Who are Like-minded: Mining User Interest Similarity in Online Social Networks

In this paper, we mine and learn to predict how similar a pair of users' interests towards videos are, based on demographic (age, gender and location) and social (friendship, interaction and group membership) information of these users. We use the video access patterns of active users as ground truth (a form of benchmark). We adopt tag-based user profiling to establish this ground truth, and justify why it is used instead of video-based methods, or many latent topic models such as LDA and Collaborative Filtering approaches. We then show the effectiveness of the different demographic and social features, and their combinations and derivatives, in predicting user interest similarity, based on different machine-learning methods for combining multiple features. We propose a hybrid tree-encoded linear model for combining the features, and show that it out-performs other linear and treebased models. Our methods can be used to predict user interest similarity when the ground-truth is not available, e.g. for new users, or inactive users whose interests may have changed from old access data, and is useful for video recommendation. Our study is based on a rich dataset from Tencent, a popular service provider of social networks, video services, and various other services in China

Identifying Professional Photographers Through Image Quality and Aesthetics in Flickr

In our generation, there is an undoubted rise in the use of social media and specifically photo and video sharing platforms. These sites have proved their ability to yield rich data sets through the users' interaction which can be used to perform a data-driven evaluation of capabilities. Nevertheless, this study reveals the lack of suitable data sets in photo and video sharing platforms and evaluation processes across them. In this way, our first contribution is the creation of one of the largest labelled data sets in Flickr with the multimodal data which has been open sourced as part of this contribution. Predicated on these data, we explored machine learning models and concluded that it is feasible to properly predict whether a user is a professional photographer or not based on self-reported occupation labels and several feature representations out of the user, photo and crowdsourced sets. We also examined the relationship between the aesthetics and technical quality of a picture and the social activity of that picture. Finally, we depicted which characteristics differentiate professional photographers from non-professionals. As far as we know, the results presented in this work represent an important novelty for the users' expertise identification which researchers from various domains can use for different applications

Describing Images by Semantic Modeling using Attributes and Tags

This dissertation addresses the problem of describing images using visual attributes and textual tags, a fundamental task that narrows down the semantic gap between the visual reasoning of humans and machines. Automatic image annotation assigns relevant textual tags to the images. In this dissertation, we propose a query-specific formulation based on Weighted Multi-view Non-negative Matrix Factorization to perform automatic image annotation. Our proposed technique seamlessly adapt to the changes in training data, naturally solves the problem of feature fusion and handles the challenge of the rare tags. Unlike tags, attributes are category-agnostic, hence their combination models an exponential number of semantic labels. Motivated by the fact that most attributes describe local properties, we propose exploiting localization cues, through semantic parsing of human face and body to improve person-related attribute prediction. We also demonstrate that image-level attribute labels can be effectively used as weak supervision for the task of semantic segmentation. Next, we analyze the Selfie images by utilizing tags and attributes. We collect the first large-scale Selfie dataset and annotate it with different attributes covering characteristics such as gender, age, race, facial gestures, and hairstyle. We then study the popularity and sentiments of the selfies given an estimated appearance of various semantic concepts. In brief, we automatically infer what makes a good selfie. Despite its extensive usage, the deep learning literature falls short in understanding the characteristics and behavior of the Batch Normalization. We conclude this dissertation by providing a fresh view, in light of information geometry and Fisher kernels to why the batch normalization works. We propose Mixture Normalization that disentangles modes of variation in the underlying distribution of the layer outputs and confirm that it effectively accelerates training of different batch-normalized architectures including Inception-V3, Densely Connected Networks, and Deep Convolutional Generative Adversarial Networks while achieving better generalization error

Computational Aesthetics for Fashion

The online fashion industry is growing fast and with it, the need for advanced systems able to automatically solve different tasks in an accurate way. With the rapid advance of digital technologies, Deep Learning has played an important role in Computational Aesthetics, an interdisciplinary area that tries to bridge fine art, design, and computer science. Specifically, Computational Aesthetics aims to automatize human aesthetic judgments with computational methods. In this thesis, we focus on three applications of computer vision in fashion, and we discuss how Computational Aesthetics helps solve them accurately

From community to commerce? Analytics, audience 'engagement' and how local newspapers are renegotiating news values in the age of pageview-driven journalism.

Local newspapers were once bastions of their communities – reporting everything from church fetes to factory closures and speaking truth to power to councillors and business leaders. But growing competition from online media, combined with advertising downturns, technological challenges and repeated consolidations in ownership, has seen a steady drift away from conventional community news values towards a hard-nosed approach to story-telling, as ever-dwindling resources have forced editors to focus on audience-driven content. A recent manifestation of this market-driven local journalism has been the rise of analytics-focused newsgathering, in which articles receiving the most web ‘clicks’, online comments and Facebook ‘likes’ determine decisions about how future stories are selected and framed. Drawing on in-depth interviews with five local web editors from around the UK, this chapter explores how journalists are increasingly ordered to embellish and follow up tales that engage and enrage audiences the most – turning the newsgathering process on its head, as commercial ‘clickability’, not public interest or normative news values, determine what is covered (and how)

INSTAGRAM: HOW DO STUDENTS VIEW ON IT IN SPEAKING CLASSROOM

The emergence of the 4.0 era requires the world of education to adapt to technology. Practicing and learning English can take the advantage of the sophisticated technology, especially applications that can be downloaded from students' smart phone. Students acknowledge that English learning done in the classroom is easy for them to forget because it is rarely used in everyday life. Practices done in the classroom do not have enough time for all students to speak English, and students are less motivation to speak English outside the classroom. Integrating Instagram into the process of teaching English speaking is believed could motivate students to speak and increase their speaking ability. Various features on Instagram can help students in doing assignments. Tasks may be packaged attractively within the variety of videos based on a certain theme and uploaded to Instagram. This study aimed to determine students' perceptions related to the use of social media Instagram in learning English speaking. This descriptive study used forty-four students Communication Science in academic year 2019/2020 who took Bahasa Inggris Keahlian. To determine students' perceptions, researchers used questionnaire adopted from Dornyei, 2011. The results of the study showed a positive or good response on students' perceptions towards the use of Instagram in learning English Speaking. Furthermore, Instagram can be used as another medium in teaching speaking. This is strengthened by the increase in self-confidence, learning motivation, and student interest in speaking in English. &nbsp

Image Understanding by Socializing the Semantic Gap

Several technological developments like the Internet, mobile devices and Social Networks have spurred the sharing of images in unprecedented volumes, making tagging and commenting a common habit. Despite the recent progress in image analysis, the problem of Semantic Gap still hinders machines in fully understand the rich semantic of a shared photo. In this book, we tackle this problem by exploiting social network contributions. A comprehensive treatise of three linked problems on image annotation is presented, with a novel experimental protocol used to test eleven state-of-the-art methods. Three novel approaches to annotate, under stand the sentiment and predict the popularity of an image are presented. We conclude with the many challenges and opportunities ahead for the multimedia community

Feasibility of Webcam Implementation in Acadia National Park

Acadia National Park, one of the smallest national parks geographically, is visited by millions of people each summer. In response to the significantly increased visitor volume, the National Park Service (NPS) and the Friends of Acadia, a non-profit conservation organization, have been experimenting with several methods in an attempt to reduce traffic congestion. One promising approach is to establish a webcam monitoring system to allow for real-time traffic updates in the most visited areas in the park. To examine this approach, our team implemented a proof-of-concept webcam system to monitor visitor traffic in some of the park’s most congested areas. This report includes an evaluation of the performance of the associated webcam network