Memoirs of Napoleon Bonaparte,

Added engraved title-page: Constable's miscellany ... Vol. LVII-LIX.

How Vladimir Putin’s Divorce Story Was Constructed and Received, or When the President Divorced His Wife and Married the Country Instead

When the Russian president divorced his wife in 2013, social media crowds coined plenty of the Internet memes to interpret the news. Anastasia Denisova, Doctoral Researcher at CAMRI, examined the framing of the story in traditional and new media and came to the surprising findings.

The role of context in image annotation and recommendation

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).

El potencial creativo de la remediación en la literatura digital hispánica

El objetivo fundamental de esta tesis ha sido situar la literatura digital hispánica como un eslabón más en la cadena de la historia de la literatura hispánica. Las obras literarias creadas en el entorno digital remedian multitud de recursos, memes y figuras retóricas heredadas de la literatura analógica, del mismo modo que la literatura impresa hizo con la tradición anterior alojada en manuscritos, rollos e incluso la literatura oral. Los nuevos medios técnicos ofrecen posibilidades preconizadas por autores analógicos que ahora adquieren corporeidad. La literariedad de estas obras concebidas para el medio electrónico reside en la remediación de la literatura impresa y su labor de adaptación a los nuevos marcos electrónicos hasta llegar al hipermedia. Los moldes que contienen las obras literarias digitales revisten a estas creaciones de propiedades que las caracterizan como tales obras de literatura digital y que a lo largo de esta tesis se han tratado de apuntar. Asimismo, partiendo de la indagación sobre estos textos, la lectura y selección de los mismos, se ofrece una propuesta de canon abierto de obras de literatura digital hispánica representativas de dichas propiedades específicas. Esta investigación se cierra con un fruto tangible, el repositorio de literatura digital hispánica Ciberia, concebido y desarrollado por varios miembros del grupo de investigación de la Facultad de Filología de la UCM LEETHI Literaturas Españolas y Europeas del Texto al Hipertexto. En el último capítulo de esta tesis se presenta y describe un proyecto que ha cumplido el objetivo de ofrecer en enero de 2015 un corpus de obras de literatura digital en español con una serie de metadatos exportables a través del consorcio internacional CELL Consortium on Electronic Literature. De este modo las creaciones de literatura electrónica en español y su análisis crítico adquieren una mayor visibilidad en el ámbito internacional...

Impacto de la innovación tecnológica en las estrategias de YouTube y Google

El concepto de administración ha venido cambiando constantemente a través del tiempo y de las ideologías de diferentes autores que han marcado dicho concepto, en donde se ha podido establecer de mejor forma el significado que ésta ciencia tiene con la innovación y la estrategia. El objetivo de este trabajo es mostrar los fundamentos teóricos de la administración, la innovación y la estrategia, para así mostrar los alcances e impactos que estos tienen en las organizaciones visitadas durante la misión empresarial al Silicon Valley, principalmente a YouTube y Google. Para cumplir con dicho objetivo se realizaron encuestas a diferentes trabajadores pertenecientes a las organizaciones, obteniendo así, información valiosa para el estudio realizado. Con la información obtenida de la entrevistas, se logró recopilar información pertinente que permitió concluir que la innovación es un pilar fundamental para el buen funcionamiento de las empresas pertenecientes al Silicon Valley y así mismo ser base indispensable para la creación y formulación de las principales estrategias de las empresas.