Exergy applied to lunar base design

This paper explores design considerations for energy efficiency in lunar habitats. It considers several previous lunar energy studies in regards to energy types and stages of energy requirements. If we are to obtain true sustainability in energy processes, we will need to design according to the principles “exergy”, considering both the first and the second laws of thermodynamics in a holistic and thorough evaluation of energy capture, transformation, and use. Such an evaluation will ascertain the source of energy, its processing and energy potential stages, as well as the task required. Traditional designs of facility thermal systems are frequently extremely wasteful: they dramatically increase both first costs and operating costs because they treat heating and cooling systems as separate entities, instead of an integrated energy system. Energy processes, the state of energy required to do a particular task, the embodied energy to complete or manufacture an object, and the wasted energy released are all important to conservation and obtaining an efficient and effective use (quality) of energy. If the regulation of energy processes is a concern in terrestrial habitation, it should be even more so for extra-terrestrial habitation where there is little margin for waste of any sort.

Sunlight exposure caused yellowing and increased mineral content in wool

This study determines how levels of various trace metals in wool and the colour of the fibre change as a result of sunlight exposure and treatment with chelating compounds during wool growth. Twenty-four yearling Merino sheep were clipped on the shoulders and rumps and fitted with sheep coats modified with transparent patches. Patches over the shoulder wool (one per sheep) were either polyethylene (PE) that transmits ultraviolet light or polyvinyl chloride (PVC) that excludes ultraviolet light. The rump wool on each sheep was treated either with a copper chelator treatment (kojic acid or methyl gentisate in aqueous alcohol) or aqueous alcohol only. For 12 of the sheep the rumps were exposed to sunlight through PE patches while rump wool on the other sheep was covered by the sheep coat. Wool was harvested after 11 weeks’ growth with yellowness (Y-Z) and individual mineral contents measured using the same clean wool sample. Sunlight exposure through PE patches caused a mean increase in Y-Z to 9.1 (shoulder) or 9.5–10.1 (rump) from a base level of 7.1–7.2 (shoulder) or 7.0–7.6 (rump) in wool protected by the sheep coat. In contrast, there was no significant change in Y-Z for the PVC patch (shoulder). Therefore, it appears that ultraviolet light damage caused the increased Y-Z. Most of the trace metals analysed increased in the shoulder wool exposed to sunlight but the paired differences for PVC were lower than PE. It appears that changes in fibre caused by sunlight exposure (especially ultraviolet light) facilitate adsorption of minerals from the environment, including the animal’s own suint. Application of the chelating compounds to the rump wool caused pronounced yellowing of the wool with Y-Z increase being most pronounced for kojic acid. Copper levels in the wool were reduced by kojic acid and methyl gentisate while calcium levels were increased by kojic acid and reduced by methyl gentisate. It is not clear from these findings whether minerals and copper in particular contribute to yellowing of wool. However, the different effects of sunlight and chelation on mineral contents in wool shown may well relate to alternative mechanisms of discoloration (i.e. photoyellowing versus bacterial).

Automatic genre identification for content-based video categorization

This paper presents a set of computational features originating from our study of editing effects, motion, and color used in videos, for the task of automatic video categorization. These features besides representing human understanding of typical attributes of different video genres, are also inspired by the techniques and rules used by many directors to endow specific characteristics to a genre-program which lead to certain emotional impact on viewers. We propose new features whilst also employing traditionally used ones for classification. This research, goes beyond the existing work with a systematic analysis of trends exhibited by each of our features in genres such as cartoons, commercials, music, news, and sports, and it enables an understanding of the similarities, dissimilarities, and also likely confusion between genres. Classification results from our experiments on several hours of video establish the usefulness of this feature set. We also explore the issue of video clip duration required to achieve reliable genre identification and demonstrate its impact on classification accuracy.

Between context-aware media capture and multimedia content analysis - where do we find the promised land?

Various issues related to the multimedia information retrieval and media access are discussed. The feasible solutions for automatic signal-based analysis of media content are analyzed. The extent of user involvement in the content creation process is emphasized. The applications driving the creation and usage of context and metadata are also elaborated.

Media independent knowledge indexing and retrieval

Composing a multimedia presentation may require creation or generation of suitable images and video segments, as well as animation, sound, or special effects. Obtaining images or video sequences can be prohibitively expensive when costs of travel to location, equipment, staff, etc, are considered. Those problems can be alleviated with the use of pictorial and video digital libraries, such libraries require methods for comprehensive indexing and annotation of stored items and efficient retrieval tools.

