A Location Aware Service to Minimize Travel Costs Using Dynamic Information

Automotive navigation systems have become common accessories in vehicles manufactured today. However, the information provided by these systems is quite limited in that many systems only provide static information. As a result, manufacturers of such systems have not been able to fully capitalize from the potential applications for mobile commerce (mcommerce) which is critically dependent on providing consumers with dynamic information. The objective of this paper is to discuss a novel method, known as Dynamic Location Cost Minimization (DLCM), which can be used with a vehicle’s navigation system to determine the optimum location to purchase gas. With the increasing cost of gas and the possibility of higher prices due to proposed gas price taxes, providing a means for consumers to minimize their costs to travel could prove to be very beneficial, and potentially help drive down prices due to increased competition. In addition, the proposed method could also be used in conjunction with mobile phones to facilitate real-time decisions for other services or purchases. Anecdotal evidence presented in this paper merits further investigation into the usability and acceptance of this technology

Apollo: A System for Tracking Internet Censorship

If it remains debatable whether the Internet has surpassed print media in making information accessible to the public, then it must nevertheless be conceded that the Internet makes the manipulation and censorship of information easier than had been on the printed page. In coming years and in an increasing number of countries, everyday producers and consumers of online information will likely have to cultivate a sense of censorship. It behooves the online community to learn how to detect and evade interference by governments, regimes, corporations, con-artists, and vandals. The contribution of this research is to describe a method and platform to study Internet censorship detection and evasion. This paper presents the concepts, initial theories, and future work

Decision Support for Perceived Threat in the Context of Intrustion Detection Systems

The objective of this research is to propose a novel approach for using a behavioral biometric known as keystroke analysis, to facilitate decision making in the context of an intrusion detection system (IDS). Regardless of the situation, individuals have a specific baseline or disposition to decision making based on two psychological factors: (1) indecisiveness, and (2) intolerance of uncertainty. The IDS provides a probability of intrusion and a set of objective situational characteristics. We propose a decision support system that allows the decision maker to state a level of perceived threat and to vary the security thresholds that determines the false acceptance rates of the IDS. Our hypothesis is that perceived threat depends not only on the keystroke technology but also on the social context and disposition toward decision making of the user. This research tests this hypothesis and provides guidance in the design of better security systems