1998 US Survey of Small Business Finances. It reports for the first time that: high type entrepreneurs are more likely to pledge collateral and pay a lower interest rate; and entrepreneurs who transfer good signals enjoy better contracts than those transferring bad signals. These findings suggest that the Sorting by Signalling and Self-Selection Model sheds more light on entrepreneurial debt finance than either the sorting-by-observed-risk or the sorting-by-private information paradigms on their own.

Semantic-level Visual Content Descriptor combining visual and textual cues as collaterals

In this paper, we introduce a novel high-level visual content descriptor which is devised for performing semantic-based image classification and retrieval. The work can be treated as an attempt to bridge the so called “semantic gap”. The proposed image feature vector model is fundamentally underpinned by the image labelling framework, called Collaterally Confirmed Labelling (CCL), which incorporates the collateral knowledge extracted from the collateral texts of the images with the state-of-the-art low-level image processing and visual feature extraction techniques for automatically assigning linguistic keywords to image regions. 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 indicates that our proposed semantic-based visual content descriptors outperform both traditional visual and textual image feature models.

A framework towards multi-modal fingerprinting scheme for multimedia assets

Fingerprinting is a well known approach for identifying multimedia data without having the original data present but what amounts to its essence or ”DNA”. Current approaches show insufficient deployment of three types of knowledge that could be brought to bear in providing a finger printing framework that remains effective, efficient and can accommodate both the whole as well as elemental protection at appropriate levels of abstraction to suit various Foci of Interest (FoI) in an image or cross media artefact. Thus our proposed framework aims to deliver selective composite fingerprinting that remains responsive to the requirements for protection of whole or parts of an image which may be of particularly interest and be especially vulnerable to attempts at rights violation. This is powerfully aided by leveraging both multi-modal information as well as a rich spectrum of collateral context knowledge including both image-level collaterals as well as the inevitably needed market intelligence knowledge such as customers’ social networks interests profiling which we can deploy as a crucial component of our Fingerprinting Collateral Knowledge. This is used in selecting the special FoIs within an image or other media content that have to be selectively and collaterally protected.

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.

A framework towards a multi-modal fingerprinting scheme for multimedia assets

Fingerprinting is a well known approach for identifying multimedia data without having the original data present but instead what amounts to its essence or 'DNA'. Current approaches show insufficient deployment of various types of knowledge that could be brought to bear in providing a fingerprinting framework that remains effective, efficient and can accommodate both the whole as well as elemental protection at appropriate levels of abstraction to suit various Zones of Interest (ZoI) in an image or cross media artefact. The proposed framework aims to deliver selective composite fingerprinting that is powerfully aided by leveraging both multi-modal information as well as a rich spectrum of collateral context knowledge including both image-level collaterals and also the inevitably needed market intelligence knowledge such as customers' social networks interests profiling which we can deploy as a crucial component of our fingerprinting collateral knowledge.

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.

European CMBS pricing: bond, mortgage and real estate characteristics

The recent global economic crisis is often associated with the development and pricing of mortgage-backed securities (i.e. MBSs) and underlying products (i.e. sub-prime mortgages). This work uses a rich database of MBS issues and represents the first attempt to price commercial MBSs (i.e. CMBSs) in the European market. Our results are consistent with research carried out in the US market and we find that bond-, mortgage-, real estate-related and multinational characteristics show different degrees of significance in explaining European CMBS spreads at issuance. Multiple linear regression analysis using a databank of CMBSs issued between 1997 and 2007 indicates a strong relationship with bond-related factors, followed by real estate and mortgage market conditions. We also find that multinational factors are significant, with country of issuance, collateral location and access to more liquid markets all being important in explaining the cost of secured funding for real estate companies. As floater coupon tranches tend to be riskier and exhibit higher spreads, we also estimate a model using this sub-set of data and results hold, hence reinforcing our findings. Finally, we estimate our model for both tranches A and B and find that real estate factors become relatively more important for the riskier investment products.

Oxygen/Glucose deprivation induces a reduction in synaptic AMPA receptors on hippocampal CA3 neurons mediated by mGluR1 and adenosine A3 receptors