We propose a system based on user oriented perceptions as they influence query formation in image and video retrieval. We present a method based on user dependent conceptual structures for creating and maintaining indexes to images and video sequences.

On the extraction of thematic and dramatic functions of content in educational videos

In this paper, we propose novel computational models for the extraction of high level expressive constructs related to, namely thematic and dramatic functions of the content shown in educational and training videos. Drawing on the existing knowledge of film theory, and media production rules and conventions used by the filmmakers. we hypothesize key aesthetic elements contributing to convey these functions of the content. Computational models to extract them are then formulated and their performance evaluated on a set of ten educational and training videos is presented.

Performing the technical image

The recent public multi-screen performance of formalist experimental animation by film artists such as Guy Sherwin, Bruce McLure and Greg Pope respond to the immediacy and speed of new digital technologies, the rise of Vilem Flusser’s ‘technical image’ and the consequent disappearance of reflective space identified by Prensky, Kroker, Virilio and Postman. Flusser’s ‘technical images’, benefiting from the digital’s painterly hyper-malleability structure and content, signifier and signified, so much the subject of Peter Gidal’s arguments in support of his concept of ‘materialist film’ in the 1970’s.  In the digital those formal editing strategies used to create the ‘technical image’ within analogue image construction that traditionally took place in the artist’s studio within the camers and optical printer are now executed inside the computer, having migrated into the post-production process.  Within the work of these artist’s recent multi-screen presentations these manipulations are now-elusively experienced in live ephemeral performance, re-forming and laying bare those processes that have been rendered invisible in digital technology.  The significance of this work partly lies in its ability to communicate historical information a-historically. Guy Sherwin and Lynn Loo’s method in their play with 16mm film flashes and after-images and Sherwin’s mirror performance further reproduces Goethe’s method from his Theory of Colours (1840). Greg Pope’s scratch performances re-enact the operation of Konrad Zuse’s 1930’s computing machine. Affinities are drawn between Bruce McLure’s immersive overpowering sonic and flicker performances with Edwin Land’s 1960’s experiments on colour constancy on which Land’s Retinex Theory of colour is based.

"You Tube and I Find" - personalizing multimedia content access

Recent growth in broadband access and proliferation of small personal devices that capture images and videos has led to explosive growth of multimedia content available everywhereVfrom personal disks to the Web. While digital media capture and upload has become nearly universal with newer device technology, there is still a need for better tools and technologies to search large collections of multimedia data and to find and deliver the right content to a user according to her current needs and preferences. A renewed focus on the subjective dimension in the multimedia lifecycle, fromcreation, distribution, to delivery and consumption, is required to address this need beyond what is feasible today. Integration of the subjective aspects of the media itselfVits affective, perceptual, and physiological potential (both intended and achieved), together with those of the users themselves will allow for personalizing the content access, beyond today’s facility. This integration, transforming the traditional multimedia information retrieval (MIR) indexes to more effectively answer specific user needs, will allow a richer degree of personalization predicated on user intention and mode of interaction, relationship to the producer, content of the media, and their history and lifestyle. In this paper, we identify the challenges in achieving this integration, current approaches to interpreting content creation processes, to user modelling and profiling, and to personalized content selection, and we detail future directions. The structure of the paper is as follows: In Section I, we introduce the problem and present some definitions. In Section II, we present a review of the aspects of personalized content and current approaches for the same. Section III discusses the problem of obtaining metadata that is required for personalized media creation and present eMediate as a case study of an integrated media capture environment. Section IV presents the MAGIC system as a case study of capturing effective descriptive data and putting users first in distributed learning delivery. The aspects of modelling the user are presented as a case study in using user’s personality as a way to personalize summaries in Section V. Finally, Section VI concludes the paper with a discussion on the emerging challenges and the open problems.

Competition and television news in India : a content analysis

In India, not unlike some other developing countries, ruling parties have used public service broadcasters to propagate their virtues, adversely affecting the broadcasters' image as a credible news media. The Indian public service broadcaster. Doordarshan,
which has been besieged by increased competition and government-imposed demandfor selfsufficiency in recent years, continues to struggle to shed its image as a government mouthpiece despite being run by an independent corporation. This article
presents a content analysis of news programs broadcast by Doordarshan and a foreign television network. Star News. The study examines the differences and similarities between Doordarshan and Star TV's prime time news programs broadcast at the turn ofthe century, almost a decade after the advent ofcommercial television in India broke the public service broadcaster's monopoly in the country.

