Unified Embedding and Metric Learning for Zero-Exemplar Event Detection

Event detection in unconstrained videos is conceived as a content-based video retrieval with two modalities: textual and visual. Given a text describing a novel event, the goal is to rank related videos accordingly. This task is zero-exemplar, no video examples are given to the novel event. Related works train a bank of concept detectors on external data sources. These detectors predict confidence scores for test videos, which are ranked and retrieved accordingly. In contrast, we learn a joint space in which the visual and textual representations are embedded. The space casts a novel event as a probability of pre-defined events. Also, it learns to measure the distance between an event and its related videos. Our model is trained end-to-end on publicly available EventNet. When applied to TRECVID Multimedia Event Detection dataset, it outperforms the state-of-the-art by a considerable margin.Comment: IEEE CVPR 201

The Impact of Mindfulness on Non-Malicious Spillage within Images on Social Networking Sites

Insider threat by employees in organizations is a problematic issue in today’s fast-paced, internet-driven society. Gone are the days when securing the perimeter of one’s network protected their business. Security threats are now mobile, and employees have the ability to share sensitive business data with hundreds of people instantaneously from mobile devices. While prior research has addressed social networking topics such as trust in relation to information systems, the use of social networking sites, social networking security, and social networking sharing, there is a lack of research in the mindfulness of users who spill sensitive data contained within images posted on social networking sites (SNS). The author seeks to provide an understanding of how non-malicious spillage through images relates to the mindfulness of employees, who are also deemed insiders. Specifically, it explores the relationships between the following variables: mindfulness, proprietary information spillage, and spillage of personally identifiable information (PII). A quasi-experimental study was designed, which was correlational in nature. Individuals were the unit of analysis. A sample population of business managers with SNS accounts were studied. A series of video vignettes were used to measure mindfulness. Surveys were used as a tool to collect and analyze data. There was a positive correlation between non-malicious spillage of sensitive business, both personally identifiable information and proprietary data, and a lack of mindfulness