Design and Implementation of an Architectural Framework for Web Portals in a Ubiquitous Pervasive Environment

Web Portals function as a single point of access to information on the World Wide Web (WWW). The web portal always contacts the portal’s gateway for the information flow that causes network traffic over the Internet. Moreover, it provides real time/dynamic access to the stored information, but not access to the real time information. This inherent functionality of web portals limits their role for resource constrained digital devices in the Ubiquitous era (U-era). This paper presents a framework for the web portal in the U-era. We have introduced the concept of Local Regions in the proposed framework, so that the local queries could be solved locally rather than having to route them over the Internet. Moreover, our framework enables one-to-one device communication for real time information flow. To provide an in-depth analysis, firstly, we provide an analytical model for query processing at the servers for our framework-oriented web portal. At the end, we have deployed a testbed, as one of the world’s largest IP based wireless sensor networks testbed, and real time measurements are observed that prove the efficacy and workability of the proposed framework

Dashboard Framework. A Tool for Threat Monitoring on the Example of Covid-19

The aim of the study is to create a dashboard framework to monitor the spread of the Covid-19 pandemic based on quantitative and qualitative data processing. The theoretical part propounds the basic assumptions underlying the concept of the dashboard framework. The paper presents the most important functions of the dashboard framework and examples of its adoption. The limitations related to the dashboard framework development are also indicated. As part of empirical research, an original model of the Dash-Cov framework was designed, enabling the acquisition and processing of quantitative and qualitative data on the spread of the SARS-CoV-2 virus. The developed model was pre-validated. Over 25,000 records and around 100,000 tweets were analyzed. The adopted research methods included statistical analysis and text analysis methods, in particular the sentiment analysis and the topic modeling

The future of spaceborne altimetry. Oceans and climate change: A long-term strategy

The ocean circulation and polar ice sheet volumes provide important memory and control functions in the global climate. Their long term variations are unknown and need to be understood before meaningful appraisals of climate change can be made. Satellite altimetry is the only method for providing global information on the ocean circulation and ice sheet volume. A robust altimeter measurement program is planned which will initiate global observations of the ocean circulation and polar ice sheets. In order to provide useful data about the climate, these measurements must be continued with unbroken coverage into the next century. Herein, past results of the role of the ocean in the climate system is summarized, near term goals are outlined, and requirements and options are presented for future altimeter missions. There are three basic scientific objectives for the program: ocean circulation; polar ice sheets; and mean sea level change. The greatest scientific benefit will be achieved with a series of dedicated high precision altimeter spacecraft, for which the choice of orbit parameters and system accuracy are unencumbered by requirements of companion instruments

ENHANCED MULTI-LABEL CLASSIFICATION OF HETEROGENEOUS UNDERWATER SOUNDSCAPES BY CONVOLUTIONAL NEURAL NETWORKS USING BAYESIAN DEEP LEARNING

The classification of underwater soundscapes is a challenging task for humans as well as machine learning systems. This is largely due to the heterogenous nature of these soundscapes, especially in coastal zones close to human settlements, where multiple ships and other man-made and natural sound sources are often present simultaneously. This thesis proposes a Bayesian deep learning approach that can accurately classify multiple ships simultaneously present in the vicinity of a sensor (multi-label classification) while also providing an uncertainty measurement for the classification. This is achieved by assuming a Bayesian formulation of standard convolutional neural network architectures to not only assign multi-labels per inference but also to provide per inference uncertainty. The best performing Bayesian architecture on the multi-label task achieves a weighted F1 score of 0.84, where each prediction is accompanied by a measurement of uncertainty that is used to further enhance the understanding of model predictions. Ships, submarines, and unmanned underwater vehicles can use this classification system to aid in the identification, tracking, and/or targeting of contacts to help maintain safety of navigation, to aid in the real-time interdiction of illicit activities (such as drug or human smuggling and covert vessel transits), and to provide port security monitoring while uncertainty filters can help sonar operators prioritize contacts for further analysis.Lieutenant Commander, United States NavyApproved for public release; distribution is unlimited

A COMPREHENSIVE HUMAN FACTORS ANALYSIS OF OFF-DUTY MOTOR VEHICLE CRASHES IN THE UNITED STATES MILITARY

Researchers have always had great interest in traffic safety and the phenomenon of motor vehicle crashes (MVCs). Though scores of service members are severely injured or killed in off-duty MVCs each year, few studies have addressed the MVC phenomenon within the military population and none have conducted a comprehensive evaluation of the causal factors associated with MVCs involving military personnel. The main purpose of this dissertation was to gain a greater understanding of the causal factors associated with serious and fatal off-duty personal MVCs for military service members with the ultimate goal of preventing future losses. The HFACS-MVC framework was developed based on the established human error framework HFACS and used to classify causal factors from archival narratives from Class A and B off-duty MVCs in the USAF, USN, and USMC. This study identified the human factors trends associated with off-duty military MVCs and compared main trends for four variables of interest, specifically for military branch, vehicle type, paygrade, and age group. The main human factor trends associated with off-duty MVCs were skill based technique errors related to negotiating curves/turns and regaining road positions and procedural violations related to speeding and drunk driving. Significant differences were found between human factors trends associated with MVCs for both vehicle type and military branch. For vehicle type, the human factors trends for 4W MVCs were significantly different from those for 2W MVCs, especially at the preconditions level. However, for military branch, the human factors trends suggest differences in the investigation and reporting processes for the three branches

U.S. Naval Strategy in the 1980\u27s

This volume is designed to complement and extend the previously published history of The Evolution of the U.S. Navy’s Maritime Strategy, 1977–1986, and to present publicly for the first time the detailed changes and developments that occurred during the decade in the five (now declassified) official versions of the strategy and three directly associated unclassified public statements by successive Chiefs of Naval Operations that were made in the years between 1982 and 1990.https://digital-commons.usnwc.edu/usnwc-newport-papers/1032/thumbnail.jp