Automatic Understanding of Image and Video Advertisements

There is more to images than their objective physical content: for example, advertisements are created to persuade a viewer to take a certain action. We propose the novel problem of automatic advertisement understanding. To enable research on this problem, we create two datasets: an image dataset of 64,832 image ads, and a video dataset of 3,477 ads. Our data contains rich annotations encompassing the topic and sentiment of the ads, questions and answers describing what actions the viewer is prompted to take and the reasoning that the ad presents to persuade the viewer ("What should I do according to this ad, and why should I do it?"), and symbolic references ads make (e.g. a dove symbolizes peace). We also analyze the most common persuasive strategies ads use, and the capabilities that computer vision systems should have to understand these strategies. We present baseline classification results for several prediction tasks, including automatically answering questions about the messages of the ads.Comment: To appear in CVPR 2017; data available on http://cs.pitt.edu/~kovashka/ad

A Worker Dialogue: Improving Health Safety and Security at DOE

During the summer of 2010, the Department of Energy Office of Health, Safety and Security (HSS) partnered with the National Academy of Public Administration to host an online dialogue to solicit ideas from front line union workers at DOE sites on how to improve worker safety across the DOE complex. Based on the results of the Dialogue, an expert Panel of the National Academy identified several themes that emerged from workers' suggestions and offered recommendations for HSS in following up on the issues raised as well as continuing to build its capacity for employee engagement.Key FindingsBased specifically on the Dialogue results, the Panel recommended HSS further investigate several issues and claims discussed by workers as well as assess the current state of reporting processes in DOE to determine if changes are necessary. In addition, the Dialogue revealed many knowledge gaps among workers regarding the substance of worker health and safety regulations in DOE, which should prompt HSS to consider expanding efforts to educate workers about these regulations.The Panel also issued several recommendations for HSS to build its capacity to engage union workers. These recommendations included considering alternate channels of reaching front-line workers and continuing engagement with workers by articulating and undertaking concrete next steps with the input received

From Lab Bench to Innovation: Critical Challenges to Nascent Academic Entrepreneurs

University research laboratories are important sources of the inventions and discoveries that become significant innovations with broad economic and societal impact. Invention alone is not innovation; innovation is the long, hard work of taking new technologies and bringing them to commercialization.There are many pathways for the dissemination of new knowledge that arises from basic research at universities, ranging from traditional methods such as publication and training students to licensing technology to established firms or new ventures.One way to transform new knowledge into valuable innovations is for university researchers to undertake the creation of new firms based on their discoveries through academic entrepreneurship. The problem is that university scientists and inventors with a discovery made at a laboratory bench face challenges beyond those experienced by traditional high-technology venture founders: they must finish creating the technology before they can begin using it.Academics typically start with inventions so immature that their commercial success cannot be predicted Academic entrepreneurship is an emerging and developing phenomenon, and there is a growing body of literature about new ventures based on university academic. However, limited research has been directed toward nascent academic entrepreneurs (NAEs) to understand the key challenges of bringing innovations to market. The majority of this work has focused on the institutional experience rather than the academic entrepreneurs and their individual experiences . Within the broader fields of entrepreneurship and innovation, it has been argued that high-potential startups such as academic ventures should receive particular attention from scholarsThe following research addressed this gap.Nascent academic entrepreneurship involves more than transforming an invention into a commercialized innovation. It is about the genesis of ideas and the emergence of opportunities, the birth of new organizations, their evolution into new companies, and the transformation of scientists into leaders. It also is about providing the foundation for future innovation by others. Though nascent academic entrepreneurship is increasing in frequency, it is not well understood. The dissertation examines this important topic

Thumbs up? Sentiment Classification using Machine Learning Techniques

We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classification, and support vector machines) do not perform as well on sentiment classification as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classification problem more challenging.Comment: To appear in EMNLP-200

Im2Flow: Motion Hallucination from Static Images for Action Recognition

Existing methods to recognize actions in static images take the images at their face value, learning the appearances---objects, scenes, and body poses---that distinguish each action class. However, such models are deprived of the rich dynamic structure and motions that also define human activity. We propose an approach that hallucinates the unobserved future motion implied by a single snapshot to help static-image action recognition. The key idea is to learn a prior over short-term dynamics from thousands of unlabeled videos, infer the anticipated optical flow on novel static images, and then train discriminative models that exploit both streams of information. Our main contributions are twofold. First, we devise an encoder-decoder convolutional neural network and a novel optical flow encoding that can translate a static image into an accurate flow map. Second, we show the power of hallucinated flow for recognition, successfully transferring the learned motion into a standard two-stream network for activity recognition. On seven datasets, we demonstrate the power of the approach. It not only achieves state-of-the-art accuracy for dense optical flow prediction, but also consistently enhances recognition of actions and dynamic scenes.Comment: Published in CVPR 2018, project page: http://vision.cs.utexas.edu/projects/im2flow

Theory borrowing in IT-rich contexts : lessons from IS strategy research

While indigenous theorizing in information systems has clear merits, theory borrowing will not, and should not, be eschewed given its appeal and usefulness. In this article, we aim at increasing our understanding of modifying of borrowed theories in IT-rich contexts. We present a framework in which we discuss how two recontextualization approaches of specification and distinction help with increasing the IT-richness of borrowed constructs and relationships. In doing so, we use several illustrative examples from information systems strategy. The framework can be used by researchers as a tool to explore the multitude of ways in which a theory from another discipline can yield the understanding of IT phenomena

Twelve Theses on Reactive Rules for the Web

Reactivity, the ability to detect and react to events, is an essential functionality in many information systems. In particular, Web systems such as online marketplaces, adaptive (e.g., recommender) systems, and Web services, react to events such as Web page updates or data posted to a server. This article investigates issues of relevance in designing high-level programming languages dedicated to reactivity on the Web. It presents twelve theses on features desirable for a language of reactive rules tuned to programming Web and Semantic Web applications