Penn State College of Information Sciences and Technology

About: Penn State College of Information Sciences and Technology is a based out in . It is known for research contribution in the topics: Information privacy & Artificial neural network. The organization has 1034 authors who have published 2229 publications receiving 66183 citations. The organization is also known as: College of IST & Pennsylvania State University College of Information Sciences and Technology.

Face recognition: a convolutional neural-network approach

Abstract: We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer.

Twitter power: Tweets as electronic word of mouth

Abstract: In this paper we report research results investigating microblogging as a form of electronic word-of-mouth for sharing consumer opinions concerning brands. We analyzed more than 150,000 microblog postings containing branding comments, sentiments, and opinions. We investigated the overall structure of these microblog postings, the types of expressions, and the movement in positive or negative sentiment. We compared automated methods of classifying sentiment in these microblogs with manual coding. Using a case study approach, we analyzed the range, frequency, timing, and content of tweets in a corporate account. Our research findings show that 19p of microblogs contain mention of a brand. Of the branding microblogs, nearly 20p contained some expression of brand sentiments. Of these, more than 50p were positive and 33p were critical of the company or product. Our comparison of automated and manual coding showed no significant differences between the two approaches. In analyzing microblogs for structure and composition, the linguistic structure of tweets approximate the linguistic patterns of natural language expressions. We find that microblogging is an online tool for customer word of mouth communications and discuss the implications for corporations using microblogging as part of their overall marketing strategy. © 2009 Wiley Periodicals, Inc.

Information privacy research: an interdisciplinary review

Abstract: To date, many important threads of information privacy research have developed, but these threads have not been woven together into a cohesive fabric. This paper provides an interdisciplinary review of privacy-related research in order to enable a more cohesive treatment. With a sample of 320 privacy articles and 128 books and book sections, we classify previous literature in two ways: (1) using an ethics-based nomenclature of normative, purely descriptive, and empirically descriptive, and (2) based on their level of analysis: individual, group, organizational, and societal. Based upon our analyses via these two classification approaches, we identify three major areas in which previous research contributions reside: the conceptualization of information privacy, the relationship between information privacy and other constructs, and the contextual nature of these relationships. As we consider these major areas, we draw three overarching conclusions. First, there are many theoretical developments in the body of normative and purely descriptive studies that have not been addressed in empirical research on privacy. Rigorous studies that either trace processes associated with, or test implied assertions from, these value-laden arguments could add great value. Second, some of the levels of analysis have received less attention in certain contexts than have others in the research to date. Future empirical studies-both positivist and interpretive--could profitably be targeted to these under-researched levels of analysis. Third, positivist empirical studies will add the greatest value if they focus on antecedents to privacy concerns and on actual outcomes. In that light, we recommend that researchers be alert to an overarching macro model that we term APCO (Antecedents → Privacy Concerns → Outcomes).

Accessibility of information on the web

Abstract: Search engines do not index sites equally, may not index new pages for months, and no engine indexes more than about 16% of the web. As the web becomes a major communications medium, the data on it must be made more accessible.

AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks

Abstract: In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation. With a novel attentional generative network, the AttnGAN can synthesize fine-grained details at different sub-regions of the image by paying attentions to the relevant words in the natural language description. In addition, a deep attentional multimodal similarity model is proposed to compute a fine-grained image-text matching loss for training the generator. The proposed AttnGAN significantly outperforms the previous state of the art, boosting the best reported inception score by 14.14% on the CUB dataset and 170.25% on the more challenging COCO dataset. A detailed analysis is also performed by visualizing the attention layers of the AttnGAN. It for the first time shows that the layered attentional GAN is able to automatically select the condition at the word level for generating different parts of the image.