Making the most of the self-quotient image in face recognition

The self-quotient image is a biologically inspired representation which has been proposed as an illumination invariant feature for automatic face recognition. Owing to the lack of strong domain specific assumptions underlying this representation, it can be readily extracted from raw images irrespective of the persons's pose, facial expression etc. What makes the self-quotient image additionally attractive is that it can be computed quickly and in a closed form using simple low-level image operations. However, it is generally accepted that the self-quotient is insufficiently robust to large illumination changes which is why it is mainly used in applications in which low precision is an acceptable compromise for high recall (e.g. retrieval systems). Yet, in this paper we demonstrate that the performance of this representation in challenging illuminations has been greatly underestimated. We show that its error rate can be reduced by over an order of magnitude, without any changes to the representation itself. Rather, we focus on the manner in which the dissimilarity between two self-quotient images is computed. By modelling the dominant sources of noise affecting the representation, we propose and evaluate a series of different dissimilarity measures, the best of which reduces the initial error rate of 63.0% down to only 5.7% on the notoriously challenging YaleB data set.

Automatic face recognition for film character retrieval in feature-length films

The objective of this work is to recognize all the frontal faces of a character in the closed world of a movie or situation comedy, given a small number of query faces. This is challenging because faces in a feature-length film are relatively uncontrolled with a wide variability of scale, pose, illumination, and expressions, and also may be partially occluded. We develop a recognition method based on a cascade of processing steps that normalize for the effects of the changing imaging environment. In particular there are three areas of novelty: (i) we suppress the background surrounding the face, enabling the maximum area of the face to be retained for recognition rather than a subset; (ii) we include a pose refinement step to optimize the registration between the test image and face exemplar; and (iii) we use robust distance to a sub-space to allow for partial occlusion and expression change. The method is applied and evaluated on several feature length films. It is demonstrated that high recall rates (over 92%) can be achieved whilst maintaining good precision (over 93%).

Reading ancient coins: automatically identifying denarii using obverse legend seeded retrieval

The aim of this paper is to automatically identify a Roman Imperial denarius from a single query photograph of its obverse and reverse. Such functionality has the potential to contribute greatly to various national schemes which encourage laymen to report their finds to local museums. Our work introduces a series of novelties: (i) this is the first paper which describes a method for extracting the legend of an ancient coin from a photograph; (ii) we are also the first to suggest the idea and propose a method for identifying a coin using a series of carefully engineered retrievals, each harnessed for further information using visual or meta-data processing; (iii) we show how in addition to a unique standard reference number for a query coin, the proposed system can be used to extract salient coin information (issuing authority, obverse and reverse descriptions, mint date) and retrieve images of other coins of the same type.

Multi-label classification with clustering for image and text categorization

This paper explores effective multi-label classification methods for multi-semantic image and text categorization. We perform an experimental study of clustering based multi-label classification (CBMLC) for the target problem. Experimental evaluation is conducted for identifying the impact of different clustering algorithms and base classifiers on the predictive performance and efficiency of CBMLC. In the experimental setting, three widely used clustering algorithms and six popular multi-label classification algorithms are used and evaluated on multi-label image and text datasets. A multi-label classification evaluation metrics, micro F1-measure, is used for presenting predictive performances of the classifications. Experimental evaluation results reveal that clustering based multi-label learning algorithms are more effective compared to their non-clustering counterparts.

Bayesian nonparametric multilevel clustering with group-level contexts

We present a Bayesian nonparametric framework for multilevel clustering which utilizes group- level context information to simultaneously discover low-dimensional structures of the group contents and partitions groups into clusters. Using the Dirichlet process as the building block, our model constructs a product base-measure with a nested structure to accommodate content and context observations at multiple levels. The proposed model possesses properties that link the nested Dinchiet processes (nDP) and the Dirichlet process mixture models (DPM) in an interesting way: integrating out all contents results in the DPM over contexts, whereas integrating out group-specific contexts results in the nDP mixture over content variables. We provide a Polyaurn view of the model and an efficient collapsed Gibbs inference procedure. Extensive experiments on real-world datasets demonstrate the advantage of utilizing context information via our model in both text and image domains.