Thesis (Ph. D.)--Institute of Paper Science and Technology, Georgia Institute of Technology, 1988. / Includes bibliography.

Thesis (Ph. D.)--Institute of Paper Science and Technology, Georgia Institute of Technology, 2001. / Includes bibliography.

Thesis (Ph. D.)--Institute of Paper Science and Technology, Georgia Institute of Technology, 2003. / Includes bibliography.

Thesis (Ph. D.)--Institute of Paper Science and Technology, Georgia Institute of Technology, 2005. / Includes bibliography.

Thesis (Ph. D.)--University of Illinois at Urbana-Champaign, 1996. / Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 217-229).

Thesis (Ph. D.)--Oregon State University, 2007. / Printout. Includes bibliographical references (leaves 153-161). Also available on the World Wide Web.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006. / Source: Dissertation Abstracts International, Volume: 67-11, Section: A, page: 4023. Adviser: Christine Jenkins. Includes bibliographical references (leaves 207-229) Available on microfilm from Pro Quest Information and Learning.

'Wearables' and 'self-quantifying technologies' are becoming ever more popular and normalised in society as a means of 'knowing' the self. How are these technologies implicated in this endeavour? Using insights from a four year multi-sited ethnography of the 'Quantified Self', I explore how the self is 'done' in the context of using technologies that purport to quantify the self in some way. Drawing on Science and Technology Studies (STS) sensibilities, I conduct a four- pronged investigation into 'self-making' by drawing upon, and expanding, existing theories of agency and performativity, number, data-visualisation, and enactment. I find that self-quantifying technologies are productive in the doing of the self and are implicated in the process of making boundaries around that which comes to be known as the 'self' in a particular moment. The numbers and visualisations that result from practices of self-quantification enable a new way of 'seeing' the self, and provide a way of communicating this self with others. The self is thus not a pre-existing entity that simply requires these technologies as a means to 'know' it. Rather, the self is constantly being done with these technologies and within the surrounding practices of self-quantification. In order to highlight the different parts of this process, I proffer the term 'entractment'. This term explains how these different elements come together to culminate in the production of a momentarily constant self in a particular context. It is a way of simultaneously encapsulating the processes of intra-action, extra-action and enactment with/in a community. In sum, it captures the conclusion that, in the context of self-quantification, we must understand the self as a collective enactment, achieved, at least in part, through the use of self-quantifying technologies that produce numerical data which facilitate visualisations that are imperative to the doing of the self.

With the rise of smart phones, lifelogging devices (e.g. Google Glass) and popularity of image sharing websites (e.g. Flickr), users are capturing and sharing every aspect of their life online producing a wealth of visual content. Of these uploaded images, the majority are poorly annotated or exist in complete semantic isolation making the process of building retrieval systems difficult as one must firstly understand the meaning of an image in order to retrieve it. To alleviate this problem, many image sharing websites offer manual annotation tools which allow the user to “tag” their photos, however, these techniques are laborious and as a result have been poorly adopted; Sigurbjörnsson and van Zwol (2008) showed that 64% of images uploaded to Flickr are annotated with < 4 tags. Due to this, an entire body of research has focused on the automatic annotation of images (Hanbury, 2008; Smeulders et al., 2000; Zhang et al., 2012a) where one attempts to bridge the semantic gap between an image’s appearance and meaning e.g. the objects present. Despite two decades of research the semantic gap still largely exists and as a result automatic annotation models often offer unsatisfactory performance for industrial implementation. Further, these techniques can only annotate what they see, thus ignoring the “bigger picture” surrounding an image (e.g. its location, the event, the people present etc). Much work has therefore focused on building photo tag recommendation (PTR) methods which aid the user in the annotation process by suggesting tags related to those already present. These works have mainly focused on computing relationships between tags based on historical images e.g. that NY and timessquare co-exist in many images and are therefore highly correlated. However, tags are inherently noisy, sparse and ill-defined often resulting in poor PTR accuracy e.g. does NY refer to New York or New Year? This thesis proposes the exploitation of an image’s context which, unlike textual evidences, is always present, in order to alleviate this ambiguity in the tag recommendation process. Specifically we exploit the “what, who, where, when and how” of the image capture process in order to complement textual evidences in various photo tag recommendation and retrieval scenarios. In part II, we combine text, content-based (e.g. # of faces present) and contextual (e.g. day-of-the-week taken) signals for tag recommendation purposes, achieving up to a 75% improvement to precision@5 in comparison to a text-only TF-IDF baseline. We then consider external knowledge sources (i.e. Wikipedia & Twitter) as an alternative to (slower moving) Flickr in order to build recommendation models on, showing that similar accuracy could be achieved on these faster moving, yet entirely textual, datasets. In part II, we also highlight the merits of diversifying tag recommendation lists before discussing at length various problems with existing automatic image annotation and photo tag recommendation evaluation collections. In part III, we propose three new image retrieval scenarios, namely “visual event summarisation”, “image popularity prediction” and “lifelog summarisation”. In the first scenario, we attempt to produce a rank of relevant and diverse images for various news events by (i) removing irrelevant images such memes and visual duplicates (ii) before semantically clustering images based on the tweets in which they were originally posted. Using this approach, we were able to achieve over 50% precision for images in the top 5 ranks. In the second retrieval scenario, we show that by combining contextual and content-based features from images, we are able to predict if it will become “popular” (or not) with 74% accuracy, using an SVM classifier. Finally, in chapter 9 we employ blur detection and perceptual-hash clustering in order to remove noisy images from lifelogs, before combining visual and geo-temporal signals in order to capture a user’s “key moments” within their day. We believe that the results of this thesis show an important step towards building effective image retrieval models when there lacks sufficient textual content (i.e. a cold start).

Master of Science / Food Science Institute / Fadi M. Aramouni / Known for its antioxidant activity and other health benefits, tea is the second most consumed beverage in the world, following water. With up to 6% (w/w) of phenolic compounds, sorghum has the highest content compared to other cereals. The objective of this research was to develop and analyze a sorghum tisane using two different red sorghum hybrids. Tisanes are herbal infusions composed of anything other than the leaves from the Camellia sinensis plant. The sorghum kernel was cracked using an Allis experimental roll stand equipped with a Le Page cut mill. Samples were sifted at 180 RMP- 4” diameter throw for 2 min. The two hybrids were roasted in a Whirlpool convection oven at 212°C for 13 or 15 min. Three fruit and herbal combinations were tested to increase consumer acceptability. Samples was brewed for 4 min in 8fl.oz at 100°C. Oxygen Radical Absorbance Capacity(ORAC) and Total Phenolic Content (TPC) were used to analyze the beverage along with chemical, physical and sensory tests. TPC results showed sorghum tisane to have 38.5±6.91 mg gallic acid equivalence/ 8fl oz. and 433.7 ±7.11 μM Trolox equivalence/ 236.6 mL (8 fl.oz, 1 cup) for an ORAC value. Fruit and herbal combinations were also added to the sorghum to increase overall consumer acceptability. These combinations included strawberry mixed with lemon, blood orange mixed with pear, and pineapple mixed with orange. A consumer acceptance test was performed on the three different sorghum tisanes using a 9 point hedonic scale. Results showed an overall acceptability at 6.63±1.54 for the sorghum tisane infused with a strawberry and lemon combination while the sorghum tisane with pineapple orange scored 6.72 . These results demonstrate the potential for introducing a consumer acceptable sorghum tisane into the market.