Internet

Although the sensationalized term “Revenge Porn” is used by media outlets to spark conversation about consent and digital privacy, the nonconsensual distribution of intimate media, or “image-based sexual abuse” (IBSA), is the preferred term by scholars for its more accurate depiction of the variety of modes, methods, and damages. I argue that targeted women experience many of the same damages to their socioemotional, interpersonal, and professional well-being that targets of traditional, offline, sexual violence experience, and that the nature and affordances of digital technology often allow these harms to transcend the once isolated contexts in which offline cases of sexual violence occurred. Moreover, regulatory bodies often trivialize and dismiss IBSA, deeming it inconsequential despite the devastating professional and socioemotional effects targets experience. This research explored a) how women navigate and respond to IBSA in the workplace when boundaries of personal and professional identities are crossed nonconsensually and whether demographic differences influence approaches via fifteen interviews with IBSA targets and two organizational leaders. The findings revealed that organizational environment and attitudes were the most influential factors in female employees' decisions to report, and in turn, employee turnover, organizational commitment, and job satisfaction. Demographic characteristics, such as age and gender, influenced how and from whom targets sought informal, or emotional support. Therefore, this research demonstrates the need for, IBSA and its impact to be foregrounded in how traditional sexual violence and harassment are dealt with. Finally, to extend the IBSA and workplace sexual harassment literature further, I argue for the importance of an organizational climate that is not only understanding, but supportive of IBSA targets in establishing appropriate training, regulations, and policy for sexual harassment both online and offline, structured around target support, prevention, and bystander intervention.

After comparing general architectures for accessing business databases from the Web, we propose a platform-independent, language-independent, object-oriented, fourtier CORBA-based architecture. The proposed architecture is presented in pattern format. Several security mechanisms are incorporated into the proposed architecture. A Web Reservation System (WRS) was created to test this architecture. Starting from an analysis pattern, a design model for the WRS was developed using the CORBA Object Request Broker (ORB); this was a Virtual Car Reservation System (VCRS). In the VCRS system, a user can get a reservation number when necessary information is entered from a Web browser. The user can also retrieve the reservation information by using the reservation number. All reservation information is stored in a distributed database system at a remote site. A realistic application of the proposed architecture is also described. We also provide an evaluation and comparison of the proposed architecture with other architectures.

With the advent of Geographic Information Systems and Geographic Information Science (GIS) over the last 20 years, the field of Geography, mainly the applied sector, has gained increased recognition world wide. Along with this has come an increased ability and need for the sharing of geographic data. This thesis will investigate the various means currently available to facilitate data sharing, including the availability of geographic data distributed throughout the Internet. A number of World Wide Web sites providing geographic data were chosen as a representative cross section of the current functionality available on the Internet. This document is intended to be used as a guide for more effective implementation of World Wide Web "GIS" sites and aid in conceptualizing the Internet as a Societal GIS.

The objective of this research is to determine the macroscopic behavior of packet transit-times across the global Internet cloud using an artificial neural network (ANN). Specifically, the problem addressed here refers to using a "fast-convergent" ANN for the purpose indicated. The underlying principle of fast-convergence is that, the data presented in training and prediction modes of the ANN is in the entropy (information-theoretic) domain, and the associated annealing process is "tuned" to adopt only the useful information content and discard the posentropy part of the data presented. To demonstrate the efficacy of the research pursued, a feedforward ANN structure is developed and the necessary transformations required to convert the input data from the parametric-domain to the entropy-domain (and a corresponding inverse transformation) are followed so as to retrieve the output in parametric-domain. The fast-convergent or fast-computing ANN (FC-ANN) developed is deployed to predict the packet-transit performance across the Internet. (Abstract shortened by UMI.)