12509330
Approximate Information Flows: Socially-Based Modeling of Privacy in Ubiquitous Computing
In this paper, we propose a framework for supporting sociallycompatible privacy objectives in ubiquitous computing settings. Drawing on social science research, we have developed a key objective called the Principle of Minimum Asymmetry, which seeks to minimize the imbalance between the people about whom data is being collected, and the systems and people that collect and use that data. We have also developed Approximate Information Flow (AIF), a model describing the interaction between the various actors and personal data. AIF effectively supports varying degrees of asymmetry for ubicomp systems, suggests new privacy protection mechanisms, and provides a foundation for inspecting privacy-friendliness of ubicomp systems.
858944
Ubiquitous computing for firefighters: field studies and prototypes of large displays for incident command
In this paper, we demonstrate how field studies, interviews, and low-fidelity prototypes can be used to inform the design of ubiquitous computing systems for firefighters. We describe the artifacts and processes used by firefighters to assess, plan, and communicate during emergency situations, showing how accountability affects these decisions, how their current Incident Command System supports these tasks, and some drawbacks of existing solutions. These factors informed the design of a large electronic display for supporting the incident commander, the person who coordinates the overall response strategy in an emergency. Although our focus was on firefighters, our results are applicable for other aspects of emergency response as well, due to common procedures and training.
7558919
Siren: Context-aware Computing for Firefighting
Based on an extensive field study of current firefighting practices, we have developed a system called Siren to support tacit communication between firefighters with multiple levels of redundancy in both communication and user alerts. Siren provides a foundation for gathering, integrating, and distributing contextual data, such as location and temperature. It also simplifies the development of firefighting applications using a peer-to-peer network of embedded devices through a uniform programming interface based on the information space abstraction. As a proof of concept, we have developed a prototype context-aware messaging application in the firefighting domain. We have evaluated this application with firefighters and they have found it to be useful for improving many aspects of their current work practices.
18153606
Virtual distributed environments in a shared infrastructure
We have developed a middleware system that integrates and extends virtual machine and virtual network technologies to support mutually isolated virtual distributed environments in shared infrastructures like the grid and the PlanetLab overlay infrastructure. Integrating virtual network and on-demand virtual machine creation and customization technologies makes virtual distributed environments a reality. The Violin-based middleware system integrates and enhances such technologies to create virtual distributed environments.
1475648
VIOLIN: Virtual Internetworking on Overlay Infrastructure
We propose a novel application-level virtual network architecture called VIOLIN (Virtual Internetworking on OverLay INfrastructure). VIOLINs are isolated virtual networks created on top of an overlay infrastructure (e.g., PlanetLab). Entities in a VIOLIN include virtual end-hosts, routers, and switches implemented by software and hosted by physical overlay hosts. Novel features of VIOLIN include: (1) a VIOLIN is a “virtual world” with its own IP address space. All its computation and communications are strictly con.ned within the VIOLIN. (2) VIOLIN entities can be created, deleted, or migrated on-demand. (3) Value-added network services not widely deployed in the real Internet can be provided in a VIOLIN. We have designed and implemented a prototype of VIOLIN in PlanetLab.
11399358
Constructing and training feed-forward neural networks for pattern classification
A new approach of constructing and training neural networks for pattern classification is proposed. Data clusters are generated and trained sequentially based on distinct local subsets of the training data. Obtained clusters are then used to construct a feed-forward network, which is further trained using standard algorithms operating on the global training set. The network obtained using this approach effectively inherits the knowledge from the local training procedure before improving on its generalization ability through the subsequent global training. Various experiments demonstrate the superiority of this approach over competing methods.
30106346
A Genetic Algorithm for Dynamic Routing and Wavelength Assignment in WDM Networks
In this paper, we study the challenging problem of Dynamic Routing and Wavelength Assignment (DRWA) in WDM (Wavelength-Division-Multiplexing) networks with wavelength continuity constraint, and propose an improved genetic algorithm (GA) for it. By adopting a new fitness function to consider simultaneously the path length and number of free wavelengths in cost estimation of a route, the new genetic RWA algorithm can achieve a good load balance among route candidates and result in a lower blocking probability than both the fixed alternative routing algorithm and the previous GA-based algorithm for DRWA, as verified by an extensive simulation study upon the ns-2 network simulator and some typical network topologies.
724770
Learning and inferencing in user ontology for personalized semantic web services
Domain ontology has been used in many Semantic Web applications. However, few applications explore the use of ontology for personalized services. This paper proposes an ontology based user model consisting of both concepts and semantic relations to represent users' interests. Specifically, we adopt a statistical approach to learning a semantic-based user ontology model from domain ontology and a spreading activation procedure for inferencing in the user ontology model. We apply the methods of learning and exploiting user ontology to a semantic search engine for finding academic publications. Our experimental results support the efficacy of user ontology and spreading activation theory (SAT) for providing personalized semantic services.