E-GOVERNMENT SECURITY IN MUNICIPAL GOVERNMENT: A CASE STUDY OF MUNICIPALITIES IN ORANGE COUNTY, CALIFORNIA

Ample amount of evidence is available discussing the barriers to e-government adoption and initiatives. Of the many barriers or challenges mentioned, security concerns are a recurring theme (Angelopoulos, Kitsios, Kofakis, & Papadopoulos, 2010; W. A. Conklin, 2007; Ebrahim & Irani, 2005b; Gilbert, Balestrini, & Littleboy, 2004; Pipe, 2006; Schwester, 2009; Stibbe, 2005). The majority of research however does not focus or discuss security considerations for e-government systems. This is even more notorious when looking specifically at municipal e­govemment literature. As such, this study takes an in-depth look at the e-govemment security practices of the 34 incorporated cities within the county of Orange, California through a descriptive case study. This case study yields important findings about the capabilities of municipal government agencies in implementing and maintaining secure e-government services by using federal e-government security requirements as a benchmark. This study utilized a case study research design collecting both quantitative and qualitative data from the participating municipal agencies. To date, limited research has been conducted in the area of municipal e-government research as evidenced by the literature review conducted as part of this study. Furthermore, this study proposed and responded to three (3) key research questions as follows: 1) What level of e-government security do municipalities currently have when benchmarked to federal e-government security requirements? 2) How can municipal agencies reach a federal level of e-government security? 3) Why are municipalities not fully compliant with federal e-government security requirements? To collect evidence this study asked all participants to complete a pre-interview participant survey. Subsequently, participants were interviewed and asked to respond to two interview questions. Findings from the survey indicate that average compliance with federal e-government security requirements as required by NIST SP800-44 was 38.05 percent as a totaled average. Participants were also asked to rate the degree of difficult in becoming fully compliant as easy, medium and difficult. The averaged totals for all 34 surveyed agencies were as follows: 20.59 percent (easy), 20.77 percent (medium) and 18.57 percent (difficult). Results from the first participant interview question after coding yield seven (7) themes as to what the greatest challenges are to implementing and maintaining e-government security: 1) Staffing 2) Budget/Financial 3) Training/Expertise 4) IT Contract Services 5) Vendors 6) Changing Nature of IT Security 7) Time/Resources to Monitor Security Threats Results from the second interview participant interview question in regards to what change or resource would assist municipal agencies in enhancing their e-govemment security were as follows: 1) Budgeting 2) Staffing 3) IT security training Overall, the findings from this study highlight two key issues that surround municipal e-govemment security. First it is evident that from the surveyed agencies, compliance with all federal e-govemment security requirements does not exist. Secondly, municipal agencies needed additional resources in the forms of budget, staffing and training to be able to provide a federal level of e-govemment security

Detecting the Presence of Electronic Devices in Smart Homes Using Harmonic Radar

Data about users is collected constantly by phones, cameras, Internet websites, and others. The advent of so-called ‘Smart Things\u27 now enable ever-more sensitive data to be collected inside that most private of spaces: the home. The first step in helping users regain control of their information (inside their home) is to alert them to the presence of potentially unwanted electronics. In this paper, we present a system that could help homeowners (or home dwellers) find electronic devices in their living space. Specifically, we demonstrate the use of harmonic radars (sometimes called nonlinear junction detectors), which have also been used in applications ranging from explosives detection to insect tracking. We adapt this radar technology to detect consumer electronics in a home setting and show that we can indeed accurately detect the presence of even ‘simple’ electronic devices like a smart lightbulb. We evaluate the performance of our radar in both wired and over-the-air transmission scenarios

A new algorithm to diagnose atrial ectopic origin from multi lead ECG systems - insights from 3D virtual human atria and torso

