Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel. In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation

Aggregating Private and Public Web Archives Using the Mementity Framework

Web archives preserve the live Web for posterity, but the content on the Web one cares about may not be preserved. The ability to access this content in the future requires the assurance that those sites will continue to exist on the Web until the content is requested and that the content will remain accessible. It is ultimately the responsibility of the individual to preserve this content, but attempting to replay personally preserved pages segregates archived pages by individuals and organizations of personal, private, and public Web content. This is misrepresentative of the Web as it was. While the Memento Framework may be used for inter-archive aggregation, no dynamics exist for the special consideration needed for the contents of these personal and private captures. In this work we introduce a framework for aggregating private and public Web archives. We introduce three mementities that serve the roles of the aforementioned aggregation, access control to personal Web archives, and negotiation of Web archives in dimensions beyond time, inclusive of the dimension of privacy. These three mementities serve as the foundation of the Mementity Framework. We investigate the difficulties and dynamics of preserving, replaying, aggregating, propagating, and collaborating with live Web captures of personal and private content. We offer a systematic solution to these outstanding issues through the application of the framework. We ensure the framework\u27s applicability beyond the use cases we describe as well as the extensibility of reusing the mementities for currently unforeseen access patterns. We evaluate the framework by justifying the mementity design decisions, formulaically abstracting the anticipated temporal and spatial costs, and providing reference implementations, usage, and examples for the framework

The Development of a graduate course on identity management for the Department of Networking, Security, and Systems Administration

Digital identities are being utilized more than ever as a means to authenticate computer users in order to control access to systems, web services, and networks. To maintain these digital identities, administrators turn to Identity Management solutions to offer protection for users, business partners, and networks. This paper proposes an analysis of Identity Management to be accomplished in the form of a graduate level course of study for a ten-week period for the Networking, Security, and Systems Administration department at Rochester Institute of Technology. This course will be designed for this department because of its emphasis on securing, protecting, and managing the identities of users within and across networks. Much of the security-related courses offered by the department focus primarily on security within enterprises. Therefore, Identity Management, a topic that is becoming more popular within enterprises each day, would compliment these courses. Students that enroll in this course will be more equipped to satisfy the needs of modern enterprises when they graduate because they will have a better understanding of how to address security issues that involve managing user identities across networks, systems, and enterprises. This course will focus on several aspects of Identity Management and its use in enterprises today. Covered during the course will be the frameworks of Identity Management, for instance, Liberty Identity Federation Framework and OASIS SAML 2.0; the Identity Management models; and some of the major Identity Management solutions that are in use today such as Liberty Alliance, Microsoft Passport, and Shibboleth. This course will also provide the opportunity to gain hands on experience by facilitating exemplar technologies used in laboratory investigations

User Identification and Authentication using Multi-Modal Behavioral Biometrics

Biometric computer authentication has an advantage over password and access card authentication in that it is based on something you are, which is not easily copied or stolen. One way of performing biometric computer authentication is to use behavioral tendencies associated with how a user interacts with the computer. However, behavioral biometric authentication accuracy rates are worse than more traditional authentication methods. This article presents a behavioral biometric system that fuses user data from keyboard, mouse, and Graphical User Interface (GUI) interactions. Combining the modalities results in a more accurate authentication decision based on a broader view of the user\u27s computer activity while requiring less user interaction to train the system than previous work. Testing over 31 users shows that fusion techniques significantly improve behavioral biometric authentication accuracy over single modalities on their own. Between the two fusion techniques presented, feature fusion and an ensemble based classification method, the ensemble method performs the best with a False Acceptance Rate (FAR) of 2.10% and a False Rejection Rate (FRR) 2.24%