Hippocampal CA1 pyramidal neurons are highly sensitive to ischemic damage, whereas neighboring CA3 pyramidal neurons are less susceptible. It is proposed that switching of AMPA receptor (AMPAR) subunits on CA1 neurons during an in vitro model of ischemia, oxygen/glucose deprivation (OGD), leads to an enhanced permeability of AMPARs to Ca2+, resulting in delayed cell death. However, it is unclear whether the same mechanisms exist in CA3 neurons and whether this underlies the differential sensitivity to ischemia. Here, we investigated the consequences of OGD for AMPAR function in CA3 neurons using electrophysiological recordings in rat hippocampal slices. Following a 15 min OGD protocol, a substantial depression of AMPAR-mediated synaptic transmission was observed at CA3 associational/commissural and mossy fiber synapses but not CA1 Schaffer collateral synapses. The depression of synaptic transmission following OGD was prevented by metabotropic glutamate receptor 1 (mGluR1) or A3 receptor antagonists, indicating a role for both glutamate and adenosine release. Inhibition of PLC, PKC, or chelation of intracellular Ca2+ also prevented the depression of synaptic transmission. Inclusion of peptides to interrupt the interaction between GluA2 and PICK1 or dynamin and amphiphysin prevented the depression of transmission, suggesting a dynamin and PICK1-dependent internalization of AMPARs after OGD. We also show that a reduction in surface and total AMPAR protein levels after OGD was prevented by mGluR1 or A3 receptor antagonists, indicating that AMPARs are degraded following internalization. Thus, we describe a novel mechanism for the removal of AMPARs in CA3 pyramidal neurons following OGD that has the potential to reduce excitotoxicity and promote neuroprotection

Organisational semiotics methods to assess organisational readiness for internal use of social media

The paper presents organisational semiotics (OS) as an approach for identifying organisational readiness factors for internal use of social media within information intensive organisations (IIO). The paper examines OS methods, such as organisational morphology, containment analysis and collateral analysis to reveal factors of readiness within an organisation. These models also help to identify the essential patterns of activities needed for social media use within an organisation, which can provide a basis for future analysis. The findings confirmed many of the factors, previously identified in literature, while also revealing new factors using OS methods. The factors for organisational readiness for internal use of social media include resources, organisational climate, processes, motivational readiness, benefit and organisational control factors. Organisational control factors revealed are security/privacy, policies, communication procedures, accountability and fallback.

Climate change impacts on global food security

Climate change could potentially interrupt progress toward a world without hunger. A robust and coherent global pattern is discernible of the impacts of climate change on crop productivity that could have consequences for food availability. The stability of whole food systems may be at risk under climate change because of short-term variability in supply. However, the potential impact is less clear at regional scales, but it is likely that climate variability and change will exacerbate food insecurity in areas currently vulnerable to hunger and undernutrition. Likewise, it can be anticipated that food access and utilization will be affected indirectly via collateral effects on household and individual incomes, and food utilization could be impaired by loss of access to drinking water and damage to health. The evidence supports the need for considerable investment in adaptation and mitigation actions toward a “climate-smart food system” that is more resilient to climate change influences on food security.

Civilian casualties – do we really care? The failure of the revolution in military affairs of non-lethal weapons in the U.S., Russia and Israel

Causing civilian casualties during military operations has become a much politicised topic in international relations since the Second World War. Since the last decade of the 20th century, different scholars and political analysts have claimed that human life is valued more and more among the general international community. This argument has led many researchers to assume that democratic culture and traditions, modern ethical and moral issues have created a desire for a world without war or, at least, a demand that contemporary armed conflicts, if unavoidable, at least have to be far less lethal forcing the military to seek new technologies that can minimise civilian casualties and collateral damage. Non-Lethal Weapons (NLW) – weapons that are intended to minimise civilian casualties and collateral damage – are based on the technology that, during the 1990s, was expected to revolutionise the conduct of warfare making it significantly less deadly. The rapid rise of interest in NLW, ignited by the American military twenty five years ago, sparked off an entirely new military, as well as an academic, discourse concerning their potential contribution to military success on the 21st century battlefields. It seems, however, that except for this debate, very little has been done within the military forces themselves. This research suggests that the roots of this situation are much deeper than the simple professional misconduct of the military establishment, or the poor political behaviour of political leaders, who had sent them to fight. Following the story of NLW in the U.S., Russia and Israel this research focuses on the political and cultural aspects that have been supposed to force the military organisations of these countries to adopt new technologies and operational and organisational concepts regarding NLW in an attempt to minimise enemy civilian casualties during their military operations. This research finds that while American, Russian and Israeli national characters are, undoubtedly, products of the unique historical experience of each one of these nations, all of three pay very little regard to foreigners’ lives. Moreover, while it is generally argued that the international political pressure is a crucial factor that leads to the significant reduction of harmed civilians and destroyed civilian infrastructure, the findings of this research suggest that the American, Russian and Israeli governments are well prepared and politically equipped to fend off international criticism. As the analyses of the American, Russian and Israeli cases reveal, the political-military leaderships of these countries have very little external or domestic reasons to minimise enemy civilian casualties through fundamental-revolutionary change in their conduct of war. In other words, this research finds that employment of NLW have failed because the political leadership asks the militaries to reduce the enemy civilian casualties to a politically acceptable level, rather than to the technologically possible minimum; as in the socio-cultural-political context of each country, support for the former appears to be significantly higher than for the latter.