Rapid atrial arrhythmias such as atrial fibrillation (AF) predispose to ventricular arrhythmias, sudden cardiac death and stroke. Identifying the origin of atrial ectopic activity from the electrocardiogram (ECG) can help to diagnose the early onset of AF in a cost-effective manner. The complex and rapid atrial electrical activity during AF makes it difficult to obtain detailed information on atrial activation using the standard 12-lead ECG alone. Compared to conventional 12-lead ECG, more detailed ECG lead configurations may provide further information about spatio-temporal dynamics of the body surface potential (BSP) during atrial excitation. We apply a recently developed 3D human atrial model to simulate electrical activity during normal sinus rhythm and ectopic pacing. The atrial model is placed into a newly developed torso model which considers the presence of the lungs, liver and spinal cord. A boundary element method is used to compute the BSP resulting from atrial excitation. Elements of the torso mesh corresponding to the locations of the placement of the electrodes in the standard 12-lead and a more detailed 64-lead ECG configuration were selected. The ectopic focal activity was simulated at various origins across all the different regions of the atria. Simulated BSP maps during normal atrial excitation (i.e. sinoatrial node excitation) were compared to those observed experimentally (obtained from the 64-lead ECG system), showing a strong agreement between the evolution in time of the simulated and experimental data in the P-wave morphology of the ECG and dipole evolution. An algorithm to obtain the location of the stimulus from a 64-lead ECG system was developed. The algorithm presented had a success rate of 93%, meaning that it correctly identified the origin of atrial focus in 75/80 simulations, and involved a general approach relevant to any multi-lead ECG system. This represents a significant improvement over previously developed algorithms

An Overview of STS-132 MRM1 Cargo Element Thermal Model Development and Analyses

STS-132 was launched in May 2010 and delivered the Russian Mini Research Module 1 (MRM1) cargo element to the International Space Station as part of the ULF-4 assembly flight. The cargo element consisted of the module outfitted with externally mounted Multi-purpose Laboratory Module (MLM) Airlock, MLM radiator, Portable Work Platform (PWP), and a European Robotic Arm (ERA) spare elbow. Prior to every Shuttle flight, hardware developers are required to determine compatibility of their hardware to thermal environments experienced during the Shuttle mission and once the element is integrated with the ISS. Thermal models are provided to the Shuttle program to determine the impact of the payload on the Orbiter hardware, as well as the ISS program to determine impacts on other ISS payloads in the Orbiter. Historically the Russian International Partner (IP) develops models in formats not compatible with software used by Space Shuttle or ISS programs. This prompted NASA and Lockheed Martin to develop a unique set of thermal models for the MRM1 cargo element. Subsequent ULF-4 mission analyses performed with the models assessed the launch to activation response, identified operational criteria documented in flight rules, and ensured compliance with the mission timeline and no hazards to the crew, orbiter, or ISS. This presentation provides an overview of the work performed, depicts unique approaches in model development, discusses lessons learned, and issue resolution approaches. Though development and analysis efforts spanned over four years and presented various integration challenges it provided an example of successful collaboration with our International Partners

Incremental Few-Shot Object Detection

Most existing object detection methods rely on the availability of abundant labelled training samples per class and offline model training in a batch mode. These requirements substantially limit their scalability to open-ended accommodation of novel classes with limited labelled training data. We present a study aiming to go beyond these limitations by considering the Incremental Few-Shot Detection (iFSD) problem setting, where new classes must be registered incrementally (without revisiting base classes) and with few examples. To this end we propose OpeN-ended Centre nEt (ONCE), a detector designed for incrementally learning to detect novel class objects with few examples. This is achieved by an elegant adaptation of the CentreNet detector to the few-shot learning scenario, and meta-learning a class-specific code generator model for registering novel classes. ONCE fully respects the incremental learning paradigm, with novel class registration requiring only a single forward pass of few-shot training samples, and no access to base classes -- thus making it suitable for deployment on embedded devices. Extensive experiments conducted on both the standard object detection and fashion landmark detection tasks show the feasibility of iFSD for the first time, opening an interesting and very important line of research.Comment: CVPR 202

When and where do ECMWF seasonal forecast systems exhibit anomalously low signal‐to‐noise ratio?

Seasonal predictions of wintertime climate in the Northern Hemisphere midlatitudes, while showing clear correlation skill, suffer from anomalously low signal‐to‐noise ratio. The low signal‐to‐noise ratio means that forecasts need to be made with large ensemble sizes and require significant post‐processing to correct the forecast distribution. In this study, a recently introduced statistical model of seasonal climate predictability is adapted so that it can be used to examine the signal‐to‐noise ratio in two versions of the ECMWF seasonal forecast system. Three novel features of the low signal‐to‐noise ratio are revealed. The low signal‐to‐noise ratio is present only for forecasts initialized on 1 November and not for forecasts initialized on 1 December. The low signal‐to‐noise ratio is predominantly a feature of the lower and middle troposphere and is not present in the stratosphere. The low signal‐to‐noise ratio is linked to low signal amplitude of the forecast systems in early winter. Future studies attempting to examine the signal‐to‐noise ratio should focus on the extent to which this early winter variability is predictable